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"Featuring leading scholars from philosophy and religious studies, The Bloomsbury Research Handbook of Indian Ethics dispels the myth that Indian thinkers and philosophers were uninterested in ethics. This comprehensive research handbook traces Indian moral philosophy through classical, scholastic Indian philosophy, pan-Indian literature including the Epics, Ayurvedic medical ethics, as well as recent, traditionalist and Neo-Hindu contributions. Contrary to the usual myths about India (that Indians were too busy being religious to care about ethics), moral theory constitutes the paradigmatic differentia of formal Indian philosophy, and is reflected richly in popular literature. Many of the papers make this clear by an analytic explication that draws critical comparisons and contrasts between classical Indian moral philosophy and contemporary contributions to ethics. By critically addressing ethics as a sub-discipline of philosophy and acknowledging the mistaken marginalization of Indian moral philosophy, this handbook reveals how Indian contributions can illuminate contemporary philosophical research on ethics. Unlike previous approaches to Indian ethics, this volume is organized in accordance with major topics in moral philosophy. The volume contains an extended introduction, exploring topics in moral semantics, the philosophy of thought, (metaethical and normative) ethical theory, and the politics of scholarship, which serve to show how the diversity of Indian moral philosophy is a contribution to the discipline of ethics. With an overview of Indian moral theory, and a glossary, this is a valuable guide to understanding the past, present and future research directions of a central component of Indian philosophy."--Bloomsbury Publishing

Non-linearities and dynamic interactions between state variables are characteristic of complex social systems and processes. In this thesis, we present a new methodology to model these non-linearities and interactions from the large panel datasets available for some of these systems. We build macro-level statistical models that can verify theoretical predictions, and use polynomial basis functions so that each term in the model represents a specific mechanism. This bridges the existing gap between macro-level theories supported by statistical models and micro-level mechanistic models supported by behavioural evidence. We apply this methodology to two important problems in the social sciences, the demographic transition and the transition to democracy. The demographic transition is an important problem for economists and development scientists. Research has shown that economic growth reduces mortality and fertility rates, which reduction in turn results in faster economic growth. We build a non-linear dynamic model and show how this data-driven model extends existing mechanistic models. We also show policy applications for our models, especially in setting development targets for the Millennium Development Goals or the Sustainable Development Goals. The transition to democracy is an important problem for political scientists and sociologists. Research has shown that economic growth and overall human development transforms socio-cultural values and drives political institutions towards democracy. We model the interactions between the state variables and find that changes in institutional freedoms precedes changes in socio-cultural values. We show applications of our models in studying development traps. This thesis comprises the comprehensive summary and seven papers. Papers I and II describe two similar but complementary methodologies to build non-linear dynamic models from panel datasets. Papers III and IV deal with the demographic transition and policy applications. Papers V and VI describe the transition to democracy and applications. Paper VII describes an application to sustainable development.

The 2030 agenda presents an integrated set of Sustainable Development Goals (SDGs) and targets that will shape development activities for the coming decade. The challenge now facing development organizations and governments is how to operationalize this interconnected set of goals and targets through effective projects and programs. This paper presents a micro-level modeling approach that can quantitatively assess the impacts associated with rural water interventions that are tailored to specific communities. The analysis focuses on how a multiple-use water services (MUS) approach to SDG 6 could reinforce a wide range of other SDGs and targets. The multilevel modeling framework provides a generalizable template that can be used in multiple sectors. In this paper, we apply the methodology to a dataset on rural water services from Mozambique to show that community-specific equivalents of macro-level variables used in the literature such as Cost of Illness (COI) avoided can provide a better indication of the impacts of a specific intervention. The proposed modeling framework presents a new frontier for designing projects in any sector that address the specific needs of communities, while also leveraging the knowledge gained from previous projects in any country. The approach also presents a way for agencies and organizations to design projects or programs that bridge sectors/disciplines (water, irrigation, health, energy, economic development, etc.) to advance an interconnected set of SDGs and targets. ; Published version

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.

Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values.

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach. ; Published (Publication status)

Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values. ; Published (Publication status)

Gravity-fed water systems are widely used in the rural hills of Nepal. This study identifies the systematic factors that contribute to rural households not obtaining water due to system breakdowns. The study makes use of data from a 2017 to 2018 study of 202 households served by 10 community-based water systems from three localities within the western middle hills of Nepal. A hierarchical regression model is used to capture both household- and system-level variables. The analysis identifies three household-level and three system-level predictors of the duration of water system breakdowns. The significant household-level predictors include (1) a sense of ownership toward the water system, (2) user involvement in decision making during the planning and implementation of the water system, and (3) income earned from water-based productive activities. The significant system-level predictors include (1) distance from the village to the water source, (2) the performance of the water user committee, and (3) the water system operator's level of activity. In addition, the interactions between household- and system-level variables are captured. The empirical relationship between household productive income and the duration of breakdowns is a novel finding. These findings will be valuable to the Nepalese government and other actors working to implement sustainable water systems. ; Published version

There is growing evidence to suggest that a substantial number of college and university students in the United States grapple with food insecurity during their studies. One of the most comprehensive surveys on this issue was conducted by The Hope Center with 33 participating four-year institutions. They estimated that 41% of students had low or very low food security (Goldrick-Rab et al. 2019). A review of food security studies by the U.S. Government Accountability Office (GAO) (2018) found similar results and that few students who qualified for food assistance were aware of federal food assistance programs such as the Supplemental Nutritional Assistance Program (SNAP). In response to the increasing concern over students access to food, this study aims to document food security at Virginia Tech. The study was designed with two parallel goals: to contribute to the national conversation on food access and security amongst higher education students; and to inform a strategic response through data-informed programs and policies at Virginia Tech. The first phase of the study was conducted between Fall 2017 and Spring 2018 and consisted of semi-structured key informant interviews. The second phase was conducted between December 2018 and January 2019 and consisted of an anonymous survey distributed to 32,242 students (27,421 undergraduate and 4,821 graduate) located in Blacksburg. A total of 2,441 (8.9%) undergraduate and 589 (12.2%) graduate students completed the entire survey (for a combined response rate of 9.4%). This study finds that 29% (±3.8%) of undergraduate and 35% (±7%) of graduate students were classified as having low or very low food security based on the USDA food security instrument. These findings are comparable with The Hope Center study (Goldrick-Rab et al. 2019). Students with low/very low food security status were more likely to be Hispanic/Latino or Black/African American, be receiving a Pell grant or financing their education through sources that need to be repaid, have a low GPA, and/or have a ...