Household Poverty and Contraceptive Non-Intention Among Women of Childbearing Age in Union in Burundi: Validity of the Theory of Intergenerational Flows of Wealth

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Jean François Régis Sindayihebura
Didier Nganawara
René Manirakiza


The Republic of Burundi wants to control population growth by increasing the prevalence of contraception by 1.5% per year. However, the intention to use modern contraception is declining among women of childbearing age. The proportion of women wishing to use contraception has dropped from 66% in 2010 to 53% in 2016–2017. This research aims to verify whether household poverty is at the root of this contraceptive non-intention of women whose couples hope for better wealth from a large group of offspring. Data from the 2010 and 2016–2017 Demographic and Health Surveys of Burundi were analyzed using multilevel logistic regression. There is no significant difference in contraceptive non-intention found between women from poor households and those with medium standards of living (p = .587 ˃ .05) or rich (p = .098 ˃ .05) in 2010 or between women from wealthy households and those from poor (p = .101 ˃ .05) or medium (p = .689 ˃ .05) standards of living in 2016–2017. Standard of living does not count among the principal factors of contraceptive non-intention in Burundi. Instead, attention should be paid to sociocultural factors.

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Sindayihebura, J. F. R., Nganawara, D., & Manirakiza, R. (2022). Household Poverty and Contraceptive Non-Intention Among Women of Childbearing Age in Union in Burundi: Validity of the Theory of Intergenerational Flows of Wealth. Journal of Population and Social Studies [JPSS], 31, 80–94. Retrieved from
Research Articles
Author Biographies

Jean François Régis Sindayihebura, Doctoral School of the University of Burundi, Burundi

Corresponding author

Didier Nganawara, Institute of Demographic Training and Research, University of Yaoundé II, Cameroon

Prof. Didier Nganawara is a Senior Lecturer at the University of Yaounde II (Cameroon). He is also the Director of Studies and Training at the Institute of Training and Demographic Research. This article has benefited from his experiences in statistical data analysis, especially multilevel modeling.

René Manirakiza, Center for Research and Studies on the Development of Societies in Reconstruction, University of Burundi, Burundi

Pr. René Manirakiza is an Associate Professor at the University of Burundi. He is our colleague at the Center for Research and Studies on the Development of Societies in Reconstruction (CREDSR). This research benefited from his critical thinking of the results and his mastery of subjects of population and development in the Burundian context


• Ahuja, M., Frimpong, E., Okoro, J., Wani, R., & Armel, S. (2020). Risk and protective factors for intention of contraception use among women in Ghana. Health Psychology Open, 7(2).

• Babalola, S., John, N., Ajao, B., & Speizer, I. (2015). Ideation and intention to use contraceptives in Kenya and Nigeria. Demographic Research, 33, 211–238.

• Becker, G. S. (1960). An economic analysis of fertility. In National Bureau of Economic Research (Ed.), Demographic and economic change in developed countries (pp. 209–240). Columbia University Press.

• Bringé, A., & Golaz, V. (2017). Manuel pratique d’analyse multiniveau [A practical handbook of multilevel analysis]. Ined Éditions.

• Caldwell, J. C. (1976). Toward a restatement of demographic transition theory. Population and Development Review, 2(3/4), 321–366.

• Callahan, R., & Becker, S. (2013). Contraceptive intentions and use in rural Bangladesh. Population Association of America.

• De Bourmont, M. (2012). La résolution d’un problème de multi-colinéarité au sein des études portant sur les déterminants d’une publication volontaire d’informations: Proposition d’un algorithme de décisions implifié basé sur les indicateurs de Belsley, Kuh et Welsch (1980) [Resolving a multi-collinearity problem within studies of the determinants of voluntary news release: Proposition of an implicit decision algorithm based on Belsley, Kuh, and Welsch indicators (1980)]. Comptabilités et innovation, Article hal-00691156.

• Dibaba, Y. (2009). Factors influencing women’s intention to limit child bearing in Oromia, Ethiopia. Ethiopian Journal of Health Development, 23(1), 28–33.

• Doliger, C. (2008). La fécondité et ses déterminants économiques: Becker vs Easterlin [Fertility and its economic determinants: Becker vs Easterlin]. Revue économique, 59(5), 955–971.

• Easterlin, R. A. (1975). An economic framework for fertility analysis. Studies in Family Planning, 6(3), 54–63.

• Gendreau, F. (1993). La population de l’Afrique: Manuel de démographie [The population of Africa: A handbook of demography]. Karthala; CEPED.

• ISTEEBU, Government of Burundi (BDI), & ICF International. (2012). Deuxième Enquête Démographique et de Santé Burundi 2010 [Second Demographic and Health Survey of Burundi 2010]. Archive Nationale des Données du Burundi.

• Kamuragiye, A., & Buzingo, D. (2019). Maitriser la croissance de la population pour profiter du dividende demographique en Afrique subsaharienne: La cas du Burundi [Contain the Population Growth to Benefit from the Demographic Dividend in Sub-Saharan Africa: The Case of Burundi]. Les Editions l’Empreinte du Passant.

