# Vaccination strategies and transmission of COVID-19: evidence across
advanced countries††thanks: We thank our editor, Owen O’Donnell, two anonymous
referees, and also Alexander Karaivanov and Hiro Kasahara for excellent
comments and suggestions.

Dongwoo Kim
Simon Fraser University Corresponding author. Department of Economics, Simon
Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada. Email:
<EMAIL_ADDRESS>Young Jun Lee
University of Copenhagen Department of Economics, University of Copenhagen,
Øster Farimagsgade 5, Building 26, DK-1353 Copenhagen K, Denmark. Email:
<EMAIL_ADDRESS>
(August 27, 2024)

###### Abstract

Given limited supply of approved vaccines and constrained medical resources,
design of a vaccination strategy to control a pandemic is an economic problem.
We use time-series and panel methods with real-world country-level data to
estimate effects on COVID-19 cases and deaths of two key elements of mass
vaccination – time between doses and vaccine type. We find that new infections
and deaths are both significantly negatively associated with the fraction of
the population vaccinated with at least one dose. Conditional on first-dose
coverage, an increased fraction with two doses appears to offer no further
reductions in new cases and deaths. For vaccines from China, however, we find
significant effects on both health outcomes only after two doses. Our results
support a policy of extending the interval between first and second doses of
vaccines developed in Europe and the US. As vaccination progresses, population
mobility increases, which partially offsets the direct effects of vaccination.
This suggests that non-pharmaceutical interventions remain important to
contain transmission as vaccination is rolled out.

Keywords: COVID-19, vaccination, dose interval, time-series, panel data,
counterfactual analysis

JEL classification: I18, I12, C22, C23

##  1 Introduction

Vaccination has been perceived as key to reaching herd immunity since the
COVID-19 pandemic was declared in 2020. This paper investigates the effects of
two key elements of mass vaccination–allocation and timing of first and second
doses and the type of vaccine implemented. Mass vaccination programs have been
launched in advanced countries since the UK first approved the use of
Comirnaty (previously the Pfizer–BioNTech vaccine) in December 2020. Due to
the very limited supply of approved vaccines in the early stage, however, even
the world’s wealthiest countries had difficulties rolling out vaccine doses
quickly. Most COVID-19 vaccines require two doses to be administered per
person within a time window recommended by manufacturers. Three or four weeks
are recommended for Comirnaty and Spikevax (previously the Moderna vaccine)
and 8–12 weeks for Vaxzevria (previously the AstraZeneca-Oxford vaccine).
Under these supply constraints, countries adopted different vaccine roll-out
strategies. The UK and Canada extended the interval between first and second
doses up to 12–16 weeks in order to maximize the share of (at least partially)
vaccinated people, whereas many other countries, such as Israel and the US,
followed the recommended dosing interval. Some countries, such as Bahrain,
Chile, Turkey, the UAE, and Uruguay, relied on less widely approved vaccines
developed by Chinese firms.

Implementing a longer interval between doses sparked a heated debate among
medical experts and policy makers. In the UK, the British Medical Association
(BMA) stood strongly against the idea of delaying the second dose (Mahase,
(2020)) as there is no clinical evidence that this strategy works. A similar
criticism arose in Canada (Miller, (2021)). Nonetheless, both governments
implemented longer intervals to vaccinate as many people as possible. There
have been a handful of papers in the medical literature that suggest extending
the interval between two doses might be beneficial. Moghadas et al., (2021)
use agent-based modeling to compare vaccination strategies. They found that
delaying the second dose can avert more infections, hospitalizations, and
deaths than the standard dosing if the efficacy of the first dose does not
wane before the delayed second dose. Similar results using simulation-based
methods are found by Saad-Roy et al., (2021), Romero-Brufau et al., (2021) and
Tuite et al., (2021). To the best of our knowledge, however, there is no
research on real-world outcomes across different countries. This paper aims to
provide observational cross-country evidence on the effectiveness of
vaccination strategies using real-world data.

The choice of vaccine is potentially even more critical to the effectiveness
of a mass vaccination program than the timing of vaccine doses. After
inoculating more than a half of the population, a few countries, such as
Chile, the UAE, and Seychelles, still experienced high rates of infection.
These countries heavily relied on Chinese vaccines (Sinopharm, Sinovac
Biotech, and CanSino).111The recommended dosing interval for these vaccines is
2–4 weeks, according to the World Health Organization, (2021)). Vaccines from
China can be a useful tool to fight the pandemic for low- and middle-income
countries, as the supply of vaccines developed in the US and Europe is limited
and low- and middle-income countries cannot compete with the wealthiest
countries to secure enough stocks of those vaccines. We include countries
relying on Chinese vaccines in our analysis to see whether and how much those
vaccines contributed to mitigating disease transmission.

The choice of a vaccination strategy is essentially a general economic
problem. The resources for mass vaccination are constrained. Not only is the
supply of vaccines limited, medical facilities and healthcare workers are
capacity constrained. Moreover, the effectiveness of vaccines tends to wane
over time. Therefore, policy makers should devise an optimal allocation
strategy under these constraints to maximize the overall effect of mass
vaccination (Kitagawa and Wang, (2021)). In this regard, our analysis can
provide lessons that go beyond the context of the COVID-19 pandemic. We
deliver evidence on whether the interval between first and second doses
matters to contain the transmission of COVID-19. We also take the types of
vaccines used in mass vaccination into account.

We employ standard time-series and panel data frameworks with cross-country
data to estimate the effects of first- and second-dose vaccination on the
COVID-19 transmission conditional on potential confounders. We first estimate
time-series models of new infections and deaths for 8 selected countries with
high vaccination rates (Canada, Israel, the US, the UK, Chile, Uruguay, the
UAE, and Bahrain). Then, we closely follow the approaches employed by
Chernozhukov et al., (2021) and Karaivanov et al., (2021) for panel data
analysis in which we investigate the impact of vaccination on new cases and
deaths, as well as behavior i.e. people’s mobility. Member countries in the
OECD and the European Union that publicly release daily epidemiological data
are considered. Bahrain and Uruguay are added to the country panel to evaluate
the effects of vaccines from China, along with Chile and Turkey, which are
already included in the OECD.222Other countries that heavily relied on Chinese
vaccines, such as the UAE and Seychelles, are omitted in the panel data
analysis as they do not provide daily vaccination statistics.

The main findings from our empirical analysis are as follows. First, growth of
new infections and deaths are significantly negatively associated with
progress in administering at least one dose. Larger reductions in both these
health outcomes are found for countries delaying the second dose (Canada and
the UK). Conditional on first-dose progress, two-dose coverage does not give
further reductions in new cases and deaths. These findings are consistent in
both time-series and panel data analyses. No reductions in health outcomes are
found for first-dose progress in countries heavily relying on vaccines from
China, whereas two-dose progress offers significant effects on new cases and
deaths in these countries. Second, we find that progress in vaccination
induces people to be more mobile. As higher mobility leads to more new
infections and deaths, this indirect effect of vaccination partially offsets
its direct effect. Last, our counterfactual simulations for selected countries
suggest that substantially extending the interval between two doses reduces
new cases and deaths.

Our findings can be useful to draw policy implications for low-income
countries that fall behind in mass vaccination. Only 11.84 vaccine doses per
100 people were administered overall in low-income countries as of December
31, 2021, according to Our World in
Data.333https://ourworldindata.org/grapher/cumulative-covid-vaccinations-
income-
group?country=High+income~Low+income~Lower+middle+income~Upper+middle+income.
As many advanced countries have started administering booster shots (third
doses), the supply of mRNA-based vaccines and alternative vaccines will
continue to be limited. Thus, it is important to vaccinate as many people as
possible with one dose using secured vaccine stocks. Our analysis, alongside
the evidence found in the medical literature, suggests that extending the
dosing interval is an effective strategy to mitigate the transmission of
COVID-19. Especially in low-income countries, not only is the supply of
vaccines constrained, the medical capacity to administer the vaccine doses is
also limited. Thus, this strategy can further help low-income countries
maximize the level of protection among the population more quickly. As
observed in many countries, the progress in single-dose vaccination starts to
flatten out at a certain point. Thus, countries can begin to vaccinate with
the second dose as resources become available. Public health measures are
still very important amid vaccination progress as they can further contain the
spread of the virus by reducing mobility and potential interactions among
people. There is a caveat in our findings for the use of vaccines from China:
single-dose vaccination is not enough to alleviate the spread of the virus.
Therefore, the importance of second-dose vaccination should be emphasized if
those vaccines are used in a significant portion of mass vaccination.

The remainder of this paper is structured as follows. Section 2 describes the
rationale behind the idea of delaying the second dose. Section 3 provides
empirical analysis using time series and panel data models. Section 4
discusses the robustness of our findings. Section 5 presents counterfactual
simulations for selected countries using hypothetical vaccine allocations.
Section 6 concludes. Additional empirical results, robustness analysis using
alternative data frequency/model specifications, and additional counterfactual
simulations are provided in Supplementary Materials for online publication.

##  2 Rationale Behind Delaying the Second Dose

The UK government decided to extend the interval between the first and second
doses of Comirnaty up to 12 weeks at the end of December 2020. Two letters
sent to health professionals (NHS, (2020) and Atherton et al., (2020)) lay out
the rationale for delaying the second dose. The main reason is that the great
majority of protection comes after the first dose. The second dose is
important for the duration of protection, but in the short run, the additional
protection afforded by the second dose is likely to be marginal. Later, in
Canada, the National Advisory Committee on Immunization, (2021) recommended
that the mRNA-based vaccines and Vaxzevria should be given to as many people
as possible by extending the dosing interval up to 4 months.

These decisions are based on three assumptions: 1. the first dose provides
good enough protection (the first dose’s efficacy is higher than the marginal
efficacy gain from the second dose); 2. protection after the first dose does
not wane too quickly; 3. delaying the second dose does not lower the efficacy
of full vaccination. Under these assumptions, we conduct a very simple thought
experiment, without a complicated epidemiological model. Suppose that a
country secures an amount of mRNA-based vaccines that can inoculate $70$% of
the population with one dose. There will be a second batch of vaccines
delivered to this country in 12–16 weeks. The country has to allocate its
vaccine stock to the first and second doses before the delivery of the second
batch. We consider two cases in which the level of protection from the first
dose substantially varies. In Case 1, the first dose provides 90% vaccine
efficacy (VE), which does not wane in 12–16 weeks, whereas first-dose VE is
only 50% effective in Case 2. In both cases, the VE after the second dose
reaches 95%, so that the marginal efficacy gain from the second dose is 5% and
45% respectively. Case 1 resembles the vaccine efficacy results for the mRNA-
based vaccines from the clinical trials.444Polack et al., (2020) reported that
the vaccine efficacy of Comirnaty between the first and second doses is
$52.4\%.$ However, they used data collected during the first two weeks after
the first dose in their calculation. Skowronski and De Serres, (2021)
reanalyzed Comirnaty efficacy between first and second doses using data
submitted to the Food and Drug Administration (FDA) and found that first-dose
efficacy is $92.6\%$ from two weeks after the first dose and before the second
dose. This is similar to the first-dose efficacy of Spikevax ($92.1\%$). Case
2 supposes a situation where variants of the virus substantially lower first-
dose VE.

Table 1: Vaccine allocations and overall protection

Case 1 |  | Case 2
---|---|---
(first dose VE: 90%, second dose VE: 95%) |  | (first dose VE: 50%, second dose VE: 95%)
first dose | second dose | protection |  | first dose | second dose | protection
70% | 0% | 63.0% |  | 70% | 0% | 35.0%
60% | 10% | 54.5% |  | 60% | 10% | 34.5%
50% | 20% | 46.0% |  | 50% | 20% | 34.0%
40% | 30% | 37.5% |  | 40% | 30% | 33.5%
35% | 35% | 33.3% |  | 35% | 35% | 33.25%

Note: The table shows the average protection levels given different vaccine
allocations. The protection levels are computed by (first dose VE $\times$
first dose fraction) + (second dose marginal VE $\times$ second dose
fraction).

We compute the average protection levels among the population given different
allocations of the first and second doses in Table 1. In both cases,
allocating all the vaccine stock to the first dose is the dominant strategy.
Delaying the second dose is obviously much more effective when the first dose
gives stronger protection, as shown in Case 1. The allocation strategy does
not matter if first-dose efficacy is identical to the marginal efficacy gained
from the second dose. This simple calculation illustrates why the longer
interval between doses can be beneficial under supply constraints if the
underlying assumptions hold. Recent findings in the medical literature support
our assumptions. Carazo et al., (2021) found that first-dose mRNA-based
vaccines provide solid protection until 16 weeks after vaccination using data
from Quebec, Canada. There is also medical evidence that the longer dosing
interval increases the overall efficacy. Voysey et al., (2021) showed that the
longer interval ($\geq 12$ weeks) between doses of Vaxzevria provided a
greater efficacy than the shorter interval ($<6$ weeks). A recent study in the
UK (Payne et al., (2021)) found that longer dosing intervals (6–14 weeks)
resulted in stronger immune responses than the standard regimen (3–4 weeks)
for Comirnaty. Tauzin et al., (2021) also support that a longer (16 weeks)
interval induced more robust immune responses than a shorter interval (4
weeks) for Cominarty. Pouwels et al., (2021), on the other hand, observed no
effect of the dosing interval on vaccine effectiveness. Another possibility is
that one dose of vaccine could provide stronger protection for people
previously infected. Angyal et al., (2022) reported that previously infected
healthcare workers in the UK showed strong immune responses after only one
dose, equivalent to those of people receiving two doses with no past history.
Similar findings are shown in recent studies (Stamatatos et al., (2021),
Samanovic et al., (2021), and Tauzin et al., (2021)). In the latter two
studies, the authors find that the second dose did not offer significant
antibody responses for individuals with prior exposure. This means that
single-dose vaccination can give full protection to a significant part of the
population for countries with a large number of cumulative cases.

Figure 1: Vaccination strategies and overall protection over time

(a) Three vaccination strategies

Note: The figures plot the number of people vaccinated with at least one dose
per hundred (red line) and the number of people vaccinated with two doses per
hundred (black line) over time given different allocation strategies.

