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Consumer Expenditure Survey Program

How does the variability of Consumer Expenditure data impact your analysis?

Aaron Cobet

Bureau of Labor Statistics

January 18, 2017

The Consumer Expenditure Surveys Program (CE) collects data on all reported expenditures and most

types of income in the United States. CE provides these data by geographic region and many 

demographic characteristics. 

However, some CE estimates may not be precise enough for your needs because CE may only provide a 

wide range of possible values rather than an exact data point.

i This report addresses these related 

questions.

Contents

1. What creates variability in sample data?..........................................................................................2

2. How does CE measure variability?....................................................................................................3

3. How does variability affect you?.......................................................................................................3

4. When can you compare two data points with confidence?.............................................................4

5. How do you decrease the variability of CE estimates?.....................................................................5

6. What additional resources does CE provide? ...................................................................................5

 
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1. What creates variability in sample data? 

Data that are based on surveys have some degree of variability, which means that the reported value is 

an approximation of the actual value. Generally, the reported value is an average of the sampled 

observations. The size of the variability depends on two factors: 

• Sample size

The sample size refers to the number of 

observations from the whole. Generally, 

the larger the sample is the smaller the 

variability (See chart 1). 

• Dispersion of the sample

Dispersion refers to how closely or 

widely the dollar amounts for an 

expenditure are dispersed. Generally, 

the larger the dispersion the larger the 

variability. In chart 2, the blue sample

has a smaller dispersion than the red 

sample. Thus, the variability of the blue 

sample is smaller than the variability of 

the red sample. 

 -50 -40 -30 -20 -10 0 10 20 30 40 50

Chart 2: Clustered and dispersed samples

 Clustered

sample with

high precision

 Dispersed

sample with

low precision

Average

Chart 1: Distribution of different sample sizes

5 observations

10 observations

20 observations

50 observations
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2. How does CE measure variability? 

The CE tables provide two measures of variability: 

• Standard error (SE) 

A SE measures the absolute dispersion of an estimate. 

For CE, SE shows how close the dollar average of the 

sample is to the average of the population. CE 

calculates a modified version of the standard SE

formula due to its sample design.ii

 

• Coefficient of variation (CV) 

A CV measures the relative dispersion of an estimate. 

CVs are expressed as percentages, dividing an 

estimate’s standard deviation (SD) by the estimate. 

The CV is also known as relative standard deviation 

(RSD). 

3. How does variability affect you? 

Variability limits your ability to use the 

data with certainty. Measures of variability 

inform you about the range of possible 

values for a particular data point. 

Generally, the larger that range is the 

lower your confidence in the data point. iii

Chart 3 shows the effect of increasing 

ranges on the range of possible values with 

three different CVs: 

• 5 percent

• 25 percent

• 100 percent 

The range of the possible estimates 

expands with a 5 percent CV to values 

between $9.50 and $10.50, with a 25 

percent CV to $12.50 to $7.50, and with a

100 percent CV to $0.00 and $20.00. The third example shows how a large CV may affect your analysis. 

The CE tables show the degree of precision with CVs. CE staff strongly recommend using estimates with 

CVs that are below 25 percent. CV’s of 25 percent allow you to state with certainty that the actual value 

is no larger or smaller than 25 percent of the estimate.

iv However, there is no rule about what precision 

is sufficient for your analysis. For more information on this topic, see “When can you compare two data 

points with confidence?”

-$5

$0

$5

$10

$15

$20

$25

5 percent CV 25 percent CV 100 percent CV

Coefficient of variation

Chart 3: Impact of coefficient of variation on estimates

Estimate
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4. When can you compare two data points with confidence? 

You can determine which data point is 

larger if there is a gap between two points 

after you consider their variation or CV. By 

contrast you cannot determine which 

estimate is larger if the two data points 

with their CVs overlap. 

Charts 4 and 5 provide examples for both 

scenarios. Both charts show on the left a 

$10 estimate and on the right a $20 

estimate. 

In chart 4, the two estimates have a gap 

after taking their CV into account. In this 

case, you can determine that the $10 

estimate is definitively smaller than the $20 

estimate because the biggest possible value 

of the $10 estimate is $12.50 and the 

smallest possible value of the $20 estimate 

is to $17.50. Since $12.50 is smaller than 

$17.50, the $10 estimate is smaller than the 

$20 estimate. 

