2022 U.S. Data Release Notes
New MSA List Released
As part of the 2020 Decennial Census, the U.S. Census Bureau has released its latest delineations for the nation’s Metropolitan Statistical Areas (MSAs). The changes include 16 new MSAs and 15 lost MSAs, for a net gain of one MSA. 37 MSAs lost a single county, 30 MSAs gained one county, and 5 MSAs gained more than one county. The Lubbock, TX MSA and the Mayagüez, PR MSA both gained three counties, while the Bend, OR, Ponce, PR, and Louisville/Jefferson County, KY-IN MSAs each gained two counties.
2020 Decennial Census ZCTA Land Area and ZCTA-to-County Relationship File Incorporated
The 2020 Decennial Census data on ZCTA land area have been released, allowing us to incorporate these new values into our annual processes for IMPLAN’s Zip-Code Data. In addition to being an interesting data point in the Area Demographics section of IMPLAN, zip-code-level land area values are also used in the estimation of a small set of other zip-code-level values and to determine which county each zip-code belongs to, which is a critical factor in the geographic balancing of the zip-code data.
CEW Data Released in 2022 NAICS Code Scheme
The Bureau of Labor Statistic’s 2022 Census of Employment and Wages (CEW) data were released using the new 2022 NAICS scheme. In the transition to the new scheme, various industries have been given new codes, have been consolidated, or have been expanded. Examples of each type of change include:
- The NAICS Code for Software Publishers, has been changed from 511210 to 513210.
- NAICS Code 335911 - Storage Battery Manufacturing and NAICS Code 335912 - Primary Battery Manufacturing have been consolidated into a single NAICS Code: 335910 - Battery Manufacturing.
- NAICS Code 325315 - Compost Manufacturing is a completely new NAICS Code
Full details about the changes are available on the Census Bureau’s NAICS website.
Now Using County-Specific Average Household Size Data When Estimating Total Households by County
Prior to the 2022 IMPLAN Data, we were using table “B25010 - Average Household Size of Occupied Housing Units by Tenure” from the 1-year American Community Survey (ACS), which only contains national and state-level data. We would then apply state-level growth rates in average household size since the last Decennial Census to county-level values of average household size from that Decennial Census to obtain county-level estimates of household counts by county. Beginning with the 2022 IMPLAN Data, we now obtain average household size data for the nation, all states, Puerto Rico, and all counties directly from the 5-year version of the same ACS table, thereby doing away with the need to use state growth rates applied to older county-level values.
Incorporation of 2016-2020 5-Year ACS Commuting Flows Data
Every five years, the Census Bureau releases new 5-Year ACS Commuting Flows data. Knowing workers’ place of work relative to their place of residence is important for more accurately attributing payroll taxes as accruing to the place of work and household spending as originating from the place of residence. The ACS Commuting Flows data account for remote working but only ask about the primary place of work. These data are important not only for MRIO analyses, where the commuting flow geography pairs come into play but also in single-region analyses where any net outflows of commuter earnings (net of payroll taxes) will be properly removed from the analysis results as one of several forms of leakage.
GOVERNMENT RESOURCES
ZIP Code Tabulation Areas (ZCTAs)
2021 U.S. Data Release Notes
Now Using More-Detailed State-Level PCE Data
Beginning with the 2017 IMPLAN data, we began incorporating state-level personal consumption expenditure (PCE) data in addition to the national-level data we had been using, with both sets of data coming from the Bureau of Economic Analysis’ (BEA) National Income and Product Accounts (NIPA) tables. Relative to the national-level data, the state-level data are lagged a year and contain much less commodity detail. Thus, we use the national data to split the state-level data into more commodity detail and to project them to the current data year.
Prior to the 2021 data year, the state-level table had just 16 commodities compared to 338 commodities in the national table. New this year, an additional state-level table is available that has 74 commodities – more than four times the commodity detail of previous years’ state-level tables. This additional precision at the state level will allow for even more state-specific PCE estimates, and therefore, more accurate induced impact results at the state and sub-state levels.
Resumption of Use of USFS Timber Sales
In general, national-level Federal Government commodity sales are derived by distributing total Federal Government sales from BEA NIPA Table 3.10.5 (Government Consumption Expenditures and General Government Gross Output) to IMPLAN commodities based on Federal Government commodity sales from the latest BEA Benchmark tables. Prior to IMPLAN data year 2018 , values for Federal Government sales of IMPLAN commodity 3015 (Forest, timber, and forest nursery products), at the national, state, and county levels, were obtained directly from the U.S. Forest Services (USFS).
