Are Stringent Containment and Closure Policies Associated with a Lower COVID-19 Spread Rate? Global Evidence
Abstract
:1. Introduction
2. Data, Samples, and Variables
3. Empirical Results
3.1. Government Stringent Responses and COVID-19 Spread
3.2. Containment and Closure Policies and the COVID-19 Spread
- Regarding the government policies on workplace closure, WORKPLACE_CLOSING had a coefficient of −0.0785 (t = −3.45), as shown in column (2). The result indicates that for every one level escalation of strictness in response to COVID-19 cases, that is, (i) from “no measures” to “recommend work from home”, or (ii) from “recommend work from home” to “require work from home for some sectors or categories of workers”, or (iii) from “require work from home for some sectors or categories of workers” to “require work from home for all-but-essential workplaces”, the COVID-19 spread rate decreased approximately by 7.85% daily. Given that the average daily new cases was about 437.3359 in our sample (reported in Table 1), this is equivalent to a reduction of 34.33 new cases on a daily basis. The effect would be even greater if the policy is escalated to the highest level of “require work from home for all-but-essential workplaces” from the lowest level of “no measures”. That is, 102.99 new cases would have been reduced daily in our sampling countries.
- Regarding the government policies on restrictions on gatherings, the result in column (4) implies that for every one level increase of strictness in response to COVID-19 cases, the COVID-19 spread rate decreased approximately by 5% daily, equivalent to a reduction of 21.87 new cases daily. The effect would be even greater if the policy is escalated to the highest level of “restrictions on gatherings of 10 people or less” from the lowest level of “no restrictions”. That is, 87.47 new cases would have been reduced daily in our samples.
- As reported in column (5), regarding the government policies on closing public transportation, for every one level increase of strictness in response to COVID-19 cases, the COVID-19 spread rate decreased approximately by 15.21% daily, equivalent to a reduction of 66.52 new cases daily. The effect would be even greater if the policy is escalated to the highest level of “require closing (or prohibit most citizens from using it)” from the lowest level of “no restrictions”. That is, 133.04 new cases would have been reduced daily in the sampling countries.
- Column (6) shows the result of government policies on the stay-at-home requirement. For every one level increase of strictness in response to COVID-19 cases, the COVID-19 spread rate decreased approximately by 11.02% daily, equivalent to a reduction of 48.19 new cases daily. The effect would be even bigger if the policy is escalated to the highest level of “require not leaving the house with minimal exceptions (e.g. allowed to leave once a week, or only one person can leave at a time, etc.)” from the lowest level of “no restrictions”. That is, 144.58 new cases would have been reduced daily in our sample.
- Regarding the government policies on domestic travel, the result in column (7) indicates that for every one level increase of strictness in response to COVID-19 cases, the COVID-19 spread rate decreased by approximately 9.75% daily, equivalent to a reduction of 42.64 new cases daily. The effect would be even greater if the policy is escalated to the highest level of “internal movement restrictions in place” from the lowest level of “no restrictions”. That is, 85.28 new cases would have been reduced daily in the sample.
- As shown in column (8), government policies on international travel were also associated with COVID-19 spread. For every one level increase of strictness in response to COVID-19 cases, the COVID-19 spread rate decreased by approximately 8.13% daily, equivalent to a reduction of 35.56 new cases daily. The effect would be even greater if the policy is escalated to the highest level of “ban on all regions or total border closure” from the lowest level of “no restrictions”. That is, 142.22 new cases would have been reduced daily in our sampling countries.
Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
SCHOOL_CLOSING | −0.0261 | 0.0196 | |||||||
(−0.86) | (0.60) | ||||||||
WORKPLACE_CLOSING | −0.0785 *** | −0.0228 | |||||||
(−3.45) | (−0.78) | ||||||||
CANCEL_PUBLIC_EVENTS | −0.0323 | 0.0831 | |||||||
(−0.65) | (1.39) | ||||||||
RESTRICTIONS_ON_GATHERINGS | −0.0500 *** | −0.0220 | |||||||
(−2.64) | (−1.01) | ||||||||
CLOSE_PUBLIC_TRANSPORT | −0.1521 *** | −0.0976 ** | |||||||
(−3.99) | (−2.51) | ||||||||
STAY_AT_HOME_REQUIREMENTS | −0.1102 *** | −0.0509 | |||||||
(−4.05) | (−1.55) | ||||||||
RESTRICTIONS_ON_INTERNAL_MOVEMENT | −0.0975 *** | −0.0093 | |||||||
(−2.87) | (−0.23) | ||||||||
INTERNATIONAL_TRAVEL_CONTROLS | −0.0813 *** | −0.0644 ** | |||||||
(−2.95) | (−2.17) | ||||||||
LN_POPULATION | −0.0108 * | 0.0302 ** | −0.0101 | 0.0220 | 0.0212 * | 0.0098 | −0.0115 * | −0.0197 *** | 0.0413 * |
(−1.96) | (2.21) | (−1.61) | (1.54) | (1.80) | (1.32) | (−1.96) | (−3.13) | (1.68) | |
POPULATION_DENSITY | 0.0108 *** | 0.0094 *** | 0.0106 *** | 0.0102 *** | 0.0098 *** | 0.0098 *** | 0.0104 *** | 0.0100 *** | 0.0089 *** |
(79.97) | (21.86) | (34.59) | (38.75) | (30.93) | (32.92) | (54.31) | (33.27) | (16.65) | |
AGED_70_OLDER | 0.5620 *** | 0.5804 *** | 0.5515 *** | 0.6081 *** | 0.6218 *** | 0.5951 *** | 0.5605 *** | 0.5286 *** | 0.6348 *** |
(59.45) | (67.20) | (37.61) | (33.06) | (39.50) | (59.52) | (81.78) | (41.75) | (17.44) | |
LN_MEDIAN_AGE | −7.2584 *** | −7.0737 *** | −7.0568 *** | −7.9806 *** | −7.2911 *** | −7.3770 *** | −7.0144 *** | −6.5031 *** | −7.4625 *** |
(−83.15) | (−72.85) | (−22.53) | (−29.80) | (−97.57) | (−97.32) | (−57.75) | (−24.04) | (−11.78) | |
LN_GDP_PER_CAPITA | −1.2989 *** | −1.0633 *** | −1.2899 *** | −1.0221 *** | −1.0922 *** | −1.0983 *** | −1.3117 *** | −1.4881 *** | −1.0178 *** |
(−41.15) | (−15.59) | (−54.12) | (−9.97) | (−20.55) | (−22.54) | (−72.21) | (−21.07) | (−5.22) | |
LN_CVD_DEATH_RATE | −0.5560 *** | −0.2637 *** | −0.5378 *** | −0.2365** | −0.1501 | −0.2394 *** | −0.5176 *** | −0.7659 *** | −0.1070 |
(−26.79) | (−3.33) | (−51.34) | (−2.09) | (−1.56) | (−3.24) | (−42.08) | (−9.86) | (−0.49) | |
DIABETES_PREVALENCE | 0.6505 *** | 0.5839 *** | 0.6395 *** | 0.6096 *** | 0.6067 *** | 0.6040 *** | 0.6389 *** | 0.6655 *** | 0.5929 *** |
(70.76) | (30.10) | (61.79) | (39.08) | (51.42) | (49.02) | (106.82) | (80.30) | (18.34) | |
MALE_SMOKERS | −0.0776 *** | −0.0701 *** | −0.0767 *** | −0.0684 *** | −0.0728 *** | −0.0715 *** | −0.0770 *** | −0.0802 *** | −0.0683 *** |
(−61.34) | (−31.35) | (−100.59) | (−19.66) | (−49.86) | (−44.06) | (−107.25) | (−67.05) | (−12.90) | |
FEMALE_SMOKERS | −0.0516 *** | −0.0727 *** | −0.0498 *** | −0.0680 *** | −0.0819 *** | −0.0664 *** | −0.0536 *** | −0.0532 *** | −0.0949 *** |
(−15.84) | (−10.39) | (−19.04) | (−9.49) | (−10.15) | (−15.04) | (−19.96) | (−22.04) | (−7.77) | |
HOSPITAL_BEDS_PER_100K | 0.2336 *** | 0.1789 *** | 0.2258 *** | 0.2070 *** | 0.1795 *** | 0.1687 *** | 0.2222 *** | 0.2541 *** | 0.1706 *** |
