Corona and the Cross: Religious Affiliation, Church Bans, and Covid Infections

Holger Strulik; Slava Yakubenko

doi:10.1515/ger-2022-0131

Published by De Gruyter August 15, 2023

Corona and the Cross: Religious Affiliation, Church Bans, and Covid Infections

Holger Strulik and Slava Yakubenko

From the journal German Economic Review

https://doi.org/10.1515/ger-2022-0131

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Abstract

We examine the effectiveness of church service bans in containing the spread of Covid-19 in Germany. We furthermore investigate how differences in the local religious affiliations affect infections and the effectiveness of church bans and other church-related restrictions. We find that, without a ban, infections per capita are higher in districts (Landkreise) with larger shares of religious population. In panel analysis, controlling for district fixed effects and a host of potential confounders, we find that church bans effectively reduce infections. For a ban in place for 14 days before a considered day, the predicted growth factor of infections is lower by 0.9 of its standard deviation. Finally, we show that Easter contributed significantly to the growth of infections in 2020 and 2021. The growth factor of infections was lower in regions with larger shares of Catholics and Protestants during Easter 2020 (when a church ban was in place) but not in 2021 (without a ban).

Keywords: COVID-19; infections; religious affiliations; religious service bans; social distancing

JEL Classification: I12; I18; R12; Z12

Corresponding author: Holger Strulik, Department of Economics, University of Göttingen, Platz der Göttinger Sieben 3, 37073 Göttingen, Germany, E-mail: holger.strulik@wiwi.uni-goettingen.de

Acknowledgments

We would like to thank two anonymous reviewers for helpful comments.

Appendix A

Table 4:

Summary statistics of used variables.

Variable	Obs	Mean	Std. dev.	Min	Max
Panel A: Cross-sectional variables
Cases per capita (wave I)	399	0.002204	0.0016541	0.0003011	0.0156681
Cases per capita (wave II)	399	0.0257026	0.009272	0.0056667	0.0624468
Cases per capita (wave III)	399	0.0145733	0.005444	0.0034591	0.0412494
Cases per capita (waves II & III)	399	0.0402758	0.0134526	0.0091257	0.0901965
Cases per capita (all waves)	399	0.043651	0.0139895	0.0104472	0.0928824
Share of Catholics	399	0.3352353	0.2488237	0.0191933	0.887439
Share of Protestants	399	0.3167559	0.1750839	0.0454727	0.7588371
Share of Free Evangelicals	399	0.0075983	0.0081158	0	0.0621516
Share of Orthodox	399	0.0107221	0.0092562	0	0.0645703
Share of other religions	399	0.0225975	0.0146717	0	0.0957371
ln(population)	399	11.96711	0.6309765	10.44024	14.2018
GDP_pc	399	3.561412	1.587353	1.59209	17.87063
Accessibility	399	0.2267669	0.1599366	0	0.69
Share 65+	399	0.209948	0.0234753	0.1510057	0.2821923
Share 29−	399	0.3024633	0.0285184	0.221618	0.3866958
Share w/o degree	399	0.0914463	0.0704263	0.0087515	0.4720327
Share w degree	399	0.0953563	0.0732854	0.016784	0.4460912
Share females	399	0.5057835	0.0063432	0.4872	0.5249
Share migrants	399	0.1673506	0.0947615	0.0181919	0.4972176
Panel B: Panel variables
R (wave I)	31,278	0.990247	0.7371988	0.0352052	20.45963
R (wave III)	34,887	0.982866	0.506134	0.004058	5.585498
Church ban	31,278	0.5969858	0.4529527	0	1
Church restrictions	31,278	0.2885	0.4281776	0	1
1.5 m distance	31,278	0.8488997	0.3319456	0	1
Shop closure	31,278	0.4666617	0.4621128	0	1
Private gatherings	31,278	0.8210393	0.3589873	0	1
Past temeprature	31,278	15.02278	4.089088	4.324472	23.45356

Table 5:

Past infections and governmental measures.

