/meta
GET /meta
GET /metaLists all datasets and their versions that you have access to. Your contract with Lightcast will determine which datasets you have access to in Agnitio.
Code Examples
curl --request GET \
--url https://agnitio.emsicloud.com/meta \
--header 'Authorization: bearer <access_token>'Response Examples
{
"datasets": [
{
"name": "emsi.us.industry",
"versions": [
"2023.4",
"2024.1",
"2024.2",
"2024.3"
]
},
{
"name": "emsi.us.unemployment.age",
"versions": [
"2023.4",
"2024.1",
"2024.2",
"2024.3"
]
},
...
]
}GET /definitions
GET /definitionsReturns detailed definitions of the datasets including titles, descriptions, and available versions.
Code Examples
curl --request GET \
--url https://agnitio.emsicloud.com/meta/definitions \
--header 'Authorization: bearer <access_token>'Response Examples
{
"datasets": [
{
"name": "EMSI.ww.Population",
"versions": [
"2019.10",
"2020.4",
"2019.12",
"2020.1",
"2019.13",
"2020.3",
"2021.1",
"2019.15",
"2019.14",
"2020.2",
"2019.11",
"2021.2"
],
"description": "# Description\n\nThis dataset contains current year population estimates by nation and metropolitan area, including a non-metropolitan catch-all area.\n\n# Questions answered by this dataset\n\n* What is the current population for the nation?\n* What is the current population for a given metropolitan area?\n\n# Metrics\n\n* Population: The number of people living in the area.\n\n# Filters\n\n* Area (nation, metropolitan/non-metropolitan area)\n",
"title": "Global Population"
},
{
"name": "EMSI.ww.Supply",
"versions": [
"2019.10",
"2020.4",
"2019.12",
"2020.1",
"2019.13",
"2020.3",
"2021.1",
"2019.15",
"2019.14",
"2020.2",
"2019.11",
"2021.2"
],
"description": "# Description\n\nThis dataset includes current year job estimates by nation and metropolitan area (including a non-metropolitan catch-all area) and Emsi global occupations. Jobs are expressed as a low, mid, and high estimate. A list of global occupations can be found here: https://global.economicmodeling.com/emsi-global-occupation-list\n\n# Questions answered by this dataset\n\n* How many mechanical engineers are employed in a particular metropolitan area?\n* What is the low estimate for the number of nurses employed for the whole nation?\n\n# Metrics\n\n* Supply.Low: The low employment estimate.\n* Supply.Mid: The mid employment estimate.\n* Supply.High: The high employment estimate.\n\n# Filters\n\n* Area (nation, metropolitan/non-metropolitan area)\n* Occupation (Emsi global occupations)\n",
"title": "Global Talent Supply"
},
{
"name": "EMSI.ww.WorkforceEstimationV2",
"versions": [
"2025.1"
],
"description": "\n # Workforce Estimation Model\n\n - [Knowledge Base](https://kb.lightcast.io/en/articles/8582979-workforce-estimation-model-wemo)\n - [Release Notes](https://kb.lightcast.io/en/articles/9252367-global-data-release-notes)\n ",
"title": "Workforce Estimation"
},
{
"name": "EMSI.ww.GlobalNationalSupply",
"versions": [
"2024.1"
],
"description": "# Description\r\n\r\n\r\n\r\nThis dataset includes current year job estimates by nation and Emsi global occupations. Jobs are expressed as a low, mid, and high estimate. A list of global occupations can be found here: https://global.economicmodeling.com/emsi-global-occupation-list\r\n\r\n\r\n\r\n# Questions answered by this dataset\r\n\r\n\r\n\r\n* How many mechanical engineers are employed?\r\n\r\n* What is the low estimate for the number of nurses employed for the whole nation?\r\n\r\n\r\n\r\n# Metrics\r\n\r\n\r\n\r\n* Supply.Low: The low employment estimate.\r\n\r\n* Supply.Mid: The mid employment estimate.\r\n\r\n* Supply.High: The high employment estimate.\r\n\r\n\r\n\r\n# Filters\r\n\r\n\r\n\r\n* Area (nation)\r\n\r\n* Occupation (Emsi global occupations)\r\n\r\n",
"title": "Global National Talent Supply"
},
{
"name": "EMSI.ww.GlobalNationalPopulation",
"versions": [
"2024.1"
],
"description": "# Description\r\n\r\n\r\n\r\nThis dataset contains current year population estimates by nation.\r\n\r\n\r\n\r\n# Questions answered by this dataset\r\n\r\n\r\n\r\n* What is the current population for the nation?\r\n\r\n\r\n\r\n# Metrics\r\n\r\n\r\n\r\n* Population: The number of people living in the area.\r\n\r\n\r\n\r\n# Filters\r\n\r\n\r\n\r\n* Area (nation)\r\n\r\n",
"title": "Global National Population"
},
{
"name": "EMSI.lf.Occupation",
"versions": [
"2023.2",
"2024.5",
"2025.1",
"2024.3",
"2024.1",
"2024.2",
"2024.4",
"2023.4"
],
"description": "# Description\n\nEmsi's occupation dataset contains information on occupations back to 2011. Worker counts are projected 10 years beyond the current calendar year.\n\n# Questions answered by this dataset\n\n* How many restaurant manager workers are projected to exist over the next 10 years in the Lodi province of the Lombardy region, Italy?\n* Which region in Germany has the most insurance representatives?\n\n# Metrics\n\n* Workers: The number of workers resident in a given region.\n\n# Filters\n\n* Area (Nation to Admin 3 level)\n* Occupation (ISCO 08)\n* Year\n",
"title": "Global Occupation"
},
{
"name": "EMSI.lf.Industry",
"versions": [
"2023.2",
"2024.5",
"2025.1",
"2024.3",
"2024.1",
"2024.2",
"2024.4",
"2023.4"
],
"description": "# Description\n\nEmsi's industry dataset contains information on industries back to 2011. Job counts are projected 10 years beyond the current calendar year.\n\n# Questions answered by this dataset\n\n* How many manufacturing workers are projected to exist over the next 10 years in the Lodi province of the Lombardy region, Italy?\n* Which region in Germany has the highest employment in the Insurance industry?\n\n# Metrics\n\n* Workers: The number of Workers resident in the region.\n\n# Filters\n\n* Area (Nation to Admin 3 level)\n* Industry (NACE 2)\n* Year\n",
"title": "Global Industry"
},
{
"name": "EMSI.lf.CountryIndicators",
"versions": [
"2024.5",
"2025.1",
"2024.3",
"2024.4"
],
"description": "# Description\r\n\r\nThis dataset shows national economic and demographic indicators for countries with data from The World Factbook published by the CIA. Data is available at the nation level only (using ISO-2 codes).\r\n\r\n# Questions answered by this dataset\r\n\r\n* Which country has the highest rate of unemployment?\r\n* How does Germany's GDP compare to other EU countries?\r\n* How do labor force numbers correlate to unemployment numbers in countries in Europe versus Asia?\r\n* Which country has the highest median age?\r\n\r\n# Metrics\r\n\r\n* Population: Population compares estimates from statistics from population censuses, vital statistics registration systems, or sample surveys pertaining to the recent past and on assumptions about future trends.\r\n\r\n* IndustrialProductionGrowthRate: Industrial production growth rate compares the annual percentage increase in industrial production (includes manufacturing, mining, and construction).\r\n\r\n* InternetUsers: Internet users compares the number of users within a country that access the Internet. Statistics vary from country to country and may include users who access the Internet at least several times a week to those who access it only once within a period of several months.\r\n\r\n* LaborForce: Labor force compares the total number of employed individuals.\r\n\r\n* MedianAge: Median age of total population.\r\n\r\n* GdpGrowthRate: GDP - real growth rate compares GDP growth on an annual basis adjusted for inflation and expressed as a percent.\r\n\r\n* GdpPerCapita: GDP - per capita (PPP) compares GDP on a purchasing power parity basis divided by population as of 1 July for the same year.\r\n\r\n* GdpPurchasingPower: GDP (purchasing power parity) compares the gross domestic product (GDP) or value of all final goods and services produced within a nation in a given year. A nation's GDP at purchasing power parity (PPP) exchange rates is the sum value of all goods and services produced in the country valued at prices prevailing in the United States.\r\n\r\n* Unemployment: Unemployement rate compares the percent of the labor force that is without jobs.\r\n\r\n# Filters\r\n\r\n* Area (Nation: ISO-2)\r\n* Year\r\n\r\n",
"title": "Country Indicators"
},
{
"name": "EMSI.uk.ExposureIndex.Industry",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains Covid-19 exposure index by industries for the UK for the current year. This index is a 0-100 scale index of how much different industries have been exposed to the disruptions caused by the coronavirus pandemic and the resulting lockdown. It reflects the extent of furlough in different industries, the need for physical proximity in jobs, and the key worker role status\n\n# Questions answered by this dataset\n\n* How much affect has Covid-19 had on the agriculture industry?\n* Which industry has the highest exposure index?\n\n# Metrics\n\n* ExposureIndex: 0-100 scale for a specific industry\n\n# Filters\n\n* Industry\n",
"title": "UK Exposure Index by Industry"
},
{
"name": "EMSI.uk.Apprenticeships",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains apprenticeship achievements data as reported by the UK Department for Education statistics service for further education. Data is reported by institution, award level, learner home area, delivery area, age group, Program (SSA), Framework code or Standard code (Standard codes will be replacing Framework codes), and provider type. Note that the latest year may not contain all four quarters of data, in which case the partialCurrentYear field in the metadata will be set to true and currentYearQuarters will define which quarters of data the current year represents.\n\n# Questions answered by this dataset\n\n* How many people completed a legal services apprenticeship last year from various providers?\n* How has the number of environmental conservation apprenticeships completed by people under age 19 changed year by year?\n* How many apprenticeships are completed in the same area as the learner's home area?\n\n# Metrics\n\n* Achievements: The number of people who complete an apprenticeship (achievements) that match the given dimensions.\n\n# Filters\n\n* Institution: UKPRN\n* AwardLevel: NVQ\n* HomeArea : The location of the learner home\n* DeliveryArea : The location where the learning occurred. If unknown, the location of the institution is used.\n* AgeGroup\n* Program: Sector Subject Area Tier 2\n* Framework\n* Standard\n* Year\n* ProviderType\n",
"title": "UK Apprenticeships"
},
{
"name": "EMSI.uk.ExposureIndex.Occupation",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains Covid-19 exposure index by occupation for the UK for the current year. This index is a 0-100 scale index of how much different occupations have been exposed to the disruptions caused by the coronavirus pandemic and the resulting lockdown. It reflects the extent of furlough, the need for physical proximity in jobs, and the key worker role status\n\n# Questions answered by this dataset\n\n* How much affect has Covid-19 had on the nursing occupation?\n* Which occupation has the highest exposure index?\n\n# Metrics\n\n* ExposureIndex: 0-100 scale for a specific occupation\n\n# Filters\n\n* Occupation\n",
"title": "UK Exposure Index by Occupation"
},
{
"name": "EMSI.uk.Staffing",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThe staffing dataset shows the occupational composition of an industry for a given class-of-worker/industry/area/year combination. This data can also be used to find all of the industries that employ a given occupation and the percentage of those industries accounted for by that occupation.\n\nHistorical staffing patterns are available from 2003 to the current year and projected staffing patterns from current year to max year. Geographies available include the nation (United Kingdom), country (England, Scotland, Wales, and Northern Ireland), and NUTS1 regions.\n\n# Questions answered by this dataset\n\n* What occupations make up the Logging industry in Scotland?\n* What industries employ the most web design and development professionals?\n* Has the occupational makeup of the Fitness facilities industry changed over time?\n\n# Metrics\n\n* Jobs: The number of industry jobs accounted for by the occupation\n\n# Filters\n\n* Class of Worker\n* Area (Nation, Country, and NUTS1)\n* Industry (1 to 5-digit SIC)\n* Occupation (4-digit SOC)\n* Year\n",
"title": "UK Staffing Patterns"
},
{
"name": "EMSI.uk.Unemp",
"versions": [
"2023.2",
"2023.1",
"2022.2",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains the number of people claiming Jobseeker's Allowance (JSA) as well as those who claim Universal Credit and are required to seek work and be available for work. This is the headline indicator of the number of people claiming benefits principally for the reason of being unemployed. Emsi updates Claimant Counts every month with the latest data available. Data is available for the United Kingdom (England, Scotland, Wales, and Northern Ireland).\n\n# Questions answered by this dataset\n\n* What city has the highest number of unemployment claims?\n* Have unemployment claims in Manchester increased or decreased over time?\n\n# Metrics\n\n* Counts: The number of people who applied for Jobseeker's Allowance or Universal Credit benefits in the year-month-area combination specified.\n\n# Filters\n\n* Area (Nation, Country, NUTS1, NUTS3, and LAU1)\n* Year\n* Month\n",
"title": "UK Unemployment by Area"
},
{
"name": "EMSI.uk.Demographics",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains population demographics data by area, age group, race, and gender. Data is available for the United Kingdom (England, Scotland, Wales, and Northern Ireland).\n\n# Questions answered by this dataset\n\n* What is the racial breakout of Manchester?\n* What is the gender breakout of Leeds?\n* Has the millennial population in our area grown or declined over the past three years?\n* Is our area more or less racially diverse than England at large?\n\n# Metrics\n\n* Population: The number of people living in the area who match the given dimensions.\n\n# Filters\n\n* Area (Nation, Country, NUTS1, NUTS3, and LAU1)\n* Age Band (19 bands)\n* Race (16 groups)\n* Gender\n* Year\n",
"title": "UK Population Demographics"
},
{
"name": "EMSI.uk.Occupation.AgeGender",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains national job counts by occupation, class of worker, age band, and gender.\n\n# Questions answered by this dataset\n\n* What is the age breakdown for nurses?\n* Is the percentage of females employed as welders growing or declining?\n\n# Metrics\n\n* Jobs: The number of jobs for the class-year-occupation-age-gender combination selected.\n\n# Filters\n\n* Occupation (1 to 4-digit SOC)\n* Class of Worker\n* Age (6 age bands)\n* Gender\n* Year\n",
"title": "UK Occupation by Age and Gender"
},
{