(b) Overall protection over time

Note: The figures plot the average protection levels over time given different
allocation strategies for Cases 1 and 2 described in Table 1.

Consider another thought experiment in which a country secures 100 doses per
hundred people and will be inoculating its population with the vaccine stock
over 120 days. On top of the limited supply of vaccines, health care capacity
limits the number of daily doses that can be administered. We assume that
medical capacity allows to deliver $100/120$ doses per hundred people daily.
Three vaccination strategies on the dosing interval are considered. In
Strategy 1, the dosing interval is standard 3 weeks, so the country delivers
first doses from day 1 and starts to inoculate second doses on day 21. From
this time, the stocks are equally allocated to the first and second doses. In
strategies 2 and 3, the dosing intervals are 3 months (90 days) and 4 months
(120 days) respectively. We compute the overall protection levels under these
strategies for both cases in Table 1. As shown in Figure 1, longer dosing
intervals help achieve higher protection levels in the given time period. It
is obvious that Strategy 3 gives the best results in both cases, followed by
Strategy 2.

In sum, there is evidence in the medical literature from which one can expect
that the idea of delaying the second dose may work in practice. Allocating the
available vaccine stocks to first doses could help maximize the level of
protection in the population given the limited supply. Furthermore, there
might be healthcare capacity constraints. Focusing on the first doses by
extending the interval between doses could also help reach a higher level of
protection more quickly. Extending the dosing interval is particularly more
effective when the first-dose vaccine efficacy is much higher than the
marginal efficacy gain from the second dose. This could be the case in low-
income countries where a significant part of the population is previously
infected. However, it is still uncertain whether this strategy actually works
in the real-world setting, as the previous studies rely on small-scale
experiments or simulations. Therefore, it is important to investigate the
performances of different vaccination strategies using observational data
across countries employing different vaccination strategies.

##  3 Empirical Analysis

###  3.1 Data

Our empirical analysis mainly relies on the country-level epidemiological data
from Our World in Data (https://ourworldindata.org/) which is a collaborative
project between the University of Oxford and Global Change Data Lab. The
worldwide country-level database on many aspects of the COVID-19 pandemic is
in its GitHub repository
(https://github.com/owid/covid-19-data/tree/master/public/data). We use the
daily counts of new cases, total cumulative cases, new tests, total cumulative
tests, vaccinated people with at least one dose per hundred, fully vaccinated
people per hundred, and total vaccine doses administered per hundred.555The
Janssen (Johnson and Johnson) vaccine only requires a single dose. If people
were immunized using Janssen vaccines, they are counted as fully vaccinated.
However, this vaccine was used only for a very small fraction of mass
vaccination in most countries (3.8% in the US, 2.3% in the European Union, and
0% for Canada, Israel, Chile, and Uruguay as of July 8, 2021), so we suppose
the effect of vaccination mainly comes from the other vaccines, on which
countries heavily relied. For government policy responses, we employ the
Containment and Health Index (https://ourworldindata.org/policy-responses-
covid#containment-and-health-index) developed by the Oxford COVID-19
Government Response Tracker (OxCGRT).666This index is built upon OxCGRT’s
Stringency Index which is a composite measure based on nine policy response
indicators including school closures, workplace closures, public event bans,
restrictions on gatherings, public transit closure, public information
campaigns, stay at home order, domestic and international travel bans rescaled
to a value from 0 to 100 (100 = strictest). Containment and Health Index
further includes testing policy, contact tracing, face coverings, and vaccine
availability. If policies vary at the sub-national level, the index is shown
as the response level of the strictest sub-region. There are some missing
values in this data set. We impute the missing values using linear
interpolation for weekdays; if values in the weekends are missing, we take the
previous value to impute the missing values. We also use the country-level
data from Google COVID-19 Community Mobility Reports
(https://www.google.com/covid19/mobility/) to take people’s behavioral
responses into account. These data show mobility trends over time in each
country across different categories of places compared to the baseline period
(Jan 3–Feb 6, 2020) before the pandemic. Following Karaivanov et al., (2021),
we construct a mobility index by calculating the arithmetic average of three
indexes (‘retail,’ ‘grocery and pharmacy,’ and ‘workplace’).

We use the period from June 1, 2020, to July 8, 2021, for our analysis. The
first wave of the pandemic was almost over across most countries around the
start date chosen. Many countries did not have enough testing–tracing
infrastructure early in the pandemic, so the data would have missed a
significant amount of infection cases. After the first wave, most countries
were well equipped with testing–tracing facilities, as one can see from the
substantially increased number of daily new tests. This period includes the
second wave, and the wide spread of the Delta variant first identified in
India is captured in some countries, such as the UK and Israel.

We estimate time-series and panel data models to investigate the effects of
vaccination on health outcomes controlling for policy and behavioral factors.
In our time-series analysis, we use a sample of countries with high
vaccination rates. We select eight nations, four of which (Canada, Israel, the
US, and the UK) have used vaccines developed in the US and Europe, while the
others (Chile, Uruguay, the UAE, and Bahrain) have been heavily reliant on
vaccines produced by China.777Canada, Israel, and the US relied on mRNA-based
vaccines (Comirnaty and Spikevax) to inoculate most of the population (the
whole population for Israel). The UK has used both mRNA-based vaccines and
Vaxzevria. Chile, Uruguay, the UAE, and Bahrain have vaccinated a significant
part of the population with vaccines from China. According to Our World in
Data, As of July 8, 2021, Chile administered $76.3\%$ of its total doses with
Chinese vaccines (17.72 million doses of Sinovac and 0.47 million doses of
CanSino out of total 23.93 million doses), and Uruguay’s share of Sinovac is
$73.6\%$ (3.12 million doses of Sinovac out of total 4.24 million doses). The
UAE and Bahrain also inoculated a majority of the population using the
Sinopharm vaccine, but the exact numbers are not publicly available. These
countries have achieved the highest vaccination rates in the world, and
relevant data are well kept and gathered at daily frequency. Then, we employ a
larger panel of high-income countries (OECD and EU members, Bahrain and
Uruguay) to further investigate the impact of vaccination progress exploiting
both time-series and cross-country variations.

Figure 2: Trends in new cases, vaccinations, and policy and mobility indexes

Note: Time period – June 1, 2020 to July 8, 2021. The UAE does not publicly
report daily numbers of first and second doses administered until early July,
2021.

We draw trends in variables of primary interest (new confirmed cases per
million, vaccination progress, Containment and Health Index, and the mobility
index) in Figure 2 for eight selected countries we use in our time-series
analysis. Among those countries, only Canada and the UK extended the dosing
interval for mRNA-based vaccines up to 12–16 weeks. The other countries
recommended the standard regimen (3–4 weeks; the US allowed up to a 6-week
interval in exceptional circumstances). As these countries adopted different
vaccination strategies, the effectiveness of mass vaccination could vary
across countries. In the upper four panels (Canada, Israel, the US, and the
UK), the daily new cases per million (smoothed by a 7-day moving average)
decreased as the vaccination program progressed. The government policy index
responded to the spike in new cases. All four countries eased restrictions as
vaccination progressed and the case number declined. The mobility index showed
the opposite trend, as it dropped in line with the surge in new cases and
recovered up as the case number decreased. It is not clear whether people’s
behavior responded to government restrictions or case numbers. Mass
vaccination obviously led to lower new infections, but at the same time,
government restrictions were eased and people’s mobility increased. Therefore,
the estimated effects of vaccination on new infections are likely to be
attenuated if policy and behavioral factors are not taken into account.

Different trends were observed in the lower four panels (Chile, Uruguay, the
UAE, and Bahrain). These countries heavily relied on vaccines from China and
experienced the surge of new cases while mass vaccination continued through
the period at a steady pace. Declines in the growth of new cases were seen
only after very high vaccination rates were achieved and stronger restrictions
were implemented. These trends cast some doubt on the effectiveness of Chinese
vaccines. Mobility and policy indexes responded to new cases similarly to the
way they did in the countries in the upper four panels. This may suggest that
countries relying on vaccines from China have to employ a different dosing
strategy.

###  3.2 Time series analysis

We analyze the evolution of COVID-19 transmission using standard time-series
models. We use the ARIMA model (autoregressive integrated moving average
models with exogenous variables) to estimate the effect of first and second
doses on new infections. The outcome variable is log of daily new cases per
million. The number of people vaccinated per hundred (at least one dose given)
and the number of people fully vaccinated per hundred (two doses) are included
as exogenous variables. To control for test intensity and the weekend effect,
the log of the number of new tests and a weekend dummy are also
included.888For Israel, the UAE, and Bahrain, the weekend dummy takes 1 if the
day is either Friday or Saturday. For the other countries, the dummy variable
equals 1 when the day is either Saturday or Sunday. Government policies and
people’s behavioral changes are also taken into account by including the
OxCGRT Containment and Health Index and the mobility index from Google
Community Mobility Reports.

As vaccinations, government policies, and behavioral responses do not
immediately affect infections, it is important to choose appropriate lags for
exogenous variables in the ARIMA models. For first and second vaccine doses,
we choose 21-day and 7-day lags, respectively. These lags are chosen based on
scientific evidence from clinical trials and real-world outcomes. The first
dose of approved vaccines begins to provide substantial protection against
infection after 2–3 weeks, and at least 14 days are required to see protection
start (see National Advisory Committee on Immunization, (2021) for a detailed
survey and Hunter and Brainard, (2021) for evidence on Comirnaty from Israel,
where the estimated vaccine effectiveness reached its peak at day 21 after the
first dose). Then, 7 days after the second dose, the vaccine efficacy further
enhances as shown in Polack et al., (2020) and clinical trials. Medical
studies such as Hall et al., (2021) also used 21-day and 7-day thresholds for
the first and second dose, respectively, to evaluate the vaccine
effectiveness. We choose 14-day lags for the government policy index and the
mobility index, as the same lags were used in Chernozhukov et al., (2021) and
Karaivanov et al., (2021) after careful investigation.

The order $(p,d,q)$ of the ARIMA model is selected by R’s forecast package
(Hyndman et al., (2020)) in which the command auto.arima chooses the optimal
order based on various criteria while ensuring that the chosen model behaves
well numerically. The outcome variable for each country becomes stationary
after the first difference, so the order $d$ is set at $1$. Note that all the
exogenous variables are also first differenced in the estimation procedure.
The econometric model we estimate is

$\displaystyle\Delta\log Y_{t}=$ $\displaystyle c+\beta_{1}\Delta
V1_{t-21}+\beta_{2}\Delta V2_{t-7}+\beta_{3}\Delta\log T_{t}+wkd_{t}+\Delta
P_{t-14}+\Delta M_{t-14}+n_{t},$ (1) $\displaystyle n_{t}=$
$\displaystyle\sum_{i=1}^{p}\phi_{i}n_{t-i}+\varepsilon_{t}-\sum_{i=1}^{q}\theta_{i}\varepsilon_{t-i},$
(2)

where $Y_{t}$ is daily new cases per million, $V1_{t}$ is population
vaccinated with at least one dose per hundred, $V2_{t}$ is population fully
vaccinated per hundred, $T_{t}$ is log daily new tests, $wkd_{t}$ is a weekend
dummy, $P_{t}$ is OxCGRT Containment and Health Index, and $M_{t}$ is the
mobility index. There certainly is day-to-day noise in the data, but the
selected ARIMA models fit this daily fluctuation very well as shown in Figure
3. Note that the UAE does not provide daily first- and second-dose progress,
so we use the number of total dose administered per hundred. The resulting
regression residuals are all mean-zero stationary processes.

Figure 3: Daily new cases and fitted values

Note: Time period – June 1, 2020 to July 8, 2021. The black dots are logs of
daily new cases. The red solid lines are fitted values computed by optimally
chosen ARIMA models.

Table 2: ARIMA model results for new cases – countries relying on vaccines
developed in the US and Europe

| Canada | Israel | US | UK
---|---|---|---|---
$(p,d,q)$ | $(2,1,3)$ | $(5,1,3)$ | $(3,1,3)$ | $(2,1,1)$
$\Delta V1_{t-21}$ |  $-$0.062∗∗∗ |  $-$0.041 |  $-$0.018 |  $-$0.086∗∗∗
| (0.018) | (0.043) | (0.041) | (0.032)
$\Delta V2_{t-7}$ |  $-$0.011 |  $-$0.006 |  $-$0.020 | 0.040
| (0.020) | (0.045) | (0.046) | (0.033)
$\Delta\log T_{t}$ | 0.060 | 0.557∗∗∗ | 0.826∗∗∗ | 0.0004
| (0.048) | (0.057) | (0.057) | (0.061)
$wkd_{t}$ |  $-$0.104∗∗∗ |  $-$0.135∗∗∗ | 0.007 |  $-$0.101∗∗∗
| (0.031) | (0.044) | (0.009) | (0.037)
$\Delta P_{t-14}$ |  $-$0.031∗∗ |  $-$0.006 | 0.014∗ | 0.004
| (0.014) | (0.005) | (0.007) | (0.013)
$\Delta M_{t-14}$ | 0.004 | 0.005 | 0.008 | 0.018
| (0.007) | (0.008) | (0.007) | (0.011)

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. $(p,d,q)$ is the optimal ARIMA order chosen by auto.arima in
R. Standard errors are in parentheses. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and
90% confidence level respectively.

The estimation results are displayed in Tables 2–3. For the countries that
mainly vaccinated their populations with widely approved vaccines developed in
the US and Europe, the growth of new cases is negatively associated with
progress in vaccination. It is noticeable that the spread of COVID-19 is
particularly strongly associated with the share of people vaccinated with at
least one dose for Canada and the UK, where the delaying strategy was
employed. A $1$% increase of the share of people vaccinated with at least one
dose is associated with around $6.2$% and $8.6$% reductions in the daily
growth of new cases, respectively. In both countries, the effect of full
vaccination progress is not significant. For Israel and the US, where the
standard dosing schedule was followed, an insignificant negative association
between vaccination progress and new infections is found. The weekend dummy is
in general negatively associated with new infections, whereas the number of
new tests is positively associated. The government policy index and the
mobility index are not significant, though the signs of their coefficients are
generally consistent with expected directions.

