An Integrated Gender Data Policy: A Case for Plugging Gender Data Gaps and Revamping Gender Statistics in India

Introduction

Despite consistent increase in the size of India’s Gender Budget, significant gaps in the existence of Sex-Disaggregated Data (SDD) within our statistical system impair policymakers’ ability to effectively navigate towards improved gender outcomes. This article analyses the institutional architecture behind the generation of SDD and delves deeper into the major gender data gaps as they exist in the large-scale surveys conducted by the Ministry of Statistics and Programme Implementation and other line ministries as well as the administrative data systems maintained by them. Based on this analysis, the article attempts to lay down the broad contours of the much-needed gender data policy in India along with a proposed mix of carrot-and-stick policy implementation solutions.

Context

India’s Gender Budget has almost doubled at a Compound Annual Growth Rate (CAGR) of 10.4% from $10.84 billion in FY 2015–2016 (Ministry of Finance, Government of India, 2016) to $19.62 billion in FY 2020–2021 (Ministry of Finance, 2020a). Contrastingly, global gender indices based on macro-level indicators suggest little improvement in gender outcomes despite incorporating lagging effects of expenditure. India ranked 123rd of 162 nations on the Gender Inequality Index (GII) (UNDP, 2020) in UNDP’s Human Development Report 2020, showing no improvement since 2018. On the World Economic Forum’s Global Gender Gap Index (GGGI) (World Economic Forum, 2020), India slipped four positions to rank 112th in 2020 compared to 2018. India scored in the bottom 20 percentile in the former and among the five worst countries on the latter on aspects such as women’s reproductive and sexual health, education, market participation, survival rates and political participation.

To bridge this widening gap between increased financial resources and stagnant gender outcomes, policymakers need sector-specific, micro-level, Sex-Disaggregated Data (SDD) to identify bottlenecks and design relevant solutions. Therefore, persistent gender data gaps can attenuate gender outcomes.

Globally, such data gaps adversely impact gender-specific reforms in countries, rich and poor alike. A UN Women’s brief highlights that only 12 out of 54 gender-specific indicators are tracked regularly enough for them to be classified as Tier I by the IAEG-SDGs (UN Women, 2018a). India’s efforts towards better gender statistics have been limited to organising international consultations, for example, the last workshop on Data Generation for SDG Gender Indicators (Ministry of Statistics and Programme Implementation [MoSPI], 2018) to improve performance on the expanded list of 80 gender-relevant indicators within 14 of the 17 SDGs as per the UN Statistical Division (UN Statistics Division and Inter-Agency Expert Group on Gender Statistics [IAEG-GS], 2018), and a lot more remains to be done.

Indian Institutional Architecture

Indian statistical system, like that of most other countries, includes two major types of data: (a) large-scale survey and census data covering socio-economic and developmental indicators collected by the MoSPI and (b) administrative and sector-specific survey data collected by respective line ministries. While women-specific policy action is the mandate of the Ministry of Women and Child Development (MWCD), SDD, given its cross-sectional nature, is produced across ministries in a decentralised manner (MoSPI, n.d.a).

Such fragmentation of gender data mandate, without an overarching policy framework integrating gender-sensitive indicators at the design stage of surveys and administrative data systems creates significant data gaps. MoSPI’s Social Statistics Division (MoSPI, n.d.b) generates and compiles SDD in its annual publication Men and Women in India (MoSPI, 2019a). A detailed analysis of this report and scheme-specific administrative data of line ministries reveals significant gender data gaps.

Gender Data Gaps: Large-Scale Surveys

Various large-scale surveys in India collect data at the household level, which obscure intra-household variations.

Thematically, data on women’s economic contribution is insufficient and infrequent. The annual Periodic Labour Force Surveys (PLFS) and decennial census fail to capture women’s time spent on unpaid domestic work. MoSPI’s 2019 Time Use Survey, which shows that women spend nearly thrice (299 minutes per day) the amount of time than men (97 minutes) on domestic services (MoSPI, 2019b), was only conducted after 20 years. MoSPI’s annual survey of industries reports SDD on direct but not contractual employment. Given the $770 billion worth potential annual additional GDP opportunity by 2025 (Mckinsey & Company, 2018) from increased economic participation of women, India needs an urgent revamp of its gender-specific labour statistics to granularly measure and course correct on the way.

In women’s access to their entitlements, similar data gaps exist. The PLFS captures the proportion of women ineligible for social security benefits but, given that 90% of the workforce in India operates in informal sectors (Mehrotra, 2019), it leaves out a vast section of women who despite their need have no access to social security benefits. In the formal sector, there are no datasets tracking gender-sensitive hiring practices and access to decent work-related services such as lactation rooms, creches and working women hostels. In banking, SDD on access to credit and other financial products/services, and in public health, utilisation of improved healthcare services is not available. SDD on access to Information and Communication Technology (ICT) services is also not available. Data on access to public services such as safe drinking water, sanitation, cooking fuel and so on is only captured at the household-level.

