Contract choice, incentives and political capture in public transport services

We consider a framework of contractual interactions between urban transport authorities and transport operators. We estimate simultaneously the choice of contract by the authorities and the effect of regulation on the cost reducing activity of the operators. We test whether regulatory schemes currently implemented in the industry are the observable items of a more general menu of second best contracts. We suggest that the generation process of the data we have in hand is better explained by the political aspects of regulation. Moreover, the cost reducing effort of the operators is greater under fixed-price regimes, compared to the cost-plus case

Multilevel (ML-ICLV) & Single Level Integrated Discrete Choice and Latent Variable (ICLV) Models Using Alternative Latent Structures' Conceptualizations

The aim of the present endeavor is to experiment on integrating discrete choice with latent variable (ICVL) models using alternative factorial structures’ conceptualizations and do so at both Single Level (Level 0) and Multilevel (ML-ICVL). In doing, specific independent variables amenable to alternative latent variables’ conceptualization were selected. These included: a) 1st-order latent variables (1st-order factors) (FM; FW), b) 1st-order latent variables (1st-order factors) (FM; FW) forming a 2nd-order factor (F), c) Multi-level (two-level) factorial structures (FML0; FML1 and FWL0; FWL1), and d) Bi-Factor factorial structures (FM; FW; FG). The results may be of use to researchers interested in using valid, reliable, and accurate structures of latent variables in ICLV models. We confirm that alternative latent structures of divergent factorial nature exist for the same observed variables, and may have different impact upon the dependent observed choice variable in the ICLV models. Second, DCE utility is conceptualized and estimated at both Level 0 and Level 1 and the differences are evident

Multilevel (ML-ICLV) & Single Level Integrated Discrete Choice and Latent Variable (ICLV) Models Using Alternative Latent Structures' Conceptualizations

The aim of the present endeavor is to experiment on integrating discrete choice with latent variable (ICVL) models using alternative factorial structures’ conceptualizations and do so at both Single Level (Level 0) and Multilevel (ML-ICVL). In doing, specific independent variables amenable to alternative latent variables’ conceptualization were selected. These included: a) 1st-order latent variables (1st-order factors) (FM; FW), b) 1st-order latent variables (1st-order factors) (FM; FW) forming a 2nd-order factor (F), c) Multi-level (two-level) factorial structures (FML0; FML1 and FWL0; FWL1), and d) Bi-Factor factorial structures (FM; FW; FG). The results may be of use to researchers interested in using valid, reliable, and accurate structures of latent variables in ICLV models. We confirm that alternative latent structures of divergent factorial nature exist for the same observed variables, and may have different impact upon the dependent observed choice variable in the ICLV models. Second, DCE utility is conceptualized and estimated at both Level 0 and Level 1 and the differences are evident

Incentive Regulatory policies: The Case of Public Transit Systems in France

We assess the empirical relevance of the new theory of regulation, using a principal-agent framework to study the regulatory schemes used in the French urban transport industry. Taking the current regulatory schemes as given, the model of supply and demand provides estimates for the firms’ inefficiency, the effort of managers, and the cost of public funds. It allows us to derive the first-best and second-best regulatory policies for each network and compare them with the actual situation in terms of welfare loss or gain. Fixed-price policies lie between fully informed and uninformed second-best schemes. Cost-plus contracts are dominated by any type of second-best contract. From these results, we may conjecture that fixed-price contracts call for better-informed regulators.Publicad

Using blind analysis for software engineering experiments

Context: In recent years there has been growing concern about conflicting experimental results in empirical software engineering. This has been paralleled by awareness of how bias can impact research results. Objective: To explore the practicalities of blind analysis of experimental results to reduce bias. Method : We apply blind analysis to a real software engineering experiment that compares three feature weighting approaches with a na ̈ıve benchmark (sample mean) to the Finnish software effort data set. We use this experiment as an example to explore blind analysis as a method to reduce researcher bias. Results: Our experience shows that blinding can be a relatively straightforward procedure. We also highlight various statistical analysis decisions which ought not be guided by the hunt for statistical significance and show that results can be inverted merely through a seemingly inconsequential statistical nicety (i.e., the degree of trimming). Conclusion: Whilst there are minor challenges and some limits to the degree of blinding possible, blind analysis is a very practical and easy to implement method that supports more objective analysis of experimental results. Therefore we argue that blind analysis should be the norm for analysing software engineering experiments

Social norms, morals and self-interest as determinants of pro-environment behaviours : the case of household recycling

The first author gratefully acknowledges the support of the Polish Ministry of Science and Higher Education and the Foundation for Polish Science.This paper considers the role which selfish, moral and social incentives and pressures play in explaining the extent to which stated choices over pro-environment behaviours vary across individuals. The empirical context is choices over household waste contracts and recycling actions in Poland. A theoretical model is used to show how cost-based motives and the desire for a positive self and social image combine to determine the utility from alternative choices of recycling behaviour. We then describe a discrete choice experiment designed to empirically investigate the effects such drivers have on stated choices. A hybrid logit model is used to link statements over attitudes to recycling to choices, dealing with a potential endogeneity problem caused by the joint effects of un-observables on attitudes and choices. We find that a substantial share of our respondents prefer to sort their waste at home rather than in a central sorting facility. This preference is associated with a moral/intrinsic motivation, involving a belief that sorting at home is more thorough than central sorting.Publisher PDFPeer reviewe

Social norms, morals and self-interest as determinants of pro-environment behaviours: the case of household recycling.

This paper considers the role which selfish, moral and social incentives and pressures play in explaining the extent to which stated choices over pro-environment behaviours vary across individuals. The empirical context is choices over household waste contracts and recycling actions in Poland. A theoretical model is used to show how cost-based motives and the desire for a positive self and social image combine to determine the utility from alternative choices of recycling behaviour. We then describe a discrete choice experiment designed to empirically investigate the effects such drivers have on stated choices. A hybrid logit model is used to link statements over attitudes to recycling to choices, dealing with a potential endogeneity problem caused by the joint effects of un-observables on attitudes and choices. We find that a substantial share of our respondents prefer to sort their waste at home rather than in a central sorting facility. This preference is associated with a moral/intrinsic motivation, involving a belief that sorting at home is more thorough than central sorting

Predicting with sparse data

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction