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Workshop on Capturing Women’s Work (CWW) held at India Habitat Centre, New Delhi on July 24, 2024

Workshop on Capturing Women’s Work (CWW) held at India Habitat Centre,
New Delhi on July 24, 2024

 

The workshop on Capturing Women’s Work (CWW) took place on July 24, 2024, at the Indian Habitat Centre, New Delhi. Hosted by IWWAGE, the event aimed to address the complexities and challenges in accurately measuring women’s work.

 

The inaugural session featured key insights from Radha Chellappa, Executive Director, IWWAGE, Neeta Goel, Country Lead – Measurement, Learning and Evaluation, Bill & Melinda Gates Foundation Foundation and Sona Mitra, Director – Policy and Research, IWWAGE focusing on findings from the IWWAGE study.

 

Led by Sona Mitra, the IWWAGE research team showcased their findings from the study titled ‘Capturing Women’s Work to Measure Better’ which aimed at developing better mechanisms for data collection by employing innovative probing techniques and sampling frames tailored to capture the nuances of women’s work. Additionally, the session emphasized on the importance of creating a robust framework for conducting women-specific surveys that could be aligned with national Labour Force Surveys (LFS). This would help in obtaining more comprehensive estimates of women’s labor force participation. The session concluded with a series of participant inquiries. The presentation of time use findings sparked discussions about how women’s time allocation evolves with age, specifically when unpaid domestic work becomes a daily routine, and the factors contributing to the transition from ‘girl’ to ‘woman’.

The second half of the presentation focused on findings around identifying and addressing the significant perception bias that often underestimates women’s economic contributions (in cases where the respondent is not the woman herself) were presented. Through these efforts, the sessions aimed to advance methodologies that more accurately reflect women’s roles in the economy.

Findings from the CWW study revealed notable gaps between self-reported data and societal perceptions, highlighting the need to include unpaid domestic work in workforce measurements for greater accuracy.

 

Discussions also covered the economic valuation of unpaid work, the impact of household characteristics on perceptions, and the significance of detailed recovery questions. Key points included discrepancies between the PLFS 2022-2023 and CWW survey estimates of female labor force participation rates, as well as concerns about the lack of a 180-day principal activity benchmark and the survey’s ability to accurately capture women’s work, particularly in Jharkhand.

 

 

The workshop ended with a panel discussion, moderated by Yamini Atmavilas, bringing together experts like Jeemol Unni, Madhura Swaminathan, Rosa Abraham, Neetha N, and PC Mohanan. They discussed innovations in measurement methods and the limitations of current survey instruments. Emphasis was placed on the need for regular Time Use Surveys (TUS) and refining survey tools to capture the dynamic nature of women’s work, including unpaid care and domestic activities. The panel concluded that improving measurement accuracy and recognizing the economic value of women’s work are essential for addressing historical underreporting and better informing policy decisions.

Related Resources
CWW Summary of Findings
CWW Report

Improving Women’s Employment Possibilities: A Sectoral Analysis

This research paper explores the intersection of sectoral growth and gendered employment in India, analysing how economic changes impact women’s participation in the workforce. Using time-series data from CPHS, CMIE CAPEX, PLFS, and NAS, the study forecasts employment trends across key sectors from 2024 to 2027, with a particular lens on sectors employing large numbers of women. It highlights an overall projected decline in women’s employment, especially in agriculture, education, ICT, and several manufacturing industries due to mechanisation and automation. However, it also identifies potential growth in sectors such as wholesale and retail trade, and selected manufacturing sub-sectors like footwear and detergents, driven by women’s increasing entrepreneurial presence in e-commerce. The paper calls for urgent upskilling and targeted investments in sectors with high potential to absorb women workers.

Why should we follow a cautious approach while interpreting the ‘usual status’ employment measures?

According to the Periodic Labour Force Survey (PLFS) rounds, the Indian economy witnessed a significant rise in female work force participation rate (FWPR) over the recent years, reaching 36% in 2022-23 from 22% in 2017-18.This rise is notably higher in rural areas, increasing from 24% to 41%, compared to the urban FWPR, which rose from 18% to 24% over the same period.However, concluding that this increase represents an unambiguous improvement in women’s labour market conditions would be misleading. This is partially due to these employment measures not informing on various other aspects of quality of employment, including the extent of underemployment. These FWPR estimates are based on identifying economic participation under the ‘usual principal and subsidiary status’ or interchangeably called the ‘usual status’ measure of employment.

