{
  "manifest": "Unequal World connections engine, source registry",
  "purpose": "Every data series, scholarly source, and API this tool draws on, so any claim it makes can be traced to a named, licensed, dated source. Built to be checked by a skeptical reviewer. 'verified' means an author hit the endpoint or read the embedded provenance on the stated date; it is not a guess.",
  "lastReviewed": "2026-07-04",
  "access": {
    "open": "Public, no key required.",
    "public_client_key": "A fixed key distributed inside a public client package; not a personal secret.",
    "needs_key": "Works but requires a free API key for reliable use; not yet obtained.",
    "needs_application": "Restricted access requiring registration or a data-use agreement; not yet obtained.",
    "closed": "Proprietary or all-rights-reserved; used locally only, never redistributed."
  },
  "dataSources": [
    {
      "id": "worldbank-wdi",
      "name": "World Bank World Development Indicators",
      "org": "World Bank",
      "category": "data",
      "endpoint": "https://api.worldbank.org/v2/",
      "access": "open",
      "license": "CC BY 4.0",
      "verified": "2026-07-03 (HTTP 200, SI.POV.GINI/USA, lastupdated 2026-07-01)",
      "whatWeUse": "GDP per capita PPP, extreme poverty at $2.15/day, Gini, life expectancy, CO2 per capita (wb-timeseries corpus), plus the wdi-expansion snapshot (accessed 2026-07-03): income shares (bottom 20%, top 10%), poverty at $3.65 and $6.85, poverty gap, unemployment, female and male labour force participation, women in parliament, maternal and infant mortality, child stunting.",
      "auditGap": "closed 2026-07-03: embedded source block added to wb-timeseries.json."
    },
    {
      "id": "worldbank-data360",
      "name": "World Bank Data360",
      "org": "World Bank",
      "category": "data",
      "endpoint": "https://data360api.worldbank.org/data360/",
      "access": "open",
      "license": "CC BY 4.0",
      "verified": "2026-07-03 (HTTP 200, indicators list for WB_WDI)",
      "whatWeUse": "Verify links on every measured card resolve to Data360. Placed 2nd in the World Bank 360 Challenge. Candidate for live indicator fetch."
    },
    {
      "id": "wid",
      "name": "World Inequality Database (WID.world)",
      "org": "World Inequality Lab / Paris School of Economics (Piketty, Saez, Zucman, Chancel)",
      "category": "data",
      "endpoint": "https://rfap9nitz6.execute-api.eu-west-1.amazonaws.com/prod/ (the public endpoint the WID R/Stata package calls); mirror via ourworldindata.org/grapher",
      "access": "public_client_key",
      "license": "WID.world data, free to use with attribution; verify commercial terms before any paid offering",
      "verified": "2026-07-03 (HTTP 200, wcsnni/US)",
      "whatWeUse": "Top 1%/10% income and wealth shares, bottom-50% share, capital share, private/national wealth-to-income ratio (beta), plus the wid-capital2 snapshot (2026-07-05): capital-income concentration (spkkin, France 1970-2014), top-1% capital-income share of total income (sptkin, US 1913-2019), and WID-native wealth shares as a first-party cross-check. All tax-data based (DINA), pre-tax concept. Probed and documented as absent from the public API: inheritance flow as share of national income (lives in the country papers, not the distributed database)."
    },
    {
      "id": "owid-gcp",
      "name": "Consumption-based CO2 emissions",
      "org": "Our World in Data, based on the Global Carbon Project",
      "category": "data",
      "endpoint": "https://ourworldindata.org/consumption-based-co2",
      "access": "open",
      "license": "CC BY 4.0",
      "verified": "embedded provenance in consumption-emissions.json",
      "whatWeUse": "Production vs consumption (footprint) emissions per capita, decoupling and carbon-inequality cards."
