Contributions of Proximate Determinants to Fertility Transition in Bangladesh: An Analysis of Bongaarts’ Fertility Model

Document Type : Original Article

Authors

1 Department of Agricultural Statistics, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh

2 Bangladesh Bureau of Statistics (BBS), Statistics and Informatics Division, Ministry of Planning, Dhaka, Bangladesh

3 Department of Statistics, Jagannath University, Dhaka, Bangladesh

10.15171/ijtmgh.2019.06

Abstract

Introduction: Fertility transition is outright by prime four proximate determinants (marriage, contraception, postpartum infecundability, and abortion). The present study examines the contributions of proximate determinants on fertility decline and quantifies inhibiting the effect of major proximate determinants according to the socioeconomic characteristics in Bangladesh.
Methods: The current study was based mainly on the three Bangladesh Demographic and Health Surveys (BDHSs) carried out in 1993-1994, 2004, and 2014. Bongaarts’ fertility framework was applied to analyze the proximate determinants of fertility in the socioeconomic status of women in Bangladesh.
Results: In 1993-1994, contraception was the greatest impediment to fertility followed by postpartum infecundability, marriage, and induced abortion, respectively. In 2014, contraception was the highest fertility obstructing effect followed by marriage, postpartum infecundability, and abortion, respectively, in both rural and urban areas of Bangladesh. Throughout the study period and even now, fertility is revered in the Sylhet and Chittagong divisions of Bangladesh. The fertility-inhibiting effect of marriage, contraception, and abortion has an affirmative relationship with the educational status of women. Postpartum infecundability, however, displays an inverse relationship with the educational status of women.
Conclusion: The current study suggests that contraception plays a vital role in fertility reduction in Bangladesh. In particular, special attention should be placed on those regions (Chittagong and Sylhet divisions) that register low contraception prevalence rates. Special programs should focus on creating an awareness of the disadvantages of child marriage among women who reside in the division of Chittagong and Sylhet of Bangladesh.

