The impact of government nonmarket policy on a firm's financial performance: a lesson from COVID-19 pandemic lockdown's policy

PurposeThis paper examines the effect of lockdown on a firm's financial performance. The authors aim to fill in the debate over the corporate world's repercussions from governments' COVID-19 response. Therefore, it is imperative to understand what effect the lockdown policy has on firm financial performance.Design/methodology/approachThe study data are cross-sectional, covering a sample of 246 listed firms in Indonesia. The lockdown policy and period data were retrieved from the Indonesian Ministry of Health COVID-19 special task force website. The authors' empirical model for performance specification is based on annual data, following a common performance function in economics and finance literature. In addition to controlling for the standard error and province effect, the authors also controlled the COVID-19 cases and the province effect.FindingsThe lockdown deteriorates the firm's profitability, but it is not up to making the firms at financial distress level. Simply put, lockdown erodes the profitability significantly, leading to declining performance;however, it does not mean the firms generate default.Research limitations/implicationsSeveral shortcomings in the authors' empirical setup need to be tackled for future research. For example, the study findings may limit the short-run effect but not the long-run effect (5–10 years after the pandemic). The findings also do not give room to justify that lockdown should not be imposed due to its deteriorating effect on the corporate world. Therefore, the authors leave this as a scope for future research.Originality/valueThis research is among the pioneer papers evaluating the effect of the government policy for mitigating the repercussions of COVID-19, and it reveals how this policy affects corporations.

Covid-19 Lockdown in Ecuador: Are there Gender Differences in Unemployment?

To fight Covid-19, governments have imposed restrictions on personal mobility and social interactions which may have negative consequences in the labor market. These consequences may be different across demographic groups particularly for female workers. We examine whether the policy that restricted operations in some economic sectors affected formal employment for Ecuadorian female workers differently. We use a difference-in-differences-in-differences model to compare female employees working in restricted economic sectors with other workers, before and after the lockdown policy. The results show that the number of unemployment spells rose by approximately 15 per cent for women working in the restricted economic activities. We also document a decrease in the probability of being employed, which is particularly strong for the youngest women (15–24 years-old), oldest women (45–65 years-old), and less educated female workers. We conclude that the lockdown policy imposed in Ecuador is a plausible explanation for women's job loss in the formal sector. [ FROM AUTHOR] Copyright of Journal of Development Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Language and COVID-19: A discourse analysis of resistance to lockdown in Indonesia.

Communities in Indonesia were resistant to lockdown policies, Large-Scale Social Restrictions (PSBB) and the Enactment of Restrictions on Community Activities (PPKM). Both policies were implemented numerous times in the country during the COVID-19 pandemic, and these caused widespread unrest. Language with the terms PSBB and PPKM, which several times extended suddenly, not informed to the community, inconsistent in its implementation, makes the community feel mad, neglected the needs of their life, and severe rejections. This research was conducted with a qualitative approach sourced from primary and secondary data. Primary data were obtained from electronic media news that shows public resistance and government policies published through the official government web. Meanwhile, secondary data were obtained from journal articles discussing community resistance related to policies to prevent the spread of the COVID-19 pandemic. The results showed that various terms translated from the term lockdown to the time PSBB and PPKM had consequences for policy misalignment with community expectations. The switching of language from lockdown to PSBB and PPKM has caused resistance in the community because it has allowed the government to be economically irresponsible. Therefore, the government needs to inform and be responsible, so that policies can run effectively.

The discrepancies in the impacts of COVID-19 lockdowns on electricity consumption in China: Is the short-term pain worth it?

The COVID-19 pandemic caused severe economic contraction and paralyzed industrial activity. Despite a growing body of literature on the impacts of COVID-19 mitigation measures, scant evidence currently exists on the impacts of lockdowns on the economic and industrial activities of developing countries. Our study provides an empirical assessment of lockdown measures using 298,354 data points on daily electricity consumption in 396 sub-industries. To infer causal relationships, we employ difference-in-differences models that compare cities with and without lockdown policies and provide quantitative evidence on whether the long-term gain of lockdowns outweighs the short-term loss. The results show that lockdown policies led to a significant short-term drop in electricity consumption of 15.2% relative to the control group. However, the electricity loss under the no-lockdown scenario is 2.6 times larger than that under the strict lockdown scenario within 4 months of the outbreak. Discrepancies in the impacts among industries are identified, and even within the same industry, lockdowns have heterogeneous effects. The impact of lockdowns on small and medium-sized enterprises in developing countries is seriously underestimated, raising concerns about the distributional impact of subsidy measures. This study serves as a crucial reference for the government when facing public health emergencies and shocks to support better policies.

