BLS WORKING PAPERS 

 U.S. Department of Labor 

U.S. Bureau of Labor Statistics

Office of Prices and Living Conditions 

Do the Poor Pay More Store-By-Store?

Gregory Kurtzon, U.S. Bureau of Labor Statistics

Robert McClelland, U.S. Bureau of Labor Statistics

Working Paper 437

March 2010

All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the

U.S. Bureau of Labor Statistics.
-1- 

Do the Poor Pay More Store-By-Store?

By

Gregory Kurtzon

Robert McClelland

U.S. Bureau of Labor Statistics 

Preliminary Draft

December 2007 

We would like to thank Aylin Kumcu for invaluable research assistance, Robert Cage, 

John Greenlees, and the participants of the BLS seminar series and the Division of Price 

and Index number seminar series for valuable comments and suggestions. All views 

expressed in this paper are those of the authors and do not necessarily reflect the views or 

policies of the U.S. Bureau of Labor Statistics. 
-2- 

Introduction

 

There is a long standing interest in the question of whether or not low income households 

face the same prices as households with higher incomes. If low income households face 

lower or higher prices than high income households, then income inequality may be 

greater or lesser than is suggested by simple comparisons of incomes. 

A priori, an argument can be made for either case. Low income households may face 

higher prices if they are less mobile than other households and that difference reduces 

their ability search for stores with lower prices. They may also tend to shop at small, 

independent stores that cannot purchase wholesale goods in enough volume to receive 

volume discounts. In addition, higher crime rates may force local stores to pay greater

insurance and security costs.

But low income households may have lower opportunity costs of search, allowing them 

to spend more time traveling to outlets with lower prices. Stores in low income areas 

may face lower wage costs. Finally, those stores may spend less on store maintenance 

and upkeep. 

Interest in the topic developed after the unambiguously titled “The Poor Pay More: 

Consumer Practices of Low-Income Families” was published in 19631. Among many 

studies of regrettable quality, a few academic and federal studies stand out. Summarized 

in Sexton (1971), those studies generally found either no difference in prices paid, or that 

the poor paid more for similar goods. Most of the studies examined specific cities, such 

as St. Louis or Detroit. Sexton summarizes two notable studies of Philadelphia. Dixon 

and McLaughlin (1968) examine the cost of 20 items in stores located in a low income 

inner city area of North Philadelphia and stores located in high income areas throughout 

the city. They concluded that the poor pay less for the same goods. Goodman (1968) 

found that 92% of low income families left the neighborhood during their principal 

shopping. He concludes that there is no evidence that the poor pay more for similar 

items.

That result rebuts an inference sometimes drawn from a BLS study conducted in 19662

. 

Examining the prices in 18 food items in high and low income areas of six cities, the 

study finds no evidence that chain stores charged higher prices in low income areas. But 

it did find that small independent stores charged higher prices and were more common in 

low income areas. Those results suggest that the poor might pay more if they shop near 

their residences. In a related study the Department of Agriculture also found no evidence 

that chain stores charged higher prices in low income areas. However, a Federal Trade 

Commission study of San Francisco and Washington, DC found that chain stores charged 

higher prices in low income areas. 

Most of the more recent literature is summarized in Kaufman, et. al., (1997). Many of 

the studies either find no significant difference in prices, or find that the poor pay more. 

 

1 See Caplovitz (1963)

2 See National Commission on Food Marketing (1966) Technical Report #10 
-3- 

The exception is Ambrose (1979), who finds that prices were lowest in small food stores 

in the inner city. Kaufman, et. al. remark that “[t]he findings on prices in small urban 

stores run counter to the strong findings of all other studies.” 

The Kaufman study also describes some areas of concern in any study of this topic. The 

main points that need to be addressed are: (1) selection of items; (2) choice of geographic 

areas; (3) choice of stores in geographic areas; (4) the method of averaging prices over 

items and across stores, and (5) how to treat missing items. 

Their points may be summarized as follows: The selection of items in each store must be 

similar so that researchers are not literally comparing apples and oranges. More 

importantly, the items must be of similar quality to avoid mistakenly inferring that low 

income households pay less when they are simply purchasing lower quality items. The 

choice of geographic area is important for studies that assume that households shop in 

nearby outlets. In particular, many low income households do not live in ‘low-income’ 

areas, so that prices in low-income areas may not reflect prices faced by many lowincome households. The choice of stores is important because low income households 

appear to shop more frequently at independent stores, rather than chain stores. Studying 

a sample of chain stores would then provide an unrepresentative picture of the shopping 

experience of low-income households. The method of averaging prices across outlets 

and items is important because some outlets are more frequently patronized than others, 

and some items are more frequently purchased than others. If possible, prices should be 

weighted by expenditures or sales volume. Missing values may be a problem in small 

samples, requiring authors to carefully consider how the values might be imputed. 

Recently, two papers have found that the poor pay less. Hayes (2000) examines

individual prices collected for use in the Consumer Price Index (CPI). Using data from 

1998, she tests for the difference across zip codes in average prices for a set of relatively 

homogeneous items—whole chickens, eggs, milk, bananas, oranges and lettuce.3

 Zip 

codes are defined as being poor or not with several different measures. She finds no 

evidence that average prices in poor zip codes are higher than other zip codes. In fact, 

she typically finds that 2 of 5 items have prices that are statistically significantly lower. 

Overall, prices in poor zip codes are up to 6 percent lower than prices in affluent zip 

codes.

Aguiar and Hurst (forthcoming) use A.C Nielsen Homescan data covering purchases in 

Denver from January 1993 through March 1995. The Homescan survey contains data

such as prices, UPCs and outlet names, for items purchased by families volunteering to 

enter the Homescan panel. Volunteers also answer a multiple-choice question about their 

income. Differences in quality can be identified with the UPC code. Their sample relies 

heavily on chain stores; 85 percent of grocery stores purchases were made at outlets of 

four chain stores. Although the authors do not provide information about specific items, 

they conclude that the prices paid by high income households are 2.1 percent higher on 

average than those paid for low income households. 

 

3 Her sample is limited to nonkosher, broiler/fryer whole chickens; a dozen large grade A white eggs; loose 

navel oranges; and individually packaged iceberg lettuce
-4- 

 In this paper we use data from the Telephone Point of Purchase Survey (TPOPS), 

previously unused for this subject, which collects information about household 

expenditures at outlets they patronize. The BLS uses the survey to create a frame of 

outlets from which it collects prices. In 2001, respondents were also asked a question 

about their income. Combining data from that survey with price data from the CPI 

Research Database (RDB), we compare prices charged at outlets patronized by families 

in each of three income categories, and calculate differences in expenditure weighted 

average prices. We also use detailed information in the RDB about the characteristics of 

each item being priced to adjust for possible differences in the quality of items sold. 

This allows us to control for many of the problems with the previous literature. First, we 

avoid the problem of defining “poor” stores or “poor” neighborhoods, because we have 

exact data on how much consumers spend at various outlets – we know where they shop. 

Second, the detailed data on goods characteristics allows us to compare control for all 

prices determinants of the goods, meaning we effectively compare prices the same goods. 

Therefore, lower prices paid by low income families do not represent lower quality 

goods. In addition, the expenditure data allows us to avoid taking simple averages of 

prices across stores. 

Unlike Aguiar and Hurst (forthcoming), the data we use spans many years and they are 

recent. It avoids possible accuracy issues with Homescan, such as small sample sizes for 

the poor. We are also able to look at areas all over the country, instead of just Denver, 

and in fact control for differences in average prices across areas. However, because we do 

not observe what families buy at a given store, we can only measure price differences 

between stores. We cannot measure average price differences due to more or less intense 

shopping for sales or bargains within a store. 

Overall, we find that the poor pay neither more nor less than the rich at the stores they 

shop at. This varies by good – for some goods the poor pay more, some less, but for most 

items there is no discernable difference. The differences by good could be due to 

sampling variation. 

Data

Established in 1997, the TPOPS is a quarterly survey of households (or more correctly, 

consumer units) used to create a frame of outlets from which the BLS may collect prices. 

