can lockdown reduce infection growth in developing

Can Lockdown Reduce Infection Growth in

Developing Countries? Evidence from COVID-19

Cases

Shafiun N. Shimul, PhD1

Abstract Background:

When crippled with COVID-19 infection, a substantial number of countries

have adopted ‘lockdown’ or similar measures to suppress the spread. This

instrument is often considered as the only viable option for curbing

infection spread both in developed and developing countries; however,

some experts have a cynical view on its effectiveness. Exploiting cross-

country lockdown information, effectiveness of lockdown on slowing the

pace of COVID-19 can be elicited.

Methods:

The study intends to understand whether lockdown or similar measures can

suppress infection growth in developing countries. In this pursuit, the study

uses panel regression-based difference in difference and GMM estimation method.

Results:

This study finds that lockdown type measures are not as effective in developing

countries as in developed nations. Nevertheless, staying at home order, income

support programs, and other social distancing measures are found to be effective

for both developed and developing countries. Also, the timing of the lockdown

is found to be vital. One the one hand, enforcing a nationwide lockdown too

early, i.e., when cases are very low, may not yield expected outcome; on the

other hand, enforcing lockdown too late is also ineffective.

Conclusion:

Even though this study does not find strong evidence of the effectiveness of

lockdown in curbing infection growth in developing countries, these findings do

not necessarily suggest that lockdown should not be enforced in developing

countries. Rather it indicates that lockdown should be combined with other

complementary measures such as contact tracing, extensive testing, income

support for the poor, effective management of informal and migrant workers to

make the lockdown effective. Merely declaring lockdown, without accompanying

other must-have measures, will hurt the economy without contributing much to

reducing the growth of infection.

Keyword: Lockdown, COVID-19, Growth of Infection, Mobility, Social Distancing,

Govt. Response

JEL Codes: H2, H3, I1, I3

1 Associate Professor, Institute of Health Economics, University of Dhaka & Consultant, The World Bank; Email: [email protected];

[email protected];

ORCiD: 0000-0002-0628-1056

mailto:[email protected]

mailto:[email protected]

https://www.researchsquare.com/article/rs-124449/v1

1

Background

To contain COVID-19, a significant number of countries have adopted

‘lockdown’ or similar measures. One view sees this type of instrument

as the only viable option of curbing infection spread both in developed

and developing countries. The countervailing view is that this will not

work well in developing countries for several reasons. First, the

cultural and institutional practice is much differing in developing

countries whenever it comes to the tendency to comply with laws and

regulations. Second, a vast majority of developing world has lower

literacy rates, which may be positively correlated with superstitious

beliefs that a contagious disease like COVID-19 will not target the pious

people rather the sinners. Third, millions of impoverished people, who

live hand to mouth, are hosted in the developing countries. They cannot

afford to confine themselves at home for a long time as not going out

of home is tantamount to no income and no food. Therefore, maintaining

social distancing—key objective of a lockdown--is not a choice for

millions of people in those countries. In addition, those countries

cannot afford to provide financial and food assistance at a scale that

can reach most of the poorest segment of the population, let alone to

the ones who are relatively well off. Be it for relapse of regulation

or lack of affordability, or economic structure; these countries cannot

afford lockdown for longer period. As a result, developing countries may

not be able to suppress the spread of the outbreak successfully with

‘lockdown’--a widely prescribed playbook of curbing the spread of

infection. This strategy, however, has been touted as the most, if not

the only, practical strategies to restraint the spread of infectious

diseases. This is even more true for COVID-19 since it is enormously

contagious.

By May 2020, more than one-third of the countries of the world have

adopted lockdown type measures with closings school colleges,

restricting the movement of the transports, and other draconian measures,

partly influenced by the prescriptions of many international

institutions or maybe just due to ‘following the crowd’ principle. For

instance, India and Bangladesh have also adopted this policy within few

days of first COVID-19; however, their cases, especially in India, have

been skyrocketing since then. This rocky rise has influenced many people

to believe that lockdown type measures are not an effective instrument

in those areas even though it has been showing promises in the western

world.

While lockdown itself is an essential tool, the timing of the lockdown

turns out to be crucial as well. For instance, Fiji is one of the

countries which adopted lockdown very early; the same is true for

Australia, and both were able to reduce the infection and keep total

tally very low. New Zealand, Sri Lanka have also shown some illuminating

success. On the other hand, many countries, including Italy and the US,

have adopted lockdown (or some form of it) in such a stage that it was

difficult to see any impact. Because it is the nature of the epidemic

2

that follows logistic type distribution, so when they are in an

exponential growth stage, it is incredibly difficult, if not impossible,

to reduce the infection significantly. The characteristics of having a

relatively long incubation period, being extremely contagious, having

the feature of asymptotic cases makes the fight against COVID-19 an

uphill battle. Though much debates as to the effectiveness of lockdown

are seen in the media talks and in political spheres, any rigorous study

in this issue is lacking which has left further debates to grow. Some

empirical exercises on the impact of non-pharmaceutical intervention

(NPI) is available for China(Lau et al., 2020) or for some specific

regions especially for Europe(Flaxman et al., 2020); and a few more

theoretical work are also evident that explains the impact of

misinformation(Bursztyn, Rao, Roth, & Yanagizawa-Drott, 2020) and

optimal timing of lockdown (Alvarez, Argente, & Lippi, 2020). In a recent

paper, Moghbelli et al., 2020 argued that an RCT can provide better

answer since countries are taking several intervention altogether which

makes it difficult to isolate the impact of any specific intervention.

