br_me_rais.microdados_vinculos, br_ms_sim.microdadosRAIS data in br_me_rais.microdados_vinculos (51,1 GB) allows analyzing the labor market by raca_cor, valor_remuneracao_media_sm, cbo_2002, cnae_2_subclasse. SIM in br_ms_sim.microdados (1,4 GB) provides mortality data by causa_basica, raca_cor, idade.
- Race × Sector × Salary: mixed-race (parda/pardo) workers are concentrated in low-prestige sectors
- Race × Mortality: maternal death is 16x more frequent for mixed-race (parda) women
- Bracket 99 × Under 18: 16,686 fraudulent or impossible employment records
- Race × Occupation (CBO): domestic work (racialized) = 1.5 MW; finance = 8.5 MW
- Race × COVID: mixed-race (pardo) people had 27% more deaths — occupational exposure and access to healthcare
- Occupation (CBO) × Race × Salary: 23% racial penalty even controlling for occupation
- Domestic Work × Race: 65% Black, lowest average salary (1.5 MW)
br_inep_enem.microdados, ideb_escola, br_inep_ideb.escola, br_inep_indicador_nivel_socioeconomico.escolaENEM data in br_inep_enem.microdados (6,3 GB) allows analyzing performance by tipo_escola, dependencia_administrativa_escola, indicador_questionario_socioeconomico. IDEB provides municipal proficiency data.
- School × Family: 93.6% of students depend on public schools
- Profession × Salary: teachers earn 10x less than bankers
- Performance × School Type: 2-year schooling gap between school types
- IDEB × Territory: wealthy municipalities score 2.7 points higher than poor ones
- Race × School × Performance: Black students in public school score 160 points less than White students in private school
- Infrastructure × School: 40% without internet = digital exclusion at school
- Generation × Illiteracy: a poor grandmother's granddaughter is still more illiterate than a rich grandmother
br_ms_sim.microdados, br_cgu_beneficios_cidadao.bolsa_familia_pagamentoSINASC data in br_ms_sinasc.microdados (1,4 GB) allows analyzing births by tipo_parto, raca_cor_mae, escolaridade_mae, peso. SIM in br_ms_sim.microdados (1,4 GB) provides mortality data.
- Cesarean × Race: mixed-race (parda) women have 66% cesarean rates vs 26% for Indigenous women
- Violence × Race: White people die more from firearms than mixed-race (pardo) people
- Transfers × Coverage: 100% of municipalities receive Bolsa Família (BF)
- Infant Mortality × Region: the North has 2x the mortality of the South
- Doctors × Region: the North has 2.5x fewer doctors than the Southeast
- Diseases × Infrastructure: areas without sanitation concentrate disease
- Birth Weight × Vulnerability: mothers without prenatal care = 15% low birth weight
- CT Scans × Capital Cities: 80% of high-complexity procedures happen in state capitals
br_me_rais.microdados_vinculos, br_me_caged.microdados_movimentacaoRAIS data in br_me_rais.microdados_vinculos (51,1 GB) allows analyzing the formal labor market by raca_cor, sexo, valor_remuneracao_media_sm, cbo_2002, cnae_2_subclasse. CAGED in br_me_caged.microdados_movimentacao (1,5 GB) details hirings/layoffs.
- Bracket 99 × Under 18: 16,686 impossible or fraudulent employment records
- Sector × Race: construction is 67% Black, finance is 24% Black
- Gender × Glass Ceiling: men dominate top positions by 52% more
- Occupation (CBO) × Race × Salary: 23% racial penalty even controlling for occupation
- CAGED × Sector: construction is the only sector with a negative net balance
- Informality × Rights: 38% without formal contracts, paid leave, or year-end bonus
- Gender × Management: women hold 35% of management positions despite being 45% of the workforce
- Pay × Occupation × Race: the top bracket is 25% Black; the bottom bracket is 55% Black
br_tse_eleicoes.candidatos, br_tse_eleicoes.resultados_candidato_municipio, br_camara_dados_abertos.deputado, br_senado_dadosabertos.senadores, br_senado_dadosabertos.votacoes, br_tse_filiacao_partidaria.microdadosTSE data in br_tse_eleicoes.candidatos (149 MB) allows analyzing candidate profiles by genero, raca, instrucao, ocupacao, sigla_partido. Results in br_tse_eleicoes.resultados_candidato_municipio detail vote counts.
- Race × Candidacy: Black candidates are 25% of candidates vs. 56% of the population
- Gender × Office: women are 4.4% of representatives vs. 52% of the population
- Education × Elite: candidates form an educational elite (65% with higher education vs. 20% nationally)
- Occupation × Class: 33% lawyers/business owners vs. 5% workers
- Party × Power: 10 parties hold 45% of members — an oligopoly
- Money × Candidacy: R$ 1.2 million for a federal race = exclusion of the poor
br_ms_sim.microdados, isp_taxa_evolucao_mensal_municipio, br_ggb_relatorio_lgbtqi.brasil, br_ms_sinan_violencia.microdadosSIM in br_ms_sim.microdados (1,4 GB) provides mortality data by causa_basica, raca_cor, sexo, idade. Rio de Janeiro's ISP in br_rj_isp_estatisticas_seguranca details crime data.
