Social Security Administration Public Use Microdata (SSA)

Research extracts provided by the Social Security Administration.

Tables contain either one record per person or one record per person per year.
The entire population of either social security number holders (most of the country) or social security recipients (just beneficiaries). One-percent samples should be multiplied by 100 to get accurate nationwide count statistics, five-percent samples by 20.
No expected release timeline.
Released by the United States Social Security Administration (SSA).

Simplified Download and Importation

The R lodown package easily downloads and imports all available SSA microdata by simply specifying "ssa" 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( "ssa" , output_dir = file.path( path.expand( "~" ) , "SSA" ) )

Analysis Examples with base R

Load a data frame:

ssa_df <- readRDS( file.path( path.expand( "~" ) , "SSA" , "ssr_data/SSIPUF.rds" ) )

Variable Recoding

Add new columns to the data set:

ssa_df <- 
    transform( 
        ssa_df , 
        
        mental_disorder = as.numeric( diag %in% 1:2 ) ,
        
        program_eligibility =
            factor( 
                prel , 
                
                levels = 0:5 , 
                
                labels =
                    c( "Unspecified" ,
                    "Aged individual" ,
                    "Aged spouse" ,
                    "Disabled or blind individual" ,
                    "Disabled or blind spouse" ,
                    "Disabled or blind child" )
            )
            
    )

Unweighted Counts

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

nrow( ssa_df )

table( ssa_df[ , "stat" ] , useNA = "always" )

Descriptive Statistics

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

mean( ssa_df[ , "fpmt" ] )

tapply(
    ssa_df[ , "fpmt" ] ,
    ssa_df[ , "stat" ] ,
    mean 
)

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

prop.table( table( ssa_df[ , "program_eligibility" ] ) )

prop.table(
    table( ssa_df[ , c( "program_eligibility" , "stat" ) ] ) ,
    margin = 2
)

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

sum( ssa_df[ , "fpmt" ] )

tapply(
    ssa_df[ , "fpmt" ] ,
    ssa_df[ , "stat" ] ,
    sum 
)

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

quantile( ssa_df[ , "fpmt" ] , 0.5 )

tapply(
    ssa_df[ , "fpmt" ] ,
    ssa_df[ , "stat" ] ,
    quantile ,
    0.5 
)

Subsetting

Limit your data.frame to females:

sub_ssa_df <- subset( ssa_df , sex == "F" )

Calculate the mean (average) of this subset:

mean( sub_ssa_df[ , "fpmt" ] )

Measures of Uncertainty

Calculate the variance, overall and by groups:

var( ssa_df[ , "fpmt" ] )

tapply(
    ssa_df[ , "fpmt" ] ,
    ssa_df[ , "stat" ] ,
    var 
)

Regression Models and Tests of Association

Perform a t-test:

t.test( fpmt ~ mental_disorder , ssa_df )

Perform a chi-squared test of association:

this_table <- table( ssa_df[ , c( "mental_disorder" , "program_eligibility" ) ] )

chisq.test( this_table )

Perform a generalized linear model:

glm_result <- 
    glm( 
        fpmt ~ mental_disorder + program_eligibility , 
        data = ssa_df
    )

summary( glm_result )

Analysis Examples with `dplyr`

The R dplyr library offers an alternative grammar of data manipulation to base R and SQL syntax. dplyr offers many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, and the tidyverse style of non-standard evaluation. This vignette details the available features. As a starting point for SSA users, this code replicates previously-presented examples:

library(dplyr)
ssa_tbl <- tbl_df( ssa_df )