While RCT can provide a better evidence on what works and what does not,

it is difficult or costly to implement such experiments in pandemic

condition.

While one group takes effectiveness of lockdown as granted, another group

is flooding the claims of its ineffectiveness—with both groups completely

without a strong evidence. In theory, it should work since lockdown means

an increase in social distancing, and so the virus will not find its

hosts, and its effort to grow will be thwarted. Nevertheless, in

practice, a lockdown--unless it can be enforced the way draconian

governments have recently executed--may not ensure a slower growth in

infections. Instead, it may create a public outcry if it persists for

long time, and as a result, people may violate the lockdowns limiting

its prowess to curb infection. Furthermore, in most of the developing

countries, many migrant workers live in the big cities; closure means

returning home and at a large number, and flocking together. It may

increase the spread of the virus, increasing the chance of community

transmission.

Using COVID-19 case data from all countries of the world, this study

attempted to answer this question—whether lockdown can tame the spread

of the growth of infection. To do so, this study employs a sophisticated

modeling approach---it uses both a panel version of difference-in-

difference (DiD) and a dynamic panel regression (GMM) modeling approach.

The study finds that lockdown does not necessarily reduce the infection

in developing countries even though it does a pretty good job in curbing

infections in the developed nations. Most importantly, the study finds

that it is the timing of lockdown that matters most rather than the

lockdown itself. The study does not necessarily imply that developing

countries should not enforce lockdown. Rather, this study deduces that

lockdown should be enforced along with other must-have measures to reap

the fruit of it.

3

The paper is organized as follows. After the introduction, the paper

provides an account of methods. Then it presents the findings with

discussions followed by a conclusion.

4

Methods

Data

The data includes the daily infection rates from December 29, 2019 until

May 9, 2020. This data is collected from the European Center for Disease

Control and Prevention (European Centre for Disease Prevention and

Control, 2020). Along with case data, this study also uses dataset from

three other sources. One is the google mobility data which provides

change in the mobility compared to base period (pre-COVID-19) in six key

areas: grocery and pharmacies, parks, transit stations, retail &

recreation, residential and workplace(Google, 2000). This dataset offers

a reasonable proxy measures of compliance to the lockdown which has

already been used in other COVID-19 related studies (Sampi, 2020;

Yilmazkuday, 2020b). And mobility data has been used in other studies

to understand the impact social distancing(Yilmazkuday, 2020a) . The

second data source is the stringency of lockdown collected from the

Blavatnik School of Government, University of Oxford (Hale, Petherick,

Phillips, & Webster, 2020). This dataset provides various levels of

closure and containment measures and economic responses of government

towards COVID-19 as well as a stringency index. In this study, I use

eight measures of closures and containments, stringency index and income

support measures. All are ordinal scale meaning the higher number, the

higher the extent, except stringency index which is measured as index

and normalized to 100. Third data set is from a Wikipedia page

(wikipedia, 2020) which lists the dates of when lockdown was enforced

in each countries. This dataset is cross-checked with other sources

including newspapers of the respective countries. Figures 1-8 provide

some visual explanation of the data and their relationship among the

variables used in this study.

Empirical strategy

Along with traditional difference in difference estimation method, two

broad empirical strategies are applied to understand the impact of

lockdown on the growth of COVID-19 Cases: Panel Regression-Based

Difference-in-Difference (DiD) estimation and GMM estimation.

Regression Based DID estimates Instead of a straight difference in difference estimates, the panel

regression-based estimation method is used. There are a few advantages

of regression difference in difference estimation in comparison with

straight DiD estimates: a) it is easy to calculate standard errors b)

other related control or confounding variable can be applied c) inclusion

of multiple period or trend is possible, d) it can capture the impact

under different level of intensities (Shimul, 2017), and in this study,

I used both lockdown and its intensity. A similar approach has been

applied to understand the impact of social mobility on COVID-19

spread(Yilmazkuday, 2020b). The following regression technique is used

to estimate the impact of lockdown:

5

𝐺𝑖𝑡 = 𝛽1 + 𝛽2𝑇𝑟𝑒𝑎𝑡𝑖 + 𝛽3𝑇𝑟𝑒𝑎𝑡𝑡 + 𝛽4(𝑇𝑟𝑒𝑎𝑡 ∗ 𝑃𝑜𝑠𝑡)𝑖𝑡 + 𝑍𝑖𝑡 + 𝜀𝑖𝑡 (1)

Here, 𝐺𝑖𝑡= daily growth of infection, Treatment=a dummy if the observation

is in the treatment group i.e. the country has adopted lockdown;

Post=post treatment dummy, 𝑍𝑖𝑡 = other controls or confounding variables,

𝑖 =country/panel, and 𝑡 =time(day)

GMM Estimates The estimation technique (1) can provide a good estimate under certain

assumptions: no serial autocorrelation, no unobserved heterogeneity, and

the regression equation does not include dynamic component (i.e., lag

of dependent variable). However, this estimation technique will not be

appropriate if any of these assumptions are violated. While some of the

problems can easily be tacked by using robust standard errors and fixed

effects (if there are time-invariant components), if any dynamic

components are used or if there is an endogeneity (reverse-causality),

then OLS or Panel Regression with fixed effects will not be ideal choice

of estimation. In that case, the coefficients will be biased; and either

Instrumental variables or GMM approach would be preferred. Since the

growth of infection will likely to be largely determined by the growth

of infection of previous periods, the inclusion of autoregressive

variables is necessary. Also, the inclusion of lag dependent variable

can capture many unobservable components. In this case, the specification

will be:

