Consumer Expenditure Survey (CES)
A household budget survey designed to guide major economic indicators like the Consumer Price Index.
One table of survey responses per quarter with one row per sampled household (consumer unit). Additional tables containing one record per expenditure.
A complex sample survey designed to generalize to the civilian non-institutional U.S. population.
Released annually since 1996.
Administered by the Bureau of Labor Statistics.
Please skim before you begin:
Consumer Expenditure Surveys Public Use Microdata Getting Started Guide
This human-composed haiku or a bouquet of artificial intelligence-generated limericks
# price indices and
# you spent how much on beans, jack?
# pocketbook issues
Download, Import, Preparation
Download both the prior and current year of interview microdata:
library(httr)
<- tempfile()
tf_prior_year
<- "https://www.bls.gov/cex/pumd/data/stata/intrvw21.zip"
this_url_prior_year
<- GET( this_url_prior_year , user_agent( "email@address.com" ) )
dl_prior_year
writeBin( content( dl_prior_year ) , tf_prior_year )
<- unzip( tf_prior_year , exdir = tempdir() )
unzipped_files_prior_year
<- tempfile()
tf_current_year
<- "https://www.bls.gov/cex/pumd/data/stata/intrvw22.zip"
this_url_current_year
<- GET( this_url_current_year , user_agent( "email@address.com" ) )
dl_current_year
writeBin( content( dl_current_year ) , tf_current_year )
<- unzip( tf_current_year , exdir = tempdir() )
unzipped_files_current_year
<- c( unzipped_files_current_year , unzipped_files_prior_year ) unzipped_files
Import and stack all 2022 quarterly files plus 2023’s first quarter:
library(haven)
<- grep( "fmli2[2-3]" , unzipped_files , value = TRUE )
fmli_files
<- lapply( fmli_files , read_dta )
fmli_tbls
<- lapply( fmli_tbls , data.frame )
fmli_dfs
<-
fmli_dfs lapply(
fmli_dfs , function( w ){ names( w ) <- tolower( names( w ) ) ; w }
)
<- lapply( fmli_dfs , names )
fmli_cols
<- Reduce( intersect , fmli_cols )
intersecting_cols
<- lapply( fmli_dfs , function( w ) w[ intersecting_cols ] )
fmli_dfs
<- do.call( rbind , fmli_dfs ) ces_df
Scale the weight columns based on the number of months in 2022:
c( 'qintrvyr' , 'qintrvmo' ) ] <-
ces_df[ , sapply( ces_df[ , c( 'qintrvyr' , 'qintrvmo' ) ] , as.numeric )
<- grep( 'wt' , names( ces_df ) , value = TRUE )
weight_columns
<-
ces_df transform(
ces_df ,mo_scope =
ifelse( qintrvyr %in% 2022 & qintrvmo %in% 1:3 , qintrvmo - 1 ,
ifelse( qintrvyr %in% 2023 , 4 - qintrvmo , 3 ) )
)
for ( this_column in weight_columns ){
is.na( ces_df[ , this_column ] ) , this_column ] <- 0
ces_df[
paste0( 'popwt_' , this_column ) ] <-
ces_df[ , * ces_df[ , 'mo_scope' ] / 12 )
( ces_df[ , this_column ]
}
Combine previous quarter and current quarter variables into a single variable:
<-
expenditure_variables gsub( "pq$" , "" , grep( "pq$" , names( ces_df ) , value = TRUE ) )
# confirm that for every variable ending in pq,
# there's the same variable ending in cq
stopifnot( all( paste0( expenditure_variables , 'cq' ) %in% names( ces_df ) ) )
# confirm none of the variables without the pq or cq suffix exist
if( any( expenditure_variables %in% names( ces_df ) ) ) stop( "variable conflict" )
for( this_column in expenditure_variables ){
<-
ces_df[ , this_column ] rowSums( ces_df[ , paste0( this_column , c( 'pq' , 'cq' ) ) ] , na.rm = TRUE )
# annualize the quarterly spending
<- 4 * ces_df[ , this_column ]
