Ekonomik Büyüme Enerji Tüketimi Göç ve İklim Değişikliğinin Doğurganlık Üzerindeki Etkisi: G-7 Ülkelerinden Kanıtlar

Öz

Bu araştırmanın amacı, G7 ülkelerinde (Almanya, Amerika Birleşik Devletleri, İngiltere, İtalya, Fransa, Japonya ve Kanada) ekonomik büyüme, enerji tüketimi, göç ve iklim değişikliğinin doğurganlık üzerindeki etkilerinin yanı sıra bu değişkenler arasındaki nedensellik ilişkisini araştırmaktır. Bu araştırma, panel veri yöntemlerinde olan birim kök testleri, eşbütünleşme testi, katsayı tahmin testi ve nedensellik testi kullanılarak yapılmıştır. Araştırmanın dönemini 1980-2022 yılları arası oluşturmaktadır. Araştırmanın sonuçlarına göre değişkenler arasında eşbütünleşme ilişkisine rastlanmıştır. Panelin geneli için yapılan katsayı tahminlerine göre G-7 ülkelerinde ekonomik büyüme ile doğurganlık arasında kısa ve uzun dönemde negatif bir ilişkiye rastlanmıştır. Enerji tüketim ile doğurganlık arasında ise kısa ve uzun dönemde pozitif bir ilişkiye rastlanmıştır. Göçe bakıldığında ise kısa dönemde göç ile doğurganlık arasında anlamlı bir ilişkiye rastlanmazken, uzun dönemde göç doğurganlık oranını pozitif yönde etkilemektedir. Son olarak araştırmada iklim değişikliği ile doğurganlık arasında kısa ve uzun dönemde anlamlı bir ilişkiye rastlanmamıştır

Anahtar Kelimeler

• Büyüme
• Doğurganlık
• Enerji
• Göç
• G-7
• İklim değişikliği

Destekleyen Kurum

Yok

Etik Beyan

Bu çalışma etik kurul gerektirmemektedir. Etik Beyanı: Yazar, bu çalışmanın tüm hazırlanma süreçlerinde etik kurallara uyduğunu beyan etmektedir. Bilimsel etik konuları ile ilgili aksi bir durumun tespiti halinde tüm sorumluluk çalışmanın yazarlarına ait olup, Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi’nin hiçbir sorumluluğu bulunmamaktadır.

Teşekkür

Yok

The Impact of Economic Growth, Energy Consumption, Migration, and Climate Change on Fertility: Evidence from G-7 Countries

Öz

The purpose of this study is to investigate the effects of economic growth, energy consumption, migration, and climate change on fertility in G7 countries (Germany, the United States, the United Kingdom, Italy, France, Japan, and Canada), as well as the causal relationships between these variables. This research was conducted using unit root tests, cointegration tests, coefficient estimation tests, and causality tests in panel data methods. The period of the research covers the years 1980-2022. The results of the study indicate that there is a cointegration relationship between the variables. According to the coefficient estimates for the panel as a whole, a negative relationship was found between economic growth and fertility in G-7 countries in both the short and long term. A positive relationship was found between energy consumption and fertility in both the short and long term. Regarding migration, no significant relationship was found between migration and fertility in the short term, while migration positively affected the fertility rate in the long term. Finally, the study found no significant relationship between climate change and fertility in either the short or long term.

Anahtar Kelimeler

• Growth
• Fertility
• Energy
• Migration
• G-7
• Climate Change

Etik Beyan

Bu çalışma etik kurul gerektirmemektedir. Etik Beyanı: Yazar, bu çalışmanın tüm hazırlanma süreçlerinde etik kurallara uyduğunu beyan etmektedir. Bilimsel etik konuları ile ilgili aksi bir durumun tespiti halinde tüm sorumluluk çalışmanın yazarlarına ait olup, Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi’nin hiçbir sorumluluğu bulunmamaktadır.

