Canada-USA macro divergence (annual)

Usage commercial OKÀ jourLicence: CC-BY-4.0

Le jeu complet n’est pas encore publié — revenez après la prochaine actualisation.Télécharger l'exemple CSV Télécharger l'exemple JSONLignes d’exemple seulement (jusqu’à 20) — pas le jeu de données complet.

Annual intelligence on how far Canada and the USA have drifted apart across 25 curated World Bank WDI macro indicators (growth, prices, labor, fiscal, external, structure, demographics, digital, energy, health, finance). For each indicator the keyless WDI API supplies CAN + USA annual series (2000-2026); the connector keeps the common years, computes gap = CAN - USA in native units at the latest common reference_year, z-scores that gap against its trailing-20-year history, and scores divergence_score = 100 * min(|gap_z|, 3) / panel-max — a within-snapshot relative 0-100 measure ranked as divergence_rank with d1-d4 tiers. Also: 5-year gap change, a widening flag (|gap| growing vs 5y ago), value_leader (higher reference value) and favorable_leader (documented higher/lower-is-better map; null where 'better' is ambiguous). Primary key: (reference_year, indicator_code); join keys: indicator_code, reference_year, theme_category; country codes are ISO alpha-3 (CAN/USA) in value_leader/favorable_leader. Caveats: reference_year varies by indicator (WDI vintages lag 1-3 years); scores are within-snapshot relative, not comparable across snapshots; WDI revisions change row hashes and mint new snapshots by design; 'favorable' is an editorial direction map, not a normative judgment. Who joins this: a macro analyst joins divergence_score/divergence_rank to a country-allocation model on indicator_code + reference_year; a policy team joins on theme_category for briefing packs. Sample use: order by divergence_rank to find where the two economies diverged most this vintage, or filter theme_category = 'labor' for the jobs picture.

Les titres et les descriptions proviennent des sources de données, en anglais.

Source: Canada-USA macro divergence (agent-curated)
Lignes: 25
Colonnes: 17
Cadence de la source: Annuelle
Dernière actualisation: 27 sept. 2026
Thème: economy

Colonne	Type	Description
reference_year	integer	Latest year with both CAN and USA values for this indicator (WDI vintages lag 1-3 years, so it varies by indicator). Part of the primary key with indicator_code. (unit: year)
indicator_code	string	World Bank WDI indicator code (e.g. NY.GDP.MKTP.KD.ZG). Part of the primary key; the join key for macro models. (unit: code)
indicator_name	string	Short human-readable indicator name from the connector's curated table. (unit: text)
theme_category	string	Closed theme vocabulary from the connector: growth, prices, labor, fiscal, external, structure, demographics, digital, energy, health, finance. A join key for briefing packs. (unit: category)
unit	string	Native unit of can_value/usa_value/gap (e.g. 'annual %', '% of GDP', 'years'). (unit: unit)
can_value	float	Canada's value at reference_year, native units. (unit: native)
usa_value	float	USA's value at reference_year, native units. (unit: native)
gap	float	CAN minus USA at reference_year, in native units. Positive = Canada higher. (unit: native)
gap_z	float	Z-score of the reference-year gap against its trailing up-to-20-year gap history (sample stdev, ddof=1); 0.0 when the history is degenerate. Winsorized at +/-3.0 for scoring. (unit: z-score)
gap_change_5y	string	Change in the gap vs 5 years earlier (gap_ref - gap_{ref-5}), native units. Null when the 5-year-ago common year is missing. (unit: native)
widening	string	True when \|gap\| at reference_year exceeds \|gap\| 5 years earlier (the economies are drifting apart on this indicator). Null when the 5-year-ago common year is missing. (unit: boolean)
value_leader	string	Country with the higher reference value: CAN, USA, or TIE. ISO alpha-3. (unit: ISO alpha-3)
favorable_leader	string	Country leading on the documented higher/lower-is-better direction for this indicator (CAN/USA/TIE). Null for indicators where 'better' is ambiguous (exchange rate, CPI index level, trade shares, consumption share, population growth, urban share, health-expenditure share). (unit: ISO alpha-3)
divergence_score	float	0-100 composite = 100 * min(\|gap_z\|, 3) / panel-max(min(\|gap_z\|, 3)). Within-snapshot relative — not comparable across snapshots. (unit: score)
divergence_rank	integer	Rank by divergence_score desc (1 = most diverged); ties broken by \|gap_z\| desc, then indicator_code asc. (unit: rank)
divergence_tier	string	d1 wide (>=75), d2 material (50-75), d3 moderate (25-50), d4 narrow (<25). (unit: tier)
row_hash	string	Deterministic 16-hex-char content hash over (reference_year, indicator_code, can_value, usa_value, gap, gap_z) — identical input yields an identical snapshot. (unit: hash)