Due to a revamp of their methodology for producing data sets with this information, the USFS was not able to provide these data to IMPLAN during data years 2018 - 2020, such that IMPLAN’s values for this commodity were estimates generated by the method used for all other commodities described above. Beginning with the 2021 data, this special USFS data set is once again available and IMPLAN’s values for this commodity once again come directly from the USFS.
Name and/or Code Changes to Counties or County Equivalent Entities
No new or lost counties, and no changes to county names or codes.
GOVERNMENT RESOURCES
NOAA Commercial Fisheries Landings
U.S. Census Commodity Flow Survey (CFS)
U.S. Census Relationship Files
U.S. Energy Information Administration Residential Energy Consumption Survey
Written November 28, 2022
Written November 29, 2023
2020 U.S. Data Release Notes
INTRODUCTION
The 2020 Data Year is now available in the IMPLAN application! As you can probably imagine, this Data Year is going to have some unique caveats that will reflect the state of the national economy during the global pandemic. This article will cover some of that weirdness that is evident in the new dataset, why that weirdness is there, and considerations for using it in your economic impact studies.
STIMULUS CHECKS, UNEMPLOYMENT BENEFITS, & HOUSEHOLD SPENDING
There were two main forms of government stimulus in 2020. The first was the stimulus checks sent to individuals and families coupled with expanded unemployment benefits. Some of this money was spent and some was saved. The levels and types of spending and saving in the Household Social Accounting Matrix column will include what Households did with the stimulus and unemployment benefits. Changes in the relative amounts that Households spend among the various commodities is also captured in the 2020 data (e.g., more on grocery stores, less on restaurants).
While gains in Household Income due to stimulus checks and unemployment benefits will be captured in the underlying data in the new dataset and are accounted for in the relationship of spending per dollar of new income, IMPLAN will not assume households receive more payments from these sources in your analysis because government payments are not by default internalized in the IMPLAN multipliers. If you would like to analyze additional gains in Household Income, these gains can be analyzed using a Household Income Event or by modeling the specific spending as individual Events.
Note, even with the fiscal efforts to support household spending, there were exceptionally high personal savings rates in 2020 as people spent less money due to quarantine and concern over less economic security. The increased savings rates will be applied to income analyzed in IMPLAN. So, all else equal, this will lead to lower Induced Effects.
PPP LOANS
The second government initiative was the Paycheck Protection Program (PPP). As this money was treated as subsidies for Industries, it shows up as reductions in TOPI. Remember, TOPI is reported as a net of subsidies. The decreases in TOPI were offset by an equal increase in OPI, which explains why some Industries see losses across all pieces of Value Added except for OPI. This OPI is the infusion of the government money that is to be used to cover operations and maintain employment levels. All else equal, when a firm gets a subsidy, it has a negative effect on net TOPI and a positive effect on OPI. For more information on this, check out the BEA. Again, read the cautions below on reporting tax results.
EXAMINING LOSSES
For some Industries, there are very high losses evident from anecdotal evidence as well as the 2020 data. We expect to see decreases in restaurants, travel, and amusement activities. Decreases in Output, Value Added and its components, and spending on Intermediate Inputs frame the story for each Industry. Many Industries were highly subsidized already, and then added additional monies through the PPP. Many Industries had to shut down (at least temporarily), decrease workforces, or pivot their entire business model. Many spent less money overall on Intermediate Inputs and a few saw international export markets dry up. All of these moving pieces need to be brought to mind when examining the losses seen in the data.
Regions and states that put stay-at-home orders in place earliest will show the largest losses. So if your county or state was quick to shut down, your Results will be more dramatic than other areas.
CAUTIONS WITH TAX IMPACTS
Because TOPI is net of subsidies by definition and the second quarter of 2020 saw excessive subsidies due to the CARES act, TOPI values in IMPLAN will be lower in many industries than they would have been otherwise. Thus, these subsidies are correctly accounted for in the IMPLAN Regions data.
However, this correct accounting in the Regions data can have undesired effects on the tax impact results. Because Federal TOPI has gotten smaller due to these subsidies, the ratio between Federal TOPI and State/Local TOPI has fallen. Thus, when calculating tax impacts, the model divvies up TOPI among these two levels of government using this smaller ratio, Federal Government is given a smaller share of any TOPI impacts and State/Local Government is given a larger share.
The consequence of this is that when the TOPI impact is positive, State/Local TOPI impacts will be overstated (too large a positive) relative to Federal, and when the TOPI impacts are negative, the State/Local TOPI impacts will be understated (too large a negative) relative to Federal. Thus, the magnitude of State/Local Government TOPI impacts will be overstated relative to Federal TOPI impacts in either case - being either too positive or too negative.