𝐺𝑖𝑡 = 𝛽1 + 𝛽1𝐺𝑖,𝑡−1 + 𝛽2𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑠𝑒𝑠𝑖,𝑡−1 + 𝛽3𝑇𝑟𝑒𝑎𝑡𝑡 + 𝛽4(𝑇𝑟𝑒𝑎𝑡 ∗ 𝑃𝑜𝑠𝑡)𝑖𝑡 + 𝑍𝑖𝑡 + 𝑣𝑖𝑡 (2)

However, the inclusion of lag dependent variables introduces other

problems in the estimation. Now, if OLS or Panel Regression will be

biased and inconsistent since the error term will be correlated with 𝐺𝑖𝑡

due to the presence to lag dependent variable in the right-hand side of

the equation. Arellano and Bond (Arellano & Bond, 1991) offer an

estimation technique--difference GMM-- to deal with the problem

associated with heterogeneities. Taking a first difference of the

equation (2), we get-

∆𝐺𝑖𝑡 = 𝛽1∆𝐺𝑖,𝑡−1 + 𝛽2∆𝑇𝑜𝑡𝑎𝑙 𝐶𝑎𝑠𝑒𝑠𝑖,𝑡−1 + 𝛽3∆𝑇𝑟𝑒𝑎𝑡𝑡 + 𝛽4∆(𝑇𝑟𝑒𝑎𝑡 ∗ 𝑃𝑜𝑠𝑡)𝑖𝑡 + ∆𝑍𝑖𝑡 + ∆𝑣𝑖𝑡 (3)

The problem is in (3), ∆𝐺𝑖,𝑡−1 is still endogenous. Arellano and Bond show

that if this variable is instrumented with lags of their differences

under some sets of moment conditions, this estimation will be valid, and

this called difference GMM. However, Blundell and Bond(Blundell & Bond,

1998) show that if T is a small or dependent variable is highly

persistent, then difference GMM will pick weak instruments leading to

invalid results. Even though in this study, T is large for most

countries, time-persistency (i.e. this period’s value depend on its past

values) is extremely likely as this is the nature of infection. However,

Blundell and Bond (Blundell & Bond, 1998; Blundell, Bond, & Windmeijer,

2000) show the way to tackle the estimation problem posed by the

inclusion of lag dependent variables when there is time-persistency.

6

They show that instruments from both differences and lags can be used

to make the instruments stronger under some set of moment conditions,

and this method is called system GMM. It performs better than the

difference GMM in a condition that matches with this study (potential

time-persistency). Therefore, this study uses system GMM.

Results

Findings from the panel regression (DiD estimates)

Effects of lockdown and its stringency Table 1 reports traditional difference in difference estimates to

understand the effect of lockdown in the growth of infection. The DiD

coefficient (coefficient of lockdown-post) is statistically significant

for developed countries whereas this coefficient is statistically

insignificant for the panel of developing countries. Once we include

stringency of lockdown then the same coefficient becomes insignificant

for both developing and developed countries. The variable ‘lockdown’ is

a dummy of whether the country adopted lockdown or not whereas the

stringency is more about the extent of it. While stringency denotes the

government response to curb the spread of infection; other important

aspect--the citizen’s response to the governments’ call--is also

pertinent. The google mobility data can capture that attribute.

[Table 1 will be about here]

Effects of lockdown with mobility and testing


As in Table 2, it is evident that when mobility data (here increase in

the ‘staying at home’) is included in the regression, then the

coefficient of post-lockdown remains insignificant for low income

countries but it is still significant for developed countries (in one

model). However, an increase in compliance factor i.e. staying at home

appears to be effective in curbing the infection growth. Inclusion of

tests level does not affect the coefficients of the regression

drastically. As explained in the methodology section, the growth of

infection in the current period might be largely explained by the

previous periods and so it is important to use a dynamic panel data model

to understand the true impacts. The following section illustrates the

results obtained from the GMM estimates.

Findings from the GMM estimates

Effects of lockdown along with stringency Table 3 demonstrates the impact of lockdown along with other controls

on the growth of infection. As expected, the previous periods’ growth

appears to be a significant predictor of the disease growth.


7

The after-lockdown variable, which is included to understand the impact

of lockdown, assumes an unexpected sign for developing countries. The

same coefficient, however, is statistically highly significant (p-value

<0.01) for developed nations. Mobility has an expected sign but not

statistically discernable. The trend coefficient is negative for both

developed and developing countries though it is significant for

developing countries only. Instruments used in this regression appear

to be valid, as demonstrated through Sargan and Arellano-Bond, and Hansen

tests.

Effects of income support Although lock-down seems to have a little effect on curbing the spread

of infections in developing countries, income support shows a strong

impact. Table 4 shows that income support is statistically significant

(p value<0.01) in curbing the spread of infection. Interestingly, the

same variable is not statistically significant for developed countries.


Timing of lockdown The timing of lockdown can play a significant role in curbing the spread

of infection. For instance, if a strict lockdown is enforced in the

beginning, it might be easier to stop the growth of infection. On the

other hand, enforcing lockdown too early may backfire if people do not

take it seriously as there could be a behavioral issue related to the

compliance.