In that survey households report their spending on specific goods and services in every 

outlet they patronized. Telephone numbers are selected at random. The consumer units 

that are called are intended to be in the survey for four consecutive quarters. Every 

commodity and service belongs to one of 16 groups. For each geographic area (the 

Primary Sampling Unit, or PSU) one group is selected and all households in that PSU are 

asked about their expenditures in that group. In each new quarter a new group is 

assigned to a PSU, so that households which stay in the survey for the full four quarters 

report expenditures for four different groups. 
-5- 

In 2001 only, consumer units new in the survey were asked to place their incomes into 

one of the following three income groups, where CUSIZE is the number of people in the 

consumer unit: 

1 Lower

less than $8,000 for CUSIZE < 2; less than $18,000 for CUSIZE = 2;

less than $18,000 for CUSIZE = 3; less than $24,000 for CUSIZE > 3 

2 Middle 

$8,000-$30,000 for CUSIZE < 2; $18,000-$57,000 for CUSIZE = 2;

$18,000-$64,000 for CUSIZE = 3; $24,000-$66,000 for CUSIZE > 3 

3 Upper 

greater than $30,000 for CUSIZE < 2; greater than $57,000 for CUSIZE = 2; 

greater than $64,000 for CUSIZE = 3; greater than $66,000 for CUSIZE > 3

The proportions are roughly 15% for group 1 (poor), 50% for group 2 (middle), and 35% 

for group 3 (upper), which roughly match the proportions in 2001 March Current 

Population Survey. 

Because only consumer units meant to be in the first of four quarters in the survey were 

asked the income question (not those replacing units that left the survey), and due to a 

response rate of about 80% of those asked and an attrition rate of about 17% after each 

quarter, most units cannot be assigned an income group. Because units stay in the survey 

for up to four quarters, the number of units that can be assigned an income group rises 

from about 20% in Q1 2001 to about 55% in Q4 2001 before falling in Q3 2002. 

The CPI RDB contains all price quotes obtained by the BLS for all commodities and 

services, excluding housing, monthly from 1987 to the present. The data we use is from 

May 2002 to April 2007. Because new outlets are chosen in each PSU for each POPS 

category every four years, each good is in the database for at most four years.

The BLS categorizes goods as follows. One or more checklists make up an ELI (entry 

level item), which are then grouped into POPS categories. Each checklist is a set a goods 

characteristics that serve to (1) list all characteristics that need to be disaggregated, in 

order to select an exact good to price, and (2) aid in choosing a comparable substitute if 

the good selected should no longer be available. Each characteristic variable on the 

checklist is called a spec. For example, if one checklist item is serving size, large or 

small, a size will be decided on based on the revenue share in that store for large or small 

versions of that good. Then the next checklist item, such as flavor, will be selected on, 

until eventually a large, strawberry, organic, low-fat, etc. item is chosen, which should 

have no further price variation within those characteristics. Therefore, these checklist 

items list all characteristics that matter for a good's price, as well as some that don't. 

(Footnote: some checklist items are catchall categories, such as other, misc., etc., and are 

listed as a "99" field). 

The BLS chooses the outlets to visit by first deciding how many quotes to get for each 

ELI, and then randomly selecting the outlet to price it at with the probability of selection 

being equal to that outlet's share of all expenditure for the POPS group that the ELI is 
-6- 

part of.4 When the outlet is visited for the first time, the exact goods to be priced are 

chosen randomly in several stages. At each stage the probability of selection equals the 

revenue share as reported by the outlet. This process is called disaggregation. After a 

good is chosen to be priced, it is repriced every month for four years until a new TPOPS 

is conducted which begins the selection again, or until that good is discontinued. Our 

study is necessarily limited by the fact that the disaggregation occurs separately from the 

TPOPS. Consequently, while we observe the prices charged by outlets patronized by 

families in each income group, we do not observe the actual prices paid by those families.

Methodology

We calculate average prices for specific goods for each income group. Because of the 

sampling process, an unweighted average implicitly weights each price quote by total 

expenditures at the given outlet. We re-weight prices so that the average reflects the 

expenditures by income group at that outlet. The new average price is what the average 

price level would have been if only that income group had been included in the TPOPS 

survey. This re-weighting is needed because the price quotes have already been chosen 

based on probabilities from all income groups combined. 

Let 1it denote an indicator function for whether good i was chosen for pricing at time t. 

