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This study re-examines the oil and democracy nexus, which is central to the political resource curse by applying the latest democracy dataset, V-DEM, into the analysis in a sample of 100 Developing Countries, over the period 1935-2014. Our study is a contribution to this taxonomic literature where we improve on previous studies not only by employing a novel democratic data source, but also because we use two definitions of oil wealth which renders our results more robust, besides delineating the sample into small and large oil endowments and looking into the experience of two regions, Latin America and the Middle East. Our analysis highlights nuances in the oil- democracy relationship. First, that there is prima facie evidence for a political resource curse if we do not control for pre-existing institutions that promote democracy. Second, once we decompose the sample into small and large oil endowments, the political resource curse vanishes, and also for Latin America, whilst for oil dependent economies in the Middle East and North Africa it still remains. Third, after controlling for pre-existing institu-tional quality, measured in our case by the rule of law, chances of the political resource curse seem to diminish. We also calculate threshold levels for the quality of the rule of law to be at in society before they turn a curse into a blessing. The converse is equally true, a deterioration in the quality and pervasiveness of the rule of law will cause the political resource curse to reappear, and democratic quality will decline.

Literature on political support broadly offers three micro-level models: socio-economic status, democratic process evaluations, and political performance evaluations explain people's differences in satisfaction with democracy. While tests show that these explanations complement each other, we do not know how. We combine for the first time all three models into one common longitudinal framework by explicitly considering aspects of time. We argue that relatively stable factors, such as socio-economic status, only explain general levels, whereas more time-sensitive factors, such as evaluations, explain differences between citizens at specific points in time. The results of latent growth curve modelling applied to nine-wave panel data support our general hypothesis of a common longitudinal framework. These results also show that economic evaluations play a prominent role as do some (but not all) electoral results. The findings have theoretical and methodological implications, and they offer a new perspective on the meaning of 'satisfaction with democracy'.

"There have been significant changes in public attitudes towards surveillance in the last few years as a consequence of the Snowden disclosures and the Cambridge Analytica scandal. This book re-evaluates competing arguments between national security and personal privacy. The increased assimilation between intelligence service and police investigatory powers and revelations of unauthorised surveillance have resulted in increased demands for transparency in information gathering and for greater control of personal data. Recent legal reforms have attempted to limit the risks to freedom of association and expression associated with electronic surveillance. This book looks at the background to recent reforms and explains how courts and the legislature are attempting to effect a balance between security and personal liberty within a social contract. It asks what drives public concern when other aspects seem to be less contentious. In view of our apparent willingness to post on social media and engage in on line commerce, it considers if we are truly consenting to a loss of privacy and how this reconciles with concerns about state surveillance"--

Politics in its electoral dimension is profoundly conditioned by this interrelation between science and technology, especially in that activity dedicated to the analysis of multimedia content, to "social engineering", that is, studies of desires that through effective translation of quantitative data but, above all, qualitative, they allow reaching short, medium and long term objectives. Prevailing to the interior of the societies from the charismatic imposition of the political actors to the social subjects, to the manipulation and metabolization of revolutionary processes and with them the ideology that these represent, even, contributing to start up action and position taking processes Social. In a sense, the digital landscape is increasingly dominated by the websites of the ideological right, making Trump look like a consensus, it is an idea that behind it there is a coordinated propaganda strategy. Steve Bannon had become the chief strategist for Trump, the parent company of Cambridge Analytica, SCL, in the midst of the company had created psychological profiles of 230 million Americans, in addition to search and collect data to develop commercial and political communication schemes ; La política en su dimensión electoral, está profundamente condicionada por esta interrelación entre ciencia y tecnología, especialmente en aquella actividad dedicada al análisis de contenido multimedial, a la "ingeniería social", o sea, los estudios de deseos que mediante traducción efectiva de datos cuantitativos pero, sobre todo cualitativos, permiten alcanzar objetivos a corto, mediano y largo plazo. Prevaleciendo al interior de las sociedades desde la imposición carismática de los actores políticos a los sujetos sociales, hasta la manipulación y metabolización de procesos revolucionarios y con ellos la ideología que estos representan, incluso, contribuyendo a poner en marcha procesos de acción y toma de posición social. En sentido, el panorama digital está cada vez más dominado por los sitios web de la derecha ideológica, hacer que Trump parezca un consenso, es una idea de que detrás de ello existe una estrategia de propaganda coordinada. Steve Bannon se había convertido en el principal estratega de Trump, la empresa matriz de Cambridge Analytica, SCL, en medio de la compañía había creado perfiles psicológicos de 230 millones de estadounidenses, además de buscar y recopilar datos para desarrollar esquemas de comunicación comercial y política.