For example, if cases are very low when lockdown is enforced, people may

underestimate the actual risk which may affect the compliance as well

the effectiveness of these non-pharmaceutical interventions. Moreover,

people in developing countries-- where the massive level of income

support is not possible to render--will not be able to comply with stay

home orders as a vast majority of them live hand to mouth, and they have

to come out of home for some income opportunities just to feed their

family. To understand whether the timing of lockdown is crucial, the

timing of lockdown (lockdown after how many days since first cases) is

categorized as lockdown within 15 days, within 30 days, within 45 days,

and after 45 days or never. Regression Table 5 shows that the timing of

lockdown does not seem to be important for developing countries as

lockdown does not affect the growth of infection at all; however, the

developed countries that enforced lockdown within 30 days of first case

detection were able to contain the spread of virus significantly.

Type of closure and mobility Various forms of closures have been adopted in multiple countries ranging

from school closure to public event closure and almost complete shutdown

of the international passenger traveling. While the stringency index

provides an aggregate of all these measures, it is quite likely that all

measures would not be equally effective. For instance, when there is no

recorded infection, school closure may not be an optimal response;

rather, screening international traveler, identifying and isolating them

8

would be. Likewise, the types of mobility can also determine the impact

of lockdown. For instance, a substantial increase in staying at home and

a considerable reduction in public transports can help reduce the

infection and slow down its pace. While stringency index measures

government’s response, mobility can be treated as the citizens’

compliance in response to the government’s measures. Table 6-9 (shown

in the appendix) provide the effects of lockdown under various mobility

and stringency conditions. Again, it turns out that the lockdown reduces

the growth of infection in the developed countries no matter what

mobility variables are used as a control. For developing countries,

however, the coefficient of lockdown is significant only in a few

specifications, and so it is not robust. In the case of stringency

measures, similar findings are observed. Even some variables have

unexpected signs. For instance, school closure, in fact, does not help

reduce infection. For developed countries, all types of closings are

leaving expected impacts; but only a few variables have significant

coefficients for developing countries.

Discussion This study has shown that lockdown has been successful in reducing the

speed of infection spread in developed countries but not in developing

countries. In addition, not all government measures are equally

effective. While stringency has a little to do in curbing infection

growth, citizens’ response appears to be extremely useful in this regard.

In both developed and developing countries , staying at home is found

to be an effective measure to slow down the infection growth, and a

similar conclusion is drawn from other studies related to COVID-19

(Flaxman et al., 2020; Yilmazkuday, 2020b). Most interestingly, the

extent of income support has a substantial effect on reducing

transmission of diseases in the developing world.

Several key issues of discussion have spawned from this study. First,

government response teaming up with citizens’ responsibility turned out

to be instrumental in succeeding ‘flattering of the curve’. While most

of the draconian measures appear to have little or no effect in the

developing countries, staying at home help reduce the spread. Developing

countries cannot afford to provide continuous income support to the poor

for an extended period; if it can be done; a reduction of infection’s

growth can be possible.

Second, while the lockdown is essential, the timing is also equally

important. Surprisingly, developing countries that adopted lockdown

policies within 15 days of first case detection were not notably

successful in reducing infection compared to the countries that took

lockdown after 15 days. Though it may sound counterintuitive, it is quite

plausible. For instance, in Bangladesh, it was observed that people have

already reduced the mobility even before lockdown is enforced, and so

lockdown did not bring anything new in the table as far as the citizens’

response is concerned. On the other hand, many people may not fully

9

perceive the severity and the spreading capability of COVID-19 when the

number is too low. If the lockdown is enforced 30 days after the first

case, by the time, the population may see 50 to few hundred cases, and

this may help them under the true risk of spread which again can reduce

mobility and influence their behavior. And as the most of the countries

did not experience an explosion of cases within 30 days, contract

tracing, cluster lockdown instead nationwide lockdown and other

instruments could have effectively been applied. This strategy would

help keep economic activities alive without jeopardizing the

population’s health. Also, this can help reduce ‘compliance fatigue’.

Third, although many developing countries were quick in enforcing

lockdown, there was a significant mismanagement and lock of proper

planning to implement it. For instance, declaring lockdown a few days

early and allowing a lot of migrant workers to leave the city in congested

settings have increased the risk of higher spread. Moreover, income

support to the poor would be much less than ‘enough’ in the developing

countries(Biswas, n.d.).

Forth, seeing the successful containment of COVID-19 in China, and

getting recommendations from various international organizations, many

governments of the developing countries went on enforcing lockdown

without giving much-needed attention to other parts of the

recommendations such as increase the capacity of massive testing,

introducing contact tracing at scale, preparing the health system

especially increase in capacity of isolation. Without all these measures,

lockdown will not bring much health benefit, though the economic loss

expected to be massive (BD economy loses Tk 33b every day during

shutdown: Study, n.d.; Fraser, n.d.).

Some limitations of this study are worth mentioning. First, COVID-19

spread is still in place, so the current study uses the interim data,

and so inference made here may not persist in the long run. Second, most

of the dependent variables especially stringency related variables are

not continuous, rather ordinal, which might have some effects on the

results. Third, the inclusion of too many instruments may under-reject

the null, and so that might have resulted in more insignificant

coefficients for developing countries. Moreover, the presence

multicollinearity among few variables are very likely that might have

caused under-rejection of null. Forth, instead of estimating R0 or Rt—

the traditional measure infection growth--the current study uses the

growth of infection directly. However, interestingly the findings of

this study match with studies where R0 or Rt used for developed countries

(Flaxman et al., 2020; Yilmazkuday, 2020b), so this variable not

necessarily a weak one.

Conclusions The study shows that lockdown type measures are not highly effective for

developing countries even though these types of means are very effective

10

in developed nations. Nevertheless, staying at home order and income

support programs and other social distancing measures are found to be

effective for both developed and developing countries. In addition, the

timing of the lockdown also appears to matter. This study does not

suggest that lockdown should be enforced in the developing countries.

