Effects of modernisation on desired fertility in Egypt

Abstract

Using a conceptual framework that borrows notions both from the economic theory of fertility and social interaction theory, this paper assesses the relative importance of social and economic modernisation at the individual and community level in explaining geographical differentials in desired fertility in Egypt. Using the 2000 Egyptian Demographic Health Survey and an up-todate map of land cover in Egypt, this paper provides an application of an advanced methodology which uses a combination of multilevel modelling and geographical information system (GIS) techniques. The paper shows how GIS techniques facilitate the construction of several variables representing the level of economic modernisation, such as land use, road density and urbanisation. It illustrates how GIS techniques and multilevel modelling can help us to move forward a step in substantiating theories of community influences on fertility. This study also analyses the effect of current family composition on desired fertility in Egypt and reveals the desire of Egyptian society to have at least two children and at least one boy.

Women’s Agency and Fertility: Recent Evidence from Egypt

“…As anticipated, the women in both rural and urban Upper Egypt have higher fertility as compared to women in the Cairo area. This result is consistent with research that shows that women in Upper Egypt are consistently significantly worse off across most women’s health outcomes (Ambrosetti et al 2013; Yount and Li 2010) and have higher fertility (Baschieri 2007; Casterline et al 2003; Weeks et al 2004). Patriarchy and development exert a major influence on regional variations in fertility (Malhotra et al 1995).…”

“…Women in Upper Egypt often have worse outcomes on health and social indicators as compared to women in other regions of Egypt (Ambrosetti, Abu Amara and Condon 2013; El-Zeini 2008; Govindasamy and Malhotra 1996). It is well established that fertility rates are also high in rural Upper Egypt (Baschieri 2007; Casterline, El-Zanaty and El-Zeini 2003; Weeks et al 2004). However, little is known about regional differences in women’s agency in Egypt, and the pathway by which agency affects fertility may depend on geographic region (Malhotra and Mather 1997; Mason and Smith 2000).…”

Further evidence of community education effects on fertility in sub-Saharan Africa

“…In addition, it has been concluded in some recent studies from subSaharan Africa that, when women with the same level of education are compared, those who live in communities where others have relatively high education tend to have the lowest fertility (Kravdal 2002;DeRose and Kravdal 2007). Such associations that are indicative of externality effects of education have also been reported in fertility studies from other regions (Hirschman and Young 2000) and in investigations of fertility desires (Moursund and Kravdal 2003;Baschieri 2007) or contraceptive use (Benefo 2006(Benefo , 2010DeRose, Wu, and Dodoo 2010;McNay et al 2003;Moursund and Kravdal 2003;Stephenson et al 2007;Stephenson, Beke, and Tshibangu 2008) in various developing countries, with a variety of additional factors included (more or less appropriately) in the models. There are also studies, however, where associations have not shown up (Gupta and Mahy 2003;Yabiku 2006).…”

“…While it is possible that increases in education will have a particularly modest impact on the number of jobs in the modern sector in rural settings with an initially dominant agricultural sector (as mentioned by Baschieri 2007), such a pattern is far from obvious. Another possible contribution to an interactive effect is that the impact of a higher educational level on women's general level of autonomy may depend on the educational level at the outset (e.g., Jejeebhoy and Sathar 2001).…”

Influence of Demographic and Health Survey Point Displacements on Raster-Based Analyses

“…Demographic and Health Survey (DHS) spatial data are widely used to evaluate the effects of environmental or contextual exposures on health outcomes (for example, see Balk et al (2004); Simler (2006); Baschieri (2007); Feldacker et al (2010); Jankowska et al (2012); Messina et al (2010, 2011); De Castro and Fisher (2012)). One of the most common uses of this data involves linking DHS clusters to environmental or contextual data in order to generate new covariates of interest.…”