Academic journal article Population

Regional Patterns of Sex Bias and Excess Female Child Mortality in India

Academic journal article Population

Regional Patterns of Sex Bias and Excess Female Child Mortality in India

Article excerpt

Female disadvantage in child mortality has been a subject of particular interest for researchers dealing with demography, epidemiology and sociology. A recent UN (1998a) study observed that 50% of humanity today lives in countries where gender inequalities result in excess mortality of girl children. In developing regions, excess female child mortality reaches mild to moderate levels in many countries of Central America and moderate to high levels in several countries of Sub-Saharan Africa and West Asia (Hill and Upchurch, 1995; United Nations, 1998b). Excess female child mortality is not specific to developing countries. Historical assessments have indicated the existence of excess female child mortality in Europe and North America for the age range 0-14 until the early twentieth century (Tabutin, 1978).

Although levels of child mortality have declined in the last three decades, sex differentials in child mortality actually worsened during the 1980s and 1990s compared with the 1970s in many countries of Southcentral Asia. In this region, which includes major contributors such as China and India, excess female mortality in childhood is estimated to result in about 250,000 preventable deaths among girls under age 5 (United Nations, 1998a). The biggest contribution to this striking female disadvantage comes from India. This issue remains a great challenge for achieving gender equity, and eliminating such differences will also substantially reduce child mortality.

The sex differentials in child mortality in the northern states of India are amongst the highest ever recorded in demographic history. Since the 1970s, India's sample registration system (SRS) has indicated the extent of sex differences in child mortality. A number of studies, both analytical and field studies, have extensively covered the subject of postnatal discrimination against female children and its impact on excess female child mortality (see Bardhan, 1974; Das Gupta, 1987; Kishor, 1993; Miller, 1981; Sen, 1988). Sex differentials in child mortality are the primary factor accounting for the historically low sex ratio in the Indian population (Visaria, 1967; Bardhan, 1974). The trends in excess female child mortality constructed from SRS data show that sex discrimination continues unabated in the northern states of India (see e.g. Premi, 2002). The failure to remove female disadvantage in child survival persists today, and that is one major reason why the female-male ratio remains high, despite improved survival chances of women with respect to men in the older age groups (see Drèze and Sen, 2002). In addition, recent analyses have documented the rise in sex ratios at birth (number of males per 100 females) during the late 1990s in the northern and western states due to prenatal discrimination in the form of sex-selective abortion (see Arnold, Kishor and Roy, 2002; Parasuraman, 2001). However, analyses have not so far demonstrated any evidence of a major substitution effect whereby prenatal discrimination significantly reduces postnatal discrimination(1). The impact of sex-selective abortion on the overall number of missing girls was not very significant until the early 1990s (see Visaria, 1994).

The country-wide first round of the National Family Health Survey of India (NFHS-I, 1992-93) indicated about 43% excess mortality for female children for India as a whole (Table 1). The recent second round of NFHS (NFSH-2, 1998-99) also indicates about 47% excess female child mortality. Arnold et al. (1998) recently explored the family building pattern in selected states of India, but full data of this magnitude have not so far been used to study the scale of this phenomenon, the regional patterns of excess female child mortality and the gender differentials in childcare. More importantly, the NFHS survey provides an array of socio-economic status data and broad sample coverage. These data can be used for indepth analysis of the influence of development factors on gender bias in inter-regional (macro) and intra-regional (micro) contexts. …

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