Measuring and Mapping Micro Level Earning Inequality towards Addressing the Sustainable Development Goals – A Multivariate Small Area Modelling Approach

Abstract

The earning inequality in India has unfavorably obstructed underprivileged in accessing elementary needs like health and education. Periodic labour force survey conducted by National Statistical Office of India generates estimates on earning status at national and state level for both rural and urban sectors separately. However, due to small sample size problem, these surveys cannot generate reliable estimates at micro-level viz. district or block. Thus, owing to unavailability of district-level estimates, analysis of earning inequality is restricted to the national and the state level. Therefore, the existing variability in disaggregate-level earning distribution often goes unnoticed. This article describes multivariate small area estimation method to generate precise and representative district-wise estimate of earning distribution in rural and urban areas of the Indian State of Bihar by linking Periodic labour force survey data of 2018–2019 and 2011 Population Census data of India. These disaggregate-level estimates and spatial mapping of earning distribution are essential for measuring and monitoring the goal of reduced inequalities related to the sustainable development of 2030 agenda. They expected to offer insightful information to decision-makers and policy experts for identifying the areas demanding more attention.

Keywords

Multivariate small area estimation earning inequality SDG periodic labour force survey NSO census

References

Benavent, R., and D. Morales. 2016. “Multivariate Fay-Herriot models for small area estimation”. Computational Statistics and Data Analysis 94: 372–390. DOI: https://doi.org/10.1016/j.csda.2015.07.013.

Brown, G., R. Chambers, P. Heady, and D. Heasman. 2001. “Evaluation of small area estimation methods: an application to unemployment estimates from the UK LFS”. In Proceedings of Statistics Canada Symposium 2001. Achieving Data Quality in a Statistical Agency: A Methodological Perspective, Ottawa: Statistics Canada. Available at: https://www150.statcan.gc.ca/n1/pub/11-522-x/2001001/session6/6247-eng.pdf (accessed April 2021).

Census. 2011. Primary Census Abstracts, Registrar General of India, Ministry of Home Affairs, Government of India. Available at: https://www.censusindia.gov.in/2011-Common/CensusData2011.html (accessed April 2021).

Chandra, H., N. Salvati, and U.C. Sud. 2011. “Disaggregate-level Estimates of indebtedness in the state of Uttar Pradesh in India-an application of small area estimation technique”. Journal of Applied Statistics 38(11): 2413–2432. DOI: https://doi.org/10.1080/02664763.2011.559202.

Datta, G.S., R.E., Fay, and M. Ghosh. 1991. “Hierarchical and empirical Bayes multivariate analysis in small area estimation”. In Proceedings of Bureau of the Census 1991 Annual Research Conference, US Bureau of the Census, January, Washington, D.C., U.S.A.: 63–79 Available at: https://play.google.com/books/reader?id=Lr3rAAAAMAAJ&pg=GBS.PA64&hl=en (accessed April 2021).

Datta, G., T. Kubokawa, I. Molina, and J.N.K. Rao. 2011. “Estimation of mean squared error of model-based small area estimators”. TEST 20(2): 367–388. DOI: https://doi.org/10.1007/s11749-010-0206-2.

Fay, R.E., and R. Herriot. 1979. “Estimates of income for small places: an application of James stein procedures to census data”. Journal of the American Statistical Association 74: 269–277.DOI: https://doi.org/10.2307/2286322.

Fay, R.E. 1987. “Application of multivariate regression of small domain estimation”. In Small Area Statistics, edited by R. Platek, J.N.K. Rao, C.E. Sa¨rndal, and M.P. Singh: 91–102. New York: Wiley. DOI: https://doi.org/10.1002/bimj.4710300737.

Franco, C., and W.R. Bell. 2021. “Using American community survey data to improve estimates from smaller U.S. surveys through bivariate small area estimation models”. Journal of Survey Statistics and Methodology. DOI: https://doi.org/10.1093/jssam/smaa040.

