How Official Data Miss Details On Half Of India’s Citizens
Lack of sex-disaggregated data and other gender-related gaps in Indian government’s official data sources is making it difficult to track issues such as girls’ and women’s employment, asset ownership, health, sanitation and education, our analysis shows. This results in limited understanding of gender issues and poorly designed policies and programmes.
In the second story in our Data Gaps series, we examine which women-specific data points are not collated or made public, and how this makes women invisible and hinders progress towards gender-equality goals.
India ranked 112th of 153 countries on the Global Gender Gap Index 2020, a drop of four places since 2018. India was among the five worst-performing countries on the economic participation, opportunity, and health and survival sub-indices of the index. Gaps in data mean increased difficulty in identifying existing gender disparities and formulating policies to close the gap.
In India, the gaps in data exist due to three major reasons, our analysis found: (i) most surveys look at household-level data but do not assess women’s ownership of assets or their access to basic amenities in the household–asset ownership is an indicator of the power an individual holds within a household; (ii) many crucial data points are not sex-disaggregated and are not periodic; (iii) definitions are unclear and underreporting is routine, particularly in cases of crimes against women.
Gender data gaps are prevalent across the world, says a 2018 brief by UN Women. Only a little over a third (37%) of the 126 countries had a coordinating body for gender statistics, found a 2012 review; only 13% countries had a regular dedicated budget for gender statistics. The brief emphasised the need to plug data gaps in order to remove gender biases in concepts and methodologies, and ensure that policies and interventions address the “lived reality of women and girls”.
Disaggregated data are divided into detailed sub-categories such as region, gender and ethnicity, and can reveal inequalities between different sub-categories that aggregated data cannot. Here is a detailed analysis of the key domains that lack sex-disaggregated data:
Jobs and livelihoods
Nearly half (48%) of India’s population are women, yet their participation in the workforce is low and has been on the decrease. Only 20.3% of women above the age of 15 are in the workforce–a decline of almost 12 percentage points from 31.7% in 2005.
India’s data on employment and access to credit miss crucial data points on women’s work conditions, wage discrimination, the type of credit that women receive and its interest rates, as per a 2019 study by the Initiative for What Works to Advance Women and Girls in the Economy (IWWAGE).
While the National Statistical Office (NSO) releases hundreds of pages of data on employment, labour force and active participation annually in the form of periodic labour force surveys and quarterly reports for urban areas, the reports do not adequately take into account women’s unpaid labour. Time-use surveys that highlight this issue–by collecting data on how each household member spends time–are less frequent.
Since a six-state survey in 1998-99, the NSO conducted a similar survey in 2019, after a gap of 20 years. This survey shows how women spend nearly five hours each day on unpaid domestic services for household members against 98 minutes spent by men.
Presence of relevant data, however, is only one part of the puzzle; periodicity (or collection and availability of data at regular time intervals) is the other. The relevant data, in the same form, has to be available periodically to enable assessment of progress over time. For example, it is difficult to analyse whether women have spent more time in unpaid labour over 20 years between 1999 and 2019 using the two time-use surveys because while the 2019 survey covers all of India, the 1999 survey has data for only six states.
The lack of periodicity of data on gender is a concern across the world. Only 23% of the available data is from 2010 or later and only 16% is available for two or more points in time, said the UN Women brief.
“Gender statistics on unpaid work have an essential role in developing and monitoring policies on expanding women’s participation in paid work,” said Ruchika Chaudhary, senior research fellow at IWWAGE and co-author of its study on gender data gaps. “And, lack of such data could lead to the making of ineffective policies on employment and care economy [the economy of unpaid care work].”
Low participation of women in the workforce also demands analysis of how social and economic norms affect hiring practices, working conditions and social security benefits for women.
However, the available data do not allow for analysis of demand-side factors that could provide insights on the discrimination women face in being hired and in pay.
“To meaningfully understand the reasons for the low levels of labour force participation, we need evidence on both demand and supply sides,” Ashwini Deshpande, professor of economics and the founding director of the Centre for Economic Data and Analysis at Ashoka University, told IndiaSpend. Data on supply-side factors answer questions on why women may not be working or why they drop out of the labour force, Deshpande said, adding, “But to answer questions like–do employers discriminate against women, is there a skill mismatch, and are there not enough jobs that can utilise women’s expertise, skill and education, data on demand-side factors are essential.”
