nl2sqlproj/spider2-nl2sql huggingface dataset:
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_mother_race`
(
  Year DATE OPTIONS(description="Year"),
  County_of_Residence STRING OPTIONS(description="County of Residence"),
  County_of_Residence_FIPS STRING OPTIONS(description="County of Residence Code"),
  Mothers_Single_Race STRING OPTIONS(description="Mother's Single Race 6"),
  Mothers_Single_Race_Code STRING OPTIONS(description="Mother's Single Race 6 Code"),
  Births INT64 OPTIONS(description="Number of Births"),
  Ave_Age_of_Mother FLOAT64 OPTIONS(description="Average Age of Mother (years)"),
  Ave_OE_Gestational_Age_Wks FLOAT64 OPTIONS(description="Average OE Gestational Age (weeks)"),
  Ave_LMP_Gestational_Age_Wks FLOAT64 OPTIONS(description="Average LMP Gestational Age (weeks)"),
  Ave_Birth_Weight_gms FLOAT64 OPTIONS(description="Average Birth Weight (grams)"),
  Ave_Pre_pregnancy_BMI FLOAT64 OPTIONS(description="Average Pre-pregnancy BMI"),
  Ave_Number_of_Prenatal_Wks FLOAT64 OPTIONS(description="Average Number of Prenatal Visits")
)
OPTIONS(
  labels=[("freebqcovid", "")]
);

INSERT INTO `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_mother_race` (Year, County_of_Residence, County_of_Residence_FIPS, Mothers_Single_Race, Mothers_Single_Race_Code, Births, Ave_Age_of_Mother, Ave_OE_Gestational_Age_Wks, Ave_LMP_Gestational_Age_Wks, Ave_Birth_Weight_gms, Ave_Pre_pregnancy_BMI, Ave_Number_of_Prenatal_Wks) VALUES ('2017-01-01', 'Humboldt County, CA', '06023', 'Asian', 'A', 60, 28.88, 38.95, 39.15, 3329.15, 26.28, 11.86), ('2017-01-01', 'Boulder County, CO', '08013', 'Asian', 'A', 187, 31.56, 38.87, 38.9, 3119.82, 23.03, 10.28), ('2017-01-01', 'Weld County, CO', '08123', 'Asian', 'A', 120, 27.89, 38.79, 38.76, 3084.29, 23.84, 10.97), ('2017-01-01', 'Tolland County, CT', '09013', 'Asian', 'A', 110, 31.35, 39.03, 39.06, 3250.52, 23.59, 12.03), ('2017-01-01', 'Lake County, FL', '12069', 'Asian', 'A', 86, 31.3, 37.98, 38.12, 3086.8, 24.15, 9.21);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_maternal_morbidity`
(
  Year DATE OPTIONS(description="Year"),
  County_of_Residence STRING OPTIONS(description="County of Residence"),
  County_of_Residence_FIPS STRING OPTIONS(description="County of Residence Code"),
  Maternal_Morbidity_Desc STRING OPTIONS(description="Maternal Morbidity Checked"),
  Maternal_Morbidity_YN INT64 OPTIONS(description="Maternal Morbidity Checked Code"),
  Births INT64 OPTIONS(description="Number of Births"),
  Ave_Age_of_Mother FLOAT64 OPTIONS(description="Average Age of Mother (years)"),
  Ave_OE_Gestational_Age_Wks FLOAT64 OPTIONS(description="Average OE Gestational Age (weeks)"),
  Ave_LMP_Gestational_Age_Wks FLOAT64 OPTIONS(description="Average LMP Gestational Age (weeks)"),
  Ave_Birth_Weight_gms FLOAT64 OPTIONS(description="Average Birth Weight (grams)"),
  Ave_Pre_pregnancy_BMI FLOAT64 OPTIONS(description="Average Pre-pregnancy BMI"),
  Ave_Number_of_Prenatal_Wks FLOAT64 OPTIONS(description="Average Number of Prenatal Visits")
)
OPTIONS(
  labels=[("freebqcovid", "")]
);

INSERT INTO `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_maternal_morbidity` (Year, County_of_Residence, County_of_Residence_FIPS, Maternal_Morbidity_Desc, Maternal_Morbidity_YN, Births, Ave_Age_of_Mother, Ave_OE_Gestational_Age_Wks, Ave_LMP_Gestational_Age_Wks, Ave_Birth_Weight_gms, Ave_Pre_pregnancy_BMI, Ave_Number_of_Prenatal_Wks) VALUES ('2017-01-01', 'Mobile County, AL', '01097', 'None checked', 1, 5549, 27.08, 38.07, 38.27, 3156.37, 27.9, 10.83), ('2017-01-01', 'Morgan County, AL', '01103', 'None checked', 1, 1410, 26.71, 38.11, 38.15, 3196.95, 27.77, 10.78), ('2017-01-01', 'Tuscaloosa County, AL', '01125', 'None checked', 1, 2520, 27.43, 38.16, 38.31, 3181.1, 28.01, 9.66), ('2017-01-01', 'Butte County, CA', '06007', 'None checked', 1, 2366, 28.77, 38.81, 38.89, 3340.57, 27.35, 11.22), ('2017-01-01', 'Humboldt County, CA', '06023', 'None checked', 1, 1352, 29.17, 38.95, 39.1, 3377.52, 26.98, 12.84);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_congenital_abnormalities`
(
  Year DATE OPTIONS(description="Year"),
  County_of_Residence STRING OPTIONS(description="County of Residence"),
  County_of_Residence_FIPS STRING OPTIONS(description="County of Residence Code"),
  Congenital_Abnormality_Checked_Desc STRING OPTIONS(description="Congenital Anomalies Checked"),
  Congenital_Abnormality_Checked_YN INT64 OPTIONS(description="Congenital Anomalies Checked Code"),
  Births INT64 OPTIONS(description="Number of Births"),
  Ave_Age_of_Mother FLOAT64 OPTIONS(description="Average Age of Mother (years)"),
  Ave_OE_Gestational_Age_Wks FLOAT64 OPTIONS(description="Average OE Gestational Age (weeks)"),
  Ave_LMP_Gestational_Age_Wks FLOAT64 OPTIONS(description="Average LMP Gestational Age (weeks)"),
  Ave_Birth_Weight_gms FLOAT64 OPTIONS(description="Average Birth Weight (grams)"),
  Ave_Pre_pregnancy_BMI FLOAT64 OPTIONS(description="Average Pre-pregnancy BMI"),
  Ave_Number_of_Prenatal_Wks FLOAT64 OPTIONS(description="Average Number of Prenatal Visits")
)
OPTIONS(
  labels=[("freebqcovid", "")]
);