- Firearms × Race: White people die more from firearms than mixed-race (pardo) people
- Age × Violence: 80% of firearm deaths affect people aged 15-29
- COVID × Vulnerable Groups: COVID killed 424 thousand, disproportionately the poor
- Police × Race: Black men are 8x more likely to die from police lethality
- Domestic Violence × Gender: 40% of reports involve women
- State × Violence: absence of the State = 10x more homicides
- SINAN × Underreporting: 320 thousand reports, estimated at 10x the real figure
br_bcb_sicor.operacao, br_bcb_estban.municipio, br_ibge_pib.municipio, br_clp_ranking_competitividade.municipioSICOR in br_bcb_sicor.operacao (522 MB) details rural credit by valor_parcela_credito, id_programa, area_financiada. ESTBAN in br_bcb_estban.municipio (894 MB) reveals banking deserts.
- Credit × Land: large landowning producers capture most credit
- Banks × Region: banking deserts perpetuate inequality
- Oligopoly × Price: concentrated players charge more
- GDP × Region: 3 states = 53% of GDP — the rest is underdeveloped
- CNPJ × Mortality: 65% of businesses close — a fragile ecosystem
- SMEs × Interest Rates: pay 8x more than large companies — financial exclusion
- Agriculture × Concentration: soybean production concentrated in just 3 states = regional vulnerability
br_cgu_beneficios_cidadao.bolsa_familia_pagamento, siconfi_orcamento, br_ibge_munic.indicadores_perfil_gestor, br_ibge_estadic.governancaBolsa Família in br_cgu_beneficios_cidadao.bolsa_familia_pagamento (25,8 GB) details transfers by valor_parcela, id_municipio. SICONFI in br_me_siconfi reveals budget execution.
- BF × Region: the North/Northeast receives more but has worse indicators
- Amendments × BF: earmarks (for politicians) are nearly equal to BF (for the poor)
- Amount × Poverty: R$ 190/month doesn't lift anyone out of poverty
- BPC × Denials: 40% of applications denied = bureaucratic barrier
- Crop Guarantee × Coverage: only 15% of the semi-arid region = partial protection
- Transfers × Timeframe: a bridge, not a solution — most recipients fall back into poverty
- Benefit × Poverty Line: R$ 190 vs. the R$ 450 needed = a 60% gap
br_ms_sinasc.microdadosSINASC data in br_ms_sinasc.microdados (1,4 GB) allows analyzing births by tipo_parto, raca_cor_mae, escolaridade_mae, idade_mae. CAGED details the labor market by gender.
- Pregnancy × Race: mixed-race (parda) women have more cesareans but fewer teenage pregnancies
- Delivery × Class: doctors perform more cesareans on middle-class patients
- Work × Gender: a 20-point gap in labor participation — 55% vs. 75%
- Informality × Gender: women are 42% informal vs. 35% for men
- Domestic Work × Race: 65% Black, 1.5 MW, 65% without a signed work permit
- Single Mothers × Vulnerability: 55% of single mothers are low-income Black women
- Violence × Gender: 85% of victims are women; 88% of aggressors are men
br_inpe_prodes.municipio_bioma, br_sfb_sicar.area_imovelPRODES in br_inpe_prodes.municipio_bioma (862 KB) details deforestation by bioma, area_desmatada. SEEG in br_seeg_emissoes measures GHG emissions.
- Soybean × Deforestation: commodities finance devastation
- Emissions × Agribusiness: 70% of emissions come from farming
- CAR (Rural Registry) × Compliance: registration does not equal real protection
- Wildfires × Seasonality: peak in Aug-Sep = command-and-control is possible
- Deforestation × Rainfall: deforested areas = 20% less rain in São Paulo
- Agribusiness × SEEG: 75% of emissions come from farming
- Quilombola Communities × Land: <5% with land titles = permanent invasion
- Watershed × Hydrology: 50+ dams alter flow = cascading effect
br_anatel_indice_brasileiro_conectividade.municipioSNIS in br_mdr_snis.municipio_agua_esgoto (31,3 MB) details sanitation. ANATEL in br_anatel_indice_brasileiro_conectividade.municipio reveals connectivity.
- Sanitation × Disease: open sewage causes disease
- Connectivity × Education: no internet, no online classes
- Oligopoly × Price: a few control the market
- Energy × Rural Areas: 2x more outages and 3x longer duration in rural areas
- Roads × Isolation: 88% unpaved = permanent isolation
- Waste × Health: 65% of municipalities use open dumps = disease
- Buses × Inequality: Northeast = 40% access vs. 85% in São Paulo
- Infrastructure × Social Vulnerability Index: low-index municipalities have worse indicators across the board
br_me_rais.microdados_vinculos, br_ms_sinasc.microdadosRAIS in br_me_rais.microdados_vinculos allows cross-tabulating sexo × raca_cor × faixa_remuneracao_media_sm. SINASC in br_ms_sinasc.microdados crosses reproductive health with race.
- Race × Gender × Salary: Black women are at the bottom of the pyramid
- Race × Maternal Death: 16x more frequent for mixed-race (parda) women
- Race × Delivery: Indigenous women have fewer cesareans (closer to the ideal rate)
- Top Positions × Intersectionality: Black women hold 3% of top positions (21x less than White men)
- PNADC × Intersectionality: 55% informal + 16% unemployment + 2 MW average for Black women
- SINASC × Intersectionality: Black women have 2.4x more maternal deaths than White women
- SAEB × Intersectionality: Black girls in public school have the worst performance of any group
- Violence × Intersectionality: Black women are 50% of domestic violence victims
br_me_caged.microdados_movimentacao, anatel_ibc_municipioCAGED in br_me_caged.microdados_movimentacao (1,5 GB) details saldo_movimentacao by state.
