National Beneficiary Survey (NBS)

The principal microdata for U.S. disability researchers interested in Social Security program performance.

• One table with one row per respondent.

• A complex sample designed to generalize to Americans between age 18 and full retirement age, covered by either Social Security Disability Insurance (SSDI) or Supplemental Security Income (SSI).

• Released at irregular intervals, with 2004, 2005, 2006, 2010, 2015, 2017, and 2019 available.

• Administered by the Social Security Administration.

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Recommended Reading

Four Example Strengths & Limitations:

✔️ Instrument designed to reduce challenges related to communication, stamina, cognitive barriers

✔️ Longitudinal 2019 sample includes beneficiaries working at prior round (2017) interview

❌ Not designed to produce regional or state-level estimates

❌ May overstate beneficiary poverty status and understate beneficiary income

Three Example Findings:

1. Large gaps in income and expenditure between Social Security Disability Insurance recipient households and working households generally increase with the number of dependents.

2. The share of Social Security Disability Insurance beneficiaries who had work goals or work expectations rose from 34% in 2005 to 43% in 2015.

3. In 2010, 9% of disabled-worker beneficiaries had a 4-year degree, 28% less than high school.

Two Methodology Documents:

National Beneficiary Survey: Disability Statistics, 2015

National Beneficiary Survey - General Waves Round 7: User’s Guide

One Haiku:

# social safety net
# poverty acrobatics
# trap or trampoline

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Download, Import, Preparation

Download and import the round 7 file:

library(haven)

zip_tf <- tempfile()

zip_url <- "https://www.ssa.gov/disabilityresearch/documents/R7NBSPUF_STATA.zip"
    
download.file( zip_url , zip_tf , mode = 'wb' )

nbs_tbl <- read_stata( zip_tf )

nbs_df <- data.frame( nbs_tbl )

names( nbs_df ) <- tolower( names( nbs_df ) )

nbs_df[ , 'one' ] <- 1

Save Locally  

Save the object at any point:

# nbs_fn <- file.path( path.expand( "~" ) , "NBS" , "this_file.rds" )
# saveRDS( nbs_df , file = nbs_fn , compress = FALSE )

Load the same object:

# nbs_df <- readRDS( nbs_fn )

Survey Design Definition

Construct a complex sample survey design:

library(survey)

options( survey.lonely.psu = "adjust" )

# representative beneficiary sample
nbs_design <-
    svydesign(
        id = ~ r7_a_psu_pub , 
        strata = ~ r7_a_strata , 
        weights = ~ r7_wtr7_ben , 
        data = subset( nbs_df , r7_wtr7_ben > 0 ) 
    )
    
# cross-sectional successful worker sample
nbs_design <- 
    svydesign(
        id = ~ r7_a_psu_pub , 
        strata = ~ r7_a_strata , 
        weights = ~ r7_wtr7_cssws , 
        data = subset( nbs_df , r7_wtr7_cssws > 0 ) 
    )
    
# longitudinal successful worker sample
lngsws_design <-
    svydesign(
        id = ~ r7_a_psu_pub , 
        strata = ~ r7_a_strata , 
        weights = ~ r7_wtr7_lngsws , 
        data = subset( nbs_df , r7_wtr7_lngsws > 0 ) 
    )

Variable Recoding

Add new columns to the data set:

nbs_design <- 
    update( 
        nbs_design , 
        
        male = as.numeric( r7_orgsampinfo_sex == 1 ) ,
        
        age_categories = 
            factor( 
                r7_c_intage_pub ,
                labels = 
                    c( "18-25" , "26-40" , "41-55" , "56 and older" )
            )
        
    )

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Analysis Examples with the survey library  

Unweighted Counts

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

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

svyby( ~ one , ~ age_categories , nbs_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , nbs_design )

svyby( ~ one , ~ age_categories , nbs_design , svytotal )

Descriptive Statistics

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

svymean( ~ r7_n_totssbenlastmnth_pub , nbs_design , na.rm = TRUE )

svyby( ~ r7_n_totssbenlastmnth_pub , ~ age_categories , nbs_design , svymean , na.rm = TRUE )

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

svymean( ~ r7_c_hhsize_pub , nbs_design , na.rm = TRUE )

svyby( ~ r7_c_hhsize_pub , ~ age_categories , nbs_design , svymean , na.rm = TRUE )

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

svytotal( ~ r7_n_totssbenlastmnth_pub , nbs_design , na.rm = TRUE )

svyby( ~ r7_n_totssbenlastmnth_pub , ~ age_categories , nbs_design , svytotal , na.rm = TRUE )

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

svytotal( ~ r7_c_hhsize_pub , nbs_design , na.rm = TRUE )

svyby( ~ r7_c_hhsize_pub , ~ age_categories , nbs_design , svytotal , na.rm = TRUE )

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

svyquantile( ~ r7_n_totssbenlastmnth_pub , nbs_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ r7_n_totssbenlastmnth_pub , 
    ~ age_categories , 
    nbs_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE , na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ r7_n_ssilastmnth_pub , 
    denominator = ~ r7_n_totssbenlastmnth_pub , 
    nbs_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to currently covered by Medicare:

sub_nbs_design <- subset( nbs_design , r7_c_curmedicare == 1 )

Calculate the mean (average) of this subset:

svymean( ~ r7_n_totssbenlastmnth_pub , sub_nbs_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( ~ r7_n_totssbenlastmnth_pub , nbs_design , na.rm = TRUE )