In contrast, chart 5 shows two data points 

that have an overlap after taking their CV 

into account. In this case, you cannot

determine with certainty that the $10 

estimate is smaller than the $20 estimate 

because the biggest possible value of the 

$10 estimate is $17, and the smallest 

possible value of the $20 estimate is $13. 

Since $17 is larger than $13, the $10 

estimate might be larger than the $20 

estimate. 

 

 

$0

$5

$10

$15

$20

$25

$30

$10 estimate $20 estimate

Overlap

Estimate

Coefficient of variation

$0

$5

$10

$15

$20

$25

$30

$10 estimate $20 estimate

Estimate

Coefficient of variation

Gap

Chart 4: Estimates with gap

Chart 5: Estimates with overlap
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5. How do you decrease the variability of CE estimates? 

You can increase the precision of estimates or decrease its range of possible values by increasing the 

number of observations in your sample. You can increase the sample size by expanding your research 

question and dropping aspects from consideration that are less important to your analysis. 

Let’s say you want to look at expenses for men’s shirts in the Northeast by households earning $50,000 

to $69,999 in 2015. However, you determine that the data are not precise enough for your analysis. You 

could drop the least important aspect until the precision meets your needs. 

For CE data, you may be able increase your sample with these changes: 

• Enlarge the region, if you assume that buying habits are sufficiently alike in different regions. For 

example, if the Northeast has too few observations, maybe include the Midwest. This method is 

possible with CE tables and Public-use Microdata (PUMD). 

• Increase the income range, if you assume that households with a broader income range follow

an adequately similar spending pattern. For example, you could consider households earning 

between $30,000 and $69,999. This method is possible with tables and PUMD.

• Broaden the item category, if you assume that the broader category behaves sufficiently similar. 

For example, you may look at clothes instead of just men’s shirts. This method is possible with 

tables and PUMD.

• Expand the time period, if you assume that expenses do not change significantly over time. For 

example if the data for one year are not precise enough, consider aggregating two years into 

one data point. This method is possible with PUMD.

You can use these methods to a varying degree with two CE products: 

• CE tables provide some flexibility with respect to the item’s detail, income groups, age groups, 

and a few other characteristics. 

• CE Public-use Microdata (PUMD) provides you the maximum flexibility to change aspects and

add years. However to use PUMD, you need to calculate estimates with a statistical software,

such as SAS or R. For more information on PUMD, see the Getting Started Guide. 

6. What additional resources does CE provide? 

For additional information on this issue, CE provides these resources: 

• The methodology that CE uses to calculate the standard error is described in the “Standard 

errors in the 2015 Consumer Expenditure Survey” by David Swanson at 

http://www.bls.gov/cex/ce_se_2015.pdf. 

• A summary of the precision of CE data is presented in the “Calculation Precision” section of the 

Handbook of Methods on page 18 at http://www.bls.gov/opub/hom/cex/pdf/cex.pdf. 

• How can you increase the precision of estimates by pooling data from several years is described 

in the “estimation procedure” section of the Interview Survey Documentation at 

http://www.bls.gov/cex/2015/csxintvw.pdf.
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i CE data can be used to derive two types of estimates. CE tables and the database provide weighted population 

estimates, which refer to estimates that have been weighted to be representative of the entire population. In 

addition, users can use Public-use Microdata to calculate not only other weighted population estimates, but also 

unweighted sample estimates, which provide an estimate for the respondents who made this expenditure or had 

this income type. 

ii CE does not calculate the standard error with the standard formula because this formula requires that the sample 

is collected as a simple random sample. However, the CE surveys draw stratified random samples of geographic 

areas and a systematic sample of households within the selected areas. To estimate an unbiased variance with this 

sampling method, CE uses the balanced repeated replication (BRR) method. For more information on this issue, 

see “Standard Errors in the 2015 Consumer Expenditure Survey”. iii This range is called confidence interval, which measures the probability that a population parameter falls

between two values. 

iv CE estimates do not provide 100 percent certainty. For more information on the degree of certainty of CE 

estimates, see Standard Errors in the 2015 Consumer Expenditure Survey by David Swanson.