sipp.Rmd

# Survey of Income and Program Participation (SIPP) {-}

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) <a href="https://github.com/asdfree/sipp/actions"><img src="https://github.com/asdfree/sipp/actions/workflows/r.yml/badge.svg" alt="Github Actions Badge"></a> 

The primary longitudinal assessment of income fluctuation, labor force participation, social programs.

* Annual tables with one record per month per person per sampled household, time period weights.

* A complex sample generalizing to the U.S. civilian non-institutionalized across varying time periods.

* Multi-year panels since 1980s, its current and now permanent [four year rotation](https://www2.census.gov/programs-surveys/sipp/tech-documentation/methodology/2023_SIPP_Users_Guide_OCT24.pdf#page=15) beginning in 2018.

* Administered and financed by the [US Census Bureau](http://www.census.gov/).

---

## Recommended Reading {-}

Four Example Strengths & Limitations:

✔️ [Annual interviews capture changes precisely, with some questions about each month of the prior year](https://www.census.gov/programs-surveys/sipp/tech-documentation/questionnaires.html)

✔️ [Overlapping panel structure allows year-to-year comparisons](https://www2.census.gov/about/partners/cac/sac/meetings/2024-03/presentation-sipp-seamless.pdf)

❌ [Each income source topcoded for privacy, no individual amounts above a certain threshold revealed](https://ceprdata.s3.amazonaws.com/data/sipp/set_f_memo.pdf)

❌ [The 2017-2019 population experiencing a gap in coverage slightly lower than a health-focused survey](https://doi.org/10.1073/pnas.2222100120)

<br>

Three Example Findings:

1. [Among individuals that experienced at least two consecutive months of poverty during 2022, 57% experienced poverty during all twelve months of the year](https://www.census.gov/library/publications/2024/demo/p70br-196.html).

2. [Americans reporting a disability start date of 2020 were more likely to have stronger employment histories than those reporting disabilities that began pre-COVID](https://www.nber.org/programs-projects/projects-and-centers/retirement-and-disability-research-center/center-papers/nb23-03).

3. [Among 24-64 year old workers in 2018-2023 earning below the median income, those who had ever received unemployment insurance had median net liquid wealth 2.5x larger than those who had not](https://doi.org/10.26509/frbc-ec-202416).

<br>

Two Methodology Documents:

> [2023 Survey of Income and Program Participation Users' Guide](https://www2.census.gov/programs-surveys/sipp/tech-documentation/methodology/2023_SIPP_Users_Guide_OCT24.pdf)

> [2023 Data User Notes](https://www.census.gov/programs-surveys/sipp/tech-documentation/user-notes/2023-usernotes.html)

<br>

One Haiku:

```{r}
# federal programs
# poverty oversample
# monthly dynamics
```

---

## Download, Import, Preparation {-}

Determine which variables from the main table to import:
```{r eval = FALSE , results = "hide" }
variables_to_keep <-
	c( 'ssuid' , 'pnum' , 'monthcode' , 'spanel' , 'swave' , 'erelrpe' , 
	'tlivqtr' , 'wpfinwgt' , 'rmesr' , 'thcyincpov' , 'tfcyincpov' ,
	'tehc_st' , 'rhicovann' , 'rfpov' , 'thnetworth' , 'tftotinc' )
```

Download and import the latest main file:
```{r eval = FALSE , results = "hide" }
library(httr)
library(data.table)

main_tf <- tempfile()

main_url <-
	paste0(
		"https://www2.census.gov/programs-surveys/sipp/" ,
		"data/datasets/2023/pu2023_csv.zip"
	)

GET( main_url , write_disk( main_tf ) , progress() )

main_csv <- unzip( main_tf , exdir = tempdir() )

sipp_main_dt <- fread( main_csv , sep = "|" , select = toupper( variables_to_keep ) )

sipp_main_df <- data.frame( sipp_main_dt )

names( sipp_main_df ) <- tolower( names( sipp_main_df ) )

```

Download and import the appropriate replicate weights file:
```{r eval = FALSE , results = "hide" }
rw_tf <- tempfile()

rw_url <-
	paste0(
		"https://www2.census.gov/programs-surveys/sipp/" ,
		"data/datasets/2023/rw2023_csv.zip"
	)

GET( rw_url , write_disk( rw_tf ) , progress() )

rw_csv <- unzip( rw_tf , exdir = tempdir() )

sipp_rw_dt <- fread( rw_csv , sep = "|" )

sipp_rw_df <- data.frame( sipp_rw_dt )

names( sipp_rw_df ) <- tolower( names( sipp_rw_df ) )
```

Limit both files to December records for a point-in-time estimate, then merge:
```{r eval = FALSE , results = "hide" }

sipp_df <-
	merge(
		sipp_main_df[ sipp_main_df[ , 'monthcode' ] %in% 12 , ] ,
		sipp_rw_df[ sipp_rw_df[ , 'monthcode' ] %in% 12 , ] ,
		by = c( 'ssuid' , 'pnum' , 'monthcode' , 'spanel' , 'swave' )
	)
	
stopifnot( nrow( sipp_df ) == sum( sipp_rw_df[ , 'monthcode' ] %in% 12 ) )
```

