PRICE INDEX COMPARISONS FOR GROCERY STORES IN ONTARIO AND MONTCLAIR, CALIFORNIA - ASHLEY MCCOY POMONA COLLEGE SEPTEMBER 10, 2010

Price Index Comparisons for Grocery Stores

   In Ontario and Montclair, California

               Ashley McCoy
              Pomona College
             September 10, 2010

Introduction

The Economic Research Service (ERS), a branch of the USDA which deals with the economics of food,

farming, and nutrition in the United States, conducted a study concerning the amount of people who

suffer from food insecurity. As of 2008, 5.7% of the US population (6.7 million people) reported that

they experienced very low food security, or food insecurity with hunger, in their households. This was a

1.6% increase from the 4.1% of the population that reported this amount of food insecurity in 2007. 1

        Food insecurity, as defined by the ERS, is “a household-level economic and social condition of

limited or uncertain access to adequate food”. 2 Food security or insecurity is measured by the ERS in

order to gauge how much assistance they may need to provide to communities in the US. The ERS, upon

receiving data showing food insecurity in a community, may be able to provide assistance to this

community through food security programs like community gardens, community-supported agriculture

programs, or farmer’s markets. 3 But the ERS cannot monitor the food security levels for every city,

town, or community in the US. This is where community-based organizations come into play.

The Organization

Uncommon Good, a non-profit organization based in Claremont, California, has made it their mission to

help low-income youth and families. 4 Noticing how many families in the Inland Empire need assistance

with simple expenses such as food, Uncommon Good has taken on the responsibility of collecting food

security data for six cities in the Inland Empire metropolitan area. The ERS provides a Community Food

Security Assessment (CFSA) Toolkit, which is free to all and can be found on their main website. This

toolkit is comprised of different methods with which to research food security in one’s community. Once

1
  USDA Economic Research Service "Statistics and Graphics." 16 Nov. 2009. 1 Sept. 2010.
.
2
  USDA Economic Research Service "Definitions of Hunger and Food Security." 16 Nov. 2009. 1 Sept. 2010.
.
3
  Linda Kantor. "Community Food Security Programs Improve Food Access." Food Review 24.1 (2001): 20‐21. 2
Sept. 2010.
4
  Uncommon Good. 2 Sept. 2010. .

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completed, the research will be drafted into a proposal for grant money which would provide enough

monetary assistance to start up the organization’s Pomona Valley Urban Agriculture Initiative (PVUAI).

This initiative will create one-family farms in which each family will be given health benefits and will

have salary pay in exchange for cultivating organic crops that are in demand. These crops will in-turn be

sold to local markets and made accessible, both in location and price, to low-income families in the area

in hopes of alleviating some food insecurity from those communities. 5

Project Location and Partners

The Inland Empire (IE) is a metropolitan area in Southern California consisting of cities in Los Angeles,

San Bernardino, and Riverside Counties. Uncommon Good has chosen to study the cities of Pomona,

Montclair, Ontario, Chino, Fontana, and Rialto, all of which fall within LA or San Bernardino Counties.

There are ten million people living in LA County, but there are no farms. San Bernardino County is the

largest (area-wise) county in the US and has the fourth highest ranking for rates of obesity in the country.

“The PVUAI will be the nearest producer of organic fruits and vegetables for the entire County of Los

Angeles” and “will be one of the few organic producers for San Bernardino County”. 6 These cities are in

dire need of a cheap, local, healthy food option. So far, only data collection for Pomona has been

completed. The Community Food Security Assessment of Pomona, California, an undergraduate thesis,

was completed in April of 2010. 7 This has been the basis of all work in Montclair and Ontario done

during the months of June through August 2010 and will be the basis of much work for the remaining

cities. For purposes of my research, I will only be discussing work done in Montclair and Ontario.

        Partners interested in and working on this project include the LA County Fairplex, the Draper

Center for Community Partnerships at Pomona College, California State Polytechnic University at

Pomona, the US Green Building Council, the Tongva Tribe, the Claremont Chamber of Commerce,

Amy’s Farm, and The Garden in Pomona. 8 Much of the data collected for the food assessment through

5
  Nancy Mintie, Pomona Valley Urban Agriculture Initiative.
6
  ibid.
7
  Sam Hanft, "Community Food Security Assessment of Pomona, California." Thesis. Pomona College, 2010.
8
  Mintie

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the methods prescribed by the CSFA Toolkit is being collected by the student volunteers at the Draper

Center, Pomona College.