"name": "EMSI.uk.Industry",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains industry data by area, industry, class of worker, and year. Data is available for the United Kingdom (England, Scotland, Wales, and Northern Ireland).\n\nNote that, due to a lack of data, Northern Ireland self-employment is estimated using Northern Ireland employment and Welsh self-employment-to-employment ratios. For more information see https://kb.economicmodelling.co.uk/emsis-uk-data-process-overview/\n\n# Questions answered by this dataset\n\n* What is the fastest-growing industry in London?\n* In what region of England are earnings for the finance industry the lowest?\n* How many hospitals are there in London? How many were there in 2013?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings: Total earnings for the industry.\n* EPW: Earnings Per Worker is current year earnings divided by current year jobs. (EPW = Earnings / Jobs).\n* Establishments: The number of physical business locations.\n\n# Filters\n\n* Area (Nation, Country, NUTS1, NUTS3, and LAU1)\n* Industry (1 to 5-digit SIC)\n* Class of Worker\n* Year\n",
"title": "UK Industry"
},
{
"name": "EMSI.uk.EducationalAttainment",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains population by area and education level. Data is available for the United Kingdom (England, Scotland, Wales, and Northern Ireland).\n\n# Questions answered by this dataset\n\n* How is the number of people in Plymouth with trade apprenticeships changing over time?\n* How many people in England had a higher education degree in 2004?\n* What is the current number of persons at each education level in Eastern Scotland?\n\n# Metrics\n\n* Population: The number of people living in the area who match the given dimensions.\n\n# Filters\n\n* Area (Nation, Country, NUTS1, NUTS3, and LAU1)\n* Education level (8 levels)\n* Year\n",
"title": "UK Education Attainment"
},
{
"name": "EMSI.uk.EconActivity.Quarterly",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains population by area and economic activity. Data is available for the United Kingdom (England, Scotland, Wales and Northern Ireland) from the first quarter of 2006 to the most recent year and quarter. Data comes from the Annual Population Survey (APS) and is updated quartery, except for Northern Ireland, which comes from the Labour Force Survey (LFS) and is updated annually. The quarterly data represents a complete year (four quarters) of data ending in the given quarter.\n\n# Questions answered by this dataset\n\n* How many people are currently unemployed in Plymouth?\n* How did the ratio of the unemployed vs. employed in Wales change from the first quarter to the last quarter of 2020?\n* What percentage of the working-age population of Scotland was employed the last quarter of last year?\n\n# Metrics\n\n* Population: The number of people living in the area who match the given dimensions.\n\n# Filters\n\n* Area (Nation, Country, NUTS1, NUTS3, and LAU1)\n* EconActivity (economic activity indicator)\n* Year\n* Quarter\n",
"title": "UK Economic Activity Quarterly"
},
{
"name": "EMSI.uk.FurtherEducation",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains further education achievements and enrolments data (including apprenticeships) as reported by the UK Department for Education statistics service. Data is reported by institution, award level, program, learner home area, delivery area, age group, and provider type. Note that the latest year may not contain all four quarters of data, in which case the partialCurrentYear field in the metadata will be set to true and currentYearQuarters will define which quarters of data the current year represents.\n\n# Questions answered by this dataset\n\n* Which learner aims had zero achievements despite a large number of enrolments?\n* Which NVQ levels had the highest number of apprenticeship achievements?\n* How many programs for a particular institution were delivered in an area different from the learner's home area?\n* Which NVQ levels with people over age 60 had no enrolments?\n\n# Metrics\n\n* Achievements\n* Apprenticeship achievements\n* Enrolments\n* Apprenticeship enrolments\n* Full-time learner equivalents for achievements\n* Full-time learner equivalents for enrolments\n\n# Filters\n\n* Institution: UKPRN\n* AwardLevel: NVQ\n* Program: Learning aim reference number (rolled up to SSA hierarchy)\n* HomeArea : The location of the learner home\n* DeliveryArea : The location where the learning occurred. If unknown, the location of the institution is used.\n* AgeGroup\n* Year\n* ProviderType\n",
"title": "UK Further Education"
},
{
"name": "EMSI.uk.Industry.AgeGender",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains national job counts by industry, class of worker, age band, and gender.\n\n# Questions answered by this dataset\n\n* What is the age breakdown of workers in the travel agency industry?\n* Is the percentage of females employed in the landscape service activities industry growing or declining?\n\n# Metrics\n\n* Jobs: The number of jobs for the class-year-industry-age-gender combination selected.\n\n# Filters\n\n* Industry (1 to 5-digit SIC)\n* Class of Worker\n* Age (6 age bands)\n* Gender\n* Year\n",
"title": "UK Industry by Age and Gender"
},
{
"name": "EMSI.uk.BusinessCounts.LocalUnits",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n \nThis dataset shows business locations (establishments) by area, industry, and number of employees by size band. Data is available for the United Kingdom (England, Scotland, Wales, and Northern Ireland).\n\n# Questions answered by this dataset \n\n* How many mining establishments are there in Wales, and how many employees do they have?\n* How quickly is the number of small businesses in Bristol growing?\n\n# Metrics\n\n* LT: Total number of establishments for the industry-area-year combination selected.\n* L0: Number of establishments with 1-4 employees for the industry-area-year combination selected.\n* L5: Number of establishments with 5-9 employees for the industry-area-year combination selected.\n* L10: Number of establishments with 10-19 employees for the industry-area-year combination selected.\n* L20: Number of establishments with 20-49 employees for the industry-area-year combination selected.\n* L50: Number of establishments with 50-99 employees for the industry-area-year combination selected.\n* L100: Number of establishments with 100-249 employees for the industry-area-year combination selected.\n* L250: Number of establishments with 250-499 employees for the industry-area-year combination selected.\n* L500: Number of establishments with 500-999 employees for the industry-area-year combination selected.\n* L1000: Number of establishments with 1000 or more employees for the industry-area-year combination selected.\n\n# Filters\n\n* Area (Nation, Country, NUTS1, NUTS3, and LAU1)\n* Industry (1 to 5-digit SIC)\n* Year\n",
"title": "UK Business Counts"
},
{
"name": "EMSI.uk.Occupation",
"versions": [
"2023.2",
"2023.1",
"2025.1",
"2024.1",
"2024.2"
],
"description": "# Description\n\nThis dataset contains occupation data by area, occupation, class of worker, and year. Data is available for the United Kingdom (England, Scotland, Wales, and Northern Ireland). Earnings data is available from 2011 to current year.\n\n# Questions answered by this dataset\n\n* How many pharmacist jobs were there in London in 2016? How many are there projected to be in 2026?\n* What are the median earnings for chief executives in Birmingham? How about the 75th percentile earnings?\n* How many job openings will there be due to replacement needs for civil engineers in England between now and 2028?\n* Is the number of librarian jobs in Liverpool growing or declining?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Growth: Growth is the net (non-negative) change for each year, and is calculated at the lowest level and aggregated up. For these reasons, it often differs from job change as calculated by subtracting an earlier year's jobs from a later year's job counts. The growth metric is a component of the openings metric (growth + replacements = openings).\n* Replacements: Replacements is the number of existing jobs that are estimated to have been vacated in the year. Replacements takes into account job openings that are created but that are not due to growth in the occupation.\n* Openings: Openings is the sum of growth and replacement, and it refers to the estimated number of job openings that exist both due to growth in the occupation and replacement needs in the occupation.\n* HistoricalEarnings: Average hourly earnings.\n* Earnings.Median: 50th percentile (median) hourly earnings per worker.\n* Earnings.Percentile10: 10th percentile hourly earnings per worker.\n* Earnings.Percentile25: 25th percentile hourly earnings per worker.\n* Earnings.Percentile50: 50th percentile (median) hourly earnings per worker.\n* Earnings.Percentile75: 75th percentile hourly earnings per worker.\n* Earnings.Percentile90: 90th percentile hourly earnings per worker.\n* HistoricalEarnings.Annual: Average annual earnings.\n* Earnings.Median.Annual: 50th percentile (median) annual earnings per worker.\n* Earnings.Percentile10.Annual: 10th percentile annual earnings per worker.\n* Earnings.Percentile25.Annual: 25th percentile annual earnings per worker.\n* Earnings.Percentile50.Annual: 50th percentile (median) annual earnings per worker.\n* Earnings.Percentile75.Annual: 75th percentile annual earnings per worker.\n* Earnings.Percentile90.Annual: 90th percentile annual earnings per worker.\n\n# Filters\n\n* Area (Nation, Country, NUTS1, NUTS3, and LAU1)\n* Occupation (SOC)\n* Class of Worker\n* Year\n",
"title": "UK Occupation"
},
{
"name": "EMSI.us.Nioem.Raw",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2020.2",
"2024.4",
"2025.2"
],
"description": "# Description \n\nThe NIOEM raw dataset shows the Bureau of Labor Statistics' National Industry-Occupation Employment Matrix raw employment projection data. Employment data are available at the national level for the most recent NIOEM year and are projected out 10 years. The BLS rounds employment values in NIOEM to the nearest hundred. Industries covered in the National Employment Matrix reflect NAICS industry codes. Occupation codes are based on the structure used by the OES program, which includes detailed occupations from the Standard Occupational Classification (SOC) system. Go to https://www.bls.gov/emp/documentation/about-the-numbers.htm to read more about the NIOEM dataset.\n\nIn order to preserve the integrity of the raw data, no aggregations are performed. Consequently, queries can only include identity mappings; i.e. users can only request a single occupation code and a single industry code per mapping. If users wish to query a particular occupation at the total industry level, they should constrain the industry ID to the total industry code 'TE1000'. To exclude self-employment from that total, use the industry code 'TE1200'. For more details, view the industry definition endpoint.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many forest fire inspectors does the BLS show working for state government?\n* What is the expected percent growth in wind turbine service technicians for ten years in the future?\n\n# Metrics\n\n* Base employment: The current number of jobs in a given industry-occupation pair.\n* Projected employment: The expected number of jobs for an industry-occupation pair ten years in the future.\n* Percent change: The expected percent change in employment from base to projected.\n\n# Filters\n\n* Industry (6-digit NIOEM)\n* Occupation (5-digit SOC)\n* Year\n",
"title": "US NIOEM Employment Projection"
},
{
"name": "EMSI.us.Ipeds_Disability",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains information on percent of the student body with reported disabilities. The data is reported by institution. The earliest year for which the data is available is 2009. The data is available through the latest year published by IPEDS.\n\nNo aggregations are performed for institutions, so only identity mappings are allowed along the institution dimension.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What institution in Virginia has the highest percentage of disabled students enrolled?\n* How has the percentage of disabled students enrolled at a particular institution changed over time?\n\n# Metrics\n\n* DisabilityPercent: The percentage of enrolled students that have reported a disability (includes physical handicaps as well as learning disabilities).\n\n# Filters\n\n* Institution\n* Year\n",
"title": "US Disability Enrollments by Institution"
},
{
"name": "EMSI.us.EducationalAttainmentZip",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows the resident population who have achieved various levels of education. The data is displayed by area, race, and gender. It is available from 2011 to the most recent year of data in the ACS API. Data for the nation, states, and ZIP Code Tabulation Areas are available. The data presented includes the population aged 25 years and above.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What is the educational attainment breakout for the Asian population in Seattle?\n* Has the percentage of people living in our ZIP code who hold at least a bachelor's degree changed?\n* What percentage of the population of Chicago has less than a high school diploma?\n\n# Metrics\n\n* Population: The number of people living in the area who match the designated dimensions.\n\n# Filters\n\n* Area (Nation, State, ZIP)\n* Race\n* Gender\n* Education level\n* Year\n",
"title": "US ZIP Educational Attainment"
},
{
"name": "EMSI.us.EducationalAttainment.Age",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThe educational attainment by age dataset shows American Community Survey (ACS) data for population by year, county, gender, age, and educational attainment for the most recent ACS dataset. The ACS sorts respondents into five different age categories and seven different educational attainment categories. Data comes from the ACS 5-year surveys and goes back to 2009.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* Which counties have the highest proportion of people under 35 with a college degree?\n* In which counties are women earning degrees earlier than men?\n* What is the average age for a woman to get her bachelor's degree in Pennsylvania?\n\n# Metrics\n\n* Population: population for the given category\n\n# Filters\n\n* Year (2009 - most recent data)\n* Area (County)\n* Gender\n* Age (five age categories)\n* Educational attainment (seven attainment categories)\n",
"title": "US Educational Attainment by Age"
},
{
"name": "EMSI.us.Completers.Locations",
"versions": [
"2025.1",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows college completions data as reported to the NCES IPEDS program integrated with NC-SARA reporting. Data is available from 2018 through the latest year available, and is reported by institution, program, award level, and area.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many degrees and certificates did Stanford confer in 2018?\n* Is the number of bachelor's degrees in electrical engineering increasing in our institution over time?\n* Which program is producing the most graduates in the state of Alabama? Which county is producing the most?\n* What is the geographical shift in distance graduates from mathematical programs at our institution?\n* How does the number of out of state graduates compare to our in state?\n\n# Metrics\n\n* Completions: The number of degrees and certificates conferred.\n\n# Filters\n\n* Institution\n* Program (2, 4, or 6-digit CIP)\n* Award Level\n* Area (Nation, State, County)\n* Year\n",
