Determinants of Abortion Behavior of the Population of Russian Regions

Cover Page

Cite item

Full Text

Abstract

Induced abortion remains among main methods of birth control. This method, called "abortive (abortion) behavior", is one of the reasons for the low birth rate in Russia in the late 20th century and the first quarter of the 20th century. The relevance of this study is due to the need, firstly, to constantly focus public discourse on abortive behavior as a threat to Russia's demographic security and, secondly, to search for reserves for Russia's demographic development in the context of a decrease in the number of women of reproductive age. This article, written by an economist, is devoted to the determinants of the abortion rate in Russia and its regions. To econometrically test the author's assumptions about the determinants from official statistics, a panel of 82 regions for 2000-2022 was formed. Some determinants turned out to be non-stationary. To test the idea of a long-term, equilibrium relationship between the non-stationary abortion rate (the number of abortions per 1,000 women aged 15–49) and non-stationary determinants, a cointegration analysis was used. The result is a cointegrating equation for the abortion rate, which showed that conservatives, if not catalysts, of the abortion rate were the growth in the share of the population with less than a college education and (to a lesser extent) the growth in the incidence of major disease classes, while the inhibitor was the growth in total living space per capita. Among the determinants selected by the author, there are stationary ones. Having transformed the abortion rate into a stationary variable by taking the first difference, the author regressed it on these determinants to identify the short-term relationship between them. Per capita consumer spending had the greatest short-term effect. As it increased, the abortion rate decreased. Per capita cash income, the volume of paid services to the population, the share of the population with cash income below the poverty line and the share of alcohol in consumer spending turned out to be stimulators of abortion behavior in the short term.

About the authors

Boris I. Alekhin

Email: b.i.alekhin@gmail.com
ORCID iD: 0000-0002-9571-4836
SPIN-code: 8540-7080
ResearcherId: LKO-3051-2024
Doctor of Economic Sciences, Professor, Independent Researcher