Montclair-Ontario Price Indexing Project

This paper is based on the portion of research done on the food assessment during summer, 2010, which

was conducted in the cities of Montclair and Ontario. Two projects were done simultaneously on these

two cities based on the Toolkit: a project done by a fellow Pomona College student, John Bonacorsi,

based on survey and focus group work with Ontario and Montclair residents, and my specific project

based on the pricing of food in grocery stores recommended by the Toolkit. Price indexing is a method

given by the CSFA Toolkit for price comparison of multiple grocery stores for different types of food

items in each city. This data will show what the pricing trends are for food in a given city and which

types of grocery stores (large, small, supermarket) have the lowest cost items. This information will be

used in the creation of the PVUAI for produce pricing baselines.

                                                 Methods

Throughout the many phases of this project, the methods have changed slightly—shifting away from

some aspects of the food assessment survey and leaning on more effective methods of gathering data.

During the data gathering process for the city of Pomona, we started with five different types of data:

price indexes, internet research, focus groups, surveying, and GIS mapping. For purposes of my research,

any information gathered from focus groups, surveying, or GIS mapping for the cities of Montclair and

Ontario will be omitted.

Price Indexes

A price index includes a basic list of foods that can be found in an average household at any time. All

foods are divided into sections or categories based on the type of food (i.e. Fruit, Vegetable, Fats and

Oils, etc.). The purpose of a price index was to record the brand or variety and weight or packaging

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information about the lowest cost option for each food on the list. This was done at multiple stores in

each city being researched for this project.

        This method has proven to be fairly helpful in the data gathering process. Uncommon Good’s

goal is to sell the produce grown on plots funded by a USDA grant they are applying for, so the price

index information will give them a basic idea as to how to price their produce when marketing it to

consumers. This essay uses the price index information for the cities of Montclair and Ontario in order to

show the prices of produce, as well as other categories of food from the price index. Unfortunately, price

index information for Pomona was not included in Sam Hanft’s thesis, which was a large contribution to

Uncommon Good’s knowledge for purposes of their grant proposal. It was not seen at the time as a major

need for the project’s sake.

        For the city of Montclair we price indexed four stores categorized as either supermarkets or large

stores: Costco, Stater Brother’s, 99¢ Only, and Target. Although the 99¢ Only store and Target sell

limited amounts of food and are not on the same scale of food sales as Costco or Stater Brothers, for the

purpose of this research they have all been analyzed as a group. Montclair is a fairly small city and lacks

grocery stores with large varieties of produce.

        Stores indexed in Ontario include the following, which range from supermarkets and large stores

to small grocery and convenience stores: Stater Brother’s, Liborio, Fresh and Easy, Albertson’s, Jax

Market, Ontario Farmer’s Market (a store for mostly produce, not an outdoor farmer’s market), Mother’s

Nutritional Center, and 7-11. Although data was collected for Mother’s Nutritional Center, the number of

items missing from the price index for that store was almost 100% and therefore will not be reported. I

will discuss some of the issues with this store and other health food stores later in the essay.

        Many problems arose from price indexing due to the fact that we had so many people work on

this project and no one had any formal training on how to carry out the research techniques. There were

many inconsistencies in data, which made it difficult to transcribe and analyze. Specific inconsistencies

in data will be discussed in the Results section to come.

Internet Research

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Another component listed in the Toolkit, we were assigned multiple data tables about each city. Data

table themes included city demographics, agricultural production or manufacturing that occurs in each

city, and existence or lack of urban gardens/agriculture in each city, as well as many other topics. The

demographic tables and other census data are useful for all cities and can help to establish the income,

primary race/ethnicity, and poverty levels in a city. Many of the other tables, on the other hand, have not

proven to be very helpful for this project. The manufacturing of products does not occur in these cities

and, although there may still be agricultural centers in Chino, Ontario’s agriculture has, for the most part,

been bought out by housing tracts. After compiling all tables for Pomona and partial tables for other

cities, our research group decided that these tables would be little help for our project. Other data

gathered from internet research include lists of stores selling food for both Montclair and Ontario, and

lists of fast food locations for both cities. 9 This data was to be used for our GIS mapping, but the duration

of research did not allow enough time to create legible maps.

          The demographic tables used in this project are Tables A1 and A2. 10 The US Census Bureau

provides all information used in these tables. 11 All data for these tables was taken from the 2000 US

Census.