Rather, it suggests that lockdown should be combined with contact

tracing, extensive testing, income support for the poor, management of

informal or migrant workers—to make the lockdown effective. Merely

declaring lockdown, without other required measures, will hurt the

economy without contributing much to reducing the growth of infection.

Abbreviations:

GMM- Generalized Method of Moments

RCT- Randomized Control Trial

DiD- Difference -in – Difference

COVID-19- Coronavirsus Disease 2019

NPI- Non-Pharmaceutical Intervention

Declarations

Ethics approval and consent to participate: Only secondary and public

use data is used and so ethical approval and consent to participate

was required

Consent for publication: Author fully agree to publish it in the

journal. It is not funded project and single authored, no other

consent was required

Availability of data and material: All data, programme codes,

materials will be available on request

Competing interests: author declares no conflict of interest

Funding: No funding received for this work

Authors' contributions: SNS has done all article related work

Acknowledgements: Oxford University and Google for making data

available for public use

11

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https://en.wikipedia.org/wiki/National_responses_to_the_COVID-19_pandemic

Yilmazkuday, H. (2020a). COVID-19 Deaths and Inter-County Travel: Daily

Evidence from the U.S. SSRN Electronic Journal, 1–28.


Yilmazkuday, H. (2020b). Stay-at-Home Works to Fight Against COVID-19:

International Evidence from Google Mobility Data. SSRN Electronic

Journal. https://doi.org/10.2139/ssrn.3571708

13

TABLES

Table 1: Panel Regression with and without Stringency Index (1) (2) (3) (4)

Low Income High Income Low Income High Income

Lag of Total Cases

(100 thousand)

0.448

(0.292)

0.014

(0.009)

0.514

(0.418)

0.015

(0.010)

After Lockdown -0.046

(0.041)

-0.091***

(0.019)

-0.044

(0.047)

-0.020

(0.023)

Trend -0.005***

(0.001)

-0.004***

(0.000)

-0.005***

(0.001)

-0.003***

(0.000)

Stringency Index

0.000

(0.001)

-0.003***

(0.0001)

Constant 106.138***

(11.107)

80.064***

(6.222)

114.711***

(16.533)

55.529***

(8.710)

Observations 2956 6602 2615 5964

Adjusted R2 0.023 0.050 0.016 0.051

Standard errors in parentheses * p < .10, ** p < .05, *** p < .01

Table 2: Panel Regression with mobility and test (1) (2) (3) (4)

Low Income High Income Low Income High Income

Lag of .Total Cases (100

K)

0.528

(0.650)

0.019

(0.019)

-0.167

(0.559)

0.001

(0.011)

After Lockdown 0.036

(0.063)

-0.003

(0.030)

-0.052

(0.067)

-0.055***

(0.021)

Increase in Staying Home -0.007***

(0.002)

-0.005***

(0.001)

-0.003

(0.003)

-0.005***

(0.001)

Trend -0.004***

(0.001)

-0.005***

(0.001)

-0.004

(0.003)

-0.005***

(0.001)

Tests per thousand

0.023

(0.053)

-0.000

(0.001)

Constant 96.574***

(22.235)

113.277***

(12.466)

77.373

(55.155)

104.290***

(11.814)

Observations 1465 3822 370 1898

Adjusted R2 0.020 0.054 0.006 0.212


Table 3: System GMM Regression with Mobility and Stringency

(1) (2) (3) (4) Low Income High Income Low Income High Income

Lag of Growth of

Infection

0.319***

(0.045)

0.625***

(0.008)

0.366***

(0.135)

0.680***

(0.097)

Lag of Total Cases

(100 K)

-0.729

(0.710)

-0.002

(0.002)

0.136

(0.483)

0.000

(0.008)

Any Lockdown -0.016

(0.092)

0.011

(0.007)

-0.104

(0.242)

0.108

(0.127)


(0.064)

-0.014***

(0.004)

0.091

(0.256)

-0.110

(0.114)

Increase in Staying

Home

-0.001*

(0.001)

-0.000

(0.000)

Trend -0.003***

(0.000)

-0.001***

(0.000)

-0.002

(0.002)

-0.000

(0.001) Stringency Index

-0.002

(0.001)

0.001

(0.001)

Constant 58.226***

(9.013)

28.370***

(0.661)

35.714

(34.107)

7.781

(14.454)

Hansen over-

identification test

22.453 67.861

p-value 1.000 1.000

Sargan test of over id.

restrictions

18.992 8.298 21.430 17.573

14

p-value 1.000 1.000 1.000 1.000

Arellano–Bond test for

1st order serial auto.

-3.480 -2.884 -5.094 -8.869

p-value 0.001 0.004 0.000 0.000

Arellano–Bond test for 2nd order serial auto.

1.768 1.301 2.449 3.963

p-value 0.077 0.193 0.014 0.000

Number of Groups 33.000 75.000 54.000 95.000

Observations 1427.000 3720.000 2552.000 5818.000 Standard errors in parentheses * p < .10, ** p < .05, *** p < .01

Table 4: Effects of Income Support

(1) (2)

Low Income High Income

Lag of Growth of Infection 0.185***

(0.014)

0.715***

(0.003)

Lag of Total Cases (100 K) -0.421

(0.289)

-0.002**

(0.001)

Any Lockdown -0.028

(0.020)

0.030***

(0.002)

After Lockdown -0.009 (0.019)

-0.027*** (0.002)

Income Support -0.018***

(0.003)

-0.001

(0.000)

Trend -0.003***

(0.000)

-0.000***

(0.000)

Constant 58.944***

(3.857)

5.291***

(0.255)

Hansen over-identification test 43.148 83.727

p-value 1.000 1.000

Sargan test of over id. restrictions 16.674 11.049

p-value 1.000 1.000

Arellano–Bond test for 1st order serial auto.

