# Section 4: Quantifying the impact of sector reform

Three approaches to the measurement of the benefits of sector reform are found in the literature (Jamasb, Mota, Newbery and Pollitt, 2005). Case studies of individual country or utility experience (Galal, Jones, Tandon and Vogelsang, 1994; Newbery and Pollitt, 1997) base calculations about the size and distribution of costs and benefits of reform on assumptions made about the working of that economy, rather than on statistical estimation based on a sample of experiences. Efficiency and productivity analysis, such as Data Envelopment Analysis (DEA), is well suited for the case where there several firms within the same economy, and the focus of reform is the impacts on efficiency measures of performance. Econometric analysis is attractive because it can include the effect of non-reform factors, as they change over time, or differ between countries.

In the literature search undertaken for this study attention was paid primarily to studies on developing countries. An earlier survey (Jamasb, Mota, Newbery and Pollitt, 2005) had identified several empirical studies on the impacts of sector reform, but few were on developing countries. The last decade has seen a sharp increase in empirical work on the impacts of sector reform in developing countries, with the largest part of this concentrated on econometric studies.

Section 4.1 outlines the hypotheses about the impacts of sector reform linked to unbundling, private sector participation, regulation, and competition that are tested, and issues involved in doing so. Section 4.2 describes the results obtained from the regression and other studies surveyed for each of the hypotheses outlined. Studies are first evaluated by the significance (or not) of the various reform policies on various performance indicators, and then for the studies where private sector participation was found to be significant, the magnitude (materiality) of the effect is calculated.

4.1 Hypotheses about the impact of sector reform on sector performance The hypotheses concerning the impacts of sector reform policies are usually stated in their positive form: e.g. the efficiency of the sector improves after privatization. It should be noted that in formal statistical hypothesis testing the “null” hypothesis (e.g. sector efficiency is unaffected by privatization) is assumed to be correct and is rejected in favor of the alternative hypothesis (sector efficiency is affected by privatization) only if the evidence is sufficiently strong. In this survey the hypotheses will be stated in their positive form to be consistent with the reform literature.

The data used to test these hypotheses come in one of three forms. A time series of data on a single utility (or country) covers the period before and after the implementation of the reform policy. Changes in the performance indicator are linked to the level of the policy variable as well as other variables expected to impact performance. The policy variable may be assumed to have the same impact in each year or may change if the intensity of the policy changes. In a simple cross-section model there are data on a given year for all the countries in the sample. Those countries where the policy is on are expected to have a higher and common value of the performance indicator once allowance has been made for differences among countries on the other determining variables. Panel analysis combines time series and cross section data, and in the simplest case the impact of 18