Interpretive Methods as Policy Tools: The COVID-19 Migration Crisis in India and the Case for Policy Innovation

Abstract

Attempts to quantify and measure migration in India have proved inadequate as far as policy change is concerned. The Census of India 2011 and the National Sample Surveys (particularly the 64th Round conducted between 2007 and 2008), while useful, are too infrequent and temporally limited to offer insights into real-time movements and seasonal migration. On the other hand, even when data is available, it can be difficult to use it to understand future patterns and reactions to policy changes, as the COVID-19 pandemic-induced crisis showed. It was not just the number of migrants residing in major cities that was unanticipated. The government and local authorities also failed to anticipate the impetus for migrant labourers to move back to their hometowns once their revenue streams were cut off. This suggests that the government had not considered the subjective, emotional concerns that migrants had about their well-being, and consequently failed to predict how they would react. This article argues that a paradigm shift towards a deeper, more wide-ranging use of qualitative-interpretive methods is in order if we are to obtain a fuller picture of migrant needs and actions. This qualitative data may then be used in tandem with surveys, enumerations and quantitative studies to predict both the quantum and the direction of migrant movements, actions and reactions. These methods are, therefore, not just supplementary, but critical policy tools in themselves, allowing policymakers to craft interventions and responses that account for the subjective considerations surrounding migrant mobility.

Introduction

In March 2020, millions of migrants in India congregated at bus and railway stations, clamouring for a way home after the government had announced a strict lockdown. One element that underwrote the government’s chaotic response that followed was a lack of quantitative data. It was the number of migrants that was unprecedented. This is not surprising, given that the most comprehensive source of data on migrants came from the Census of India in 2011, putting the number of migrants at about 450 million (Government of India, 2011a, 2011b). Operating with data from nine years prior, it is unsurprising that any sense of the scale and magnitude of migrant mobility was relatively unknown at the time that the pandemic hit. Other quantitative sources on migration data were also relatively dated, and none matched the precision of the census. For example, the Economic Survey of India put India’s interstate migrant population at ‘about 60 million’ and its inter-district migrant population at ‘as high as 80 million’ and used data from the Indian Railways to put the average annual flow of people between states at 9 million people (Government of India, 2017).

In previous work, I have argued that these sources are imprecise, dated and partial in scope (Rajan et al., 2020), which limits the capacity of both the union and state governments to frame an informed, comprehensive migrant welfare policy. The intensity of the crisis, which prompted large-scale movement across the country by any means possible, thus, elicited a haphazard, incomplete response whose roots primarily lay in a lack of authoritative quantitative information (for a summary of key migrant data sources, see Rajan & Bhagat, 2021). Although several scholars have pointed out the characteristics of India’s internal migrants and extracted incredible value from the data available (for a comprehensive compilation, see Rajan & Sumeetha, 2020), the sources are generally acknowledged to be limited. It is near-impossible to paint a full picture of the quantum of India’s migrant population given the vast proportion of informal labourers, temporary migrants, circular migrants and seasonal workers. These populations are intrinsically illegible to state authorities, and their movements are often too sporadic to track consistently (Deshingkar, 2021).

It is critical to remember, however, that a lack of quantitative data is only one side of the story. The massive attempted exodus had not been accounted for at all, and it was clear that the government’s attempt at a strict lockdown had completely failed to factor in migrant needs. Moreover, the government’s policy to stop all mobility only served to incite further panic, with increased crowds at transit points, frantic attempts to return home resulting in accidents and heartbreaking reports of official apathy towards lives cut short (Iyer, 2020; Savelife Foundation, 2020). Later, the government even suggested that compensation for the families of migrants who died during the COVID-19 pandemic was not in order because there was ‘no data’ on those deaths (Prabhu, 2020). It took until May 2020 for the government to begin organising return transport in earnest, with ‘Shramik Special’ trains ferrying 5.8 million migrants to a number of sending states across India (Press Information Bureau, 2020a, 2020b). Between March and May 2020, the plight of India’s internal migrants worsened exponentially, even as India and countries across the world dealt with the most significant public health crisis in decades.

