Skip to main content
SpringerLink
Log in

The Economic-Adjusted Age Dependency Ratio in India: A New Measure for Understanding Economic Burden of Aging

  • Living reference work entry
  • First Online:
Handbook of Aging, Health and Public Policy

Abstract

The rising share of the elderly in the population has several economic implications, including alterations in workforce participation and economic growth. The enhanced economic growth in India during the last three decades did not make a corresponding improvement in the access to social security for the aging population, which has compelled most elderly people to continue working till late in their life span. And, this, in turn, has an implication for dependency ratios computed only based on the age structure of the population. Therefore, this chapter proposes to estimate the economic burden of aging based on workforce participation of all adult population aged 15–64 years and elderly population aged 65 years and above. The newly proposed measures, economic-adjusted age dependency ratio (EADR) and economic-adjusted old-age dependency ratio (EAODR), estimate the financial burden of the older adults more correctly by adjusting to out-of-workforce population for all adult population aged 15–64 years and 64 years and above. Our findings suggest that the states with higher age dependency ratio (OADR), such as Himachal Pradesh, Andhra Pradesh, Telangana, Tamil Nadu, Maharashtra, etc., have lower EADR as a result of the better workforce participation rate among population aged 15 years and above. Across the states, the discordance in the ranks of OADR and EADR in NCT of Delhi (OADR 0.13; EADR 1.06) has the highest EADR but exhibits lowest OADR, whereas Himachal Pradesh (OADR 0.19; EADR 0.28) has the lowest EADR but accounts for the highest OADR. Similarly, the estimates of EAODR show that majority of the states have a high OADR but relatively low EAODR. And, this difference is largest for Himachal Pradesh (OADR 19.6; EAODR 8.8) followed by Andhra Pradesh (OADR 18.5; EAODR 10.5) and Tamil Nadu (OADR 20.5; EAODR 13.6). These instances imply that not all working-age population are working, while all older population are not economically dependent. Therefore, EADR and EAODR are potential measures to estimate the actual economic burden of age dependency and help in strengthening economic policies and social safety net programs and further help in designing employment policies to promote healthy and active aging societies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    United Nations uses three dependency ratios: young, old, and total dependency ratios. Young dependency ratio is defined as ratio of population aged ≤14 years divided by working-age population aged 15–65 years, while old-age dependency ratio is defined as population aged ≥65 years divided by working-age population aged 15–65 years. Total dependency ratio comprises both young- and old-age dependency ratios: sum (aged ≤14 years and aged ≥65 years)/working-age population aged 15–65 years.

  2. 2.

    Self-reported dependency ratio is estimated using the information from National Sample Survey Office estimates of 2017–2018. The survey asks the respondents whether they are fully (1) or partially (2) dependent on others or not independent (3). Using these three categories, we have estimated the ratio of self-reported dependency as follows: ((1 + 2)/3 * adjusted to workforce participation rate in adults).

References

  • Abyad, A. (2018). From active ageing to health ageing. QJM: An International Journal of Medicine, 111(1), hcy200.067. https://doi.org/10.1093/qjmed/hcy200.067

    Article  Google Scholar 

  • Balachandran, A. (2020). Population ageing in Europe and Asia: Beyond traditional perspectives. Doctoral dissertation, University of Groningen.

    Google Scholar 

  • Balachandran, A., & James, K. S. (2019). A multi-dimensional measure of population ageing accounting for quantum and quality in life years: An application of selected countries in Europe and Asia. SSM-Population Health, 7, 100330.

    Article  Google Scholar 

  • Balachandran, A., de Beer, J., James, K. S., van Wissen, L., & Janssen, F. (2020). Comparison of population aging in Europe and Asia using a time-consistent and comparative aging measure. Journal of Aging and Health, 32(5–6), 340–351.

    Article  Google Scholar 

  • Balachandran, A., James, K. S., Van Wissen, L., Samir, K. C., & Janssen, F. (2022). Can changes in education alter future population ageing in Asia and Europe? Journal of Biosocial Science, 54(3), 398–410.

    Article  Google Scholar 

  • Ballod, C. (1913). Grundriss der Statistik: enthaltend Bevoelkerungs-, Wirtschafts-, Finanz- und Handels-Statistik. J. Guttentag.