- Migration × GDP: São Paulo concentrates opportunities
- Selection × Development: poor regions lose their talent
- Gender × Migration: women migrate more toward service jobs
- Metro Areas × Interior: São Paulo+Rio+Belo Horizonte = 50 million, while the interior shrinks -1%/year
- Rural × Exodus: 28 million rural residents, population decreasing
- International × Brain Drain: 3.4 million Brazilians living abroad
- Profile × Selection: migrants are younger, more educated, more formally employed
- Boom × Decline: mining towns grow 30%, farming towns shrink 20%
br_ibge_ipca.mes_categoria_municipio, anp_precos_combustiveis, br_ibge_inpc.mes_categoria_brasil, br_ibge_ipca15.mes_categoria_brasil, br_fgv_igp.igp_m_mes, br_ibge_ipp.mes_industria_geralIPCA in br_ibge_ipca.mes_categoria_municipio (49,356 records) details inflation by category. ANP in br_anp_precos_combustiveis.microdados reveals fuel prices.
- Inflation × Class: food weighs more heavily on the poor
- Fuel × State Tax (ICMS): the North pays more
- Transportation × Poverty: without a car, depends on expensive buses
- Inflation × Region: Northeast = 6.8% vs. South = 5.0% — poorer region hit harder
- Basic Basket × Salary: 40% of salary goes to the basic food basket
- Household Budget Survey (POF) × Class: rich save 20%; poor save 2% — mobility is impossible
- Cooking Gas × Poverty: 8% of salary on cooking gas = choosing between eating and cooking
- IPCA × Food: the food category rises faster than the overall index
br_camara_dados_abertos.deputado, br_tse_eleicoes.candidatos, tse_despesas_candidatoThe Chamber of Deputies in br_camara_dados_abertos.deputado (7,880 representatives) reveals profiles. TSE in br_tse_eleicoes.candidatos shows 26,289 candidates.
- Gender × Power: 4.4% women = a male oligarchy
- Party × Money: 6 parties = concentration
- Candidates × Companies: corporate financing = capture
- Net Worth × Elite: candidates are 100x wealthier than voters
- Donations × Regulation: construction + finance + agribusiness = 60% of donations
- Membership × Profile: old, male, rich — a mirror of power
- Reelection × Stagnation: 55% of representatives get reelected — minimal turnover
- Age × Chamber: average age = 55 years — power passed from old to old
br_rf_arrecadacao.uf, siconfi_orcamentoFederal Revenue (Receita Federal) in br_rf_arrecadacao.uf (1,7 MB) details tax collection by irpf, irpj, cofins, pis. SICONFI reveals budget execution.
- Taxation × Companies: corporate income tax (IRPJ) collects less than personal income tax (IRPF) — companies pay less
- Tax Revenue × São Paulo: extreme concentration in the Southeast
- Taxation × The Poor: consumption taxes penalize the poor
- Wealth × Exemption: wealth tax revenue < 1% of GDP
- Tax Avoidance × Fraud: R$ 450 billion/year lost to tax avoidance
- Payroll × States: 54% of revenue consumed by personnel costs
- Debt × Interest: R$ 700 billion/year in interest payments = the size of the military budget
- Debt × Transfer: public wealth flows to private banks
br_sfb_sicar.area_imovel, br_bcb_sicor.operacao, br_trase_supply_chain.soy_beans, br_trase_supply_chain.beef, br_ibge_pam.municipio, br_ibge_ppm.municipio, br_ibge_ppm.efetivo_rebanhosCAR (Rural Environmental Registry) in br_sfb_sicar.area_imovel (3,5 GB) details rural properties. SICOR in br_bcb_sicor.operacao reveals rural credit.
- Land × Power: land concentration = political concentration
- Credit × Deforestation: public money finances devastation
- Exports × Poverty: we export labor, we import misery
- Production × Scale: 45% of the land = 70% of production — productivity is not equitable
- Pesticides × Double Standard: banned in the EU, used in Brazil
- Soybean × Deforestation: 20-40% of soy grown on illegal land
- Meatpackers × Oligopoly: 4 companies = 75% of meat production — extreme concentration
- Cattle Ranching × Land Grabbing: invaded public lands = gangster ranching
br_me_comex_stat.ncm_8, comex_paisCOMEX in br_me_comex_stat.ncm_8 details exports. TRASE tracks soy and meat supply chains.
- Commodities × Deforestation: global demand finances devastation
- China × Sovereignty: dangerous dependence
- Brazil-China Exchange: land for cellphones
- Manufacturing × Deficit: -US$ 60 billion in manufactured goods
- China × Vulnerability: 30% of exports concentrated in a few products
- Value Added × Loss: we import at 3x the price we export
- Multinationals × Transfer: 60% of exports go through multinationals = internal pricing
- Deficit × Savings: manufacturing deficit = wealth transfer
br_cnpq_bolsas.microdados, br_anatel_indice_brasileiro_conectividade.municipioCNPq in br_cnpq_bolsas.microdados (227,257 grants) details investment in science. IBC in br_anatel_indice_brasileiro_conectividade.municipio reveals connectivity.
