Probit panel-data models are used to generate a suitable instrument for each of the eight CCMs, separately. The general specification for the models is: (4) i 1C C i 2 i 3D i 4D i2 5 i + 6C C i 7C C C i 8 i 9 C i 10 i i ii Pr(yy = 1) = αα + ββ CC + ββ DDC OOhCC + ββ OO + ββ OO + ββ HHCCHH ββ CCh + ββ + ββ TTCCTTCC + ββ DDCCTTD DDyy + ββ DDCCTTDDf CCDDOO + εε where i = 1 indicates that at time _t_ the country implemented the CCM. Parameter indicates the existence of idiosyncratic characteristics at the country level, that prompts the use of the ii random effects models to avoid biasing the estimation. yy αα

**We include the same set of control variables when modeling the probability of a country having implemented a CCM** . The first two controls are the number of daily cases and daily deaths, for which we naturally expect 1 2 > 0. By nature, pandemics tend to spread in the population with an “S” shape which, in turn, depends on the time elapsed since the beginning of the contagion period (see Gatto et al, 2020; Scally 2020). We use the time in days elapsed since ββ , ββ the outbreak (i.e., the date when the first case was officially reported in each country), which we label as _DSO_ , and its squared value to capture the non-linear diffusion of the disease. Our prior is that 3 > 0 and 4 < 0. **Response measures to the pandemics reveal, to a large extent, the stance of the** ββ ββ

**Response measures to the pandemics reveal, to a large extent, the stance of the health system in each country** . Kandel et al (2020) evaluate health security capacities in 182 countries, using 18 indicators that reflect the ability of each country to prevent, detect, and respond to the pandemic, as well as its operational readiness. Their results show that countries vary widely in terms of their capacity to prevent, detect, and respond to outbreaks. Half of all countries analyzed have strong operational readiness capacities in place, which suggests that an effective response to potential health emergencies could be enabled, including to COVID-19. Therefore, we expect that stronger health capacities would allow authorities to deal with the pandemic without necessarily resorting to harsh restrictions on the activities and liberties of the population 5 < 0. We proxy health capacities with a _Health Capacity Index_ (HCI) which, as described in the appendix, comprises of a standardized measure of the health personnel, infrastructure and financial resources available in each country at the start of the pandemic.  ββ

**In addition to the general state of the health system, we also control the pressure on health systems caused by the increase in cases.** Indeed, we include time-varying stress levels of health workers by including the number of daily infections per physician and per nurses, which we label as _CperPh_ and _CperNr_ , respectively. As noted by Ma et al. (2020) and others, health care professionals caring for COVID-19 patients have been contaminated or experienced substantial psychological distress. Authorities ought to take into consideration the stress levels of health workers when assessing the implementation of a CCM and we expect that more stressed systems tend to increase the probability of adoption of CCMs ( 6 7 > 0). **Temperature has also been identified as a potential accelerant of the spread of the** ββ , ββ **disease.** Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. Xie and Zhu (2020) find that

regressors are generated in the first stage, the standard IV standard errors are still asymptotically valid (Wooldridge, 2002).

10