    Book  Google Scholar 

  • Becker, G. S. (1965). A theory of the allocation of time. The Economic Journal, 75(299), 493–517.

    Article  Google Scholar 

  • Behrendt, C., & Nguyen, Q. A. (2018). Innovative approaches for ensuring universal social protection for the future of work. Future of Work Research Paper Series, 1.

    Google Scholar 

  • Bloom, D. E., & Canning, D. (1999). Economic development and the demographic transition: The role of cumulative causality. Center for International Development at Harvard University.

    Google Scholar 

  • Bloom, D. E., Canning, D., & Malaney, P. N. (2000). Population dynamics and economic growth in Asia. Population and Development Review, 26, 257–290.

    Google Scholar 

  • Bloom, D., Canning, D., & Sevilla, J. (2003). The demographic dividend: A new perspective on the economic consequences of population change. Rand Corporation.

    Book  Google Scholar 

  • Bloom, D. E., Canning, D., Fink, G., & Finlay, J. E. (2007). Does age structure forecast economic growth? International Journal of Forecasting, 23, 569–585.

    Article  Google Scholar 

  • Bloom, D. E., Canning, D., & Fink, G. (2010). The greying of the global population and its macroeconomic consequences. Twenty-First Century Society, 5(3). Special Section on Economics of an Ageing World.

    Google Scholar 

  • Bloom, D. E., Börsch-Supan, A., McGee, P., & Seike, A. (2012a). Population ageing: Macro challenges and policy responses. In Global population ageing: Peril or promise? (p. 35). World Economic Forum.

    Google Scholar 

  • Bloom, D. E., Canning, D., Fink, G., & Finlay, J. E. (2012b). Microeconomic foundations of the demographic dividend (No. 9312). Program on the Global Demography of Aging.

    Google Scholar 

  • Cutler, D. M., Poterba, J. M., Sheiner, L. M., Summers, L. H., & Akerlof, G. A. (1990). An aging society: opportunity or challenge?. Brookings Papers on Economic Activity, 1990(1), 1–73.

    Google Scholar 

  • Deshpande, A., & Sharma, S. (2013). Entrepreneurship or survival? Caste and gender of small business in India. Economic and Political Weekly, 48, 38–49.

    Google Scholar 

  • Deshpande, A., Goel, D., & Khanna, S. (2018). Bad karma or discrimination? Male–female wage gaps among salaried workers in India. World Development, 102, 331–344.

    Article  Google Scholar 

  • Dogra, S., Dunstan, D. W., Sugiyama, T., Stathi, A., Gardiner, P. A., & Owen, N. (2021). Active aging and public health: Evidence, implications, and opportunities. Annual Review of Public Health, 43, 439–459.

    Article  Google Scholar 

  • Goli, S., & Pandey, A. (2010). Is India getting old before getting rich: Beyond demographic assessment. In Challenges of the twenty first century: A trans-disciplinary perspective (Macmillan Advance Research Series). Macmillan.

    Google Scholar 

  • Goli, S., Reddy, A. B., James, K. S., & Srinivasan, V. (2019). Economic independence and social security among India’s elderly. Economic and Political Weekly, 54(39), 32–41.

    Google Scholar 

  • Goli, S., Hossain, B., Singh, A., & James, K. (2023). Mortality and epidemiological transition: India and its concerns. In K. James & T. Sekher (Eds.), India population report (pp. 110–170). Cambridge University Press. https://doi.org/10.1017/9781009318846.006

    Chapter  Google Scholar 

  • Günther, E. (1931). Der Geburtenrückgang als Ursache der Arbeitslosigkeit? Untersuchung einiger Zusammenhänge zwischen Wirtschaft und Bevölkerungsbewegun. Jahrbücher für Nationalökonomie und Statistik, 79(134), 921–973.

    Article  Google Scholar 

  • Han, X., & Cheng, Y. (2020). Consumption-and productivity-adjusted dependency ratio with household structure heterogeneity in China. The Journal of the Economics of Ageing, 17, 100276.

    Google Scholar 

  • Harasty, C., & Ostermeier, M. (2020). Population Ageing: Alternative measures of dependency and implications for the future of work (No. 5). ILO Working Paper.