10.

GHI. 2020. Global Hunger Index 2020. Available at: https://www.globalhungerindex.-org/results.html (accessed September 2021).

11.

Guha, S., and H. Chandra. 2021a. “Measuring and Mapping Disaggregate Level Disparities in Food Consumption and Nutritional Status via Multivariate Small Area Modelling”. Social Indicators Research 154(2): 623–646. DOI: https://doi.org/10.1007/s11205-020-02573-8.

12.

Guha, S., and H. Chandra. 2021b. “Measuring disaggregate level food insecurity via multivariate small area modelling: evidence from rural districts of Uttar Pradesh, India”. Food Security 13(3): 597–615. DOI: https://doi.org/10.1007/s12571-021-01143-1.

13.

Johnson, F.A., H. Chandra, J. Brown, and S. Padmadas. 2010. “Estimating district-level births attended by skilled attendants in ghana using demographic health survey and census data: an application of small area estimation technique”. Journal of Official Statistics 26(2): 341–359. Available at: https://www.scb.se/contentassets/ca21efb41-fee47d293bbee5bf7be7fb3/district-level-estimates-of-institutional-births-in-ghana-application-of-small-area-estimation-technique-using-census-and-dhs-data.pdf

14.

MoHFW. 2021. COVID-19 State wise Status. Available at: https://www.mohfw.gov.in (accessed September 2021).

15.

MoSPI. 2020a. Annual Report: PLFS, 2018–19. Government of India, Ministry of Statistics and ProgrammeImplementation, New Delhi. Available at: http://mospi.nic.in/sites/default/files/publicationreports/Annual_Report_PLFS_2018_19_HL.pdf (accessed April 2021).

16.

MoSPI. 2020b. Quarterly Bulletin, PLFS: April–June 2020. Government of India, Ministry of Statistics and Programme Implementation, New Delhi. Available at: http://mospi.nic.in/sites/default/files/publication_reports/PLFS_Quarterly_Bulletin_April-June_2020.pdf (accessed September 2021).

17.

MoSPI. 2021. Press note: On estimates of gross domestic product for the first quarter (april 2 june) 2021–2022. Government of India, Ministry of Statistics and Programme Implementation, New Delhi. Available at: http://mospi.nic.in/sites/default/files/press-release/PRESS_NOTE-Q1_2021-22.pdf (accessed September 2021).

18.

NITI Aayog. 2019. SDG India Index. Available at: https://sdgindiaindex.niti.gov.in (accessed September 2021).

19.

Prasad, N.G.N., and J.N.K. Rao. 1990. “The estimation of the mean squared error of small-area estimators”. Journal of the American Statistical Association 85: 163–171. DOI: https://doi.org/10.2307/2289539.

20.

Rao, J.N.K., and I. Molina. 2015. Small area estimation. New York: Wiley. DOI: https://doi.org/10.1002/9781118735855.

21.

Särndal, C.E., B. Swensson, and J.H. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer-Verlag.

22.

Schall, R. 1991. “Estimation in generalized linear models with random effects”. Biometrika 78(4): 719–727. DOI: https://doi.org/10.2307/2336923.

23.

UNCDP. 2018. Official Records of the Economic and Social Council, 2018. Available at: https://undocs.org/pdf?symbol=en/E/2018/33 (accessed September 2021).

24.

UNDP. 2015. Sustainable Development Goals. Available at: https://www.undp.org (accessed April 2021).

25.

U.S. Census Bureau. 2016. Theil Index. Available at: https://www.census.gov/topics/income-poverty/income-inequality/about/metrics/theil-index.html (accessed September 2021).

26.

World Bank. 2019. Open Data. Available at: https://www.worldbank.org/en/home (accessed September 2021).

27.

World Bank. 2020. Open Data. Available at: https://www.worldbank.org/en/home (accessed September 2021).