Analysis of North Carolina Forest Industry Earnings: Adapting Household-Level Data from the American Community Survey to a Social Accounting Matrix. Issue 2 (14th September 2017)

• Record Type: Journal Article
• Authors: Scouse, Adam; McConnell, Eric; Kelley, Stephen S; Venditti, Richard
• Is Part Of: Journal of forestry. Volume 116:Issue 2(2018)
• Page Start: 101
• Page End: 108
• Publication Date: 2017-09-14
• Subjects: input-output analysis -- social accounting matrix -- forest products -- income distribution analysis -- North Carolina; Forests and forestry -- Periodicals; Forests and forestry -- Societies, etc; Forests and forestry -- Terminology; Forests and forestry; Forests and forestry -- Societies, etc; Periodicals; Terminology; 634.905
• DOI: 10.5849/jof-2017-032
• Languages: English
• ISSNs: 0022-1201

Abstract

Abstract: There is a significant need to not only understand how different industries contribute to overall wealth but how they affect certain segments of society. This study augments input-output social account matrix (SAM) modeling techniques with American Community Survey (ACS) Public Use Microdata Samples (PUMS) to better characterize North Carolina forest products industry earnings' impact on low-, medium-, and high-income households. A 2014 North Carolina SAM was created using IMpact Analysis for PLANning (IMPLAN) and customized so that industry-specific earnings were allocated to household income classes according to the distributions contained within the ACS-PUMS data set. Multipliers were determined to describe earnings distributions per dollar change of final demand. These multipliers were then contextualized by perturbing the SAM model with a 10% change in final demand for relevant forest product industries. The results of the analysis indicate that SAM analysis methods based on unmodified IMPLAN models underestimate earnings paid to low-income and overestimate earnings paid to high-income households resulting from economic growth in the study area. Scenario results obtained using our updated SAM model highlight the improved analytical capabilities of this approach for measuring impacts across income class.