1. The Candidate Universe

Who Runs for Municipal Office in Brazil in 2024?

Show code 
source(here::here("code", "00_setup.R"))
df <- readRDS(paths$analysis_full_rds)
df <- df %>%
  mutate(ideology_category = factor(ideology_category, levels = ideology_levels))

1 Overview

Before we can understand LGBTQ+ candidates, we need to understand the universe they inhabit. Brazil’s 2024 municipal elections saw candidates competing for city council seats (vereadores) and mayoral positions (prefeitos) across 5,570 municipalities — from megacities like São Paulo (12M population) to towns with fewer than 1,000 residents.

This chapter provides a baseline portrait of all 463,601 candidates.

2 Sample Composition

2.1 By Position

The table below shows how candidates are distributed between the two types of municipal office: city council (vereador/a) and mayor (prefeito/a).

Show code 
df %>%
  count(position_simple) %>%
  mutate(pct = format_pct(n / sum(n))) %>%
  rename(Position = position_simple, N = n, `%` = pct) %>%
  kable(align = c("l", "r", "r"))
Table 1: Candidates by Position
Position N %
City Councilor 432005 93.2%
Mayor 15676 3.4%
Vice-Mayor 15920 3.4%

The overwhelming majority are city councilor candidates. This reflects Brazil’s institutional structure: each municipality has one mayor but many council seats (ranging from 9 in small towns to 55 in the largest cities).

2.2 By Candidacy Status

Note

The integrated dataset does not contain disaggregated candidacy status information (all rows share a single code). Candidacy filtering was performed upstream during data integration.

3 Demographics

3.1 Gender

Gender is recorded on TSE registration forms using the categories Feminino (Female) and Masculino (Male). The table below shows the gender distribution separately by position type.

Show code 
df %>%
  mutate(gender_en = translate_gender(gender)) %>%
  count(position_simple, gender_en) %>%
  group_by(position_simple) %>%
  mutate(pct = format_pct(n / sum(n))) %>%
  ungroup() %>%
  rename(Position = position_simple, Gender = gender_en, N = n, `%` = pct) %>%
  kable(align = c("l", "l", "r", "r"))
Table 2: Gender Distribution by Position
Position Gender N %
City Councilor Female 152949 35.4%
City Councilor Male 279017 64.6%
City Councilor Undisclosed 39 0.0%
Mayor Female 2396 15.3%
Mayor Male 13277 84.7%
Mayor Undisclosed 3 0.0%
Vice-Mayor Female 3718 23.4%
Vice-Mayor Male 12199 76.6%
Vice-Mayor Undisclosed 3 0.0%

Despite mandatory gender quotas requiring parties to field at least 30% women candidates for proportional races (city council), women remain underrepresented — particularly in executive positions (mayor races).

3.2 Race/Ethnicity

Race/ethnicity is self-reported by candidates on TSE registration forms, using Brazil’s official census categories (Branca, Parda, Preta, Amarela, Indigena). We translate these as White, Brown, Black, Asian, and Indigenous, respectively, and group the latter two plus unreported cases as “Other” for analytical simplicity.

Show code 
df %>%
  mutate(race_en = translate_race(race)) %>%
  count(race_en, sort = TRUE) %>%
  mutate(pct = format_pct(n / sum(n))) %>%
  rename(Race = race_en, N = n, `%` = pct) %>%
  kable(align = c("l", "r", "r"))
Table 3: Race/Ethnicity Distribution
Race N %
White 217171 46.8%
Brown 186781 40.3%
Black 52468 11.3%
Not Reported 2763 0.6%
Indigenous 2578 0.6%
Asian 1795 0.4%
Undisclosed 45 0.0%
Show code 
df %>%
  filter(!is.na(race_simple)) %>%
  count(position_simple, race_simple) %>%
  group_by(position_simple) %>%
  mutate(pct = n / sum(n)) %>%
  ggplot(aes(x = race_simple, y = pct, fill = race_simple)) +
  geom_col(alpha = 0.9, show.legend = FALSE) +
  geom_text(aes(label = format_pct(pct)), vjust = -0.5, size = 3.5) +
  scale_y_continuous(labels = percent, expand = expansion(mult = c(0, 0.15))) +
  scale_fill_manual(values = pal_race) +
  facet_wrap(~position_simple) +
  labs(x = NULL, y = "Proportion",
       title = "Race/Ethnicity of Candidates by Position",
       subtitle = "TSE self-reported race categories")

save_figure(last_plot(), "01_race_by_position")
Figure 1: Race Distribution by Position

