Numerous structural vulnerabilities put developing regions at a disadvantage as they confront the prospect of increasingly frequent extreme shocks. Typical of these regions, South Asia had several characteristics that suggested it would be badly hit by COVID-19: a sizeable informal sector, growing inequalities in access to health services and social protection, and high levels of hunger and malnutrition. This Special Issue focuses on the South Asian experience through COVID-19 and distills forward-looking lessons for the developing world. Included papers point to the importance of strengthening individual resilience, building basic infrastructure and institutional capacity, and implementing inclusive social protection measures.
On May 5, 2023, the World Health Organization declared that COVID-19 ceased to be a global health emergency. The pandemic dominated global policy discourse for more than two years following the first quarter of 2020 when the health and economic impacts of the virus began to affect countries worldwide. Concerns around viral transmission and overburdened health systems caused many governments to adopt strict containment strategies to slow the spread of the virus, including direct restrictions on the movement of people and goods and the enforcement of COVID-19 protocols such as social distancing and mask adherence (Hale et al., 2020). The resulting slowdown in economic activities, particularly those deemed non-essential or that relied heavily on migrant workers, transport, and traded inputs, caused immense distress. Unsurprisingly, across the world, many of the poorest and most marginalized were particularly hard-hit (Bottan et al., 2020; Bundervoet et al., 2022; Desai et al., 2021).
Within this broader context, countries in South Asia emerged with both successes and severe challenges, yielding a range of lessons for the future. Early estimates predicted that, all else equal, a 5% contraction in per capita incomes induced by the pandemic could push more than 80 million below the US$1.9/day poverty line globally, with the bulk of the potentially new poor concentrated in Sub-Saharan and South Asia (Sumner et al., 2020). The World Bank’s South Asia Economic Focus report from the Fall of 2020 stated that South Asia was facing its worst-ever recession and estimated that regional economies would contract drastically, from 9.6% in India to 19.5% in the Maldives (World Bank, 2020). Despite this, the report concluded that the South Asian economies were “beaten” but not “broken”.
At the start of 2021, it appeared that this reading was accurate, with some sectors, especially agriculture, proving resilient to the pandemic. Even as overall growth faltered, the agricultural sector in several countries performed well (see for example, Government of India, 2022), bolstered in part by strong monsoon and winter season harvests. This, combined with low official rates of infection and COVID-related deaths and a ramping up of expenditure on social safety nets, raised hopes of an early economic recovery. Unfortunately, the devastating second wave of infection from March 2021 brought a spike in rates of infections and death and the reinstitution of lockdown-type restrictions across the region. Even as the pandemic retreated after the second wave, the emergence of the Omicron strain of COVID-19 suggested persistent economic stress, casting a long shadow over recovery.
This Special Issue brings together a diverse collection of 14 papers on various aspects of the COVID-19 crisis in South Asia that speak to the remit of Applied Economic Policy and Perspectives. Collectively, these papers address themes such as agricultural value chains, food security and vulnerability, coping strategies of rural households, social protection, and macroeconomic impacts, with evidence from Bangladesh, Nepal, India, Myanmar, and Pakistan. Khan et al. (2023) use bilateral migration flows between countries to model the spatial spread of the virus and develop a risk index that can be used to target resources and containment efforts. Gupta et al. (2023) and Kabir et al. (2023) investigate the impacts of COVID-19 on agricultural value chains in India and Bangladesh respectively and identify the correlates of recovery. Kharel et al. (2023) document the key role of migration and remittances as coping strategies of rural households in Nepal and Bangladesh; Siwach et al. (2023) note the role of women’s collectives in India. Other papers highlight the role of social protection, including cash transfers in Bangladesh (Ahmed et al., 2023), a rural workfare guarantee in India (Varshney & Meenakshi, 2023), and a livestock transfer program in Nepal (Ekstrom et al., 2023). Two papers provide an assessment on the impact of the pandemic on vulnerable groups: Seager et al. (2023) examine adolescent hunger in Bangladesh, and Ramachandran and Deshpande (2023) discuss the heterogenous impacts of the pandemic on employment outcomes in India. Finally, Boughton et al. (2023), Davies et al. (2023), Ecker et al. (2023) and Dorosh et al. (2023) use modeling approaches to provide sectoral or economy-wide insights into the medium- and longer-term economic impacts of the pandemic, including when coupled with other shocks as was the case in Myanmar. Boughton et al. (2023) and Davies et al. (2023) further explore the role and distributional consequences of social protection.
