National Financial Capability Study (NFCS)

A study of financial knowledge and behavior, like making ends meet, planning ahead, managing assets.

One state-by-state survey table with one row per sampled respondent, a separate investor survey.
An online non-probability sample of U.S. adults (18+) calibrated to the American Community Survey.
Released triennially since 2009.
Funded by the FINRA Investor Education Foundation and conducted by FGS Global.

Download, Import, Preparation

Download and import the latest state-by-state microdata:

library(haven)

zip_tf <- tempfile()

zip_url <- 
    'https://finrafoundation.org/sites/finrafoundation/files/2021-SxS-Data-and-Data-Info.zip'

download.file( zip_url , zip_tf , mode = 'wb' )

unzipped_files <- unzip( zip_tf , exdir = tempdir() )

stata_fn <- grep( "\\.dta$" , unzipped_files , value = TRUE )

nfcs_tbl <- read_dta( stata_fn )

nfcs_df <- data.frame( nfcs_tbl )

names( nfcs_df ) <- tolower( names( nfcs_df ) )

Add a column of all ones, add labels to state names, add labels to the rainy day fund question:

nfcs_df[ , 'one' ] <- 1

nfcs_df[ , 'state_name' ] <-
    factor(
        nfcs_df[ , 'stateq' ] , 
        levels = 1:51 , 
        labels = sort( c( 'District of Columbia' , state.name ) ) 
    )

nfcs_df[ , 'rainy_day_fund' ] <-
    factor(
        nfcs_df[ , 'j5' ] ,
        levels = c( 1 , 2 , 98 , 99 ) ,
        labels = c( 'Yes' , 'No' , "Don't Know" , "Prefer not to say" )
    )

Save Locally

Save the object at any point:

# nfcs_fn <- file.path( path.expand( "~" ) , "NFCS" , "this_file.rds" )
# saveRDS( nfcs_df , file = nfcs_fn , compress = FALSE )

Load the same object:

# nfcs_df <- readRDS( nfcs_fn )

Survey Design Definition

Construct a complex sample survey design:

library(survey)

nfcs_design <- svydesign( ~ 1 , data = nfcs_df , weights = ~ wgt_n2 )

divison_design <- svydesign( ~ 1 , data = nfcs_df , weights = ~ wgt_d2 )

state_design <- svydesign( ~ 1 , data = nfcs_df , weights = ~ wgt_s3 )

Variable Recoding

Add new columns to the data set:

nfcs_design <- 
    update( 
        nfcs_design ,
        
        satisfaction_w_finances =
            ifelse( j1 > 10 , NA , j1 ) ,
            
        risk_taking =
            ifelse( j2 > 10 , NA , j2 ) ,
        
        difficult_to_pay_bills =
            factor(
                j4 ,
                levels = c( 1 , 2 , 3 , 98 , 99 ) ,
                labels = 
                    c( 
                        'Very difficult' , 
                        'Somewhat difficult' , 
                        'Not at all difficult' , 
                        "Don't know" , 
                        'Prefer not to say' 
                    )
            ) ,
                
        spending_vs_income =
            factor(
                j3 ,
                levels = c( 1 , 2 , 3 , 98 , 99 ) ,
                labels = 
                    c( 
                        'Spending less than income' , 
                        'Spending more than income' , 
                        'Spending about equal to income' , 
                        "Don't know" , 
                        'Prefer not to say' 
                    )
            ) ,
        
        unpaid_medical_bills =
            ifelse( g20 > 2 , NA , as.numeric( g20 == 1 ) )
    )

Analysis Examples with the `survey` library

Unweighted Counts

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

sum( weights( nfcs_design , "sampling" ) != 0 )

svyby( ~ one , ~ spending_vs_income , nfcs_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , nfcs_design )

svyby( ~ one , ~ spending_vs_income , nfcs_design , svytotal )

Descriptive Statistics

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

svymean( ~ satisfaction_w_finances , nfcs_design , na.rm = TRUE )

svyby( ~ satisfaction_w_finances , ~ spending_vs_income , nfcs_design , svymean , na.rm = TRUE )

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

svymean( ~ difficult_to_pay_bills , nfcs_design )

svyby( ~ difficult_to_pay_bills , ~ spending_vs_income , nfcs_design , svymean )