3.3 Age

Age is calculated from the candidate’s date of birth as of election day (October 2024). The table reports summary statistics by position type.

Show code 
df %>%
  filter(!is.na(age)) %>%
  group_by(position_simple) %>%
  summarise(
    N = n(),
    Mean = round(mean(age), 1),
    SD = round(sd(age), 1),
    Median = median(age),
    Min = min(age),
    Max = max(age),
    .groups = "drop"
  ) %>%
  rename(Position = position_simple) %>%
  kable(align = c("l", rep("r", 6)))
Table 4: Age Distribution Summary
Position N Mean SD Median Min Max
City Councilor 431963 47.0 11.5 47 17 96
Mayor 15673 50.7 11.3 50 21 96
Vice-Mayor 15917 50.5 11.7 50 19 92
Show code 
df %>%
  filter(!is.na(age), position_simple %in% c("City Councilor", "Mayor")) %>%
  ggplot(aes(x = age, fill = position_simple)) +
  geom_histogram(binwidth = 5, alpha = 0.7, position = "identity", color = "white") +
  scale_fill_manual(values = c("City Councilor" = "#3498DB", "Mayor" = "#E74C3C"),
                    name = "Position") +
  labs(x = "Age", y = "Count",
       title = "Age Distribution of Municipal Candidates",
       subtitle = "City councilor candidates skew younger than mayoral candidates")

save_figure(last_plot(), "01_age_distribution")
Figure 2: Age Distribution by Position

3.4 Education

Education level is self-declared on TSE registration forms. We simplify the original 8 TSE categories into three groups: Less than High School (illiterate through incomplete middle school), High School (complete middle school through complete high school), and College+ (incomplete or complete higher education).

Show code 
df %>%
  filter(!is.na(education_simple)) %>%
  count(position_simple, education_simple) %>%
  group_by(position_simple) %>%
  mutate(pct = format_pct(n / sum(n))) %>%
  ungroup() %>%
  rename(Position = position_simple, Education = education_simple, N = n, `%` = pct) %>%
  kable(align = c("l", "l", "r", "r"))
Table 5: Education Level Distribution
Position Education N %
City Councilor Less than HS 56252 13.0%
City Councilor High School 241400 55.9%
City Councilor College+ 134314 31.1%
Mayor Less than HS 710 4.5%
Mayor High School 4930 31.5%
Mayor College+ 10033 64.0%
Vice-Mayor Less than HS 1353 8.5%
Vice-Mayor High School 6557 41.2%
Vice-Mayor College+ 8007 50.3%
Show code 
df %>%
  filter(!is.na(education_simple)) %>%
  count(position_simple, education_simple) %>%
  group_by(position_simple) %>%
  mutate(pct = n / sum(n)) %>%
  ggplot(aes(x = education_simple, y = pct, fill = position_simple)) +
  geom_col(position = "dodge", alpha = 0.9, width = 0.7) +
  geom_text(aes(label = format_pct(pct)),
            position = position_dodge(width = 0.7), vjust = -0.5, size = 3.5) +
  scale_y_continuous(labels = percent, expand = expansion(mult = c(0, 0.15))) +
  scale_fill_manual(values = c("City Councilor" = "#3498DB", "Mayor" = "#E74C3C"),
                    name = NULL) +
  labs(x = NULL, y = "Proportion",
       title = "Education Levels of Municipal Candidates",
       subtitle = "Mayoral candidates are significantly more educated")

save_figure(last_plot(), "01_education_by_position")
Figure 3: Education Distribution by Position

4 Party and Ideology

4.1 Top Parties

Party affiliation is recorded on TSE registration forms. Brazil has a large and fragmented party system; the table below lists the 20 largest parties by number of municipal candidates.