Quantifying the impact of pandemic lockdown policies on global port calls.

The recent experience of lockdowns during COVID-19 highlights the prolonged impact a pandemic could have on ports and the shipping industry. This paper uses port call data derived from the Automatic Identification System (AIS) reports from the world's 30 largest container ports to quantify both the immediate and longer-term impact of national COVID-19 lockdown policies on global shipping flows. The analysis uses the Difference-in-Difference (DID) and combined regression discontinuity design (RDD)-DID models to represent the effects of lockdown policies. The combination of RDD and DID models is particularly effective because it can mitigate time trends in the data, e.g., the Chinese New Year effect on Chinese ports. This study further examines the potential shock propagation effects, namely, how lockdown policy in one country (i.e., China) can affect the number of port calls in other countries. We categorize ports in other countries into a high-connectivity (with Chinese ports) group and a low-connectivity group, using a proposed connectivity index with China derived from individual vessel trajectories obtained from the AIS data. The results provide a clearly measurable picture of the kinds of trade shocks and consequent pattern changes in port calls over time caused by responses to lockdown policies of varying levels of stringency. We further document the existence of significant shock propagation effects. As the risk of pandemics rises in the twenty-first century, these results can be used by policy makers to assess the potential impact of different levels of lockdown policy on the maritime industry and trade flows more broadly. Maritime players can also use findings such as these to manage their capacity during lockdowns more effectively and to respond more flexibly to changing demand in seaborne transportation.

Effective Lockdown and Role of Hospital-Based COVID-19 Transmission in Some Indian States: An Outbreak Risk Analysis.

Several reports in India indicate hospitals and quarantined centers are COVID-19 hotspots. To study the transmission occurring from the hospitals and as well as from the community, we developed a mechanistic model with a lockdown effect. Using daily COVID-19 cases data from six states and overall India, we estimated several important parameters of our model. Moreover, we provided an estimation of the effective (RT ), the basic (R0 ), the community (RC ), and the hospital (RH ) reproduction numbers. We forecast COVID-19 notified cases from May 3, 2020, till May 20, 2020, under five different lockdown scenarios in the seven locations. Our analysis suggests that 65% to 99% of the new COVID-19 cases are currently asymptomatic in those locations. Besides, about 1-16% of the total COVID-19 transmission are currently occurring from hospital-based contact and these percentage can increase up to 69% in some locations. Furthermore, the hospital-based transmission rate (ß2 ) has significant positive (0.65 to 0.8) and negative (-0.58 to -0.23) correlation with R0 and the effectiveness of lockdown, respectively. Therefore, a much larger COVID-19 outbreak may trigger from the hospital-based transmission. In most of the locations, model forecast from May 3, 2020, till May 20, 2020, indicates a two-times increase in cumulative cases in comparison to total observed cases up to April 29, 2020. Based on our results, we proposed a containment policy that may reduce the threat of a larger COVID-19 outbreak in the future.

Stock markets response to contagious disease: Evidence on the impact of COVID-19 in the three worst hit African economies

This study analyses whether Coronavirus health shocks and government responses in terms of lockdown policy and stringency measures impact stock markets in Africa. We found that stock markets appeared to be more negatively responsive to growth in total number of COVID-19 reported cases than the growth in deaths in the case of Nigeria and South Africa. While for Egypt, the stock market reacted significantly negative to both COVID-19-related indicators. Our results further show that government stringency policy has significant negative effect on stock market returns in the case of Nigeria and South Africa, but positive in the case of Egypt.

Rethinking Lockdown Policies in the Pre-Vaccine Era of COVID-19: A Configurational Perspective.

The significance of lockdown policies for controlling the COVID-19 pandemic is widely recognized. However, most studies have focused on individual lockdown measures. The effectiveness of lockdown policy combinations has not been examined from a configurational perspective. This research applies fuzzy-set qualitative comparative analysis (fsQCA) to examine different lockdown policy combinations associated with high-epidemic situations in 84 countries. A high-epidemic situation can occur through three different "weak-confined" patterns of lockdown policy combinations. The findings demonstrate that a combination of lockdown policies is more successful than any single lockdown policy, whereas the absence of several key measures in policy combinations can lead to a high-epidemic situation. The importance of international travel controls can become obscured when they are the only measures adopted, and a high-epidemic situation can still arise where restrictions are placed on international travel but not on public transport or when workplaces are closed but schools remain open.

Assessing impact of the COVID-19 pandemic on China's TFP growth: Evidence from region-level data in 2020.