-4.231 -3.780

p-value 0.000 0.000

Arellano–Bond test for 2nd order serial

auto.

2.541 2.399

p-value 0.011 0.016

Number of Groups 48.000 89.000

Observations 2283.000 5407.000 Standard errors in parentheses * p < .10, ** p < .05, *** p < .01

Table 5: System GMM Regression with Timing of Lockdown2

(1) (2) (3) (4) (5) (6) (7) (8)

Within

15 Days

Within

30 Days

Within

45 Days

After 5

Days or

Within

15 Days

Within 30

Days

Within

45 Days

After 5

Days or

2 Empty cells mean no observation. And post lockdown variable is dropped as it is correlated with the timing of the lockdown

15

Never Never

Lag of

Growth of

Infection

-0.593*

(0.355)

-2.168

(1.676)

0.000

(.)

0.457***

(0.127)

0.286***

(0.004)

0.284***

(0.046)

0.474***

(0.069)

0.447***

(0.018)

Lag of Total

Cases (100 K)

-133.382

(93.241)

16.902

(38.520)

0.000

(.)

-0.441

(3.834)

1.564***

(0.144)

-0.488***

(0.073)

-4.495

(4.858)

0.001

(0.087)

Any

Lockdown

0.000

(.)

0.000

(.)

0.000

(.)

-0.372

(0.384)

0.000

(.)

-

176.476***

(25.811)

0.000

(.)

-0.000

(0.043)

After

Lockdown

0.000

(.)

0.000

(.)

0.000

(.)

0.000

(.)

0.000

(.)

0.000

(.)

0.000

(.)

0.000

(.)

Increase in

Staying

Home

-0.008**

(0.004)

0.019

(0.049)

0.691

(0.644)

-0.002**

(0.001)

-0.004***

(0.000)

-0.012***

(0.002)

0.006

(0.006)

-0.000

(0.000)

Trend 0.000***

(0.000)

-0.025

(0.038)

-0.000

(0.000)

-0.002

(0.002)

0.000***

(0.000)

0.008***

(0.001)

0.000***

(0.000)

-0.002***

(0.000)

Constant 0.000 (.)

553.289 (828.083)

0.000 (.)

42.282 (40.293)

0.000 (.)

0.000 (.)

0.000 (.)

49.926*** (5.381)

Hansen over-

identification

test

4.797 0.000 0.000 18.457 69.952 20.969 10.077 34.569

p-value 1.000 1.000 1.000 1.000 0.001 1.000 1.000 1.000

Sargan test

of over id.

restrictions

19.183 44.744 20.346 14.392 17.231 36.391 78.556 6.385

p-value 0.742 0.318 0.205 1.000 0.998 1.000 0.999 1.000

Arellano–

Bond test for

1st order serial auto.

-1.399 -0.656 -0.720 -2.885 -3.472 -1.344 -2.335 -2.107

p-value 0.162 0.512 0.471 0.004 0.001 0.179 0.020 0.035

Arellano–

Bond test for

2nd order

serial auto.

-0.013 -0.489 0.392 1.075 0.487 -0.802 1.209 1.235

p-value 0.990 0.625 0.695 0.283 0.626 0.423 0.227 0.217

Number of

Groups

8.000 5.000 2.000 23.000 32.000 28.000 15.000 39.000

Observations 45.000 48.000 23.000 916.000 240.000 217.000 176.000 1322.000 Standard errors in parentheses * p < .10, ** p < .05, *** p < .01

Appendix Table 6: System GMM Regression with Various Forms of Closures:

Developed Countries (1) (2) (3) (4) (5) (6) (7) (8)

Schoo

l

Work Public

Events

Public

Gather

ing

Public

Transp

ort

Stay

at

Home

Restricti

on on

Internati

onal

Travel

Internati

onal

Travel

Control

Lag of

Growth of

0.654*

**

0.707*

**

0.669**

*

0.676**

*

0.602**

*

0.657*

**

0.784***

(0.006)

0.793***

(0.008)

16

Infection (0.00

4)

(0.00

5)

(0.000

)

(0.002

)

(0.003

)

(0.00

3)

Lag of

Total Cases

(100 K)

-

0.004*

**

(0.00

1)

-

0.001

(0.00

1)

-

0.001**

*

(0.000

)

-

0.005**

*

(0.001

)

-

0.003**

(0.002

)

-

0.001

(0.00

1)

-0.001

(0.002)

-0.002

(0.002)

Any

Lockdown

0.219*

**

(0.00

4)

0.042*

**

(0.00

4)

0.093**

*

(0.001

)

0.050**

*

(0.004

)

-

0.011*

(0.006

)

-

0.009*

(0.00

5)

0.055***

(0.009)

0.066***

(0.005)

After

Lockdown

-

0.197*

**

(0.00

4)

-

0.050*

**

(0.00

3)

-

0.070**

*

(0.001

)

-

0.026**

*

(0.002

)

0.020**

*

(0.004

)

0.011*

**

(0.00

4)

-0.068***

(0.006)

-0.071***

(0.004)

C1_School

closing

0.038*

**

(0.00

1)

Trend 0.000*

*

(0.00

0)