Dep. variable	Church ban
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
New cases yesterday (’000)	0.00355	0.00001
	(0.01050)	(0.02781)
Total cases (’000)			0.00024	0.00011
			(0.00037)	(0.00045)
Total cases last week(’000)					0.00075	0.00246
					(0.00154)	(0.00448)
Total cases last 4 weeks(’000)							0.00044	0.00110
							(0.00065)	(0.00109)
N	109,874	109,874	109,874	109,874	107,468	107,468	99,047	99,047
Number of regions	401	401	401	401	401	401	401	401
Adj. R ²	0.938	0.939	0.938	0.939	0.938	0.939	0.945	0.947

Dep. variable	Restrictions
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
New cases yesterday (’000)	0.10131	0.12875
	(0.14532)	(0.31274)
Total cases (’000)			0.00062	0.00043
			(0.00046)	(0.00282)
Total cases last week(’000)					0.01653	0.02242
					(0.02223)	(0.05654)
Total cases last 4 weeks(’000)							0.00427	0.00640
							(0.00461)	(0.01821)
N	180,847	180,847	180,847	180,847	178,441	178,441	170,020	170,020
Number of regions	401	401	401	401	401	401	401	401
Adj. R ²	0.780	0.843	0.780	0.843	0.778	0.842	0.773	0.843

LPM regressions. Odd columns include Date fixed effects, even columns include both Date and Bundesland fixed effects. Standard errors clustered at the Bundesland level in parentheses. ^a p < 0.10, ^b p < 0.05, ^c p < 0.01.

Table 6:

Governmental measures and religious affiliation: panel regressions.

Dep. variable	R(t)
	(1)	(2)	(3)	(4)
Ban	−0.130^c	−0.015
	(0.023)	(0.038)
Ban × Catholic		−0.343^c
		(0.057)
Ban × Protestant		−0.122
		(0.081)
Ban × Free Evangelic		−1.164
		(1.539)
Ban × Orthodox		−2.216
		(1.868)
Ban × other		2.902^c
		(1.083)
Restrictions			−0.049^b	−0.119^c
			(0.021)	(0.040)
Restrictions × Catholic				0.040
				(0.053)
Restrictions × Protestant				0.160^a
				(0.091)
Restrictions × Free Evangelic				1.840
				(1.468)
Restrictions × Orthodox				−0.019
				(1.986)
Restrictions × other				−0.362
				(1.159)
Past temperature	−0.025^c	−0.025^c	−0.024^c	−0.024^c
	(0.002)	(0.002)	(0.002)	(0.002)
1.5 m distance	−0.589^c	−0.601^c	−0.593^c	−0.595^c
	(0.044)	(0.044)	(0.042)	(0.043)
Shops closure	−0.129^c	−0.122^c	−0.262^c	−0.262^c
	(0.023)	(0.022)	(0.020)	(0.020)
Private gatherings	0.143^c	0.156^c	0.147^c	0.148^c
	(0.035)	(0.035)	(0.036)	(0.036)
Landkreis FE	Yes	Yes	Yes	Yes
Mean R(t)	0.990	0.990	0.990	0.990
SD R(t)	0.737	0.737	0.737	0.737
N	31,278	31,278	31,278	31,278
Number of regions	401	401	401	401
Adj. R-squared	0.116	0.118	0.114	0.114

OLS regressions. Unaffilaited are the omitted category in columns (2) and (4). Standard errors clustered at the Landkreis level in parentheses. ^a p < 0.10, ^b p < 0.05, ^c p < 0.01.

Table 7:

Easter and spread of COVID-19: joint sample.

Dep. variable	R(t)
	(1)	(2)
Past R	0.105^c	−0.089^c
	(0.016)	(0.017)
Easter	1.326^c
	(0.177)
Wave III	−0.007
	(0.009)
Easter × Wave III	0.563^c
	(0.206)
Easter 2020 × Catholic		−4.112^c
		(1.353)
Easter 2021 × Catholic		−1.634
		(1.037)
Easter 2020 × Protestant		−2.132
		(1.692)
Easter 2021 × Protestant		−1.080
		(1.271)
Easter 2020 × Free Evangelic		−13.610
		(15.969)
Easter 2021 × Free Evangelic		−4.339
		(15.149)
Past temperature	−0.039^c	0.000
	(0.001)	(0.005)
Landkreis FE	Yes	Yes
Bundesland × date FE	No	Yes
N	60,150	59,850
Number of regions	401	399
Adj. R ²	0.071	0.194

OLS regressions. The baseline category includes unaffiliated, Orthodox and others. Column (1) includes past measures of social distancing. Standard errors clustered at the Landkreis level in parentheses. ^a p < 0.10, ^b p < 0.05, ^c p < 0.01.

Table 8:

Governmental measures and religious affiliation: Bundesland-level SEs.

