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.

To view the commuting data for your region, go to Explore > Social Accounts > IxC Social Accounting Matrix. If the Employee Compensation column makes a payment to the Domestic Trade row, then there is net in-commuting into the region (more income earned by workers who work in the area and live outside the area than vice-versa). The ratio of this value divided by the Employee Compensation column total gives you an estimate of the net in-commuting rate. If the cell is empty, then the region has net out-commuting, which will show up as a payment from the Domestic Trade column to the household rows.  To get a net out-commuting rate, divide the sum of these payments (across the 9 HH types) by the sum of the household column totals.

The equation below allows you to adjust IMPLAN’s estimated regional commuting rate to your known regional commuting rate.

 

IMPLAN offers a wide variety of data options to meet every user’s local study area needs. Below is a description of the type of data files we offer, so you can easily select which is best to custom design your study. We also include a quick reference table so you can easily visualize whats available in each package.

Data Purchase	Zip code Information	County Information	Congressional Districts	State Totals	National Totals
County Totals	No	Yes	No	No	No
County Plus	Yes	Yes	Yes	No	No
Individual Congressional Districts	No	No	Yes	No	No
State Totals	No	No	No	Yes	No
State Package	No	Yes	No	Yes	Yes
State Package Plus	Yes	Yes	Yes	Yes	Yes
51 State Totals	No	No	No	Yes	Yes
National Totals	No	No	No	No	Yes
National Congressional Disctricts	No	No	Yes	Yes	Yes
National Package	No	Yes	No	Yes	Yes
National Package Plus	Yes	Yes	Yes	Yes	Yes

 

 

COUNTY LEVEL

County Totals

 

The County Totals Data File provides IMPLAN information for analysis at the level of an individual county. Multiple county level files can be combined into a single analysis or compared in multi-regional analysis. County Totals are perfect for users purchasing 1-4 county regions in the same state or multiple counties scattered through states, but who do not need to view impacts at metropolitan or zip code levels. 

 

County Plus

The County Plus Data File provides IMPLAN information for analysis at the level of an individual county with the ability to break the county down into specific zip code regions. County Plus files also include the Totals information so that county information can be accessed and compared to other county regions without having to aggregate all the zip code information. County Plus packages provide you with the power to custom aggregate zip codes to create metropolitan areas within the selected county. As with Totals files, these are a great choice for users examining 1-4 county regions in the same state or multiple counties scattered through states.

STATE LEVEL

State Totals

The State Totals Data File provides IMPLAN information for analysis at an aggregated state level. Multiple State Totals files can be combined into a single analysis or can be compared in multi-regional analysis. State Totals are for users who do not need to view impacts at county, metropolitan, or zip code levels but are interested in seeing only how impacts affect a state or a group of states as a whole.

 

 State Packages

State Packages provide IMPLAN information for analysis for every county within the selected state. These packages should be considered by those wishing to model information for multiple County Totals within a state, especially those looking to purchase 4 or more County Totals files. State Packages provide the ability to break the state down into individual counties or groups of counties, as well as, provide State Totals for the selected state and National Totals information.

 

State Package Plus

State Package Plus files provide IMPLAN information for analysis for every county within the selected state and the ability to break each county into its zip code regions. These packages should be considered by those wishing to model information for multiple County Plus files within a state, especially those looking to purchase 4 or more County Plus files. State Package Plus files provide the ability to break the state down into individual counties, groups of counties, zip codes or groups of zip codes for complete customization of the study area. State Totals for the selected state and National Totals are also provided.

 

51 State Totals

The 51 State Totals Data provides IMPLAN information for analysis of all 51 states at an aggregated state level. With this package users can receive all the aggregated state level information for the entire nation in a single purchase. State Totals data provides 51 files that can be studied independently or combined to perform impact analyses. 51 State Totals do not have information for modeling at the county, metropolitan, or zip code level. This information is designed for those needing to perform models on 5 or more individual states or state groupings. National Totals are also provided.

NATIONAL LEVEL

 

National Totals

The National Totals provide IMPLAN information for analysis at the aggregated national level. National Totals are for users who do not need to view impacts at state, county, metropolitan, or zip code levels, but are interested in seeing only how impacts affect the nation as a whole.

 

National Package

The National Package is designed to provide IMPLAN information for analysis for every county in all 51 states in the nation. This package includes all the County Totals and all State Totals that IMPLAN produces, organized by state. The National Package provides the ability to break the state down into individual counties or groups of counties, as well as, individual states or groupings of states, or, in IMPLAN Pro, to create multi-regional analyses for any region in the nation for those doing large numbers of studies on geographically diverse regions. National Totals are also included.

 

National Package Plus

Designed to provide all the U.S. data information IMPLAN has to offer, the National Package Plus includes the County Plus, State Totals, and National Totals data so that users can create studies on any area in the nation from the zip code level up. This package is for users who do large amounts of impact analysis on geographically diverse regions and who need to view impacts at every local level.

There are three primary datasets containing non-disclosed elements that are used to estimate IMPLAN employment and labor income data: The BLS’ Quarterly Census of Employment & Wages (QCEW), the BEA’s Regional Economic Accounts (REA), and the Census Bureau’s County Business Patterns (CBP). Therefore, in order to complete the IMPLAN database, the non-disclosed values must be estimated and controlled for each regional dataset. This article describes these methods.

 

THREE STEPS TO ESTIMATING MISSING COUNTY BUSINESS PATTERN (CBP) ELEMENTS

While the CBP data lag one year behind the IMPLAN data year, they provide the number of establishments by employee-size classification for all NAICS codes in all places, even in those cases where the employment is not disclosed.  For this reason, CBP estimates provide initial estimates of non-disclosed QCEW data in some cases.

1. Estimate the missing elements by taking the midpoints of each employment-size-class and multiplying these by the number of establishments in each size class. This provides an initial estimate of employment.
2. Add the 6-, 5-, 4- ,3- and 2-digit NAICS estimates from the bottom-up to make the first adjustment to the non-disclosed elements with the corresponding NAICS. This ensures that the 6-digit NAICS add to the 5-digit NAICS, the 5-digit NAICS add to the 4-digit NAICS, the 4-digit NAICS add to the 3-digit NAICS, and the 3-digit NAICS add to the 2-digit NAICS.
3. A top-down pass is made so that non-disclosed elements are adjusted again to ensure that all data add to the overall total. This procedure is performed on the national, state, and county data. This adjusting provides a complete set of CBP employment data that is internally consistent within a county, state, or nation.

ESTIMATING NON-DISCLOSURES IN THE QCEW DATA

QCEW data are current but also come with non-disclosures and, unlike the CBP data, provide only total establishment counts (i.e., they do not provide the number of establishments by employee-size classification) for non-disclosed items. To estimate the non-disclosed elements of the QCEW data, a number of methods are used, depending on data availability.  The first option is to turn to recent past years’ disclosed raw QCEW values, applying a growth rate based on state or U.S. data.  If no recent past raw data are disclosed for a particular NAICS code in a particular geography, the next option is to turn to the CBP data.  If there are no CBP data available for this particular NAICS code and geography, we use ratios from the parent NAICS code or geography applied to the QCEW establishment counts, which are always disclosed.   

Values for all lower-level NAICS codes are then controlled to higher-level parent NAICS codes, across all NAICS levels.  Adjustments are made only to the elements that are non-disclosed; adjustments are not made to disclosed elements.

DERIVING REA STATE NON-DISCLOSURE ADJUSTMENTS

BEA Regional Economic Accounts (REA) data are integral to the IMPLAN data creation process because they cover all sectors and are one of the few sources of Proprietor Employment and Income data, as well as Employee Compensation. U.S. 3-digit REA employment and income data are reported without non-disclosures; however, the state and county level data do have non-disclosed elements. Estimation of non-disclosed state values are made while ensuring that the state values add up to the U.S. values and that the individual state sectors also sum to the more aggregated state sectors.

