Are they Surgeons? or are they Plumbers? Assessing the Role of Private Sector Consultants in Place Branding and Other Place-Based Economic Development Strategies

Public Organization Review - This study takes an in-depth look at the roles and utility of consultants in local economic development, focusing on whether there is ultimately a need for this...     

Mindfulness and Subjective Well-being in the Sustainability Movement: A Further Elaboration of Multiple Discrepancies Theory

In a previous interpretation of multiple discrepancies theory (MDT), the authors operationalized mutiple discrepancies as gaps between respondents' values and their performance. Using a sample of back-to-the-landers from the larger sustainability movement, the first-stage of the data analysis (Jacob and Brinkerhoff, 1997) confirmed that multiple discrepancies between values and do have a significant impact on subjective well-being (SWB), but this initial analysis also demonstrated that there are intervening variables that can act as compensating factors to attenuate the influence of multiple discrepancies on SWB. In this, the second-stage of data analysis, the authors identify and further explore the interaction of phenomena that might serve as compensation variables in respondents' calculus of their SWB. The present study identifies two salient compensating factors: mindfulness (a calm, yet focused, engagement with the present, similar to a meditative experience), and a sense of an absence of time constraints. Operationalized as Mindfulness and Time for Self scales, these two interrelated variables explain, in multiple regression equations, the major share of variation in multiple measures of SWB. Nevertheless, the multiple discrepancies between values and performance are still significant, if secondary, factors in explaining variance in SWB for the sample of back-to-the- landers.     

Biodiversity,coastal protection and resource endowment: Policy options for improving ocean health

The paper develops a production function for the Global Ocean Health Index (OHI) for 2013. Data from the Ocean Health Statistics, plus from the Human Development Index (HDI) for 151 countries are used. We employ two-stage regression model to conduct this evaluation. The Tobit model, used to obtain the estimated dependent variable, results show Coastal Protection, Livelihoods and Economies, Tourism and Recreation, Iconic Species, Clean Water and Biodiversity, Food Provision, Artisanal Fisheries Opportunities, Natural Products, and Carbon Storage are significant variables. The rank regression in the second stage showed that HDI and Marine Protected Areas (MPAs) significantly influenced the predicted value of the OHI. Policy makers should note that biodiversity increases have the greatest effect on OHI, and its improvement is within reach of even the poorest country. Countries with varying levels of resource endowment may choose different techniques to improve OHI, but the implementation of MPAs should be priority.     

Nonparametric Estimation of the Probability of Detection of Flaws in an Industrial Component,from Destructive and Nondestructive Testing Data,Using Approximate Bayesian Computation

We consider the problem of estimating the probability of detection (POD) of flaws in an industrial steel component. Modeled as an increasing function of the flaw height, the POD characterizes the detection process; it is also involved in the estimation of the flaw size distribution, a key input parameter of physical models describing the behavior of the steel component when submitted to extreme thermodynamic loads. Such models are used to assess the resistance of highly reliable systems whose failures are seldom observed in practice. We develop a Bayesian method to estimate the flaw size distribution and the POD function, using flaw height measures from periodic in‐service inspections conducted with an ultrasonic detection device, together with measures from destructive lab experiments. Our approach, based on approximate Bayesian computation (ABC) techniques, is applied to a real data set and compared to maximum likelihood estimation (MLE) and a more classical approach based on Markov Chain Monte Carlo (MCMC) techniques. In particular, we show that the parametric model describing the POD as the cumulative distribution function (cdf) of a log‐normal distribution, though often used in this context, can be invalidated by the data at hand. We propose an alternative nonparametric model, which assumes no predefined shape, and extend the ABC framework to this setting. Experimental results demonstrate the ability of this method to provide a flexible estimation of the POD function and describe its uncertainty accurately.     

Changes that cause changes

Beyond determining whether procedures can be manipulated, the real goal for any analysis of “strategic behavior” is to identify all settings where and when this can be done, who can do it, and what they should do. By applying the geometric approach of Saari [7, 8] to the Kemeny's Rule (KR), we demonstrate how surprisingly simple this analysis can be, we identify all three candidate KR strategic behavior, and we show how an almost identical analysis answers most other multiple profile concerns (e.g., the abstention paradox and when voters just make errors). We also introduce new measures, which can be used with any procedure, to compare strategic and other behavior involving “changes.” These measures help to identify settings where it may be more important to worry about honest mistakes than strategic voting. Received: 16 April 1999/Accepted: 29 September 1999