Information is also not available on how many women work in “decent” work conditions, on access to working women’s hostels and crèche facilities, and on how many lactating mothers get feeding breaks during work timings, the IWWAGE study found. Pregnancy, child care, elderly care, lack of family support, and unsupportive work environment are the main reasons for Indian women quitting the workforce, we reported in August 2018. Hence, robust data on access to safe working conditions and child care facilities could help create policies that can address these issues.
While the Annual Survey of Industries provides sex-disaggregated data for employed workers, no similar data exist for contract workers. Also, no sex-disaggregated data are available on wages. Similarly, no data exist on the large proportion of self-employed women workers in the informal sector. While the NSO’s annual reports collect data on women workers ineligible for social security benefits, job contracts and paid leave, they do not identify the proportion of women who need social security benefits and those who receive them.
The dashboard of the world’s largest employment programme, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), provides sex-disaggregated data only for the total person-days worked. Information on supportive facilities, wages, and duration of work are not available by sex, according to the IWWAGE report.
Female workers are highly disadvantaged in the labour market–they are in large part low-skilled informal workers, engaged in low-productivity, low-paying work, IndiaSpend reported in March 2018. While the government’s flagship Skill India programme provides sex-disaggregated data on the number of beneficiaries trained and training sessions completed, it does not provide the details of the types of training imparted, the IWWAGE report showed. Such data, if made available, could help in understanding if there is a skill mismatch amongst women workers that is shrinking their employment opportunities, and then help design programmes to fix this mismatch.
Collecting information on gender norms and perceptions affecting labour market participation is new to quantitative surveys and official data sources do not include it yet, said the IWWAGE report.
The current ‘static’ methodology in collecting data is inappropriate to capture the different constraints women face at different stages of their lives, the IWWAGE report said. Women in different age groups, at different stages of their lives, and whether married, single, divorced women or single parents, have different problems.
Issues of safety and security, wage gaps and child care policies affect each category of women differently. The current system of data collection fails to capture these multiple issues and results in gender-blind policies, said Chaudhary.
Access to credit and asset ownership
In addition to employment, access to easy credit can open up economic opportunities for women. The National Rural Livelihood Mission (NRLM)–a government programme for easy finance to self-help groups–increased household incomes by 19% and savings by 28%, we reported in October. While the Ministry of Rural Development, under which the programme runs, provides annual state-wise data on the progress of the scheme, it does not provide sex-disaggregated data on member-wise credit access and utilisation, and their participation and performance. This makes it difficult to analyse how many women benefit from the programme.
The Pradhan Mantri Mudra Yojana (PMMY)–the government’s flagship programme providing loans upto Rs 10 lakh to small and micro enterprises–only provides data on the number of women entrepreneurs availing the benefit. It does not give detailed information on how many women have availed the loan under each of its three amount-based categories, the IWWAGE report found. While the scheme guidelines mention that banks may provide loans to women entrepreneurs at lower rates, no data about this are available.
Besides the lack of sex-disaggregated data points, gender biases in definitions and classifications, sample selection for population surveys and the way questions are asked and data are collected also affect the robustness of the data source, said the UN Women brief.
Decline in labour force participation also depends on how women’s work is categorised. While the government’s national sample surveys show a decline of nearly 25% in participation between 2004-05 and 2011-12, the India Human Development Survey (IHDS) records a 5% decline over the same period. The National Council of Applied Economic Research (NCAER) that conducted IHDS says that this is because the sample surveys’ questions tend to miss out on obtaining data on women’s family-based work. The survey respondents and interviewers may wrongly categorise women farmers or women who run small businesses as homemakers, while men in similar occupations are considered employed.
Suicides by women farmers in India are underreported as they may be categorised as suicides by housewives because conventional societies mostly do not acknowledge women as farmers, IndiaSpend reported in August 2015.
