This book reveals the myriad aspects of Big Data collection and analysis, by defining and clarifying the meaning of Big Data and its unique characteristics in a non-technical and easy-to-follow way. Moreover, it discusses critical issues and problems related to the Big Data revolution and their implications for both Statistics as a discipline and for our everyday lives. The author identifies various problems and limitations in the quantitative analysis of Big Data, with regard to e.g. its volume, velocity and variety, as well as its reliability and veridicity. Dedicated chapters focus on the epistemological aspects of data-based knowledge and ethical aspects of the use of Big Data, while also addressing paradigmatic cases such as Cambridge Analytica and the use of data from social networks to influence election outcomes.
This book focuses on economic inequality, its measurement, and its relationship with economic growth and development. The current literature uses multiple points of view, ranging from ethical, legal, philosophical, to political and economic, to understand the nature of (in)equality. Presenting the problem objectively, this book shows how to measure the phenomenon statistically along with an international comparison of the level of income inequality and economic growth and of their complex relationship. The book also analyzes three decades of theoretical and empirical evidence to understand this phenomenon and discusses a number of political measures to reduce economic disparities while stimulating economic growth.
A Primer for Spatial Econometrics aims to meet a growing demand in the field by introducing basic spatial econometrics methodologies to a wide variety of researchers. Spatial econometrics is a relatively new topic that is becoming increasingly popular in many of the social sciences. Readers will find this text to be an approachable, informative springboard for their own research and an invaluable support for those that want to start working immediately with the methods. It moves beyond previous studies as it is aimed explicitly at bridging the gap between a basic econometric textbook and more specialized texts in the subject. This book provides a practical guide that illustrates the potential of spatial econometric modelling, discusses problems and solutions and enables the reader both to interpret empirical results correctly and to start working with the methods. It provides essential notions and key insights as well as providing references for further reading to more in-depth discussions. Readers will appreciate the extensive presentation of examples in 'R', which has emerged as the software of choice for model builders in this area. The text is integrated with real numerical examples, problem sets and practical exercises and also contains a description of the essential computer codes of the statistical software 'R'.
Spatial Econometrics is a rapidly evolving field born from the joint efforts of economists, statisticians, econometricians and regional scientists. The book provides the reader with a broad view of the topic by including both methodological and application papers. Indeed the application papers relate to a number of diverse scientific fields ranging from hedonic models of house pricing to demography, from health care to regional economics, from the analysis of R&D spillovers to the study of retail market spatial characteristics. Particular emphasis is given to regional economic applications of spatial econometrics methods with a number of contributions specifically focused on the spatial concentration of economic activities and agglomeration, regional paths of economic growth, regional convergence of income and productivity and the evolution of regional employment. Most of the papers appearing in this book were solicited from the International Workshop on Spatial Econometrics and Statistics held in Rome (Italy) in 2006.
This book bridges the gap between economic theory and spatial econometric techniques. It is accessible to those with only a basic statistical background and no prior knowledge of spatial econometric methods. It provides a comprehensive treatment of the topic, motivating the reader with examples and analysis. The volume provides a rigorous treatment of the basic spatial linear model, and it discusses the violations of the classical regression assumptions that occur when dealing with spatial data.
Figure 1. 1. Map of Great Britain at two different scale levels. (a) Counties, (b)Regions. '-. " Figure 1. 2. Two alternative aggregations of the Italian provincie in 32 larger areas 4 CHAPTER 1 d . , b) Figure 1. 3 Percentage of votes of the Communist Party in the 1987 Italian political elections (a) and percentage of population over 75 years (b) in 1981 Italian Census in 32 polling districts. The polling districts with values above the average are shaded. Figure 1. 4: First order neighbours (a) and second order neighbours (b) of a reference area. INTRODUCTION 5 While there are several other problems relating to the analysis of areal data, the problem of estimating a spatial correlO!J'am merits special attention. The concept of the correlogram has been borrowed in the spatial literature from the time series analysis. Figure l. 4. a shows the first-order neighbours of a reference area, while Figure 1. 4. b displays the second-order neighbours of the same area. Higher-order neighbours can be defined in a similar fashion. While it is clear that the dependence is strongest between immediate neighbouring areas a certain degree of dependence may be present among higher-order neighbours. This has been shown to be an alternative way of look ing at the sca le problem (Cliff and Ord, 1981, p. l 23). However, unlike the case of a time series where each observation depends only on past observations, here dependence extends in all directions.
Spatial Microeconometrics introduces the reader to the basic concepts of spatial statistics, spatial econometrics and the spatial behavior of economic agents at the microeconomic level. Incorporating useful examples and presenting real data and datasets on real firms, the book takes the reader through the key topics in a systematic way. The book outlines the specificities of data that represent a set of interacting individuals with respect to traditional econometrics that treat their locational choices as exogenous and their economic behavior as independent. In particular, the authors address the consequences of neglecting such important sources of information on statistical inference and how to improve the model predictive performances. The book presents the theory, clarifies the concepts and instructs the readers on how to perform their own analyses, describing in detail the codes which are necessary when using the statistical language R. The book is written by leading figures in the field and is completely up to date with the very latest research. It will be invaluable for graduate students and researchers in economic geography, regional science, spatial econometrics, spatial statistics and urban economics.
