Non-pharmaceutical interventions in the State of Georgia: Economic implications

RC White; R Luo; R Rothenberg

doi:10.1016/j.jemep.2023.100891

. 2023 Apr 20;28:100891. doi: 10.1016/j.jemep.2023.100891

Non-pharmaceutical interventions in the State of Georgia: Economic implications

RC White ^1,^⁎, R Luo ¹, R Rothenberg ¹

PMCID: PMC10116346 PMID: 37095763

Graphical abstract

Keywords: Covid, Lockdown, Non-pharmaceutical intervention, Public health policy

Abstract

Background

As Covid-19 spread rapidly, many countries implemented a strict shelter-in-place to “flatten the curve” and build capacity to treat in the absence of effective preventative therapies or treatments. Policymakers and public health officials must balance the positive health effects of lockdowns with economic, social, and psychological costs. This study examined the economic impacts of state and county level restrictions during the 2020 Covid-19 pandemic for two regions of Georgia.

Methods

Taking unemployment data from the Opportunity Insights Economic Tracker with mandate information from various sites, we examined trends before and after a mandate's implementation and relaxation using joinpoint regression.

Results

We found mandates with the largest impact on unemployment claims rates were the shelters-in-place (SIPs) and closures of non-essential businesses. Specific to our study, mandates had an effect where first implemented, i.e., if the state implemented an SIP after the county, the state-wide SIP had no additional measurable effect on claims rates. School closures had a consistent impact on increasing unemployment claims rates, but to a lesser degree than SIPs or business closures. While closing businesses did have a deleterious effect, implementing social distancing for businesses and restricting gatherings did not. Notably, the Coastal region was less affected than the Metro Area. Additionally, our findings indicate that race ethnicity may be a larger predictor of adverse economic effects than education, poverty level, or geographic area.

Conclusions

Our findings coincided with other studies in some areas but showed differences in what indicators may best predict adverse effects and that coastal communities may not always be as impacted as other regions in a state. Ultimately, the most restrictive measures consistently had the largest negative economic impacts. Social distancing and mask mandates can be effective for containment while mitigating the economic impacts of strict SIPs and business closures.

Background

By March 2020, countries around the world began locking down their communities in an attempt to flatten the epidemic curve of Covid-19 [1]. Lockdowns typically consisted of placing restrictions on gatherings, closing schools and workplaces, canceling public events, and issuing shelter-in-place (SIP) orders [2]. The state of Georgia's lockdowns and other non-pharmaceutical interventions (NPIs) were implemented via Governor executive orders.

Lockdowns are controversial because of the potential clash between public health benefits and adverse economic consequences. The poorer, more vulnerable individuals and communities benefit the least from lockdowns as they can cause increased deprivation [3], [4]. Although lockdowns showed benefits to curbing Covid incidence [5], [6], [7], [8], [9], [10] and improving air quality [11], [12], [13], there were severe adverse effects on economic indicators such as Gross Domestic Product (GDP) per capita, trade, and tourism [14]. Several studies estimated the costs to GDP between $2.2 to $7.2 trillion [15], [16], [17], [18]. These lockdowns are not sustainable and can have long-term implications for individuals and communities, widening inequalities [19]. Targeted and coordinated restrictions may be the most beneficial to improve public health and limit economic impacts [16], [20], [21], [22], [23].

To better understand the economic county-level predictors within Georgia, we chose to analyze counties within Metro Atlanta and the Coastal District (Appendix A; see supplementary materials associated with this article on line) as these counties differ vastly in population and sociodemographic characteristics (Table 1 ). We conducted this study to answer these primary research questions: 1) Did county-level restrictions adversely affect the economies in the Atlanta Metro and Coastal District in addition to state-level restrictions? 2) Did statewide government restrictions affect the economies in the regions equally? 3) Which state and county-level restrictions were most economically deleterious? Which ones were the least?

Table 1.

Metro and Coastal county characteristics.

