# Results from regression‐based models

This group of 16 studies used very similar approaches to estimation and testing, allowing a comparison of results across a number of studies based on common assumptions and method. Although there are overlaps in country and time period coverage, and in variables utilized, there are enough differences between studies to argue that finding similar results across a number of such studies strengthens the overall conclusions drawn.

In this group of studies, the impact (performance) variables were linked to variables measuring whether policies were “on” or “off” and in some cases to the degree to which policies were “on”. In addition, other main determining (control) variables were added in order to allow for shifts in the performance variables not due to policy changes. Coefficients were estimated by variants of least squares.

All of these studies used a panel framework—that is each country was represented by observations for a number of years, and it was assumed that the impact of adopting a reform policy had the same marginal effect on performance irrespective of the year in which the reform was adopted or the country in which it was adopted. Differences in the overall levels of the performance indicators were accounted for by the level of the control variables. In addition, country specific dummy variables were included. Within a country, the policy on variable represents the change in performance when the policy was adopted, while between countries the variable measures the difference in performance between countries with or without the policy. The inclusion of the control variables allows for the possibility that performance of an SOE improved over time, even though no reform step had been adopted.

The problem of endogeneity was raised by some authors who varied their estimation technique to allow for it. As pointed out by Bacon and Besant-Jones (2001), some countries appeared more willing to adopt sector reform than others, and these countries had some common features— notably a higher level of GDP per capita. Extending this to allow for other determinants of the number and extent of reforms adopted, it is possible that the current levels of certain performance variables encouraged some governments to adopt reform, which itself might then impact performance. For example, the existence of a badly-run distribution sector within the SOE may have acted as an inducement to unbundle and privatize in order to improve performance via the pressures of profit maximization and competition. If this were so, the adoption of policy would not be exogenous with respect to performance, and estimation methods should be used that take account of the simultaneity between them. For example, Sen et al. (2016), Cubbin and Stern (2006), Sen and Jamasb (2012), and Urpelainen and Yang (2017) all used instrumental variables estimation or three-stage least squares in order to avoid endogeneity bias. Urpelainen (2017) used two-stage least squares estimation and compared the results to ordinary least squares. For transmission and distribution losses the benefits of undertaking a reform step were underestimated by four-fifths using ordinary least squares, while for generation capacity the ordinary least squares estimator was not significant, while the IV estimator was. These results confirm the importance of allowing for endogeneity in estimating the impacts of sector reform.

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