This Special Issue highlights two broad sets of learnings. COVID-19 revealed challenges researchers face in producing credible and timely inputs to guide the management of large-scale shocks; this introduction describes the diverse strategies that researchers used to overcome these challenges. We then discuss five consistent policy lessons that emerge despite considerable diversity in geographical scope, empirical approaches, and the types of data employed. First, risk-sharing and consumption-smoothing devices that help individuals, households, and enterprises tide over idiosyncratic shocks may fail to protect them in the face of larger more correlated shocks as posed by the pandemic, but building household resilience can stand them in good stead. Second, recovery is faster when the quality of infrastructure is better and when public and private institutions are robust. This includes state capacity to roll out and administer safety nets at short notice. Third, broad social safety nets are indispensable in mitigating the negative impacts of pandemic-scale shocks, even if they fell short of fully mitigating the economic impacts of COVID-19. Fourth, without active attention to social inclusion large-scale social protection programs may accentuate pre-existing inequalities. Fifth, multiple simultaneous crises can compound impacts and restrict policy options. In South Asia, Sri Lanka faced a massive macroeconomic crisis, Myanmar witnessed a military coup, and Pakistan found itself in a governance gridlock; each of these complicating conditions magnified the consequences of the pandemic.
After providing a brief background on South Asian economies in the context of COVID-19 in the next section, the third section of this paper indicates the range of methodological options for addressing research challenges related to pandemic-like shocks by highlighting constraints researchers of COVID-19 in South Asia faced and how they overcame them. Section 3 examines the five lessons we extract from the South Asian experience which may have relevance for future shocks in that region and elsewhere. Section 4 concludes.
SOUTH ASIAN POLICIES DURING THE PANDEMIC
South Asia encompasses large and small economies with varying levels of economic and social development and divergent degrees of state capacity. Despite emerging as among the fastest-growing regions in the world in recent decades, the region also faces challenges of hunger, nutrition, and significant inequalities (Table 1). Even within India, there are states with social indicators comparable to countries in the developed world and others that are consistent with the poorest and worst-performing national economies (Drèze & Sen, 1999). South Asian economies differ in the extent to which they depend on global capital, trade, labor flows, and remittances. Though participation in global value chains tends to be low, some countries have sectors that depend heavily on exports, such as garments in Bangladesh or tea in Sri Lanka (World Bank, 2020). Some have achieved food self-sufficiency and others continue to depend heavily on international trade for food supplies. Further, whereas South Asian countries share a similar trajectory of structural transformation, with the tertiary sector dominating in GDP shares, the extent to which this is the case varies significantly (Table 1). Most of them also have substantial rural populations and large informal sectors.TABLE 1. A profile of South Asian economies.
|Total population (in Millions as of 2021)||166.3||1393.4||29.7||225.2||22.2||0.5||0.8||54.8|
|Number of poor (in Millions)b||21.3||136.8||2.2||10.5||0.3||0.0||0.0||1.1|
|Poverty Headcount ratio (at $2.15 a day)||13.5||10.0||8.2||4.9||1.3||0.0||0.9||2.0|
|Female literacy rate||72.0||66.0||63.0||46.0||92.0||98.0||63.0||86.0|
|Infant Mortality rate||22.9||25.5||22.8||52.8||5.8||5.1||22.5||33.7|
|Child stunting rates (% of children under age 5)||30.0||30.1||30.0||36.7||16.0||14.2||22.4||25.2|
|Per capita income (in $)||2457.9||2256.6||1208.2||1505.0||4013.7||10366.3||3266.4||1209.9|
|Trade Opennessa (%)||36.8||38.3||54.9||29.6||52.4||147.0||79.4||61.4|
|Trade as % of GDP||27.7||45.3||43.8||27.1||41.1||143.1||84.6||53.1|
|Primary sector share in GDP||11.6||16.8||21.3||22.7||8.7||5.3||19.2||23.4|
|Secondary sector share in GDP||21.2||14.0||4.7||11.9||17.9||1.9||5.9||24.8|
|Tertiary sector share in GDP||51.3||47.5||52.6||52.1||55.1||73.2||44.0||41.5|
|Informal sector employment (share of employed, and/or absolute numbers)c||94.7||89.1||81.6||84.3||67.2||48.1||n/a||81.0|
|Proportion of population living in urban areas||38.9||35.4||21.0||37.4||18.9||41.1||43.0||31.5|
|Hospital beds per 10,000 population||8.0||5.3||3.0||6.3||41.6||43.0||17.4||10.4|
|Health expenditure as % of GDP||2.5||3.0||4.5||3.4||4.1||8.0||3.6||4.7|
|Official COVID-19 deathsd||29,446||531,152||12,025||30,656||16,835||311||21||19,490|
- Note: “n/a” indicates data is not available.
