# 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 Michiganâ€™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
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
'er33802' , # sequence number, 2005
'er34302' , # sequence 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 = 'er33802' ,
by.y = 'er25002'
)
ind_fam_2015 <-
merge(
individual_df ,
family_2015_df ,
by.x = 'er34302' ,
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 ) )
```