Is Healthy Food a Luxury for the Low-Income

Households in the U.S.?

Olga Kozlova*†

Duke University

November 23, 2016

Job market paperFor the most updated version, visit: https://sites.duke.edu/olgakozlova/

Abstract

This paper studies how the quality of food purchased by low-income house-holds changes after a positive income shock. Using induced changes in thehousehold budget due to exogenous variation in the winter temperature thatdirectly affects heating bills, I show that households do not improve the nu-tritional quality of their food purchases. Households below 130 percent of thepoverty threshold increase total calorie amount without changing the compo-sition of food purchases. Households above 130 percent of the poverty thresh-old purchase different products, but these products are of mixed nutritionalquality. My findings suggest that policies that provide food subsidies face atrade-off subsidizing not just the increased consumption of healthy food butalso the increased consumption of unhealthy food.

*olga.kozlova@duke.edu†I am grateful to Patrick Bayer for his supervision and numerous suggestions. I have benefited

from the comments of Peter Arcidiacono, Federico Bugni, Allan Collard-Wexler, Margaux Luflade,Matthew Panhans, James Roberts, Juan-Carlos Suarez Serrato, Modibo Sidibe, John Singleton, An-drew Steck, Christopher Timmins, and Daniel Xu. I am also grateful to Nicolas-Aldebrando Benellifor the helpful criticism of my work. Any opinions, findings, recommendations, or conclusions arethose of the author and do not necessarily reflect the views of the U.S. Department of Agriculture,its Food and Nutrition Service, or its Economic Research Service and IRI. The analysis, findings,and conclusions expressed in this paper also should not be attributed to either Nielsen or Informa-tion Resources, Inc. (IRI). Data for this study were provided by the Duke-UNC USDA Center forBehavioral Economics and Healthy Food Choice Research (BECR) through third-party agreementswith the U.S. Department of Agriculture (USDA) and Information Resources, Inc. (IRI).

I. Introduction

Diet quality is a strong predictor of various public health issues, including obesity,

type 2 diabetes, and cardiovascular diseases. In the U.S., the correlation between

income and food quality has led to research on the determinants of household diet

choices. Among the various hypotheses, the existence of food deserts, which may

restrict the choice set of some households, has received a lot of attention (Larson

et al., 2009). Alternatively, the price effect of food cannot be understated, as well as

the potentially high marginal willingness to pay for low-nutrient food. The former

would imply that healthy food is a luxury good whose demand increases with

income, while the latter suggests a flat gradient between quality and income.

Recent empirical studies on nutritional disparities suggest that food access alone

cannot account for the differences in nutritional quality across income (Handbury

et al., 2015; Kozlova, 2016; Michele and Rahkivsky, 2016). In this paper, I explore

alternative mechanisms. Namely, I investigate whether healthy food is a luxury for

low-income households or whether a preference for unhealthy food is attributable

to persistent factors like habits.

In this paper, I study how the quality of low-income households’ food purchases

changes in response to small variation in their budget constraints. I use a reduced-

form equation that allows me to identify the sign of the effect of the budget change

on food expenditures using exogenous variation in the outside temperature during

winter. I show that outside temperature predicts heating bills and, thus, affects

the households’ monthly budgets. I consider different outcome measures. First, I

split food items into two groups: (1) an unhealthy group that contains processed

food (e.g., potato chips) and beverages (e.g., soda), and (2) a healthy group that

contains vegetables, fruits, meat, dairy, condiments, and grains. I study the effect

of the exogenous budget increase on expenditures in the two food groups, and on

expenditure shares. Second, I study the effect of the exogenous budget increase at

a more disaggregated level, considering food groups like fruits, grains, and pro-

cessed food. Finally, I analyze nutrient quality within the food groups (e.g., fruits,

2

grains).

I find that when households face looser budget constraints, their food expendi-

tures rise; an increase in the average monthly outside temperature from 32°F to 36°F

(0°C to 2.2°C) increases total monthly food expenditures by 4.00 percent. House-

holds whose income lies below 130 percent of the poverty line do not respond to

an increase in budget by increasing their share of expenditures on healthy food,

but instead, increase the total calorie amount. In contrast, households above 130

percent of the poverty line do increase their share of expenditures on healthy food

by 0.7 percentage points, substituting processed food with vegetables and dairy.

However, these households choose mixed-quality products within a food group.

On average households above 130 percent of the poverty line buy: (a) beverages

with a higher amount of sugar (4.3 percent more sugar per serving) and (b) dairy

products with a higher amount of sugar (1 percent). The only exceptions are grain

products, where on average households buy grains with a lower amount of sodium

(2.3 percent). My results suggest that households with very low income use a rise in

budget to increase the total number of calories in their diet, while households with

low income use a rise in budget to purchase different products of mixed nutritional

quality.

This paper contributes to the research on the diet quality of low-income house-

holds. In particular, I show that low-income households choose to purchase more of

both healthy and unhealthy food when their budget increases. My findings suggest

that policies providing food subsidies, like the Supplemental Nutrition Assistance

Program (SNAP), face a trade-off, subsidizing not just the increased consumption

of healthy food but also the increased consumption of unhealthy food.

Part II of this paper explores the literature around food choices and household

income. In Part III, I summarize the datasets used. Part IV presents my empirical

strategy. Part V explains my results. In Part VI, I test the robustness of the model.

The paper concludes in Part VII.

3

II. Literature Review

This paper is related to the literature on (a) nutritional disparities across income,

(b) the high relative price of healthy food, (c) household habits in food product

choice, and (d) the effect of winter temperature (through heating bills) on the food

security of low-income households.

The importance of food access in nutritional disparities has been widely stud-

ied, while the importance of other factors has been explored only recently. Early

studies on food access concluded that low-income households have low food ac-

cess and therefore low access may cause worse diet quality. More recent studies

have found that food access explains some fraction of nutritional disparities, other

factors, particularly product choice, play an important role in explaining why low-

income households purchase lower nutritional-quality food.

Data challenges in the past have prevented researchers from finding reliable

evidence of the effect of food access on nutritional disparities. Researchers have

used business registry data on store locations and surveys on food intake and

have shown that food access (measured by the number of supermarkets/stores

that offer fresh produce in the neighborhood) is positively related to diet quality

(see literature surveys of Larson et al. (2009) and Walker et al. (2010)). A set of

studies collected data on a store’s product offerings and linked them to small-scale

survey data on food intake. They found a positive relationship between healthy

food offerings and consumption (e.g., Bodor et al. (2008)).

Recently, a complete picture of the effect of food access and product quality

choice has emerged. Cummins et al. (2014) study the effect of the entry of a super-

market in a food desert, as part of the Philadelphia Fresh Food Financing Initiative,

on nutritional quality using a difference-in-difference method. They find no ef-

fect of increased food access on the purchases of nutritional food (as measured by

fruit and vegetable purchases) in the six months following the supermarket open-

ing. Using Nielsen scanner data, Handbury et al. (2015) show that food access

plays a limited role in explaining nutritional disparities, and nutritional dispari-

4

ties between low- and high-income households persist when conditioning on the

retailer. Finally, they show that changing the retail environment (e.g., the entry of

a new store) does not change the nutritional quality of a low-income household’s

purchased bundle. These papers reach similar conclusions to Kozlova (2016), who

also shows that within-product quality choice is important. Finally, Michele and

Rahkivsky (2016) use Nielsen scanner data and the FoodAPS dataset to show that

(a) when households travel further, they buy only slightly better food, and (b) low-

income households are more sensitive to prices than to the distance to a store. My

work builds on these papers exploring other factors that could explain the reason

why low-income households purchase lower-quality food.

Studies on the cost of healthy food in the U.S. indicate that healthy food is more

expensive than unhealthy food (though this is sensitive to the price metric used)

and the high price of healthy food threatens the food security of low-income house-

holds.1 These findings form one of the potential mechanisms of my research—low-

income households are constrained by their income to lower diet quality.

Drewnowski and Barratt-Fornell (2004); Drewnowski (2010) show that healthy

food is more expensive than unhealthy food.2 In particular, grains and fats provide

dietary energy at a lower cost than vegetables. Within food groups, Todd et al.