Table 3: ARIMA model results for new cases – countries relying on vaccines
from China

| Chile | Uruguay | UAE | Bahrain
---|---|---|---|---
$(p,d,q)$ | $(2,1,4)$ | $(2,1,2)$ | $(0,1,3)$ | $(1,1,2)$
$\Delta V1_{t-21}$ | 0.011 |  $-$0.020 |  |  $-$0.020
| (0.010) | (0.035) |  | (0.023)
$\Delta V2_{t-7}$ |  $-$0.008 |  $-$0.041 |  |  $-$0.012
| (0.010) | (0.040) |  | (0.010)
$\Delta V_{t-21}$ |  |  |  $-$0.013∗∗ |
|  |  | (0.006) |
$\Delta\log T_{t}$ | 0.686∗∗∗ | 0.004 | 0.189∗∗∗ | 0.001
| (0.017) | (0.005) | (0.039) | (0.003)
$wkt_{t}$ |  $-$0.074∗∗∗ |  $-$0.242∗∗∗ |  $-$0.014 |  $-$0.032∗∗
| (0.010) | (0.040) | (0.017) | (0.014)
$\Delta P_{t-14}$ |  $-$0.007 | 0.012 | 0.011 | 0.009
| (0.004) | (0.010) | (0.007) | (0.007)
$\Delta M_{t-14}$ | 0.004∗ |  $-$0.003 |  $-$0.006 |  $-$0.011
| (0.002) | (0.011) | (0.013) | (0.012)

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. $(p,d,q)$ is the optimal ARIMA order chosen by auto.arima in
R. Standard errors are in parentheses. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and
90% confidence level respectively. The UAE does not publicly announce first
and second dose progress separately so we include total doses administered
($V$) instead.

The results are mixed for the countries relying on vaccines from China. These
countries had already vaccinated a significant part of the population (as of
July 8, 2021, the shares of fully vaccinated people are the UAE 65%, Chile
58.8%, Uruguay 54.7% Bahrain 59.6%). However, significant negative association
between new cases and vaccine coverage is only found in the UAE, where the
total dose administered per hundred ($V$) is included instead of $V1$ and
$V2.$ One additional dose per hundred is associated with a $1.3$% reduction in
new cases in the UAE.

We also estimate the ARIMA models for new death counts. The results in
Supplementary Materials A (Tables A1 and A2) are in general very similar to
the results for new cases, except Israel where we find a significant effect of
full vaccination progress.999In Israel, death counts started sharply dropping
shortly after the mass vaccination program had launched. The number of daily
new deaths was close to 0 between Apr 1 and Jul 8, 2021. Therefore, the
outcome, log differenced daily new deaths, becomes very noisy.

Our results from time-series analysis suggest that extending the interval
between first and second doses might be an effective strategy for the vaccines
developed in the US and Europe. Larger negative effects of vaccination
progress are found in the countries adopting this strategy. It is not clear
whether this strategy works for vaccines from China. The purpose of estimating
time-series models is to obtain some limited descriptive evidence of how the
choice of the dosing interval and types of vaccines matter to contain the
spread of COVID-19. We only use 8 countries in the analysis and have relied on
daily fluctuations, which are noisy due to the stationarity concern. To
investigate relationships between vaccination and new infections, we will
further exploit cross-country variations in vaccination, policies, and
mobility using multi-country panel data models.

###  3.3 Panel data analysis

We use OECD and EU member countries to construct our country panel, adding
Bahrain and Uruguay in order to examine the effect of vaccines from China.
Countries that do not publish daily COVID-19-related statistics are excluded.
In total, there are 37 countries in our data set.101010Included countries are
Australia, Austria, Belgium, Bulgaria, Bahrain, Canada, Switzerland, Chile,
Colombia, Germany, Denmark, Spain, Estonia, Finland, France, the UK, Greece,
Croatia, Hungary, Israel, Italy, Japan, South Korea, Lithuania, Luxembourg,
Latvia, Mexico, Malta, Norway, New Zealand, Poland, Portugal, Romania,
Slovenia, Turkey, Uruguay, and the US. We closely follow the estimation
strategies used in Chernozhukov et al., (2021) and Karaivanov et al., (2021).

Our regression equations are motivated by a variant of the SIRD model
introduced in Chernozhukov et al., (2021) where new infections are only
partially detected via testing. Let $S,I,R$ and $D$ denote the numbers of
susceptible, infected, recovered, and deceased individuals in a given state.
Each of these variables is a function of time. We modify the model by adding
vaccination factors, $V1$ and $V2$. We assume that vaccinated individuals
obtain marginal immunity gain against infection at the rates $\delta_{1}$ and
$\delta_{2}$ from the first and second doses, with which they exit the
susceptible class and enter the recovered class. The laws of motion for these
variables are specified as

$\displaystyle\dot{S}\left(t\right)$
$\displaystyle=-\left(\delta_{1}\dot{V1}\left(t-21\right)+\delta_{2}\dot{V2}\left(t-7\right)\right)-\frac{S\left(t\right)}{N}\beta\left(t\right)I\left(t\right)$
$\displaystyle\dot{I}\left(t\right)$
$\displaystyle=\frac{S\left(t\right)}{N}\beta\left(t\right)I\left(t\right)-\gamma
I\left(t\right),$ $\displaystyle\dot{R}\left(t\right)$
$\displaystyle=\left(1-\kappa\right)\gamma
I\left(t\right)+\left(\delta_{1}\dot{V1}\left(t-21\right)+\delta_{2}\dot{V2}\left(t-7\right)\right),$
$\displaystyle\dot{D}\left(t\right)$ $\displaystyle=\kappa\gamma
I\left(t\right),$

where $N$ is the population, $\beta\left(t\right)$ is the rate of infection
spread between $S_{t-\ell}$ and $I_{t-\ell}$, $\gamma$ is the rate of recovery
or death, and $\kappa$ is the probability of death conditional on infection.
The total number of confirmed cases, $C_{t}$, evolves as

$\displaystyle\dot{C}\left(t\right)$
$\displaystyle=\tau\left(t\right)I\left(t\right)\left(=\frac{\tau\left(t\right)}{\kappa\gamma}\dot{D}\left(t\right)\right),$

where $\tau_{j}\left(t\right)$ is the rate that infections are detected. Note
that we only observed $C\left(t\right)$ and $D\left(t\right)$, but not
$I\left(t\right)$. The unobserved $I\left(t\right)$ can be eliminated in two
ways:

$\displaystyle\frac{\ddot{C}\left(t\right)}{\dot{C}\left(t\right)}=\frac{S\left(t\right)}{N}\beta\left(t\right)-\gamma+\frac{\dot{\tau}\left(t\right)}{\tau\left(t\right)},\quad\frac{\ddot{D}\left(t\right)}{\dot{D}\left(t\right)}=\frac{S\left(t\right)}{N}\beta\left(t\right)-\gamma.$

We note that the rate of infection, $\beta\left(t\right)$, can be affected by
individual behaviors and observed policies through social distancing and
lockdown. We specify $\frac{S\left(t\right)}{N}\beta\left(t\right)$ as a
linear function of vaccination, policies, behavioral responses, information,
and confounders other than testing.

Let $C_{it}$ denote the cumulative number of confirmed cases per million in
country $i$ at time $t$. We define $\Delta C_{it}$ as the 7-day new COVID-19
cases reported at time $t$:

$\Delta C_{it}:=C_{it}-C_{i,t-7}.$ (3)

Our dependent variable

$Y_{it}^{C}=\Delta\log\left(\Delta C_{it}\right)=\log\left(\Delta
C_{it}\right)-\log\left(\Delta C_{i,t-7}\right)$ (4)

approximates the weekly growth rate in new cases in country $i$ from $t-7$ to
$t$. Similarly, we denote $\Delta\log\left(\Delta T_{it}\right)$ as the 7-day
growth rate in new tests with the cumulative number of tests, $T_{it}$.

To analyze the impact of first- and second-dose vaccination, government policy
responses, and people’s behavioral changes on $Y_{it}^{C}$, we estimate

$\displaystyle Y_{it}^{C}$ $\displaystyle=$
$\displaystyle\alpha_{0i}+\alpha_{V1}V1_{i,t-21}+\alpha_{V2}V2_{i,t-7}+\alpha_{P}P_{i,t-14}+\alpha_{M}M_{i,t-14}$
(5) $\displaystyle+\alpha_{C1}\Delta\log\left(\Delta
C_{i,t-14}\right)+\alpha_{C2}\log\left(\Delta
C_{i,t-14}\right)+\alpha_{T}\Delta\log\left(\Delta
T_{it}\right)+\varepsilon_{it}^{C},$

where $V1_{it}$ is the number of people vaccinated with at least one dose per
hundred, $V2_{it}$ is the number of fully vaccinated people per hundred, and
$P_{it}$ and $M_{it}$ are policy and behavioral variables. We use the same lag
specifications for the exogenous variables as in our time series analysis. For
the growth rate in new deaths, $Y_{it}^{D}=\Delta\log\left(\Delta
D_{it}\right)$, we use lags that are 14 days behind those in the case equation
following Karaivanov et al., (2021):

$\displaystyle Y_{it}^{D}$ $\displaystyle=$
$\displaystyle\beta_{0i}+\beta_{V1}V1_{i,t-35}+\beta_{V2}V2_{i,t-21}+\beta_{P}P_{i,t-28}+\beta_{M}M_{i,t-28}$
(6) $\displaystyle+\beta_{D1}\Delta\log\left(\Delta
D_{i,t-28}\right)+\beta_{D2}\log\left(\Delta
D_{i,t-28}\right)+\varepsilon_{it}^{D}.$

To examine the potentially distinct effects of vaccines from China, we further
include Chinese vaccine variables in the above regression equations (5) and
(6) with appropriate lags. We first define a Chinese vaccine dummy, $D_{i},$
which takes value 1 if the country $i$ is Chile, Uruguay, Turkey, or
Bahrain.111111Turkey started its mass vaccination program using the Sinovac
vaccine from China. According to Bridge Beijing (https://bridgebeijing.com/),
31.4 million doses of the Sinovac vaccine has been delivered to Turkey as of
Sep 6, 2021. Turkey shifted to Comirnaty as shipments of the Sinovac doses
kept delayed but until Jul 8, 2021, the Sinovac vaccine accounted for more
than a half of total inoculated doses. Then the Chinese vaccine variables
$V1^{CHN}_{it}$ and $V2^{CHN}_{it}$ are generated by $D_{i}\times V1_{it}$ and
$D_{i}\times V2_{it}$ respectively.

Table 4: The direct effects of vaccination, policy and behavior on new cases

| Dependent variable: $\Delta\log\Delta C_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-21}$ | -0. | 0074∗∗∗ | -0. | 0077∗∗∗ | -0. | 0173∗∗∗ | -0. | 0185∗∗∗
| (0. | 0021) | (0. | 0022) | (0. | 0043) | (0. | 0041)
$V2_{t-7}$ | 0. | 0029 | 0. | 0024 | 0. | 0038 | 0. | 0066
| (0. | 0025) | (0. | 0029) | (0. | 0057) | (0. | 0064)
$V1_{t-21}^{CHN}$ |  | 0. | 0149∗∗∗ |  | 0. | 0108
|  | (0. | 0033) |  | (0. | 0087)
$V2_{t-7}^{CHN}$ |  | -0. | 0138∗∗∗ |  | -0. | 0180∗∗
|  | (0. | 0042) |  | (0. | 0074)
$P_{t-14}$ | -0. | 0020 | -0. | 0019 | -0. | 0027∗ | -0. | 0027∗
| (0. | 0012) | (0. | 0013) | (0. | 0015) | (0. | 0015)
$M_{t-14}$ | 0. | 0052∗∗∗ | 0. | 0053∗∗∗ | 0. | 0101∗∗∗ | 0. | 0098∗∗∗
| (0. | 0012) | (0. | 0012) | (0. | 0014) | (0. | 0013)
$\Delta\log\Delta C_{t-14}$ | 0. | 1102∗∗∗ | 0. | 1056∗∗ | 0. | 0901∗∗ | 0. | 0908∗∗
| (0. | 0395) | (0. | 0394) | (0. | 0350) | (0. | 0343)
$\log\Delta C_{t-14}$ | -0. | 0374∗∗∗ | -0. | 0403∗∗∗ | -0. | 1496∗∗∗ | -0. | 1509∗∗∗
| (0. | 0077) | (0. | 0077) | (0. | 0214) | (0. | 0214)
$\Delta\log\Delta T_{t}$ | 0. | 5094∗∗∗ | 0. | 5064∗∗∗ | 0. | 4090∗∗∗ | 0. | 4079∗∗∗
| (0. | 1775) | (0. | 1767) | (0. | 1499) | (0. | 1496)
Country fixed effects | yes | yes | yes | yes
Country specific trend in days | no | no | quadratic | quadratic
${\rm R}^{2}$ | 0. | 2838 | 0. | 2868 | 0. | 3987 | 0. | 3999
Adjusted ${\rm R}^{2}$ | 0. | 2834 | 0. | 2864 | 0. | 3953 | 0. | 3964
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 382 | 382 | 382 | 382