- a Data on trade openness is from https://ourworldindata.org/trade-and-globalizationzation – Our World in Data.
- b Data on the number of poor people is from Poverty Data Explorer of World Bank data – Our World in Data.
- c Data on the informal Sector is from Statistics on the informal economy – ILOSTAT. All other indicators are from the World Development Indicators (WDI) database, accessed on 04/19/2023. The numbers reported here are for the latest year for which data is available on each indicator (see Appendix S1 for details).
- d Data on the official number of COVID-19 deaths is sourced from WHO Coronavirus (COVID-19) Dashboard (Link: https://COVID-19.who.int/data). Source last updated: 04/19/2023.
In 2020, as the first cases of COVID-19 emerged, South Asian governments took swift and strong measures, usually in the form of mobility restrictions to slow the spread of infection or “flatten the COVID-19 curve”. Relative to the global average, South Asian countries tended to have more severe restrictions than the global average (Figure 1).
In addition to lockdowns, South Asian Governments employed external trade policies both to support the availability of medical and testing equipment and to regulate the domestic availability and prices of strategic commodities. Evenett et al. (2021)’s compilation of trade policy changes before and after January 2020 indicates considerable policy churn post-pandemic (Figure 2). Unlike the highly restrictive trade policy response to the global food price spike of 2008–09, when kneejerk protectionist policies led to a Great Trade Collapse, the response to COVID-19 was more measured (Baldwin and Toimura, 2020). Though South Asian countries clearly leveraged trade policies in the face of the pandemic, policy changes were not consistently or overwhelmingly restrictive or protectionist.
Beyond mobility and trade policies, most countries in South Asia introduced social protection measures, with many governments leveraging existing programs to either expand eligibility or expand benefits for those identified as eligible. By February 2022, the six largest countries in South Asia had implemented 159 Social Protection and Labor measures. An estimated 79% of aid disbursed was in the form of social assistance and 11% each was directed to social insurance and to labor market assistance (Almenfi & Iyengar, 2022). These countries had ramped up expenditure on assistance to 1.8% of Gross Domestic Product in 2021–22, compared to 1.13% of GDP during 2020–21.1 While some countries scaled up the value of such assistance substantially, others extended benefits without necessarily scaling up their value (Gentilini, 2022). Despite the rapid and significant expansion, in general, these measures were inadequate (see Drèze and Somanchi (2021a) for a discussion of India). Overall, the South Asia region spent US$46 per capita (unadjusted for Purchasing Power Parity) relative to the US$314 per capita in the 194 countries for which these data were compiled (Almenfi & Iyengar, 2022).
These three broad sets of policy reactions – restrictions on mobility within and between countries, changes in trade policy, and the ramping up of social assistance – in addition to public health interventions like vaccination programs, set the context for researching the economic, social, and nutritional impacts of COVID-19 and the trajectories of recovery.
RESEARCHING COVID-19 IN SOUTH ASIA: DATA AND METHODS
The papers in this Special Issue exemplify the formidable challenges to providing timely and credible analysis of the impacts of and recovery after COVID-19. In this section, we provide an account of these challenges and how contributors to this collection addressed them.
Many South Asian countries field routine surveys that generate data on household expenditure, prices, trade, and formally registered enterprises.2 Nonetheless, high-quality, nationally representative data were either dated or not available at a frequency necessary to assess the short- and medium-term impacts of COVID-19. In the case of India, for example, the most recent household expenditure survey is from 2011−12. Most national statistical agencies rely on in-person methods of data collection, efforts that were interrupted or delayed on account of COVID-19-related restrictions. The lack of data is particularly acute when it relates to individuals or entities that fall outside the formal sector; and as discussed above, almost all South Asian countries have large informal sectors comprising casual workers and unregistered enterprises that are not systematically tracked even in ‘normal’ times. A few surveys by trade bodies, civil society organizations, and researchers emerged during the pandemic to fill this gap, but they were often restricted to specific geographies and sub-populations.
