2013-2017 U.S. Data Release Notes


This article contains the U.S. Annual Release Notes for the 536 Industry set, years 2013-2017.  



The Bureau of Economics (BEA) recently developed a state-level personal consumption expenditures (PCE) data set.  The state-level data do not have the same level of commodity detail as the national-level data and are one year lagged relative to the national-level-data (and IMPLAN gdata); therefore, the national-level data are used to split the state-level expenditure categories into more detailed expenditure categories and to project the state-level data to the current IMPLAN data year.  Therefore, for each state, the values for the detailed expenditure categories (which are estimated using national ratios of each detailed expenditure as a proportion of its parent expenditure category) will sum to the state’s value for the parent category, thereby incorporating state-level information while retaining the necessary detail.  The states’ values for each expenditure category are then forced to sum to the national value for that expenditure category, effectively projecting the state-level data to the current data year.  Thus, we still rely heavily on the national-level data, while incorporating some state-level information.

While this change allows us to adhere more closely to state-level parent sector values (and state-level overall total values) from the BEA, which confers regional specificity, it is possible that in some cases the difference among states in parent category values could be due to just a few of the many detailed categories that make up that parent category rather than being a proportional difference among all of them, in which case using the national ratios to split the parent category value among the detailed categories will result in skewed values for the detailed categories.  Thus, there could be a trade-off in some cases – the gain of a better sum of detailed categories while introducing inferior estimates of some individual detailed categories.   


In July 2018, the BEA released the initial results of the 15th comprehensive, or benchmark, update of the National Income and Product Accounts (NIPAs), which occurs roughly every 5 years.  Details can be found here: https://apps.bea.gov/scb/2018/04-april/0418-preview-2018-comprehensive-nipa-update.htm.  These revisions have been incorporated into the 2017 IMPLAN data, so there may be some noticeable changes from previous years’ data sets.  For example, the reclassification of payments by the Federal Reserve banks to the U.S. government as dividend payments rather than as tax payments will reduce the Federal government’s tax receipts but will increase its dividend receipts, both of which will be reflected in the IMPLAN SAM and will affect the tax impact report.  Interest payments by State and Local governments have been revised upward, reflecting the higher imputed interest on plans’ claims on employers, which will be reflected in the IMPLAN SAM.  In addition, the change in measurement of government benefit pension plans results in reduced government expense on labor, which has the effect of increasing government surplus.


There have been changes to the composition of several Metropolitan Statistical Areas (MSAs), as defined by the U.S. Census Bureau.  These changes affect our MSA data product.  More details can be found here: https://www.census.gov/geographies/reference-files/time-series/demo/metro-micro/delineation-files.html.