References

  1. Bearak, J. M. Country-Specific Estimates of Unintended Pregnancy and Abortion Incidence: A Global Comparative Analysis of Levels in 2015-2019 / J. M. Bearak, A. Popinchalk, C. Beavin [et al.] // BMJ Global Health. 2022. Vol. 7, No. 3. Pp. 24–53. doi: 10.1136/bmjgh-2021-007151.
  2. Репродуктивное здоровье населения России, 2011 : итоговый отчет, май, 2013 / Федеральная служба гос. статистики (Росстат), М-во здравоохранения Российской Федерации, Фонд ООН в обл. народонаселения (ЮНФПА), Отд. Репродуктивного Здоровья, Центр по контролю и профилактике заболеваний (DRH/CDC, Атланта, США) ; [пер. с англ.: Н. Елагина, Ю. Лазарева]. Москва : Статистика России, 2013. 343 с.
  3. Денисов, Б. П. Аборты в России (по материалам выборочного обследования) / Б. П. Денисов, В. И. Сакевич // Доказательная медицина и клиническая эпидемиология. 2009. Т. 2, № 2. С. 32–37. EDN LLROSU.
  4. Рязанцев, С. В. Аборты и абортивное поведение в контексте поиска резервов демографического развития в России / С. В. Рязанцев, Т. К. Ростовская, Е. П. Сигарева, С. Ю. Сивоплясова // Экология человека. 2019. № 7. С. 17–23. doi: 10.33396/1728-0869-2019-7-17-23. EDN VFJSRT.
  5. Davis, K. Social Structure and Fertility: An Analytic Framework / K. Davis, J. Blake // Economic Development and Cultural Change. 1956. Vol. 4. No. 4. Pp. 211–235. doi: 10.1086/449714.
  6. Trent, K. Structural Determinants of the Abortion Rate: A Cross-Societal Analysis / K. Trent, A. W. Hoskin // Social Biology. 1999. Vol. 46, No. 1–2. Pp. 62–81.
  7. Llorente-Marrón, M. Contextual Determinants of Induced Abortion: A Panel Analysis / M. Llorente-Marrón, M. Díaz-Fernández, P. Méndez-Rodríguez // Revista de Saúde Pública. 2016. Vol. 50, No. 8. Pp. 30–45. doi: 10.1590/S1518-8787.2016050005917.
  8. Bonnen, K.I. Determinants of First and Second Trimester Induced Abortion – Results from a Cross-Sectional Study Taken Place 7 Years after Abortion Law Revisions in Ethiopia / K. I. Bonnen, D. N. Tuijje, V. Rasch // BMC Pregnancy Childbirth. 2014. Vol. 14, No. 416. Pp. 58–70. doi: 10.1186/s12884-014-0416-9.
  9. Huneeus, A. Induced Abortion According to Socioeconomic Status in Chile / A. Huneeus, D. Capella, B. Cabieses, G. Cavada // Journal of Pediatric and Adolescent Gynecology. 2020. Vol. 33, No. 4. Pp. 415–420. doi: 10.1016/j.jpag.2020.03.003.
  10. Hosseini, H. Factors Associated with Incidence of Induced Abortion in Hamedan / H. Hosseini, A. Erfani, M. Nojomi // Archives of Iranian Medicine. 2017. Vol. 20, No. 5. Pp. 282–287.
  11. Korejo, R. Sociocultural Determinants of Induced Abortion / R. Korejo, K. J. Noorani, S. Bhutta // Journal of College of Physicians and Surgeons Pakistan. 2003. Vol. 13, No. 5. Pp. 260–262.
  12. Elul, B. Determinants of Induced Abortion: An Analysis of Individual, Household and Contextual Factors in Rajasthan, India // Journal of Biosocial Science. 2011. Vol. 43, No. 1. Pp. 1–17. doi: 10.1017/s0021932010000490.
  13. Ahmed, S. Determinants of Pregnancy and Induced and Spontaneous Abortion in a Jointly Determined Framework: Evidence from a Country-Wide, District-Level Household Survey in India / S. Ahmed, R. Ray // Journal of Biosocial Science. 2014. Vol. 46, No. 4. Pp. 480–517. doi: 10.1017/S0021932013000369.
  14. Kant, S. Induced Abortion in Villages of Ballabgarh HDSS: Rates, Trends, Causes and Determinants / S. Kant, R. Srivastava, S. K. Rai [et al.] // Reproductive Health. 2015. Vol. 12, No. 51. Pp. 1–7. doi: 10.1186/s12978-015-0040-9.
  15. Gil-Lacruz A.I. Socio-Economic Determinants of Abortion Rates / A. I. Gil-Lacruz, M. Gil-Lacruz, E. B. Cuenca // Sexuality Research and Social Policy. 2010. Vol. 9, No. 2. Pp. 1–28. doi: 10.1007/s13178-011-0056-z.
  16. Sousa, A. Exploring the Determinants of Unsafe Abortion: Improving the Evidence Base in Mexico / A. Sousa, R. Lozano, E. Gakidou // Health Policy Plan. 2010. Vol. 25, No. 4. Pp. 300–310. doi: 10.1093/heapol/czp061.
  17. Moscelli, G. Socioeconomic Inequality of Access to Healthcare: Does Choice Explain the Gradient? / G. Moscelli, L. Siciliani, N. Gutacker, R. Cookson // Journal of Health Economics. 2018. Vol. 57. Pp. 290–314. doi: 10.1016/j.jhealeco.2017.06.005.
  18. Alamneh, T. S. Socioeconomic Inequality in Barriers for Accessing Health Care among Married Reproductive Aged Women in Sub-Saharan African Countries: A Decomposition Analysis / T. S. Alamneh, A. B. Teshale, Y. Yeshaw [et al.] // BMC Women’s Health. 2022. Vol. 22, No. 1. Pp. 3–25. doi: 10.1186/s12905-022-01716-y.
  19. Dehlendorf, C. Disparities in Abortion Rates: A Public Health Approach / C. Dehlendorf, L. H. Harris, T. A. Weitz // American Journal of Public Health. 2013. Vol. 103, No. 10. Pp. 1772–1779. doi: 10.2105/AJPH.2013.301339.
  20. Лупекина, Е. А. Социальные и личностные особенности женщин, планирующих прерывание беременности / Е. А. Лупекина, М. С. Бондаренко // Право. Экономика. Психология. 2022. № 2(26). С. 64–70. EDN IZZTAV.
  21. Канторович, Г. Г. Анализ временных рядов // Экономический журнал Высшей школы экономики. 2003. Т. 7, № 1. С. 79–103. EDN YYSZVZ.
  22. Horga, M. How Can the Rates of Induced Abortion Be Reduced? / M. Horga, F. Ludicke // Towards Better Reproductive Health in Eastern Europe. Budapest : Central European University Press, 1999. Pp. 99–128. doi: 10.1515/9789633865064-010.
  23. Farrar, D. E. Multicollinearity in Regression Analysis: The Problem Revisited / D. E. Farrar, R. R. Glauber // The Review of Economics and Statistics. 1967. Vol. 49, No. 1. Pp. 92–107. doi: 10.2307/1937887.
  24. Engle, R. Co-Integration and Error Correction: Representation, Estimation, and Testing / R. Engle, C. W. J. Granger // Econometrica. 1987. Vol. 55, No. 2. Pp. 251–276. doi: 10.2307/1913236.
  25. Алехин, Б. И. Монетарная бедность и образование в России // Финансовый журнал. 2023. Т. 15, № 4. С. 43–62. doi: 10.31107/2075-1990-2023-4-43-62. EDN MUCBLP.
  26. Kao, C. International R&D Spillovers: An Application of Estimation and Inference in Panel Cointegration / C. Kao, M.-H. Chiang, B. Chen // Oxford Bulletin of Economics and Statistics. 1999. Vol. 61, No. S1. Pp. 691–709. doi: 10.1111/1468-0084.0610s1691.
  27. Алехин, Б. И. Человеческий капитал и рост региональных экономик // Пространственная экономика. 2021. Т. 17, № 2. С. 57–80. doi: 10.14530/se.2021.2.057-080. EDN BQCTHT.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).