"title": "US Completions by Institution, Distance/Non-Distance, Area"
},
{
"name": "EMSI.us.Occupation.RaceEthnicity",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description \n\nThis dataset contains job counts by class of worker, area, occupation, race, and ethnicity. The areas available are nation, state, MSA, and county. All historic years are available, and the data is projected out two years beyond the current industry year. 'Current industry year' refers to the latest year for which data is available from QCEW, and is not always equivalent to the current calendar year. ZIP-level figures are not available for occupation-specific race/ethnicity data.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nNote: The ethnicity category should be thought of as a layer on top of the race category. In other words, each race has a Hispanic/non-Hispanic dimension. For ease of presentation, Emsi's Analyst and Developer tools treat the Hispanic category as an additional race category alongside the other races. To duplicate this methodology and produce figures that match Analyst or Developer, 'Hispanic' should be treated as an additional race category, and only the 'non-Hispanic' portions of the other race categories should be counted. Essentially this methodology removes the Hispanic population from the other race categories and makes it into its own racial category.\n\nSee emsi.us.occ.hires.seps for data regarding hires and separations.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many Hispanic workers are there in Nursing in Ohio?\n* What is the ethnicity breakout of welders in Kit Carson County, CO?\n* What is the racial and ethnic breakout of those workers?\n\n# Metrics\n\n* Jobs: number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, MSA, County)\n* Occupation (5-digit SOC)\n* Race\n* Ethnicity\n* Year\n",
"title": "US Occupation by Race and Ethnicity"
},
{
"name": "EMSI.us.Occupation.Detailed",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains job counts by class of worker, area, occupation, age band, and gender. The areas available are nation, state, MSA, and county. All historic years are available, and the data is projected out two years beyond the current industry year. 'Current industry year' refers to the latest year for which data is available from QCEW, and is not always equivalent to the current calendar year. ZIP-level figures are not available for occupation-specific age and gender data.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nSee emsi.us.occ.hires.seps for data regarding hires and separations.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many female workers are there in computer-related occupations in Ohio?\n* What is the age breakout of welders in Kit Carson County, CO?\n* How many construction managers might be retiring soon in the ZIP codes that make up Denver, CO?\n\n# Metrics\n\n* Jobs: number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, MSA, County)\n* Occupation (5-digit SOC)\n* Age (8 age bands)\n* Gender\n* Year\n",
"title": "US Occupation by Age and Gender"
},
{
"name": "EMSI.us.Industry.Month",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nEmsi's monthly industry dataset contains job count data for industries by month from January 2001 to the latest month available from QCEW. Available areas are nation, states, MSAs, counties, and ZIPs. Specific monthly data is derived from QCEW employment data.\n\nWhen requesting MSA codes or ZIP codes as the area constraint in a query, the code must be prepended by MSA or ZIP, respectively (e.g. for ZIP code 98102, use 'ZIP98102')\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many people were employed in sound recording studios in July, 2012 in Los Angeles county?\n* How do residential property manager jobs change from the summer to the winter months?\n* How did new single-family housing construction jobs change by month in Michigan from 2007 through 2009?\n* How many purchasing managers are employed in the ZIP codes at the heart of Minneapolis?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n\n# Filters\n\n* Area (Nation, State, MSA, County, ZIP)\n* Industry (2 to 6-digit NAICS)\n* Year\n* Month\n",
"title": "US Industry Month (Nation, State, MSA, County, ZIP)"
},
{
"name": "EMSI.us.Demographics",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains population demographics data by area, age group, race, and gender. Data is available at the nation, state, MSA, county, ZIP, and census tract. Data is available back to 2001 and is projected out 10 years beyond the current calendar year.\n\nWhen requesting MSA codes or ZIP codes as the area constraint in a query, the code must be prepended by MSA or ZIP, respectively (e.g. for ZIP code 98102, use 'ZIP98102')\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What is the racial breakout of Miami-Dade County?\n* How does the racial breakout of the populations of different sections of Miami differ?\n* What is the gender breakout of ZIP-code 01603?\n* Are gender breakouts in small towns in Kentucky different than gender breakouts in small towns in Washington?\n* How many women live in census tract 005400?\n* Are there denser pockets of boomers living in certain census tracts within San Francisco?\n* Has the millennial population in our area grown or declined over the past three years?\n* Is our area more or less racially diverse than the US at large?\n\n# Metrics\n\n* Population: The number of people living in the area who match the designated dimensions.\n\n# Filters\n\n* Area (Nation, State, MSA, County, ZIP, Census tract)\n* Race\n* Gender\n* AgeGroup (single ages 0-85 and 5-year age groups)\n* Year\n",
"title": "US Demographics (Nation, State, MSA, County, ZIP, Census Tract)"
},
{
"name": "EMSI.us.LaborForce",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains employment, unemployment, and population figures by area. Together these metrics can be used to create a labor force participation rate, which measures how much of the population either has a job, or does not have a job but is seeking one. Producing a labor force participation rate can be done by dividing the sum of employment and unemployment by population:\n\n labor force participation rate = (Employment + Unemployment) / WorkingAgePopulation\n\nThis dataset is monthly, and is provided for each month from January 2010 through the latest month available in the version selected. Data is available at the nation, state, MSA, and county levels.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nUnemployed persons are defined by the BLS (from which Emsi collects unemployment data) as 'all persons who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment sometime during the 4-week period ending with the reference week. Persons who were waiting to be recalled from a job from which they had been laid off need not have been looking for work to be classified as unemployed' (https://www.bls.gov/lau/laufaq.htm#Q03).\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What is the labor force participation rate for Indianapolis?\n* How has the unemployment rate changed over time in Portland?\n* What cities in the US have the highest labor force participation rate? What cities have the lowest rate?\n* How does the LFPR for the working age population differ from the LFPR for everyone 16 and older?\n\n# Metrics\n\n* Employment: The number of jobs in the area.\n* Unemployment: The number of unemployed people in the area.\n* UnemploymentRate: Unemployment/(Empoyment+Unemployment)\n* WorkingAgePopulation: The civilian non-institutional population aged 16-64 living in the area.\n* LaborForcePopulation: The civilian non-institutional population aged 16+ living in the area.\n* WorkingAgeLFPR: (Empoyment+Unemployment)/WorkingAgePopulation\n* LFPR: (Empoyment+Unemployment)/LaborForcePopulation\n* EmployablePopulation (deprecated name): The civilian non-institutional population aged 16-64 living in the area.\n* LaborForceParticipationRate (deprecated name): (Empoyment+Unemployment)/WorkingAgePopulation\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Year\n* Month\n",
"title": "US Labor Force Participation"
},
{
"name": "EMSI.us.Occupation.EducationalAttainment",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows educational attainment by occupation in percentages. Data is available by detailed SOC, but only at the national level. Data is only available for the root occupation (00-0000) and most detailed occupations (e.g. 11-1031).\n\nIn order to preserve the integrity of the raw data, no aggregations are performed along the Occupation dimension. Consequently, queries can only include identity mappings for Occupation constraints; i.e. users can only request a single occupation code per mapping.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What percent of Registered Nurses have Associate's degrees? What percent have Bachelor's degrees?\n\n# Metrics\n\n* Percent: What percent of the occupation selected has achieved the education level indicated\n\n# Filters\n\n* Occupation (5-digit SOC)\n* Education level\n",
"title": "US Occupation Educational Attainment Percent Breakouts"
},
{
"name": "EMSI.us.DemographicsCDP",
"versions": [
"2025.1",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains population demographics data by area, age group, race, and gender. Data is available at the nation, state, and census-designated place. Data is available back to 2001 and is projected out 10 years beyond the current calendar year.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What is the racial breakout of Miami-Dade County?\n* How does the racial breakout of the populations of Miami differ to Tallahasee?\n* What is the gender breakout of ZIP-code 01603?\n* Are gender breakouts in small towns in Kentucky different than gender breakouts in small towns in Washington?\n* How many women live in census tract 005400?\n* Are there denser pockets of boomers living within San Francisco?\n* Has the millennial population in our area grown or declined over the past three years?\n* Is our area more or less racially diverse than the US at large?\n\n# Metrics\n\n* Population: The number of people living in the area who match the designated dimensions.\n\n# Filters\n\n* Area (Nation, State, Census-Designated Place)\n* Race\n* Gender\n* AgeGroup (single ages 0-85 and 5-year age groups)\n* Year\n",
"title": "US Demographics (Nation, State, Census-Designated Place)"
},
{
"name": "EMSI.us.Staffing",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThe staffing dataset shows the occupational composition of an industry. This data can also be used to find all of the industries that employ a given occupation and the percentage of those industries accounted for by that occupation. Data is available at the nation, state, MSA, county, ZIP, and census tract level.\n\nWhen requesting MSA codes or ZIP codes as the area constraint in a query, the code must be prepended by MSA or ZIP, respectively (e.g. for ZIP code 98102, use 'ZIP98102')\n\nHistorical staffing patterns are available back to 2001 and are projected out 10 years beyond the current calendar year.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What occupations make up the General Medical and Surgical Hospitals industry in Los Angeles?\n* What industries employ the most web developers in the ZIP code area of downtown Seattle?\n* Has the occupational makeup of the Colleges, Universities, and Professional Schools industry changed over time?\n\n# Metrics\n\n* Jobs: The number of jobs in the industry that are accounted for by the occupation.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, MSA, County, ZIP, Census Tract)\n* Industry (6-digit NAICS)\n* Occupation (5-digit SOC)\n* Year\n",
"title": "US Staffing Patterns (Nation, State, MSA, County, ZIP, Census Tract)"
},
{
"name": "EMSI.us.GrossRegionalProduct",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nGross Regional Product is simply GDP for a smaller area: state, county, group of counties, etc. This dataset provides dollar amounts for the components that make up Gross Regional Product. GRP is the sum of earnings, taxes and profits, less subsidies:\n\n GRP = earnings + taxes + profits - subsidies\n\nThese make up the 'component' dimension of this dataset. The data is available by industry for the nation, states, MSAs, and counties, for years 2007 through the most current industry year.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nAll Emsi GRP data is presented in real dollars, i.e. 2009 GRP figures are presented in 2009 dollars and are not modified to be equivalent to the current dollar.\n\nThis dataset contains one additional 'industry', comprised of Government Federal Enterprises, Government State and Local Enterprises, and Owner-Occupied Dwellings (989999). This sector contributes to the GRP of a region alongside NAICS industries and is therefore included in the GRP dataset. To read more about this sector, visit https://kb.economicmodeling.com/glossary/other-vectors-i-o/.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What was Alaska's GRP in 2012?\n* What is the most heavily subsidized industry in the United States?\n* What is the total earnings figure for the Computer Systems Design Services industry in King County, Washington (Seattle)?\n\n# Metrics\n\n* Dollars: Total value of all goods and services produced in the region.\n\n# Filters\n\n* Component\n* Area (Nation, State, MSA, County)\n* Industry (2 to 6-digit NAICS)\n* Year\n",
"title": "US Gross Regional Product"
},
{
"name": "EMSI.us.Occ.Hires.Seps",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nEmsi's occupation hires and separations dataset provides hires and separations for employees by area and occupation. All historic years back to 2001 are available, and the data is projected out two years beyond the current industry year. 'Current industry year' refers to the latest year for which data is available from QCEW, and is not always equivalent to the current calendar year. Data is available at the nation, state, MSA, and county level.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many cashiers were hired in Sanoma County in 2010?\n* How is the number of airline pilots hired in the nation expected to change in the next two years?\n* How did the number of separations of retail salespersons compare with that of first-line supervisors last year in Texas?\n\n# Metrics\n\n* Hires: The number of hires made in that occupation. For more information, see https://kb.economicmodeling.com/glossary/hires/\n* Separations: The number of separations from that occupation. Like hires, separations come from the Census Bureau's Quarterly Workforce Indicators (QWI) dataset.\n\n# Filters\n\n* Class of Worker (1, 2)\n* Year\n* Area (Nation, State, MSA, County)\n* Occupation (5-digit SOC)\n",
"title": "US Occupation Hires and Separations by Area and Occupation"
},
{