Dayton-Kettering, OH

Poughkeepsie-Newburgh-Middletown, NY

Prescott Valley-Prescott, AZ

Twin Falls, ID

Yauco, PR



Dayton, OH       

Prescott, AZ      



Albany, GA

Ames, IA

Baton Rouge, LA

Beaumont-Port Arthur, TX

Birmingham-Hoover, AL

Bismarck, ND

Blacksburg-Christiansburg-Radford, VA

Bloomington, IL

Carbondale-Marion, IL

Champaign-Urbana, IL

Charleston, WV

Charlotte-Concord-Gastonia, NC-SC

Charlottesville, VA

Cincinnati, OH-KY-IN

Clarksville, TN-KY

Columbia, MO

Columbus, GA-AL

Corpus Christi, TX

Dallas-Fort Worth-Arlington, TX

Des Moines-West Des Moines, IA

Duluth, MN-WI

Durham-Chapel Hill, NC

Fayetteville, NC

Fayetteville-Springdale-Rogers, AR-MO

Fort Wayne, IN

Gainesville, FL

Grand Island, NE

Gulfport-Biloxi-Pascagoula, MS

Hagerstown-Martinsburg, MD-WV

Hattiesburg, MS

Jackson, MS

Jackson, TN

Kalamazoo-Portage, MI

Knoxville, TN

Lafayette-West Lafayette, IN

Lansing-East Lansing, MI

Longview, TX

Louisville/Jefferson County, KY-IN

Manhattan, KS

Mayagüez, PR

Memphis, TN-MS-AR

Minneapolis-St. Paul-Bloomington, MN-WI

Mobile, AL

Monroe, LA

Morristown, TN

Nashville-Davidson--Murfreesboro--Franklin, TN

New York-Newark-Jersey City, NY-NJ-PA

Panama City, FL

Peoria, IL

Pocatello, ID

Ponce, PR

Rapid City, SD

San Angelo, TX

Shreveport-Bossier City, LA

Sioux City, IA-NE-SD

Spartanburg, SC

Spokane-Spokane Valley, WA

Springfield, MA

Sumter, SC

Terre Haute, IN

Toledo, OH

Tuscaloosa, AL

Virginia Beach-Norfolk-Newport News, VA-NC

Walla Walla, WA

Warner Robins, GA

Washington-Arlington-Alexandria, DC-VA-MD-WV

Wausau, WI

Wichita, KS



These changes affect IMPLAN’s (Covered Employment and Wages) CEW data product, as well as growth rates based on CEW data, which are used to project some lagged raw data elements to the current IMPLAN data year.  For more details, please see the Census Bureau’s NAICS website: https://www.census.gov/eos/www/naics/.

  • 4 existing NAICS codes underwent title changes:
    • NAICS codes 7213, 72131, and 721310 changed titles from “Rooming and boarding houses” to “Rooming and boarding houses, dormitories, and workers’ camps”
    • NAICS code 33522 changed title from “Major appliance manufacturing” to “Major household appliance manufacturing”
  • 43 NAICS codes from the 2012 scheme were lost and 30 new NAICS codes were added
    • Creating a net loss of 13 NAICS codes, as listed below.  NAICS categories that only underwent a code change, with no change to the title, are bolded and italicized:
    • The following 43 2012 NAICS codes were lost:
    • 21111 - Oil and gas extraction
    • 211111 - Crude petroleum and natural gas extraction
    • 211112 - Natural gas liquid extraction
    • 212231 - Lead ore and zinc ore mining
    • 212234 - Copper ore and nickel ore mining
    • 333911 - Pump and pumping equipment manufacturing
    • 333913 - Measuring and dispensing pump manufacturing
    • 335221 - Household cooking appliance manufacturing
    • 335222 - Household refrigerator and home freezer mfg.
    • 335224 - Household laundry equipment manufacturing
    • 335228 - Other major household appliance manufacturing
    • 4521 - Department stores
    • 45211 - Department stores
    • 452111 - Department stores, except discount
    • 452112 - Discount department stores
    • 4529 - Other general merchandise stores
    • 45291 - Warehouse clubs and supercenters
    • 452910 - Warehouse clubs and supercenters
    • 45299 - All other general merchandise stores
    • 452990 - All other general merchandise stores
    • 454111 - Electronic shopping
    • 454112 - Electronic auctions
    • 454113 - Mail-order houses
    • 51221 - Record production
    • 512210 - Record production
    • 51222 - Integrated record production and distribution
    • 512220 - Integrated record production and distribution
    • 5171 - Wired telecommunications carriers
    • 51711 - Wired telecommunications carriers
    • 517110 - Wired telecommunications carriers
    • 5172 - Wireless telecommunications carriers (except Satellite)
    • 51721 - Wireless telecommunications carriers (except Satellite)
    • 517210 - Wireless telecommunications carriers (except Satellite)
    • 53222 - Formal wear and costume rental
    • 532220 - Formal wear and costume rental
    • 53223 - Video tape and disc rental
    • 532230 - Video tape and disc rental
    • 53229 - Other consumer goods rental
    • 532291 - Home health equipment rental
    • 532292 - Recreational goods rental
    • 532299 - All other consumer goods rental
    • 541711 - Research and development in biotechnology
    • 541712 Other physical and biological research
    • The following 30 new 2017 NAICS codes were gained:
      • 21112 - Crude Petroleum Extraction
      • 211120 - Crude Petroleum Extraction
      • 21113 - Natural Gas Extraction
      • 211130 - Natural Gas Extraction
      • 212230 - Copper, Nickel, Lead, and Zinc Mining
      • 333914 - Measuring, Dispensing, and Other Pumping Equipment Manufacturing
      • 335220 - Major Household Appliance Manufacturing
      • 4522 - Department Stores
      • 45221 - Department Stores
      • 452210 - Department Stores
      • 4523 - General Merchandise Stores, including Warehouse Clubs and Supercenters
      • 45231 - General Merchandise Stores, including Warehouse Clubs and Supercenters
      • 452311 - Warehouse Clubs and Supercenters
      • 452319 - All Other General Merchandise Stores
      • 454110 - Electronic Shopping and Mail-Order Houses
      • 51225 - Record Production and Distribution
      • 512250 - Record Production and Distribution
      • 5173 - Wired and Wireless Telecommunications Carriers
      • 51731 - Wired and Wireless Telecommunications Carriers
      • 517311 - Wired Telecommunications Carriers
      • 517312 - Wireless Telecommunications Carriers (except Satellite)
      • 53228 - Other Consumer Goods Rental
      • 532281 - Formal Wear and Costume Rental
      • 532282 - Video Tape and Disc Rental
      • 532283 - Home Health Equipment Rental
      • 532284 - Recreational Goods Rental
      • 532289 - All Other Consumer Goods Rental
      • 541713 - Research and Development in Nanotechnology
      • 541714 - Research and Development in Biotechnology (except Nanobiotechnology)
      • 541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)