Area	Population	% Bachelor's degree or higher	% Households with internet	% White Non-Hispanic	% < 65 uninsured	Median household income	% in poverty
Metro Region	4,456,715	37.1	86.1	41.4	15.0	$70,008	10.4
Fulton	1,063,937	52.9	85.6	39.6	13.7	$69,673	13.8
DeKalb	759,297	44.2	85.0	29.3	17.1	$62,399	12.9
Gwinnett	936,250	36.9	88.6	35.4	18.1	$71,026	9.2
Cobb	760,141	47.4	91.6	51.1	14.2	$77,932	8.3
Clayton	292,256	19.5	80.2	9.1	18.5	$47,864	16.0
Coweta	149,509	30.3	85.5	70.5	13.9	$75,913	9.0
Douglas	146,343	28.2	80.3	37.4	14.9	$63,835	10.9
Fayette	114,421	46.2	90.1	60.6	10.8	$90,145	5.4
Henry	234,561	28.5	88.4	40.0	13.7	$71,288	8.1
Coastal Region	628,683	24.2	81.9	61.0	15.4	$57,292	13.5
Bryan	39,627	33.1	87.2	72.2	12.7	$72,624	7.8
Camden	54,666	23.9	84.3	69.3	14.2	$56,951	13.8
Chatham	289,430	33.6	85.2	47.8	16.5	$56,842	14.8
Effingham	64,296	20.0	80.7	77.7	13.5	$66,822	9.1
Glynn	85,292	29.9	77.9	63.6	17.6	$52,977	15.4
Liberty	61,435	18.3	87.0	37.7	13.1	$48,007	14.5
Long	19,559	17.4	80.8	57.2	17.8	$54,605	14.2
McIntosh	14,378	17.6	72.3	62.2	17.9	$49,504	18.7

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We hypothesized that statewide government restrictions would show a steeper positive relationship with unemployment claims in the Coastal counties as opposed to Metro Atlanta. We also hypothesized the county-level restrictions would have a greater association with unemployment claims than the statewide restrictions alone. We assumed that the shelter-in-place and closure of non-essential businesses would have the greatest effects on the outcome and take the longest to recover from for both regions. In contrast, we believed school closures would have the least effect on the economies of either region.

Methods

Weekly rates of initial unemployment claims served as a proxy to estimate the economic health of the counties and came from the Opportunity Insights Economic Tracker (OIET) [24], [25]. All data was publicly available, aggregated, and deidentified. Therefore, no IRB approval was necessary. Demographic and socioeconomic data came from Census data [26]. Executive orders from the Governor of Georgia were located on the Office of the Governor's website [27]. Each Metro and Coastal county had restriction-related information on county sites and school district sites (Appendix B; see supplementary materials associated with this article on line).

The primary outcome of interest was the weekly rate of initial unemployment claims. Each mandate would have had a lag time before it affected the outcome, so the lag time was adjusted according to theory and the literature. Since our data indicated the week that the claim was filed, the lag time was thought to be less than the 2 to 4 weeks claims can take to be approved. After using multiple lag times for the knots, a five-day lag time proved to produce the better fitting models per the Akaike Information Criteria (AIC).

The exposures of interest were the various government restrictions as interventions. The frequencies for these mandates were analyzed (Appendix C see supplementary materials associated with this article on line). Since demographic and socioeconomic indicators were constant for the period under investigation, they were not included as variables in the joinpoint models but were used to determine Pearson correlation coefficients (Table 2 ).

Table 2.

County-level correlations for unemployment claims and rates, and socioeconomic indicators.

