On 24th March, 2020, the Indian government announced the imposition of a complete lockdown in the country as a preventive measure against the spread of the Covid-19 virus. As important as it was to enforce a lockdown, it was equally important to think of the unintended consequences that could result from it. In particular, it was important to think about the effect of a hastily implemented lockdown on vulnerable sections of society who live a hand-to-mouth existence and already lie on the margins of government support. The migrant workers are one such section, who were left to suffer in starvation, stranded, with no income, food and shelter. A lack of information and political will, exacerbated by the rigid framework for disaster management characterized the government’s slow response, forcing Civil Society Organizations (CSOs) across the country to immediately intervene and take up the challenge of delivering relief to those in need. However, confronted with resource and infrastructural constraints, this type of relief delivery proved to be unsustainable. The suffering that resulted was nothing less than unimaginable, and can make one see, more clearly than ever, that when a crisis breaks out, relief delivery must be as fast as possible. In this context, I attempt to emphasize three interwoven gaps which need to be addressed, as a stepping stone towards quicker and more efficient relief delivery – Discretion, Data and Trust.

Understanding the Hurdles

As it usually does during disasters, the Indian government invoked the National Disaster Management Act (2005) as a framework for disaster management. In light of how the situation was handled, it becomes apparent to us that the administrative and operational procedures under this act are far too rigid to effectively address the mass hunger crisis that the lockdown imposed on migrant welfare. It took over five days for the Centre to declare the provision of any kind of relief for these people, and even with this announcement of free food, problems of information asymmetry and beneficiary identification still persisted. How would migrants know where to go to avail of these provisions? Did all of them know of its existence? This relief response characterizes the myopic top-down approach that the government tends to take, with heavy paperwork and a lack of urgency. For a response to be quick and meaningful, we need insights that are inspired by the ground reality of the situation. However, the lack of discretionary powers at the local levels of government prevents the realization of these insights. This stems from a lack of trust in local governments from the upper levels of the bureaucracy.

The next problem we are confronted with is a lack of credible data on potential beneficiaries. Without complete information on which people need relief and which areas are affected, it would be difficult for any agent of relief delivery to identify beneficiaries and efficiently allocate resources to different localities. This issue can be seen in the way the Public Distribution System (PDS) functioned to distribute dry rations to poverty-stricken populations. The limitation with PDS was that workers were only entitled to these rations in their home states, which essentially excludes migrant workers from accessing these benefits. Although the PDS system was eventually extended to include temporary beneficiaries, it remains that there was no clear cut mechanism to identify who needs it. There is also no credible data on the number of people who actually benefited from this provision. This problem is not even solved by CSOs in the way they operate, in that they lack the infrastructure to conduct essential surveys to gauge the extent of the problem in each area and coordinate amongst each other. As a consequence, all CSOs and State agents could potentially be targeting the same forty-percent of the population repeatedly, leaving the remaining sixty percent with no access. On top of this, CSOs had faced backlash from law enforcement officials for violating lockdown norms, restricting them from reaching people in need.

The major constraints faced by CSOs and governments are in the form of information and infrastructure. Moreover, due to a lack of credible data and the precedent of slow responses by the government, CSOs will not stand aside and trust the government to do its job. These are gaps that those on the ground are well aware of, and is in fact the very reason that they exist to fill them. Trust is a nuance of the problem that shows up in many ways in the intricate picture of relief delivery. We see a lack of trust not only in lower levels of government from the bureaucracy, but also a lack of trust in the bureaucracy from first-responder CSOs. Overall, it appears that Discretion, Data, and Trust, lie at the root cause of slow relief delivery.

Overcoming the Hurdles

In crisis situations, there is some potential in collaboration between CSOs and governments, where CSOs can serve as a channel for surveying and data collection, and the government can provide them with infrastructural support in the form of access and resources. The most effective way to mobilize CSOs would be through local governments. However, we see from the problem articulated above that discretion becomes a necessary condition to leverage local governments. A decentralized bottom-up approach would allow local governments to facilitate a process of community mobilization and collaboration through CSOs and engage a much larger group of people to help facilitate quicker relief. It would also give local government officials a more participatory role in judgement and decision making on the ground. Here of course, the problem of trust and accountability creeps up once again. With the right checks and balances in place, giving more discretion to local governments to utilize government funds would help to provide infrastructural support to CSOs. These organizations can then be sent on the ground in addition to existing government human resources to survey areas and collect data on affected population. The additional government backing would allow CSOs to reach areas that were previously inaccessible. It would also allow us to capture a more realistic picture of the on-ground situation within its own context, and enable us to respond in a more appropriate, effective manner through local governments. These surveys should be designed in a manner that holistically captures the need of a potential beneficiary, keeping in mind that the notion of ‘poverty’ is a fluid state.  The data collected from these surveys could be reported to local government officials, who can mobilize the necessary resources and funds, after understanding the situation in their areas. This increase in coordination would help in understanding which areas are getting too much attention at the expense of others. Moreover, since the CSOs themselves are collecting the data, it would add a significant amount of credibility, trust and understanding of the situation on-ground.

Conclusion

The nature of collaboration suggested here is intended to be inclusive, collaborative and is founded on trust. With trust, comes the willingness to allow discretion. With discretion, there is a strong possibility of more effective data collection. However, a simple relaxation of bureaucratic hurdles is not a sufficient condition for this. The system is plagued with a lack of transparency and accountability, both of which are detrimental to any form of trust. Issues on this front become increasingly complex with each day, in a world where realities are constantly distorted by conflict and propaganda. One thing that is certain, however, is that people have suffered, and continue to suffer. It is time for us to realistically and practically acknowledge what is going wrong and commit to putting aside our vested interests, in pursuit of collective good. We must develop the political will to step up, and come together in times of crisis.

The views expressed in the post are those of the author and in no way reflect those of the ISPP Policy Review or the Indian School of Public Policy. Images via open source.