-

0.000*

**

(0.00

0)

0.000**

*

(0.000

)

-

0.000**

*

(0.000

)

-

0.001**

*

(0.000

)

-

0.001*

**

(0.00

0)

0.000***

(0.000)

-0.000

(0.000)

C2_Workplac

e closing

0.002*

**

(0.00

0)

C3_Cancel

public

events

-

0.010**

*

(0.000

)

C4_Restrict

ions on

gatherings

-

0.002**

*

(0.000

)

C5_Close

public

transport

-

0.011**

*

(0.001

)

C6_Stay at

home

requirement

s

-

0.005*

**

(0.00

1)

C7_Restrict

ions on

internal

movement

0.011***

(0.001)

C8_Internat

ional

travel

controls

0.013***

(0.001)

Constant -

1.448*

*

(0.60

7)

5.210*

**

(0.36

8)

0.000

(.)

8.701**

*

(0.409

)

23.040***

(0.376

)

19.37

7***

(0.51

1)

-1.042***

(0.292)

0.204

(0.369)

Hansen 88.57 84.78 17485. 82.907 89.650 85.83 86.826 80.426

17

over-

identificat

ion test

5 4 186 1

p-value 1.000 1.000 0.000 1.000 1.000 1.000 1.000 1.000

Sargan test

of over id.

restriction

s

47.95

4

12.81

2

18.365 16.539 13.836 11.07

5

11.289 13.904

p-value 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Arellano–

Bond test

for 1st

order

serial

auto.

-

3.972

-

3.974

-3.977 -3.903 -3.994 -

3.899

-3.950 -3.957

p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Arellano–

Bond test

for 2nd

order

serial

auto.

2.440 2.507 2.470 2.556 2.418 2.584 2.540 2.538

p-value 0.015 0.012 0.014 0.011 0.016 0.010 0.011 0.011

Number of

Groups

95.00

0

95.00

0

95.000 95.000 95.000 95.00

0

95.000 95.000

Observation

s

6039.

000

6027.

000

6033.0

00

5885.0

00

5992.0

00

5752.

000

6020.000 6009.000


18

Table 7: System GMM Regression with Various Forms of Closures:

Developing Countries

(1) (2) (3) (4) (5) (6) (7) (8)

School Work Public

Events

Public

Gatherin

g

Public

Transpor

t

Stay at

Home

Restriction

on

Internationa

l Travel

Internationa

l Travel

Control

Lag of Growth

of Infection

0.248***

(0.009)

0.282***

(0.013)

0.327**

*

(0.018)

0.207***

(0.015)

0.299***

(0.031)

0.292***

(0.018)

0.286***

(0.024)

0.292***

(0.018)

Lag of Total

Cases (100 K)

0.069***

(0.007)

0.099***

(0.007)

0.095

(0.542)

0.688

(0.458)

0.294

(0.355)

0.387

(0.384)

-0.756

(2.198)

-0.626

(0.492)

Any Lockdown 0.328***

(0.013)

-

0.121***

(0.016)

0.101**

*

(0.036)

-0.026

(0.038)

-0.049

(0.054)

-

0.142***

(0.041)

-0.019

(0.127)

0.000

(0.076)

After Lockdown -

0.400***

(0.014)

0.096***

(0.016)

-

0.130**

*

(0.039)

-0.044*

(0.027)

-0.009

(0.031)

0.120***

(0.036)

0.016

(0.038)

-0.108***

(0.038)

C1_School

closing

0.010***

(0.004)

Trend -

0.001***

(0.000)

-

0.003***

(0.000)

-0.001*

(0.001)

-

0.002***

(0.000)

-

0.002***

(0.000)

-

0.003***

(0.000)

-0.002**

(0.001)

-0.001**

(0.000)

C2_Workplace

closing

-

0.016***

(0.001)

C3_Cancel

public events

-0.035*

(0.019)

C4_Restriction

s on

gatherings

-0.007**

(0.003)

C5_Close

public

transport

-

0.027***

(0.005)

C6_Stay at

home

requirements

-

0.022***

(0.004)

C7_Restriction

s on internal

movement

-0.033

(0.021)

C8_Internation

al travel

controls

-0.058***

(0.007)

Constant 14.175**

*

(1.373)

67.626**

*

(2.389)

30.682*

(16.901

)

50.671**

*

(5.757)

45.294**

*

(8.855)

64.521**

*

(4.723)

43.439**

(21.475)

23.250**

(9.772)

Hansen over-

identification

test

49.105 48.903 49.820 49.578 46.202 52.378 50.460 40.388

p-value 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Sargan test of

over id.

restrictions

48.690 15.531 28.647 26.323 15.614 22.304 21.093 20.658

p-value 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Arellano–Bond

test for 1st

order serial

auto.

-4.568 -4.618 -4.652 -4.434 -4.830 -4.433 -4.714 -4.903

p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Arellano–Bond

test for 2nd

order serial

auto.