While State/Local TOPI actually stayed relatively stable, the share of these of total TOPI looks to increase in the data. Therefore, we recommend using your own tax estimates for the local and state levels or using tax estimates based on the 2019 Data Year in lieu of the 2020 Data Year.
For more information on how the PPP affects the BEA NIPAs, please see this article from the BEA.
ECONOMIC IMPACTS USING 2020 DATA
When using the 2020 Data Year, always add as much information as possible to your Industry Event in the Advanced Menu. This process is now easier than ever with the addition of the new Industry Impact Analysis Event Type (IIA) in IMPLAN V6! With the IIA Event Type, you can specify any value in an Industry’s Leontief Production Function (LPF) as your event value. With the IIA Event you can also customize Industry Spending Patterns. Note that this Event Type means that it is no longer necessary to utilize Analysis-by-parts in order to input those values.
Referencing an industry’s Leontief Production Function from 2019 can be helpful if an Industry’s production decreased during the year and their LPF changed in a way that does not reflect their operational LPF. For example, if Employment has decreased in an Industry, particularly among their lower wage employees, the Industry’s Average Employee Compensation (EC) likely increased and will potentially lead to an overestimation of Direct EC in an impact analysis. In reality, that Industry will probably need to hire for the positions lost to ramp up operations again.
Consider adding a footnote to your report or presentation that notes not only which dataset was utilized, but also includes a disclaimer about the potential implications of using it.
Run your analysis using the 2019 Data Year and the 2020 Data Year to see a range of the potential economic impacts. If the economy shifts back to a pre-pandemic state, this may help estimate future impacts. Although, our economy may never return to the “old normal” and it is unclear how the recovery will look. Brookings outlined some potential options that are worth considering when modeling your impact.
Finally, if you’d like to venture into painting your own picture of the current or future state of the economy, you can make edits to Industry levels and per-worker values in the Study Area data by Customizing a Region.
ECONOMIC CONTRIBUTIONS USING 2020 DATA
Running an Industry Contribution Analysis using the 2020 data is a great way to see what the effects of a business or industry are in the COVID economy. Run your analysis at a 100% contribution to estimate the effects supported by an entire Industry. If you are running just a firm or business, use the current level of Output they produce.
Comparing a 2020 Industry Contribution to a 2019 Industry Contribution is recommended for estimating the before and after effects of COVID-19 (keeping in mind it's unknown what changes we still might see). However, remember to note that the changes in taxes between these two Data Years will be dramatic because of the large subsidies in the 2020 data.
CONSIDER USING THE 2019 DATA FOR FUTURE IMPACTS
Using the annual 2019 IMPLAN dataset might still be the best option if you are trying to model anything before the pandemic or any events occurring in 2021 or later. We always recommend using the Data Year that looks most like the year you want to analyze. Because of the uniqueness of the 2020 Data Year in IMPLAN, it is possible that this data does not accurately reflect the economic conditions you are trying to model. So keep that in mind when deciding whether or not to utilize the 2020 Data Year in your analyses.
GENERAL DATA UPDATES INCORPORATION OF 2020 DECENNIAL CENSUS
The 2020 annual data set incorporated National, State, and Puerto Rico population numbers from the 2020 Decennial Census rather than from the usual estimation source. County data was not available at the time of completing demographics. Population data used comes from census apportionment tables, taken from the Census, published April 26, 2021.
Our county population numbers for 2020 come from the U.S. Census Bureau's Population Estimates series, which is the same source used for any regular IMPLAN data set.
COUNTY CHANGES
Valdez-Cordova Census Area, Alaska (State FIPS 02, County FIPS 261) was split to form Chugach Census Area (County FIPS 063) and Copper River Census Area (County FIPS 066), effective January 2, 2019. This change will be reflected in the IMPLAN annual data starting with the 2020 annual IMPLAN data.
SIGNIFICANT ECONOMIC CHANGES
As we are all aware, the U.S. (and global) economy experienced significant changes in 2020 associated with the Covid-19 pandemic. The 2020 IMPLAN data will reflect many of these changes. With very few exceptions, any lagged raw data are controlled to non-lagged data points at more aggregate levels. IMPLAN data are not adjusted to be smoother over time; rather, our goal is to publish data that are as accurate as reasonably possible.
We understand that a large shift one year may not reflect the long-term average for an industry, and therefore may pose difficulties when running impact analyses. Some suggestions and considerations, each of which depends on the specific circumstances and goals of the analysis:
- The Industry Impact Analysis (Detailed) analysis type, allows full customization of the direct effects when setting up an analysis, wherein one could use a several-year average for some values, if such customizing is appropriate and documented.
- A comparison across industries or geographies may be useful in highlighting regional or industry-group strengths relative to other regions or industry groups. Similarly, an individual business or project may have fared well relative to the overall economy in the region or nation.
