MEPLAR | INDIA FINDEX 2024
Frozen source retrieval: 17 October 2026 UTC. Data release: Global Findex 2025.

WHAT THE CHART SAYS
India's estimated account-ownership rates are close: women 89.2482328079147%, men 88.8052472379392%. The estimated share reporting a digital merchant payment is women 11.1096702109148%, men 25.3681855789872%.
Women minus men, account ownership: +0.4429855699755 percentage points.
Men minus women, digital merchant payment: +14.2585153680724 percentage points.
Merchant-payment rate, men / women: 2.283432820000722... .
The chart rounds percentages and the payment gap to one decimal place. It plots unrounded horizontal coordinates on one 0-100% scale. Both gender points use the same vertical row, with distinct shapes. Their near-overlap on account ownership is intentional and reflects the source estimates.

SOURCE FILES AND SELECTION
Official World Bank country-level download page:
https://www.worldbank.org/en/publication/globalfindex/download-data
Full CSV:
https://thedocs.worldbank.org/en/doc/be6615202d1f08a25855c8ac2d615122-0050012025/related/GlobalFindexDatabase2025.csv
SHA256: dce89f60a9aceaf60a7be62b56b6a52e4d47405ee48fe2e2227da60f2a9d51cd
17,643,094 bytes; 8,577 data rows; 438 columns. UTF-8 with BOM decoding used successfully (HTTP's charset label differs). Original bytes are retained.
Select codewb=IND, year=2024, group=gender, group2=men or women. There is exactly one source row for each gender. Read account_t_d and merchant_pay; each is a proportion. Multiply by 100 to obtain percentages. No weighting or aggregation by Meplar is needed because the CSV supplies the survey estimates. Do not average the two gender percentages to derive a national total.
Original two-row extract: source/india-2024-gender-source-extract.csv.
Exact plotted values: india-findex-2024-plotted.csv.
Python Decimal calculations: qa/numeric-audit.json.
Independent Node CSV parser and calculations: independent_check.mjs and qa/independent-numeric-check.json. It reads the full original CSV independently rather than the Python output.

DEFINITIONS
Official glossary:
https://thedocs.worldbank.org/en/doc/be6615202d1f08a25855c8ac2d615122-0050012025/related/GlobalFindex2025-glossary.xlsx
SHA256: 8eb99754e6e18d6831c133cc743d8207cc657baedc3447588a574fd3d935f923
Excel row 2: account.t.d, Account (%, age 15+). CSV maps dots to underscores: account_t_d. Covers a self-reported individual or joint account at a bank or similar financial institution, or personal use of a mobile money service in the past year. It is broader than bank accounts alone.
Excel row 119: merchant.pay, Made a digital merchant payment (%, age 15+). CSV: merchant_pay. Respondents report a debit/credit card or mobile phone purchase in-store, or online payment for an internet purchase. This is a share of all adults in the gender group, not only account owners. It is not restricted to UPI, and is not payment count, spending value, frequency, or all digital financial activity.
The questionnaire's in-store item FIN25E_2 and online-buying stem FIN26 use the past 12 months. FIN27 distinguishes online payment from cash on delivery. The reporting window is relative to the interview; it is not an administrative calendar-year transaction total.
https://thedocs.worldbank.org/en/doc/be6615202d1f08a25855c8ac2d615122-0050012025/original/Findex-English.pdf
Selected glossary cells are preserved in source/selected-glossary-rows.json; the original workbook is retained.

SURVEY SCOPE AND LIMITS
Official methodology:
https://www.worldbank.org/en/publication/globalfindex/methodology
https://thedocs.worldbank.org/en/doc/be6615202d1f08a25855c8ac2d615122-0050012025/related/Survey-methodology.pdf
Target population: civilian, noninstitutionalized adults age 15+. India: 3,086 face-to-face interviews, 1 August-29 December 2024 (Table A.1, PDF page 14 / printed page 280). The northeast states, remote islands, and Jammu and Kashmir were excluded; the methodology says excluded areas represent less than 10% of the population. These are weighted survey estimates, not a census or transaction ledger.
The methodology lists 2.1 percentage points as the maximum whole-sample margin of error for India. This is not a valid uncertainty interval for either gender/indicator estimate. Gender-specific confidence intervals are not supplied in the aggregate CSV; none is invented. The chart describes point estimates and makes no significance claim about the small account difference. The two marginal indicators are not an individual-level conversion funnel; they do not explain who uses an owned account or why people differ.

LICENCE AND ATTRIBUTION
World Bank open aggregate data: CC BY 4.0, with the World Bank's published additional mandatory dispute terms. The Findex Data360 catalogue entry identifies CC BY 4.0. The World Bank's official public-licences policy also states this is the default for World Bank-produced open datasets. This chart uses the public country-level aggregate CSV, not licensed individual microdata. No contrary restriction appears in the supplied CSV or glossary.
Dataset catalogue: https://data360.worldbank.org/en/dataset/WB_FINDEX
World Bank policy and additional terms: https://datacatalog.worldbank.org/public-licenses
Creative Commons licence: https://creativecommons.org/licenses/by/4.0/
Full attribution for a companion page/download:
World Bank. 2025. Global Findex Database 2025: Connectivity and Financial Inclusion in the Digital Economy. Country-level data, India, 2024, account_t_d and merchant_pay, men and women age 15+. Source: World Bank Global Findex country-level CSV and indicator glossary, retrieved 17 October 2026. Licensed CC BY 4.0, subject to the World Bank's published additional terms. Adaptation by Meplar: selected country/year/gender/indicators; converted proportions to percentages; calculated point and ratio differences; rounded display labels; redesigned chart. No World Bank endorsement implied.
Concise chart credit: World Bank · Global Findex 2025 / CC BY 4.0 · Chart: Meplar.
Meplar's chart, source code and companion text in this package are also provided under CC BY 4.0. Retain source and transformation attribution when redistributing.

REPRODUCTION
Python 3.12, matplotlib 3.11.2, Pillow 12.3.0; Node built-in fs for independent checking. No proprietary design software or paid data. Run build_chart.py from any directory, then node independent_check.mjs. The builder resolves all inputs relative to itself and works offline. The PNG is 1800x3240; SVG live text is editable, outlined SVG is portable, PDF is vector. Font: bundled DejaVu Sans. Minimum text at 390px display width is 14.44px. All source data, glossary and scripts are bundled. Build caches are excluded from the review ZIP.