    Google Scholar 

  • Jain, N., & Goli, S. (2022). Potential demographic dividend for India, 2001 to 2061: A macro-simulation projection using the spectrum model. SN Social Sciences, 2, 171.

    Article  Google Scholar 

  • James, K. S., & Goli, S. (2016). Demographic changes in India: Is the country prepared for the challenge. The Brown Journal of World Affairs, 23, 169.

    Google Scholar 

  • Keating, N. (2022). A research framework for the United Nations Decade of Healthy Ageing (2021–2030). European Journal of Ageing, 19(3), 775–787.

    Article  Google Scholar 

  • Ladusingh, L. (2013). Lifecycle deficit, intergenerational public and familial support system in India. Asian Population Studies, 9(1), 78–100.

    Article  Google Scholar 

  • Lee, R. (2002). The demographic transition: Three centuries of fundamental change. Journal of Economic Perspectives, 17(4), 167–190.

    Article  Google Scholar 

  • Lee, R. D., & Mason, A. (Eds.). (2011). Population aging and the generational economy: A global perspective. Edward Elgar Publishing.

    Google Scholar 

  • Loichinger, E., Hammer, B., Prskawetz, A., Freiberger, M., & Sambt, J. (2017). Quantifying economic dependency. European Journal of Population, 33, 351–380.

    Article  Google Scholar 

  • Ministry of Health and Family Welfare. (2022). Press Information Bureau report, Delhi (26 July 2022). Government of India.

    Google Scholar 

  • Ministry of Statistics and Programme Implementation. (2022). Periodic Labour Force Survey (PLFS) annual report (July 2021–June 2022). National Sample Survey Office Ministry of Statistics and Programme Implementation, Government of India.

    Google Scholar 

  • MoSPI. (2018). Periodic Labour Force Survey (PLFS) annual report (July 2017- June 2018), National Sample Survey Office Ministry of Statistics and Programme Implementation (MoSPI), Government of India.

    Google Scholar 

  • National Commission on Population. (2020). Population Projection Report 2011–2036. Ministry of Home Affairs. https://main.mohfw.gov.in/sites/default/files/Population%20Projection%20Report%202011-2036%20-%20upload_compressed_0.pdf

    Google Scholar 

  • Notestein, F. W. (1944). Problems of policy in relation to areas of heavy population pressure. The Milbank Memorial Fund Quarterly, 22(4), 424–444.

    Google Scholar 

  • Office of RGI. (1971). Sample Registration System (SRS) 1971–72. Office of the Registrar General & Census Commissioner (08 December 1971), Government of India.

    Google Scholar 

  • Office of RGI. (2020). Sample Registration System (SRS) 2020–21. Office of the Registrar General & Census Commissioner (24 October 2020), Government of India.

    Google Scholar 

  • Prasad, R. D., Ram, H., & Goli, S. (2023). Multitude effects of intersection of gender, marital status and economic dependency on the health status of the elderly in India. Journal of Social and Economic Development, 1–27. https://doi.org/10.1007/s40847-023-00234-3

  • Rajan, S. I., Sarma, P. S., & Mishra, U. S. (2003). Demography of Indian aging, 2001–2051. Journal of Aging & Social Policy, 15(2–3), 11–30.

    Article  Google Scholar 

  • Raju, S. (2011). Gendered geographies: Space and place in the South Asia. Oxford University Press.

    Google Scholar 

  • Reddy, A. B., & Goli, S. (2023). Self-employment among the elderly in India. In Handbook of aging, health and public policy. Springer. https://doi.org/10.1007/978-981-16-1914-4_164-1

    Chapter  Google Scholar 

  • Sanderson, W. C., & Scherbov, S. (2015). Are we overly dependent on conventional dependency ratios? Population and Development Review, 41(4), 687–708.

    Article  Google Scholar 

  • Skirbekk, V. (2022). Eight billion ageing citizens. In R. Muttarak & J. Wilde (Eds.), The world at 8 billion (pp. 24–26). Population Council.