Significant revision to the estimation method for IMPLAN farm sectors 6, 10, and 14 

Data on ERS annual cash receipts includes several commodities in the miscellaneous crops and miscellaneous animals sectors that are not classified as "miscellaneous" according to NAICS or according to IMPLAN. In previous years, IMPLAN has attempted to "disentangle" these miscellaneous categories, but as ERS includes an increasing number of commodities in those categories with each passing year, our ability to disentangle this data deteriorates. In response to this, we began using a new method for these three sectors in the 2016 IMPLAN data set, beginning with data from the Census of Agriculture and projecting it to the IMPLAN reference year. Using this data has allowed us to better match estimates from other sources and to use more precise data in estimating county-level production in these sectors.

Improved wage and salary employment and income methodology 

When developing the 2014 IMPLAN data set, we spoke with the BEA about the difference between the BEA’s REA SA27 and the BLS’s CEW data for several industries in which there is a significant difference, but which BLS does not acknowledge any coverage gap. These industries include: fishing/hunting/trappingmembership organizations, and private education. CEW data does acknowledge coverage gaps in military, private households, farms, and railroads. The BEA informed us that they do adjust estimates for these industries due to coverage gaps. Thus, in that year, we incorporated corresponding adjustments for 4 sectors: religious organizationscommercial fishingprivate higher education, and private households. After the release of the 2015 data set, we were made aware that there is also a discrepancy between the two data sources for the Agriculture and Forestry Support Services sector. Accordingly, beginning with the 2016 data release, we've included this sector in the adjustment process.   

Slight modification to commercial fishing’s and private colleges’ adjustment ratio process and addition of adjustment ratio for support activities for agriculture and forestry

When estimating wage and salary employment, there are a number of sectors that the BLS’s CEW program does not completely cover. IMPLAN correspondingly adjusts those values upward according to the ratio of the previous year’s CEW employment to the BEA’s REA wage and salary employment (which is lagged one year) for that sector. The affected sectors include commercial fishingprivate higher educationreligious organizations, and private households. Since REA sectoring is more aggregate than CEW sectoring for some of these sectors, we had been calculating the adjustment ratios at the aggregate sector level and applying them to each of the less aggregate IMPLAN sectors that belong to the aggregate REA sector. With the 2015 IMPLAN release, we took a more nuanced approach to calculating the adjustment ratios for religious organizations so that they would more accurately reflect the specific sector being undercovered. Beginning with the 2016 release, we now do the same for commercial fishing and private education. We also incorporated an adjustment ratio for support activities for agriculture and forestry

