Release notes from IMPLAN’s historical data releases. Data years 2009 & 2011 did not have noteworthy changes.
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.
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.
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.
130 Ketchikan Gateway Borough
201 Prince of Wales-Outer Ketchikan Census Area
130 Ketchikan Gateway Borough
198 Prince of Wales-Hyder Census Area
105 Hoonah-Angoon Census Division
230 Skagway Borough
280 Wrangell-Petersburg Census Area
195 Petersburg Census Area
275 Wrangell Borough
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.
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.