- IMPLAN’s new Data Library contains IMPLAN data dating back to 2001, projected quarterly data, and tools for comparing regions and industries (location quotients, shift-share, etc.), all of which can help to provide a richer context to any analysis or report.
- Detailed information about the IMPLAN data production processes, educational articles and videos, expert Q&A, and more support for your analysis can be found From the Data Team.
- You can also check out some considerations for analyzing your impacts with the 2020 dataset as outlined in Using the Evolving Economy - COVID 2020 Q2 & Q3 Data.
GOVERNMENT RESOURCES
NOAA Commercial Fisheries Landings
U.S. Census Commodity Flow Survey (CFS)
U.S. Census Relationship Files
U.S. Energy Information Administration Residential Energy Consumption Survey
Written December 7, 2021
2019 U.S. Data Release Notes
ACCOUNTING FOR CHANGES IN SOURCE DATA: NOAA FISH LANDINGS
- We use NOAA’s landings data for initial estimates of commercial fishing output for those states covered by NOAA (coastal states and the states surrounding the Great Lakes). However, starting in calendar year 2020, NOAA changed their data provider for the Gulf States (AL, LA, MS, TX) to GulfFIN, and according to our contact at NOAA, GufFIN redacts their confidential data instead of including it as some form of aggregate. As such, we no longer use the current NOAA data for these states; instead, we project the 2017 NOAA values for these states using the change in Employment and Employee Compensation in this sector in these states.
- We had been relying on BLS CEW data to a) estimate commercial fishing output for states not covered by NOAA and b) project the lagged output estimates to the current IMPLAN year. However, since a) CEW does not fully cover the Commercial Fishing sector, b) the degree of coverage very likely varies by state, and c) CEW data do not include proprietors, we now use Employment and Employee Compensation from later stages in the data production process to account for proprietors and adjustments for such under-coverage.
- NOAA’s new species-specific data (and thus also their aggregations across species, by state) include numerous instances of pounds reported with no accompanying dollar value. We assume these are a mixture of onsite consumption (similar to on-farm consumption of production) and by-catch (fish caught as a byproduct and either sold but not reported or thrown back). We estimate an average dollar per pound for these species across all states and apply that value to those pounds missing a dollar value. Any remaining instances lacking dollar values are assumed to have been catches thrown back (i.e., true NULLS) and are not further adjusted.
IMPROVED STATE-LEVEL DISTRIBUTION OF PERSONAL PROPERTY TAXES
Personal property taxes are not distinguished from other types of property taxes in the Annual Survey of State and Local Government Finances (including in Census years) or the Annual Survey of State Government Tax Collections Data. We had been using this aggregate property tax to distribute state-level personal property tax, but beginning with the 2019 IMPLAN data set, we now use more-detailed data from the BEA REA SA50 table for this initial state-level distribution.
IMPROVED STATE-LEVEL DISTRIBUTION OF PERSONAL CAPITAL INCOME
Beginning with the 2019 IMPLAN data set, national values for personal capital income, consisting of dividend income, interest income, and rental income of persons, is distributed using data from BEA REA table SA40, which reports each of these items for states. Previously, we had used a single, state-level, aggregate value to distribute those three components of personal income.
IMPROVED OUTPUT ESTIMATES FOR OIL & GAS EXTRACTION
Because the price of crude oil is so volatile, growth in Employment and Labor Income in this industry does not always reflect growth in Output as closely as in other industries whose products do not experience such price volatility. Thus, using growth rates based on Employment or Labor Income to project the lagged BEA Output values is not ideal for these sectors. Therefore, starting with the 2019 IMPLAN data, we will be using the Energy Information Administration (EIA) data on the prices and physical production of crude oil and natural gas to project the lagged BEA Output value for the oil and gas extraction sector.
The benefits of this change are two-fold: in addition to improving the Output estimates for the oil and gas extraction industry itself, these improved Output estimates will result in more accurate estimates of the petroleum refinery industry's purchases of crude oil, thereby yielding improved impact estimates.
NO LONGER ESTIMATING NON-DISCLOSED CBP VALUES
The Census Bureau has adopted a new policy under which they no longer provide establishment counts for cases in which the establishment count is less than three, as the number of establishments is now considered sensitive. This new decision to omit from their tables all records with fewer than three establishments was a new policy that was set in place to protect the confidentiality of businesses.
This new practice makes a missing record of 1-2 establishments (an existing industry) indistinguishable from a missing record of zero establishments (a non-existent industry). The omission of records with less than three establishments, in addition to making it impossible to estimate their employment and income values, also makes it impossible for us to obtain high quality estimates for the non-disclosed records with three or more establishments, since we are not able to roll up across NAICS levels without having values for sibling sectors.
