# **3. Data**

**This paper presents new global evidence on the key determinants of public-private partnership (PPP) investment in electricity transmission and distribution based on a panel data analysis  for 105 developing countries over a 16 years period from 1993 to 2008.**

**Our study aims to identify the key factors affecting the private investor’s decision to enter electricity transmission and distribution and  the amount of investment sunk in this market segment.**

**The analysis of the key determinants of the private investor’s decision to enter is done using the probit model** , where the dependent variable is a dummy equal to 1 if PPPs were introduced in electricity transmission and distribution and 0 if not.

**The treatment of the modeling of the key factors affecting the private investor’s amount of investment sunk in the renewable energy market is based on Heckman’s approach to sample selection** . This distinguishes between (1) the decision on whether to enter the generation segment of the electricity market (selection equation), and (2) the decision on how much investment to commit to (investment equation).<sup>1</sup> Unlike the Tobit model, the factors that affect the two decisions need not be identical and, where identical, could even be different in the sign of their effect on the decisions. The selection equation relates the choice of whether to attract PPPs (via a zero/one dummy variable) as a function of the governance variables and the price of oil is estimated over the complete set of countries using a probit model. The second-stage equation, relating PPP investment to the full set of short run and long run variables described in Table 1, is estimated over the sub-set of countries. Because the group has been selected by the first-stage equation, the possibility of selection bias would be introduced if a standard regression were used at the second stage. This bias is related to the magnitude of the correlation between the errors (that include the omitted variables) in the selection and investment equations. Where there is no correlation between these errors, there is no selection bias. The direction of the bias depends on the sign of the inter-equation error correlation.

The condition for identification of the Heckman estimation procedure is that the selection equation contains a significant variable(s) not included in the investment equation. The significance of the Mills’ ratio in the second stage indicates whether there would have been selection bias in its absence. If the Mills’ ratio is insignificant, a simple regression of the quantity of investment on explanatory variables would be unbiased. The canonical Heckman model also assumes that the errors are jointly normal. If that assumption fails, the estimator is generally inconsistent and can provide misleading inference in small samples.

**Data used in this paper are composed of both micro and macro level data.** The micro, projectlevel data are from the Private Participation in Infrastructure (PPI)<sup>2</sup> database. The PPI database is managed by the World Bank and the Private-Public Infrastructure Advisory Facility (PPIAF). The data are collected at the project level. The following information of each project recorded in

> 1 The data on the dependent variable consist of observations related to the investment. This would be zero for countries that had not attracted PPPs, and positive for those countries that had decided to use it. This observation is split into two components: a zero or one variable indicating the lack of entry or entry of PPPs, and a variable measuring investment for the subset of countries that attracted PPPs.

> 2 <u>http://www.ppiaf.org/ppiaf/page/private-participation-infrastructure-database</u>

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