Notes from the Field| Recording the Unorganized Sector: Reflections on the e-Shram Database

On 24 March 2020, the Government of India announced one of the most stringent lockdowns to combat the spread of Covid-19. Overnight, workers in the unorganized sector — comprising over 90 % of the country’s population —  lost their jobs as projects that employed them came to a halt and supply chains got interrupted. The subsequent lockdowns triggered one of the worst migrant crisis India has ever seen. With no means of transport, workers were forced to return to their villages on foot and many of them lost their lives on their way back home. 
As 78% of the unorganized workers lost their livelihood during the pandemic, the need to strengthen safety nets became a pressing matter. In response to the Supreme Court’s suo moto order to address their needs, the Government of India launched the e-Shram portal — India’s national database of unorganized workers. 

Using field data from the villages in Hunsur and HD Kote blocks of Mysuru district, Karnataka, this essay reflects on the e-Shram database and asks what is left out from the database and what are its implications? 

The power of digitized databases in enabling large-scale data collection, storage, and management cannot be undermined. By stitching each data point into rows and columns, databases make it easy to retrieve and verify information. The process of classification (of things, people, species, diseases, etc.) is fundamental to constructing any database. Whether a certain piece of information finds a unique place in a database is determined by certain pre-set categories or classification principles. For instance, only an Indian citizen who is above the age of 18 can be recorded in India’s voter database. In this case, nationality and age become classification categories. Essentially, national databases classify people and each database is monitored and utilized by the state to extend benefits to the citizens. 

Politics of Databases 

The e-Shram portal, according to its official website, intends to create a “centralized” and “comprehensive” database that can help central and state governments enhance unorganized workers’ access to social security and “tackle Covid-19 like crises in the future”. It employs classification systems such as age, Aadhaar card number, educational qualifications, address, etc., to record the unorganized workforce. A worker can also select their primary occupation from the National Classification of Occupations (NCO) codes and mention the skills they have acquired. While on the surface, the categories seem to be fair and broad, the database risks excluding populations. 

Susan Leigh Star and Geoffery C. Bowker argue that classification principles employed in any information system (including databases) are never neutral. The process of creating categories is a series of choices that the classifier (the state in the case of e-Shram)  makes. It is no surprise then, that a lot of information gets “discarded” while recording “messy human data”.  Star and Bowker draw our attention to what's left out of the database or what they call, the “residual” data. They insist on a need for deeper “ethical and political” enquiry into residuality as it carries “stories [that] have peculiar possibilities and qualities”, which are crucial to center and recenter in the discourse around information systems. Residual data remain between categories or outside of the database and hence invisible, perhaps even invisibilized. 

Using reflective and purposive samples gathered from seven villages in HD Kote and Hunsur blocks of Mysuru district, this essay reflects on the residuality in the e-Shram database. To what extent does the portal encapsulate the realities of the unorganized sector? What about the unorganized sector that is left out of the database? What governmental and ethical concerns does this residuality leave us with? 

The Residual Category of the Aged Workers 

Traditionally, the participation of senior citizens in the country’s workforce has been invisibilized. We tend to consider 60 years as the age of ‘retirement’ after which most people do not ‘contribute to the economy’. The e-Shram portal, too, registers workers who are between the ages of 16 and 59 only. But most people, especially those who work in the unorganized sector, continue to work even after the age of 60. Table 1 shows a data set of a few people above the age of 59 in HD Kote Block, Mysuru district, who are involved in at least one of the occupations that fall under the unorganized sector. 

Table 1: People above the age of 59 who are involved in at least one of the occupations that fall under the unorganized sector (HD Kote block) 

By limiting the category of age to the range 16-59, the e-Shram portal overlooks one of the most important characteristics of the unorganized sector: informalized work spills over into old age. With limited scope for economic mobility, poor social protection, and low wages, these workers do not have enough savings to spare in their old age. As their children, who are also likely to be engaged in informal work, often migrate, and the elderly have to support themselves. A study reports that between 1983 and 2012, the labor force participation rate among old men and women in rural areas remained unchanged. In the rural areas, informal work in sectors such as agriculture, service, and animal husbandry is the primary source of income and state welfare schemes act as crucial safety nets for the elderly. e-Shram’s classification criteria of age not only leaves a huge population of aged workers out of the database but also excludes them from the benefits it plans to offer. 