(2011) finds that whole grains are more expensive than refined grains and fresh

and frozen dark green vegetables are more expensive than starchy vegetables.

Several studies show that in markets where the price of healthy food is higher,

low-income households are restricted in their access to healthy food (Nord and

Leibtag, 2005; Leibtag, 2007; Nord and Hopwood, 2007; Gregory and Coleman-

Jensen, 2013).3 These studies conclude that a high relative price of healthy food is

a barrier for household ability to buy healthy food.

1The USDA defines food insecurity as reduced quality, variety, or desirability of diet.2Other studies criticize the use of price per calorie as a metric for price comparison across prod-

ucts (e.g., Carlson and Frazao (2012)).3Various measures of what defines healthy food used in different studies. Leibtag (2007); Gre-

gory and Coleman-Jensen (2013) use the price index based on a balanced diet (e.g., Gregory andColeman-Jensen (2013) use the required food for the Thrifty Food Plan—the least expensive USDA-designed food plan that is consistent with dietary guidelines). Nord and Leibtag (2005); Nord andHopwood (2007) use the household self-described basket.

5

An alternative explanation of food insecurity in low-income households is that

the long-term habits of households form the preferences of food choice, suggesting

that a budget increase alone cannot improve the diet quality of households. This

has been confirmed in empirical studies on habits of households. Bronnenberg et al.

(2012) analyze households across all income groups in the U.S. and find that brand

preferences are highly persistent over individuals’ lifetimes and are particularly

important in food categories with high levels of advertising.4 Similar findings

on brand persistence have been found in studies with a shorter horizon (Keane,

1997; Dube et al., 2010). Logan and Rhode (2010) study the food expenditure data

of nineteenth-century immigrants and find that the relative food prices in their

countries of origin predict the food expenditure shares (e.g., for eggs and beef).

A crucial assumption of my empirical design is that heating bills affect the

monthly budget of households and, thus, constitute an exogenous budget change

necessary to study the effect on food quality. Using the Consumer Expenditure

Survey and the National Health and Nutrition Examination Survey, Bhattacharya

et al. (2003) show that in unexpectedly cold winter months, food expenditures of

low-income households fall as expenditures on heating increases. Nord and Kantor

(2006) indicate that variation in food security is higher in states with colder winters.

III. Data and Summary Statistics

I use the data from the National Consumer Panel Data (NCP) combined with data

on heating degree days from the Climate Prediction Center of the National Weather

Service. The former dataset allows me to observe a panel of food purchases for a

representative sample of households that reside in different locations in the U.S.

In the second dataset, I observe the exogenous temperature variation in the winter

months that I use in my empirical design. I restrict my sample to households from

the Northeast because I expect that the variation in heating bills in this region will

be of larger importance to the monthly budgets of low-income households than in

4Note that processed foods that are on average less healthy are more likely to be advertised than,for example, fruits and vegetables.

6

other regions that have higher winter temperatures.

III.1. Sample of Households

The NCP contains several important features for my research: (1) a representative

household sample of the urban U.S. population, (2) information on the location of

households, (3) panel data on food items purchased, and (4) the nutritional content

of products.

The sample is selected to be representative of the urban U.S. population. Panel

enrollment is voluntary, but participating households are chosen based on demo-

graphics, with the goal of having a population representative of the Metropolitan

Statistical Area (MSA). I observe the block-group-level location of households that

are mostly located in urban areas across the U.S. and have been enrolled in a 2008–

2012 panel. The sample covers the ten largest MSAs (see Figure 1). I restrict my

sample to households (a) with income below $50,000 (the approximate median

household income in the U.S.), and (b) that are consistent reporters.5

I observe a broad range of demographics, including income group, household

size, race, ethnicity, education, employment, occupation, and location. My chosen

subsample of households from the Northeast fits well with the demographic distri-

bution from the American Community Survey (ACS). I present summary statistics

for household size, marital status, the presence of children, age, race, ethnicity, and

house ownership. There are slightly more households with children in the sample,

and households in the sample have on average higher education (see education

distribution in Figures 3a and 3b and other demographics in Table 3). Importantly,

the NCP sample income distribution fits the ACS sample income distribution very

well (see Figure ??). In my sample of households with income below $50,000, I

split households according to the poverty threshold.6 I will use three groups—

households under 130 percent of the poverty threshold, households between 130

5To qualify for this sample, a household needs to report at least once in 4 weeks and spendsmore than a fixed minimum amount per person per week (e.g., $25 for a single household member.

6The poverty threshold is determined by household income and size and is used as one ofthe criteria for several federal welfare programs. See income-household size cutoffs across years:https://aspe.hhs.gov/poverty-guidelines.

7

percent and 200 percent of the poverty threshold, and households above 200 percent

of the poverty threshold—in my empirical estimation. Importantly, these cutoffs

have direct policy implications. The income criterion for SNAP is that a household

has to fall below 130 percent of the poverty threshold. Households that are below

200 percent of the poverty threshold are considered to be struggling economically.

I follow the shopping behavior of households over an extended period of time.

On average, households stay in the sample for 2.3 years (see Table 3). Households

scan the food items they purchase. In particular, I observe the date of purchase, the

price, and the quantity of each product. I expand on the way the data is collected in

Appendix VIII.1. The monthly expenditure levels reported in the dataset are close

to the ones reported in the Consumer Expenditure Survey (see Figure 4).

For most product purchases, I have access to the information on the nutritional

facts label.7 In particular, I observe most macronutrients like carbohydrates (includ-

ing fiber and sugar), fats (including saturated fats), and protein.8 I focus on four

nutrients—fiber, sugar, sodium and saturated fats. In its 2015 dietary guidelines,

the USDA writes that foods that contain dietary fiber “may have positive health

effects.” The research has shown that diets rich in fiber are linked to a reduced

risk of various health conditions, including cardiovascular diseases and type 2 di-

abetes (McKeown et al., 2002; Pereira et al., 2004; Brownell and Frieden, 2009). In

contrast, high intake of dietary sugars is associated with a higher risk of cardiovas-

cular diseases, type 2 diabetes, and obesity (Yang et al., 2014; Johnson et al., 2007).

High intake of sodium is associated with higher blood pressure, higher risk of car-

diovascular diseases, stomach cancer, and osteoporosis (Sacks et al., 2001; Marmot

et al., 2007; Cook et al., 2009; He and MacGregor, 2009). Therefore, the USDA

recommends restricting the intake of sugar, sodium in the diet. Also, the USDA

recommends restricting the intake of saturated fats. However, there is an ongoing

discussion about the negative effects on health outcomes of high intake of saturated

fats, especially, in light of substitution towards diet in high in carbohydrates and

7See details on how I match nutrient data to product data in Appendix VIII.1.8I impute missing values in the nutrient data using an approach similar to that of Dubois et al.

(2014) (see Appendix VIII.1 for details).

8

sugars, in particular (Hu et al., 2001; Hu, 2010; Siri-Tarino et al., 2010).

I split food items into eight groups: (1) meat, (2) dairy, (3) fruits, (4) vegetables,

(5) grains, (6) sweeteners and oils, (7) processed food, and (8) beverages.9 On

average, households spend the most per month on processed food ($73.6), and

meat is the second largest category ($55.1) (see Figure 7a). I then combine the

processed food and beverages in what I call a “relatively unhealthy food group.”

Elements of this group are worse on average in terms of nutritional composition,

do not require cooking, and are storable. Households spend $151.7 on average in

the healthy product group and $95.9 in the unhealthy group (see Figure 7b).

Across months and years, there is slight variation in the composition of the

food purchases in terms of “healthy” and “unhealthy.” In Figures 8a and 8b, I

present average spending per month (year) in the sample. Households spend on

average more on groceries in January and March, which are longer months, and

their share of “healthy” food is lower in March (64.6 percent of total expenditures)

than in the other two months (64.9 percent in both January and February). Across

years, variation is larger. The lowest total expenditure is in 2012 ($241.3) and the

highest in 2009 ($256.3), which is likely explained by the economic environment.

The lowest share of “healthy” expenditures is in 2008 (34.6 percent) and the highest

in 2010 (35.7 percent).

