📈 econ

U.S. economic indicators from Federal Reserve (FRED), Bureau of Labor Statistics (BLS), Bureau of Economic Analysis (BEA), and Treasury Department. Includes employment statistics, inflation metrics, GDP components, interest rates, regional economic data, and over 800,000 time series from FRED. Enables macroeconomic analysis, economic forecasting, policy research, and cross-domain correlation with geographic and demographic data. Uses DuckDB quackformers extension for native embedding generation.

60 datasets · 316 columns

fred_indicators_enriched · table

Enriched fred_indicator observations with metadata such as series names, units, frequency, or others.

View — columns are resolved by the query engine at runtime.

national_accounts_enriched · table

National accounts (NIPA) data enriched with table metadata. Columns: table_id (BEA table code), line_number (line within table), line_description (component description), series_code (BEA series code), year, value (millions of dollars), units, frequency (A=Annual/Q=Quarterly), table_description (full table description from BEA). Enables semantic queries without manual joins. (The nipa_tables reference catalog exposes only table_name/description from the BEA API; its former section categorization was never populated — the partition was changed section->type in commit a2c400aaa — so no section column is emitted.)

View — columns are resolved by the query engine at runtime.

regional_income_enriched · table

Regional income data enriched with LineCode descriptions from the reference catalog. Columns include all regional_income fields plus line_code_description (what the BEA line code represents). Enables semantic queries without needing to know specific LineCode values. (The regional_linecodes reference catalog exposes only key/desc from the BEA API, so table_prefix/data_category/geography_level/table_frequency are not available.)

View — columns are resolved by the query engine at runtime.

interest_rate_spreads · table

Full yield curve with spreads and breakeven inflation. Columns: date, three_month_yield, two_year_yield, five_year_yield, ten_year_yield, thirty_year_yield (all Treasury constant maturity rates %), term_spread_10y_2y (canonical recession signal, negative historically precedes downturns), term_spread_10y_3m, breakeven_inflation_10y (market-implied 10Y inflation expectations %), fed_funds_rate (FOMC policy rate %), corporate_spread_bps (investment-grade spread, widening signals credit stress). Critical for recession forecasting, monetary policy analysis, and credit market assessment.

View — columns are resolved by the query engine at runtime.

housing_indicators · table

Comprehensive housing market health dashboard. Columns: date, housing_starts (total new housing units started in thousands, annualized), single_family_starts (single-family housing starts subset in thousands), building_permits (authorized building permits in thousands, leading indicator), existing_sales (existing home sales in millions annualized), case_shiller_index (S&P CoreLogic Case-Shiller U.S. National Home Price Index), median_sale_price (median sales price of houses sold in USD), mortgage_rate_30y (30-year fixed rate mortgage average %), rental_vacancy_rate (percentage of rental units vacant). Essential for real estate analysis, construction sector tracking, and housing affordability assessment.

View — columns are resolved by the query engine at runtime.

monetary_aggregates · table

Federal Reserve monetary aggregates and velocity analysis. Columns: date, m1_money_supply (narrow money supply including currency and demand deposits in billions), m2_money_supply (broad money supply including M1 plus savings deposits and money market funds in billions), monetary_base (currency in circulation plus bank reserves in billions), nominal_gdp (gross domestic product in billions), m2_velocity (calculated as GDP/M2, measures money turnover rate, declining velocity may indicate liquidity hoarding). Critical for Federal Reserve policy analysis, inflation expectations, and monetary transmission mechanism assessment.

View — columns are resolved by the query engine at runtime.

business_indicators · table

Business cycle and economic activity dashboard. Columns: date, industrial_production_index (index of manufacturing and industrial output, 2017=100), capacity_utilization_pct (percentage of total industrial capacity in use, high values indicate potential inflation pressure), retail_sales_millions (advance retail sales in millions USD, excludes services, leading consumer spending indicator), bank_credit_billions (total credit extended by banks in billions, measure of lending activity), lending_standards_net_pct (net percentage of banks tightening lending standards, positive values indicate credit tightening). Essential for business cycle analysis, recession/expansion identification, and credit conditions monitoring.

View — columns are resolved by the query engine at runtime.

trade_balance_summary · table

U.S. international trade balance aggregated by category from BEA. Columns: year (calendar year), quarter (Q1-Q4 for quarterly data, NULL for annual), line_description (trade category such as 'Goods', 'Services', 'Foods feeds and beverages', etc.), total_exports (sum of exports in millions USD), total_imports (sum of imports in millions USD), net_trade_balance (exports minus imports, negative indicates trade deficit). Derived from BEA NIPA Table 4.2.5B (Foreign Transactions section), enables analysis of trade composition, bilateral balances, and sectoral trade patterns. Critical for understanding U.S. competitiveness, currency impacts, and GDP contribution from net exports.

View — columns are resolved by the query engine at runtime.

trade_statistics · table

U.S. international trade statistics from BEA NIPA Table 4.2.5B (Foreign Transactions section). Provides detailed breakdown of exports and imports of goods and services with computed columns: quarter (1-4 extracted from time_period for quarterly data, NULL for annual), trade_type (exports/imports/trade_balance derived from line_description). This is a convenience view filtering national_accounts for trade-specific data. For enriched version with section metadata, query national_accounts_enriched WHERE table_id = 'T40205B'.

