---
title: "Monthly Poverty Analysis with Annual PNADC Data"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Monthly Poverty Analysis with Annual PNADC Data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!--
MAINTAINER NOTE:
The code chunks in this vignette must match the code in:
  mensalizacao_pnad/code/generate_annual_poverty_figures.R

Look for comments like "# VIGNETTE CODE: chunk-name" in that script.
To regenerate figures, run that script.
-->

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  eval = FALSE,
  echo = TRUE,
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE,
  fig.width = 10,
  fig.height = 6,
  purl = FALSE
)
```

## Introduction

When exactly did poverty spike during COVID-19? Official annual statistics tell us that 2020 was a difficult year---but they can't tell us whether the crisis peaked in April or June, whether the Auxilio Emergencial reduced poverty immediately or with a delay, or what the month-by-month recovery path looked like. **Monthly data can.**

This vignette combines the mensalization algorithm with **annual PNADC data** to produce monthly poverty statistics. The annual PNADC releases contain comprehensive household income measures (`VD5008`) that aren't available in the quarterly releases. By applying a mensalization crosswalk (built from quarterly data) to annual income data, we get monthly temporal precision with comprehensive income measurement.

![Data Workflow: Monthly Poverty Analysis with Annual PNADC](figures/annual-poverty-analysis/fig-workflow-diagram.png){width=100%}

IBGE's PNADC uses a rotating panel design where the same households appear in both quarterly and annual data. The workflow is:

1. Build a **crosswalk** from quarterly data using `pnadc_identify_periods()`
2. **Apply** the crosswalk to annual income data with `pnadc_apply_periods()` (which handles the merge internally and calibrates weights)
3. Analyze detailed income and poverty measures at monthly frequency

---

## Prerequisites

```{r prerequisites}
library(PNADCperiods)
library(data.table)
library(fst)
library(readxl)      # Read IBGE deflator Excel files
library(deflateBR)   # INPC deflator
library(ggplot2)
library(scales)
```

You also need:

- **Quarterly PNADC data** (2015-2024) in `.fst` format for creating the mensalization crosswalk
- **Annual PNADC data** (2015-2024) in `.fst` format with income supplement variables
- **Deflator file** from IBGE documentation (`deflator_pnadc_2024.xls`)

---

## Complete Workflow

### Step 1: Create Mensalization Crosswalk

Load stacked quarterly PNADC data and run the mensalization algorithm. See the [Download and Prepare Data](download-and-prepare.html) vignette for details on obtaining and formatting PNADC microdata.

```{r create-crosswalk}
# Define paths
pnad_quarterly_dir <- "path/to/quarterly/data"

# List quarterly files (2015-2024)
quarterly_files <- list.files(
  path = pnad_quarterly_dir,
  pattern = "pnadc_20(1[5-9]|2[0-4])-[1-4]q\\.fst$",
  full.names = TRUE
)

# Variables needed for mensalization
quarterly_vars <- c(
  "Ano", "Trimestre", "UPA", "V1008", "V1014",
  "V2008", "V20081", "V20082", "V2009",
  "V1028", "UF", "posest", "posest_sxi", "Estrato"
)

# Load and stack quarterly data
quarterly_data <- rbindlist(
  lapply(quarterly_files, function(f) {
    read_fst(f, as.data.table = TRUE, columns = quarterly_vars)
  }),
  fill = TRUE
)

# Build the crosswalk (identifies reference periods)
crosswalk <- pnadc_identify_periods(
  quarterly_data,
  verbose = TRUE
)

# Check determination rate (expect ~96% with 40 quarters of data)
crosswalk[, mean(determined_month, na.rm = TRUE)]
```

### Step 2: Load Annual PNADC Data

Annual PNADC files follow a specific naming convention. Note that 2020-2021 use visit 5 (due to COVID-related field disruptions), while other years use visit 1:

```{r load-annual-data}
pnad_annual_dir <- "path/to/annual/data"

# Define which visit to use for each year
visit_selection <- data.table(
  ano = 2015:2024,
  visita = c(1, 1, 1, 1, 1, 5, 5, 1, 1, 1)  # 2020-2021 use visit 5
)
```

> **Why Visit 5 for 2020-2021?**
>
> During COVID-19, IBGE suspended in-person data collection. Visit 1 interviews
> for 2020-2021 have significant quality issues or are unavailable entirely.
> Visit 5 interviews were conducted later under improved conditions and are
> the standard choice for COVID-era income and poverty analysis.

