Discussion Papers

Andreas Hense, Marcus Stronzik

Produktivitätsentwicklung der deutschen Strom- und Gasnetzbetreiber –Untersuchungsmethodik und empirische Ergebnisse
Nr. 268 / September 2005

Summary

Within the framework of an incentive regulation the proper choice of the X-factor is crucial. The X-factor reflects the capability of the regulated industry or company to achieve a higher productivity growth than the national economy. Since, up to now, company data is not available the German regulatory authority has to use aggregated data. Against this background the total factor productivity approach (TFP) is the most common measure in order to get first evidence on the efficiency of the electricity and gas network operators.

TFP is an index number technique computing the ratio of all outputs (weighted by revenue shares) to all inputs (weighted by cost shares). The growth of TFP gives an indication of the size of productivity gains that are achievable in subsequent regulatory periods. An appropriate TFP measure is the Tornquist index. This index is a weighted geometric average of the quantity relatives with weights given by the simple average of the value shares in two successive periods.

By means of data from the national accounts TFP calculation reveals a productivity growth difference of 0.5% between the energy sector and the national economy. This result has to be construed with care since the energy aggregate in the national account does not only encompass electricity and gas but also district heating. Moreover, the data from the national accounts does not allow for a focused view on network operation.Thus the application of more technical data could provide a helpful approximation.

Based on this approach the difference in TFP growth between the national economy and the electricity network operators amounts to 1.3%. The wedge for the gas network operators is 1.7%. Nevertheless, the consistency of the technical data set is not ensured because of different data sources.

The use of more sophisticated regression or non-parametric approaches, like for example Data Envelopment Analysis (DEA), requires the collection of company data. With this information the individual efficiency level of each network operator could be determined and specific X-factors could be derived. However, the X-factor should not only reflect the historically achieved productivity growth. If a network operator is expected to attain higher growth rates in the future, more demanding productivity targets could be set. Particularly with the beginning of an effective incentive regulation productivity generally increases due to higher market pressure to innovate and to adapt to the continuously changing business environment. [Full text available in German only]

Diskussion Paper is available for download.

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