Keywords

References

  1. United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects; 2017.
  2. National Institute of Population Research and Training (NIPORT), Mitra and Associates, and ICF International. Bangladesh Demographic and Health Survey 2014. Dhaka, Bangladesh, and Rockville, Maryland, USA: NIPORT, Mitra and Associates, and ICF International; 2016.
  3. Cleland J. Fertility levels and trends in Bangladesh. In: Cleland J, Edward Ebanks G, Wai L, Nawab Ali M, Rashid MA, eds. Bangladesh Fertility Survey, 1989, Secondary Analysis. Dhaka: National Institute of Population Research and Training (NIPORT); 1994.
  4. Hasan MM, Islam M, Sakib S, Haq I. Influences of Proximate Determinates on Fertility Among Urban and Rural Women in Bangladesh. Dhaka Univ J Sci. 2018;66(1):49-54.
  5. Islam MM, Al Mamun A, Bairagi R. Fertility and its proximate determinants in Bangladesh: evidence from the 1993/94 Demographic and Health Survey. Asia Pac Popul J. 1998;13(3):3- 22. doi:10.18356/d2ac171b-en.
  6. Caldwell JC, Barkat EK, Caldwell B, Pieris I, Caldwell P. The Bangladesh fertility decline: an interpretation. Popul Dev Rev. 1999;25(1):67-84. doi:10.1111/j.1728-4457.1999.00067.x.
  7. Chola M, Michelo C. Proximate determinants of fertility in Zambia: Analysis of the 2007 Zambia Demographic and Health Survey. Int J Popul Res. 2016;2016:5236351. doi:10.1155/2016/5236351.
  8. Laelago T, Habtu Y, Yohannes S. Proximate determinants of fertility in Ethiopia; an application of revised Bongaarts model. Reprod Health. 2019;16(1):1-9. doi:10.1186/s12978-019-0677-x.
  9. Islam R, Islam N, Rahman M, Hossain G, Islam R. Fertility situation in Bangladesh: application of revised Bongaarts model. Sci Technol. 2015;5(2):33-38.
  10. Lai SL, Tey NP. Socio-economic and proximate determinants of fertility in the Philippines. World Appl Sci J. 2014;31(10):1828-1836. doi:10.5829/idosi.wasj.2014.31.10.591.
  11. Majumder N, Ram F. Explaining the role of proximate determinants on fertility decline among poor and non-poor in Asian countries. PLoS One. 2015;10(2):e0115441. doi:10.1371/journal.pone.0115441.
  12. Bongaarts J. The end of the fertility transition in the developed world. Completing the Fertility Transition. Department of Economic and Social Affairs, Population Division. ESA/P/WP. 172/ Rev. 1. New York: United Nations; 2002:288-307.
  13. Bongaarts J, Watkins SC. Social interactions and contemporary fertility transitions. Popul Dev Rev. 1996;22(4):639-682. doi:10.2307/2137804.
  14. Cleland J, Wilson C. Demand theories of the fertility transition: An iconoclastic view. Popul Stud. 1987;41(1):5-30. doi:10.1080/0032472031000142516.
  15. Caldwell JC. Theory of fertility decline. New York: Academic Press; 1982.
  16. Bongaarts J. A framework for analyzing the proximate determinants of fertility. Popul Dev Rev. 1978;4(1):105-132. doi:10.2307/1972149.
  17. Bongaarts J, Potter R. Fertility, biology, and behavior: An analysis of the proximate determinants. Academic Press; 2013.
  18. Islam MA, Sabu SP, Padmadas SS, Kabir M. Evaluation of reported induced abortion in Bangladesh: evidence from the recent DHS. In: The 132nd Annual Meeting; Washington, DC; 2004.
  19. Boerma JT, Weir SS. Integrating demographic and epidemiological approaches to research on HIV/AIDS: the proximate-determinants framework. J Infect Dis. 2005;191 Suppl 1:S61-67. doi:10.1086/425282.
  20. Bongaarts J. The fertility-inhibiting effects of the intermediate fertility variables. Stud Fam Plann. 1982;13(6-7):179-189. doi:10.2307/1965445.
  21. Madhavan S. An analysis of the proximate determinants of fertility in sub-Saharan Africa with a focus on induced abortion. Baltimore: Johns Hopkins University; 2014.
  22. Mitra SN, Nawab Ali M, Islam S, Cross AR, Saha T. Bangladesh Demographic and Health Survey, 1993-1994. Calverton, Maryland: National Institute of Population Research and Training (NIPORT), Mitra and Associates, Macro International Inc; 1994.
  23. National Institute of Population Research and Training (NIPORT), Mitra and Associates, ORC Macro. Bangladesh Demographic and Health Survey 2004. Calverton, Maryland, USA: National Institute of Population Research and Training (NIPORT), Mitra and Associates, ORC Macro; 2005.
  24. Wang SX, Chen YD, Chen CH, Rochat RW, Chow LP, Rider RV. Proximate determinants of fertility and policy implications in Beijing. Stud Fam Plann. 1987;18(4):222-228. doi:10.2307/1966873.
  25. Moses LYA, Kayizzi JB. Using the Bongaarts model in explaining fertility decline in Urban areas of Uganda. In: Fifth African Population Conference; Arusha; 2007.
  26. Haq I. Relationship between age at marriage, education and fertility among residence of Bangladesh. Am J Soc Sci Res. 2018;4(2):33-39.
  27. Mboup G, Saha T. Fertility levels trends and differentials: Demographic and health surveys, Comparative Study No. 28. Calverton, Maryland: Macro International; 1998.
  28. Moultrie TA, Sayi TS, Timaeus IM. Birth intervals, postponement, and fertility decline in Africa: a new type of transition? Popul Stud (Camb). 2012;66(3):241-258. doi:10.1080/00324728.2012.701660.
  29. Islam MM, Islam MA, Chakroborty N. Fertility transition in Bangladesh: understanding the role of the proximate determinants. J Biosoc Sci. 2004;36(3):351-369. doi:10.1017/S0021932003006333.