At the time, I wrote about how the government not only had failed to account for the number and real-time mobility of migrants but had actually displayed a deep apathy towards their subjective fears, aspirations and policy concerns (Srinivasan, 2020). This apathy cannot be explained only by a lack of quantitative data on mobility, just as the overwhelming desire of migrants to return to their hometowns cannot be understood as an ‘irrational’ response to a near-total restriction on mobility. Had migrants trusted the authorities to serve their interests and address their vulnerabilities, they would have responded to the numerous arrangements and appeals from administrators, such as Delhi Chief Minister Arvind Kejriwal’s fervent appeal to migrants to stay put, and his use of schools and stadiums as night shelters (Barman, 2020; Khanna, 2020). A deeper reflection on the crisis merits a different perspective—one that accounts for the subjective, interpretive considerations underlying the government’s apathy, the reaction of migrants and the exacerbation of difficult circumstances.

In this article, I argue that it is not just (a lack of) data in general that worsened the crisis, but also a poor qualitative understanding of what drove crisis response at the time, on the part of both the authorities and the migrants in question. As we reflect on policy lessons from this tumultuous episode, I suggest that qualitative data-collection methods—such as participant observation, interviews, ethnographic theory-building and several others—have an important role to play. Used in conjunction with quantitative demographic data, these methods could allow us to predict, respond to and, perhaps, prevent another crisis in the future. In other words, our approach to migration policy might benefit enormously from jettisoning a total attachment to a ‘legal-rational way of getting things done’ (Darrouzet et al., 2022), and leaning into interpretive methods as a means of understanding the subjective aspirations and vulnerabilities of India’s migrants. Even if we lack the exact numbers that could inform a comprehensive policy response, we could benefit from clear pointers on which responses should demand maximum priority. A ‘ear to the ground’ approach could, thus, help overcome some of the policy challenges associated with gathering accurate demographic data on mobility in a country as complex as India and enable improved, scalable crisis safeguards for migrants. In the following section, I briefly examine some of the subjective sociological factors that may have exacerbated the crisis beyond a lack of accurate quantitative data.

Understanding the Subjective Concerns of India’s Migrants in Crisis

When the most significant imponderable during a crisis is mobility, why move? At the time that the COVID-19 virus first forced India into a lockdown—in March 2020—this seemed to be the basic question confronting scholars of migration. Here was a virus whose spread, effects and future trajectory were completely unknown at the time. Recall that the now-common COVID-19 vaccine would only become available to the general public over a year later, in May 2021. India—indeed, nations worldwide—had never seen such sweeping restrictions on mobility in peacetime. At a time when everything was deeply uncertain, only one maxim remained: wherever you are, stay put. In that context, the enormous crowds of labour migrants at bus terminals, train stations and other transit points seemed to belie basic rationality. Why the sudden anxiety and the irresistible impetus to return to hometowns, despite the danger?

To understand the now-infamous ‘migrant crisis’ that ensued, we must move beyond accepted notions of rationality and interrogate the burdens that migrants faced at the time. There is broad scholarly consensus that migrants in India were more susceptible to intense mental health challenges wrought by the uncertainty and unpredictability of the COVID-19 pandemic (Chander et al., 2021; Ministry of Health and Family Welfare, 2020; Rajan et al., 2022; Thatavarthi et al., 2021). Given that migrants are in partially or fully unfamiliar spaces, employed (in several cases) as informal, uncontracted workers, financially insecure and bereft of institutional support for mental health concerns, this is eminently understandable. On the one hand, an obvious policy response is to expand messaging on mental health and make high-quality counselling services available and accessible (Rajan et al., 2022). However, that would only be a mitigation measure, intended to treat the symptoms instead of the root cause. On the other hand, it stands to reason that the anxieties of migrant workers—and the consequent desire to return to their hometowns—stemmed from the government’s seeming apathy towards this enormous, important group (Bhagat et al., 2020). The authorities, though, had moved quickly to reassure migrants that their needs would be addressed and asked them to stay put—a response exemplified by Chief Minister Arvind Kejriwal’s announcement guaranteeing food and shelter for migrants in Delhi. Why, then, were migrants unconvinced?

I argue that the answer to this question lies in understanding the narrative that the initial apathy had generated amongst migrants. The desire to return to hometowns—presumably for basic sustenance amongst job, food and housing insecurity—was a constant during the summer months of 2020. Had the policy responses been adequate, a basic understanding of causal policy chains and cost–benefit ratios would have led us to believe that the migrants would stay put. Migrant workers were just as aware as the general public that COVID-19 was highly transmissible, dangerous and best countered by limiting interpersonal contact of all kinds. A basic evaluation of costs and benefits, under this rationale, should have spurred migrants to stay put, at least temporarily. Yet, the costs of staying put—uncertainty, financial insecurity and institutional apathy—were outweighed by the benefits of returning to hometowns. This means that the government’s evaluation of costs and benefits deviated from that of migrant communities. If the deviance cannot be explained using the standard logic of the cost–benefit ratio of ‘evidence-based’ policymaking, another lens is necessary: that of the policy narrative, driven by qualitative-interpretive methodological approaches. Herein lies both an explanatory framework for the deviance and a possible policy framework for future evaluations in crisis situations.

Methodology and Summary

In the following sections, I make the case for interpretive methods as policy tools. First, I briefly review some of the literature on interpretive approaches, highlighting the implications for questions of rationality, costs, benefits and predictability. In particular, I propose that even analytical models that foreground ‘rational’ choice inevitably do so within specific contexts of rationality. I then apply a narrative analysis to the events of the summer of 2020, outlining the overarching narratives that emerged from the government’s lockdown efforts and the subsequent reactions of migrants in India’s cities. I articulate three narratives: the narrative of forgetting, the narrative of apathy and the narrative of distrust. Broadly, these narratives, beyond being merely stories about one’s circumstances, articulate a coherent version of events and bind governance to lived realities. This reveals the need for interpretive understandings of decision-making that account for contextual flexibility. Subsequently, I provide an overview of the various uses of interpretive policy tools, stressing their utility for policy formulation when the quantum of migration or migrant populations is poorly understood. I conclude by summarising the case for interpretive tools as turnkeys for policy analysis. In doing so, I highlight an example of a method (narrative analysis) and shed light on others that could yield deeper policy insights in the absence of reliable quantitative data.

Framing the Narrative of ‘Forgotten’ Migrants

Modern scholarship on public policy questions the supremacy of the cost–benefit analysis as a policymaking tool on several grounds. The supremacy of causality in the policy chain, which presumes that policymakers and the general public will act rationally, is questionable. Causation seems to suggest ‘an airtight level of determination that simply isn’t warranted in the world of human action’ (Wagenaar, 2007). Thus, the idea that movement could ‘cause’ the spread of COVID-19 and, consequently, that ‘rational’ migrants would choose to stay put is perhaps too simplistic. ‘Rational’ choice is itself a questionable assumption, as are notions of instrumental action, self-preservation and maximised benefit (Griggs, 2007). Herbert Simon, in his seminal work on public administration, coined the term ‘satisficing’ to explain the process by which individuals might make decisions in the context of limited information (Simon, 1947, 1956). The idea is that human beings are incapable of envisioning, comprehending and evaluating the full set of choices available to them and, therefore, optimise by making choices within the constraints involved. Like most decision-making processes, satisficing is a heuristic, or a means of simplifying and condensing a complex body of information. The implication here is that even the logic of ‘rational’ choice is not airtight; rational choice is framed by context, constraints and social-psychological extraneous factors (Dunn, 1988). If one considers the immense impact of the stress wrought by the COVID-19 pandemic on migrants, the disproportionate impact on their mental health and the unpredictability of the short and medium term, the ‘legal-rational’ (Darrouzet et al., 2022) approach to policymaking and policy reception appears downright inadequate.

One way of envisioning the gap between rational choice and observed action is to understand the narrative or discourse surrounding the initial stages of the COVID-19 pandemic. Scholars have argued that qualitative-interpretive methods are critical to policy evaluation and analysis (Fischer, 1995, 2003; Yanow, 2000, 2007). By interpreting the narratives and discourses surrounding policy frameworks, one can understand the context of what is ‘rational’ in a given policy circumstance. This makes policy intervention more meaningful, as it affords policymakers the chance to tweak their actions to suit contextual limitations.

One strain of this literature focuses specifically on narratives as stories, and policy intervention as a form of discursive storytelling by authorities (Rhodes, 2018; Roe, 1994). Rhodes argues that, within the ‘narrative policy analysis’ framework, policy is contested, contingent and constructed (Rhodes, 2018). Understanding the impact of policy is, therefore, best served by ethnographic observation, interviews and immersion. Roe (1994) views narrative as a ‘story’ and sees policy as an interaction of stories, counterstories and the metanarratives they create. In other cases, scholars have articulated ‘narrative subscription’ (Miller, 2019) to justify alignments and differences between target populations and policy frameworks. Yet other scholars attempt to bring a form of generalisability to this analysis of stories by breaking them down into consistent components, such as setting, characters, a plot and a moral (Jones et al., 2014). This generates a ‘science of stories’ (Jones et al., 2014) which offers comparability (Jones & McBeth, 2010). Given the inherent open-endedness of interpretive analysis, this approach loses out on some of the creativity and analytic capacity of ‘true’ qualitative-interpretive analysis methods. It seems unlikely that breaking down fluid, socially contingent policy contexts into consistent components is a ‘scientific’ move that might impart generalisability. Nonetheless, it is evident that scholarship on interpretive policy analysis has considered multiple forms of explanation that are not tied to neat, causal policy chains.