Therefore, beginning with the 2019 IMPLAN data set, we no longer create estimates for non-disclosed CBP data; rather, we only use the disclosed data. This will affect our estimates of non-disclosed BLS CEW values in those cases in which we need to turn to CBP (lack of disclosed raw CEW from previous year) and in which the CBP value is not disclosed; in those cases, we will now move right to using a projected CEW value from two years back, if possible, followed by the use of state-level or U.S.-level ratios as the second and third options for obtaining a first estimate for CEW, rather than having CBP as a second option.
NO LONGER USING ANNUALLY-UPDATED ZBP VALUES
The changes to the Census Bureau's disclosure practices described above also affect the Census ZIP Code Business Patterns (ZBP) data, beginning with the 2017 data. Thus, the most recent set of ZBP data that are suitable for use in IMPLAN is the 2016 ZBP data. As described in this article, the ZBP data, along with zip-code-level demographic data, are used to split county-level data into zip-code-level data. The zip-code data are then aggregated into Congressional District data. An unfortunate consequence of these changes, therefore, is that the zip-code and Congressional District data will rely on increasingly lagged ZBP data for distributing county-level industry values to zip-codes.
INCLUSION OF 2017 CFS DETAILED DATA
The Census releases new Commodity Flow Survey (CFS) data every five years, first as a small preliminary data release and then later as a larger detailed data set. IMPLAN incorporates the CFS data over two years. The 2018 IMPLAN data incorporated the Preliminary 2017 CFS data on commodity shipment characteristics. The preliminary data are used to revise estimates of average miles shipped for shippable commodities, which are used as a calibrator for the gravity model, and estimates of shipment-modes by industry. The new 2019 IMPLAN data incorporate the newly released detailed CFS data (released in July 2020), which include greater commodity specificity on average miles shipped and shipment-modes. These data are used to further revise both sets of estimates.
NO LONGER USING CENSUS COUNTY BUSINESS PATTERNS BY LEGAL FORM OF ORGANIZATION DATA
Since the 2013 IMPLAN data year, we had been using these data in combination with BLS CEW data to distribute BEA REA proprietor employment and income, which are in a more aggregate sectoring scheme, among the IMPLAN sectors that map to each BEA REA sector. Due to the Census Bureau’s new practice of omitting records with fewer than three establishments (described in more detail above) and the fact that state-level data by industry and legal form of organization often have records with fewer than three establishments, these data are not sufficiently reliable in their current form. Therefore, we have returned to the previous method of relying solely on FLS CEW data for the distribution.
IMPROVED STATE AND LOCAL GOVERNMENT SALES BEHAVIOR
Prior to the 2019 IMPLAN data year, the only State and Local Government institution that had sales was State and Local Government Non-Education (with State and Local Government Education getting its funding through State and Local Government Non-Education). With the incorporation of the new 2012 BEA Benchmark tables, beginning with the 2018 IMPLAN data set, we now have an additional State and Local Government institution: State and Local Government Hospitals and Health Services. Note: this split out is not available in IMPLAN Pro which only contains 544 Industries.
With the addition of the new Hospitals and Health Services institution, it should ideally be this new institution, rather than the State and Local Government Other Services institution, which sells the following four commodities: Outpatient care centers (3486), Home health care services (3488), Other ambulatory health care services (3489), and Hospital services (3490). Starting with the 2019 IMPLAN data set, this is now the case.
In addition to providing information on the goods and services sold by the State and Local Government Hospitals and Health Services institution, the new 2012 BEA Benchmark also provides information on the goods and services sold by the State and Local Government Education institution. Therefore, it should ideally be this institution, rather than the State and Local Government Other Services institution, which sells the following 4 commodities: News syndicates, libraries, archives and all other information services (3437), Elementary and secondary schools (3480), Junior colleges, colleges, universities, and professional schools (3481), and Other educational services (3482). Starting with the 2019 IMPLAN data set, this is now the case.
Finally, we also added NIPA control totals for the sum of the four commodities sold by each of these institutions. These changes will have no effect on impact analyses run from an industry basis. They will have very minor, if any, effect on commodity-based impact analyses (it will be case-dependent), but results (and model data) will more accurately reflect the actors involved in commodity production.
NEW OTHER VALUE-ADDED PROCEDURES
Prior to the 2019 IMPLAN data year, the BEA data we used for state-level industry-specific Gross Operating Surplus, Gross Taxes on Production and Imports, and Subsidies were lagged one year relative to the IMPLAN data. Beginning with the 2019 IMPLAN data, the BEA is now publishing these data in a much more timely fashion that allows us to use data that are in the same year as the latest IMPLAN data being produced, thereby doing away with the need for projection.
