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The use of Consumer Expenditure Survey’s (CE) sample data to estimate population quantities of interest, such as the average expenditure per consumer unit on a particular item category, is achieved through the use of weights. Each consumer unit in the survey is assigned a weight that is the number of similar consumer units in the U.S. civilian noninstitutional population the sampled consumer unit represents. Using these weights, the average expenditure per consumer unit on a particular item category is estimated with the standard weighted average formula:

_{$$ $$ $$ }

where

_{}= the average expenditure per consumer unit on the item category,

y* _{i}*= the expenditure made by the

*w _{i}*= the weight of the

*S *= the sample of consumer units that participated in the survey.

For example, if *y _{i}* is the expenditure on eggs made by the

If one wants to estimate the proportion of consumer units that purchased eggs during a given period, then the same formula is applied, where *y _{i}* is set equal to 1 if the

Several factors are involved in computing the weight of each consumer unit from which a usable interview is received. Each consumer unit is initially assigned a *base weight* equal to the inverse of the consumer unit’s probability of being selected for the sample. The total U.S. target population counts for these base weights come from the Census Current Population Survey. Base weights in the CE are typically around 10,000, which means a consumer unit in the sample represents 10,000 consumer units in the U.S. civilian noninstitutional populationâ€•itself plus 9,999 other consumer units that were not selected for the sample. The base weight is then adjusted by the following factors to correct for certain nonsampling errors:

*Weighting control factor*. This adjusts for subsampling in the field. Subsampling occurs when a data collector visits a particular address and discovers multiple housing units where only one housing unit was expected.

*Noninterview adjustment factor*. This adjusts for interviews that cannot be conducted in occupied housing units due to a consumer unit’s refusal to participate in the survey or the inability to contact anyone at the housing unit in spite of repeated attempts. This adjustment is based on region of the country, consumer unit size, number of contact attempts, and the average adjusted gross income in the consumer unit’s zip code according to a publicly available database from the Internal Revenue Service.

*Calibration factor*. This adjusts the weights to 24 “known” population counts to account for frame undercoverage. These known population counts are for age, race, household tenure (owner or renter), region of the country, and urban or rural. The population counts are updated quarterly using the Current Population Survey estimates. Each consumer unit is given its own unique calibration factor. There are infinitely many sets of calibration factors that can make the weights add up to the 24 known population counts, and the CE uses nonlinear programming to select the set that minimizes the amount of change made to the “initial weights” (initial weight = base weight x weighting control factor x noninterview adjustment factor).

After adjusting the base weights by these factors, the *final weights* are typically around 17,000, which means an interviewed consumer unit represents 17,000 consumer units in the U.S. civilian noninstitutional populationâ€•itself plus 16,999 other consumer units that did not participate in the survey.

The precision of the estimator _{} is measured by its standard error. Standard errors measure the sampling variability of the CE estimates. That is, standard errors measure the uncertainty in the survey estimates caused by the fact that a random sample of consumer units from across the United States is used instead of every consumer unit in the nation. See table 2.

The CE’s standard errors are estimated by using the method of “balanced repeated replication.” In this method, the sampled PSUs are divided into 43 groups (called *strata*), and the consumer units within each stratum are randomly divided into two *half samples*. Half of the consumer units are assigned to one half sample, and the other half are assigned to the other half sample. Then 44 different estimates of _{} are created using data from only one half sample per stratum. There are many combinations of half samples that can be used to create these replicate estimates, and the CE uses 44 of them that are created in a “balanced” way with a 44x44 Hadamard matrix. The standard error of _{} is then estimated by:

_{}

where

_{} is the *r ^{th}* replicate estimate of

The coefficient of variation is a related measure of sampling variability that measures the variability of the survey estimate relative to the mean. It is defined by the equation:

_{}_{.}

Item category | Average annual expenditure per consumer | Standard error, SE( | )Coefficient of variation, CV ( | ) (in percent)
---|---|---|---|

Total expenditures |
$57,311 | $594 | 1.04 |

Food |
7,203 | 95 | 1.31 |

Housing |
18,886 | 149 | 0.79 |

Apparel |
1,803 | 63 | 3.50 |

Transportation |
9,049 | 195 | 2.16 |

Healthcare |
4,612 | 73 | 1.58 |

Entertainment |
2,913 | 67 | 2.30 |

Personal care |
707 | 11 | 1.59 |

Reading |
118 | 6 | 4.86 |

Education |
1,329 | 70 | 5.24 |

Tobacco products and smoking supplies |
337 | 11 | 3.25 |

Miscellaneous |
959 | 43 | 4.53 |

Cash contributions |
2,081 | 213 | 10.24 |

Personal insurance and pensions |
6,831 | 146 | 2.14 |

Source: U.S. Bureau of Labor Statistics. |

Last Modified Date: March 28, 2018