Politicians, bureaucrats and the public-private choice in public service delivery: anybody there pushing for remunicipalization?

Empirical evidence on remunicipalization remains scarce, and even more so as regards potential differences in the roles played by politicians and bureaucrats in service delivery reform. We use information obtained from a survey of Spanish municipalities to investigate differences in the service delivery preferences of politicians and technical staff, as well as differences in their respective propensities to reform. The results we obtain suggest that bureaucrats have both a stronger preference for private participation in service delivery and for reforming services than do politicians

How Much Vertical Integration? Contractual Choice and Public–Private Partnerships in the United States

Efficiency gains in public–private partnerships (PPP) derive from risk transfer and the bundling of different tasks. We study the factors that explain bundling in single contracts. We focus on the choice between integrating operational tasks alone or construction tasks alone, versus vertically integrating both operational and construction tasks. We analyze a new data set that includes 553 PPPs that were concluded in the United States. We find evidence that some financial variables play a role in bundling decisions. In addition, market size and the type of economic sectors involved, are also important drivers of contract choice and bundling decisions

Are we there yet? Understanding the implementation of re-municipalization decisions and their duration

Studies of the drivers of the decision to re-municipalize have increased recently, but research on its implementation is very scarce. We analyse how service characteristics and institutional factors influence the implementation of re-municipalization. For that purpose, we use an extensive database on re-municipalization decisions, and analyse the available data by means of logistic and negative binomial regressions. Strong network characteristics are associated with lower probabilities of implementation and longer implementation processes. Re-municipalization of personal services is more likely to be fully implemented and is finalized faster. Interestingly, after the great recession the probability of implementing reforms increased

Weakening political connections by means of regulatory reform: Evidence from contracting out water services in Spain

One area of public policy where rent-seeking and favoritism is relatively common is the contracting out of public services. Private firms can improve their chances of obtaining contracts by bribing politicians or public servants and funding political parties. In the same vein, firms can gain access to policymakers by hiring influential former politicians—a practice commonly referred to as revolving-doors. In this paper, we use information from 922 privatizations of water services in Spanish municipalities between 1984 and 2016 and multinomial logistic regression techniques to study the association between specific firms securing contracts and the political parties ruling the municipalities. We find robust statistical evidence of an association between the Popular Party (Partido Popular or PP) and the firm Aqualia, part of the large Spanish holding company Fomento de Construcciones y Contratas (FCC), which is known to have funded the Popular Party. Furthermore, former PP politicians have been appointed to top positions in the FCC Board of Directors. However, this relationship weakened after the institutional reform of 2007 on public procurement and financing of political parties, which is empirically evaluated in this paper

Modular Multi-level Converter Hardware-in-the-Loop Simulation on low-cost System-on-Chip devices

Comunicació presentada a IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society (October 21-23, 2018 Washington D.C., USA.)System-on-Chip (SoC) devices combine powerful general purpose processors, a Field-Programmable Gate Array (FPGA) and other peripherals which make them very convenient for Hardware-in-the-Loop (HIL) simulation. One of the limitations of these devices is that control engineers are not particularly familiarized with FPGA programming, which need extensive expertise in order to code these highly sophisticated algorithms using Hardware Description Languages (HDL). Notwithstanding, there exist High-Level Synthesis (HLS) tools which allow to program these devices using more generic programming languages such as C, C++ and SystemC. This paper evaluates SoC devices to implement a Modular Multi-Level Converter (MMC) model using HLS tools for being implemented in the FPGA fabric in order to perform HIL verification of control algorithms in a single low-cost device

Photoplethysmographic Waveform in Hyperbaric Environment

The objective of this work is the identification of significant variations of morphological parameters of the photoplethysmographic (PPG) signal when the subjects are exposed to an increase in atmospheric pressure. To achieve this goal, the PPG signal of 26 subjects, exposed to a hyperbaric environment whose pressure increases up to 5 atm, has been recorded. From this record, segments of 4 minutes have been processed at 1 atm, 3 atm and 5 atm, both in the descending (D) and ascending (A) periods of the immersion. In total, four states (3D, 5, 3A and 1A) normalized to the basal state (1D) have been considered. In these segments, six morphological parameters of the PPG signal were studied. The width, the amplitude, the widths of the anacrotic and catacrotic phases, and the upward and downward slopes of each PPG pulse were extracted. The results showed significant increases in the three parameters related to the pulse width. This increase is significant in the four states analysed for the anacrotic phase width. Furthermore, a significant decrease in the amplitude and in both slopes (in the states 1A) was observed. These results show that the PPG width responds rapidly to the increase in pressure, indicating an activation of the sympathetic system, while amplitude and pulse slopes are decreased when the subjects are exposed to the hyperbaric environment for a considerable period of time

Using Data-mining Techniques for the prediction of the severity of road crashes in Cartagena, Colombia

Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low

Economic development, demographic characteristics, road network and traffic accidents in Zhongshan, China: gradient boosting decision tree model

This paper explores the joint effects of economic development, demographic characteristics and road network on road safety. Although extensive efforts have been undertaken to model safety effects of various influential factors, little evidence is provided on the relative importance of explanatory variables by accounting for their mutual interactions and non-linear effects. We present an innovative gradient boosting decision tree (GBDT) model to explore joint effects of comprehensive factors on four traffic accident indicators (the number of traffic accidents, injuries, deaths, and the economic loss). A total of 27 elaborated influential factors in Zhongshan, China during 2000–2016 are collected. Results show that GBDT not only presents high prediction accuracy, but can also handle the multicollinearity between explanatory variables; more importantly, it can rank the influential factors on traffic accidents. We also investigate the partial effects of key influential factors. Based on key findings, we highlight the practical insights for planning practice