Disclosing Wage and Salary Employment (SA27 Tables)

1. State REA values are matched to corresponding CEW employment value. The CEW problem sectors (farm, railroad, and military) are not a problem with REA data as there are few non-disclosures in these sectors at the state level in REA.
2. After plugging in the initial estimates, state values are RASed using U.S. values as controls for the row values and the 1-digit State REA values as the column control.

Disclosing Wage and Salary Income

1. The first estimate is the corresponding state level CEW income/employment ratio times the state W&S employment derived above.
2. After plugging in the initial estimates, the state values are RASed using the U.S. as controls for the row values and the 2-digit State REA values as the column control.

Disclosing Total Employment (SA25 Tables)

The four component BEA Gross State Product data is a source of information at the state level which will tell us whether there is any self-employment income in the state.

1. If no proprietor employment is reported, then total employment is equal to wage and salary employment, the value of which is derived in the previous step ‘Disclosing Wage and Salary Employment’.
2. In some cases, a single 3-digit non-disclosure remains within a 2-digit group which can be derived through subtracting all disclosed 3-digit data from the 2-digit control value. Conversely, there may be proprietor employment and no corresponding wage and salary employment. For sectors for which both wage and salary employment and proprietor employment are non-disclosed, the first estimate is based on U.S. proprietor employment to wage and salary employment ratios for that sector.
3. Initial estimates are controlled to known totals at various stages in the process.

 

DERIVING REA COUNTY NON-DISCLOSURE ESTIMATES

 

Disclosing REA Employee Compensation and W&S Income 

1. State and County 6-digit CEW income data are aggregated to the REA sectoring scheme (the estimation of CEW data is described above).
2. To get our first estimate of Employee Compensation (EC), we either project a historical disclosed REA value for that county and industry or apply the state’s ratio of REA EC to CEW W&S Income to the county’s CEW W&S Income.
3. To get our first estimate of W&S Income, we apply the state ratio of W&S Income to EC to the county’s EC value.
4. These estimates are then adjusted as necessary to ensure that more-detailed sectors sum to their more-aggregate parent sectors.

 

Disclosing REA Wage & Salary Employment

1. State and County 6-digit CEW employment data are aggregated to the REA sectoring scheme (the estimation of CEW data is described above).
2. To get our first estimate of W&S Employment we apply either the county’s ratio of CEW Employment to CEW W&S Income to the county’s W&S Income estimate or the state’s CEW W&S Employment to CEW W&S Income ratio to the county’s W&S Income estimate. 
3. These estimates are then adjusted as necessary to ensure that more-detailed sectors sum to their more-aggregate parent sectors.

 

Disclosing REA Proprietor Employment

1. If Total Employment is disclosed, we simply subtract W&S Employment from Total Employment to get Proprietor Employment.  Otherwise, if the parent sector’s Proprietor Employment is non-disclosed, we estimate the child sectors’ Proprietor Employment by applying the state’s ratio of Proprietor Employment to EC for that sector to the county’s EC value.  If the parent sector’s Proprietor Employment is disclosed, we distribute its value to its children sectors based on state proportions of children to same parent.
2. These estimates are then adjusted as necessary to ensure that more-detailed sectors sum to their more-aggregate parent sectors.

 

Disclosing REA Proprietor Income

1. If Labor Income is disclosed, we simply subtract W&S Income from Labor Income to get Proprietor Income. Otherwise, if the parent sector’s Proprietor Income is non-disclosed, we estimate the child sectors’ Proprietor Income by applying the state’s ratio of Proprietor Income for that sector to Total EC for all sectors to the county’s Total EC value. If the parent sector’s Proprietor Income is disclosed and the child sector has the opposite sign as the parent sector, we apply the state childrens’ Proprietor Income-to-EC ratios to the county childrens’ EC values. If the parent sector’s Proprietor Income is disclosed and the child sector has the same sign as the parent sector, we subtract the disclosed childrens’ values from the parent’s value and then distribute the leftover to the non-disclosed child sectors based on state ratio of Proprietor Income for that child to state’s leftover parent to distribute (that is, the state parent less the same child sectors subtracted from the county parent).
2. These estimates are then adjusted as necessary to ensure that more-detailed sectors sum to their more-aggregate parent sectors.