Spatial econometrics can be defined in a narrow and in a broader sense. In a narrow sense it refers to methods and techniques for the analysis of regression models using data observed within discrete portions of space such as countries or regions. In a broader sense it is inclusive of the models and theoretical instruments of spatial statistics and spatial data analysis to analyze various economic effects such as externalities, interactions, spatial concentration and many others. Indeed, the reference methodology for spatial econometrics lies on the advances in spatial statistics where it is customary to distinguish between different typologies of data that can be encountered in empirical cases and that require different modelling strategies. A first distinction is between continuous spatial data and data observed on a discrete space. Continuous spatial data are very common in many scientific disciplines (such as physics and environmental sciences), but are still not currently considered in the spatial econometrics literature. Discrete spatial data can take the form of points, lines and polygons. Point data refer to the position of the single economic agent observed at an individual level. Lines in space take the form of interactions between two spatial locations such as flows of goods, individuals and information. Finally data observed within polygons can take the form of predefined irregular portions of space, usually administrative partitions such as countries, regions or counties within one country.
Spatial Microeconometrics introduces the reader to the basic concepts of spatial statistics, spatial econometrics and the spatial behavior of economic agents at the microeconomic level. Incorporating useful examples and presenting real data and datasets on real firms, the book takes the reader through the key topics in a systematic way. The book outlines the specificities of data that represent a set of interacting individuals with respect to traditional econometrics that treat their locational choices as exogenous and their economic behavior as independent. In particular, the authors address the consequences of neglecting such important sources of information on statistical inference and how to improve the model predictive performances. The book presents the theory, clarifies the concepts and instructs the readers on how to perform their own analyses, describing in detail the codes which are necessary when using the statistical language R. The book is written by leading figures in the field and is completely up to date with the very latest research. It will be invaluable for graduate students and researchers in economic geography, regional science, spatial econometrics, spatial statistics and urban economics.
Figure 1. 1. Map of Great Britain at two different scale levels. (a) Counties, (b)Regions. '-. " Figure 1. 2. Two alternative aggregations of the Italian provincie in 32 larger areas 4 CHAPTER 1 d . , b) Figure 1. 3 Percentage of votes of the Communist Party in the 1987 Italian political elections (a) and percentage of population over 75 years (b) in 1981 Italian Census in 32 polling districts. The polling districts with values above the average are shaded. Figure 1. 4: First order neighbours (a) and second order neighbours (b) of a reference area. INTRODUCTION 5 While there are several other problems relating to the analysis of areal data, the problem of estimating a spatial correlO!J'am merits special attention. The concept of the correlogram has been borrowed in the spatial literature from the time series analysis. Figure l. 4. a shows the first-order neighbours of a reference area, while Figure 1. 4. b displays the second-order neighbours of the same area. Higher-order neighbours can be defined in a similar fashion. While it is clear that the dependence is strongest between immediate neighbouring areas a certain degree of dependence may be present among higher-order neighbours. This has been shown to be an alternative way of look ing at the sca le problem (Cliff and Ord, 1981, p. l 23). However, unlike the case of a time series where each observation depends only on past observations, here dependence extends in all directions.
This book bridges the gap between economic theory and spatial econometric techniques. It is accessible to those with only a basic statistical background and no prior knowledge of spatial econometric methods. It provides a comprehensive treatment of the topic, motivating the reader with examples and analysis. The volume provides a rigorous treatment of the basic spatial linear model, and it discusses the violations of the classical regression assumptions that occur when dealing with spatial data.
This book focuses on economic inequality, its measurement, and its relationship with economic growth and development. The current literature uses multiple points of view, ranging from ethical, legal, philosophical, to political and economic, to understand the nature of (in)equality. Presenting the problem objectively, this book shows how to measure the phenomenon statistically along with an international comparison of the level of income inequality and economic growth and of their complex relationship. The book also analyzes three decades of theoretical and empirical evidence to understand this phenomenon and discusses a number of political measures to reduce economic disparities while stimulating economic growth.
This book reveals the myriad aspects of Big Data collection and analysis, by defining and clarifying the meaning of Big Data and its unique characteristics in a non-technical and easy-to-follow way. Moreover, it discusses critical issues and problems related to the Big Data revolution and their implications for both Statistics as a discipline and for our everyday lives. The author identifies various problems and limitations in the quantitative analysis of Big Data, with regard to e.g. its volume, velocity and variety, as well as its reliability and veridicity. Dedicated chapters focus on the epistemological aspects of data-based knowledge and ethical aspects of the use of Big Data, while also addressing paradigmatic cases such as Cambridge Analytica and the use of data from social networks to influence election outcomes.
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