In information systems like the national database, classification schemes such as age might appear self-descriptive, but in the process, what’s silenced are narratives of those who do not “fit” into the categories. Since elderly workers are one of the most vulnerable groups in our country, by not including them in the e-Shram database, are we not invisibilizing them even further?

Difficulty in Defining Primary Occupation

Informalized nature of work is one of the most prominent markers of the unorganized sector and the uncertainty associated with employment is an immense cause of distress among the workers. To address this issue and enhance their job prospects, the e-Shram portal allows workers to list their primary occupation (the activity which is their major source of income) and secondary occupation (a minor but significant source of income) according to the NCO codes. The former appears on the e-Shram card which can then be used to seek jobs anywhere in the country. 

While it may be a useful exercise to gather data on workers’ occupations, what the database overlooks is that most workers in the unorganized sector have multiple jobs. With no secure employment, poor work conditions and economic compensations, people are forced to juggle between occupations across different sectors to earn a living, as shown in Table 2.

Table 2: People who have at least two or more occupations that fall under the unorganized sector (HD Kote and Hunsur block)

It is important to note that informal work intrinsically suggests that most jobs are temporary, and this affects people’s livelihoods and patterns of income. Labor in the agriculture sector, for instance, is highly seasonal and it depends on factors such as rainfall and the yield of crops. Jobs at construction sites are on a project and/or contract basis, too. Since, workers gather income from various sources, defining which source is primary and which is not, is difficult, perhaps even impossible. Channabasappa* (42), who lives in a village in Hunsur block, has different occupations throughout the year. During the marriage season, he works as a cook at weddings. For the rest of the year, he alternates between being a laborer at construction sites and on agricultural farms. Moreover, the unorganized sector is characterized by significant occupational segregation based on gender and caste that restricts people to multiple low-paying jobs. Across villages, several women work as daily-wage laborers on agricultural land but are also involved in home-based work such as tailoring, making baskets, and packing incense sticks.

While it is understandable that a database cannot capture all the occupations of a worker. It is perhaps also impossible for it to capture the entire breadth of the unorganized sector. And this is precisely why it becomes all the more important to pay attention to what’s left out of the database. To ask “the perennially difficult question: what is not here, unrepresentable in the apparent plenum of information we subsist in?” By ascribing a primary status to one occupation over several others, do we obscure the precarity of employment in the informal economy? Does a nomenclature such as this forcefully package the scattered experiences of unorganized work into units that are measurable and finite? How do we ensure narratives that don’t find a place in the information system are not forgotten? If and when e-Shram introduces occupation-specific benefits, how will the government facilitate its delivery based on data that is only indicative and severely limited? It is helpful to bring back the work of Star and Bowker, who write that everyone occupies a residual category in one classification system or the other. “No one is an insider everywhere. However, when a residual category comes to substitute for lived experience and is imposed by one group or person or another, there is violence done.”

Lessons from e-Shram 

As digital databases get embedded deep into the state’s welfare delivery system, a couple of facets that are not discussed in the essay but are important to flag are that databases are increasingly being used as an instrument of surveillance. In a strategy meeting held in September 2021, several civil society organizations reported roadblocks in the e-Shram registration process, exclusions due to mandatory Aadhaar-seeding, and concerns over data rights. The participants insisted that the Ministry of Labor and Employment must expand the scope of the e-Shram portal by altering classification categories such as age; not making Aadhaar “mandatory by default” to register on the portal; and ensuring workers have the “right to access, audit, and correct their personal data”. The ministry should also clarify the “quantum and kinds of benefits” that are available for the workers.

Finally, while it is crucial to review the database to ensure it reflects the realities of the unorganized sector, it is equally important to question the increasing centrality of databases in welfare delivery. Why does the government seek to enumerate the citizens but not address the various concerns that come in the way of welfare delivery? After all, constructing a database is merely a step to welfare delivery and not its substitute.  

*Names have been changed to protect people’s privacy.