Viewed as such, it is suffice to say that policy implementation is not an exact, rational science. It is also a craft that works to manipulate public narrative and perception, convey authority and incentivise compliance (Dunn, 2013; Majone, 1989; Fischer & Forester, 1987). Crucial to the COVID-19 crisis, however, is the inverse: that policy may be created with one narrative or set of goals in mind, but end up reflecting quite another. In crisis situations, these dynamics are fast-moving, unpredictable and difficult to control. Narrative creation is, thus, a double-edged sword. During the COVID-19 crisis, attempts to incite conformity to the lockdown, juxtaposed with an apparent apathy for migrant needs generated a contradictory narrative. Thus, trust—a fundamental motivator for any sort of policy compliance—was broken. In subsequent paragraphs, I examine some of the factors that spurred the narratives that harmed trust, and the potential impacts on policy implementation vis-à-vis migrants.

The first factor to consider is the evident surprise of the government at the crowds of migrants at transit points. This was reflected in the unusually large crowds, limited transport measures, repressive policing and crowd management and fervent appeals by administrators to remain in place. The context suggested that the government, in instituting a lockdown that fundamentally affected mobility, had entirely forgotten the mobile. The basis of this overstep did not lie, primarily, in a lack of quantitative data. Although the census and National Sample Survey data on migrants was outdated by this point, the data on migration within these sources was still available to administrators and would have allowed them to craft migrant-centric policy interventions that could assist lockdown efforts, however limited. The surprise at the upswell at transit points seemed to reveal a deeper, more concerning issue: that migrant workers were not a factor at all in lockdown preparations. Naturally, this would create anxiety that the government did not have their best interests at heart and had crafted the lockdown from a ‘sedentarist’ perspective. To seek the social, institutional and emotional support required, migrants would need to return to their hometowns. This ‘narrative of forgetting’ of the migrants adds some depth to the basic argument that migrants had no incentive to stay in unfamiliar, precarious spaces where basic sustenance and income was not guaranteed (Duggal et al., 2022).

One might also consider, equally, the reaction of the government. Because crowds were deemed a threat, the government reacted by dispelling crowds at transit points, policing those walking home on foot and belatedly offering migrant workers last-minute incentives to stay put. It took a few days for state governments to react, and responses were not coordinated. For example, the Delhi government pushed buses into service to drop migrants to the border of the city, ‘expecting’ the Uttar Pradesh government to handle transport from there (News 18, 2020). This suggests that, in a crisis situation, disparate authorities are not prepared to take collective responsibility for vulnerable groups. Given the fluidity of the situation, attempts to journey back to hometowns on foot or in makeshift transport arrangements, despite the danger of road accidents and extreme heat, were not altogether infeasible. Had the government—whose attention was squarely on mitigating crowding—chosen to understand this logic from an ‘ear on the ground’ perspective, this ‘narrative of apathy’ might have been understood more clearly, prompting more sympathetic messaging and coordinated policy responses. However, the nature of the pandemic and its unpredictability made such an investment impossible. Both the government and the migrants reacted out of panic. However, the panic of the government also communicated that its priorities were elsewhere; specifically, on forcefully restricting movement and crowding, not on facilitating movement in the short term to assuage concerns and balance risk in the medium to long term. This demonstration of apathy, thus, generated a visible public narrative that framed the lockdown as an inherently unequal policy measure.

Finally, these chaotic circumstances came together to create a ‘narrative of distrust’. As state governments and the centre began to realise that transport measures would prove inadequate, they responded with harsh crackdown measures and attempted to shelter migrants in situ wherever possible. The Home Ministry, for example, circulated an order that offered protection from eviction, paused rent collection and kick-started the construction of temporary shelters (Ministry of Home Affairs, 2020). However, what migrants experienced was a juxtaposition of their importance to urban life and economy upon their patent exclusion from the right to the city (Attoh, 2011). Moreover, those who did manage to return home faced a double vulnerability: Having survived such arduous ordeals during the journey, they also faced the ignominy of suspicion and discrimination in their hometowns. This was exemplified by an infamous incident where migrants were sprayed with a ‘disinfectant’ in Bareilly, Uttar Pradesh (BBC News, 2020). The ‘moral outrage’ (Krishnan, 2020) amongst India’s prosperous middle class did little to assuage what observers have called a ‘deep distrust of the state’ (Mukhopadhyay & Naik, 2020). In fact, the relative ‘data invisibility’ of migrants to state governments through the census and other evaluation tools offers some insight into their invisibility as a group in general. The government’s oversight in not factoring in migrants at all, leave alone at such a large scale, was complicated by the disinformation and advice surrounding COVID-19. Governments rapidly changed protocols, re-evaluated how the virus spreads and extended lockdowns without warning, the latter being an extreme measure almost never seen in peacetime India, and certainly not since the 1975 Emergency. In such an environment, a distrust of the state was amplified along with the paranoia around rising cases, pushing migrants to return to hometowns where stability and familiarity would outweigh the costs of staying put in places that were already short-term or circular migrant destinations, to begin with. Finding safe passage to hometowns, despite the danger, was likely both a response to the narrative of distrust and a self-preservation measure. As nations around the world have resumed free movement and vaccines are widely available, it is easy to forget the intensity and unusualness of the national lockdown. These were unpredictable times, and they exacerbated class differences significantly.

It bears mentioning that this distrust was not a feature of the crisis everywhere. Kerala, for example, was lauded for its humane and comprehensive response to the crisis—an observation backed by first-hand migrant experiences (Rao et al., 2020). The provision of ration cards, healthcare, accommodation and other support measures by employers and the government helped assuage concerns and ensured that migrants could stay put without facing undue hardship. It is, therefore, critical to note that policy narratives are flexible and, as Rhodes (2018) argues, ‘contingent’. Understanding these contingent narratives analytically involves both a deep attention to discursive context and information from the ground. This is why, in crisis situations, policy can be informed by interpretive fieldwork, journalistic reportage and on-ground policy enforcement and evaluation teams. The possibilities of this change in approach for the creation and analysis of migration policy are offered in the following section.

Using Interpretive Tools to Frame Migration Policy

As the analysis above suggests, policy creation and evaluation are not linear processes based on rational actors and causal chains. They are also rooted in contingent circumstances, public messaging, administrative choices and political exigencies. Modern policymaking vocabulary, such as ‘evidence-based’ or ‘empirical’, usually serves as a proxy for quantitative data. These method-neutral terms mask the dominance of quantitative data as the basis for policy framing. Interpretive narrative data, although qualitative, is also based on empirical realities. When strengthened by fieldwork, interviews and ethnographic observation, these narrative analyses can become powerful tools for policy creation. Migrant workers suffer from a series of specific vulnerabilities, and these were elevated during the COVID-19 pandemic—a state of exception—to catastrophic extents, creating what has been called ‘pandemic citizenship’ (Chowdhory & Poyil, 2021). Surveys may help policymakers understand which vulnerabilities are most prevalent, but predicting a consequent reaction, especially in fraught circumstances, can be much more difficult. This is where narrative analyses can complement survey-based data and cost–benefit analyses by contextualising vulnerabilities across time. They also offer the benefit of extended, subjective insights into the fears and opportunities migrants perceive within crisis surroundings. For example, a survey might tell us that access to food is the primary problem faced by migrants. An interview, or ethnographic participant observation at a shelter, might tell us that the consequence of this vulnerability would be fear, desperation and/or a search for food or a return home. Similarly, even though the census might tell us how many migrants we might expect to find in a given city, we have no way of knowing how and when they might choose to move back to their hometowns unless we have some insight into the subjectivities that spur reactions to crises. Mixed-methods policymaking, which employs both quantitative-statistical data and interpretive tools, thus, allows us to predict both the quantum and the direction of migrant flows. This is especially important in the context of ‘invisible’ informal migrants and vulnerabilities associated with particular demographic characteristics. For example, if we understand gender vulnerability—a critical factor in marginalisation—not just as a problem of access, but also as a distinct set of psychosocial anxieties of those ‘on the margins’ (Neetha, 2016), we improve the ‘evidence’ base that could drive evidence-based policymaking.