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

grouped_result <-
    svyby( 
        ~ r7_n_totssbenlastmnth_pub , 
        ~ age_categories , 
        nbs_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( nbs_design )

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

svyvar( ~ r7_n_totssbenlastmnth_pub , nbs_design , na.rm = TRUE )

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

# SRS without replacement
svymean( ~ r7_n_totssbenlastmnth_pub , nbs_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ r7_n_totssbenlastmnth_pub , nbs_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( ~ male , nbs_design ,
    method = "likelihood" )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( r7_n_totssbenlastmnth_pub ~ male , nbs_design )

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

svychisq( 
    ~ male + r7_c_hhsize_pub , 
    nbs_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        r7_n_totssbenlastmnth_pub ~ male + r7_c_hhsize_pub , 
        nbs_design 
    )

summary( glm_result )

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Replication Example

This example matches the percentages and t-tests from the final ten rows of Exhibit 4:

ex_4 <-
    data.frame(
        variable_label =
            c( 'coping with stress' , 'concentrating' , 
            'getting around outside of the home' , 
            'shopping for personal items' , 'preparing meals' , 
            'getting into or out of bed' , 'bathing or dressing' , 
            'getting along with others' , 
            'getting around inside the house' , 'eating' ) ,
        variable_name =
            c( "r3_i60_i" , "r3_i59_i" , "r3_i47_i" , "r3_i53_i" , 
            "r3_i55_i" , "r3_i49_i" , "r3_i51_i" , "r3_i61_i" , 
            "r3_i45_i" , "r3_i57_i" ) ,
        overall =
            c( 61 , 58 , 47 , 39 , 37 , 34 , 30 , 27 , 23 , 14 ) ,
        di_only =
            c( 60 , 54 , 47 , 36 , 35 , 36 , 30 , 23 , 24 , 13 ) ,
        concurrent =
            c( 63 , 63 , 47 , 43 , 41 , 34 , 33 , 31 , 23 , 15 ) ,
        concurrent_vs_di =
            c( F , T , F , F , F , F , F , T , F , F ) ,
        ssi =
            c( 61 , 62 , 47 , 40 , 39 , 33 , 29 , 31 , 22 , 15 ) ,
        ssi_vs_di =
            c( F , T , F , F , F , F , F , T , F , F )
    )

Download, import, and recode the round 3 file:

r3_tf <- tempfile()

r3_url <- "https://www.ssa.gov/disabilityresearch/documents/nbsr3pufstata.zip"
    
download.file( r3_url , r3_tf , mode = 'wb' )

r3_tbl <- read_stata( r3_tf )

r3_df <- data.frame( r3_tbl )

names( r3_df ) <- tolower( names( r3_df ) )

r3_design <- 
    svydesign(
        id = ~ r3_a_psu_pub , 
        strata = ~ r3_a_strata , 
        weights = ~ r3_wtr3_ben , 
        data = subset( r3_df , r3_wtr3_ben > 0 ) 
    )
    
r3_design <-
    update(
        r3_design ,
        
        benefit_type =
            factor(
                r3_orgsampinfo_bstatus ,
                levels = c( 2 , 3 , 1 ) ,
                labels = c( 'di_only' , 'concurrent' , 'ssi' )
            )

    )

Calculate the final ten rows of exhibit 4 and confirm each statistics and t-test matches:

for( i in seq( nrow( ex_4 ) ) ){

    this_formula <- as.formula( paste( "~" , ex_4[ i , 'variable_name' ] ) )

    overall_percent <- svymean( this_formula , r3_design )
    
    stopifnot( 100 * round( coef( overall_percent ) , 2 ) == ex_4[ i , 'overall_percent' ] )
    
    benefit_percent <- svyby( this_formula , ~ benefit_type , r3_design , svymean )
    
    stopifnot(
        all.equal( 
            100 * as.numeric( round( coef( benefit_percent ) , 2 ) ) , 
            as.numeric( ex_4[ i , c( 'di_only' , 'concurrent' , 'ssi' ) ] )
        )
    )
    
    ttest_formula <- as.formula( paste( ex_4[ i , 'variable_name' ] , "~ benefit_type" ) )
    
    di_only_con_design <-
        subset( r3_design , benefit_type %in% c( 'di_only' , 'concurrent' ) )
    
    con_ttest <- svyttest( ttest_formula , di_only_con_design )

    stopifnot(
        all.equal( 
            as.logical( con_ttest$p.value < 0.05 ) , 
            as.logical( ex_4[ i , 'concurrent_vs_di' ] )
        )
    )
    
    di_only_ssi_design <-
        subset( r3_design , benefit_type %in% c( 'di_only' , 'ssi' ) )
    
    ssi_ttest <- svyttest( ttest_formula , di_only_ssi_design )

    stopifnot(
        all.equal(
            as.logical( ssi_ttest$p.value < 0.05 ) , 
            as.logical( ex_4[ i , 'ssi_vs_di' ] ) 
        )
    )

}

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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 NBS users, this code replicates previously-presented examples:

library(srvyr)
nbs_srvyr_design <- as_survey( nbs_design )

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

nbs_srvyr_design %>%
    summarize( mean = survey_mean( r7_n_totssbenlastmnth_pub , na.rm = TRUE ) )

nbs_srvyr_design %>%
    group_by( age_categories ) %>%
    summarize( mean = survey_mean( r7_n_totssbenlastmnth_pub , na.rm = TRUE ) )