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”.

The U.S. control totals for the Household Personal Consumption Expenditures (PCE) come from the BEA’s National Income and Product Accounts (NIPA) for the current year.  Beginning with the 2017 IMPLAN data, we now also incorporate state-level PCE data from the BEA (which are lagged one year but controlled to the current NIPA totals).  There are about 100 NIPA expenditure categories,  which we distribute to the IMPLAN sectoring scheme using more detailed PCE data from the latest BEA Benchmark I-O.  

The BEA data are for all household income groups combined; to break these out by household income group we turn to the Bureau of Labor Statistics’ Consumer Expenditure Survey (CES) data, which are lagged but provide estimates of expenditures by various income categories.  

These expenditure data are in terms of purchaser prices, so we must margin the data; that is, we must split the purchaser values among the producer price and any transportation, wholesale, and retail margins. This is done using data from the latest BEA Benchmark. These values are then matched to the appropriate producing, transportation, wholesale, and retail sectors, resulting in an allocation of PCE spending across IMPLAN sectors for each IMPLAN household income group.

For each household income category, these national spending values by IMPLAN sector are converted to coefficients (proportions of total PCE). To get state-level and county-level PCE expenditures by IMPLAN sector and household income category, these national coefficients are multiplied by each state and county’s spending by household income category. Therefore, the spending pattern for each income class is constant across the U.S. While the CE data is reported by large region, analysis performed by IMPLAN showed no statistically significant difference between regions.

INTRODUCTION:

Both Margins and Deflators are included in the IMPLAN database. Margins allow for consumer expenditures to be traced though retail, wholesale, and transportation Industries back to the industries who manufactured the product, allowing the appropriate allocation to the producing Industries. Built-in Deflators allow for adjustments of the Dollar Year you enter on the Impacts screen and the Dollar Year you see in your Results. Note: margins and deflators in IMPLAN are not regionally specific.

 

MARGINS:

Most Input-Output models, including IMPLAN, record expenditures in producer prices. This allocates expenditures to the Industries that produce the goods or services. Any Output or sales you want to apply to multipliers that are in purchaser prices (prices paid by final consumers) need to be converted to producer prices or allocated to the producing Industries. Margins enable the move from producer to purchaser prices or vice-versa. Data on margins comes from the BEA Benchmark I-O tables.

Below is an example to show how a purchase is allocated with Margins. Assume that a consumer spends $150 at a retail store. A portion of that price, $20 in this case, is retained by the retailer. Another portion, $30 goes to transportation costs, and $100 goes to the producing Industry that actually made the item.  

 

 

The only Industries that can be margined in IMPLAN are retail and wholesale. Nearly all Commodities, on the other hand, can be margined.  This file shows the 2018 Margins for Industries and Commodities.

 

 

INDUSTRY EVENTS

This feature is only available for retail and wholesale Industry Events. To apply margins for an Industry Event, open the menu. Here you will have the option to choose between Total Revenue (Purchaser Price) and Marginal Revenue (Producer Price). Most times, we only know the Purchaser Price, so leaving Total Revenue selected is the right choice. The Results in this case will only show the retail margin and its impact. Your Direct Effect to the retail Industry will be smaller than the Total Revenue you entered on the Impacts screen. All of other pieces of the value chain are lost (production, transportation, and wholesale).

If Marginal Revenue was chosen, the full $1M would be the Direct Output in your Results. This implies that you are modeling what the retailer is keeping (instead of just a portion of the item cost).

 

 

COMMODITY EVENTS

To apply margins in a Commodity Event, open up the menu icon and select between Total Revenue (Purchaser Price) and Marginal Revenue (Producer Price). Again, we usually know the Total Revenue, so the default selection is fine. The Results in this case will show us the impacts on the entire value chain for this Commodity; production, transportation, wholesale, and retail, with Direct Effects in each (when applicable).

If Marginal Revenue was chosen, you would again expect to see the full $1M applied to the Commodity 3366. The only deduction you might encounter is if some of the product was taken from inventory or produced by the government. 

 

 

 

DEFLATORS:

Deflators are used to adjust for relative price changes over time. Output deflators are Industry specific and are used to adjust Industry Output. GDP deflators are not Industry specific and are used to adjust Final Demand and Value Added. Output and GDP deflators from the BEA are used for all past years, while BLS output deflators are used for future years.

