# Current Population Survey - Basic Monthly (CPSBASIC)

The Current Population Survey - Basic Monthly is the monthly labor force survey of the United States.

One table with one row per sampled youth respondent.

A complex sample survey designed to generalize to the civilian non-institutional population of the United States

Released monthly since 1994.

Administered jointly by the US Census Bureau and the Bureau of Labor Statistics.

## Simplified Download and Importation

The R `lodown`

package easily downloads and imports all available CPSBASIC microdata by simply specifying `"cpsbasic"`

with an `output_dir =`

parameter in the `lodown()`

function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

```
library(lodown)
lodown( "cpsbasic" , output_dir = file.path( path.expand( "~" ) , "CPSBASIC" ) )
```

`lodown`

also provides a catalog of available microdata extracts with the `get_catalog()`

function. After requesting the CPSBASIC catalog, you could pass a subsetted catalog through the `lodown()`

function in order to download and import specific extracts (rather than all available extracts).

```
library(lodown)
# examine all available CPSBASIC microdata files
cpsbasic_cat <-
get_catalog( "cpsbasic" ,
output_dir = file.path( path.expand( "~" ) , "CPSBASIC" ) )
# march 2017 only
cpsbasic_cat <- subset( cpsbasic_cat , year == 2017 & month == 3 )
# download the microdata to your local computer
lodown( "cpsbasic" , cpsbasic_cat )
```

## Analysis Examples with the `survey`

library

Construct a complex sample survey design:

```
library(survey)
cpsbasic_df <-
readRDS( file.path( path.expand( "~" ) , "CPSBASIC" , "2017 03 cps basic.rds" ) )
# construct a fake survey design
warning( "this survey design produces correct point estimates
but incorrect standard errors." )
cpsbasic_design <-
svydesign(
~ 1 ,
data = cpsbasic_df ,
weights = ~ pwsswgt
)
```

### Variable Recoding

Add new columns to the data set:

```
cpsbasic_design <-
update(
cpsbasic_design ,
one = 1 ,
pesex = factor( pesex , levels = 1:2 , labels = c( 'male' , 'female' ) ) ,
weekly_earnings = ifelse( prernwa == -.01 , NA , prernwa ) ,
# exclude anyone whose hours vary
weekly_hours = ifelse( pehrusl1 < 0 , NA , pehrusl1 ) ,
class_of_worker =
factor( peio1cow , levels = 1:8 ,
labels =
c( "government - federal" , "government - state" ,
"government - local" , "private, for profit" ,
"private, nonprofit" , "self-employed, incorporated" ,
"self-employed, unincorporated" , "without pay" )
) ,
part_time = ifelse( pemlr == 1 , as.numeric( pehruslt < 35 ) , NA )
)
```

### Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

```
sum( weights( cpsbasic_design , "sampling" ) != 0 )
svyby( ~ one , ~ pesex , cpsbasic_design , unwtd.count )
```

### Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

```
svytotal( ~ one , cpsbasic_design )
svyby( ~ one , ~ pesex , cpsbasic_design , svytotal )
```

### Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

```
svymean( ~ weekly_earnings , cpsbasic_design , na.rm = TRUE )
svyby( ~ weekly_earnings , ~ pesex , cpsbasic_design , svymean , na.rm = TRUE )
```

Calculate the distribution of a categorical variable, overall and by groups:

```
svymean( ~ class_of_worker , cpsbasic_design , na.rm = TRUE )
svyby( ~ class_of_worker , ~ pesex , cpsbasic_design , svymean , na.rm = TRUE )
```

Calculate the sum of a linear variable, overall and by groups:

```
svytotal( ~ weekly_earnings , cpsbasic_design , na.rm = TRUE )
svyby( ~ weekly_earnings , ~ pesex , cpsbasic_design , svytotal , na.rm = TRUE )
```

Calculate the weighted sum of a categorical variable, overall and by groups:

```
svytotal( ~ class_of_worker , cpsbasic_design , na.rm = TRUE )
svyby( ~ class_of_worker , ~ pesex , cpsbasic_design , svytotal , na.rm = TRUE )
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:

```
svyquantile( ~ weekly_earnings , cpsbasic_design , 0.5 , na.rm = TRUE )
svyby(
~ weekly_earnings ,
~ pesex ,
cpsbasic_design ,
svyquantile ,
0.5 ,
ci = TRUE ,
keep.var = TRUE ,
na.rm = TRUE
)
```

Estimate a ratio:

```
svyratio(
numerator = ~ weekly_earnings ,
denominator = ~ weekly_hours ,
cpsbasic_design ,
na.rm = TRUE
)
```

### Subsetting

Restrict the survey design to california residents:

`sub_cpsbasic_design <- subset( cpsbasic_design , gestfips == 6 )`

Calculate the mean (average) of this subset:

`svymean( ~ weekly_earnings , sub_cpsbasic_design , na.rm = TRUE )`

### 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 <- svymean( ~ weekly_earnings , cpsbasic_design , na.rm = TRUE )
coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )
grouped_result <-
svyby(
~ weekly_earnings ,
~ pesex ,
cpsbasic_design ,
svymean ,
na.rm = TRUE
)
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )
```

Calculate the degrees of freedom of any survey design object:

`degf( cpsbasic_design )`

Calculate the complex sample survey-adjusted variance of any statistic:

`svyvar( ~ weekly_earnings , cpsbasic_design , na.rm = TRUE )`

Include the complex sample design effect in the result for a specific statistic:

```
# SRS without replacement
svymean( ~ weekly_earnings , cpsbasic_design , na.rm = TRUE , deff = TRUE )
# SRS with replacement
svymean( ~ weekly_earnings , cpsbasic_design , na.rm = TRUE , deff = "replace" )
```

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See `?svyciprop`

for alternatives:

```
svyciprop( ~ part_time , cpsbasic_design ,
method = "likelihood" , na.rm = TRUE )
```

### Regression Models and Tests of Association

Perform a design-based t-test:

`svyttest( weekly_earnings ~ part_time , cpsbasic_design )`

Perform a chi-squared test of association for survey data:

```
svychisq(
~ part_time + class_of_worker ,
cpsbasic_design
)
```

Perform a survey-weighted generalized linear model:

```
glm_result <-
svyglm(
weekly_earnings ~ part_time + class_of_worker ,
cpsbasic_design
)
summary( glm_result )
```

## Analysis Examples with `srvyr`

The R `srvyr`

library calculates summary statistics from survey data, such as the mean, total or quantile using dplyr-like syntax. srvyr allows for the use of many verbs, such as `summarize`

, `group_by`

, and `mutate`

, the convenience of pipe-able functions, the `tidyverse`

style of non-standard evaluation and more consistent return types than the `survey`

package. This vignette details the available features. As a starting point for CPSBASIC users, this code replicates previously-presented examples:

```
library(srvyr)
cpsbasic_srvyr_design <- as_survey( cpsbasic_design )
```

Calculate the mean (average) of a linear variable, overall and by groups:

```
cpsbasic_srvyr_design %>%
summarize( mean = survey_mean( weekly_earnings , na.rm = TRUE ) )
cpsbasic_srvyr_design %>%
group_by( pesex ) %>%
summarize( mean = survey_mean( weekly_earnings , na.rm = TRUE ) )
```