INCORPORATION OF THE 116TH CONGRESSIONAL DISTRICTS
A new relationship file between Zip Code Tabulation Areas (ZCTAs) and Congressional Districts has been published by the Census Bureau. This new relationship file has been incorporated into the development of IMPLAN’s 2019 Congressional District data.
INCORPORATION OF THE 2017 CENSUS OF GOVERNMENTS
The Census of Governments is conducted every 5 years, with the latest one being conducted in 2017 and published in 2020. We use the Census of Government data on State and Local Government Finances to supplement the Annual Survey of State and Local Government Finances, the latter of which is released two years lagged relative to the IMPLAN data. We start with values from the latest Census of Government (inflated to the same year as the annual survey data. We then replace any Census values with values from the Survey, where available. These values are used to distribute U.S. control values to the states and counties. The incorporation of the new Census data may cause some significant changes to tax impacts.
OCCUPATION DATA
The Occupation Data for 2019 was updated February 10, 2021.
GOVERNMENT RESOURCES
NOAA Commercial Fisheries Landings
U.S. Census Commodity Flow Survey (CFS)
U.S. Census Relationship Files
U.S. Energy Information Administration Residential Energy Consumption Survey
Written November 11, 2020
Updated February 11, 2021
2018 U.S. Data Release Notes
INCORPORATION OF THE NEW 2012 BEA BENCHMARK I-O TABLES
- With the release of the 2007 BEA BM, IMPLAN decided to no longer tie itself so tightly to the BEA BM sectoring scheme, as this continued to decline in sector number. Instead, we decided to keep virtually every BEA BM sector that had ever existed and only add new sectors as the BEA supplied data for them or we obtained data for them elsewhere. However, it became apparent with the release of the 2012 BEA BM that not all BEA sectors could be persisted because in addition to being deprecated by the BEA, some sectors are also deprecated within the NAICS classification system, meaning that we no longer have CBP or CEW data for them; these are our most detailed data sources and without those data we would have no means with which to persist those sectors aside from using split ratios from old CBP or CEW data, ratios that would get very stale very quickly. Thus, there are 3 cases in which sectors had to be combined in the new IMPLAN sectoring scheme. Fortunately, there are more cases of new sectors (i.e., more sector detail) than cases of combined sectors (i.e., less sector detail). Each of these cases is listed below.
- Previous IMPLAN sectors 20 and 21 will comprise a single sector (20) in the new 546 sectoring scheme. This is due to 2012 NAICS codes 211111 and 211112 being rearranged into NAICS codes 211120 and 211130, which do not have enough detail to keep these two sectors separate.
- Previous IMPLAN sectors 26 and 27 will comprise a single sector (22) in the new 546 sectoring scheme. This is due to 2012 NAICS codes 212231 and 212234 being combined into 2017 NAICS code 212230.
- Previous IMPLAN sectors 287 and 289 will comprise a single sector (285) in the new 546 sectoring scheme. This is due to 2012 NAICS codes 333911 and 333913 being combined into a single 2017 NAICS code (333914).
- Previous sector 395 will be split into 10 sectors in the new 546 sectoring scheme: sectors 392-401. There is sufficient 2017 NAICS code detail to allow for this.
- Wholesale - Motor vehicle and motor vehicle parts and supplies
- Wholesale - Professional and commercial equipment and supplies
- Wholesale - Household appliances and electrical and electronic goods
- Wholesale - Machinery, equipment, and supplies
- Wholesale - Other durable goods merchant wholesalers
- Wholesale - Drugs and druggists’ sundries
- Wholesale - Grocery and related product wholesalers
- Wholesale - Petroleum and petroleum products
- Wholesale - Other nondurable goods merchant wholesalers
- Wholesale - Wholesale electronic markets and agents and brokers
- Previous sector 437 will be split into 2 sectors in the new 546 sectoring scheme: sectors 443 and 444. There is sufficient 2017 NAICS code detail to allow for this.
- Insurance carriers, except direct life
- Insurance agencies, brokerages, and related activities
- Previous sector 440 will be split into 2 sectors in the new 546 sectoring scheme: sectors 447 and 448. There is sufficient 2017 NAICS code detail to allow for this.