  30. Mahjabeen T, Imran Khan I, Amit Khan I. Analyzing Bongaarts model and its applications in the context of Bangladesh. 19th International Congress on Modelling and Simulation; Perth, Australia; 2011.
  31. Haq I. Decomposition of the Change in Proximate Determinants and Its Impacts on Fertility in Bangladesh: An Evidence from National Surveys. Int J Math Comput Sci. 2018;4(1):8-17
  32. Mekonnen W, Worku A. Determinants of fertility in rural Ethiopia: the case of Butajira Demographic Surveillance System (DSS). BMC Public Health. 2011;11:782. doi:10.1186/1471-2458-11-782.
  33. Alene GD, Worku A. Estimation of the total fertility rates and proximate determinants of fertility in North and South Gondar zones, Northwest Ethiopia: An application of the Bongaarts’ model. Ethiop J Health Dev. 2009;23(1):19-27. doi:10.4314/ejhd.v23i1.44833.
  34. Mekonnen W, Worku A. Levels and proximate determinants of fertility in Butajira District, South Central Ethiopia. Ethiop J Health Dev. 2011;25(3):184-191.
  35. Islam MM, Dorvlo AS, Al-Qasmi AM. Proximate determinants of declining fertility in Oman in the 1990s. Can Stud Popul. 2012;38(3-4):133-152. doi:10.25336/P6731K.
  36. Palamuleni M. Fertility decline in Malawi: an analysis of the proximate determinants. J Soc Dev Afr. 2010;25(1):9-38. doi:10.4314/jsda.v25i1.54277.
  37. Kabir A, Ali R, Islam MS, Kawsar LA, Islam MA. A comparison of regional variations of fertility in Bangladesh. Int Q Community Health Educ. 2008;29(3):275-291. doi:10.2190/IQ.29.3.f.
  38. Islam MM, Rob U, Chakroborty N. Regional variations in fertility in Bangladesh. Genus. 2003;59(3-4):103-145.
  39. Islam MN, Islam MM. Biological and behavioural determinants of fertility in Bangladesh: 1975-1989. Asia Pac Popul J. 1993;8(1):3- 18. doi:10.18356/a538a363-en.
  40. Hossain F, Karim R. Determination of Total Fertility Rate of Bangladesh using Bongaarts Model. J Biom Biostat. 2013;4(5):1-4. doi:10.4172/2155-6180.1000176.
  41. Rutaremwa G, Galande J, Nviiri HL, Akiror E, Jhamba T. The contribution of contraception, marriage and postpartum insusceptibility to fertility levels in Uganda: an application of the aggregate fertility model. Fertil Res Pract. 2015;1:16. doi:10.1186/s40738-015-0009-y.
  42. Drèze J, Murthi M. Fertility, education, and development: evidence from India. Popul Dev Rev. 2001;27(1):33-63. doi:10.1111/j.1728-4457.2001.00033.x.
  43. Hailemariam T, Sebhatu A, Gebreselassie T. Components of Fertility Change in Ethiopia: Further Analysis of the 2000, 2005, and 2011 Demographic and Health Surveys. DHS Further Analysis Reports No. 80. Calverton. Maryland, USA: ICF International; 2013.
  44. Ko EY, Siddiqi K, Brannigan RE, Sabanegh ES, Jr. Empirical medical therapy for idiopathic male infertility: a survey of the American Urological Association. J Urol. 2012;187(3):973-978. doi:10.1016/j.juro.2011.10.137.
  45. Shapiro D, Gebreselassie T, Strunk L. Fertility transition in sub- Saharan Africa: evidence from the demographic and health surveys. Minneapolis: Paper presented at the Annual Meeting of the Population Association of America; 2003
  46. Adhikari R. Demographic, socio-economic, and cultural factors affecting fertility differentials in Nepal. BMC Pregnancy Childbirth. 2010;10:19. doi:10.1186/1471-2393-10-19.
  47. Karakaya Ayalp E. Socioeconomic determinants on fertility rate at an aggregate level: a linear regression model for Turkish provinces. Public Knowledge Journal. 2014;6:1-6.
  48. Kalule-Sabiti I. Socio-economic factors affecting fertility in Kenya. S Afr J Soc. 1992;23(2):46-52. doi:10.1080/02580144.1992.10429816.
  49. Chola M, Michelo C. Examining underlying determinants of fertility rates in Zambia: Evidence from the 2007 Zambia Demographic and Health Survey. Etude Popul Afr. 2016;30(2):2318-2327. doi:10.11564/30-2-829.
  50. Mutharayappa R. Factors affecting fertility among tribals. Man & Development. 1994;16(4):63-79.
  51. Gwatkin DR, Rutstein S, Johnson K, Suliman E, Wagstaff A, Amouzou A. Socio-economic differences in health, nutrition, and population within developing countries. Washington, DC: The World Bank; 2007:287.
  52. Yazbeck AS, Gwatkin DR, Wagstaff A, Qamruddin J. Why were the reaching the poor studies undertaken? In: Gwatkin DR, Wagstaff A, Yazbeck AS, eds. Reaching the poor with health, nutrition, and population services: what works, what doesn’t, and why. Washington, DC: The World Bank; 2005:3-26. doi:10.1596/978-0-8213-5961-7.
  53. Worku AG, Tessema GA, Zeleke AA. Trends of modern contraceptive use among young married women based on the 2000, 2005, and 2011 Ethiopian Demographic and Health Surveys: a multivariate decomposition analysis. PLoS One. 2015;10(1):e0116525. doi:10.1371/journal.pone.0116525.
  54. Adebowale SA, Adedini SA, Ibisomi LD, Palamuleni ME. Differential effect of wealth quintile on modern contraceptive use and fertility: evidence from Malawian women. BMC Womens Health. 2014;14(1):40. doi:10.1186/1472-6874-14-40.

Files

History

  • Receive Date: 10 November 2018
  • Revise Date: 20 January 2019
  • Accept Date: 10 February 2019
  • First Publish Date: 01 March 2019

Share

How to cite

Statistics