                                         Results and Discussion

Averages and Graphical Analysis

All grocery stores price indexed in Montclair and Ontario were entered into two separate Excel files based

on the city and then were analyzed. Each item’s weight or packaging type as indicated by the price index

was calculated so that particular item at any store had the same measurement. For example, if a fruit is

usually measured per pound but one researcher found a type of fruit that came in a 3.5 pound bag, I would

take the price of the 3.5 pound bag and divide it by 3.5 in order to get the price per pound. The prices of

9
  ReferenceUSA. "U.S. Business Search." 29 July 2010. .
10
   USDA Economic Research Service. "Community Food Security Assessment Toolkit." 1 July 2002. 3 May 2010.
. pp 75‐76
11
   American FactFinder. U.S. Census Bureau. 1 Sept. 2010.

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each type of fruit listed on the price index have been graphed according to store name (Figures 1 and 2).

Vegetables were also graphed in the same manner (Figures 3 and 4).

           It is interesting to see the fluctuations in price at different stores for the exact same items.

Although Stater Brother’s is usually the cheaper option when choosing between grocery stores, Figures 1

and 2 seem to show a different story. When in Montclair, it’s better to buy in bulk at Costco for most

produce items, but especially fruit. This specific Target does not have produce and the 99 Cent Only

store’s fruit prices are on par with those of Stater Brother’s. The 99 Cent Only store has cheaper

vegetables than Stater Brother’s, though.

           When shopping for fruit in Ontario, Stater Brother’s is still not the cheapest option. The cheapest

possible options for fruit are Ontario Farmer’s Market and Jax Market. The quality of produce at these

smaller, non-chain stores is very questionable. Ontario Farmer’s Market has low-quality produce which

goes bad quickly, so although the fruit is inexpensive, one would have to shop for fruit much more

often. 12 The cheapest option for vegetables also includes Jax Market, but Liborio tends to be second

cheapest or on par with Jax.

           In Figure 5, average food prices in Montclair are compared with average food prices in Ontario.

Because the only stores price indexed in Montclair were larger stores or supermarkets, I only considered

stores of the same type (Stater Brother’s, Liborio, Fresh and Easy, and Albertson’s) for the comparison of

Ontario stores. When taking the averages, not all items within a category had the same units, which may

make the graphical data more difficult to interpret. Fresh fruit and fresh meat can be assumed to be

measured per lb with the exception of eggs (in the fresh meat category) which is measured by the dozen.

12
     Personal observation through price indexing and personal shopping trips.

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Figure 1: Price of fresh fruit at Montclair markets by type of fruit

 Figure 2: Price of fresh fruit at Ontario markets by type of fruit

                                                                       8

Figure 3: Price of fresh vegetables in Montclair markets by type of vegetable

 Figure 4: Price of fresh vegetables in Ontario markets by type of vegetable

                                                                                9

Figure 5 shows that average prices for food in Montclair are generally lower than those of

Ontario. The two cities have approximately the same average for fresh vegetables and canned dairy

products (condensed milk). Instances where Ontario’s average is less expensive occur with fresh fruit and

fresh meat/meat alternatives.

Discrepancies in Graphical Data

        Where there is no bar for a given item at a given store, the item was either missing at that location

or the data was unreasonable, meaning that it was not feasible to convert its weight or packaging type into

units comparable with the other stores’ units for that item. One such instance occurred in Figure 2 for the

                Figure 5: Comparison of average food prices at supermarkets and large grocery stores in
                                    Montclair and Ontario by price index category

price of apples at Liborio. The price was recorded as $8.99 for one pound of apples. Outliers that I did

not cut out of the data, such as the price for apples at Costco (Figure 1), are due to inconsistencies in

measurement of weight or packaging. Note that for apples I analyzed it as “per bag” rather than by a

weight. This was due to the fact that no weight was specified for the package. The price of melons at

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Liborio also created an outlying data point. The price was recorded at $9.00 for 2 lbs, which seems

unreasonable depending on the type of melon, but I had no reason to believe it was any different so I left

it in for analysis.

         Unfortunately many of these discrepancies in weight or packaging type for each item were due to

lack of experience with the type of field research. I only began to work with this project in February and

was not put into a position where I had to know price indexing well until June. My research partner had

very limited experience with it as well. Many people collecting this data were new to the methods in

June. It is very hard to get items to have the same units when the researchers do not know what goes into

analyzing the data and do not know that it would be easier to get as close to the recommended weight or

packaging suggested on the price index template. Many people also may have forgotten to write down

the packaging type on the price index. Other discrepancies are caused by the fact that so many people

were entering data and I did not have access to many of the hard copies. I relied solely on Excel files

transcribed by myself, my research partner, and a high school program earlier in the summer, which could

very well have been transcribed from hard copy incorrectly.

