The Evolution of the Weekly Gasoline Price Survey through Changes in 

Design and Frame 

Paula Weir, Benita J. O’Colmain, Pedro J. Saavedra 

Weir, Energy Information Administration; Saavedra and O’Colmain, Macro International Inc., 

Paula.Weir@eia.doe.gov 

Abstract 

The Energy Information Administration (EIA) weekly survey of gasoline prices produces estimates of gasoline pump 

prices at the national and regional levels, as well as estimates for several states and cities, two formulations and three 

grades of gasoline. This survey originated as a response to the First Gulf War and was later expanded to monitor the 

effects of the Clean Air Act on the price of gasoline. The original design was a two-phase sample, with a monthly 

survey’s sample of gasoline refiners and resellers serving as the first phase, and the individual gasoline stations owned by 

the monthly respondents serving as the second. Subsequent expansions to produce estimates at lower geographic levels 

required the use of two cycles of the monthly survey sample and the Census County Business Patterns database. The 

most recent expansion of state and city estimates, however, required a new approach to the sampling frame and sample 

design. The new design is a two-stage sample where the first stage is an area sample in which counties are selected and 

the second stage is the sample of stations from the selected counties. The design made use of a newly constructed outlet 

level frame.. However, because the old corporate level frame and the new outlet level frame approached the population 

differently, the potential existed for discontinuity in the survey estimates. In order to attenuate the discontinuity, the 

sample drawn under the new design was combined with a subsample of the old design. Variance estimates of the new 

sample were obtained taking into account the procedure that combined the two samples and frames. 

Survey Background and History 

The August 1990 Iraqi invasion of Kuwait and the resulting rise in gasoline prices led to a need for more frequent 

monitoring of motor gasoline prices by an independent source. Pump price information was needed on a weekly basis (or 

more often) for unleaded, regular gasoline at the national level that was not only accurate, but could be obtained quickly 

and inexpensively. A survey to collect the data needed to be operational within a week. Commercial data were found to 

be insufficient in satisfying the timeliness, coverage and independence requirements. As a result of these limitations, it 

was decided that the EIA would conduct its own weekly price survey that would produce a national estimate of the price 

of regular gasoline at the pump. 

The Weekly Motor Gasoline Price Survey was initially a survey of retail motor gasoline outlet prices drawn from the 

sample that was already in place for the monthly price survey of petroleum products. The new survey was intended to 

monitor consumer prices during the Persian Gulf War in 1990 and 1991 (Saavedra and Weir, 1991). The principal 

objective was to collect, process, and release the data to a variety of users, including policy makers and citizens, in a very 

rapid turn around mode. Specifically, Monday morning’s prices should be available by the end of the same day. A twophase sample was used, with a subset of the monthly survey respondents as the first phase of the sample (Saavedra, 

1988). In particular, monthly survey respondents who sold gasoline through retail outlets were sampled with 

probabilities proportional to sample weighted sales’ volumes in each state, as reported in the monthly survey. For the 

second phase, one or more gasoline stations in a given reported state were sampled, according to the size of each 

company selected in the first phase. The appeal of the design was that it: 1) permitted the use of a simple average as the 

price estimator, thereby simplifying the development of an over-night survey processing system; 2) allowed for quick 

implementation of the sample because of the ongoing monthly contact with those companies. 

The survey was later iteratively expanded in response to the Clean Air Act and, eventually, estimates for Conventional, 

Oxygenated, Reformulated and OPRG gasoline for the five geographic regions known as Petroleum Administration for 

Defense Districts (PADDs), three sub-regions or sub-PADDs, and the State of California were added, as well as 

estimates for midgrade and premium grades of gasoline. These expansions in detail required an increase in sample size 

and presented the difficulty that the first-phase sample from the monthly survey was not sufficiently large to provide the 

allocations needed to meet the targeted accuracy. As a result, two survey rotation cycles of the monthly survey (current 

and previous samples) were combined in order to form Phase 1 (Weir and Saavedra, 1998). 
More recently, EIA conducted a two-part expansion of the weekly gasoline price survey. As part of the first expansion, 

the sample was augmented minimally to allow release of average prices for 5 states (one in each PADD) and 6 cities, in 

addition to the regional and U.S. average prices previously released. The first expansion also included the collapsing of 

formulation designations into just two types. The newly combined categories were then backcast to provide the data 

users a historical continuous database 

The second part of the expansion required a complete redesign of the weekly price survey (Saavedra et. al., 2002). In 

this expansion, several more cities and states were added to the required estimates rendering a two-phase design no 

longer viable for the estimates required. The previous expansion was performed for the most part in cities and states that 

already satisfied allocations that would provide the required CVs. For this expansion, the monthly survey, which 

collected company/state level prices, was no longer sufficiently large, nor could it target the sample at the city level. As 

a result, it was determined that a new frame was needed in order to develop a sample that would meet the necessary 

requirements and be flexible in meeting future requirements. In response to this need, the EIA developed a retail 

gasoline outlet frame and designed a new version of the weekly gasoline sample. The new sample design entailed using 

a totally new outlet level frame that only overlapped with the old company level frame when a company reporting retail 

gasoline sales from the monthly survey was represented by retail gasoline outlets on the new frame. However, 

identifying and measuring this overlap was virtually impossible, increasing the possibility for discontinuity between 

comparable price estimates produced from the old and new designs. 