INSERT INTO `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_congenital_abnormalities` (Year, County_of_Residence, County_of_Residence_FIPS, Congenital_Abnormality_Checked_Desc, Congenital_Abnormality_Checked_YN, Births, Ave_Age_of_Mother, Ave_OE_Gestational_Age_Wks, Ave_LMP_Gestational_Age_Wks, Ave_Birth_Weight_gms, Ave_Pre_pregnancy_BMI, Ave_Number_of_Prenatal_Wks) VALUES ('2017-01-01', 'Bibb County, GA', '13021', 'None checked', 1, 2240, 27.06, 38.0, 38.38, 3075.78, 28.39, 12.05), ('2017-01-01', 'Muscogee County, GA', '13215', 'None checked', 1, 2786, 27.23, 38.0, 38.22, 3122.8, 27.56, 11.3), ('2017-01-01', 'Sebastian County, AR', '05131', 'None checked', 1, 1826, 26.97, 38.25, 38.43, 3238.38, 27.95, 9.44), ('2017-01-01', 'Unidentified Counties, AR', '05999', 'None checked', 1, 20626, 26.12, 38.25, 38.49, 3212.04, 28.03, 10.44), ('2017-01-01', 'St. Clair County, IL', '17163', 'None checked', 1, 3209, 27.98, 38.25, 38.43, 3202.52, 28.53, 10.41);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.sdoh_cms_dual_eligible_enrollment.dual_eligible_enrollment_by_county_and_program`
(
  State_Abbr STRING OPTIONS(description="State of Beneficiary"),
  County_Name STRING OPTIONS(description="County of Beneficiary"),
  QMB_Only INT64 OPTIONS(description="Number of Qualified Medicare Beneficiary Program (QMB) Only Enrollees"),
  QMB_plus_Full INT64 OPTIONS(description="Number of Qualified Medicare Beneficiary Program (QMB) plus Full Medicaid Enrollees"),
  SLMB_only INT64 OPTIONS(description="Number of Specified Low-Income Medicare Beneficiary (SLMB) Program Only Enrollees"),
  SLMB_plus_Full INT64 OPTIONS(description="Number of Specified Low-Income Medicare Beneficiary (SMLB) plus Full Medicaid Enrollees"),
  QDWI INT64 OPTIONS(description="Number of Qualifying Disabled Working Individual (QDWI) Enrollees"),
  QI INT64 OPTIONS(description="Number of Qualifying Individual (QI) Enrollees"),
  Other_full INT64 OPTIONS(description="Number of Other Dual Full Medicaid Enrollees"),
  Public_Total INT64 OPTIONS(description="Total Enrollees Across All Dual Eligible Programs"),
  Date DATE OPTIONS(description="Date of Enrollment Snapshot"),
  FIPS STRING OPTIONS(description="Geo FIPS Code")
)
OPTIONS(
  description="This document presents counts of Medicare-Medicaid dual enrollees (including those in Medicare Savings Programs), by eligibility type, who were enrolled in a given month (to be continuously updated on a quarterly basis).  They include numbers of individuals at both the state and county level.   Note that these are monthly snapshots and are inherently lower than ever-enrolled counts which include persons enrolled at any time during a calendar year.  The underlying source data are the Medicare Modernization Act (MMA) data that states submit to CMS on an at-least monthly basis. These data are subject to retroactive adjustment.  Analyses were conducted within the Chronic Condition Data Warehouse (CCW) environment.   For more information on identifying dually eligible beneficiaries within CMS data, please refer to the following website: https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/Downloads/MMCO_Definition_of_Dual_Eligible.pdf.",
  labels=[("freebqcovid", "")]
);

INSERT INTO `bigquery-public-data.sdoh_cms_dual_eligible_enrollment.dual_eligible_enrollment_by_county_and_program` (State_Abbr, County_Name, QMB_Only, QMB_plus_Full, SLMB_only, SLMB_plus_Full, QDWI, QI, Other_full, Public_Total, Date, FIPS) VALUES ('AL', 'Autauga', 668, 558, 332, 35, 0, 244, 132, 1969, '2018-12-01', '01001'), ('AL', 'Autauga', 662, 549, 328, 36, 0, 236, 139, 1950, '2018-09-01', '01001'), ('AL', 'Autauga', 661, 551, 330, 36, 0, 241, 129, 1948, '2018-06-01', '01001'), ('AL', 'Autauga', 671, 567, 323, 33, 0, 234, 119, 1947, '2018-03-01', '01001'), ('AL', 'Autauga', 676, 572, 324, 30, 0, 229, 126, 1957, '2017-12-01', '01001');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.sdoh_snap_enrollment.snap_enrollment`
(
  GeoDesc STRING OPTIONS(description="Substate/Region with Program"),
  SNAP_PA_Participation_Persons INT64 OPTIONS(description="SNAP All Persons Public Assistance Participation"),
  SNAP_NPA_Participation_Persons INT64 OPTIONS(description="SNAP All Persons Non-Public Assistance Participation"),
  SNAP_All_Participation_Persons INT64 OPTIONS(description="Calc: SNAP Total PA and Non-PA People"),
  SNAP_PA_Participation_Households INT64 OPTIONS(description="SNAP All Households Public Assistance Participation"),
  SNAP_NPA_Participation_Households INT64 OPTIONS(description="SNAP All Households Non-Public Assistance Participation"),
  SNAP_All_Participation_Households INT64 OPTIONS(description="Calc: SNAP Total PA and Non-PA Households"),
  SNAP_All_Issuance INT64 OPTIONS(description="SNAP All Total Actual PA & Non-PA Issuance"),
  Date DATE OPTIONS(description="Data of Enrollment Data"),
  FIPS STRING OPTIONS(description="County FIPS Designation"),
  SubCounty STRING OPTIONS(description="SubCounty Assigned Number"),
  Program STRING OPTIONS(description="Program Abbreviation")
)
OPTIONS(
  labels=[("freebqcovid", "")]
);