    },
    {
      "id": "gdim",
      "name": "Global Database on Intergenerational Income Mobility (GDIM)",
      "org": "World Bank (Munoz & Van der Weide, 2025)",
      "category": "data",
      "endpoint": "https://datacatalog.worldbank.org/search/dataset/0066878",
      "access": "open",
      "license": "CC BY 4.0",
      "verified": "embedded provenance in ige-income.json",
      "whatWeUse": "Intergenerational income elasticity (the Great Gatsby curve), mobility cards."
    },
    {
      "id": "undp-ihdi",
      "name": "Inequality-adjusted Human Development Index (IHDI)",
      "org": "UNDP Human Development Report Office",
      "category": "data",
      "endpoint": "https://hdr.undp.org/data-center/documentation-and-downloads",
      "access": "open",
      "license": "CC BY 3.0 IGO",
      "verified": "embedded provenance in ihdi-global.json; page HTTP 200 on 2026-07-03",
      "whatWeUse": "Development lost to inequality (IHDI vs HDI) across 165 countries."
    },
    {
      "id": "brainlat-exposome",
      "name": "Exposome of brain aging (BrainLat / ReDLat)",
      "org": "Legaz, Ibanez et al., BrainLat, Universidad Adolfo Ibanez",
      "category": "data",
      "endpoint": "https://www.nature.com/articles/s41591-026-04302-z",
      "access": "closed",
      "license": "derived indicators; cite the paper; confirm reuse terms with the authors",
      "verified": "paper DOI resolves; used by the production globe methodology link",
      "whatWeUse": "Health lens: inequality and environment linked to brain aging. Partnership already in motion.",
      "auditGap": "exposome-data.json lacks an embedded source block; add one and confirm data-reuse terms with the BrainLat authors before any publication."
    },
    {
      "id": "ilostat",
      "name": "ILOSTAT Labour Statistics",
      "org": "International Labour Organization (ILO Department of Statistics)",
      "category": "data",
      "endpoint": "https://rplumber.ilo.org/data/indicator?id={INDICATOR}_A&format=.csv.gz",
      "access": "open",
      "license": "CC BY 4.0 (commercial use permitted)",
      "verified": "2026-07-06 (keyless bulk-CSV facility, codes checked against the ILOSTAT indicator table of contents; labour share USA 55.8%/Germany 62.9%, working poverty Madagascar 86%/China 0.1%, informality DR Congo 96%/Switzerland 1.1%)",
      "whatWeUse": "Labour income share of GDP (SDG 10.4.1), working poverty rate at the extreme $2.15/day line (SDG 1.1.1), and informal employment rate. The labour lane WEF leans on most. 8,675 country-year points."
    },
    {
      "id": "unu-wider-wiid",
      "name": "UNU-WIDER World Income Inequality Database (WIID) Companion",
      "org": "UNU-WIDER (United Nations University World Institute for Development Economics Research)",
      "category": "data",
      "endpoint": "https://www.wider.unu.edu/sites/default/files/WIID/wiidcountry_4.xlsx",
      "access": "open",
      "license": "CC BY-NC-SA 3.0 IGO (NON-COMMERCIAL, share-alike). Compatible with this project's non-commercial status; revisit before any paid offering.",
      "verified": "2026-07-06: HTTP 200, xlsx, 7,474,552 bytes; parsed 2,797 rows into 195 mapped countries, 1950-2023; net Gini US 0.417, Nordics 0.27-0.34, ZAF 0.67, BRA 0.48; license confirmed verbatim at wider.unu.edu/about/copyright",
      "whatWeUse": "Harmonised survey-based net Gini plus top-10/bottom-40/quintile income shares and the Palma ratio, as an adversarial cross-check against WID.world and a coverage backfill (the standing adversarial-validation mandate). DOI 10.35188/UNU-WIDER/WIIDcomp-290425. 15,159 points."
