--- pretty_name: ISORA — International Survey on Revenue Administration (FY2014–FY2024) license: other license_name: isora-data-portal-terms license_link: LICENSE language: - en tags: - isora - international-survey-on-revenue-administration - tax-administration - revenue-administration - tax-authority - taxation - tax - tax-policy - tax-compliance - tax-collection - vat - corporate-income-tax - personal-income-tax - public-finance - fiscal-policy - government-revenue - revenue-statistics - economics - macroeconomics - development-economics - public-sector - government - e-government - digitalization - imf - international-monetary-fund - oecd - adb - ciat - iota - ra-fit - cross-country - comparative - panel-data - time-series - survey-data - open-government-data - sdmx annotations_creators: - expert-generated multilinguality: - monolingual source_datasets: - original size_categories: - 100K **Unofficial redistribution.** This dataset is not produced or endorsed by the IMF, ADB, CIAT, > IOTA, the OECD or any tax administration. The data remain subject to the > [ISORA Data Portal Terms and Conditions](https://data.imf.org/en/Datasets/RAFIT-Consolidated/Terms-and-Conditions) > and the [IMF Copyright and Usage policy](https://www.imf.org/en/about/copyright-and-terms) — > read the [`LICENSE`](LICENSE) file. You may publish ISORA data provided the source is > acknowledged; see [Citation](#citation-and-acknowledgement). | | | |---|---| | Observations | **796,601** (`observations` table) | | Jurisdictions | **182** tax administrations (alpha-3 codes, IMF practice) | | Indicator codes | **1,868** distinct question/answer codes with data | | Fiscal years | **FY2014 – FY2024** (eight survey rounds: ISORA 2016 → ISORA 2025) | | Tables | `observations` · `indicators` · `indicator_history` · `jurisdictions` · `coverage` · `revisions` | | Formats | Parquet (`data/`), gzip CSV copies (`csv/`), build metadata (`metadata/`) | | Source snapshot | IMF SDMX API, retrieved 2026-09-20T04:42:35Z | | Keywords | ISORA, RA-FIT, tax administration, revenue administration, tax authority, tax agency, IMF Fiscal Affairs Department, OECD Tax Administration Series, CIAT, IOTA, ADB, tax compliance, tax collection, VAT, CIT, PIT, PAYE, tax arrears, tax audit, taxpayer registration, e-filing, e-payment, tax administration staffing, tax administration budget, TADAT, public finance, government revenue, panel data, cross-country comparison | ## Quick start ```python from datasets import load_dataset obs = load_dataset("FrenchCastle/isora-tax-administration", "observations", split="train").to_pandas() ind = load_dataset("FrenchCastle/isora-tax-administration", "indicators", split="train").to_pandas() hist = load_dataset("FrenchCastle/isora-tax-administration", "indicator_history", split="train").to_pandas() ``` ```python import pandas as pd # 1. Pick a question and look at its history across questionnaire generations first. hist.set_index("indicator_code").loc["80040_3", ["label_2016", "label_2018", "label_2020plus", "comparability_flag"]] # 2. Build a country × year panel of one indicator (values already in base local-currency units). net_revenue = ( obs[obs.indicator_code == "80040_3"] .pivot(index="jurisdiction_code", columns="fiscal_year", values="value_local_currency_units") ) # 3. Wide table of every numeric indicator for one jurisdiction-year. fra_2023 = obs[(obs.jurisdiction_code == "FRA") & (obs.fiscal_year == 2023) & (obs.value_status == "value")] fra_2023.pivot(index="indicator_code", columns="fiscal_year", values="value_numeric") # 4. Categorical answers: the closed list of options is in indicators.answer_categories. ind.loc[ind.indicator_code == "80700", ["questionnaire_generation", "label", "answer_categories"]] ``` DuckDB works directly on the Parquet files: ```sql SELECT jurisdiction_name, fiscal_year, value_numeric FROM 'data/observations/*.parquet' WHERE indicator_code = '337_001' -- net revenue collected as % of