BUILD_PNADC_PANEL

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

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

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.

Attrition for Panel 2

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

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