This paper examines the first wave spread of COVID-19 in China and its impact on TFP growth for 2020, and assess the role of anti-epidemic lockdown policy in suppressing the pandemic. Methodologically, we systematically quantify the disparity in the pandemic's productivity impact and the role of lockdown policies across regions, by combining the prefecture-level TFP growth for 422 regions (including 276 municipal cites and 146 county regions) with the daily statistics on the pandemic. Our results show that the negative impact of the COVID-19 pandemic on TFP growth are more likely to occur in municipal cities, compared to rural areas. Moreover, the anti-epidemic quarantine policy succeeded to bring the COVID-19 pandemic down in China, but it may generate additional costs through dampening TFP growth if overused. Given the regions either with a relative higher resilience level or in the remote rural areas suffered more from the strict regulation. A more flexible policy is required to be designed so as to mitigate the ongoing COVID-19 impacts in future. These findings provide useful insights for China, as well as other Asian developing countries, to cope with its continuing episodes.

The impact of government nonmarket policy on a firm's financial performance: a lesson from COVID-19 pandemic lockdown's policy

Purpose This paper examines the effect of lockdown on a firm's financial performance. The authors aim to fill in the debate over the corporate world's repercussions from governments' COVID-19 response. Therefore, it is imperative to understand what effect the lockdown policy has on firm financial performance. Design/methodology/approach The study data are cross-sectional, covering a sample of 246 listed firms in Indonesia. The lockdown policy and period data were retrieved from the Indonesian Ministry of Health COVID-19 special task force website. The authors' empirical model for performance specification is based on annual data, following a common performance function in economics and finance literature. In addition to controlling for the standard error and province effect, the authors also controlled the COVID-19 cases and the province effect. Findings The lockdown deteriorates the firm's profitability, but it is not up to making the firms at financial distress level. Simply put, lockdown erodes the profitability significantly, leading to declining performance;however, it does not mean the firms generate default. Research limitations/implications Several shortcomings in the authors' empirical setup need to be tackled for future research. For example, the study findings may limit the short-run effect but not the long-run effect (5-10 years after the pandemic). The findings also do not give room to justify that lockdown should not be imposed due to its deteriorating effect on the corporate world. Therefore, the authors leave this as a scope for future research. Originality/value This research is among the pioneer papers evaluating the effect of the government policy for mitigating the repercussions of COVID-19, and it reveals how this policy affects corporations.

Flow of goods to the shock of COVID-19 and toll-free highway policy: Evidence from logistics data in China

Using high-frequency logistics data from China, this paper quantitatively examines the negative impact of the COVID-19 pandemic on logistics. Meanwhile, our research focuses on the toll-free highway policy during the COVID-19 pandemic, analyzing the promoting effect of this policy on road freight in China. Three main conclusions are drawn from the study. Firstly, the COVID-19 pandemic led to an average daily drop of 0.67% in road freight volume and an increase of 0.48% in logistic cost compared to the pre-pandemic period. Secondly, the toll-free highway policy had a significant offset effect of pandemic on freight volume and price, stimulating the resumption of work and production. However, the dynamic effect shows that the toll-free highway policy is only temporarily effective rather than the long term. Thirdly, the effectiveness of the toll-free highway policy is moderated by the severity of the epidemic and the transportation distance. This paper contributes to research on economy recovery and transportation policy under the COVID-19 pandemic shock.

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19.

Following the outbreak of the COVID-19 pandemic, various lockdown strategies restrained global economic growth bringing a significant decline in maritime transportation. However, the previous studies have not adequately recognized the specific impacts of COVID-19 on maritime transportation. In this study, a series of analyses of the Baltic Dry Index (BDI), the China Coastal Bulk Freight Index (CCBFI) and of container throughputs with and without the impact of COVID-19 were carried out to assess changing trends in dry bulk and container transportation. The results show that global dry bulk transportation was largely affected by lockdown policies in the second month during COVID-19, and BDI presented a year-on-year decrease of approximately 35.5% from 2019 to 2020. The CCBFI showed an upward trend in the second month during COVID-19, one month ahead of the BDI. The container throughputs at Shanghai Port, the Ports of Hong Kong, the Ports of Singapore and the Ports of Los Angeles from 2019 to 2020 presented the largest year-on-year drops of approximately 19.6%, 7.1%, 10.6% and 30.9%, respectively. In addition, the authors developed exponential smoothing models of BDI, CCBFI, and container transportation, and calculated the percentage prediction error between the observed and predicted values to examine the impact of exogenous effects on the shipping industry due to the outbreak of COVID-19. The results are consistent with the conclusions obtained from the comparison of BDI, CCBFI, and container transportation during the same period in 2020 and 2019. Finally, on the basis of the findings, smart shipping and special support policies are proposed to reduce the negative impacts of COVID-19.

Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China.

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%-48.8% drop in the concentration of NO2 has been observed in the four metropolises compared with the same period in 2018-2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (µg/m3) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality.

Optimal Immunity Control and Final Size Minimization by Social Distancing for the SIR Epidemic Model.

The aim of this article is to understand how to apply partial or total containment to SIR epidemic model during a given finite time interval in order to minimize the epidemic final size, that is the cumulative number of cases infected during the complete course of an epidemic. The existence and uniqueness of an optimal strategy are proved for this infinite-horizon problem, and a full characterization of the solution is provided. The best policy consists in applying the maximal allowed social distancing effort until the end of the interval, starting at a date that is not always the closest date and may be found by a simple algorithm. Both theoretical results and numerical simulations demonstrate that it leads to a significant decrease in the epidemic final size. We show that in any case the optimal intervention has to begin before the number of susceptible cases has crossed the herd immunity level, and that its limit is always smaller than this threshold. This problem is also shown to be equivalent to the minimum containment time necessary to stop at a given distance after this threshold value.

The Improvement of Air Quality and Associated Mortality during the COVID-19 Lockdown in One Megacity of China: An Empirical Strategy.

Although the lockdown policy implemented during the COVID-19 pandemic indeed improved the air quality and reduced the related health risks, the real effects of the lockdown and its resulting health risks remain unclear considering the effects of unobserved confounders and the longstanding efforts of the government regarding air pollution. We compared air pollution between the lockdown period and the period before the lockdown using a difference-in-differences (DID) model and estimated the mortality burden caused by the number of deaths related to air pollution changes. The NO2 and CO concentrations during the lockdown period (17 days) declined by 8.94 µg/m3 (relative change: 16.94%; 95% CI: 3.71, 14.16) and 0.20 mg/m3 (relative change: 16.95%; 95% CI: 0.04, 0.35) on an average day, respectively, and O3 increased by 8.41 µg/m3 (relative change: 32.80%; 95% CI: 4.39, 12.43); no meaningful impacts of the lockdown policy on the PM2.5, PM10, SO2, or the AQI values were observed. Based on the three clearly changed air pollutants, the lockdown policy prevented 8.22 (95% CI: 3.97, 12.49) all-cause deaths. Our findings suggest that the overall excess deaths caused by air pollution during the lockdown period declined. It is beneficial for human health when strict control measures, such as upgrading industry structure and promoting green transportation, are taken to reduce emissions, especially in cities with serious air pollution in China, such as Shijiazhuang.

Computer Vision Syndrome During SARS-CoV-2 Outbreak in University Students: A Comparison Between Online Courses and Classroom Lectures.

Purpose: To compare the prevalence of computer vision syndrome in university students of different teaching modes during the SARS-CoV-2 outbreak period. Methods: A cross-sectional survey study using the validated Computer Vision Syndrome Questionnaire in Chinese medical students of Sichuan University who took classroom lectures and the same-grade foreign students from a Bachelor of Medicine and Bachelor of Surgery (MBBS) program who took online lectures with similar schedules. Results: A total of 137 responses from 63 Chinese students and 74 MBBS students were obtained. The highest frequency of digital screen time was 7-9 h (43.24%, 32/74) for MBBS students and 2-4 h (46.03%, 29/63) for Chinese students. The prevalence of computer vision syndrome among Chinese students and MBBS students were 50.79% and 74.32%, respectively (P = 0.004). The average numbers of reported symptoms were 5.00 ± 2.17 in Chinese students and 5.91 ± 1.90 in MBBS students (P = 0.01). The three most highly reported symptoms were "heavy eyelids" (53.97%), "dryness" (50.79%), and "feeling of a foreign body" (46.03%) in Chinese students and "dryness" (72.97%), "feeling of a foreign body" (62.16%), and "heavy eyelids" (58.11%) in MBBS students. The sum grades of computer vision syndrome had a moderate positive correlation with screen time (Spearman's correlation coefficient = 0.386, P < 0.001). The grades of symptoms of "feeling of a foreign body," "heavy eyelids," and "dryness" showed a weak positive correlation with screen time (Spearman's correlation coefficients were 0.220, 0.205, and 0.230, respectively). Conclusion: Online study may contribute to the prevalence of computer vision syndrome among university students.

An interrupted time series analysis of the lockdown policies in India: a national-level analysis of COVID-19 incidence.