III.2. Weather Data

I use data on heating degree days, which is an index based on the outside temper-

ature that predicts heating bill amounts. I present empirical evidence that heating

degree days predict heating bills of households using cross-sectional survey data.

I show that across markets and time there is variation in heating degree days that I

will use for identification in my empirical design. I further present summary statis-

tics on the amount of households’ heating bills during the winter months, and I

argue that variation in amounts is substantial for low-income households.

I use data on heating degree days provided by the Climate Prediction Center

9See more details on the products in each group in Table 1.

9

of the National Weather Service. The number of heating degree days is an index

that reflects the demand for energy to heat houses. This index is defined as the

summations of negative differences between the average daily temperature and the

65°F base. For example, if the average daily temperatures are 63, 65, and 60 over

three consecutive days, then there are a total of 7 heating degree days. Therefore,

I use the variation in the number of heating degree days in the previous month as

an exogenous variation in income for this month.

Heating bills are approximated by a linear function of heating degree days.10 In

particular, the following formula is used:

B = P × Q × HDD (1)

where HDD - heating degree days, Q - heat loss per degree day in British thermal

units, and P - price per British thermal unit.

I test this formula using the Residential Energy Consumption Survey provided

by the U.S. Energy Information Administration. The dataset is at a household level,

and I observe the heating bills for the winter season, as well as the heating degree

days within season. The regression of heating bills on heating degree days suggests

that heating bills are well predicted by the heating degree days; an increase in

heating degree days by 100 increases the heating bill by $7.169 (see Table 5).

I show that heating bills can be a substantial cost for low-income households us-

ing the households survey data from the U.S. Energy Information Administration.

Although I do not observe heating bill data in the NCP data, I use this secondary

source to present the approximate costs that households from the NCP data might

be facing. In Table 4, I present summary statistics on average monthly bills for the

October–March in 2009.11 The average monthly bill of households with an annual

income below $50,000 is $150.6, with a standard deviation of $86.4. Households

below 130 percent of the poverty threshold have an average lower heating bill than

10See, for example: http://hyperphysics.phy-astr.gsu.edu/hbase/thermo/heatloss.html.11Note that because the dataset presents total heating cost for the season, the average monthly bill

in the months of January and February is likely higher because they are, on average, colder months.

10

the other two groups ($143.8). The households that fall between 130–200 percent of

the poverty threshold have on average the highest heating bill within the sample

($159.1). The households above 200 percent of the poverty threshold are on average

smaller, which explains the non-monotonicity in heating bills across the poverty

groups. Figure 5a presents the source of heating bill payment. More than 50 per-

cent of households pay for heating themselves, and households above 130 percent

of the poverty threshold are around 20 percentage points more likely to pay for

heating themselves. In Figure 5b, I plot the fraction of households using each fuel

type. Natural gas is the most common choice, followed by fuel oil. The price of the

heating depends on the heating fuel; therefore, households using different types of

fuel might be paying different heating bills in similar housing.

There is substantial variation in heating degree days across and within markets

over time. In Figure 6, I plot the variation in heating degree days across time in

the Northeast. There is variation within a year (with January being on average the

coldest month) and across years. The average number of heating degree days from

December to February of 2008 to 2012 in the Northeast was 977.5 (corresponding

to an average temperature of 32°F), with a standard deviation of 171.

IV. Empirical Strategy

In this section, I derive, from the relevant structural equation, the reduced-form

equation that I estimate. I am interested in the effect of monthly budget on dif-

ferent outcome variables characterizing food quality. Monthly budget changes are

potentially endogenous and could be correlated with increase in leisure time (for

example, due to unemployment) or unobserved ability that also predict changes in

food quality. I show that I can use the exogenous variation in the number of heating

degree days,12 which predicts the heating bill and, thus, affects the monthly bud-

get, to sign the effect of the monthly budget. I show that I can obtain a consistent

12As explained in the previous section, heating degree days is an index that reflects the demandfor energy to heat houses. In particular, heating degree days are summations of negative differencesbetween the average daily temperature and the 65°F base. For example, heating degree days withdaily temperature averages of: 63, 65, 60 are 2, 0, 5, for a total of 7 heating degree days.

11

estimate, controlling for a set of covariates. The outcome variables I am interested

in are (1) the expenditure on different food groups, and (2) the nutritional quality

of products purchased within groups.

IV.1. Identification and Estimation

I am interested in estimating the causal effect of a budget change on various mea-

sures of food quality. In particular, the structural equation that I am interested in

is as follows:

Yit = β0 + β1Xit + δi + εit (2)

where Yit is an outcome variable (e.g., expenditure on produce) of household i in

month t, Xit is the disposable budget (after paying rent, utilities, and other bills) of

a household i in month t, δi is the household-level fixed effect, and εit is the error

term. The parameter of my interest is β, which shows how the outcome variable

responds to an increase in the monthly disposable budget. The household-level

fixed effect addresses the potential endogeneity of demographics. For example,

larger households have, on average, a higher monthly budget, but also might pur-

chase higher-quality food. However, there are still two problems associated with

estimating Equation 2. First, a disposable budget is endogenous; for example, if a

woman leaves the labor market to stay at home, then the disposable budget would

decrease, but the quality of food might improve, because there is a person at home

who can do the cooking. Second, I do not observe in the data the monthly dispos-

able budget of households.

To deal with endogeneity problem, I can use exogenous variation in heating

bills. I consider the following equation:

Xit = α0 + α1Bit + εit (3)

where Bit is the heating bill of household i in month t. The heating bill predicts

the disposable budget of households and, thus, can be a valid instrument. How-

12

ever, heating bill variation in itself is not exogenous. For example, if a person in

a household is out of work, they might spend more time at home, thus increas-

ing the heating bill, at the same time that the disposable budget reduces due to

unemployment. Furthermore, I do not observe heating bills in my data.

The exogenous variation in heating bills comes from the variation in weather,

in particular, heating degree days. I introduce the following equation:

Bit = θ0 + θ1Hmt−1 + ξm + ωt + εit (4)

where Hmt−1 is the number of heating degree days in market m where household

i resides in the previous month t − 1, ξm is the market-level fixed effect, and ωt is

the time-level fixed effect. The number of heating degree days predicts the heating

bill (as discussed in Section III.2), making it a valid instrument. I also observe it in

the data. I control for market and time-level fixed effects. Therefore, the number

of heating degree days in a given month can be seen as a random draw from

the location-specific weather distribution. Therefore, I assume that the number

of heating degree days in the previous month is orthogonal to the error terms in

Equations 3 and 4:

E[Hmt−1εit] = 0 (5)

E[Hmt−1εit] = 0 (6)

Therefore, had I observed the heating bill and the monthly disposable budget, I

could use the number of heating degree days as an instrument.

I derive my reduced-form equation. I substitute Equation 4 in Equation 3, and

then substitute it in Equation 2, and I obtain the following reduced form equation:

Yit = γ0 + γ1Hmt−1 + δi + ωt + uit (7)

where γ1 = β1α1θ1, and uit is a linear function of the error terms (εit, εit, εit) in

Equations 2, 3, and 4. I have assumed that the number of heating degree days is

13

orthogonal to the error terms in Equations 3 and 4. I assume that any weather

effects on the prices of products that could affect the ourcome Yit are captured by

the year-month fixed effects.

However, I argue that there is potentially a correlation between Hmt−1 and εit,

because the previous month’s heating degree days are correlated with the current

month’s heating degree days, and the current month’s heating degree days might

affect the food choices for the month. For example, households might eat more

soup in colder months. This introduces a potential endogeneity in Equation 7

through the error term uit. I introduce in the reduced-form Equation 7, the number

of heating degree days in the current month:

Yit = γ0 + γ1Hmt−1 + γ2Hmt + δi + ωt + uit (8)

I assume that in Equation 8:

E[Hmt−1εit] = 0 (9)

Given the assumptions in Equations 5, 6, and 10, it follows that:

E[Hmt−1uit] = 0 (10)

Therefore, I can run an OLS to estimate Equation 8 and obtain a consistent estimate

of γ1. Hence, the structural parameter β can be signed and interpreted from the

reduced-form parameter γ1.