The ‘usual status’ is the most widely reported statistical measure of employment across all domains. Although the recent ‘usual status’ trends indicate higher engagement of women in economic activities overall, these estimates should be interpreted with caution as these measures assign the status of being employed to  individuals with significantly different durations of economic engagements. The usual activity status of a person is determined based on both usual principal status and usual subsidiary status.  According to the definition of usual status, a person is considered employed if they meet either the principal status criterion (employed for at least six months in a year) or the subsidiary status criterion (employed for at least 30 days but less than six months in a year).  The subsidiary economic engagement is considered to define the employment status of a person only when the individual isn’t employed according to the principal status criterion. But the duration of   engagement in subsidiary activities is mostly significantly less than principal employment.  The usual status approach doesn’t differentiate between principal status and subsidiary status workers and adds them together to estimate the workforce participation rates. Consequently, the usual status measures fail to reveal the underemployment existing among subsidiary workers without any principal engagement. Therefore the drawback of a broad measure like usual status which includes the subsidiary engagement in defining employment is the inability to capture the underemployment.

We understand the risk with narrower measures like usual principal status as it undercounts the extent of activities taking place in the informal and subsistence economies, which are mostly seasonal in nature.  Thus the usual principal status measure gives us a closer picture for only those with stable employment conditions all throughout the year as it happens largely for working-age men. But it fails to measure women’s workforce participation adequately and this underestimation is significant for rural women because of their high share of engagement in short-term seasonal opportunities majorly in the agricultural sector. As the PLFS 2022-23 reveals, among the rural women solely engaged in subsidiary activities, approximately 82% are involved in agricultural activities majorly as unpaid family workers/own account workers. So, while it is important not to gloss over the subsidiary engagements where women participate significantly and capture the various activities extensively, as the broad employment measures do, we must also be mindful of the perils of interpreting the changes in these broad measures without looking into the granular details. We need to delve deeper to understand whether the change is driven by principal or subsidiary engagements. This is imperative for a better understanding of the extent of underemployment among the workers as the duration of economic engagement is one of the metrics of underemployment and it differs significantly between principal and subsidiary activities.

As we distinguish among women workers based on the principal and subsidiary engagements, we find that over the period of 2017-18 to 2022-23, the share of women solely in subsidiary engagement has risen from 10% to 23% at all-India level, with the share rising from 12% to 26% among rural women and 6% to 12% among urban women. This indicates that the increase in FWPR over the recent years, is significantly driven by an increase in subsidiary engagements. These shares are much lower for men as the shares of male workers engaged only in subsidiary activities are 3% at all-India level, 3% in rural areas, and 2% in urban areas in 2022-23. The shares reveal the higher underemployment existing among women workers, and more so in rural areas, in comparison to male  workers.

Also, when we compare women’s labour market participation across the states based on usual status estimates, we need to tread with caution. According to PLFS 2022-23 usual status measures, in case of few states with rural FWPR above national average like Karnataka, Andhra Pradesh, Maharashtra, Telangana, Tamil Nadu, and Gujarat, the shares of women with sole engagement in subsidiary activities range between 2-14%. And, in case of few other states, similarly with rural FWPR above national average like Jharkhand, Madhya Pradesh, Odisha, Uttarakhand, a very high share of rural women workers are engaged only in subsidiary activities, with the shares lying between 36-52%. Thus, the nature of women’s labour market participation is very different between these two sets of states, but it remains uncaptured if one looks at the usual status estimates alone.

In India’s context, because of the empirical realities of a developing nation like high prevalence of informal employment, seasonal activities, the broad employment measures  especially underemployment often don’t reveal the various aspects of quality of employment including underemployment. Any attempt to interpret these employment estimates and changes in these estimates should be undertaken with granular level inspection, otherwise it would be inadequate and misleading. This is particularly true for women who are majorly engaged in these ill-paid or unpaid short-term marginal activities where the increase in their participation is more often distress-driven and less in response to generation of good-quality, long-term employment opportunities. It is therefore critical that policymaking takes into account the usual status estimates in conjunction with usual principal status estimates in order to ensure a comprehensive consideration of women’s work.