View — columns are resolved by the query engine at runtime.

real_gdp_growth · table

Real GDP (chained 2017 dollars) with quarter-over-quarter growth rate. Columns: date, real_gdp_billions (real GDP in billions of chained 2017 dollars), prior_quarter_gdp, qoq_growth_pct (percentage change from prior quarter). Use GDPC1 instead of GDP for growth analysis to remove inflation distortion. Two consecutive negative qoq_growth_pct values is the informal recession definition.

View — columns are resolved by the query engine at runtime.

pce_inflation · table

PCE inflation dashboard — the Federal Reserve's preferred inflation framework. Columns: date, pce_index (Personal Consumption Expenditures price index, headline), core_pce_index (PCE excluding food and energy — the FOMC's primary 2% inflation target). Complements CPI-based views; the Fed explicitly targets Core PCE, making this view critical for monetary policy analysis.

View — columns are resolved by the query engine at runtime.

consumer_spending · table

Consumer spending, income, and saving dashboard. Columns: date, personal_consumption_billions (PCE in billions USD — largest single component of GDP at ~70%), personal_income_billions (total personal income in billions USD), saving_rate_pct (personal saving as % of disposable income, low values indicate consumer stress, high values can signal precautionary behavior). Essential for consumer health assessment, GDP forecasting, and tracking post-shock household balance sheet recovery.

View — columns are resolved by the query engine at runtime.

labor_market_conditions · table

Comprehensive labor market conditions dashboard. Columns: date, unemployment_rate_u3 (official U-3 unemployment rate %), unemployment_rate_u6 (broad U-6 rate including underemployed and marginally attached %), labor_slack_gap (U6 minus U3, higher values indicate hidden slack), initial_claims (weekly initial jobless claims — most timely leading labor indicator), continuing_claims (workers still collecting unemployment benefits — measures labor market absorption), avg_weekly_hours_mfg (average weekly hours in manufacturing, a Conference Board leading indicator — employers cut hours before cutting workers). Essential for assessing true labor market health beyond the headline unemployment rate.

View — columns are resolved by the query engine at runtime.

fed_policy_dashboard · table

Federal Reserve policy stance dashboard. Columns: date, fed_funds_rate (FOMC policy rate %), core_pce (Fed primary inflation target — PCE ex-food-and-energy %), real_fed_funds_rate (nominal rate minus Core PCE — negative values indicate accommodative policy, positive values restrictive), fed_balance_sheet_billions (Fed total assets in billions — tracks QE/QT cycles), in_recession (NBER recession indicator, 1=recession, 0=expansion). Directly maps to the Fed's dual mandate framework (price stability + maximum employment) and balance sheet normalization tracking.

View — columns are resolved by the query engine at runtime.

global_conditions · table

Global commodity and currency conditions. Columns: date, wti_crude_price (West Texas Intermediate crude oil spot price in USD/barrel — leading input cost and inflation driver), usd_broad_index (trade-weighted US dollar index against broad basket of trading partners, 2006=100 — strong dollar suppresses exports and commodity prices, weak dollar does the opposite). Useful for cost-push inflation analysis, export competitiveness, and macro regime identification.

View — columns are resolved by the query engine at runtime.

employment_statistics · table

U.S. employment and unemployment statistics from BLS including national unemployment rate, labor force participation, job openings, and employment by sector. Updated monthly with seasonal adjustments.

Column	Type	Null	Description
series	string	yes	BLS series identifier (e.g., 'LNS14000000' for unemployment rate)
year	integer	yes	Data year from BLS API
period	string	yes	BLS period identifier (M01-M12 for monthly, Q01-Q04 for quarterly)
periodName	string	yes	Human-readable period name (e.g., 'January', 'February')
value	double	yes	Employment statistic value (e.g., unemployment rate as percentage)
latest	boolean	yes	Whether this is the latest data point in the series
footnotes	string	yes	Any footnotes or qualifiers for the data point

inflation_metrics · table

Consumer Price Index (CPI) and Producer Price Index (PPI) data tracking inflation across different categories of goods and services. Includes urban, regional, and sector-specific inflation rates.

Column	Type	Null	Description
series	string	yes	BLS series identifier (e.g., 'CUUR0000SA0' for CPI All Urban, 'WPUFD49207' for PPI)
year	integer	yes	Data year from BLS API
period	string	yes	BLS period identifier (M01-M12 for monthly)
periodName	string	yes	Human-readable period name (e.g., 'January', 'February')
value	double	yes	Price index value (typically base period = 100)
latest	boolean	yes	Whether this is the latest data point in the series
footnotes	string	yes	Any footnotes or qualifiers for the data point

regional_cpi · table

Consumer Price Index for 4 U.S. Census regions (Northeast, Midwest, South, West). Enables regional inflation comparisons and analysis of cost-of-living differences across major geographic areas.

Column	Type	Null	Description
date	date	yes	Observation date (ISO 8601 format)
series	string	yes	BLS series identifier
area_code	string	yes	Census region code
area_name	string	yes	Census region name
value	double	yes	CPI value (base period = 100)
percent_change_month	double	yes	Month-over-month percentage change
percent_change_year	double	yes	Year-over-year percentage change

metro_cpi · table

Consumer Price Index for 20 major U.S. metropolitan areas including NYC, LA, Chicago, Houston, Phoenix, and others. Critical for understanding local cost-of-living variations and metro-specific inflation trends.

