Issues of Gender Development in India range from the traditional issues like meta-preference for sons (
The scale of conventional metrics and indices quantifying gender equality in India are based on Health, Education and Quality of Life. They take into account the life expectancy, the mean year of schooling and the income levels. Gender Inequality Index attempts to broad base the index using more precise metrics focusing on Health: Maternal Mortality Rate, Adolescent Birth Rate as measure of female reproductive health index, Empowerment: percentage of women with secondary education and share of seats in Parliament, and lastly labour force participation rate as measure of employability and economic opportunities. The index is meant to highlight areas of policy making that can be focused to reduce inequalities in development indicators between men and women.
“The GII sheds new light on the position of women in 162 countries; it yields insights in gender gaps in major areas of human development. The component indicators highlight areas in need of critical policy intervention and it stimulates proactive thinking and public policy to overcome systematic disadvantages of women” (HDR, 2020).
However, such indices cover nations, and in doing so miss out the variations across the States. It leads to policy makers focusing on a top down approach, wherein the interventions for gender development are institutional such as Gender based Budgeting or creating separate funds catering to a Gender issue or amending a criminal law. However, at the core of gender inequality lies social stereotypes, social positioning of a woman and family dynamics that affect women by hindering their access to institutional arrangements. An illustration of this trend is the “
Currently, Gender Issues and Policy challenges are framed at National level. The policies are based on indices which are not easy to track and set targets at State or district level. Secondly, many such indices take into account institutional and macroscopic indicators like Life Expectancy and Mean year of schooling. Similarly, such indicators are institutional and something that can be corrected by mere provisioning. However, subjugation of women, manifestation of patriarchy and status of women in family, society are more nuanced, deep rooted. Therefore, the society has deep seated gender issues which need to be addressed, broad basing the intent of existing Gender Development Indicators
In this regard, the study attempts to develop Gender Development Index based on aspects that are fundamental for development. Such issues cover not only tangible aspects such as nutrition and well being but also socio-economic factors that can describe the status of women in the society. The index is a point of convergence of multiple Sustainable Development Goals: SDG-3 for “Good Health and Well Being”, SDG-5 for “Gender Equality”, SDG-2 for “Zero Hunger” and SDG-4 “Quality Education “. Therefore, the index is an opportunity to track the performance of individual States with respect to multiple SDGs. More importantly, it gives the States the opportunity to develop tangible targets for Gender Equality moving from Gender Budgeting to a more elaborate institutional attempt to tackle issues hampering Gender Equality in India.
This index therefore attempts to include indicators that are broader based, that capture social dynamics, are more granular. They assume that core areas of governance such as Health, Education are implemented on ground by the States and therefore a measure of the states will not only track performance, delineate the weak spots for every state but can also foster competitiveness among States by becoming a criterion for horizontal devolution through finance commissions.
Gender Development Indices have been developed by multilateral organizations like United Nations to track levels of gender development since 1995. Such gender development indices are based on Education, health and quality of life. However, such gender indices were created to attract attention towards gender equality and not for policy planning in itself. This has led to misinterpretation of GDI (
In case of India the initial gender-based index were mostly in the form of human development reports of the states. The most common mistakes found in these was that the GDI was used to interpret it as a measure of gender inequality. Many such studies present both correct and incorrect interpretations. This establishes the fact that computation of the GDI is not understood very well. In case of India works like
If existing indices are meant to track development, they are inadequate. Firstly, the indices do not offer State wise perspective. Therefore, as a tool for policy making it lacks the granularity to allow decentralized policy making. This moves the focus away from States and puts the burden of gender empowerment policies on the Central government. This in turn can lead to one size fits all, which can be counterproductive as the causes of gender inequality in different states are different as shown in the index levels. Secondly, there is no mechanism to track all such metrics for independent States or districts, mostly because of data deficiency. This hinders the State from tracking gender development levels in the State, and creating a lacuna as far as gender development policies are concerned. Thirdly, most of these indicators are institutional or macroscopic, not touching upon issues that affect “common women”.