Inconsistencies are also found in data on asset ownership. These data are only available at the household level and, hence, there are no data on what assets women in the household own, the IWWAGE report showed. “Most of the available statistics are not granular enough for disaggregation,” said Chaudhary, adding, “Household data cannot always be divided into individuals to reveal gender dimensions.”
For instance, the Agricultural Census consolidates data from state land revenue surveys that consider operational holdings at the household level as the primary unit. Therefore, there are no sex-disaggregated data for title holders within the household. In India, land ownership is integral to the definition of a farmer, but many women do not own the land they work on. This alienates them from access to government benefits, we reported in September 2019.
Health, sanitation and education
Women are responsible for over 70% of water-related chores and its management globally; and access to clean water, adequate sanitation and hygiene facilities are especially essential for health and survival of women and girls.
While both the Census and NSO provide data on water availability and distance travelled to fetch water, information is not available on the quality of water, the number of days for which water is not available or is not safe, and individual access to water. India has 69,258 “water-quality-affected habitations”, affecting nearly 46 million people, we reported in November 2018. However, these data, which could have helped address health and sanitation issues within a household, are not available at the household level.
The NSSO, NFHS and Census collect information on whether a household has access to a latrine (owned/shared), but there is no information in any of these surveys on whether women use the latrine facility or have access to it throughout their lives. There is no information available on what toilet amenities are available at the workplace.
There are also not enough data on caregiving within households. Caregiving at home helps save time and cost to the public health system. Hence, appropriate indicators are needed to capture months of care provisioning, support provided by the state, and distribution of the care burden between men and women, according to the report.
While the NFHS and other official data sources on health indicators such as the Sample Registration System (SRS) provide robust data on infant and maternal health, data on adolescent girls are lacking. If India were to focus on adolescent health for the country’s 236.5 million children between the ages of 10 and 19 years, it would create a healthier and more productive working population to benefit the country’s growth and development, we reported in August 2019.
Education also plays an important role in improving health outcomes and fewer dropouts can mean fewer child brides and, hence, fewer infant deaths. While the Unified District Information System for Education (U-DISE) collects data on enrollment and attendance, it does not look at average daily attendance of girls and boys in schools–data that could help track girls with low attendance.
Also, data on girls who are socially excluded due to race, ethnicity, religion, location or disability are important so as to understand why girls drop out, since these girls are likely to face multiple forms of discrimination and exclusion, the IWWAGE report said.
Crimes against women are underreported
Along with missing data points and lack of sex-disaggregated data, underreporting is a concern when it comes to data on crimes against women.
The National Crime Records Bureau’s (NCRB) Crime In India is the only comprehensive data source of crimes across India. The report is collated based on administrative sources or police records and has extensive data on different categories of crimes against women.
However, crimes against women in India are considerably underreported, which is a fundamental and common obstruction in addressing these crimes, we reported in August 2019. The underreporting of these crimes puts a question mark over how representative NCRB’s data are.
The NCRB’s report does not reveal the correct levels and pervasiveness of crime against women in the country, stated a 2011 report by a committee constituted by the ministry of statistics and programme implementation (MOSPI) to review crime statistics.
The extent of underreporting of crimes against women is important to understand the factors that prevent women from reporting these crimes. Only one in 13 cases of sexual harassment in Delhi and one in nine in Mumbai were reported to the police, found a 2015 survey of close to 5,000 households by the Commonweath Human Rights Initiative (CHRI). A majority of the respondents said they did not report the crime because they did not want to be entangled in a legal case or feared retaliation.
The MOSPI committee expressed the need for a nationwide household level survey to have reliable estimates of the crimes in India. In addition to the survey, data should be collated from various statutory bodies like the National Commission for Women and be made available at regular intervals, said the report.
Besides the NCRB, the NFHS releases data on violence against women and girls between the ages of 15 and 49 across rural and urban areas, religions and castes. However, the NFHS does not include any information on such violence against girls younger than 15 or women older than 49. The survey also does not specify the severity of violence.
Yet, as many as 99.1% of cases of sexual violence against women are not reported, with the perpetrator being the victim’s husband in most cases, a 2018 analysis of the NCRB and NFHS data by Livemint found. Only about 15% of the cases of sexual violence where perpetrators are not the victim’s current husbands are reported.
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