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

Table 5: The direct effects of vaccination, policy, and behavior on new deaths

| Dependent variable: $\Delta\log\Delta D_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-35}$ | -0. | 0096∗∗∗ | -0. | 0093∗∗∗ | -0. | 0125∗∗∗ | -0. | 0132∗∗∗
| (0. | 0021) | (0. | 0023) | (0. | 0040) | (0. | 0041)
$V2_{t-21}$ | 0. | 0036 | 0. | 0014 | 0. | 0040 | 0. | 0061
| (0. | 0029) | (0. | 0033) | (0. | 0043) | (0. | 0046)
$V1_{t-35}^{CHN}$ |  | 0. | 0169∗∗∗ |  | 0. | 0045
|  | (0. | 0034) |  | (0. | 0076)
$V2_{t-21}^{CHN}$ |  | -0. | 0116∗∗ |  | -0. | 0097
|  | (0. | 0050) |  | (0. | 0059)
$P_{t-28}$ | -0. | 0008 | -0. | 0005 | -0. | 0013 | -0. | 0013
| (0. | 0014) | (0. | 0015) | (0. | 0013) | (0. | 0013)
$M_{t-28}$ | 0. | 0057∗∗∗ | 0. | 0057∗∗∗ | 0. | 0077∗∗∗ | 0. | 0075∗∗∗
| (0. | 0016) | (0. | 0015) | (0. | 0016) | (0. | 0015)
$\Delta\log\Delta D_{t-28}$ | 0. | 0584∗∗ | 0. | 0560∗ | 0. | 0574∗∗ | 0. | 0567∗∗
| (0. | 0281) | (0. | 0280) | (0. | 0258) | (0. | 0259)
$\log\Delta D_{t-28}$ | -0. | 0429∗∗∗ | -0. | 0479∗∗∗ | -0. | 1308∗∗∗ | -0. | 1308∗∗∗
| (0. | 0096) | (0. | 0085) | (0. | 0112) | (0. | 0112)
Country fixed effects | yes | yes | yes | yes
Country specific trend in days | no | no | quadratic | quadratic
${\rm R}^{2}$ | 0. | 0966 | 0. | 1016 | 0. | 1747 | 0. | 1749
Adjusted ${\rm R}^{2}$ | 0. | 0962 | 0. | 1010 | 0. | 1698 | 0. | 1699
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 368 | 368 | 368 | 368

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

We estimate our regression equations with country fixed effects to account for
country-specific heterogeneity. We do not include week or month fixed effects,
as each country experienced heterogeneous evolution of the pandemic. Including
country-specific time-fixed effects results in too many parameters to
estimate, which lead to imprecise estimates. Instead, country-specific time
trends are included to control for heterogeneous trends across countries. We
consider quadratic time trends as a baseline specification. Alternative
specifications (linear and cubic time trends) are also considered, and our
main findings hold under those specifications, as shown in Supplementary
Materials B.

The results are displayed in Tables 4–5 for new infections and deaths
respectively. It is notable that weekly growth rates of both new infections
and deaths are significantly negatively associated at the $99\%$ confidence
level with first-dose vaccination progress across all the specifications
considered. With no time trends, the estimates suggest that a $1\%$ increase
in the share of people vaccinated with at least one dose leads to around
$0.77\%$ reduction in the weekly case growth rate and around $0.93\%$
reduction in the weekly death growth rate. When quadratic time trends are
included, the magnitudes of first-dose vaccination effect estimates increase
to $1.85\%$ and $1.32\%$ for new cases and deaths respectively. The estimated
effect of second-dose vaccination progress is not significantly different from
$0$ for both health outcomes under any of the specifications considered.

The estimated effects of first and second doses of Chinese vaccines are the
sums of coefficients on $V1$ and $V1^{CHN}$, and $V2$ and $V2^{CHN}$,
respectively. Table 6 reports these estimates. Contrary to the results for
mRNA-based and adenoviral vaccines, first-dose progress in countries relying
on Chinese vaccines is not effective in reducing either case or death counts.
Only full vaccination progress leads to significant reductions in new cases
and deaths, although the effect on deaths becomes insignificant when quadratic
time trends are controlled for. The estimated magnitude of the vaccine
effectiveness is smaller than that of vaccines from the US and Europe. These
results are in line with the evidence from clinical trials that these vaccines
have a lower vaccine efficacy than mRNA-based counterparts and
Vaxzevria.121212The World Health Organization, (2021) approved the Sinovac
vaccine for emergency use and stated that the vaccine efficacy of this vaccine
is 51% for symptomatic infection. Dashdorj et al., (2021) also report that the
Sinopharm vaccine induced weaker antibody responses than Comirnaty and
Vaxzevria. These findings suggest that keeping the recommended dosing interval
is a better strategy for countries relying on Chinese vaccines.

Table 6: Test for the effects of the Chinese vaccines on confirmed cases and
deaths

| $\Delta\log\Delta C_{t}$ |  | $\Delta\log\Delta D_{t}$
---|---|---|---
| (2) | (4) |  | (2) | (4)
$V1_{t-21}+V1_{t-21}^{CHN}$ | 0. | 0072∗∗∗ | -0. | 0077 | $V1_{t-35}+V1_{t-35}^{CHN}$ | 0. | 0075∗∗ | -0. | 0087
| (0. | 0025) | (0. | 0080) |  | (0. | 0028) | (0. | 0064)
$V2_{t-7}+V2_{t-7}^{CHN}$ | -0. | 0114∗∗∗ | -0. | 0114∗∗∗ | $V2_{t-21}+V2_{t-21}^{CHN}$ | -0. | 0102∗∗ | -0. | 0036
| (0. | 0027) | (0. | 0028) |  | (0. | 0038) | (0. | 0035)

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using the estimates in Tables 4 and 5 under specifications (2) and (4).
Standard errors in parentheses are clustered at the country level. ∗∗∗, ∗∗,
and ∗ denote the 99%, 95% and 90% confidence level respectively.

The signs and magnitudes of the other coefficient estimates are, in general,
very consistent across the specifications. The weekly growth rate of new cases
is positively related to the growth of new tests. The weekly case growth rate
is negatively associated with government policies, whereas both cases and
deaths are positively associated with the mobility index. Note that government
policy and mobility indexes are insignificant in our time series analysis for
each country. These factors are now significant when time trends are taken
into account, because we further exploit the cross-country variations in
government policies and people’s behavioral changes. Our results mean that
less stringent policy measures and more mobile people can be translated into a
higher $\beta(t)$ in the SIRD model, which results in more cases and deaths.

Table 7: The direct effects of vaccination, policy, and information on
mobility

|  Dependent variable: $M_{t}$
---|---
| (1) | (2) | (3) | (4)
$\Delta V1_{t}$ | 0. |  5839∗∗∗ | 0. |  6642∗∗∗ | 0. |  5260∗∗∗ | 0. |  5937∗∗∗
| (0. | 1260) | (0. | 1623) | (0. | 1266) | (0. | 1636)
$\Delta V2_{t}$ | 0. |  2641∗ | 0. |  3941∗∗ | 0. |  3033∗∗ | 0. |  4396∗∗
| (0. | 1316) | (0. | 1636) | (0. | 1439) | (0. | 1865)
$V1_{t-7}$ | 0. | 0494 | 0. | 0314 | 0. | 0377 | 0. | 0204
| (0. | 0324) | (0. | 0372) | (0. | 0318) | (0. | 0373)
$V2_{t-7}$ | -0. | 0084 | 0. | 0032 | 0. | 0120 | 0. | 0220
| (0. | 0349) | (0. | 0379) | (0. | 0315) | (0. | 0346)
$\Delta V1_{t}^{CHN}$ |  | -0. |  3956∗ |  | -0. | 3474
|  | (0. | 2144) |  | (0. | 2233)
$\Delta V2_{t}^{CHN}$ |  | -0. |  4834∗ |  | -0. |  5110∗
|  | (0. | 2568) |  | (0. | 2762)
$V1_{t-7}^{CHN}$ |  | 0. | 0693 |  | 0. | 0736
|  | (0. | 0676) |  | (0. | 0641)
$V2_{t-7}^{CHN}$ |  | -0. | 0380 |  | -0. | 0414
|  | (0. | 0742) |  | (0. | 0652)
$\Delta P_{t}$ | -0. |  3450∗∗∗ | -0. |  3464∗∗∗ | -0. |  3583∗∗∗ | -0. |  3590∗∗∗
| (0. | 0449) | (0. | 0448) | (0. | 0428) | (0. | 0428)
$P_{t-7}$ | -0. |  1120∗∗∗ | -0. |  1141∗∗∗ | -0. |  1028∗∗∗ | -0. |  1049∗∗∗
| (0. | 0173) | (0. | 0175) | (0. | 0174) | (0. | 0175)
$\Delta\log\Delta C_{t}$ | 0. |  4354∗ | 0. |  4702∗ |  |
| (0. | 2520) | (0. | 2560) |  |
$\log\Delta C_{t}$ | -0. |  7026∗∗∗ | -0. |  6950∗∗∗ |  |
| (0. | 0849) | (0. | 0924) |  |
$\Delta\log\Delta D_{t}$ |  |  | -0. | 0742 | -0. | 0624
|  |  | (0. | 1715) | (0. | 1717)
$\log\Delta D_{t}$ |  |  | -0. |  8039∗∗∗ | -0. |  7976∗∗∗
|  |  | (0. | 1150) | (0. | 1211)
$M_{t-7}$ | 0. |  7635∗∗∗ | 0. |  7637∗∗∗ | 0. |  7469∗∗∗ | 0. |  7472∗∗∗
| (0. | 0220) | (0. | 0219) | (0. | 0239) | (0. | 0239)
Country fixed effects | yes | yes | yes | yes
Time effects | no | no | no | no
${\rm R}^{2}$ | 0. | 8363 | 0. | 8367 | 0. | 8366 | 0. | 8369
Adjusted ${\rm R}^{2}$ | 0. | 8362 | 0. | 8366 | 0. | 8365 | 0. | 8368
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 396 | 396 | 396 | 396

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

We also examine how vaccination, policies, and the evolution of pandemic
affect people’s mobility which is related to social distancing behaviors by
estimating the following equation:

$\displaystyle M_{it}$ $\displaystyle=$
$\displaystyle\gamma_{0i}+\gamma_{d1}\Delta V1_{it}+\gamma_{d2}\Delta
V2_{it}+\gamma_{1}V1_{i,t-7}+\gamma_{2}V2_{i,t-7}+\gamma_{dP}\Delta
P_{it}+\gamma_{P}P{}_{i,t-7}$ (7) $\displaystyle+\gamma_{dC}\Delta\log\Delta
C_{it}+\gamma_{C}\log\Delta
C_{it}+\gamma_{M}M_{i,t-7}+\varepsilon_{it}^{Mob}.$

This regression equation includes the weekly case growth rate and the number
of weekly new infections as information variables. We estimate the regression
coefficients using many different specifications, all of which include country
fixed effects. Time trends are omitted as they are in general insignificant
(Results with country-specific linear time trends are provided in
Supplementary Materials B). In an alternative specification, we replace the
weekly case number and growth rate with the weekly death count and growth
rate. Table 7 displays the estimation results. Most explanatory variables are
highly significant across all the specifications. The values of R-squared are
close to $0.84$ in all the specifications. Our results imply that vaccination
and government policies have large effects on people’s behaviors. Weekly
progresses in first- and second-dose vaccination lead to a large observed
increase in people’s mobility. When the weekly case counts, weekly case growth
rates, and the Chinese vaccine dummy are included, a $1\%$ weekly growth in
first- and second-dose vaccination increases the mobility index by $0.66$ and
$0.39$, respectively. These results are consistent with findings in Andersson
et al., (2021).131313Swedish survey data collected before vaccination was
launched (Dec 10–13, 2020) show that providing vaccine information, such as
the safety, effectiveness, and availability, reduces people’s voluntary social
distancing. On the other hand, more stringent government responses lead to a
substantial reduction in people’s mobility. Information also matters as
people’s mobility is negatively associated with both weekly growth and counts
of new infections and deaths. The magnitudes of estimates for the weekly
growth and counts of deaths are larger than those for new cases. This
indicates that people responded more sensitively to new death counts than new
case numbers, which conforms to Chernozhukov et al., (2021). Interestingly, no
significant increases in mobility due to vaccination are observed in countries
relying on vaccines from China. This may be related to the perceived
credibility of the effectiveness of those vaccines.

The magnitudes of vaccination and policy effects are not very sensitive to
whether or not we include cases or deaths as information variables and across
different specifications. As we show in Tables 4–5, people’s mobility in the
past is significantly positively associated with new infections and deaths.
Therefore, there is an indirect effect of vaccination, which partially offsets
its direct effects on the transmission of COVID-19 by inducing people to be
more mobile. Many countries with high vaccination rates loosened public health
measures amid high vaccination rates. Our findings imply that this could be a
risky move, as loosened restrictions and vaccination progress collectively
push mobility up. Public health measures are still key to containing the
spread of the virus, by restraining people’s mobility and social interactions.
Note that our focus is solely given to epidemiological outcomes here. When
overall social welfare is taken into account, it may be still optimal to relax
non-pharmaceutical interventions to support economic activity in the presence
of effective vaccination.

##  4 Robustness Checks

###  4.1 Alternative lags

We conduct sensitivity analysis with different lag specifications for
vaccination centered around the baseline lags of 21 and 7 days for first- and
second-dose progress respectively. In alternative specifications, we use the
baseline lags $\pm\ d$ ($d=1,2,3$), for both vaccination variables. The
results are provided in Supplementary Materials C (Figure A1). All the
estimates across the alternative lag specifications are consistent with our
baseline findings. The magnitudes of estimates slightly vary with lags, and
the coefficient estimates of first-dose progress always remain highly
significant. The effect of the share of vaccinated people with at least one
dose becomes slightly larger when the lag employed is longer. Full vaccination
progress is insignificant with all the alternative lags. The effects of
vaccines from China are not sensitive to the lag specifications.

###  4.2 Alternative periods

We also estimate our regression coefficients for alternative initial and end
dates. First, we change the initial date from June 1 to July 1, 2020, so that
the first month in the full period is omitted. During the omitted period, the
cases numbers were stable in many countries. Secondly, we exclude the data
after May 31, 2021, from the full period. In the omitted period, some
countries experienced a surge in new cases due to the spread of the Delta
variant. The results are reported in Supplementary Materials D (Table A6). In
the first sub-period (July 1, 2020–July 8, 2021), the coefficients estimates
remain very similar to Table 4. The magnitude of the effect of first-dose
progress shrinks in the second sub-period (June 1, 2020–May 31, 2021), but it
is still negative and significant. This would be naturally the case, because
most countries in our sample did not achieve high enough vaccination rates to
effectively contain transmission until May 31, 2021.