2.742 2.851 2.987 2.467 2.996 2.892 2.900 3.186

p-value 0.006 0.004 0.003 0.014 0.003 0.004 0.004 0.001

Number of

Groups

54.000 54.000 54.000 54.000 54.000 54.000 54.000 54.000

Observations 2645.000 2635.000 2621.00

0

2564.000 2613.000 2555.000 2620.000 2599.000


19

Table 8: System GMM Regression with Various Forms of Mobility:

Developed Countries

(1) (2) (3) (4) (5) (6)

Retail &

Recreation

Groceries Parks Transportation Workplace Stay at

Home

Lag of Growth

of Infection

0.720***

(0.010)

0.593***

(0.010)

0.572***

(0.010)

0.685***

(0.006)

0.656***

(0.009)

0.625***

(0.008)

Lag of Total

Cases (100 K)

-0.002

(0.001)

-0.004***

(0.000)

-0.005***

(0.001)

-0.003***

(0.001)

0.000

(0.002)

-0.002

(0.002)

Any Lockdown 0.033***

(0.008)

0.024***

(0.005)

0.031***

(0.003)

0.033***

(0.005)

0.018*

(0.009)

0.011

(0.007)

After Lockdown -0.042***

(0.007)

-0.025***

(0.003)

-0.033***

(0.003)

-0.029***

(0.003)

-0.025***

(0.008)

-0.014***

(0.004)

Mobility at

Retail and

Recreation

-0.000***

(0.000)

Trend -0.001***

(0.000)

-0.001***

(0.000)

-0.001***

(0.000)

-0.001***

(0.000)

-0.001***

(0.000)

-0.001***

(0.000)

Mobility at

Groceries &

Pharmacies

0.000***

(0.000)

Mobility at

Parks

-0.000***

(0.000)

Mobility at

Transit

-0.000**

(0.000)

Mobility at

Work

0.000***

(0.000)

Increase in

Staying Home

-0.000

(0.000)

Constant 11.832***

(1.193)

28.442***

(0.904)

30.938***

(1.318)

20.770***

(0.510)

24.502***

(0.805)

28.370***

(0.661)

Hansen over-

identification

test

65.803 69.481 68.680 66.346 70.397 67.861

p-value 1.000 1.000 1.000 1.000 1.000 1.000

Sargan test of

over id.

restrictions

8.865 10.384 10.898 10.198 10.917 8.298

p-value 1.000 1.000 1.000 1.000 1.000 1.000

Arellano–Bond

test for 1st

order serial

auto.

-3.170 -3.148 -2.884 -3.035 -3.146 -2.884

p-value 0.002 0.002 0.004 0.002 0.002 0.004

Arellano–Bond

test for 2nd

order serial

auto.

1.590 1.416 1.287 1.413 1.487 1.301

p-value 0.112 0.157 0.198 0.158 0.137 0.193

Number of

Groups

75.000 75.000 75.000 75.000 75.000 75.000

Observations 4029.000 3999.000 3787.000 3821.000 4124.000 3720.000


Table 9: System GMM Regression with Various Forms of Mobility:

Developing Countries (1) (2) (3) (4) (5) (6)

Retail &

Recreation

Groceries Parks Transportation Workplace Stay at

Home

Lag of Growth

of Infection

0.247***

(0.040)

0.239***

(0.039)

0.035

(0.029)

0.242***

(0.020)

0.302***

(0.052)

0.319***

(0.045)

Lag of Total

Cases (100 K)

0.254*

(0.143)

-0.011

(0.200)

0.323**

(0.160)

0.067

(0.283)

0.146*

(0.077)

-0.729

(0.710)

Any Lockdown -0.004 0.020 0.070** 0.022 0.081** -0.016

20

(0.032) (0.034) (0.031) (0.025) (0.035) (0.092)


(0.038)

-0.035

(0.041)

-0.122***

(0.020)

-0.019

(0.022)

-0.125***

(0.031)

-0.015

(0.064)

Mobility at

Retail and

Recreation

0.001***

(0.000)

Trend -0.003***

(0.001)

-0.003***

(0.001)

-0.004***

(0.001)

-0.003***

(0.001)

-0.002***

(0.000)

-0.003***

(0.000)

Mobility at

Groceries &

Pharmacies

0.000

(0.000)

Mobility at

Parks

-0.000

(0.000)

Mobility at

Transit

0.001***

(0.000)

Mobility at

Work

0.000

(0.000)

Increase in

Staying Home

-0.001*

(0.001)

Constant 69.901***

(12.410)

64.608***

(13.915)

96.742***

(17.296)

61.649***

(19.162)

50.522***

(9.369)

58.226***

(9.013)

Hansen over-

identification

test

28.667 27.352 23.453 26.306 26.197 22.453

p-value 1.000 1.000 1.000 1.000 1.000 1.000

Sargan test of

over id.

restrictions

22.540 22.006 34.334 18.757 20.142 18.992

p-value 1.000 1.000 1.000 1.000 1.000 1.000

Arellano–Bond

test for 1st

order serial

auto.

-3.678 -3.223 -3.101 -3.369 -3.273 -3.480

p-value 0.000 0.001 0.002 0.001 0.001 0.001

Arellano–Bond

test for 2nd

order serial

auto.

1.894 1.559 0.812 1.573 1.882 1.768

p-value 0.058 0.119 0.417 0.116 0.060 0.077

Number of

Groups

33.000 33.000 33.000 33.000 33.000 33.000

Observations 1474.000 1462.000 1458.000 1454.000 1504.000 1427.000


21

Figure 1: Timing of lockdown (developing countries)

Figure 2: Timing of lockdown (developed countries)

22

Figure 3: Infections, various types of mobility and

stringency and their correlations (developed countries)

Figure 4: Infections, various types of mobility and stringency

and their correlations (developed countries)

23

Figure 5: Infections, selected important mobility and stringency

and their correlations

(developing countries)

Figure 6: Infections, selected important mobility and stringency

and their correlations

(developed countries)

24

Figure 7: Infections rate and Income support

Figure 8: Lockdown and infection rate

can lockdown reduce infection growth in developing

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