"name": "EMSI.us.Industry.Detailed",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nEmsi's detailed industry dataset provides job counts, hires, separations, and turnover rate by class of worker, area, industry, age group, and gender. All historic years back to 2001 are available, and the data is projected out two years beyond the current industry year. 'Current industry year' refers to the latest year for which data is available from QCEW, and is not always equivalent to the current calendar year. Data is available at the nation, state, MSA, and county level.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What is the male-to-female employment ratio in the Travel Agencies industry?\n* How many 25-34 year olds are employed in Government industries in New York?\n* How many 19-21 year olds were hired in 2010?\n* Has the number of separations for women in the General Medical and Surgical Hospitals industry increased or decreased over the last 5 years?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Hires: The number of hires made in that industry. For more information, see https://kb.economicmodeling.com/glossary/hires/\n* Separations: The number of separations from an industry. Both quits and fires are included in the separations figure. Like hires, separations comes from the Census Bureau's Quarterly Workforce Indicators (QWI) dataset.\n* TurnoverRate: The rate of separations per job.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, MSA, County)\n* Industry (2 to 6-digit NAICS)\n* Age (8 age bands)\n* Gender\n* Year\n",
"title": "US Industry by Age and Gender"
},
{
"name": "EMSI.us.Staffing.Earn",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThe dataset shows employment and percentile earnings by year, industry, and occupation for QCEW and Non-QCEW employees (excludes self-employed).\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* Which industry employs the most special effects animators?\n* What is the earnings difference for general contractors employed in residential vs nonresidential building construction?\n* What are the median earnings for purchasing managers working at nursing care facilities? How about the 75th percentile earnings?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings\n** Average: Average hourly earnings per job.\n** Median: 50th percentile (median) hourly earnings per job.\n** Percentile10: 10th percentile hourly earnings per job.\n** Percentile25: 25th percentile hourly earnings per job.\n** Percentile50: 50th percentile (median) hourly earnings per job.\n** Percentile75: 75th percentile hourly earnings per job.\n** Percentile90: 90th percentile hourly earnings per job.\n** Average.Annual: Average annual earnings per job.\n** Median.Annual: 50th percentile (median) annual earnings per job.\n** Percentile10.Annual: 10th percentile annual earnings per job.\n** Percentile25.Annual: 25th percentile annual earnings per job.\n** Percentile50.Annual: 50th percentile (median) annual earnings per job.\n** Percentile75.Annual: 75th percentile annual earnings per job.\n** Percentile90.Annual: 90th percentile annual earnings per job.\n\n# Filters\n\n* Class of Worker (QCEW and Non-QCEW employees only)\n* Industry (6-digit NAICS)\n* Occupation (5-digit SOC)\n* Year\n",
"title": "US Industry by Occupation Earnings"
},
{
"name": "EMSI.us.Ind.Firm",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nEmsi's industry firm job change dataset provides the number of jobs that firms created or destroyed within a particular area and industry. All historic years back to 2001 are available, and the data is projected out two years beyond the current industry year. 'Current industry year' refers to the latest year for which data is available from QCEW, and is not always equivalent to the current calendar year. Data is available at the nation, state, MSA, and county level.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many new positions opened up in the manufacturing industries in Sanoma County in 2010?\n* How many jobs in the airline industry have been created and destoyed by firms in the last 5 years?\n\n# Metrics\n\n* CreatedJobs: The number of jobs in that occupation created by firms.\n* DestroyedJobs: The number of jobs in that occupation destroyed by firms.\n\n# Filters\n\n* Class of Worker (1, 2)\n* Year\n* Area (Nation, State, MSA, County)\n* Industry (6-digit NAICS)\n",
"title": "US Industry Firm Job Change"
},
{
"name": "EMSI.us.EducationalAttainment",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows the resident population who have achieved various levels of education. The data is displayed by area, race, and gender. It is available from 2001 and is projected 10 years out beyond the current calendar year. Data for the nation, states, MSAs, and counties are available. The data presented includes the population aged 25 years and above.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What is the educational attainment breakout for the Asian population in Seattle?\n* Has the percentage of people living in our town who hold at least a bachelor's degree changed?\n* What percentage of the population of Chicago has less than a high school diploma?\n\n# Metrics\n\n* Population: The number of people living in the area who match the designated dimensions.\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Race\n* Gender\n* Education level\n* Year\n",
"title": "US Educational Attainment by Demographic"
},
{
"name": "EMSI.us.Enrollments.Demographics",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains enrollments data by institution, race, gender, and enrollment level. Data is available back to 2003, and through the latest year available from IPEDS. Enrollment level refers to whether the student is a degree/non-degree-seeking student, a graduate or undergraduate student, and whether a first professional degree is sought.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* Is enrollment for institutions in our area increasing or declining?\n* Is degree-seeking enrollment for institutions in our area growing or declining?\n* How has the racial makeup of non-degree-seeking students enrolling at institutions in our area changed over time?\n* Where is female degree-seeking enrollment growing the fastest? Where is it declining the fastest?\n\n# Metrics\n\n* Enrollments: The number of students enrolled that match the criteria specified.\n\n# Filters\n\n* Institution\n* Race\n* Gender\n* Enrollment level\n* Year\n",
"title": "US Enrollments by Institution and Demographic"
},
{
"name": "EMSI.us.OccupationCDP",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows (by year, class of worker, occupation, and area) occupational employment and earnings data by place of work for Census-designated places (CDPs), as well as states and the nation. The data is available back to 2001, and is projected 10 years out beyond the current calendar year.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many pharmacist jobs existed in Florida in 2016? How many are projected to exist in 2026?\n* Is the number of librarian jobs in Houston growing or declining?\n* How do employment trends for registered nurses in Boston compare with trends in surrounding cities like Brookline or Cambridge?\n* Which cities in the Seattle Metropolitan Area are showing the highest levels of job growth?\n\n# Metrics\n\n* Jobs by place of work: The number of occupied positions in an area. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings (current occupation year) or HistoricalEarnings (2005 - current occupation year)\n** Percentile10: 10th percentile hourly earnings per worker.\n** Percentile25: 25th percentile hourly earnings per worker.\n** Percentile50: 50th percentile (median) hourly earnings per worker.\n** Percentile75: 75th percentile hourly earnings per worker.\n** Percentile90: 90th percentile hourly earnings per worker.\n** Median.Annual: 50th percentile (median) annual earnings per worker.\n** Percentile10.Annual: 10th percentile annual earnings per worker.\n** Percentile25.Annual: 25th percentile annual earnings per worker.\n** Percentile50.Annual: 50th percentile (median) annual earnings per worker.\n** Percentile75.Annual: 75th percentile annual earnings per worker.\n** Percentile90.Annual: 90th percentile annual earnings per worker.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, Census-designated place)\n* Occupation (5-digit SOC)\n* Year\n",
"title": "US Occupation (Nation, State, Census-designated place)"
},
{
"name": "EMSI.us.Unemployment.Occupation",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset provides unemployment figures by area and occupation. Data is available by month from January 2010 through the latest month available in the version selected. Data is available at the national, state, MSA, and county levels. Only the total figure and two-digit occupation data are available. Unemployment is provided in both numbers and percentages, and is not seasonally adjusted.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nThe BLS definitions for many of our metrics can be found at [the BLS unemployment FAQ](https://www.bls.gov/lau/laufaq.htm#Q03).\n\nOur twelve month averages are a less volatile metric to measure employment and unemployment, and are made by averaging the current month of employment/unemployment data with the previous eleven months' data.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What major occupation group had the highest employment and unemployment in Ohio in January 2018?\n* What state has the most employment and unemployment in healthcare occupations?\n* How has occupation employment and unemployment changed over time in Kansas City?\n* Which occupation in Missouri had the largest growth in employment in May 2021?\n\n# Metrics\n\n* Employment: The number of employed people in the area.\n* Unemployment: The number of unemployed people in the area.\n* TwelveMonthAverage: The average of the last 12 months' unemployment.\n* TwelveMonthEmpAverage: The average of the last 12 months' employment.\n* LaborForce: The number of employed and unemployed people in the area.\n* UnemploymentRate: Unemployment divided by the LaborForce.\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Occupation (2-digit SOC)\n* Year\n* Month\n",
"title": "US Unemployment by Occupation"
},
{
"name": "EMSI.us.Occ.Firm",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nEmsi's occupation firm job change dataset provides the number of jobs that firms created or destroyed within a particular area and occupation. All historic years back to 2001 are available, and the data is projected out two years beyond the current industry year. 'Current industry year' refers to the latest year for which data is available from QCEW, and is not always equivalent to the current calendar year. Data is available at the nation, state, MSA, and county level.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many new cashier positions opened up in Sanoma County in 2010?\n* How many airline pilot jobs have been created and destoyed by firms in the last 5 years?\n* How did the number of new retail salesperson positions compare with that of first-line supervisors last year in Texas?\n\n# Metrics\n\n* CreatedJobs: The number of jobs in that occupation created by firms.\n* DestroyedJobs: The number of jobs in that occupation destroyed by firms.\n\n# Filters\n\n* Class of Worker (1, 2)\n* Year\n* Area (Nation, State, MSA, County)\n* Occupation (5-digit SOC)\n",
"title": "US Occupation Firm Job Change"
},
{
"name": "EMSI.us.Completers.Demographics",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows college completions data as reported to the NCES IPEDS program. Data is available from 2003 through the latest year available, and is reported by institution, program, award level, race, and gender.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many degrees and certificates did Stanford confer in 2016?\n* Is the number of bachelor's degrees in electrical engineering increasing in our institution over time?\n* Which program is producing the most graduates in the state of Alabama? Which program is producing the fewest graduates?\n* Is the share of female graduates in mathematical programs increasing at our institution?\n* Is the share of graduates of racial minority at our institution increasing?\n\n# Metrics\n\n* Completions: The number of degrees and certificates conferred.\n* Completers (deprecated): The number of degrees and certificates conferred.\n\n# Filters\n\n* Institution\n* Program (2, 4, or 6-digit CIP)\n* Award Level\n* Race\n* Gender\n* Year\n",
"title": "US Completions by Institution and Demographic"
},
{
"name": "EMSI.us.Commuting",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains commuting data (employment by class of worker, year, place of residence and place of work). Data is available at the nation, state, MSA, county, and zip code levels, from 2001 to the current year.\n\nWhen requesting MSA codes or ZIP codes as the area constraint in a query, the code must be prepended by MSA or ZIP, respectively (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many people who work in the zip code area for downtown Seattle live outside of that area?\n* How many people who work in the Kansas City MSA live in Missouri?\n* How many people who live in LA County work outside of Californa this year compared to previous years?\n\n# Metrics\n\n* Jobs: The number of jobs in the area.\n\n# Filters\n\n* ClassOfWorker\n* PlaceOfResidence (Nation, State, MSA, County, ZIP)\n* PlaceOfWork (Nation, State, MSA, County, ZIP)\n* Year\n",
"title": "US Commuting (Nation, State, MSA, County, ZIP)"
},
{
"name": "EMSI.us.Industry.RaceEthnicity",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThe race/ethnicity industry dataset provides job counts, hires, separations, and turnover rate by class of worker, area, industry, race, and ethnicity. All historic years back to 2001 are available, and the data is projected out two years beyond the current industry year. 'Current industry year' refers to the latest year for which data is available from QCEW, and is not always equivalent to the current calendar year. Data is available at the nation, state, MSA, and county level.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nNote: The ethnicity category should be thought of as a layer on top of the race category. In other words, each race has a Hispanic/non-Hispanic dimension. For ease of presentation, Emsi's Analyst and Developer tools treat the Hispanic category as an additional race category alongside the other races. To duplicate this methodology and produce figures that match Analyst or Developer, 'Hispanic' should be treated as an additional race category, and only the 'non-Hispanic' portions of the other race categories should be counted. Essentially this methodology removes the Hispanic population from the other race categories and makes it into its own racial category.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What is the racial breakdown of the finance industry in New York City?\n* What percentage of workers in the custom computer programming services industry in Phoenix are Hispanic?\n* How has the racial makeup of the workforce in Denver changed over time?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Hires: The number of hires made in that industry. For more information, see https://kb.economicmodeling.com/glossary/hires/\n* Separations: The number of separations from an industry. Both quits and fires are included in the separations figure. Like hires, separations comes from the Census Bureau's Quarterly Workforce Indicators (QWI) dataset.\n* TurnoverRate: The rate of separations per job.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, MSA, County)\n* Industry (2 to 6-digit NAICS)\n* Race\n* Ethnicity\n* Year\n",