ces_df[ , this_column ]
is.na( ces_df[ , this_column ] ) , this_column ] <- 0
ces_df[
}
Append any interview survey UCC found at https://www.bls.gov/cex/ce_source_integrate.xlsx:
<- c( "450110" , "450210" )
ucc_exp
<- grep( "mtbi2[2-3]" , unzipped_files , value = TRUE )
mtbi_files
<- lapply( mtbi_files , read_dta )
mtbi_tbls
<- lapply( mtbi_tbls , data.frame )
mtbi_dfs
<-
mtbi_dfs lapply(
mtbi_dfs , function( w ){ names( w ) <- tolower( names( w ) ) ; w }
)
<- lapply( mtbi_dfs , function( w ) w[ c( 'newid' , 'cost' , 'ucc' , 'ref_yr' ) ] )
mtbi_dfs
<- do.call( rbind , mtbi_dfs )
mtbi_df
<- subset( mtbi_df , ( ref_yr %in% 2022 ) & ( ucc %in% ucc_exp ) )
mtbi_df
<- aggregate( cost ~ newid , data = mtbi_df , sum )
mtbi_agg
names( mtbi_agg ) <- c( 'newid' , 'new_car_truck_exp' )
<- nrow( ces_df )
before_nrow
<-
ces_df merge(
ces_df ,
mtbi_agg ,all.x = TRUE
)
stopifnot( nrow( ces_df ) == before_nrow )
is.na( ces_df[ , 'new_car_truck_exp' ] ) , 'new_car_truck_exp' ] <- 0 ces_df[
Save locally
Save the object at any point:
# ces_fn <- file.path( path.expand( "~" ) , "CES" , "this_file.rds" )
# saveRDS( ces_df , file = ces_fn , compress = FALSE )
Load the same object:
# ces_df <- readRDS( ces_fn )
Survey Design Definition
Construct a multiply-imputed, complex sample survey design:
Separate the ces_df
data.frame into five implicates, each differing from the others only in the multiply-imputed variables:
library(survey)
library(mitools)
# create a vector containing all of the multiply-imputed variables
# (leaving the numbers off the end)
<- gsub( "5$" , "" , grep( "[a-z]5$" , names( ces_df ) , value = TRUE ) )
mi_vars
# loop through each of the five variables..
for ( i in 1:5 ){
# copy the 'ces_df' table over to a new temporary data frame 'x'
<- ces_df
x
# loop through each of the multiply-imputed variables..
for ( j in mi_vars ){
# copy the contents of the current column (for example 'welfare1')
# over to a new column ending in 'mi' (for example 'welfaremi')
paste0( j , 'mi' ) ] <- x[ , paste0( j , i ) ]
x[ ,
# delete the all five of the imputed variable columns
<- x[ , !( names( x ) %in% paste0( j , 1:5 ) ) ]
x
}
assign( paste0( 'imp' , i ) , x )
}
<-
ces_design svrepdesign(
weights = ~ finlwt21 ,
repweights = "^wtrep[0-9][0-9]$" ,
data = imputationList( list( imp1 , imp2 , imp3 , imp4 , imp5 ) ) ,
type = "BRR" ,
combined.weights = TRUE ,
mse = TRUE
)
Variable Recoding
Add new columns to the data set:
<-
ces_design update(
ces_design ,
one = 1 ,
any_food_stamp = as.numeric( jfs_amtmi > 0 ) ,
bls_urbn = factor( bls_urbn , levels = 1:2 , labels = c( 'urban' , 'rural' ) ) ,
sex_ref = factor( sex_ref , levels = 1:2 , labels = c( 'male' , 'female' ) )
)
Analysis Examples with the survey
library
Unweighted Counts
Count the unweighted number of records in the survey sample, overall and by groups:
MIcombine( with( ces_design , svyby( ~ one , ~ one , unwtd.count ) ) )
MIcombine( with( ces_design , svyby( ~ one , ~ bls_urbn , unwtd.count ) ) )
Weighted Counts
Count the weighted size of the generalizable population, overall and by groups:
MIcombine( with( ces_design , svytotal( ~ one ) ) )
MIcombine( with( ces_design ,
svyby( ~ one , ~ bls_urbn , svytotal )
) )
Descriptive Statistics
Calculate the mean (average) of a linear variable, overall and by groups:
MIcombine( with( ces_design , svymean( ~ totexp ) ) )
MIcombine( with( ces_design ,
