datazoom.social

The datazoom.social package facilitates access to official Brazilian social data.

This package is in development stage - more datasets will be released soon.

In this first version of the package, the focus is only on the Continuous PNAD. We allow for many quarters to be easily downloaded and read, as well as identifying individuals across time, forming a panel.

Installation

You can install the development version of datazoom.social from GitHub with:

install.packages("devtools")
devtools::install_github("datazoompuc/datazoom.social")

Data

Continuous PNAD Download PNADC of a range of quarters Panel Identification Build a Panel of PNADC individuals

Continuous PNAD

The load_pnadc function is a wrapper for get_pnadc from the package PNADcIBGE, with added identification algorithms to build a Panel.

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Usage:

To download PNADC data for all quarters of 2022 and 2023, with advanced identification, simply run

load_pnadc(
  save_to = "Directory/You/Would/like/to/save/the/files",
  years = 2022:2023
)

To download PNADC data for all of 2022, but only the first quarter of 2023, run

load_pnadc(
  save_to = "Directory/You/Would/like/to/save/the/files",
  years = 2022:2023,
  quarters = list(1:4, 1)
)

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Options:

1. save_to: The directory in which the user desires to save the downloaded files.

2. years: picks the years for which the data will be downloaded

3. quarters: The quarters within those years to be downloaded. Can be either a vector such as 1:4 for consistent quarters across years, or a list of vectors, if quarters are different for each year.

4. panel: Which panel algorithm to apply to this data. There are three options:

   • none: No panel is built. If raw_data = TRUE, returns the original data. Otherwise, creates some extra treated variables.
   • basic: Performs basic identification steps for creating households and individual identifiers for panel construction
   • advanced: Performs advanced identification steps for creating households and individual identifiers for panel construction.

5. raw_data: A command to define if the user would like to download the raw or treated data. There are two options:

   • TRUE: if you want the PNADC variables as they come.
   • FALSE: if you want the treated version of the PNADC variables.

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Details:

The function performs the following steps:

1. Loop over years and quarters using PNADcIBGE::get_pnadc to download the data and save in the save_to directory, in files named pnadc_year_quarter.rds. If the raw_data option is FALSE, some PNADC variables are treated at this stage.

2. Split the data into panels, by reading each .rds file and filtering by the quarter variable V1014. Data from each panel x is saved to pnad_panel_x.csv. The use of .csv allows for data from each quarter to be appended on top of the previous ones, making the process faster.

3. Read each panel file and apply the identification algorithms defined in the build_pnadc_panel.

• The identification algorithms in build_pnadc_panel are drawn from Ribas, Rafael Perez, and Sergei Suarez Dillon Soares (2008): “Sobre o painel da Pesquisa Mensal de Emprego (PME) do IBGE”.

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PNAD Panel Identification

Description

Our load_pnadc function uses the internal function build_pnadc_panel to identify households and individuals across quarters. The method used for the identification is based on the paper of Ribas, Rafael Perez, and Sergei Suarez Dillon Soares (2008): “Sobre o painel da Pesquisa Mensal de Emprego (PME) do IBGE”.

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Basic Identification

The household identifier – stored as id_dom – combines the variables:

• UF – State;

• UPA – Primary Sampling Unit - PSU;

• V1008 – Household;

• V1014 – Panel Number;

In order to create a unique number for every combination of those variables.

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The basic individual identifier – stored as id_ind – combines the household id with:

• V2003 – Order number: individual's unique number within their household;

• V2007 – Sex;

• Date of Birth – [V20082 (year), V20081 (month), V2008 (day)];

In order to create an unique number for every combination of those variables.

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Advanced Identification

The advanced identifier is saved as id_rs. On individuals who were not matched on all interviews, we relax some assumptions to increase matching power. Under the assumption that the date of birth is often misreported, we take individuals who are either:

1. Head of the household or their partner

2. Child of the head of the household, 25 or older

For these observations, we run the basic identification again, but allowing the year of birth to be wrong. We also include the order number.

Attrition

The tables below show the levels of attrition obtained using the basic and advanced identification algorithms, and compares them to the attrition levels obtained in the Stata datazoom_social package.

Interview	Percentage found (R)	Percentage found (Stata)
1	100.0	100.0
2	86.2	85.7
3	78.5	77.5
4	73.2	71.6
5	69.1	66.8

Attrition for Panel 2

Each cell is the percentage of PNADC observations that are identified by the advanced algorithm in each interview.

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Credits

DataZoom is developed by a team at Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Department of Economics. Our official website is at: https://www.econ.puc-rio.br/datazoom/.

To cite package datazoom.social in publications use:

Data Zoom (2023). Data Zoom: Simplifying Access To Brazilian Microdata.
https://www.econ.puc-rio.br/datazoom/english/index.html

A BibTeX entry for LaTeX users is:

@Unpublished{DataZoom2024,
    author = {Data Zoom},
    title = {Data Zoom: Simplifying Access To Brazilian Microdata},
    url = {https://www.econ.puc-rio.br/datazoom/english/index.html},
    year = {2024}}