India implemented a national mandatory lockdown policy (Lockdown 1.0) on 24 March 2020 in response to Coronavirus Disease 2019 (COVID-19). The policy was revised in three subsequent stages (Lockdown 2.0-4.0 between 15 April to 18 May 2020), and restrictions were lifted (Unlockdown 1.0) on 1 June 2020. This study evaluated the effect of lockdown policy on the COVID-19 incidence rate at the national level to inform policy response for this and future pandemics. We conducted an interrupted time series analysis with a segmented regression model using publicly available data on daily reported new COVID-19 cases between 2 March 2020 and 1 September 2020. National-level data from Google Community Mobility Reports during this timeframe were also used in model development and robustness checks. Results showed an 8% [95% confidence interval (CI) = 6-9%] reduction in the change in incidence rate per day after Lockdown 1.0 compared to prior to the Lockdown order, with an additional reduction of 3% (95% CI = 2-3%) after Lockdown 4.0, suggesting an 11% (95% CI = 9-12%) reduction in the change in COVID-19 incidence after Lockdown 4.0 compared to the period before Lockdown 1.0. Uptake of the lockdown policy is indicated by decreased mobility and attenuation of the increasing incidence of COVID-19. The increasing rate of incident case reports in India was attenuated after the lockdown policy was implemented compared to before, and this reduction was maintained after the restrictions were eased, suggesting that the policy helped to 'flatten the curve' and buy additional time for pandemic preparedness, response and recovery.

Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States.

The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, social stability, and economic development. Since the first case was recorded on December 29, 2019, in Wuhan of China, the disease has rapidly extended to other nations of the world to claim many lives, especially in the USA, the United Kingdom, and Western Europe. To stay ahead of the curve consequent of the continued increase in case and mortality, predictive tools are needed to guide adequate response. Therefore, this study aims to determine the best predictive models and investigate the impact of lockdown policy on the USA' COVID-19 incidence and mortality. This study focuses on the statistical modelling of the USA daily COVID-19 incidence and mortality cases based on some intuitive properties of the data such as overdispersion and autoregressive conditional heteroscedasticity. The impact of the lockdown policy on cases and mortality was assessed by comparing the USA incidence case with that of Sweden where there is no strict lockdown. Stochastic models based on negative binomial autoregressive conditional heteroscedasticity [NB INGARCH (p,q)], the negative binomial regression, the autoregressive integrated moving average model with exogenous variables (ARIMAX) and without exogenous variables (ARIMA) models of several orders are presented, to identify the best fitting model for the USA daily incidence cases. The performance of the optimal NB INGARCH model on daily incidence cases was compared with the optimal ARIMA model in terms of their Akaike Information Criteria (AIC). Also, the NB model, ARIMA model and without exogenous variables are formulated for USA daily COVID-19 death cases. It was observed that the incidence and mortality cases show statistically significant increasing trends over the study period. The USA daily COVID-19 incidence is autocorrelated, linear and contains a structural break but exhibits autoregressive conditional heteroscedasticity. Observed data are compared with the fitted data from the optimal models. The results further indicate that the NB INGARCH fits the observed incidence better than ARIMA while the NB models perform better than the optimal ARIMA and ARIMAX models for death counts in terms of AIC and root mean square error (RMSE). The results show a statistically significant relationship between the lockdown policy in the USA and incidence and death counts. This suggests the efficacy of the lockdown policy in the USA.

A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19.

Since the start of the pandemic caused by the novel coronavirus, COVID-19, more than 106 million people have been infected and global deaths have surpassed 2.4 million. In Chile, the government restricted the activities and movement of people, organizations, and companies, under the concept of dynamic quarantine across municipalities for a predefined period of time. Chile is an interesting context to study because reports to have a higher quantity of infections per million people as well as a higher number of polymerize chain reaction (PCR) tests per million people. The higher testing rate means that Chile has good measurement of the contagious compared to other countries. Further, the heterogeneity of the social, economic, and demographic variables collected of each Chilean municipality provides a robust set of control data to better explain the contagious rate for each city. In this paper, we propose a framework to determine the effectiveness of the dynamic quarantine policy by analyzing different causal models (meta-learners and causal forest) including a time series pattern related to effective reproductive number. Additionally, we test the ability of the proposed framework to understand and explain the spread over benchmark traditional models and to interpret the Shapley Additive Explanations (SHAP) plots. The conclusions derived from the proposed framework provide important scientific information for government policymakers in disease control strategies, not only to analyze COVID-19 but to have a better model to determine social interventions for future outbreaks.