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Forecasting number of births and sex ratio at birth in Iran using deep neural network and ARIMA: implications for policy evaluations

Author

Listed:
  • Nasibeh Esmaeili

    (University of Tehran)

  • Mohammad Jalal Abbasi-Shavazi

    (Vienna Institute of Demography, Austrian Academy of Sciences
    Australian National University
    University of Tehran (on leave))

Abstract

Once fertility falls to a low level, the number of births declines, affecting the future of population growth and age structure. In low-fertility settings where sex preference is culturally rooted in society, the sex ratio at birth is usually higher than the normal average leading to an imbalanced age structure in the long run. Low fertility and its negative consequences have led to the implementation of pronatalist programs aimed at increasing fertility rates in Iran. In this context, the number of births and sex ratio at birth are matters of concern for policymakers. The main objective of this paper is to forecast the trends of the total number of births by gender and predict the sex ratio at birth (SRB) in Iran over 10 years (2021–2030) using two modeling approaches: Deep Neural Networks-DNNs and Autoregressive Integrated Moving Average—ARIMA. The results are compared to examine the performance of these forecasting methods. The findings from both DNN and ARIMA approaches suggest a 20.6% and 3.5% reduction in the number of births, respectively, and a changing trend within the normal range for sex ratios at birth. The results show the superiority of DNN model as compared with ARIMA for predictions. We recommend the utilization of the DNN approach and its derivations to visualize the outcomes of population policies based on accurate and long-term predictions. This approach can serve as an initial validation of policy impacts to enhance policymakers’ confidence in their proposed programs.

Suggested Citation

  • Nasibeh Esmaeili & Mohammad Jalal Abbasi-Shavazi, 2024. "Forecasting number of births and sex ratio at birth in Iran using deep neural network and ARIMA: implications for policy evaluations," Journal of Population Research, Springer, vol. 41(4), pages 1-21, December.
    • Handle: RePEc:spr:joprea:v:41:y:2024:i:4:d:10.1007_s12546-024-09348-9
    DOI: 10.1007/s12546-024-09348-9
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    References listed on IDEAS

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    1. Xiaoyun Xie & Ming Xie & Ata Jahangir Moshayedi & Mohammad Hadi Noori Skandari & Zuowei Cai, 2022. "A Hybrid Improved Neural Networks Algorithm Based on L2 and Dropout Regularization," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-19, November.
    2. Zhen Zhang & Qiang Li, 2020. "Population aging caused by a rise in the sex ratio at birth," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(32), pages 969-992.
    3. Andrea Nigri & Susanna Levantesi & Jose Manuel Aburto, 2022. "Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(8), pages 199-232.
    4. Raftery, Adrian E. & Ševčíková, Hana, 2023. "Probabilistic population forecasting: Short to very long-term," International Journal of Forecasting, Elsevier, vol. 39(1), pages 73-97.
    5. Trond Husby & Hans Visser, 2021. "Short- to medium-run forecasting of mobility with dynamic linear models," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(28), pages 871-902.
    6. Nico Keilman & Dinh Quang Pham & Arve Hetland, 2002. "Why population forecasts should be probabilistic - illustrated by the case of Norway," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 6(15), pages 409-454.
    7. Tom Wilson & Martin Bell, 2004. "Australia's uncertain demographic future," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 11(8), pages 195-234.
    8. Peter McDonald & Meimanat Hosseini-Chavoshi & Mohammad Jalal Abbasi Shavazi & Arash Rashidian, 2015. "An assessment of recent Iranian fertility trends using parity progression ratios," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 32(58), pages 1581-1602.
    9. Ridhi Kashyap & Francisco Villavicencio, 2016. "The Dynamics of Son Preference, Technology Diffusion, and Fertility Decline Underlying Distorted Sex Ratios at Birth: A Simulation Approach," Demography, Springer;Population Association of America (PAA), vol. 53(5), pages 1261-1281, October.
    10. Han Lin Shang, 2012. "Point and interval forecasts of age-specific life expectancies," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(21), pages 593-644.
    11. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    12. Zhongwei Zhao & Yuan Zhu & Anna Reimondos, 2013. "Could changes in reported sex ratios at birth during China's 1958-1961 famine support the adaptive sex ratio adjustment hypothesis?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(33), pages 885-906.
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