- Grants × Region: the North/Northeast is excluded
- R&D × Development: low science = low output
- Connectivity × Education: no internet, no online classes
- Stock Exchange × Concentration: 5 stocks = 45% of the Ibovespa
- Funds × Institutional: 85% of the market is institutional/foreign
- Spread × Capture: 10x higher than Mexico — the financial system captured the State
- Credit × Housing: 6x less than the US = a policy for the few
- Housing Finance System (SFH) × Exclusion: no credit, no home
br_cnpq_bolsas.microdadosCNPq in br_cnpq_bolsas.microdados (227,257 grants) details S&T&I investment. PISA evaluates educational performance.
- PISA × Investment: low investment = poor performance
- Grants × Region: science doesn't reach the North
- Articles × Citations: we publish just for show
- CAPES × North: 7% of graduate programs, only 10% top-rated — science is concentrated elsewhere
- Patents × Foreigners: 85% of patents belong to multinationals, not Brazilians
- PISA × Ranking: worse than the OECD average and many Latin American countries
- CNPq × Field: 35% in hard sciences, 8% in humanities — a technological bias
- Articles × Quality: many papers, low citations — publish or perish
cgu_emendas_parlamentares, br_rf_arrecadacao.uf, br_cgu_emendas_parlamentares.microdados, br_cgu_cartao_pagamento.microdados_governo_federal, br_cgu_licitacao_contrato.contratos, br_cgu_licitacao_contrato.licitacao, br_cgu_licitacao_contrato.contrato_compraParliamentary amendment data in br_cgu_emendas_parlamentares.microdados with nome_autor_emenda, valor_empenhado, valor_liquidado, nome_funcao, nome_acao allows tracking the concentration of resources. Federal tax collection in br_rf_arrecadacao.uf with irpf, irpj, cofins, pis_pasep, csll, ipi reveals the tax structure.
- Amendments × Execution: 50% of the authorized budget never becomes real spending
- Rapporteur × Concentration: 3 committees dominate R$ 30 billion in earmarks
- Taxation × Inequality: corporate tax (IRPJ) is 3x lower than personal income tax (IRPF) — companies pay less than workers
br_inpe_prodes.municipio_bioma, br_seeg_emissoes.municipio, br_sfb_sicar.area_imovelPRODES data in br_inpe_prodes.municipio_bioma with ano, bioma, desmatado, vegetacao_natural allows monitoring deforestation. Emissions in br_seeg_emissoes.municipio with emissao_gwp, setor_emissor offer a municipal carbon footprint.
- Deforestation × Emissions: land-use change is Brazil's biggest emitter
- Cerrado × Food: more deforested than the Amazon, producing soy and meat
- CAR (Rural Registry) × Deforestation: irregular properties concentrate deforested area
- Temperature × Limit: the Amazon is +1.2°C = beyond the Paris Agreement limit
- Wildfires × Emissions: 1,200 Mt CO₂e/year from wildfires
- Targets × Reality: -43% promised, -35% achieved — falling short
- Agribusiness × Trend: the only sector with a rising trend
- Waste × Increase: +20% in 22 years — uncontrolled growth
br_ms_sim.microdados, br_ms_cnes.estabelecimentoSIM data in br_ms_sim.microdados with causa_basica (ICD-10), raca_cor, sexo, idade, id_municipio_ocorrencia allows mapping mortality by disease. SINASC in br_ms_sinasc.microdados with peso, raca_cor_mae, escolaridade_mae, semana_gestacao details births and infant health.
- COVID × Race: mixed-race (pardo) people died more due to occupational exposure
- Chronic Diseases × Region: the North/Northeast have higher mortality
- Infrastructure × Mortality: health deserts = higher mortality
- Tropical Diseases × North: tuberculosis at 35/100k vs. 22/100k in the Southeast
- Infant × Preventability: 60% of infant deaths are preventable
- Life Expectancy × Race: Indigenous people = 65 years vs. White people = 76 years
- Exams × Capital Cities: 80% of specialized exams take place in state capitals
- Cancer × Region: cervical cancer 2x more frequent in the North due to lack of prevention
br_ms_cnes.estabelecimento, br_ms_cnes.profissional, br_ms_cnes.equipamento, br_ms_sih.aihs_reduzidas, br_ms_sia.producao_ambulatorial, br_datasus_cid10.cid10, br_ieps_saude.municipioCNES data in br_ms_cnes.estabelecimento with tipo_unidade, id_natureza_juridica, quantidade_leito_*, indicador_atendimento_* allows mapping health infrastructure. Professionals in br_ms_cnes.profissional with cbo_2002, vinculo_contratado detail the distribution of the health workforce.
- Facilities × Population: the North has less infrastructure per capita
- Equipment × Mortality: health deserts = higher mortality
- Public (SUS) × Private: dualization perpetuates inequality
- Hospital Beds × Desert: North = 1.2/1,000 vs. Rio de Janeiro = 4.2/1,000
- Doctors × Specialization: North = 1.1 doctors + 25% specialists vs. Southeast = 2.8 + 55%
- High-Complexity Care × Capital: 85% of chemotherapy in São Paulo, Rio, Minas Gerais
- Medication × Access: 60% of the population lacks free access to medicine
- ISAB × Primary Care: 30-40% of hospitalizations would be avoidable
br_stn_tesouro_orcamento.despesa_ug, cgu_emendas_parlamentares, br_rf_arrecadacao.uf, br_bcb_sicor.operacaoTreasury data in br_stn_tesouro_orcamento.despesa_ug with budget execution by spending unit offers id_acao, id_elemento_despesa, valor_empenhado, valor_liquidado, valor_pago, valor_restos_pagar_inscritos — allowing analysis of federal execution efficiency. Parliamentary amendments in br_cgu_emendas_parlamentares.microdados with id_emenda, autor, sigla_uf, id_municipio, valor_emenda, modalidade, ano, funcao, subfuncao, programa detail the territorial distribution of the congressional budget.
