Regulated price reforms and unregulated substitutes: the case of residential piped gas in Argentina

This paper examines the impact of regulated piped gas price changes on connection decisions and households’ welfare. The analysis explicitly considers bottled gas as a substitute for piped gas and therefore uses a simple utility maximization model that yields both a gas-type choice model and a demand specification whose parameterization allows examining households’ responses to regulated price changes. The results show that relative gas prices and household features explain households’ piped gas usage. They also provide insights on the distribution of welfare changes. The findings illustrate the impact of tariff rebalancing and unregulated substitutes on further access to regulated network services.     

Diffusion of Kyoto's clean development mechanism

To date, developed countries can only tap mitigation opportunities in developing countries by investing in projects under the Clean Development Mechanism (CDM). Yet CDM investments have so far failed to reach all of the high-potential sectors identified in IPCC reports. This raises doubts about whether the CDM will be able to generate an adequate supply of credits from the limited areas where it has proved successful. Our paper examines the current trajectory of potential mitigation entering the CDM pipeline and projects it forward under the assumption that the diffusion of the CDM will follow a path similar to other kinds of innovations. Projections are then compared to pre-CDM predictions of the mechanism's potential market size used to assess Kyoto's cost, in order to discern whether limits on the types of project entering the pipeline will also limit the eventual supply of certified emission reductions (CERs). The main finding of the paper is that the mechanism is on track to deliver an average annual flow of roughly 700 million CERs by the close of 2012 and nearly to 1100 million tons by 2020. Parameter tests suggest that currently identified CDM investments will exceed early model predictions of the potential market for CDM projects.     

Translating Corporate Social Responsibility into Action: A Social Learning Perspective

Journal of Business Ethics - Interest in the microfoundations of corporate social responsibility (CSR) has grown over the past decade. In this study, we draw on social learning theory to examine...     

A Balancing Act: Work–Life Balance and Multiple Stakeholder Outcomes in Hospitals

This article examines the direct and indirect effects of work–life balance (WLB) practices on multiple stakeholder outcomes in hospitals. The authors examine the direct and indirect effects of WLB practices in 173 hospitals in the United Kingdom on organizational, patient care and employee outcomes. The article proposes a model in which the effects of WLB practices on patient care outcomes and financial performance are mediated by employee turnover intentions. The authors provide strong support for the potential vested in WLB practices in the healthcare setting. Results indicate that greater use of WLB practices enhances outcomes for hospitals, their employees and the patients they care for.     

Productivity throughout regulatory cycles in gas utilities

This paper examines productivity patterns in price cap regulated utilities around price reviews. We specify a variable cost function that we estimate using alternative specifications of technical change. Results show that the pattern of pure technical change differs within and between regulatory cycles. They also provide evidence that exogenous investment reduces the ratchet-problem, that strategic cost cutting behavior is reduced when regulatory cycles are short and that, absent these two features, industry productivity is consistent with strategic cost cutting behavior.     

Markov decision processes under model uncertainty

We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle, we obtain a local-to-global paradigm, namely solving a local, that is, a one time-step robust optimization problem leads to an optimizer of the global (i.e., infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the . We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.