INSERT INTO `bigquery-public-data.sdoh_snap_enrollment.snap_enrollment` (GeoDesc, SNAP_PA_Participation_Persons, SNAP_NPA_Participation_Persons, SNAP_All_Participation_Persons, SNAP_PA_Participation_Households, SNAP_NPA_Participation_Households, SNAP_All_Participation_Households, SNAP_All_Issuance, Date, FIPS, SubCounty, Program) VALUES ('1000112 DE EBT KENT', 35208, 294, 35502, 17745, 93, 17838, 7330601, '2019-01-01', '10001', '12', 'EBT'), ('1000112 DE EBT KENT', 35749, 188, 35937, 17932, 74, 18006, 4026835, '2018-01-01', '10001', '12', 'EBT'), ('1000112 DE EBT KENT', 34698, 229, 34927, 17401, 72, 17473, 3867530, '2018-07-01', '10001', '12', 'EBT'), ('1000112 DE EBT KENT', 36177, 141, 36318, 17979, 52, 18031, 4101182, '2017-01-01', '10001', '12', 'EBT'), ('1000112 DE EBT KENT', 35763, 142, 35905, 17694, 54, 17748, 4230965, '2016-07-01', '10001', '12', 'EBT');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.census_utility.fips_codes_all`
(
  summary_level STRING,
  summary_level_name STRING,
  state_fips_code STRING,
  county_fips_code STRING,
  county_subdivision_fips_code STRING,
  place_fips_code STRING,
  consolidated_city_fips_code STRING,
  area_name STRING
);

INSERT INTO `bigquery-public-data.census_utility.fips_codes_all` (summary_level, summary_level_name, state_fips_code, county_fips_code, county_subdivision_fips_code, place_fips_code, consolidated_city_fips_code, area_name) VALUES ('050', 'state-county', '01', '01001', NULL, NULL, NULL, 'Autauga County'), ('050', 'state-county', '01', '01003', NULL, NULL, NULL, 'Baldwin County'), ('050', 'state-county', '01', '01005', NULL, NULL, NULL, 'Barbour County'), ('050', 'state-county', '01', '01007', NULL, NULL, NULL, 'Bibb County'), ('050', 'state-county', '01', '01009', NULL, NULL, NULL, 'Blount County');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.census_utility.lsad_codes`
(
  lsad_code STRING,
  lsad_description STRING,
  associated_geographic_entities STRING
)
OPTIONS(
  description="The legal/statistical area description (LSAD) codes describe the particular typology for each geographic entity. For legal entities, the LSAD reflects the term that appears in legal documentation pertaining to the entity, such as a treaty, charter, legislation, resolution, or ordinance. For statistical entities, the LSAD is the term assigned by the Census Bureau or other agency defining the entity. The LSAD code is a two-character field that corresponds to a description of the legal or statistical type of entity and identifies whether the LSAD term should be capitalized and should precede or follow the name of the geographic entity. Note that the same LSAD code is assigned to entities at different levels of the geographic hierarchy when they share the same LSAD. For example, the Census Bureau assigns the same LSAD code (\"21\") to boroughs in New York and Connecticut, although they are county subdivisions in the former and incorporated places in the latter.\n\nThese codes can be found in the TIGER/Line products, gazetteer files, and other products. The table below describes each code and which geographic entities each code is valid for."
);

INSERT INTO `bigquery-public-data.census_utility.lsad_codes` (lsad_code, lsad_description, associated_geographic_entities) VALUES ('0', NULL, 'American Indian Area, Congressional District, Consolidated City, County or Equivalent Feature, County Subdivision, Economic Census Place, Elementary School District, Incorporated Place, Local/Tribal Legislative District, Military Installation, Nation, Secondary School District, Special School Administrative Area, Special-Purpose District, State Legislative District (Lower Chamber), State Legislative District (Upper Chamber), State or Equivalent Feature, Tribal Subdivision, Unified School District, Voting District'), ('76', 'Urban Cluster (suffix)', 'Urban Area'), ('75', 'Urbanized Area (suffix)', 'Urban Area'), ('BG', 'Block Group (prefix)', 'Block Group'), ('CT', 'Census Tract (prefix)', 'Census Tract');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.census_utility.mtfcc_feature_class_codes`
(
  feature_class_code STRING,
  feature_class STRING,
  superclass STRING,
  point BOOL,
  linear BOOL,
  areal BOOL,
  description STRING
);

INSERT INTO `bigquery-public-data.census_utility.mtfcc_feature_class_codes` (feature_class_code, feature_class, superclass, point, linear, areal, description) VALUES ('C3022', 'Mountain Peak or Summit', 'Miscellaneous Topographic Features', True, False, False, 'A prominent elevation rising above the surrounding level of the Earth''s surface.'), ('C3061', 'Cul-de-sac', 'Miscellaneous Topographic Features', True, False, False, 'An expanded paved area at the end of a street used by vehicles for turning around. The placement of addressed structures located along the street may wrap around the end of the cul-de-sac.'), ('C3062', 'Traffic Circle', 'Miscellaneous Topographic Features', True, False, False, 'A circular intersection allowing for continuous movement of traffic at the meeting of roadways, when the circle is represented as a point.'), ('C3066', 'Gate', 'Miscellaneous Topographic Features', True, False, False, 'A movable barrier across a road.'), ('C3067', 'Toll Booth', 'Miscellaneous Topographic Features', True, False, False, 'A structure or barrier where a fee is collected for using a road.');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.census_utility.fips_codes_states`
(
  state_fips_code STRING,
  state_postal_abbreviation STRING,
  state_name STRING,
  state_gnisid STRING
);

INSERT INTO `bigquery-public-data.census_utility.fips_codes_states` (state_fips_code, state_postal_abbreviation, state_name, state_gnisid) VALUES ('01', 'AL', 'Alabama', '01779775'), ('02', 'AK', 'Alaska', '01785533'), ('04', 'AZ', 'Arizona', '01779777'), ('05', 'AR', 'Arkansas', '00068085'), ('06', 'CA', 'California', '01779778');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.geo_us_boundaries.railways`
(
  geo_id INT64,
  full_name STRING,
  mtfcc_feature_class_code STRING,
  railway_geom STRING
)
PARTITION BY DATE(_PARTITIONTIME)
CLUSTER BY full_name, mtfcc_feature_class_code;

INSERT INTO `bigquery-public-data.geo_us_boundaries.railways` (geo_id, full_name, mtfcc_feature_class_code, railway_geom, _PARTITIONTIME) VALUES (11020438805, NULL, 'R1011', '{"type":"LineString","coordinates":[[-121.367074,37.94801299999999],[-121.3665969999999,37.94804999999999],[-121.366438,37.948053],[-121.3592349999999,37.94809899999999],[-121.3590149999999,37.9481],[-121.3588539999999,37.94809499999999],[-121.3584869999999,37.948065]]}', NULL), (11020438883, NULL, 'R1011', '{"type":"LineString","coordinates":[[-121.315573,37.93698899999999],[-121.315729,37.936768],[-121.3157939999999,37.936692],[-121.315866,37.93662099999999],[-121.3159469999999,37.93655499999999],[-121.316034,37.936495],[-121.3161769999999,37.93641799999999],[-121.316333,37.93635499999999],[-121.3164969999999,37.93630699999999],[-121.3166669999999,37.93627699999999],[-121.316838,37.93626199999999],[-121.316958,37.936261]]}', NULL), (11020439210, NULL, 'R1011', '{"type":"LineString","coordinates":[[-121.274568,37.91270999999999],[-121.274528,37.9125]]}', NULL), (11020439400, NULL, 'R1011', '{"type":"LineString","coordinates":[[-121.3206119999999,37.94135699999999],[-121.3204329999999,37.941468]]}', NULL), (11020439664, NULL, 'R1011', '{"type":"LineString","coordinates":[[-121.3255379999999,37.950721],[-121.3258289999999,37.95087999999999]]}', NULL);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.geo_us_boundaries.adjacent_states`
(
  state STRING,
  state_name STRING,
  state_fips_code STRING,
  neighbors_state ARRAY<STRING>,
  neighbors_fips_code ARRAY<STRING>
);

