<img src="//bat.bing.com/action/0?ti=5564067&amp;Ver=2" height="0" width="0" style="display:none; visibility: hidden;">

IMPLAN Blog

2015 Data is Here!

November 28, 2016 by Phil Cheney

IMPLAN is happy to announce that our annual data products are now available for the 2015 Data Year!

As the pioneer of automated economic impact modeling, IMPLAN has a rich history of producing quality data, and we’re excited to continue bringing you the most relevant and accurate data available with the 2015 release.

This release includes improvements in data classification regarding household income categories, updates to several output figures from multiple IMPLAN Sectors, and much more.

 

Datasets and Subscriptions:

2015 IMPLAN Pro datasets are available at every one of our multiple levels of granularity (ZIP Code, county,congressional district, MSA, state, and national), and both datasets and subscriptions are available in each of our familiar data packages.

Update your IMPLAN data to the 2015 Data Year to bring even more accuracy and reliability to your studies.

 

Covered Employment and Wages (CEW):

IMPLAN’s covered employment and wages (CEW) data have also undergone a 2015 update.

CEW data offers fully-disclosed establishment counts, employment numbers, and wages and salaries at all NAICS levels (2-digit to 6-digit). Additionally, IMPLAN’s CEW data spans all four ownership types (Private, Federal, State, and Local) and is available for all states and all counties in the United States, giving you the option to choose whichever region best fits your needs!

Spanning 2001 to 2015, and in both 2007 and 2012 NAICS schemes, IMPLAN’s CEW data is complete, comprehensive, and ideal for trend and time series analyses.

 

Shannon-Weaver (S-W) Index:

The Shannon-Weaver Index is a measure of economic diversity based on the number of industries in a given region and the distribution of employment throughout those given industries. Its value ranges from a minimum of 0 (no diversity, all employment concentrated in a single industry) to a maximum of 1 (maximum diversity, employment distributed evenly across all possible industries).

IMPLAN's Shannon-Weaver Index data is available at a ZIP Code, county, state, and national level, and spans from 2001 to 2015.  

 

International Datasets:

Beginning in 2017, IMPLAN is expanding our selection of international datasets to offer over 100 international models!

We are launching a growing collection of international models drawing from three main international data sources: OECD (Organisation for Economic Co-operation and Development), Eurostat, and Statistics Canada. The datasets included in the 2015 release will exist in varying, and select, Data Years but will serve as the first installation in what will be a continued series of annually updated international models.

An exhaustive description of all of the updates and improvements made to IMPLAN products for the 2015 data release can be found below:

 

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.

 

Updating your IMPLAN data to our newest Data Year makes your information, your analyses, and your impacts even more accurate.

You work hard to make an impact. Let IMPLAN help you prove it.

Order your 2015 data here or give us a call at (800) 507-9426.

 

Topics: Data

Why IMPLAN?

Put simply, IMPLAN is built for everyone.

Together, our software and data give you a window into your region of study — like one gigantic transaction log for the local economy. Chances are that if your project or business has a financial component, then IMPLAN can reveal some sometimes surprising detail about how your project relates to the local, state, or national economy.

What used to take economists weeks can be done in minutes. By anyone!

But you're not alone, IMPLAN's best benefits go beyond the work done in the tool:

  • Easy to learn and use
  • Outstanding customer support
  • Access to orientations, trainings, and project consultations
  • Instills confidence in your analyses

Recent Posts