The Frame Development 

Nine potential sources of nationwide lists of gasoline outlets with names and addresses were evaluated to determine the 

basis for the sampling frame. The primary focus of the evaluation was comprehensiveness of the data, although other 

factors such as availability of volumetric information, frequency of updates to the database, and expense in terms of 

database costs and level of effort needed to convert the data into a useable format were also considered (See Table 1). 

Comprehensiveness was assessed based on the total number of outlets contained in each database, and, also by 

conducting a “zip code” test for each database. Twelve zip codes were randomly selected, six from urban areas and six 

from rural areas. Each vendor was then asked to provide a count of the number of retail outlets in those zip code areas. 

Comprehensiveness of the final sampling frame was assessed at the regional level using two different sources: 1) 

Regional estimates of retail gasoline stations published in the May 2002 issue of the National Petroleum News, and 2) 

1999 U.S. Commerce Department County Business Patterns (CBP) estimates. 

Table 1. Criteria for Evaluation of Available Data Sources 

 Main Issue Variable Evaluated 

Availability of SIC codes Availability of Volumetric 

Data Availability of a variable that denotes volume of 

gasoline sold 

Comprehensiveness 

Coverage 

Absence of systematic bias 

Format of the Data Availability of a useable flat file 

Cost 

Expense 

Level of effort 

The advantages and disadvantages of each of the initial nine data sources based on these criteria are presented in Table 2. 

The combination of all factors was considered in the selection of the source to be used for the sampling frame, however 

comprehensiveness and expense were weighted most heavily in the choice of the final data source. 

The results of the zip code test to assess coverage in randomly selected urban and rural areas for each of the data sources 

are shown in Table 3, as well as the counts of outlets for each zip code obtained from the U.S. Census CBP database. 
Table 2. Advantages and Disadvantages of Available Data Sources 

Data Source Advantages Disadvantages 

Dun & 

Bradstreet 

• Flat file available 

• More comprehensive 

• Primary and secondary SIC codes available 

• Low level of effort required 

• No known systematic bias exists 

• Expensive 

• Variable that denotes volume of gasoline 

sold not available 

Survey 

Sampling, Inc. 

• Flat file available 

• Inexpensive to obtain list of 3,000 records 

• Primary and secondary SIC codes available 

• Low level of effort required 

• Expensive to obtain entire database 

• Inexpensive option requires that SSI selects 

sample records on our behalf 

• Less comprehensive 

• Variable that denotes volume of gasoline 

sold not available 

• Systematic bias exists for stations with P.O. 

box only, no phone number, no annual report 

OPIS 

• Flat file available 

• Medium expense 

• More comprehensive 

• Low level of effort required 

• SIC codes not available; categorical data 

used instead 

• Variable that denotes volume of gasoline 

sold not available 

• Systematic bias may exist for stations that do 

not allow credit card usage 

New Image 

Marketing 

• Flat file available 

• More comprehensive 

• Variable that denotes volume of gasoline 

sold available (number of fueling positions) 

• Low level of effort required 

• Expensive 

• SIC codes not available; categorical data 

used instead 

• Systematic bias exists as data are not 

collected for rural areas 

Reference 

USA 

• Inexpensive 

• Primary and secondary SIC codes available 

• No known systematic bias exists 

• Flat file not available 

• Less comprehensive 

• Variable that denotes volume of gasoline 

sold not available 

• High level of effort required 

Powerfinder 

• Inexpensive 

• Primary and secondary SIC codes available 

• No known systematic bias exists 

• Flat file not available 

• Less comprehensive 

• Variable that denotes volume of gasoline 

sold not available 

• High level of effort required 

InfoUSA 

• Flat file available 

• Medium expense 

• Primary and secondary SIC codes available 

• Low level of effort required 

• No known systematic bias exists 

• Less comprehensive 

• Variable that denotes volume of gasoline 

sold not available 

Acxiom/ 

USA Data 

• Flat file available 

• Medium expense to obtain entire file 

• Inexpensive to obtain list of 3,000 records 

• Low level of effort required 

• Inexpensive option requires that 

Acxiom/USA Data randomly selects sample 

records on our behalf 

• Less comprehensive 

• Secondary SIC code not available as 

selection criterion 

• Variable that denotes volume of gasoline 

sold not available 

• Systematic bias exists to the extent that 

secondary SIC codes are not available as 

selection criterion 

Internet 

Yellow Pages 

• Free • Flat file not available 

• Less comprehensive 

• SIC codes not available 

• Variable that denotes volume of gasoline 

sold not available 
• High level of effort required 

Table 3. Zip Code Comparison for Available Data Sources 

 Urban Rural 

City 

New York City Chicago Houston Denver Los Angeles San Francisco Theresa Walnut Grove Lostant Queen City Fountain Norwalk 

