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What is Input-Output Modeling?
What is Input-Output Modeling?
Updated over a week ago

Input-Output modeling is a specialized branch of economics that has been practiced for over 100 years. I-O models are highly technical and can only be thoroughly explained in terms of advanced economic concepts and applied linear algebra.

What follows is a non-technical introduction to the theory of input-output modeling, as well as a list of the sources that go into Lightcast’s Input-Output model.

I-O Overview

I-O models represent the flow of money in an economy (area of study), primarily among industries. The interactions among industries in an economy can be arranged according to a particular accounting system called “input-output accounts.” A portion of the output (i.e. sales) of one industry will appear as the input (i.e. purchase) of other industries. These input-output accounts are used to build models that display the relationships between industries.

The main source of all I-O models in the United States is the Industry Economic Accounts produced by the Bureau of Economic Analysis (BEA). These tables provide a summary of how industries produce and consume commodities at the national level, showing which industries produce and consume which commodities, and in what amounts.

The tables outlined above are combined and customized for smaller regions of the country using each region’s own industry mix and information from other data sources. This process is called "regionalizing" the model, and it is crucial for being able to estimate how much of each industry’s inputs are obtained locally (within the region), and how much of each industry’s outputs are exported outside the region. All major I-O models today depend heavily on non-survey techniques that use various regional data sources, including the region’s industry mix, to estimate regional values.

The regionalization process results in a customized table for a region which shows what percentages of each industry’s inputs depend on the outputs from other industries. This table is the heart of any regional I-O model.Lightcast’s multi-regional model is a non-survey model that can analyze the transactions and ripple effects (multipliers) of multiple regions interacting with each other. Regions in this case are made up of collections of counties. If a user elects to create a multi-regional model using the tool, the relevant regional data are combined and the amount and kind of transactions that occur among the selected regions is also calculated.

Lightcast I-O Model Data Sources

To produce regional data, the Lightcast model relies on a number of internal and external data sources, mostly compiled by the federal government. What follows is a listing and short explanation of our sources.

  • Lightcast Data is produced from many data sources to produce detailed industry, occupation, and demographic jobs and earnings data at the local level. This information (especially sales-to-jobs ratios derived from jobs and earnings-to-sales ratios) is used to help regionalize the national matrices as well as to disaggregate them into more detailed industries than are normally available.

  • BEA Make and Use Tables (MUTs) are the basis for input-output models in the US. The make table is a matrix that describes the amount of each commodity made by each industry in a given year. Industries are placed in the rows and commodities in the columns. The use table is a matrix that describes the amount of each commodity used by each industry in a given year. In the use table, commodities are placed in the rows and industries in the columns. The MUTs are used in the Lightcast model to produce an industry-by-industry matrix describing all industry purchases from all industries.

  • BEA Gross Domestic Product by State (GSP) describes gross domestic product. The Lightcast model makes use of this data as a control and pegs certain pieces of the model to values from this dataset.

  • BEA National Income and Product Accounts (NIPA) cover a wide variety of economic measures for the nation. NIPA data is used in many of the Lightcast multi-regional processes as both controls and seeds.

  • BEA Local Area Income (LPI) contains a table called CA05 (Personal income and earnings by industry), which is used in several processes to help with place-of-work and place-of-residence differences, as well as to calculate personal income, transfers, dividends, interest, and rent.

  • BLS Consumer Expenditure Survey (CEX) reports on the buying habits of consumers along with some information as to their income, consumer unit, and demographics. Lightcast utilizes this data heavily in the creation of the national demographic by income type consumption on industries.

  • Census of Government’s (CoG) state and local government finance dataset is used specifically to aid breaking out state and local data that is reported in the MUTs. This allows Lightcast to have unique production functions for each of its state and local government sectors.

  • Census LODES Data is a collection of three datasets for the census block level for multiple years.

    • Origin-Destination (OD) offers job totals associated with both home census blocks and a work census block.

    • Residence Area Characteristics (RAC) offers jobs totaled by home census block.

    • Workplace Area Characteristics (WAC) offers jobs totaled by work census block.

All three of these are used in the commuting submodel to gain better estimates of earnings by industry that may be counted as commuting. This dataset has holes for specific years and regions. These holes are filled with ACS’s Residence County to Workplace County Commuting Work Flows dataset, described later.

  • Census Current Population Survey (CPS) is used as the basis for the demographic breakout data of the Lightcast multi-regional model. This set is used to estimate the ratios of demographic cohorts and their income for the three different income categories (i.e. wages, property income, and transfers).

  • Census American Community Survey Residence County to Workplace County Commuting Flows for the United States and Puerto Rico describes the amount of commuting jobs between counties. This set is used to fill in the areas where LODES does not have data.

  • Census American Community Survey (ACS) Public Use Microdata Sample (PUMS) is the replacement for Census’ long form and is used by Lightcast to fill the holes in the CPS data.

  • Oak Ridge National Lab (ORNL) County-to-County Distance Matrix (Skim Tree) “contains a matrix of distances and network impedances between each pair of county centroids via highway, railroad, water, and combined highway-rail paths.” Also included in this set are minimum impedances utilizing the best combination of paths. This is used in Lightcast’s gravitational flows model that estimates the amount of trade between counties in the United States.

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