Column	Type	Null	Description
date	date	yes	Observation date (ISO 8601 format)
series	string	yes	BLS series identifier
area_code	string	yes	Metro area code
area_name	string	yes	Metro area name
value	double	yes	CPI value (base period = 100)
percent_change_month	double	yes	Month-over-month percentage change
percent_change_year	double	yes	Year-over-year percentage change

state_industry · table

Employment by industry sector for all 53 U.S. jurisdictions (50 states + DC, plus Puerto Rico and the Virgin Islands) across 22 NAICS supersector codes. Includes total nonfarm employment, private sector employment, and breakdowns by major industry categories like manufacturing, construction, retail, healthcare, and government. Enables state-level industry analysis and cross-state economic comparisons.

Column	Type	Null	Description
series	string	yes	BLS series ID (e.g., SMU01000000000000001 = Alabama, statewide, total nonfarm, all employees)
year	integer	yes	Data year
period	string	yes	BLS period identifier (M01-M12 monthly, M13 annual average)
periodName	string	yes	Human-readable period name derived from period
value	double	yes	Employment in thousands
footnotes	string	yes	BLS footnote codes for the data point

state_wages · table

Average weekly wages for all 51 U.S. jurisdictions (50 states + DC) from BLS QCEW (Quarterly Census of Employment and Wages). Provides state-level compensation data for understanding regional wage differences, cost-of-living adjustments, and economic competitiveness analysis.

Column	Type	Null	Description
area_fips	string	yes	State FIPS code with 000 suffix (e.g., 01000 for Alabama)
own_code	string	yes	Ownership code (0=all, 5=private)
industry_code	string	yes	NAICS industry code (10=total)
agglvl_code	string	yes	Aggregation level code
annual_avg_estabs	int	yes	Average annual establishment count
annual_avg_emplvl	int	yes	Average annual employment level
total_annual_wages	long	yes	Total annual wages in dollars
annual_avg_wkly_wage	int	yes	Average weekly wage in dollars
state_fips	string	yes	2-digit state FIPS code derived from area_fips
state_name	string	yes	State name derived from FIPS code

metro_industry · table

Employment by industry sector for 27 major U.S. metropolitan areas across 22 NAICS supersector codes. Covers major metros including NYC, LA, Chicago, Houston, and others with detailed industry breakdowns for manufacturing, retail, healthcare, financial activities, and government sectors. Enables metro-level industry analysis and cross-metro economic comparisons.

Column	Type	Null	Description
series	string	yes	BLS series ID (e.g., SMU36935610000000001 = New York metro, total nonfarm, all employees)
year	integer	yes	Data year
period	string	yes	BLS period identifier (M01-M12 monthly, M13 annual average)
periodName	string	yes	Human-readable period name derived from period
value	double	yes	Employment in thousands
footnotes	string	yes	BLS footnote codes for the data point

metro_wages · table

Average weekly wages for 27 major U.S. metropolitan areas from BLS QCEW (Quarterly Census of Employment and Wages). Provides metro-level compensation data for understanding local wage differences, cost-of-living adjustments, and metropolitan economic competitiveness analysis.

Column	Type	Null	Description
area_fips	string	yes	Metro/CSA area FIPS code
own_code	string	yes	Ownership code (0=all, 5=private)
industry_code	string	yes	NAICS industry code (10=total)
agglvl_code	string	yes	Aggregation level code
annual_avg_estabs	int	yes	Average annual establishment count
annual_avg_emplvl	int	yes	Average annual employment level
total_annual_wages	long	yes	Total annual wages in dollars
annual_avg_wkly_wage	int	yes	Average weekly wage in dollars
avg_annual_pay	int	yes	Average annual pay in dollars
metro_code	string	yes	Metro area publication code (e.g., A419 for Atlanta)
metro_name	string	yes	Metro area name

county_qcew · table

County-level employment and wages from BLS QCEW (Quarterly Census of Employment and Wages) for all ~3,142 U.S. counties. Comprehensive data includes establishment counts, employment levels, total wages, and average weekly wages by industry (NAICS codes) and ownership type (private, federal, state, local government). Enables detailed county-level labor market analysis, industry concentration studies, and regional economic development research.

Column	Type	Null	Description
area_fips	string	yes	County FIPS code
own_code	string	yes	Ownership code (private, federal, state, local government)
industry_code	string	yes	NAICS industry code
agglvl_code	string	yes	Aggregation level code
annual_avg_estabs	int	yes	Average annual establishment count
annual_avg_emplvl	int	yes	Average annual employment level
total_annual_wages	long	yes	Total annual wages in dollars
annual_avg_wkly_wage	int	yes	Average weekly wage in dollars

county_wages · table

Average weekly wages for all 6,038 U.S. counties from BLS QCEW (Quarterly Census of Employment and Wages). Most granular geographic wage data available, enabling county-level compensation analysis, local labor market studies, and rural-urban wage comparisons. Essential for detailed regional economic analysis.

Column	Type	Null	Description
area_fips	string	yes	County FIPS code (5 digits)
own_code	string	yes	Ownership code (0=all, 5=private)
industry_code	string	yes	NAICS industry code (10=total)
agglvl_code	string	yes	Aggregation level code
annual_avg_estabs	int	yes	Average annual establishment count
annual_avg_emplvl	int	yes	Average annual employment level
total_annual_wages	long	yes	Total annual wages in dollars
annual_avg_wkly_wage	int	yes	Average weekly wage in dollars
county_fips	string	yes	County FIPS code alias
state_fips	string	yes	State FIPS code derived from county FIPS

jolts_regional · table

Job Openings and Labor Turnover Survey (JOLTS) data for 4 U.S. Census regions (Northeast, Midwest, South, West). Tracks job openings rate, hires rate, total separations rate, quits rate, and layoffs/discharges rate. Provides critical insights into labor market dynamics, worker mobility, and regional employment trends.

