Crisis lifecycle, policy response, and policy effectiveness

ABSTRACT

The dimension of time has been neglected in the practice and research of public administration for decades. By developing a framework to guide the quantification and operationalization of the crisis lifecycle model, this study explores how the timing, sequence, and tempo of government policy response impact policy effectiveness. Quantitative analysis of COVID-19-related data in 152 countries/regions from 1 January to 31 July 2020 shows that direct policies on curtailing infection sources in the early outbreak stage are key to controlling the pandemic. This article concludes by identifying different time tactics that may help policymakers improve strategic decision-making.

KEYWORDS:

• Crisis lifecycle
• policy response
• policy effectiveness
• time tactics
• COVID-19

Acknowledgments

The authors would like to thank the three anonymous reviewers from Public Management Review for their helpful comments on a previous draft of this paper.

Data availability statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. 31 December 2019 is the date of the first public notification of the COVID-19 outbreak by the CSSE COVID-19 Data source up to 31 July 2020 (https://github.com/CSSEGISandData/ COVID-19). Therefore, 1 January 2020 was used in this study as the starting point of the prodromal stage for all countries/regions.

2. Phan and Narayan (2020) compared the number of days it took for countries to record the 100 cases, 1,000 cases, 10,000 cases, and 100,000 cases from the day of the first case in 25 countries.

3. The University of Oxford tracked policy data from more than 160 countries and regions worldwide. Given the lack of socioeconomic data in some countries or regions, 152 countries or regions were retained as the final sample.

4. Nehrt (1996) and Lee et al. (2000) measured the timing variable by calculating the gap between the action time and the reference timepoint. Considering that the pandemic policy response is a dynamic and continuous process, this study modified the statistical method for the measurement of policy response timing on this basis.

5. George et al. (2020) argued that COVID-19-related deaths have been by far the most reported measurement both in the media and by policymakers. Therefore, COVID-19-related deaths per 100,000 population was used as the proxy variable for the robustness test.

6. As of 31 July 2020, the COVID-19 pandemic had not yet completed a lifecycle in many countries. Thus, we used the days consumed in each subdivision stage (Age_t_i) to replace the duration of the entire crisis lifecycle (Age). The Age_t_i representing the speed of crisis destruction in the empirical models is different from the Age representing the social recovery time in the analytical framework.

7. The system collects national and regional policy measures to prevent and control the COVID-19 outbreaks from various sources, such as news reports and government announcements, and standardizes them for cross-country comparison and tracking over time.

8. Each policy indicator is scored on whether it was introduced, recommended or mandated. For example, the indicator of ‘restrictions on international travel’ was divided into five categories, including no measures, screening, quarantine of arrivals from high-risk regions, ban on high-risk regions, and total border closure, which were assigned values of 0, 1, 2, 3, or 4, respectively. The daily Government Response Index is the daily average of 13 indicators (rescale each indicator 0−100) as of 31 July 2020. See https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/index_methodology.md for more details on how each indicator/index was calculated.

9. The legacy indicator chooses a single indicator between ‘cancelled public events’ and ‘restrictions on gathering size’, and between ‘shelter-in-place and home confinement orders’ and ‘restrictions on internal movement’, selecting whichever of those pairs provides a higher sub-index score. See https://github.com/OxCGRT/covid-policy-tracker/blob/master/documentation/index_methodology.md for more details on how each indicator/index is calculated.

10. The confirmed cases are considered to be closely related to the detection capacity of each country/region. Due to the limited detection capability data collection (only 85 countries/regions), this study included it as a control variable only in the robustness test. The cumulative COVID-19 test data were obtained from https://ourworldindata.org/grapher/full-list-total-tests-for-covid-19.

11. Population size (lnPopu) is not included as a control variable in the empirical models with Peak (cumulative confirmed cases per 100,000 population) as the dependent variable.

12. The test results in this study showed that the detection capability was significantly positively or negatively correlated with the cumulative confirmed cases per 100,000 population or the consumed days in the crisis stage.

13. Various organizational behaviour scholars regard time as a social construct and argue that actors can exploit it in ‘time tactics’ (Fleischer 2013). Pollitt (2008, 177–178) lists a series of ‘time tactics’ that include ‘delaying’, ‘speeding up’, and ‘claiming it is too late to do any more’. Other time tactics, including ‘in advance’, ‘pre-emptive’, ‘prompt’, ‘reactive’, and ‘slowing down’, have been discussed in previous literature (Rosenthal and Kouzmin 1997; Fleischer 2013; Schedler and Santiso 1998).

Additional information

Funding

No funds, grants, or other support were received.

Notes on contributors

Shiming Zheng

Shiming Zheng is a full professor at the School of Public Administration and Emergency Management at Jinan University, No.601, West Huangpu Avenue, Guangzhou, China 510632 (email: [email protected]). His research areas include policy process, emergency management, and environmental policy.

Hongxia Li

Hongxia Li is a doctoral candidate at the School of Public Administration at South China University of Technology, 381 Wushan Road, Tianhe District, Guangzhou, China 510641 (email: [email protected]). Her research areas include crisis management, policy decision-making, and collaborative governance.

Hao Sun

Hao Sun is an assistant professor in the Department of Government and Public Affairs at the School of Civic Leadership, Business and Social Change at Gallaudet University, 800 Florida Ave NE, Washington, Dc 20002, USA (email: [email protected]). His research areas include public budgeting and financial management, public policy and research methodology.