(50.52) | (11.45) | (32.55) | (20.69) | (12.55) | (10.58) | (47.04) | (32.27) | (6.03) | |
CONS | 33.2319 *** | 28.6568 *** | 32.4776 *** | 30.7495 *** | 28.9014 *** | 29.9807 *** | 32.4805 *** | 34.0489 *** | 28.3462 *** |
(54.11) | (21.05) | (45.59) | (31.03) | (24.47) | (34.18) | (75.34) | (68.80) | (12.94) | |
WEEK FIXED EFFECTS | YES | YES | YES | YES | YES | YES | YES | YES | YES |
COUNTRY FIXED EFFECTS | YES | YES | YES | YES | YES | YES | YES | YES | YES |
N | 6411 | 6395 | 6390 | 6391 | 6369 | 6392 | 6368 | 6389 | 6316 |
R2 | 0.0302 | 0.0310 | 0.0291 | 0.0303 | 0.0321 | 0.0316 | 0.0309 | 0.0315 | 0.0358 |
3.3. A Lagged Effect of Government Policies on the COVID-19 Spread
3.4. Cultural Tightness vs. Looseness
3.5. Population Density
3.6. Endogeneity Issue
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Definitions, Data Sources, and References of Main Variables in the Regression Equation
Notation | Description | Data Source | Reference |
---|---|---|---|
CASES_GROW | New case growth rate for a country i on a day t, calculated as follows: CASES_GROWi,t = (NEW_CASESi,t − NEW_CASESi,t−1)/NEW_CASESi,t−1. | Our World in Data website | - |
NEW_CASES | The count of confirmed COVID-19 cases on a certain date | Our World in Data website | - |
STRINGENCY_INDEX | Government policy stringency index: a composite measure based on four types of governmental policies with a total of 17 indicators: (1) containment and closure policies, (2) economic policies, (3) health system policies, and (4) miscellaneous policies. The index is rescaled to a value from 0 to 100 (100 = strictest response). In particular, containment and closure policies include eight indicators, with the detailed information provided in Appendix B. | Oxford COVID-19 Government Response Tracker, Blavatnik School of Government | - |
STRINGENCY_TERCILE | A category variable, created by splitting the sample into three subsamples based on the original stringency index of a government and assigning a measure of 1, 2, and 3 if its stringency index is in the bottom, medium, and top terciles of all sample countries on a certain day t, corresponding to three different strictness levels of a government’s policy response to the COVID-19 spread: below average, average, and above average. | Oxford COVID-19 Government Response Tracker, Blavatnik School of Government | - |
STRINGENCY_MEDIAN_ADJUSTED | A country’s stringency index adjusted by all the country’s median level of the index on a day t. | Oxford COVID-19 Government Response Tracker, Blavatnik School of Government | - |
STRINGENCY_STANDARDIZED | A normalized index by considering both mean and standard deviation of the original stringency index on day t. Note that here the day t refers to the tth day since a country reports its first case of COVID-19, not the calendar day. | Oxford COVID-19 Government Response Tracker, Blavatnik School of Government | - |