    },
    {
      "id": "owid-expansion",
      "name": "Our World in Data grapher series (ILO, V-Dem, Global Carbon Project, World Bank mirrors)",
      "org": "Our World in Data",
      "category": "data",
      "endpoint": "https://ourworldindata.org/grapher/{slug}.csv",
      "access": "open",
      "license": "CC BY 4.0 (OWID); underlying sources retain their own terms (ILO, V-Dem, GCP)",
      "verified": "2026-07-03 (fetched by scripts/build-owid-expansion.py, 74,843 points, provenance embedded in owid-expansion.json)",
      "whatWeUse": "Identity lens: gender wage gap (ILO). Politics lens: V-Dem egalitarian and liberal democracy indices. Economics: labour share, energy access. Climate: CO2 intensity of GDP. Feeds the xp- expansion threads."
    },
    {
      "id": "ipu-parline",
      "name": "IPU Parline: Women in National Parliaments",
      "org": "Inter-Parliamentary Union (IPU)",
      "category": "data",
      "endpoint": "https://api.data.ipu.org/v1/chambers",
      "access": "open",
      "license": "CC BY-NC-SA 4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/); Terms of use https://www.ipu.org/terms-use. NON-COMMERCIAL.",
      "verified": "2026-07-06 (keyless JSON:API; 193 countries lower house, 87 upper, 1997-2026, 7,929 points; Rwanda 63.75%, Nordics 42-46%, Yemen 0%; matches IPU's published ranking)",
      "whatWeUse": "Share of parliamentary seats held by women per country over time (womenInParliament lower/single house, womenInParliamentUpper senate), the canonical political-empowerment series, filling our weakest lane. Women-in-ministerial-positions is not exposed by this API."
    },
    {
      "id": "who-gho",
      "name": "WHO Global Health Observatory",
      "org": "World Health Organization",
      "category": "data",
      "endpoint": "https://ghoapi.azureedge.net/api/",
      "access": "open",
      "license": "CC BY-NC-SA 3.0 IGO (verify per indicator)",
      "verified": "2026-07-03 (fetched by scripts/build-who-expansion.py, 29,696 points across 6 indicators, provenance embedded in who-expansion.json; wealth-quintile dimension pinned to totals after a clobbering bug was caught and fixed)",
      "whatWeUse": "Health lens: WHO life expectancy (total, female, male), healthy life expectancy (HALE), under-5 mortality, UHC service coverage index. Feeds the xp-health expansion threads."
    },
    {
      "id": "ipi-g20-report",
      "name": "G20 Extraordinary Committee of Independent Experts on Global Inequality (Global Inequality Report)",
      "org": "G20 South Africa Presidency; committee chaired by Joseph Stiglitz",
      "category": "editorial",
      "endpoint": "https://www.gov.za/sites/default/files/gcis_document/202511/g20-global-inequality-report-full-and-summary.pdf",
      "access": "open",
      "license": "public government document; cite the committee and the G20 South Africa Presidency (November 2025)",
      "verified": "2026-07-06 (PDF resolves, 69 pages; committee membership and title cross-checked against UNRISD, Wits and PERI-UMass; the report's own data backbone is WID.world plus World Bank, LIS and OECD, the same sources this project uses)",
      "whatWeUse": "The emerging authoritative statement of the inequality-is-policy-choice position, backing the ipi-policy-choice camp. The report also recommended the standing International Panel on Inequality (an IPCC for inequality, founding meeting March 2026), which this project positions itself as complementary to: a public claim-testing instrument beside their scientific synthesis."
    },
    {
      "id": "unctad",
      "name": "UNCTAD illicit financial flows and Global-South macro-finance",
      "org": "UNCTAD (UN Trade and Development)",
      "category": "data",
      "endpoint": "https://unctadstat-api.unctad.org/datamart-api/ (OData); key figures from Economic Development in Africa Report 2020 (aldcafrica2020_en.pdf)",
      "access": "open",
      "license": "CC BY 3.0 IGO",
      "verified": "2026-07-06 (UNCTADstat OData API reverse-engineered and read anonymously; the machine-readable SDG 16.4.1 illicit-flow series is a sparse pilot of a few countries, so we use the citable report figures traced verbatim to the 2020 PDF: $88.6bn/yr Africa capital flight = 3.7% of GDP, $40bn extractive underinvoicing 2015, $200bn SDG gap)",
      "whatWeUse": "Illicit financial flows as an inequality mechanism: the ~$88.6bn/year drained from Africa, sourced to UNCTAD 2020, as fact-threads that pair with the Hickel unequal-exchange camp. Opens the Global-South extraction dimension the corpus lacked."