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

reference_year	indicator_code	indicator_name	theme_category	unit	can_value	usa_value	gap	gap_z	gap_change_5y	widening	value_leader	favorable_leader	divergence_score	divergence_rank	divergence_tier	row_hash
2 024	FP.CPI.TOTL	Consumer price index (2010 = 100)	prices	index	138,108	143,857	-5,749	-2,373	-5,262	true	USA	—	100	1	d1	77fd9ecc768af494
2 025	NY.GDP.MKTP.PP.CD	GDP, PPP	growth	current international $	2 780 073 936 107,75	30 769 700 000 000	-27 989 626 063 892,25	-2,094	-8 462 185 062 427,641	true	USA	USA	88,21	2	d1	303a21de16ee2b73
2 025	SP.URB.TOTL.IN.ZS	Urban population	demographics	% of total	82,876	80,192	2,684	1,663	0,73	true	CAN	—	70,07	3	d2	9c1112ed138f59eb
2 025	PA.NUS.FCRF	Official exchange rate	external	LCU per US$, period average	1,398	1	0,398	1,376	0,057	true	CAN	—	57,97	4	d2	6ff7d808eacb05b8
2 025	SL.UEM.TOTL.ZS	Unemployment, total (modeled ILO)	labor	% of labor force	6,907	4,198	2,709	1,221	1,107	true	CAN	USA	51,43	5	d2	1782ea0b8f645565
2 024	IT.NET.USER.ZS	Internet users	digital	% of population	94,353	94,694	-0,341	-1,191	-2,823	false	USA	USA	50,17	6	d2	d77d98b9a70036c3
2 025	SL.UEM.TOTL.NE.ZS	Unemployment, total (national estimate)	labor	% of labor force	6,819	4,282	2,537	1,103	0,935	true	CAN	USA	46,46	7	d3	4376843f5aa14c25
2 024	GC.DOD.TOTL.GD.ZS	Central government debt	fiscal	% of GDP	64,131	115,768	-51,637	-1,016	-4,758	true	USA	CAN	42,79	8	d3	c55c8a518f7fc199
2 025	FB.AST.NPER.ZS	Bank nonperforming loans	finance	% of gross loans	0,706	0,96	-0,254	0,857	0,285	false	USA	CAN	36,1	9	d3	de75f5ac162066bd
2 024	NE.IMP.GNFS.ZS	Imports of goods and services	external	% of GDP	32,652	14,041	18,611	0,829	-0,738	false	CAN	—	34,91	10	d3	454f219f1ee759b1

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/ca_usa_macro_divergence/ca_usa_macro_divergence_annual" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/ca_usa_macro_divergence/ca_usa_macro_divergence_annual").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API : https://datazimuts.com/v1/datasets/ca_usa_macro_divergence/ca_usa_macro_divergence_annual

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

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APA

Canada-USA macro divergence (agent-curated). (2026). Canada-USA macro divergence (annual) [Data set, snapshot 20260927T205446Z-adc220f986de, sha256 adc220f986de]. Datazimuts. Retrieved 2026-09-27, from https://datazimuts.com/fr/datasets/ca_usa_macro_divergence/ca_usa_macro_divergence_annual?snapshot=20260927T205446Z-adc220f986de

BibTeX

@misc{dz_ca_usa_macro_divergence_ca_usa_macro_div_adc220f9,
  title        = {{Canada-USA macro divergence (annual)}},
  author       = {{Canada-USA macro divergence (agent-curated)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/fr/datasets/ca_usa_macro_divergence/ca_usa_macro_divergence_annual?snapshot=20260927T205446Z-adc220f986de}},
  note         = {Snapshot 20260927T205446Z-adc220f986de, sha256 adc220f986dec79a32476962649003396ce14239781ad34b6750e0e8bf7356c5; accessed 2026-09-27}
}

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