IV.2. Outcome Variables

To describe the changes in the household food purchases in response to positive

budget changes, I analyze (1) the expenditure on different food groups, and (2)

quality within food groups. First, I analyze whether households increase expendi-

tures on food and how it differs across food groups. Because I show that nutritional

quality within product groups is important to grocery shoppers in my previous

work (Kozlova, 2016), I analyze quality change within food groups. In particular,

14

I analyze the change in the amounts of nutrients in the food products purchased

and how it differs across food groups.

V. Results

In this section, I present results that confirm that in response to an increase in

the budget low-income households increase the total food expenditure. House-

holds right above 130 percent poverty threshold increase the share of healthy food.

This increase comes from substituting away from processed food to vegetables and

dairy. However, within each group (e.g., dairy) these households purchase prod-

ucts of mixed nutritional quality. I interpret the direction of the effect of the budget

change using the exogenous variation in heating degree days, as explained in Sec-

tion IV.1. The previous month’s increase in the heating degree days reduces the

disposable monthly budget of a household because they have to pay higher heat-

ing bills.

V.1. Total Food Expenditure

I present results showing that an increase in budget increases the total food ex-

penditure. I consider different functional specifications. I show that the result

holds only for low-income households and not for households with income above

$50,000. The effect is larger for households who are above 130 percent of the

poverty threshold. The number of heating degree days does not affect the food

expenditure in other regions besides the Northeast.

I consider linear and quadratic specifications of the number of heating degree

days. In Table 6, the linear and quadratic specifications both predict the total food

expenditure. I choose to use the quadratic specification because it could capture

any non-linear effects of the number of heating degree days on the outcome vari-

ables. The coefficients on the heating degree days suggest that, at the average level

of heating degree days (977), the effect of a decrease in the number of heating

degree days by 100 increases the total monthly expenditure by 4.00 percent.

15

The effect of heating degree days is concentrated in low-income households and

does not affect high-income households. In Table 7, I present results across income

groups. First, the results are small in magnitude and statistically insignificant for

households both with income above $50,000. This result is consistent with an intu-

ition that heating bills do not affect the monthly budget of these households, given

the relatively small amount of the heating bill compared to their monthly income.

Among the households in my sample, the effect is the largest for the households

with income between $38K–$50K. The coefficients on the heating degree days sug-

gest that, at the average level of heating degree days, the effect of a decrease in

the number of heating degree days by 100 increases the total monthly expenditure

by 4.6 percent. For the other two income groups, it is also negative but smaller

and insignificant. Given that the effective poverty of a household is determined

not just by its total annual income but also by the household’s size, I consider the

heterogeneity of the effect across poverty groups in Table 8. The point estimate is

largest for the households right above 130 percent of the poverty threshold. For

these households, the effect of a decrease in the number of heating degree days

by 100 (at the average level of heating degree days) increases the total monthly

expenditure by 5.7 percent.

The effect of heating degree days is larger for the households in the Northeast

region. In Table 9, I present results for other regions outside the Northeast. The

results are statistically insignificant for Midwest and South. In the West the result

on total expenditure is smaller than in the Northeast: the effect of a decrease in the

number of heating degree days by 100 (at the average level of heating degree days)

increases the total monthly expenditure by 1.9 percent. This is consistent with an

intuition that heating bill variation affects low-income households more in colder

regions. Similar findings have been shown by Nord and Kantor (2006).

V.2. Healthy and Unhealthy Food Groups

I find that, in response to an exogenous budget increase (due to a decrease in

heating degree days), households do not change the share of healthy food, but

16

households who are right above the 130 percent poverty threshold slightly increase

their share of healthy food.13

I find that, in response to an exogenous budget increase, households increase

their expenditure on unhealthy food, but this increase does not translate into a

changed share of healthy food. In the first two columns of Table 10, I present

results for the monthly expenditures on the healthy and the unhealthy groups for

all households in my sample. The coefficients on heating degree days suggest that,

at the average level of heating degree days, the effect of a decrease in the number of

heating degree days by 100 increases the total monthly expenditure on unhealthy

food by 4.2 percent. However, there is no statistically significant effect on the share

of expenditure for the healthy group. Therefore, households do not change the

composition of their food purchases in response to an exogenous budget change.

I show that the healthy food share increases for households that are above the

130 percent poverty threshold level. In Table 11, the coefficients on heating degree

days suggest that, at the average level of heating degree days, the effect of a de-

crease in the number of heating degree days by 100 increases the share of healthy

food by 0.007 (or 0.7 percentage point). This increase corresponds to increase of the

share of healthy food by 1.1 percent.

V.3. Food Groups within Healthy and Unhealthy

I study whether there is a change in the composition of food expenditures at a more

disaggregated level. Households across poverty threshold change the composition

in response to an exogenous budget increase in different ways. Households who

are above 130 percent of the poverty threshold decrease the share of processed

fruits substituting towards vegetables and dairy.

In Table 12, I report the results for the expenditure shares of different food

groups. Households below 130 percent of the poverty threshold increase the share

of fruits by 0.003. Households right above 130 percent of the poverty threshold

13I split food products into healthy and unhealthy groups, as discussed in Section III.1. I putprocessed food and beverages in the unhealthy group, and fruits, vegetables, meat, grains, dairy,and sweeteners and oil in the healthy group.

17

increase the share of vegetables (0.006) and dairy (0.006) and decrease the share

of processed food (0.009). The decrease in processed food is significantly different

to change of households in other poverty groups. Households above 200 percent

of the poverty threshold increase the share of fruits (0.002), dairy (0.005) and oils

(0.003) and decrease the share of meat (0.0014).

V.4. Quality of Products within Food Groups

I study the quality dimension within food groups to understand whether house-

holds improve the quality of food at a more disaggregated level—at the level of

UPCs (i.e., unique products). In particular, I consider nutritional quality in terms

of fiber, sugar, sodium and saturated fats. I find that households above 130 per-

cent poverty threshold respond to an exogenous budget increase (due to the lower

number of heating degree days) by substituting to different products, but these are

not necessarily more nutritious. In fact, some products are of a lower nutritional

quality.

As an outcome variable, I consider four nutrients: (a) sugar, (b) fiber, (c) sodium,

and (4) saturated fats. The empirical evidence from the health literature suggests

that a diet rich in fiber has positive health effects, whereas sugar, fiber and saturated

fats has negative effects (see Section III.1). I observe the amount of each nutrient

per serving of a product. I consider whether the amount of nutrient per serving of

a product changes in response to an exogenous budget change. Because nutrients

are distributed differently across products (e.g., there is sugar in soda, but there are

few other nutrients), I consider, for each nutrient, the two food groups that have

a high amount of this nutrient and the nutrient data has few missing values (see

Table 2). To study substitution within a set of similar products (e.g., purchasing a

different type of yogurt in terms of brand or flavor), I present results with category-

level fixed effects.

I find that, in response to an exogenous budget increase, households above 130

percent poverty threshold purchase dairy products and beverages with a higher

amount of sugar. In the first six columns of Table 13, I present the results for sugar

18

as an outcome variable, and I consider two food groups that have a high amount

of sugar: (a) dairy, and (b) beverages. I present results across poverty groups.

Households below 130 percent poverty threshold do not significantly change the

average sugar amount (possibly, because they do not switch products). In contrast,

households right above 130 percent poverty threshold purchase products with a

higher amount of sugar per serving. At the average level of heating degree days, in

response to a decrease in the number of heating degree days by 100, these house-

holds purchase beverage and dairy products with 4.3 percent and 1 percent more

sugar per serving, respectively. Households above 200 percent of poverty threshold

purchase dairy products with a higher amount of sugar per serving (2 percent).

The results for fiber are mixed. In the second six columns of Table 13, I present

the results for fiber as an outcome variable, and I consider two food groups that

have a high amount of fiber: (a) grains, and (b) processed food. Households below

130 percent of poverty threshold purchase grain products with a lower amount of

fiber per serving (1 percent). Households above 200 percent of poverty threshold

purchase processed food with a higher amount of fiber per serving (0.6 percent).

