Hugging Face's logo

Datasets:
FrenchCastle
/
isora-tax-administration

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
isora
international-survey-on-revenue-administration
tax-administration
revenue-administration
tax-authority
taxation
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
isora-tax-administration
File size: 4,434 Bytes
cbd273f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9f0fcd7
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
"""Constants shared by the pipeline: API endpoints, dataflows, rounds, sentinel codes, paths."""

from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parents[2]
RAW_DIR = PROJECT_ROOT / "raw"
RAW_DATA_DIR = RAW_DIR / "data"
RAW_STRUCTURES_DIR = RAW_DIR / "structures"
OUT_DIR = PROJECT_ROOT / "hf_dataset"

SDMX3_BASE = "https://api.imf.org/external/sdmx/3.0"
SDMX21_BASE = "https://api.imf.org/external/sdmx/2.1"
AGENCY = "ISORA"
USER_AGENT = "isora-hf/0.1 (+https://huggingface.co; data pipeline; contact via dataset card)"

ISORA_PORTAL_URL = "https://data.imf.org/en/datasets/ISORA:ISORA_LATEST_DATA_PUB"
TERMS_URL = "https://data.imf.org/en/Datasets/RAFIT-Consolidated/Terms-and-Conditions"
DISCLAIMER_URL = "https://data.imf.org/en/Datasets/RAFIT-Consolidated/Disclaimer"
ABOUT_URL = "https://data.imf.org/en/Datasets/RAFIT-Consolidated/About-RA-FIT"


@dataclass(frozen=True)
class Dataflow:
    """One published ISORA dataflow (a questionnaire generation)."""

    id: str
    version: str
    dsd_version: str
    generation: str  # human-readable questionnaire generation
    fiscal_years: tuple[int, ...]
    geo_dimension: str  # COUNTRY (numeric IMF codes) or JURISDICTION (alpha-3)
    indicator_codelist: str
    form_status_attr: str


DATAFLOWS: tuple[Dataflow, ...] = (
    Dataflow(
        id="ISORA_2016_DATA_PUB",
        version="2.0.0",
        dsd_version="1.0.0",
        generation="ISORA 2016",
        fiscal_years=(2014, 2015),
        geo_dimension="COUNTRY",
        indicator_codelist="CL_INDICATOR",
        form_status_attr="FORM_STATUS",
    ),
    Dataflow(
        id="ISORA_2018_DATA_PUB",
        version="2.0.0",
        dsd_version="2.0.0",
        generation="ISORA 2018",
        fiscal_years=(2016, 2017),
        geo_dimension="JURISDICTION",
        indicator_codelist="CL_ISORA_TAX",
        form_status_attr="FORMSTATUS",
    ),
    Dataflow(
        id="ISORA_LATEST_DATA_PUB",
        version="5.0.0",
        dsd_version="6.0.0",
        generation="ISORA 2020+",
        fiscal_years=(2018, 2019, 2020, 2021, 2022, 2023, 2024),
        geo_dimension="JURISDICTION",
        indicator_codelist="CL_ISORA_TAX",
        form_status_attr="FORMSTATUS",
    ),
)

# Older published versions of the consolidated dataflow, still served by the API.
# They are earlier release vintages of the same FY2018+ series and let us track revisions.
VINTAGES: tuple[tuple[str, str, str], ...] = (
    # (dataflow version, release label, fiscal years covered)
    ("2.0.0", "ISORA 2023 release (FY2018-FY2022)", "2018-2022"),
    ("4.0.0", "ISORA 2024 release (FY2018-FY2023)", "2018-2023"),
    ("5.0.0", "ISORA 2025 release (FY2018-FY2024)", "2018-2024"),
)

# Fiscal year -> survey round in which that fiscal year was first collected.
FISCAL_YEAR_TO_ROUND: dict[int, str] = {
    2014: "ISORA 2016",
    2015: "ISORA 2016",
    2016: "ISORA 2018",
    2017: "ISORA 2018",
    2018: "ISORA 2020",
    2019: "ISORA 2020",
    2020: "ISORA 2021",
    2021: "ISORA 2022",
    2022: "ISORA 2023",
    2023: "ISORA 2024",
    2024: "ISORA 2025",
}

# Published participation counts (About ISORA page + ISORA 2025 news item), for the card.
ROUND_PARTICIPATION: dict[str, int] = {
    "ISORA 2016": 135,
    "ISORA 2018": 159,
    "ISORA 2020": 156,
    "ISORA 2021": 156,
    "ISORA 2022": 165,
    "ISORA 2023": 166,
    "ISORA 2024": 164,
    "ISORA 2025": 166,
}

# Sentinel strings found in the OBSERVATION column.
NOT_AVAILABLE_CODES = frozenset({"D"})
NOT_APPLICABLE_STRINGS = frozenset({"not applicable", "n/a", "na"})
UNRECOGNIZED_CODES = frozenset({"P"})  # ISORA 2016 only; not documented in surviving material

# DissemScale annotation -> normalized indicator type
INDICATOR_TYPE_MAP: dict[str, str] = {
    "binary": "binary",
    "counting": "count",
    "currency": "currency",
    "percent": "percent",
    "nominal": "nominal",
    "ordinal": "ordinal",
    "text": "text",
    "date": "date",
    "na": "unspecified",
    "": "unspecified",
}

# Derived level aggregates in the consolidated dataflow that are published in thousands of local
# currency with SCALE=0 (verified: they equal ~1/1000 of net revenue x cost-of-collection ratio),
# unlike the raw money questions which carry SCALE=3 and are already in base units.
LATEST_THOUSANDS_CODES = frozenset({"337_176", "337_177", "337_178", "337_179"})