Such indices describe the general sense of gender inequality in the country as a whole and not as a measure that can be used to compare and cluster the state. With policy making decentralizing, and State based indices being developed by NITI Aayog such as indices on Health, Water and overall SDG indices, a gender index in the country can help in mapping out, comparing and tracking each state. Moreover, with development in the country becoming more centred around Sustainable Development Goals, it is imperative that issues of women that are represented in the SDGs are also targeted by policymakers. The index therefore, also offers the opportunity to link pillars of index with Sustainable Development Goals. The index developed in the past decades have not been able to actualize any of the earmarked issues. Moreover, instances of low utilization such as gender-based budget indicate towards a lower priority accorded to the societal issues that perpetuate gender-based injustices. So, while we have laws, funds and provisions, unless we do not have focus on indicators that measure current state of affairs of women as part of society and levels of their agency, we cannot determine how empowered they are. In this paper we try to define the scope of index reflecting a particular theme with the to give a clear picture of the “state of affairs” of women and compare the States to find the best and the worst.
The methodology of this study is divided into three steps. The study focuses on developing an index and assess the state wise performance based on the index, cluster them on basis of the performance and use dimensionality reduction to arrive at fundamental dimensions for policy targeting.
The study aims to develop an index based on six broad pillars: health, reproductive and child health, social, capacity, nutrition and economic factors. The six pillars have a total of 24 variables in total. The description of each variable and its pillar are given at the end of the paper. The study uses secondary data from the Ministry of Health and Family Welfare, National Family Health Survey 4 (2015-16), co-ordinated by International Institute for Population Sciences, Mumbai.
According to literature the states have often been grouped together on the geographic location, socio-economic performance as well as cultural connection. Hence to bring out a nuanced regional aspect the states in this case the states have been grouped in the following manner:
The index determines the scores for each aspect. In calculation of score in each aspect a State is scored on a scale of one to four based on the quartile in which it is placed. As the score of the state moves from first to fourth quartile the score moves from 1 to 4. The States are scored for all variables in the pillar and then the scores are aggregated and divided by the total score possible within a pillar. This gives a score on the scale of 1 for every state on every pillar. These scores are used to determine aspirant and performing States in each aspect. This score is the basis for identification of areas of intervention in each aspect. The final GDI is determined as the geometric mean of the scores in each pillar.
Geometric mean is used because poor performance in any dimension is directly reflected in the geometric mean. In other words, a low achievement in one dimension is not linearly compensated for by a higher achievement in another dimension. The geometric mean reduces the level of substitutability between dimensions and at the same time ensures that a unit decline in the index is same as one percent decline in the any other parameter of the index. Thus, as a basis for comparisons of achievements, this method is also more respectful of the intrinsic differences across the dimensions than a simple average. The GDI was computed in R and the all the parameters were given equal weights in the geometric mean index.
To achieve dimensionality reduction, the study uses Principal Component Analysis to identify principal components which can describe the variations in the original index scores and act as broad indicators that encapsulate more than one original indicators. It is used to determine the fundamental dimensions for policy intervention.
Based on their performance on the index, the states are clustered using K-Means clustering. The basic idea behind k-means clustering consists of defining clusters so that the total intra-cluster variation (known as total within-cluster variation) is minimized.
These clusters are then identified as “Achievers”, “Performers” and “Aspirants “based on the index.
For each state the following steps were undertaken:
Identifying those pillars in which the state performs below a benchmark (0.5 for Aspirant and 0.625 for Performers) OR the score of the state in that pillar is lower than the final index score of the state.
Based on these criteria, we identify the original pillar that the state needs to improve in and based on them, the fundamental dimensions in which the intervention is required. The heatmaps, the PCA Analysis, Clustering and Index preparation was conducted on R. Data Compilation and Charting was prepared on Excel.
The state wise performances for each of the six pillars have been computed to highlight the issues. The pillar wise assessment is as follows:
Social parameter tries to gauge the societal position through indicators like literacy, sex ratio and violence faced. The social index performance is given in the figure
State wise grouping based on social index performance in terms of quartile
Heat map of the state wise performance in social pillar
There is not a very strong linkage of the region and the scoring pattern. The West Indian belt include varying degree of performances. Similarly, the southern states have a spectrum of scores across the states.
