Emerging Meta-Governance as a Regulation Framework for Public-Private Partnerships: An Examination of the European Union's Approach

This article extends previous research on public-private partnerships (PPPs), which has primarily been case study or national context oriented, by examining how these PPPs are regulated in the framework of the European Union (EU). While a number of partnership models have been identified in the academic literature, this study focuses on three significant types of PPP: the contract-PPP, the concession-PPP, and the institutional-PPP. Based on a notion of the EU as a meta-governance framework that guides, steers, and controls PPP activity at national, sector, and project level, the article draws a number of lessons on the EU’s role in regulating the formation phase of PPP. The research demonstrates that this meta-governance framework provides the EU with no direct regulations for the use of the PPP model in the 27 member states, but two sets of regulations which apply if a public authority decides to sign a PPP deal. As the EU hitherto has engaged in regulation of PPP at a somewhat abstract and conceptual level, national and local public administrations are given considerable room for manoeuvre to craft regulations and policies to support or hinder uptake of PPPs. More recently, however, the Commission has raised its stakes by launching a European Partnership Excellence Centre to support policy learning, the spread of best practice, and PPP expert networks

Public-Private Partnerships as Converging or Diverging Trends in Public Management? A Comparative Analysis of PPP Policy and Regulation in Denmark and Ireland

The utilization of the public-private partnership (PPP) model as a means of delivering various types of asset-based public services and infrastructure is often seen in academic research as part of a globally spread public management reform trend. This view is often suggested with reference to the staggering amount of attention and public money, which is now being dedicated to the formation of PPPs worldwide. This article, however, proceeds from the observation that if we look beyond the reports from a small handful of primarily Anglo-Saxon countries, which have so far attracted widespread attention in the PPP literature, we observe a much more divergent and heterogeneous pattern in various national governments’ policy and regulation for PPP and the amount of actually implemented PPP projects. By comparing the initiatives taken by the Irish government, which has embraced PPPs, with those of the Danish government, which has been PPP sceptic, the article draws on two in-depth country case studies to examine how and why PPPs developed so differently in the two countries. The research illustrates that whereas PPPs in Denmark have been subject to a loosely organized institutional framework with a number of fundamental policy and regulation issues being either unresolved or not very supportive to the uptake of PPPs, Ireland, on the other hand, now presides over one of the most ambitious PPP programs in the world, with major policy, regulation and procurement functions centralized within the Ministry of Finance and the Treasury. As research on PPPs continues to proliferate, this article illustrates that academic PPP literature would benefit from adopting a more explicit comparative focus to account for these significant comparative differences in national governments’ PPP approaches

dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R

Data cleaning and validation are important steps in any data analysis, as the validity of the conclusions from the analysis hinges on the quality of the input data. Mistakes in the data can arise for any number of reasons, including erroneous codings, malfunctioning measurement equipment, and inconsistent data generation manuals. Ideally, a human investigator should go through each variable in the dataset and look for potential errors - both in input values and codings - but that process can be very time-consuming, expensive and error-prone in itself. We describe an R package, dataMaid, which implements an extensive and customizable suite of quality assessment aids that can be applied to a dataset in order to identify potential problems in its variables. The results are presented in an auto-generated, nontechnical, stand-alone overview document intended to be perused by an investigator with an understanding of the variables in the data, but not necessarily knowledge of R. Thereby, dataMaid aids the dialogue between data analysts and field experts, while also providing easy documentation of reproducible data quality screening. Moreover, the dataMaid solution changes the data screening process from the usual ad hoc approach to a systematic, well-documented endeavor. dataMaid also provides a suite of more typical R tools for interactive data quality assessment and screening, where the data inspections are executed directly in the R console