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2016 IMPLAN Data Release Notes

November 8, 2017 by Phil Cheney

It's that time of year again... It's data season at IMPLAN!

IMPLAN data has gotten better with every single release. That didn't stop in 2016. We've been hard at work refining our estimation techniques, improving our methodologies, and expanding our data sources. We think it shows in this year's dataset.

That's why we're especially excited to share the Release Notes for IMPLAN's 2016 data set and let everybody know exactly what improvements we've introduced with this year's release.

Here's what's new:

Significant revision to the estimation method for IMPLAN farm sectors 6, 10, and 14 

Data on ERS annual cash receipts includes several commodities in the miscellaneous crops and miscellaneous animals sectors that are not classified as "miscellaneous" according to NAICS or according to IMPLAN. In previous years, IMPLAN has attempted to "disentangle" these miscellaneous categories, but as ERS includes an increasing number of commodities in those categories with each passing year, our ability to disentangle this data deteriorates. In response to this, we began using a new method for these three sectors in the 2016 IMPLAN data set, beginning with data from the Census of Agriculture and projecting it to the IMPLAN reference year. Using this data has allowed us to better match estimates from other sources and to use more precise data in estimating county-level production in these sectors.

Improved wage and salary employment and income methodology 

When developing the 2014 IMPLAN data set, we spoke with the BEA about the difference between the BEA’s REA SA27 and the BLS’s CEW data for several industries in which there is a significant difference, but which BLS does not acknowledge any coverage gap. These industries include: fishing/hunting/trapping, membership organizations, and private education. CEW data does acknowledge coverage gaps in military, private households, farms, and railroads. The BEA informed us that they do adjust estimates for these industries due to coverage gaps. Thus, in that year, we incorporated corresponding adjustments for 4 sectors: religious organizations, commercial fishing, private higher education, and private households. After the release of the 2015 data set, we were made aware that there is also a discrepancy between the two data sources for the Agriculture and Forestry Support Services sector. Accordingly, beginning with the 2016 data release, we've included this sector in the adjustment process.   

Slight modification to commercial fishing’s and private colleges’ adjustment ratio process

When estimating wage and salary employment, there are a number of sectors that the BLS’s CEW program does not completely cover. IMPLAN correspondingly adjusts those values upward according to the ratio of the previous year’s CEW employment to the BEA’s REA wage and salary employment (which is lagged one year) for that sector. The affected sectors include commercial fishing, private higher education, religious organizations, and private households. Since REA sectoring is more aggregate than CEW sectoring for some of these sectors, we had been calculating the adjustment ratios at the aggregate sector level and applying them to the less aggregate IMPLAN sector. With the 2015 IMPLAN release, we took a more nuanced approach to calculating the adjustment ratios for religious organizations so that they would more accurately reflect the specific sector being undercovered. Beginning with the 2016 release, we now do the same for commercial fishing and private education

Improvement to the BEA Benchmark ratios used to estimate annual EC for some farm sectors  

Prior to the 2016 release, the ratios being used to estimate employee compensation for 4 farm sectors were calculated based on assigning the entirety of NAICS code 111191 to grain farming and the entirety of NAICS code 111336 to fruit farming. However, NAICS code 111191 represents combined oilseed and grain farming, and NAICS code 111336 represents combined fruit and tree nut farming. Beginning with the 2016 release, we now assume an even 50:50 split between each of the two IMPLAN sectors that correspond to each of these combination NAICS codes. This has increased the benchmark employee compensation per wage and salary worker for oilseed farming and decreased the benchmark employee compensation per wage and salary worker for the grain farming and tree nut farming sectors.

Improved classification of government-owned establishments of NAICS code 488

Prior to the 2016 release, we had been classifying this activity under administrative government. The choice of which government enterprise or institution to classify a given activity under is based on the BEA Benchmark Make table, the latest of which combines NAICS codes 487 and 488. It also classifies the combined activity under Other State and Local Government EnterprisesWe previously treated these two NAICS codes individually, classifying the former under Other State and Local Government Enterprises (in line with the BEA Benchmark Make table) and the latter under administrative government. Without evidence to support this separate treatment, we decided to better align our data with the BEA Benchmark by treating the two NAICS codes in the same manner. Therefore, beginning with the 2016 release, IMPLAN data will now classify NAICS code 488 under Other State and Local Government Enterprises.

Improved method for distributing state-level State and Local (S/L) Government hospital services sales to counties

Prior to the 2016 release, all state-level non-stumpage sales by S/L Government were distributed to counties based on administrative government employment. However, this resulted in some cases of sales of hospital services in counties where there were no state government-owned or local government-owned hospitals. Thus, beginning with the 2016 release, we distribute the sales of hospital services based on CEW data for state government-owned and local government-owned hospitals.

Updating your IMPLAN data to our newest Data Year makes your information and your studies better. It's as simple as that. So, click here to order now!

Topics: Data


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