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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.

Economic violence refers to any act or behavior causing economic harm to an individual and generally involves coercive control of economic resources of a person . It is one of the many interconnected forms of violence often taking place at the domestic realm in the context of intimate relationships leading to adverse consequences on mental, physical, and financial well-being as well as other development opportunities of the victims and their dependents both in the short and long-term. This blog attempts to provide estimates of the incidence of economic violence in India based on the National Family Health Survey 5, conducted during 2019-21.

Although economic violence is now legally recognized in a few of the European Union member states like Belgium, Bulgaria, Croatia, Lithuania, Hungary, Malta, Romania, Slovenia and Slovakia and it is a commonly used tactic by perpetrators for coercive control over the victims and co-occurs with other forms of violence, it is less talked about and underreported. The under-reporting arises majorly due to the general lack of awareness about what constitutes economic violence. The gap in empirical understanding of economic violence and the factors influencing the occurrence of this form of violence also leads to a vacuum in the policy-making space through prohibitive measures to support the survivors.

There are majorly three types of economic violence categorized by the European Institute of Gender Equality: economic control which includes preventing, limiting, or controlling a victim’s finances and related decision-making; economic exploitation which means using the economic resources of a victim to the abuser’s advantage; and economic sabotage which involves preventing a victim from pursuing, obtaining, or maintaining employment and/or education. However, the identified indicators from NFHS 5 allows us to explore only the extent of economic control and economic exploitation experienced by women in India.

According to NFHS 5, 49% of women, aged between 15 to 49 years, don’t have the decision-making power on how to spend their own money. The situation is relatively better for urban women as compared to their rural counterparts since the share is relatively lower at 43% for urban women and 51% for rural women. This is because urban women face less restrictive socio-cultural norms and enjoy better agency as compared to rural women. Also, there exists vast state-wise variation when it comes to women’s control over their own money. The three states with highest shares of women with decision-making power over their own money, are Himachal Pradesh, West Bengal, and Karnataka with the shares being 62%, 61%, and 59% respectively, whereas the worst performing states are Telangana, Andhra Pradesh and Assam with the shares being 32%, 29% and 28% respectively. The state-wise patterns indicate varying levels of women’s agency and the associated socio-cultural norms across the states. 

The control over one’s own money also varies among women in different age-cohorts, with the control steadily increasing with age. While only 35% women in the age-cohort of 15-19 years can decide how to spend their own money, this share rises to 59% for women in the age-cohort of 45-49 years. As women transition from young adulthood to middle age, their growing social network often make them collectively empowered, help them challenge the restrictive social norms and exercise better agency. Moreover, financial distress has been a contributing factor to the prevalence of economic violence as 54% women in the ‘poorest quintile’ reported not having the control over their own financial resources. This percentage goes down among women belonging to upper expenditure quintiles.

The partner pay gap- the difference in earnings between the partners – turns out to be a significant influencer of economic violence as women’s relative earning position tends to determine the interpersonal power dynamics within the couple. Around 65% of women who earn equal to their husbands or more than their husbands, are found to have command on how they spend their own money, and the share is 59% for those earning less than their male spouses. The findings from NFHS 5 reveal a U-shaped relationship between women’s control over their own economic resources and their education level. This implies that women at very low levels of education have better command over their economic resources as compared to women in secondary/higher-secondary levels of education; the control increases for women with graduation/post-graduation levels of education. As NFHS 5 reveals, this too can be explained by the partner pay gap. According to the data, the share of women earning more or less similar to their male spouses is high among women with lower levels of education and thus on average they enjoy better command over their economic resources. Women at secondary/higher secondary levels of education, earn much lower than their spouses on average, leading to lower agency and less control on their own economic resources, and again the ‘partner pay gap’ declines for women with tertiary level of education, leading to better agency. Additionally, as the education levels of the male spouses increases, the females are found to enjoy more decision-making power over their own earnings as educated men are supposed to conform to more gender-equal norms. Approximately 50% of women whose male spouses received no education, don’t have any command over their own money. This share goes down to 36% for women with male partners educated above the secondary level.

Another commonly found example of financial abuse is the male spouse depleting the wife’s savings without her knowledge/consent. According to NFHS 5, around 44% of women stated that they have a bank savings account of their own, but they are not in control of the money in it. This share again varies by education levels, with the share being 44% for women without any education level and the share declining to 36% for those with education level above secondary level, reflecting higher agency among educated women.

Despite the pervasiveness of low agency of women when it comes to controlling their own incomes and financial resources, the recognition of it is lower. This lack of recognition is often due to not acknowledging the exclusion as another form of discrimination and assault on women. This points to the need for generating more evidence and awareness around the various forms of exclusion both in the legal and scholarly spheres. Criminalizing economic abuse would help to send a firm message about the unacceptability of this form of violence. Allocating significant budgetary resources for training legal professionals for increasing their capacity to recognize, investigate, and prosecute economic violence would be needed to address and prevent its occurrence. Along with this, the access to the services to combat familial violence must be provided to its victims too. Lastly, we need more sophisticated surveys capturing the various dimensions of economic violence to fully comprehend the subject.