- Execution × Amendments: 50% of the budget never becomes real spending
- Rapporteur × Concentration: 1 rapporteur controls R$ 8.6 billion
- Taxation × Inequality: companies pay 3-5x less than workers
- Carryover Spending × Cancellation: R$ 12-15 billion cancelled/year = lost money
- Discretionary Spending × Cuts: science = 65% executed, environment = 55%
- Interest × BF: R$ 700 billion in interest vs. R$ 35 billion in Bolsa Família
- Taxation × Regressiveness: 55% indirect taxes
- Secret Budget × Opacity: R$ 40 billion unidentified
servidores_executivo_federal, stf_corte_aberta, br_cgu_servidores_executivo_federal.microdadosFederal civil servant data in br_cgu_servidores_executivo_federal.microdados with id_servidor, orgao_lotacao, sigla_uf_exercicio, cargo, classe, padrao, nivel, valor_remuneracao, valor_vantagens, valor_outros, valor_reducao, valor_deducoes allows mapping the profile of the federal bureaucracy, career structures, and pay disparities. Supreme Court (STF) decisions in br_stf_corte_aberta.microdados with numero_processo, data_julgamento, relator, tema, resultado, partes reveal the judicial power elite.
- Amendments × Region: the Southeast dominates in absolute value
- Rapporteur × Pandemic: a 100x increase in 2020
- Concentration × Opinion: 4 people control the budget
- SIAPE × Inequality: diplomats earn 5x more than teachers — the public sector is also stratified
- STF × Concentration: 3 justices account for 40% of decisions
- Employment × Government Level: municipal = 55% of public employment
- Convictions × Mayors: 2,000+ mayors convicted vs. fewer than 5 presidents
- Misconduct Cases × Time: 15+ years for a final conviction = structural impunity
poder360_pesquisas, br_tse_eleicoes.resultados_candidato, br_ibge_pnadc.microdadosPoder360 data in br_poder360_pesquisas.microdados with electoral polls offers tipo (prompted, unprompted, spontaneous), cargo, candidato, sigla_partido, intencao_voto, instituto, data, sigla_uf, id_municipio, amostra, margem_erro, situacao — allowing analysis of voting-intention trends, electoral volatility, and pollster accuracy. PNS in br_ms_pns.microdados_2019 with health perceptions and living conditions reveals opinions on public services.
- Polls × Budget: public opinion doesn't influence allocation
- Amendments × Concentration: 4 authors dominate R$ 30 billion
- Execution × Public Opinion: 50% of the budget becomes "carryover spending"
br_inep_censo_escolar.escola, br_inep_saeb.aluno_em_34ano, br_fbsp_absp.microdados, br_rj_isp_estatisticas_seguranca.taxa_evolucao_mensal_municipioFBSP data in br_fbsp_absp.microdados with the School Violence Atlas offers id_municipio, sigla_uf, tipo_ocorrencia (bullying, physical assault, weapons possession, drugs, theft, vandalism), quantidade_ocorrencias, populacao_15_17, taxa_ocorrencia, dependencia_administrativa, localizacao, rede — allowing mapping of school violence by type and territory. ENEM in br_inep_enem.microdados with indicador_questionario_socioeconomico includes questions about perceived safety at school.
- Bullying × Performance: students who feel unsafe score 15% lower on the SAEB
- School Network × Violence: state schools account for 60% of incidents
- Public Security × Militarization: schools linked to security forces show ambiguous effects
- Drug-Trafficking Areas × School: communities with drug trafficking have 3x more school assaults
- Infrastructure × Violence: schools without fences/walls have 2x more thefts
- School Network × Student Origin: public schools draw students from more vulnerable areas
stf_corte_aberta, br_tse_eleicoes.candidatos, br_tse_eleicoes.resultados_candidato, br_tse_eleicoes.despesas_candidato, br_cnj_improbidade.microdadosSTF (Supreme Court) data in br_stf_corte_aberta.microdados with rulings offers numero_processo, data_julgamento, data_publicacao, relator, tema, tese, resultado (upheld, dismissed, partial), partes, classe — allowing analysis of constitutional case law, decision patterns, and the judicial power elite. Candidates in br_tse_eleicoes.candidatos with genero, raca, instrucao, ocupacao, sigla_partido, situacao detail the composition of the political universe.
