# Pesquisa Nacional por Amostra de Domicilios (SIPP)

*Contributed by Dr. Djalma Pessoa <pessoad@gmail.com>*

Brazil’s previous principal household survey, the Pesquisa Nacional por Amostra de Domicilios (PNAD) measures general education, labor, income, and housing characteristics of the population.

One table with one row per sampled household and a second table with one row per individual within each sampled household.

A complex sample survey designed to generalize to the civilian non-institutional population of Brazil, although the rural north was not included prior to 2004.

Released annually since 2001 except for years ending in zero, when the decennial census takes its place.

Administered by the Instituto Brasileiro de Geografia e Estatistica.

## Simplified Download and Importation

The R `lodown`

package easily downloads and imports all available SIPP microdata by simply specifying `"sipp"`

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( "sipp" , output_dir = file.path( path.expand( "~" ) , "SIPP" ) )
```

`lodown`

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

function. After requesting the SIPP 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 SIPP microdata files
sipp_cat <-
get_catalog( "sipp" ,
output_dir = file.path( path.expand( "~" ) , "SIPP" ) )
# 2011 only
sipp_cat <- subset( sipp_cat , year == 2011 )
# download the microdata to your local computer
sipp_cat <- lodown( "sipp" , sipp_cat )
```

## Analysis Examples with the `survey`

library

Construct a database-backed complex sample survey design:

```
library(DBI)
library(RSQLite)
library(survey)
options( survey.lonely.psu = "adjust" )
prestratified_design <-
svydesign(
id = ~v4618 ,
strata = ~v4617 ,
data = sipp_cat[ 1 , "db_tablename" ] ,
weights = ~pre_wgt ,
nest = TRUE ,
dbtype = "SQLite" ,
dbname = sipp_cat[ 1 , "dbfile" ]
)
sipp_design <-
lodown:::pnad_postStratify(
design = prestratified_design ,
strata.col = 'v4609' ,
oldwgt = 'pre_wgt'
)
```

### Variable Recoding

Add new columns to the data set:

```
sipp_design <-
update(
sipp_design ,
age_categories = factor( 1 + findInterval( v8005 , seq( 5 , 60 , 5 ) ) ) ,
male = as.numeric( v0302 == 2 ) ,
teenagers = as.numeric( v8005 > 12 & v8005 < 20 ) ,
started_working_before_thirteen = as.numeric( v9892 < 13 )
)
```

### Unweighted Counts

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

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

### Weighted Counts

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

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

### Descriptive Statistics

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

```
svymean( ~ v4720 , sipp_design , na.rm = TRUE )
svyby( ~ v4720 , ~ region , sipp_design , svymean , na.rm = TRUE )
```

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

```
svymean( ~ age_categories , sipp_design )
svyby( ~ age_categories , ~ region , sipp_design , svymean )
```

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

```
svytotal( ~ v4720 , sipp_design , na.rm = TRUE )
svyby( ~ v4720 , ~ region , sipp_design , svytotal , na.rm = TRUE )
```

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

```
svytotal( ~ age_categories , sipp_design )
svyby( ~ age_categories , ~ region , sipp_design , svytotal )
```

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

```
svyquantile( ~ v4720 , sipp_design , 0.5 , na.rm = TRUE )
svyby(
~ v4720 ,
~ region ,
sipp_design ,
svyquantile ,
0.5 ,
ci = TRUE ,
keep.var = TRUE ,
na.rm = TRUE
)
```

Estimate a ratio:

```
svyratio(
numerator = ~ started_working_before_thirteen ,
denominator = ~ teenagers ,
sipp_design ,
na.rm = TRUE
)
```

### Subsetting

Restrict the survey design to married persons:

`sub_sipp_design <- subset( sipp_design , v4011 == 1 )`

Calculate the mean (average) of this subset:

`svymean( ~ v4720 , 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:

```
this_result <- svymean( ~ v4720 , sipp_design , na.rm = TRUE )
coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )
grouped_result <-
svyby(
~ v4720 ,
~ region ,
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:

`degf( sipp_design )`

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

`svyvar( ~ v4720 , sipp_design , na.rm = TRUE )`

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

```
# SRS without replacement
svymean( ~ v4720 , sipp_design , na.rm = TRUE , deff = TRUE )
# SRS with replacement
svymean( ~ v4720 , 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:

```
svyciprop( ~ male , sipp_design ,
method = "likelihood" )
```

### Regression Models and Tests of Association

Perform a design-based t-test:

`svyttest( v4720 ~ male , sipp_design )`

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

```
svychisq(
~ male + age_categories ,
sipp_design
)
```

Perform a survey-weighted generalized linear model:

```
glm_result <-
svyglm(
v4720 ~ male + age_categories ,
sipp_design
)
summary( glm_result )
```

## Poverty and Inequality Estimation with `convey`

The R `convey`

library estimates measures of income concentration, poverty, inequality, and wellbeing. This textbook details the available features. As a starting point for SIPP users, this code calculates the gini coefficient on complex sample survey data:

```
library(convey)
sipp_design <- convey_prep( sipp_design )
sub_sipp_design <-
subset(
sipp_design ,
!is.na( v4720 ) & v4720 != 0 & v8005 >= 15
)
svygini( ~ v4720 , sub_sipp_design , na.rm = TRUE )
```

## Replication Example

```
svytotal( ~one , sipp_design )
svytotal( ~factor( v0302 ) , sipp_design )
cv( svytotal( ~factor( v0302 ) , sipp_design ) )
```