• Lemessa, R., & Wencheko, E. (2014). Factors affecting the intention of women to limit childbearing in rural Ethiopia. Ethiopian Journal of Health Development, 28(2), 75–80.

• Leridon, H. (2015). Théories de la fécondité: Des démographes sous influence [The development of fertility theories: A multidisciplinary endeavor]. Population, 70(2), 309–348.

• Machiyama, K., & Cleland, J. C. (2013, February). Insights into unmet need in Ghana (STEP UP Research Report). London School of Hygiene and Tropical Medicine.

• Manirakiza, R. (2008). Population et développement au Burundi [Population and development in Burundi]. Harmattan.

• Masuy, A. J. (2013). L’exploration: Évaluer, préparer et décrire [Exploration: Assessing, preparing and describing]. In G. Masuy-Stroobant & R. Costa (Eds.), Analyser les données en Sciences sociales: De la préparation des données à l’analyse multivariée [Analyzing Data in the Social Sciences: From Data Preparation to Multivariate Analysis] (pp. 49–74). P.I.E. Peter Lang.

• Ministry to the Presidency Responsible for Good Governance and Planning [Burundi] (MPBGP), Ministry of Public Health and the Fight against AIDS [Burundi] (MSPLS), Institute of Statistics and Economic Studies in Burundi (ISTEEBU), & ICF International. (2017). Troisième Enquête Démographique et de Santé 2016–2017 [Third demographic and health survey 2016–2017].

• Nganawara, D. (2016). Famille et scolarisation des enfants en âge scolaire obligatoire au Cameroun: Une analyse à partir du recensement de 2005 [Family and schooling of children of compulsory school age in Cameroon: An analysis based on the 2005 census]. Observatoire démographique et statistique de l’espace francophone (ODSEF).

• Nganawara, D. (2017). Analyse de fécondité [Fertility analysis] (Les documents pédagogiques de l’IFORD [IFORD educational documents]).

• Programme Alimentaire Mondial (PAM). (2017, March). République du Burundi: Analyse de la sécurité alimentaire en situation d’urgence au Burundi [Republic of Burundi: Emergency food security in Burundi]. United Nations.

• Republic of Burundi. (2018, June). Plan National de Développement du Burundi (PND Burundi 2018–2027) [National Development Plan of Burundi (PND Burundi 2018–2027)].

• Schaalma, H., Aarø, L. E., Flisher, A. J., Mathews, C., Kaaya, S., Onya, H., Ragnarson, A., & Klepp, K. I. (2009). Correlates of intention to use condoms among Sub-Saharan African youth: The applicability of the theory of planned behaviour. Scandinavian Journal of Public Health, 37(2_suppl), 87–91.

• Schoumaker, B. (2013). La régression linéaire multiple [Multiple linear regression]. In G. Masuy-Stroobant & R. Costa (Eds.), Analyser les données en Sciences sociales: De la préparation des données à l’analyse multivariée [Analyzing Data in the Social Sciences: From Data Preparation to Multivariate Analysis] (pp. 227–252). P.I.E. Peter Lang.

• Sindayihebura, J. F. R., Nganawara, D., Bouba Djourdebbe, F., & Manirakiza, R. (2022). Family planning services supply and non-intention to use the modern contraception among women of childbearing age in union in Burundi. International Journal of Mathematical Analysis, 16(2), 81–88.

• Sindayihebura, J. F. R., & Nkunzimana, A. (2020). Changement climatique et anémie chez les femmes en âge de procréer au Burundi: Approche par la région de residence [Climate change and anemia among women of childbearing age in Burundi: Approach by the region of residence]. Revue de l’Université du Burundi: Séries Sciences Humaines et Sociales, 18(1), 160–173.

• Smuseneto, A. (2019). Determinants of childbearing among Thai-Muslim Generation Y in Thailand. Journal of Population and Social Studies, 27(4), 321–333.

• Tabutin, D., & Schoumaker, B. (2020). La démographie de l’Afrique subsaharienne au XXIe siècle: Bilan des changements de 2000 à 2020, perspectives et défis d’ici 2050 [The demography of Sub-Saharan Africa in the 21st Century: Transformations from 2000 to 2020, Prospects and Challenges to 2050]. Population, 75(2-3), 165–286.

• Tiruneh, F. N., Chuang, K. Y., Ntenda, P. A. M., & Chuang, Y. C. (2016). Factors associated with contraceptive use and intention to use contraceptives among married women in Ethiopia. Women & Health, 56(1), 1–22.

• United Nations Development Programme (UNDP). (2020, December). République du Burundi: Rapport National sur le Développement Humain – Edition 2019: Cohésion sociale, dividende démographique et développement humain durable [Republic of Burundi: National Human Development Report – Edition 2019: Social cohesion, demographic dividend and sustainable human development].