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Table 2 Regression analysis

From: The median age of a city’s residents and population density influence COVID 19 mortality growth rates: policy implications

Variables

(1)

(2)

(3)

\(\Delta \mathrm{ln}(Cum\_Deaths)\)

\(\Delta \mathrm{ln}(Cum\_Deaths)\)

\(\Delta \mathrm{ln}(Cum\_Deaths)\)

Constant

0.000114

− 0.000196

− 0.00142***

(0.324)

(0.158)

(< 0.01)

PopulationSize

1.41 × 10–8***

1.29 × 10–8***

1.24 × 10–8***

(< 0.01)

(< 0.01)

(< 0.01)

(MedianAge 11) × .t

1.64 × 10–7***

2.06 × 10–7***

4.28 × 10–7***

(6.68 × 10–10)

(< 0.01)

(< 0.01)

Dum_vaccine × (MedianAge 11) × .t

3.33 × 10–8***

3.88 × 10–8***

1.67 × 10–7***

(0.00659)

(0.00315)

(< 0.01)

Population_Density

8.80 × 10–8***

9.23 × 10–8***

(0.000104)

(5.24 × 10–5)

Lockdowns

0.00229***

(< 0.01)

Holidays

− 0.000118

(0.191)

Observations

71,580

63,555

63,555

R-squared between estimators

0.6118

0.6372

0.6461

Calculated wald chi2 for the regression significance

363.61***

365.25***

1,197.41***

Number of CityCode

173

152

152

  1. Estimation outcomes are based on the empirical model given by Eq. (1). MedianAge = 11 is the minimum age. The R-squared between estimators gives the goodness of fit for the general equation \({\overline{y} }_{i}=\alpha +{\overline{x} }_{i}\beta +{\nu }_{i}+{\overline{\varepsilon }}_{i}\) where \({\overline{y} }_{i}={\sum }_{t}{y}_{it}/{T}_{i}\); \({\overline{x} }_{i}={\sum }_{t}{x}_{it}/{T}_{i}\); \({\overline{\epsilon }}_{i}={\sum }_{t}{\epsilon }_{it}/{T}_{i}\) (the sample mean of cities across time) and \({\nu }_{i}\) reflect generic differences across cities. p-values are given in parentheses
  2. ***p < 0.01