This blog is written by Aneek Choudhury, Research Associate and Bidisha Mondal, Research Fellow at IWWAGE.

Picture credit: Paula Bronstein/Getty Images/Images of Empowerment

The Sveriges Riksbank Prize in Economic Sciences in memory of Alfred Nobel, was awarded to Claudia Goldin, professor of economics at Harvard University, on October 9^th 2023 for ‘having advanced our understanding of women’s labour market outcomes’. The recognition of Claudia Goldin’s work is expected to strengthen the discourse around gender inequalities in the Indian labour market too. Although Goldin’s work majorly focuses on high income economies, it holds important insights for the gender differences in labour market returns in India. Her work underscores the need for data collection and evidence generation on identity-based differences in labour market outcomes and critiques identity-insensitive policy-making. Using PLFS 2021-22 data, this blog explores the nature and quality of work that women in India are engaged in and how it is influenced by demographic and socio-economic factors.

In India, along with the low female workforce participation rate at 25% (PLFS 2021-22), another major concern is women’s engagement in job opportunities of poor quality in terms of wages and other benefits. A striking feature of the Indian labour market is the overwhelming presence of women engaged as unpaid family workers. According to Periodic Labour Force Survey 2021-22, 37% (all India) of total employed women are working as unpaid family workers and this percentage rises up to 43% in case of rural women. Unpaid family workers are those who are working without any pay or profit in a family operated farm or a business owned by any household member with whom the person is related by kinship/marriage/adoption etc. The unpaid family workers are considered to be part of the labour force and their contribution gets counted in national income, but their work doesn’t get remunerated and the profit belongs to the owner of the family business. The worker engages and contributes to the business considering it a part of household responsibility/obligation. Thus, in spite of being a part of the workforce, this form of engagement isn’t expected to lead to financial empowerment due to the absence of remuneration unlike the mainstream labour market activities. This non-monetized nature of the activities of unpaid family workers leads to lack of recognition of women’s work and women’s agency and often leads to underreporting of women’s work. Thus, understanding the factors influencing women’s decision to work as unpaid workers in family businesses and hindering them from entering paid work opportunities is imperative to enable informed policy-making for ensuring remunerative engagement of these women.

Women’s engagement as unpaid family workers is concentrated in agriculture and related activities, as 89% of them are in these farm-related activities. However, women’s participation as unpaid family workers differs by demographic variables like age-group, education level, skill training, care responsibilities and also characteristics of the households. The share of working women engaged as unpaid family helpers is much higher at 47% among those aged between 15-25 years, as compared to 34-38% among the older age-cohorts. This reflects that social norms are more restrictive for younger women when it comes to working outside the family. The education level also influences women’s working status. The share of working women engaged in unpaid family business is highest at 42% among those who are illiterate. Although, the share goes down with rise in education level, a share as high as 37% of working women with middle to higher-secondary level of education and 13% of working women with as highly qualified as graduates, post-graduates and above, are engaged as unpaid helpers in family businesses. This fact points towards the lack of paid job opportunities for those with mid to high-levels of education, along with constraining social norms. The PLFS data however indicates that those who received formal vocational training are less likely to work as unpaid family workers and tend to engage in remunerative engagements. This is indicated by the share of working women with formal vocational training engaged as unpaid family workers which is 10%,  much lower than others.

Along with these characteristics, women’s care responsibilities also influence their decision to work inside or outside their homes. The PLFS 2021-22 shows that the share of working women engaged in family businesses as unpaid helpers is 45% for those with children aged below 5 years, whereas the share is much lower at 34% for others. Additionally, as the household’s income rises, the likelihood of women being engaged as unpaid helpers in family businesses declines. According to the PLFS 2021-22, the share of working women engaged as unpaid family workers is 44% in the lowest income class and the share declines to 26% for those women belonging to the uppermost income class. This is evidently due to the inability of poorer households to hire paid workers from outside and instead have to engage household members in their family businesses.

As the factors leading to women’s engagement in these non-remunerative activities are many, including restrictive social norms, lack of job opportunities, lack of skills, care responsibilities etc, a multifaceted approach is needed to shift these women to remunerative opportunities. As the data indicate that vocational training is effective to ensure women’s remunerative engagements, raising awareness among women about these programmes, strengthening the existing programmes for higher outreach, skill training of women for the emerging non-traditional sectors, would be impactful policy measures. The availability of job opportunities for highly skilled and qualified women in the non-farm sector would also encourage women to take up these remunerative opportunities instead of working as unpaid family workers. Thus, creation of good quality job opportunities in the secondary and tertiary sectors and women-friendly work environment would be needed to address these concerns. As childcare responsibilities often act as a constraint for women to work outside their homes, state provisioning of childcare facilities would free up women’s time for commitments beyond the domestic sphere. Above all, making women aware of the implications of economic empowerment for their agency is of utmost importance for encouraging them to taking up remunerative engagements.

The blog is authored  by Dr. Bidisha Mondal, Research Fellow, IWWAGE