Improvement to the BEA Benchmark ratios used to estimate annual EC for some farm sectors  

Prior to the 2016 release, the ratios being used to estimate employee compensation for 4 farm sectors were calculated based on assigning the entirety of NAICS code 111191 to grain farming and the entirety of NAICS code 111336 to fruit farming. However, NAICS code 111191 represents combined oilseed and grain farming, and NAICS code 111336 represents combined fruit and tree nut farming. Beginning with the 2016 release, we now assume an even 50:50 split between each of the two IMPLAN sectors that correspond to each of these combination NAICS codes. This has increased the benchmark employee compensation per wage and salary worker for oilseed farming and decreased the benchmark employee compensation per wage and salary worker for the grain farming and tree nut farming sectors.

Improved classification of government-owned establishments of NAICS code 488

Prior to the 2016 release, we had been classifying this activity under administrative government. The choice of which government enterprise or institution to classify a given activity under is based on the BEA Benchmark Make table, the latest of which combines NAICS codes 487 and 488. It also classifies the combined activity under Other State and Local Government EnterprisesWe previously treated these two NAICS codes individually, classifying the former under Other State and Local Government Enterprises (in line with the BEA Benchmark Make table) and the latter under administrative government. Without evidence to support this separate treatment, we decided to better align our data with the BEA Benchmark by treating the two NAICS codes in the same manner. Therefore, beginning with the 2016 release, IMPLAN data will now classify NAICS code 488 under Other State and Local Government Enterprises.

Improved method for distributing state-level State and Local (S/L) Government hospital services sales to counties

Prior to the 2016 release, all state-level non-stumpage sales by S/L Government were distributed to counties based on administrative government employment. However, this resulted in some cases of sales of hospital services in counties where there were no state government-owned or local government-owned hospitals. Thus, beginning with the 2016 release, we distribute the sales of hospital services based on CEW data for state government-owned and local government-owned hospitals.



New Household Income Categories

We updated our household income categories to reflect the categories of the BLS Consumer Expenditure Survey. The new categories are:

                Households LT15k

                Households 15-30k

                Households 30-40k

                Households 40-50k

                Households 50-70k

                Households 70-100k

                Households 100-150k

                Households 150-200k

                Households 200k+


Improved Disclosure Routines for CEW and CBP

NAICS sector 10 (All Industry Total) includes the values in NAICS sector 99 (Unclassified). Previously, we had not included NAICS sector 99 in our CEW and CBP estimation processes. The consequences of this were:

  1. If all other 2-digit level NAICS codes were disclosed, the sum of our 2-digit NAICS sectors’ values would not equal the All Industry Total, since we did not report NAICS sector 99.
  2. If any non-99 2-digit level NAICS codes were non-disclosed, we would give them a first estimate and then control them to the All Industry Total. This resulted in them being over-estimated since the All Industry Total includes the sector 99 value.

Thus, we are now treating NAICS sector 99 as any other; that is, we are giving it an estimate if non-disclosed and not distributing its value amongst other non-disclosed sectors. This means that all NAICS sectors in all places will roll up all the way to the All Industry Total and our non-disclosed sectors will no longer be overestimated in those cases mentioned above.

Improved Estimates of Commercial Fishing Output

 It was brought to our attention that NOAA’s U.S. value for fish production is the sum of NOAA’s state values, but NOAA’s state values do not include all states for which there is BLS CEW employment in fisheries. Therefore, the NOAA U.S. total value is not a true total – i.e., it does not include the value of the output in states for which NOAA does not report production values. Thus, we have developed a methodology for estimating a U.S. total that includes estimates for all states for which there is BLS CEW employment, not just those for which NOAA reports a value. This change will lead to an increase in fish output in most states.