With the development of urbanization and industrialization, the degradation of ambient air quality has become a serious issue that impacts human health and the environment; thus, it has attracted more attention from scholars. Usually, the mass concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and particulate matter with an aerodynamic diameter less than 10 μm and 2.5 μm (PM10 and PM2.5) are used to evaluate air quality. A commonly used data-driven regionalization framework for studying air quality issues, identifying areas with similar air pollution behavior and locating emission sources involves an incorporation of the principal component analysis (PCA) with cluster analysis (CA) methods. However, the traditional PCA does not consider spatial variations, which is a notable issue in geographic studies. This article focuses on extracting the local principal components (PCs) of air quality indicators based on a geographically weighted principal component analysis (GWPCA), which is superior to the PCA when considering spatial heterogeneity. Then, a spatial cluster analysis (SCA) is used to identify the areas with similar air pollution behavior based on the results of the GWPCA. The results are all visualized and show that the GWPCA has a higher explanatory ability than the traditional PCA. Our modified framework based on the GWPCA and SCA for assessing air quality can effectively guide environmentalists and geographers in evaluating and improving air quality from a new perspective. Furthermore, the visualization results can be used by city planners and the government for monitoring and managing air pollution. Finally, policy suggestions are recommended for mitigating air pollution via regional collaboration.

With the development of urbanization and industrialization, the degradation of ambient air quality has become a serious issue that impacts human health and the environment; thus, it has attracted more attention from scholars. Usually, the mass concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and particulate matter with an aerodynamic diameter less than 10 μm and 2.5 μm (PM10 and PM2.5) are used to evaluate air quality. A commonly used data-driven regionalization framework for studying air quality issues, identifying areas with similar air pollution behavior and locating emission sources involves an incorporation of the principal component analysis (PCA) with cluster analysis (CA) methods. However, the traditional PCA does not consider spatial variations, which is a notable issue in geographic studies. This article focuses on extracting the local principal components (PCs) of air quality indicators based on a geographically weighted principal component analysis (GWPCA), which is superior to the PCA when considering spatial heterogeneity. Then, a spatial cluster analysis (SCA) is used to identify the areas with similar air pollution behavior based on the results of the GWPCA. The results are all visualized and show that the GWPCA has a higher explanatory ability than the traditional PCA. Our modified framework based on the GWPCA and SCA for assessing air quality can effectively guide environmentalists and geographers in evaluating and improving air quality from a new perspective. Furthermore, the visualization results can be used by city planners and the government for monitoring and managing air pollution. Finally, policy suggestions are recommended for mitigating air pollution via regional collaboration.

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the real-world problems often deal with imprecise or ambiguous data. In this paper, we propose a novel robust data envelopment model (RDEA) to investigate the efficiencies of decision-making units (DMU) when there are discrete uncertain input and output data. The method is based upon the discrete robust optimization approaches proposed by Mulvey et al. (1995) that utilizes probable scenarios to capture the effect of ambiguous data in the case study. Our primary concern in this research is evaluating electricity distribution companies under uncertainty about input/output data. To illustrate the ability of proposed model, a numerical example of 38 Iranian electricity distribution companies is investigated. There are a large amount ambiguous data about these companies. Some electricity distribution companies may not report clear and real statistics to the government. Thus, it is needed to utilize a prominent approach to deal with this uncertainty. The results reveal that the RDEA model is suitable and reliable for target setting based on decision makers (DM's) preferences when there are uncertain input/output data.

Die Beiträge des Sammelbandes sind überwiegend aus Vorträgen hervorgegangen, die auf der Cologne Computer Conference 1988 gehalten wurden. Das Hauptanliegen dieses Kongresses war es, die methodischen und methodologischen Konsequenzen informationstechnologischer Entwicklungen in den Geistes- und Sozialwissenschaften darzustellen. Die Autoren verbindet folgende Intention: Die oft gepriesenen "einfachen" Methoden (wie prozentuierte Kontingenztabellen, bivariate Korrelations- und Regressionsrechnung) reichen in der historischen Forschungspraxis nicht aus. Sie werden der üblichen Komplexität theoretischer Konzepte und den unterschiedlichen Niveaus verfügbarer oder erreichbarer Daten nicht gerecht; sie schöpfen den informativen Gehalt der Daten nicht optimal aus. Vorgestellt werden daher komplexere Ansätze zur Analyse historischer Daten. (pmb)

