Religious affiliation, religiosity, and male and female fertility

Zhang, Li

doi:10.4054/demres.2008.18.8

Cited by 111 publications

(114 citation statements)

References 46 publications

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“…In line with the present study, some studies have also emphasized the positive and meaningful effect of religious beliefs on women's childbearing and reproductive behavior (26)(27)(28). In an empirical study, Lund demonstrated that introducing and developing the religious concepts in people would increase the childbearing rate (29).…”

Section: Discussionsupporting

confidence: 61%

Relationship Between Demographic Factors and Religious Orientation and the Reproductive Behavior of Employed Women in Tehran

Ozgoli¹,

saeigarenaz²,

Fahimeh³

et al. 2017

International Journal of Women’s Health and Reproduction Scienc

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Section: Discussionsupporting

confidence: 61%

Relationship Between Demographic Factors and Religious Orientation and the Reproductive Behavior of Employed Women in Tehran

Ozgoli¹,

saeigarenaz²,

Fahimeh³

et al. 2017

International Journal of Women’s Health and Reproduction Scienc

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“…The final dependent variable is religiosity, which has been shown to be positively related to fertility: on average, more religious women have more children (Hayford and Morgan 2008;Zhang 2008).…”

Section: Independent Variablesmentioning

confidence: 99%

Using Split-Population Models to Examine Predictors of the Probability and Timing of Parity Progression

et al. 2009

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Survival models are widely used in demography to analyse the timing of events such as death or leaving school. However, for events such as marriage or childbirth that are not experienced by everyone, standard survival analysis conflates the speed of progressing to an event with the proportion that never experience the event. The problem can be overcome by applying a 'split population' or 'cure' survival time model which splits the population into those who eventually experience the event and those who do not, and determines the speed of progression for the former. This paper demonstrates the use of split-population models in examining variables which affect the propensity and timing of additional births. The data analysed are from a sample of women from the 2001 Household, Income and Labour Dynamics in Australia (HILDA) Survey (N = 4,611). We model the propensity and time to have another child given sex composition of existing children, number of siblings and age at first birth for three cohorts of women. The study finds evidence of a preference for a mixed-sex composition, and an increased propensity for women with two boys to try for a third child. Women in later cohorts tend to have more children if they themselves come from larger families. Age at first birth is strongly associated with the propensity to have another child and with the speed of progression.

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“…The interaction hypothesis considers religious groups as social institutions through which people learn religious doctrines and are influenced by other members' health behaviour (Zhang, 2008;Yeatman and Trinitapoli, 2008). It may even happen that some members adopt the predominant group behavior without understanding the real reasons, in order to maintain a good relationship with the group.…”

Section: Theoretical Considerationmentioning

confidence: 99%

Religious differences in child vaccination rates in urban Africa: Comparison of population surveillance data from Ouagadougou, Burkina Faso

Soura

Pison

Senderowicz

et al. 2013

APS

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Religious affiliation, religiosity, and male and female fertility

Cited by 111 publications

References 46 publications

Relationship Between Demographic Factors and Religious Orientation and the Reproductive Behavior of Employed Women in Tehran

Relationship Between Demographic Factors and Religious Orientation and the Reproductive Behavior of Employed Women in Tehran

Using Split-Population Models to Examine Predictors of the Probability and Timing of Parity Progression

Religious differences in child vaccination rates in urban Africa: Comparison of population surveillance data from Ouagadougou, Burkina Faso

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