This chapter ran a penel regression model to explore the effect of the Organisation for Economic Co-operation and Development's (OECD) country risk classifications and Standard & Poor's (S&P) sovereign ratings on the number of financially closed infrastructure public-private partnerships (PPPs). The PPP projects are taken from the World Bank's Private Participation in Infrastructure Database and complemented with data from the World Development Indicators, with variables including gross domestic product (GDP) growth, inflation, and country trade openness.
Following Araya, Schwartz, and Andres (2013), a Poisson regression model was used.
In(ʎi) | = | β0 + β1 Risk measuresi + β2 GDP growthit-1 + β3Inflationit-1 |
where ʎi. is the number of projects in country i from 1991 to 2015. Risk measuresi are the OECD's country risk classification or S&P's sovereign ratings. Most econometric specifications dealing with GDP and investments suffer from endogeneity. This is addressed by assuming the investments are being affected by previous year events. GDP growthit-1 is GDP growth for country i in the year t-1, and is expected to have a positive impact on investments in PPPs. Inflationit-1 captures the monetary instability for country i in the year t-1 and is expected to have a negative impact. Trade opennessit-1 is a proxy for the openness of the country calculated as the sum of exports and imports over the GDP for country i in the year t-1; it is expected to have a positive impact on investments. Inflation and openness are log transformed.
To test the participation of multilateral development banks (MDBs) in catalyzing the private financing of infrastructure projects through various schemes, including credit enhancement products, a dummy variable 1 was introduced, if an MDB participates in any of the PPP projects of a given country or 0 otherwise. The interaction variable between PPP participation of MDBs and the OECD's credit risk and S&P's rating was introduced in the model.