BANS

We initially undertook a narrative review of the current situation regarding COVID-19 in India and on suggested treatments. We subsequently undertook quantitative research in the form of a survey. The narrative review included a review of current and proposed treatment approaches including vaccines and recommendations for preventing and managing COVID-19, including the role of community pharmacists as well as issues of misinformation. We did not systematically review the papers or other information sources for their quality using well-known scales such as the Newcastle-Ottawa scale as some of the papers quoted are in pre-publication format and we have used a considerable number of internet sources (Almeida et al., 2018 (link)). However, the publications and internet sources were filtered by the co-authors to add robustness to the paper and its suggestions.
The information sourced from the pragmatic review of the literature was combined with a questionnaire survey among community pharmacies (Appendix 1) to assess the situation regarding prices, availability and usage patterns of carefully selected medicines that could potentially be used in the management of COVID-19, as well as PPE, soon after the start of the pandemic.
For this rapid analysis, we selected via purposive sampling representation of pharmacies from across India. This included Ahmedabad, Jodhpur, and Pune as part of Western India, West Singhbhum and Kolkata as part of Eastern India, and Delhi in northern India. Convenience sampling in these cities was used to select pharmacists through emails, telephone contact, personal contacts and other mechanisms. There was no sample size calculation as there was no previous data in India to base calculations upon. However, the intention was to undertake the research among an appreciable number of community pharmacies across India to gain good insights and provide a basis for future studies if needed.
Key questions were to assess patterns of demand, availability, and price changes of selected medicines and equipment, as well as the potential future role of pharmacists to reduce misinformation. These are contained in Box 2 (building on Appendix 1). Those conducting the research were provided with an Excel spreadsheet of the questions to complete. The questions were open ended as we were aware that in a number of situations we would be unable to obtain exact details of changes in utilisation patterns and prices; however, we wanted to capture data including more general information for this initial study. The answers were collated where possible into logical bands for comparisons with other countries such as Bangladesh (Haque et al., 2020 ). These bands were not pre-defined as this was an exploratory rapid pilot study, with changes in prices based on local prices. In addition, general information would be sufficient if the pharmacists were unable to be specific given the exploratory nature of this study.
The pharmacists were briefed on the objectives of the study with the option to participate or not, with confidentiality maintained throughout. Our hypothesis, based on findings in other countries, was that there would be shortages of some of the medicines, although countered in India as a chief producer of medicines with export bans in place certainly initially (Duffy & Hussain, 2020 ) Price rises especially for medicines potentially tempered though by the Ministry of Pharma and Consumer Affairs being instructed to take necessary action to regulate these for PPE and other health related materials alongside existing regulations for (Table 1) (WHO India, 2020e ). The findings were compiled into a tabular format. No formal statistical analysis was performed as the level of detail varied considerably across the pharmacies.
We subsequently combined the data collected using the experience of the co-authors regarding key issues including pharmaceutical care, health policy and self-purchasing in LMICs to provide future direction, building on comments from the interviewed pharmacists. We have previously successfully used this approach to provide future direction in LMICs (Godman et al., 2018 (link); Godman et al., 2019a (link); Godman et al., 2020a (link); Godman et al., 2020b (link); Godman et al., 2020c (link); Godman et al., 2020d (link)).
Ethical approval for this study was not required according to national legislation and institutional guidelines. However, all pharmacists freely provided the requested information having been given the opportunity to refuse to participate if wished. This is in line with previous studies undertaken by the co-authors in related areas including analysis of policies to enhance the rationale use of medicines and biosimilars, pricing policies and issues surrounding generics, which involved direct contact with health authority personnel and other key stakeholders (Moorkens et al., 2017 (link); Godman et al., 2019b ; Gad et al., 2020 (link); Godman et al., 2020a (link); Godman et al., 2020c (link); Miljković et al., 2020 (link)).

Policy interventions may have increased measured systemic risk in the long or the short run due to costs associated with regulatory restrictions. In many situations, bonuses were prohibited, and dividends could be distributed only to the government. Additionally, the government occupied seats on some supervisory boards, especially in the case of recapitalizations. In addition, interventions could imply a series of commitments, such as divestments, acquisition bans, or price leadership bans, which can affect investors’ expectations about the future profitability of the bank.17 For the banks in our sample, the restrictions were imposed temporarily by the regulator until the intervention was unwound.
To explore the impact of regulatory restrictions on the relation between emergency rescue actions and systemic risk, we consider the following constraints: supervisory board intrusions, management pay limitations, and capital payout bans. Intrusions on supervisory boards may ensure stricter supervision of investment and lending practices, while caps on executive compensation are likely to reduce portfolio risk (Dam and Kotter 2012 ). Refraining from paying dividends or from buybacks could improve banks’ financial health, as retained earnings increase banks’ capacity to rebuild capital buffers and promote lending. On the other hand, as capital payout limits have strong implications for shareholders, it is likely that they incentivize managers to increase portfolio risk to repay the bailout faster so that the regulator removes the restriction. Acharya and Yorulmazer (2008 (link)) show that the government’s stake in the bank should be large enough to overcome risk-taking incentives.
As our MES measure captures both systemic risk realizations and forward-looking systemic risk, the effect of regulatory restrictions on the relation between interventions and systemic risk could be twofold. If shareholders perceive regulatory restrictions to be effective tools for moving portfolio risk toward optimal levels and assuring stricter monitoring of the bank, then the market valuation of more restricted rescued banks is likely to be higher than that of less restricted rescued banks, leading to lower measured systemic importance for the former. In turn, if investors perceive the regulatory burden to be costly, then stricter restrictions may lead to underperforming stock returns and therefore higher measured systemic risk for more restricted than for less restricted bailed-out banks. This translates into a higher systemic importance for bailed-out banks when regulators impose tighter restrictions.
The impact of restrictions on the link between policy interventions and systemic risk is examined with the following specification: $${\mathit{SystemicRisk}}_{ij,t}={\beta }_0+{\beta }_1\times {Policyinterventions}_{ij,eventwindow}+{\beta }_2\times {Policyinterventions}_{ij,eventwindow}\times {\mathit{Restrictions}}_{ij,t-1}+{\beta }_3\times {\mathit{Restrictions}}_{ij,t-1}+{\beta }_4\times {\mathit{IMR}}_{ij,t-1}+\mathrm{\Phi }\times Bank{\mathit{controls}}_{ij,t-1}+\mathrm{\Psi }\times Market\&{Macrocontrols}_{j,t-1}+{\mu }_{\mathit{jt}}+{\epsilon }_{ij,t}$$
In addition to Eq. (2), we include the interaction term of policy interventions with the restrictions imposed by the regulator. The latter are captured by dummy variables that reflect the following dimensions: supervisory board intrusions, management pay limitations, and capital payout bans. The coefficient β₂ should be negative and significant if such restrictions reduce the systemic importance of banks when interventions are implemented and positive otherwise. As in the baseline specification, we use the same bank-level, market and macro controls. Additionally, we account for sample selection bias by including the inverse Mills ratio generated by the Probit model in Eq. (1). The strategy involves estimating the empirical models separately for each interaction of policy interventions with the restrictions using OLS FE for the restricted sample of rescued banks.