Name and/or Code Changes or Corrections to Counties or County Equivalent Entities

  • Wade Hampton Census Area, Alaska (02-270):
    Changed name and code to Kusilvak Census Area (02-158), effective July 1, 2015.
  • Shannon County, South Dakota (46-113):
    Changed name and code to Oglala Lakota County (46-102), effective May 1, 2015.
    • *Special Note* On the regions map, you will be unable to select Shannon County SD, and will only be able to use the search box to find Shannon County for data years 2014 and prior. Conversely, you can only find Oglala Lakota County SD, in the search box and map for data years 2015 and beyond.

Improvement in ZIP Code Railroad Sector Data

Since the 2012 Data Year, we have incorporated county-level railroad employment data from the official Railroad Retirement Board website. As of the 2015 Data Year, we also now incorporate the ZIP Code-level data from this same source as an enhancement to our ZIP Code data.

Improvement in New Construction Output

It came to our attention in Data Year 2015 that the NIPA “Private fixed investment in structures” figure in NIPA Table 5.4.5. includes net purchases of used structures and brokers' commissions and other ownership transfer costs. Upon further investigation, we learned the following:

  • Estimates of private fixed investment (PFI) on Table 5.4.5 include expenditures by private businesses on new nonresidential structures and on net purchases of used nonresidential structures from governments (line 34). Similarly, estimates of government investment in structures on Table 3.9.5 include expenditures by governments on new nonresidential structures and on net purchases of used nonresidential structures from private businesses. Each unit of used nonresidential structures included in estimates of net purchases by private businesses on Table 5.4 is also included in estimates of net purchases by governments on Table 3.9. These transactions offset and, therefore, have no combined effect on GDP – but they are necessary to keep track of the stocks of structures in each sector over time.
  • Brokers’ commissions are included in estimates of net purchases of used nonresidential structures. These commissions represent the value of purchased services that add to and are reflected in the value of the structures being bought and sold.
  • For the federal government, the source data used to estimate net purchases of used nonresidential structures is administrative data from various federal agencies, primarily from the Government Services Administration.

Therefore, we no longer control to the “Private fixed investment in structures” value, but rather to the "Private fixed investment in new structures" value. This will have the effect of reduction Output for the new construction sectors, all else equal.

Improvement in Farm Value Added

In Data Year 2015, we updated our source data and method for forecasting lagged state GDP data for farm sectors (IMPLAN sectors 1-14):

  • At the state level, we use growth in total farm output rates to project value added growth.  We have empirical sources for current-year agricultural output by state.  This has the result of better approximating future BEA estimates of farm value added.  Previously, we used only EC, which was extrapolated from REA total farm EC and current-year output estimates.
  • These state-level projections are then controlled to the national projections.

In Data Year 2015, we also incorporated USDA ERS Agriculture Resource Management Survey (ARMS) data to estimate components of value added by commodity at the national and state levels, which are then used to distribute the projected BEA “Farm” GDP data amongst the 14 IMPLAN farm sectors.

Modification to Farm Output

We have opted to not control farm sector estimates to BEA for several reasons. The BEA release of farm cash receipts was released after we produced agricultural estimates. Additional discussions with BEA revealed that they primarily use ERS data, which is one of IMPLAN’s primary sources, so controlling to BEA estimates adds relatively little value. Furthermore, BEA’s commodity-level estimates are for cash receipts, which excludes crops put into inventory and home consumption, both of which drive intermediate expenditures. However, one benefit of controlling to BEA that we wanted to keep is that it theoretically corrects for ERS’ tendency to overestimate the output of the Miscellaneous Crops sector; therefore, we implemented an adjustment factor based on the ratio of 2007 BEA Benchmark output to 2007 ERS output.

