Talent Supply Data

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Description

This 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

Questions answered by this dataset

How many mechanical engineers are employed in a particular metropolitan area?
What is the low estimate for the number of nurses employed for the whole nation?

Metrics

Supply.Low: The low employment estimate.
Supply.Mid: The mid employment estimate.
Supply.High: The high employment estimate.

Filters

Area (nation, metropolitan/non-metropolitan area)
Occupation (Emsi global occupations)

Core LMI Metadata

This dataset is offered via the Core LMI API. The metadata for this Core LMI API dataset is below.

Attribute	Description
Dataset ID	`EMSI.ww.Supply`
Dataset URL	`https://agnitio.emsicloud.com/meta/dataset/EMSI.ww.Supply/{version}`

Area Aggregation Path

The area aggregation path refers to the hierarchy that shows how data is aggregated, starting from the lowest-level geography and progressing to the highest-level geography.

The levels in the aggregation path include:

		Aggregation Path
		1	2	3	4
Level	1	Nation	Metropolitan US	Nation	Metropolitan US
	2	State	MSA	State	MSA
	3	County	County	County	County
	4	Zip Code	Zip Code	Census Tract	Census Tract

Datasets with lower granularity aggregates only up to Level 1

Versions

http://agnitio.emsicloud.com/meta/dataset/EMSI.ww.Supply/2021.2

Metrics

[
{
  "name": "Supply.Low"
},
{
  "name": "Supply.Mid"
},
{
  "name": "Supply.High"
}
]

Dimensions

[
{
  "name": "Area",
  "levelsStored": [
     "0",
     "1",
     "2"
  ]
},
{
  "name": "Occupation",
  "levelsStored": [
     "0",
     "1"
  ]
}
]

Attributes

{
"minYearInclusive": "2020",
"name": "Supply",
"type": "dataset",
"path": "Supply",
"maxYearInclusive": "2025",
"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",
"countryCode": "ww",
"displayName": "Global Talent Supply",
"currentYear": "2020",
"releaseDate": "2021-07-26 21:46:00.1667546Z",
"datarun": "2021.2"
}

Updated 11 days ago