This blog is written by Bidisha Mondal[1] works as a Senior Research Fellow with IWWAGE,
Aneek Choudhury[2] works as a Research Associate with IWWAGE.

karunakar
Karunakar Rao

Communication Manager

Karunakar Rao is a Communication & Convenings Manager at LEAD. Previously, he worked with organisations including ACCESS Development Services, AIACA and Oxfam India.

Karunakar holds a Master’s and Bachelor’s Degree in Journalism and Mass Communication from Guru Gobind Singh Indraprastha University, Delhi.

His core experience lies in brand communications. Karunakar is passionate about strategic planning, design, content development and dabbles in photography and videography. In his free time, he likes to pamper dogs, travel, binge-watch on OTT platforms and party.

Lack of census data and use of electoral roll-based sampling frame in specific studies

Selection of households and individuals is one of the most important tasks of developing sampling designs. Traditionally, in developing countries where population data is mostly available in the censuses conducted by the government, this information forms the basis of the house-listing done for the purpose of selection of households in a sample survey. In India as well, researchers mostly depend on the census data for constituting robust and representative sampling frames using a house-listing exercise to implement a particular sampling technique, unlike in the USA or other similar countries which already have readily available sampling frames. The house-listing process is a cumbersome process and often requires time and financial resources, which may, at times, deter researchers from using these probability-based sampling techniques and resort to purposive sampling which often may not be representative and may not provide unbiased information and data.

In this context, IWWAGE explored using electoral rolls as an alternative to using population census as sampling frames in the selection of household or individuals for some of our recent studies. Using electoral rolls for studies aimed at policy-making is a relatively new trend. In the absence of updated census data as well as to ensure minimised time and resource requirements, exploring electoral rolls as sampling frames may be useful, although its use for household surveys is relatively sparse in India (Vaishnav, 2021; Joshi et al. 2020). At IWWAGE, we have used electoral roll sampling frame for selection of individuals for the surveys in two of our studies on labour force participation. The data collection for one study, viz., ‘Women’s Labour Force Participation in Select States in India’, was conducted during November, 2021 and January, 2022 and the other one is an ongoing study on ‘Capturing women’s work to measure better’.

The completed study majorly aimed at unpacking the enablers and barriers of women’s labour force participation and suggesting actionable points based on the findings. For this study, approximately 5000 females and 1000 males were interviewed, from five states of India, namely Jharkhand, Karnataka, Delhi, Rajasthan, and Madhya Pradesh. The main objectives of the ongoing study are to capture women’s work comprehensively by identifying the varied, yet major, forms of paid and unpaid activities, listing activities by categories of work, and developing mechanism of estimating the simultaneity of engagement of women. Approximately 4000 females and 800 males have been surveyed from the states of Jharkhand and Karnataka for the study.

It is worth mentioning that, in a multi-stage cluster sampling context, selection of individuals (or households) from the electoral roll frame entails selection of polling booth in the previous stage of sample selection as opposed to more traditional approach of selecting villages in rural areas and census enumeration blocks (CEB) or urban frame survey (UFS) blocks in urban areas. In this note, we outline the advantages and challenges of using electoral rolls in selecting individuals based on our experience of conducting the two studies mentioned above.

Advantages of using electoral rolls in constructing the sampling frame

In addition to being time and cost efficient, there were multiple other advantages of using electoral-rolls as an alternative sampling frame, particularly in our studies. This technique provides us direct access to individual-level information like age, gender etc, that enables selection of a random sample, stratified on the basis of individual characteristics. It also allows us to minimise respondent bias by enabling enquiry from each individual rather than elicit information from only one member of the sample household who may then be the representative of the sampling unit and respond ‘on behalf’ of others, which may carry certain biases.

Also, the electoral-roll based sampling is a better alternative to the non-probabilistic sampling methods where sample selection often relied upon the ease of access to respondents and thus leads to a non-random, non-representative sample.

  1. Challenges of using electoral-rolls in constructing the sampling frame:

However, there exist a few challenges of the electoral roll-based sampling, as described below.

