The Effect of Health and Economic Costs on Governments’ Policy Responses to COVID‐19 Crisis under Incomplete Information

• Published date: 01 November 2021
• Author: Germà Bel,Óscar Gasulla,Ferran A. Mazaira‐Font
• Date: 01 November 2021
• DOI: http://doi.org/10.1111/puar.13394

Research Article

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 1131

Abstract: The COVID-19 pandemic has become an unprecedented health, economic, and social crisis. The present

study has built a theoretical model and used it to develop an empirical strategy, analyzing the drivers of policy-

response agility during the outbreak. Our empirical results show that national policy responses were delayed, both by

government expectations of the healthcare system capacity and by expectations that any hard measures used to manage

the crisis would entail severe economic costs. With decision-making based on incomplete information, the agility of

national policy responses increased as knowledge increased and uncertainty decreased in relation to the epidemic’s

evolution and the policy responses of other countries.

Evidence for Practice

• Governments had incomplete information when they responded to COVID-19.

• Confidence in healthcare-system capacity and expected costs delayed their responses.

• Federal countries were more agile than unitary countries in developing policy responses.

• Healthcare-system capacity does not fully guarantee epidemic management.

The coronavirus outbreak has produced an

unprecedented health, economic, and social

crisis, developing into a transboundary crisis,

as characterized in the study by Boin (2019). Global

leaders, including Antonio Guterres (Secretary

General of United Nations) and Angela Merkel

(Chancellor of Germany), have compared its impact

to World War II.

In a crisis, authorities must engage in coherent

analysis and search for proper responses, despite

time limitations, uncertainty, and intense

pressure (Boin et al. 2005); this has been the case

during the COVID-19 crisis (Van Dooren and

Noordegraaf 2020). The rapid spread of the pandemic

has forced countries to take unprecedented measures.

More than 90 percent of the world’s population lives

in countries that have placed restrictions on people

arriving from other countries. Many of these countries

have closed their borders completely to noncitizens

and nonresidents, according to the Pew Research

Center (see Connor 2020). Quarantines, social

distancing, and isolating infected populations can

contain the epidemic.

There is no clear consensus on the specific impact

of each measure used to mitigate propagation (see

Anderson et al. 2020; Koo et al. 2020). At present,

the literature includes few policy analyses related to

COVID-19. Among these, Moon (2020) has analyzed

the policy response in Korea; Huang (2020) has

shown that collaborative governance (cooperation

between different levels of government and non-

governmental organizations) was a key factor in

Taiwan’s fight against COVID-19; and Gupta

et al. (2020) have analyzed behavioral responses

to policies mandated in the United States. Any

analysis of COVID-19 policy is restricted, given the

provisional character and limitations of the existing

data (Stock 2020).

Despite this, there is a widespread consensus among

researchers and international organizations that early

prevention and response are critical (Grasselli, Pesenti,

and Cecconi 2020), especially given the acute effect of

pandemics on disadvantaged sectors of the population

(Cénat et al. 2020; Deslatte, Hatch, and Stokan 2020;

Furceri and Ostry 2020; Kapiriri and Ross 2020;

Menifield, Charles, and Clark 2020; Scott, Crawford-

Browne, and Sanders 2016).

The available information allows us to analyze why

some national policy responses have been more agile

than others. Within the domain of policy decision-

making and implementation, agility is defined

as “speed in responding to variety and change”

(Gong and Janssen 2012, S61). Lai (2018, 459)

defines agility as the “iterative, successive process

Germà Bel

Óscar Gasulla

Ferran A. Mazaira-Font

Universitat de Barcelona

The Effect of Health and Economic Costs on Governments’

Policy Responses to COVID-19 Crisis under Incomplete

Information

Ferran A. Mazaira-Font holds a degree

in Mathematics and is a doctoral candidate

in Economics at the Universitat de

Barcelona. He worked as a consultant at

McKinsey&Company and is the Director of

Arcvi, a consulting firm specialized in Big

Data and Advanced Analytics.

Email: ferranmazaira@gmail.com

Oscar Gasulla holds a bachelor’s degree

in Medicine by Universitat de Barcelona’s

Hospital Clinic faculty. He is currently

working as a physician at Hospital

Universitari de Bellvitge (UB) where he

undergoes a specialization in diagnostic and

interventional radiology.

Email: ogasulla@bellvitgehospital.cat

Germà Bel is a professor of Economics

and Public Policy at Universitat de

Barcelona. Director of the Observatory of

Analysis and Evaluation of Public Policies

at UB (OAP-UB). His research focuses on

government reform, local public services,

transportation, and infrastructure. On these

topics he has published several books

and more than one hundred peer-referred

articles. Editor of

Local Government Studies

.

Email: gbel@ub.edu

Public Administration Review,

Vol. 81, Iss. 6, pp. 1131–1146. © 2021

TheAuthors. Public Administration Review

published by Wiley Periodicals LLC on behalf of

American Society for Public Administration.

DOI: 10.1111/puar.13394.