 

DISTRIBUTING DISCLOSED 3-DIGIT REA EMPLOYMENT AND INCOME DATA TO IMPLAN SECTORING

With a complete disclosed set of 3-digit REA income (national income being adjusted to NIPA) and employment data, it is now possible to distribute data to the 536 IMPLAN sectors using the disclosed CEW data.

Distributing W&S Employment and Employee Compensation to IMPLAN Sectoring

1. The 3-digit adjusted REA W&S employment is distributed to IMPLAN sectors based on the CEW data that has been aggregated to the IMPLAN sectoring scheme.
2. State estimates are forced to sum to the national value.
3. County data are then forced to sum to the corresponding state values.
4. A proportion of some sectors’ activity (employment, output, income, etc.) gets reclassified into other sectors. This follows the BEA “redefinitions” practice and is designed to reassign products from producing industries in which they are secondary product to the industries where those products are primary.

 

Distributing Proprietor Employment and Income to IMPLAN Sectoring

1. Census data on employing proprietors per establishment and non-employing proprietors per establishment are used along with CEW establishment counts (aggregated to IMPLAN sectoring) to give a first estimate of proprietors.
2. These first estimates are used to distribute the REA control.
3. These final estimates are then multiplied by the REA control’s Proprietor Income per Proprietor to obtain Proprietor Income for each IMPLAN sector.

 

Special Considerations for Distribution

• Agricultural and Construction sectors are not defined by 6-digit NAICS in IMPLAN. Agriculture is a commodity based sector and construction is based off of Census construction type categories. Therefore, other distributors are used (see Special Sectors) instead of CEW data.

• CEW data also does not cover Railroad transportation, but there is a one-to-one correspondence between 3-digit REA data and IMPLAN sectoring, so no distribution is necessary.

• There are industries, at the county level, for which 3-digit REA income and employment are available but there are no corresponding 6-digit CEW data (indicating self-employment in the industry for the county but no wage and salary workers). For these cases, the 3-digit REA data are distributed to industries based on the state distribution. If this distribution places less than 0.4 employees to a particular industry, then that piece of the distribution is added to the largest component of the distribution. Finally, a sector with less than 0.25 employees before redefinitions and balancing is zeroed out.

In general, we leave this up to you. The multipliers are based on the structure of the economy of the year of the data. If you determine that the data year you have is an accurate representation of the current economy, then there is no need to update and purchase a more recent data year.

 

IMPLAN Does recommend updating your data in response to the following situations:

• A significant change in the local structure of the economy. Not all changes in local economies will be as obvious as an impact resulting from, for example, a nature disaster, like New Orleans before and after Hurricane Katrina. In many cases, economic changes result from growth in a subsector or even the introduction of new industries. The multipliers are based on the structure of the economy, whether an industry exists, and the relationships of the demand and supply of commodities. Due to increases in foreign imports, Output Multipliers typically decrease in size somewhat over time. Likewise, as productivity increases, this also indicates decreased employment needs per dollar of output.
• Release of a BEA benchmark. When the BEA releases a new benchmark, IMPLAN follows suit and introduces those new underlying sets of industry production functions. The economy and technology are constantly changing. When new BEA benchmarks are introduced, new industries are likely as well. If you wish to keep current with the BEA benchmark when reporting your analysis, a recent purchase of that data region is necessary to maintain consistency.
   
• Increased scrutiny of the project using the data. The more exposure an analysis has to the public, particularly a controversial proposal with an opposing side, the more politically important it is to use “the latest data”.