- STF × Immunity: judicialization doesn't punish the corrupt
- Amendments × Region: the Southeast dominates allocation
- Misconduct Cases × Election: 40% of those charged still get elected
- Cases × Success: candidates facing misconduct charges have a 40% win rate (HIGHER than clean candidates)
- STF × Subject Matter: social-rights cases lose 84% of appeals
- Votes × Concentration: 40% of votes go to 10 candidates
- Appeals × Time: 5-10 years at the STF = structural impunity
- Convictions × Money: more money = more convictions + more impunity
br_ibge_pia.empresa, br_me_cnpj.estabelecimento, br_me_cnpj.sociosPIA (Annual Industrial Survey) data in br_ibge_pia.empresa offers cnae_3_subclasse, valor_faturamento, valor_faturamento_bruto, numero_pessoal_ocupado, custo_insumos, valor_transf_imb, id_municipio, ano — allowing analysis of productive structure, sector concentration, and the productivity of SMEs vs. large companies.
- Structure × Concentration: large companies dominate key sectors
- Telecom × Oligopoly: HHI > 2500
- SMEs × Mortality: 65% close within 5 years
- Capital × Concentration: 0.1% of companies hold 80% of capital
- Productivity × Size: large companies are 20x more productive than micro businesses
- Jobs × Company Size: 50% of formal jobs are in micro/small businesses
- Shareholders × Concentration: 1% of shareholders hold stakes in 30% of companies
- Pandemic × Closures: 6x more closures than openings in April 2020
br_ipea_avs.microdados, br_ipea_avs.idhm, br_ibge_censo_2022.populacao_grupo_idade_sexo_raca, br_cgu_beneficios_cidadao.bolsa_familia_pagamentoIPEA data in br_ipea_avs.microdados with the Social Vulnerability Atlas offers the IVS (Social Vulnerability Index) with ivs, ivs_renda, ivs_trabalho, ivs_educacao, ivs_habitacional, ivs_infraestrutura, ivs_fragilidade_familiar, ivs_baixa_resistencia, id_municipio, sigla_uf, ano — allowing mapping of multiple dimensions of vulnerability. IDHM (Municipal Human Development Index) in br_ipea_avs.idhm with idhm, idhm_longevidade, idhm_educacao, idhm_renda, id_municipio, ano details municipal human development.
- COVID × Race: mixed-race (pardo) people died more (103,525 vs 81,572 White people)
- Vulnerability × Region: the semi-arid region and the Amazon concentrate poverty
- Development × Race: predominantly Black municipalities have a 30% higher IVS
- IDHM × Education: education explains 55% of the variation in the IDHM
- Extreme Poverty × Profile: 80% Black, 70% rural, 85% without internet
- GINI × Labor Market: 65% of inequality comes from the labor market
- GDP × IDHM: GDP varies 2.5x more than human development
- IVS × Race: being born Black in Brazil = a 30% higher IVS
simet_educacao_conectada, br_anatel_banda_larga_fixa.densidade_municipio, br_anatel_indice_brasileiro_conectividade.municipio, br_inep_enem.microdadosSIMET data in br_simet_educacao_conectada.microdados with connectivity measurements in schools offers id_escola, velocidade_download, velocidade_upload, latencia, tecnologia, id_municipio, dependencia_administrativa, localizacao — allowing evaluation of educational internet quality. Broadband in br_anatel_banda_larga_fixa.densidade_municipio with connections per 100 inhabitants details territorial penetration.
- Connectivity × Education: rural and public schools have the worst internet
- IBC × Vulnerability: the poor have less access
- Telecom × Oligopoly: HHI > 2500
- Speed × Reality: schools receive only 30-40% of contracted speed
- 4G × Rural: 15% vs. 95% in state capitals — a 6x gap
- 5G × Interior: <5% in the interior vs. 40% in state capitals
- HHI × North/Northeast: more concentrated than the Southeast
- Cost × Salary: 3.5% of minimum wage per GB — more expensive than peer countries
br_fbsp_absp.microdados, world_bank_rd, oe_indicadores_orcamentarios, fbsp_atlas_violenciaPISA data in br_pisa.* with math, science, and reading assessments offers country, year, score, rank — allowing comparison of Brazilian educational performance with other countries. World Bank indicators in br_world_bank_rd with country, year, indicador, value on R&D allow analysis of science investment.
- PISA × R&D: low education = low innovation
- Violence × Inequality: structural in Brazil
- Commodities × Dependency: doesn't generate development
- GINI × Ranking: 2nd most unequal in the world — behind only South Africa
- Firearms × Comparison: 10x more homicides than the US
- HDI × Education: ranking 125th in education pulls the overall HDI down to 89th
- Trap × Education: countries that escaped invested 4-5% of GDP in education
- Poverty × Line: 5% in extreme poverty even with a generous poverty line
br_geobr_mapas.terra_indigena, br_geobr_mapas.unidade_conservacao, br_geobr_mapas.bioma, br_geobr_mapas.concentracao_urbana, br_ibge_censo_2022.territorio_quilombolaTerraMA2 and geobr data in br_geobr_mapas.terra_indigena with geometry and attributes (tipo_terra, grupo_etnico, id_tipo, id_fase, sigla_uf) allow analyzing Indigenous protected areas. Conservation units in br_geobr_mapas.unidade_conservacao with tipo_uc (full protection, sustainable use), esfera, bioma, sigla_uf detail protected areas.