The results for sodium and saturated fats confirm that households above 130

percent poverty threshold are responding to changes in the budget by purchas-

ing different products. Households above 130 percent poverty threshold purchase

grain products with a lower amount of sodium (3 percent) in response to a de-

crease in the number of heating degree days by 100. Households above 200 percent

poverty threshold purchase dairy products with a higher amount of saturated fats

per serving (1.6 percent).

V.5. Total Calories

Households below 130 percent of poverty threshold increase the total amount of

calories in response to an increase in monthly budget. These households do not

change the composition of their food purchases and do not substitute towards dif-

ferent products as households above 130 percent poverty threshold do, instead,

they seem to purchase the same composition just in larger quantity. In Table 15,

19

households below 130 percent poverty threshold increase the total amount of calo-

ries by 5.3 in response to a decrease in the number of heating degree days by 100.

VI. Robustness

In this section, I show that my results are robust. First, I show that the share

of expenditure does not change in response to an exogenous budget change for

different demographic groups. I then show that my results are robust with respect

to a different split of products into healthy and unhealthy groups.

I show that the share of expenditure on healthy food does not increase in re-

sponse to an exogenous budget increase (due to a decrease in the number of heating

degree days) across a set of demographics: (a) marital status, (b) presence of kids,

and (c) education (see Table 17).

My results are robust to alternative specifications of the healthy and unhealthy

groups. In particular, I follow the USDA dietary guidelines for Americans14 in

splitting products into healthy and unhealthy. In Table 16, I present results for this

specification. The coefficients are insignificant for the expenditures. Again, I find

no change in the share of expenditure on healthy food in response to an exogenous

budget change.

VII. Conclusion

The diet quality of low-income households in the U.S. has attracted a lot of at-

tention, because of the effects of poor diet quality on health outcomes and equity

concerns with regards to food security among low-income households. There has

been a large focus on low food access among low-income households as a deter-

minant of poor diet quality. However, these households might be choosing food

of lower nutritional quality either because high-quality food is a luxury or because

they have a distaste for high-quality food. My results suggest that households be-

low 130 percent poverty threshold do not substantially improve the quality of their

14See https://www.cnpp.usda.gov/2015-2020-dietary-guidelines-americans.

20

food purchases in response to the budget increase, even though they increase their

food expenditures. Instead, they increase the total amount of calories purchased.

In contrast, households above 130 percent poverty threshold substitute towards

healthier food groups (particularly, away from processed food and towards veg-

etables and dairy). However, within the food groups households purchase food of

lower food nutritional quality.

My findings suggest that there are trade-offs in policies that subsidize food ex-

penditures because low-income households do not substantially increase the qual-

ity of their food purchases in response to a budget increase. The results for house-

holds below 130 percent poverty threshold suggest that these households might be

calorie-constrained.

Further understanding how households value the nutritional quality of food is

relevant for public policies. In particular, studying how households value nutrients

in different groups, and what is heterogeneous across different demographics is

important. Another interesting question is whether there is geographical hetero-

geneity in preferences and how it shapes nutritional outcomes.

21

References

Bhattacharya, J., T. DeLeire, S. Haider, and J. Currie (2003): “Heat or eat?

Cold-weather shocks and nutrition in poor American families,” American Journal

of Public Health, 93, 1149–1154. (Cited on 6)

Bodor, J. N., D. Rose, T. A. Farley, C. Swalm, and S. K. Scott (2008): “Neigh-

bourhood fruit and vegetable availability and consumption: the role of small

food stores in an urban environment,” Public health nutrition, 11, 413–420. (Cited

on 4)

Bronnenberg, B. J., J.-P. H. Dube, and M. Gentzkow (2012): “The evolution of

brand preferences: Evidence from consumer migration,” The American Economic

Review, 102, 2472–2508. (Cited on 6)

Brownell, K. D. and T. R. Frieden (2009): “Ounces of prevention—the public

policy case for taxes on sugared beverages,” New England Journal of Medicine, 360,

1805–1808. (Cited on 8)

Carlson, A. and E. Frazao (2012): “Are healthy foods really more expensive? It

depends on how you measure the price,” USDA-ERS Economic Information Bul-

letin. (Cited on 5)

Cook, N. R., E. Obarzanek, J. A. Cutler, J. E. Buring, K. M. Rexrode, S. K. Ku-

manyika, L. J. Appel, and P. K. Whelton (2009): “Joint effects of sodium and

potassium intake on subsequent cardiovascular disease: the Trials of Hyperten-

sion Prevention follow-up study,” Archives of internal medicine, 169, 32–40. (Cited

on 8)

Cummins, S., E. Flint, and S. A. Matthews (2014): “New neighborhood grocery

store increased awareness of food access but did not alter dietary habits or obe-

sity,” Health Affairs, 33, 283–291. (Cited on 4)

Drewnowski, A. (2010): “The cost of US foods as related to their nutritive value,”

The American journal of clinical nutrition, 92, 1181–1188. (Cited on 5)

22

Drewnowski, A. and A. Barratt-Fornell (2004): “Do healthier diets cost more?”

Nutrition Today, 39, 161–168. (Cited on 5)

Dube, J.-P., G. J. Hitsch, and P. E. Rossi (2010): “State dependence and alternative

explanations for consumer inertia,” The RAND Journal of Economics, 41, 417–445.

(Cited on 6)

Dubois, P., R. Griffith, and A. Nevo (2014): “Do Prices and Attributes Explain

International Differences in Food Purchases?” The American Economic Review, 104,

832–867. (Cited on 8, 27)

Gregory, C. A. and A. Coleman-Jensen (2013): “Do high food prices increase

food insecurity in the United States?” Applied Economic Perspectives and Policy, 35,

679–707. (Cited on 5)

Handbury, J., I. Rahkovsky, and M. Schnell (2015): “Is the focus on food deserts

fruitless? Retail access and food purchases across the socioeconomic spectrum,”

Tech. rep., Mimeo. (Cited on 2, 4)

He, F. J. and G. A. MacGregor (2009): “A comprehensive review on salt and

health and current experience of worldwide salt reduction programmes,” Journal

of human hypertension, 23, 363–384. (Cited on 8)

Hu, F. B. (2010): “Are refined carbohydrates worse than saturated fat?” The Ameri-

can journal of clinical nutrition, 91, 1541–1542. (Cited on 9)

Hu, F. B., J. E. Manson, and W. C. Willett (2001): “Types of dietary fat and

risk of coronary heart disease: a critical review,” Journal of the American College of

Nutrition, 20, 5–19. (Cited on 9)

Johnson, R. J., M. S. Segal, Y. Sautin, T. Nakagawa, D. I. Feig, D.-H. Kang, M. S.

Gersch, S. Benner, and L. G. Sanchez-Lozada (2007): “Potential role of sugar

(fructose) in the epidemic of hypertension, obesity and the metabolic syndrome,

diabetes, kidney disease, and cardiovascular disease,” The American journal of

clinical nutrition, 86, 899–906. (Cited on 8)

23

Keane, M. P. (1997): “Modeling heterogeneity and state dependence in consumer

choice behavior,” Journal of Business & Economic Statistics, 15, 310–327. (Cited

on 6)

Kozlova, O. (2016): “Food access versus product choice: How do low-income

households in the US fill their grocery baskets?” Tech. rep., Working Paper.