[ES] The opening of public statistics data is analysed in the data catalogues of the Spanish central and regional governments. It is assumed that their documentary processing is insufficient, bearing in mind that the statistical data are particularly relevant for open data. Three issues are examined with regard to official statistics: it complies with the requirements for open data, the possibility of being found in the catalogues and can be properly understood for re-use. These aspects involve defining what is an open data and what is public statistics; consider whether it is possible to identify such statistical information on the portals; and whether documentary processing would allow for reuse with guarantees. The results show a wide disparity, both in the way statistical information is called (reachability) and in the way it is provided (accessibility). Portals therefore increase dispersion in retrieval and only some describe datasets in a way that is intelligible by themselves. It is concluded that efforts need to be concentrated or at least coordinated with regard to the provision of open data in Spain. Peset Mancebo, MF.; Fernández López, LA. (2014). Information gaps on open data in Spain. ThinkEPI yearbook. 8: 318-321. http://hdl.handle.net/10251/50381 ; 318 321 8 ; [ES] The opening of public statistics data is analysed in the data catalogues of the Spanish central and regional governments. It is assumed that their documentary processing is insufficient, bearing in mind that the statistical data are particularly relevant for open data. Three issues are examined with regard to official statistics: it complies with the requirements for open data, the possibility of being found in the catalogues and can be properly understood for re-use. These aspects involve defining what is an open data and what is public statistics; consider whether it is possible to identify such statistical information on the portals; and whether documentary processing would allow for reuse with guarantees. The results show a wide ...

PurposeThe purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.Design/methodology/approachA model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.FindingsThis modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.Research limitations/implicationsThe research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.Practical implicationsThe study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).Social implicationsThe growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/valueDespite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

"Embedding the efficient bargaining model into the R. Hall (1988) approach for estimating price-cost margins shows that both imperfections in the product and labor markets generate a wedge between factor elasticities in the production function and their corresponding shares in revenue. This article investigates these two sources of discrepancies both at the industry level and the firm level using an unbalanced panel of 10646 French firms in 38 manufacturing industries over the period 1978-2001. By estimating standard production functions and comparing the estimated factor elasticities for labor and materials and their shares in revenue, we are able to derive estimates of average price-cost mark-up and extent of rent sharing parameters. For manufacturing as a whole, our estimates of these parameters are of an order of magnitude of 1.17 and 0.44 respectively. Our industry-level results indicate that industry differences in these parameters and in the underlying estimated factor elasticities and shares are quite sizeable. Since firm production function, behavior and market environment are very likely to vary even within industries, we also investigate firm-level heterogeneity in estimated mark-up and rent-sharing parameters. To determine the degree of true heterogeneity in these parameters, we adopt the P.A. Swamy (1970) methodology allowing to correct the observed variance in the firm-level estimates from their sampling variance. The median of the firm estimates of the price-cost mark-up ignoring labor market imperfections is of 1.10, while as expected it is higher of 1.20 when taking them into account and the median of the corresponding firm estimates of the extent of rent sharing is of 0.62. The Swamy corresponding robust estimates of true dispersion are of about 0.18, 0.37 and 0.35, showing indeed very sizeable within-industry firm heterogeneity. We find that firm size, capital intensity, distance to the industry technology frontier and investing in R&D seem to account for a significant part of this heterogeneity"--National Bureau of Economic Research web site

AbstractThe American states offer a wealth of variation across time and space to understand the sources, dynamics, and consequences of public policy. As laboratories of socioeconomic and political differences, they enable both wide-scale assessments of change and studies of specific policy choices. To leverage this potential, we constructed and integrated a database of thousands of state-year variables for designing and executing social research: the Correlates of State Policy Project (CSPP). The database offers one-stop shopping for accurate and reliable data, allows researchers to assess the generalizability of the relationships they uncover, enables assessment of causal inferences, and connects state politics researchers to larger research communities. We demonstrate CSPP's use and breadth, as well as its limitations. Through an applied empirical approach familiar to the state politics literature, we show that researchers should remain attentive to regional variation in key variables and potential lack of within-state variation in independent and dependent variables of interest. By comparing commonly used model specifications, we demonstrate that results are highly sensitive to particular research design choices. Inferences drawn from state politics research largely depend on the nature of over time variation within and across states and the empirical leverage it may or may not provide.

AbstractSustainable development goals adopted by the United Nations in 2015 emphasize on poverty reduction and inclusive growth. In India, development policies focusing on poverty reduction have been implemented since independence. However, there is evidence of rising inequality and slowing down of the rate of poverty reduction, after the introduction of economic reforms. The state governments in India have an important role to play in poverty alleviation. Thus, it is necessary to measure the performance of the states in respect of the same. Data envelopment analysis is used by considering input variables, namely, growth, development expenditure, irrigation, and government performance. Percentage of non‐poor is used as an output variable. This paper measures efficiency with which poverty has been alleviated in 19 states covering 90% population, for the years 2006, 2010, and 2014. Resource‐rich states are found to be inefficient, whereas states with resource scarcity use them efficiently. Moreover, poor states with lower per capita gross domestic product (GDP) are found to be more efficient as compared to those with higher per capita GDP. The states with high incidence of poverty are found to be catching up with states with low incidence of poverty. It is found that inequality and high indebtedness adversely impact the efficiency of states.