William B. Stuart & Laura A. Grace - Department of Forestry Mississippi State University
The Logging Cost Index is a 20-year long study gathering the actual costs and productivity of a stable set of logging contractors in the Eastern United States, begun at Virginia Tech and now housed at Mississippi State, with support from the Wood Supply Research Institute.
What Is the Logging Cost Index Project?
The Logging Cost Index is the result of a two-decade effort to document the actual costs of harvesting and delivering forest products. The Project was started at Virginia Tech and moved to Mississippi State in 2000.
We collect cost and productivity information directly from contractors willing to participate. Our graduate students have traditionally visited each firm annually to collect the data and discuss the state of the business environment in the contractor’s locale. Participating contractors are located from Michigan to Florida and west to Texas, and we havetried to involve a balanced mix of small, medium, and large contractors. The smallest producer in 2007 delivered 26,000 tons, the largest just over 450,000 tons. We have also tried to get a mix of operation types: some produce primarily pine, others a mix of pine and hardwood, others specialize in thinning pine stands. Some produce cut-to-length material by mechanized systems, others by manual. Several produce residue chips as well as roundwood, but the majority produce tree-length as the dominant form and log-length material as the tract and markets require.
We gather cost information from accounting records—income statements and balance sheets—or tax filings. We summarize these cost data into six categories:
1) Equipment - depreciation and interest onequipment loans,
2) Labor - direct wages plus fringes, workers compensation insurance, retirement program contribution, uniform allowances, and other related expenses,
3) Consumable supplies - fuel, oil, tires, repair and maintenance parts and materials, service calls and charges, and any equipment that can be expensed,
4) Contract services - trucking, road building, BMP or other work by an external contractor or a companion firm with common ownership,
5) Insurance - fire, theft, vandalism, liability, andrelated policies, and
6) Administrative overheads - office employees, office and shop rent and/or maintenance, mechanics, accounting and legal services, phone, electricity, training and entertainment expenses, licenses and permits, property and business taxes, heating and any other costs not directly tied to production.
There is no allowance for profit in these costs, although we add a wage allowance for the contractor when he is employed full time running the business.
Production data come directly from delivery records. Deliveries measured volumetrically—cords, MBF, or other convention—are converted to tons using local conversion factors.
The economic contraction that began in 2008 has placed pressures both on loggers and on university research budgets that have made collecting this information increasingly difficult. During the past three years there has been an unusually high level of turnover among participants. Some have left the industry, some have retired, and some have restructured due to a mill closure’s reducing or eliminating markets for their services. When a firm leaves our base of participants, we try to replace it with a firm in the same region with similar equipment and production. Introducing a new firm entails visiting candidate firms, getting them to agree to participate, collecting the necessary supporting information, and establishing a working relationship with their bookkeeper or accountant, all of which requires both time and travel expense.
What Have Been the Project’s Main Insights?
We’ll examine some cost data the Project has tracked over time. The costs shown in the following examples have all been adjusted to 2010 dollars to remove the effects of inflation.
Average logging cost per ton, as displayed in Figure 1, has changed very little over the life of the study. Nominal costs (dotted line) have risen, but when adjusted for inflation (solid line) they have hovered between $18 and $21per ton for the 20-year period that concluded in 2007. Fuel costs appear to be the major factor driving year-to-year variation.
Business management strategies
Figures 2a-e shows the annual percentage of total costs for each of the six summary categories. The “Big Four”—equipment, consumable supplies, labor, and contract services—account for approximately 95% of the total costs (for the purposes of this article, we have combined insurance and administration). Contractors develop strategies for shifting costs between capital assets (equipment), operating costs (consumable supplies and labor), and outsourcing (contract services) to survive, since rate structures for their services tend to move slowly in times of stress. The effects are apparent in the Figures: as consumable supplies’ cost rises, investment in equipment tends to diminish, and contract trucking increases.
Equipment
As shown in Figure 2a, expenditures for equipment rose in the first 10 years of the study but dropped by roughly one third during the past decade. This observation may reflect the longer productive life of the equipment, but more likely is the result of strategies to offset the rising costs of consumable supplies and the increased use of contract trucking (Figure 2b and Figure 2d). In either case the trend line demonstrates that contractors have reduced their capital investment or equity in their businesses by 25%-30% over the period. Repair and maintenance costs rise with older equipment, adding to consumable supplies’ costs. “Getting out of the trucking business” involves concentrating available capital on assets crucial to the business.