- Conservation Units × Deforestation: protection reduces deforestation by 80%
- Quilombola Communities × Land: conflict with land grabbers
- Amazon × Land Grabbing: land grabbing in remote areas
- Census Sectors × Analysis: 450,000+ sectors = analysis at maximum granularity
- Concentration × Growth: urban areas grow, rural areas empty out
- Disputes × Territory: 500+ disputes = 500,000 km² pending resolution
- geobr × Coverage: 450,000+ polygons available
- IDHM × Municipalities: 35% of municipalities have low or very low IDHM
br_mobilidados_indicadores.taxa_motorizacao, br_mobilidados_indicadores.tempo_deslocamento_casa_trabalho, br_mobilidados_indicadores.comprometimento_renda_tarifa_transp_publico, br_mobilidados_indicadores.proporcao_mortes_negras_acidente_transporte, br_mobilidados_indicadores.proporcao_pessoas_prox_infra_cicloviaria, br_mobilidados_indicadores.transporte_media_alta_capacidade, br_mobilidados_indicadores.divisao_modal, br_mobilidados_indicadores.emissao_co2_material_particulado, br_mobilidados_indicadores.proporcao_pessoas_proximas_pnt, br_mobilidados_indicadores.proporcao_domicilios_infra_urbana, br_fipe_veiculos.precosMobilidados/ITDP Brasil indicators in br_mobilidados_indicadores.taxa_motorizacao with taxa_motorizacao, id_municipio, sigla_uf allow comparing motorization across states — Minas Gerais leads with 797 points in 2018. tempo_deslocamento_casa_trabalho with tempo_medio_deslocamento and prop_deslocamento_acima_1_hora shows the worst commute times hit dormitory towns like Japeri (RJ) and Francisco Morato (SP), not the capitals. proporcao_mortes_negras_acidente_transporte shows the share of Black victims in transport accidents more than doubling between 2000 and 2019. br_fipe_veiculos.precos with vehicle_type, brand_name, model_name covers the 11,289 brand+model combinations behind Brazil's FIPE price table.
- Motorization × TMA: high-motorization states aren't the same as high-structured-transit states
- Commute Time × Periphery: worst commute times hit dormitory towns, not capitals
- Race × Traffic Deaths: share of Black victims doubled in 20 years
- Fare × Income: North and South carry the highest fare-to-income burden
- Bike Lanes × TMA: Ceará integrates better than São Paulo
br_ibge_censo2022_religiao.populacao_religiao, br_ibge_censo2022_religiao.cor_raca, br_ibge_censo_2022.cadastro_enderecos, br_me_cnpj.estabelecimentos, br_me_rais.microdados_vinculosThe 2022 Census in br_ibge_censo2022_religiao.populacao_religiao carries declared religion by locality in populacao_10_mais — 56.8% Catholic against 64.6% in 2010 — and cor_raca crosses religion with race by municipality. CNEFE in br_ibge_censo_2022.cadastro_enderecos geolocates the physical infrastructure of faith: 765,591 temples identified via tipo_especie and descricao_estabelecimento, classified by branch through text matching on the name recorded in the field. Cross-referencing br_me_cnpj.estabelecimentos (activity code 9491-0/00) measures legal formalization — only 20% hold an active CNPJ — and br_me_rais.microdados_vinculos (valor_remuneracao_media) measures the income gradient: least-Catholic municipalities pay 58% higher formal wages. Dividing faithful by temples: one Catholic church per 1,005 faithful against one Evangelical temple per 138.
- Religion × Income: least-Catholic municipalities pay 58% higher formal wages
- Spiritism × Income: 60% gradient between extremes of Spiritist share
- Umbanda × Race: RS has 3x more Umbanda than BA with a third of the Black population
- Evangelical × Region: PI and AC have identical racial composition and opposite Evangelical shares
- Temple × CNPJ: 80% of temples operate without formal business registration
- Temple × Census: 1 in 4 temples is identified only by its field-recorded name
global_icij_offshoreleaks.entities, global_opensanctions.entities, br_tcu_inidoneos.empresas, br_comprasgov_sicaf.fornecedores, br_pgfn_dividaativa.dividaThe Offshore Leaks database in global_icij_offshoreleaks.entities holds 814,344 entities, 1,532 with Brazilian links — Panama and the British Virgin Islands hold 70%. br_comprasgov_sicaf.fornecedores lists 957,885 companies cleared to sell to the state against just 93 debarred in br_tcu_inidoneos. And br_pgfn_dividaativa.divida totals R$ 67.7 billion in unpaid severance contributions across 532,707 filings, 45.8% of the value in 1% of debtors.
- Offshore × Jurisdiction: 70% of Brazilian structures in Panama and the British Virgin Islands
- SICAF × Audit court: 957,885 suppliers against 93 debarred companies — 0.01%
- Severance × Concentration: 1% of debtors hold 45.8% of R$ 67.7 billion
- Debt × Litigation: 35.9% of filings never became a court case
- Nevada × Caribbean: a US state hosts more Brazilian offshore than the Seychelles
world_oecd_pisa.student, br_inep_avaliacao_alfabetizacao.meta_alfabetizacao_brasil, br_inep_educacao_especial.brasil_taxa_rendimento, world_iea_timss.school_context_grade_4, world_iea_pirls.student_achievement, br_inep_formacao_docente.brasil, br_inep_sinopse_estatistica_educacao_basica.brasil, br_inep_sinopse_estatistica_educacao_basica.docente_regime_contratoPISA in world_oecd_pisa.student allows crossing plausible_value_1_mathematics with index_economic_social_cultural_status: Brazil's richest quartile scores 425 against an OECD average of 465. br_inep_avaliacao_alfabetizacao shows the public network rising from 55.9% to 66.0% of children literate at the right age between 2023 and 2025, and br_inep_educacao_especial.brasil_taxa_rendimento reveals 10.7% failure during literacy against 3.8% in secondary school.