Kaynakça

1. Aboulhosn, A., Aksoy, C., G. ve Ozcan, B. (2025). Refugee immigration and natives fertility. RF Berlin-CReAM Discussion Paper Series, No. 05/25, Rockwool Foundation Berlin (RF Berlin) and Centre for Research & Analysis of Migration (CReAM), Berlin and London.
2. Adsera, A. ve Ferrer, A. (2016). The Fertility of married ımmigrant women to Canada. International Migration Review, 50(2), 475–505. https://doi.org/10.1111/imre.12114
3. Adsera, A. ve Ferrer, A. (2011). Age at migration, language and fertility patterns among migrants to Canada. IZA Discussion Paper. Paper No: 5552, Institute for the Study of Labor (IZA), Bonn. https://hdl.handle.net/10419/51781
4. Akinlo, T. ve Oyeleke, O., J. (2020). Human capital formation and economic growth in Sub-Saharan African countries: an empirical ınvestigation. The Indian Economic Journal, 68, 1-20. https://doi.org/10.1177/0019466220972848
5. Amuedo-Dorantes, C., ve Arenas-Arroyo, E. (2021). Immigration policy and fertility: Evidence from undocumented migrants in the US. Journal of Economic Behavior & Organization, 189, 274–297. https://doi.org/10.1016/j.jebo.2021.06.027
6. Anyawie, M., Lichter, D., T. ve Qian, Z. (2025). Immigration and fertility in the united states. International Migration Review, First published online April 1. https://doi.org/10.1177/01979183251325189
7. Baltagi, B. H., Feng, Q. ve Kao, C. (2012). A Lagrange multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170(1), 164–177. https://doi.org/10.1016/j.jeconom.2012.04.004
8. Bagavos, C. (2019). On the multifaceted impact of migration on the fertility of receiving countries: Methodological insights and contemporary evidence for Europe, the United States, and Australia. Demographic Research, 41, 1–36. https://doi.org/10.4054/DemRes.2019.41.1