```{r load-annual-continued}
# Build file paths
annual_files <- visit_selection[, .(
  file = file.path(pnad_annual_dir, sprintf("pnadc_%d_visita%d.fst", ano, visita))
), by = ano]

# Variables to load
annual_vars <- c(
  # Join keys
  "ano", "trimestre", "upa", "v1008", "v1014",
  # Demographics
  "v2005", "v2007", "v2009", "v2010", "uf", "estrato",
  # Weights and calibration
  "v1032", "posest", "posest_sxi",
  # Household per capita income (IBGE pre-calculated)
  "vd5008"
)

# Load and stack annual data
annual_data <- rbindlist(
  lapply(annual_files[file.exists(file), file], function(f) {
    dt <- read_fst(f, as.data.table = TRUE)
    setnames(dt, tolower(names(dt)))
    cols_present <- intersect(annual_vars, names(dt))
    dt[, ..cols_present]
  }),
  fill = TRUE
)
```

### Step 2b: Standardize Column Names

The annual data has lowercase column names, but `pnadc_apply_periods()` requires
specific casing for the join keys. Standardize before applying the crosswalk:

```{r standardize-columns}
# pnadc_apply_periods() expects uppercase join keys
key_mappings <- c(
  "ano" = "Ano", "trimestre" = "Trimestre",
  "upa" = "UPA", "v1008" = "V1008", "v1014" = "V1014",
  "v1032" = "V1032", "uf" = "UF", "v2009" = "V2009"
)

for (old_name in names(key_mappings)) {
  if (old_name %in% names(annual_data)) {
    setnames(annual_data, old_name, key_mappings[[old_name]])
  }
}
```

> **Note:** The calibration columns `posest` and `posest_sxi` stay lowercase---the
> package expects them in that case. Only the join keys (`Ano`, `Trimestre`, `UPA`,
> `V1008`, `V1014`) and weight column (`V1032`) need uppercase.

### Step 3: Apply Crosswalk and Calibrate Weights

Apply the crosswalk to annual data and calibrate weights using `pnadc_apply_periods()`.
The function handles the merge internally using the five join keys (`Ano`, `Trimestre`,
`UPA`, `V1008`, `V1014`):

```{r apply-crosswalk}
d <- pnadc_apply_periods(
  annual_data,
  crosswalk,
  weight_var = "V1032",
  anchor = "year",
  calibrate = TRUE,
  calibration_unit = "month",
  smooth = TRUE,
  verbose = TRUE
)

# Check match rate (expect ~97% with year anchor)
mean(!is.na(d$ref_month_in_quarter))
```

> **Why `anchor = "year"`?** Annual PNADC data contains only one visit per
> household (e.g., visit 1 or visit 5), not all rotation groups like quarterly
> data. The `"year"` anchor calibrates the annual weight `V1032` to monthly SIDRA
> population totals while preserving yearly totals.

### Step 4: Construct Per Capita Income

Use IBGE's pre-calculated household per capita income variable:

```{r construct-income}
# Filter to household members only
d <- d[v2005 <= 14 | v2005 == 16]

# Use IBGE's pre-calculated per capita household income
d[, hhinc_pc_nominal := fifelse(is.na(vd5008), 0, vd5008)]
```

### Step 5: Apply Deflation

Convert nominal income to real values using IBGE deflators:

```{r apply-deflation}
# Load deflator data (from IBGE documentation)
deflator <- readxl::read_excel("path/to/deflator_pnadc_2024.xls")
setDT(deflator)
deflator <- deflator[, .(Ano = ano, Trimestre = trim, UF = uf, CO2, CO2e, CO3)]

# Merge deflators with data
setkeyv(deflator, c("Ano", "Trimestre", "UF"))
setkeyv(d, c("Ano", "Trimestre", "UF"))
d <- deflator[d]

# INPC adjustment factor to reference date (December 2025)
inpc_factor <- deflateBR::inpc(1,
                               nominal_dates = as.Date("2024-07-01"),
                               real_date = "12/2025")

# Apply deflation
d[, hhinc_pc := hhinc_pc_nominal * CO2 * inpc_factor]
```

### Step 6: Define Poverty Line

Calculate the World Bank PPP-based poverty threshold:

```{r define-poverty-lines}
# World Bank poverty line: USD 8.30 PPP per day (upper-middle income threshold)
poverty_line_830_ppp_daily <- 8.30

# 2021 PPP conversion factor (World Bank)
# https://data.worldbank.org/indicator/PA.NUS.PRVT.PP?year=2021
usd_to_brl_ppp <- 2.45
days_to_month <- 365/12

