Data and processes, particularly public-facing ones, are the cornerstones of modern states. Governments collect data to understand population demographics or a particular topic, document & validate important information about individuals e.g. age or citizenship, and for various other reasons. Closely related are public-facing bureaucratic processes that define the way interactions between the public & government happen, such as applying for government-sponsored work, availing maternal health & child-care services, registering one’s property, or paying fines.
When & why new schemes and tech don’t help
While government efforts on matters of data and processes have focused on efficiency and digital accessibility, there is not enough attention paid to whether the data collected reflects ground realities or if processes make things better for the general public. Take for example the Prime Minister’s Internship Scheme, aimed at providing India’s youth, particularly the less privileged, with both employment opportunities and work experience. Despite these intentions, centering registration for the scheme through conventional mediums (like the internet) has made it less accessible for the very people that it was targeted towards.
Technology is used to improve government processes, particularly service delivery, but is quite possibly excluding sections of the population who very much need government assistance. Technology can also have unintended consequences—such as denial of rations—due to poor design & embedding. Work by economist Jean Dreze has showcased this problem on a larger scale in the public distribution system in Jharkhand, specifically how the Adhaar-based Biometric Authentication (ABBA) has created barriers for households in accessing their entitled foodgrains. And though proponents of technology-aided government processes argue about the efficiency brought about by using technology, recent evidence has emerged suggesting that this isn’t always the case.
Misunderstanding people and their identities
Poor design also extends to issues around data, whether it is collected for administrative purposes e.g. collecting one’s date of birth to issue birth certificates, or as part of surveys to help guide policy. A noticeable way this manifests is how identities are documented and thus, intentional or not, acknowledged. As per the WHO, sex is defined by one’s biology or physiology, while gender is a construct determined by society that may or may not relate to one’s sex at birth. Work by the Centre for Internet and Society (CIS) has reportedly shown how sex and gender are poorly understood—and thus poorly documented and validated by government workers, making it difficult for those outside the male-female binary to procure even ordinary documents like proof of identity.
There has been some positive change such as the inclusion of an ‘Other’ category in the 2011 census for people wishing to identify as other than male or female; but the lack of further progress, particularly in distinguishing between sex & gender, disregarding the views of trans & non-binary individuals in data collection, and non-inclusivity in processes and in policymaking show how data & processes fail to reflect the lived realities of individuals. The government’s passage of legislation defining trans identities from its perspective rather than that of trans individuals only underscores this inflexible & inconsiderate approach to data & processes.
Aside from gender, caste is another domain where poor design manifests itself. The methods to document it have been far from accurate and this has been true since the time of the British Raj. The attempts have not greatly improved since then as we still rely upon simple surveys to understand caste, only few of which (such as IHDS) probe further, such as on matters of sub-caste. At a time when caste identities have taken center stage due to its association with affirmative action, understanding & properly documenting caste is important when such identities become less definite and shift with changing economic circumstances. Simple enumeration can only count and that too poorly at times, which does not help in the understanding of the more complex & location varying nature of caste.
How do we make processes helpful & data collection better?
But the need for data and processes cannot be denied. Measurement or documentation are necessary for an objective understanding of any matter; similarly processes are important for work to be done in a non-arbitrary and accountable manner. For all its criticisms, technology can have a real & positive impact on government processes; and even with marginal impacts be perceived favourably by the public. Thus efficiency or innovation should not be sidelined, but due consideration should be given in the planning of data collection or design of processes to those who would be affected by them.
For instance, welfare-related processes can focus on seeking accountability from intermediaries rather than end-users. By tying intermediaries to rapidly assessable indicators linked to goods or services that they’re responsible for provisioning, we provide an alternative to placing the burden of fraud prevention on end-users. Policymakers can also take this a step further and design policies or interventions in a holistic manner, giving local officials a selection of tools and flexibility in tackling developmental concerns, but rigorously and rapidly assessing how these concerns are actually tackled.
With regards to gender & sex, the best approach would be to distinguish between the two and acknowledge that the former is not a binary concept. By taking into confidence experts on gender & sex, and representatives of marginalised sexes, a framework to record data can be designed that is fit for policy purposes but is also accommodative. Fringe concerns such as self-determination of sex leading to misuse can be dealt with by pattern recognition and can be circumvented entirely by the aforementioned shift to indicator-centric schemes.
And in some cases an entirely new approach is not just advisable but necessary, such as for documenting & understanding caste. Troubles with the previous attempts at a caste census has shown how methods such as surveys can run into issues with documentation—simple spelling errors resulting in an exponential build-up of data points. Qualitative approaches and community-based social audits conducted with the aid of researchers and social impact organisations could provide a ‘base’ upon which regular enumeration could be conducted, rather than relying entirely on enumeration as a method to understand caste.
Documenting complexities of caste can also perhaps be done by asking questions on attitudes towards other castes, experiences because of one’s caste, social networks, and the perceived place of a person’s caste within their local community. Questions like these can help map region-specific caste networks and even develop targeted affirmative action, distinct for those who are economically marginalised and those that are both economically & socially marginalised.
What being less ‘rigid’ really means
Thus an inclusive—or rather an effective approach to data & processes, is one where actions are well-thought and well-reasoned. What are the intended goals? What are the means to achieve it? Who will be affected and how, both within and beyond what is intended? What are the thoughts of stakeholder representatives, experts or researchers; individuals who have spent years working with or trying to understand the topic at hand? Will adding more documentation/steps truly help—and at what human cost?
The suggestions outlined above are not a comprehensive set of recommendations for how governments should approach data & processes differently. Rather, they illustrate how things can be done differently, show that there are ways to govern and administer in a more inclusive manner. While inertia can have a strong effect on continuing with previous practices, policymakers must endeavour to try & break this cycle to implement policies that match the needs of the citizens that they serve.
Abhishek Vajjala, Ishani Kadu, T Roy
