Amartya Sen set off a debate in development economics when he estimated that there are 100 million ‘missing women’ in the world, referring to the magnitude of female survival disadvantage due to unequal treatment in the intra-household allocation of



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Declining Juvenile Sex Ratio in India: Trends and Determinants
Lekha S Chakraborty* and Darshy Sinha
Abstract

The paper examines the intertemporal and spatial trends in juvenile sex ratio and the socioeconomic determinants of the spatial variations in the relative neglect of girl child in India due to unequal treatment in the intrahousehold allocation of survival related commodities. Using fixed effects model of pooled least squares for the last four decennial census data across fifteen major states in India, the paper reveals that higher socio-economic characteristics viz. female literacy, female work force participation and economic growth has not been translated effectively in terms of containing the female sustenance and survival disadvantage. The disaggregated panel data analysis by geographic units reveals that lower juvenile sex ratio is not an isolated phenomenon of rural India; rather the matter is dismal in urban units. It is alarming to note that sex ratio of age cohort 0-6 is inversely related to female education and female economic activity rate with relatively higher elasticity coefficients for urban India. The spatial spillover effects associated with either geographic delineation or aggregation of States in terms of juvenile sex ratio is controlled in the models, however the spatial dependence of the phenomenon was found insignificant. The argument that economic value of women increases their bargaining power in the intrahousehold decision making and the hypothesis of ‘women’s agency and empowerment’ through higher educational attainment and participation in economic activity may not be refuted as the econometric results depicted an inverse relationship between labour force participation and juvenile sex ratio. Rather, the aggregate evidence could be interpreted as that despite the improving socioeconomic characteristics, the monotonic decline in the juvenile sex ratio reinforces the existence of gender discriminatory practices which starts even before birth; further catalyzed by the spread of sex determination tests and sex selective abortions. This subject matter requires urgent attention of public policy, as improving literacy and economic value of woman is necessary but not sufficient for enhancing the relative life chances of girl child.


Amartya Sen set off a debate in development economics when he estimated that there are 100 million ‘missing women’ in the world, referring to the magnitude of female survival disadvantage due to unequal treatment in the intra-household allocation of survival-related commodities1. India has the dubious distinction of having the largest share of ‘missing women’ in the world along with China (Sen, 1989; Coale, 1991 and Klasen, 1994 and Klasen and Claudia, 2003). It is important to analyse the process by which they went ‘missing’; irrespective of the debate whether the excess death due to the unequal access to the intrahousehold resources and sex selective abortions should been treated ethically equivalent2. The aim of the paper is to examine the trends and determinants of cross state variations in the relative life chances of girls in India.

The relative life chances of girls is captured through the estimates of juvenile sex ratio (0-6 age group), which is the age cohort most sensitive to the gender discriminatory practices, whether it is via intrahousehold distribution of resources or through sex selective abortions. Although the overall sex ratio in India improved to 933 in Census 2001 by six percentage points compared to that of 1991, the juvenile sex ratio declined to 927 in 2001 relative to 945 in 1991. However this figure conceals the wide variations across states in India and a distinct geographical pattern. It is alarming to note that the number of States/UTs in the Northern belt (relatively rich States in terms of economic growth) with child sex ratio below 900 has almost doubled over the last one decade, from three (Punjab, Haryana and Chandigarh) in 1991 to six (Delhi and Himachal Pradesh in addition to the earlier three) in 2001. This points to the hypothesis that economic growth and human development seldom move together, when it comes to improving gender relations. However, it is to be noted that though Kerala’s overall sex ratio is favourable to women at 1058, the juvenile sex ratio is only 963, as per Census 2001. The paper analyses the socioeconomic determinants of these interstate variations in the relative neglect of girl child.

In explaining the trends and determinants in the juvenile sex ratio, link between the economic value of women and relative survival chances of girls is emphasized (Bardhan, 1974; Sen, 1991; Berik, 2000). It is often traced that gender discriminatory practices in the intrahousehold distribution of resources have roots in lower economic value of women. In this paper, we examine the hypothesis whether female economic activity rate and literacy rate improves the relative survival chances of girls.

The paper is organized as follows. Apart from introduction, section II presents a review of literature on the determinants of high mortality due to the relative neglect of girl child. Section III interprets data and discusses methodology. Section IV presents the empirical model and provides the results of regression analysis. Section V summarizes and draws policy conclusions.