LN_POPULATION | Natural logarithm of a country’s population in 2020 | United Nations, Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2019 Revision | Rubin et al. [34] |
POPULATION_DENSITY | The number of people divided by land area, measured in square kilometers, most recent year available. | World Bank—World Development Indicators, sourced from the Food and Agriculture Organization and World Bank estimates | Hamidi et al. [33]; Rubin et al. [34]; Kadi and Khelfaoui [35] |
AGED_70_OLDER | Share of the population that is 70 years and older in 2015. | United Nations, Department of Economic and Social Affairs, Population Division (2017), World Population Prospects: The 2017 Revision | Dowd et al. [36]; Liang et al. [15] |
LN_MEDIAN_AGE | The median age of the population, UN projection for 2020 | UN Population Division, World Population Prospects, 2017 Revision | Dowd et al. [36]; Rubin et al. [34] |
LN_GDP_PER_CAPITA | Natural logarithm of a country’s Gross domestic product at purchasing power parity (constant 2011 international dollars), most recent year available | World Bank—World Development Indicators, source from World Bank, International Comparison Program database | Cepaluni et al. [14] |
LN_CVD_DEATH_RATE | Natural logarithm of death rate from cardiovascular disease in 2017 (annual number of deaths per 100,000 people) | Global Burden of Disease Collaborative Network, Global Burden of Disease Study 2017 Results | Mehra et al. [41] |
DIABETES_PREVALENCE | Diabetes prevalence (% of population aged 20 to 79) in 2017 | World Bank—World Development Indicators, sourced from International Diabetes Federation, Diabetes Atlas | Rubin et al. [34]; Dowd et al. [36] |
MALE_SMOKERS | Percentage of men who smoke, most recent year available | World Bank—World Development Indicators, sourced from the World Health Organization, Global Health Observatory Data Repository | Hamidi et al. [33]; Rubin et al. [34] |
FEMALE_SMOKERS | Percentage of women who smoke, most recent year available | World Bank—World Development Indicators, sourced from the World Health Organization, Global Health Observatory Data Repository | Hamidi et al. [33]; Rubin et al. [34] |
HOSPITAL_BEDS_PER_100K | Hospital beds per 100,000 people, most recent year available since 2010 | OECD, Eurostat, World Bank, national government records, and other sources | Hamidi et al. [33]; Liang et al. [15] |
Appendix B. Definitions of Containment and Closure Policies Used in the Government Response Stringency Index
Variable | Description | Measurement | Coding |
---|---|---|---|
SCHOOL_CLOSING | Record closings of schools and universities | Ordinal scale | 0—no measures 1—recommend closing 2—require closing (only some levels or categories, e.g., just high school, or just public schools) 3—require closing all levels Blank—no data |
WORKPLACE_CLOSING | Record closings of workplaces | Ordinal scale | 0—no measures 1—recommend closing (or recommend work from home) 2—require closing (or work from home) for some sectors or categories of workers 3—require closing (or work from home) for all-but-essential workplaces (e.g., grocery stores, doctors) Blank—no data |