State NY IL TX CO CA CA NY MN IL TX CO CA 

Zip Code 

10033 60615 77058 80226 90057 94112 13691 56180 61334 75572 80817 90650 

Total 

number 

of 

stations 

in all 12 

zip 

codes 

Total 

number 

of 

stations 

availabl

e from 

source 

Dun & 

Bradstreet 2 6 9 7 1 9 3 1 0 3 1 15 57 108,073 

Survey 

Sampling Inc. 3 6 8 11 1 9 3 0 0 2 2 17 62 78,674 

OPIS 2 6 5 10 0 8 1 2 0 5 4 20 63 114,861 

New Image 

Marketing 5 8 10 26 1 22 NA NA NA NA 4 24 100 86,000 

Reference 

USA 5 5 3 13 1 13 0 0 0 1 2 20 63 81,392 

Powerfinder 5 5 11 6 2 11 0 1 0 2 4 3 50 78,651 

InfoUSA 4 5 3 13 1 11 0 0 0 0 2 20 59 69,209 

Acxiom/ 

USA Data 3 6 7 16 1 7 1 0 0 2 1 14 58 63,341 

Yellow Pages 4 5 4 11 0 12 0 0 0 0 1 20 57 NA 

Census 5 4 6 17 1 13 2 1 0 3 3 25 80 121,082 

Based on the evaluation of all factors and the zip code comparison for comprehensiveness, the Oil Price Information 

Service (OPIS) database of gasoline outlets was chosen as the basis for the sampling frame. However, the OPIS list 

excluded gasoline outlets owned by hypermarkets, including mass retailers such as supermarkets, discount retailers, and 

warehouse clubs, which had begun selling gasoline at their locations in the last few years. To adjust for this known area 

of under coverage, lists of hypermarket-branded gasoline outlets were obtained from nine top hypermarket companies 

and were used to supplement the OPIS database. One source, New Image Marketing (NIM), which had limited market 

coverage but other variables, was used for analytic purposes to determine the relationship between the type of station, 

sales volume, and number of pumps. Table 4 presents the regional and national comparison of retail gasoline outlet 

counts from the National Petroleum News and the U.S. Census Bureau with those from the selected and augmented 

sampling frame, OPIS augmented by the hypermarket data. The National Petroleum News data suggests there were just 

over 170,000 outlets in the year 2002. 

Table 4. Number of Outlets by Region and Percentage of National Total 

Number Percent 

PADD Region NPN1 CBP2 OPIS+HM3 NPN CBP OPIS+HM

1 East Coast 61,842 44,560 42,912 36.2% 36.8% 37.0%

2 Midwest 50,731 37,216 35,787 29.7% 30.7% 30.9%

3 South Central 34,490 20,492 20,968 20.2% 16.9% 18.1%

4 Mountain 7,177 4,911 4,971 4.2% 4.1% 4.3%

5 West Coast 16,438 13,903 11,292 9.6% 11.5% 9.7%

 

1 Source: National Petroleum News, Volume 94, Number 5 (May 2002), p. 6 and previous years of Market Facts (mid-July). 

Underlying survey is conducted in the first quarter of each year. 

2 CBP 1999 numbers are from the U.S. Department of Commerce and only include a fraction of outlets selling gasoline due to 

definition restrictions, i.e. outlet must have employees, payroll and more than half of outlet income must be from gasoline sales. 

3 National total includes 114,861 stations from OPIS and 1,069 stations from the aggregated hypermarket (HM) data file. 
 Number Percent 

PADD Region NPN1 CBP2 OPIS+HM3 NPN CBP OPIS+HM

 National Total 170,678 121,082 115,930 100.0% 100.0% 100.0%

The Sample Design 

In addition to the newly constructed outlet frame, other data sources were used for sampling and weighting purposes. 

These included the counts of gasoline stations per county obtained from the Census Bureau’s County Business Patterns 

(CBP) database, unpublished EIA data from the monthly survey used to assign a percentage of sales by grade to each 

station within a state, the NIM data, and the current weekly gasoline survey prices used for estimating unit variances for 

various sampling cells. A comparison of the OPIS data set and the CBP indicated a very close level of agreement in 

terms of the number of stations per county, with the exception of the state of California, where the CBP indicates a larger 

number of stations. To handle this issue the maximum of the two numbers was assumed as the number of stations in a 

county. This meant that if a county had 20 stations listed in OPIS, 24 in the CBP and 1 station is allocated to the county, 

it was assumed that the one station was sampled from 24, even though only 20 were available in the frame. 

The new sample design was driven by the definitions of Publication Cells and Sampling Cells. A Publication Cell is one 

defined by a PADD, state, city and attainment status. Hence, PADD 2 reformulated gasoline is a publication cell. 

Another example of a publication cell is New York State, conventional gasoline. Sampling cells are the smallest units 

whose borders are defined by publication cells, but for which estimates are not published. Sampling cells contribute to 

publication cells. For example, the part of New York State where reformulated gasoline is required, but is not in New 

York City would be a sampling cell, contributing to the state estimate. 