Show code 
df %>%
  count(party_abbrev, sort = TRUE) %>%
  head(20) %>%
  mutate(
    pct = format_pct(n / nrow(df)),
    rank = row_number()
  ) %>%
  select(rank, Party = party_abbrev, N = n, `% of All` = pct) %>%
  kable(align = c("r", "l", "r", "r"))
Table 6: Top 20 Parties by Number of Candidates
rank Party N % of All
1 MDB 44505 9.6%
2 PP 39920 8.6%
3 PSD 38944 8.4%
4 UNIÃO 36623 7.9%
5 PL 36074 7.8%
6 REPUBLICANOS 34040 7.3%
7 PT 30150 6.5%
8 PSB 26560 5.7%
9 PODE 23816 5.1%
10 PDT 22937 4.9%
11 PSDB 22104 4.8%
12 PRD 17096 3.7%
13 AVANTE 16527 3.6%
14 SOLIDARIEDADE 15105 3.3%
15 NOVO 7615 1.6%
16 AGIR 7375 1.6%
17 DC 7172 1.5%
18 MOBILIZA 6628 1.4%
19 CIDADANIA 5038 1.1%
20 PV 4796 1.0%

4.2 Ideology Distribution

Party ideology scores are drawn from Bolognesi et al.’s expert survey, which rates each party on a 0–10 left-right scale. We classify scores below 4.0 as Left, 4.0–7.1 as Center, and above 7.1 as Right. Each candidate inherits the ideology score and category of their party.

Show code 
df %>%
  filter(!is.na(ideology_category)) %>%
  count(ideology_category) %>%
  mutate(pct = format_pct(n / sum(n))) %>%
  rename(Ideology = ideology_category, N = n, `%` = pct) %>%
  kable(align = c("l", "r", "r"))
Table 7: Candidates by Ideology Category
Ideology N %
Left 60730 13.1%
Center 166746 36.0%
Right 236125 50.9%
Show code 
df %>%
  filter(!is.na(ideology_score)) %>%
  ggplot(aes(x = ideology_score)) +
  geom_histogram(binwidth = 0.25, fill = "#3498DB", alpha = 0.7, color = "white") +
  geom_vline(xintercept = c(4, 7.1), linetype = "dashed", color = "gray40") +
  annotate("text", x = 2, y = Inf, label = "Left", vjust = 2, color = "#E74C3C", fontface = "bold") +
  annotate("text", x = 5.5, y = Inf, label = "Center", vjust = 2, color = "#95A5A6", fontface = "bold") +
  annotate("text", x = 8.5, y = Inf, label = "Right", vjust = 2, color = "#3498DB", fontface = "bold") +
  labs(x = "Ideology Score (0 = Far Left, 10 = Far Right)", y = "Count",
       title = "Distribution of Party Ideology Scores",
       subtitle = "Based on Bolognesi et al. expert survey scores",
       caption = "Dashed lines mark Left/Center and Center/Right thresholds.")

save_figure(last_plot(), "01_ideology_distribution")
Figure 4: Party Ideology Score Distribution

5 Geography

5.1 By Region

Brazil’s 27 states are grouped into five macro-regions: North, Northeast, Center-West, Southeast, and South. The table below shows the distribution of candidates across regions.