9. Becker, G. S., Murphy, K. M. ve Tamura, R. (1990). Human capital, fertility, and economic growth. Journal of Political Economy, 98(5), 12–37. https://doi.org/10.1086/261723
10. Behrman, J. A. ve Weitzman, A. (2022). Point of reference: A multisited exploration of African migration and fertility in France. International Migration Review, 56(3), 911–940. https://doi.org/10.1177/01979183211046571.
11. Belmin , C., Hoffmann, R., Pichler, P. P. ve Weisz, H. (2022).   Fertility transition powered by women’s access to electricity and modern cooking fuels. Nature Sustainability, 5(March 2022), 245–25. https://doi.org/10.1038/s41893-021-00830-3
12. Breusch, T. S. ve Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, 47(1), 239–253. https://doi.org/10.2307/2297111
13. Budolfson, M. ve Spears, D. (2021). Population ethics and the prospects for fertility policy as climate mitigation policy. The Journal of Development Studies, 57(9), 1499-1510. https://doi.org/10.1080/00220388.2021.1915481
14. Chatterjee, S. ve Vogl, T. (2018). Escaping Malthus: Economic growth and fertility change in the developing world. American Economic Review, 108(6), 1440–1467. https://doi.org/10.1257/aer.20170748
15. Chen, S. (2024). Fertility rate, fertility policy, and climate policy: A case study in China. Structural Change and Economic Dynamics, 69(2024), 339–348. https://doi.org/10.1016/j.strueco.2024.01.004
16. Chen, D. ve Ma, Q. (2025). Household energy transition, health and fertility in China. Economic Modelling, 151(2025), 107231, 1-14. https://doi.org/10.1016/j.econmod.2025.107231
17. Chudik, A. ve Pesaran, M. H. (2015). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. Journal of Econometrics, 188(2), 393–420. https://doi.org/10.1016/j.jeconom.2015.03.007
18. Çelik, H., Alev, N. ve Erdemli, M. (2024). Is the Romer Hypothesis valid for newly ındustrialized countries? Evidence frompanel ARDL. Economic Journal of Emerging Markets, 16(2), 124-135. https://doi.org/10.20885/ejem.vol16.iss2.art3
19. Demir, C. (2019). Dışa açılma ve kamu harcamaları: OECD ülkeleri için panel veri analizi. Kırklareli Üniversitesi Sosyal Bilimler Dergisi, 3(2), 80-96.
20. Dumitrescu, E. I. ve Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460. https://doi.org/10.1016/j.econmod.2012.02.014
21. Durgun, B. ve Kızılkaya, S. (2025). Determinants of fertility rates in Türkiye: the role of women’s income, human capital, participation in civil society and life expectancy. Discover Social Science and Health. 5(92), 1-11. https://doi.org/10.1007/s44155-025-00244-y
22. Esaku, S. ve Mugoda, S. (2025). Does education spur economic growth? Fresh insights from Uganda. Journal of the Knowledge Economy, Published 11 July 2025, 1-25. https://doi.org/10.1007/s13132-025-02818-2
23. Ertürkmen, G. ve Çelik, H. (2023). Ekonomik büyüme, ticari dışa açıklık ve gıda güvenliği ilişkisi: Akdeniz havzası ülkeleri için ampirik bir analiz. Üçüncü Sektör Sosyal Ekonomi Dergisi, 58(3), 1917-1929. https://doi.org/10.15659/3.sektor-sosyal-ekonomi.23.08.2194
24. Gautam, C. ve Shah, B. N. (2025). Economic growth and fertility rebound: Evidence from developing countries. Journal of Economics, Marketing and Management, 13(1), 23-30. http://doi.org/10.20482/jemm.2024.13.1.23
25. Göksoy-Sevinçli B. (2024). Göçmenlerin entegrasyonunda yerel yönetimlerin rolü. Erat, V., Demirkanoğlu, Y. ve Demirci, K, (Ed.), Göç ve Entegrasyon Politikalar ve Aktörler içinde (177 -192). Ankara: Nobel Kitabevi.
26. Granger, C.W. J. (1969). Investigating causal relations by econometric models and crossspectral methods. Econometrica, 37(3), 424–438.
27. Hafner, K. A. ve Mayer-Foulkes, D. (2013). Fertility, economic growth, and human development causal determinants of the developed lifestyle. Journal of Macroeconomics, 38(2013), 107–120. http://dx.doi.org/10.1016/j.jmacro.2013.04.001
28. Huang, K. (2024). Fertility and long‐term economic growth. Economic Inquiry. 62, 1152–1171. https://doi.org/10.1111/ecin.13216
29. Karra, M., Canning, D. ve Wilde, J. (2017). The effect of fertility decline on economic growth in Africa: A macrosimulation model. Population and Development Review, 43(Fertility Transition in Sub-Saharan Africa 2017), 237-263. https://www.jstor.org/stable/26622881
30. Keivabu, R. C., Cozzani, M. ve Wilde, J. (2025). Temperature and fertility: Evidence from Spain, Population Studies, 79(2), 283-297, https://doi.org/10.1080/00324728.2024.2382152
31. Konatar, M., Đurašković, J., Popović, N. ve Radović, M. (2025). Does public debt crowd out public ınvestment in Central and Eastern European economies? A dynamic approach using CS-ARDL. Prague Economic Papers, 34(1), 26-44. https://doi.org/10.18267/j.pep.887
32. Lakhoo, D., P. (2025). A systematic review and meta-analysis of heat exposure impacts on maternal, fetal and neonatal health. Nature Medicine, February, 31(2), 684-694. https://doi.org/10.1038/s41591-024-03395-8
33. Epub 2024 Nov 5. PMID: 39500369; PMCID: PMC11835737. Nature Medicine | Volume 31 | February 2025 | 684–694
34. Maitra, B. ve Ganguli, D. (2025). Impact of fertility decline, gender, and social development on economic growth in India. Journal of Social and Economic Development, 2(2025), 450–471. https://doi.org/10.1007/s40847-024-00345-5