(Cited on 2, 5, 14)

Larson, N. I., M. T. Story, and M. C. Nelson (2009): “Neighborhood environ-

ments: disparities in access to healthy foods in the US,” American journal of pre-

ventive medicine, 36, 74–81. (Cited on 2, 4)

Leibtag, E. (2007): “Stretching the Food Stamp Dollar: Regional Food Prices Affect

Affordability of Food,” Economic Information Bulletin 29, 2. (Cited on 5)

Logan, T. D. and P. W. Rhode (2010): “Moveable Feasts: A New Approach to

Endogenizing Tastes,” manuscript (The Ohio State University). (Cited on 6)

Marmot, M., T. Atinmo, T. Byers, J. Chen, T. Hirohata, A. Jackson, W. James,

L. Kolonel, S. Kumanyika, C. Leitzmann, et al. (2007): “Food, nutrition, phys-

ical activity, and the prevention of cancer: a global perspective,” . (Cited on 8)

McKeown, N. M., J. B. Meigs, S. Liu, P. W. Wilson, and P. F. Jacques (2002):

“Whole-grain intake is favorably associated with metabolic risk factors for type

2 diabetes and cardiovascular disease in the Framingham Offspring Study,” The

American journal of clinical nutrition, 76, 390–398. (Cited on 8)

Michele, V. P. and I. Rahkivsky (2016): “Recent Evidence on the Effects of Food

Store Access on Food Choice and Diet Quality,” Tech. rep., United States Depart-

ment of Agriculture, Economic Research Service. (Cited on 2, 5)

Nord, M. and H. Hopwood (2007): “Higher cost of food in some areas may affect

food stamp households’ ability to make healthy food choices,” . (Cited on 5)

24

Nord, M. and L. S. Kantor (2006): “Seasonal variation in food insecurity is as-

sociated with heating and cooling costs among low-income elderly Americans,”

The Journal of nutrition, 136, 2939–2944. (Cited on 6, 16)

Nord, M. and E. Leibtag (2005): “Is the” Cost of Enough Food” Lower in Rural

Areas?” The Review of Regional Studies, 35, 291. (Cited on 5)

Pereira, M. A., E. O’Reilly, K. Augustsson, G. E. Fraser, U. Goldbourt, B. L.

Heitmann, G. Hallmans, P. Knekt, S. Liu, P. Pietinen, et al. (2004): “Dietary

fiber and risk of coronary heart disease: a pooled analysis of cohort studies,”

Archives of internal medicine, 164, 370–376. (Cited on 8)

Sacks, F. M., L. P. Svetkey, W. M. Vollmer, L. J. Appel, G. A. Bray, D. Har-

sha, E. Obarzanek, P. R. Conlin, E. R. Miller, D. G. Simons-Morton, et al.

(2001): “Effects on blood pressure of reduced dietary sodium and the Dietary

Approaches to Stop Hypertension (DASH) diet,” New England journal of medicine,

344, 3–10. (Cited on 8)

Siri-Tarino, P. W., Q. Sun, F. B. Hu, and R. M. Krauss (2010): “Saturated fatty

acids and risk of coronary heart disease: modulation by replacement nutrients,”

Current atherosclerosis reports, 12, 384–390. (Cited on 9)

Sweitzer, M., D. Levin, A. Okrent, M. K. Muth, D. Brown, K. Capogrossi, S. A.

Karns, P. Siegel, C. Zhen, et al. (2016): “Understanding IRI Household-Based

and Store-Based Scanner Data,” Tech. rep., United States Department of Agricul-

ture, Economic Research Service. (Cited on 27)

Todd, J. E., E. Leibtag, and C. Penberthy (2011): Geographic differences in the relative

price of healthy foods, DIANE Publishing. (Cited on 5)

Walker, R. E., C. R. Keane, and J. G. Burke (2010): “Disparities and access to

healthy food in the United States: a review of food deserts literature,” Health &

Place, 16, 876–884. (Cited on 4)

25

Yang, Q., Z. Zhang, E. W. Gregg, W. D. Flanders, R. Merritt, and F. B. Hu

(2014): “Added sugar intake and cardiovascular diseases mortality among US

adults,” JAMA Internal Medicine, 174, 516–524. (Cited on 8)

26

VIII. Appendix

VIII.1. Construction of the Data

In this Appendix, I provide details on the construction of the data. The dataset is

collected by Information Resources Inc. and the Nielsen Company. The core NCP

consists of 100,000 active panelists within the U.S. The household selection criteria

are: (1) household size, (2) age of head of household, (3) household income, (4)

household ethnicity, (5) household race, (6) the presence of children, and (7) urban

or rural location. Household information is collected through the annual survey,

but the demographics of 2012 are applied to all previous years. Income is measured

in brackets. A sample of households answers an annual survey about their medical

conditions.

For the purchase data, households scan each bar code and record the quantity

of items they purchase and the store where they purchase. Price information is

obtained either from the retailer or the households.15

For the nutrient data, the information on products is provided by InfoScan.

The information contains everything that is on the package, including the macro

nutrients. Slightly above 95 percent of the UPCs in the NCP have a match in

InfoScan. However, there are missing values for nutrient data. For the nutrients

of my interest (fiber, sugars, sodium and saturated fats) 9 - 13 percent of values

are missing. I follow the procedure of Dubois et al. (2014) to impute the values.

If there is no match, I use the average nutrient amount within the same brand,

product, size, and flavor. In the next step, I use the average within the same brand,

product, and size. For the random weight, I manually fit the information from the

USDA National Nutrient Database for Standard Reference.

15See more details on this dataset in Sweitzer et al. (2016).

27

Food group Example itemsFruits Fruits (apples, bananas, etc.) and juicesVegetables Beans, broccoli, carrots, cucumbers, and other vegetablesMeat and nuts Sausage, lunch meat, seafood, meat, eggsDairy Milk, cheese, yogurtGrains Cereal, bread, pasta, riceOils and sweeteners Butter, oil, sugarProcessed food Candy, cookies, frozen dinners, pastry, condimentsBeverages Soda

Table 1: Definition of food groups.

Food group Share of missing valuesSugar Fiber Sodium Saturated fats

Fruits 0.253 0.242 0.242 0.252Vegetables 0.200 0.200 0.200 0.200Meat and nuts 0.164 0.365 0.365 0.365Dairy 0.059 0.057 0.057 0.057Grains 0.155 0.154 0.154 0.154Oils and sweeteners 0.164 0.106 0.107 0.143Processed food 0.043 0.035 0.037 0.044Beverages 0.124 0.021 0.021 0.101

Table 2: Missing values in food groups.

28

VIII.2. Summary Statistics

37.5

40.0

42.5

45.0

−80 −76 −72 −68

Figure 1: Locations of the MSAs in the U.S. Includes the following MSAs: (1) New York-Newark-Jersey City, NY-NJ-PA, (2) Philadelphia-Camden-Wilmington, PA-NJ-DE-MD, (3)Boston-Cambridge-Newton, MA-NH, (4) Pittsburgh, PA, (5) Providence-Warwick, RI-MA,(6) Hartford-West Hartford-East Hartford, CT, (7) Buffalo-Cheektowaga-Niagara Falls, NY,(8) Rochester, NY, (9) Albany, NY, and (10) Syracuse, NY.

29

0

10

20

30

40

Gra

de s

chool

Som

e h

igh s

chool

Gra

duate

d h

igh s

chool

Som

e c

olle

ge

Gra

duate

d c

olle

ge

Post gra

duate

school

Share

Female, NCP Female, ACS

(a) Female education distribution in NCP and ACS.

0

10

20

30

40

Gra

de s

chool

Som

e h

igh s

chool

Gra

duate

d h

igh s

chool

Som

e c

olle

ge

Gra

duate

d c

olle

ge

Post gra

duate

school

Share

Male, NCP Male, ACS

(b) Male education distribution in NCP and ACS.

Figure 3: I obtained ACS data for 2008–2012 from the IPUMS-USA website and computethe statistics for education distribution. I compute the statistics for education distributionfor the NCP subsample that I use in my analysis. I use the education groups that are givenin the NCP dataset.

30

NCP ACSMean St.dev Median Mean St.dev Median

Household size 2.243 1.473 2 2.135 1.402 2Single 0.605 0.488 1 0.763 0.425 1Age of female head 49.823 15.717 50 54.954 19.352 55Age of male head 51.078 15.663 50 53.796 18.309 53White 0.774 0.418 1 0.691 0.462 1Black 0.132 0.338 0 0.168 0.374 0Asian 0.019 0.135 0 0.045 0.208 0Hispanic 0.111 0.314 0 0.154 0.361 0Own house 0.458 0.498 1 0.425 0.494 0Have kids 0.258 0.437 0 0.167 0.373 0Hispanic 0.111 0.314 0 0.154 0.361 0< 130% poverty 0.306 0.461 0 0.378 0.484 0130% - 200% poverty 0.354 0.478 0 0.333 0.471 0≥ 200% poverty 0.340 0.474 0 0.291 0.454 0Years in panel 2.327 1.436 2 - - -Number of households 3055 124,803

Table 3: Demographics of the households in the NCP sample compared to the AmericanCommunity Survey household demographics. I obtained ACS data for 2008–2012 from theIPUMS-USA website and compute the statistics of relevant demographics for households.I compute the statistics of the same demographics for the NCP subsample that I use in myanalysis.

