IMPLAN Data Release Notes

Release notes from IMPLAN's historical data releases. Data years 2009 & 2011 did not have noteworthy changes.

Time Series

Time Series Release Notes

IMPLAN’s new Time Series Data product was produced using our latest methodologies, which have been honed over the past 20 years of data development. A few special tactics were required for some data elements/years/places, which are noted later in this document. Benefits of this new data product include the following:

 

• Use of revised raw data (many government data sources are later revised after the annual IMPLAN data creation process – this product takes advantage of the improved raw data!)
• Use of current raw data (many of our annual data sources come to us a year lagged – this is of course not the case when going back and estimating past years, so no projections needed!)
• Consistent estimation methodologies (incorporates all of our best practices and improved data sources learned throughout the years)
• Consistent and more-detailed sectoring scheme (this is the only way to see 2001-2012 data in the current 536 sectoring scheme – the most sectors we’ve ever had!)
• Statistical Analysis with IMPLAN data is now possible and easy!

 

Major Methodology Improvements and Changes Incorporated over Time

All years from 2001 – 2014 were based on the latest (2007) BEA Benchmark Make and Use tables.

County Changes

2002

On November 15, 2001, Broomfield County (State FIPS 08, County FIPS 014) separated from Boulder County to become the newest and smallest county of Colorado.

 

2008

Four existing Alaskan boroughs underwent transformation from mid-2007 to mid-2008 creating five re-named and re-coded FIPs codes and a net gain of two boroughs, as shown in the table below.

 

2007 Boroughs	2008 Boroughs
130 Ketchikan Gateway Borough 201 Prince of Wales-Outer Ketchikan Census Area	130 Ketchikan Gateway Borough 198 Prince of Wales-Hyder Census Area
232 Skagway-Angoon	105 Hoonah-Angoon Census Division 230 Skagway Borough
280 Wrangell-Petersburg Census Area	195 Petersburg Census Area 275 Wrangell Borough

 

2014

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.

 

Incorporation of State-Level GSP Data

 

The BEA provides data on TOPI by GSP sector (81 of them), by state. Previous to the original 2012 data year, we were only making use of the U.S.-level data, using U.S. ratios to estimate state-level data. In the 2012 and later IMPLAN Data, as well as the Time Series Data, we improved our process of incorporating the state-level BEA TOPI data.

 

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.

 

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.

 

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.   The Economic Research Service (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 for value of production for certain agricultural products from the Department of Agriculture’s National Agricultural Statistics Service (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 NASS, ERS, and the Census of Agriculture continue to be our primary data sources for estimating state- and county-level agricultural output.

 

Improved Redefinitions

 

The NIPA control totals for Government Gross Investment in structures (from table 3.9.5) and Private Fixed Investment in structures (from table 5.3.5) have already been redefined – that is, they include all activity related to the construction of structures, regardless of which industry performed that construction. Thus, when redefining the Output of each sector, while we still need to take construction activity out of the other sectors, we do not need to add that activity to the construction sectors (since their output figures for the construction sectors presumably already includes that activity).   Thus, in the Time Series Data set and all annual IMPLAN Data sets beginning with 2012 R2, we no longer add the non-construction-sector construction output to the construction sectors. However, the other sectors’ Employment, EC, PI, OPI, and IBT will continue to be moved into the construction sectors because the data for these factors is not redefined.

 

Revision of IMPLAN SAM Accounts to More Closely Conform to the Current BEA NIPAs

 

Starting in the 2010 data year, indirect business taxes (IBT) have been converted to taxes on production and imports net of government subsidy (TOPI). This removes business transfers to government from GDP. It also subtracts government subsidy to business from IBT. Thus, it is possible for TOPI to be negative for some industries, meaning that government subsidy exceeds taxes paid by the industry. This change has been incorporated into all annual IMPLAN Data sets since 2010, as well as the Time Series Data sets.

 

New ERS process

 

For agricultural sector Output, in the 2012 data year we shifted from using sales data to production data multiplied by the average price for that commodity for that year. The reason for this change is that agricultural commodities are not always sold in the same year that they are produced, making revenues an imprecise measure of Output. The same can be said for other manufacturing sectors; however, we get the Output data for those sectors from the Anuual Survey of Manufactures, which includes data on net inventory changes, which allows us to separate sales from production for those sectors. This improvement has been incorporated into all annual IMPLAN Data sets since 2012, as well as the Time Series Data sets.

 

2015
2015 Release Notes

                                                                                                                                                                                  November 18, 2016
2015 IMPLAN Data Release Notes

New Household Income Classes:

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

                Number of Households LT15k

                Number of Households 15-30k

                Number of Households 30-40k

                Number of Households 40-50k

                Number of Households 50-70k

                Number of Households 70-100k

                Number of Households 100-150k

                Number of Households 150-200k

                Number of Households 200k+

 

Slight modification to CEW and CBP disclosure routines:

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.

 

Improvement to Fish Output estimates:

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.

 

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.

 

2014

2014 Release Notes

2014 Release Notes                                                                                                                                                                            November 2015

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. 

 

2013

2013 R1 Release Notes

2013 R1 Release Notes

Highlights

• 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.

http://www.slate.com/articles/business/moneybox/2013/04/nipa_revisions_bea_will_treat_intangibles_as_capital_goods_and_boost_gdp.html
http://www.bea.gov/scb/pdf/2014/03%20March/0314_r&d_in_economic_accounts_and_in_business_accounts.pdf

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

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

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.

