Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-23T20:58:32.064Z Has data issue: false hasContentIssue false
  • English
  • Français

Age heaping among individuals in selected South Asian countries: evidence from Demographic and Health Surveys

Published online by Cambridge University Press:  08 June 2021

Manish Singh ,
Gyan Chandra Kashyap  and
Madhumita Bango Open the ORCID record for Madhumita Bango [Opens in a new window]
Manish Singh
Affiliation:
International Institute for Population Sciences, Mumbai, India
Gyan Chandra Kashyap
Affiliation:
Institute of Health Management Research, Bangalore, India
Madhumita Bango*
Affiliation:
School of Health Systems Studies, Tata Institute of Social Sciences, Mumbai, India
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Age misreporting is a common phenomenon in Demographic and Health Surveys, and there are numerous reasons for this. The trend and pattern of disparity in age heaping vary between countries. The present study assesses age heaping in the selected South Asian countries of Afghanistan, India, Nepal, Bangladesh and Pakistan using data from the most recent round of the Demographic and Health Survey. The respondent sample sizes were 203,703 for Afghanistan, 2,869,043 for India, 49,064 for Nepal, 81,618 for Bangladesh and 100,868 for Pakistan. Age heaping was assessed by respondent’s age, education level, sex and level of education. Whipple’s index was calculated to assess systematic heaping on certain ages as a result of digit preference. Bangladesh, Afghanistan and India showed stronger preference for ages ending with the digits ‘0’ and ‘5’ compared with Pakistan and Nepal among uneducated respondents. On the other hand, strong avoidance of ages ending in the digits ‘1’, ‘4’ and ‘9’ was observed in Bangladesh, Afghanistan and India. However, urban–rural place of residence was not found to be associated with digit preference in the study countries. Among males, age misreporting with the final digits ‘0’ and ‘5’ was highest in Bangladesh, followed by Afghanistan and India, and Nepal showed the least displacement. Strong digit preference and avoidance, and upper age displacement, were witnessed in the surveys conducted in Bangladesh, Afghanistan and India on the parameters of sex and education level. Innovative methods of data collection with the measurement and minimization of errors using statistical techniques should be used to ensure accuracy of age data.