###  4.3 Data frequency

A few countries (Costa Rica, Cyprus, Iceland, and the Netherlands) release key
epidemiological variables weekly. We re-estimate our regression coefficients
by aggregating the daily-level data to the weekly level to include these
countries. In total, 41 countries are included in our weekly country panel.
The results are almost identical to our findings in the daily frequency
estimation in terms of the magnitudes and significance of coefficient
estimates. We provide the results in Supplementary Materials E (Tables A7–A9).

###  4.4 Interaction between vaccine distribution and mobility

Our baseline specifications do not allow for interactions between included
variables. We extend these specifications to include the interaction terms
between vaccine variables and the mobility index to test whether the effects
of mobility on case/death growth are reduced when more people are vaccinated.
The results are provided in Supplementary Materials F (Tables
A10–A11).141414We use the same lags (14-day and 28-day lags respectively for
cases and deaths) for vaccine variables and the mobility index to produce the
interaction terms. This specification allows us to interpret the coefficients
on interaction terms as differential effects of mobility conditional on
vaccination rates. All the coefficients on interaction terms are insignificant
at the 95% confidence level when the quadratic time trends are controlled for.

###  4.5 Countries adopting Chinese vaccines

The baseline specifications include only four countries that use Chinese
vaccines. Hungary, Mexico, and Colombia also adopted Chinese vaccines but
those countries are not included because they did not use Chinese vaccines for
the majority of total doses.151515According to Our World in Data, Wilson
Center (https://www.wilsoncenter.org/), and Observer Research Foundation
(https://www.orfonline.org/), the ratios of Chinese vaccines to total vaccine
doses for three countries are as follows: 20.4% (Hungary, as of Jul 2, 2021),
30.1% (Mexico, as of Jul 1, 2021), and 53.6% (Colombia, as of Jul 5, 2021). We
re-estimate including these three countries among those using Chinese
vaccines. The results in Supplementary Materials G (Table A12) are consistent
with our baseline findings.

##  5 Counterfactual Vaccine Allocations

We use our estimates in Tables 4–5 to evaluate counterfactuals where the
actual vaccine allocations between first and second doses are replaced by
alternative hypothetical allocations. Saad-Roy et al., (2021) consider the
rate of first-dose progress as an increasing function of the dosing interval.
In a similar spirit, we also assume that the total amount of available
vaccines does not change:

$\displaystyle V1_{it}+V2_{it}=V1_{it}^{*}+V2_{it}^{*},$

where $V1_{it}^{*}$ and $V2_{it}^{*}$ are sequences of counterfactual first-
and second-dose vaccination rates for country $i$.

We focus on Canada and the US for our counterfactual experiments as both
countries mainly relied on mRNA-based vaccines but used very different dosing
intervals. When it comes to the US, we extend the dosing interval to 8 weeks,
resulting in faster growth of first-dose vaccination. For Canada, we reduce
the interval between two doses to 8 weeks. This means that first-dose progress
rises less steeply and full vaccination progress grows more quickly than the
actual data. Following the actual vaccination rates for the two countries, we
set the maximum rate of first-dose vaccination to 65% for Canada and 55% for
the US. The hypothetical sequences of vaccination rates are shown in Figure 4.

Figure 4: Actual and hypothetical vaccination rates (8-week dosing interval)

(a) Canada

(b) US

Note: Black solid lines are actual vaccination rates, the share of people
vaccinated and the share of people fully vaccinated per hundred. Red dotted
lines are hypothetical vaccination rates we consider in our counterfactual
experiments.

We compute the counterfactual outcomes using Equations (5)–(7) for new cases,
deaths and mobility. We assume that government policies and all other
variables remain fixed at their observed values in the data. As recent
vaccination rates, case counts and growth affect people’s behavior, we compute
the counterfactual case growth and mobility iteratively conditional on the
lagged values of counterfactual outcomes and the hypothetical sequences of
vaccination allocations. Therefore, the information effects of counterfactual
case and death counts and the indirect effects of vaccination via mobility are
taken into account in such calculations. The 90% confidence bands are computed
using simulations.

To compute counterfactual outcomes, Equations (7) and (5) are rewritten in
terms of $\log\Delta C_{i,t}$:

$\displaystyle M_{i,t-14}=$
$\displaystyle\gamma_{0i}+\gamma_{M}M_{i,t-21}+\left(\gamma_{dC}+\gamma_{C}\right)\log\Delta
C_{i,t-14}-\gamma_{dC}\log\Delta C_{i,t-21}$
$\displaystyle+\gamma_{d1}V1_{i,t-14}+\gamma_{d2}V2_{i,t-14}+\left(\gamma_{1}-\gamma_{d1}\right)V1_{i,t-21}+\left(\gamma_{2}-\gamma_{d2}\right)V2_{i,t-21}$
$\displaystyle+\gamma_{dP}P_{i,t-14}+\left(\gamma_{P}-\gamma_{dP}\right)P_{i,t-21}+\varepsilon_{i,t-14}^{Mob},$
$\displaystyle\log\Delta C_{i,t}=$ $\displaystyle\log\Delta
C_{i,t-7}+\alpha_{0i}+\left(\alpha_{C1}+\alpha_{C2}\right)\log\left(\Delta
C_{i,t-14}\right)-\alpha_{C1}\log\left(\Delta C_{i,t-21}\right)$
$\displaystyle+\alpha_{V1}V1_{i,t-21}+\alpha_{V2}V2_{i,t-7}+\alpha_{P}P_{i,t-14}+\alpha_{M}M_{i,t-14}+\alpha_{T}\Delta\log\left(\Delta
T_{it}\right)+\varepsilon_{it}^{C},$

We use these two equations to iteratively compute counterfactual outcomes,
$\\{M_{i,t}^{*}\\}$ and $\\{\log\Delta C_{i,t}^{*}\\}$, given
$\\{V1_{t}^{*},V2_{t}^{*}\\}$ and $\\{P_{i,t},\varepsilon_{i,t}^{Mob}\\}$. We
draw the parameters, $\tilde{\alpha}_{j}$, in Equations (5) and (6) from their
asymptotic distribution and compute the associated residuals,
$\tilde{\varepsilon}_{j}^{C}$, while the parameters for country specific time
trends, country fixed effects in (5) and all parameters in (7) remain fixed.
We then compute the counterfactual outcomes with
$\left(\tilde{\alpha}_{j},\tilde{\varepsilon}_{j}^{C}\right)$ for
$j=1,\ldots,200$. The point estimate of each counterfactual outcome is the
mean across 200 replications. We plot the 90% point-wise confidence bands by
taking 5th and 95th percentiles across the replications at each $t$.

From the estimates in Tables 4–5, we expect that converting second doses
administered to partially vaccinated people to first doses for unvaccinated
individuals will lead to substantially lower numbers of new cases and deaths
in the US. Analogously, reducing the interval between doses in Canada is
expected to result in higher counts for both health outcomes. Effects of
changing the vaccination strategy on cases and deaths are expected to be
partially moderated via effects on mobility.

Figure 5: Counterfactual case counts

(a) Canada

(b) US

Note: In the left panels, red solid lines are changes in log of weekly case
counts due to altered vaccine schedules. In the right panels, black solid
lines are actual case numbers and red solid lines are counterfactual counts.
Shaded areas are 90% confidence bands.

Figure 6: Counterfactual death counts

(a) Canada

(b) US

Note: In the left panels, red solid lines are changes in log of weekly case
counts due to altered vaccine schedules. In the right panels, black solid
lines are actual case numbers and red solid lines are counterfactual counts.
Shaded areas are 90% confidence bands.

Figure 5 shows the counterfactual simulations for cases counts. We draw
changes in log of weekly case counts due to altered vaccine schedules in the
left panels. Actual and counterfactual case numbers are plotted in the right
panels. For Canada, the case counts substantially increase compared to their
actual counterparts under the hypothetical vaccination schedule. The
counterfactual outcomes are significantly different from the actual outcomes.
The daily case counts decrease by 274 on average between Feb 1 and Apr 30,
2021 as first-dose progress is slightly faster in the hypothetical vaccination
schedule in the earlier period. The case numbers increase on average by 1,014
between May 1 and Jul 8, 2021 as the counterfactual first-dose progress goes
below the actual progress. On the contrary, the counterfactual outcomes become
significantly lower than the actual values for the US as first-dose progress
is much faster in the hypothetical scenario. Between Feb 1 and Jul 8, 2021,
the estimated reductions in the daily case numbers are 21,587. In terms of
cumulative cases, those are translated into a 7% increase for Canada and a 43%
reduction for the US between Feb 1 and July 8, 2021.

Similar patterns are observed in the counterfactual death counts as shown in
Figure 6. For Canada, the daily death counts initially decrease on average by
1.4 between Feb 1 and Apr 30, 2021 and subsequently increase on average by 6.2
between May 1 and Jul 8, 2021. The average reduction in daily death numbers is
271 for the US from Feb 1 to Jul 8, 2021. In terms of cumulative death counts,
those are translated into a 4.6% increase for Canada and a 25.9% reduction for
the US between Feb 1 and July 8, 2021.

In alternative hypothetical scenarios, we adjust the dosing interval to 6
weeks and 12 weeks. The results are provided in Supplementary Materials H. For
the US, gaps between counterfactuals and observed outcomes widen as the dosing
interval becomes longer. When it comes to Canada, the 6-week dosing regimen
results in worse health outcomes than the 8-week interval. Under the 12-week
dosing regimen, the hypothetical vaccination rates closely follow the actual
rates, but the hypothetical first-dose progress is slightly faster in the
earlier vaccination stage, as Canada delayed the second dose in March 2021.
Hence reductions in cases and deaths are observed when the 12-week dosing
interval is imposed from the beginning.

The counterfactual outcomes are calculated under the assumption that
government policies remain at their actual values observed in the data. As
government policies were eased in response to the decline in new cases, this
would be also the case in the counterfactual situations where the case numbers
still drop along with vaccination progress. If we adjust the government policy
index according to the relative changes in case and death counts, the gaps
between the counterfactual outcomes and the actual outcomes will shrink.

##  6 Discussion

###  6.1 Vaccination strategy across heterogeneous populations

There were elevated concerns about vulnerable sub-populations such as the
elderly, healthcare workers, and immunocompromised people when the UK and
Canada decided to extend the dosing interval. The COVID-19-related mortality
risk has been reported to be higher for the elderly and clinically vulnerable
people with underlying medical conditions. Front-line healthcare providers
work in direct contact with COVID-19 infected patients as well as other
vulnerable populations. Therefore, these groups have to be prioritized for
full vaccination if the marginal efficacy gain from the second dose is large.
Nonetheless, the UK and Canada both extended the dosing interval for the whole
population based on the rationale described in Section 2 (see NHS, (2020),
Atherton et al., (2020), and National Advisory Committee on Immunization,
(2021)). Skowronski et al., (2021) report that single-dose vaccination
provides robust protection against the original strain and the variants of
concern for adults 70 years or older using data from British Columbia, Canada.
More recently, similar results have been reported by Shrotri et al., (2021)
using the UK data. Thus the vulnerable groups can benefit from receiving their
first doses more quickly under the extended dosing interval scheme.
Furthermore, maximizing the number of vaccinated people can reduce community
transmission and overcrowding in hospitals. A larger fraction of vaccinated
people in the population lowers the risk of exposure for the vulnerable
people, as our panel data analysis suggests. The reduced healthcare burden
also helps the elderly and vulnerable people access non-COVID-19-related
medical services.

###  6.2 Variants of concern

Each country suffers from different variants of concern, some of which are
known to be more easily transmitted than the original strain. We try to
control this by using country fixed effects and country-specific time trends.
With or without time trends, our estimates remain with the same signs across
different model specifications. In the period of our analysis, a number of
countries such as the UK and Israel experienced another wave due to the Delta
variant. When we exclude the period of the new wave, all the estimates still
remain very similar. As the Delta variant has become the most dominant variant
in many countries, there may be a structural break in the evolution of the
pandemic. The period after the rise of the Delta variant is an avenue of
future research. Provided single-dose vaccination is still highly effective
against new variants (although somewhat weaker than against the original
strain), the policy implications drawn from our findings remain valid.

The evidence on single-dose effectiveness of approved vaccines against
symptomatic infection is mixed, but more recent studies tend to report that
the vaccine effectiveness against the Delta variant is solid. Bernal et al.,
(2021) report that one dose effectiveness against the Delta variant is much
lower for Comirnaty (35.6%) and Vaxzevria (30%) than the effectiveness with
two doses (Comirnaty: 88%, Vaxzevria: 67%) using data until May 16, 2021, from
England. However, more recent research (Pouwels et al., (2021)) from the UK
reports that the marginal efficacy gain from the first dose against the Delta
variant (Comirnaty: 58%, Spikevax: 75%, Vaxzevria: 43%) is larger.161616The
reported vaccine effectiveness 14 days after the second dose is Comirnaty: 82%
and Vaxzevria: 67%. They have insufficient data for the second dose of
Spikevax. Nasreen et al., (2021) also find similar vaccine effectiveness with
one dose against the Delta variant (Comirnaty: 57%, Spikevax: 70%, Vaxzevria:
68%) using Canadian data. Tang et al., (2021) observe that the marginal
efficacy gain from the second dose is negligible for mRNA-based vaccines using
Qatari data.

Vaccine effectiveness against severe illness (hospitalization or death) caused
by the Delta variant is shown to be much higher than for symptomatic infection
after partial vaccination. Nasreen et al., (2021) estimate that vaccine
effectiveness against severe outcomes after single-dose inoculation of
Comirnaty, Spikevax, and Vaxzevria was 81%, 90%, and 91% respectively. A
technical report by Public Health England (Stowe et al., (2021)) and Tang et
al., (2021) also strongly agree with the Canadian results on vaccine
effectiveness against hospitalization only after dose 1. This implies that
maximizing the number of at least partially vaccinated people is an effective
strategy to prevent worse health outcomes, which are of primary policy
interest.