The probability of a particular good i being chosen in period t, prt(i), at outlet o is given 

by:

( ) ( ) ∑∑

∑

∑ ===

o I

Io

I

Io

o

o

o tit

e

e

e

e iprE

,

,

1

where eo is total expenditures on outlet o and eo.I is the expenditures by income group I on 

outlet O. The estimate of the expenditure share weighted average price, pt , taken across 

price quotes, pit, for specific good i in period t is:

 . 

∑ =

I

titit 1 pp ˆ

We calculate an adjustment factor for each outlet o and income group I

 

4

 Various weights are used to compensate for the imperfect match between ELIs, POPS categories, and 

item stratum, but as the total effects of these are small, we ignore them here.
-7- 

Io

o I

Io

I

Io

o

Io

Io

A

e

e

e

e

,

,

,

,

,

=

∑∑

∑

∑ . 

We multiply each price quote by this adjustment factor in order to yield an implicit 

weight equal in expected value to the probability of selection for that income group. Let 

It pˆ denote the estimated average price for income group I.

= ∑i itIoitIt pˆ 1 , pA

The expected value of It pˆ is

= ∑ ∑= i It itIoit i it PAEpAEpE itoI )1()1() ˆ ( ,

∑ ∑

∑ ∑

∑

∑

∑ ∑ = = i it

o Io

Io

it

o I Io

I Io

o

Io

Io

i

o

o

o p

e

e

p

e

e

e

e

e

e

,

,

,

,

,

,

 , 

which is the expenditure share weighted average price if only members if income group I 

were in the BLS sample. For a specific good this is taking the average price of that good 

across different outlets weighted by that income groups’ expenditure at those outlets. 

 

Those averages may also be obtained through weighted least squares. To estimate 

averages with weighted least squares, we create a separate observation for each income 

group for each price quote. Each of these three observations differs only in the weight it 

is given, which is the adjustment factor AoI, and the values of dummy variables for 

income. Regressing price on just those dummy variables would estimate coefficients 

equal to the average price in outlets patronized by each income group, using weights AoI. 

Regressing price on an intercept and dummy variables for medium and high incomes 

would estimate coefficients equal to the difference between prices at medium income and 

low income stores and the difference between price at high income and low income 

stores.

However, using regression analysis allows us to use dummy variables to control for 

regional effects, general inflation over time, and variation in the quality of goods. We 

therefore create dummy variables for publication region and all of the important 

characteristics described on the BLS checklists. When possible we also create dummy 

variables for many of the written characteristics, such as brand. 
-8- 

Because the monthly price for any given good is subject to serial correlation, we take the 

average price for a particular good chosen from dissagregation over all months it is priced 

as one observation. A dummy variable is created to indicate the middle period for which 

the good was priced, in order to control for general inflation. 

We perform two types of analyses. In the first, we use the characteristic variables to 

select a unique item that can be priced in many areas and time periods. For example, 

apples are limited to red delicious, not organic, loose (not in a bag), not wax-free nor 

pesticide-free apples.

Because every observation of the good should be exactly the same, price variation across 

income groups cannot be attributed to quality differences. 

The list of goods includes one example from each checklist used in the combined 

regressions described below. Let βi and Di denote the coefficient and dummy variables, 

where i can be H for high income, M for medium income, t represents the period that a 

particular quote centers on, a represents the area, and s represents checklist variables. For 

each homogenous good, a weighted regression is estimated using the following equation: 

it DDp MMHH t Dtt a Daa it ln ββ ++= ∑ + ∑ + εββ

where εit is an error term. In this model the coefficients on the income group dummies, βH

and βM, are the percentage differences between that income group’s average prices and 

those faced by low income households.

 

In our second analysis we do not attempt to identify an unique item. Instead we use the 

characteristic variables to account for price variation due to differences in quality: 

it DDp MMHH ++= ∑t Dtt + ∑ ∑ a Daa s Dss ++ it ln ββ ββ εβ

To minimize the chance that quality variation explains the variation in prices, we include 

dummy variables for all of the characteristics and all variations of the ‘99’ fields. Two 

spellings of a brand name would therefore be assigned two different dummy variables. 

