What does productivity measure

This is because competition favours firms that are more productive, and so these firms' market share expands, while that of less productive firms contracts. In the process, the average level of productivity is increased. This process of competitive dynamics is important for keeping the economy close to its production possibility frontier.

Policies and market behaviour that undermine competition may cause the economy to slip below its potential. There is also potential for 'spillovers' between firms that mean productivity improvements can be contagious. That is, the things that firms do to benefit themselves benefits other firms as well. Proponents of proactive industry policies such as government support for innovation hubs and clusters often cite the importance of spillovers as a source of productivity growth.

However, proposals for public expenditure in this area need careful scrutiny to ensure that spillovers are indeed generated, they are from activity that otherwise would not have occurred, and the benefits exceed the public cost.

Figure 1 Australia's multifactor productivity statistics have been flat — how should this be interpreted? Measured productivity is the ratio of a measure of total outputs to a measure of inputs used in the production of goods and services. Productivity growth is estimated by subtracting the growth in inputs from the growth in output — it is the residual.

There are a number of ways to measure productivity. In Australia, the most common productivity measures used are:. The calculation of MFP using the traditional accounting methods requires independent measures of inputs and outputs. For Australia, this is calculated for 16 industries, which the ABS terms the market sector of the economy. Hence, economy-wide MFP estimates reflect productivity growth in only around 80 per cent of the economy the share of the 16 industries in total GDP.

LP can be measured for both the market and non-market sectors of the economy. This is because labour input can be measured in real volume terms as hours worked. MFP is a measure closer to the concept of productive efficiency than LP as it removes the contribution of capital deepening from the residual. Two potential sources of change in measured productivity warrant special attention: unmeasured inputs that affect real costs, and capacity utilisation.

There are also a number of measurement problems associated with estimating output and input volumes. In some industries, inputs other than capital and labour and knowledge can have a strong influence on output. Where these inputs are not purchased in the market, as is the case with some natural resource inputs and volunteer effort, they are not included in the measure of inputs. If the availability or quality of these inputs is changing then productivity estimates, as the residual, will be affected.

Recent Commission research has identified Mining, Utilities, and Agriculture as industries where the MFP estimates are affected by changes in unmeasured inputs. These industries are all dependent on natural resource inputs. Deterioration in the quality of the natural resource input, or more stringent regulatory restrictions on the uses of such inputs, can reduce measured productivity despite the productive efficiency of the firms in the industry remaining unchanged or even improving.

Senior management gave high visibility to the new system and even used it to calculate a large portion of the bonuses it paid to line managers. So the line managers computerized everything in sight. The result was increased productivity in every department but one—data processing.

While staff was shrinking in the rest of the bank, data processing came under incredible pressure. It boosted its staff as well as its spending on hardware and software. If that expansion in overhead was best for the bank, executives could never say for sure; their measurement system focused only on the productivity of direct labor. The trouble with single-factor productivity measures whether output per labor hour, output per machine, or output per ton of material is that it is easy to increase the productivity of one factor by replacing it with another.

Labor, capital, and materials are all potential substitutes for each other. Effective productivity measurement requires the development of an index that identifies the contribution of each factor of production and then tracks and combines them.

Take a hypothetical plant that machines purchased castings as one step in its production of motors. Now the company decides to purchase this component premachined. What happens to productivity? Output has remained constant, but the number of workers has fallen, so labor productivity is up. So too is capital productivity, by virtue of the lower asset base. But with top management pushing hard for identifiable productivity increases, there is a real risk that defining productivity too narrowly will lead to unsound decisions by subordinates.

A multifactor view of productivity is important, therefore, but it is difficult for one index to encompass all inputs. Using several different single-factor measures can also yield a multifactor perspective. Indeed, even if a plant uses one aggregate measure, it still makes sense to use single-factor measures because they help identify the sources of aggregate productivity trends.

A big change in a multifactor productivity measurement raises obvious questions: Is the change due to simultaneous shifts in the productivity of labor, capital, and materials, or has only one dimension changed?

Economists and productivity specialists like to use sophisticated functional forms when they combine labor, materials, and capital into one index. Rather than simply adding everything up or averaging inputs, they prefer logarithmic and multiplicative techniques.