 New National GDP Controls

The BEA industry series releases estimates for national GDP by industry at approximately the 3-digit NAICS level for the IMPLAN reference year (that is, it releases estimates of 2015 GDP in time for the production of 2015 IMPLAN data). We incorporated these GDP controls since they do appear to be consistent with REA data, which we use for (lagged) state GDP, and we have no better alternative for national GDP besides our own predications. Also, the GDP forecasts can take better account of changes that do not involve EC. For example, a decline in gasoline prices will reduce output and profits, but likely will not cause a decline in EC of nearly the same rate.


New methodology for the Oil & Gas Extraction sectors (sectors 20 and 21):  Our source for Output for sectors 20 (Extraction of natural gas and crude petroleum) and 21 (Extraction of natural gas liquids) had been the U.S. Energy Information Administration (EIA).  However, upon investigating some sizable differences between EIA values and BEA values, we discovered that the EIA data represent commodity output, while the BEA figures capture industry output.  However, we cannot use BEA figures directly because they are lagged a year and they do not have the same level of industry detail as IMPLAN (in this case, the two extraction sectors are combined as one in the BEA data).  Thus, our new methodology involves using the ratio of “Extraction of natural gas and crude petroleum” output to “Extraction of natural gas liquids” output from the latest Economic Census to split out the lagged BEA value into the two IMPLAN sectors, and then project the two BEA figures using the EIA data.

County Changes: Bedford City, Virginia (State FIPS 51, County FIPS 515) changed from independent city status to town status and was added to Bedford County (State FIPS 51, County FIPS 019), effective July 1, 2013.

Improved Employment and Labor Income Methodology:  We inquired with the Bureau of Economic Analysis (BEA) about the difference between their Regional Economic Accounts (REA) state-level wage and salary employment (SA27) and the Bureau of Labor Statistic (BLS)’s Census of Employment and Wages (CEW) wage and salary employment counts for the few industries where there is a significant difference but which the BLS does not acknowledge any coverage gap – Fishing/Hunting/Trapping, Membership Organizations, and Private Education (the BLS does acknowledge a coverage gap with military, private households, farms, and railroads).  We were informed that BEA upwardly adjusts the employment and income estimates for these sectors due to coverage gaps.

  • The adjustment for Membership Organizations is for religious organizations, so we now adjust this IMPLAN sector according to state-specific REA/CEW ratios.
  • The Small Business Job Protection Act of 1996 exempted a lot of employees in shellfishing and finfishing from unemployment insurance coverage.  This adjustment affects GA, RI, LA, TX, OR, and MA.  Thus, we now adjust this IMPLAN sector according to state-specific REA/CEW ratios as well.
  • There is an adjustment for Private Education, which applies primarily to student workers at universities.   Thus, we now adjust this IMPLAN sector according to state-specific REA/CEW ratios as well.
  • There is an adjustment for Private Households.  Thus, we now adjust this IMPLAN sector according to state-specific REA/CEW ratios as well.  

Commuter Flows:  We obtained new Journey-To-Work data from the 2009-2013 American Community Survey and have incorporated it into the 2014 IMPLAN data.

Incorporating BEA data into the farm sectors:  We added a control of the sum of our state-level estimates to BEA’s national estimates for the value of crop sales.   ERS, which is BEA’s primary initial source of cash receipts by commodity, estimates include adjustments for Commodity Credit Corporation (CCC) loans, and do not account for home consumption or inventory, all of which need to be addressed when estimating output based on cash receipts.   We obtain estimates value of production for certain agricultural products from NASS; these values don’t require adjustments for CCC or inventory.  BEA adds the value of intra-state livestock sales to its estimates, which should be included in output, so this tends to increase our estimates.  We do not control individual state values to BEA values since we generally can obtain and process more current ERS and NASS data before they are incorporated into BEA’s data.  Although BEA’s “other crops” category includes sugar cane, BEA does not produce any detailed estimate of sugarcane output, which is well-measured by NASS and ERS, so we do not apply the control to that IMPLAN sector.  The Department of Agriculture’s National Agricultural Statistics Service, Economic Research Service, and the Census of Agriculture continue to be our primary data sources for estimating state- and county-level agricultural output.