Sample sizes were determined using the historical survey data, and auxiliary data where necessary to obtain the desired 

coefficients of variation for all three grades of gasoline and for totals for each publication cell. These sample sizes were 

first converted into an allocation for each sampling cell, made up of counties, and then transformed into allocations 

(possibly fractional) for each county. More specifically, using the unit variance in each sampling cell from the historical 

weekly prices, Chromy’s Allocation Algorithm (Chromy, 1987, Zayatz and Sigman, 1995) was employed to calculate 

allocations for each sampling cell. A minimum of five stations was assigned to each sampling cell. Using the 

maximum outlet count described above, each county’s proportion of outlets in the sample cell was calculated. Each 

county’s proportion was then multiplied by the allocation for the sample cell to derive the county allocation. The county 

allocation helped to mitigate the potential frames coverage issues mentioned above. However, because these allocations 

were not necessarily integers, only the integer part of the allocation was assigned to the county, and counties were 

selected (with replacement) with probabilities proportional to the fractional part of the allocation. Thus, if a county had 

an allocation of 2.3 stations, the sampling procedure assigned it at least two stations, and the third station was assigned 

with a probability of .3. 

Once integer allocations were assigned for each county, stations were selected from each sampling cell in two stages. 

For the first stage, a Goodman-Kish PPS sampling method was used, ordering counties within states by the number of 

stations in that state. Counties were selected with probabilities proportional to the number of stations that did business in 

the county, based on the CBP, allowing the possibility of more than one station in the county if the formula for the 

probability of selection of the county yields a number greater than one. For the second stage, stations were randomly 

selected from the selected counties using the new outlet frame, which had been mapped to county designations. 

The Chromy algorithm yielded an allocation of 875 stations. Table A1 in the Appendix presents the allocations for the 

new sample, and a comparison between the number of stations reported by CPB and the number on the constructed frame 

for each of the 38 sampling cells in the design. It should be noted that some of the sampling cells (e.g. cities contained 

in only one State, the state of Minnesota, the reformulated part of PAFDD 1C and the conventional part of New York 

State) are also publication cells, while others are not. However, every publication cell can be expressed as the union of 

sampling cells (for example, combining cells 7, 9, and 10 yields New York State, and combining cells 30 to 38 yields 

PADD 5. 

For the purpose of estimating average prices for sampling cells, sample weights were constructed using information 

obtained from the sampled outlet during initiation on the number of gas pumps at the outlet. The number of pumps 

served as a proxy for sales’ volume based on the research conducted using the NIM data. These weights were applied to 
the reported weekly outlet prices to obtain averages for the various grades within the sampling cells. An outlet’s 

proportion of sales by grade was assigned using the monthly survey volume data based on the State where the outlet was 

located. In other words, the weights were used to estimate total volume and the proportion of sales in the state assigned 

the appropriate amount to each of the three grades. Similarly, the sampling cells were combined to form publication 

cells using the cell’s volume from the monthly survey. 

One advantage of the above approach is that should early variance estimates indicate that standard errors are too large for 

some publication cells, the sampling cells forming the publication cells can be examined, and the sample can be 

augmented in one or more sampling cells. 

Implementation and Data Continuity 

As part of the process used each time that a new sample is implemented, prices were collected initially from both the new 

and the old samples. This insures continuity as the overlapping data are used to study and smooth any discontinuities that 

might result with frame, sample, and respondent changes. A comparison of prices was made between the old sample and 

the new sample for the 200 publication cells that the two surveys had in common after the necessary formulation cells 

were combined and the new areas included. The comparison indicated that a number of cells were unacceptably 

discrepant. Further examination revealed wide differences in coverage between the sample cycles. The old cycle 

contained a number of stations owned by refiners or large resellers not present in the new cycle, while the new cycle 

contained mostly smaller independent outlets. As shown in Table 5, for conventional gasoline, the average differences 

appeared consistent across grade (with the exception of the Gulf Coast) but varied among the regions with the largest 

differences occurring in the Lower Atlantic and Central Atlantic regions. 

Table 5. Average Regional Price Differences for Overlap-Weeks, Conventional Gasoline 

 Grade 

Region Regular Midgrade Premium 

New England $0.015 $0.018 $0.014 

Central Atlantic $0.021 $0.039 $0.039 

Lower Atlantic $0.035 $0.027 $0.035 

Midwest $0.022 $0.019 $0.020 

Gulf Coast $0.008 $0.008 $0.020 

Rocky Mountain $0.011 $0.012 $0.010 

West Coast $0.020 $0.019 $0.006 

U.S. $0.016 $0.015 $0.013 

Likewise, the price differences at the city level were larger (not previously targeted or published) and were consistent 

across grade with the exception of premium in Houston, which deviated less than the other grades, contrary to the 

regional Gulf Coast difference, as shown in Table 6. The largest city differences occurred in Los Angeles. 

Table 6. Average City Price Differences for Overlap-Weeks 

 Grade 

City Regular Midgrade Premium 

Chicago $0.028 $0.029 $0.020 

Denver $0.013 $0.006 $0.015 

Houston $0.021 $0.017 $0.005 

Los Angeles $0.030 $0.034 $0.030 

New York City $0.009 $0.012 $0.010 

San Francisco $0.013 $0.019 $0.023 
This same pattern was exhibited at the state level, varying from state to state, consistent across grades with the exception 

of premium grade in Texas, as well as Minnesota midgrade, as shown in Table 7. The largest differences occurred in 

California. 

Table 7. Average State Price Differences for Overlap-Weeks 

 Grade 

State Regular Midgrade Premium 

California $0.036 $0.038 $0.035 

Colorado $0.017 $0.024 $0.020 

Minnesota $0.015 $0.025 $0.015 

New York $0.021 $0.037 $0.034 

Texas $0.006 $0.006 $0.020 

Three of these states (California, New York, and Texas) sell both reformulated and conventional gasoline, which might 

have been a contributing factor to discontinuity as a result of the change in the formulation weights. In fact, the 

combined formulation effect was further demonstrated as shown in Table 8, which presents average regional price 

differences for all formulations, as compared to Table 5, which presents only conventional formulation. The differences 

almost doubled in the New England and West Coast regions, but were cut in half for the Central Atlantic region. These 

regions contain significant clusters of reformulated gasoline. The other regions contain either only small amounts of 

reformulated gasoline, or none at all. 

Table 8. Average Regional Price Differences for Overlap-Weeks, All Formulations 

 Grade 

Region Regular Midgrade Premium 

New England $0.036 $0.033 $0.035 

Central Atlantic $0.005 $0.022 $0.024 

Lower Atlantic $0.036 $0.029 $0.034 

Midwest $0.020 $0.020 $0.019 

Gulf Coast $0.006 $0.006 $0.014 

Rocky Mtn. $0.011 $0.012 $0.010 

West Coast $0.039 $0.039 $0.032 

U.S. $0.019 $0.022 $0.018 

The effect of reformulated gasoline in the regions was further demonstrated by examining the regional price differences 

for reformulated gasoline. In all regions and grades, with the exception of all grades in the Central Atlantic region, and 

premium grade for the Lower Atlantic, Midwest, and Gulf Coast, the differences are even larger, as shown in Table 9. 

Table 9. Average Regional Price Differences for Overlap-Weeks, Reformulated 

 Grade 

Region Regular Midgrade Premium 

New England $0.053 $0.051 $0.051 

Central Atlantic $0.002 $0.020 $0.020 

Lower Atlantic $0.043 $0.036 $0.009 

Midwest $0.024 $0.040 $0.016 

Gulf Coast $0.019 $0.020 $0.005 

West Coast $0.029 $0.032 $0.028 

U.S. $0.030 $0.034 $0.026 
To further understand these price differences, a bootstrap was used to estimate standard errors for each sample using data 

from the third overlap week. For each bootstrap sample, the number of stations allocated to a cell was sampled with 

replacement from the cell, and the estimates calculated. The resulting standard errors and CVs were in accordance with 

what the design had targeted. A z- test was then used to test the differences between the samples for significance, where 

z was calculated as: 

z = Mnew - Mold)/(s2

old+s2

new)

1/2

In 46 of the 200 publication cells common to the old and the new sample, the value of z was greater than 1.96 (i.e., the 

probability of the differences happening by chance was less that .05). The median difference across the cells was 1.8 

cents, but varied across the overlap weeks as shown in Table 10. The number of cells for each overlap week that were 

significantly different between the two samples, and the number of cells for which the coefficient of variation was less 

than .01 are also shown in Table 10. 

The price difference for one week and the standard deviation by grade for the 38 sampling cells are shown in Appendix 

Table A2.

Table 10. Median Absolute Difference and CVs for 200 Common Cells 

Week Median 

Diff. 

Old, Median 

CV 

New, Median 

CV 

# signif. 

Diff. 

P < .05 

# cells old 

CV < .01 

# cells new 

CV < .01 

Week 1 Overlap $0.019 0.007 0.007 37 163 158 

Week 2 Overlap $0.027 0.007 0.007 72 163 161 

Week 3 Overlap $0.018 0.006 0.007 46 172 152 

Coverage Issues Identified after Sample Initiation 

Due to the large discontinuities in price estimates evidenced in some areas after the new sample was initiated, the 

sampling frame was examined further to determine whether coverage issues might account for some of the price 

differences. A review was conducted to determine whether stations from the old sample in six cities with published 

prices were represented on the sampling frame. Additionally, the sampling frame was analyzed to determine whether 

stations owned by the largest five companies (according to sales volume) in each state were included. 

Several strategies were used to determine whether companies and specific outlets were represented on the frame. 

Companies were identified primarily by name and brand of gasoline sales. When searching for individual stations, the 

file was sorted on various variables (e.g., station name, state, city, address, phone number) and then visually examined 

for the street address of the station in question. But it was often difficult to determine exactly which outlets and how 

many were owned and operated by a particular company due to differences in naming conventions for the individual 

stations and the inability to determine whether some stations sold branded gasoline but were independently owned. 

Ownership status for only the sampled stations was ascertained during the initial phone contact with each station. 