35. Majelantle, R., G. ve Navaneetham, K. (2013). Migration and Fertility: A review of theories and evidences. Journal Glob. Economics, 1(1), 1-3, https://doi.org/10.4172/2375-4389.1000101
36. Moscone, F., Madia, J. E., Nicodemo, C., An, J-C. ve Lee, C. (2024), Addressing fiscal uncertainty: Proposing policy pathways for enhancing economic growth and fertility rates in South Korea, Research in Economics, 78(2024), 100975, 1-10.
37. Mu, R., Zhang, F., He, S. ve Zhang, J. (2025). The role of environmental efficiency and economic development in fertility: implications for public health and sustainability among OECD nations. Frontiers in Public Health, 13(1551413), 1-14. https://doi.org/10.3389/fpubh.2025.1551413
38. Mussino, E. ve Strozza, S. (2012). The fertility of immigrants after arrival: The Italian case. Demographic Research, 26(Article 4), 99-130. https://doi.org/10.4054/DemRes.2012.26.4
39. National Aeronautics and Space Administration (NASA). (2023). NASA Summer 2023 temperature media resources. https://svs.gsfc.nasa.gov/14407
40. Nkalu, C., N. ve Nwosu, E. O. (2024). Environmental issues, economic growth, and fertility rate in developing regions: From theory to robust empirical policies. Health Care for Women International, 45(2), 190-216, https://doi.org/10.1080/07399332.2022.2164283
41. Okumuş, I., Guzel, A. E. ve Destek, M. A. (2021). Renewable, non-renewable energy consumption and economic growth nexus in G7: Fresh evidence from CS-ARDL. Environmental Science and Pollution Research, 2021(28), 56595-56605. https://doi.org/10.1007/s11356-021-14618-7
42. Peseran, H. M. ve Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50–93. https://doi.org/10.1016/j.jeconom.2007.05.010
43. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels, CESifo Working Paper, Paper No. 1229, Center for Economic Studies and ifo Institute (CESifo), Munich. https://hdl.handle.net/10419/18868
44. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-sectiondependence. Journal of Applied Econometrics. 22(2), 265–312. https://doi.org/10.1002/jae.951
45. Robards, j. ve Berrington, A. (2016). The fertility of recent migrants to England and Wales. Demographic Research, 34(Article 36), 1037−1052. https://doi.org/10.4054/DemRes.2016.34.36
46. Sadiq, M. (2024). The impact of green finance, eco-innovation, renewable energy and carbon taxes on CO2 emissions in BRICS countries: Evidence from CS ARDL estimation. Geoscience Frontiers, 15(2024), 101689, 1-10. https://doi.org/10.1016/j.gsf.2023.101689
47. Sato, Y. (2007). Economic geography, fertility and migration. Journal of Urban Economics, 61(2007), 372–387. https://doi.org/10.1016/j.jue.2006.08.002
48. Savaş, Y. (2025). Examining the link between load capacity and income inequality: Evidence from OECD countries. Environment, Development and Sustainability, 27, 8065–8083. https://doi.org/10.1007/s10668-025-06025-6
49. Sellers, S. ve Gray. (2019). Climate shocks constrain human fertility in Indonesia. World Development 117 (2019) 357–369. https://doi.org/10.1016/j.worlddev.2019.02.003
50. Sultana, T., Dey, S., R. ve Tareque, M. (2022). Exploring the linkage between human capital and economic growth: A look at 141 developing and developed countries. Economic Systems, 46(2022), 101017, 1-17. https://doi.org/10.1016/j.ecosys.2022.101017
51. Swamy, P. A. V. B. (1970). Efficient Inference in a Random Coefficient Regression Model. Econometrica, 38(2), 311–323. https://doi.org/10.2307/1913012
52. Tasciotti, L., Sulehria, F. ve Wagner, N. (2024). Fertility, electricity, and television: Is there a link? Evidence from Pakistan, 1990–2018. Journal of Demographic Economics, 90(2024), 285–312. http://dx.doi.org/10.1017/dem.2022.6
53. The Food and Agriculture Organization. (2025). FAOSTAT: Food and Agriculture Data. https://www.fao.org/faostat/en/#data (E.T. 14.08.2025).
54. The World Bank. (2025). Databank: World Development Indicators. https://databank.worldbank.org/source/world-development-indicators, (E.T.01.07.2025).
55. Tutgun, S. ve Savaş, Y. (2023). Kurumsal kalite ve finansal gelişmenin ekonomik büyüme üzerindeki etkisi: sahra altı Afrika ülkeleri örneği. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(3), 879-904. https://doi.org/10.18074/ckuiibfd.1280373.
56. Uddin, I. vd. (2023). Enhancing institutional quality to boost economic development in developing nations: New insights from CS-ARDL approach, Research in Globalization, 7(2023), 100137, 1-11. https://doi.org/10.1016/j.resglo.2023.100137
57. Usman, A. vd. (2022). Revealing the nexus between nuclear energy and ecological footprint in STIRPAT model of advanced economies: Fresh evidence from novel CS-ARDL model. Progress in Nuclear Energy, 148(2022), 104220, 1-10. https://doi.org/10.1016/j.pnucene.2022.104220
58. Yerdelen-Tatoğlu, F. (2020). Panel zaman serileri analizi (3. Baskı). İstanbul: Beta Basım Yayım.
59. Zhang, X., Zhang, J. ve Chen, F. (2025). Climate shocks and fertility intentions: Evidence from extreme temperature events. Economic Modelling, 151 ( October, 2025) 107223, 1-18. https://doi.org/10.1016/j.econmod.2025.107223
60. Westerlund, J. ve Edgerton, D. L. (2007). A panel bootstrap cointegration test. Economics Letters, 97(3), 185–190. https://doi.org/10.1016/j.econlet.2007.03.003
61. Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69(6), 0305-9049, 709-748. https://doi.org/10.1111/j.1468-0084.2007.00477.x