  1. Categorizing polling booths into rural and urban centers: In case of a few states, the rural-urban bifurcated list of polling booths is not directly available anywhere. In those cases, each polling booth has to be located in the Geographic Information System (GIS) software maps and categorized on the basis of information provided in the software. For example, in case of Karnataka, to know the rural/urban location of a polling booth, it has to be located in the GIS map and then the rural/urban location has to be decided depending on whether the polling booth is falling under a Hubli (indicating a rural area) or town (indicating an urban area).
  2. Translation from local language: In case of a few states (for example, Karnataka), where the list of polling booths and the electoral rolls are available in local languages only, translating in English and digitizing them, increase the risk of errors, and require robust monitoring and quality checks.
  3. Unavailability of voter rolls in convertible PDFs: Voter rolls are sometimes available online in standard PDF but in many cases, they are available as scanned copies of voter lists. These are difficult to convert into excel files, and hence sometimes entries of the listed individuals need to be done manually – increasing the cost and time in the digitization process. It also inbuilds a cost of manual supervision after the entries are completed in the excel file. In case of a large-scale coverage/nationally representative study, the manual process of making entries will be challenging.

 

  1. Challenges arising for electoral roll-,zbased sampling method while implementing the survey:
  2. Less frequent updating of the voter rolls: Less frequent updating of the voter rolls leads to difficulties in locating the respondents, especially in urban areas with high intra-city or inter-city out migration. Combining two of our studies, in about 20-30% instances, the respondents could not be located due to out-migration. However, as the geographical area of survey expands, this percentage comes down.
  3. Difficulty in locating respondents in dense settlements: In case of the densely populated urban areas, the houses located near the boundaries of the polling booths often get excluded from the electoral rolls corresponding to their own polling booth and get enrolled in the electoral rolls of the adjacent polling booths. This arises due to the fact that there is a cap on the number of voters in a polling booth and once the limit is reached, the remaining voters are to be enrolled in the neighbouring polling booth.
  4. Difficulty in locating women in younger age-cohort: It is also realized that locating women in the age-cohort of 18-24 years is far more difficult as compared to others. Younger women are much less likely to be listed in the voter rolls than other individuals and they also relocate more often after marriage, rendering themselves as untraceable in that particular polling booth.
  5. False entries: Sometimes the names or other information like age of the individuals does not match exactly leading to minor mismatch between the electoral roll entry and the original information of individuals. Also, the existence of false entries is found in the voter’s list.
  6. Voters not residing in the delimited area of a particular polling booth: In some cases, it is found that most of the respondents selected from the voter list of a particular booth, actually reside in a village far from the polling booth demarcated area. This is because voters in a particular area are assigned to other polling booths besides the one officially demarcated for the area.

To tackle the challenges of non-response and difficulty in locating the respondents, digitizing data of extra polling booths as buffers and preparing a list of respondents which include more numbers in addition to the required sample size for each group of respondents in each polling booth, would be a mitigating mechanism.

  1. Suggestions to facilitate a more convenient use of electoral rolls in constructing sampling frame:

Below are a few suggestions from our experience of using electoral rolls for constructing sampling frame to make the process more efficient:

  • providing the list of polling booths and electoral rolls in English;
  • indicating the rural/urban location of the polling booths in Chief electoral officer’s website;
  • making the electoral rolls available in convertible PDFs; and
  • more frequent updating of the electoral rolls.

Lastly an important limitation of using electoral rolls pertains to specific age cohorts. Since the electoral rolls include only the eligible voters, the sampling frame thus includes only those individuals who are 18 years and above. It would thus be relevant mainly for surveys that include specific age groups above a certain threshold.

 

This blog has been authored by Dr. Sona Mitra, Director- Policy and Research, IWWAGE; Dr. Bidisha Mondal, Research Fellow, IWWAGE; Prakriti Sharma, Senior Research Associate, IWWAGE; and Aneek Chowdhury, Research Associate, IWWAGE[1].

We are very thankful to Dr. Santanu Pramanick for his guidance through the process of developing a sampling frame. We are also grateful to Shri P C Mohanan for his comments in both phases of using the electoral rolls for our purpose.