100

150

200

250

1: <$20K 2: $20K−$38K 3: $38K−$61.5

Month

ly e

xpenditure

NCP home CES home

Figure 4: Monthly expenditure comparison: National Consumer Panel and Consumer Ex-penditure Survey (CES). For the CES, I use the Bureau of Labor Statistics Spotlight on Statis-tics Bulletin (November 2010) that contains expenditures on food at home across quintilesof household income distribution in 2009. I then split households in NCP in the same binsand compute the average monthly expenditure on food for each bin.

31

Mean St.dev MedianAll 150.578 86.354 137.167< 130% poverty 143.754 79.850 135130% - 200% poverty 159.076 86.886 141.667≥ 200% poverty 152.597 92.439 137.168Number of households 974

Table 4: Monthly heating bills are obtained from the 2009 Residential Energy ConsumptionSurvey provided by the U.S. Energy Information Administration.

0.00

0.25

0.50

0.75

1.00

< 130% 130 − 200% > 200%Poverty group

Fra

ctio

n o

f hh

s

Household Rent Other

(a) Heating bill payment source

0.0

0.2

0.4

Nat

ural g

as

Fuel o

il

Electric

ity

Woo

d

Keros

ene

Oth

er

Propa

ne

Fra

ctio

n o

f h

hs

(b) Heating fuel type

Figure 5: Data is obtained from the 2009 Residential Energy Consumption Survey providedby the U.S. Energy Information Administration. Abbreviation ”hhs” represents house-holds.

32

2008 2009 2010 2011 2012

800

1000

1200

Dec Jan Feb Dec Jan Feb Dec Jan Feb Dec Jan Feb Dec Jan Feb

Year−month

Month

ly H

DD

Average HDD in the Northeast

Figure 6: I plot the average number of heating degree days in the Northeast and corre-sponding standard deviation in each year and month. I obtain heating degree data bymarkets and months from the Climate Prediction Center of the National Weather Service.I use the Census definition of U.S. regions.

33

0

20

40

60

Proce

ssed

Mea

t

Gra

ins

Dairy

Fruits

Vege

tables

Bever

ages

Oils

Month

ly e

xpenditure

, U

SD

(a) Food expenditure across food groups

0

50

100

150

Healthy Unhealthy

Month

ly e

xpenditure

, U

SD

Processed

Meat

Grains

Dairy

Fruits

Vegetables

Beverages

Oils

(b) Food expenditure across healthy and unhealthy food groups

Figure 7: I compute the monthly food expenditure for the households in my sample acrossfood groups.

34

0

100

200

Jan Feb Mar

Month

ly e

xpenditure

, U

SD

Healthy Unhealhy

(a) Food expenditure on healthy and unhealthy food across months

0

100

200

2008 2009 2010 2011 2012

Month

ly e

xpenditure

, U

SD

Healthy Unhealhy

(b) Food expenditure on healthy and unhealthy food across years

Figure 8: I compute the monthly food expenditure for the households in my sample.

35

Dependent variable Average monthly heating bill

Heating degree daysHDD (00s) 7.169***

(2.079)Constant 84.096***

(19.486)

Observations 974R2 0.014Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses.

Table 5: Effect of heating degree days on heating bills. Data is from the 2009 ResidentialEnergy Consumption Survey provided by the U.S. Energy Information Administration.

VIII.3. Results

VIII.3.1. Total Food Expenditure

Dependent variable Log of total monthly expenditure

Linear Non-linear

Heating degree daysHDD (00s) -0.041* -0.069

(0.021) (0.039)HDD2 (00s) 0.002

(0.002)

Marginal effect of HDD -0.041* -0.040*p-value 0.09

Observations 21,846 21,846R2 0.663 0.664Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 6: Effect of heating degree days on total monthly expenditure.

36

Dependent variable Log of total monthly expenditure

<$20K $20K-$38K $38K-$50K > $50K

Heating degree daysHDD (00s) -0.057 -0.018 -0.150*** 0.002

(0.053) (0.075) (0.040) (0.030)HDD2 (00s) 0.0004 -0.0004 0.005** -0.0004

(0.002) (0.004) (0.002) (0.002)

Marginal effect of HDD -0.049 -0.026 -0.046** -0.006p-value 0.20 0.32 0.01 0.62

Observations 58,848R2 0.676

Average expenditure 226.736 249.240 283.118 316.143Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 7: Effect of heating degree days on total monthly expenditure across income groups.

Dependent variable Log of total monthly expenditure

<130 poverty 130%-200% poverty >200% poverty

Heating degree daysHDD (00s) -0.072* -0.040 -0.089

(0.035) (0.079) (0.056)HDD2 (00s) 0.002 -0.001 0.004

(0.001) (0.004) (0.002)

Marginal effect of HDD -0.039 -0.057** -0.020p-value 0.14 0.02 0.39

Observations 21,846R2 0.665

Average expenditure 266.005 274.229 240.272Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 8: Effect of the heating degree days on total monthly expenditure across povertythreshold.

37

Dependent variable Log of total monthly expenditure

Northeast Midwest South West

Heating degree daysHDD (00s) -0.069* 0.018 -0.0003 0.008

(0.037) (0.042) (0.011) (0.021)HDD2 (00s) 0.001 -0.00003 -0.0002 -0.001**

(0.002) (0.002) (0.001) (0.001)

Marginal effect of HDD -0.040** 0.018 -0.005 -0.019*p-value 0.04 0.18 0.51 0.01

Observations 111,494R2 0.662

Average expenditure 258.762 234.471 239.471 237.293Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 9: Effect of the heating degree days on total monthly expenditure across regions.

VIII.3.2. Expenditure on Healthy and Unhealthy Food Groups

Dependent variable Log of Share ofexpenditure expenditure

Food group: Healthy Unhealthy Healthy

Heating degree daysHDD (00s) -0.041 -0.093** 0.005

(0.062) (0.046) (0.012)HDD2 (00s) 0.0004 0.003* -0.000

(0.003) (0.002) (0.001)

Marginal effect of HDD -0.032 -0.042** 0.005F-value for HDD 0.51 5.86 0.16

Observations 21,826 21,826 21,826R2 0.638 0.649 0.622

Average 166.476 92.718 0.641Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 10: Effect of heating degree days on the monthly expenditure in the healthy andunhealthy food groups.

38

Dependent variable Healthy-food expenditure share

Poverty threshold

<130% 130%-200% >200%

Heating degree daysHDD (00s) 0.037 -0.021** -0.006

(0.021) (0.009) (0.016)HDD2 (00s) -0.002 0.001 0.001

(0.001) (0.0004) (0.001)

Marginal effect of HDD 0.006 -0.007* 0.006p-value 0.37 0.09 0.33∆ with < 130% -0.014 0.0005p-value 0.10 0.94

Observations 21,780R2 0.623

Average share 0.635 0.643 0.643Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 11: Effect of heating degree days on the monthly share of healthy food group acrosspoverty threshold.