The income group coefficients in these regressions are effectively weighted averages of 

the coefficients that would have been obtain if separate ‘homogenous’ regressions were 

estimated for every checklist good included.
-9- 

Results

The results for our list of 27 homogenous goods are presented in table 1 below. The signs 

of the coefficients are mixed.5

 For the high income variable, one is negative and 

significant, meaning the high income households pay less than low income households, 

and four are positive and significant, meaning high income households pay more. For the 

medium income group, one is negative and significant and one is positive. The results for 

the regressions using characteristic variables are presented in table 2. One is negative and 

significant and two are positive and significant for the high income group, while for the 

medium group they are four and three respectively. In the majority of cases for both 

medium and high income groups, prices are not statistically significant different than 

prices offered by low income stores. 

The results are consistent with the true difference being zero for all groups and goods, 

with the coefficient variation arising from sampling variation. In other words, if there was 

no difference in average prices between high income and low income stores, sampling 

variation would lead to a two tailed distribution of coefficient results, the result would 

some positive, some negative, but most insignificant. 

Conclusion

Table 5 summarizes our preliminary results. We do not find strong evidence that high or 

low income households shop at outlets charging different prices. This is consistent with 

the findings of Aguiar & Hurst (forthcoming). They find that for roughly similar income 

groups to our own, high income households pay 2.1% more than low income households, 

which is a very small difference. Our data only allows us to look at across store variation 

in prices, not whether different income groups pay differently at the same stores, perhaps 

because they shop more or less intensely for bargains. Thus we would find less than a 

2.1% difference, which is difficult to statistically detect.

 

5 Some coefficients are very small, especially for table 1, and the standard errors are 

small enough to be reported as zero. Because these coefficients are so small, we count 

them similar to statistically insignificant coefficients.
-10- 

Table 1: Homogeneous goods

Panel A

Standard errors below coefficients

Item Med High N adj. R2

Apples 0.01021 -0.04771 170 0.591

0.03891 0.04999 170

Bacon 0.02343 0.05821 1158 0.456

0.01819 0.02776 1158

Bananas 0.06380 0.07590 222 0.734

0.02301 0.02908 222

Beef 0.00390 0.07505 2398 0.444

0.00839 0.01280 2398

Bread -0.01748 0.05953 871 0.457

0.02369 0.02486 871

Butter 0.00000 0.00000 14 1.000

0.00000 0.00000 14

Catsup 0.00000 0.00000 14 1.000

0.00000 0.00000 14

Chicken 0.00000 0.00000 14 1.000

0.00000 0.00000 14

Chops -0.03289 -0.03159 2995 0.433

0.01062 0.01357 2995

Coffee 0.00000 0.00000 21 1.000

0.00000 0.00000 21

Eggs -0.03910 -0.01941 57 0.898

0.05649 0.06301 57

Fish 0.00000 0.00000 20 1.000

0.00000 0.00000 20

Flour -0.01184 0.01943 57 0.865

0.03212 0.05112 57

Franks 0.02108 0.00875 415 0.532

0.01991 0.02357 415

 
-11- 

Table 1: Homogeneous goods

Panel B

Standard errors below coefficients

Item Med High N adj. R2

Green Beans 0.00000 0.00000 15 1.000

0.00000 0.00000 15

Lettuce -0.01343 -0.00249 320 0.395

0.03874 0.05733 320

Margarine 0.00000 0.00000 12 1.000

0.00000 0.00000 12

Whole Milk 0.01200 0.03159 109 0.791

0.02770 0.04324 109

Low Fat Milk 0.00000 0.00000 26 1.000

0.00000 0.00000 26

Peanut Butter -0.02381 0.02398 25 0.947

0.03656 0.04296 25

Potatoes 0.01089 -0.09449 56 0.999

0.06789 0.11184 56

Soda A 0.00000 0.00000 73 0.772

0.00442 0.00622 73

Soda B 0.05441 -0.00090 11 1.000

0.03079 0.04401 11

Soup 0.00000 0.00000 19 1.000

0.00000 0.00000 19

Sugar 0.00000 0.00000 3

0.00000 0.00000 3

Tea 0.00000 0.00000 12 1.000

12

Tuna 0.00000 0.00000 15 1.000

0.00000 0.00000 15
-12- 

Table 2: Characteristic variables included in regression

Panel A

Standard errors below coefficients

Item Med High N

adj. 