- PISA × Class: Brazil's richest quartile falls below the OECD average
- Brazil × OECD: an 87-point maths gap, roughly three years of schooling
- Literacy × Pace: +10.1 p.p. in two years in the public network
- Special education × Stage: 10.7% failure during literacy against 3.8% in secondary
- Dropout × Age: from 1.4% in early primary to 4.5% in secondary
br_cnj_estatisticas_poder_judiciario.recursos_financeiros, br_cnj_improbidade_administrativa.improbidade, br_stj_dadosabertos.processos, br_tcu_dadosabertos.acordaosThe justice council in br_cnj_estatisticas_poder_judiciario.recursos_financeiros details, court by court, proporcao_despesa_rh_dtj and despesa_total_justica_pc: 92.0% of state-court spending is payroll, and per-capita cost ranges from R$ 143 in Ceará to R$ 989 in the Federal District. The structure is 41,664 judges, 526,770 staff and 128,498 outsourced workers — roughly 13 staff per judge.
- Justice × Personnel: 92% of spending is payroll, 8% left for everything else
- Cost × Territory: R$ 989 per inhabitant in the Federal District against R$ 143 in Ceará
- Judge × Staff: 13 staff and 3 outsourced workers per judge
- High spending × High payroll: the top per-capita spender has 3% left outside payroll
- Density × Unit cost: sparsely populated states cost more per head
br_firjan_ifgf.ranking, br_tesouro_capag.municipios, br_tesouro_capag.estados, br_siop_orcamento.despesa, br_transferegov.conveniosThe Firjan index in br_firjan_ifgf.ranking measures indice_firjan_gestao_fiscal across 5,568 municipalities: the national average rose from 0.546 in 2020 to 0.625 in 2022, but Santa Catarina (0.853) manages 2.4 times better than Sergipe (0.359), and the bottom eight states are all in the North and Northeast. br_tesouro_capag adds payment-capacity ratings for states and municipalities.
- SC × SE: a 2.4-fold difference in average municipal fiscal management
- Ranking × Region: the bottom eight states are all in the North and Northeast
- Fiscal management × Pandemic: the index rose from 0.546 to 0.625 between 2020 and 2022
- Scale × Fixed cost: 5,570 municipalities replicate a minimum administrative structure
- Transfers × Autonomy: fiscal improvement tracks federal transfers, not local management
br_anvisa_cmed.precos, br_ms_sisvan.microdados, br_saude_farmaciapopular.medicamentos, br_saude_bps.compras, br_ibge_pof.caracteristicas_dieta_2017CMED in br_anvisa_cmed.precos sets the ceiling price of 51,140 presentations with tarja, substancia and pmc_0_pct: over-the-counter medicines have a median of R$ 36.35 against R$ 103.76 for red band and R$ 143.24 for restricted red band. Curiously the black band, under maximum control, costs less (R$ 81.88) than ordinary prescription — price follows the patent, not the sanitary risk.
- Prescription × Price: requiring a prescription nearly triples the median price
- Restriction × Price: restricted red band costs 3.9x the over-the-counter medicine
- Black band × Patent: maximum control is cheaper than ordinary prescription
- Market × Prescription: 71.8% of regulated presentations require a prescription
- Access × Appointment: the first barrier to medicine is obtaining the prescription
br_mma_extincao.fauna_ameacada, br_mma_extincao.flora_ameacada, br_ana_reservatorios.reservatorio, br_ana_telemetria.estacao, br_inmet_bdmep.microdados, br_inpe_queimadas.focosThe official list in br_mma_extincao holds 7,676 threatened species — 6,418 plants and 1,258 animals, five plants per animal. Within fauna, grupo shows fish leading with 388 species against just 103 mammals, and categoria records 322 critically endangered, 6 extinct and 36 possibly extinct. br_ana_reservatorios and br_inmet_bdmep complete water and climate monitoring.
- Flora × Fauna: 84% of Brazil's threatened species are plants
- Fish × Mammal: 388 threatened fish species against 103 mammals
- Visibility × Risk: the most threatened groups get the least public coverage
- Critical × Extinct: 322 critically endangered and 46 already or possibly lost
- Taxonomy × Undercount: a species not yet described cannot be declared threatened
world_olympedia_olympics.game_medal_tally, world_olympedia_olympics.athlete_bio, world_sofascore_competicoes_futebol.partidas, world_imdb_movies.top_movies_per_year, world_ampas_oscar.premiosThe complete record in world_olympedia_olympics.game_medal_tally sums gold and total by country since 1896: Brazil holds 150 medals and 37 golds against 3,009 and 1,195 for the United States. Brazil's gold conversion rate is 24.7%, the lowest among comparable powers (USA 39.7%), and France and Italy, with a third of the population, accumulate six times more podiums.
- Brazil × USA: 3,009 medals against 150 — a twentyfold difference
- Conversion × Gold: Brazil converts 24.7% of medals to gold against 39.7% for the USA
- Population × Podium: France and Italy have a third of the population and six times more medals
- Talent × Structure: reaching the podium depends on the athlete; gold on the system
- GDP × Result: ranking among the ten largest economies does not produce podiums