<p>I think the data you're interested in can be found in the source Our World in Data uses for their index calculations (<a href="https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker" rel="nofollow noreferrer">this is the website of the researchers</a> dedicated to the dataset, and <a href="https://github.com/OxCGRT/covid-policy-tracker/tree/master/data" rel="nofollow noreferrer">this is the github page</a> with all the data downloads.) Cross referencing that dataset against major news outlets' Covid timelines (for example <a href="https://www.nytimes.com/article/coronavirus-timeline.html" rel="nofollow noreferrer">this one from the NY Times</a> or <a href="https://www.washingtonpost.com/graphics/2020/world/coronavirus-2020-timeline/" rel="nofollow noreferrer">this one from the Washington Post</a>) should be enough to get a pretty good idea of the dates of lockdowns and other major milestones. (That dataset has been used in other peer-reviewed articles, <a href="https://www.nature.com/articles/s41598-021-81442-x" rel="nofollow noreferrer">like this one in Nature</a> and <a href="https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01872-8" rel="nofollow noreferrer">this one in the BMC</a>, so at least it's been used successfully in a peer-reviewed context.) The only other comprehensive datasets I've seen are <a href="https://www.nature.com/articles/s41597-020-00609-9" rel="nofollow noreferrer">this open-source one</a> and <a href="https://www.nature.com/articles/s41597-021-00878-y" rel="nofollow noreferrer">this one generated using AI-assisted tracking</a>. I know much less about those two, so I'm not sure if they're useful and/or accurate.</p>
<p>Alternatively, there seem to be a few other published papers that look at the effectiveness of different government measures that don't seem to reference the BSG dataset. <a href="https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(20)30208-X/fulltext" rel="nofollow noreferrer">This one in the Lancet says</a> their data was collected from public sources and is available upon request, so it might be useful to shoot them an email. Others (<a href="https://www.nature.com/articles/s41598-021-82873-2" rel="nofollow noreferrer">like this one in Nature</a>) aren't that explicit about where they gather lockdown data from, but don't directly cite the BSG working paper, so they might have additional data sources to potentially ask about.</p>
<p>If you're looking for more datasets, they're usually labeled as measuring &quot;non-pharmaceutical interventions.&quot;</p>
<p>The last thing I'd mention is to offer a word of caution regarding modeling the effects of lockdowns (and other NPIs). The specifics surrounding the context of lockdown implementation is far from uniform across countries, which can have substantial impacts on estimating the impacts of NPIs. (For example, some countries imposed lockdowns after an outbreak occurred, others implemented them ahead of time.  Similarly, some countries imposed lockdowns with no real warning, while in others the government debated and passed legislation well ahead of implementation.) Other research <a href="https://link.springer.com/article/10.1007%2Fs00148-020-00799-x" rel="nofollow noreferrer">has highlighted the difficulties in using these measures</a> for causal analysis without adjustment. Finally, certain NPIs are particularly sensitive to adoption by a population, which can lead to behavioral responses not captured by government level policies. For example, some research into mask mandates (as seen about halfway down in <a href="https://www.nature.com/articles/d41586-021-01394-0" rel="nofollow noreferrer">this non-peer reviewed article on Nature</a>) suggests that, while the mandates might be effective in boosting mask wearing among hesitant populations, widespread mask adoption occurred before mandates were implemented, and continued after mandates were lifted, in other areas.</p>
<p>Let me know if any of that wasn't clear, and good luck with the project!</p>

CC BY-SA 4.0
2021-08-02T15:15:11.937Z
Regarding the first date restrictions were imposed anywhere in the world is actually as easy as 1muflon1 suggests. There is daily data.
BrsG
CC BY-SA 4.0
2021-08-02T20:26:31.510Z
@BrsG It's actually not, since the index's calculations aren't that precise. For example, Botswana didn't impose any Covid19 restrictions on January 1, 2020, however their index number is greater than 0 on that date.
AndrewC
CC BY-SA 4.0
2021-08-02T10:14:52.670Z
2021-08-02T10:14:52.670Z
AndrewC
<p>Regarding COVID-19 response, we have <a href="https://ourworldindata.org/grapher/covid-stringency-index" rel="nofollow noreferrer">the stringency index</a>. But I am wondering is there any source that <strong>documented the first law implementation date or month regarding society restriction</strong> (social distance, school closures, workplace closures, and travel bans) in every country.</p>