### Save Locally \ {-}

Save the object at any point:

```{r eval = FALSE , results = "hide" }
# sipp_fn <- file.path( path.expand( "~" ) , "SIPP" , "this_file.rds" )
# saveRDS( sipp_df , file = sipp_fn , compress = FALSE )
```

Load the same object:

```{r eval = FALSE , results = "hide" }
# sipp_df <- readRDS( sipp_fn )
```

### Survey Design Definition {-}
Construct a complex sample survey design:

```{r eval = FALSE , results = "hide" }
library(survey)

sipp_design <- 
	svrepdesign(
			data = sipp_df ,
			weights = ~ wpfinwgt ,
			repweights = "repwgt([1-9]+)" ,
			type = "Fay" ,
			rho = 0.5
		)
	
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE , results = "hide" }
rmesr_values <-
	c( 
		"With a job entire month, worked all weeks",
		"With a job all month, absent from work without pay 1+ weeks, absence not due to layoff",
		"With a job all month, absent from work without pay 1+ weeks, absence due to layoff",
		"With a job at least 1 but not all weeks, no time on layoff and no time looking for work",
		"With a job at least 1 but not all weeks, some weeks on layoff or looking for work",
		"No job all month, on layoff or looking for work all weeks",
		"No job all month, at least one but not all weeks on layoff or looking for work",
		"No job all month, no time on layoff and no time looking for work"
	)

sipp_design <- 
	
	update( 
		
		sipp_design , 
		
		one = 1 ,
		
		employment_status = factor( rmesr , levels = 1:8 , labels = rmesr_values ) ,
			
		household_below_poverty = as.numeric( thcyincpov < 1 ) ,
		
		family_below_poverty = as.numeric( tfcyincpov < 1 ) ,
		
		state_name =
			
			factor(
				
				as.numeric( tehc_st ) ,
				
				levels = 
					c(1L, 2L, 4L, 5L, 6L, 8L, 9L, 10L, 
					11L, 12L, 13L, 15L, 16L, 17L, 18L, 
					19L, 20L, 21L, 22L, 23L, 24L, 25L, 
					26L, 27L, 28L, 29L, 30L, 31L, 32L, 
					33L, 34L, 35L, 36L, 37L, 38L, 39L, 
					40L, 41L, 42L, 44L, 45L, 46L, 47L, 
					48L, 49L, 50L, 51L, 53L, 54L, 55L, 
					56L, 60L, 61L) ,
		
				labels =
					c("Alabama", "Alaska", "Arizona", "Arkansas", "California", 
					"Colorado", "Connecticut", "Delaware", "District of Columbia", 
					"Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", 
					"Iowa", "Kansas", "Kentucky", "Louisiana", "Maine", "Maryland", 
					"Massachusetts", "Michigan", "Minnesota", "Mississippi", "Missouri", 
					"Montana", "Nebraska", "Nevada", "New Hampshire", "New Jersey", 
					"New Mexico", "New York", "North Carolina", "North Dakota", "Ohio", 
					"Oklahoma", "Oregon", "Pennsylvania", "Rhode Island", "South Carolina", 
					"South Dakota", "Tennessee", "Texas", "Utah", "Vermont", "Virginia", 
					"Washington", "West Virginia", "Wisconsin", "Wyoming", "Puerto Rico",
					"Foreign Country")
			)
			
	)
```

---

## Analysis Examples with the `survey` library \ {-}

### Unweighted Counts {-}

Count the unweighted number of records in the survey sample, overall and by groups:
```{r eval = FALSE , results = "hide" }
sum( weights( sipp_design , "sampling" ) != 0 )

svyby( ~ one , ~ state_name , sipp_design , unwtd.count )
```

### Weighted Counts {-}
Count the weighted size of the generalizable population, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ one , sipp_design )

svyby( ~ one , ~ state_name , sipp_design , svytotal )
```

### Descriptive Statistics {-}

Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ tftotinc , sipp_design , na.rm = TRUE )

svyby( ~ tftotinc , ~ state_name , sipp_design , svymean , na.rm = TRUE )
```

Calculate the distribution of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ employment_status , sipp_design , na.rm = TRUE )

svyby( ~ employment_status , ~ state_name , sipp_design , svymean , na.rm = TRUE )
```

Calculate the sum of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ tftotinc , sipp_design , na.rm = TRUE )

svyby( ~ tftotinc , ~ state_name , sipp_design , svytotal , na.rm = TRUE )
```