Variable	Degree	Internet	Income	Pop	% White	Uninsured	Poverty	Claims	Unemp
Degree	1.0	-	-	-	-	-	-	-	-
Internet	0.62^a	1.0	-	-	-	-	-	-	-
Income	0.69^a	0.69^a	1.0	-	-	-	-	-	-
Pop	0.74^a	0.43	0.29	1.0	-	-	-	-	-
% White	−0.12	−0.10	0.27	−0.50^a	1.0	-	-	-	-
Uninsured	−0.31	−0.57^a	−0.65^a	0.15	−0.40	1.0	-	-	-
Poverty	−0.44	−0.73^a	−0.91^a	−0.11	−0.25	0.67^a	1.0	-	-
Claims	0.34	0.12	−0.02	0.39	−0.67^a	0.12	0.08	1.0	-
Unemp	0.18	−0.06	−0.24	0.41	−0.82^a	0.34	0.29	0.93^a	1.0

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Degree: percent of population with a bachelor's degree or higher; Internet: percent of population with internet; Income: county-level median income; Pop: total population at the county level; % White: percentage of population that is White Non-Hispanic; % Uninsured < 65 years: percentage of the county population under 65 years of age; Poverty: percent of population below the poverty level; Claims: county-level rate of initial unemployment claims; Unemp: county-level unemployment rate

P-value < 0.05.

Analysis was performed in SAS 9.4 using joinpoint regression, also known as spline or segmented regression. To test whether the implementation of a restriction produced a statistically significant change in the acceleration of initial unemployment claims rates, the data were evaluated stepwise at the region level, then the county level. The day of a mandate's implementation and relaxation (with the lag time) were used as joinpoints or knots (k) for the models according to the structure below:

$y = β_{0} + β_{1} x + β_{2} x^{2} + β_{3} x^{3} + β_{4} {(x - k_{1})}_{+}^{3} + β_{5} {(x - k_{2})}_{+}^{a} + β_{6} {(x - k_{3})}_{+}^{3} + \dots + ε$

Where

${(x - k)}_{+} = \{_{x - k,}^{0,} (_{i f x \geq k}^{i f x < k}\})$

For each region, we assessed the impact each individual state mandate had on initial unemployment claims rates. Then we ran full models that included all the state mandates. For each county, we assessed the impact each individual state mandate and county mandate had on initial unemployment claims rates. Like the regional models, we ran full models that included all the state and county mandates.

Initially, we explored using segmented models with degrees of one, two, and three with the PROC GLMSELECT SAS procedure. Each spline has a degree of transformation with one indicating a linear relationship (i.e., change in slope of segments), two indicating the difference in curvature of the segments, and three indicating the rate of change (i.e., change of change) between the segments. Although a degree of one would produce more easily interpretable parameter coefficients, the polynomial segments produced a better fitting model in every scenario.

The LIST KNOTMETHOD in PROC GLMSELECT was used as it allowed us to input specific days for the mandates of interest. Parameters should be interpreted in the context of the segments (i.e., time intervals) preceding and after the joinpoint and the joinpoint in this case is the date of the mandate that includes the lag time for the outcome. Since the joinpoint is represented by cubic splines, the parameters relate to the rate of change of acceleration in the outcome (i.e., change of change).

Ultimately, our exploration of multiple lag times and degrees of transformation allowed us to set up subsequent analyses in a way to maximize the fit of our models to best assess the trends observed from the data.

Results

Trends for initial unemployment claims rates for the Metro and Coastal regions are in Appendix D. Rates followed similar trends over time for the Metro and Coastal regions. Both rates increased dramatically in March and April, coinciding with the stricter government mandates. Then both decreased, the Coastal region more steeply, with another smaller increase in June. Rates continued to decrease for the remainder of the year, but never returned to pre-pandemic levels. Although both regions show similar trends, rates for the Coastal District were lower throughout the year.

Since the sociodemographic measurements were constant throughout the time period, we could not include them in the models as variables. However, since these county level attributes can affect economic outcomes, we ran Pearson correlations for each. Findings from the Pearson correlations (Table 2) show statistically significant moderate positive correlations between having a degree and income (0.69), and internet access (0.62). Not surprisingly, poverty had strong negative correlation to internet access (−0.73) and income (−0.91) and a moderately positive correlation to the percent of people under 65 that are uninsured (0.67). A striking non-correlation was that with poverty and claims (0.08). Interestingly, the percentage of the population who identified as White Non-Hispanic was strongly negatively correlated to unemployment rates (−0.82) and claims (−0.67).

Results for the Metro (Table 3 ) and Coastal (Table 4 ) regions are organized by date of a mandate's implementation in chronological order (column 1). Every subsequent column represents a specific mandate and the dates (i.e., joinpoints) contained in the model. The last column, All Mandates, contains the full model for all applicable mandates for that area. For example, results for state school closures (column 2) have parameters for two joinpoints: the day the state closed schools (Day 76) and the day they reopened (Day 168). Those two joinpoints were the only knots included in the model assessing school closures for the region. State SIPs and state business closures are one model as these mandates were implemented simultaneously. As stated, the parameters should be interpreted in the context of the segments. (i.e., time intervals) preceding the joinpoint and the joinpoints are the dates of the mandate (including the lag time). For example, looking at the State SIP & Business column in Table 3, we can say that after the implementation of the statewide shelter-in-place for the vulnerable and distancing for businesses (Day 88), the rate of acceleration of unemployment claims decreased by −0.006 in the Metro area as compared to the previous time period (Days 0 to 88). Not surprisingly, multiple mandates implemented simultaneously had greater impacts on unemployment claims rates than standalone restrictions. The time intervals for the final models are slightly different as they include more segments than the initial models with only individual mandates and explain why some of the parameters are different for the same mandates. The full timeline of state and county level mandates is in Appendix E (see supplementary materials associated with this article on line). Results for the county models are in Appendix F (see supplementary materials associated with this article on line). Full models incorporate all state and county level mandates; individual models assess single mandates. For brevity, only the cubic terms for the joinpoints are listed in the results tables. See Appendix G (see supplementary materials associated with this article on line) for notes on interpreting the joinpoint parameters.

Table 3.

Results of Metro region joinpoint analyses of initial unemployment claims.

Mandate (Joinpoint)	State schools	State gatherings	State SIP & businesses	State masks	All mandates
GA Closes Schools (Day 76)	0.00004^a				0.002^a
GA implements SIP for the vulnerable, distancing for businesses, & limits gatherings to < 10 (Day 83)		0.00005^a	−0.0006^a		0.004^a
GA implements full SIP & closes businesses (Day 94)			0.0007^a		0.003^a
GA Recommends Masks (Day 114)				0.00001^a	−0.0005
GA relaxes SIP to vulnerable & opens businesses with distancing (Day 122)			−0.0001^a		−0.0001
GA relaxes gathering restrictions to < 50 (Day 153)		−0.00001^a			0.0001
GA allows schools some F2F (Day 168)	−0.00001^a				−0.00003
R² AIC	0.72 74.5	0.73 71.3	0.91 18.2	0.60 90.2	0.96 −18.2

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P-value < 0.05.

Table 4.

Results of Coastal Region joinpoint analyses of initial unemployment claims.

Mandate (Joinpoint)	State Schools	State Gatherings	State SIP & Businesses	State Masks	All Mandates
GA Closes Schools (Day 76)	0.00004^a				0.001^a
GA implements SIP for the vulnerable, distancing for businesses, & limits gatherings to <10 (Day 83)		0.00004^a	0.0005^a		−0.003^a
GA implements full SIP & closes businesses (Day 94)			0.0006^a		0.002^a
GA Recommends Masks (Day 114)				0.000005^a	−0.0006
GA relaxes SIP to vulnerable & opens businesses with distancing (Day 122)			−0.0001^a		0.00002
GA relaxes gathering restrictions to <50 (Day 153)		−0.00001^a			0.00005
GA allows schools some F2F (Day 168)	−0.00001^a				−0.00001
R² AIC	0.72 54.8	0.74 51.2	0.92 −10.0	0.56 74.8	0.97 −50.7

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P-value < 0.05

Overall, mandates had larger impacts on increasing rates of acceleration of unemployment claims rates in the Metro area than the Coastal region. For example, the full SIP and business closures (Day 94) indicated an acceleration in claims rates by 0.003 in the Metro area (Table 3) vs. 0.002 on the Coast (Table 4) (All Mandates column).

