**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

proposed are rather limited, there are no time or country effects, no controls for macroeconomic reform or the size of the stock market, and not much is said about the error structure. In addition, the main explanatory variables are dummies.<sup>4</sup> At this stage we can draw some conclusions from this brief review of econometric studies. First, at the current state of reforms in the electricity sector one should be able to work with panel datasets rather than simple cross-section models, preferably containing pre- and post-reform data. As observed in Fink, Mattoo, and Rathindran (2002), estimating a model containing time-series cross-section data usually implies a complicated error structure. Problems such as serial and/or contemporaneous correlation and heteroskedasticity should be addressed in the estimation procedure. Ignoring presence of serial correlation results in consistent but inefficient estimators. Similarly, assuming homoskedastic disturbances when heteroskedasticity is present will still result in consistent estimators, but correction for heteroskedasticity makes the estimation far more efficient (Baltagi 1995). Dealing with contemporaneous correlation across panels is a more complicated matter, considering the availability of data. The appropriate estimation that incorporates contemporaneous correlation would require as many time series as there are panels (in order to satisfy matrix invertibility conditions), which is usually not possible. For example, a dataset with panel of 30 countries would also require data for 30 years.

Second, especially when dealing with a varied sample of (developed and developing) countries and a larger time series, country and time effects should be included in the model. These should be included in the form of fixed effects when we are focusing on a specific set of countries. When we are drawing individual countries randomly from a large population, the use of a random effects specification is appropriate. To strengthen our choice we should perform a specification test (Hausman’s test).

Finally, further empirical research should concentrate efforts in targeting the problem of endogeneity. This has been attempted for the telecommunications sector (Gutiérrez (2003)), but not so much for the electricity supply industry (with the exception of Zelner and Henisz (2000) of the papers reviewed in this Subsection). As suggested by our conceptual reform model outlined in Section 3, performance is not only influenced by reforms but also itself influences reform decisions. Gutiérrez (2003) proposes a dynamic model that uses lagged variables as instruments and show that neglecting the effect of lagged dependent variables may distort or overestimate the effects of all variables, including reform variables. Dynamic panel data modelling and estimation is one way to tackle the problem; however the estimation itself is not so straightforward when the time span is limited (as is usually the case for policy analysis). However, this problem becomes more tractable over time as datasets become larger.

> 4 The proposed regulatory index is actually the result of the interaction of three dummy variables. In addition, there is a dummy for vertical integration.

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