R2

Apples -0.05615 -0.02802 1040 0.503

0.01729 0.02195 1040

Bacon 0.00551 -0.00260 14077 0.798

0.00471 0.00643 14077

Bananas 0.02711 0.03679 661 0.597

0.01637 0.02102 661

Beef 0.02995 0.02366 28450 0.666

0.00310 0.00439 28450

Bread 0.01192 0.02782 14484 0.954

0.00467 0.00608 14484

Butter 0.00319 -0.00001 228 0.883

0.00769 0.00894 228

Catsup 0.00000 0.00000 96 0.739

0.00000 0.00000 96

Chicken -0.01455 -0.00694 837 0.888

0.01642 0.02274 837

Chops 0.01353 -0.02889 21509 0.422

0.00472 0.00620 21509

Coffee 0.00051 -0.00105 487 0.933

0.00332 0.00420 487

Eggs 0.00000 0.00000 544 0.838

0.00645 0.00836 544

Fish -0.00602 -0.03099 434 0.883

0.01203 0.01817 434

Flour -0.00371 -0.00169 425 0.809

0.00801 0.01079 425

Franks -0.05615 -0.02802 5734 0.903

0.01729 0.02195 5734
-13- 

Table 2: Characteristic variables included in regression

Panel B

Standard errors below coefficients

Item Med High N adj. R2

Gas 0.00105 0.00424 117 0.976

0.00331 0.00530 117

Green Beans -0.00079 0.00208 74 0.828

0.00359 0.00470 74

Lettuce -0.00431 0.00705 907 0.805

0.01125 0.01607 907

Margarine 0.00034 -0.00092 287 0.887

0.00437 0.00582 287

Whole Milk -0.02810 -0.02881 392 0.916

0.01338 0.01804 392

Low Fat Milk -0.00365 -0.01821 379 0.948

0.01140 0.01604 379

Peanut Butter 0.00000 0.00000 158 0.808

0.00000 0.00000 158

Potatoes 0.02995 0.02366 953 0.768

0.00310 0.00439 953

Soda -0.02075 -0.00969 537 0.803

0.01595 0.01950 537

Soup -0.00715 -0.01079 470 0.925

0.00515 0.00651 470

Sugar -0.05401 -0.00641 141 0.989

0.02738 0.03752 141

Tea -0.01710 -0.00098 426 0.985

0.00827 0.00946 426

Tuna 0.01606 -0.00415 117 0.932

0.02642 0.03840 117

 
-14- 

References

Bureau of Labor Statistics (2007): BLS Handbook of Methods, “Chapter 17: The 

Consumer Price Index.” Available online at 

http://www.bls.gov/opub/hom/pdf/homch17.pdf. 

Caplovitz, David (1963): The Poor Pay More. New York: Free Press of Glencoe 

Sexton, Donald E (1971): “Comparing the Cost of Food to Blacks and to Whites. A 

Survey,” Journal of Marketing 35 #3 (July), pp. 40-46 

Kaufman, Phillip R., MacDonald, James M., Lutz, Steve M. and David M. Smallwood 

(1997): “Do the Poor Pay More for Food? Item Selection and Price Differences Affect 

Low-Income Household Food Costs,” U.S. Department of Agriculture. Agriculture 

Economic Report No. 759 

National Commission on Food Marketing (1966) “Retail Food Prices in Low and Higher 

Income Areas: A Study of Prices Charged in Food Stores Located in Low and Higher 

Income Areas of Six Large Cities,” in Special Studies in Food Marketing, Technical 

Report #10. Washington, D.C.: Government Printing Office 

Ambrose, David M. (1979) “Retail Grocery Pricing: Inner City, Suburban, and Rural 

Comparisons,” Journal of Business 52 #1 (January), pp. 95-102 

Hayes, Lashawn R. (2000) “Do the Poor Pay More? An Empirical Investigation of Price 

Dispersion in Food Retailing,” Princeton University, Department of Economics Industrial 

Relations Working Paper 446 

Aguiar, Mark and Erik Hurst, “Lifecycle Prices and Production,” AER forthcoming