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Get Started Free →Access World Bank development indicators and country statistics
.claude/skills/brycewang-stanford-world-bank-data-guide/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 137% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 53% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 116% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 14% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 32% | 0% |
The World Bank Data API provides free access to one of the most comprehensive collections of global development data available. The Indicators API covers over 16,000 development indicators across 217 countries and territories, with time series data spanning several decades. Topics include poverty and inequality, health outcomes, education statistics, infrastructure, trade, environmental metrics, and governance indicators.
This API is a cornerstone resource for researchers in development economics, public health, education policy, environmental science, and political science. The World Bank's data is widely cited in academic publications and policy documents, making it an authoritative source for cross-country comparative research.
The API is entirely free, requires no authentication, and returns data in JSON, XML, or other formats. The v2 API supports pagination, filtering by date ranges and income levels, and provides metadata about indicators and data sources.
No authentication is required. The World Bank Data API is free and open.
bash# No API key needed curl "https://api.worldbank.org/v2/country/US/indicator/NY.GDP.MKTP.CD?format=json&date=2020:2024"
GET https://api.worldbank.org/v2/country/{country_code}/indicator/{indicator_code}?format=jsonParameters:
country_code: ISO 2-letter or 3-letter code; use all for all countriesindicator_code: World Bank indicator codeformat: json or xml (default xml)date: Year range (e.g., 2015:2024)per_page: Results per page (default 50, max 32500)page: Page numberCommon Indicators:
NY.GDP.MKTP.CD: GDP (current US$)NY.GDP.PCAP.CD: GDP per capita (current US$)SP.POP.TOTL: Total populationSI.POV.DDAY: Poverty headcount ratio at $2.15/daySE.ADT.LITR.ZS: Adult literacy rateSH.XPD.CHEX.GD.ZS: Current health expenditure (% of GDP)EN.ATM.CO2E.PC: CO2 emissions (metric tons per capita)Example: GDP per capita for BRICS nations:
bashcurl -s "https://api.worldbank.org/v2/country/BR;RU;IN;CN;ZA/indicator/NY.GDP.PCAP.CD?format=json&date=2019:2023&per_page=100" \ | python3 -m json.tool
bashcurl -s "https://api.worldbank.org/v2/indicator?format=json&per_page=50" \ | python3 -m json.tool
bashcurl -s "https://api.worldbank.org/v2/topic/8/indicator?format=json&per_page=20" \ | python3 -m json.tool
Topics include: 1=Agriculture, 3=Economy, 4=Education, 6=Environment, 8=Health, 11=Poverty, etc.
bashcurl -s "https://api.worldbank.org/v2/country/CN?format=json" \ | python3 -m json.tool
pythonimport requests import time BASE_URL = "https://api.worldbank.org/v2" def get_indicator(indicator_code, countries="all", date_range="2015:2023", per_page=500): """Fetch World Bank indicator data.""" url = f"{BASE_URL}/country/{countries}/indicator/{indicator_code}" params = { "format": "json", "date": date_range, "per_page": per_page } resp = requests.get(url, params=params) resp.raise_for_status() data = resp.json() if len(data) < 2: return [] return data[1] # Compare health expenditure vs life expectancy health_exp = get_indicator("SH.XPD.CHEX.GD.ZS", countries="US;GB;DE;JP;BR;IN", date_range="2020") for record in health_exp: if record["value"] is not None: country = record["country"]["value"] year = record["date"] value = record["value"] print(f"{country} ({year}): {value:.1f}% of GDP on health")
pythonimport requests import csv def build_panel(indicators, countries, years): """Build a panel dataset from multiple World Bank indicators.""" panel = {} for ind_code, ind_name in indicators.items(): url = f"https://api.worldbank.org/v2/country/{countries}/indicator/{ind_code}" params = {"format": "json", "date": years, "per_page": 5000} resp = requests.get(url, params=params) data = resp.json() if len(data) < 2: continue for record in data[1]: key = (record["country"]["id"], record["date"]) if key not in panel: panel[key] = {"country": record["country"]["value"], "year": record["date"]} panel[key][ind_name] = record["value"] return list(panel.values()) indicators = { "NY.GDP.PCAP.CD": "gdp_per_capita", "SP.POP.TOTL": "population", "SE.ADT.LITR.ZS": "literacy_rate" } data = build_panel(indicators, "BR;IN;NG;ID", "2018:2023") for row in data[:10]: print(row)
Development Panel Regressions: Combine multiple indicators across countries and years to construct panel datasets for econometric analysis. Study relationships between education spending, health outcomes, and economic growth.