Improved methodology for estimating proprietor employment:  In 2014, we developed a method to estimate Wage and Salary Employment separately from Proprietor Employment.  As part of this process, we incorporated new data sources (Census Non-Employer Statistics and CBP Organizations with Employees by Ownership Type) and more involved processes for estimating the proprietor count. 



    • Incorporates new Bureau of Economic Analysis (BEA) Benchmark input-output  (I-O) tables, which were released in 2014.
    • Reflects latest methodological revisions to BEA National Income and Product Accounts.
    • Enhanced use of demographic data from the Census Bureau's American Community Survey on county- and zip code-level estimates of household income distributions.
    • Includes data from the latest BEA Regional Economic Accounts, the 2012 Economic Census, the 2012 Census of Agriculture, Bureau of Labor Statistics QCEW dataset, preliminary 2012 Commodity Flow Survey results, among many more.

Detailed Release Notes

New data source for Railroad Employment: The 2013 data year is the first year we incorporated independent railroad employment data (from the U.S. Railroad Retirement Board).

New Census of Agriculture: The Census of Agriculture is released every 5 years; thus, there may be some sizeable changes in some farm sectors in some regions. The 2012 Census of Agriculture was released in 2014 and is incorporated into the 2013 IMPLAN data set. Census of Agriculture data are used to disclose data missing from USDA Economic Research Service (ERS) and National Agricultural Statistics Service (NASS) data sources.

NASS Data for Agriculture Output: We use NASS sales and production data as a supplement to ERS sales data, where available, since the ERS sales data may omit inventory changes, home consumption, and production used in the production process of another agricultural good (e.g. hay used to feed animals). Large differences between the datasets tend to occur with products that are likely to be added or removed from inventory (grains) or consumed on a farm (hay, meat).

New BEA Benchmark: The BEA's Benchmark I-O tables are also released every 5 years. These tables set the course for IMPLAN's sectoring scheme, production functions, by-product coefficients, and market share coefficients. The 2007 Benchmark was released in 2014 and incorporated into the 2013 IMPLAN data set. This will cause changes across many sectors and regions.

New Household by Income Group Counts: Beginning with the 2013 IMPLAN data set, we now incorporate raw data for the counts of households by income group at the county and zip code levels. We previously used more aggregate-level distributions.

Foreign Trade of NAICS 115 (Support Activities for Agriculture and Forestry): As of the 2013 data year, the Department of Commerce recoded all commodities previously assigned to NAICS Code 115 to other NAICS codes. This simply means that this commodity is now being correctly classified as a service. As such, we obtain the export/import values from the BEA Benchmark.

New Sectoring Scheme: As of 2013 data year, we have expanded the IMPLAN sectoring scheme from 440 sectors to 536 sectors. As a result, you will likely notice a change in the Top Ten lists in the Model Overview Screen. For example, when looking at the Top Ten Industries by Employment, sector 413 - Food services and drinking places - appeared near the top of this list in most regions. In the new sectoring scheme, this sector has been split into 3 specific types of food and drinking places:

    • 501 – Full-service restaurants
    • 502 – Limited-service restaurants
    • 503 – All other food and drinking places

If you were to sum the Employment of these three sectors they would likely still appear in the Top Ten Industries by Employment; however, each of these sectors individually is now smaller, and thus may not appear in the Top Ten list.

Another result of splitting sectors is that ratios like output per worker, income per worker, etc. may differ for the more-detailed sector from the previously more-aggregate sector since the more-aggregate sector is a weighted average of its more-detailed parts. For example, suppose sector 501 has a very high income per worker, while sectors 502 and 503 have low income per worker. This would result in the old sector 413 having an income per worker ratio somewhere in the middle – not too high, not too low. Comparing 501 to the old 413, you would see an increase in income per worker, while comparing sector 502 or 503 to the old 413, you would see a decrease in income per worker. These changes do not necessarily reflect a change in workers' earnings, but rather just reflect a more-detailed allocation of the workers into more specific sectors, each of which has its own earnings rate.