svyby( ~ totexp , ~ bls_urbn , svymean )
) )
Calculate the distribution of a categorical variable, overall and by groups:
MIcombine( with( ces_design , svymean( ~ sex_ref ) ) )
MIcombine( with( ces_design ,
svyby( ~ sex_ref , ~ bls_urbn , svymean )
) )
Calculate the sum of a linear variable, overall and by groups:
MIcombine( with( ces_design , svytotal( ~ totexp ) ) )
MIcombine( with( ces_design ,
svyby( ~ totexp , ~ bls_urbn , svytotal )
) )
Calculate the weighted sum of a categorical variable, overall and by groups:
MIcombine( with( ces_design , svytotal( ~ sex_ref ) ) )
MIcombine( with( ces_design ,
svyby( ~ sex_ref , ~ bls_urbn , svytotal )
) )
Calculate the median (50th percentile) of a linear variable, overall and by groups:
MIcombine( with( ces_design ,
svyquantile(
~ totexp ,
0.5 , se = TRUE
) ) )
MIcombine( with( ces_design ,
svyby(
~ totexp , ~ bls_urbn , svyquantile ,
0.5 , se = TRUE ,
ci = TRUE
) ) )
Estimate a ratio:
MIcombine( with( ces_design ,
svyratio( numerator = ~ totexp , denominator = ~ fincbtxmi )
) )
Subsetting
Restrict the survey design to california residents:
<- subset( ces_design , state == '06' ) sub_ces_design
Calculate the mean (average) of this subset:
MIcombine( with( sub_ces_design , svymean( ~ totexp ) ) )
Measures of Uncertainty
Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:
<-
this_result MIcombine( with( ces_design ,
svymean( ~ totexp )
) )
coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )
<-
grouped_result MIcombine( with( ces_design ,
svyby( ~ totexp , ~ bls_urbn , svymean )
) )
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )
Calculate the degrees of freedom of any survey design object:
degf( ces_design$designs[[1]] )
Calculate the complex sample survey-adjusted variance of any statistic:
MIcombine( with( ces_design , svyvar( ~ totexp ) ) )
Include the complex sample design effect in the result for a specific statistic:
# SRS without replacement
MIcombine( with( ces_design ,
svymean( ~ totexp , deff = TRUE )
) )
# SRS with replacement
MIcombine( with( ces_design ,
svymean( ~ totexp , deff = "replace" )
) )
Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop
for alternatives:
# MIsvyciprop( ~ any_food_stamp , ces_design ,
# method = "likelihood" )
Regression Models and Tests of Association
Perform a design-based t-test:
# MIsvyttest( totexp ~ any_food_stamp , ces_design )
Perform a chi-squared test of association for survey data:
# MIsvychisq( ~ any_food_stamp + sex_ref , ces_design )
Perform a survey-weighted generalized linear model:
<-
glm_result MIcombine( with( ces_design ,
svyglm( totexp ~ any_food_stamp + sex_ref )
) )
summary( glm_result )
Replication Example
This example matches the number of consumer units and the Cars and trucks, new rows of Table R-1:
<-
result MIcombine( with( ces_design , svytotal( ~ as.numeric( popwt_finlwt21 / finlwt21 ) ) ) )
stopifnot( round( coef( result ) , -3 ) == 134090000 )
<-
results sapply(
weight_columns , function( this_column ){
sum( ces_df[ , 'new_car_truck_exp' ] * ces_df[ , this_column ] ) /
sum( ces_df[ , paste0( 'popwt_' , this_column ) ] )
}
)
stopifnot( round( results[1] , 2 ) == 2195.30 )
<- sqrt( ( 1 / 44 ) * sum( ( results[-1] - results[1] )^2 ) )
standard_error
stopifnot( round( standard_error , 2 ) == 174.02 )
# note the minor differences
MIcombine( with( ces_design , svymean( ~ cartkn ) ) )