When the chief goal is to study productivity behavior, as in statistical research, these approaches have theoretical advantages. Within Northern Telecom, some divisions make sure managers and workers understand multifactor productivity by including them in the design of department-specific indexes and by keeping the indexes simple. A department develops several performance ratios no fewer than three, no more than seven that it believes capture the essence of its mission.

For example, one design engineering team proposes six ratios, among which are: reworked drawings as a percentage of total drawings, overdue drawings as a percentage of total drawings, and overtime hours as a percentage of total hours. Next the department identifies current performance, long-term goals, and interim goals for each ratio. Finally, managers assign weights to the ratios to reflect their relative importance, with the sum totaling This approach is not analytically perfect; there is no statistical reason to limit the number of ratios to seven, for example, and the weighting scheme is undeniably subjective.

But Northern Telecom follows a basic principle that many other companies fail to appreciate: when deciding whether you need greater measurement precision, ask first whether greater precision will make a real difference in subsequent actions to improve productivity. Executives should seek the measure that promises the greatest impact, not the measure boasting the greatest accuracy or technical elegance. The same principle applies to data collection. There are real costs associated with developing and implementing elaborate productivity systems.

My research suggests that the point comes—sometimes very early on—where increased accuracy is not worth additional cost. For example, the mismatch between the information provided by some accounting systems and what is needed for productivity analysis often means that bypassing the accounting data and developing data specifically for the productivity index will raise accuracy. But it is seldom worth the cost.

The costs can go even higher if you consider another factor: the time it takes to develop and implement a productivity measurement system. But the challenge goes further. Conventional systems to measure productivity often overlook two aspects of the production process that are becoming very important in determining international competitiveness: production time and the role of employees other than shop-floor workers.

Since neither lends itself well to direct measurement, productivity technicians often prefer to look the other way. Managers do so at their peril. The first oversight, time, is not purchased, so it is usually ignored. If two businesses use identical machines, the same number of people, and equivalent materials to produce identical products, most productivity indexes would produce identical scores. But suppose one business ships orders within three days of receiving them and the other takes three weeks.

Is their productivity the same? Obviously not. This is not an exaggerated example. Increasingly, companies are discovering the competitive power of shortening their production cycles—or the dangers of not doing so. But unless a productivity index assigns some value to the amount of time consumed, it is unrealistic to expect managers to focus on shortening turnaround times.

Assigning inventory carrying costs is a step in the right direction, although most companies record carrying charges far below their true competitive costs. Carrying costs should not only be realistic but they should also reflect where the inventory sits in terms of value added as well as how long it sits. An additional time-based charge that captures how long it takes to complete an order can focus attention even more directly on possible gains in turnaround time.

The new head of a sheet metal plant owned by a major electronics company learned this lesson soon after he took over. But that represented a misunderstanding of its mission.

So the new manager introduced a productivity index that focused on turnaround time, and he posted the results prominently. Eventually, the plant cut prototype production time from 20 weeks to three days. The second crucial but often overlooked aspect of many productivity measurement systems regards whose performance is being measured. Most systems target inputs on the shop floor, but manufacturing efficiency is not only a function of who and what are located there.

Engineers, supervisors, and other white-collar employees make significant contributions to manufacturing productivity, but few systems measure their roles.

The Northern Telecom system cited earlier is a notable exception. To a large extent, the absence of such measures reflects two principal difficulties of quantifying productivity in any service setting: measuring output and connecting employee actions to outputs.

For the line worker in an auto plant, output is basically the number of cars or components produced. The connection between worker activity and output is also straightforward—the person tightens three bolts on every car, and this action helps complete the car. Measuring the productivity of product designers is a much more subtle problem. Designing an item to make production smoother will improve the efficiency of the entire plant, for example.

If such a design takes twice as long to complete as a simpler approach, it certainly does not mean that the engineer is less productive. It does mean, however, that managers must be creative and open to new ways of thinking about an operation. A plant manager of an important supplier to the auto industry met with resistance at headquarters to his request to augment his engineering staff. Most companies need to establish specific benchmarks for themselves.

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Labor productivity, or how productive a company's workers are, is an important factor for ongoing profitability.

Measuring productivity can be done in several ways, with newer methods relying on software tracking and monitoring. Compare Accounts.