Given this paucity of data, those researching COVID-19 and its impacts employed a variety of methods, including innovative uses of existing datasets, conducting fit-for-purpose surveys, often by telephone, and building simulation models. Across the region, researchers relied on price and trade data, and information on the domestic market arrivals of agricultural commodities to assess impacts; papers in this Special Issue review these studies in detail. Other researchers made creative use of secondary data such as night-time lights, electricity consumption, air quality, and Google mobility indices to proxy economic activity, as outcome variables, and as proxies for policy restrictions on movement, social and economic activities (for example, Beyer et al., 2021; Ravindran & Shah, 2023). Researchers also leveraged data from private entities and web-based platforms to infer impacts on specific sectors (Lall et al., 2022; Mahajan & Tomar, 2021). A notable example of private sector data is the Consumer Pyramids Household Survey (CPHS) collected independently by the Centre for Monitoring the Indian Economy (CMIE), a private entity. Despite limitations regarding representativeness and methodological departures from official statistics (Drèze & Somanchi, 2021b), researchers of India have come to rely heavily on the CPHS, including the authors of three papers in this Special Issue (Gupta et al., 2023; Ramachandran & Deshpande, 2023; Varshney & Meenakshi, 2023). The unprecedented nature of the COVID-19 pandemic justified researchers’ efforts to extract meaning from limited data that were by no means ideal for economic analysis.
Beyond the creative exploitation of existing datasets, researchers benefited from investments by international agencies to fund specific surveys mapping COVID-19 impacts. For example, the World Food Program (WFP) rolled out monthly remote surveys to provide regular insight into household food security in Sri Lanka. USAID funded several studies of value chain disruptions across commodities and countries (Dejene et al., 2022), one of which is represented in this Special Issue (Kabir et al., 2023). Other international organizations also stepped in to collect data.
A bulk of the recent COVID-19-related research, including papers in this Special Issue, features primary data that researchers have collected on their own. These range from telephone surveys of carefully crafted representative samples to large-scale dipstick surveys, to the recording of narratives using community-led radio programming or personal diaries.3 Several papers in this volume rely on phone surveys of households and enterprises (Ahmed et al., 2023; Boughton et al., 2023; Kabir et al., 2023; Kharel et al., 2023; Seager et al., 2023), including some that re-interview respondents from previous rounds. Collectively, the diversity of metrics they use to measure hunger, poverty, income, and resilience illustrates both the potential and the pitfalls of phone surveys. However, where phone surveys were used to reach respondents previously covered by in-person surveys, the rate of response is typically quite low. Kabir et al. (2023) report that the response rate for shrimp value chain actors was only 57%–71% in their second round of surveys. Ahmed et al. (2023) note that their third round of phone surveys had a response rate of 71.9%. This means that researchers using phone surveys must carefully account for attrition bias and other data quality issues. The authors of phone survey-based papers in this Special Issue are acutely aware of the limitations of the data and discuss methods to ensure that inferences are nevertheless robust.
In the absence of rich data, researchers have relied on economic models to construct scenarios and predict macroeconomic impacts of COVID-19. Modeling approaches like Social Accounting Matrices and Computable General Equilibrium models can predict consequences for growth and distribution, sectoral impacts, and impacts across different types of households and enterprises. In this issue, Boughton et al. (2023), Davies et al. (2023), Ecker et al. (2023) and Dorosh et al. (2023) use these methods. Additionally, models of the spread of the disease itself can enable policymakers to better direct their limited resources. In this Special Issue, Khan et al. (2023) use international and internal migrant data for Bangladesh and Nepal to estimate the risk of COVID-19 incidence spatially, in the spirit of earlier papers by Lee et al. (2021) and Imbert (2020).
Related to the challenges of data availability, researchers have had to address impediments to causal inference. Because COVID-19 was a common shock, it proved difficult to construct appropriate counterfactuals and comparison groups to assess impacts. For sound ethical reasons, randomized experiments have been largely confined to designing interventions that could improve health behaviors, such as the use of masks or social distancing. Instead, much of the rest of the literature relies on non-experimental techniques. Papers in this issue follow that trend, adopting a range of statistical techniques such as difference-in-differences (Ahmed et al., 2023; Gupta et al., 2023; Siwach et al., 2023) or regression discontinuity designs (Varshney & Meenakshi, 2023) to establish causality. Others leverage the randomized allocation of resources pre-pandemic to uncover the longer-term impact of these interventions on households’ ability to cope with COVID-19 (Ekstrom et al., 2023). The rest rely on correlational analysis and refrain from making claims of causality (Kabir et al. (2023), for example).
FIVE POLICY LESSONS FROM COVID-19 IN SOUTH ASIA
The papers in this Special Issue offer perspectives on the pandemic’s effects in South Asia across issues such as agricultural trade and value chains, economic growth, food security, and social protection. The South Asian experience provides salient lessons for the future; we summarize these along five connected themes.
Households’ usual coping strategies fail during widespread covariate shocks
The COVID-19 pandemic represented a covariate rather than an idiosyncratic shock. This means that unlike, say, the theft of livestock, a shock that typically affects only a few individuals at the same time, the pandemic simultaneously affected many people. Several papers in this volume highlight that strategies that enable individuals, households, and enterprises to share risk, smooth their consumption over the course of the year and thereby tide over idiosyncratic shocks may fail to protect them in the face of covariate shocks, and could on occasion leave them more vulnerable than those who did not have these strategies to begin with. Papers in this Special Issue provide specific examples of strategies that work for limited shocks but performed poorly during pandemic, three of which we discuss below: private remittances, self-help groups (SHGs), and clustered development.
Kharel et al. (2023) document in detail the return of migrants to Nepal and Bangladesh. In usual times, migration can be a reliable strategy for poor households to augment and smooth incomes via remittances. The authors note a perceptible increase in the number of migrants who returned home and a sharp dip in remittances and show that this led to an increase in seasonal hunger even though COVID-19 occurred during the relatively prosperous agricultural season. Ahmed et al. (2023) note that both domestic and international remittances, key sources of consumption smoothing for households in normal times, failed to protect food security in the aftermath of COVID-19.
Kabir et al. (2023) explore the survival of businesses and the sustainability of fish and shrimp value chain actors in Bangladesh. They find that the ability of economic actors to tolerate a sustained crisis is limited, especially when most rely on informal sources of finance and social networks. These informal networks may offer a buffer when individual enterprises face shocks, but not when everyone in the network faces the same shock. Kabir et al. (2023) also note that enterprises in production clusters, normally associated with benefits from cooperation and sharing within the cluster, can end up competing for scarce resources during a crisis. Somewhat counterintuitively, firms located in production clusters may be rendered more vulnerable than those that were not.4
Siwach et al., (2023)’s study of savings and credit-based SHGs in India suggest similar patterns. While financial SHGs usually offer reliable risk-sharing mechanisms for members, Siwach et al. (2023) find that during the pandemic these SHGs were severely hampered, with repayment rates plummeting and SHGs unable to lend as they would under normal circumstances. However, Siwach et al. (2023) demonstrate that it is not always the case that these risk-sharing mechanisms break down completely and there may be modest benefits even in the face of shared shocks.
Although localized risk-sharing mechanisms were often overwhelmed by the pandemic, other interventions supported greater coping capacity. Investments to address structural conditions of poverty or sources of chronic poverty contributed to greater coping under COVID-19, even though it is unlikely that the risk of widespread shocks motivated these programs. Ekstrom et al. (2023) take advantage of a long-term randomized controlled trial (RCT) setting to show that a livestock transfer program in Nepal by Heifer International enhanced the resilience of households to COVID-19, improving their financial position relative to non-beneficiaries, and enhancing their ability to cope through the sale of livestock and improved access to new loans.
Quality of infrastructure and of institutions matters for recovery
Many papers in this Special Issue provide clear evidence that past investments in social and economic institutions and infrastructure determine the speed and trajectory of recovery.
There is a large literature showing that domestic agrifood value chains collapsed across countries in the early months of the pandemic, but evidence of their subsequent recovery is limited. Gupta et al. (2023) and Kabir et al. (2023) add to this body of evidence and identify contextual conditions that enable recovery. Gupta et al. (2023) find that the states in India that had better logistics recovered faster than others in terms of both farm-to-market supply and consumption expenditure. They use an index of logistics ease developed in the context of international trade to capture both physical infrastructure and the regulatory environment and apply this to their state-level analysis that relates the quality of logistics to the recovery of arrivals in spot markets for agricultural commodities and to consumer expenditure.
In a somewhat different context and setup, Kabir et al. (2023) note across- and within-value chain differences in Bangladesh. Segments that depended on international rather than domestic trade, such as shrimp, suffered disproportionately. Within value chains, those facing markets, dependent on the movement of commodities, and those far away from Dhaka were more likely to report negative impacts on business operations and profits, emphasizing the role of transportation infrastructure.
The role of prior conditions extends to the quality of private and public institutions. One example is the role of SHGs in India. Following the rich tradition of microcredit and SHGs in Bangladesh, the National Rural Livelihoods Mission (NRLM) of the Government of India focused first on mobilizing communities of women, organized them into groups for savings and lending and eventually linked groups to the formal financial system for credit to support livelihood activities. Siwach et al. (2023) note that despite the disruption to SHG functioning, highlighted in the previous section, SHG members fared better than non-members in terms of household food insecurity. The resilience of individuals can also be a function of the strength of the existing social protection architecture and the implementation capacity of the state.
State capacity and social protection are crucial, even if inadequate
The extent to which public institutions and public support can boost resilience, especially when other risk-sharing mechanisms fail, depends crucially on state capacity and the ability of governments to quickly roll out or scale up social protection programs. Bangladesh, India, and Pakistan already had broad-based social protection programs in operation and leveraged these effectively to address the fallout of COVID-19.
Ahmed et al. (2023) use panel data on rural and urban households in Bangladesh from 2018−19 to late 2021 and estimate differences-in-differences models with household fixed effects to show that pre-pandemic access to social protection is associated with significant reductions in food insecurity in all post-pandemic survey rounds, especially in the urban sample. Private remittances did not have a similar impact. In the Indian context, evidence suggests that variation in the ability of states to implement the large national workfare program, the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), is related more to historical performance and state capacity than demand. In contrast to this notion of a languid administration, Varshney and Meenakshi (2023) find that states within India rose to the occasion and the expansion of MGNREGA had substantive impacts on employment. Moreover, the MGNREGA proved to be cost-effective as well.
Yet, political and fiscal priorities might dismantle crucial social protection programs at the time of greatest need. The starkest example of this is the dramatic collapse of social protection in Myanmar after the May 2021 coup, even in the face of increased poverty and food insecurity consequent to COVID-19 (Boughton et al., 2023).
The success of social protection programs depends on the ability of the state to reach not only pre-existing beneficiaries but also the “new poor”, those pushed into poverty on account of the crisis. Major South Asian countries used national identity or social registries (National Identity or NID in Bangladesh), administrative beneficiary lists from pre-existing programs (such as the Pradhan Mantri Jan Dhan Yojana or the MGNREGA and old age pensions in India, the BISP in Pakistan) and online applications to expand or introduce social protection to cover the vulnerable and the new poor (Almenfi & Iyengar, 2022: Gentilini, 2022). Papers in this issue address various aspects of this problem. Khan et al. (2023) attempt to obtain subnational estimates of COVID-19 exposure to estimate how many are likely to be affected by the pandemic, which would help governments better prepare to support and identify geographies for targeting. Davies et al. (2023) document the Ehsaas program that leveraged a 2008 transfer program called the Benazir Income Support Programme (BISP).
Broadening the scope of interventions considered, Ecker et al. (2023) offer an ex-ante assessment of the potential effectiveness of alternative social protection measures. They model the potential impact of five hypothetical social protection interventions in Myanmar and Bangladesh and note that the mitigation effects of cash transfer, in-kind food transfer, and food voucher programs with a typical monetary value ($13 per-household per-month) and common population coverage is expected to be quite modest in terms of dietary and nutritional outcomes but could be substantially increased through food fortification. Davies et al. (2023) compare the relative benefits and distributional consequences of cash transfers and a rural construction program in Pakistan.
The dangers of exclusion from relief
The ability of governments to administer large-scale social protection programs matters, and the inclusion of marginalized groups like women, children, adolescents, the disabled, historically disadvantaged communities, and seasonal migrants in these programs is critically important (Banks et al., 2021; Desai et al., 2021; Kharel et al., 2023). Without active attention to social inclusion, there is a risk that relief may accentuate and aggravate pre-existing inequalities, contributing to “K-shaped” economic recoveries, that is, situations where different sub-populations recover from an economic shock at different rates, leading to divergent trajectories (Dalton et al., 2021).