For the Metro counties, the state mandates to shelter the vulnerable, limit gatherings to < 10, and implement social distancing for businesses had statistically significant negative parameters reducing the acceleration of claims rates. The full SIP with non−essential business closures had a positive effect on acceleration of rates. Relaxing the full SIP back to only the vulnerable population and reopening businesses decreased the acceleration of claims rates for all counties. All Metro counties had a decrease of acceleration of claims rates when limits to gatherings were relaxed from < 10 to < 50. School closures had positive parameters while the state allowing some face-to-face (F2F) had negative parameters.

Other than school closures, only four Metro counties implemented restrictions outside of the state mandates: DeKalb, Clayton, Coweta, Douglas, and Henry. All the county level SIPs had positive parameters for the acceleration of claims rates. And all these counties implemented their full SIP after the state had the vulnerable SIP, but before the state implemented the full SIP. Limiting gatherings to < 10 and business closures at the county level all showed positive statistically significant parameters. Having social distancing in businesses had negative parameters for all Metro counties except Douglas, but Douglas went from completely open to distanced while the other counties went from closed to open with distancing. When Clayton opened businesses, it had the largest change in parameters (from 0.0001 to −0.0007) compared to the other Metro counties. The county level school closures all had positive parameters, but lesser than the state school closures. County level mask recommendations all had positive parameters, while mask mandates all had negative parameters.

For the Coastal counties, the state mandates to shelter the vulnerable, limit gatherings to < 10, and implement social distancing for businesses had statistically significant negative parameters reducing the acceleration of claims rates. The full SIP with non-essential business closures had a positive effect on acceleration of rates. Relaxing the full SIP back to only the vulnerable population and reopening businesses decreased the acceleration of claims rates for all counties. All Coastal counties had an increase of acceleration of claims rates and a decrease when limits to gatherings were relaxed from < 10 to < 50. School closures had positive parameters while the state allowing some F2F school had negative parameters. Mask recommendations had all positive parameters.

Only Bryan, Chatham, and Glynn implemented county-level restrictions other than school closures. Of those, limiting gatherings to < 10 had a positive parameter value for both Bryan and Chatham. Relaxing the gathering restriction was negative for Chatham and non-significant for Bryan. Closing non-essential businesses had an increase in acceleration of claims rates for both Chatham and Glynn. County-level school closures were all positive and returning for some face-to-face classes had all negative parameters. Mask recommendations and mandates were non-significant for all counties.

The mandates with the largest impact on accelerating unemployment claims rates in both regions for all counties were the full SIP mandates and closures of non-essential businesses at the state and county levels. These mandates had an effect at the level they were first implemented, i.e., if the county implemented an SIP and afterwards the state, the state SIP had no additional measurable effect on claims rates. School closures had a consistent impact on accelerating unemployment claims rates, but to a lesser degree than the SIP orders or business closures. Restricting gatherings to either < 10 or < 50 did not have a negative economic impact on the areas of study. While closing businesses did have a deleterious effect, implementing social distancing for businesses did not.

Discussion

The analyses showed that the statewide restrictions affected the economies in the Metro and Coastal regions differently. During 2020, the trends in unemployment claims rates were similar but rates were consistently lower for the Coast. We hypothesized that the restrictions would have steeper positive associations (i.e., more negative economic impacts) for the Coastal region and counties than for the Metro area. The analyses showed the opposite to be true. Restrictions consistently had larger impacts for both increasing and decreasing the accelerations of claims rates in Metro region. The hypothesis was made due to the Coast heavily depending upon tourism with industries potentially more impacted by lockdowns and other restrictions. Additionally, previous studies have found that coastal communities were hit hard by Covid restrictions [28], [29]. However, port-related operations and manufacturing in this region may have been considered essential work thereby mitigating some of the adverse economic effects.