The Bureau of Economic Analysis (BEA) provides historical Output deflators which we use for past to current years. The BEA Output deflators are provided with the BEA Gross Output data.

The BEA also has historical GDP deflators which we use for past to current years. The BEA GDP deflators come from NIPA Table 1.1.9 – Implicit Price Deflators for Gross Domestic Product. The BLS produces time-series of Output estimates for its Employment Growth Model. The Outputs are projected in real and constant dollars. This gives implicit price index projections which are the basis for projections of the IMPLAN deflators.

Both the BEA and BLS deflator data have fewer Industries than the IMPLAN Industry scheme; therefore, all IMPLAN Industries within a single BEA or BLS Industry will have the same deflator. This file shows the 2018 Deflators for Industries and Commodities.

 

DETAILED INFORMATION:

The purchasing power of a dollar changes over time (typically decreasing) due to inflation, a cyclical phenomenon by which prices of goods and services increase[1], which spurs workers to demand higher wages, which in turn increases demand for goods and services, thereby spurring additional price increases, and so on.  Due to inflation, a dollar in 2017 cannot purchase as much as did a dollar in 2001, for example; as such, a 2017 dollar is not the same thing as a 2001 dollar.  IMPLAN’s deflators are indexes of inflation, with the deflator for the model data year set at 1.00. 

The deflators are not used to create the social accounts or multipliers but are necessary for impact analysis whenever the Dollar Year of the event differs from the Data Year being used. The same model year multipliers are used regardless of the Dollar Year of the event; it is the value applied to those multipliers that changes when the Dollar Year of the event differs from the Data Year.

All the relationships in the multipliers are based on model year prices, so the Direct Effects applied to those multipliers need to also be in the correct Dollar Year – this is accomplished via the deflators. The value applied to the multipliers is the user-entered value divided by the deflator. The deflators also allow impact results to be viewed in years other than the model year, regardless of whether or not the Dollar Year of the event differs from the Data Year. 

While the Event values and/or result values can be inflated or deflated, depending on whether the index value being applied is less than 1.00 or greater than 1.00 (i.e., depending on the Industry or Commodity and whether one is adjusting to a future or past value), we use a single term – deflators – to refer to all of these index values.

The Output deflators are specific to the Industry or Commodity and are applied to the Output value, while GDP deflators are the same for every Industry and Commodity and are applied to all of the value-added components.

Margins are derived from the Bureau of Economic Analysis Input-Output tables.  Margins are particularly important for Personal Consumption Expenditures (PCE) values as nearly all household purchases of goods are through a retail Industry. The Margins used to form the PCE data elements are compiled from the BEA Detailed Benchmark tables. This data provides the Margins associated with each of the different Personal Consumption categories. These PCE categories are modified to fit IMPLAN Industry definitions.

 

DOLLAR YEAR & DATA YEAR:

By default, impacts will be reported in current year dollars; however, because IMPLAN data are typically lagged a year (i.e., 2018 data were released in 2019), it is handy to be able to report the results in current year dollars. This can be achieved by changing the Dollar Year on the Results screen. This is just an option, and is not the same thing as changing the Dollar Year on the Impacts screen. The Dollar Year must match the year that the dollars represent – this ensures that the correct value is applied to the multipliers.  

Suppose you are going to model the impact of the 500,000 visitors that came to your tourist attraction in 2020.  Also suppose that you didn’t conduct your own visitor expenditures survey and are thus borrowing a survey that was conducted on a similar tourist attraction in a similar region but way back in 2010.  That survey gives you the per-tourist expenditures on things like lodging, food, transportation, and entertainment.  For example, each tourist spent $200 on lodging during their 3-night trip to that attraction. If you were to set Dollar Year to 2020 and put in $200 you would be understating your impact because 3 nights at a similar hotel would cost more than $200 in 2020 due to inflation!  So you’d want to set the Dollar Year to 2010, since that is the year that those $200 represent.  IMPLAN will inflate accordingly and apply the value to the multipliers. 

More information on on this can be found in the article Dollar Year & Data Year.

 

2018 MARGINS Margins represent the value of the wholesale and retail trade services provided in delivering commodities from producers’ establishments to purchasers. This file contains the four components of the Value Chain: retail, wholesale, and transportation margins for each Industry along with the Producer value.	Download
2018 DEFLATORS Deflators are used by the software whenever the Event Year is set to a year that differs from the model Data Year. This file has the deflators/inflators for 1997-2060.	Download