Comparative Efficiency Studies

  • Simona AlfieroEmail author
  • Alfredo Esposito
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-31816-5_3292-1

Synonyms

Definition

The organization’s efficiency is about the comparison between the outputs (services or products) it produces and the inputs (resources) it uses. An efficient organization would be one that produces the maximum possible outputs given its inputs, or one that produces a certain level of output with the minimum amount of inputs.

Generally, the efficiency can be achieved under the conditions of maximizing the results of an action in relation to the resources used, and it is calculated by comparing the effects obtained in their efforts. Measuring the efficiency requires: (a) estimating the costs, the resources consumed the effort, in general, found in the literature as the input; (b) estimating the results, or the outputs (services, projects, products); (c) comparing the two.

Introduction

The efficiency issue is critical to evaluate the performance of an organization.

Measuring and managing performance is relevant to anyone – individuals, firms, and organizations. No matter how good you think you are, you can always be better. However, it requires you to measure performance properly and understand what drives performance. In this way, it is possible to learn better practices, to make better decisions, and motivate improved performance.

In order to contribute to the knowledge of performance, in recent decades, many researchers have focused on studying and analyzing efficiency.

Firms and organizations use multiple inputs (resources) to pursue multiple outputs (services or products). From the relationship between input and output, it is possible to evaluate the reached level of efficiency.

Moreover, the input and output interact in complicated ways. Two resources, say labor and capital, may, for instance, both substitute and complement each other.

Such interactions imply that simple, key performance indicators, including simple financial ratios, do not suffice to measure performance or guide decision-making.

The measurement of efficiency is even more relevant in the public sector. The effective use of public resources is important to guarantee the economic growth, the stability and the individual well-being.

The adequate measurement of public sector efficiency is a difficult empirical concept and the literature on it, particularly when it comes to aggregate and international data, is rather scarce. The measurement of the costs of public activities, the determination of goals and the evaluation of efficiency via appropriate cost and outcome measures of public policies are very thorny issues. Academics and international organizations have made some progress in this regard by paying more attention to the costs of public activities, by looking at the composition of public expenditure and by monitoring the level of reached results and the satisfaction of users.

Economists are affected about the efficient use of scarce resources. The issue of efficiency finds a prominent place in the study of the spending and taxing activities of governments and public entities. Economists believe that these activities should generate the maximum potential benefits for the population and they penalize those organizations when, in their view, they use resources inefficiently.

The measurement of efficiency generally requires: (a) an estimation of costs or resources; (b) an estimation of output or services or projects; and (c) the relationship between the two. Applying this concept to the spending activities of public entities, it is possible to declare that public expenditure is efficient when, given the amount spent, it produces the largest possible benefit (output) for the population.

Often efficiency is identified in a comparative sense: the relation between input and output of an organization alpha is compared with the others. This can be done for total public expenditure, or for expenditure related to specific functions such as health, education, poverty alleviation, building of infrastructures, waste or water provision, and so on. If in the organization alpha the output exceed the input by a larger margin than in other entities, then public expenditure in organization alpha is considered more efficient.

The simple comparison outlined above requires that both input and output be measured in acceptable ways. This is easy, or easier, for private sector or for a single asset (machines, cars, furnaces) but difficult for public entities. It is often difficult to measure the services or output from a public expenditure. But, one could assume that, at least the costs (i.e., the resources used) should be easy to determine. Unfortunately, this is not always so. Deficient budgetary classifications, lack of reliable data, difficulties in allocating fixed costs to a specific function, and failure to impute some value to the use of public assets used in the activity can also hamper the determination of real costs (Table 1) Mihaiu et al. (2010).
Table 1

Determining the efficiency indicators

Efficiency

Public sector

Private sector

Input (resources)

Difficult to identify, especially when it is a hybrid (private/public) financing form

Easy to indicate

Output (services, projects, products)

Hard to quantify and to compare

Easy to determine and to make explicit quantity of money

In order to evaluate the performance, in terms of efficiency, several methods and approach are developed.

Measuring Efficiency: Methodologies

There are two main methodologies used to measure the efficiency: namely parametric and nonparametric approach. Nonparametric methods differ from parametric methods by not requiring any specification of production or cost function in advance, which is a great advantage (Zhu 2009). The parametric approach requires the specification of functional form of the production, cost and profit frontier and some distributional assumptions about the error term. On the other hand, nonparametric approach does not assume any specific functional form for evaluating efficiency, and therefore, does not take into account the error term (Cummins and Xie 2008).

Another relevant distinction is between the deterministic and stochastic models.

In stochastic models, we allow for the fact that the individual observations may be somewhat affected by random noise and we attempt to identify the underlying mean structure stripped from the impact of the random elements. In deterministic models, the possible noise is suppressed, and any variation in the data is considered to contain significant information about the efficiency of the firms and the shape of the technology.

The most widely used methodology in comparative efficiency studies is Data Envelopment Analysis.

Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a nonparametric linear frontier method that is widely used in efficiency measurements, especially in studies of utility industries (i.e., waste and water provision), education and health. In a DEA model, the measure of efficiency of any organization is obtained using the ratio of weighted outputs to weighted inputs subject to the condition that similar ratios for every company are equal to or less than unity (Zhu 2009).