Column	Type	Null	Description
date	date	yes	Observation date (ISO 8601 format)
series	string	yes	BLS series identifier
region_code	string	yes	Census region code (00=US, NE/MW/SO/WE for regions)
region_name	string	yes	Census region name
metric_type	string	yes	JOLTS metric (hires, separations, openings, quits, layoffs)
value	double	yes	JOLTS metric value (levels in thousands, rates as percentage)

jolts_industry · table

Job Openings and Labor Turnover Survey (JOLTS) data by industry sector at the national level. Tracks job openings, hires, total separations, quits, and layoffs/discharges (rates and levels) per JOLTS industry. Series ID encodes the industry at positions 4-9 with national geography (state=00) at positions 10-11.

Column	Type	Null	Description
industry_code	string	yes	JOLTS industry code at series_id positions 4-9 (000000 = total nonfarm, excluded here)
year	int	yes	Year
date	string	yes	Observation date - period field (M01, M02, etc.)
series	string	yes	BLS series identifier
metric_type	string	yes	JOLTS metric code (last 3 chars - element + rate/level, e.g. JOR, HIR, HIL)
value	double	yes	JOLTS metric value (levels in thousands, rates as percentage)

jolts_state · table

Job Openings and Labor Turnover Survey (JOLTS) data for all 51 U.S. jurisdictions (50 states + DC). Tracks job openings rate, hires rate, total separations rate, quits rate, and layoffs/discharges rate at state level. Provides state-specific insights into labor market dynamics, worker mobility, and employment trends for state-level policy analysis.

Column	Type	Null	Description
state_fips	string	yes	State FIPS code at series_id positions 10-11 (positions 4-9 are the industry field)
state_name	string	yes	State name from the state FIPS at series_id positions 10-11; unmapped codes captured as-is
year	int	yes	Year
date	string	yes	Observation date - period field (M01, M02, etc.)
series	string	yes	BLS series identifier
metric_type	string	yes	JOLTS metric code (last 3 chars - JOR, HIR, TSR, QUR, LDR)
value	double	yes	JOLTS metric value (levels in thousands, rates as percentage)

wage_growth · table

Average hourly and weekly earnings from BLS Current Employment Statistics (CES). Tracks wage growth trends across all private sector employees.

Column	Type	Null	Description
date	date	yes	Observation date (ISO 8601 format)
series	string	yes	BLS series identifier for wage/earnings data
value	double	yes	Earnings value (hourly or weekly depending on series)

regional_employment · table

State-level LAUS employment statistics including unemployment rates, employment levels, and labor force participation for all 51 U.S. jurisdictions. Source: BLS Local Area Unemployment Statistics (LAUS) program.

Column	Type	Null	Description
series	string	yes	BLS LAUS series identifier
year	integer	yes	Observation year
period	string	yes	BLS period identifier (M01-M12 for monthly)
value	double	yes	Employment statistic value
state_fips	string	yes	2-digit state FIPS code derived from series ID (LASST{fips}...)
measure	string	yes	Employment measure type derived from series ID

treasury_yields · table

Daily U.S. Treasury yield curve rates from 1-month to 30-year maturities. Includes nominal yields, TIPS yields, and yield curve shape indicators. Source: U.S. Treasury Direct API.

Column	Type	Null	Description
record_date	date	yes	Date of the interest rate record (YYYY-MM-DD format)
security_type_desc	string	yes	Type of Treasury security (e.g., 'Treasury Bills', 'Treasury Notes')
security_desc	string	yes	Description of the security
avg_interest_rate_amt	double	yes	Average interest rate as a percentage
src_line_nbr	string	yes	Source line number identifier

federal_debt · table

U.S. federal debt statistics including total public debt, debt held by public, and intragovernmental holdings. Tracks debt levels, composition, and trends. Source: Treasury Fiscal Data API.

Column	Type	Null	Description
record_date	date	yes	Date of the debt record (YYYY-MM-DD format)
debt_held_public_amt	double	yes	Debt held by the public in dollars
intragov_hold_amt	double	yes	Intragovernmental holdings in dollars
tot_pub_debt_out_amt	double	yes	Total public debt outstanding in dollars
src_line_nbr	string	yes	Source line number identifier

world_indicators · table

International economic indicators from World Bank for all countries. Includes GDP, inflation, unemployment, government debt, and population statistics. Uses bulk download with response partitioning: one API call per indicator returns all countries/years, then partitioned on write.

Column	Type	Null	Description
countryiso3code	string	yes	ISO 3-letter country code (e.g., 'USA', 'CHN')
date	integer	yes	Year of observation (e.g., 2022)
value	double	yes	Indicator value for the given country and year
unit	string	yes	Unit of measurement (e.g., 'current US$', 'percent')
obs_status	string	yes	Observation status flag
decimal	int	yes	Decimal precision indicator

fred_indicators · table

Federal Reserve Economic Data (FRED) time series raw observations. For metadata (series name, units, frequency, etc.), JOIN with reference_fred_series or use fred_indicators_enriched view.

Column	Type	Null	Description
series	string	yes	FRED series identifier (foreign key to reference_fred_series)
date	date	yes	Observation date (ISO 8601 format)
value	double	yes	Observed value for this date

national_accounts · table

National Income and Product Accounts (NIPA) data from BEA covering comprehensive economic statistics. Includes ALL NIPA tables across 8 sections: 1=Domestic Product & Income (GDP, national income), 2=Personal Income & Outlays (wages, consumer spending), 3=Government (receipts, expenditures), 4=Foreign Transactions (exports, imports), 5=Saving & Investment (capital formation), 6=Industry (sectoral data), 7=Supplemental (per capita, additional details), 8=Not Seasonally Adjusted. Tables dynamically discovered from BEA reference catalog.