Of course, worse variants can arise. The Omicron variant is quickly becoming a
dominant variant in many countries. There have been elevated concerns that
vaccine effectiveness could be substantially lower against the Omicron variant
than against the Delta variant (see Karim and Karim, (2021) and Callaway and
Ledford, (2021)). However, information on first-dose vaccine effectiveness
against the new variant is very limited.171717Public Health England, (2021)
reports vaccine effectiveness against the Omicron variant using the UK data.
First-dose effectiveness of Comirnaty against symptomatic infections was
slightly lower than against the delta variant. However, the Omicron case
number used in this analysis for first-dose effectiveness is very small (28),
which resulting in a wide confidence band. Countries can always modify their
vaccination strategy depending upon updated medical evidence on the vaccine
effectiveness against new variants and their policy goal (minimizing new
infections or severe illness).

###  6.3 Waning effects

Countries with high vaccination rates have repeatedly reported that the
vaccine effectiveness after the second dose is decreasing over time. Andrews
et al., (2021) show that the vaccine effectiveness against symptomatic
infection peaked a week after the second dose and started to substantially
drop over time (Comirnaty: 92.4% $\to$ 69.7% (+20 weeks), Vaxzevria: 62.7%
$\to$ 47.3% (+20 weeks)) using the UK data. Nanduri et al., (2021) also report
that the effectiveness of mRNA vaccines against infection declined from 74.7%
(Mar-May 2021) to 53.1% (Jun-Jul 2021) among elderly nursing home residents in
the US. Chemaitelly et al., (2021) observe the same pattern in the Qatari
data. Goldberg et al., (2021) provide a detailed breakdown of the waning
effectiveness by age group using the Israeli data. The rate ratio for
infection among people fully vaccinated in the month when they were first
eligible was around 1.6, as compared with individuals fully immunized 2 months
later, for all age groups. When it comes to severe illness (hospitalization
and death), Andrews et al., (2021) report limited waning in effectiveness. On
the contrary, Chemaitelly et al., (2021) and Goldberg et al., (2021) show
substantial waning in protection.

It is not clear whether the waning effectiveness comes from decreased immunity
or more new infectious variants. It may be from both. An additional
confounding factor is that fully vaccinated people tend to increase social
interactions, which increases the exposure risk among the vaccinated.
Nonetheless, the recent results in the literature clearly suggest the immunity
against infection wanes in a few months after receiving the second dose. As
far as the first dose itself provides robust protection for 3–4 months,
extending the dosing interval could potentially protect vaccinated people
longer because the timing of receiving the second dose is delayed, and the
delay may produce stronger immune responses.

##  7 Concluding Remarks

We evaluate the impact of vaccination progress and types of vaccines on the
spread of COVID-19 using cross-country data. We exploit variations in
vaccination rates over time and across countries using standard time-series
and panel data models. We find a significant negative association between
vaccination progress and the transmission of COVID-19. The key factor is the
share of vaccinated people with at least one dose, which leads to significant
reductions in new infections and deaths. Second-dose progress offered no
further reductions, at least in the short run. For vaccines from China, we
find significant effects on new cases and deaths only after full vaccination.
Vaccination progress increases people’s mobility, which indirectly partially
offsets effects on growth rates of new infections and deaths. Our
counterfactual experiments show that extending the interval between two
vaccine doses can substantially reduce new cases and deaths.

Our findings support the idea of delaying the second dose. As many advanced
countries use the remaining vaccine orders to administer booster shots (the
third dose), the supply of mRNA-based vaccines will be likely to be limited
for middle- and low-income countries. Given the constraints, a more efficient
allocation strategy for available vaccine stocks would be to maximize the
number of (at least) partially vaccinated people. However, this does not
appear to apply to vaccines from China. Countries relying on those vaccines
may need to administer both shots within the recommended interval. Another
policy implication is that the indirect effects of vaccination offset its
direct effects on health outcomes via increased mobility. Many countries eased
public health restrictions in line with the decline in new infections. Lower
case numbers, vaccination progress, and eased restrictions all lead to greater
mobility. Hence maintaining policy restrictions helps contain the virus by
reducing mobility and social interactions. We do not focus on each public
health measure and only include a composite index of the overall stringency of
government responses in our analysis. As coefficient estimates on this index
remain negative and significant across all the specifications considered, our
results further support the previous studies on the effectiveness of other
policy interventions.

Since the Delta variant emerged, countries with high vaccination rates have
experienced a surge in new infections. The vaccine effectiveness of approved
vaccines against the new variant is substantially lower than against the
original strain. However, the single dose efficacy of mRNA and adenoviral
vaccines is still higher than the marginal efficacy gain from the second dose.
It is also shown that those vaccines remain highly effective against severe
illness, even with only one dose. This implies that delaying the second dose
can be an effective strategy to prevent worse health outcomes,
hospitalization, and death, amid the rise of the Delta variant. Severe illness
caused by the virus is a primary policy concern because it leads to a great
burden on the healthcare system. It may not be possible to reduce case counts
to zero by vaccination, but the widely provided protection offered by vaccines
can make the disease more manageable. The same implications could apply for
new variants provided single-dose effectiveness against severe illness remains
solid.

We conclude that vaccination has indeed been a very powerful weapon to battle
the COVID-19 pandemic. However, it does not mean that other policy
interventions can be quickly phased out as vaccination rates reach a high
enough level. Containment measures are still valuable tools to supplement the
vaccination efforts. We focus on the impact of vaccination on epidemiological
outcomes. An interesting research avenue is the impact of vaccination, along
with eased restrictions, on economic activities such as retail sales,
employment, and economic growth as recently studied in Dave et al., (2021).

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See pages 1 of appen_title.pdf

##  Appendix A Time series estimation for new death counts

Table A1: ARIMA model results for new deaths - countries relying on vaccines
developed in the US and Europe

| Canada | Israel | US | UK
---|---|---|---|---
$(p,d,q)$ | $(1,1,1)$ | $(2,1,2)$ | $(6,1,2)$ | $(4,1,2)$
$\Delta V1_{t-35}$ |  $-$0.037∗ | 0.020 |  $-$0.015 |  $-$0.131∗∗∗
| (0.022) | (0.024) | (0.058) | (0.035)
$\Delta V2_{t-21}$ |  $-$0.015 |  $-$0.061∗∗ |  $-$0.044 | 0.057
| (0.057) | (0.025) | (0.066) | (0.056)
$wkd_{t}$ |  $-$0.194∗∗∗ | 0.003 |  $-$0.611∗∗∗ |  $-$0.705∗∗∗
| (0.047) | (0.025) | (0.124) | (0.054)
$\Delta P_{t-28}$ |  $-$0.019 |  $-$0.008∗ |  $-$0.002 |  $-$0.002
| (0.029) | (0.004) | (0.012) | (0.007)
$\Delta M_{t-28}$ | 0.003 |  $-$0.005 | 0.005 |  $-$0.003
| (0.013) | (0.006) | (0.010) | (0.008)

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. We use lags of included regressors that are 14 days behind
those in the case equation following Karaivanov et al., (2021). $(p,d,q)$ is
the optimal ARIMA order chosen by auto.arima in R. Standard errors are in
parentheses. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

Table A2: ARIMA model results for new deaths - countries relying on vaccines
from China

| Chile | Uruguay | UAE | Bahrain
---|---|---|---|---
$(p,d,q)$ | $(4,1,2)$ | $(2,1,2)$ | $(2,1,1)$ | $(0,1,1)$
$\Delta V1_{t-35}$ |  $-$0.007 |  $-$0.017 |  | 0.020
| (0.021) | (0.025) |  | (0.033)
$\Delta V2_{t-21}$ | 0.017 | 0.006 |  |  $-$0.015
| (0.022) | (0.033) |  | (0.024)
$\Delta V_{t-35}$ |  |  |  $-$0.016∗∗ |
|  |  | (0.007) |
$wkd_{t}$ |  $-$0.004 | 0.016 |  $-$0.033 | 0.001
| (0.141) | (0.025) | (0.040) | (0.022)
$\Delta P_{t-28}$ | 0.002 | 0.003 | 0.020 |  $-$0.043∗∗
| (0.015) | (0.006) | (0.014) | (0.018)
$\Delta M_{t-28}$ |  $-$0.005 |  $-$0.001 |  $-$0.021 |  $-$0.038
| (0.006) | (0.005) | (0.018) | (0.025)

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. We use lags of included regressors that are 14 days behind
those in the case equation following Karaivanov et al., (2021). $(p,d,q)$ is
the optimal ARIMA order chosen by auto.arima in R. Standard errors are in
parentheses. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively. The UAE does not publicly announce first and second dose
progress separately so we include total doses administered ($V$) instead.

##  Appendix B Panel Data Regression with alternative time controls

Table A3: The direct effects of vaccination, policy, and behavior on new cases

| Dependent variable: $\Delta\log\Delta C_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-21}$ | -0. | 0176∗∗∗ | -0. | 0189∗∗∗ | -0. | 0176∗∗∗ | -0. | 0227∗∗∗
| (0. | 0036) | (0. | 0041) | (0. | 0046) | (0. | 0042)
$V2_{t-7}$ | 0. | 0066 | 0. | 0046 | 0. | 0047 | 0. | 0025
| (0. | 0041) | (0. | 0051) | (0. | 0065) | (0. | 0058)
$V1_{t-21}^{CHN}$ |  | 0. | 0179∗∗∗ |  | 0. | 0377∗∗∗
|  | (0. | 0059) |  | (0. | 0081)
$V2_{t-7}^{CHN}$ |  | -0. | 0100∗ |  | 0. | 0024
|  | (0. | 0059) |  | (0. | 0063)
$P_{t-14}$ | -0. | 0035∗ | -0. | 0037∗ | -0. | 0057∗∗ | -0. | 0060∗∗
| (0. | 0021) | (0. | 0021) | (0. | 0022) | (0. | 0023)
$M_{t-14}$ | 0. | 0058∗∗∗ | 0. | 0065∗∗∗ | 0. | 0098∗∗∗ | 0. | 0100∗∗∗
| (0. | 0016) | (0. | 0015) | (0. | 0013) | (0. | 0013)
$\Delta\log\Delta T_{t}$ | 0. | 4860∗∗∗ | 0. | 4804∗∗∗ | 0. | 3882∗∗ | 0. | 3847∗∗
| (0. | 1712) | (0. | 1696) | (0. | 1442) | (0. | 1427)
$\Delta\log\Delta C_{t-14}$ | 0. | 1022∗∗∗ | 0. | 1022∗∗∗ | 0. | 0735∗∗ | 0. | 0656∗
| (0. | 0373) | (0. | 0372) | (0. | 0346) | (0. | 0354)
$\log\Delta C_{t-14}$ | -0. | 0744∗∗∗ | -0. | 0831∗∗∗ | -0. | 1628∗∗∗ | -0. | 1622∗∗∗
| (0. | 0136) | (0. | 0132) | (0. | 0208) | (0. | 0214)
Country fixed effects | yes | yes | yes | yes
Country specific trend in days | linear | linear | cubic | cubic
${\rm R}^{2}$ | 0. | 3219 | 0. | 3266 | 0. | 4285 | 0. | 4346
Adjusted ${\rm R}^{2}$ | 0. | 3197 | 0. | 3244 | 0. | 4237 | 0. | 4298
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 382 | 382 | 382 | 382

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

Table A4: The direct effects of vaccination, policy, and behavior on new
deaths

| Dependent variable: $\Delta\log\Delta D_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-35}$ | -0. | 0202∗∗∗ | -0. | 0206∗∗∗ | -0. | 0112∗∗ | -0. | 0133∗∗∗
| (0. | 0040) | (0. | 0047) | (0. | 0043) | (0. | 0041)
$V2_{t-21}$ | 0. | 0075 | 0. | 0027 | 0. | 0083 | 0. | 0081
| (0. | 0053) | (0. | 0069) | (0. | 0050) | (0. | 0054)
$V1_{t-35}^{CHN}$ |  | 0. | 0155∗∗ |  | 0. | 0139∗∗
|  | (0. | 0074) |  | (0. | 0060)
$V2_{t-21}^{CHN}$ |  | -0. | 0017 |  | -0. | 0016
|  | (0. | 0083) |  | (0. | 0064)
$P_{t-28}$ | -0. | 0035∗ | -0. | 0037∗ | -0. | 0038∗∗ | -0. | 0039∗∗
| (0. | 0019) | (0. | 0019) | (0. | 0018) | (0. | 0018)
$M_{t-28}$ | 0. | 0053∗∗∗ | 0. | 0059∗∗∗ | 0. | 0087∗∗∗ | 0. | 0088∗∗∗
| (0. | 0019) | (0. | 0017) | (0. | 0014) | (0. | 0014)
$\Delta\log\Delta D_{t-28}$ | 0. | 0656∗∗ | 0. | 0681∗∗ | 0. | 0520∗ | 0. | 0506∗
| (0. | 0293) | (0. | 0289) | (0. | 0262) | (0. | 0262)
$\log\Delta D_{t-28}$ | -0. | 0817∗∗∗ | -0. | 0942∗∗∗ | -0. | 1263∗∗∗ | -0. | 1249∗∗∗
| (0. | 0121) | (0. | 0121) | (0. | 0127) | (0. | 0127)
Country fixed effects | yes | yes | yes | yes
Country specific trend in days | linear | linear | cubic | cubic
${\rm R}^{2}$ | 0. | 1271 | 0. | 1324 | 0. | 1860 | 0. | 1864
Adjusted ${\rm R}^{2}$ | 0. | 1244 | 0. | 1295 | 0. | 1789 | 0. | 1792
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 368 | 368 | 368 | 368

