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Regional disparities in electrification of India – do geographic factors matter?
TL;DR: In this paper, the authors examined the factors that influence household and village electrification, with particular attention given to the influence of geographic factors, and found that household electrification depends on household characteristics, the degree of community electrification and the quality of electricity supply.
Abstract: Modern energy sources are important input factors for human development. Although official estimates indicate that 85% of Indian villages are electrified, fewer than 60% of Indian households actually consume electricity. Therefore, one observes a considerable spatial heterogeneity in electrification rate. This paper examines the factors that influence household and village electrification, with particular attention given to the influence of geographic factors. The analysis shows that village electrification is constrained by state area and village structure. In addition, a high share of agricultural areas seems to have a positive effect. Household electrification depends on household characteristics, the degree of community electrification, and the quality of electricity supply, and it is independent of geographic factors. Surprisingly, household expenditure and, in particular, the electricity tariff show only a relatively small effect on a household‘s choice for electricity.
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TL;DR: In this article, the authors developed an economic model that explains the decision-making problem under uncertainty of an industrial firm that wants to invest in a process technology, where the decision is between making an irreversible investment in a combined heat-and-power production (cogeneration) system, or investing in a conventional heat-only generation system (steam boiler) and to purchase all electricity from the grid.
90 citations
TL;DR: In this paper, the authors categorized the literature into four focal lenses: technology, institutional, viability and user-centric, and combined the four lenses to develop a business model framework that policy makers, practitioners and investors could use to assess RE projects or to design future rural electrification strategies.
83 citations
TL;DR: The solar photo voltaic system converts light energy into direct current power using photovoltaic effect and battery is used to store the extra power generated during the day and used during nights.
Abstract: Rural area electrification in developing countries helps to improve the quality of life of the people. It increases productivity and supports education. It also discourages people from migrating towards urban areas. In India about 70% of the population lives in rural area, hence it is necessary to electrify these villages to achieve inclusive economic growth. Transmission and distribution of power to this less densely populated areas which are located far away from the power generating stations is the major reason for not able to achieve 100% electrification in the country. Hence it is necessary to find out an energy source which can be decentralized to supply power to these hamlets. As India is blessed with solar energy which is omnipresent in almost all parts of the country, micro grid system which uses solar photo voltaic panels seems as the finest option. The solar photo voltaic system converts light energy into direct current power using photovoltaic effect. Battery is used to store the extra power generated during the day and used during nights. Inverters and power conditioning devices are used to convert direct current power generated by solar photo voltaic systems to alternative current, which is supplied to the load using power distribution network which adds to system cost. At present the capital cost and the land requirement for this system is higher than all other renewable energy power generation system. But it has very less operation and maintenance cost which makes it superior to other system. Moreover additional modules can be added to it when the power demand increases. This paper says about how rural area electrification can be achieved in India by solar photo voltaic system micro grid system and the challenges which has to be over come during implementation.
81 citations
TL;DR: In this article, the authors investigated the cost effectiveness of centralized and decentralised distributed generation (DDG) technologies to achieve universal energy access for rural households in the South Asian region.
70 citations
Additional excerpts
...Other factors which affect the choice of technology are income level of consumers (paying ability), cost and maturity of technology and ease of maintenance of infrastructure (Kemmler, 2006; World Bank, 2011)....
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TL;DR: In this article, the authors have set a target, going beyond the MDGs, of energizing all households by the year 2012, in view of the differentiated responsibilities of various ministries to the Government of India, the strategy for reaching this target may not address itself to the larger development goals.
68 citations
References
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Book•
01 Jan 2001
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Abstract: The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
28,298 citations
Book•
06 Aug 1999
TL;DR: In this article, the authors present a regression analysis with time series data using OLS asymptotics and a simple regression model in Matrix Algebra, which is based on the linear regression model.
Abstract: 1. The Nature of Econometrics and Economic Data. Part I: REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA. 2. The Simple Regression Model. 3. Multiple Regression Analysis: Estimation. 4. Multiple Regression Analysis: Inference. 5. Multiple Regression Analysis: OLS Asymptotics. 6. Multiple Regression Analysis: Further Issues. 7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables. 8. Heteroskedasticity. 9. More on Specification and Data Problems. Part II: REGRESSION ANALYSIS WITH TIME SERIES DATA. 10. Basic Regression Analysis with Time Series Data. 11. Further Issues in Using OLS with Time Series Data. 12. Serial Correlation and Heteroskedasticity in Time Series Regressions. Part III: ADVANCED TOPICS. 13. Pooling Cross Sections across Time: Simple Panel Data Methods. 14. Advanced Panel Data Methods. 15. Instrumental Variables Estimation and Two Stage Least Squares. 16. Simultaneous Equations Models. 17. Limited Dependent Variable Models and Sample Selection Corrections. 18. Advanced Time Series Topics. 19. Carrying out an Empirical Project. APPENDICES. Appendix A: Basic Mathematical Tools. Appendix B: Fundamentals of Probability. Appendix C: Fundamentals of Mathematical Statistics. Appendix D: Summary of Matrix Algebra. Appendix E: The Linear Regression Model in Matrix Form. Appendix F: Answers to Chapter Questions. Appendix G: Statistical Tables. References. Glossary. Index.
6,120 citations
TL;DR: The generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more, and a new method is offered that is both easier to implement and produces accurate standard errors.
Abstract: We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.
5,670 citations
TL;DR: The Elements of Econometrics as mentioned in this paper is a textbook for upper-level undergraduate and master's degree courses and may usefully serve as a supplement for traditional Ph.D. courses in economics.
Abstract: This classic text has proven its worth in university classrooms and as a tool kit in research--selling over 40,000 copies in the United States and abroad in its first edition alone. Users have included undergraduate and graduate students of economics and business, and students and researchers in political science, sociology, and other fields where regression models and their extensions are relevant. The book has also served as a handy reference in the "real world" for people who need a clear and accurate explanation of techniques that are used in empirical research.Throughout the book the emphasis is on simplification whenever possible, assuming the readers know college algebra and basic calculus. Jan Kmenta explains all methods within the simplest framework, and generalizations are presented as logical extensions of simple cases. And while a relatively high degree of rigor is preserved, every conflict between rigor and clarity is resolved in favor of the latter. Apart from its clear exposition, the book's strength lies in emphasizing the basic ideas rather than just presenting formulas to learn and rules to apply.The book consists of two parts, which could be considered jointly or separately. Part one covers the basic elements of the theory of statistics and provides readers with a good understanding of the process of scientific generalization from incomplete information. Part two contains a thorough exposition of all basic econometric methods and includes some of the more recent developments in several areas.As a textbook, "Elements of Econometrics" is intended for upper-level undergraduate and master's degree courses and may usefully serve as a supplement for traditional Ph.D. courses in econometrics. Researchers in the social sciences will find it an invaluable reference tool.A solutions manual is also available for teachers who adopt the text for coursework.Jan Kmenta is Professor Emeritus of Economics and Statistics, University of Michigan.
3,838 citations