Data Ecosystem for Women's Land Rights in India: An Analysis in the Context of SDG Reporting

Abstract

Women’s access to and control over productive resources including land is central to women’s economic empowerment and gender equality. Several of the Sustainable development goals (SDG 1, 2 and 5) which the world is trying to achieve collectively are linked to equitable ownership of land by women. The progress in achieving these goals depends substantially on the effective data collection systems in member nations.
To ensure that land tenure security is a firm part of SDGs and annual policy reviews, indicators need to reach tier one by 2020. Reaching tier one requires 50 per cent of the countries and 50 per cent of the total population for each region to report regularly. While indicator 1.4.2 has already reached tier I, indicators 2.4.1 and 5.a.1 are now in tier II. This makes their reporting from India very important, considering its population and developed data ecosystem.
Aligning its SDG agenda with Sabka Saath Sabka Vikaas paradigm, India has adopted a localization model centered on implementation and monitoring at the three levels of government structures. It comprises of National SDG Indicator framework (NIF), State SDG Indicator framework (SIF) and District Indicator frameworks (DIF) at national, state and district level respectively. NIF, led by the NITI Aayog, constitutes 297 indicators across all 17 goals and 60 percent of the states now reports SIF. However, indicator 1.4.2 and 2.4.1 in India’s SDG Dashborad report statistics other than land while 5.a.1(a) is only reported.
Target 5.a of SDGs aims to “Undertake reforms to give women equal rights to economic resources, as well as access to ownership and control over land and other forms of property, financial services, inheritance and natural resources, in accordance with national laws.” Indicator 5.a.1 under target 5.a measures (a) proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure.
With focus on the indicator 5.a.1(a), this paper will analyse the Indian reporting and existing data sets to see if they match the official standards of UNSTAT’s metadata . Monitoring of this indicator requires periodic and reliable gender disaggregated data collected at the lowest possible spatial resolution, coupled with detailed information on agricultural population (including information about formal and customary land tenure regimes, land rights, and their social and spatial distribution).
In the specific case of indicator 5.a.1(a), UNSTAT recommends use of household surveys since they are broad in scope and are cost effective. They take the example of Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) , the Demographic and Health Surveys (DHS) , National Survey of Household Income and Expenditure , Household budget surveys (HBS), Labour Force Surveys (LFS) and Multiple Indicator Cluster Surveys (MICS).
In India, this indicator is reported at NITI Aayog’s NIF dashboard and is drawn from Ministry of Statistics and Programme Implementation (MoSPI), which uses data from Agriculture Census, done every five years. On MoSPI dashboard, it is described as ‘Operational land holdings - gender wise (percentage of female operated operational holdings)’ and reports the national value as 13.6 drawing from 2015-16 Agriculture census. Though available state and district wise, SIF don’t report this indicator now.
The Gender and Land Rights Database (GLRD) of FAO, which also reports indictor 5.a.1a , uses Agriculture Census data, it being conducted globally as per FAO’s prescription. FAO is also the custodian agency of this SDG indicator. However, operational holding data remains only a proxy and it prescribes a metadata substantially different from the UNSTAT. MoSPI is now considering integrating the land indicators in National Sample Survey (NSS) data collection process, while it is still unclear if there will be annual reporting, as desired, which is not the frequency at which NSS is conducted.
While India’s data ecosystem on land-related information is quite rich and there exist a range of databases, all of them have variable levels of granularity, sampling techniques, periodicity and dissemination methods. As a usual practice, most of these databases consider family as a unit and record land in the name of head of the household. Gender column is still missing in the India’s land records. In spite of its substantive computerization, online land records in almost all states lack gender disaggregation, though Department of Land Resources, Government of India, had asked the states to add gender column in 2015. Women’s identity and land ownership remains subsumed in the identity of the household.
Consequentially, India as a country, lacks substantially in recording the sex disaggregated data of land ownership. The deep-seated patriarchal beliefs and value systems embedded in the data collection eco-systems further blight the quality of whatever little sex-disaggregated data, that exists.
Apart from the Agricultural Census, there is Population Census , the Socio-Economic Caste Census , as well as National Family Health Survey and India Human Development Survey which contain women land rights information. They all are reliable, robust and open access databases but none gives a comprehensive picture of women land rights and does not cater to the needs of SDG indicator 5.1.1a. Instead, together they portray a conflicting canvas, add chaos to the existing understanding, and differentially inform and influence the policy and practice around aiming furthering land rights of women.
This paper will provide a critical overview of such relevant data sources on women land rights available in India, and discuss the strengths and limitations of each of them vis-à-vis global and national reporting goals, needs and metadata. It will also delve into these data ecosystem to check if state-wise variations align along relevant legal-institutional reforms brought in to correct gender-inequity as well as the socio-cultural diversities, that conspire to perpetuate it. With alternate datasets like PRINDEX and crowd-sourcing options like LANDex emerging, the paper would explore if harmonization or interoperability is possible in India’s Women Land Rights data ecosystem to report SDG indicator with firm figures around land ownership by women in India.

