Family Formation 1976.

Westoff, C. F.; Dunnell, Karen

doi:10.2307/2175201

Cited by 9 publications

(2 citation statements)

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“…Despite the Sustainable Development Goal (SDG) focusing on lessening under-five mortality rates to below 25 deaths per 1000 live births by the end of 2030 [7], under-five mortalities are still alarmingly high in sub-Saharan Africa (SSA) and South Asia [6]. To curve the current population growth, understanding fertility choice is important to realize the demographic transitions of various nations [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Nearly 90% of the differences between countries in actual fertility are accounted for solely by differences in desired fertility [11]. Fertility preferences reflect women's or couples' desire for children which could have a direct impact on contraception demand [10,12] and population growth [11]. In 2015, more than 90% of developing countries with rapid population growth instituted policies to check the growth rate [3].…”

Section: Introductionmentioning

confidence: 99%

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Residential and wealth-related disparities of high fertility preferences in Ethiopia: A decomposition analysis

Alemu,

Debie,

Alemu

et al. 2024

PLoS ONE

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Background Fertility preference significantly influences contraceptive uptake and impacts population growth, especially in low and middle-income countries. In the previous pieces of literature, variations in fertility preference across residence and wealth categories and its contributors were not assessed in Ethiopia. Therefore, we decomposed high fertility preferences among reproductive-aged women by residence and wealth status in Ethiopia. Methods We extracted individual women’s record (IR) data from the publicly available 2016 Ethiopian Demographic Health Survey (EDHS) dataset. A total of 13799 women were included in the study. Multivariate decomposition analysis was conducted to identify the factors that contributed to the differences in the percentage of fertility preferences between rural and urban dwellers. Furthermore, we used an Erreygers normalized concentration index and curve to identify the concentration of high fertility preferences across wealth categories. The concentration index was further decomposed to identify the contributing factors for the wealth-related disparities in high fertility preference. Finally, the elasticity of wealth-related disparity for a change in the socioeconomic variable was estimated. Results The weighted percentage of women with high fertility preference among rural and urban residents was 42.7% and 19%, respectively, reflecting a 23.7 percentage point difference. The variations in fertility preference due to the differences in respondents’ characteristics accounted for 40.9%. Being unmarried (8.4%), secondary (14.1%) and higher education (21.9%), having more than four children (18.4%), having media exposure (6.9%), middle (0.4%), richer (0.2%) and richest (0.1%) wealth were the positive and city administration (-30.2%), primary education (-1.3%) were the negative contributing factors for the variations in high fertility preferences due to population composition. Likewise, about 59% of the variations in fertility preference were due to variations in coefficients. City administration (22.4%), primary (7.8%) and secondary (7.4%) education, poorer wealth (0.86%) were the positive and having media exposure (-6.32%) and being unmarried (-5.89%), having more than four children (-2.1%) were the negative factors contributing to the difference in high fertility preferences due to the change in coefficients across residents. On the other hand, there was a pro-poor distribution for high fertility preferences across wealth categories with Erreygers normalized concentration index of ECI = -0.14, SE = 0.012. Having media exposure (17.5%), primary (7.3%), secondary (5.4%), higher (2.4%) education, being unmarried (8%), having more than four children (7.4%), rural residence (3%) and emerging (2.2%) were the positive and city administration (-0.55) was the negative significant contributor to the pro-poor disparity in high fertility preference. Conclusion The variations in high fertility preferences between rural and urban women were mainly attributed to changes in women’s behavior. In addition, substantial variations in fertility preference across women’s residences were explained by the change in women’s population composition. In addition, a pro-poor distribution of high fertility preference was observed among respondents. As such, the pro-poor high fertility preference was elastic for a percent change in socioeconomic variables. The pro-poor high fertility preference was elastic (changeable) for a percent change in each socioeconomic variables. Therefore, women’s empowerment through education and access to media will be important in limiting women’s desire for more children in Ethiopia. Therefore, policymakers should focus on improving the contributing factors for the residential and wealth-related disparities in high fertility preferences.

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Section: Introductionmentioning

confidence: 99%