Column	Type	Null	Description
table_name	string	yes	BEA table identifier (e.g., 'T10101')
line_number_raw	string	yes	Line number within the BEA table (raw string)
line_description	string	yes	Description of the GDP component
series_code	string	yes	BEA series code for this component
time_period	string	yes	BEA time period (e.g., '2023' for annual, '2023Q1' for quarterly)
data_value	string	yes	Raw data value from BEA API
cl_unit	string	yes	Units of measurement (e.g., 'Millions of Dollars')
unit_mult	string	yes	Unit multiplier
note_ref	string	yes	Note reference
table_id		yes	BEA table identifier for GDP components
line_number		yes	Line number within the BEA table
year		yes	Year extracted from time period
value		yes	Component value in millions of dollars (NULL for unavailable data)
units		yes	Units of measurement
frequency		yes	Data frequency (A=Annual, Q=Quarterly)

state_personal_income · table

State personal income from BEA Regional Economic Accounts (SAINC tables). Uses bulk download approach - single 16MB ZIP file contains all data since 1929. Much more efficient than per-year/geo/linecode API calls.

Column	Type	Null	Description
geo_fips	string	yes	FIPS code for the geographic area (e.g., '01000' for Alabama)
geo_name	string	yes	Name of the geographic area (state name)
region	string	yes	Census region
table_name	string	yes	BEA table identifier (e.g., SAINC1, SAINC30)
line_code	string	yes	BEA line code for specific metric
industry_classification	string	yes	Industry classification code
description	string	yes	Description of the metric
unit	string	yes	Unit of measurement
year	integer	yes	Year (unpivoted from column headers)
data_value	double	yes	Value for this year (unpivoted from wide format)

state_gdp · table

State GDP from BEA Regional Economic Accounts (SAGDP tables). Uses bulk download approach - single 9.5MB ZIP file contains all data.

Column	Type	Null	Description
geo_fips	string	yes	FIPS code for the geographic area
geo_name	string	yes	Name of the geographic area (state name)
region	string	yes	Census region
table_name	string	yes	BEA table identifier (e.g., SAGDP1, SAGDP2)
line_code	string	yes	BEA line code for specific metric
industry_classification	string	yes	Industry classification code
description	string	yes	Description of the metric
unit	string	yes	Unit of measurement
year	integer	yes	Year (unpivoted from column headers)
data_value	double	yes	Value for this year (unpivoted from wide format)

state_quarterly_income · table

State quarterly personal income from BEA Regional Economic Accounts (SQINC tables). Uses bulk download approach - single 16.7MB ZIP file contains all data.

Column	Type	Null	Description
geo_fips	string	yes	FIPS code for the geographic area
geo_name	string	yes	Name of the geographic area (state name)
region	string	yes	Census region
table_name	string	yes	BEA table identifier (e.g., SQINC1, SQINC35)
line_code	string	yes	BEA line code for specific metric
industry_classification	string	yes	Industry classification code
description	string	yes	Description of the metric
unit	string	yes	Unit of measurement
year	string	yes	Year/Quarter (unpivoted from column headers, e.g., 2020Q1)
data_value	double	yes	Value for this period (unpivoted from wide format)

state_quarterly_gdp · table

State quarterly GDP from BEA Regional Economic Accounts (SQGDP tables). Uses bulk download approach - single 3.2MB ZIP file contains all data.

Column	Type	Null	Description
geo_fips	string	yes	FIPS code for the geographic area
geo_name	string	yes	Name of the geographic area (state name)
region	string	yes	Census region
table_name	string	yes	BEA table identifier (e.g., SQGDP1, SQGDP2)
line_code	string	yes	BEA line code for specific metric
industry_classification	string	yes	Industry classification code
description	string	yes	Description of the metric
unit	string	yes	Unit of measurement
year	string	yes	Year/Quarter (unpivoted from column headers, e.g., 2020Q1)
data_value	double	yes	Value for this period (unpivoted from wide format)

state_consumption · table

State personal consumption expenditures from BEA Regional Economic Accounts (SAPCE tables). Uses bulk download approach - single 3.7MB ZIP file contains all data.

Column	Type	Null	Description
geo_fips	string	yes	FIPS code for the geographic area
geo_name	string	yes	Name of the geographic area (state name)
region	string	yes	Census region
table_name	string	yes	BEA table identifier (e.g., SAPCE1, SAPCE2)
line_code	string	yes	BEA line code for specific metric
industry_classification	string	yes	Industry classification code
description	string	yes	Description of the metric
unit	string	yes	Unit of measurement
year	integer	yes	Year (unpivoted from column headers)
data_value	double	yes	Value for this year (unpivoted from wide format)

regional_income · table

State and regional personal income statistics from BEA Regional Economic Accounts. Includes total income, per capita income, and population by state.

Column	Type	Null	Description
geo_fips	string	yes	FIPS code for the geographic area (e.g., '01000' for Alabama)
geo_name	string	yes	Name of the geographic area
time_period	string	yes	Year or Quarter - e.g. 2020 or 2023Q1
cl_unit	string	yes	Income metric type (e.g., 'Personal Income', 'Per Capita Income')
unit_mult	string	yes	Unit multiplier (e.g., thousands, millions)
data_value	double	yes	Income value in thousands of dollars or dollars (depending on metric)
year	string	yes	Year partition from dimension iteration
geo_fips_set	string	yes	Geographic level - STATE, COUNTY, MSA, etc.
tablename	string	yes	BEA table identifier (e.g., SAGDP1, SAINC1)
line_code	string	yes	BEA line code for specific metric

ita_data · table

International Transactions Accounts (ITA) from BEA providing balance of payments statistics. Includes trade balance, current account, capital account, and income balances.