GDP (derived by ISORA) ORDER BY 1, 2; ``` ## About ISORA ISORA collects tax administration data from national or federal tax administrations through an online platform administered by the IMF, using common questions and definitions agreed by the five partner organisations. Participation is voluntary; after collection the partners review the data for accuracy, completeness and consistency, then publish the finalised round. Participants in ISORA 2020 and later rounds agree in advance that all data they provide can be placed in the public domain; every row in this dataset carries the source flag `PUBLIC_DATA = true`. | Survey round | Collected in | Fiscal years covered | Participating administrations (as published) | |---|---|---|---| | ISORA 2016 | 2016 | 2014, 2015 | 135 | | ISORA 2018 | 2018 | 2016, 2017 | 159 | | ISORA 2020 | 2020 | 2018, 2019 | 156 | | ISORA 2021 | 2021 | 2020 | 156 | | ISORA 2022 | 2022 | 2021 | 165 | | ISORA 2023 | 2023 | 2022 | 166 | | ISORA 2024 | 2024 | 2023 | 164 | | ISORA 2025 | 2025 | 2024 | 166 | Until 2021 the survey ran every two years and collected two fiscal years at a time. After ISORA 2018 the questionnaire was redesigned: a smaller **annual** core is asked every year and a larger **periodic** module (governance, human resources, compliance risk management, taxpayer services, tax operations) is asked every four years — it was included in ISORA 2023 (FY2022), which is why that year has roughly twice as many indicators as its neighbours. The IMF exposes the published data as three SDMX dataflows, one per **questionnaire generation**. This dataset keeps that distinction because the question codes and wording differ between them: | Questionnaire generation | Source dataflow | Fiscal years | Rows | Jurisdictions | Indicator codes | |---|---|---|---|---|---| | ISORA 2016 | `ISORA_2016_DATA_PUB` v2.0.0 | FY2014–FY2015 | 185,360 | 131 | 1,000 | | ISORA 2018 | `ISORA_2018_DATA_PUB` v2.0.0 | FY2016–FY2017 | 254,942 | 155 | 1,072 | | ISORA 2020+ | `ISORA_LATEST_DATA_PUB` v5.0.0 | FY2018–FY2024 | 356,299 | 182 | 701 | Coverage by fiscal year: | Fiscal year | Collected in | Jurisdictions | Indicator codes | Rows | |---|---|---|---|---| | 2014 | ISORA 2016 | 131 | 1000 | 92,679 | | 2015 | ISORA 2016 | 131 | 999 | 92,681 | | 2016 | ISORA 2018 | 155 | 1072 | 127,395 | | 2017 | ISORA 2018 | 155 | 1072 | 127,547 | | 2018 | ISORA 2020 | 157 | 302 | 40,898 | | 2019 | ISORA 2020 | 157 | 304 | 41,306 | | 2020 | ISORA 2021 | 165 | 312 | 41,571 | | 2021 | ISORA 2022 | 174 | 314 | 43,580 | | 2022 | ISORA 2023 | 174 | 675 | 92,313 | | 2023 | ISORA 2024 | 164 | 353 | 48,181 | | 2024 | ISORA 2025 | 167 | 349 | 48,450 | ## The six tables ### `observations` (default config) One row per *jurisdiction × indicator × fiscal year*. Keys are unique within each questionnaire generation and, because fiscal years do not overlap between generations, unique overall. | Column | Type | Description | |---|---|---| | `jurisdiction_code` | string | Alpha-3 code as used in the IMF ISORA codelists (ISO 3166-1 alpha-3 except `KOS` for Kosovo). ISORA 2016/2018 published numeric IMF codes; they were mapped through the ISO annotation of the IMF codelist. | | `jurisdiction_name` | string | Name exactly as published by the IMF (IMF naming practice, without prejudice to the status of any territory). | | `fiscal_year` | int16 | Fiscal year the answer refers to. Fiscal-year definitions differ by jurisdiction. | | `survey_round` | string | Round in which that fiscal year was first collected (`ISORA 2016` … `ISORA 2025`). Values for earlier years may have been revised in later rounds. | | `questionnaire_generation` | string | `ISORA 2016`, `ISORA 2018` or `ISORA 2020+` — which codelist / questionnaire family the `indicator_code` belongs to. **Join to `indicators` on both this and `indicator_code`.