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This book describes official statistics as a tool to hold up a mirror to society – but also as an instrument for those who can manipulate this mirror. It addresses the precarious interaction of politics, official statistics, and ethical principles. Three sets of themes can be derived from this relationship, which are the focus of this book: Political systems and guiding principles, official statistics as a science of the state, and ethical issues arising from them. Ultimately, the determining factor is the political system that exists in each case. The book contains eleven chapters. The first three focus on the key concepts of the book: power and morality, official statistics and policy making, and ethical principles for statistical work. Three further chapters focus on episodes that illustrate, as "drastic" examples, the misuse of official statistics over the past hundred years, covering the situation in the Soviet Union, the Third Reich and Greece. The remaining five chapters take up current topics that pose particular challenges to official statistics. These are the phenomena paraphrased by digitalisation, globalisation, happiness research, overpopulation, migration, the Covid-19 pandemic and climate change. The book is primarily aimed at statisticians working in national and international statistical institutions, but also at readers interested in statistics, national accounts, economic and statistics history, and ethical issues.

Statistical services. --- Data collection services --- Information services --- Statistics --- Estadística --- Serveis estadístics --- Política

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With the primary goal of expanding access to spatial data science tools, this book offers dozens of minimal or low-code functions and tutorials designed to ease the implementation of fully reproducible Spatial Socio-Econometric Modeling (SSEM) analyses. Designed as a University of Pennsylvania Ph.D. level course for sociologists, political scientists, urban planners, criminologists, and data scientists, this textbook equips social scientists with all concepts, explanations, and functions required to strengthen their data storytelling. It specifically provides social scientists with a comprehensive set of open-access minimal code tools to: •Identify and access place-based longitudinal and cross-sectional data sources and formats •Conduct advanced data management, including crosswalks, joining, and matching •Fully connect social network analyses with geospatial statistics •Formulate research questions designed to account for place-based factors in model specification and assess their relevance compared to individual- or unit-level indicators •Estimate distance measures across units that follow road network paths •Create sophisticated and interactive HTML data visualizations cross-sectionally or longitudinally, to strengthen research storytelling capabilities •Follow best practices for presenting spatial analyses, findings, and implications •Master theories on neighborhood effects, equality of opportunity, and geography of (dis)advantage that undergird SSEM applications and methods •Assess multicollinearity issues via machine learning that may affect coefficients' estimates and guide the identification of relevant predictors •Strategize how to address feedback loops by using SSEM as an identification framework that can be merged with standard quasi-experimental techniques like propensity score models, instrumental variables, and difference in differences •Expand the SSEM analyses to connections that emerge via social interactions, such as co-authorship and advice networks, or any form of relational data The applied nature of the book along with the cost-free, multi-operative R software makes the usability and applicability of this textbook worldwide.

Social sciences. --- Sampling (Statistics). --- Computer simulation. --- Machine learning. --- Society. --- Methodology of Data Collection and Processing. --- Computer Modelling. --- Machine Learning.

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This volume teaches readers how to sort through the vast mountain of climate and environmental science data to extract actionable insights. With the advancements in sensing technology, we now observe petabytes of data related to climate and the environment. While the volume of data is impressive, collecting big data for the sake of data alone proves to be of limited utility. Instead, our quest is for actionable data that can drive tangible actions and meaningful impact. Yet, unearthing actionable insights from the accumulated big data and delivering them to global stakeholders remains a burgeoning field. Although traditional data mining struggles to keep pace with data accumulation, scientific evolution has spurred the emergence of new technologies like numeric modeling and machine learning. These cutting-edge tools are now tackling grand challenges in climate and the environment, from forecasting extreme climate events and enhancing environmental productivity to monitoring greenhouse gas emissions, fostering smart environmental solutions, and understanding aerosols. Additionally, they model environmental-human interactions, inform policy, and steer markets towards a healthier and more environment-friendly direction. While there's no universal solution to address all these formidable tasks, this book takes us on a guided journey through three sections, enriched with chapters from domain scientists. Part I defines actionable science and explores what truly renders data actionable. Part II showcases compelling case studies and practical use scenarios, illustrating these principles in action. Finally, Part III provides an insightful glimpse into the future of actionable science, focusing on the pressing climate and environmental issues we must confront. Embark on this illuminating voyage with us, where big data meets practical research, and discover how our collective efforts move us closer to a sustainable and thriving future. This book is an invitation to unlock the mysteries of our environment, transforming data into decisive action for generations to come.

Climatology. --- Quantitative research. --- Sampling (Statistics). --- Climate Sciences. --- Data Analysis and Big Data. --- Methodology of Data Collection and Processing.

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Lenders can exploit households' payment data to infer their creditworthiness. When households value privacy, they then face a tradeoff between protecting such privacy and credit conditions. We study how the introduction of an informationally more intrusive digital payment vehicle affects households' cash use, credit access, and welfare. A tech monopolist controls the intrusiveness of the new payment method and manipulates information asymmetries among households and oligopolistic banks to extract data contracts that are more lucrative than lending on its own. The laissez-faire equilibrium entails a digital payment vehicle that is more intrusive than socially optimal, providing a rationale for regulation.

Macroeconomics --- Economics: General --- Industries: Financial Services --- Money and Monetary Policy --- Databases --- Asymmetric and Private Information --- Macroeconomics: Consumption --- Saving --- Wealth --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Financial Institutions and Services: Government Policy and Regulation --- Monetary Policy, Central Banking, and the Supply of Money and Credit: General --- Monetary Systems --- Standards --- Regimes --- Government and the Monetary System --- Payment Systems --- Data Collection and Data Estimation Methodology --- Computer Programs: General --- Economic & financial crises & disasters --- Economics of specific sectors --- Finance --- Monetary economics --- Data capture & analysis --- Credit --- Money --- Digital financial services --- Technology --- Loans --- Financial institutions --- Consumer credit --- Data collection --- Economic and financial statistics --- Currency crises --- Informal sector --- Economics --- Financial services industry --- Technological innovations --- Economic statistics

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This paper presents Cambodia’s technical assistance (TA) report on Government Finance Statistics (GFS) Mission. The mission noted a limited progress in the compilation and dissemination of GFS and Public Sector Debt Statistics (PSDS) since the previous TA mission. The mission advised on the treatment of social security benefits and government support to public financial corporations in line with the Government Finance Statistics Manual 2014. The mission discussed with the gross domestic product (GDP) GFS Team the treatment of other accounts receivable/payable in GFS time series. The mission reviewed the joint work of the Ministry of Economy and Finance GDP and the National Bank of Cambodia on reconciling the coverage of government deposits, but progress here has been limited. The mission discussed quality issues in classifying government expenditure according to Classification of the Functions of Government. Additional limitations relate to the availability and detail of source data on net acquisition of nonfinancial assets. The mission also reviewed the first draft of the GFS/PSDS Technical Compilation and Dissemination Guidelines for Cambodia. The draft includes general overview and advantages of using GFS/PSDS framework, selected provisions on the existing institutional arrangements for GFS/PSDS in Cambodia, and the description of the general government sector.

Money and Monetary Policy --- International Economics --- Statistics --- Monetary Policy --- International Agreements and Observance --- International Organizations --- Data Collection and Data Estimation Methodology --- Computer Programs: Other --- Monetary economics --- International institutions --- Econometrics & economic statistics --- Monetary policy --- International organization --- Government finance statistics --- Economic and financial statistics --- International agencies --- Finance --- Cambodia