Column	Type	Null	Description
data_value	double	yes	Transaction value
area_or_country	string	yes	Geographic area or country name for the transaction
time_series_description	string	yes	Description of the time series
series_code	string	yes	Unique identifier for the time series
time_period	string	yes	Time period for the data point (e.g., '2022', '2022Q1')
cl_unit	string	yes	Classification unit (e.g., 'USD')
unit_mult	string	yes	Unit multiplier (e.g., '6' for millions)
metric_name	string	yes	Type of metric (e.g., 'Current Dollars')
frequency	string	yes	Data frequency ('A' for Annual, 'Q' for Quarterly)
note_ref	string	yes	Reference to explanatory notes

fdi_direct_investment · table

BEA Direct Investment (DI) statistics from the Multinational Enterprises (MNE) dataset. Balance-of-payments direct investment: position (stock), income, financial transactions, equity, debt instruments, and reinvested earnings. Inward (foreign investment in the U.S.) and outward (U.S. investment abroad), by partner country or by industry. Tall layout: one row per statistic (series_id) x area/industry (row_code) x year.

Column	Type	Null	Description
series_id	string	yes	BEA MNE series identifier (e.g. 30=U.S. position abroad, 22=foreign position in U.S., 27=direct investment income)
series_name	string	yes	Human-readable statistic name
row_code	string	yes	Country or industry code (matches the classification partition)
row_label	string	yes	Country or industry name
column_code	string	yes	Statistic sub-breakdown code
column_label	string	yes	Statistic sub-breakdown name
data_value	double	yes	Statistic value in the units given by table_scale (null when BEA suppressed the value)
table_scale	string	yes	Units for data_value (e.g. 'Millions of Dollars', 'Thousands of Employees')
time_period	string	yes	Data year (YYYY)

fdi_activities · table

BEA Activities of Multinational Enterprises (AMNE) statistics from the MNE dataset, majority-owned affiliates (bank and nonbank). Operations of U.S. affiliates of foreign firms (inward) and foreign affiliates of U.S. firms (outward): employment, total sales, total assets, value added, net income, capital expenditures, R&D expenditures, and affiliate trade in goods. By partner country or by industry. Same tall layout as fdi_direct_investment; note table_scale mixes dollars and headcounts.

Column	Type	Null	Description
series_id	string	yes	BEA MNE series identifier (e.g. 30=U.S. position abroad, 22=foreign position in U.S., 27=direct investment income)
series_name	string	yes	Human-readable statistic name
row_code	string	yes	Country or industry code (matches the classification partition)
row_label	string	yes	Country or industry name
column_code	string	yes	Statistic sub-breakdown code
column_label	string	yes	Statistic sub-breakdown name
data_value	double	yes	Statistic value in the units given by table_scale (null when BEA suppressed the value)
table_scale	string	yes	Units for data_value (e.g. 'Millions of Dollars', 'Thousands of Employees')
time_period	string	yes	Data year (YYYY)

iip_positions · table

BEA International Investment Position (IIP): U.S. external assets and liabilities by type of investment, reporting position (stock) and the change in position decomposed by driver (transactions, price changes, exchange-rate changes, other). Tall layout: one row per type_of_investment x component x period.

Column	Type	Null	Description
type_of_investment	string	yes	Asset/liability category code (e.g. CurrAndDepAssets, DirectInvAssets)
component	string	yes	Measure code (Pos=position; ChgPos*=change in position by driver)
time_series_id	string	yes	BEA series identifier (e.g. TSI_IipCurrAndDepAssetsChgPos_A)
time_series_description	string	yes	Human-readable series description
time_period	string	yes	Data period ('2022' annual, '2022Q1' quarterly)
data_value	double	yes	Position or change value in the units given by cl_unit/unit_mult (null when BEA suppressed)
cl_unit	string	yes	Classification unit (e.g. 'USD')
unit_mult	string	yes	Unit multiplier (e.g. '6' for millions)
frequency	string	yes	Data frequency ('A' for Annual)
note_ref	string	yes	Reference to explanatory notes

gdp_statistics · table

Quarterly and annual GDP growth rates, nominal and real GDP values.

Column	Type	Null	Description
year	int	yes	Year of the GDP observation (extracted from TimePeriod)
quarter	int	yes	Quarter number (1-4) for quarterly data, null for annual data
metric	string	yes	GDP metric name (e.g., 'Nominal GDP', 'Real GDP', 'Personal Consumption')
value	double	yes	GDP value in millions of dollars (BEA returns comma-formatted numbers)
percent_change	double	yes	Percent change from previous period (not available in raw NIPA data)
seasonally_adjusted	boolean	yes	Whether the data is seasonally adjusted

industry_gdp · table

GDP by Industry data from BEA showing value added by NAICS industry sectors. Provides comprehensive breakdown of economic output by industry including agriculture, mining, manufacturing, services, and government sectors. Available at both annual and quarterly frequencies for detailed sectoral analysis.