Data Envelopment Analysis (DEA) was first introduced by Charnes et al. (1978) and extended by Banker et al. (1984). The purpose of this approach was to measure the relative efficiency of each organizations or DMU (Decision Making Unit) with the best practices entity.

DEA decomposes the cost efficiency (CE) into two components. One is technical efficiency (TE) (either maximizing output for a given level of inputs or minimizing inputs for a given level of output). The other is allocative efficiency (AE) (using input in optimal proportions given the input prices and output quantities). Technical Efficiency (TE) can be further decomposed into Pure Technical Efficiency (PTE) and Scale Efficiency (SE). SE occurs when firm operates at Constant Returns to Scale (CRS) and PTE occurs when firm maximizes its output with Variable Returns to Scale (VRS). The resultant efficiency measure, ranging between zero (least efficient) and one (most efficient), depicts the distance from each unit to frontier.

Throughout the quantitative analysis, the efficiency scores computed under both the variable returns to scale (VRS) and the constant return to scale (CRS) assumptions will be used. Efficiency under CRS is deemed to be overall efficiency, as it can be deconstructed into two components, VRS and scale efficiency (SE), providing an insight into the source of inefficiencies. VRS, also known as “pure efficiency,” has its boundary within CRS and reflects the managerial ability to organize inputs in the production process (Thanassoulis 2000). The ratio of these two efficiency measures (CRS/VRS) shows the impact of scale on efficiency for each company. The SE describes that part of inefficiency which can be attributed to a company because it diverged from its most productive operating scale size (Banker et al. 1984).

Conclusion

Despite the complexities in measuring efficiency in the public sector and the problem of isolating the effects on efficiency from other external influences, empirical evidence suggests that the following three institutional factors may improve public sector performance:
  • Decentralization of political power and spending responsibility.

  • Arrangements that increase flexibility, agencification.

  • Methods for strengthening competitive pressures through privatization and other means.

  • Appropriate human resource management practice.

  • In the education and health sectors, there is evidence that increasing the scale of operations may improve efficiency. This effect is attributed to economies of scale that result from savings in overhead costs and fixed costs in tangible assets. However, the impact on other public sector values such as equity, access to services, and the quality of services needs to be taken into account.

    Moreover, several are the benefits from the use of performance information and the monitoring of level of efficiency:

  • It generates a sharper focus on results within the management of entities.

  • It provides more and better information on organization goals and priorities, and on how different programs contribute to achieve these goals.

  • It encourages a greater emphasis on planning and acts as a signaling device that provides key actors with details on what is working and what is not.

  • It improves transparency by providing more and better information to users and to the public and has the potential to improve public management and efficiency.

Functional and political decentralization (i.e., spending responsibility) to subnational governments also seems beneficial for efficiency. In principle, devolution of functional responsibilities, if accompanied by appropriate fiscal and political decentralization, provides incentives for subcentral governments to deliver locally preferred services more efficiently, as the burden and the benefits of public service delivery both accrue in the communities. Evidence from federal countries shows that decentralized taxation reduces the size of government; however, evidence on the comparison of countries is inconclusive in this regard.

Also the human resource management practices also matter a great deal. The soft aspects of human resource management, such as employee satisfaction and morale, are considered to be the most important drivers of performance. While wages are still important for staff, nonmonetary incentives are also essential. High wage levels – compared to similar work in the private sector – could lead to inefficiencies, although governments often are model employers and their wage policies reflect equity concerns as well. Wages are also important for attracting and retaining qualified staff, especially in case of skill shortages. Performance-related pay initiatives appear to have a low impact on staff motivation.

Findings are more inconclusive on the impact of ownership, competition, and agencification. While private ownership is not a guarantee of higher efficiency, public ownership does not necessarily lead to higher inefficiencies either. Rather than ownership per se, it is the importance of competitive pressure on efficiency that matters. However, there is a need to further explore for what and with whom public organizations compete. The nature of service delivery, e.g., whether it has features such as low asset specificity (high levels of alternative uses for resources) and low information costs, is crucial for successful competition in public services.

Regarding agencification, there is some evidence that a reduction of input controls combined with steering for results, financial incentives and competition could lead to increased efficiency. However, the impact on the quality of service delivery and policy effectiveness is unclear. The literature also calls attention to the major risks of agencification, including the exposure of government to financial and employment risks and opportunities for political patronage and corruption. The effects of new intragovernmental coordination mechanisms are also not known.

Cross-References

References

  1. Banker R, Charnes A, Cooper W (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092CrossRefGoogle Scholar
  2. Charnes A, Cooper W, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444CrossRefGoogle Scholar
  3. Cummins D, Xie X (2008) Mergers and acquisitions in the US property-liability insurance industry: productivity and efficiency effects. J Bank Financ 32:30–55CrossRefGoogle Scholar
  4. Mihaiu, D. M., Opreana, A., & Cristescu, M. P. (2010). Efficiency, effectiveness and performance of the public sector. Romanian Journal of Economic Forecasting, 4(1), 132–147.Google Scholar
  5. Thanassoulis E (2000) The use of data envelopment analysis in the regulation of UK water utilities: water distribution. Eur J Oper Res 126(2):436–453CrossRefGoogle Scholar
  6. Zhu J (2009) Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets. Springer, New YorkCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of ManagementUniversity of TurinTurinItaly