# Monthly value in 2021 BRL
poverty_line_830_brl_monthly_2021 <- poverty_line_830_ppp_daily *
                                     usd_to_brl_ppp * days_to_month

# Deflate to December 2025 reference
poverty_line_830_brl_monthly_2025 <- deflateBR::inpc(
  poverty_line_830_brl_monthly_2021,
  nominal_dates = as.Date("2021-07-01"),
  real_date = "12/2025"
)

d[, poverty_line := poverty_line_830_brl_monthly_2025]
```

> **Why USD 8.30/day?** This is the World Bank's upper-middle income poverty
> threshold, appropriate for Brazil. We use the 2021 PPP conversion factor
> (2.45 BRL per USD) because 2021 is the World Bank's reference year for
> the current poverty lines.

---

## Analysis Examples

### Helper Functions

Before computing poverty measures, we define the FGT family of poverty indices:

```{r helper-functions}
# FGT poverty measure family (Foster-Greer-Thorbecke)
# alpha = 0: Headcount ratio (share below line)
# alpha = 1: Poverty gap (average shortfall)
# alpha = 2: Squared poverty gap (sensitive to inequality among poor)
fgt <- function(x, z, w = NULL, alpha = 0) {
  if (is.null(w)) w <- rep(1, length(x))
  if (length(z) == 1) z <- rep(z, length(x))

  idx <- complete.cases(x, z, w)
  x <- x[idx]; z <- z[idx]; w <- w[idx]

  g <- pmax(0, (z - x) / z)
  fgt_val <- ifelse(x < z, g^alpha, 0)

  sum(w * fgt_val) / sum(w)
}
```

### Example 1: Monthly FGT Poverty Measures

Calculate monthly poverty rates using the FGT family:

```{r example-fgt-family}
# Filter to determined observations
d_monthly <- d[!is.na(ref_month_yyyymm)]

# Use calibrated monthly weight (from pnadc_apply_periods())
d_monthly[, peso := weight_monthly]

# Compute monthly poverty statistics
monthly_poverty <- d_monthly[, .(
  # FGT-0 (Headcount ratio)
  poverty_rate = fgt(hhinc_pc, poverty_line, peso, alpha = 0),

  # FGT-1 (Poverty gap)
  poverty_gap = fgt(hhinc_pc, poverty_line, peso, alpha = 1),

  # Mean income
  mean_income = weighted.mean(hhinc_pc, peso, na.rm = TRUE),

  # Sample size
  n_obs = .N

), by = ref_month_yyyymm]

# Add date for plotting
monthly_poverty[, period := as.Date(paste0(
  ref_month_yyyymm %/% 100, "-",
  ref_month_yyyymm %% 100, "-15"
))]
```

<details>
<summary>Show plotting code</summary>

```{r fgt-plot}
# Prepare data for plotting
fgt_data <- melt(
  monthly_poverty[, .(period,
                      `PPP 8.30/day` = poverty_rate)],
  id.vars = "period",
  variable.name = "poverty_line",
  value.name = "rate"
)

fgt_gap_data <- melt(
  monthly_poverty[, .(period,
                      `PPP 8.30/day` = poverty_gap)],
  id.vars = "period",
  variable.name = "poverty_line",
  value.name = "gap"
)

# Panel A: Headcount ratio (FGT-0)
p1 <- ggplot(fgt_data, aes(x = period, y = rate, color = poverty_line)) +
  geom_line(linewidth = 0.8) +
  geom_point(size = 1) +
  scale_y_continuous(labels = percent_format(accuracy = 1),
                     limits = c(0, NA)) +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  scale_color_manual(values = c("PPP 8.30/day" = "#b2182b")) +
  labs(title = "A. Poverty Headcount (FGT-0)",
       subtitle = "Share of population below poverty line",
       x = NULL, y = "Poverty Rate",
       color = "Poverty Line") +
  theme_minimal(base_size = 11) +
  theme(legend.position = "bottom",
        panel.grid.minor = element_blank(),
        plot.title = element_text(face = "bold"))

# Panel B: Poverty gap (FGT-1)
p2 <- ggplot(fgt_gap_data, aes(x = period, y = gap, color = poverty_line)) +
  geom_line(linewidth = 0.8) +
  geom_point(size = 1) +
  scale_y_continuous(labels = percent_format(accuracy = 0.1),
                     limits = c(0, NA)) +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  scale_color_manual(values = c("PPP 8.30/day" = "#ef8a62")) +
  labs(title = "B. Relative Poverty Gap (FGT-1)",
       subtitle = "Average shortfall as share of poverty line",
       x = NULL, y = "Relative Poverty Gap",
       color = "Poverty Line") +
  theme_minimal(base_size = 11) +
  theme(legend.position = "bottom",
        panel.grid.minor = element_blank(),
        plot.title = element_text(face = "bold"))