Column	Type	Null	Description
table_id	string	yes	BEA table identifier for industry GDP data
quarter	string	yes	Quarter identifier (e.g., 'Q1', 'Q2') or empty for annual data
industry_code	string	yes	NAICS industry code
industry_description	string	yes	Full description of the industry
value	double	yes	GDP value for the industry in millions of dollars
units	string	yes	Units of measurement (e.g., 'Millions of Dollars')
note_ref	string	yes	Reference to footnotes or data quality notes

trade_exports · table

Monthly U.S. goods exports by HS-6 commodity and destination country (Census International Trade timeseries, exports/hs endpoint).

Column	Type	Null	Description
country_code	string	yes	Destination country code
country_name	string	yes	Destination country name
hs6	string	yes	HS 6-digit commodity code
hs2	string	yes	HS 2-digit chapter (derived from hs6)
value_usd	long	yes	Total export value for the month (USD)
quantity	double	yes	Quantity shipped in the first unit of measure
quantity_unit	string	yes	Unit of measure for quantity

trade_imports · table

Monthly U.S. goods imports by HS-6 commodity and origin country, including CIF charges (Census International Trade timeseries, imports/hs endpoint).

Column	Type	Null	Description
country_code	string	yes	Origin country code
country_name	string	yes	Origin country name
hs6	string	yes	HS 6-digit commodity code
hs2	string	yes	HS 2-digit chapter (derived from hs6)
value_usd	long	yes	General customs import value for the month (USD)
cif_charges	long	yes	Cost, insurance, and freight charges for the month (USD)
quantity	double	yes	Quantity imported in the first unit of measure
quantity_unit	string	yes	Unit of measure for quantity

trade_by_state · table

Monthly U.S. goods exports by origin state, HS-6 commodity, and destination country (Census International Trade timeseries, statehs endpoint). state='-' rows are U.S. totals; country_code='-' rows are all-countries totals.

Column	Type	Null	Description
state	string	yes	Origin state (USPS 2-char abbreviation); '-' = U.S. total roll-up
country_code	string	yes	Destination country code (4-char Census); '-' = all-countries roll-up
country_name	string	yes	Destination country name
hs6	string	yes	HS 6-digit commodity code
hs2	string	yes	HS 2-digit chapter (derived from hs6)
commodity_desc	string	yes	Short HS commodity description
value_usd	long	yes	Total export value for the month, all transport modes (USD)
vessel_value_usd	long	yes	Export value shipped by vessel for the month (USD)
air_value_usd	long	yes	Export value shipped by air for the month (USD)
container_value_usd	long	yes	Export value shipped in containers for the month (USD)

comtrade_flows · table

UN Comtrade bilateral merchandise trade: reported value and quantity per reporter country x partner country x HS-6 commodity x flow x year. Tall. reporter_code and partner_code are ISO alpha-3 (FK to ref.countries); partner_code='WLD' and other non-country codes are world/aggregate roll-ups (is_aggregate=true). Complements the one-sided U.S. Census trade_exports/ trade_imports with the partner side of every flow. Fan-out is a curated major-reporter list (comtrade/comtrade-reporters.json) x flow x year: one (reporter, flow, year) fetch returns all partners x HS-6, accumulated into the year partition and replaced wholesale on re-run (overwritePartitions).

Column	Type	Null	Description
reporter_code	string	yes	Reporting country ISO alpha-3 (FK to ref.countries.iso_alpha3)
partner_code	string	yes	Partner country ISO alpha-3 (FK to ref.countries.iso_alpha3; 'WLD'=world total)
flow_desc	string	yes	Trade flow (Import / Export / Re-import / Re-export)
hs6	string	yes	HS 6-digit commodity code
hs2	string	yes	HS 2-digit chapter (derived from hs6 by the transformer)
hs_description	string	yes	Commodity description
hs_revision	string	yes	HS classification vintage of the reported code (H0..H6)
trade_value_usd	double	yes	Reported trade value (USD)
net_weight_kg	double	yes	Net weight (kg) when reported
quantity	double	yes	Quantity in quantity_unit when reported
quantity_unit	string	yes	Supplementary quantity unit
is_aggregate	boolean	yes	True for world/aggregate partner roll-ups (exclude from partner-level joins)

ilostat_indicators · table

UN ILO ILOSTAT labor indicators: value per country x indicator x sex x classification x year. Tall — the international analog of the econ BLS tables (employment_statistics, wages, jolts). country_code is ISO alpha-3 (FK to ref.countries); ILO regional/income aggregates are flagged is_aggregate. Fan out one bulk CSV per indicator (each carries all countries/years); year is split on write (responsePartitioning) and replaced wholesale (overwritePartitions).

Column	Type	Null	Description
indicator_code	string	yes	ILOSTAT indicator id (the fan-out key)
indicator_name	string	yes	Indicator label
country_code	string	yes	Country ISO alpha-3 (FK to ref.countries.iso_alpha3; from ILO ref_area)
sex	string	yes	Sex classification (Total / Male / Female)
classif1	string	yes	Primary classification value (age band, sector, ...); "Total" when none
classif2	string	yes	Secondary classification value when present
year	integer	no	Reference year (partition column; emitted from the ILO time field)
value	double	yes	Indicator value (unit implied by indicator; null when unreported)
unit	string	yes	Unit / measure (rate %, thousands, local currency, ...)
obs_status	string	yes	ILO observation status flag
is_aggregate	boolean	yes	True for ILO regional/income-group ref_area rows (exclude from country joins)