Dep. variable	R(t)
	(1)	(2)
Ban	−0.655^b	−0.385
	(0.268)	(0.222)
Restrictions	−0.586^b	−0.433^a
	(0.268)	(0.221)
Ban × Catholic		−1.084^c
		(0.191)
Ban × Protestant		−0.328
		(0.302)
Ban × Free Evangelic		3.134
		(5.146)
Ban × Orthodox		−7.805
		(6.780)
Ban × other		7.004
		(4.161)
Restrictions × Catholic		−0.880^c
		(0.190)
Restrictions × Protestant		−0.164
		(0.417)
Restrictions × Free Evangelic		4.327
		(5.437)
Restrictions × Orthodox		−6.866
		(6.192)
Restrictions × other		6.184
		(4.250)
Past temperature	−0.023^c	−0.022^c
	(0.003)	(0.002)
1.5 m distance	−0.270	−0.199
	(0.203)	(0.165)
Shops closure	−0.112^c	−0.098^c
	(0.028)	(0.025)
Private gatherings	0.216^c	0.218^c
	(0.062)	(0.049)
N	31,278	31,278
Number of regions	401	401
Adj. R ²	0.127	0.137

OLS regressions. Unaffiliated are the omitted category. Standard errors clustered at the bundesland level in parentheses. ^a p < 0.10, ^b p < 0.05, ^c p < 0.01.

Table 9:

Easter and spread of COVID-19: Bundesland-level SEs.

Dep. variable	R(t)
Wave	I		III
	(1)	(2)	(3)	(4)
Past R	−0.118^c	−0.143^c	0.266^c	−0.069^c
	(0.027)	(0.030)	(0.046)	(0.016)
Easter	0.245		1.876^c
	(0.221)		(0.126)
Easter × Catholic		−5.411^a		−0.756
		(2.552)		(1.707)
Easter × Protestant		−3.804		0.103
		(2.398)		(2.050)
Easter × Free Evangelic		−5.085		−9.843
		(23.878)		(11.384)
Past temperature	−0.015^c	−0.009	−0.043^c	0.002
	(0.002)	(0.018)	(0.004)	(0.005)
Landkreis FE	Yes	Yes	Yes	Yes
Bundesland × date FE	No	Yes	No	Yes
Mean R(t)	0.899	0.899	0.983	0.983
SD R(t)	0.640	0.641	0.506	0.507
N	25,664	25,536	34,887	34,713
Number of regions	401	399	401	399
Adj. R ²	0.013	0.040	0.162	0.364

OLS regressions. The baseline category includes unaffiliated, Orthodox and others. Column (1) includes past measures of social distancing. Standard errors clustered at the bundesland level in parentheses. ^a p < 0.10, ^b p < 0.05, ^c p < 0.01.

Table 10:

Deaths of COVID-19.

Dep. variable	Deaths per 1000 inhabitants
Wave	I	II	III	II & III	All
	(1)	(2)	(3)	(4)	(5)
Catholic	0.270^a	0.887^b	0.222^a	1.109^b	0.001^c
	(0.138)	(0.450)	(0.120)	(0.475)	(0.000)
Protestant	0.165	1.139^b	0.229^a	1.368^b	0.002^c
	(0.120)	(0.505)	(0.137)	(0.532)	(0.001)
Free Evangelic	0.763	−1.800	0.481	−1.318	−0.001
	(0.920)	(3.031)	(0.758)	(3.146)	(0.003)
Other	−1.037	0.170	0.319	0.489	−0.000
	(1.157)	(2.765)	(0.649)	(3.004)	(0.003)
Orthodox	1.374	−4.239	−0.558	−4.797	−0.003
	(1.348)	(3.735)	(1.033)	(3.917)	(0.004)
ln(population)	0.008	−0.038	0.006	−0.033	−0.000
	(0.018)	(0.054)	(0.010)	(0.055)	(0.000)
GDP_pc	0.013^a	−0.035^b	0.003	−0.032^a	−0.000
	(0.007)	(0.017)	(0.004)	(0.018)	(0.000)
Accessibility	−0.142^b	−0.258	−0.085^a	−0.343^b	−0.000^c
	(0.072)	(0.162)	(0.043)	(0.174)	(0.000)
Share 29−	−0.462	−5.468^c	−1.212^b	−6.680^c	−0.007^c
	(0.920)	(1.954)	(0.521)	(2.069)	(0.002)
Share 65+	0.423	−0.173	0.378	0.205	0.001
	(0.544)	(2.018)	(0.458)	(2.194)	(0.002)
Share w/o degree	0.781	1.112	0.248	1.359	0.002
	(0.945)	(1.705)	(0.471)	(1.799)	(0.002)
Share w degree	−0.229	−0.274	−0.345^a	−0.619	−0.001
	(0.274)	(0.641)	(0.182)	(0.669)	(0.001)
Share female	1.103	2.684	0.626	3.311	0.004
	(1.613)	(3.329)	(0.994)	(3.620)	(0.004)
Share migrants	−0.305	2.511^c	0.322	2.833^c	0.003^c
	(0.311)	(0.826)	(0.250)	(0.899)	(0.001)
N	399	399	399	399	399
Adj. R ²	0.198	0.495	0.467	0.564	0.533

OLS regressions. All regressions include Bundesland FE and a constant term. Robust standard errors in parentheses. ^a p < 0.10, ^b p < 0.05, ^c p < 0.01.