** | | `indicator_code` | string | Source code of the question or answer cell (e.g. `80040_3`, `337_001`, `PARTICIPATION_RATE`). Codes are reused across generations, sometimes with different wording. | | `indicator_label` | string | Label of the code **in that generation's codelist** (denormalised for convenience). | | `indicator_value_kind` | string | What this indicator's answers look like in the data: `numeric`, `binary`, `categorical`, `free_text`, `mixed`, `no_values` (inferred from the published values, see below). | | `value_raw` | string | The published value, verbatim (before any cleaning). | | `value_numeric` | float64 | Parsed number when the published value is numeric, else null. Stored exactly as published (see [Units](#units-and-currency)). | | `value_text` | string | Cleaned text answer (HTML fragments removed, encoding glitches repaired, whitespace collapsed), else null. | | `value_status` | string | `value`, `not_available`, `not_applicable`, `empty`, `unrecognized_code` (table below). | | `unit_multiplier` | int8 | The source `SCALE` attribute (`0` or `3`). | | `monetary_unit` | string | `thousands of local currency` (ISORA 2016/2018 money questions), `local currency units` (ISORA 2020+ money questions, i.e. every row with `unit_multiplier = 3`), or null for non-monetary indicators. | | `value_local_currency_units` | float64 | **Harmonised money amount in base units of the jurisdiction's currency** (2016/2018 values × 1 000; 2020+ values unchanged). Null for non-monetary indicators. Not converted across currencies. | | `form_status` | string | Source workflow flag on the observation (`CERTIFY`, `EDIT`, `REEDIT`) or null. | | `footnote` | string | Free-text note published with the observation (cleaned), or null. | | `source_dataflow`, `source_dataflow_version` | string | Provenance: which IMF dataflow version the row came from. | | `value_status` | Rows | Share | Meaning | |---|---|---|---| | `value` | 664,349 | 83.4% | a numeric or text answer is present | | `not_available` | 113,785 | 14.3% | the administration answered `D` (data not available) to a numeric question | | `not_applicable` | 9,203 | 1.2% | the administration answered *Not Applicable* | | `empty` | 7,738 | 1.0% | the cell was published empty | | `unrecognized_code` | 1,526 | 0.2% | the published value is the undocumented code `P` (ISORA 2016 only) | | `indicator_value_kind` | Rows | What it means | |---|---|---| | `numeric` | 455,528 | every published answer is a number | | `binary` | 253,326 | answers are Yes / No | | `categorical` | 82,533 | answers come from a closed list (≤ 25 distinct values) | | `mixed` | 2,856 | numbers and text both occur (usually a category plus a numeric ‘other’) | | `no_values` | 2,358 | only `D`, empty or not-applicable cells were published | ### `indicators` One row per *questionnaire generation × indicator code* (3,569 rows), i.e. the three source codelists flattened with every annotation the IMF attaches to a code, plus statistics computed from the observations. Key columns: `label`, `display_label`, `description` (rarely filled at source), `form_code` / `form_name` (the survey form, e.g. *Form F. Operational metrics*), `question_ref` (e.g. *Form D - Q2*, ISORA 2020+ only), `section`, `topic_group` / `topic_subgroup` (the IMF *Indicators by Topic* hierarchy, ISORA 2020+ only), `report_table_index` / `report_table_title` (where the code appears in the IMF review tables), `indicator_type` (the source's declared type: binary, count, currency, percent, nominal, ordinal, text, date, unspecified — unreliable, see caveats), `observed_value_kind` (inferred from data), `answer_categories` (the closed list of answers actually observed, most frequent first), `is_derived` / `formula` / `numerator` / `denominator` / `legend` (ISORA-computed ratios such as `337_001` *net revenue as % of GDP*), `is_monetary`, `is_local_currency`, `label_mentions_thousands`, `is_periodic` (periodic-module question), `is_review_indicator`, `n_observations`, `n_observations_with_value`, `n_jurisdictions`, `fiscal_years_with_data`, `has_observations` (codelists