From the Pearson correlations (Table 2), the only socioeconomic variable correlated to either unemployment claims or rates was the percentage of the population who was White Non-Hispanic. Race ethnicity may have a larger association with unemployment than education or income alone. The joinpoint analyses also showed larger parameters for counties with lower percentages of White Non-Hispanic, e.g., Clayton and Douglas. This could be because Black or Hispanic populations could have professions more likely to be adversely affected by government restrictions (e.g., food workers, hairdressers, childcare workers, etc.).

The hypothesis that shelters-in-place and business closures would have the greatest effects on unemployment claims rates was supported. However, the hypothesis that county-level restrictions would have a greater negative impact on unemployment claims rates than state mandates alone was not supported by the results. County-level restrictions did have an impact, but primarily when implemented before similar state mandates. Additionally, school closures did have a negative economic impact contrary to our hypothesis, which is something other studies have also found [30]. This could be due to parents being unable work or find childcare for children once the schools closed.

This study has several limitations. Adherence to the restrictions was not measured. Some municipalities recommended their own restrictions at the city-level and were not represented in the county-level data. County-level restrictions were superseded by state restrictions and were not enforceable. Since some mandates were implemented simultaneously, estimating an individual mandate's contribution is difficult. The lag time of 5 days was estimated from analyses, theory, and research. This lag time may differ for different mandates or regions. Additionally, the economic health of an area is complex and difficult to measure. The outcomes used are only a proxy for estimation. Finally, the joinpoint analysis cannot tell us causation of the trend, only that the acceleration of rates either increased or decreased compared to the previous time period.

Conclusions

Even with the limitations, this study adds to the body of knowledge for gauging the economic impacts of NPIs and is the only one known to specifically evaluate and compare regions in Georgia. Lockdowns were previously shown to significantly increase unemployment, but only after being in place for at least 10 consecutive days in the U.S [31], [32]. Our research shows that adverse effects may begin more quickly.

Our findings indicate that the most restrictive measures consistently have the largest negative economic impacts, while less restrictive measures like limiting gatherings and implementing social distancing in businesses have minimal impact. Protecting the vulnerable, practicing social distancing, and mandating masks can be effective countermeasures while mitigating the economic impacts of strict shelters-in-place. Further, state governments should consider allowing local municipalities flexibility to enact NPIs more or less restrictive than the state-level mandates. This could be beneficial under some conditions where the data indicate it is necessary to protect communities from disease or undue economic burden. The sociodemographics of an area can play a big part in how a local municipality may choose to respond to a pandemic. Our findings indicated that race ethnicity may play a bigger role in the adverse effects of NPIs than education, poverty level, or geographic location. Local authorities are more aware of their population's demographics, healthcare infrastructure, and economies. And they should also have also more freedom to respond appropriately locally rather than having a one-size-fits-all statewide approach.

Our findings can be built upon for future research into socioeconomic predictors and spillover effects. Another methodology (e.g., event panel or difference-in-difference designs) could be more appropriate to explore geographic areas along with their sociodemographics and control for more potential confounders. More research is necessary to better understand the extent of how much the socioeconomic predictors contribute to the variability in economic trends. Additionally, studies have shown both positive and negative economic spillover effects to surrounding areas during lockdowns [33], [34]. Further research as to what indicators for an area can produce positive economic spillover effects would be helpful to aid in mitigating future impacts associated with mandated NPIs.

The year 2020 reminded us that we are still vulnerable to infectious disease. As new pathogens emerge, we must prepare to protect our health in traditional ways when preventive therapies and treatments do not yet exist. Although this study concentrated on Covid-19, the findings should still be useful to decision makers when evaluating what kinds of restrictions may be helpful and which may cause more harm than good.