39

Dependent variable Share of expenditure

Food group: Vegetables Fruits Meat Dairy Grains Oils Processed Beverages

Heating degree days< 130%

HDD (00s) 0.005 -0.012* 0.020 -0.001 0.010 0.009 -0.032* 0.001(0.006) (0.006) (0.017) (0.008) (0.007) (0.009) (0.015) (0.006)

HDD2 (00s) -0.0003 0.0004 -0.00004 0.0004 -0.0003 -0.0003 0.001* 0.00001(0.0002) (0.0003) (0.009) (0.0002) (0.0002) (0.0004) (0.001) (0.0003)

130% - 200%HDD (00s) -0.004 -0.005 0.005 -0.007* 0.0001 -0.011*** 0.021* 0.002

(0.005) (0.010) (0.008) (0.004) (0.005) (0.003) (0.011) (0.004)HDD2 (00s) -0.0001 -0.0001 -0.0008 0.0001 -0.0001 0.001*** -0.001 -0.0001

(0.0002) (0.0004) (0.0004) (0.0002) (0.0002) (0.0001) (0.001) (0.0001)> 200%

HDD (00s) 0.005 -0.016** -0.001 0.003 -0.003 0.004 0.013 -0.005(0.006) (0.005) (0.010) (0.008) (0.005) (0.005) (0.013) (0.006)

HDD2 (00s) -0.0003 0.0001 -0.0004 0.0004 0.0003 -0.0004 -0.001 0.00001(0.0003) (0.0002) (0.0004) (0.0004) (0.0003) (0.0003) (0.001) (0.0002)

Marginal effect of HDD <130% -0.001 -0.003* 0.005 -0.0004 0.003 0.003 -0.010 0.004p-value 0.62 0.07 0.28 0.93 0.47 0.49 0.16 0.25Marginal effect of HDD 130%-200% -0.006** 0.002 0.002 -0.006*** -0.001 0.003 0.009* -0.001p-value 0.02 0.94 0.51 0.01 0.74 0.25 0.08 0.78Marginal effect of HDD >200% -0.001 -0.002* 0.014** -0.005* 0.002 -0.003* -0.002 -0.003p-value 0.68 0.24 0.01 0.09 0.59 0.07 0.69 0.16

∆ 130%-200% and <130% 0.002 0.004 -0.003 -0.005 -0.004 -0.001 0.019* -0.005p-value 0.25 0.32 0.69 0.23 0.51 0.92 0.073 0.31∆ >200% and <130% 0.001 0.001 -0.003 -0.004 -0.001 -0.006 0.008 -0.007p-value 0.85 0.72 0.26 0.24 0.81 0.17 0.31 0.13

Observations 21,846 21,846 21,846 21,846 21,846 21,846 21,846 21,846R2 0.516 0.559 0.536 0.554 0.434 0.440 0.580 0.552

Average share 0.073 0.087 0.214 0.105 0.109 0.053 0.305 0.054Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included. Marg. effect computed at average HDD (977.5).

Table 12: Effect of heating degree days on the share of monthly expenditure across food groups.

40

VIII.3.3. Quality of Products within Food Group

Dependent variable Log of sugar per serving Log of fiber per serving

Food group: Beverages Dairy Grains Processed

Poverty group: <130% 130%-200% >200% <130% 130%-200% >200% <130% 130%-200% >200% <130% 130%-200% >200%

Heating degree daysHDD (00s) 0.031 -0.065 -0.105 -0.029 -0.012 -0.014 0.003 -0.016 -0.008 -0.031 0.010 -0.023*

(0.095) (0.135) (0.110) (0.038) (0.023) (0.021) (0.022) (0.015) (0.015) (0.020) (0.009) (0.011)HDD2 (00s) -0.001 0.006 0.008 0.001 0.0001 -0.0003 0.0004 0.001 0.0004 0.001 -0.001 0.001

(0.005) (0.007) (0.005) (0.002) (0.001) (0.001) (0.001) (0.001) (0.0004) (0.001) (0.0004) (0.001)

ME of HDD 0.012 -0.043*** 0.018 -0.003 -0.010* -0.020*** 0.010*** 0.005 -0.001 -0.007 -0.002 -0.006*p-value 0.21 0.004 0.53 0.66 0.001 0.002 0.01 0.24 0.82 0.27 0.99 0.13

∆ with < 130% -0.055*** 0.006 -0.006 -0.017 -0.005 -0.011*** 0.006 0.001p-value 0.01 0.85 0.40 0.11 0.31 0.001 0.35 0.86

Observations 115,698 231,402 250,171 717,446R2 0.437 0.812 0.314 0.339Average, g 17.903 16.410 13.689 7.941 7.964 8.177 1.805 1.893 2.052 1.331 1.324 1.397Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level, year-month and category F.E. are included. Marg. effect computed at average HDD (977.5).

Table 13: Effect of heating degree days on the amount of sugar and fiber across poverty threshold.

Dependent variable Log of sodium per serving Log of saturated fat per serving

Food group: Grains Processed Dairy Processed

Poverty group: <130% 130%-200% >200% <130% 130%-200% >200% <130% 130%-200% >200% <130% 130%-200% >200%

Heating degree daysHDD (00s) -0.013 0.025 0.047 0.047* -0.008 -0.019 0.004 0.002 -0.031 0.011 0.013 0.004

(0.074) (0.038) (0.056) (0.024) (0.035) (0.024) (0.036) (0.046) (0.029) (0.015) (0.022) (0.012)HDD2 (00s) 0.0003 -0.0002 -0.002 -0.002* -0.0001 0.001 -0.0001 0.0004 0.001 -0.001 -0.001 0.0001

(0.003) (0.002) (0.003) (0.001) (0.002) (0.001) (0.002) (0.002) (0.001) (0.001) (0.001) (0.001)

ME of HDD -0.007 0.023*** 0.016 -0.002 -0.009 -0.005 0.003 0.002 -0.016** -0.00003 -0.005 0.002p-value 0.66 0.01 0.23 0.76 0.11 0.18 0.63 0.77 0.05 0.99 0.24 0.44

∆ with < 130% 0.030** 0.023 -0.007 -0.003 -0.001 -0.019* -0.005*** 0.002p-value 0.04 0.35 0.44 0.70 0.94 0.08 0.01 0.66

Observations 250,248 720,743 231,387 716,555R2 0.657 0.706 0.514 0.475Avg g 0.010 0.010 0.010 0.012 0.012 0.011 2.738 2.665 2.500 2.357 2.239 2.224Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level, year-month and category F.E. are included. Marg. effect computed at average HDD (977.5).

Table 14: Effect of heating degree days on the amount of sodium and saturated fats across poverty threshold.

VIII.3.4. Total Calories

Dependent variable Log of total calories

<130 poverty 130%-200% poverty >200% poverty

Heating degree daysHDD (00s) -0.085* -0.111 -0.143**

(0.042) (0.089) (0.087)HDD2 (00s) 0.002 0.004 0.005**

(0.002) (0.005) (0.002)

Marginal effect of HDD -0.053* -0.038 -0.039p-value for ME 0.06 0.34 0.21

Observations 21,805R2 0.665Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 15: Effect of the heating degree days on total calories across poverty threshold.

VIII.4. Robustness

Dependent variable Log of Share ofexpenditure expenditure

Food group: Healthy Unhealthy Healthy

Heating degree daysHDD (00s) -0.095 -0.054 -0.010

(0.057) (0.042) (0.009)HDD2 (00s) 0.003 0.001 -0.0001

(0.003) (0.002) (0.0003)

Marginal effect of HDD -0.047 -0.032 -0.002F-value for HDD 3.45 3.04 1.33

Observations 21,826 21,826 21,826R2 0.664 0.639 0.565Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 16: Effect of heating degree days on monthly expenditures in healthy and unhealthyfood groups using USDA dietary guidelines.

43

Dependent variable Share of expenditure

Marital status Kids Education

Married Not married No kids Kids < college ≥ college

Heating degree daysHDD (00s) 0.004 0.004 0.004 -0.009 0.009 -0.002

(0.019) (0.013) (0.012) (0.013) (0.016) (0.015)Square of HDD (00s) -0.0002 -0.0001 -0.0001 -0.0003 0.0002 0.0001

(0.001) (0.0005) (0.001) (0.001) (0.001) (0.001)

Marginal effect of HDD 0.001 0.002 0.002 -0.001 0.003 0.003p-value 0.67 0.62 0.66 0.51 0.42 0.38Difference 0.001 0.003 -0.001p-value 0.71 0.15 0.72

Observations 21,826 21,826 19,167R2 0.623 0.623 0.61Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

S.E. in parentheses. S.E. are clustered at the market level.Household-level and year-month F.E. are included.

Marg. effect computed at average HDD (977.5).

Table 17: Effect of heating degree days on monthly expenditures in the healthy and un-healthy food groups across demographics.

44