Modelling fertility: an application of count regression models

Pandey, Ranjita; Kaur, Charanjit

doi:10.1080/10042857.2015.1111573

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Fertility preferences stated by the respondents constitute a numerical discrete variable. As such, it can be analysed with multiple regression models such as Poisson Regression (Pandey and Kaur 2015) or grouped in classes. The choice of the model depends upon the approach and the objective of the research.…”

Section: Statistical Approach and Analysismentioning

confidence: 99%

The heterogeneous effects of socioeconomic and cultural factors on fertility preferences: evidence from Rwanda and Kenya

Muhoza

2019

J Pop Research

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This research uses the latest Demographic and Health Surveys to investigate the heterogeneity of effects of socioeconomic and cultural factors on fertility preferences in Rwanda and Kenya, two countries undergoing fertility transition. The results show that the preference for high fertility in Rwanda is associated with few factors namely low education, protestant religion and high child mortality. In this country, economic factors are not significant. In Kenya, however, both socioeconomic and cultural factors are important. The propensity for high fertility is correlated with low economic status, low education, Muslim religion, large family size of origin, high child mortality, etc. While Rwanda tends to be homogenous with the attitude towards low fertility Kenya displays enormous variations. The negative attitude for high fertility in Rwanda, even among the poor and rural populations, has been linked with poverty awareness and scarcity of land, which undermine the traditional values attached to large families. The conclusion is that the relationships between socioeconomic and cultural factors and fertility preferences are not unidirectional; they may vary with local context. What is significant in one country may be with less important in another. Thus, the strategies to enhance the ongoing fertility transition should be country based, designed and implemented.

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Section: Statistical Approach and Analysismentioning

confidence: 99%

The heterogeneous effects of socioeconomic and cultural factors on fertility preferences: evidence from Rwanda and Kenya

Muhoza

2019

J Pop Research

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“…The variance (0.708) of the dependent variable is greater than the mean (0.570) indicating that number of children with respiratory infection are far from its mean. Figure 1 also shows that our number of children with respiratory infection variable is over-dispersed with mean 0.570 and variance 0.708 (Fenta, Fenta, & Ayenew, 2020;Pandey & Kaur, 2015;Shaaban, Peleteiro, & Martins, 2021). It implies that poisson model is not suitable for our data.…”

Section: Empirical Results On the Effect Of Household Cooking Energy ...mentioning

confidence: 95%

Effect of Household Cooking Energy Poverty on Child Respiratory Health in Pakistan

Qurat-ul-Ann

Mahfooz

2022

iRASD J. Eco.

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Household cooking energy poverty can lead to serious health concerns for the household members generally and children under age five specifically. To attain sustainable development goal 3 of ensuring healthy lives and promoting well-being for all of all ages, Pakistan has to strive for development in the child health sector. The recent pandemic has amplified the significance of this issue many folds. This study aims to identify the effect of household cooking energy poverty on the respiratory health of children under the age of five using Pakistan demographic and health survey data, 2017-18. 26.4 percent of the sample households are cooking energy poor in Pakistan. Child health is at greater risk in Balochistan and Gilgit Baltistan due to a greater multidimensional incidence of cooking energy poverty. Rural areas with a higher incidence of energy poverty are also vulnerable in terms of child wellbeing. Negative binomial regression analysis estimates the effect of household cooking energy poverty on number of children with respiratory issues in Pakistan. Empirical results suggest that a one-unit increase in household cooking energy poverty in Pakistan leads to a significant increase in the expected log (count) of number of children with respiratory infection by 0.21. Household cooking energy poverty directly affects respiratory health of children under age five in Pakistan. The study recommends that through the development and adoption of the use of modern stoves and clean fuels, improvement in the indoor environment and health of children in Pakistan can be attained.

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“…Several studies in the literature support the use of the models mentioned above for count regression [ 13 ] used PR and NB regression models to predict the average number of children ever born to women in the U.S. [ 14 ] offered a practical demonstration of regression models recommended for count outcomes using longitudinal predictors of children’s medically attended injuries. Similarly, [ 15 ] made an attempt to model fertility behavior using various count regression models based on religious, educational, economic, and occupational characteristics.…”

Section: Introductionmentioning

confidence: 99%

Count data regression modeling: an application to spontaneous abortion

et al. 2020

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Background In India, around 20,000 women die every year due to abortion-related complications. In count data modeling, there is sometimes a prevalence of zero counts. This article is concerned with the estimation of various count regression models to predict the average number of spontaneous abortions among women in Punjab and few northern states in India. The study also assesses the factors associated with the number of spontaneous abortions. Methods This study includes 27,173 married women of Punjab obtained from the DLHS-4 survey (2012–13) to train the count models. The study predicts the average number of spontaneous abortions using various count regression models, and also identifies the determinants affecting the spontaneous abortions. Further, the best model is validated with other northern states of India using the latest data (NFHS-4, 2015–16). Results Statistical comparisons among four estimation methods reveals that the ZINB model provides the best prediction for the number of spontaneous abortions. The study suggests total children born to a woman, antenatal care (ANC) place, place of residence, woman’s education, and economic status are the most significant factors affecting the instance of spontaneous abortion. Conclusions This article offers a practical demonstration of techniques designed to handle count outcome variables. The statistical comparisons among four estimation models revealed that the ZINB model provides the best prediction for the number of spontaneous abortions, and it suggests policymakers to use this model to predict the number of spontaneous abortions. The study recommends promoting higher education among women in Punjab and other northern states of India. It also suggests that women must receive institutional antenatal care and have a limited number of children.

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Modelling fertility: an application of count regression models

Cited by 7 publications

References 11 publications

The heterogeneous effects of socioeconomic and cultural factors on fertility preferences: evidence from Rwanda and Kenya

The heterogeneous effects of socioeconomic and cultural factors on fertility preferences: evidence from Rwanda and Kenya

Effect of Household Cooking Energy Poverty on Child Respiratory Health in Pakistan

Count data regression modeling: an application to spontaneous abortion

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