The World Bank,INGOs, and Civil Society: Converging Agendas? The Case of Universal Basic Education in Niger

This paper explores, through a case study of the World Bank's pursuit of universal basic education, the gulf between the Bank's dialogue with international civil society elites and its treatment of grassroots civil society in its development practice. It argues that the World Bank is pursuing a conscious program to build a global elite governance system similar to Bank vice-president J. F. Rischard's concept of global issues networks, in which experts from business, government, and civil society will set globally binding social and economic policies. There is a risk of co-optation of international NGOs into this autocratic global managerial system.     

Validation of the Chinese version of the Oswestry Disability Index

This study aimed to collect evidence on the structural and substantive validity, and test-retest reliability of the Chinese version of the Oswestry Disability Index (CODI). Seventy-nine patients suffering from chronic low back pain were assessed with the CODI. The results of explorative factor analysis primarily suggested a single-factor structure with nine out of 10 items (factor loading = 0.66-0.79). The sex life item was found to load on a different factor. The Cronbach's alpha of all 10 items was 0.81 (p<0.05). When the sex life item was removed from the analysis, the alpha value was increased to 0.89 (p<0.05). The test-retest reliability was estimated based on 56 participants who completed two administrations of CODI in 48 hours. The intraclass correlation coefficient (ICC) computed for the total CODI scores was 0.86 (95% C.I. = 0.81-0.91). The reliability estimated for the item scores using Kappa statistics ranged from a high of k=0.80 for the sitting item to a low of k=0.49 for the traveling item. Kappa statistics were not available for three items. The Chinese version of the Oswestry Disability Index demonstrated satisfactory validity and test-retest reliability, and so could be considered as an appropriate instrument for assessing chronic back pain-related disability in Chinese patients in Hong Kong. Further research should address the cross-cultural and measurement issues in regard to sex life in order to further improve the test content of the instrument.     

Rural studies: Modernism, postmodernism and the ‘post-rural’

In response to Philo [(1992b), Neglected rural geographies: a review. Journal of Rural Studies8, 193–207), who calls for rural studies to take the study of ‘others’ more seriously, we argue the need to take postmodernism more seriously. The paper focuses upon the production of knowledge about rural areas by academics. In the narrative that we provide here, the ‘rural’ had a strong presence until Pahl's critique of the rural-urban continuum which both diminished the status of the rural and emphasised the role of class in shaping particular spaces. Newby and his colleagues applied class analysis to agriculture, likewise undermining the significance of the rural. Further applications of general social theory, such as the political economy and restructuring approaches, show how modernist rural studies seem to be fighting a losing battle to posit the indispensability of the significance of the urban-rural division as an explanation; articulating and rearticulating the divide within a whole range of processes: economic, social and cultural. Rural social scientists have woven this modernist narrative, but, as Philo shows, one effect has been the neglect of certain social groups, cultures and identities. However, in contrast to Philo, we argue that a rather fundamental reassessment of social scientific approaches to the rural is required if these ‘neglected others’ are to be satisfactorily considered. We believe a ‘sociology of postmodernism’ would offer a more reflexive perspective on the processes which give rise to ‘the rural’. We thus call for an end to the use of universal or global concepts such as ‘rural’ (or the ‘urban’) and for a concern with the way places are ‘made’. This will entail a focus on ‘power’ as certain actors impose ‘their’ rurality on others. We term this the study of the ‘post-rural’.     

Attitudes Toward Managing Hazardous Waste: What Should Be Cleaned Up and Who Should Pay for It?

Hazardous waste policy in the United States uses a liability-based approach, including strict, retroactive, and joint and several liability. To assess attitudes toward these basic principles of liability, and toward priorities for clean-up of wastes, a questionnaire was mailed to legislators, judges, executives of oil and chemical companies, environmentalists, and economists. The questionnaire consisted of abstract, simplified cases, which contrasted basic principles rather than dealing with real-world scenarios. Subjects were asked how they would allocate clean-up costs between companies and government as a function of such factors as adherence to standards, adoption of best available technology (BAT), and influence of penalties on future behavior. Most subjects felt that, if the company followed government standards or used the best available technology (BAT), it should pay for only a portion of the clean-up cost, with the government paying the rest. In general, responses did not support the principles underlying current law–strict, retroactive, and joint-and-several liability. Most subjects were more interested in polluters paying for damages than in deterrence or future benefit–even to the extent that they would have "harmless" waste sites cleaned up. A bias was found toward complete clean-up of some sites, or "zero risk." Different groups of subjects gave similar answers, although more committed environmentalists were more willing to make companies pay and to clean up waste regardless of the cost.     

Confidence Intervals for Univariate Impulse Responses With a Near Unit Root

This article proposes a method for constructing confidence intervals for the impulse response function of a univariate time series with a near unit root. These confidence intervals control coverage, whereas the existing techniques can all have coverage far below the nominal level. I apply the proposed method to several measures of U.S. aggregate output.     

Inferactive data analysis

We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis. We see this as a useful step in concrete providing tools (with statistical guarantees) for current data scientists. The basis of inference we use is (a conditional approach to) selective inference, in particular its randomized form. The relevant reference distributions are constructed from what we call a DAG-DAG—a Data Analysis Generative DAG, and a selective change of variables formula is crucial to any practical implementation of inferactive data analysis via sampling these distributions. We discuss a canonical example of an incomplete cross-validation test statistic to discriminate between black box models, and a real HIV dataset example to illustrate inference after making multiple queries on data.     

Learning to Manage the Multirisk World

Risk assessment, perception, and management tend to focus on one risk at a time. But we live in a multirisk world. This essay in honor of the 40th anniversary of the Society for Risk Analysis (SRA) and the journal Risk Analysis suggests that we can—and have already begun to—strengthen risk analysis and policy outcomes by moving from a focus on the single to the multiple—multiple stressors, multiple impacts, and multiple decisions. This evolution can improve our abilities to assess actual risks, to confront and weigh risk-risk trade-offs and innovate risk-superior moves, and to build learning into adaptive regulation that adjusts over time. Recognizing the multirisk reality can help us understand complex systems, foresee unintended consequences, design better policy solutions, and learn to improve.     

Subnational Location Capital: The Role of Subnational Institutional Actors and Socio‐spatial Factors on Firm Location

Firms do not simply locate, but rather seek to accrue location‐based advantages such as knowledge, market insidership and resource utilization. Adopting the lens of social capital, this paper explores how subnational institutional actors facilitate location capital for firms. Using qualitative case study analysis of six multinational companies (MNCs), we highlight the important role of subnational institutional actors in fostering three dimensions of subnational location capital – structural, relational and cognitive. We show that subnational location capital, defined as the economic and social assets accessible through relationships within a subnational location, enable firms to derive advantages via subnational engagement. These findings contribute to the growing literature on the dynamic interaction of firms with subnational location, particularly the nuanced role of subnational institutional actors with MNCs.     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.