In this Special Issue, Ramachandran and Deshpande (2023) note that marginalized communities in India fared worse in the immediate aftermath of the first lockdown and were between 9.6 and 18% more likely to be unemployed, relative to the historically privileged groups. They show that much of this difference is on account of social identity rather than selection into industry or job-type, although human capital differences appear to drive some of the caste gap. Their work offers a cautionary note that crises may deepen existing inequalities, though these caste gaps all but disappear after 20 months.
Seager et al. (2023) focus on adolescents, another potentially vulnerable group. Using data collected immediately before and during the COVID-19, they show that a year into the pandemic, adolescents were three times more likely to report hunger, and households were twice as likely to report cutting back food to adolescents compared to before COVID-19 restrictions. Vulnerable households experienced larger increases in hunger and reductions in food consumption, with girls more adversely affected than boys. Significantly, they note that neither cash nor food aid was able to mitigate these negative trends.
The targeting and identification of beneficiaries and the new poor might inadvertently lead to exclusion errors, whereby deserving individuals are denied access to benefits. Davies et al. (2023) note the challenges of the Pakistani Government to identify the new poor for the enhanced cash transfer program. In contrast, Varshney and Meenakshi (2023) highlight the potential of the MGNREGA, a universal scheme that self-selects participants without the state trying to identify and target individual beneficiaries. Whether universal programs fare better in these circumstances is a question that needs further consideration (see Banerjee et al., 2022 for a discussion on targeting in general).
Multiple crises have compounding effects
Finally, the South Asia experience also has lessons for the impacts of multiple concurrent crises. Sri Lanka faced a massive macroeconomic and humanitarian crisis caused by a series of policy misadventures and economic issues (Bhowmick, 2022). In eastern South Asia, Myanmar witnessed a military coup in February 2021 and in Pakistan, tensions between the federal and provincial governments have implied a governance gridlock that frustrated prompt and effective action in COVID-19 management.
Contemporaneous crises compound challenges for governments and international donors in multiple ways. First, the policy space that countries have for independent action may shrink and their ability to extend social protection can be compromised by political and macroeconomic exigencies. Boughton et al. (2023) note the collapse of social protection in the aftermath of regime change in Myanmar. Second, ensuing civilian conflicts can trigger additional challenges that further compound movement restrictions and market access. Third, tackling shocks, especially multiple ones, can be detrimental to longer-term fiscal health, as highlighted in Dorosh et al. (2023).
This Special Issue anchors the vast body of scholarship related to South Asia that grew out of the COVID19 crisis and offers new insights from one of the most populous regions in the world to draw clear, forward-looking, generalizable suggestions for policy.
A key actionable finding is that investments in data collection are crucial. Most datasets and methods have well-known limitations that were either aggravated in the context of the pandemic or prevented researchers from capturing the true extent of impacts, especially on certain sub-populations. The lack of timely and credible data on different actors in the economy hampers our ability to fully understand the ramifications of shocks. In addition to plugging this gap, inclusive, updated, and error-free data registries can serve as credible bases for reaching the poor and the marginalized in times of crisis, provided that adequate safeguards are built in around their access and use. Filling these data gaps would allow for the formulation of policies that could prevent or mitigate the negative consequences of crises.
As we look to a future with more frequent covariate shocks, the South Asian experience shows that multiple crises can occur contemporaneously and without warning. Just as routine investment in both the physical and institutional infrastructure of value chains (their “bones” and “blood”) is needed to make food systems resilient to shocks (Reardon et al., 2022), continuous investments in public safety nets and private household capacity may be needed to build broad resilience to wide-scale shocks.
Investing in state capacity to design and implement social protection programs seems to have high returns. As there is no simple rule to identify the type of support that is optimal or even how best to track the new or emerging poor, it is essential to invest in building capacity for informed design and management of social protection programs in dynamic contexts. At the core of this investment is the trade-off between investing in identifying new poor for inclusion, at the risk of incurring potential exclusion errors, and providing universal coverage at a lower level of average benefit. When the scale of humanitarian crises is large, as it was with COVID-19, the implications of this trade-off are both ethically and fiscally significant. The strategies adopted will depend in part on the state’s capacity to effectively administer whatever program is chosen.
Finally, there is a clear need to create and nurture opportunities for regional learning and cooperation. Such regional cooperation can help countries that face multiple crises and have limited financial or state capacity to overcome overwhelming challenges. It can also help build robust systems for supporting transnational or internal migrants, promoting regional trade, and coordinating national and regional policy that can minimize the collective impact of shocks.
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