Public Private Partnerships and economic growth in developing countries: An empirical analysis
The study analysed the impact of Public Private Partnership (PPP) investment on economic growth in 39 developing countries, and used a traditional growth model. Using the system Generalised Method of Moments (GMM) estimation technique, the analysis was carried out in two ways. First, the study analysed the effect of total PPP investment on economic growth, measured in GDP per capita. Secondly, PPP investment was disaggregated into the three PPP sectors, namely energy, transport, and water and sanitation. This was done to identify the most productive sectors for PPP investment. This study used the World Bank’s Private Participation in Infrastructure (PPI) database and covered a period between 1997 – 2016. The findings suggest that PPP investment positively contributes to economic growth. When disaggregated by sector, the results of the study suggest that none of PPP investment in the selected sectors positively contribute to economic growth. PPP investment in the energy and transport sectors were found to contribute negatively to economic growth. In contrast, PPP investment in the water and sanitation sector was found to be insignificant when it comes to explaining economic growth in these countries. The sectoral results of PPP investment were unexpected and could be attributed to limitations of data as some sectoral data was not reported on in the database. This finding points to the importance of data that is adequate and consistently available over a long period. PPPs are becoming a necessary solution for strengthening infrastructure and generating economic growth in developing countries. Thus, understanding the empirical links, through research, that exists between infrastructure investment using PPPs and economic growth, is essential. Research such as these could enhance debate in developing countries on how best to use PPP models as propellers of economic growth. As such, how data is reported is important as it affects the credibility of the model and the results produced by it. It is therefore important that the shortcomings of inconsistency in the reporting of data be corrected to ensure that meaningful and accurate conclusions could be drawn from it.
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