Get Started with PNADCperiods

Overview

The PNADCperiods package converts Brazil’s quarterly PNADC (Pesquisa Nacional por Amostra de Domicilios Continua) survey data into sub-quarterly time series. PNADC quarterly statistics are actually moving averages of three months, which obscures the true timing of economic shocks, the actual magnitude of changes, and turning points in trends.

The package offers two complementary approaches:

  1. Microdata mensalization — Identify which specific month, fortnight, or week each survey observation refers to, then calibrate weights for sub-quarterly analysis. Use this for custom variables, subgroup analysis, or individual-level regressions.

  2. SIDRA mensalization — Convert IBGE’s published rolling quarter aggregate series (86+ indicators) into exact monthly estimates. No microdata needed — just 3 lines of code.

For a detailed explanation of the algorithm, see How PNADCperiods Works.

Installation

# Install from GitHub
devtools::install_github("antrologos/PNADCperiods")
library(PNADCperiods)
library(data.table)

Dependencies: data.table, checkmate, and sidrar (for weight calibration and SIDRA API access).

Microdata Mensalization

Use the microdata workflow when you need custom variable definitions, subgroup analysis, individual-level regressions, or indicators not available via SIDRA. This requires PNADC microdata files — see Download and Prepare Data for how to obtain them.

Required Columns

The algorithm needs these columns from your PNADC data:

Column Description
Ano Survey year
Trimestre Quarter (1-4)
UPA Primary Sampling Unit
V1008 Household identifier
V1014 Panel identifier (rotation group 1-8)
V2008 Birth day (1-31, or 99 for unknown)
V20081 Birth month (1-12, or 99 for unknown)
V20082 Birth year (or 9999 for unknown)
V2009 Age

For weight calibration, you also need: V1028 (or V1032 for annual data), UF, posest, and posest_sxi.

Step 1: Build the Crosswalk

# Load your stacked quarterly PNADC data
pnadc <- fread("pnadc_stacked.csv")
# Identify reference periods (month, fortnight, week)
crosswalk <- pnadc_identify_periods(pnadc, verbose = TRUE)

Stack multiple quarters for best results. The algorithm exploits PNADC’s rotating panel design — each household (UPA + V1014) is interviewed across 5 consecutive quarters at the same relative month position. Cross-quarter aggregation achieves ~97% month determination from the full 2012-2025 history, vs ~70% from a single quarter.

# Check determination rates
crosswalk[, .(
  month_rate = mean(determined_month),
  fortnight_rate = mean(determined_fortnight),
  week_rate = mean(determined_week)
)]

See ?pnadc_identify_periods for full documentation of all crosswalk output columns.

Step 2: Apply Crosswalk and Calibrate Weights

result <- pnadc_apply_periods(
  pnadc_2023q1,
  crosswalk,
  weight_var = "V1028",
  anchor = "quarter",
  calibrate = TRUE,
  calibration_unit = "month"
)

Key parameters:

Parameter Values Default
weight_var "V1028" (quarterly) or "V1032" (annual) Required
anchor "quarter" or "year" Required
calibrate TRUE / FALSE TRUE
calibration_unit "month", "fortnight", "week" "month"
smooth TRUE / FALSE FALSE

Weight calibration adjusts survey weights to match known population benchmarks from SIDRA, ensuring monthly totals are consistent. The result includes all original columns plus reference period indicators and calibrated weights (e.g., weight_monthly).

Step 3: Compute Monthly Estimates

# Monthly unemployment rate
monthly_unemployment <- result[determined_month == TRUE, .(
  unemployment_rate = sum((VD4002 == 2) * weight_monthly, na.rm = TRUE) /
                      sum((VD4001 == 1) * weight_monthly, na.rm = TRUE)
), by = ref_month_yyyymm]

# Monthly population
monthly_pop <- result[, .(
  population = sum(weight_monthly, na.rm = TRUE)
), by = ref_month_yyyymm]

Use determined_month == TRUE (or equivalently, !is.na(weight_monthly)) to filter to observations with determined reference months.

For complete analysis examples with plots, see Applied Examples.

Build Once, Apply Many

The crosswalk only needs identification columns, so you can build it once and reuse it:

# Build crosswalk once from stacked data
crosswalk <- pnadc_identify_periods(pnadc_stacked)
saveRDS(crosswalk, "crosswalk.rds")

# Apply to any quarterly or annual dataset
crosswalk <- readRDS("crosswalk.rds")
result_q1 <- pnadc_apply_periods(pnadc_2023q1, crosswalk,
                                  weight_var = "V1028", anchor = "quarter")
result_annual <- pnadc_apply_periods(pnadc_annual_2023, crosswalk,
                                      weight_var = "V1032", anchor = "year")

Monthly Series from SIDRA — No Microdata Needed

If you need aggregate monthly labor market statistics (unemployment rate, employment levels, income), you can get them directly from IBGE’s published data without any microdata. The SIDRA module converts the rolling quarter series published by IBGE into exact monthly estimates.

Quick Start

# Step 1: Fetch rolling quarter data from SIDRA API
rolling_quarters <- fetch_sidra_rolling_quarters()

# Step 2: Convert to exact monthly estimates
monthly <- mensalize_sidra_series(rolling_quarters)

# Step 3: Use your monthly data
head(monthly[, .(anomesexato, m_popocup, m_taxadesocup)])

fetch_sidra_rolling_quarters() downloads 70+ economic indicators from IBGE’s SIDRA API (Tables 4093, 6390, 6392, 6399, 6906). mensalize_sidra_series() applies the mensalization formula using pre-computed starting points bundled with the package, converting rolling quarter averages into exact monthly values.