"title": "US Industry by Race and Ethnicity"
},
{
"name": "EMSI.us.Unemployment.Race",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset provides unemployment figures by area and race. Data is available by month from January 2010 through the latest month available in the version selected. Data is available at the national, state, MSA, and county levels. Unemployment is provided in both numbers and percentages, and is not seasonally adjusted.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nThe BLS definitions for many of our metrics can be found at [the BLS unemployment FAQ](https://www.bls.gov/lau/laufaq.htm#Q03).\n\nOur twelve month averages are a less volatile metric to measure employment and unemployment, and are made by averaging the current month of employment/unemployment data with the previous eleven months' data.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How has total employment and unemployment changed in Idaho compared to California?\n* What is the ratio of Black to White employment and unemployment in my area?\n* Has unemployment hit Asians more than Whites in the nation in 2020?\n* What percentage of the labor force in Canadian County, OK is American Indian?\n\n# Metrics\n\n* Employment: The number of employed people in the area.\n* Unemployment: The number of unemployed people in the area.\n* TwelveMonthAverage: The average of the last 12 months' unemployment.\n* TwelveMonthEmpAverage: The average of the last 12 months' employment.\n* LaborForce: The number of employed and unemployed people in the area.\n* UnemploymentRate: Unemployment divided by the LaborForce.\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Race (Total, American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White)\n* Year\n* Month\n",
"title": "US Unemployment by Race"
},
{
"name": "EMSI.us.Occupation",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows (by year, class of worker, occupation, and area) occupation data including job counts by place of work, openings and replacement figures, earnings, and job counts by place of residence. The data is available back to 2001, with the exception of earnings, which is available for historical data from the current occupation year back to 2005. Jobs by place of work, replacements, and openings data are projected 10 years out beyond the current calendar year. Data is available at the nation, state, MSA, county, census tract, and ZIP code levels.\n\nWhen requesting MSA codes or ZIP codes as the area constraint in a query, the code must be prepended by MSA or ZIP, respectively (e.g. for ZIP code 98102, use 'ZIP98102')\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many pharmacist jobs existed in Florida in 2016? How many are projected to exist in 2026?\n* What are the median earnings for purchasing managers in the ZIP codes or census tracts in the heart of Minneapolis?\n* How many job openings will there be due to replacement needs for marketing managers in the US between now and 2028?\n* Is the number of librarian jobs in Houston growing or declining?\n* Do self-employed web developers in Santa Clara County, CA make more than web developers who are employees of companies?\n* What are the 90th percentile earnings for graphic designers in the Washington DC metropolitan area?\n* How many registered nurses live in the counties that make up Spokane, WA?\n\n# Metrics\n\n* Jobs by place of work: The number of occupied positions in an area. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings.Average: Average hourly earnings for each job.\n* Growth: Growth is the net (non-negative) change for each year, and is calculated at the lowest level and aggregated up. For these reasons, it often differs from job change as calculated by subtracting an earlier year's jobs from a later year's job counts. The growth metric is a component of the openings metric (growth + replacements = openings).\n* Replacements: Replacements is the number of existing jobs that are estimated to have been vacated in the year. Replacements takes into account job openings that are created but that are not due to growth in the occupation.\n* Openings: Openings is the sum of growth and replacement, and it refers to the estimated number of job openings that exist both due to growth in the occupation and replacement needs in the occupation.\n* Jobs by place of residence: The number of people living in an area that hold a job within the occupation in question (not available at the census tract level)\n* Earnings (current occupation year) or HistoricalEarnings (2005 - current occupation year)\n** Percentile10: 10th percentile hourly earnings per worker.\n** Percentile25: 25th percentile hourly earnings per worker.\n** Percentile50: 50th percentile (median) hourly earnings per worker.\n** Percentile75: 75th percentile hourly earnings per worker.\n** Percentile90: 90th percentile hourly earnings per worker.\n** Median.Annual: 50th percentile (median) annual earnings per worker.\n** Percentile10.Annual: 10th percentile annual earnings per worker.\n** Percentile25.Annual: 25th percentile annual earnings per worker.\n** Percentile50.Annual: 50th percentile (median) annual earnings per worker.\n** Percentile75.Annual: 75th percentile annual earnings per worker.\n** Percentile90.Annual: 90th percentile annual earnings per worker.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, MSA, County, Census Tract, ZIP code)\n* Occupation (5-digit SOC)\n* Year\n",
"title": "US Occupation (Nation, State, County, MSA, ZIP, Census Tract)"
},
{
"name": "EMSI.us.Crime",
"versions": [
"2025.1",
"2024.3",
"base",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains counts of crimes reported by type. Data is available at the nation, state, MSA, and county levels. Data is available back to 2001.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What county had the most murders in 2005?\n* What counties in the US have more violent crime than property crime?\n* Has the number of robberies in New York been increasing or decreasing over the last 5 years?\n\n# Metrics\n\n* Violent Crimes: The number of violent crimes reported\n* Property Crimes: The number of property crimes reported\n* Murders: The number of murders reported\n* ForcibleRapes: The number of forcible rapes reported\n* Robberies: The number of robberies reported\n* Assaults: The number of assaults reported\n* Burglaries: The number of burglaries reported\n* Larcenies: The number of larcenies reported\n* MotorVehicleThefts: The number of motor vehicle thefts reported\n* Arsons: The number of arsons reported\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Year\n",
"title": "US Crime Counts"
},
{
"name": "EMSI.us.Enrollments.Distance",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains enrollments data by institution, enrollment level, and distance type. Data is available back to 2012, and through the latest year available from IPEDS. Enrollment level refers to whether the student is a graduate or undergraduate student, and if undergraduate whether a degree or non-degree is sought. Distance type refers to whether all, some, or none of the students are enrolled in distance education courses, and where students enrolled exclusively in distance education courses are located in relation to the institution.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* Is total enrollment for our institution increasing or declining over time?\n* How many undergraduates in our institution are seeking a degree? How many of these are enrolled in distance education courses?\n* How is the number of students enrolled in some but not all distance education courses changing over time?\n* What percentage of students enrolled exclusively in distance education are located in the same state as the institution? \n\n# Metrics\n\n* Enrollments: The number of students enrolled that match the criteria specified.\n\n# Filters\n\n* Institution\n* EnrollmentLevel\n* DistanceType\n* Year\n",
"title": "US Distance Enrollments by Institution"
},
{
"name": "EMSI.us.IRS.Migration",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows domestic taxpayer migration among all states, MSAs, and counties in the United States. The data goes back to 2012 (2012 tracks migration from 2011-2012) and is updated as available data is released by the IRS, generally lagging by two calendar years.\n\nThe source and design of this dataset excludes certain groups of people and thus does not represent the entire population, but rather is a good indicator of migrating workers within the laborforce, based on taxpayers. Specifically, the follow demographics are under-represented:\n* Youth (not required to file taxes)\n* Elderly (not required to file taxes)\n* The poor (not required to file taxes)\n* The very wealthy (complicated returns often get extensions and are excluded)\n* New filers (excluded because they did not file the previous year)\n* Former filers (excluded because they filed in the previous year but not the current year)\n* Some joint filers (only the primary taxpayer is included)\n* Mistakes (errors on a tax return can cause it to be excluded)\n\nAdditionally, on recommendation from the migration expert at the IRS, we multiply the published number of migrations by 0.9 to better approximate the actual number of taxpayers that are moving. Their recommendation is based on the assumption that 90% of exemptions claimed on tax returns actually represent a person, while the remaining 10% do not.\n\nWarning: non-migrants are included in this dataset as datapoints where a county is included in both the OutCounty and InCounty constraints. This means that any summation of counties will likely include some non-migrants. For example, if you want to retrieve the number of migrants from Los Angeles county to the state of California, you will also need to subtract migration from Los Angeles county to itself. Another example, if you want to calculate total outmigration from Washington, DC MSA to any other county, you will also need to subtract any flows between the counties that comprise the MSA itself.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many taxpayers moved from Kansas to Texas in 2014?\n* How many taxpayers moved among the counties in the Dallas-Fort Worth metro in 2015?\n* Is the trend of taxpayers moving from California to Idaho growing?\n\n# Metrics\n\n* Population: Number of taxpayers that moved from OutCounty to InCounty in the given year.\n\n# Filters\n\n* OutCounty (Nation, State, MSA, County taxpayers are migrating from)\n* InCounty (Nation, State, MSA, County taxpayers are migrating to)\n* Year\n",
"title": "US Population Migration"
},
{
"name": "EMSI.us.Unemployment.Ethnicity",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset provides unemployment figures by area and ethnicity. Data is available by month from January 2010 through the latest month available in the version selected. Data is available at the national, state, MSA, and county levels. Unemployment is provided in both numbers and percentages, and is not seasonally adjusted.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nThe BLS definitions for many of our metrics can be found at [the BLS unemployment FAQ](https://www.bls.gov/lau/laufaq.htm#Q03).\n\nOur twelve month averages are a less volatile metric to measure employment and unemployment, and are made by averaging the current month of employment/unemployment data with the previous eleven months' data.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How has total employment and unemployment changed in Idaho compared to California?\n* What is the ratio of Hispanic to non-Hispanic employment and unemployment in my area?\n* Has unemployment hit Latinos more than non-Latinos in the nation in 2020?\n* What percentage of the labor force in Ventura County, CA is Hispanic?\n\n# Metrics\n\n* Employment: The number of employed people in the area.\n* Unemployment: The number of unemployed people in the area.\n* TwelveMonthAverage: The average of the last 12 months' unemployment.\n* TwelveMonthEmpAverage: The average of the last 12 months' employment.\n* LaborForce: The number of employed and unemployed people in the area.\n* UnemploymentRate: Unemployment divided by the LaborForce.\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Ethnicity (Total, Hispanic or Latino, Not Hispanic or Latino)\n* Year\n* Month\n",
"title": "US Unemployment by Ethnicity"
},
{
"name": "EMSI.us.MinimumWage",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows minimum wage by state by year, from 2001 to the most recent year available. Data is available for all 50 states plus the District of Columbia, as well as the US minimum wage. In cases where cities within the state have different minimum wage laws than the state itself, the state's minimum wage is shown. In all cases where the state's minimum wage is less than the US minimum wage, the US minimum wage is shown.\n\nOnly identity mappings are allowed along StateID.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What was the minimum wage in Oregon in 2011?\n* What is the minimum wage in the state of Washington?\n* What state has the highest minimum wage?\n\n# Metrics\n\n* MinimumWage: Shows the minimum wage for the state and year selected; if the state minimum wage is less than the nation, then the nation is shown.\n\n# Filters\n\n* Area (Nation, State)\n* Year\n",
"title": "US Minimum Wage"
},
{
"name": "EMSI.us.CostOfLivingIndex",
"versions": [
"2025.1",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows cost of living index for the current year. Data is available at the nation, state, MSA, and county levels. Emsi's cost of living data is based on the Cost of Living Index published by the Council for Community and Economic Research (C2ER).\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What county has the lowest cost of living index?\n* How does the cost of living in the Bronx compare to New York state?\n\n# Metrics\n\n* COLI: Cost of Living Index\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Year\n",
"title": "US Cost of Living Index"
},
{
