Efficiency and Profitability

Marginal analyses were performed to analyze the relationship between efficiency and profitability. These analyses served to point out the approximate scale size associated with maximum efficiency and revenue. Efficiency is an important prerequisite of profitability, but the point of maximum efficiency is not always where efficiency is maximized, when green tons of wood produced is the output of the process. For example, contractors harvesting a larger proportion of hardwood may have lower

efficiency but will be more profitable if high value species and products are encountered.

The relationship between the most efficient operation size, also referred to as the most productive scale size (mpss), and profitability was also explored to make this point.

Marginal and average cost curves were found using methods discussed in LeBel (1996).

Least squares regression procedures were used to develop quadratic equations that expressed total costs as a function of yearly production for observations located in the efficient, partially efficient, and least efficient groups. The residuals associated with these regression lines were analyzed, and it was determined that a non-constant variance problem was present. Weighted least squares, using one over the square root of

production (x) as the weight, was used to remedy the non-constant variance problem.

Marginal cost and average cost curves were then developed from the resulting regression equations (Figures 7.33 – 7.35).

The average cost curve is concave upward in each of the three figures, showing differences in scale efficiency relating to high and low production (Stuart 1997). The rapid drop of the average cost curves on the left-hand side of the figures is due in part to the distribution of fixed costs. The average cost curve is negatively sloped until the point of least cost is reached, which represents the general location of the operation size where efficiency is maximized (mpss). DEA deemed several observations efficient that had average costs higher than the least cost point. These observations had relatively low or high production levels; therefore, the differences were due in part to variable returns to scale.

The average cost curve then rises as it moves beyond the area of lowest average cost, which, for the most part, signified a loss in efficiency. The marginal cost curve is a straight line with a positive slope, suggesting that costs, including equipment, are

variable. The study included multiple contractors over multiple years, allowing all costs to become variable. The marginal revenue line was chosen arbitrarily to be $12 per ton, which is represented by a straight line across each figure.

Marginal analyses of the least efficient observations showed that the point where

marginal costs equals marginal revenue at $12 per ton is located at a scale size lower than the area of least cost (Figure 7.33). Therefore, these observations must increase

efficiency, by reducing average cost per ton, to minimize losses. To be profitable, these observations would have to receive a marginal revenue rate higher than $12 per ton.

Figures 7.34 and 7.35 show that the point of maximum profitability, where marginal costs equals marginal revenue, is located at a larger production level than that of the most efficient operation size (mpss). In this example, for contractors operating at the most efficient operation size to maximize profits, they must increase production. This

production increase would result in higher average cost per ton, thus lowering efficiency.

A larger number of observations included in the partially efficient group were located near the scale where marginal revenue equals marginal costs (Figure 7.34). It is likely that these observations are focusing on profits rather than maximum efficiency. Several observations in the least efficient category have moved beyond the area of least cost as well, perhaps because of the relatively low slope of the marginal cost curve.

As in Stuart (1997), more observations were located in the area of least costs on the average cost curve. It is possible that these operations are being constrained to operate in this region from administrative, managerial, or natural forces. It is important for all parties involved that contractors produce as close to the point of maximum profits as possible, to guarantee future participation. Contractors in this position could, in turn, be able to provide increased production during periods of high demand, and reduce

production during periods of low demand without being driven to the point of bankruptcy.

Figure 7.33. Marginal and Average Cost Curves for Least Efficient Observations.

(TE < 0.74). (Total Cost = 1.6562E-05x² + 9.9755x + 138004)

0 2 4 6 8 10 12 14 16 18 20

0 50,000 100,000 150,000 200,000 250,000

Production (Tons)

$/Ton

Average Costs Marginal Costs

Figure 7.34. Marginal and Average Cost Curves for Partially Efficient Observations.

(0.89 > TE > 0.75). (Total Cost = 4.2979E-05x² + 4.178488x + 211747)

Figure 7.35. Marginal and Average Cost Curves for Efficient Observations (TE > 0.90).

(Total Cost = 2.9980E-05x² + 5.238750x + 125517)

7.6 Summary

Technical efficiency, an indicator of the green tons of wood produced per dollar of expenditure, was used to measure the performance of participating contractors. This information was used to explore the influence of technical, organizational, natural, administrative, and regulatory forces on contractor performance. Quarterly and yearly efficiency trends and the relationship between efficiency and profitability were also explored.

The efficiency scores were separated into three groups, efficient (0.90 – 1), partially efficient (0.80 – 0.89), and least efficient (obs < 0.79). The least efficient category

0

0 50,000 100,000 150,000 200,000 250,000

Production (Tons)

0 50,000 100,000 150,000 200,000 250,000

Production (Tons)

$/Ton

Average Costs Marginal Costs

included most of the observations obtained from the BCC model (59%), followed by the efficient (23%) and the partially efficient categories (18%). Efficiency scores for

consumables and labor were considerably higher than those for equipment. This difference indicates that equipment reinvestments have a considerable impact on technical efficiency.