usitc_tariffs · table

USITC DataWeb imports-for-consumption with the duty breakout: one row per HTS-8 x partner country x year. The tariff counterpart to econ.trade_imports — hs6 (first 6 of hts8) is the join key back to the Census HS-6 import flows. Carries customs value, calculated duties, and the effective duty rate (calculated_duties / customs_value). Country is the DataWeb country NAME (the runReport grid reports names, not codes) — join hts_description via econ.usitc_tariff_schedule. Because a single DataWeb runReport is hard-capped at 20,000 rows AND a broad all-commodities year query is too slow to compute, the transformer CHUNKS BY HTS CHAPTER: one query per chapter (01..99, ex-77) at HTS-8 x country, merged into the year partition (replaced wholesale on re-run, overwritePartitions). Zero-trade rows are suppressed; requests are paced for DataWeb's rate limit. Requires a free DataWeb token (TRADE_USITC_API_TOKEN). NOTE: a single very large chapter can still exceed the 20k cap (the transformer WARNs and that chapter is a top slice); dutiable value, first-unit quantity, and import-program breakout are additional DataWeb measures not yet wired (their dataToReport codes require a DataWeb-UI network capture to confirm).

Column	Type	Null	Description
hts8	string	yes	HTS 8-digit commodity code (U.S. tariff-line detail)
hs6	string	yes	HS-6 prefix (first 6 digits of hts8) — join key to econ.trade_imports.hs6
country_name	string	yes	Partner country name (DataWeb reports country names, not codes)
customs_value_usd	double	yes	Customs (import) value for consumption imports (USD)
calculated_duties_usd	double	yes	Calculated duties collected (USD)
ave_duty_rate	double	yes	Effective duty rate = calculated_duties / customs_value; null when customs_value is 0/absent (not fabricated). Denominator is customs value (DataWeb dutiable-value measure not yet wired).

usitc_tariff_schedule · table

USITC statutory Harmonized Tariff Schedule rate lines (the published general/special/column-2 rates, as opposed to duties actually collected in usitc_tariffs): one row per HTS-8 tariff line of the current HTS revision. hs6 is the first 6 digits of hts8 (join key). general_ave (ad-valorem equivalent) and effective_date are left null when not derivable from the export — never fabricated. Slowly-changing; the whole schedule is one fetch, stamped with the current revision year and overwritten each January.

Column	Type	Null	Description
hts8	string	yes	HTS 8-digit commodity code
hs6	string	yes	HS-6 prefix (first 6 digits of hts8) — join key
description	string	yes	Tariff-line description
unit_of_quantity	string	yes	Statutory unit(s) of quantity (comma-joined when multiple)
general_rate	string	yes	Column-1 General (MFN / normal trade relations) rate text
general_ave	double	yes	Ad-valorem equivalent of the general rate; null for non-ad-valorem or complex rates (not fabricated) and null in the current export
special_rate	string	yes	Column-1 Special (FTA / preference) rate text
column2_rate	string	yes	Column-2 (non-NTR) rate text
effective_date	date	yes	Effective date of this rate line (null when not published in the export)
year	integer	no	HTS revision year (partition column; stamped by the transformer)

comtrade_bilateral_trade · view

Enriched UN Comtrade bilateral flows: one row per year x reporter x partner x HS-6 x flow, with reporter/partner country names and M49 regions joined from ref.countries. Country-level only (aggregate/world-total partner rows filtered out). Use for supply-chain and partner-exposure analysis; complements the one-sided U.S. trade_exports/trade_imports with the partner side of each flow.

View — columns are resolved by the query engine at runtime.

comtrade_partner_balances · view

Bilateral trade balance from UN Comtrade, aggregated across commodities: one row per year x reporter x partner with total exports, total imports, and net balance (exports - imports) in USD. Country-level only. Enables bilateral surplus/deficit analysis and pairs with econ.trade_balance_summary (U.S. macro).

View — columns are resolved by the query engine at runtime.

ilostat_labor_snapshot · view

Headline ILOSTAT labor indicators pivoted wide: one row per country x year with unemployment rate, employment-to-population ratio, labour force participation rate, and youth NEET rate (total sex, all ages). The international analog of the econ BLS labor tables — join to ref.countries for names/regions, or compare a given year against U.S. employment_statistics.

View — columns are resolved by the query engine at runtime.

state_economic_snapshot · view

State-level economic snapshot joining BLS QCEW state wages and JOLTS state labor market data, enriched with geographic identifiers. QCEW carries no "series" — a state/year has multiple rows keyed by own_code (0=all, 5=private) and industry_code (10=total); filter those to select a specific wage aggregate. One row per state+year+own_code+industry_code+jolts_series. Use state_fips to join with geo.states, census, or health schemas.

View — columns are resolved by the query engine at runtime.

fdi_data · view

Unified tall view over the two BEA MNE foreign-direct-investment tables: fdi_direct_investment (stat_type='DI' — position, income, financial transactions) and fdi_activities (stat_type='AMNE' — employment, sales, assets, value added). One row per stat_type x direction x classification x year x series_id x row_code. data_value is expressed in the units named by table_scale (dollars for financial series, thousands of employees for employment). Use fdi_by_country for a country-level wide pivot.

View — columns are resolved by the query engine at runtime.

fdi_by_country · view

Country-level wide pivot of BEA foreign direct investment: one row per direction (inward/outward) x partner country x year, with the headline DI and AMNE statistics as columns. Position and income are historical-cost balance-of-payments direct investment; employment/sales/assets/value_added are majority-owned affiliate activities. Dollar figures are in millions; employment is in thousands of employees. Each source series is a single country row (verified), so the MAX pivot is a lossless reshape.

View — columns are resolved by the query engine at runtime.