"name": "EMSI.us.Completers",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows college completions data as reported to the NCES IPEDS program. Data is available from 2003 through the latest year available in IPEDS or College Navigator, whichever is more recent, and is reported by institution, program, and award level.\nNOTE: This dataset delivers completions numbers through 'BrickAndMortar' and 'Distance' dimensions. These categories are binary. If a program is offered in a distance capacity at a particular institution, all completions for that program will be listed in the 'Distance' category. Despite this, in reality, the school may have students in the program that completed their studies on-campus. The presence of completions in the 'Distance' dimension only indicates that the program is offered in a distance capacity. It may or may not also be offered on-campus, and 'Distance' may or may not equate to 'online.'\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many of the University of Maryland's programs are offered in a distance capacity?\n* Is the number of distance-offered programs at our institution increasing? How quickly?\n\n# Metrics\n* Distance: The number of degrees and certificates conferred that were offered in a distance capacity. If this number is greater than 0, 'BrickAndMortar' will be 0.\n* BrickAndMortar: The number of degrees and certificates conferred that were only offered on-campus. If this number is greater than 0, 'Distance' will be 0.\n* Completions: The number of degrees and certificates conferred. This figure will match the non-zero 'Distance' or 'BrickAndMortar' figure.\n* Completers (deprecated): The number of degrees and certificates conferred. This figure will match the non-zero 'Distance' or 'BrickAndMortar' figure.\n* CompletionlessDistancePrograms: The number of programs with no completions that were offered in a distance capacity.\n* CompletionlessBrickAndMortarPrograms: The number of programs with no completions that were only offered on-campus.\n* CompletionlessPrograms: The number of programs with no completions.\n\n# Filters\n\n* Institution\n* Program (2, 4, or 6-digit CIP)\n* Award Level\n* Year\n",
"title": "US Completions by Institution, Distance/Non-Distance"
},
{
"name": "EMSI.us.Industry",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nEmsi's industry dataset contains information on industries back to 2001. Job counts are projected 10 years beyond the current calendar year; earnings are not projected. Available areas are nation, states, MSAs, counties, ZIPs, and tracts. Establishment data is not available for ZIP or tract level.\n\nWhen requesting MSA codes or ZIP codes as the area constraint in a query, the code must be prepended by MSA or ZIP, respectively (e.g. for ZIP code 98102, use 'ZIP98102')\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many manufacturing jobs are projected to exist over the next 10 years in Calvert County, MD?\n* How many Insurance Carriers establishments are there in Nebraska?\n* What is the average wage for workers in hospitals in Ann Arbor, Michigan?\n* How much higher is the average wage today for workers in hospitals than it was in 2005?\n* How many health care jobs exist in the ZIP codes that make up downtown Seattle?\n* How do earnings for Registered Nurses differ in the various parts of Los Angeles County?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Wages: Total base earnings for the industry. Note that this figure is for the whole industry, not for the average worker in the industry.\n* Supplements: Total supplements for the industry. This figure is also for the whole industry, not for the average worker in the industry.\n* Establishments: The number of physical business locations.\n* Earnings: Total earnings for the industry. Sum of wages and supplements. This figure is also for the whole industry, not for the average worker in the industry.\n* EPW: Earnings Per Worker. The earnings figure (sum of wages and salaries) divided by the number of jobs in the industry.\n* SPW: Supplements Per Worker. Supplements for the industry divided by the number of jobs in the industry.\n* WPW: Wages Per Worker. Wages for the industry divided by the number of jobs in the industry.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, MSA, County, ZIP, tract)\n* Industry (2 to 6-digit NAICS)\n* Year\n",
"title": "US Industry (Nation, State, MSA, County, ZIP, Census Tract)"
},
{
"name": "EMSI.us.Unemployment.Industry",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset provides unemployment figures by area and industry. Data is available by month from January 2010 through the latest month available in the version selected. Data is available at the national, state, MSA, and county levels. Only the total figure and two-digit industry data are available. Unemployment is provided in both numbers and percentages, and is not seasonally adjusted.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nThe BLS definitions for many of our metrics can be found at [the BLS unemployment FAQ](https://www.bls.gov/lau/laufaq.htm#Q03).\n\nOur twelve month averages are a less volatile metric to measure employment and unemployment, and are made by averaging the current month of employment/unemployment data with the previous eleven months' data.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What industry sector had the highest employment and unemployment in Ohio in January 2018?\n* What state has the most employment and unemployment in the manufacturing sector?\n* How has industry employment and unemployment changed over time in Kansas City?\n* Which industry in Missouri had the largest growth in employment in May 2021?\n\n# Metrics\n\n* Employment: The number of employed people in the area.\n* Unemployment: The number of unemployed people in the area.\n* TwelveMonthAverage: The average of the last 12 months' unemployment.\n* TwelveMonthEmpAverage: The average of the last 12 months' employment.\n* LaborForce: The number of employed and unemployed people in the area.\n* UnemploymentRate: Unemployment divided by the LaborForce.\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Industry (2-digit NAICS)\n* Year\n* Month\n",
"title": "US Unemployment by Industry"
},
{
"name": "EMSI.us.IndustryCDP",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nEmsi's industry dataset contains information on industries back to 2001. Job counts are projected 10 years beyond the current calendar year; earnings are not projected. Available areas in this dataset are nation, states, and Census-designated places (CDPs).\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many manufacturing jobs are projected to exist over the next 10 years in Baltimore, MD?\n* What is the average wage for workers in hospitals in Flint, Michigan?\n* How much higher is the average wage today for workers in hospitals than it was in 2005?\n* How many health care jobs exist in the cities surrounding Seattle?\n* How do earnings for Registered Nurses differ in the various parts of the Los Angeles metro area?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Wages: Total base earnings for the industry. Note that this figure is for the whole industry, not for the average worker in the industry.\n* Supplements: Total supplements for the industry. This figure is also for the whole industry, not for the average worker in the industry.\n* Earnings: Total earnings for the industry. Sum of wages and supplements. This figure is also for the whole industry, not for the average worker in the industry.\n* EPW: Earnings Per Worker. The earnings figure (sum of wages and salaries) divided by the number of jobs in the industry.\n* SPW: Supplements Per Worker. Supplements for the industry divided by the number of jobs in the industry.\n* WPW: Wages Per Worker. Wages for the industry divided by the number of jobs in the industry.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, Census-designated place)\n* Industry (2 to 6-digit NAICS)\n* Year\n",
"title": "US Industry (Nation, State, Census-designated place)"
},
{
"name": "EMSI.us.Oes.Raw",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2020.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThe OES raw dataset shows the Occupational Employment Statistics (OES) employment and annual earnings data. Data are available at the national and state level for the most recent OES year. Occupation codes include detailed occupations from the Standard Occupational Classification (SOC) system. Go to https://www.bls.gov/oes/oes_emp.htm#overview to read more about the OES dataset.\n\nIn order to preserve the integrity of the raw data, no aggregations are performed. Consequently, queries can only include identity mappings; i.e. users can only request a single occupation code and a single area per mapping.\n\nOES does not disclose earnings percentiles above their topcode value. In such cases, the earnings percentiles values will show zero, but the user can find the topcode value in the /meta/ endpoint for this dataset.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many pharmacist jobs are there in Florida?\n* Which state has the most special effects animators jobs after California?\n* What are the median earnings for purchasing managers in Minneapolis? How about the 75th percentile earnings?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings.Average.Annual: Average annual earnings per worker.\n* Earnings.Median.Annual: 50th percentile (median) annual earnings per worker.\n* Earnings.Percentile10.Annual: 10th percentile annual earnings per worker.\n* Earnings.Percentile25.Annual: 25th percentile annual earnings per worker.\n* Earnings.Percentile50.Annual: 50th percentile (median) annual earnings per worker.\n* Earnings.Percentile75.Annual: 75th percentile annual earnings per worker.\n* Earnings.Percentile90.Annual: 90th percentile annual earnings per worker.\n\n# Filters\n\n* Area (Nation, State)\n* Occupation (5-digit SOC)\n* Year\n",
"title": "US OES (Nation, State)"
},
{
"name": "EMSI.us.Enrollments.Migration",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows the origin of freshman college students by institution and state of origin. Areas include all states and US Territories. All freshmen from foreign countries are pooled in one 'Foreign countries' category. 'Unknown' and 'Residence not reported' categories are included as well. The data is available from 2003 through the most current year available.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* Which institution has the most freshmen migrating from New York?\n* Where do freshmen from Nebraska go?\n* Is UC-Berkeley's percent of foreign freshmen increasing over time?\n* What percent of freshmen from Idaho are enrolled at an in-state institution?\n\n# Metrics\n\n* Enrollments: The number of freshman students enrolled at the institution(s) selected from the state(s) selected\n\n# Filters\n\n* Institution\n* Area (States, US Territories)\n* Year\n",
"title": "US Freshman Home State by Institution"
},
{
"name": "EMSI.us.Qcew.Quarter",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains unsuppressed quarterly industry data from the Quarterly Census of Employment and Wage (QCEW). The area and industry hierarchies for all historical data have been transformed to match the definitions of the latest quarter of QCEW. It shows quarterly job counts and earnings data for industries by quarter from January 2001 to the latest month available from QCEW. Available areas are nation, states, and counties. Go to https://www.bls.gov/cew/overview.htm to read more about the QCEW dataset.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many people were employed in sound recording studios during Quarter 3, 2012 in Los Angeles county?\n* How do residential property manager jobs change from the third to fourth quarter?\n* How did new single-family housing construction jobs change by quarter in Michigan from 2007 through 2009?\n* How many purchasing managers are employed in certain Michigan counties?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings: Total earnings for the industry.\n* Establishments: The number of physical business locations.\n\n# Filters\n\n* Area (Nation, State, County)\n* Industry (2 to 6-digit NAICS)\n* Year\n* Quarter\n",
"title": "US Quarterly QCEW Industry"
},
{
"name": "EMSI.us.FinancialAid",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset contains amounts of financial aid reported by type. Data is reported by institution. The sample for this dataset is financial aid recipients, not total student population. Data is available back to 2009.\n\nNo aggregations are performed for institutions, so only identity mappings are allowed along the institution dimension.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many financial aid recipients received Pell grants in 2004?\n* What percentage of financial aid recipients received non-federal loans from 2014-2016?\n* What percentage of aid provided to recipients at UNC was provided by the institution last year?\n\n# Metrics\n\n* Recipients: The number of financial aid recipients\n* TotalGrantDollars: The amount of grant aid\n* FederalGrantDollars: The amount of federal grant aid\n* PellGrantDollars: The amount of federal grant aid that is in the form of Pell grants\n* OtherFederalGrantDollars: The amount of non-Pell federal grant aid\n* StateLocalGrantDollars: The amount of state and local grant aid\n* InstitutionGrantAid: The amount of institutional grant aid\n* LoanDollars: The amount of loan aid\n* FederalLoanDollars: The amount of federal load aid\n* OtherLoanDollars: The amount of non-federal loan aid\n\n# Filters\n\n* Institution\n* Year\n",
"title": "US Financial Aid by Institution"
},
{
"name": "EMSI.us.Unemployment.Age",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset provides unemployment figures by area and age group. Data is available by month from January 2010 through the latest month available in the version selected. Data is available at the national, state, MSA, and county levels. Unemployment is provided in both numbers and percentages, and is not seasonally adjusted.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nThe BLS definitions for many of our metrics can be found at [the BLS unemployment FAQ](https://www.bls.gov/lau/laufaq.htm#Q03).\n\nOur twelve month averages are a less volatile metric to measure employment and unemployment, and are made by averaging the current month of employment/unemployment data with the previous eleven months' data.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* What is the current unemployment for people under age 22 in California? In Idaho?\n* Which age group in the nation had the highest unemployment change from January to April 2020?\n* How has the unemployment rate changed in Oregon for people aged 45-54 in 2020?\n* Which age group makes up the largest share of the labor force?\n\n# Metrics\n\n* Employment: The number of employed people in the area.\n* Unemployment: The number of unemployed people in the area.\n* TwelveMonthEmpAverage: The average of the last 12 months' employment.\n* TwelveMonthAverage: The average of the last 12 months' unemployment.\n* LaborForce: The number of employed and unemployed people in the area.\n* UnemploymentRate: Unemployment divided by the LaborForce.\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Age (9 age groups)\n* Year\n* Month\n",
"title": "US Unemployment by Age"
},
{