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

Table A5: The direct effects of vaccination, policy and information on
mobility

|  Dependent variable: $M_{t}$
---|---
| (1) | (2) | (3) | (4)
$\Delta V1_{t}$ | 0. |  4922∗∗∗ | 0. |  6020∗∗∗ | 0. |  4196∗∗∗ | 0. |  4985∗∗∗
| (0. | 1103) | (0. | 1472) | (0. | 1150) | (0. | 1548)
$\Delta V2_{t}$ | 0. | 1511 | 0. | 2597 | 0. | 1710 | 0. | 2787
| (0. | 1291) | (0. | 1579) | (0. | 1365) | (0. | 1798)
$V1_{t-7}$ | 0. |  0619∗ | 0. | 0549 | 0. |  0650∗∗ | 0. | 0590
| (0. | 0356) | (0. | 0397) | (0. | 0306) | (0. | 0353)
$V2_{t-7}$ | -0. | 0244 | -0. | 0130 | -0. | 0023 | 0. | 0071
| (0. | 0382) | (0. | 0450) | (0. | 0302) | (0. | 0345)
$\Delta V1_{t}^{CHN}$ |  | -0. |  4470∗∗ |  | -0. |  3085∗
|  | (0. | 1658) |  | (0. | 1761)
$\Delta V2_{t}^{CHN}$ |  | -0. | 3209 |  | -0. | 3235
|  | (0. | 2853) |  | (0. | 3159)
$V1_{t-7}^{CHN}$ |  | -0. | 0517 |  | -0. | 0405
|  | (0. | 0965) |  | (0. | 0937)
$V2_{t-7}^{CHN}$ |  | 0. | 0493 |  | 0. | 0364
|  | (0. | 0759) |  | (0. | 0654)
$\Delta P_{t}$ | -0. |  3474∗∗∗ | -0. |  3491∗∗∗ | -0. |  3576∗∗∗ | -0. |  3584∗∗∗
| (0. | 0452) | (0. | 0449) | (0. | 0432) | (0. | 0430)
$P_{t-7}$ | -0. |  1744∗∗∗ | -0. |  1765∗∗∗ | -0. |  1546∗∗∗ | -0. |  1572∗∗∗
| (0. | 0203) | (0. | 0199) | (0. | 0187) | (0. | 0185)
$\Delta\log\Delta C_{t}$ | 0. |  7572∗∗∗ | 0. |  7607∗∗∗ |  |
| (0. | 2356) | (0. | 2389) |  |
$\log\Delta C_{t}$ | -1. |  0037∗∗∗ | -0. |  9634∗∗∗ |  |
| (0. | 1176) | (0. | 1190) |  |
$\Delta\log\Delta D_{t}$ |  |  | 0. |  2439∗ | 0. |  2339∗
|  |  | (0. | 1361) | (0. | 1333)
$\log\Delta D_{t}$ |  |  | -1. |  1954∗∗∗ | -1. |  1445∗∗∗
|  |  | (0. | 1565) | (0. | 1588)
$M_{t-7}$ | 0. |  7065∗∗∗ | 0. |  7034∗∗∗ | 0. |  6735∗∗∗ | 0. |  6721∗∗∗
| (0. | 0230) | (0. | 0234) | (0. | 0242) | (0. | 0243)
Country fixed effects | yes | yes | yes | yes
Country specific trend in days | linear | linear | linear | linear
${\rm R}^{2}$ | 0. | 8432 | 0. | 8438 | 0. | 8443 | 0. | 8447
Adjusted ${\rm R}^{2}$ | 0. | 8427 | 0. | 8433 | 0. | 8438 | 0. | 8442
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 396 | 396 | 396 | 396

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

##  Appendix C Panel Data regression with alternative lags

Figure A1: Alternative lags for vaccination variables

(a) No time trends

(b) Cubic time trends

Note: The orange squares are estimates from the baseline specifications. The
dark-blue dots are estimates from alternative lag specifications. The solid
lines are the 95% confidence intervals.

##  Appendix D Panel Data regression with alternative periods

Table A6: Early and late sub-periods

| Dependent variable: $\Delta\log\Delta C_{t}$
---|---
| July 1, 2020 - July 8, 2021 | June 1, 2020 - May 31, 2021
$V1_{t-21}$ | -0. | 0164∗∗∗ | -0. | 0185∗∗∗ | -0. | 0132∗∗∗ | -0. | 0139∗∗∗
| (0. | 0042) | (0. | 0041) | (0. | 0043) | (0. | 0049)
$V2_{t-7}$ | 0. | 0035 | 0. | 0058 | 0. | 0006 | 0. | 0006
| (0. | 0055) | (0. | 0059) | (0. | 0038) | (0. | 0053)
$V1_{t-21}^{CHN}$ |  | 0. | 0203∗∗∗ |  | 0. | 0059
|  | (0. | 0055) |  | (0. | 0083)
$V2_{t-7}^{CHN}$ |  | -0. | 0199∗∗∗ |  | -0. | 0029
|  | (0. | 0067) |  | (0. | 0063)
$P_{t-14}$ | -0. | 0031∗∗ | -0. | 0030∗∗ | -0. | 0027 | -0. | 0027
| (0. | 0014) | (0. | 0015) | (0. | 0019) | (0. | 0019)
$M_{t-14}$ | 0. | 0106∗∗∗ | 0. | 0106∗∗∗ | 0. | 0104∗∗∗ | 0. | 0105∗∗∗
| (0. | 0012) | (0. | 0012) | (0. | 0012) | (0. | 0012)
$\Delta\log\Delta C_{t-14}$ | 0. | 0950∗∗∗ | 0. | 0939∗∗∗ | 0. | 0927∗∗ | 0. | 0918∗∗
| (0. | 0318) | (0. | 0318) | (0. | 0379) | (0. | 0376)
$\log\Delta C_{t-14}$ | -0. | 1412∗∗∗ | -0. | 1412∗∗∗ | -0. | 1672∗∗∗ | -0. | 1665∗∗∗
| (0. | 0204) | (0. | 0208) | (0. | 0210) | (0. | 0213)
$\Delta\log\Delta T_{t}$ | 0. | 3942∗∗ | 0. | 3939∗∗ | 0. | 3569∗∗ | 0. | 3569∗∗
| (0. | 1467) | (0. | 1467) | (0. | 1377) | (0. | 1376)
Country fixed effects | yes | yes | yes | yes
Quadratic time trends | yes | yes | yes | yes
${\rm R}^{2}$ | 0. | 4054 | 0. | 4067 | 0. | 3950 | 0. | 3951
Adjusted ${\rm R}^{2}$ | 0. | 4018 | 0. | 4032 | 0. | 3911 | 0. | 3911
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 373 | 373 | 344 | 344

Note: The results are produced using daily data. Standard errors in
parentheses are clustered at the country level. ∗∗∗, ∗∗, and ∗ denote the 99%,
95% and 90% confidence level respectively.

##  Appendix E Panel Data Regression with weekly data

Table A7: The direct effects of vaccination, policy and behavior on new cases

| Dependent variable: $\Delta\log\Delta C_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-21}$ | -0. | 0077∗∗∗ | -0. | 0078∗∗∗ | -0. | 0166∗∗∗ | -0. | 0180∗∗∗
| (0. | 0020) | (0. | 0021) | (0. | 0044) | (0. | 0043)
$V2_{t-7}$ | 0. | 0025 | 0. | 0015 | 0. | 0011 | 0. | 0032
| (0. | 0023) | (0. | 0027) | (0. | 0057) | (0. | 0062)
$V1_{t-21}^{CHN}$ |  | 0. | 0164∗∗∗ |  | 0. | 0152∗∗
|  | (0. | 0032) |  | (0. | 0073)
$V2_{t-7}^{CHN}$ |  | -0. | 0145∗∗∗ |  | -0. | 0171∗∗
|  | (0. | 0042) |  | (0. | 0072)
$P_{t-14}$ | -0. | 0024∗ | -0. | 0022∗ | -0. | 0034∗∗ | -0. | 0033∗∗
| (0. | 0012) | (0. | 0013) | (0. | 0015) | (0. | 0015)
$M_{t-14}$ | 0. | 0054∗∗∗ | 0. | 0055∗∗∗ | 0. | 0108∗∗∗ | 0. | 0108∗∗∗
| (0. | 0012) | (0. | 0012) | (0. | 0015) | (0. | 0015)
$\Delta\log\Delta C_{t-14}$ | 0. | 0787 | 0. | 0732 | 0. | 0522 | 0. | 0515
| (0. | 0517) | (0. | 0518) | (0. | 0469) | (0. | 0467)
$\log\Delta C_{t-14}$ | -0. | 0329∗∗∗ | -0. | 0365∗∗∗ | -0. | 1399∗∗∗ | -0. | 1403∗∗∗
| (0. | 0076) | (0. | 0073) | (0. | 0224) | (0. | 0226)
$\Delta\log\Delta T_{t}$ | 0. | 5589∗∗∗ | 0. | 5543∗∗∗ | 0. | 4391∗∗∗ | 0. | 4387∗∗∗
| (0. | 1434) | (0. | 1426) | (0. | 1244) | (0. | 1243)
Country fixed effects | yes | yes | yes | yes
Country specific trend in weeks | no | no | quadratic | quadratic
${\rm R}^{2}$ | 0. | 2837 | 0. | 2879 | 0. | 3980 | 0. | 3987
Adjusted ${\rm R}^{2}$ | 0. | 2813 | 0. | 2848 | 0. | 3726 | 0. | 3728
Number of countries | 41 | 41 | 41 | 41
Obs. per country | 54 | 54 | 54 | 54

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using weekly data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

Table A8: The direct effects of vaccination, policy and behavior on new deaths

| Dependent variable: $\Delta\log\Delta D_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-35}$ | -0. | 0097∗∗∗ | -0. | 0094∗∗∗ | -0. | 0123∗∗∗ | -0. | 0130∗∗∗
| (0. | 0022) | (0. | 0023) | (0. | 0038) | (0. | 0039)
$V2_{t-21}$ | 0. | 0039 | 0. | 0018 | 0. | 0044 | 0. | 0068
| (0. | 0030) | (0. | 0032) | (0. | 0042) | (0. | 0044)
$V1_{t-35}^{CHN}$ |  | 0. | 0172∗∗∗ |  | 0. | 0035
|  | (0. | 0038) |  | (0. | 0069)
$V2_{t-21}^{CHN}$ |  | -0. | 0121∗∗ |  | -0. | 0098∗
|  | (0. | 0055) |  | (0. | 0055)
$P_{t-28}$ | -0. | 0010 | -0. | 0007 | -0. | 0016 | -0. | 0016
| (0. | 0014) | (0. | 0015) | (0. | 0013) | (0. | 0013)
$M_{t-28}$ | 0. | 0056∗∗∗ | 0. | 0055∗∗∗ | 0. | 0083∗∗∗ | 0. | 0081∗∗∗
| (0. | 0016) | (0. | 0016) | (0. | 0017) | (0. | 0016)
$\Delta\log\Delta D_{t-28}$ | 0. | 0636∗∗∗ | 0. | 0611∗∗∗ | 0. | 0639∗∗∗ | 0. | 0633∗∗∗
| (0. | 0223) | (0. | 0224) | (0. | 0227) | (0. | 0228)
$\log\Delta D_{t-28}$ | -0. | 0448∗∗∗ | -0. | 0492∗∗∗ | -0. | 1317∗∗∗ | -0. | 1319∗∗∗
| (0. | 0107) | (0. | 0100) | (0. | 0121) | (0. | 0122)
Country fixed effects | yes | yes | yes | yes
Country specific trend in weeks | no | no | quadratic | quadratic
${\rm R}^{2}$ | 0. | 1017 | 0. | 1063 | 0. | 1852 | 0. | 1855
Adjusted ${\rm R}^{2}$ | 0. | 0990 | 0. | 1028 | 0. | 1500 | 0. | 1494
Number of countries | 41 | 41 | 41 | 41
Obs. per country | 52 | 52 | 52 | 52

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using weekly data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

Table A9: The direct effects of vaccination, policy and information on
mobility

|  Dependent variable: $M_{t}$
---|---
| (1) | (2) | (3) | (4)
$\Delta V1_{t}$ | 0. |  5894∗∗∗ | 0. |  6601∗∗∗ | 0. |  5188∗∗∗ | 0. |  5778∗∗∗
| (0. | 1216) | (0. | 1504) | (0. | 1223) | (0. | 1521)
$\Delta V2_{t}$ | 0. |  2504∗ | 0. |  3451∗∗ | 0. |  2876∗∗ | 0. |  3860∗∗
| (0. | 1277) | (0. | 1528) | (0. | 1388) | (0. | 1725)
$V1_{t-7}$ | 0. | 0531 | 0. | 0390 | 0. | 0424 | 0. | 0292
| (0. | 0327) | (0. | 0371) | (0. | 0316) | (0. | 0364)
$V2_{t-7}$ | -0. | 0133 | -0. | 0032 | 0. | 0072 | 0. | 0163
| (0. | 0361) | (0. | 0396) | (0. | 0320) | (0. | 0355)
$\Delta V1_{t}^{CHN}$ |  | -0. | 3402 |  | -0. | 2865
|  | (0. | 2269) |  | (0. | 2347)
$\Delta V2_{t}^{CHN}$ |  | -0. | 3465 |  | -0. | 3598
|  | (0. | 2477) |  | (0. | 2670)
$V1_{t-7}^{CHN}$ |  | 0. | 0410 |  | 0. | 0395
|  | (0. | 0645) |  | (0. | 0597)
$V2_{t-7}^{CHN}$ |  | -0. | 0176 |  | -0. | 0180
|  | (0. | 0731) |  | (0. | 0625)
$\Delta P_{t}$ | -0. |  2323∗∗∗ | -0. |  2320∗∗∗ | -0. |  2559∗∗∗ | -0. |  2547∗∗∗
| (0. | 0505) | (0. | 0507) | (0. | 0485) | (0. | 0489)
$P_{t-7}$ | -0. |  1182∗∗∗ | -0. |  1204∗∗∗ | -0. |  1096∗∗∗ | -0. |  1120∗∗∗
| (0. | 0179) | (0. | 0182) | (0. | 0183) | (0. | 0186)
$\Delta\log\Delta C_{t}$ | 0. | 2696 | 0. | 2993 |  |
| (0. | 2639) | (0. | 2663) |  |
$\log\Delta C_{t}$ | -0. |  7097∗∗∗ | -0. |  6992∗∗∗ |  |
| (0. | 0909) | (0. | 0976) |  |
$\Delta\log\Delta D_{t}$ |  |  | -0. | 1759 | -0. | 1645
|  |  | (0. | 2216) | (0. | 2211)
$\log\Delta D_{t}$ |  |  | -0. |  7710∗∗∗ | -0. |  7585∗∗∗
|  |  | (0. | 1279) | (0. | 1334)
$M_{t-7}$ | 0. |  7547∗∗∗ | 0. |  7547∗∗∗ | 0. |  7402∗∗∗ | 0. |  7405∗∗∗
| (0. | 0248) | (0. | 0249) | (0. | 0272) | (0. | 0272)
Country fixed effects | yes | yes | yes | yes
Time effects | no | no | no | no
${\rm R}^{2}$ | 0. | 8232 | 0. | 8235 | 0. | 8232 | 0. | 8234
Adjusted ${\rm R}^{2}$ | 0. | 8225 | 0. | 8225 | 0. | 8225 | 0. | 8224
Number of countries | 41 | 41 | 41 | 41
Obs. per country | 56 | 56 | 56 | 56

