National Financial Capability Study (NFCS)

License: GPL v3 Github Actions Badge

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.

Please skim before you begin:

  1. 2021 National Financial Capability Study: State-by-State Survey Methodology

  2. Financial Capability Insights: What the NFCS Reveals

  3. A haiku regarding this microdata:

# lady madonna
# laid bank balance goose egg, loves
# gold unrequited

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 ) )