"name": "EMSI.us.IRS.ForeignMigration",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows taxpayer migration out of the US in the past year (anywhere overseas, US territories, and APO/FPO ZIP codes where the taxpayer still files taxes in the US). This dataset does not include former taxpayers who no longer pay taxes in the US. This data is based on IRS taxpayer migration data, so people who move to Puerto Rico are considered foreign migrants to be consistent with IRS classifications. Data is available starting in 2012 (2012 tracks migration from 2011-2012) and is updated whenever new IRS data is available, generally lagging by two calendar years.\n\nThe source and design of this dataset excludes certain groups of people and thus does not represent the entire population, but rather is a good indicator of migrating workers within the laborforce, based on taxpayers. Specifically, the follow demographics are under-represented:\n* Youth (not required to file taxes)\n* Elderly (not required to file taxes)\n* The poor (not required to file taxes)\n* The very wealthy (complicated returns often get extensions and are excluded)\n* New filers (excluded because they did not file the previous year)\n* Former filers (excluded because they filed in the previous year but not the current year)\n* Some joint filers (only the primary taxpayer is included)\n* Mistakes (errors on a tax return can cause it to be excluded)\n\nAdditionally, on recommendation from the migration expert at the IRS, we multiply the published number of migrations by 0.9 to better approximate the actual number of taxpayers that are moving. Their recommendation is based on the assumption that 90% of exemptions claimed on tax returns actually represent a person, while the remaining 10% do not.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* Which counties show the highest rates of foreign out-migration?\n* Are taxpayers in New York leaving the country at the same rate as taxpayers in Nebraska?\n\n# Metrics\n\n* Population: Number of taxpayers who moved from the geography in question to somewhere outside of the US in the given year.\n\n# Filters\n\n* Year\n* AreaID (county, state, nation that taxpayers are migrating from)\n",
"title": "US Population Total Foreign Out Migration"
},
{
"name": "EMSI.us.Unemployment.Gender",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset provides unemployment figures by area and gender. Data is available by month from January 2010 through the latest month available in the version selected. Data is available at the national, state, MSA, and county levels. Unemployment is provided in both numbers and percentages, and is not seasonally adjusted.\n\nWhen requesting an MSA code as the area constraint in a query, the code must be prepended by MSA (e.g. for MSA code 10540, use 'MSA10540').\n\nThe BLS definitions for many of our metrics can be found at [the BLS unemployment FAQ](https://www.bls.gov/lau/laufaq.htm#Q03).\n\nOur twelve month averages are a less volatile metric to measure employment and unemployment, and are made by averaging the current month of employment/unemployment data with the previous eleven months' data.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How has total employment and unemployment changed in Idaho compared to California?\n* What is the ratio of male to female employment and unemployment in my area?\n* Has unemployment hit women more than men in the nation in 2020?\n* Are there more men or women in the labor force in Utah?\n\n# Metrics\n\n* Employment: The number of employed people in the area.\n* Unemployment: The number of unemployed people in the area.\n* TwelveMonthAverage: The average of the last 12 months' unemployment.\n* TwelveMonthEmpAverage: The average of the last 12 months' employment.\n* LaborForce: The number of employed and unemployed people in the area.\n* UnemploymentRate: Unemployment divided by the LaborForce.\n\n# Filters\n\n* Area (Nation, State, MSA, County)\n* Gender (Total, Male, Female)\n* Year\n* Month\n",
"title": "US Unemployment by Gender"
},
{
"name": "EMSI.us.Tuition",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows tuition and fees as reported by institutions in the United States to the NCES IPEDS program. Data is available from 2003 through the most recent year available. The data is reported and displayed here by individual institution.\n\nNo aggregations are performed for institutions, so only identity mappings are allowed along the institution dimension.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How much is in-state tuition for Stanford?\n* How much is out-of-state tuition for Stanford?\n* How much have prices for books and supplies increased for our institution over the years?\n* How does off-campus room and board compare among the various campuses of the University of Maryland?\n\n# Metrics\n\n* Tuition.Instate: In-state undergraduate tuition and fees for the institution.\n* Tuition.OutState: Out-of-state undergraduate tuition and feeds for the institution.\n* RoomBoard.OnCampus: On-campus room and board for the institution.\n* RoomBoard.OffCampus: Off-campus room and board for the institution.\n* BookSupplies.Latest: The cost of books and supplies for the institution.\n* CreditHourCharge.Graduate.InDistrict\n* CreditHourCharge.Graduate.InState\n* CreditHourCharge.Graduate.OutOfState\n* CreditHourCharge.Undergraduate.InDistrict\n* CreditHourCharge.Undergraduate.InState\n* CreditHourCharge.Undergraduate.OutOfState\n* Fees.Graduate.InDistrict\n* Fees.Graduate.InState\n* Fees.Graduate.OutOfState\n* Fees.Undergraduate.InDistrict\n* Fees.Undergraduate.InState\n* Fees.Undergraduate.OutOfState\n* Tuition.Graduate.InDistrict\n* Tuition.Graduate.InState\n* Tuition.Graduate.OutOfState\n* Tuition.Undergraduate.InDistrict\n* Tuition.Undergraduate.InState\n* Tuition.Undergraduate.OutOfState\n\n# Filters\n\n* Institution\n* Year\n",
"title": "US Tuition and Fees by Institution"
},
{
"name": "EMSI.us.Veterans_Affairs",
"versions": [
"2025.1",
"2024.3",
"2024.2",
"2024.4",
"2025.2"
],
"description": "# Description\n\nThis dataset shows, by institution, the number of GI Bill recipients as well as the amount of money received through the GI Bill. The data is available back to 2013 and is updated annually.\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* Which institutions in the US have the most GI Bill beneficiaries enrolled?\n* What institutions are seeing increasing enrollment of GI beneficiaries over time?\n\n# Metrics\n\n* GiBill: The number of GI Bill beneficiaries enrolled at the institution in the year selected.\n* YellowRibbon: The number of students at the institution receiving yellow ribbon scholarships in the year selected.\n* YellowRibbonAmount: Total yellow ribbon amount paid to the institution in the year selected.\n* GiBill911: The number of Post-9/11 recipients at the institution receiving tuition payments under the program in the year selected.\n* GiBill911TuitionFees: Total Post-911 amount paid to the institution for tuition and fees in the year selected.\n\n# Filters\n\n* Institution\n* Year\n",
"title": "US GI Bill Enrollments by Institution"
},
{
"name": "EMSI.us.PopulationMigration",
"versions": [
"base"
],
"description": null,
"title": null
},
{
"name": "EMSI.us.Occupation.Example",
"versions": [
"example"
],
"description": "# Description\n\nThis example dataset shows (by year, class of worker, occupation, and area) occupation data including job counts by place of work, openings and replacement figures, earnings, and job counts by place of residence. This example data is available only for class of worker 1, and for the years 2012, 2013, and 2014. Data is available at the nation, state, MSA, county, census tract, and ZIP code levels, for the following artificial geography set:\nA total of 54 distinct census tracts (not displayed for brevity);\nZIPs 60203, 60301, 60304, 60411, 60416, 60450, 60459;\nCounties 17015, 17031, 17037;\nMSA 169800;\nState 17;\nNation 0\n\nWhen requesting MSA codes or ZIP codes as the area constraint in a query, the code must be prepended by MSA or ZIP, respectively (e.g. for ZIP code 60203, use 'ZIP60203')\n\nThe geographies aggregate as follows:\n* the census tracts aggregate to the state total (as do the ZIPs and counties);\n* county 17031 consists of ZIPs 60203, 60301, 60304;\n* county 17037 consists of ZIPs 60411, 60416;\n* county 17015 consists of ZIPs 60450, 60459;\n* MSA 169800 consists of counties 17031, 17037 (county 17015 has no associated MSA);\n* state 17 consists of counties 17015, 17031, 17037;\n* nation 0 is equivalent to state 17\n\n# Questions answered by this dataset\n\n* How many pharmacist jobs were there in Illinois in 2012?\n* What are the median earnings for purchasing managers in various ZIP codes around Chicago? How about the 75th percentile earnings?\n* How many job openings where there due to replacement needs for marketing managers in Cook County in 2014?\n* How did the 90th percentile earnings for graphic designers in the Charleston-Mattoon metropolitan area change from 2012 to 2014?\n\n# Metrics\n\n* Jobs by place of work: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings.Average: Average hourly earnings for each job.\n* Growth: Growth is the net (non-negative) change for each year, and is calculated at the lowest level and aggregated up. For these reasons, it often differs from job change as calculated by subtracting an earlier year's jobs from a later year's job counts. The growth metric is a component of the openings metric (growth + replacements = openings).\n* Replacements: Replacements is the number of existing jobs that are estimated to have been vacated in the year. Replacements takes into account job openings that are created but that are not due to growth in the occupation.\n* Openings: Openings is the sum of growth and replacement, and it refers to the estimated number of job openings that exist both due to growth in the occupation and replacement needs in the occupation.\n* Jobs by place of residence: The number of people living in an area that hold a job within the occupation in question. (The total will not be the same as jobs by place of work at the nation, since this is only a sample dataset.)\n* Earnings (current occupation year) or HistoricalEarnings (2005 - current occupation year)\n * Percentile10: 10th percentile hourly earnings per worker.\n * Percentile25: 25th percentile hourly earnings per worker.\n * Percentile50: 50th percentile (median) hourly earnings per worker.\n * Percentile75: 75th percentile hourly earnings per worker.\n * Percentile90: 90th percentile hourly earnings per worker.\n * Median.Annual: 50th percentile (median) annual earnings per worker.\n * Percentile10.Annual: 10th percentile annual earnings per worker.\n * Percentile25.Annual: 25th percentile annual earnings per worker.\n * Percentile50.Annual: 50th percentile (median) annual earnings per worker.\n * Percentile75.Annual: 75th percentile annual earnings per worker.\n * Percentile90.Annual: 90th percentile annual earnings per worker.\n\n# Filters\n\n* Class of Worker: 1\n* Area (Nation, State, MSA, County, Census tract, ZIP code)\n* Occupation (5-digit SOC)\n* Year\n* Jobs by place of work: 2012 - 2014\n* Earnings: 2012 - 2014\n* Growth: 2012 - 2014\n* Replacements: 2012 - 2014\n* Jobs by place of residence: 2012 - 2014\n ",
"title": "US Occupation Example"
},
{
"name": "EMSI.ca.Unemp",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset contains the total number of unemployed persons by two-digit NAICS industry code. Data is monthly and goes back to 2001. The dataset contains data for the nation and the ten provinces. Statistics Canada does not at this time record data for the three territories, and territorial unemployment is not included in the national total.\n\n# Questions answered by this dataset\n\n* Which provinces have the fastest increase in unemployment rates?\n* Which industries have the lowest unemployment rates?\n* Which provinces are outperforming others in terms of dealing with unemployment?\n\n# Metrics\n\n* Unemployment: population of unemployed persons\n\n# Filters\n\n* Area: Nation or province\n* Industry (NAICS 1- to 2-digit code)\n* Year (2001-present)\n* Month\n",
"title": "CA Unemployment by Industry (Nation, Province)"
},
{
"name": "EMSI.ca.Locations",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset shows business location (establishment) counts by area, industry, and number of employees by size band. Data is available from June 2009 through the latest data available from CBP. Data is available down to the Census Subdivision level for geography, and down to 6-digit NAICS for industry.\n\nUsers should note that Canada business location data is only published every June and December. API constraints are such that all months must be displayed; however, data is only available for the months June and December. All other months should be disregarded.\n\n# Questions answered by this dataset\n\n* How many mining establishments are there in Alberta, and how many employees do they have?\n* How quickly is the number of small businesses in Toronto growing?\n\n# Metrics\n\n* LT: Total. Count of all businesses in all other categories (including Indeterminate). Equivalent to the sum of all other ## metrics.\n* LI: Indeterminate. Count of businesses for which a size could not be determined or disclosed.\n* LD: Determinate. Count of businesses with a known size.\n* L1: Number of establishments with 1-4 employees for the industry-area combination selected.\n* L5: Number of establishments with 5-9 employees for the industry-area combination selected.\n* L10: Number of establishments with 10-19 employees for the industry-area combination selected.\n* L20: Number of establishments with 20-49 employees for the industry-area combination selected.\n* L50: Number of establishments with 50-99 employees for the industry-area combination selected.\n* L100: Number of establishments with 100-199 employees for the industry-area combination selected.\n* L200: Number of establishments with 200-499 employees for the industry-area combination selected.\n* L500: Number of establishments with 500 or more employees for the industry-area combination selected.\n\n# Filters\n\n* Area (Nation, Province, Census Division, Census Subdivision)\n* Industry (2 to 6-digit NAICS)\n* Year\n* Month\n",
"title": "CA Business Establishments by Size Band"
},
{
"name": "EMSI.ca.WorkforceDemographics",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset shows working population by year, area, occupation, race, age, and gender. Data is available for the latest Census year. Data is available down to the Census Division, 5-digit NOC, 12 race categories, and 5 age bands.\n\n# Questions answered by this dataset\n\n* How many male 35-44 year old Korean pharmacists were employed in Ottawa in 2016?\n\n# Metrics\n\n* Jobs: The number of employed persons.\n\n# Filters\n\n* Area (Nation, Province/Territory, Census Division)\n* Occupation (4-digit NOC)\n* Race (12 race categories)\n* Age (5 age bands)\n* Gender\n* Year\n",
"title": "CA Workforce Demographics"
},
{
