# Panel Study of Income Dynamics (PSID)

The Panel Study of Income Dynamics is the longest running longitudinal household survey in the world.

One cross-year individual with one record per respondent in participating household, many family data tables with one record per family per timepoint.

A complex sample survey designed to generalize to residents of the United States.

Released either annually or biennially since 1968.

Administered by the University of Michigan’s Institute for Social Research and funded by consortium.

## Simplified Download and Importation

The R `lodown`

package easily downloads and imports all available PSID microdata by simply specifying `"psid"`

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( "psid" , output_dir = file.path( path.expand( "~" ) , "PSID" ) ,
your_email = "email@address.com" ,
your_password = "password" )
```

`lodown`

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

function. After requesting the PSID 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 PSID microdata files
psid_cat <-
get_catalog( "psid" ,
output_dir = file.path( path.expand( "~" ) , "PSID" ) ,
your_email = "email@address.com" ,
your_password = "password" )
# download the microdata to your local computer
psid_cat <- lodown( "psid" , psid_cat ,
your_email = "email@address.com" ,
your_password = "password" )
```

## Analysis Examples with the `survey`

library

Construct a complex sample survey design:

```
options( survey.lonely.psu = "adjust" )
library(survey)
# identify the cross-year individual filename
cross_year_individual_rds <-
grep(
"cross-year individual" ,
list.files(
file.path( path.expand( "~" ) , "PSID" ) ,
recursive = TRUE ,
full.names = TRUE
) ,
value = TRUE
)
individual_df <- readRDS( cross_year_individual_rds )
ind_variables_to_keep <-
c(
'one' , # column with all ones
'er30001' , # 1968 interview number
'er30002' , # 1968 person number
'er31997' , # primary sampling unit variable
'er31996' , # stratification variable
'er33801' , # interview number, 2005
'er34301' , # interview number, 2015
'er32000' , # sex
'er34305' , # age in 2015
'er33813' , # employment status in 2005
'er34317' , # employment status in 2015
'er33848' , # 2005 longitudinal weight
'er34413' # 2015 longitudinal weight
)
individual_df <- individual_df[ ind_variables_to_keep ] ; gc()
family_2005_df <-
readRDS( file.path( path.expand( "~" ) , "PSID" , "family files/2005.rds" ) )
fam_2005_variables_to_keep <-
c(
'er25002' , # 2005 interview number
'er28037' # 2005 total family income
)
family_2005_df <- family_2005_df[ fam_2005_variables_to_keep ] ; gc()
family_2015_df <-
readRDS( file.path( path.expand( "~" ) , "PSID" , "family files/2015.rds" ) )
fam_2015_variables_to_keep <-
c(
'er60002' , # 2015 interview number
'er65349' # 2015 total family income
)
family_2015_df <- family_2015_df[ fam_2015_variables_to_keep ] ; gc()
ind_fam_2005 <-
merge(
individual_df ,
family_2005_df ,
by.x = 'er33801' ,
by.y = 'er25002'
)
ind_fam_2015 <-
merge(
individual_df ,
family_2015_df ,
by.x = 'er34301' ,
by.y = 'er60002'
)
psid_df <- merge( ind_fam_2005 , ind_fam_2015 , all = TRUE )
psid_design <-
svydesign(
~ er31997 ,
strata = ~ er31996 ,
data = psid_df ,
weights = ~ er33848 ,
nest = TRUE
)
```

### Variable Recoding

Add new columns to the data set:

```
psid_design <-
update(
psid_design ,
employment_2005 =
factor( er33813 , levels = 1:8 ,
labels = c( 'working now' , 'only temporarily laid off' ,
'looking for work, unemployed' , 'retired' , 'permanently disabled' ,
'housewife; keeping house' , 'student' , 'other' )
) ,
employed_in_2015 =
factor( er34317 , levels = 1:8 ,
labels = c( 'working now' , 'only temporarily laid off' ,
'looking for work, unemployed' , 'retired' , 'permanently disabled' ,
'housewife; keeping house' , 'student' , 'other' )
) ,
female = as.numeric( er32000 == 2 )
)
```

### Unweighted Counts

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

```
sum( weights( psid_design , "sampling" ) != 0 )
svyby( ~ one , ~ employment_2005 , psid_design , unwtd.count )
```

### Weighted Counts

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

```
svytotal( ~ one , psid_design )
svyby( ~ one , ~ employment_2005 , psid_design , svytotal )
```

### Descriptive Statistics

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

```
svymean( ~ er28037 , psid_design , na.rm = TRUE )
svyby( ~ er28037 , ~ employment_2005 , psid_design , svymean , na.rm = TRUE )
```

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

```
svymean( ~ employed_in_2015 , psid_design , na.rm = TRUE )
svyby( ~ employed_in_2015 , ~ employment_2005 , psid_design , svymean , na.rm = TRUE )
```

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

```
svytotal( ~ er28037 , psid_design , na.rm = TRUE )
svyby( ~ er28037 , ~ employment_2005 , psid_design , svytotal , na.rm = TRUE )
```

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

```
svytotal( ~ employed_in_2015 , psid_design , na.rm = TRUE )
svyby( ~ employed_in_2015 , ~ employment_2005 , psid_design , svytotal , na.rm = TRUE )
```

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

```
svyquantile( ~ er28037 , psid_design , 0.5 , na.rm = TRUE )
svyby(
~ er28037 ,
~ employment_2005 ,
psid_design ,
svyquantile ,
0.5 ,
ci = TRUE ,
keep.var = TRUE ,
na.rm = TRUE
)
```

Estimate a ratio:

```
svyratio(
numerator = ~ er28037 ,
denominator = ~ er65349 ,
psid_design ,
na.rm = TRUE
)
```

### Subsetting

Restrict the survey design to senior in 2015:

`sub_psid_design <- subset( psid_design , er34305 >= 65 )`

Calculate the mean (average) of this subset:

`svymean( ~ er28037 , sub_psid_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( ~ er28037 , psid_design , na.rm = TRUE )
coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )
grouped_result <-
svyby(
~ er28037 ,
~ employment_2005 ,
psid_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( psid_design )`

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

`svyvar( ~ er28037 , psid_design , na.rm = TRUE )`

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

```
# SRS without replacement
svymean( ~ er28037 , psid_design , na.rm = TRUE , deff = TRUE )
# SRS with replacement
svymean( ~ er28037 , psid_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( ~ female , psid_design ,
method = "likelihood" )
```

### Regression Models and Tests of Association

Perform a design-based t-test:

`svyttest( er28037 ~ female , psid_design )`

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

```
svychisq(
~ female + employed_in_2015 ,
psid_design
)
```

Perform a survey-weighted generalized linear model:

```
glm_result <-
svyglm(
er28037 ~ female + employed_in_2015 ,
psid_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 PSID users, this code replicates previously-presented examples:

```
library(srvyr)
psid_srvyr_design <- as_survey( psid_design )
```

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

```
psid_srvyr_design %>%
summarize( mean = survey_mean( er28037 , na.rm = TRUE ) )
psid_srvyr_design %>%
group_by( employment_2005 ) %>%
summarize( mean = survey_mean( er28037 , na.rm = TRUE ) )
```