Full text

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Data Ecosystem for Women’s Land Rights in India:
An Analysis in the Context of SDG Reporting
Pranab R. Choudhury
1
and Shipra Deo
2
4.6 Introduction
Women’s access to and control over productive resources, including land, is central to women’s
economic empowerment. Several of the Sustainable Development Goals
3
(SDGs 1, 2, and 5),
which the world is trying to achieve collectively, are linked to equitable ownership of land by
women. The progress in achieving these goals depends substantially on the effectiveness of data
collection systems in member nations.
Aligning its SDG agenda with the Sabka Saath Sabka Vikaas paradigm, India has adopted a
localisation model centred on implementation and monitoring at three levels of government
structures. It comprises the National SDG Indicator Framework (NIF), the State SDG Indicator
Framework (SIF), and the District SDG Indicator Frameworks (DIFs). The NIF, led by NITI
Aayog, constitutes 297 indicators across all 17 goals and 60 per cent of all States in India now
report for SIF. In this paper, we analyse India’s reporting of indicator 5.a.1(a) and compare the
existing Indian data sets with the official standards prescribed by UNSTAT’s metadata.
4.7 Reporting of Land Indicators in the SDGs
Target 5.a of the SDGs aims to “undertake reforms to give women equal rights to economic
resources, as well as access to ownership of and control over land and other forms of property,
financial services, inheritance and natural resources, in accordance with national laws.” Indicator
5.a.1 under target 5.a measures: (a) the proportion of total agricultural population with ownership
or secure rights over agricultural land, by sex; and (b) the share of women among owners or rights-
bearers of agricultural land, by type of tenure. The monitoring of this indicator requires periodic
and reliable gender disaggregated data collected at the lowest possible administrative resolution
1
!Founder!and!Coordinator,!NRMC!Center!for!Land!Governance,!Bhubaneswar;!pranabrc@nrmc.co!!
2
!Director!for!Women’s!Land!Rights!with!Landesa!in!India;!shiprad@landesa.org!!
Authors!acknowledge!the!critical!feedbacks!received!during!the!presentation!of!the!paper!at!the!India!Land!Forum!and!
place!on!record!their!appreciation!for!the!organizers!and!the!participants.!
3
!See:!https://sustainabledevelopment.un.org/post2015/transformingourworld.!