Poverty and Inequality Analysis: Track poverty headcount ratios, Gini coefficients, and income share data across countries and over time to study the effectiveness of development interventions.
Environmental-Economic Nexus: Combine CO2 emissions, renewable energy, and GDP data to study the Environmental Kuznets Curve or the decoupling of economic growth from environmental degradation.
Policy Evaluation: Compare indicator trajectories before and after major policy reforms across treated and comparison countries using difference-in-differences or synthetic control methods.
per_page=32500 for single-page retrieval of most queriesformat=json as the default is XMLEUU (EU), WLD (World), LIC (Low Income) for pre-computed aggregatesMRV=1 to get the most recent value for each country when the latest year varies| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 14,594 | 43,691 | +199% | 1 | 1 | 0% | 3,112 | 4,118 | +32% | 0 | 0 | — |
case-02 | pass→pass | 45,010 | 11,324 | -75% | 1 | 1 | 0% | 2,818 | 4,275 | +52% | 0 | 0 | — |
case-03 | fail→pass | 14,379 | 23,937 | +66% | 1 | 1 | 0% | 2,319 | 5,490 | +137% | 0 | 0 | — |
case-04 | pass→pass | 9,924 | 42,109 | +324% | 1 | 1 | 0% | 1,432 | 3,796 | +165% | 0 | 0 | — |
case-05 | pass→pass | 5,790 | 5,453 | -6% | 1 | 1 | 0% | 1,098 | 2,833 | +158% | 0 | 0 | — |
case-06 | pass→pass | 10,151 | 5,248 | -48% | 1 | 1 | 0% | 1,562 | 2,903 | +86% | 0 | 0 | — |
case-07 | pass→pass | 36,877 | 5,054 | -86% | 1 | 1 | 0% | 1,416 | 2,799 | +98% | 0 | 0 | — |
case-08 | pass→pass | 11,146 | 2,877 | -74% | 1 | 1 | 0% | 1,944 | 2,497 | +28% | 0 | 0 | — |
case-09 | fail→pass | 9,946 | 4,688 | -53% | 1 | 1 | 0% | 1,849 | 2,824 | +53% | 0 | 0 | — |
case-10 | pass→pass | 8,060 | 6,793 | -16% | 1 | 1 | 0% | 1,586 | 3,260 | +106% | 0 | 0 | — |
case-11 | pass→pass | 8,955 | 4,490 | -50% | 1 | 1 | 0% | 1,367 | 2,559 | +87% | 0 | 0 | — |
case-12 | fail→pass | 6,275 | 4,931 | -21% | 1 | 1 | 0% | 1,251 | 2,700 | +116% | 0 | 0 | — |
case-13 | pass→pass | 12,097 | 11,044 | -9% | 1 | 1 | 0% | 2,356 | 4,068 | +73% | 0 | 0 | — |
case-14 | pass→pass | 8,373 | 4,816 | -42% | 1 | 1 | 0% | 1,752 | 2,924 | +67% | 0 | 0 | — |
case-15 | pass→pass | 7,250 | 4,493 | -38% | 1 | 1 | 0% | 1,036 | 2,778 | +168% | 0 | 0 | — |
case-16 | pass→pass | 7,413 | 4,002 | -46% | 1 | 1 | 0% | 1,486 | 2,520 | +70% | 0 | 0 | — |
case-17 | pass→pass | 8,122 | 3,650 | -55% | 1 | 1 | 0% | 1,248 | 2,578 | +107% | 0 | 0 | — |
case-18 | fail→pass | 11,937 | 3,367 | -72% | 1 | 1 | 0% | 2,167 | 2,476 | +14% | 0 | 0 | — |
case-19 | pass→pass | 10,024 | 3,833 | -62% | 1 | 1 | 0% | 1,968 | 2,706 | +38% | 0 | 0 | — |
case-20 | pass→pass | 2,188 | 2,406 | +10% | 1 | 1 | 0% | 420 | 2,368 | +464% | 0 | 0 | — |
case-21 | pass→pass | 13,199 | 7,334 | -44% | 1 | 1 | 0% | 2,108 | 3,336 | +58% | 0 | 0 | — |
case-22 | pass→pass | 7,932 | 7,371 | -7% | 1 | 1 | 0% | 1,314 | 3,190 | +143% | 0 | 0 | — |
case-23 | pass→pass | 5,831 | 2,932 | -50% | 1 | 1 | 0% | 901 | 2,325 | +158% | 0 | 0 | — |
case-24 | pass→pass | 5,240 | 3,069 | -41% | 1 | 1 | 0% | 918 | 2,361 | +157% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +17 percentage points is the difference between those two pass rates over the 24 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.