CC BY-SA 4.0
2021-08-02T08:57:41.777Z
The index has daily data the first fay where the index went above zero is the first day of any covid-19 related restriction
1muflon1
<international-economics><covid-19-crisis>
Asking for paper or data source mentioning the social restriction due to COVID-19 globally
Phil Nguyen
# Asking for paper or data source mentioning the social restriction due to COVID-19 globally

(Asked by: username_0 on 2021-08-02)

Regarding COVID-19 response, we have [the stringency index](https://ourworldindata.org/grapher/covid-stringency-index). But I am wondering is there any source that **documented the first law implementation date or month regarding society restriction** (social distance, school closures, workplace closures, and travel bans) in every country.

**Tags:** international-economics, covid-19-crisis

> **Comments:**
> **username_1**: The index has daily data the first fay where the index went above zero is the first day of any covid-19 related restriction  

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## 1 answers

### Answer by username_2 on 2021-08-02. Score: 3 | Accepted answer

I think the data you're interested in can be found in the source Our World in Data uses for their index calculations ([this is the website of the researchers](https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker) dedicated to the dataset, and [this is the github page](https://github.com/OxCGRT/covid-policy-tracker/tree/master/data) with all the data downloads.) Cross referencing that dataset against major news outlets' Covid timelines (for example [this one from the NY Times](https://www.nytimes.com/article/coronavirus-timeline.html) or [this one from the Washington Post](https://www.washingtonpost.com/graphics/2020/world/coronavirus-2020-timeline/)) should be enough to get a pretty good idea of the dates of lockdowns and other major milestones. (That dataset has been used in other peer-reviewed articles, [like this one in Nature](https://www.nature.com/articles/s41598-021-81442-x) and [this one in the BMC](https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01872-8), so at least it's been used successfully in a peer-reviewed context.) The only other comprehensive datasets I've seen are [this open-source one](https://www.nature.com/articles/s41597-020-00609-9) and [this one generated using AI-assisted tracking](https://www.nature.com/articles/s41597-021-00878-y). I know much less about those two, so I'm not sure if they're useful and/or accurate.

Alternatively, there seem to be a few other published papers that look at the effectiveness of different government measures that don't seem to reference the BSG dataset. [This one in the Lancet says](https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(20)30208-X/fulltext) their data was collected from public sources and is available upon request, so it might be useful to shoot them an email. Others ([like this one in Nature](https://www.nature.com/articles/s41598-021-82873-2)) aren't that explicit about where they gather lockdown data from, but don't directly cite the BSG working paper, so they might have additional data sources to potentially ask about.

If you're looking for more datasets, they're usually labeled as measuring "non-pharmaceutical interventions."

The last thing I'd mention is to offer a word of caution regarding modeling the effects of lockdowns (and other NPIs). The specifics surrounding the context of lockdown implementation is far from uniform across countries, which can have substantial impacts on estimating the impacts of NPIs. (For example, some countries imposed lockdowns after an outbreak occurred, others implemented them ahead of time. Similarly, some countries imposed lockdowns with no real warning, while in others the government debated and passed legislation well ahead of implementation.) Other research [has highlighted the difficulties in using these measures](https://link.springer.com/article/10.1007%2Fs00148-020-00799-x) for causal analysis without adjustment. Finally, certain NPIs are particularly sensitive to adoption by a population, which can lead to behavioral responses not captured by government level policies. For example, some research into mask mandates (as seen about halfway down in [this non-peer reviewed article on Nature](https://www.nature.com/articles/d41586-021-01394-0)) suggests that, while the mandates might be effective in boosting mask wearing among hesitant populations, widespread mask adoption occurred before mandates were implemented, and continued after mandates were lifted, in other areas.

Let me know if any of that wasn't clear, and good luck with the project!

> **Comments:**
> **username_3**: Regarding the first date restrictions were imposed anywhere in the world is actually as easy as username_1 suggests. There is daily data.  
> **username_2**: @username_3 It's actually not, since the index's calculations aren't that precise. For example, Botswana didn't impose any Covid19 restrictions on January 1, 2020, however their index number is greater than 0 on that date.  

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