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with detailed information on population engaged in agriculture (including information about
formal and customary land tenure regimes, land rights, and their social distribution).
For indicator 5.a.1(a), UNSTAT recommends the use of household surveys such as the Living
Standards Measurement Study—Integrated Surveys on Agriculture (LSMS-ISA),
4
the
Demographic and Health Surveys (DHS),
5
the National Survey of Household Income and
Expenditure,
6
Household Budget Surveys (HBS), Labour Force Surveys (LFS), and Multiple
Indicator Cluster Surveys (MICS).
The UNSTAT Dashboard currently reports 5.a.1 for 11 countries. For India, it uses the India
Human Development Survey (IHDS, 2012) as the main source. At the same time, the Gender and
Land Rights Database (GLRD) of FAO, which also reports Indictor 5.a.1, uses Agriculture Census
data, which is also endorsed by UN Women. Importantly, FAO is the custodian agency of this SDG
indicator, but UNSTAT recommends the use of additional modules for the Agriculture Census to
ensure collection and reporting of relevant data.
4.8 India’s Data Ecosystem around Women’s Land Rights
The gender-disaggregated land information in India is available through different databases,
collected by different agencies with varied granularity, sampling intensity, and periodicity. Table
4.1 mentions various such data sets and their characteristics.
Table 4.1: Characteristics of Various Datasets Reporting
Women’s Land Rights in India
Indian Open
Datasets
Data Characteristics
Frequency
of
C
ollection
Collecting
Agency
Sampling
Method
Survey
Methodolo
gy
Sample
Size
Unit of
Enumera
-
tion
Disaggre-
gation
Agricultural
Census
5 years
Agricultural Census
Division, Ministry of
Agriculture, GoI
Two-stage
sampling
Re-tabulated from
land record States
and by household
enquiry from Non-
land record States
All villages in land
record States*, 20%
sample villages in
non-land record
States§
Household
Gender,
caste, farm
size, etc.
4
!See:!http://econ.worldbank.org.!!
5
!See:!https://dhsprogram.com/.!
6
!For!India,!see:!http://www.ncaer.org/study_details.php?cID=4&pID=48.!
!
!

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IHDS
2 rounds
conducted
(2005-06
and 2011-
12)
National Council of
Applied Economic
Research, University of
Maryland
Stratified random
sampling
Door-to-door
interviews
42,152 households
Individual
Gender, age,
caste, mode
of acquisition
NFHS
5 rounds
conducted
since 1992-
1993
IIPS & Ministry of
Health and Family
Welfare (MOHFW),
Government of India
Stratified random
sampling
Door-to-door
interviews
5,68,200
households (NFHS-
4)
Individual
Gender, age,
spatial
Census
10 years
Office of the Registrar
General and Census
Commissioner,
Ministry of Home
Affairs
Full population
covered
Door-to-door
interviews
Total Population
Individual
Caste,
gender,
religion,
occupation
SECC
once in
2011
Socio-economic Caste
Census, Ministry of
Rural Development
Full Population
covered in the
Enumeration
Blocks
Door-to-door
interviews
17.91 crore
households
Individual
Caste,
gender,
primary
source of
income
PRIndex
Started in
2017
Global Land Alliance
and Overseas
Development Institute
Multi-stage
stratified cluster
sampling using
Census data
Face-to-face or
telephone interviews
2017- 16,000
samples in 24
States and UTs;
2019 data set ~
about 3150
Individual
Gender,
income, age
Note: * States and Union Territories where comprehensive land records are maintained.
§ In States and UTs where comprehensive land records do not exist, viz., Arunachal Pradesh, Goa, Kerala, Manipur,
Mizoram, Meghalaya, Nagaland, Odisha, Sikkim, Tripura, and West Bengal, and the Union Territories of Daman and
Diu, and Lakshadweep.
Source: Different databases as provided in Column 1; Author’s analysis of the metadata of the databases
Various censuses like the Population Census, Agriculture Census, and Socio-Economic Caste
Census (SECC) indirectly report information on agriculture land ownership and management.
While the Population Census provides information on the population engaged in agriculture, the
Agricultural Census records operational land holdings, and the SECC, which was conducted in
2011, provides data on land ownership.
Administrative data on land is obtained through annual reports of the State Land Departments.
Online databases maintained by States under the flagship programme, Digital India Land Record
Modernisation Programme (DILRMP) has not yet universalised the availability of gender-
disaggregated information. As per the DILRMP website, only five States reported gender
disaggregated data as of November 2017.
As a usual practice, most of these databases consider the family as a unit and record ownership of
land in the name of the head of the household, which, in most cases, is presumed to be a male.