Keywords

Age heapingMyers’ indexWhipple’s index
Type
Research Article
Information
Journal of Biosocial Science , Volume 54 , Issue 4 , July 2022 , pp. 725 - 734
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agrawal, G and Khanduja, P (2015) Influence of literacy on India’s tendency for age misreporting: evidence from Census 2011. Journal of Population and Social Studies 23(1), 47–56.Google Scholar
Blum, M and Krauss, KP (2018) Age heaping and numeracy: looking behind the curtain. Economic History Review 71(2), 464–479.CrossRefGoogle Scholar
Borkotoky, K and Unisa, S (2014) Indicators to examine quality of large-scale survey data: an example through district level household and facility survey. PLoS One 9(3), e90113.CrossRefGoogle ScholarPubMed
CBS (2014) National Population and Housing Census 2011 (Village Development Committee/Municipality). Central Bureau of Statistics of Nepal.Google Scholar
Fayehun, O, Ajayi, AI, Onuegbu, C and Egerson, D (2020) Age heaping among adults in Nigeria: evidence from the Nigeria Demographic and Health Surveys 2003–2013. Journal of Biosocial Science 52(1), 132–139.CrossRefGoogle ScholarPubMed
Friesen, J, Baten, J and Prayon, V (2012) Women count: gender (in-)equalities in the human capital development in Asia, 1900–60. University of Tübingen Working Papers in Economics and Finance No. 29.Google Scholar
James, KS and Rajan, SI (2004) Respondents and quality of survey data. Economic and Political Weekly 39(7), 659–663.Google Scholar
Kashif, M, Khan, MI, Iqbal, Z and Riaz, M (2012) Measuring quality of age reporting data in Pakistan. Paper presented at the 9th International Conference on Statistical Sciences, p. 191.Google Scholar
Lyons-Amos, M and Stones, T (2017) Trends in Demographic and Health Survey data quality: an analysis of age heaping over time in 34 countries in Sub Saharan Africa between 1987 and 2015. BMC Research Notes 10(1), 1–7.CrossRefGoogle ScholarPubMed
Mahalanobis, PC (1959) The National Sample Survey. Number 12. A Technical Note on Age Grouping. Sankhyā: The Indian Journal of Statistics (1933–1960), 21(1/2), 53–90.Google Scholar
Mukhopadhyay, BK and Majumdar, PK (2009). A multivariate statistical analysis of reporting error in age data of India. Journal of Social Sciences 19(1), 57–61.CrossRefGoogle Scholar
Nasir, JA and Hinde, A (2014) An extension of modified Whipple index-further modified Whipple index. Pakistan Journal of Statistics 30(2).Google Scholar
NIPORT, Mitra and Associates and ICF International (2014) 2016 Bangladesh Demographic and Health Survey. NIPORT, Mitra and Associates and ICF International, Dhaka, Bangladesh, and Rockville, MD, USA.Google Scholar
NIPORT, Mitra and Associates, and ICF International (2013) Bangladesh Demographic and Health Survey 2011. NIPORT, Mitra and Associates and ICF International, Dhaka, Bangladesh, and Rockville, MD, USA.Google Scholar
Noumbissi, A (1992) L’indice de Whipple modifié: une application aux données du Cameroun, de la Suède et de la Belgique. Population (French edition), 1038–1041.CrossRefGoogle Scholar
Pullum, TW (2005) A statistical reformulation of demographic methods to assess the quality of age and date reporting, with application to the Demographic and Health Surveys. Paper presented at the Annual Meeting of the Population Association of America, Philadelphia, March 2005.Google Scholar
Retherford, RD and Mirza, GM (1982) Evidence of age exaggeration in demographic estimates for Pakistan. Population Studies 36(2), 257–270.CrossRefGoogle ScholarPubMed
Roger, G, Waltisperger, D and Corbille-Guitton, C (1981) Les structures par sexe et âge en Afrique. Groupe de démographie africaine. Les structures par sexe et âge en Afrique. URL: https://core.ac.uk/download/pdf/39876119.pdf Google Scholar
Siegel, JS and Swanson, DA (2004) The Methods and Materials of Demography. Elsevier Academic Press, London.Google Scholar
Shryock, HS and Siegel, JS (1976) The Materials and Methods of Demography. Academic Press, New York.Google Scholar
Singh, M (2017). Understanding digit preferences in India using Modified Whipple Index: an analysis of 640 districts of India. Journal of Current Research 9(1), 45144–45152.Google Scholar
Spoorenberg, T (2009) Assessing the quality of age reporting at a time of general data quality improvement: going beyond the original Whipple’s index. Paper presented at the XXVI IUSSP International Population Conference, Morocco, 27th September 2009, Session P-5.Google Scholar
Spoorenberg, T and Dutreuilh, C (2007) Quality of age reporting: extension and application of the modified Whipple’s index. Population 62(4), 729–741.CrossRefGoogle Scholar
Tollnek, F and Baten, J (2016) Age-heaping-based human capital estimates. Handbook of Cliometrics pp. 131–154, doi:10.1007/978-3-642-40406-1CrossRefGoogle Scholar
Unisa, S Dwivedi, LK Reshmi, RS and Kumar, K(2009) Age reporting in Indian census: an insight. Paper presented at the XXVI IUSSP International Population Conference, Morocco.Google Scholar
Yadav, A, Vishwakarma, M and Chauhan, S (2020) The quality of age data: comparison between two recent Indian censuses 2001–2011. Clinical Epidemiology and Global Health 8(2), 371–376.CrossRefGoogle Scholar