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

svytotal( ~ satisfaction_w_finances , nfcs_design , na.rm = TRUE )

svyby( ~ satisfaction_w_finances , ~ spending_vs_income , nfcs_design , svytotal , na.rm = TRUE )

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

svytotal( ~ difficult_to_pay_bills , nfcs_design )

svyby( ~ difficult_to_pay_bills , ~ spending_vs_income , nfcs_design , svytotal )

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

svyquantile( ~ satisfaction_w_finances , nfcs_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ satisfaction_w_finances , 
    ~ spending_vs_income , 
    nfcs_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE , na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ satisfaction_w_finances , 
    denominator = ~ risk_taking , 
    nfcs_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to persons receiving pandemic-related stimulus payment:

sub_nfcs_design <- subset( nfcs_design , j50 == 1 )

Calculate the mean (average) of this subset:

svymean( ~ satisfaction_w_finances , sub_nfcs_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( ~ satisfaction_w_finances , nfcs_design , na.rm = TRUE )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    svyby( 
        ~ satisfaction_w_finances , 
        ~ spending_vs_income , 
        nfcs_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( nfcs_design )

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

svyvar( ~ satisfaction_w_finances , nfcs_design , na.rm = TRUE )

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

# SRS without replacement
svymean( ~ satisfaction_w_finances , nfcs_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ satisfaction_w_finances , nfcs_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( ~ unpaid_medical_bills , nfcs_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( satisfaction_w_finances ~ unpaid_medical_bills , nfcs_design )

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

svychisq( 
    ~ unpaid_medical_bills + difficult_to_pay_bills , 
    nfcs_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        satisfaction_w_finances ~ unpaid_medical_bills + difficult_to_pay_bills , 
        nfcs_design 
    )

summary( glm_result )

Replication Example

This example matches the unweighted count shown on PDF page 4:

stopifnot( nrow( nfcs_df ) == 27118 )

This example matches the PDF page 7 estimate that 53% have three months of rainy day funds:

national_rainy_day <- svymean( ~ rainy_day_fund , nfcs_design )
stopifnot( round( coef( national_rainy_day )[ 'rainy_day_fundYes' ] , 2 ) == 0.53 )

This example matches counts and rainy day estimates from The Geography of Financial Capability:

state_counts <-
    svyby(
        ~ one ,
        ~ state_name ,
        state_design ,
        unwtd.count
    )
    
stopifnot( state_counts[ 'California' , 'counts' ] == 1252 )
stopifnot( state_counts[ 'Missouri' , 'counts' ] == 501 )
stopifnot( state_counts[ 'Oregon' , 'counts' ] == 1261 )

state_rainy_day <-
    svyby(
        ~ rainy_day_fund ,
        ~ state_name ,
        state_design ,
        svymean
    )
    
stopifnot( round( state_rainy_day[ 'California' , 'rainy_day_fundYes' ] , 2 ) == 0.57 )
stopifnot( round( state_rainy_day[ 'Missouri' , 'rainy_day_fundYes' ] , 2 ) == 0.51 )
stopifnot( round( state_rainy_day[ 'Oregon' , 'rainy_day_fundYes' ] , 2 ) == 0.52 )

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 NFCS users, this code replicates previously-presented examples:

library(srvyr)
nfcs_srvyr_design <- as_survey( nfcs_design )

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

nfcs_srvyr_design %>%
    summarize( mean = survey_mean( satisfaction_w_finances , na.rm = TRUE ) )

nfcs_srvyr_design %>%
    group_by( spending_vs_income ) %>%
    summarize( mean = survey_mean( satisfaction_w_finances , na.rm = TRUE ) )

Analyze Survey Data for Free

National Financial Capability Study (NFCS)

Recommended Reading

Download, Import, Preparation

Save Locally

Survey Design Definition

Variable Recoding

Analysis Examples with the `survey` library

Unweighted Counts

Weighted Counts

Descriptive Statistics

Subsetting

Measures of Uncertainty

Regression Models and Tests of Association

Replication Example

Analysis Examples with `srvyr`

National Financial Capability Study (NFCS)

Recommended Reading

Download, Import, Preparation

Save Locally

Survey Design Definition

Variable Recoding

Analysis Examples with the survey library

Unweighted Counts

Weighted Counts

Descriptive Statistics

Subsetting

Measures of Uncertainty

Regression Models and Tests of Association

Replication Example

Analysis Examples with srvyr

Analysis Examples with the `survey` library

Analysis Examples with `srvyr`