Results of searching the frame for each outlet in the old sample that contributed to the six previously published city 

prices indicated that stations were omitted from the frame with rates ranging from 10.8 percent in Denver to 58.8 percent 

in Los Angeles. Of the stations used to generate the Chicago estimate, 14.3 percent of stations were not found on the 

sampling frame. In Houston 36.6 percent were omitted from the frame, in New York, 21.2 percent were not found on the 

frame, and 31.0 percent of stations in San Francisco were missing from the frame. In Los Angeles, a single company was 

identified that owned 70 percent of the missing stations. Additional review revealed that none of more than 350 Los 

Angeles area stations from this company were included on the sampling frame. It was later learned that this company did 

not allow credit card transactions which was the reason for not appearing on the OPIS outlet frame. Among the 21.2 

percent of New York stations not found on the frame, more than half were from one major company that is a top seller in 

eight states, including three published States. 

Results of the frame search for the top five companies in sales volumes for each state indicated that at least six of these 

companies were not included on the sampling frame. Additionally, it was difficult to discern whether one of the largest 

five companies in Washington, D.C. was included on the frame. These seven companies were top volume sellers across 
15 states and the District of Columbia, including two states with published prices, Florida and Texas. An additional eight 

of the top five companies in at least one State were under-sampled, suggesting these companies were under-covered on 

the frame. These eight companies were among the top five companies across seventeen unpublished States and seven 

published States. Additionally, two of the top five sellers were under-represented in seven States, including three 

published States. Finally, in New York, a published State, three of the top five companies appeared to be under-covered. 

After targeted matching of the old and new samples and the new frame, it was determined that the price differences were 

primarily attributable to incomplete frame coverage (see O’Colmain, Churchill, Weir and Saavedra). These coverage 

issues impacted the city, state, regional and U.S. level estimates. 

Revising the Sample 

It appeared from this analysis that the two samples represented different populations of outlets, neither representing the 

complete population. As a result, the alternative of combining the two samples was considered. This not only was 

expected to reduce the discontinuity, but also was expected to produce more accurate estimates as the coverage of the 

combined frame would be greater than that of either frame by itself. The following steps were taken to draw the 

composite sample. 

1) The outlets in the old sample were classified into the new sample’s sampling cells. 

2) Within each sampling cell, the largest weight was identified, and all the outlets in the cell were subsampled with 

probabilities proportional to the ratio of the weight in the old sample to the maximum weight in the old sample 

for outlets in that cell. This step had the effect of making the old sample stations in each new sample-sampling 

cell have the same probability of selection. Thus, if in a given cell some of the stations had a probability of .02, 

and some a probability of .06, we would select all the stations with .06 probability and one third of the stations 

with .02 probability. This would give all the stations in the cell the same probability of selection (.06). 

3) New unit variance estimates were obtained for each sampling cell using all stations in the cell from the old or 

new sample. The Chromy algorithm was then rerun and new allocations obtained. 

4) If a sufficient number of outlets were available from each sample, fifty percent of the allocations were filled by 

old sample outlets and fifty percent by new sample outlets. 

5) For some of the new publication cells (e.g. Miami and Cleveland) an insufficient number of outlets existed in 

the old sample, so the bulk of the outlets in the revised sample came from the new sample. 

6) Outlets from the old sample were presumed to represent larger companies and outlets, so the number of pumps 

used as the estimation weight was set to the maximum. 

7) To smooth the transition from the old sample estimates to the composite sample estimates, estimates for the 

third overlap week were recalculated using the composite sample. The ratios of the composite sample 

estimates to the old sample estimates for that week were then applied to the first non-overlap week of the 

composite sample. The following week the ratios were adjusted so that r’= (2r+1)/3, and the following week 

adjusted again to r’’= (r+2)/3. This procedure minimized the discontinuity in the estimates due to the change in 

samples. 

8) Bootstraps were conducted for the composite sample, preserving the proportion of old and new sample 

companies per stratum for every bootstrap sample. 

The estimates that were generated for the third overlap week using the composite sample were compared to the old 

sample estimates for that week. This comparison showed: 

• 165 of 200 cell differences were reduced 

• The mean difference between the new and old sample of 1.74 cents was reduced to a 0.05-cent difference 

between the composite sample and the old sample 

• The median difference between the new and old sample of 1.8 cents was reduced to a 0.0-cent difference 

between the composite sample and the old sample. 

Table 11 shows the differences in regional prices for reformulated gasoline for the third overlap week for the originally 

selected new sample and the composite sample. 

The median price, standard error, and coefficient of variation, across all cells for the composite sample, for the last 

overlap week and the two weeks after smoothing was completed are shown in Table 12. These composite sample 
estimates and their errors satisfied the original sample requirements. 