Similarly, SDD on women’s empowerment outcomes such as asset ownership, health expenditure, crime and violence, and improved agency is also not available. Since the primary sampling unit for the National Sample Survey Office’s land and livestock surveys (National Sample Survey Office, 2013a) and debt and investment surveys (National Sample Survey Office, 2013b) capturing information on area of land, number, type and value of the livestock owned is a household, SDD on the same is unavailable. Land title information in the Agriculture Census conducted by the Ministry of Agriculture and Farmers’ Welfare (2016) aggregates all land held by the household as a single operational holding while reporting it. In public health, the National Health Accounts (Ministry of Health and Family Welfare, 2019) provide no data on intra-household output-of-pocket health expenditure variations. SDD on mental health ailments impacting several women is not available. Data on improved women’s agency in the corporate world, trade unions and cooperatives is not available. The National Family Health Survey captures data on violence against women/girls in the age group of 15–49 years but leaves out other age groups and provides no insights on the severity of the violence.

Globally as well, as per the UN Statistics Division’s 2012 survey, only 30%–40% of the 126 countries regularly produced sex-disaggregated statistics on unpaid work, informal employment, domestic violence, access to clean water and sanitation (UN Statistical Commission, 2012).

Gender Data Gaps: Administrative Data

Administrative data, typically captured for internal programme monitoring by ministries, is often not sex-disaggregated in the absence of an external mandate or incentives. Of about 1,600 indicators for 160 major Indian government schemes being tracked through the Output-Outcome Monitoring Framework (Ministry of Finance, 2020b), about 50% are beneficiary-oriented indicators, and only 8% of those are gender disaggregated.

Ownership in the flagship urban housing scheme PM Awas Yojana (Ministry of Housing & Urban Affairs, 2020), member-wise credit access and utilisation information in Self-Help Groups (SHGs) within the National Rural Livelihood Mission (Ministry of Rural Development, n.d.a), duration of work, wages and so on provided under Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) (Ministry of Rural Development, n.d.b), and beneficiary profile of the $2.5 billion annual budget income support scheme for farmers, PM KISAN (Ministry of Agriculture and Farmers’ Welfare, n.d.), are some examples of administrative datasets with no sex-disaggregated reporting.

Globally, there is increasing traction towards administrative data as a cost-effective tool for generating gender statistics. A UN Women study in Africa suggests that 63% of the 54 gender-specific SHG indicators can be derived from administrative sources (UN Women, 2019). Consequently, an Advisory Group within the IAEG-GS was established in 2019 to provide guidance on leveraging administrative data systems for improving SDD (UNICEF, 2020).

Gender Data Policy: The Way Forward

To align institutional mandates and incentive structures for better gender statistics, an integrated gender data policy with a mix of push-and pull-based solutions needs to be formulated.

For top-down push, the policy can constitute a high-level standing committee of the Secretaries of the concerned ministries, that is, MoSPI, MWCD, NITI Aayog and line ministries. To drive gender-sensitive administrative data collection, a separate gender-relevant Output-Outcome Monitoring Framework, linking gender budget allocations to ministries’ promise on measurable gender outcome indicators, should be tabled in the Parliament. Further, competitive mechanisms such as scorecards measuring compliance to standard protocols and maturity of gender data systems in central ministries and indigenisation of global indices (GII and GGGI) for state-level comparisons can be created.

To generate a bottom-up pull, dedicated cells should be constituted within the statistical divisions of each line ministry to mainstream gender-sensitive indicators. On the field, gender data officer positions should be created in each of the 700 district offices to facilitate district-wise cross-sectoral gender target setting on a set of minimum indicators. These minimum indicators can be modelled on the ‘Ready to Measure’ (R2M) 20 indicators drawn by a joint paper by Open Data Watch and Data2X (2015) and a master list of 54 indicators identified by the IAEG-SDG (UN Women, 2018b). By quickly improving compliance on SDG gender data monitoring, this step can motivate governments across different tiers. For capacity-building, dedicated modules on gender-sensitive data collection and reporting mechanisms should be included in the curricula of government training institutions.

Operationally, for themes with minimal gender data gaps such as health, education and measurement of unpaid work, existing household surveys can include gender-disaggregated indicators. For areas such as mental health, domestic violence and crime, entrepreneurship, work conditions and asset ownership, new periodic surveys need to be institutionalised. For access to digital and financial products, Big Data analysis of existing private sector datasets can be considered.

Considering that only 37% of the UNSD-surveyed 126 countries had a coordinating body for gender statistics and 13% had a dedicated budget (UN Statistical Commission, 2012), India can promote an international coalition focused on increasing public–private partnership investments in national gender statistical systems, advocating long-term panel surveys for time-series gender data and building shared knowledge repositories.

In summary, the integrated gender data policy framework can ensure that India truly fulfils the SDG 2030 commitment of ‘leave no one behind’ from the gender perspective.