CANCEL_PUBLIC_EVENTS | Record canceling public events | Ordinal scale | 0—no measures 1—recommend canceling 2—require canceling Blank—no data |
RESTRICTIONS_ON_ GATHERINGS | Record limits on private gatherings | Ordinal scale | 0—no restrictions 1—restrictions on very large gatherings (the limit is above 1000 people) 2—restrictions on gatherings between 101–1000 people 3—restrictions on gatherings between 11–100 people 4—restrictions on gatherings of 10 people or less Blank—no data |
CLOSE_PUBLIC_TRANSPORT | Record closing of public transport | Ordinal scale | 0—no measures 1—recommend closing (or significantly reducing volume/route/means of transport available) 2—require closing (or prohibit most citizens from using it) Blank—no data |
STAY_AT_HOME_ REQUIREMENTS | Record orders to “shelter-in-place” and otherwise confined to the home | Ordinal scale | 0—no measures 1—recommend not leaving the house 2—require not leaving the house with exceptions for daily exercise, grocery shopping, and ‘essential’ trips 3—require not leaving the house with minimal exceptions (e.g., allowed to leave once a week, or only one person can leave at a time, etc.) Blank—no data |
RESTRICTIONS_ON_ INTERNAL_ MOVEMENT | Record restrictions on internal movement between cities/regions | Ordinal scale | 0—no measures 1—recommend not to travel between regions/cities 2—internal movement restrictions in place Blank—no data |
INTERNATIONAL_TRAVEL_ CONTROLS | Record restrictions on international travel for foreign travelers, not citizens | Ordinal scale | 0—no restrictions 1—screening arrivals 2—quarantine arrivals from some or all regions 3—ban arrivals from some regions 4—ban on all regions or total border closure Blank—no data |
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Variables | N | Mean | SD | Min | P25 | P50 | P75 | Max |
---|---|---|---|---|---|---|---|---|
CASES_GROW | 6684 | 0.2241 | 1.2537 | −1.0000 | −0.3750 | 0.0000 | 0.3813 | 7.8824 |
NEW_CASES | 6684 | 437.3359 | 1001.4436 | 0.0000 | 8.0000 | 50.0000 | 295.0000 | 4805.0000 |
STRINGENCY_INDEX | 6684 | 71.4082 | 23.8832 | 0.0000 | 61.2400 | 79.4900 | 88.7500 | 100.0000 |
STRINGENCY_TERCILE | 6684 | 2.1142 | 0.7937 | 1.0000 | 1.0000 | 2.0000 | 3.0000 | 3.0000 |
STRINGENCY_MEDIAN_ADJUSTED | 6684 | −4.6471 | 20.9930 | −83.6000 | −12.4400 | 0.3325 | 8.3300 | 67.6000 |
STRINGENCY_STANDARDIZED | 6675 | 0.0273 | 0.9297 | −4.3526 | −0.3909 | 0.2835 | 0.6828 | 2.4794 |
LN_POPULATION | 6684 | 16.6855 | 1.6737 | 11.4962 | 15.5129 | 16.6859 | 17.8121 | 21.0454 |
POPULATION_DENSITY | 6684 | 286.1975 | 983.7277 | 3.0780 | 46.7540 | 97.9990 | 214.2430 | 7915.7310 |
AGED_70_OLDER | 6684 | 7.3985 | 4.5071 | 0.6170 | 3.2620 | 6.9380 | 11.5800 | 16.2400 |
LN_MEDIAN_AGE | 6684 | 3.5258 | 0.2578 | 2.7973 | 3.3776 | 3.5723 | 3.7377 | 3.8691 |
LN_GDP_PER_CAPITA | 6684 | 9.8069 | 1.0346 | 6.6238 | 9.2669 | 10.0331 | 10.5904 | 11.4540 |
