A Stochastic Production Frontier Analysis of Factors That Affect Productivity and Efficiency of Logging Businesses in Virginia

Abstract

Abstract Understanding the effect of the relationship between timber harvesting attributes on loggers’ productivity and efficiency is crucial for the feasibility and expansion of sustainable forest management and logging. We applied a stochastic production frontier model to firm-level operational data collected from 202 loggers in Virginia, United States, in 2019. Logging equipment value, physiographic region, tract area, number of workers and crews in the woods, college education level, and harvest type statistically increase harvesting productivity. Harvesting productivity in the Coastal Plain was the greatest of all physiographic regions, and pine clearcut productivity was statistically greater than that of hardwood thinning. On the other hand, manual felling reduces harvesting productivity. We found an average efficiency rate of 67% among firms in our sample, which is similar to that found in the literature. The estimated values can show factors that improve forest harvest productivity through better planning and investments while improving the sustainable use of inputs and resources. Study Implications: We empirically analyzed factors affecting logging productivity and efficiency in the southern US state of Virginia. Increased productivity was associated with working in the Coastal Plain physiographic region, investing in logging equipment, increasing the number of workers and crews in the woods, increasing pine clearcut as opposed to hardwood thinning, choosing optimal harvesting tract size, and having a college education as opposed to no high school degree. Manual felling reduces harvesting productivity, and average BMP implementation time does not affect harvesting productivity. Our results can be used as a guide in planning future decisions to increase logging productivity.