INSERT INTO `bigquery-public-data.geo_us_boundaries.adjacent_states` (state, state_name, state_fips_code, neighbors_state, neighbors_fips_code) VALUES ('AL', 'Alabama', '01', '[''Florida'' ''Georgia'' ''Mississippi'' ''Tennessee'']', '[''12'' ''13'' ''28'' ''47'']'), ('AR', 'Arkansas', '05', '[''Tennessee'' ''Texas'' ''Missouri'' ''Mississippi'' ''Louisiana'' ''Oklahoma'']', '[''47'' ''48'' ''29'' ''28'' ''22'' ''40'']'), ('AZ', 'Arizona', '04', '[''Nevada'' ''New Mexico'' ''Colorado'' ''California'' ''Utah'']', '[''32'' ''35'' ''08'' ''06'' ''49'']'), ('CA', 'California', '06', '[''Arizona'' ''Nevada'' ''Oregon'']', '[''04'' ''32'' ''41'']'), ('CO', 'Colorado', '08', '[''Kansas'' ''New Mexico'' ''Nebraska'' ''Utah'' ''Wyoming'' ''Arizona'' ''Oklahoma'']', '[''20'' ''35'' ''31'' ''49'' ''56'' ''04'' ''40'']');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.geo_us_boundaries.adjacent_counties`
(
  county STRING,
  state STRING,
  county_fips_code STRING,
  neighbors ARRAY<STRING>,
  neighbors_state ARRAY<STRING>,
  neighbors_fips_code ARRAY<STRING>
);

INSERT INTO `bigquery-public-data.geo_us_boundaries.adjacent_counties` (county, state, county_fips_code, neighbors, neighbors_state, neighbors_fips_code) VALUES ('Audubon County', 'Iowa', '19009', '[''Shelby'' ''Adair'' ''Guthrie'' ''Crawford'' ''Cass'' ''Carroll'']', '[''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'']', '[''19165'' ''19001'' ''19077'' ''19047'' ''19029'' ''19027'']'), ('Boone County', 'Iowa', '19015', '[''Polk'' ''Dallas'' ''Webster'' ''Greene'' ''Story'' ''Hamilton'']', '[''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'']', '[''19153'' ''19049'' ''19187'' ''19073'' ''19169'' ''19079'']'), ('Bremer County', 'Iowa', '19017', '[''Floyd'' ''Chickasaw'' ''Buchanan'' ''Butler'' ''Fayette'' ''Black Hawk'']', '[''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'']', '[''19067'' ''19037'' ''19019'' ''19023'' ''19065'' ''19013'']'), ('Calhoun County', 'Iowa', '19025', '[''Sac'' ''Carroll'' ''Buena Vista'' ''Webster'' ''Greene'' ''Pocahontas'']', '[''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'']', '[''19161'' ''19027'' ''19021'' ''19187'' ''19073'' ''19151'']'), ('Cedar County', 'Iowa', '19031', '[''Scott'' ''Jones'' ''Clinton'' ''Johnson'' ''Muscatine'' ''Linn'']', '[''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'' ''Iowa'']', '[''19163'' ''19105'' ''19045'' ''19103'' ''19139'' ''19113'']');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.wm`
(
  series_id STRING NOT NULL,
  year INT64,
  period STRING,
  value FLOAT64,
  footnote_codes STRING,
  date DATE,
  series_title STRING
)
OPTIONS(
  description="WM  Dataset"
);

INSERT INTO `bigquery-public-data.bls.wm` (series_id, year, period, value, footnote_codes, date, series_title) VALUES ('WMU00217801020000003530310001', 2020, 'A01', 8.55, 'nan', '2020-12-31', 'Average hourly wage for waiters and waitresses, in Evansville, IN-KY, Level 01'), ('WMU00217801020000003530312601', 2020, 'A01', 8.55, 'nan', '2020-12-31', 'Average hourly wage for waiters and waitresses, part-time, in Evansville, IN-KY, Level 01'), ('WMU37000031020000003530312601', 2020, 'A01', 8.56, 'nan', '2020-12-31', 'Average hourly wage for waiters and waitresses, part-time, in Piedmont North Carolina nonmetropolitan area, Level 01'), ('WMU37000031020000003530310001', 2020, 'A01', 8.59, 'nan', '2020-12-31', 'Average hourly wage for waiters and waitresses, in Piedmont North Carolina nonmetropolitan area, Level 01'), ('WMU55000001020000003530310001', 2020, 'A01', 8.6, 'nan', '2020-12-31', 'Average hourly wage for waiters and waitresses, in Wisconsin, Level 01');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.employment_hours_earnings`
(
  series_id STRING NOT NULL,
  year INT64,
  period STRING,
  value FLOAT64,
  footnote_codes STRING,
  date DATE,
  series_title STRING
)
OPTIONS(
  description="Employment_Hours_Earnings Dataset"
);

INSERT INTO `bigquery-public-data.bls.employment_hours_earnings` (series_id, year, period, value, footnote_codes, date, series_title) VALUES ('CES3133660001', 1939, 'M01', 61.4, 'nan', '1939-01-01', 'All employees, thousands, ship and boat building, seasonally adjusted'), ('CES9000000001', 1939, 'M01', 3988.0, 'nan', '1939-01-01', 'All employees, thousands, government, seasonally adjusted'), ('CEU3000000007', 1939, 'M01', 36.7, 'nan', '1939-01-01', 'Average weekly hours of production and nonsupervisory employees, manufacturing, not seasonally adjusted'), ('CES3200000007', 1940, 'M01', 37.0, 'nan', '1940-01-01', 'Average weekly hours of production and nonsupervisory employees, nondurable goods, seasonally adjusted'), ('CES3000000006', 1940, 'M01', 8593.0, 'nan', '1940-01-01', 'Production and nonsupervisory employees, thousands, manufacturing, seasonally adjusted');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.cpi_u`
(
  series_id STRING NOT NULL,
  year INT64,
  period STRING,
  value FLOAT64,
  footnote_codes STRING,
  survey_abbreviation STRING,
  seasonal_code STRING,
  periodicity_code STRING,
  area_code STRING,
  area_name STRING,
  item_code STRING,
  item_name STRING,
  date DATE
)
OPTIONS(
  description="CPI_U Dataset"
);

