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Determinants of regional fertility in Russia: a dynamic panel data analysis

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  • Ichiro Iwasaki
  • Kazuhiro Kumo

Abstract

The aim of this paper is to empirically examine the regional determinants of the fertility rate in Russia using panel data for the period of 2005–2015. The estimation results of a system GMM dynamic model revealed that economic growth, employment opportunity, favourable local business conditions, educational opportunity, quality of social infrastructure, and housing supply serve to increase the fertility rate in Russian regions, while the presence of a Slavic population, migration inflow, poverty and ecological risks tend to suppress it. Furthermore, we found that combinations of factors that strongly affect the reproductive behaviour of Russian women vary greatly among age groups and regions. To mitigate the declining trend of fertility in Russia, it is necessary to implement policies that take generational differences and regional heterogeneity into serious consideration.

Suggested Citation

  • Ichiro Iwasaki & Kazuhiro Kumo, 2020. "Determinants of regional fertility in Russia: a dynamic panel data analysis," Post-Communist Economies, Taylor & Francis Journals, vol. 32(2), pages 176-214, February.
    • Handle: RePEc:taf:pocoec:v:32:y:2020:i:2:p:176-214
    DOI: 10.1080/14631377.2019.1678333
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    Cited by:

    1. KUMO, Kazuhiro & 雲, 和広, 2020. "Fertility in Russia: A Re-examination Using Microdata," CEI Working Paper Series 2020-8, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    2. Ichiro Iwasaki & Yuko Adachi, 2024. "Legal Weakness, Investment Risks, and Distressed Acquisitions: Evidence from Russian Regions," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 66(1), pages 1-69, March.
    3. Iwasaki, Ichiro & 岩﨑, 一郎 & Kumo, Kazuhiro & 雲, 和広, 2020. "Regional Determinants of Marriage Rates in Russia: A Dynamic Panel Data Analysis," RRC Working Paper Series 85, Russian Research Center, Institute of Economic Research, Hitotsubashi University.
    4. KOMATSU Sho & MA Xinxin & SUZUKI Aya, 2022. "Influence of E-commerce on Birth Rate: Evidence from rural China based on county-level longitudinal data," Discussion papers 22101, Research Institute of Economy, Trade and Industry (RIETI).
    5. João Plínio Juchem Neto & Bruno Paese, 2023. "An intersectoral migration and growth model with distinct population growth rates," Economics Bulletin, AccessEcon, vol. 43(1), pages 413-428.

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    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • P25 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Urban, Rural, and Regional Economics
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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