These strategies, in their own right, carry risk. Used logging equipment has a smaller resale market than that for construction and agricultural equipment and has fewer alternative uses. Contract trucking not only reduces capital investment but may increase efficiency by enabling the logger to schedule only the number of trucks needed by day or week. Realizing those savings depends on the availability of a pool of properly equipped, willing contractors.
The declining share of expenditures for equipment is of concern because most of the equity of these businesses is in depreciable, personal property. Reduced spending for machinery is an indication of decreasing business equity, which will in time make it increasingly difficult for the firms to survive and grow.
Consumable Supplies
Consumable supply costs (Figure 2b) rose from the mid-1990s through 2006, even with the shift to contract trucking, a move that did not reduce fuel costs, only shifted them between classifications. Much of the increase was due to rising fuel and lubrication costs, but a share was also attributable to the increased repair and maintenance on aging equipment.
Labor
Labor costs fell as a percentage of total costs (Figure 2c). If the change was due to a historical force, such as more productive machines and systems, the change should have been more consistent and stable. The most likely cause was the increase in contract trucking—the labor costs are still there but shifted to another category. Another likely reason is that crew size has been reduced to cut costs, and older skilled workers are retiring, being replaced with younger, less highly paid workers. The work force in many of the firms is aging, and finding skilled, dependable replacements is difficult.
Contract Services
Contract services (Figure 2d) are a safety valve for most operations. It is one category where adding or reducing capacity can be accomplished without long-term commitments in machines or labor. There is an indication of a link between expenditures for consumable supplies and contract services. Contract trucking tends to increase when fuel prices rise. Changes in and disappearances of markets have increased haul distances for some of the firms. Longer hauls reduced trucking capacity, and many operations chose to use contract trucking instead of investing in additional equipment. Each manager makes decisions based on his situation at a particular point in time, and the scope and methodology of this study are not adequate for attributing causality; a full investigation would require more detailed background data than we have on hand.
Insurance and Administration
These “overhead costs,” shown in Figure 2e, are minimal for most operations, accounting for 5%-7% of the total costs. Insurance in this case is exclusive of Workers’ Compensation because, in most cases, Workers’ Comp is tied directly or indirectly to wages and therefore was considered a labor expense. Insurance costs peaked in 1995, fell back through 1999 and have been rising slowly since. We would like to believe that the reduction was due to the improved awareness of safety and health issues arising from the education programs of the SFI effort, but a significant share resulted from the increase in contract trucking and aging of logging equipment. Administrative costs have been rising as recordkeeping and reporting requirements have become more onerous.
Are There Economies of Scale?
We were able to test an old hypothesis that there are economies of scale (that larger operations are more cost-efficient than smaller ones) in logging. We published a paper in the Journal of Forest Policy and Economics based on 737 well-documented observations demonstrating that the hypothesis did not hold for the logging industry.
We found that total annual costs were linear across the range of operations in the study, and that production alone explained 85% percent of the variation (Figure 3). Large variations above or below the line usually reflected a short-term business change, such that the firm moved back closer to the average in the next year or two, as the operation re-stabilized. For example, a firm might make a major equipment investment in one year, and then move back closer to the average as the equipment depreciated and the loan was repaid. In some cases, contractors incurred higher costs when weather or markets curtailed their production and they restructured their costs in response. By the same token, observations below the line often reflected firms operating older equipment scheduled for replacement in following year or years. When the equipment was replaced, those loggers’ costs moved closer to the trend line.
The smaller operations tend to be hardwood loggers producing grade hardwood logs and pulpwood, although there are pine-thinning and small-tract loggers in the group. The majority of the firms produced between 50 and 150 thousand tons per year, usually with a single crew. The larger operations tended to have multiple operations serving different markets.
Most of these contractors have been in business for decades. They have concentrated on “right sizing”— choosing an equipment spread and crew size that matches the timber, markets, and labor force in their work area. Some have moved between the small and medium or between the medium and large categories over the life of the study, searching for a system and size that best fit their situation.
Conclusions
Collecting logging cost information over time, in a consistent manner, from a stable base of logging firms occurs under many encumbrances. Mills’ concern about preserving independent contractor relationships, through an “arms length” relationship, inhibits procurement staff from gathering information about contractors’ businesses, as does concern over possible allegations of price fixing or constraint of trade. But apart from these legitimate concerns, the simple expense and effort involved in collecting these data and maintaining a long-term, cohesive database are also a factor to reckon with.
