Diminishing marginal returns: what does another kitchen worker add?

Sources checked 31 October 2026. The catering kitchen and both production tables are hypothetical. The lab calculates differences in entered output; it does not forecast production or recommend a staffing optimum.

In a hypothetical catering kitchen, four people prepare 240 finished meals during a four-hour shift. The owner adds a fifth person before a busy event. The team completes 250 meals. There is another wage to pay, and the owner wants to know whether a sixth person would help.

The same oven is still running. The preparation bench has not grown. Someone can wash vegetables while someone else packs, but there are only so many people who can reach the bench at once.

That is a reason to investigate the arrangement. It is too early to conclude that the fifth worker was ineffective, or that the business should never hire again.

Keep the comparison narrow

OpenStax describes marginal product as the additional output associated with an extra unit of an input, holding the other inputs fixed. With labor increasing against fixed equipment, successive additions can eventually contribute less. The early additions may contribute more before that decline begins. [1]

For this kitchen, compare the same four-hour shift, menu, quality standard, oven, and preparation area. Assume ingredients and orders are sufficient for the output shown. Count completed meals that meet the agreed standard, rather than trays started or meals waiting for inspection. A change in what counts as output would change the comparison.

Headcount also needs context. Two people working different hours are not two equal additions of labor. Experience, task allocation, and breaks matter. The example holds these conditions comparable so that the arithmetic is readable; a real observation sheet needs to record them.

Read three quantities separately

Suppose the entered totals for one through six workers are 60, 150, 210, 240, 250, and 240 meals. Zero workers produce zero in this manual-work example. Each total describes a separate staffing condition for one shift; these totals are not cumulative meals across the week.

The additions are therefore 60, 90, 60, 30, 10, and minus 10. Adding the third worker raises total output from 150 to 210, while adding less than the second worker did. The fourth and fifth additions are smaller again. This is the pattern the owner needs to examine.

Average output answers another question. At four workers it is 240 divided by four, or 60 meals per worker per shift. The increment from three to four workers is 30 meals. Using the average to predict another worker would assume a contribution the table does not support.

One fixed kitchen. The discrete totals rise through five workers while the additions shrink after the second. The sixth entry falls. All figures are assumed meals completed in a four-hour shift.

A smaller gain can still be useful

The fifth addition is positive: ten more meals. The sixth is negative: ten fewer. Falling increments and falling total output describe different conditions. A zero increment would leave the total unchanged. Keep the signs visible rather than labeling every small addition a failure.

The table alone cannot decide whether ten meals justify another worker. The owner needs the contribution from those meals, the labor cost, and any effects on quality, overtime, or delivery commitments. A worker might also reduce strain or cover absences without raising this output count. Neither the highest total nor the highest average in the lab is a profit-maximizing recommendation.

If these were observations, the negative sixth increment would need checking. A more complicated menu, an oven interruption, fewer orders, or different staff could explain it. Crowding is one possible mechanism. A declining sequence of measured differences does not establish that adding labor caused the decline.

Change the equipment as a separate assumption

OpenStax's long-run discussion allows equipment to change and then considers a new short-run production relationship. [2] For the kitchen, an additional oven and more preparation space would define a different arrangement. Its output still needs evidence.

The production lab offers a second assumed table: 60, 150, 240, 320, 380, and 420 meals for one through six workers. These numbers illustrate another possible arrangement. They are not calculated benefits of buying an oven. Edit either table, switch between them, and inspect each staffing step. Your edits stay with their respective arrangement.

Returns to scale asks about changing all inputs in the same proportion and comparing the proportional change in output. MIT's production notes distinguish constant, increasing, and decreasing returns on that basis. [3] Adding workers to one oven does not answer that question. Neither does our second table: it changes selected equipment without specifying proportional changes in every input.

Before the next staffing decision, spend ten minutes writing the shift length, menu, output standard, and fixed equipment beside recent staffing observations. Calculate each adjacent difference. Circle a smaller increment and list two explanations besides headcount. Then assign a short, comparable production trial with a review date. Use its results, together with costs and service requirements, to decide what to change next.

Sources

• [1] OpenStax, Principles of Economics 3e, section 7.2, “Production in the Short Run”. Definitions of fixed inputs, total product, and marginal product. The catering figures are an original illustration, not textbook data or an estimated production function.

• [2] OpenStax, Principles of Economics 3e, section 7.4, “Production in the Long Run”. Explains how changing equipment creates a different production relationship. No equipment-effect estimate is borrowed.

• [3] MIT OpenCourseWare, 14.01, “Lecture Summary 5: Production” (Fall 2023), section 3.1.4. Defines returns to scale through proportional changes in all inputs. Used only for this distinction.