LN_CVD_DEATH_RATE | 6684 | 5.2926 | 0.5085 | 4.4515 | 4.8542 | 5.2939 | 5.6289 | 6.3920 |
DIABETES_PREVALENCE | 6684 | 7.7999 | 3.6503 | 1.9100 | 5.5000 | 7.1100 | 9.5900 | 22.0200 |
MALE_SMOKERS | 6684 | 31.8790 | 12.6190 | 8.5000 | 21.4000 | 30.9000 | 40.8000 | 76.1000 |
FEMALE_SMOKERS | 6684 | 11.8606 | 10.4430 | 0.2000 | 1.9000 | 7.8000 | 20.0000 | 35.3000 |
HOSPITAL_BEDS_PER_100K | 6684 | 3.4920 | 2.6515 | 0.3000 | 1.6000 | 2.7700 | 4.5100 | 13.0500 |
Variables | (1) | (2) | (3) | (4) |
---|---|---|---|---|
STRINGENCY_INDEX | −0.0043 *** | |||
(−3.22) | ||||
STRINGENCY_TERCILE | −0.0752 *** | |||
(−3.50) | ||||
STRINGENCY_MEDIAN_ADJUSTED | −0.0052 *** | |||
(−3.72) | ||||
STRINGENCY_STANDARDIZED | −0.1166 *** | |||
(−3.93) | ||||
LN_POPULATION | −0.0192 *** | −0.0160 *** | −0.0216 *** | −0.0288 *** |
(−4.06) | (−3.26) | (−4.54) | (−4.73) | |
POPULATION_DENSITY | 0.0097 *** | 0.0114 *** | 0.0095 *** | 0.0097 *** |
(27.22) | (50.29) | (26.07) | (31.64) | |
AGED_70_OLDER | 0.5510 *** | 0.5725 *** | 0.5497 *** | 0.5380 *** |
(75.38) | (81.11) | (75.81) | (58.09) | |
LN_MEDIAN_AGE | −6.8121 *** | −7.5662 *** | −6.7360 *** | −6.6900 *** |
(−41.74) | (−66.63) | (−41.10) | (−39.80) | |
LN_GDP_PER_CAPITA | −1.1774 *** | −1.3147 *** | −1.1569 *** | −1.1710 *** |
(−27.26) | (−77.69) | (−26.02) | (−29.38) | |
LN_CVD_DEATH_RATE | −0.3711 *** | −0.4990 *** | −0.3360 *** | −0.3615 *** |
(−6.64) | (−30.72) | (−5.74) | (−7.26) | |
DIABETES_PREVALENCE | 0.5974 *** | 0.6701 *** | 0.5882 *** | 0.5912 *** |
(34.13) | (82.90) | (32.89) | (36.02) | |
MALE_SMOKERS | −0.0727 *** | −0.0795 *** | −0.0719 *** | −0.0722 *** |
(−41.98) | (−87.33) | (−40.32) | (−43.31) | |
FEMALE_SMOKERS | −0.0604 *** | −0.0458 *** | −0.0622 *** | −0.0572 *** |
(−16.28) | (−17.22) | (−16.42) | (−20.87) | |
HOSPITAL_BEDS_PER_100K | 0.2110 *** | 0.2481 *** | 0.2069 *** | 0.2129 *** |
(26.77) | (40.74) | (25.03) | (31.33) | |
CONS | 30.3346 *** | 33.9560 *** | 29.6910 *** | 29.8882 *** |
(29.74) | (78.37) | (27.39) | (29.92) | |
WEEK FIXED EFFECTS | YES | YES | YES | YES |
COUNTRY FIXED EFFECTS | YES | YES | YES | YES |
N | 6684 | 6684 | 6684 | 6675 |
R2 | 0.0309 | 0.0299 | 0.0318 | 0.0318 |
Variables | (1) LOOSE | (2) TIGHT |
---|---|---|
STRINGENCY_INDEX | −0.0038 | −0.0105 ** |
(−1.48) | (−2.95) | |
LN_POPULATION | 0.0772 | 0.0635 *** |
(1.18) | (7.27) | |
POPULATION_DENSITY | 0.0005 ** | −0.0000 *** |
(2.81) | (−3.31) | |
AGED_70_OLDER | 0.2777 * | −0.0092 |
(2.13) | (−1.23) | |
LN_MEDIAN_AGE | −6.0024 * | 1.1522 *** |
(−2.19) | (4.61) | |
LN_GDP_PER_CAPITA | −0.2915 ** | −0.1471 ** |
(−2.73) | (−2.29) | |
LN_CVD_DEATH_RATE | 0.4367 * | −0.0993 |
(2.02) | (−0.92) | |
DIABETES_PREVALENCE | −0.0095 | −0.0230 *** |
(−0.60) | (−5.01) | |
MALE_SMOKERS | −0.0219 | 0.0099 ** |
(−1.18) | (3.01) | |
FEMALE_SMOKERS | 0.0223 | −0.0011 |
(1.46) | (−0.29) | |
HOSPITAL_BEDS_PER_100K | −0.0265 *** | −0.0772 *** |
(−4.36) | (−4.52) | |
CONS | 19.0952 ** | −2.8299 * |
(2.39) | (−2.03) | |