    },
    {
      "id": "ihme-gbd-haq",
      "name": "IHME Global Burden of Disease: Healthcare Access and Quality (HAQ) Index",
      "org": "Institute for Health Metrics and Evaluation (IHME), University of Washington",
      "category": "data",
      "endpoint": "https://ghdx.healthdata.org/record/ihme-data/gbd-2019-healthcare-access-and-quality-1990-2019 (GBD 2019 HAQ, downloaded file)",
      "access": "needs_application",
      "license": "IHME Free-of-Charge NON-COMMERCIAL User Agreement (account accepted 2026-07-06). NON-COMMERCIAL: see LICENSING.md, a commercial pivot needs a paid IHME license or drops this lane.",
      "verified": "2026-07-06 (file downloaded with Johnnie's IHME account; overall HAQ Index, age-standardized, 204 countries, 1990 and 2019; China 35.0 to 70.2, sanity-consistent)",
      "whatWeUse": "The Healthcare Access and Quality Index: how well a health system prevents deaths that timely quality care should avoid. A health-system-inequality series across ~200 countries, and the 1990-to-2019 gain shows which countries closed the access gap. No free equivalent for the HAQ Index."
    },
    {
      "id": "unesco-uis",
      "name": "UNESCO Institute for Statistics (UIS) education indicators",
      "org": "UNESCO Institute for Statistics",
      "category": "data",
      "endpoint": "https://download.uis.unesco.org/bdds/202602/SDG.zip",
      "access": "open",
      "license": "CC BY-SA 3.0 IGO (attribution, share-alike; commercial use permitted)",
      "verified": "2026-07-06 (keyless Bulk Data Download Service, Feb 2026 release; 15 series, 210 countries, 1970-2025, ~69k points; Sahel literacy 30-42%, gender gaps clear, rich-country tertiary 76-112%)",
      "whatWeUse": "Adult literacy (total/female/male), gross enrolment by level (primary/secondary/tertiary, each by sex), and primary out-of-school rate, from the keyless UIS bulk CSVs. The education backbone, and the gender-split series feed the identity lens directly."
    },
    {
      "id": "oecd-sigi",
      "name": "OECD Social Institutions and Gender Index (SIGI) 2023",
      "org": "OECD Development Centre",
      "category": "data",
      "endpoint": "https://sdmx.oecd.org/public/rest/data/OECD.DEV.NPG,DSD_SIGI@DF_SIGI_2023,1.0/....?dimensionAtObservation=AllDimensions",
      "access": "open",
      "license": "OECD terms: free reuse with attribution (https://www.oecd.org/termsandconditions/); commercial use permitted; some third-party content may carry restrictions",
      "verified": "2026-07-06 (keyless OECD SDMX; SIGI 2023 composite + 4 sub-indices, 0-100 where HIGHER means MORE legal discrimination; Norway 6.7 least, Mauritania 67.4 most; IDD Gini USA 0.394 secondary)",
      "whatWeUse": "Legal, de jure gender discrimination our income and wealth data cannot see: the SIGI composite and its family / assets / physical-integrity / civil-liberties sub-indices, plus OECD IDD disposable-income Gini. Scale is inverted (higher is worse), recorded so threads and stances read correctly."
    },
    {
      "id": "opportunity-atlas",
      "name": "The Opportunity Atlas",
      "org": "Opportunity Insights (Chetty, Friedman, Hendren, Jones, Porter), Harvard / US Census Bureau",
      "category": "data",
      "endpoint": "https://opportunityinsights.org/data/ (county_outcomes_simple.csv)",
      "access": "open",
      "license": "publicly posted research data; cite Chetty, Friedman, Hendren, Jones and Porter, The Opportunity Atlas (NBER w25147)",
      "verified": "2026-07-04 (CSV downloaded, 3,208 counties; measure kfr_pooled_pooled_p25 checked against the published codebook, Table 2)",
      "whatWeUse": "Urban lens: mean adult household income rank for children raised in poor (25th percentile) families, by US county. Powers the oa-mobility threads, including the Detroit Wayne vs Oakland vs Macomb pairing beside the Grosse Pointe barricade photographs."