Variance Estimation for the New Sample 

Estimating variance for the new sample required the use of a bootstrap, where the outlets that came from the old frame 

and the ones that came from the new frame were each selected with replacement in the same number per stratum as in the

Table 11. Overlap Week 3, Regional Price Differences, Reformulated, New vs. Composite 

Region Sample Regular Midgrade Premium 

New England new 0.058 0.056 0.051 

comp 0.015 0.012 0.011 

Central Atlantic new 0.004 0.020 0.018 

comp 0.014 0.003 0.009 

Lower Atlantic new 0.040 0.033 0.006 

comp 0.027 0.019 0.005 

Midwest new 0.008 0.020 0.003 

comp 0.001 0.007 0.005 

Gulf Coast new 0.023 0.022 0.004 

comp 0.015 0.008 0.002 

West Coast new 0.033 0.033 0.023 

comp 0.019 0.018 0.018 

U.S. new 0.028 0.031 0.021 

comp 0.000 0.003 0.007 

actual sample. The one exception was a stratum with only one old sample outlet, where the two samples were 

bootstrapped as if they came from one source. Appendix A presents the resulting standard errors and CVs for one month. 

Table 12. Composite Sample Median Price, SE and CV (all cells) 

Week Price SE CV 

Overlap Week 3 (5/19) $1.583 $0.010 0.006 

7/14 $1.603 $0.009 0.006 

7/21 $1.617 $0.009 0.006 

Summary 

In order to meet formulation changes in the industry reflecting regulatory requirements, as well as, increased user 

requirements for more city and state level estimates of gasoline pump prices, the sample design of the gasoline price 

survey has evolved to meet these changing requirements. Originally, the survey requirement was to produce a timely 

U.S. price of regular gasoline in the first Gulf war. This requirement was later expanded to other grades and then to 

multiple formulations to monitor the effects of regulatory requirements stemming from the Clean Air Act. More recent 

requirements have centered on producing lower geographic level prices, select cities and states. In order to 

accommodate this last change in requirements, a more targeted gasoline outlet frame was constructed. The initial list of 

outlets from OPIS was purchased after examining and comparing nine sources, but was immediately augmented to cover 

hypermarket outlets not adequately represented on the list. This frame appeared to be fairly comprehensive, containing 

115,930 gasoline stations after augmentation for hypermarket outlets in contrast to the 121,082 gasoline stations reported 

by the Census Bureau’s County Business Patterns. 

After selecting the outlet sample, using first an area selection of counties and then selecting outlets within the counties, 

the two samples were overlapped and estimates were compared. The differences between the two sample estimates over 
a three-week period motivated the selection of a composite sample. The composite sample, including stations from both 

the old and new sample, mitigated the effects of the new frame’s under-coverage of some of the larger gasoline sellers. 

The composite sample produced a data series more continuous with the old series and reduced the CVs while still 

satisfying the new requirement for accurate gasoline price estimates to be produced for an extended number of states and 

cities, three grades, and two formulations. 

 

References 

O’Colmain, B., T. Churchill, P. J. Saavedra, and P. Weir. (2003). “Non-sampling Error in the New Cycle of the Weekly 

EIA Gasoline Price Survey.” Presented at the Joint Statistical Meeting, American Statistical Association, San Francisco, 

CA, Section on Survey Research Methods. 

Saavedra, P. J. (1988). “Linking multiple stratifications: Two petroleum surveys”, 1998 Proceedings of the American 

Statistical Association, Section on Survey Research Methods [CD-ROM], Alexandria, VA: American Statistical 

Association, pp. 777–781. 

Saavedra, P. J., and P. Weir. (1991). “A Telephone Survey of Gasoline Retailers Drawn as a Subsample of a National 

Survey”, 1991 Proceedings of the American Statistical Association, Section on Survey Research Methods [CD-ROM], 

Alexandria, VA: American Statistical Association. 

Saavedra, P. J., P. Weir, B. O’Colmain, T. Churchill, and E. Carlton. (2002). “The New Design for the EIA-878 

Gasoline Price Survey”, 2002 Proceedings of the American Statistical Association, Section on Survey Research Methods 

[CD-ROM], Alexandria, VA: American Statistical Association. 

Weir, P. and P. J. Saavedra. (1998). “Two Multiple-Phase Surveys that Combine Overlapping Sample Cycles at Phase 

1”, 1998 Proceedings of the American Statistical Association, Section on Survey Research Methods [CD-ROM], 

Alexandria, VA: American Statistical Association, pp. 443-447. 

Weir, P. , Saavedra, P.J. , O’Colmain, B. and Churchill T., “Quality of Estimates in the New Cycle of the Weekly EIA 

Gasoline Price Survey”, Presented at the Joint Statistical Meeting, American Statistical Association, San Francisco, CA, 

