Using The High Low Method The Fixed Cost Is Calculated

Using the High-Low Method to Calculate Fixed Costs

The high-low method is a simple technique used in cost accounting to separate fixed and variable costs from a mixed cost. A mixed cost, also known as a semi-variable cost, contains both fixed and variable components. Understanding these components is crucial for accurate budgeting, pricing decisions, and performance analysis. This comprehensive guide will delve deep into the high-low method, explaining its application, limitations, and how to interpret the results.

Understanding Mixed Costs

Before diving into the high-low method, let's define what a mixed cost is. A mixed cost includes a fixed portion that remains constant regardless of the activity level and a variable portion that changes proportionally with the activity level. For example, the cost of running a factory includes both fixed costs (rent, insurance) and variable costs (direct materials, direct labor).

Examples of Mixed Costs:

• Utility Bills: A base fee (fixed) plus a charge based on consumption (variable).
• Sales Representatives' Compensation: A base salary (fixed) plus commission based on sales (variable).
• Telephone Expenses: A monthly line rental (fixed) plus charges for calls made (variable).
• Maintenance Costs: A regular maintenance fee (fixed) plus costs for repairs based on usage (variable).

The High-Low Method: A Step-by-Step Guide

The high-low method uses the highest and lowest activity levels within a relevant range to estimate the fixed and variable cost components. The relevant range is the band of activity where the cost behavior is consistent. Outside this range, the cost relationship may change.

Here's a step-by-step breakdown of how to apply the high-low method:

Step 1: Identify the Highest and Lowest Activity Levels

First, you need data showing total costs at various activity levels. The activity level could be units produced, machine hours, labor hours, sales revenue, or any other relevant measure. Identify the period with the highest activity level and the period with the lowest activity level within the relevant range.

Step 2: Calculate the Variable Cost per Unit

The formula for calculating the variable cost per unit is:

(Highest Activity Cost – Lowest Activity Cost) / (Highest Activity Level – Lowest Activity Level)

This formula essentially calculates the change in cost divided by the change in activity. The result represents the variable cost per unit.

Step 3: Calculate the Fixed Cost

Once the variable cost per unit is determined, you can calculate the fixed cost using either the highest or lowest activity data point. The formula is:

Total Cost – (Variable Cost per Unit * Activity Level)

Let's illustrate this with an example:

Example:

A company's total production costs over the past six months are as follows:

Solution:

1. Identify High and Low Points: The highest activity level is 20,000 units in May with a total cost of $40,000. The lowest activity level is 10,000 units in January with a total cost of $25,000.

2. Calculate Variable Cost per Unit:

   ($40,000 - $25,000) / (20,000 - 10,000) = $1.50 per unit

3. Calculate Fixed Cost: Using the highest activity level data:

   $40,000 - ($1.50 * 20,000) = $10,000

Alternatively, using the lowest activity level data:

$25,000 - ($1.50 * 10,000) = $10,000

In this example, the fixed cost is $10,000, and the variable cost is $1.50 per unit. This means that the total cost equation is:

Total Cost = $10,000 + ($1.50 * Number of Units Produced)

Limitations of the High-Low Method

While the high-low method is simple and easy to understand, it has some significant limitations:

• Sensitivity to Outliers: The method relies heavily on the highest and lowest data points. If these points are outliers (unusual or inaccurate data), the resulting cost estimations will be skewed and unreliable. A single inaccurate data point can significantly distort the results.

• Ignoring Other Data Points: The method only utilizes the highest and lowest data points, ignoring all other data. This can lead to a less accurate cost estimate compared to methods that consider all data points. More sophisticated methods like regression analysis utilize all available data points for a more robust estimation.

• Assumption of Linearity: The high-low method assumes a linear relationship between cost and activity. However, in reality, the relationship may be curvilinear or step-wise. The method may not be appropriate if the cost behavior is non-linear within the relevant range.

• Relevance of the Relevant Range: The accuracy of the high-low method heavily depends on the selection of the relevant range. If the chosen range includes data points outside the consistent cost behavior pattern, the estimates will be inaccurate.

• Inaccurate Data: The accuracy of the high-low method is completely reliant on the accuracy of the underlying cost and activity data. Errors or omissions in the data will directly impact the results.

Alternatives to the High-Low Method

Several other methods offer more accurate cost estimations than the high-low method, particularly when dealing with non-linear cost behavior or outliers:

• Scattergraph Method: This method plots all data points on a graph to visually identify the relationship between cost and activity. It's less precise than regression analysis but provides a visual representation of the cost behavior.

• Regression Analysis: This statistical method uses all available data points to determine the line of best fit, providing a more accurate estimation of fixed and variable costs. Regression analysis considers the strength of the relationship between cost and activity and accounts for potential outliers.

• Least Squares Method: A specific type of regression analysis which minimizes the sum of the squared differences between the observed data points and the predicted values from the regression line. This leads to a more robust and precise estimate compared to the high-low method.

When to Use the High-Low Method

Despite its limitations, the high-low method remains a valuable tool under specific circumstances:

• Simplicity and Ease of Use: Its simplicity makes it ideal for quick cost estimations when a high degree of precision isn't necessary.

• Limited Data Availability: If only limited cost data is available, the high-low method may be the only practical option for cost separation.

• Educational Purposes: It is frequently used in introductory accounting courses to illustrate the basic concepts of cost behavior.

Conclusion

The high-low method provides a straightforward approach to separating fixed and variable costs from a mixed cost. However, its limitations concerning outliers, the assumption of linearity, and the dependence on only two data points should be carefully considered. While useful for quick estimations and introductory understanding, more sophisticated methods like regression analysis should be preferred for higher accuracy and reliability, particularly when dealing with complex cost behaviors and a larger dataset. Understanding both the strengths and weaknesses of each method will empower you to choose the most appropriate tool for your specific needs. Remember to always critically evaluate the results and consider using multiple methods to validate your findings.