INSERT INTO `bigquery-public-data.bls.cpi_u` (series_id, year, period, value, footnote_codes, survey_abbreviation, seasonal_code, periodicity_code, area_code, area_name, item_code, item_name, date) VALUES ('CUSR0000SA0', 1997, 'M01', 159.4, 'nan', 'CU', 'S', 'R', '0', 'U.S. city average', 'SA0', 'All items', '1997-01-01'), ('CUSR0000SA0', 1997, 'M02', 159.7, 'nan', 'CU', 'S', 'R', '0', 'U.S. city average', 'SA0', 'All items', '1997-02-01'), ('CUSR0000SA0', 1997, 'M03', 159.8, 'nan', 'CU', 'S', 'R', '0', 'U.S. city average', 'SA0', 'All items', '1997-03-01'), ('CUSR0000SA0', 1997, 'M04', 159.9, 'nan', 'CU', 'S', 'R', '0', 'U.S. city average', 'SA0', 'All items', '1997-04-01'), ('CUSR0000SA0', 1997, 'M05', 159.9, 'nan', 'CU', 'S', 'R', '0', 'U.S. city average', 'SA0', 'All items', '1997-05-01');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.unemployment_cps`
(
  series_id STRING NOT NULL,
  year INT64,
  period STRING,
  value FLOAT64,
  footnote_codes STRING,
  date DATE,
  series_title STRING
)
OPTIONS(
  description="Unemployment_CPS Dataset"
);

INSERT INTO `bigquery-public-data.bls.unemployment_cps` (series_id, year, period, value, footnote_codes, date, series_title) VALUES ('LNU05023554', 1940, 'A01', 44200.0, 'nan', '1940-12-31', '(unadj) Not in labor force - 14 years and over'), ('LNU02323554', 1943, 'A01', 57.6, 'nan', '1943-12-31', '(Unadj) Employment-population ratio - 14 years and over'), ('LNU05023554', 1945, 'A01', 40230.0, 'nan', '1945-12-31', '(unadj) Not in labor force - 14 years and over'), ('LNU02023554', 1946, 'A01', 55250.0, 'nan', '1946-12-31', '(unadj) Employed - 14 years and over'), ('LNU05023554', 1946, 'A01', 45550.0, 'nan', '1946-12-31', '(unadj) Not in labor force - 14 years and over');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.c_cpi_u`
(
  series_id STRING NOT NULL,
  year INT64,
  period STRING,
  value FLOAT64,
  footnote_codes STRING,
  survey_abbreviation STRING,
  seasonal_code STRING,
  periodicity_code STRING,
  area_code STRING,
  area_name STRING,
  item_code STRING,
  item_name STRING,
  date DATE
)
OPTIONS(
  description="C_CPI_U Dataset"
);

INSERT INTO `bigquery-public-data.bls.c_cpi_u` (series_id, year, period, value, footnote_codes, survey_abbreviation, seasonal_code, periodicity_code, area_code, area_name, item_code, item_name, date) VALUES ('SUUR0000SA0', 1999, 'M12', 100.0, 'nan', 'SU', 'U', 'R', '0', 'U.S. city average', 'SA0', 'All items', '1999-12-01'), ('SUUR0000SA0', 2000, 'M01', 100.3, 'nan', 'SU', 'U', 'R', '0', 'U.S. city average', 'SA0', 'All items', '2000-01-01'), ('SUUR0000SA0', 2000, 'M02', 100.9, 'nan', 'SU', 'U', 'R', '0', 'U.S. city average', 'SA0', 'All items', '2000-02-01'), ('SUUR0000SA0', 2000, 'M03', 101.6, 'nan', 'SU', 'U', 'R', '0', 'U.S. city average', 'SA0', 'All items', '2000-03-01'), ('SUUR0000SA0', 2000, 'M04', 101.6, 'nan', 'SU', 'U', 'R', '0', 'U.S. city average', 'SA0', 'All items', '2000-04-01');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.employment_hours_earnings_series`
(
  series_id STRING NOT NULL,
  supersector_code INT64,
  industry_code INT64,
  data_type_code INT64,
  seasonal STRING,
  series_title STRING,
  footnote_codes STRING,
  begin_year INT64,
  begin_period STRING,
  end_year INT64,
  end_period STRING
)
OPTIONS(
  description="Employment_Hours_Earnings_Series Dataset"
);

INSERT INTO `bigquery-public-data.bls.employment_hours_earnings_series` (series_id, supersector_code, industry_code, data_type_code, seasonal, series_title, footnote_codes, begin_year, begin_period, end_year, end_period) VALUES ('CES0000000001', 0, 0, 1, 'S', 'All employees, thousands, total nonfarm, seasonally adjusted', 'nan', 1939, 'M01', 2021, 'M12'), ('CES0000000010', 0, 0, 10, 'S', 'Women employees, thousands, total nonfarm, seasonally adjusted', 'nan', 1964, 'M01', 2021, 'M12'), ('CES0000000025', 0, 0, 25, 'S', 'All employees, quarterly averages, seasonally adjusted, thousands, total nonfarm, seasonally adjusted', 'nan', 1939, 'M03', 2021, 'M12'), ('CES0000000026', 0, 0, 26, 'S', 'All employees, 3-month average change, seasonally adjusted, thousands, total nonfarm, seasonally adjusted', 'nan', 1939, 'M04', 2021, 'M12'), ('CES0500000001', 5, 5000000, 1, 'S', 'All employees, thousands, total private, seasonally adjusted', 'nan', 1939, 'M01', 2021, 'M12');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.wm_series`
(
  series_id STRING NOT NULL,
  seasonal STRING,
  area_code INT64,
  ownership_code INT64,
  estimate_code INT64,
  industry_code INT64,
  occupation_code INT64,
  subcell_code INT64,
  datatype_code INT64,
  level_code INT64,
  series_title STRING,
  footnote_codes STRING,
  begin_year INT64,
  begin_period STRING,
  end_year INT64,
  end_period STRING
)
OPTIONS(
  description="WM_Series Dataset"
);