Human and animal rights

The authors declare that the work described has not involved experimentation on humans or animals.

Informed consent and patient details

The authors declare that the work described does not involve patients or volunteers.

Disclosure of interest

The authors declare that they have no competing interest.

Funding

This work did not receive any grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author Contributions

R.W. contributed to the study design, wrote the main manuscript text, and performed the statistical analyses

R.L. oversaw the statistical analyses and reviewed and revised the manuscript

R.R. contributed to the study design and reviewed and revised the manuscript

All authors approved the final article.

Data Availability Statement

The data analyzed in this study are publicly available through the Opportunity Insights Economic Tracker (https://tracktherecovery.org/).

Footnotes

^{Online Supplement}

Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.jemep.2023.100891.

Online Supplement. Supplementary data

mmc1.docx^{(248.2KB, docx)}

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25.Chetty R., Hendren N., Stepner M., Opportunity Insights Team . 2020. The economic impacts of covid-19: evidence from a new public database built from private sector data. [Online]. Available: https://opportunityinsights.org/wp-content/uploads/2020/05/tracker_paper.pdf. [DOI] [PMC free article] [PubMed] [Google Scholar]
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28.Amoah J.O., Addai-Mununkum T., Oduro G.Y., Odoi A., Ansah J.W., et al. Effects of Covid-19 on coastal livelihoods in the central region of Ghana. Soc Sci Humanit Open. 2023;7:100443. doi: 10.1016/j.ssaho.2023.100443. [DOI] [PMC free article] [PubMed] [Google Scholar]
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30.Garcia KSD, Cowan BW. The Impact of U.S. School closures on labor market outcomes during the Covid-19 Pandemic. Natl Bureau Econ Res 2022; 29641.
31.Berry C.R., Fowler A., Glazer T., Handel-Meyer S., MacMillen A. Evaluating the effects of shelter-in-place policies during the Covid-19 pandemic. Proc Natl Acad Sci USA. 2021;118 doi: 10.1073/pnas.2019706118. e2019706118. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Goswami B., Mandal R., Nath K.H. Covid-19 pandemic and economic performances of the states in India. Econ Anal Pol. 2021;69:461–479. doi: 10.1016/j.eap.2021.01.001. [DOI] [Google Scholar]
33.Coccia M. The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: lessons learned to cope with future pandemics similar to Covid-19 and to constrain the deterioration of economic system. Sci Total Environ. 2021;775:145801. doi: 10.1016/j.scitotenv.2021.145801. [DOI] [Google Scholar]
34.Wang Q., Zhang F. What does the China's economic recovery after Covid-19 pandemic mean for the economic growth and energy consumption of other countries? J Cleaner Prod. 2021;295:126265. doi: 10.1016/j.jclepro.2021.126265. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.docx^{(248.2KB, docx)}

Data Availability Statement

The data analyzed in this study are publicly available through the Opportunity Insights Economic Tracker (https://tracktherecovery.org/).

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PERMALINK

Non-pharmaceutical interventions in the State of Georgia: Economic implications

RC White

R Luo

R Rothenberg

Graphical abstract

Abstract

Background

Methods

Results

Conclusions

Background

Table 1.

Methods

Table 2.

Results

Table 3.

Table 4.

Discussion

Conclusions

Human and animal rights

Informed consent and patient details

Disclosure of interest

Funding

Author Contributions

Data Availability Statement

Footnotes

Online Supplement. Supplementary data

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

Non-pharmaceutical interventions in the State of Georgia: Economic implications

RC White

R Luo

R Rothenberg

Graphical abstract

Abstract

Background

Methods

Results

Conclusions

Background

Table 1.

Methods

Table 2.

Results

Table 3.

Table 4.

Discussion

Conclusions

Human and animal rights

Informed consent and patient details

Disclosure of interest

Funding

Author Contributions

Data Availability Statement

Footnotes

Online Supplement. Supplementary data

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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