Measurement of Service Productivity

The increased importance of service industries over the last two decade and current concern over productivity growth has stimulated interest in productivity measures for this expanding sector of the economy.

The service sector, as defined here, encompasses the major industry groupings of trade, finance, insurance, communications, public utilities, transportation, and government, as well as business and personal services.

It accounts for almost three-fourths of the Nation’s employment and provides the greatest potential, as well as some of the greatest difficulties, for developing productivity measures.

Whilst concepts of productivity measurement in manufacturing have been introduced decades ago and are based on contrasting input and output, the productivity of services is a topic that is currently under intensive research.

Productivity measurement concepts established in manufacturing cannot simply be transferred to service due to its peculiarities.

The customer is always a part of the service, and hence the customer actions need to be considered on the input side, and consequently, quantifying customer co-operation is necessary.

Furthermore, service readiness, which is the major prerequisite of service delivery, also needs to be incorporated into measuring productivity,

Techniques of Productivity Measurement

These are techniques used mainly in academic studies, but they ai9 occasionally used by some statistical institutions and even by some firms and organizations:

1. Index-based Methods: Using indices to measure productivity is the simplest method in formal terms’ and the most commonly used.

However, since the numerator and denominator can both vary /whether in terms of the magnitude adopted or the method used to estimate it), there is a great diversity of indices, each of which is problematic to a greater or lesser extent.

Productivity is usually measured as a quantity index of output over a quantity index of inputs.

Indices are required because the heterogeneity of goods and services does not permit simply adding up units of different types of commodities.

However, results of index aggregation are in general sensitive to the choice of a specific index number formula, and formulae should therefore be chosen on conceptual and on practical grounds.

2. Frontier Techniques: Index-based methods of measuring productivity are favored by both national and international statistical bodies and professional actors (firms and other organizations, trade unions and employers’ associations, and so on).

However, other methods also exist. So-called frontier techniques have been used very successfully in studies, particularly when the aim has been to assess the productivity or technical efficiency of the market and non-market services.

Farrell is generally regarded as the father of frontier techniques. The basic aim of frontier techniques is to model the production process in order to explain the relative efficiency of different production units.

Thus the production frontier is made up of the most efficient production units in a given sample (whether they be firms, other organizations, or any other decision-making level).

The efficiency of the other units is assessed relative to this empirical frontier.

3. Data Envelopment Analysis: In Data Envelopment Analysis, Only quantity observations are available for inputs and outputs.

This could, e.g., be the case for non-market activities where either no price exists or where an existing price has little economic meaning.

Output (input and productivity) indices can nonetheless be established but require econometric or linear programming techniques.

With these tools, underlying technologies and efficiency frontiers can be identified, and measured productivity growth can be split up into efficiency changes and into shifts in the technology frontier.

The price of applying these techniques is that a sufficient number of observations must be available in each period.

4. Econometric Approach: The econometric approach to productivity measurement is only based on observations of volume outputs and inputs.

It avoids postulating a relationship between production elasticity and income shares, which may or may not correspond to reality, and indeed puts researchers in a position of testing these relationships.

Further possibilities arise with econometric techniques: allowance can be made for adjustment cost (the possibility that changes in factor inputs are increasingly costly the faster they are implemented) and variations in capacity utilization.

Measures of Productivity

There are many different productivity measures. The choice between them depends on the purpose of productivity measurement and, in mam instances, on the availability of data. Broadly, productivity measures cat be classified as:

1. Input Measure: In a production process, labor, capital, and intermediate inputs are combined to produce one or several outputs:

  1. Single Factor Productivity Measures: Single-factor productivity refers to the measurement of productivity that is a ratio of output and one input factor. A most well-known measure of single-factor productivity is the measure of output per work input, describing work productivity:
    1. Labour Productivity: Labour productivity measures the number of goods and services produced by one hour of labor. More specifically, labor productivity measures the amount of real GDP produced by an hour of labor. Growing labor productivity depends on three main factors: investment and saving in physical capital, new technology, and human capital.
    2. Capital Productivity: The capital productivity index shows the time profile of how productively capital is used to generate gross value or value-added. Capital productivity reflects the joint influence of labor, intermediate inputs, technical change, and efficiency change, economies scale, capacity utilization, and measurement errors.
  2. Multifactor Productivity Measures: Multifactor productivity refers to the productivity of all the inputs used in the productivity of all the inputs used in the production process. Multifactor productivity measurement helps disentangle the direct growth contributions of labor, capital, intermediate inputs, and technology. This is an important tool for reviewing past growth patterns and for assessing the potential for future economic growth. These include labor, capital, land, and intermediate inputs (e.g., energy inputs and purchased services). Consequently, MFP measures output per unit of combined inputs and indicates the overall production efficiency of the service industry:
    1. Capital-Labour MFP: Capital-labour MFP indices show the time profile of how productively combined labor and capital inputs are used to generate value-added or gross value. Conceptually, capital-labor productivity is not, in general, an accurate measure of technical change. It is, however, an indicator of an industry’s capacity to contribute to economywide growth of income per unit of primary input.
    2. KLEMS Multifactor Productivity: The KLEMS productivity measure captures disembodied technical change. In practice, it also reflects efficiency change, economies of scale, variations in capacity utilization, and measurement errors. When capital and intermediate input measures are aggregators of detailed types of assets and products, each weighted by their respective share in total cost, and based on prices that reflect the quality change, the effects of embodied technical change are picked up by the capital and intermediate inputs terms, and only disembodied technical change enters the MFP measure.

2. Output Measure: Definition and measuring output – the output that appears in the numerator of productivity indicators can be represented by gross output or by value-added, that is, by output minus intermediate consumption (VA = P-IC).

The justification for this second alternative is simple. The productivity gains recorded may owe nothing (or very little) to more efficient utilization of the factors of production.

They may simply be a consequence of better-quality intermediate goods (raw materials, semi-finished products, and so on).

In other words, the increased productivity of a given production unit may essentially be the result of increased productivity in another entity situated upstream.

Thus using the gross output to measure productivity particularly penalizes those units that are most strongly vertically integrated.

Thus since all production units transform inputs from other units into outputs using their own factors of production, the idea is that what it really produces is the difference; that is what it adds to the inputs it consumes.

However, it is not always easy to determine the quantity added, since it is never a concrete additional quantity’:

  1. Gross Output: The goods or services that are produced within a producer unit and that become available for use outside the unit. This is a gross measure in the sense that it represents the value of sales and net additions to inventories without, however, allowing for purchases of intermediate inputs.
  2. Value-Added Concept: When purchases of intermediate inputs are deducted from the gross output, one obtains a measure of value-added. In this sense, value-added is a net measure. It may not be considered a net measure in the sense that it includes the value of depreciation or consumption of fixed capital.

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