Middle-age contractors tended to be slightly more efficient than the others. Contractors with the most experience tended to more efficient as well. Contractors with at least a high school education were also more efficient than the others. It is difficult to isolate forces influencing the efficiency differences associated with contractor age, education, or experience.

Separation of the observations according to state and region of operation showed that Georgia and Virginia were the most efficient, followed by Alabama, South Carolina, North Carolina, Florida, and Tennessee. The coastal plain was the most efficient

physiograhpic region, followed by the mountains and the piedmont. The majority of the mountain contractors harvested plantation pine; therefore, little difference was found between them and the coastal plain. Small sample sizes restricted rankings of contractors from North Carolina, Florida, and Tennessee.

Analysis of the influence of business organization on efficiency showed that observations using sub chapter S corporations were the most efficient, followed by general

partnerships, full C corporations, and sole proprietorships. These results suggest that contractors using sub S corporations and general partnerships to a lesser extent, are more likely to be aware of and take advantage of business management, and tax strategies.

Full C corporations were expected to show the same trends as sub S corporations, but scale inefficiencies are likely affecting these scores.

Efficiency generally increased as the percentage of pine harvested increased. This trend can be attributed in part to technical or natural forces relating to more difficult conditions encountered when harvesting hardwood in the southeastern U.S. Contractors who

purchased a significant portion of their stumpage tended to have lower efficiencies. This could be attributed to the fact that contractors harvesting a large share of company-or-dealer owned stumpage may have more access to larger, well-managed tracts.

Alternatively, contractors who purchase a considerable share of their own stumpage may be torn between procurement, marketing, and running a logging business, adversely affecting efficiency.

Excluding operations that were making adjustments or under producing efficiency tended to increase with higher capacity utilization. This trend indicates the negative effect that administrative and natural forces, in the form of wet conditions and production quota, can have on efficiency by reducing deliveries under normal levels.

Contractors producing between 60,000 and 100,000 tons per year had the highest median CCR technical efficiency. This range includes the most productive scale size, which spans approximately 55,000-85,000 tons. Even though observations with larger scale sizes were generally less efficient, contractors may have been focusing on profitability rather than efficiency. Inefficiencies resulting from scale size are likely a function of organizational and administrative forces.

Observations were classified by operational strategies relating to productivity, including upwardly elastic, downwardly elastic, and inelastic strategies. The inelastic operation strategy was the most efficient, followed by downwardly elastic, and upwardly elastic.

These results state that contractors with stable productivity were the most efficient, strongly suggesting that variability is indeed the enemy of efficiency.

The most efficient observations had relatively low equipment and consumable costs, and higher proportions associated with labor. Observations with lower efficiency tended to have higher proportions of their costs associated with equipment and/or consumables.

Separate DEA analyses were conducted on observations with and without contract trucking, using variable returns to scale (VRS) models from the OnFront software

package. The production frontiers obtained for the groups were similar. There does not appear to be a major difference in efficiency associated with trucking strategy.

Therefore, it appears that the use of contract trucking does not represent gains in technical efficiency, but was used instead to provide legal advantages, tax advantages, and to fill in trucking gaps without purchasing extra equipment.

Technical efficiency tended to increase slightly from the first to the third quarter, before dropping off somewhat in the fourth quarter, mainly due to inefficiencies relating to equipment and labor inefficiencies associated with the holiday season. Wilcoxon signed rank tests were performed on paired quarterly efficiency scores to determine if any of the quarters were significantly different. Only 1995 included quarters that were ruled

statistically different at the 90% confidence level. The differences are likely related to increases in productivity levels in the second and third quarters of 1995, as mills increased inventories. Overall, there is no convincing evidence that efficiency varies with calendar quarter.

Yearly efficiency fluctuated somewhat but appeared to be in a general state of decline for the period. Significant differences existed between 1996 and the previous 6 years at the 90% confidence level. The efficiency decline is due in part to the inability of

productivity increases to keep pace with inflation throughout the 1990’s and the period of pulp and paper market oversupply in the mid-1990’s. All three cost components

experienced median efficiency declines during the period. Consumables and labor had less dramatic declines in efficiency after 1992 than did equipment. The cyclic nature of equipment therefore appeared to have had the largest impact on efficiency after 1992.

Efficiency is an important prerequisite to the ultimate goal of any business, but profits and efficiency are not always maximized at the same point. Many observations were located in the area of least costs on the average cost curve, indicating that profits were not being maximized for these observations. It is possible that these operations are being constrained to operate in this region from administrative, managerial, or natural forces. It is important for all parties involved for contractors to produce as close to the point of

maximum profits. Contractors in this position could in turn provide added benefits to the wood supply system, including increased flexibility, safety, and professionalism.