The output contains anomesexato (month in YYYYMM format) and m_* columns with mensalized monthly estimates, starting from March 2012.

Discovering Available Series

# Browse all 86+ available series
meta <- get_sidra_series_metadata()
meta[, .(series_name, description, unit)]

Most commonly used series:

Column Description Unit
m_taxadesocup Unemployment rate Percent
m_popocup Employed population Thousands
m_taxapartic Labor force participation rate Percent
m_massahabnominaltodos Total nominal wage bill Millions R$
m_rendhabnominaltodos Average nominal usual income R$
m_taxacompsubutlz Composite underutilization rate Percent

Visualizing Monthly vs Rolling Quarter Data

Since we have both the original rolling quarter data and the mensalized monthly estimates, comparing them is straightforward:

Monthly estimates reveal dynamics hidden by rolling quarter averaging
Monthly estimates reveal dynamics hidden by rolling quarter averaging
Show plotting code 
library(ggplot2)

# Prepare comparison data (merge rolling quarter and monthly estimates)
plot_data <- merge(
  rolling_quarters[, .(anomesexato = anomesfinaltrimmovel, rolling = taxadesocup)],
  monthly[, .(anomesexato, monthly = m_taxadesocup)],
  by = "anomesexato"
)[anomesexato >= 201901 & anomesexato <= 202312]

plot_data[, date := as.Date(paste0(substr(anomesexato, 1, 4), "-",
                                    substr(anomesexato, 5, 6), "-01"))]

# Reshape to long format
plot_long <- melt(plot_data, id.vars = c("anomesexato", "date"),
                  variable.name = "type", value.name = "rate")
plot_long[, type := factor(type,
                           levels = c("rolling", "monthly"),
                           labels = c("Rolling Quarter", "Monthly"))]

# Plot
ggplot(plot_long, aes(x = date, y = rate, color = type)) +
  geom_line(linewidth = 0.8) +
  annotate("rect", xmin = as.Date("2020-03-01"), xmax = as.Date("2020-12-31"),
           ymin = -Inf, ymax = Inf, fill = "red", alpha = 0.08) +
  scale_color_manual(values = c("Rolling Quarter" = "#888888",
                                "Monthly" = "#E53935"),
                     name = NULL) +
  scale_x_date(date_breaks = "6 months", date_labels = "%b\n%Y") +
  scale_y_continuous(labels = function(x) paste0(x, "%")) +
  labs(
    title = "Unemployment Rate: Monthly vs Rolling Quarter (2019-2023)",
    subtitle = "Monthly estimates capture the true timing and magnitude of economic shocks",
    x = NULL,
    y = "Unemployment Rate (%)"
  ) +
  theme_minimal(base_size = 11) +
  theme(
    plot.title = element_text(face = "bold"),
    legend.position = "top",
    panel.grid.minor = element_blank()
  )

For the full SIDRA guide — including custom starting points, methodology, and a COVID case study — see the SIDRA Mensalization Guide.

Improving Determination Rates

PNADC uses a rotating panel where each household (UPA + V1014) is interviewed in 5 consecutive quarters, always at the same relative month position. This means birthday constraints from any quarter can determine the month for all quarters — which is why stacking more data dramatically improves rates:

Data Stacked Month Rate Fortnight Rate Week Rate
1 quarter ~70% ~7% ~2%
8 quarters (2 years) ~94% ~9% ~3%
55 quarters (full 2012-2025) 97.0% 9.2% 3.3%

Fortnight and week rates remain low regardless of stacking because their constraints cannot aggregate across quarters — only month benefits from the panel design.

For analyses requiring higher determination, experimental strategies make informed probabilistic assignments:

# Build crosswalk with date bounds for experimental strategies
crosswalk <- pnadc_identify_periods(pnadc, store_date_bounds = TRUE)

# Apply experimental strategies
crosswalk_exp <- pnadc_experimental_periods(
  crosswalk,
  strategy = "both",
  confidence_threshold = 0.9
)

With strategy = "both" and confidence_threshold = 0.9, month determination improves to ~97.3% and fortnight to ~13.5%. See How PNADCperiods Works for details on experimental strategies.

Annual Data

Annual PNADC data uses different weights and achieves higher determination rates (~98% month) due to more complete panel coverage:

result_annual <- pnadc_apply_periods(
  pnadc_annual,
  crosswalk,
  weight_var = "V1032",
  anchor = "year",
  calibrate = TRUE,
  calibration_unit = "month"
)

For poverty and income analysis using annual data, see Monthly Poverty Analysis with Annual PNADC Data.

Function Overview

Function Purpose
Microdata Workflow
pnadc_identify_periods() Build period crosswalk from stacked microdata
pnadc_apply_periods() Apply crosswalk + calibrate weights
pnadc_experimental_periods() Experimental strategies for higher determination
validate_pnadc() Check required columns before processing
SIDRA Mensalization
fetch_sidra_rolling_quarters() Download rolling quarter series from SIDRA API
mensalize_sidra_series() Convert rolling quarters to exact monthly estimates
get_sidra_series_metadata() Browse 86+ available series with metadata
fetch_monthly_population() Fetch monthly population totals
clear_sidra_cache() Clear cached API responses
compute_starting_points_from_microdata() Compute custom starting points for SIDRA mensalization

Next Steps

References