II. Review of Literature

The empirical literature related to declining sex ratio and its determinants can be categorized into three: the studies related to the estimation of missing women; the econometric studies related to the determinants of the declining sex ratio and microeconomic analysis of gender well being in intrahousehold resource allocation. In a series of papers in the late 1980s, Amartya Sen claimed that about 100 million women in the world are missing, referring to the number of females who had died as a result of unequal access to resources in parts of the developing world (Stephan Klasen, 1994; Klasen and Claudia, 2003). He produced such estimate by comparing the sex ratios in countries with large female deficits to the sex ratio prevailing in sub-Saharan Africa. Stephen and Claudia (2003) found that of number of missing women has increased in absolute terms to over 100 million. Regional analyses point to a better status for women in North Africa and West Asia, as compared to South Asia. Within South Asia, India now has the largest share of missing females in South Asia and next to China in the global comparison. The latest estimates of missing women across three methodologies are given in Table 1.



Table 1: Selected Estimates of Missing Women




Sen's Method

Coale's Method

Klasen's Method




No.

%

No.

%

No.

%

China

49.9

8.2

32.3

5.3

40.9

6.7

Taiwan

0.7

6.3

0.3

3.2

0.5

4.7

South Korea

0.5

2.1

0.2

1.0

0.2

0.7

India

42.6

8.6

24.6

5.0

39.1

7.9

Pakistan

6.0

9.6

3.4

5.5

4.9

7.8

Bangladesh

3.3

5.2

1.0

1.6

2.7

4.2

Nepal

0.1

1.0

0.3

2.4

0.1

0.5

Sri Lanka

0.2

1.9

0.1

1.4

0.0

0.0

West Asia:

5.3

5.7

1.8

2.0

3.8

4.2

of which




Turkey

1.1

4.0

0.2

0.7

0.7

2.4

Syria

0.4

6.1

0.1

1.6

0.2

3.1

Afghanistan

0.7

6.8

0.5

4.4

1.0

9.3

Iran

1.4

4.7

0.3

0.9

1.1

3.7

Egypt

1.8

6.2

0.7

2.4

1.3

4.5

Algeria

0.5

3.1

0.1

0.8

0.2

1.2

Tunisia

0.2

3.5

0.0

0.0

0.1

2.1

Sub-Saharan Africa

0.0

0.0

3.6

1.2

5.5

1.8

Total (Comparable)

108.9

8.0

63.6

4.7

92.8

6.8

Total (World)

113.0

6.4

60.6

3.4

101.3

5.7

Source: Klasen and Claudia, 2003, page 279.
Another study by Gunseli Berik and Cihan Bilginsoy (2000) in the context of Turkey to examine the effect of the economic value of women on the 0-9 cohort population sex ratio, indicated that Turkey does not reflect a significant deficit of women in the population as compared to the alarming figures from India and China. The study concluded a direct relationship between women’s labor force participation and the sex ratio but this effect is present only where women engagement is unpaid family labour.
Another set of studies argued that unequal access to health care leads to higher mortality rates of girls (Basu, 1992; Chen, Huq and D’Souza, 1981; Sen and Sengupta, 1983; Hill and Upchurch, 1995). However, in some of these studies, it is noted that the gender differentials in the access to nutrition appeared a negligible factor. Sex selective abortions and son preference are also identified as determinants of declining sex ratio in certain studies related to Asian countries (D’Souza and Chen, 1980; Park and Cho, 1995; Kynch and Sen, 1983; Das Gupta, 1987). These studies attributed excess female mortality to a general preference for sons, which in turn traced it to either higher expected returns to the labour of male over female children or anticipated old-age support from sons within the patrilineal kinship system (Berik and Bilginsoy, 2000). Visaria (1969) also claimed that ‘excess female mortality’ is the basic reason for declining sex ratio and excess female mortality in turn is the result of female infanticide, female foeticide, neglect of females and maternal mortality. Barbara Diane Miller (1989) examined the changes in the regional patterns of juvenile sex ratio in rural India from the censuses of 1961 and 1971. It was found that while sex differentials in childhood mortality were substantial and widely distributed in India at the time of the 1961 Census, they were even more so by the time of the 1971 census. Yet another recent study of spatial variations in sex ratio in the context of India is Klasen and Claudia (2003), where they found labour force participation rate and literacy rate of women significant in lessening sex ratio; while increasing recourse to sex selective abortions worsen it. This paper examines the trends in the juvenile sex ratio in rural and urban areas of 15 major States over the last four decades and investigates the determinants of current trends in this gender bias.



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