    },
    {
      "id": "unequal-scenes-photos",
      "name": "Unequal Scenes aerial photography",
      "org": "Johnny Miller / Unequal Scenes",
      "category": "media",
      "endpoint": "local only",
      "access": "closed",
      "license": "All rights reserved; local-only, never pushed to a public repo",
      "verified": "in-repo, gitignored",
      "whatWeUse": "The human face beside the number: aerial photographs matched to cited countries."
    },
    {
      "id": "inflection-causes",
      "name": "Inflection-point causes",
      "org": "Unequal World, researched annotations",
      "category": "editorial",
      "endpoint": "in-repo inflection-causes.json",
      "access": "open",
      "license": "each annotation carries its own primary source URL",
      "verified": "187 annotations, each with a source",
      "whatWeUse": "Why a country's line bent in a given year. Each is individually sourced."
    }
  ],
  "backDoorsVerifiedNotYetWired": [
    {
      "id": "un-sdg",
      "name": "UN SDG Indicators API",
      "org": "UN Statistics Division",
      "category": "data",
      "endpoint": "https://unstats.un.org/sdgapi/",
      "access": "open",
      "license": "open, cite UNSD",
      "verified": "2026-07-03 (HTTP 200, Series/List, release 2026.Q1)",
      "whatWeUse": "SDG 10 (reduced inequalities) and cross-lens development indicators."
    },
    {
      "id": "oecd-sdmx",
      "name": "OECD Data (SDMX)",
      "org": "OECD",
      "category": "data",
      "endpoint": "https://sdmx.oecd.org/public/rest/",
      "access": "open",
      "license": "OECD terms; mostly free reuse with attribution",
      "verified": "2026-07-03 (HTTP 200, dataflow OECD.WISE.INE)",
      "whatWeUse": "Income Distribution Database, wealth distribution, for rich-country triangulation."
    },
    {
      "id": "ilostat-sdmx",
      "name": "ILOSTAT (SDMX)",
      "org": "International Labour Organization",
      "category": "data",
      "endpoint": "https://sdmx.ilo.org/rest/",
      "access": "open",
      "license": "CC BY 4.0",
      "verified": "2026-07-03 (HTTP 200, ILO dataflows)",
      "whatWeUse": "Labour income share, informality, gender wage gap, working poverty."
    },
    {
      "id": "undp-gii",
      "name": "UNDP Gender Inequality Index",
      "org": "UNDP HDR Office",
      "category": "data",
      "endpoint": "https://hdr.undp.org/data-center/",
      "access": "open",
      "license": "CC BY 3.0 IGO",
      "verified": "2026-07-03 (page HTTP 200)",
      "whatWeUse": "Identity lens: composite gender inequality across health, empowerment, labour."
    },
    {
      "id": "vdem",
      "name": "Varieties of Democracy (V-Dem), direct dataset",
      "org": "V-Dem Institute, University of Gothenburg",
      "category": "data",
      "endpoint": "https://www.v-dem.net/data/the-v-dem-dataset/",
      "access": "open",
      "license": "V-Dem terms (free academic/non-commercial; cite the dataset and method)",
      "verified": "2026-07-03 (page HTTP 200; dataset is a versioned download, not a live API)",
      "whatWeUse": "Two V-Dem indices are already wired via the OWID mirror (see owid-expansion). The direct dataset stays here for finer politics variables (power by socioeconomic position) if needed later."