There are areas with a fairly homogenous scoring pattern. The Eastern belt has a very homogenous score on the index. The Hindi heartland has some of the lowest scores from Rajasthan, Uttar Pradesh, Madhya Pradesh, Bihar, Jharkhand having the lowest scores. The northern hilly states too have a homogenous scoring, performing better than the states of the plain.
The North Eastern states have a majority of high scores in the region and the hilly states in general, including the ones in the north have a score higher than the scores in the plains. Barring one or two states, it forms one of the best performing clusters in the country.
Capacity as an indicator is a measure of awareness and ability of a woman in a household.
State wise grouping based on capacity index performance in terms of quartile
Heat map of the state wise performance in capacity dimension
There is not a very strong linkage of the region and the scoring pattern. The West Indian belt includes varying degree of performances. Similarly, the southern states have a spectrum of scores across the states.
There are areas with a fairly homogenous scoring pattern. The Eastern belt has a very homogenous score on the index. The Hindi heartland has some of the lowest scores from Rajasthan, Uttar Pradesh, Madhya Pradesh, Bihar, and Jharkhand having the lowest scores. The northern hilly states to have a homogenous scoring, performing better than the states of the plain.
The North-eastern states have a majority of high scores in the region and the hilly states in general, including the ones in the north have a score higher than the scores in the plains. Barring one or two states, it forms one of the best performing clusters in the country.
This pillar measures indicators that track lifestyle disorders and general hygiene.
State wise grouping based on health index performance in terms of quartile
Heat map of the state wise performance in health dimension
Geographically, rural urban divide in majority areas in health is not well defined, with close score on the indicator by States.
In general, the northern Hills, Western India and Southern states have performed well on the indicator.
On the other hand, states in North East have scored below average, barring Meghalaya, with traditional high scores Mizoram and Sikkim struggling to score high on the index as well. Similarly, Eastern states have scored below average on the index.
The Northern states are a mixed bag, with Haryana and Punjab performing above national average and UP, MP and Bihar struggling with low scores.
Economic parameters Is taken into account to understand the access to economic resources to women. This includes land and also safety nets such as financial services, assets etc.
State wise grouping based on economic dimension performance in terms of quartile
Heat map of the state wise performance in Economic dimension
The top performers of the index are the Southern states, with low Rural-Urban divide hinting to the success of self-help group schemes like Kudumbashree in Kerala and Velugu in erstwhile united Andhra Pradesh. The region sees these three states (Kerala, Andhra Pradesh and Telangana) registering higher scores in rural areas probably due to such SHG movements.
In North India, Punjab performs well on percentage of women with bank accounts, while UP and Bihar perform well in land ownership percentage, a phenomenon attributed to “Feminization of Agriculture” in Economic Survey 2017, in which migration from such states and welfare scheme conditions have led to increased female land ownership.
In Hilly and North Eastern States, the performance is mostly low to average scoring barring exceptions Arunachal Pradesh and Tripura. In north hill states, land ownership among women is muted, while in low scoring North-East states, the percentage of women with access to bank accounts is low.
This indicator records the access to quality healthcare to women with respect to childbirth.
State wise grouping based on RCH index performance in terms of quartile
Overall, the performance in Northeast remains poor, barring Sikkim and Tripura.
In North India, UP and Bihar score low while Punjab and Haryana form the upper end of the region
In Southern states, all the states are in the fourth quartile, indicating excellent infrastructure and access.
Heat map of the state wise performance in reproductive and child healthcare dimension
This indicator is used to determine the quality of diet available to women in general as well as during pregnancy. It seeks to determine the likelihood of lifestyle disorder because of inadequate nutrition like obesity and malnutrition, anaemia and access to Iron Folic Acid.
State wise grouping based on nutrition index performance in terms of quartile
Heat map of the state wise performance in nutrition dimension
Southern states are divided with Andhra Pradesh and Telangana in first quartile, Karnataka in second, Kerala in third and Tamil Nadu in fourth quartile.
North Eastern states like Sikkim and Mizoram are top scorers on the index, while Meghalaya, Manipur and Nagaland are in the third quartile.
Eastern states register scores in first and second quartiles, while North Indian states of UP, MP and Bihar score the lowest in the pillar.