- Other real estate
- Tenant-occupied housing
- New State and Local Government institution[1]: Whereas there were 2 State and Local Government institutions in the old 536 sectoring scheme (State and Local Government Education and State and Local Government Non-Education), the new 546 sectoring scheme will have 3 State and Local Government institutions: State and Local Government Education, State and Local Government Hospitals and Health Services, and State and Local Government Other Services. As such, previous state government payroll sector 531 (Employment and payroll of state govt, non-education) will be split into 2 sectors (sectors 540 and 541) and previous local government payroll sector 533 (Employment and payroll of local govt, non-education) will be split into 2 sectors (sectors 543 and 544) in the new 546 sectoring scheme. There is sufficient 2017 NAICS code detail to allow for this.1
- * Employment and payroll of state govt, hospitals and health services
- * Employment and payroll of state govt, other services
- * Employment and payroll of local govt, hospitals and health services
- * Employment and payroll of local govt, other services
- Cases of aggregation:
- Cases of disaggregation:
NOW ADJUSTING NIPA FOREIGN EXPORT CONTROL TOTALS TO ACCOUNT FOR TRANSPORTATION AND WHOLESALE MARGINS
Footnote 11 on page 7–7 of the 2006 “Concepts and Methods of the U.S. Input-Output Accounts” document states that the National Income and Product Accounts (NIPAs) value exports of goods and services at the value leaving the country, which is equivalent to purchasers’ prices (this is in contrast to the NIPA values for imports, which are expressed in producer prices). Because input-output (I-O) models are based in producer prices, the two export control totals (one for goods, the other for services) must be converted to producer prices by shifting some of the NIPA control total for exports of goods to the NIPA control total for the exports of services, to account for the value of the transportation and wholesale services involved prior to exportation and thus included in the purchaser price of those exports.
We base the amount of the shift on the latest BEA Benchmark I-O tables, which contain data on the exports of goods and services expressed in both purchaser and producer prices. We had not previously made this adjustment and will be doing so beginning with the 2018 IMPLAN data, with the adjustment ratio being updated every 5 years with the release of new BEA Benchmark I-O tables.
FURTHER IMPROVEMENT TO CALCULATION OF ADJUSTMENT RATIOS
For more background on this adjustment process which we apply to the BLS’ CEW data, please refer to our data release notes for the 2014 and 2016 data years.
Upon investigation of a customer query, we determined that our method of applying state-level adjustment ratios to all counties within the state, while allowing us to use only fully-disclosed data for the calculation of the ratios, did not always yield reasonable estimates for some counties. Thus, beginning with the 2019 IMPLAN data, we are incorporating two refinements to this adjustment process: 1) we now use ratios calculated from county-level data, regardless of disclosure code and 2) incorporated a more dynamic and less subjective method for capping very large adjustment ratios.
Depending on the disclosure codes for each county and sector, this change potentially affects employment and employee compensation (EC) estimates in the following IMPLAN sectors:
- Religious organizations
- Commercial fishing
- Junior colleges, colleges, universities, and professional schools
- Private households
- Support activities for agriculture and forestry
INCORPORATION OF THE 2017 CENSUS OF AGRICULTURE
This data year marks the first year to incorporate the new 2017 Census of Agriculture. The Census of Agriculture is used to:
- Estimate benchmark proprietor employment
- Specifically, IMPLAN takes the BEA REA Farm proprietor employment for the year of the benchmark and distributes it among the 14 IMPLAN farm sectors using proprietor farm count per proprietor farm sector data from the Census of Agriculture. These benchmark-year (to make sure we don’t falsely lead people to think that these are from the Benchmark itself) proprietor employment estimates by farm sector are then combined with the BEA Benchmark output data by farm sector to calculate output-per-proprietor ratios by farm sector.
- Estimate crop production for states not reported by ERS or NASS in their annual data sets
- We supplement gaps in ERS and NASS state-level crop production data with estimates derived from the Census of Agriculture.
- Distribute state level farm sector production to counties
- State values are distributed to counties by using the ratio of county physical production to state physical production from the Census of Agriculture. For example, if County A has 10% of the state’s corn sales (or acres if sales is not available, and farms if acreages is not available), then it gets 10% of the state’s Annual Cash Receipts value for corn.
MINOR CHANGES TO FRUIT FARMING OUTPUT
New non-citrus fruit state output values and county distributors:
- In their annual data sets, the USDA’s Economic Research Service (ERS) and National Agricultural Statistics Service (NASS) have discontinued reporting for multiple non-citrus fruits (bananas, blackberries, boysenberries, figs, guavas, and pineapples). Therefore, we now use the 2017 Census of Agriculture’s values for the overall non-citrus fruit category (NAICS codes 111331, 111332, 111333, 111334, and 111339) for our state estimates, which has the following benefits:
- Reduces the number of disclosures performed by us for those individual fruit categories (i.e., reduces the number of estimations made by us).
- Avoids the need to project a continually growing and aging set of values for individual fruit commodities (i.e., avoids the reliance on a projection of the last reported value for a given specific fruit category that is no longer reported, a value which becomes more out of date with each passing year).