contain codes that were never published with data). ### `indicator_history` One row per indicator code (2,551 codes) describing how the code appears across the three questionnaire generations: `in_2016` / `in_2018` / `in_2020plus`, the label in each generation, `label_changed_2016_to_2018`, `label_changed_2018_to_2020plus`, similarity scores of the normalised labels (0–1), `n_generations`, `fiscal_years_with_data`, observation counts and a `comparability_flag`: | `comparability_flag` | Codes | Meaning | |---|---|---| | `single_generation` | 1,759 | The code exists in only one questionnaire generation. | | `label_stable` | 439 | Present in two or three generations with the same wording (after normalising case, punctuation and spacing). | | `label_changed` | 353 | Present in more than one generation **with different wording** — check whether the meaning changed before stitching a time series. | 226 codes exist in all three generations, 566 in two, 1,759 in one. 183 codes changed wording between ISORA 2016 and ISORA 2018, 215 between ISORA 2018 and ISORA 2020+. ### `jurisdictions` One row per jurisdiction with data (182 rows): `jurisdiction_code`, `jurisdiction_name`, `imf_numeric_code`, IMF region / sub-region / regional technical-assistance centre, World Bank region and **FY2015** income group (a snapshot carried in the IMF codelist, not current), WEO group, fragile / small-developing-state flags, membership flags (ADB, CIAT, IOTA, OECD, OECD Forum on Tax Administration, EU, G20, G7, WCO, WAEMU) as recorded in the IMF codelist, and participation computed from the data (`fiscal_years_with_data`, `survey_rounds_with_data`, `in_isora_2016` / `in_isora_2018` / `in_isora_2020plus`, `n_observations`). ### `coverage` One row per *generation × indicator × fiscal year* (6,752 rows) counting how many jurisdictions were published for that question in that year, split by `value_status` (`n_value`, `n_not_available`, `n_not_applicable`, `n_empty`, `n_unrecognized_code`, `n_jurisdictions_reporting`). This is the questionnaire matrix: it tells you which questions were asked (or at least published) in which year, and how well they were answered. ### `revisions` The IMF API still serves earlier published versions of the consolidated FY2018+ dataflow. Each version is the dataset as released after a survey round, so differences between versions are **revisions of previously published answers**. Three vintages were compared on every *jurisdiction × indicator × fiscal year* key they share (359,777 keys): | Vintage | Content | |---|---| | `ISORA_LATEST_DATA_PUB` v2.0.0 | ISORA 2023 release, FY2018–FY2022 | | `ISORA_LATEST_DATA_PUB` v4.0.0 | ISORA 2024 release, FY2018–FY2023 | | `ISORA_LATEST_DATA_PUB` v5.0.0 | ISORA 2025 release, FY2018–FY2024 (the vintage used for `observations`) | The table lists, in long format (one row per key × vintage), only the keys where something meaningful changed (42,552 keys), with a `change_type`: | `change_type` | Keys | Meaning | |---|---|---| | `value_revised` | 2,787 | A number was replaced by a different number (beyond published precision) or by a sentinel. | | `text_revised` | 47 | A categorical/text answer changed. | | `scale_convention_change` | 20,114 | The number changed by exactly ×1 000: the 2023 release published money in thousands, later releases in base units (see [Units](#units-and-currency)). Not a revision of the answer. | | `added_in_later_release` | 16,189 | The key was absent from an earlier release covering that year (question added, back-filled or late submission). | | `removed_in_later_release` | 3,415 | The key was present in an earlier release and dropped later (question withdrawn or answer removed). | Differences that are only formatting (thousands separators, float precision, rounding to the coarser published precision, encoding glitches, letter case) are **not** listed. Example of a real revision: Australia's `337_084` (on-time filing rate, CIT) for FY2022 was published as 72.88 in the 2023 and 2024 releases and as 68.68 in the 2025 release — the latter equals the FY2021 value of the earlier releases. Users who need "the value as first published" can rebuild it from this table; users who need "the latest view" should simply use `observations`. ## Working with questions that changed over time ISORA question codes are **not** stable identifiers of meaning across the three questionnaire generations. Three patterns occur: 1. **Same code, same question, new wording.