Census Data

Data tables from the CSFA Toolkit were used to
                                                          Figure 5: Zip code map of Montclair and Ontario
collect data from the 2000 US Census about

demographic and economic information. Tables

1 and 2 below are taken from table A1 in the

CSFA Toolkit. The total population for the city

of Montclair is 34,437, 60% of which identify as

being of Hispanic origin. Ontario’s total

population is about 4 times that of Montclair at

162,630 people, 64.3% of which identify as

being of Hispanic origin. If broken up by zip

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code, Ontario’s population is as follows: Zip code 91761, Population 56,271; Zip code 91762, Population

54,243; Zip code 91764, Population 49,461. 13

        Economic characteristics given by the US Census were put into Table A2 given by the Toolkit.

Tables 3 and 4 show the economic characteristics for Montclair and Ontario. Table 3 shows that the

median household income for Montclair is $40,797. 17.4% of Montclair’s population is under the

poverty level. Table 4 shows that the median income for Ontario is $57,184. 10.1% of Ontario’s

population is under the poverty level. Because there are three different zip codes in Ontario and

individual data sets available for each, it is easier to see the breakdown of income and poverty levels by

zip code. Figure 5 show a map broken up by zip code. 14 Within zip code 91761, the median income is

$49,430 and 12.4% of its population is under the poverty level; zip code 91762, median is $39,972 and

7.6% of people are under poverty levels; and zip code 91764, median is $39,098 and 17.3% of people are

under the poverty level. 15
                                 Table 1: Montclair demographic characteristics

13
   American FactFinder
14
   US Naviguide Home Page. "USPS Lookup and Boundary Map." Jan. 2010. 2 Sept. 2010.
.
15
   American FactFinder

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Table 2: Ontario demographic characteristics

Table 3: Montclair economic characteristics              Table 4: Ontario economic characteristics

                                                                                                     13

Grocery Prices in Relation to Census Data

So what do the census data have to do with the price index data? People in Ontario can afford to pay

more for food than residents of Montclair. Of course this is a generalization based off of the census data

(Tables 3,4), but that would be how the markets see the situation. Stores that sell their produce for less,

no matter what the quality of the product may be, are most likely marketing to low-income residents. I

suspect that supermarkets are not centrally located in (or near) low-income neighborhoods, whereas low

cost produce is sold in stores that are located closer to low-income neighborhoods. Without GIS maps

showing placement of markets against city demographics/economics it is hard to prove that these claims

are true.

                                                Conclusion

After Sam Hanft’s thesis was written, many different initiatives have been taken to see that the

city of Pomona receives land for a PVUAI. The results of the thesis have stunned many

residents and, once presented the data by Cal Poly students, even the city itself. 16 This may even

spawn future studies on the topic of poverty and food insecurity in Pomona. Ontario and

Montclair have the same potential, but both cities need completed data for the food assessment

first. Each city will be studied further for the purposes of the PVUAI grant proposal for

Uncommon Good through studies conducted by students of Cal Poly Pomona and Pomona

College. Unfortunately by the end of the summer project baseline prices for what people would

be willing to pay for produce from the PVUAI were not established, but it is safe to assume that

people will be willing to pay as much as the lowest cost stores sell their produce for, like Jax or

Ontario Farmer’s Market. Although may not be enough accurate data to help the PVUAI

proposal, there is enough data to show that the cities are in need of a PVUAI garden.

16
     Nancy Mintie, personal interview.

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Works Cited

American FactFinder. U.S. Census Bureau. Web. 1 Sept. 2010.

"Community Food Security Assessment Toolkit." USDA Economic Research Service. USDA, 1 July
       2002. Web. 3 May 2010. .

"Definitions of Hunger and Food Security." USDA Economic Research Service. USDA, 16 Nov. 2009.
       Web. 1 Sept. 2010. .

Hanft, Sam. "Community Food Security Assessment of Pomona, California." Thesis. Pomona College,
       2010. Print.

Kantor, Linda. "Community Food Security Programs Improve Food Access." Food Review 24.1 (2001):
       20-26. Web. 2 Sept. 2010.

Mintie, Nancy. Pomona Valley Urban Agriculture Initiative. Project Description.

"Statistics and Graphics." USDA Economic Research Service. USDA, 16 Nov. 2009. Web. 1 Sept. 2010.
       .

Uncommon Good. Uncommon Good. Web. 2 Sept. 2010. .

"U.S. Business Search." ReferenceUSA. Web. 29 July 2010. .

"USPS Lookup and Boundary Map." US Naviguide Home Page. Jan. 2010. Web. 2 Sept. 2010.
       .

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