Section on Survey Research Methods. 
APPENDIX A 

Table A1: Allocation and Population of Cells by CPB and Frame 

# Cell Description New 

Sample* 

CPB 

Stations 

Frame 

Stations 

1 Boston in MA 8 2,101 1,686 

2 Boston not in MA 5 373 412 

3 NYC in PADD 1A 5 702 746 

4 Mass. Not in Boston 5 365 351 

5 Rest of conventional PADD 1A 26 1,548 1,590 

6 Rest of reformulated PADD 1A 5 1,021 948 

7 NYC in New York State 14 2,787 2,221 

8 NYC in rest of PADD 1B 8 2,204 1,993 

9 Conventional NY State 18 2,836 2,272 

10 Rest of reformulated NY State 5 126 145 

11 Rest of conventional PADD 1B 6 3,977 4,027 

12 Rest of reformulated PADD 1B 17 3,591 3,499 

13 Miami 13 658 626 

14 Rest of Florida 20 5,915 6,024 

15 Rest of conventional PADD 1C 29 14,848 14,996 

16 Reformulated 1C 66 1,508 1,376 

17 Chicago 31 2,255 2,125 

18 Cleveland 13 785 729 

19 Minnesota 24 2,553 2,669 

20 Rest of Ohio 19 3,571 3,207 

21 Rest of conventional PADD 2 93 26,130 25,357 

22 Rest of reformulated PADD 2 33 1,922 1,700 

23 Houston 38 2,014 1,998 

24 Conventional Texas 20 6,806 7,040 

25 Rest of reformulated Texas 21 1,792 1,910 

26 Rest of PADD 3 30 9,880 10,020 

27 Denver 18 707 587 

28 Rest of Colorado 17 1,042 1,044 

29 Rest of PADD 4 59 2,521 2,739 

30 Los Angeles 19 3,729 2,736 

31 Alaska 5 238 203 

32 Rest of California 30 3,457 2,548 

33 Hawaii 7 334 231 

34 Rest of Washington State 16 1,340 1,337 

35 Rest of conventional PADD 5 41 2,637 2,333 

36 Rest of reformulated PADD 5 5 834 816 

37 San Francisco 50 1,251 992 

38 Seattle 36 724 697 

 *Prior to revision 
Table A2. Price Difference and Standard Deviations for One Week by Sample Stratum 

Cell Description Diff. 

Regular 

Diff. 

Midgrade

Diff. 

Premium 

SD 

Regular 

SD 

Midgrade 

SD 

Premium

Boston in MA -0.002 -0.004 -0.014 0.049 0.051 0.057

Boston not in MA 0.020 0.020 -0.003 0.049 0.049 0.049

NYC in PADD 1A -0.015 -0.023 -0.054 0.060 0.078 0.077

Mass. Not in Boston 0.008 0.006 0.008 0.077 0.087 0.081

Rest of conventional PADD 1A -0.011 -0.009 -0.006 0.075 0.076 0.072

Rest of reformulated PADD 1A -0.009 -0.015 -0.005 0.052 0.056 0.058

NYC in New York State 0.067 0.077 0.098 0.064 0.062 0.071

NYC in rest of PADD 1B 0.001 0.007 -0.005 0.038 0.047 0.047

Conventional NY State 0.013 0.006 0.000 0.030 0.030 0.032

Rest of reformulated NY State 0.001 0.021 0.011 0.048 0.050 0.050

Rest of conventional PADD 1B 0.065 0.077 0.086 0.066 0.072 0.074

Rest of reformulated PADD 1B -0.004 0.017 0.028 0.110 0.115 0.126

Miami NA NA NA 0.056 0.066 0.060

Rest of Florida 0.032 0.039 0.048 0.068 0.072 0.073

Rest of conventional PADD 1C 0.043 0.041 0.046 0.070 0.070 0.072

Reformulated 1C -0.013 -0.008 -0.034 0.089 0.095 0.094

Chicago -0.049 -0.040 -0.047 0.077 0.080 0.082

Cleveland 0.068 0.066 0.066 0.084 0.095 0.093

Minnesota 0.012 -0.002 -0.006 0.045 0.053 0.053

Rest of Ohio 0.062 0.092 0.088 0.069 0.072 0.077

Rest of conventional PADD 2 0.033 0.034 0.042 0.073 0.076 0.088

Rest of reformulated PADD 2 0.060 0.070 0.023 0.111 0.114 0.126

Houston 0.025 0.014 0.004 0.044 0.046 0.047

Conventional Texas 0.000 0.012 -0.001 0.059 0.065 0.079

Rest of reformulated Texas 0.038 0.045 0.034 0.049 0.052 0.055

Rest of PADD 3 0.046 0.018 0.009 0.058 0.063 0.063

Denver -0.028 0.001 0.002 0.067 0.068 0.067

Rest of Colorado 0.026 -0.001 0.032 0.085 0.084 0.081

Rest of PADD 4 0.040 0.029 0.034 0.065 0.064 0.070

Los Angeles 0.041 0.040 0.040 0.047 0.050 0.047

Alaska 0.019 0.085 0.005 0.071 0.063 0.056

Rest of California 0.003 -0.003 -0.005 0.071 0.069 0.072

Hawaii 0.108 0.116 0.129 0.117 0.122 0.137

Rest of Washington State 0.021 0.014 0.011 0.040 0.047 0.049

Rest of conventional PADD 5 0.041 0.053 0.040 0.092 0.090 0.096

Rest of reformulated PADD 5 0.033 0.029 0.023 0.036 0.039 0.037

San Francisco 0.004 0.003 -0.003 0.070 0.073 0.074

Seattle 0.075 0.045 0.034 0.078 0.070 0.071