This book deals with not just complex linkages, interactions and exchanges that form the relationship between the economic activities, human society and the ecosystems, but also the influences and impacts that each causes on the other. In recent times, this ecology-economy-society interface has received unprecedented attention within the broader environment-development discourse. The volume is in honour of Kanchan Chopra, one of the pioneers of research in these areas in India. She has recently been awarded the coveted Kenneth Boulding Award by the International Society for Ecological Economics (ISEE) and is the first Asian to receive it. The four sub-themes of the book reflect some of the important areas in the environment-development discourse - sustainability of development, institutions and environmental governance, environment and well-being, and ecosystem and conservation. Within each of the sub-themes, the policy and the practice as well as the macro and micro aspects are addressed. With contributions mainly from ecological economists and ecologists, the book's approach is interdisciplinary, both in spirit and content, reflecting the honoree's work, which went not just beyond the mainstream ideology of economics, but also the way she listened to ideas from disciplines like ecology and sociology. The volume also includes two reflective essays on academic life and works of Kanchan Chopra. The book is a valuable resource for students, teachers, researchers, practitioners and policy makers in the areas of development economics, ecological economics, environmental economics and related disciplines such as conservation, development, ecology, economics, environment, governance, health, sociology and public policy.

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham's tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader's R skills are gradually honed, with the help of "your turn" exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods-generalized additive models and random forests (an important and versatile machine learning method)-are introduced intuitively with applications. The book will be of great interest to economists-students, teachers, and researchers alike-who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.

This book deals with not just complex linkages, interactions and exchanges that form the relationship between the economic activities, human society and the ecosystems, but also the influences and impacts that each causes on the other. In recent times, this ecology-economy-society interface has received unprecedented attention within the broader environment-development discourse. The volume is in honour of Kanchan Chopra, one of the pioneers of research in these areas in India. She has recently been awarded the coveted Kenneth Boulding Award by the International Society for Ecological Economics (ISEE) and is the first Asian to receive it. The four sub-themes of the book reflect some of the important areas in the environment-development discourse - sustainability of development, institutions and environmental governance, environment and well-being, and ecosystem and conservation. Within each of the sub-themes, the policy and the practice as well as the macro and micro aspects are addressed. With contributions mainly from ecological economists and ecologists, the book's approach is interdisciplinary, both in spirit and content, reflecting the honoree's work, which went not just beyond the mainstream ideology of economics, but also the way she listened to ideas from disciplines like ecology and sociology. The volume also includes two reflective essays on academic life and works of Kanchan Chopra. The book is a valuable resource for students, teachers, researchers, practitioners and policy makers in the areas of development economics, ecological economics, environmental economics and related disciplines such as conservation, development, ecology, economics, environment, governance, health, sociology and public policy.

This book gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the Cobb-Douglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression. It uses data that can be downloaded from the internet, and which is also available in different R packages. With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful.

This brief views the environment through diverse lenses - those of standard economics, institutional economics, political science, environmental science and ecology. Chapter 2 discusses diverse theoretical and statistical models - constrained optimization models, game theory, differential equations, and statistical models for causal inference - in a simple manner. Developing countries have certain distinct environmental problems - traditional pollution and traditional dependence on the commons. While chapters 3 and 4 discuss these specific problems, statistical graphs of the World Development Indicators explore the macro-context of developing countries in chapter 1. Chapter 5 examines ecological systems, which are nonlinear and unpredictable, and subject to sudden regime shifts. Chapter 6 deals with the global challenges of climate change and biological invasions. The last chapter discusses sustainable development and institutions. The brief explains these topics simply; mathematics is largely confined to an appendix. The broad treatment and simple exposition will appeal to students new to the field of economics. The extension of core economic models in diverse directions will also be of interest to economists looking for a different treatment of the subject.

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham's tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader's R skills are gradually honed, with the help of "your turn" exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods-generalized additive models and random forests (an important and versatile machine learning method)-are introduced intuitively with applications. The book will be of great interest to economists-students, teachers, and researchers alike-who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.