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 for panel construction. For details on the identification algorithms, see vignette("BUILD_PNADC_PANEL").

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Panel Structure:

The table below shows the first and last quarter (ANOtrimestre, e.g. 20121 = 2012 Q1) covered by each PNADC rotating panel:

Panel	Start	End
1	20121	20124
2	20121	20141
3	20132	20152
4	20143	20163
5	20154	20174
6	20171	20191
7	20182	20202
8	20193	20213
9	20204	20224
10	20221	20241
11	20232	20252
12	20243	20263
13	20254	20274
14	20271	20291

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Usage:

Default

load_pnadc(
  save_to = getwd(),
  years,
  quarters = 1:4,
  panel = "advanced",
  raw_data = FALSE,
  save_options = c(TRUE, TRUE),
  vars = NULL
)

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)
)

To download PNADC data without any variables treatment or identification (e.g., for all quarters of 2021), run

load_pnadc(
  save_to = "Directory/You/Would/like/to/save/the/files",
  years = 2021,
  panel = "none",
  raw_data = TRUE
)

To download PNADC data, keep the quarters parquet on disk, and save panels as Parquet, run

load_pnadc(
  save_to = "Directory/You/Would/like/to/save/the/files",
  years = 2022,
  save_options = c(TRUE, FALSE)
)

To download PNADC data and save panels as CSV but discard the intermediate quarters parquet, run

load_pnadc(
  save_to = "Directory/You/Would/like/to/save/the/files",
  years = 2022,
  save_options = c(FALSE, TRUE)
)

To download only a specific subset of variables — for example, age (V2009) and habitual income (VD4019) — alongside the structural columns that PNADcIBGE always returns, run

load_pnadc(
  save_to = "Directory/You/Would/like/to/save/the/files",
  years = 2022,
  vars = c("V2009", "VD4019")
)

Note: PNADcIBGE::get_pnadc() always downloads a set of ~210 structural columns regardless of the vars argument. These include survey design weights (V1027, V1028, V1028001–V1028200, posest, posest_sxi), deflator variables (Habitual, Efetivo), and identifiers such as UF, Estrato, V1029, V1033, and ID_DOMICILIO. The vars argument adds columns on top of those; it does not restrict them. Use vars = NULL (the default) to download all available microdata columns.

If you specify vars and also request panel identification, any columns required by the identification algorithm that are absent from vars will be added automatically and a warning will tell you which ones were added. For example, when using panel = "advanced", the columns V2007, V20082, V20081, V2008, and V2003 must be present. If you omit them from vars, the function adds them for you:

# Only V2009 requested, but panel = "advanced" (the default) needs
# V2007, V20082, V20081, V2008 and V2003 — these are added automatically
# with a warning.
load_pnadc(
  save_to = "Directory/You/Would/like/to/save/the/files",
  years = 2022,
  panel = "advanced",
  vars = c("V2009", "VD4019")
)

---

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 (e.g. list(1:4, 1:2) for four quarters in the first year and two in the second).

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. The intermediate quarters parquet is always kept when panel = "none".
   • 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.

6. save_options: A logical vector of length 2 controlling file saving behaviour:

   • c(TRUE, TRUE) (default): keeps the intermediate quarters parquet after panel is built; saves panel files as .csv.
   • c(FALSE, TRUE): deletes the quarters parquet after use; saves panel files as .csv.
   • c(TRUE, FALSE): keeps the quarters parquet; saves panel files as a .parquet dataset.
   • c(FALSE, FALSE): deletes the quarters parquet after use; saves panel files as a .parquet dataset.

7. vars: A character vector of additional variable names to download, following the same convention as vars in PNADcIBGE::get_pnadc(). Use NULL (the default) to download all available microdata columns. See the note above regarding the ~210 structural columns that are always returned by PNADcIBGE::get_pnadc() regardless of this argument.

---

Details:

The function performs the following steps:

1. Loop over years and quarters using PNADcIBGE::get_pnadc to download the data. All quarters are collected in memory and saved together into a single pnadc_quarters.parquet file in save_to.

2. Split the data into panels by the panel variable V1014. Data from each panel is saved depending on save_options.

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

4. If save_options[1] = FALSE, the intermediate quarters parquet is deleted after the panels are built.

• 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

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Usage:

Basic Panel

panel_data <- build_pnadc_panel(dat = pnad_sample, panel = "basic")

Advanced Panel

panel_data <- build_pnadc_panel(dat = pnad_sample, panel = "advanced")

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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:

• 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:

• 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://datazoom.com.br/en/.

To cite package datazoom.social in publications use:

Data Zoom (2023). Data Zoom: Simplifying Access To Brazilian Microdata.
https://datazoom.com.br/en/

A BibTeX entry for LaTeX users is:

@Unpublished{DataZoom2024,
    author = {Data Zoom},
    title = {Data Zoom: Simplifying Access To Brazilian Microdata},
    url = {https://datazoom.com.br/en/},
    year = {2024}}