** `80250_3` is *Non-tax revenue - Net* in 2016 and 2018 and *Net revenue collected by the tax administration (in thousands in local currency)-Non-tax revenue* in 2020+. Comparable. 2. **Same code, narrower or broader question.** `88360` is *Administration pre-fills returns or assessments* (2016, 2018) but *Administration pre-fills PIT returns or assessments* (2020+). `85710_268` is *Other verification interventions - Total additional assessments…* (2016), *Automated audits - Total additional assessments…* (2018) and *Value of additional assessments raised from audits and verification actions… - Electronic compliance checks* (2020+). Comparability is a judgement call. 3. **Same code, unrelated question.** `92670` is *Categories of third party information used to pre-fill returns - Other income - description* (2018) and *Description of tax deductible expenses that are prefilled in PIT tax returns and assessments* (2020+). Not comparable. Recommended workflow: - Start from `indicator_history`; filter `comparability_flag == "label_stable"` for series that can be stitched with little risk, and read both labels for `label_changed` codes. - Join `observations` to `indicators` on `(questionnaire_generation, indicator_code)` so each value carries the definition that applied when it was collected. Never join on the code alone. - Use `coverage` to see in which years a question was actually asked; the periodic module (`indicators.is_periodic`) only has data for FY2022 within the consolidated generation. - Within ISORA 2020+ the questionnaire is stable across FY2018–FY2024 (the same codelist version is published for all seven years); the `revisions` table shows which earlier answers were revised in later rounds. - The ISORA 2016 → ISORA 2018 transition is smoother (605 shared codes, mostly same questions) than ISORA 2018 → ISORA 2020+ (a redesigned, much shorter questionnaire). ## Units and currency - **Money is in the jurisdiction's own currency** and is not converted. `indicators.is_local_currency` marks national-currency questions. Derived ratios (`337_*`, `398_*`, `111_*`) are unit-free. - The three generations publish money differently. ISORA 2016 and 2018 published amounts **in thousands** (as asked on the form) with `SCALE = 0`. The consolidated ISORA 2020+ dataflow publishes the same questions already multiplied out to **base currency units** and marks them with `SCALE = 3` (verified against GDP: France's `398_001` for FY2022 is published as 2 638 008 000 000 with `SCALE = 3`, i.e. EUR 2.64 trillion; the ISORA 2023 release had published 2 638 008 000, in thousands). **Do not multiply ISORA 2020+ values by 1 000.** - `value_local_currency_units` removes the ambiguity: it is always base units (41,020 rows converted from thousands, 39,451 rows taken as published). `value_numeric` stays exactly as published for traceability. - Counts (staff, taxpayers, returns), percentages and ratios are published as-is. ## What was changed relative to the source (transformation notice) Values were **not** altered. The following was done, and is reversible through `value_raw`: 1. Three SDMX dataflows were stacked into one long table with a common schema; the dataset-level and series-level attribute rows of the SDMX-CSV were dropped. 2. Numeric IMF jurisdiction codes (ISORA 2016/2018) were mapped to the alpha-3 codes used by the consolidated dataflow, via the `ISO` annotation of the IMF codelist (Kosovo: `967` → `KOS`, the IMF's current code; the 2018 codelist annotated it `UVK`). 3. The mixed-type `OBSERVATION` string was split into `value_numeric` / `value_text` / `value_status`. `D` → `not_available`; *Not Applicable* / `N/A` → `not_applicable`; `P` → `unrecognized_code`; digit strings with space grouping (`163 310 020`) → number. 4. Text answers and footnotes: HTML fragments such as `