1132 Public Administration Review • November | Decembe r 2021

of adjustment and routine-breaking actions.” Agility is related to

policy-response quality (Lai 2018); it is also an aspect of robustness

in policy design (Howlett, Capano, and Ramesh 2018). It has been

a key factor in countries like South Korea, which has dealt with

the COVID-19 crisis successfully (Moon 2020). Agility is thus

a relevant policy issue, as the time dimension is central to crisis

management. The policies that governments have implemented to

deal with COVID-19 have followed distinct national (rather than

consensual international) standards, in line with policy responses to

previous epidemic crises (Baekkeskov 2016; Vallgårda 2007).

This article investigates why some countries took longer to

institute lockdown measures than others. We present a model that

characterizes the drivers of coronavirus reaction time, namely the

number of known diagnosed cases per million people (incidence

rate) when the government approved hard measures (partial or

complete lockdowns). Our base model includes three main factors:

the expected capacity of each health system to deal with the outbreak,

the expected economic costs of hard measures, and the level of

information available to governments forming these expectations.

We extend our analysis to account for differences in governance and

political regimes, emotional beliefs and biases affecting the assessment

of pandemic-related risk, and political survival factors.

We estimate an equation derived from our modeling. Using data

from the OECD and European countries, we find that three main

factors are statistically relevant. First, the government’s expected

capacity to fight the outbreak, measured as total healthcare

expenditure per capita (adjusted for purchasing power parity), is a

factor that delays policy response, accounting for 26.6 percent of

the total delay. The higher a government’s healthcare expenditure,

the more it is likely to believe it can handle the outbreak—hence the

longer delay in responding.

When it comes to preventing economic costs, the more a country

is exposed to globalization and trade, the more (relatively) affected

it will be by hard measures, such as border closures. We use

total trade (% Gross Domestic Product) and the total travel and

tourism contribution to GDP as proxies for the expected cost of

hard measures. Both are highly significant; together, they account

for 37.0 percent of the total predictive power of the model. As

expected, the higher the cost, the slower the reaction.

To represent the level of information, we use the number

of countries that instituted hard measures before a country

experienced her first coronavirus cases. As expected, countries

that experienced their first coronavirus cases when other countries

already had lockdowns in place anticipated their responses. The

level of information is responsible for 19.5 percent of the model’s

explanatory power. The evidence also confirms the relevance of

decision-making processes and types of decision makers. Concretely,

federal states are more agile than unitary states.

In regard to emotional and perception-related factors, proximity

bias—represented by the distance from Wuhan to the capital city of

each country—accounts for 5.9 percent of response agility. Finally,

we extend our analysis by testing several variables related to values,

ideological biases, and the political survival hypothesis, finding no

systematic role for any of these factors.

The rest of the article is organized as follows. First, we outline the

theoretical framework used to model the speed of response during

the COVID-19 outbreak and formulate empirical predictions,

according to our model. Next, we discuss the data and present

empirical results derived from our base equation. We extend the

analysis by considering several additional hypotheses. We then

conduct robustness checks. Finally, we draw our main conclusions

and discuss some policy implications.

Modeling the Decision of the Policy Response to the

Crisis

We present a theoretical model developing an empirical strategy

that we later follow to analyze the drivers of policy-response

agility. We begin with a basic model, representing a cost–benefit

analysis carried out by a rational, benevolent government, which

cares only about social welfare and has incomplete information

on the pandemic. We then present two extensions. First, we allow

for different types of decision makers (governments and political

systems). Second, we consider the possibility that governments are

(1) not entirely rational and potentially emotionally biased and

(2) not fully benevolent, but driven by self-interest (i.e., stay in

office).

Base Model: Benevolent Government with Incomplete

Information

At the start of the pandemic, a set of natural features, such as the

density of population (Wong and Li 2020), the share of population

above 65 years old and with pre-existing comorbidities (Álvarez-

Mon et al. 2021; Knight et al. 2020), temperature, and humidity

(Mecenas et al. 2020), determine the virus reproductive number

under no contention measures, ρ, and the death rate, d. The

strategies used to fight the outbreak can be modeled as a sequential

decision-making process with incomplete information, where

governments, instead of observing the true parameters involved in

decision-making, achieve only partial estimations. As noted in the

introduction section, even after 7 months we lack clear knowledge

of how the virus is propagated. We do not know how effective the

various mitigation measures are (Stock 2020). Indeed, the very

first response guidelines issued by the WHO in January 2020 were

mainly addressed to communication and clinical management

(WHO 2020a; WHO 2020b), and did not consider specific

recommendations on contention measures, since due to the lack of

information it was not even clear whether the virus was transmitted

between humans.

In every time period, a government can decide to implement either

hard or soft measures to contain the virus. If the government

implements soft measures (SM) at time t (e.g., temperature

control at airports or testing people with symptoms coming from

affected countries), the transmission rate is reduced to ρt = δSρ.

If it implements hard measures, it loses π units of utility (lost

production) but reduces transmission rate to δHρ, with δH < δS < 1.

It is worth highlighting that, according to cross-country estimates

(Hilton and Keeling 2020; Katul et al. 2020), all countries in our

sample, no matter natural determinants, had reproductive numbers

far above 1 (different methodologies lead to estimates ranging from

2 to 6.5), which imply that the pandemic would collapse their

healthcare system unless massive tracking and severe contention

measures were taken.1