Generating the annual IMPLAN data involves many raw data sources at varying levels of Industry and geographic detail, sometimes with differing definitional frameworks, and most with non-disclosures. Constructing a complete IMPLAN dataset therefore entails gathering data from the various sources, estimating non-disclosed values, disaggregating into finer Industry and geographic detail, adjusting for undercoverage (we're looking at you, CEW), and projecting lagged values, all the while controlling estimated values to known totals or other more accurate or recent data to maintain accuracy. IMPLAN adds value to publicly available data by:
1. Providing estimates for non-disclosed data
2. Providing estimates for non-census and non-survey years
3. Disaggregation into finer geographic scales
4. Disaggregation into finer Industry detail
5. Projecting lagged raw data
Additionally, IMPLAN provides inter-county trade flow data, for which there is no comprehensive raw data source. This is done using a calibrated and doubly-constrained gravity model. These trade flow data (along with commuting data) allow for multi-regional input-output (MRIO) analysis, which estimates the effects of economic activity in one region across any number of linked regions.
An Industry is a group of establishments engaged in the same or similar types of economic activity. The Production Function of an Industry in IMPLAN determines how an Industry will allocate Output to continue to operate. The Leontief Production Function is used in IMPLAN to dictate the ratio of inputs needed by each Industry in order to produce a unit of Output (in terms of dollar value).
Although the IMPLAN's production functions are based on the Bureau of Economic Analysis' (BEA) Benchmark I-O Tables (which are released every five years), the absorption coefficient values change somewhat year to year as they are forced to sum to new Industry Output and Value Added totals each year, as follows:
- We start with the latest BEA Benchmark I-O tables.
- We derive current Industry Output, Value Added (VA), and Final Demands.
- Using the Benchmark Byproducts Matrix and current Industry Output, we derive current Commodity Output.
- Multiplying the current Industry Output through the Absorption Matrix gives us a first approximation of the current USE matrix (i.e., current production functions).
- The columns of the current USE matrix are forced to sum to Industry Output less Value Added (column controls).
- Then the rows of the current USE matrix are forced to sum to the Commodity Output less Final Demand (row control totals). This will be the first time the proportions of the columns change.
- This is repeated until no further adjustment is needed.
Absorption coefficients (Intermediate Input) for a particular Industry will also vary across regions because the ratio of VA to Output varies from region to region, which forces the national gross absorption coefficients to adjust (so that the sum of an Industry's absorption coefficients plus that Industry's total VA = 1.0). The assumption is that the local Output and VA data are correct, and the national coefficients need to adjust to fit the local situation. Finally, applying the Regional Purchase Coefficients (RPCs) from the trade flow data to the gross absorption coefficients yields regional absorption coefficients by pulling out the imports (which also vary by region).
In IMPLAN, Total Industry Output (TIO) is the value of production that occurred during the year. It can be measured as the total value of purchases by intermediate and final consumers or as intermediate outlay plus value added. Most output data is from the BEA's Annual Industry Accounts and the Annual Survey of Manufacturers. Retail data come from the U.S. Census Bureau's Annual Survey of Retail Trade. Farm sector output estimates come from the USDA's NASS, ERS, and Census of Agriculture data sets. Output for the electricity generation, oil and gas extraction, and petroleum refining industries rely on Energy Information Administration (EIA) data. Other Industries use information from other various surveys and censuses.
VALUE ADDED DATA
VA has four components: Employee Compensation (EC), Proprietor Income (PI), Other Property Income (OPI), and Taxes on Production & Imports Net of Subsidies (TOPI). The estimation of OPI and TOPI relies on BEA state-level GDP data, as described in detail in this article. USDA ERS Agricultural Resource Management Survey (ARMS) data are also used for farm sector OPI and TOPI.
EMPLOYMENT AND LABOR INCOME DATA
The Bureau of Labor Statistics' (BLS) Census of Employment and Wages (CEW) data serve as the source for Wage and Salary Employment and Income estimates for most IMPLAN sectors. The CEW data do not fully cover some industries, in which cases we turn to other data sources. More details can be found here.
For Proprietor Employment and Income, we turn to the BEA's Regional Economic Accounts (REA) data. The BEA REA data also give us ratios of Employee Compensation to Wage and Salary Income, which we apply to our CEW Wage and Salary Income estimates to yield current Employee Compensation estimates. The BEA REA data are one year lagged relative to the IMPLAN data year and come at a much more aggregate level of Industry detail.
In IMPLAN, Institutions are entities that create final demand in an economy including: households, federal, state, and local government institutions, capital, inventory, and trade.
National household Personal Consumption Expenditures (PCE) are estimated using the BEA Benchmark I-O-to-PCE bridge tables and current PCE data from the BEA's Nation Income and Product Accounts (NIPA) tables. The spending patterns for each of the nine household income categories are based on the BLS Consumer Expenditure Survey (CES).
NIPA PCE Data
- Annual and current
- National level
- Only one spending pattern (i.e., not separated by income group)
- The NIPA table has 100 or so expenditure categories. The BEA benchmark I-O tables are used to distribute these expenditure categories among the IMPLAN Industries
- The PCE data are in purchaser prices, so margining is necessary to obtain producer prices for use in IMPLAN
BLS CES Data
- Annual but lagged
- National level
- Gives us the expenditures by income class; we control these to the NIPA PCE totals
Federal Government sales and expenditures data are estimated using NIPA control totals and the Benchmark I-O distribution, with the exception of the timber sales data, which are from the U.S. Forest Service.
Data for State and Local Government sales and revenues are obtained from a combination of the five-year Census of State and Local Government Finances, the Annual Survey of State and Local Government Finances, and the Annual Survey of State Government Tax Collections. State and Local Government expenditures are estimated using NIPA control totals and the Benchmark I-O distribution.
For manufacturing, the Annual Survey of Manufacturers provides the inventory data. Other Industries are derived from Benchmark I-O ratios.
IMPORTS AND EXPORTS
For the U.S., data for the foreign trade of commodities come from the Department of Commerce import and export trade data, which includes a concordance that maps the data to NAICS. Service trade is based on the BEA Benchmark I-O tables and controlled to current NIPA values. For sub-national regions, foreign imports are assumed to make up the same proportion of the region's demand as for the U.S. Similarly, foreign exports are assumed to make up the same proportion of the region's supply as for the U.S. For example, if the U.S. satisfies 80% of its sugar demands with foreign imports, then each state and county will also satisfy 80% of their sugar demands with foreign imports.
After foreign trade has been removed, IMPLAN's gravity model is used to estimate domestic trade of goods and services.
We use current-year NIPA investment data by aggregated Industry making the investment and allocate that to more detailed Industries according to the latest Benchmark I-O tables.
Video: IMPLAN Data Sources
BEA Article: Where do those numbers come from?
Written August 30, 2023