Volume : 5, Issue : 10, OCT 2019

A STUDY OF CONFIDENCE INTERVAL-BASED INFERENCE FOR INDIAN ECONOMIC AND SOCIAL DATA

DR. MOHAMMED SHAHID

Abstract

The growing emphasis on evidence-based policymaking in India has highlighted the need for more robust statistical inference tools, particularly confidence intervals (CIs), to complement traditional point estimates. Although point estimates remain widely used in national discourse, they often fail to communicate the underlying uncertainty and sampling variability inherent in survey-based economic and social indicators. This study examines the application and significance of confidence interval–based inference in the Indian context by analysing selected datasets available up to 2015, including GDP growth rates (MOSPI), inflation measures (CPI/WPI from RBI), unemployment statistics (NSSO 68th Round), literacy and learning outcomes (ASER 2005–2015), health indicators (NFHS-3 and NFHS-4 preliminary findings), and household consumption and poverty statistics (NSSO 68th Round).The study adopts a descriptive and analytical research design, integrating classical statistical inference with empirical evidence drawn from national surveys and administrative datasets. Confidence intervals were constructed to evaluate the precision and reliability of key estimates. Findings indicate that economic indicators such as GDP growth and CPI inflation generally exhibit narrower confidence bands due to large sample sizes and high-frequency data collection. In contrast, social indicators, particularly health outcomes, literacy rates, and learning achievement, show wider confidence intervals, reflecting both greater heterogeneity and more complex sampling structures. For example, ASER literacy estimates across rural districts display substantial variation, while NFHS indicators like maternal health practices and child nutrition show significant confidence interval breadth, underscoring disparities across states and demographic groups.The analysis demonstrates that incorporating CIs enhances the interpretive quality of national statistics, allowing policymakers to assess the reliability of estimates and avoid overgeneralization based on isolated data points. Furthermore, CI-based inference helps identify statistically meaningful differences across states, demographic categories, and time periods, differences that might otherwise be obscured by reliance on point estimates alone. The study also highlights widespread misunderstandings in the Indian statistical ecosystem, including confusion between confidence intervals and prediction intervals and the absence of CI reporting in several official publications prior to 2015.The findings underscore the urgent need for mandatory CI reporting in major national surveys, improved statistical training for policymakers, and greater transparency in statistical communication. Future research may explore advanced methods such as bootstrap confidence intervals and Bayesian credible intervals to strengthen inference for complex Indian datasets.

Keywords

CONFIDENCE INTERVALS, STATISTICAL INFERENCE, POINT ESTIMATES, SAMPLING ERROR, STANDARD ERROR, ECONOMIC INDICATORS, SOCIAL INDICATORS, GDP GROWTH RATE, CPI INFLATION, WPI INFLATION, UNEMPLOYMENT RATE, NSSO SURVEYS, NFHS HEALTH DATA, ASER LEARNING OUTCOMES, CENSUS 2011, POVERTY ESTIMATES, HOUSEHOLD CONSUMPTION, PARAMETER ESTIMATION, SAMPLE VARIABILITY, PRECISION OF ESTIMATES, NULL HYPOTHESIS TESTING, CONFIDENCE LEVELS, SURVEY METHODOLOGY, INDIAN STATISTICAL SYSTEM, RBI HANDBOOK OF STATISTICS, MOSPI DATA, LITERACY RATE, EDUCATION STATISTICS, MATERNAL HEALTH INDICATORS, CHILD NUTRITION INDICATORS, RURAL-URBAN DISPARITY, EVIDENCE-BASED POLICYMAKING, DATA RELIABILITY, STATISTICAL SIGNIFICANCE, POLICY INTERPRETATION, CONFIDENCE INTERVAL WIDTH, NATIONAL SAMPLE SURVEYS, HEALTH AND EDUCATION STATISTICS, DATA-DRIVEN GOVERNANCE, INFERENTIAL STATISTICS, INDIAN ECONOMIC ANALYSIS.

Article : Download PDF

Cite This Article

IESRJ

International Educational Scientific Research Journal

E-ISSN: 2455-295X

International Indexed Journal | Multi-Disciplinary Refereed Research Journal

ISSN: 2455-295X

Peer-Reviewed Journal - Equivalent to UGC Approved Journal

Peer-Reviewed Journal

Article No : 15

Number of Downloads : 86

References

BOOKS AND THEORETICAL SOURCES

  1. Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson.
  2. Wooldridge, J. M. (2013). Introductory Econometrics: A Modern Approach (5th ed.). South-Western Cengage.
  3. Gujarati, D. N., & Porter, D. (2010). Basic Econometrics (5th ed.). McGraw-Hill.
  4. Casella, G., & Berger, R. L. (2002). Statistical Inference. Cengage Learning.
  5. Cochran, W. G. (2007). Sampling Techniques (3rd ed.). Wiley.
  6. Montgomery, D. C., &Runger, G. C. (2011). Applied Statistics and Probability for Engineers. Wiley.
  7. Mood, A. M., Graybill, F. A., &Boes, D. C. (1974). Introduction to the Theory of Statistics. McGraw-Hill.

INDIAN DATA SOURCES

  1. Ministry of Statistics and Programme Implementation (MOSPI). National Accounts Statistics, various issues up to 2015.
  2. Reserve Bank of India. RBI Handbook of Statistics on the Indian Economy, editions up to 2015.
  3. NSSO (National Sample Survey Office). 68th Round: Household Consumer Expenditure, 2011–12.
  4. NSSO. 68th Round: Employment and Unemployment Survey, 2011–12.
  5. Census of India 2011. Provisional Population Totals. Office of the Registrar General & Census Commissioner.
  6. NFHS-3 (2005–06) and NFHS-4 (2015–16) datasets, International Institute for Population Sciences (IIPS).
  7. ASER Centre. Annual Status of Education Report, editions 2005–2015.
  8. Labour Bureau (Government of India). Youth Employment–Unemployment Survey, 2013–14.
  9. Central Statistical Office (CSO). Statistical Yearbooks up to 2015.
  10. Planning Commission. Poverty Estimates, 2009–10 and 2011–12.

RESEARCH PAPERS AND REPORTS (UP TO 2015)

  1. Deaton, A., &Dreze, J. (2010). “Food and Nutrition in India: Facts and Interpretations.” Economic and Political Weekly, 45(7).
  2. Panagariya, A., & Mukim, M. (2014). “A Comprehensive Analysis of Poverty in India.” NBER Working Paper.
  3. Subramanian, S. (2011). “The Poverty Line: Getting It Wrong Again?” Economic and Political Weekly.
  4. Himanshu. (2011). “Employment Trends in India: A Re-examination.” Economic and Political Weekly, 46(37).
  5. Drèze, J., & Sen, A. (2013). An Uncertain Glory: India and its Contradictions. Penguin Books.
  6. OECD. (2014). Economic Survey of India 2014. OECD Publishing.
  7. World Bank. (2015). India Development Update: Fiscal Policy for Equitable Growth.
  8. UNDP. (2014). Human Development Report 2014: Sustaining Human Progress.
  9. Rangarajan Committee Report (2014). Expert Group to Review the Methodology for Measurement of Poverty.
  10. RBI. (2013–2015). Annual Reports, Reserve Bank of India.
  11. Chakraborty, L. (2010). “Public Investment and Uncertainty: The Case of India.” Levy Economics Institute Working Paper.
  12. Basu, K. (2011). “India’s Demographic Dividend.” Economic and Political Weekly.
  13. Planning Commission. (2013). Twelfth Five-Year Plan (2012–17): Faster, More Inclusive and Sustainable Growth.