"name": "EMSI.ca.Staffing",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThe staffing dataset shows the occupational composition of an industry for a given class-of-worker/industry/area/year combination. This data can also be used to find all of the industries that employ a given occupation and the percentage of those industries accounted for by that occupation.\n\nHistorical staffing patterns are available from 2001 to the current occupation year and projected staffing patterns from the current occupation year to the last COPS projection year. 'Current occupation year' refers to the latest year for which data is available from LFS, and is not equivalent to the current calendar year. Staffing patterns are available for the Nation, Provinces, and Economic Regions.\n\n# Questions answered by this dataset\n\n* What occupations make up the Logging industry in Alberta?\n* What industries employ the most civil engineers?\n* Has the occupational makeup of the software publishers industry changed over time?\n\n# Metrics\n\n* Jobs: The number of industry jobs accounted for by the occupation\n\n# Filters\n\n* Class of Worker\n* Area (Nation, Province, Economic Region)\n* Industry (4-digit NAICS)\n* Occupation (4-digit NOC)\n* Year\n",
"title": "CA Staffing Patterns"
},
{
"name": "EMSI.ca.Industry",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset contains basic industry data for Canada. Historical data are available from 2001 to the current industry year and projected data from the current industry year to the last COPS projection year. Earnings data are only available through the current industry year. 'Current industry year' refers to the latest year for which more than six months of data is available from SEPH; it thus can lag behind the current calendar year. Data is available down to the Census Subdivision level.\n\n# Questions answered by this dataset\n\n* What is the fastest-growing industry in Winnipeg?\n* In what region of Nova Scotia are earnings for the finance industry the lowest?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings: Total earnings for the industry for 2001 through the current industry year.\n* EPW: Earnings per worker for 2001 through the current industry year. EPW = Earnings / Jobs.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, Province, Census Division, Census Subdivision)\n* Industry (2 to 4-digit NAICS)\n* Year\n",
"title": "CA Industry"
},
{
"name": "EMSI.ca.Commuting",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset contains commuting data that shows total commuting by place of residence, place of work, and gender. The data is published every five years as part of the Canadian Census. Place of residence and place of work are given down to the Census Subdivision level.\n\n# Questions answered by this dataset\n\n* How many workers who live in Langley are driving to Vancouver for work?\n* How far does the average Montreal worker have to travel to and from work?\n\n# Metrics\n\n* Population: The number of people commuting from a given place of residence to a given place of work.\n\n# Filters\n\n* Place of residence (nation, province/territory, Census Division, Census Subdivision)\n* Place of work (nation, province/territory, Census Division, Census Subdivision)\n* Gender\n* Year\n",
"title": "CA Commuting"
},
{
"name": "EMSI.ca.OntarioLabourFlows",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset shows the number of jobs by place of residence and place of work for a given industry in Ontario. Data is available for 2011 and each Census year thereafter. Data is available down to the Census Subdivision level and to 4-digit NAICS.\n\n# Questions answered by this dataset\n\n* How many people in the rail transportation industry live in Toronto? \n* How many people in the rail transportation industry work in Toronto?\n\n# Metrics\n\n* ResEmp: The number of people employed in the industry that live in the given area.\n* WorkEmp: The number of people employed in the industry that work in the given area.\n\n# Filters\n\n* Area (Province, Census Division, Census Subdivision)\n* Industry (2 to 4-digit NAICS)\n* Year\n",
"title": "Ontario Employment by Place of Work and Place of Residence"
},
{
"name": "EMSI.ca.completers",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset shows enrollments and graduates by year, institution, program, award level, and credential type. Data is available from 2009 through the most current year available from PSIS.\n\n# Questions answered by this dataset\n\n* How many students are enrolled in higher education institutions in Toronto?\n* What program has the most graduates among all institutions in Alberta?\n* Is the number of enrollees and graduates in Computer Science increasing? How quickly?\n\n# Metrics\n\n* Enrollments: The number of people enrolled in a program that matches the dimensions specified.\n* Completers: The number of graduates that matches the dimensions specified.\n\n# Filters\n\n* Institution\n* Program (2, 4, and 6-digit CIP)\n* Award level\n* Credential\n* Year\n",
"title": "CA Enrollments and Graduates by Institution"
},
{
"name": "EMSI.ca.Demographics",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset contains population demographics data by area, age group, and gender. Historical data is available from 2001 to the current industry year and projected data from the current industry year to 2038. Data is available down to the Census Division level. Age is available in 18 categories including a totals category.\n\n# Questions answered by this dataset\n\n* What is the gender breakout of Vancouver?\n* Has the millennial population in our area grown or declined over the past three years?\n\n# Metrics\n\n* Population: The number of people living in the area who match the given dimensions.\n\n# Filters\n\n* Area (Nation, Province, Census Division)\n* Age (18 age bands)\n* Gender\n* Year\n",
"title": "CA Population Demographics"
},
{
"name": "EMSI.ca.Occupation",
"versions": [
"2023.1",
"2022.3",
"2025.1",
"2024.3",
"2024.1",
"2023.3"
],
"description": "# Description\n\nThis dataset shows occupation data including job counts and earnings by class of worker, year, area and occupation. Historical data is available from 2001 to the current occupation year and jobs counts are projected from the current occupation year to eight years in the future. 'Current occupation year' refers to the latest year for which data is available from LFS, and is not equivalent to the current calendar year. Data is available down to the Census Subdivision level. Earnings percentile data is not available prior to 2011.\n\n# Questions answered by this dataset\n\n* How many pharmacist jobs were there in Winnipeg in 2018? How many are there projected to be in 2028?\n* What are the median earnings for chief executives in Vancouver? How about the 75th percentile earnings?\n* How many job openings will there be due to replacement needs for civil engineers in Canada between now and 2022?\n* Is the number of librarian jobs in Calgary growing or declining?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Earnings: Equivalent to Earnings.Average * Jobs (current year)\n* EPW: Earnings per worker for the current occupation year. EPW = current year earnings divided by current year jobs (EPW = Earnings / Jobs).\n* Earnings.Average: Average hourly earnings.\n* Earnings.Percentile10: 10th percentile hourly earnings per worker.\n* Earnings.Percentile25: 25th percentile hourly earnings per worker.\n* Earnings.Percentile50: 50th percentile (median) hourly earnings per worker.\n* Earnings.Percentile75: 75th percentile hourly earnings per worker.\n* Earnings.Percentile90: 90th percentile hourly earnings per worker.\n* Earnings.Median.Annual: Median annual earnings.\n* HistoricalEarnings.Percentile10: 10th percentile hourly earnings per worker.\n* HistoricalEarnings.Percentile25: 25th percentile hourly earnings per worker.\n* HistoricalEarnings.Percentile50: 50th percentile (median) hourly earnings per worker.\n* HistoricalEarnings.Percentile75: 75th percentile hourly earnings per worker.\n* HistoricalEarnings.Percentile90: 90th percentile hourly earnings per worker.\n* HistoricalEarnings.Median.Annual: Median annual earnings.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, Province, Census Division, Census Subdivision)\n* Occupation (all levels down to 4-digit NOC)\n* Year",
"title": "CA Occupation"
}
]
}GET /dataset/{dataset}
GET /dataset/{dataset}https://agnitio.emsicloud.com/meta/dataset/\{dataset}
https://agnitio.emsicloud.com/meta/dataset/\{dataset}Available versions of a specific dataset can be retrieved by adding dataset/<name> to the path
Code Examples
curl --request GET \
--url https://agnitio.emsicloud.com/meta/dataset/emsi.us.industry \
--header 'Authorization: bearer <access_token>'Response Examples
[
"2023.4",
"2024.1",
"2024.2",
"2024.3"
]GET /dataset/{dataset}/{version}
GET /dataset/{dataset}/{version}https://agnitio.emsicloud.com/meta/dataset/]{dataset}/\{version}
https://agnitio.emsicloud.com/meta/dataset/]{dataset}/\{version}More information about a particular version of a dataset can be requested with this endpoint. Returns detailed information including dimensions, metrics, and attributes.
Code Examples
curl --request GET \
--url https://agnitio.emsicloud.com/meta/dataset/emsi.us.industry/2024.2 \
--header 'Authorization: bearer <access_token>'Response Examples
{
"datasetName": "EMSI.US.Industry",
"versionName": "2024.2",
"attributes": {
"estabStartYear": "2004",
"minYearInclusive": "2001",
"estabYear": "2023",
"name": "Industry",
"path": "Industry",
"type": "dataset",
"currentJobsQuarters": "2022Q4,2023Q1,2023Q2,2023Q3",
"maxYearInclusive": "2034",
"description": "# Description\n\nEmsi's industry dataset contains information on industries back to 2001. Job counts are projected 10 years beyond the current calendar year; earnings are not projected. Available areas are nation, states, MSAs, counties, ZIPs, and tracts. Establishment data is not available for ZIP or tract level.\n\nWhen requesting MSA codes or ZIP codes as the area constraint in a query, the code must be prepended by MSA or ZIP, respectively (e.g. for ZIP code 98102, use 'ZIP98102')\n\n# Use Cases\n\n#### Questions answered by this dataset:\n\n* How many manufacturing jobs are projected to exist over the next 10 years in Calvert County, MD?\n* How many Insurance Carriers establishments are there in Nebraska?\n* What is the average wage for workers in hospitals in Ann Arbor, Michigan?\n* How much higher is the average wage today for workers in hospitals than it was in 2005?\n* How many health care jobs exist in the ZIP codes that make up downtown Seattle?\n* How do earnings for Registered Nurses differ in the various parts of Los Angeles County?\n\n# Metrics\n\n* Jobs: The number of occupied positions. This is not quite the same as workers because one worker might fill more than one position.\n* Wages: Total base earnings for the industry. Note that this figure is for the whole industry, not for the average worker in the industry.\n* Supplements: Total supplements for the industry. This figure is also for the whole industry, not for the average worker in the industry.\n* Establishments: The number of physical business locations.\n* Earnings: Total earnings for the industry. Sum of wages and supplements. This figure is also for the whole industry, not for the average worker in the industry.\n* EPW: Earnings Per Worker. The earnings figure (sum of wages and salaries) divided by the number of jobs in the industry.\n* SPW: Supplements Per Worker. Supplements for the industry divided by the number of jobs in the industry.\n* WPW: Wages Per Worker. Wages for the industry divided by the number of jobs in the industry.\n\n# Filters\n\n* Class of Worker\n* Area (Nation, State, MSA, County, ZIP, tract)\n* Industry (2 to 6-digit NAICS)\n* Year\n",
"earnYear": "2023",
"currentYear": "2023",
"countryCode": "us",
"datarun": "2024.2",
"displayName": "US Industry (Nation, State, MSA, County, ZIP, Census Tract)",
"releaseDate": "2024-04-12 07:25:42.424191Z",
"numAggPaths_AreaID": "4",
"levelsStored_AreaID": "[1:[1,2,3,4],2:[2,3,4],3:[1,2,3,4],4:[2,3,4]]"
},
"dimensions": [
{
"name": "ClassOfWorker",
"levelsStored": [
"2"
]
},
{
"name": "Area",
"levelsStored": [
"1",
"2",
"3",
"4"
]
},
{
"name": "Industry",
"levelsStored": [
"1",
"2",
"3",
"4",
"5",
"6"
]
}
],
"metrics": [
{ "name": "Jobs.2001" },
{ "name": "Jobs.2002" },
...
{ "name": "Jobs.2034" },
{ "name": "Wages.2001" },
...
]
}GET /dataset/{dataset_name}/{version}/{dimension}
GET /dataset/{dataset_name}/{version}/{dimension}https://agnitio.emsicloud.com/meta/dataset/\{dataset}/\{version}/\{dimension}
https://agnitio.emsicloud.com/meta/dataset/\{dataset}/\{version}/\{dimension}View the hierarchy of a particular dimension of a dataset. Dimensions are hierarchical taxonomies.
For example, Lightcast's standard U.S. Area dimension description is based on the FIPS system for describing states and counties. Results can be filtered based on aggregation path and level.
Code Examples
curl --request GET \
--url https://agnitio.emsicloud.com/meta/dataset/emsi.us.industry/2024.2/Area \
--header 'Authorization: bearer <access_token>'Response Examples
{
"name": "Area",
"hierarchy": [
{
"name": "United States",
"abbr": "US",
"child": "0",
"display_id_parent": "0",
"parent": "0",
"aggregation_path": "1",
"level_name": "1"
},
{
"name": "United States",
"abbr": "US",
"child": "0",
"display_id_parent": "0",
"parent": "0",
"aggregation_path": "3",
"level_name": "1"
},
{
"name": "Alabama",
"abbr": "AL",
"child": "1",
"display_id_parent": "0",
"parent": "0",
"aggregation_path": "1",
"level_name": "2"
},
{
"name": "Alabama",
"abbr": "AL",
"child": "1",
"display_id_parent": "0",
"parent": "0",
"aggregation_path": "3",
"level_name": "2"
},
{
"name": "Delaware",
"abbr": "DE",
"child": "10",
"display_id_parent": "0",
"parent": "0",
"aggregation_path": "1",
"level_name": "2"
},
...
]
}GET /dataset/{dataset}/{version}/{dimension}/search/{predicate}
GET /dataset/{dataset}/{version}/{dimension}/search/{predicate}https://agnitio.emsicloud.com/meta/dataset/\{dataset}/\{version}/\{dimension}/search/\{predicate}
https://agnitio.emsicloud.com/meta/dataset/\{dataset}/\{version}/\{dimension}/search/\{predicate}URL Parameters
| Name | Type | Description |
|---|---|---|
dataset | string | Specify dataset to query. |
version | string | Version of the dataset. |
dimension | string | Name of the dimension. |
predicate | string | The Search term. |
Code Examples
curl --request GET \
--url https://agnitio.emsicloud.com/meta/dataset/emsi.us.industry/2024.2/Area/search/Latah \
--header 'Authorization: bearer <access_token>'Response Examples
[
{
"name": "Latah",
"abbr": "ID",
"display_id": "16057",
"display_id_parent": "16",
"child": "16057",
"parent": "16",
"aggregation_path": "1",
"level": "3"
},
{
"name": "Latah, WA",
"abbr": "WA",
"display_id": "ZIP99018",
"display_id_parent": "53063",
"child": "ZIP99018",
"parent": "53063",
"aggregation_path": "1",
"level": "4"
},
{
"name": "Latah",
"abbr": "ID",
"display_id": "16057",
"display_id_parent": "MSA34140",
"child": "16057",
"parent": "MSA34140",
"aggregation_path": "2",
"level": "3"
},
{
"name": "Latah, WA",
"abbr": "WA",
"display_id": "ZIP99018",
"display_id_parent": "53063",
"child": "ZIP99018",
"parent": "53063",
"aggregation_path": "2",
"level": "4"
},
{
"name": "Latah",
"abbr": "ID",
"display_id": "16057",
"display_id_parent": "16",
"child": "16057",
"parent": "16",
"aggregation_path": "3",
"level": "3"
},
{
"name": "Latah",
"abbr": "ID",
"display_id": "16057",
"display_id_parent": "MSA34140",
"child": "16057",
"parent": "MSA34140",
"aggregation_path": "4",
"level": "3"
}
]