 

2012

2012 R1 Release Notes

2012 R1 Release Notes                                                                                                                                                           December 18th, 2013

For 2012, there is a small change in the gravity model formulation. Tiny demands and supplies are now traded locally if possible. This will cause a slight bump to multipliers.

In the past we have made an effort to prevent negative labor income (ie, proprietor’s income losses exceeding employee compensation for a given sector). This year it was not possible for the farming sectors as it led to significant loss of those agricultural sectors for many counties. Negative labor income can lead to overall negative induced effects. Since this may not be logical for impact analysis, we suggest customizing an impact analysis by zeroing out negative proprietor income in the event window.

The BEA provides data on TOPI by GSP sector (81 of them), by state. Previous to the 2012 data year, we were only making use of the U.S.-level data, using U.S. ratios to estimate state-level data. In the 2012 Data Season, we improved our process of incorporating the state-level BEA TOPI data. As such, there may be some large changes in TOPI for some states in some sectors compared to previous years.

2012 R2 Release Notes

 
2012 R2 Release Notes

More current GSP data: Due to the government sequestration in the Fall of 2013, the latest Gross State Product (GSP) data were not released to the public; thus, the first release of the 2012 IMPLAN data used lagged GSP data. The 2012 R2 data incorporate the most current GSP data. This will affect OPI and TOPI.

Improved Redefinitions: The NIPA control totals for Government Gross Investment in structures (from table 3.9.5) and Private Fixed Investment in structures (from table 5.3.5) have already been redefined – that is, they include all activity related to the construction of structures, regardless of which industry performed that construction. Thus, when redefining the Output of each sector, while we still need to take construction activity out of the other sectors, we do not need to add that activity to the construction sectors (since their output figures for the construction sectors presumably already includes that activity).   Thus, in the 2012 R2 IMPLAN data and all future IMPLAN data sets, we will no longer add the non-construction-sector construction output to the construction sectors. However, the other sectors’ Employment, EC, PI, OPI, and IBT will continue to be moved into the construction sectors because the data for these factors is not redefined.

New ERS process: For Output for the agricultural sectors, we have shifted from using sales data to production data multiplied by the average price for that commodity for that year. The reason for this change is that agricultural commodities are not always sold in the same year that they are produced, making revenues an imprecise measure of Output. The same can be said for other manufacturing sectors; however, we get the Output data for those sectors from the Anuual Survey of Manufactures, which includes data on net inventory changes, which allows us to separate sales from production for those sectors.  

New ORNL Impedences: We use inter-county impedences (cost of transport indexes) from the Oak Ridge National Laboratory for use in our gravity model to estimate inter-county trade flows. For IMPLAN data sets 2007 – 2012 R1, we had been using the same original set of ORNL impedences. In 2013, we acquired updated impedences from ORNL which were incorporated in the 2012 R2 IMLAN data set.

New Zip-Code Population Data: It was pointed out to us by a customer that the Census Bureau showed a population of 33 for zip-code 18430, yet IMPLAN did not have a data set for that zip-code. We checked our raw 2010 Decennial Census data and did not see the zip-code there (so we did NOT make a mistake). However, a re-download of the 2010 Census Data DID have data for that zip-code – and 191 others! So we incorporated them into our list of unique zip-codes and they now show up in the new set of zip-code data files. The new Census data did not involve any changed values to zip-codes that were in the original download. In the process of ensuring that all zip-codes in a given county to sum to that county’s values, the other zip-codes in counties for which new zip-code data were created will experience a very slight decrease in their values.

2010

 2010 Release Notes

2010 Release Notes

Revision of IMPLAN SAM accounts to more closely conform to the current BEA NIPAs:

IBT (indirect business taxes) has been converted to “Taxes on production and imports net of government subsidy”. This removes business transfers to government from GDP. It also subtracts government subsidy to business from IBT. The upshot for the user is that it is now possible for IBT to be negative for some industries, meaning that government subsidy exceeds taxes paid by the industry.

REIS Data

1. Changes to Wage and Salary Income no longer calculated but rather based on CEW W&S income per W&S worker
2. Much of the controlling counties values to state totals has been eliminated to avoid spreading/compounding error. Controlling left to final IMPLAN sectoring controls.
   
   

Corrected Error in Bridge between 2002 Benchmark and IMPLAN Sectoring Scheme

The BEA codes for 3 commodities were mismarked leading to incorrect USE and Final demand distributions for these three commodities. Corrections noted below:

Construction Sector Improvement

Earnings and output per worker modified to better reflect 2002 BM relationships.

2008

2008 Release Notes

2008 Release Notes

Alaska Boroughs: 4 existing boroughs underwent transformation from mid-2007 to mid-2008 creating 5 renamed and re-coded FIPs numbers and a net gain of 2 boroughs. Much of the necessary data for these boroughs was not available, so we created corresponding zip code data files to generate the needed definition to create 2008 data.

2007 Boroughs	2008 Boroughs
130 Ketchikan Gateway Borough 201 Prince of Wales-Outer Ketchikan Census Area	130 Ketchikan Gateway Borough 198 Prince of Wales-Hyder   Census Area
232 Skagway-Angoon	105 Hoonah-Angoon Census Division 230 Skagway Borough
280 Wrangell-Petersburg Census Area	195 Petersburg Census Area 275 Wrangell Borough