    Google Scholar 

  • Skirbekk, V., Dieleman, J. L., Stonawski, M., Fejkiel, K., Tyrovolas, S., & Chang, A. Y. (2022). The health-adjusted dependency ratio as a new global measure of the burden of ageing: A population-based study. The Lancet Healthy Longevity, 3(5), e332–e338.

    Article  Google Scholar 

  • Sydenstricker, E., & King, W. I. (1921). The measurement of the relative economic status of families. Quarterly Publications of the American Statistical Association, 17(135), 842–857.

    Google Scholar 

  • Thompson, W. S., & Whelpton, P. K. (1933). Population trends in the United States. New York: Gordon and Breach Science Publishers.

    Google Scholar 

  • UNFPA. (2023). 8 billion lives, infinite possibilities: The case for rights and choices, State of World Population report 2023. United Nations Population Fund (UNFPA).

    Google Scholar 

  • United Nations. (2020). World population prospects 2019. Department of Economic and Social Affairs Population Dynamics. United Nations, 2019. https://population.UN.org/wpp/Download/. Accessed 20 Sept 2020.

    Google Scholar 

  • Visaria, A., & Dommaraju, P. (2019). Productive aging in India. Social Science & Medicine, 229, 14–21.

    Article  Google Scholar 

  • World Health Organization. (2020). Decade of healthy ageing: Baseline report. WHO.

    Google Scholar 

  • Yadav, P. N., Goli, S., Perianayagam, A., & Pou, L. M. A. (2014). Employment, chronic disease prevalence and medical care among the older population in India: an assessment of linkages. Quality in Ageing and Older Adults, 15(2), 76–89.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Bio-Statistics and Epidemiology, International Institute for Population Sciences (IIPS), Mumbai, India

    Varsha Rani

  2. International Institute for Population Sciences (IIPS), Mumbai, India

    Srinivas Goli

  3. Department of Economics and Finance, Birla Institute of Technology and Science–Pilani, Hyderabad Campus, Hyderabad, India

    A. Bheemeshwar Reddy

Authors
  1. Varsha Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Srinivas Goli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Bheemeshwar Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Varsha Rani .

Section Editor information

  1. International Institute for Population Sciences, Mumbai, India

    K. S. James

  2. Department of Sociology, University of Maryland, College Park, MD, USA

    Arun Balachandran

Appendix Tables

Appendix Tables

See Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10.

Table 1 The percent share of older (65 years and above) population, old-age dependency ratio, and workforce participation rate (65 years and above in %) of the population by Indian states and union territories of India, 2021–2022
Full size table
Table 2 The workforce participation rate (%) of the population aged 15–64 years and 65 years and above by the state and union territories of India, 2021–2022
Full size table
Table 3 The economic-adjusted age dependency ratio (%) of the population aged 15 years and above and the economic-adjusted old-age dependency ratio (%) of 65 years and above by the state and union territories of India, 2021–2022
Full size table
Table 4 The relative contribution of males and females to the total out-of-workforce population in the age group of 15–64 years, 2021–2022
Full size table
Table 5 The relative contribution of rural males and females to the total out-of-workforce population in the age group of 15–64 years, 2021–2022
Full size table
Table 6 The relative contribution of urban males and females to the total out-of-workforce population in the age group of 15–64 years, 2021–2022
Full size table
Table 7 The relative contribution of males and females to the total out-of-workforce population in the age group of 65 years and above, 2021–2022
Full size table
Table 8 The relative contribution of rural males and females to the total out-of-workforce population in the age group of 64 years and above, 2021–2022
Full size table
Table 9 The relative contribution of urban males and females to the total out-of-workforce population in the age group of 65 years and above, 2021–2022
Full size table
Table 10 Economic independence status among the people aged 15 years and above and 65 years and above by states and union territories in India, 2017–2018
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2024 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Rani, V., Goli, S., Reddy, A.B. (2024). The Economic-Adjusted Age Dependency Ratio in India: A New Measure for Understanding Economic Burden of Aging. In: Handbook of Aging, Health and Public Policy. Springer, Singapore. https://doi.org/10.1007/978-981-16-1914-4_242-1

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-1914-4_242-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1914-4

  • Online ISBN: 978-981-16-1914-4

  • eBook Packages: Springer Reference Social SciencesReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Search

Navigation