Calculate the weighted sum of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ employment_status , sipp_design , na.rm = TRUE )

svyby( ~ employment_status , ~ state_name , sipp_design , svytotal , na.rm = TRUE )
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svyquantile( ~ tftotinc , sipp_design , 0.5 , na.rm = TRUE )

svyby( 
	~ tftotinc , 
	~ state_name , 
	sipp_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE , na.rm = TRUE
)
```

Estimate a ratio:
```{r eval = FALSE , results = "hide" }
svyratio( 
	numerator = ~ tftotinc , 
	denominator = ~ rfpov , 
	sipp_design ,
	na.rm = TRUE
)
```

### Subsetting {-}

Restrict the survey design to individuals ever covered by health insurance during the year:
```{r eval = FALSE , results = "hide" }
sub_sipp_design <- subset( sipp_design , rhicovann == 1 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ tftotinc , sub_sipp_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:
```{r eval = FALSE , results = "hide" }
this_result <- svymean( ~ tftotinc , sipp_design , na.rm = TRUE )

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

grouped_result <-
	svyby( 
		~ tftotinc , 
		~ state_name , 
		sipp_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:
```{r eval = FALSE , results = "hide" }
degf( sipp_design )
```

Calculate the complex sample survey-adjusted variance of any statistic:
```{r eval = FALSE , results = "hide" }
svyvar( ~ tftotinc , sipp_design , na.rm = TRUE )
```

Include the complex sample design effect in the result for a specific statistic:
```{r eval = FALSE , results = "hide" }
# SRS without replacement
svymean( ~ tftotinc , sipp_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ tftotinc , sipp_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:
```{r eval = FALSE , results = "hide" }
svyciprop( ~ family_below_poverty , sipp_design ,
	method = "likelihood" , na.rm = TRUE )
```

### Regression Models and Tests of Association {-}

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( tftotinc ~ family_below_poverty , sipp_design )
```

Perform a chi-squared test of association for survey data:
```{r eval = FALSE , results = "hide" }
svychisq( 
	~ family_below_poverty + employment_status , 
	sipp_design 
)
```

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		tftotinc ~ family_below_poverty + employment_status , 
		sipp_design 
	)

summary( glm_result )
```

---

## Replication Example {-}

This example matches statistics and standard errors from the [Wealth and Asset Ownership for Households, by Type of Asset and Selected Characteristics: 2022](https://www2.census.gov/programs-surveys/demo/tables/wealth/2022/wealth-asset-ownership/wealth_tables_dy2022.xlsx):

Restrict the design to permanent residence-based householders to match the count in Table 4:

```{r eval = FALSE , results = "hide" }
sipp_household_design <- subset( sipp_design , erelrpe %in% 1:2 & tlivqtr %in% 1:2 )

stopifnot( round( coef( svytotal( ~ one , sipp_household_design ) ) / 1000 , -2 ) == 134100 )
```
	
Compute Household Net Worth distribution and standard errors across the Total row of Tables 4 and 4A:

```{r eval = FALSE , results = "hide" }
sipp_household_design <-
	update(
		sipp_household_design ,
		thnetworth_category =
			factor(
				findInterval( 
					thnetworth , 
					c( 1 , 5000 , 10000 , 25000 , 50000 , 100000 , 250000 , 500000 ) 
				) ,
				levels = 0:8 ,
				labels = c( "Zero or Negative" , "$1 to $4,999" , "$5,000 to $9,999" , 
				"$10,000 to $24,999" , "$25,000 to $49,999" , "$50,000 to $99,999" , 
				"$100,000 to $249,999" , "$250,000 to $499,999" , "$500,000 or over" )
			)
	)

results <- svymean( ~ thnetworth_category , sipp_household_design )

stopifnot( 
	all.equal( as.numeric( round( coef( results ) * 100 , 1 ) ) , 
	c( 11.1 , 6.8 , 3.5 , 5.7 , 5.6 , 7.8 , 15.9 , 14.4 , 29.2 ) ) 
)

stopifnot(
	all.equal( as.numeric( round( SE( results ) * 100 , 1 ) ) ,
	c( 0.3 , 0.2 , 0.2 , 0.2 , 0.2 , 0.2 , 0.3 , 0.3 , 0.3 ) )
)

```

---

## Poverty and Inequality Estimation with `convey` \ {-}

The R `convey` library estimates measures of income concentration, poverty, inequality, and wellbeing. [This textbook](https://guilhermejacob.github.io/context/) details the available features. As a starting point for SIPP users, this code calculates the gini coefficient on complex sample survey data:

```{r eval = FALSE , results = "hide" }
library(convey)
sipp_design <- convey_prep( sipp_design )

svygini( ~ tftotinc , sipp_design , na.rm = TRUE )
```

---

## Analysis Examples with `srvyr` \ {-}

The R `srvyr` library calculates summary statistics from survey data, such as the mean, total or quantile using [dplyr](https://github.com/tidyverse/dplyr/)-like syntax. [srvyr](https://github.com/gergness/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](https://cran.r-project.org/web/packages/srvyr/vignettes/srvyr-vs-survey.html) details the available features. As a starting point for SIPP users, this code replicates previously-presented examples:

```{r eval = FALSE , results = "hide" }
library(srvyr)
sipp_srvyr_design <- as_survey( sipp_design )
```
Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
sipp_srvyr_design %>%
	summarize( mean = survey_mean( tftotinc , na.rm = TRUE ) )

sipp_srvyr_design %>%
	group_by( state_name ) %>%
	summarize( mean = survey_mean( tftotinc , na.rm = TRUE ) )
```