- Simplifies our processes and reduces the risk of error.
- These 2017 state values are projected to the data year based on ERS disclosed state-specific non-citrus fruit values (either one or multiple individual fruit categories, depending on data availability for that state and years), where available, else U.S. disclosed non-citrus fruit values. State to county distributors are derived using the Non-citrus totals (excluding berries) and Berry totals data from the 2017 Census of Agriculture.
IMPROVED CALCULATION OF BENCHMARK-YEAR EMPLOYEE COMPENSATION PER WAGE AND SALARY EMPLOYMENT RATIOS FOR FARM SECTORS
We calculate this ratio for all IMPLAN farm sectors when a new BEA Benchmark is released; these ratios are used in our annual data process (see our farm data document for more details on how the ratios are used). Prior to the current (2012) Benchmark, we simply used the ratio of Benchmark EC (before redefinitions, bridged to IMPLAN sectors) to benchmark-year CEW wage and salary employment (also bridged to IMPLAN sectors). However, upon calculating the new ratios this year, it became apparent that the Benchmark has a different distribution of EC across the farm sectors relative to that of CEW wage and salary income data, which would result in strikingly different EC per wage and salary income ratios across farm sectors, as well as negative supplements to wages for some farm sectors. Thus, we now re-allocate CEW wage and salary employment to farm sectors according to the distribution implied by the Benchmark EC data.
In summary:
- We first calculate ratios of wage and salary income to wage and salary employment for each farm sector from CEW.
- We then convert those wage and salary income per wage and salary employment ratios to EC per wage and salary employment ratios using a ratio of Farm Total EC to Farm Total wage and salary income from the BEA’s REA data series.
- Next, we distribute that wage and salary employment according the Benchmark EC so that the distribution of wage and salary employment now matches the Benchmark rather than CEW, the latter of which lacks full coverage of non-proprietor farm employment.
- Finally, we use these sector-specific values to distribute the BEA REA Farm Total wage and salary employment among the 14 IMPLAN farm sectors, since the BEA REA data provide full coverage of farms but no distribution to specific farm sectors.
IMPROVEMENTS TO DISTRIBUTION OF FINAL DEMAND OF NEW CONSTRUCTION
Prior to DY 2018, we were distributing final demand for the new construction sectors geographically using data points other than output. However, in order to have equivalence between final demand and output for new construction sectors, we should be distributing final demand for these sectors based on their output; as of DY 2018 we are doing just that.
NOW INCORPORATING CONTROLS FOR BEA OUTPUT
Beginning with the 2018 IMPLAN data, we now incorporate current, but more aggregate, BEA Output data as controls for our projections of the more-detailed but lagged BEA Output data that we’ve always used, thereby giving us greater confidence in those output values.
BETTER ADJUSTMENT RATIOS FOR RETAIL SECTOR OUTPUT
The BEA BM (and IMPLAN) define retail output as retail margin + sales taxes collected on gross sales. However, the annual retail trade survey (ARTS) data on retail sales and margins do not include sales tax. Thus, we have always made an adjustment to bump the ARTS values upward to account for sales tax. However, it was discovered in DY 2017 that our adjustment process was not quite accurate, so in DY 2018 (when we needed to update the adjustment ratios anyway to incorporate the new BM), we corrected the calculation of said adjustment ratios. This change also affects the calculation of the margins as a percentage of sales that enter into the structural matrix.
INCORPORATION OF PRELIMINARY 2017 COMMODITY FLOW SURVEY DATA
The 2018 IMPLAN data are the first to incorporate the Census Bureau’s Preliminary 2017 CFS data on commodity shipment characteristics. The Census Bureau releases new CFS data every 5 years, first as a small preliminary data release and then later as a larger more-detailed data set. IMPLAN incorporates the CFS data over two years:
- In the first year (2018 IMPLAN data in this case), the preliminary data are used to revise estimates of average miles shipped for shippable commodities (which are used as a calibrator for the gravity model) and estimates of shipment-modes by industry (used to determine which combination of impedances to use for each commodity in the gravity model).
- In the following year (2019 IMPLAN data in this case), IMPLAN incorporates the later-released detailed CFS data to further revise both sets of estimates.
[1] Due to limitations of the desktop version of the IMPLAN software, this change is only available in the latest online version of the IMPLAN application.
RELATED TOPICS
BEA Benchmark & The New 546 Sectoring Scheme
GOVERNMENT RESOURCES
Comprehensive Update of Industry Accounts Now Available
Measuring the Nation's Economy: An Industry Perspective | A Primer on BEA's Industry Accounts
National Agricultural Statistics Service (NASS)
USDA Economic Research Service (ERS)
Written November 7, 2019