INSERT INTO `bigquery-public-data.bls.wm_series` (series_id, seasonal, area_code, ownership_code, estimate_code, industry_code, occupation_code, subcell_code, datatype_code, level_code, series_title, footnote_codes, begin_year, begin_period, end_year, end_period) VALUES ('WMU00000001020000001100002300', 'U', 0, 1, 2, 0, 110000, 23, 30, 0, 'Average hourly wage for union management occupations in the U.S.', 'nan', 2020, 'A01', 2020, 'A01'), ('WMU00000001020000001110212300', 'U', 0, 1, 2, 0, 111021, 23, 30, 0, 'Average hourly wage for union general and operations managers in the U.S.', 'nan', 2020, 'A01', 2020, 'A01'), ('WMU00000001020000001190212300', 'U', 0, 1, 2, 0, 119021, 23, 30, 0, 'Average hourly wage for union construction managers in the U.S.', 'nan', 2020, 'A01', 2020, 'A01'), ('WMU00000001020000001190332300', 'U', 0, 1, 2, 0, 119033, 23, 30, 0, 'Average hourly wage for union education administrators, postsecondary in the U.S.', 'nan', 2020, 'A01', 2020, 'A01'), ('WMU00000001020000001190392300', 'U', 0, 1, 2, 0, 119039, 23, 30, 0, 'Average hourly wage for union education administrators, all other in the U.S.', 'nan', 2020, 'A01', 2020, 'A01');
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.unemployment_cps_series`
(
  series_id STRING NOT NULL,
  lfst_code INT64,
  periodicity_code STRING,
  series_title STRING,
  absn_code INT64,
  activity_code INT64,
  ages_code INT64,
  class_code INT64,
  duration_code INT64,
  education_code INT64,
  entr_code INT64,
  expr_code INT64,
  hheader_code INT64,
  hour_code INT64,
  indy_code INT64,
  jdes_code INT64,
  look_code INT64,
  mari_code INT64,
  mjhs_code INT64,
  occupation_code INT64,
  orig_code INT64,
  pcts_code INT64,
  race_code INT64,
  rjnw_code INT64,
  rnlf_code INT64,
  rwns_code INT64,
  seek_code INT64,
  sexs_code INT64,
  tdat_code INT64,
  vets_code INT64,
  wkst_code INT64,
  born_code INT64,
  chld_code INT64,
  disa_code INT64,
  seasonal STRING,
  footnote_codes STRING,
  begin_year INT64,
  begin_period STRING,
  end_year INT64,
  end_period STRING,
  cert_code INT64
)
OPTIONS(
  description="Unemployment_CPS_Series Dataset"
);

INSERT INTO `bigquery-public-data.bls.unemployment_cps_series` (series_id, lfst_code, periodicity_code, series_title, absn_code, activity_code, ages_code, class_code, duration_code, education_code, entr_code, expr_code, hheader_code, hour_code, indy_code, jdes_code, look_code, mari_code, mjhs_code, occupation_code, orig_code, pcts_code, race_code, rjnw_code, rnlf_code, rwns_code, seek_code, sexs_code, tdat_code, vets_code, wkst_code, born_code, chld_code, disa_code, seasonal, footnote_codes, begin_year, begin_period, end_year, end_period, cert_code) VALUES ('LNS11000000', 10, 'M', '(Seas) Civilian Labor Force Level', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 'S', 'nan', 1948, 'M01', 2021, 'M12', 0), ('LNS11000000Q', 10, 'Q', '(Seas) Civilian Labor Force Level', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 'S', 'nan', 1948, 'Q01', 2021, 'Q04', 0), ('LNS11000001', 10, 'M', '(Seas) Civilian Labor Force Level - Men', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 'S', 'nan', 1948, 'M01', 2021, 'M12', 0), ('LNS11000001Q', 10, 'Q', '(Seas) Civilian Labor Force Level - Men', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 'S', 'nan', 1948, 'Q01', 2021, 'Q04', 0), ('LNS11000002', 10, 'M', '(Seas) Civilian Labor Force Level - Women', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 'S', 'nan', 1948, 'M01', 2021, 'M12', 0);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.bls.cpsaat18`
(
  year INT64,
  sector STRING,
  subsector STRING,
  industry_group STRING,
  industry STRING,
  total_employed_in_thousands INT64,
  percent_women FLOAT64,
  percent_white FLOAT64,
  percent_black_or_african_american FLOAT64,
  percent_asian FLOAT64,
  percent_hispanic_or_latino FLOAT64
)
OPTIONS(
  description="Current population survey 18: Employed persons by detailed industry, sex, race, and Hispanic or Latino ethnicity"
);

INSERT INTO `bigquery-public-data.bls.cpsaat18` (year, sector, subsector, industry_group, industry, total_employed_in_thousands, percent_women, percent_white, percent_black_or_african_american, percent_asian, percent_hispanic_or_latino) VALUES (2020, 'Total, 16 years and over', NULL, NULL, NULL, 147795, 0.47, 0.78, 0.12, 0.06, 0.18), (2021, 'Total, 16 years and over', NULL, NULL, NULL, 152581, 0.47, 0.78, 0.12, 0.66, 0.18), (2022, 'Total, 16 years and over', NULL, NULL, NULL, 158291, 0.47, 0.77, 0.13, 0.07, 0.19), (2023, 'Total, 16 years and over', NULL, NULL, NULL, 161037, 0.469, 0.765, 0.128, 0.069, 0.188), (2020, 'Agriculture, forestry, fishing, and hunting', NULL, NULL, NULL, 2349, 0.28, 0.92, 0.03, 0.01, 0.24);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.google_dei.dar_non_intersectional_attrition_index`
(
  workforce STRING OPTIONS(description="Overall and sub-categories"),
  report_year INT64 OPTIONS(description="The year the report was published"),
  race_asian INT64 OPTIONS(description="The attrition index score of Googlers in the U.S. who identify as Asian and zero or more other races   "),
  race_black INT64 OPTIONS(description="The attrition index score of Googlers in the U.S. who identify as Black and zero or more other races   "),
  race_hispanic_latinx INT64 OPTIONS(description="The attrition index score of Googlers in the U.S. who identify as Hispanic or Latinx and zero or more other races  "),
  race_native_american INT64 OPTIONS(description="The attrition index score of Googlers in the U.S. who identify as Native American and zero or more other races     "),
  race_white INT64 OPTIONS(description="The attrition index score of Googlers in the U.S. who identify as White and zero or more other races "),
  gender_us_women INT64 OPTIONS(description="The attrition index score of Googlers in the U.S. who are women"),
  gender_us_men INT64 OPTIONS(description="The attrition index score of Googlers in the U.S. who are men"),
  gender_global_women INT64 OPTIONS(description="The attrition index score of global Googlers who are women "),
  gender_global_men INT64 OPTIONS(description="The attrition index score of global Googlers who are men ")
)
OPTIONS(
  description="This table contains the attrition index score of Googlers in the U.S. cut by race and gender separately and the attrition index score of global Googlers cut by gender. Some data may be intentionally redacted due to security and privacy restrictions regarding smaller n-counts. In those cases, the data is displayed as a null value."
);