Show code 
df %>%
  filter(!is.na(region)) %>%
  count(region) %>%
  mutate(pct = format_pct(n / sum(n))) %>%
  rename(Region = region, N = n, `%` = pct) %>%
  kable(align = c("l", "r", "r"))
Table 8: Candidates by Region
Region N %
North 43535 9.4%
Northeast 119664 25.8%
Center-West 38455 8.3%
Southeast 179266 38.7%
South 82681 17.8%

5.2 By State

Show code 
df %>%
  filter(!is.na(region)) %>%
  count(state_abbrev, region, sort = TRUE) %>%
  ggplot(aes(x = reorder(state_abbrev, n), y = n, fill = region)) +
  geom_col(alpha = 0.9) +
  coord_flip() +
  scale_fill_manual(values = pal_region, name = "Region") +
  scale_y_continuous(labels = comma) +
  labs(x = NULL, y = "Number of Candidates",
       title = "Candidates by State",
       subtitle = "Ordered by total number of candidates")

save_figure(last_plot(), "01_candidates_by_state")
Figure 5: Candidates by State

6 Electoral Outcomes

6.1 Election Rate by Position

Election rate is the proportion of candidates who won their race (elected = 1). This includes candidates elected outright (‘ELEITO’) and those elected by average (‘ELEITO POR MEDIA’) in the proportional system. Note that election rates differ sharply by position: city council races have many seats per municipality, while mayoral races have only one winner.

Show code 
df %>%
  filter(!is.na(elected)) %>%
  group_by(position_simple) %>%
  summarise(
    N = n(),
    Elected = sum(elected),
    `Election Rate` = format_pct(mean(elected)),
    .groups = "drop"
  ) %>%
  rename(Position = position_simple) %>%
  kable(align = c("l", "r", "r", "r"))
Table 9: Election Rate by Position
Position N Elected Election Rate
City Councilor 408469 57433 14.1%
Mayor 15053 5470 36.3%
Vice-Mayor 15059 5470 36.3%

6.2 Election Rate by Key Variables

The table below disaggregates election rates among city councilor candidates by gender, race, education, and party ideology. These baseline rates provide context for evaluating LGBTQ+ candidate outcomes in subsequent chapters.

Show code 
councilors <- df %>% filter(position_simple == "City Councilor", !is.na(elected))

bind_rows(
  # Gender
  councilors %>%
    mutate(gender_en = translate_gender(gender)) %>%
    group_by(group = gender_en) %>%
    summarise(n = n(), rate = mean(elected), .groups = "drop") %>%
    mutate(dimension = "Gender"),
  # Race
  councilors %>%
    filter(!is.na(race_simple)) %>%
    group_by(group = race_simple) %>%
    summarise(n = n(), rate = mean(elected), .groups = "drop") %>%
    mutate(dimension = "Race"),
  # Education
  councilors %>%
    filter(!is.na(education_simple)) %>%
    group_by(group = education_simple) %>%
    summarise(n = n(), rate = mean(elected), .groups = "drop") %>%
    mutate(dimension = "Education"),
  # Ideology
  councilors %>%
    filter(!is.na(ideology_category)) %>%
    group_by(group = ideology_category) %>%
    summarise(n = n(), rate = mean(elected), .groups = "drop") %>%
    mutate(dimension = "Ideology")
) %>%
  mutate(rate = format_pct(rate), n = format_n(n)) %>%
  select(Dimension = dimension, Group = group, N = n, `Election Rate` = rate) %>%
  kable(align = c("l", "l", "r", "r"))
Table 10: Election Rates by Demographics (City Councilor Candidates)
Dimension Group N Election Rate
Gender Female 144,292 7.3%
Gender Male 264,176 17.8%
Gender Undisclosed 1 0.0%
Race White 188,829 16.1%
Race Brown 165,676 13.4%
Race Black 47,567 8.3%
Race Other 6,396 12.1%
Education Less than HS 52,807 10.6%
Education High School 228,404 12.9%
Education College+ 127,257 17.6%
Ideology Left 52,405 11.4%
Ideology Center 146,845 15.2%
Ideology Right 209,219 14.0%

7 Summary

This chapter establishes the baseline portrait of Brazil’s 2024 municipal candidate pool, documenting the distribution of candidates across demographic, partisan, and geographic dimensions. These patterns serve as the reference point for all subsequent comparisons.

With this baseline in hand, the next chapter asks: How do LGBTQ+ candidates differ from this picture?