For these reasons, research on the economics of the wood supply component of forestry has relied on estimates using the tools of microeconomics, “engineering economy” (the study of how changes in small operating units respond to changes within a larger operating environment), and short-term surveys.
What can the “micro-economic” approach tell us—applying supply and- demand formulas within an abstract modeling of an industry or society in the aggregate? In our view, the learnings are limited, since most “micro-economic” models avoid consideration of individual firms’ responses to markets, tax codes, financing needs, regulatory issues, insurance negotiations—the “messy” details at a small business’s ground level. The main technique of the micro-economist is to split costs into two basic components, “fixed” and “variable,” and apply various decision-making strategies to them. Our view is that only a few micro-economic tools are helpful in understanding individual firms’ behaviors; the micro-economic approach works better in examining behavior of aggregated populations (of people, firms, or markets, for instance).
The “engineering economy” approach is more specialized. The researcher using this approach will develop cost and productivity data at a very specialized level (an individual’s behavior or a machine’s production) and then apply trend data to the larger operating environment. The well-known “Machine Rate” model for logging research, which Don Matthews developed in the 1930s for comparing different machines and loggers’ operating strategies, is a common approach today. In applying this “Machine Rate” model, the researcher develops fixed and variable costs, as well as production, for various components in a system and then totals these costs to derive an estimated “joint productivity” and cost per unit of output. This approach uses straight-line depreciation, charges interest as an “opportunity cost” against the average undepreciated value of the machine over its working life, and considers only direct capital, operating, and labor costs associated with production. (Since such costs are considered “indicative of everybody’s but representative of nobody’s,” it is possible to discuss them openly without fear of price-fixing allegations.)
However, even the “engineering economy” approach has its limits, in modeling the actual costs of running a logging business. Each contractor follows a business plan of his own making, chosen to match his own situation, the capabilities of his system, and the local market for his services. The business records of two contractors in the same area, serving the same markets, may differ greatly in their allocations of expenditures across the categories we have discussed, because the strategies each adopts, to allow profitability, differ. Business plans, like battle plans, are situational; they may reflect the owner’s objectives, but they must adapt to the realities they encounter.
Expenditures tend to follow trends in the general economy. Fuel cost variability is one example. Hesitancy to invest in equipment in a climate of uncertainty is another. The points widely above the line in Figure 3 represent years when the contractor’s business plan and production were at odds. Quite often, the next year’s data found the contractor back much closer to the population average; adjustments had been made. Points well below the line often indicated that the contractor’s older machines had been fully depreciated and would soon have to be replaced but were continuing to operate satisfactorily. The next year’s observation would move that contractor closer to, or above, the line, reflecting the larger depreciation and interest costs of the replacement machinery.
In our view, the Logging Cost Index demonstrates that the era of “capital substitution for labor” and other large-scale “fixes” is over. The machines and systems employed have stabilized, and no major technological breakthroughs are on the horizon. The costs of both machines and labor tend to move in unison, indicating that both respond to the same stimuli.
So is there a “magic wand” for increasing supply chain productivity, the way the development of specialized logging machinery enabled such an increase in the late Twentieth Century? We don’t know—but we expect that the search for it will soon be underway.
Neither logging nor its transportation component is an industry in which economies of scale apply, especially in an era when average tract size is shrinking and the effect of weather is growing in relative importance. Today’s “large” logging firm is typically organized as multiple crews, each operating as a small business under a common umbrella.
Data collected for the Logging Cost Index are a valuable tool for understanding the functioning and performance of the “middle link” of production forestry. The health, vitality, and future of all three components are inevitably linked, and all must remain economically healthy if the whole is to survive.
We would like to express our special thanks to the cadre of graduate students at Virginia Tech and Mississippi State that accomplished the field work and analyses for the Logging Cost Index over the years:
Clay Altizer, Jason Cutshall, Orlando Ellerby, Brian Jackson, Luc LeBel, Easton Loving, Lee Miller, Chris Omohundro, Xiana Santos, James Shannon, Dustin Smith, Jeff Smith, Ronald Stutzman, & Mike Walter
THE AUTHORS: Bill Stuart and Laura Grace, well known for their work on the economics and structure of the wood supply business, and for their lengthy involvement in the Logging Cost Index project, serve on the Forestry faculty of Mississippi State University. FRA welcomes the opportunity for dialogue on wood supply dynamics; the authors’ views are not necessarily the views of the Forest Resources Association.