# Combine panels
library(patchwork)
fig_fgt <- p1 / p2 +
  plot_annotation(
    title = "Monthly Poverty Measures: Brazil, 2015-2024",
    caption = "Source: PNADC/IBGE. Annual data with monthly reference periods from PNADCperiods.",
    theme = theme(
      plot.title = element_text(face = "bold", size = 14),
      plot.subtitle = element_text(size = 11),
      plot.caption = element_text(size = 9, hjust = 0)
    )
  )
fig_fgt
```

</details>

![Monthly Poverty Measures: Brazil, 2015-2024](figures/annual-poverty-analysis/fig-fgt-monthly-series.png){width=100%}

The figure reveals several key dynamics:

1. **COVID-19 spike (March-April 2020)**: The poverty rate shows a sharp increase in early 2020.

2. **Auxilio Emergencial effect (May-December 2020)**: Emergency cash transfers dramatically reduced poverty below pre-pandemic levels.

3. **Post-Auxilio adjustment (2021)**: As emergency aid was reduced, poverty rates partially rebounded.

For proper inference with confidence intervals, use complex survey design with monthly weights---see the [Complex Survey Design](complex-survey-design.html) vignette.

---

## Summary

| Insight | Annual Data | Monthly Data |
|---------|-------------|--------------|
| **COVID poverty spike** | Averaged across year | Visible March-April 2020 |
| **Auxilio timing** | Effect blurred | Clear May 2020 onset |
| **Recovery dynamics** | Single 2021 estimate | Monthly trajectory |
| **Seasonal patterns** | Invisible | December income spikes |

**Limitations**: ~3% sample loss from undetermined reference months; annual PNADC is released with 18+ month delay; monthly estimates have wider confidence intervals than annual.

---

## Further Reading

- [Get Started](getting-started.html) - Basic mensalization workflow
- [How It Works](how-it-works.html) - Algorithm details
- [Complex Survey Design](complex-survey-design.html) - Variance estimation
- [Applied Examples](applied-examples.html) - Unemployment and minimum wage examples

## References

- HECKSHER, Marcos; BARBOSA, Rogerio J. "Estimation of exact months for the microdata and rolling quarter series from PNAD Continua". SocArXiv preprint, 2026. <https://osf.io/preprints/socarxiv/fra5u_v1>

  *Earlier formulations of the methodology:*

    - HECKSHER, Marcos. "Valor Impreciso por Mes Exato: Microdados e Indicadores Mensais Baseados na Pnad Continua". IPEA - Nota Tecnica Disoc, n. 62. Brasilia, DF: IPEA, 2020. <https://portalantigo.ipea.gov.br/portal/index.php?option=com_content&view=article&id=35453>
    - HECKSHER, M. "Cinco meses de perdas de empregos e simulacao de um incentivo a contratacoes". IPEA - Nota Tecnica Disoc, n. 87. Brasilia, DF: IPEA, 2020.
    - HECKSHER, Marcos. "Mercado de trabalho: A queda da segunda quinzena de marco, aprofundada em abril". IPEA - Carta de Conjuntura, v. 47, p. 1-6, 2020.

- NERI, Marcelo; HECKSHER, Marcos. "A Montanha-Russa da Pobreza". FGV Social - Sumario Executivo. Rio de Janeiro: FGV, Junho/2022. <https://www.cps.fgv.br/cps/bd/docs/MontanhaRussaDaPobreza_Neri_Hecksher_FGV_Social.pdf>
- NERI, Marcelo; HECKSHER, Marcos. "A montanha-russa da pobreza mensal e um programa social alternativo". *Revista NECAT*, v. 11, n. 21, 2022.
- IBGE. Pesquisa Nacional por Amostra de Domicilios Continua (PNADC). <https://www.ibge.gov.br/estatisticas/sociais/trabalho/>
- World Bank. Poverty and Shared Prosperity Reports. Various years.
- Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. *Econometrica*, 52(3), 761-766.
- Barbosa, Rogerio J; Hecksher, Marcos. (2026). PNADCperiods: Identify Reference Periods in Brazil's PNADC Survey Data. R package version v0.1.2. <https://CRAN.R-project.org/package=PNADCperiods>