    }
  ],
  "scholarlySources": [
    {
      "id": "openalex",
      "name": "OpenAlex",
      "org": "OurResearch",
      "category": "scholarly",
      "endpoint": "https://api.openalex.org/",
      "access": "open",
      "license": "CC0 (data)",
      "verified": "2026-07-03 (HTTP 200, works + authors search)",
      "whatWeUse": "Camp-freshness pipeline: pull each vetted thinker's new publications for the editorial workbench."
    },
    {
      "id": "crossref",
      "name": "Crossref",
      "org": "Crossref",
      "category": "scholarly",
      "endpoint": "https://api.crossref.org/",
      "access": "open",
      "license": "open metadata",
      "verified": "2026-07-03 (HTTP 200, works query)",
      "whatWeUse": "DOI resolution and citation metadata verification for camp sources."
    },
    {
      "id": "semantic-scholar",
      "name": "Semantic Scholar Academic Graph",
      "org": "Allen Institute for AI",
      "category": "scholarly",
      "endpoint": "https://api.semanticscholar.org/graph/v1/",
      "access": "open",
      "license": "open with key (key obtained 2026-07-04, stored in gitignored .env as S2_API_KEY)",
      "verified": "2026-07-04 (HTTP 200 with key: author/search + author/{id}/papers)",
      "whatWeUse": "Secondary enrichment in the camp-freshness pipeline: citation counts and a second opinion on new papers by cited thinkers. S2 author profiles are often split, so S2 items are marked and never overrule OpenAlex; a human reviews everything."
    },
    {
      "id": "lis",
      "name": "Luxembourg Income Study (LIS)",
      "org": "LIS Cross-National Data Center",
      "category": "data",
      "endpoint": "https://www.lisdatacenter.org/wp-content/uploads/files/access-key-workbook.xlsx (Key Figures workbook, no login); LISSY microdata is registration-only",
      "access": "open",
      "license": "cite as: LIS Inequality and Poverty Key Figures, (date downloaded). Luxembourg: LIS.",
      "verified": "2026-07-05 (workbook fetched by scripts/build-lis-keyfigures.py: 10,537 points, 54 countries, 1963-2024, provenance embedded in lis-keyfigures.json)",
      "whatWeUse": "Open tier (DART/Key Figures): disposable-income Gini, Atkinson indices, percentile ratios, relative poverty at 50/60 percent of median incl. child and elderly. LISSY microdata access GRANTED 2026-07-08: enables the market-income vs disposable-income redistribution measure the open tier cannot give (how much taxes and transfers shrink inequality). Computed via a LISSY remote-execution job (scripts/lissy-redistribution-job.R), which returns only aggregate Gini figures per country-year, per the data-use agreement. Non-commercial DUA; cite LIS."
    }
  ],
  "editorialLayer": {
    "camps": {
      "file": "camps.seed.json",
      "count": 63,
      "status": "seed-for-review",
      "note": "63 scholarly positions across economics, climate, moral, health, identity, politics and urban, each with its own primary-source URL checked to resolve during research. The 2026-07-05 extension added the Piketty inequality-regimes position, Saez-Zucman progressive taxation, Folbre care economics, Hickel unequal exchange, inheritance flows, and the DINA-vs-survey-harmonisation measurement pair. Selection criteria are stated in METHODOLOGY.md. Marked seed-for-review: a human editor (and ideally a partner) must vet every claim and citation before publication. This human gate is deliberate and is part of the audit story."
    },
    "validator": {
      "file": "validate.mjs",
      "note": "Numeric-trace gate: every number in a computed data claim must derive from that claim's own sourced values, or the build fails. Plus source-URL presence, em-dash/style checks, and a refusal check. This is what makes the measured cards fabrication-resistant."
    }
  },
  "keysNeededFromJohnnie": [
    "Luxembourg Income Study: DONE. LISSY access granted and the market-vs-disposable redistribution measure is computed (35 countries) and wired (xp-redistribution threads). Re-run scripts/lissy-redistribution-job.R later to refresh or add countries.",
    "BrainLat / Ibanez: confirm data-reuse terms for the exposome indicators, ideally as part of the existing collaboration, before publishing derived numbers."
  ]
}