` and entities removed, whitespace collapsed, and 64 values plus 141 footnotes with double-encoded UTF-8 (`‘` → `‘`, `Türkiye` → `Türkiye`) repaired. 5. Monetary harmonisation (`monetary_unit`, `value_local_currency_units`) as described above. 6. Indicator metadata flattened from SDMX annotations; declared types normalised (`Counting` → `count`, trailing spaces removed); observed value kinds, answer categories, coverage, cross-generation history and inter-release revisions computed. 7. Jurisdiction attributes taken from the IMF `CL_ISORA_ISO_COUNTRY` codelist; `Yes/No` flags converted to booleans. Nothing was imputed, interpolated, deduplicated or filtered out. ## Caveats and known issues in the source - **Self-reported, voluntary.** Answers are provided by the administrations and reviewed by the partners, but definitions are applied locally; read the ISORA guide before comparing countries. - **`D` and `P`.** 113,785 cells are `D` — the ISORA convention for *no data available* on a numeric question, distinct from a question that was skipped. 1,526 ISORA 2016 cells contain `P`, a code that does not appear in the surviving documentation; it occurs only on numeric questions and is treated as missing (`unrecognized_code`). - **Declared types are unreliable.** In the 2020+ codelist 817 of 1 098 codes have no declared type and several count questions (*Total number of returns received - CIT*) are typed `currency`. Use `indicator_value_kind` / `observed_value_kind`, which are inferred from data. - **Scale attribute inconsistency** between generations (see Units). The `label_mentions_thousands` flag exists because in ISORA 2016/2018 the unit is only stated in some labels. - **Categorical answers are not fully harmonised at source**: `InPlace` and `In Place`, `Implmenting` and `Implementing`, `option a)` with a stray `
`, leading spaces in ISORA 2016 answers. Cleaning removed markup and whitespace but did not merge spellings. - **Codelists include codes without data** (283 in 2016, 116 in 2018, 397 in 2020+): questions suppressed from publication or never asked. `indicators.has_observations` flags them. - **Fiscal years** are the administrations' own fiscal years and do not align across countries. - **Combined tax-and-customs administrations** sometimes report total staff or expenditure for both functions (the IMF notes this on the staff tables). - **World Bank income groups** in `jurisdictions` are the FY2015 classification stored in the IMF codelist. Join current classifications yourself if you need them. - **Revisions**: the `observations` table is the latest published view (ISORA 2025 release). If you compare with figures quoted in older IMF/OECD publications, consult `revisions`. - **Territorial names** follow IMF practice (e.g. *China, P.R.: Hong Kong*, *Taiwan*, *Kosovo, Republic of*, *Türkiye, Rep of*) and are without prejudice to the status of any territory. ## Provenance and reproducibility Everything comes from the public IMF SDMX API (`https://api.imf.org/external/sdmx/3.0`, agency `ISORA`), retrieved on 2026-09-20T04:42:35Z: | Dataflow | Version | Data structure | Last updated at source | Used here | |---|---|---|---|---| | `ISORA_2016_DATA_PUB` | 1.0.0 | `ISORA:DSD_ISORA_PUBLISHED(1.0+.0)` | 2025-03-31T14:48:21.329477Z | no | | `ISORA_2016_DATA_PUB` | 2.0.0 | `ISORA:DSD_ISORA_PUBLISHED(1.0+.0)` | 2025-06-19T04:06:43.477036Z | observations | | `ISORA_2018_DATA_PUB` | 1.0.0 | `ISORA:DSD_ISORA_PUBLISHED(2.0+.0)` | 2025-03-31T14:48:21.358333Z | no | | `ISORA_2018_DATA_PUB` | 2.0.0 | `ISORA:DSD_ISORA_PUBLISHED(2.0+.0)` | 2025-06-19T04:06:43.532576Z | observations | | `ISORA_LATEST_DATA_PUB` | 2.0.0 | `ISORA:DSD_ISORA_PUBLISHED(4.0+.0)` | 2025-03-28T16:08:59.158309Z | revisions | | `ISORA_LATEST_DATA_PUB` | 4.0.0 | `ISORA:DSD_ISORA_PUBLISHED(5.0+.0)` | 2025-07-04T18:22:18.692447Z | revisions | | `ISORA_LATEST_DATA_PUB` | 5.0.0 | `ISORA:DSD_ISORA_PUBLISHED(6.0+.0)` | 2026-06-15T17:13:23.041621Z | observations | Structures used: `DSD_ISORA_PUBLISHED` 1.0.0 / 2.0.0 / 6.0.0 with their codelists (`CL_INDICATOR` 1.0.2, `CL_ISORA_TAX` 1.0.3 and 6.0.6, `CL_COUNTRY`, `CL_JURISDICTION` 4.8.4, `CL_ISORA_ISO_COUNTRY` 2.0.1), the hierarchies `H_CL_INDICATORS_BY_TOPIC` 2.2.0, `H_CL_PERIODIC_INDICATORS` 2.0.0, `H_CL_DERIVED_INDICATORS` 2.0.0, `H_CL_REVIEW_INDICATORS` 2.1.0 and the label codelist `CL_RAFIT_LABELS`. Dataset-level attributes (license URL, citations, publication dates) were read from the SDMX 2.1 CSV endpoint and are stored in `metadata/build_summary.json`. The full pipeline (download, transformation rules, unit tests, this card's template) is in [`pipeline/`](pipeline/) and is MIT-licensed; `python -m isora_hf.sdmx_client && python -m isora_hf.build && python -m isora_hf.card` rebuilds the dataset from scratch. Re-running it after the next ISORA release (expected mid-2027 for FY2025) is how this dataset will be updated. Official documentation — questionnaires, completion guides, review and derived tables per round — is in the ISORA [Documents Catalog](https://data.imf.org/en/Documents#f:Datasets=[International%20Survey%20on%20Revenue%20Administration%20(ISORA)]) (the files are served through the portal's download buttons and are not mirrored here). The IMF also publishes analytical reports on each round (*ISORA 2016: Understanding Revenue Administration*, 2019; *ISORA 2018: Understanding Revenue Administration*, 2021; *ISORA 2023: Tax Administration: Performance and Practices*, 2026), and the OECD's annual *Tax Administration* series is built on the same data for 58 jurisdictions. ## Citation and acknowledgement Any publication using these data must acknowledge the source. The citation requested by the publisher (from the source metadata) is: > The International Survey on Revenue Administration (ISORA). http://isoradata.org. Accessed on [date]. Full source citation: > The Asian Development Bank (ADB), the Inter-American Center of Tax Administrations (CIAT); the > International Monetary Fund (IMF); the Intra-European Organisation of Tax Administrations > (IOTA); and the Organisation for Economic Co-operation and Development (OECD), International > Survey on Revenue Administration: https://ISORADATA.ORG If you also want to credit this cleaned redistribution: ```bibtex @misc{isora_hf_2026, title = {ISORA -- International Survey on Revenue Administration, FY2014--FY2024 (cleaned redistribution)}, howpublished = {Hugging Face dataset \url{https://huggingface.co/datasets/FrenchCastle/isora-tax-administration}}, year = {2026}, note = {Unofficial restructuring of data published by the IMF on behalf of ADB, CIAT, IMF, IOTA and OECD through the ISORA Data Portal (https://isoradata.org). Data subject to the ISORA Data Portal Terms and Conditions.} } ``` ## Licence `license: other` — **ISORA Data Portal Terms and Conditions of Data Access and Use** plus the IMF Copyright and Usage policy, reproduced in [`LICENSE`](LICENSE). In short: you may use and publish the data with appropriate acknowledgement of the source; the data are provided as is, without warranty; you indemnify the partner organisations against third-party claims arising from your use; the partner organisations' immunities are preserved; contact copyright@imf.org for commercial reuse questions. The pipeline code is MIT-licensed. ## Dataset version - **1.0.0** (2026-09-20): first release, built from `ISORA_2016_DATA_PUB` 2.0.0, `ISORA_2018_DATA_PUB` 2.0.0 and `ISORA_LATEST_DATA_PUB` 5.0.0 (ISORA 2025 release, FY2024 data published June 2026).