2013 Comprehensive Revisions to the BEA's National Income and Product Accounts (NIPA):
The 2013 comprehensive revision to the NIPA Accounts defines new kinds of investments:
"Recognizing expenditures by business, government, and nonprofit institutions serving households for research and development (R&D) as fixed investment, thus improving BEA's measures of fixed investment and allowing users to better measure the effects of innovation and intangible assets on the economy."
Since investment is not current accounts spending – i.e., not part of an industry's production function - output, employment, payroll and spending activity for this investment must be removed from the industry and moved to sector 456 "Scientific research and development services". This essentially doubles employment in the 2013 sector 456 when compared to the corresponding 2012 employment (sector 376 in the 440 sector scheme).

A similar new redefinition was made for sector 446 (Lessors of nonfinancial intangible assets):
"Recognizing expenditures by private enterprises for the creation of entertainment, literary, and artistic originals as fixed investment, further expanding BEA's measures of intangible assets." 1
Creation of new intangible assets in a given year is small compared to the history of such asset creation, so the redefinition's effect on employment for sector 446 is relatively small.


Reclassified several sectors from government enterprise to administrative government
Federal government, state government and local government-owned establishments in several sectors were reclassified from government enterprise to administrative government and vice-versa. The reclassifications were done to maintain consistency with the BEA's Benchmark I-O accounts. Manufacturing was reclassified to administrative government for all government types.


2013 R2 Release Notes

March 3, 2015

The second release of 2013 data reflects the most recent (2012) 5-year Census of Governments, which improves estimates of government spending and revenue. The Census of Governments had not been published early enough to integrate into the first release of 2013 data, which took place in December 2014.

The second release of 2013 data also reflects the most recent (2012) Commodity Flow Survey, which allows IMPLAN to use updated calibration data in its trade modeling system (i.e., the gravity model). The 2012 Commodity Flow Survey had not been published early enough to integrate into the first release of 2013 data, which took place in December 2014.

The initial download of raw zip-code-level CBP employment data were missing roughly 1/3 of the raw data. IMPLAN's zip-code estimation process uses disclosed CBP data as the preferred distributor for county-level employment and compensation estimates, with other variables (e.g., population) serving as back-up distributors. Thus, county-level employment and compensation in those industries that were missing CBP data (generally, NAICS codes > 51) were still allocated to zip-codes, although according to these backup distributors rather than according to disclosed CBP data. The second release of the 2013 data makes use of the full set of raw CBP data.

Finally, the second release of 2013 also corrects an error in the estimation of state- and county-level OPI. This issue affected only some sectors in some places, and resulted in over- or under-stating OPI, and, consequently, Output and Value-Added.  Because this issue did not affect Labor Income or Intermediate Expenditures, it did not affect impact analysis results except in the case of contribution analyses where Total Industry Output is used as the Event value.


2013 R3 Release Notes

June 30, 2015   

The third release of the 2013 data corrects an error in the Employment estimates of the farming sectors (sectors 1-14). This update does not affect Output, Total Value-Added, or any component of Value-Added for these sectors, nor does it affect commuting flows or trade flows for these sectors. Because this issue did not affect Labor Income or Intermediate Expenditures, it did not affect impact analysis results except in the case of contribution analyses where Total Industry Output is used as the Event value. This error can be attributed to a manual spreadsheet error.

The second release of 2013 data inadvertently omitted the Rail Transportation data for D.C.; thus, the third release of the 2013 data reinstates those data; this will cause some shifting of transportation sector values due to forcing all transportation sectors to the BEA REA parent transportation sector values and controlling states to the U.S. and counties to their respective state values.

If you wish to update your data with the newest release, please call us at 651-439-4421 or e-mail us at support@implan.com. We sincerely apologize for any trouble these errors may have caused you and will be implementing preventive measures to avoid such miscalculations in the future.


Historical U.S. Industries, Conversions, & Bridges

Written August 30, 2023