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Consequentially, India as a country is lags substantially in recording the sex disaggregated data
of land ownership. The patriarchal beliefs and value systems embedded in the data collection
ecosystems also adversely affect the quality sex-disaggregated data.
4.8.1 India’s Reporting of Women’s Land Rights for SDGs
Although India has several reliable, robust, and open access databases, none of them provides a
comprehensive picture of women’s land rights and none caters to the needs of SDG indicator 5.a.1.
Rather with different figures (as seen in Table 4.2), they may confound the policy and action,
thereby affecting SDG-envisaged goals.
Table 4.2: Variations in Women’s Land Rights Data Reported by Various National Datasets
Datasets
Women’s Land Rights Indicators
Value (%)
IHDS 2011-12 and
Population Census 2011
Share of adult women population owning agricultural land
among total adult land owners of agricultural land
4
IHDS 2011-12
Share of adult women among the top three household members
who owned any agricultural land
14.3
Socio-Economic Caste
Census 2011
Share of women-headed households engaged in cultivation
among the total households engaged in cultivation
10.2
Agriculture Census,
2015-16
Share of women operational holders among the total
operational holders of agricultural land
13.6
NFHS-5 (2020)
Women owning a house and/or land (alone or jointly with
others) (%)
41.6
(NFHS-5)*
PRINDEX 2017
Percentage of rural respondents (women) who use a separate
plot of land for agricultural purposes
60
DILRMP, Odisha, 2014
(4 districts)
Percentage of land records with names of women, singly or
jointly (excluding homestead land; 1.4 million records)
22.9
N-LRSI, 2020
Percentage of single owners (12,208 land holdings; Coverage: 12
States/UTs; 49% are single and 51% are joint owners)
6.4
9 micro studies
Percentage of women’s land holdings (agriculture and
homestead lands) (Choudhury et al, 2017)
11.05
(SD—5.85)
ICRISAT Survey, 2014
Women landowners as a percentage of all women in the given
age groups (> 15) in landed households (Agrawal et al., 2020)
8.4
Note: *For 17 states and 4 Union Territories for which the result of the first phase has been announced so far.
Source: Different databases as provided in Column 1 abs references noted in column 2; Author’s analysis of the
metadata of the databases
NITI Aayog’s NIF dashboard drawing from the Ministry of Statistics and Programme
Implementation (MoSPI), reports 5.a.1 using data from Agriculture Census. On the MoSPI

Page | 5
dashboard, it is described as ‘Operational landholdings—gender-wise (percentage of female-
operated operational holdings)’ and reports the national value as 13.6 as per the Agriculture
Census, 2015-16. However, the operational holding data remains only a proxy and prescribes a
metadata substantially different from that recommended by UNSTAT.
Following are some other discrepancies between the Agriculture Census and UNSTAT metadata:
• The agricultural population, as per the UNSTAT definition, should include agricultural
labour, but the Agriculture Census, 2011, excludes about 144 million people who work as
agricultural labourers.
• The Agriculture Census reports data on operational holdings, which denotes de facto
possession of a household. It is different from ownership, which is the de jure position of
individuals.
• The Agriculture Census considers only the sex of the heads of households, and thus
excludes enumeration of individual women’s land ownership or tenure.
• As defined by UNSTAT, agriculture land excludes lands that are not considered
‘agricultural’, including land under farm buildings and farmyards as well as forest land.
However, the Agriculture Census includes farm buildings and farmyards when they are
part of a parcel that was under agricultural use during the reference period.
There are some additional concerns too. By excluding forest land, both UNSTAT as well as the
Agriculture Census could potentially be excluding about 1.7 million hectares of forest land for
which individual titles have been allocated to 1.9 million persons under the Forest Rights Act,
2006. Most of this land is used for farming.
Further, the Agriculture Census under-reports agriculture tenancy, thereby marginalising tenant
women. In contrast to NSSO (2012-13), which reported a tenancy of 13 per cent, a figure that
researchers argue is under-reported, the Agriculture Census (2016-16) reports a corresponding
figure of only 1.8 per cent. In several States of India, women are increasingly coming together to
lease in land collectively. The failure to record their tenures contributes to a poor reflection of the
actual situation of women’s access to land.
4.9 Conclusion
None of the existing databases in India gives a real and comprehensive picture of women’s land
rights, and none caters to the needs of SDG indicator 5.1.a, in their present form. As MoSPI
considers integrating land indicators in the National Sample Survey (NSS) data collection

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process, there is a need to recognise the gaps discussed above and work towards addressing
them
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