INSERT INTO `bigquery-public-data.google_dei.dar_non_intersectional_attrition_index` (workforce, report_year, race_asian, race_black, race_hispanic_latinx, race_native_american, race_white, gender_us_women, gender_us_men, gender_global_women, gender_global_men) VALUES ('tech', 2022, 95, 92, 100, 156, 109, 77, 108, 80, 107), ('tech', 2021, 86, 130, 110, 103, 115, 76, 108, 78, 107), ('tech', 2019, 80, 120, 110, 104, 114, 76, 106, 78, 107), ('tech', 2018, 84, 155, 120, 71, 115, 81, 107, 84, 105), ('tech', 2020, 79, 121, 101, 116, 122, 79, 107, 83, 105);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.google_dei.dar_non_intersectional_representation`
(
  workforce STRING OPTIONS(description="Overall and sub-categories"),
  report_year INT64 OPTIONS(description="The year the report was published"),
  race_asian FLOAT64 OPTIONS(description="The percentage of Googlers in the U.S. who identify as Asian and zero or more other races"),
  race_black FLOAT64 OPTIONS(description="The percentage of Googlers in the U.S. who identify as Black and zero or more other races"),
  race_hispanic_latinx FLOAT64 OPTIONS(description="The percentage of Googlers in the U.S. who identify as Hispanic or Latinx and zero or more other races"),
  race_native_american FLOAT64 OPTIONS(description="The percentage of Googlers in the U.S. who identify as Native American and zero or more other races"),
  race_white FLOAT64 OPTIONS(description="The percentage of Googlers in the U.S. who identify as White and zero or more other races"),
  gender_us_women FLOAT64 OPTIONS(description="The percentage of Googlers in the U.S. who are women"),
  gender_us_men FLOAT64 OPTIONS(description="The percentage of Googlers in the U.S. who are men"),
  gender_global_women FLOAT64 OPTIONS(description="The percentage of global Googlers who are women"),
  gender_global_men FLOAT64 OPTIONS(description="The percentage of global Googlers who are men")
)
OPTIONS(
  description="This table contains the representation of Googlers in the U.S. cut by race and gender separately and the representation of global Googlers cut by gender. Some data may be intentionally redacted due to security and privacy restrictions regarding smaller n-counts. In those cases, the data is displayed as a null value."
);

INSERT INTO `bigquery-public-data.google_dei.dar_non_intersectional_representation` (workforce, report_year, race_asian, race_black, race_hispanic_latinx, race_native_american, race_white, gender_us_women, gender_us_men, gender_global_women, gender_global_men) VALUES ('leadership', 2014, 0.242, 0.017, 0.022, 0.006, 0.732, 0.206, 0.794, 0.208, 0.792), ('leadership', 2015, 0.25, 0.02, 0.02, 0.009, 0.722, 0.232, 0.768, 0.229, 0.771), ('leadership', 2016, 0.258, 0.018, 0.021, 0.007, 0.713, 0.24, 0.76, 0.242, 0.758), ('leadership', 2017, 0.271, 0.02, 0.024, 0.008, 0.696, 0.242, 0.758, 0.245, 0.755), ('leadership', 2018, 0.273, 0.024, 0.027, 0.008, 0.689, 0.253, 0.747, 0.255, 0.745);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.google_dei.dar_non_intersectional_hiring`
(
  workforce STRING OPTIONS(description="Overall and sub-categories"),
  report_year INT64 OPTIONS(description=" The year the report was published"),
  race_asian FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as Asian and zero or more other races  "),
  race_black FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as Black and zero or more other races    "),
  race_hispanic_latinx FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as Hispanic or Latinx and zero or more other races "),
  race_native_american FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as Native American and zero or more other races "),
  race_white FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as White and zero or more other races "),
  gender_us_women FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who are women "),
  gender_us_men FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who are men "),
  gender_global_women FLOAT64 OPTIONS(description="The percentage of global Googlers hired who are women "),
  gender_global_men FLOAT64 OPTIONS(description="The percentage of global Googlers hired who are men ")
)
OPTIONS(
  description="This table contains the breakdown of Googlers hired in the U.S. cut by race and gender separately and the hiring breakdown of global Googlers cut by gender. Some data may be intentionally redacted due to security and privacy restrictions regarding smaller n-counts. In those cases, the data is displayed as a null value."
);

INSERT INTO `bigquery-public-data.google_dei.dar_non_intersectional_hiring` (workforce, report_year, race_asian, race_black, race_hispanic_latinx, race_native_american, race_white, gender_us_women, gender_us_men, gender_global_women, gender_global_men) VALUES ('leadership', 2016, 0.253, 0.023, 0.034, 0.0, 0.69, 0.23, 0.77, 0.257, 0.743), ('leadership', 2020, 0.28, 0.036, 0.044, 0.007, 0.662, 0.246, 0.754, 0.261, 0.739), ('leadership', 2019, 0.327, 0.036, 0.051, 0.005, 0.597, 0.26, 0.74, 0.259, 0.741), ('leadership', 2017, 0.331, 0.015, 0.023, 0.015, 0.646, 0.275, 0.725, 0.296, 0.704), ('leadership', 2018, 0.277, 0.057, 0.043, 0.014, 0.631, 0.284, 0.716, 0.294, 0.706);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.google_dei.dar_non_intersectional_exits_representation`
(
  workforce STRING OPTIONS(description=" Overall and sub-categories"),
  report_year INT64 OPTIONS(description="The year the report was published"),
  race_asian FLOAT64 OPTIONS(description="The percentage of Googler exits in the U.S. who identify as Asian and zero or more other races"),
  race_black FLOAT64 OPTIONS(description="The percentage of Googler exits in the U.S. who identify as Black and zero or more other races"),
  race_hispanic_latinx FLOAT64 OPTIONS(description="The percentage of Googler exits in the U.S. who identify as Hispanic or Latinx and zero or more other races"),
  race_native_american FLOAT64 OPTIONS(description="The percentage of Googler exits in the U.S. who identify as Native American and zero or more other races"),
  race_white FLOAT64 OPTIONS(description="The percentage of Googler exits in the U.S. who identify as White and zero or more other races "),
  gender_us_women FLOAT64 OPTIONS(description="The percentage of Googler exits in the U.S. who are women"),
  gender_us_men FLOAT64 OPTIONS(description="The percentage of Googler exits in the U.S. who are men"),
  gender_global_women FLOAT64 OPTIONS(description="The percentage of global Googler exits who are women"),
  gender_global_men FLOAT64 OPTIONS(description="The percentage of global Googler exits who are men")
)
OPTIONS(
  description="This table contains the breakdown of Googler exits in the U.S. cut by race and gender separately and the breakdown of global Googler exits cut by gender. Some data may be intentionally redacted due to security and privacy restrictions regarding smaller n-counts. In those cases, the data is displayed as a null value."
);