Although short-term, seasonal and circular migration are well-known features of the migration landscape in India, patterns are difficult to track. It is, thus, difficult to know how many migrants are in a given city at any point in time. The census only provides a once-in-a-decade picture of internal migration that is not dynamic; in other words, a ‘stock’ perspective of a point in time, not a ‘flow’ perspective of change over time. Some schemes, such as ‘One Nation, One Ration Card’ and the tethering of Aadhaar numbers to several services, help with data collection, but a true idea of the quantum of migrants across categories is near impossible to attain. However, given the paucity of data—but the availability of a fairly accurate picture of migrant-heavy industries and labour sites—it is not actually the number of migrants that is most helpful to policymaking. We know where migrants go, but not when, or in what numbers. It, therefore, helps policymakers more if they are aware of the intentions and reactions of migrants in crisis situations and the resources likely needed to assist them. Resources such as food rations and buses can be mobilised pre-emptively, and consequently scaled up or down depending on the emergent nature of a crisis. Thus, even without a clear picture of the quantum of migrant flows, an interpretive understanding of subjective reactions to crisis situations can help ensure that the right support resources are available.

At the national level, interpretive data from multiple source and destination sites can be analysed as a whole to identify trends that inform migrant reactions. For example, vignettes are well-established as interpretive tools that help ascertain attitudes and reactions to situations. A vignette is a short story or scene presented to a person of interest that helps the analyst understand how a respondent would react to an unfolding situation. Vignettes have been used in diverse analytical contexts—from health policy (Mah et al., 2014) to police traffic stops (Phillips, 2009). One concrete way to innovate on migration policy is to present a vignette to migrants at source and destination at various locations and identify how migrants would react to changes in income, weather, family circumstances and so on. This can then be aggregated with data from other locations and time periods to form concrete policy recommendations, such as special cash transfers, transportation arrangements or new benefits. Vignettes elicit nuanced evaluations of changing circumstances, and an analyst who is trained to interpret these evaluations can then extrapolate broader trends.

Similarly, ethnography and participant observation can reveal social dynamics that can help policymakers predict reactions to change. Thachil (2017) uses participant observation and survey instruments to evaluate ethnic divisions among labour migrants. His methodology leverages the strengths of both ‘being there’ (observation) and quantitative data to predict triggers for division, based on interpretive techniques. Therefore, interpretive approaches can help ‘firm up’ quantitative data analysis by underlining the social dynamics that are generally observed. Pre-emptive crisis response stands to gain considerably from the texture afforded by interpretive analyses of migrant behaviour.

The upshot of these arguments is that narrative analysis is not just about deconstructing the stories surrounding migrant vulnerability. The method also offers genuine avenues for policy creation, implementation and correction. Embedded fieldwork and interpretive understandings of vulnerability help connect crisis response to principles of human dignity—specific responses for specific concerns—as a hallmark of empathetic, effective policymaking. When used as part of a broader mixed-methods policy evaluation framework, it holds immense possibilities for long-term policy improvement.

Conclusion: Integrating Qualitative-Interpretive Approaches into Migration Policy in India

This article re-examines the 2020 migrant ‘crisis’ precipitated by the COVID-19 pandemic, arguing that a bad situation was exacerbated by poor policy management and a series of negative narratives surrounding migrant support. Those outcomes were not just indicative of a lack of quantitative data, but also a poor understanding of migrant self-perception and the consequent reactions to narratives of forgetting, apathy and distrust. The article also offers a brief introduction to qualitative-interpretive policy analysis methods, elaborating upon their benefits and potential. These methods, when integrated into our broader frameworks for migration policy and combined with quantitative data, could enrich our policy responses immeasurably in future crisis situations. Moreover, they enhance human dignity by foregrounding subjective understandings of crisis as the basis of policy response.

Qualitative-interpretive methods, especially participant observation, are time-consuming. Feasibility may vary across contexts, and it may be especially difficult to access vulnerable groups. However, by integrating the lived realities of migrants into policymaking, these methods offer a more holistic perspective on migrants and migration. Moreover, even in limited circumstances, they can offer critical insights into discourses on the ground, thereby informing specific policy response measures and differentiated planning. Although India is in the process of collecting data on migrant workers (Press Information Bureau, 2022), the difficulty of tracking travel for informal work and the true quantum of workers in cities and agricultural tracts at any given point is considerable. It, therefore, benefits policymakers to rely on local context, invest in understanding subjective concerns and frame policy narratives that are specifically intended to alleviate those concerns. In addition to improving policy, this approach also serves to uplift human dignity and integrate an ‘ethic of care’ (Tronto, 1993) into an administrative praxis that is often detached and ineffective in its priority-setting.