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using weekly data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

##  Appendix F Interaction between vaccine variables and the mobility index

Table A10: The direct effects of vaccination, policy, behavior and their
interactions on cases

|  Dependent variable: $\Delta\log\Delta C_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-14}$ | -0. |  0085∗∗ | -0. |  0086∗∗ | -0. |  0156∗∗∗ | -0. |  0173∗∗∗
| (0. | 0038) | (0. | 0038) | (0. | 0054) | (0. | 0061)
$V2_{t-14}$ | 0. | 0040 | 0. | 0041 | 0. | 0035 | 0. | 0072
| (0. | 0055) | (0. | 0056) | (0. | 0083) | (0. | 0098)
$V1_{t-14}^{CHN}$ |  | 0. |  0185∗∗∗ |  | 0. | 0130
|  | (0. | 0061) |  | (0. | 0080)
$V2_{t-14}^{CHN}$ |  | -0. |  0255∗∗∗ |  | -0. |  0311∗∗∗
|  | (0. | 0080) |  | (0. | 0100)
$P_{t-14}$ | -0. | 0015 | -0. | 0016 | -0. | 0016 | -0. | 0013
| (0. | 0011) | (0. | 0011) | (0. | 0013) | (0. | 0012)
$M_{t-14}$ | 0. |  0059∗∗∗ | 0. |  0055∗∗∗ | 0. |  0099∗∗∗ | 0. |  0103∗∗∗
| (0. | 0015) | (0. | 0015) | (0. | 0017) | (0. | 0017)
$V1_{t-14}\times M_{t-14}$ | -0. | 0002 | -0. | 0001 | -0. | 0002 | -0. | 0002
| (0. | 0002) | (0. | 0002) | (0. | 0003) | (0. | 0003)
$V2_{t-14}\times M_{t-14}$ | 0. | 0001 | 0. | 0002 | 0. | 0002 | 0. | 0003
| (0. | 0004) | (0. | 0004) | (0. | 0004) | (0. | 0004)
$V1_{t-14}^{CHN}\times M_{t-14}$ |  | 0. | 0003 |  | 0. | 0002
|  | (0. | 0003) |  | (0. | 0003)
$V2_{t-14}^{CHN}\times M_{t-14}$ |  | -0. | 0007 |  | -0. |  0008∗
|  | (0. | 0005) |  | (0. | 0004)
$\Delta\log\Delta C_{t-14}$ | 0. |  1022∗∗ | 0. |  1004∗∗ | 0. |  0824∗∗ | 0. |  0826∗∗
| (0. | 0390) | (0. | 0391) | (0. | 0336) | (0. | 0335)
$\log\Delta C_{t-14}$ | -0. |  0369∗∗∗ | -0. |  0391∗∗∗ | -0. |  1562∗∗∗ | -0. |  1572∗∗∗
| (0. | 0105) | (0. | 0106) | (0. | 0217) | (0. | 0219)
$\Delta\log\Delta T_{t}$ | 0. |  5145∗∗∗ | 0. |  5107∗∗∗ | 0. |  4054∗∗∗ | 0. |  4025∗∗∗
| (0. | 1754) | (0. | 1745) | (0. | 1461) | (0. | 1450)
Country fixed effects | yes | yes | yes | yes
Country specific trend in days | no | no | quadratic | quadratic
${\rm R}^{2}$ | 0. | 2707 | 0. | 2743 | 0. | 3930 | 0. | 3962
Adjusted ${\rm R}^{2}$ | 0. | 2702 | 0. | 2737 | 0. | 3895 | 0. | 3926

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

Table A11: The direct effects of vaccination, policy, behavior and their
interactions on deaths

|  Dependent variable: $\Delta\log\Delta D_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-28}$ | -0. |  0084∗∗∗ | -0. |  0083∗∗∗ | -0. |  0077∗∗ | -0. |  0094∗∗
| (0. | 0027) | (0. | 0028) | (0. | 0032) | (0. | 0036)
$V2_{t-28}$ | -0. | 0013 | -0. | 0016 | -0. | 0062 | -0. | 0019
| (0. | 0042) | (0. | 0047) | (0. | 0052) | (0. | 0060)
$V1_{t-28}^{CHN}$ |  | 0. |  0314∗∗∗ |  | 0. | 0045
|  | (0. | 0040) |  | (0. | 0091)
$V2_{t-28}^{CHN}$ |  | -0. |  0357∗∗∗ |  | -0. |  0151∗
|  | (0. | 0057) |  | (0. | 0076)
$P_{t-28}$ | -0. | 0006 | -0. | 0005 | -0. | 0007 | -0. | 0006
| (0. | 0013) | (0. | 0013) | (0. | 0010) | (0. | 0010)
$M_{t-28}$ | 0. |  0075∗∗∗ | 0. |  0069∗∗∗ | 0. |  0081∗∗∗ | 0. |  0082∗∗∗
| (0. | 0017) | (0. | 0016) | (0. | 0015) | (0. | 0015)
$V1_{t-28}\times M_{t-28}$ | -0. | 0000 | 0. | 0000 | 0. | 0002 | 0. | 0001
| (0. | 0001) | (0. | 0001) | (0. | 0001) | (0. | 0001)
$V2_{t-28}\times M_{t-28}$ | -0. | 0003 | -0. | 0003 | -0. |  0004∗∗ | -0. | 0004
| (0. | 0003) | (0. | 0003) | (0. | 0002) | (0. | 0002)
$V1_{t-28}^{CHN}\times M_{t-28}$ |  | 0. |  0009∗∗∗ |  | 0. | 0001
|  | (0. | 0002) |  | (0. | 0002)
$V2_{t-28}^{CHN}\times M_{t-28}$ |  | -0. |  0012∗∗∗ |  | -0. | 0003
|  | (0. | 0003) |  | (0. | 0003)
$\Delta\log\Delta D_{t-28}$ | 0. |  0718∗∗ | 0. |  0715∗∗ | 0. |  0703∗∗ | 0. |  0691∗∗
| (0. | 0274) | (0. | 0274) | (0. | 0266) | (0. | 0267)
$\log\Delta D_{t-28}$ | -0. |  0346∗∗∗ | -0. |  0388∗∗∗ | -0. |  1282∗∗∗ | -0. |  1273∗∗∗
| (0. | 0096) | (0. | 0091) | (0. | 0117) | (0. | 0118)
Country fixed effects | yes | yes | yes | yes
Country specific trend in days | no | no | quadratic | quadratic
${\rm R}^{2}$ | 0. | 0956 | 0. | 0987 | 0. | 1728 | 0. | 1733
Adjusted ${\rm R}^{2}$ | 0. | 0951 | 0. | 0979 | 0. | 1679 | 0. | 1681

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

##  Appendix G Expansion of the list of countries adopting Chinese vaccines

Table A12: The direct effects of vaccination, policy, and behavior on new
cases

| Dependent variable: $\Delta\log\Delta C_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-21}$ | -0. | 0074∗∗∗ | -0. | 0079∗∗∗ | -0. | 0173∗∗∗ | -0. | 0192∗∗∗
| (0. | 0021) | (0. | 0023) | (0. | 0043) | (0. | 0040)
$V2_{t-7}$ | 0. | 0029 | 0. | 0033 | 0. | 0038 | 0. | 0082
| (0. | 0025) | (0. | 0029) | (0. | 0057) | (0. | 0063)
$V1_{t-21}^{CHN}$ |  | 0. | 0085 |  | 0. | 0135∗
|  | (0. | 0051) |  | (0. | 0068)
$V2_{t-7}^{CHN}$ |  | -0. | 0093∗ |  | -0. | 0230∗∗∗
|  | (0. | 0051) |  | (0. | 0082)
$P_{t-14}$ | -0. | 0020 | -0. | 0020 | -0. | 0027∗ | -0. | 0028∗
| (0. | 0012) | (0. | 0013) | (0. | 0015) | (0. | 0015)
$M_{t-14}$ | 0. | 0052∗∗∗ | 0. | 0052∗∗∗ | 0. | 0101∗∗∗ | 0. | 0098∗∗∗
| (0. | 0012) | (0. | 0012) | (0. | 0014) | (0. | 0013)
$\Delta\log\Delta C_{t-14}$ | 0. | 1102∗∗∗ | 0. | 1092∗∗∗ | 0. | 0901∗∗ | 0. | 0912∗∗
| (0. | 0395) | (0. | 0398) | (0. | 0350) | (0. | 0347)
$\log\Delta C_{t-14}$ | -0. | 0374∗∗∗ | -0. | 0386∗∗∗ | -0. | 1496∗∗∗ | -0. | 1518∗∗∗
| (0. | 0077) | (0. | 0079) | (0. | 0214) | (0. | 0213)
$\Delta\log\Delta T_{t}$ | 0. | 5094∗∗∗ | 0. | 5086∗∗∗ | 0. | 4090∗∗∗ | 0. | 4075∗∗∗
| (0. | 1775) | (0. | 1774) | (0. | 1499) | (0. | 1494)
Country fixed effects | yes | yes | yes | yes
Country specific trend in weeks | no | no | quadratic | quadratic
${\rm R}^{2}$ | 0. | 2838 | 0. | 2845 | 0. | 3987 | 0. | 4011
Adjusted ${\rm R}^{2}$ | 0. | 2834 | 0. | 2841 | 0. | 3953 | 0. | 3976
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 382 | 382 | 382 | 382

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

Table A13: The direct effects of vaccination, policy, and behavior on new
deaths

| Dependent variable: $\Delta\log\Delta D_{t}$
---|---
| (1) | (2) | (3) | (4)
$V1_{t-35}$ | -0. | 0096∗∗∗ | -0. | 0094∗∗∗ | -0. | 0125∗∗∗ | -0. | 0134∗∗∗
| (0. | 0021) | (0. | 0022) | (0. | 0040) | (0. | 0040)
$V2_{t-21}$ | 0. | 0036 | 0. | 0022 | 0. | 0040 | 0. | 0074∗
| (0. | 0029) | (0. | 0031) | (0. | 0043) | (0. | 0043)
$V1_{t-35}^{CHN}$ |  | 0. | 0061 |  | 0. | 0050
|  | (0. | 0078) |  | (0. | 0065)
$V2_{t-21}^{CHN}$ |  | -0. | 0032 |  | -0. | 0126∗∗
|  | (0. | 0077) |  | (0. | 0062)
$P_{t-28}$ | -0. | 0008 | -0. | 0006 | -0. | 0013 | -0. | 0014
| (0. | 0014) | (0. | 0014) | (0. | 0013) | (0. | 0013)
$M_{t-28}$ | 0. | 0057∗∗∗ | 0. | 0056∗∗∗ | 0. | 0077∗∗∗ | 0. | 0075∗∗∗
| (0. | 0016) | (0. | 0015) | (0. | 0016) | (0. | 0015)
$\Delta\log\Delta D_{t-28}$ | 0. | 0584∗∗ | 0. | 0584∗∗ | 0. | 0574∗∗ | 0. | 0564∗∗
| (0. | 0281) | (0. | 0284) | (0. | 0258) | (0. | 0258)
$\log\Delta D_{t-28}$ | -0. | 0429∗∗∗ | -0. | 0464∗∗∗ | -0. | 1308∗∗∗ | -0. | 1308∗∗∗
| (0. | 0096) | (0. | 0094) | (0. | 0112) | (0. | 0113)
Country fixed effects | yes | yes | yes | yes
Country specific trend in weeks | no | no | quadratic | quadratic
${\rm R}^{2}$ | 0. | 0966 | 0. | 0979 | 0. | 1747 | 0. | 1752
Adjusted ${\rm R}^{2}$ | 0. | 0962 | 0. | 0973 | 0. | 1698 | 0. | 1702
Number of countries | 37 | 37 | 37 | 37
Obs. per country | 368 | 368 | 368 | 368

Note: Time period – June 1, 2020 to July 8, 2021. The results are produced
using daily data. Standard errors in parentheses are clustered at the country
level. ∗∗∗, ∗∗, and ∗ denote the 99%, 95% and 90% confidence level
respectively.

##  Appendix H Additional counterfactuals

Figure A2: Canada (Left: 6 weeks, Right: 12 weeks)

Note: In the first row of panels, black solid lines are actual vaccination
rates, and red dotted lines are hypothetical rates we consider in our
counterfactual experiments. In the second and fourth row, red lines are
changes in the log of weekly case/death counts, respectively. In the third and
fifth row of panels, black lines are actual case/death numbers, and red lines
are counterfactual counts. Shaded areas are 90% confidence bands.

Figure A3: US (Left: 6 weeks, Right: 12 weeks)

Note: In the first row of panels, black solid lines are actual vaccination
rates, and red dotted lines are hypothetical rates we consider in our
counterfactual experiments. In the second and fourth row, red lines are
changes in the log of weekly case/death counts, respectively. In the third and
fifth row of panels, black lines are actual case/death numbers, and red lines
are counterfactual counts. Shaded areas are 90% confidence bands.