INSERT INTO `bigquery-public-data.google_dei.dar_non_intersectional_exits_representation` (workforce, report_year, race_asian, race_black, race_hispanic_latinx, race_native_american, race_white, gender_us_women, gender_us_men, gender_global_women, gender_global_men) VALUES ('tech', 2018, 0.352, 0.03, 0.046, 0.006, 0.612, 0.162, 0.838, 0.168, 0.832), ('tech', 2019, 0.358, 0.03, 0.047, 0.007, 0.593, 0.175, 0.825, 0.175, 0.825), ('tech', 2020, 0.363, 0.028, 0.05, 0.009, 0.597, 0.173, 0.827, 0.18, 0.82), ('tech', 2021, 0.405, 0.031, 0.057, 0.006, 0.547, 0.182, 0.818, 0.181, 0.819), ('tech', 2023, 0.465, 0.038, 0.056, 0.008, 0.474, 0.196, 0.804, 0.191, 0.809);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.google_dei.dar_region_non_intersectional_exits_representation`
(
  workforce STRING OPTIONS(description="Overall and sub-categories"),
  report_year INT64 OPTIONS(description="The year the report was published"),
  region STRING OPTIONS(description="Region"),
  gender_women FLOAT64 OPTIONS(description="The percentage of Googler exits in the region who are women"),
  gender_men FLOAT64 OPTIONS(description="The percentage of Googler exits in the region who are men")
)
OPTIONS(
  description="This table contains the breakdown of Googler exits by region (Americas, APAC, and EMEA) cut by gender. “Americas” includes all countries in North and South America in which we operate, excluding the U.S. Some data may be intentionally redacted due to security and privacy restrictions regarding smaller n-counts. In those cases, the data is displayed as a null value."
);

INSERT INTO `bigquery-public-data.google_dei.dar_region_non_intersectional_exits_representation` (workforce, report_year, region, gender_women, gender_men) VALUES ('non_tech', 2024, 'AMERICAS (non-US)', 0.491, 0.509), ('non_tech', 2024, 'APAC', 0.486, 0.514), ('non_tech', 2024, 'EMEA', 0.485, 0.515), ('non_tech', 2023, 'APAC', 0.46, 0.54), ('non_tech', 2022, 'APAC', 0.45, 0.55);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.google_dei.dar_selfid_representation`
(
  workforce STRING OPTIONS(description="Self-identification category"),
  report_year INT64 OPTIONS(description="The year the report was published"),
  global FLOAT64 OPTIONS(description=" The percentage of global Googlers who identify as being part of the self-identification category (i.e., “workforce” type)")
)
OPTIONS(
  description="This table contains the representation of Googlers globally who identify as LGBTQ+, members of the military or veterans, people with disabilities, or non-binary genders. Some data may be intentionally redacted due to security and privacy restrictions regarding smaller n-counts. In those cases, the data is displayed as a null value."
);

INSERT INTO `bigquery-public-data.google_dei.dar_selfid_representation` (workforce, report_year, global) VALUES ('selfid_disability', 2020, 0.061), ('selfid_disability', 2019, 0.075), ('selfid_disability', 2022, 0.054), ('selfid_disability', 2021, 0.056), ('selfid_disability', 2023, 0.065);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.google_dei.dar_region_non_intersectional_attrition_index`
(
  workforce STRING OPTIONS(description="Overall and sub-categories"),
  report_year INT64 OPTIONS(description="The year the report was published"),
  region STRING OPTIONS(description="Region"),
  gender_women INT64 OPTIONS(description="The attrition index score of Googlers in the region who are women"),
  gender_men INT64 OPTIONS(description="The attrition index score of Googlers in the region who are women")
)
OPTIONS(
  description="This table contains the breakdown of Googler exits by region (Americas, APAC, and EMEA) cut by gender. “Americas” includes all countries in North and South America in which we operate, excluding the U.S. Some data may be intentionally redacted due to security and privacy restrictions regarding smaller n-counts. In those cases, the data is displayed as a null value."
);

INSERT INTO `bigquery-public-data.google_dei.dar_region_non_intersectional_attrition_index` (workforce, report_year, region, gender_women, gender_men) VALUES ('non_tech', 2021, 'APAC', 75, 118), ('overall', 2021, 'APAC', 79, 111), ('tech', 2021, 'APAC', 79, 106), ('tech', 2022, 'APAC', 87, 105), ('non_tech', 2022, 'APAC', 90, 104);
-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.google_dei.dar_intersectional_hiring`
(
  workforce STRING OPTIONS(description="Overall and sub-categories"),
  report_year INT64 OPTIONS(description="The year the report was published"),
  gender_us STRING OPTIONS(description="Gender of Googlers in the U.S."),
  race_asian FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as Asian and zero or more other races"),
  race_black FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as Black and zero or more other races"),
  race_hispanic_latinx FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as Hispanic or Latinx and zero or more other races"),
  race_native_american FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as Native American and zero or more other races "),
  race_white FLOAT64 OPTIONS(description="The percentage of Googlers hired in the U.S. who identify as White and zero or more other races ")
)
OPTIONS(
  description="This table contains the breakdown of Googlers hired in the U.S. cut by race and gender combined. Some data may be intentionally redacted due to security and privacy restrictions regarding smaller n-counts. In those cases, the data is displayed as a null value."
);

INSERT INTO `bigquery-public-data.google_dei.dar_intersectional_hiring` (workforce, report_year, gender_us, race_asian, race_black, race_hispanic_latinx, race_native_american, race_white) VALUES ('leadership', 2021, 'men', 0.168, 0.045, 0.045, nan, 0.45), ('leadership', 2024, 'men', 0.208, nan, nan, nan, 0.377), ('leadership', 2024, 'women', 0.156, nan, nan, nan, 0.175), ('leadership', 2023, 'men', 0.222, 0.043, 0.043, nan, 0.363), ('leadership', 2022, 'women', 0.119, 0.047, nan, nan, 0.178);