WEEK FIXED EFFECTS | YES | YES |
COUNTRY FIXED EFFECTS | YES | YES |
N | 798 | 1009 |
R2 | 0.0398 | 0.0512 |
Variables | (1) Least | (2) Most |
---|---|---|
STRINGENCY_INDEX | −0.0016 | −0.0060 *** |
(−0.49) | (−3.09) | |
LN_POPULATION | −0.0174 | −0.2599 *** |
(−0.59) | (−4.12) | |
POPULATION_DENSITY | 0.1173 *** | −0.0000 *** |
(10.80) | (−3.80) | |
AGED_70_OLDER | −0.3395 *** | 0.0537 *** |
(−5.37) | (3.44) | |
LN_MEDIAN_AGE | 5.0990 *** | −1.1509 |
(4.48) | (−1.33) | |
LN_GDP_PER_CAPITA | −0.8176*** | 0.1703 |
(−4.17) | (0.94) | |
LN_CVD_DEATH_RATE | −1.2313 *** | 0.6281 |
(−3.24) | (1.00) | |
DIABETES_PREVALENCE | −0.0125 *** | −0.0251 |
(−3.63) | (−1.11) | |
MALE_SMOKERS | 0.0082 *** | −0.0048 |
(3.62) | (−1.42) | |
FEMALE_SMOKERS | 0.0412 *** | −0.0242 *** |
(3.40) | (−5.61) | |
HOSPITAL_BEDS_PER_100K | 0.1875 ** | 0.0289 * |
(2.28) | (1.91) | |
CONS | −3.0251 *** | 3.9827 |
(−2.87) | (0.56) | |
WEEK FIXED EFFECTS | YES | YES |
COUNTRY FIXED EFFECTS | YES | YES |
N | 1286 | 1155 |
R2 | 0.0348 | 0.0492 |
Variables | (1) STRINGENCY_INDEX | (2) CASES_GROW |
---|---|---|
FREEDOM_INDEX | −16.5872 *** | |
(−75.1304) | ||
STRINGENCY_INDEX_PREDICTED | −0.0353 *** | |
(−111.2981) | ||
LN_POPULATION | −39.9735 *** | −0.1015 *** |
(−114.0754) | (−18.9480) | |
POPULATION_DENSITY | −0.6830 *** | 0.0025 *** |
(−68.3462) | (46.4547) | |
AGED_70_OLDER | 194.4369 *** | 0.4856 *** |
(82.5026) | (78.9334) | |
LN_MEDIAN_AGE | −1905.1409 *** | −3.6214 *** |
(−79.4920) | (−64.8338) | |
LN_GDP_PER_CAPITA | 165.9606 *** | −0.4306 *** |
(59.5568) | (−37.5322) | |
LN_CVD_DEATH_RATE | −303.1061 *** | 0.8499 *** |
(−65.5361) | (51.9923) | |
DIABETES_PREVALENCE | 29.9748 *** | 0.2453 *** |
(99.0861) | (114.7384) | |
MALE_SMOKERS | 2.9409 *** | −0.0424 *** |
(43.8874) | (−95.6032) | |
FEMALE_SMOKERS | −17.6555 *** | −0.1311 *** |
(−91.9176) | (−61.5783) | |
HOSPITAL_BEDS_PER_100K | 16.1073 *** | 0.0740 *** |
(77.8955) | (27.6377) | |
CONS | 6894.5887 *** | 11.6673 *** |
(84.8811) | (56.7092) | |
WEEK FIXED EFFECTS | YES | YES |
COUNTRY FIXED EFFECTS | YES | YES |
N | 6684 | 6684 |
R2 | 0.6727 | 0.0287 |
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Xiu, Z.; Feng, P.; Yin, J.; Zhu, Y. Are Stringent Containment and Closure Policies Associated with a Lower COVID-19 Spread Rate? Global Evidence. Int. J. Environ. Res. Public Health 2022, 19, 1725. https://doi.org/10.3390/ijerph19031725
Xiu Z, Feng P, Yin J, Zhu Y. Are Stringent Containment and Closure Policies Associated with a Lower COVID-19 Spread Rate? Global Evidence. International Journal of Environmental Research and Public Health. 2022; 19(3):1725. https://doi.org/10.3390/ijerph19031725
Chicago/Turabian StyleXiu, Zongfeng, Pengshuo Feng, Jingwei Yin, and Yingjun Zhu. 2022. "Are Stringent Containment and Closure Policies Associated with a Lower COVID-19 Spread Rate? Global Evidence" International Journal of Environmental Research and Public Health 19, no. 3: 1725. https://doi.org/10.3390/ijerph19031725