Table 11:

Correlation matrix of used variables.

	Catholic	Protestant	Free	other	Orthodox	ln(population)	ln(GDP)	Accessibility	Share	Share	Share	Share	Share	Share
			Evangelic						25−	65+	w/o	w/degree	female	migrants
											degree
Catholic	1.0000
Protestant	−0.5140^c	1.0000
Free Evangelic	−0.2350^c	0.4036^c	1.0000
Other	0.1281^b	0.1375^c	0.2295^c	1.0000
Orthodox	0.2012^c	−0.0168	0.0995^b	0.7158^c	1.0000
ln(population)	−0.0602	−0.0460	0.2406^c	0.3212^c	0.2503^c	1.0000
ln(GDP)	0.1384^c	−0.0696	−0.0239	0.4096^c	0.5637^c	0.0725	1.0000
Accessibility^c	−0.1409^c	0.1760^c	0.1465^c	−0.4235^c	−0.4811^c	−0.1287^c	−0.4753^c	1.0000
Share 25−	0.4905^c	0.0060	0.1440^b	0.4386^c	0.4938^c	0.1884^c	0.4058^c	−0.3612^c	1.0000
Share 65+	−0.5299^c	0.0918^b	−0.0782	−0.2922^c	−0.3859^c	−0.2618^c	−0.2257^c	0.1708^c	−0.8277^c	1.0000
Share w/o	0.4282^c	0.2243^c	0.1997^c	0.7054^c	0.6167^c	0.0867	0.3214^c	−0.2806^c	0.6439^c	−0.4749^c	1.0000
degree
Share	−0.0890	−0.1577^b	−0.0591	0.2990^c	0.4715^c	0.3694^c	0.5351^c	−0.5515^c	0.3768^c	−0.2314^c	0.0123	1.0000
w/degree
Share	−0.2845^c	0.1198^b	−0.0137	0.1259^b	0.0580	0.0642	0.0244	−0.2656^c	−0.2054^c	0.3764^c	−0.0467	0.3235^c	1.0000
females
Share	0.2819^c	0.0880	0.2465^c	0.8639^c	0.8522^c	0.2649^c	0.5819^c	−0.4874^c	0.604^c0	−0.4392^c	0.8188^c	0.3859^c	0.0753	1.0000
migrants

^a p < 0.10, ^b p < 0.05, ^c p < 0.01.

The Model

To analyze the dynamics of the epidemics we refer to a simple SEIR model. Under the standard formulation the model consist of as a set of differential equations that describe evolution of susceptible, exposed, infectious and recovered populations. As we are primarily interested in the analysis of contagiousness we focus on the infected individuals and assume the share of susceptible individuals to be equal to 1. We believe that this assumption is reasonable in our setting. For analysis of the efficacy of the ban and restrictions of religious services, we use data from Wave I (until 1 June, 2020). By this time in total 182 thousand cases of COVID-19 were registered in Germany, what is roughly equal to 0.2 percent of the total population. Even though it is not yet exactly known what share of infected does not reveal symptoms, we have reasons to believe that the real number of infected individuals has not substantially exceeded 1 percent of the total population (Mizumoto et al. 2020).

The evolution of a number of cases diagnosed with the disease is:

(A1) α I ̇ = ( 1 − α ) I r ( t ) γ + α I r γ h − ( 1 − α ) I γ − α I γ h .

I is the total size of the currently infected population and α is the share of infected with revealed symptoms. We assume that only symptomatic cases (αI) were registered, but asymptomatic cases ((1 − α)I) are contagious and an infected individual generates r new cases while she or he is contagious. γ is the mean communicability period for asymptomatic cases and γ _h is the mean time from infection to isolation of individuals with symptoms – the incubation period. The last two characters in Eq. (A1) stand for recovery of asymptomatic and isolation of symptomatic individuals, respectively.

We rearrange Eq. (A1) as:

(A2) α I ̇ = I λ , where λ ≡ r ( t ) ( 1 − α ) γ 2 γ h + α γ γ h 2 − ( 1 − α ) γ h − α γ γ γ h .

Assuming α to be constant, the solution to Eq. (A1) is:

(A3) α I ( t ) = α I ( 0 ) e y ( t ) , where y ( t ) ≡ ∫ λ ( t ) d t ,

and I(0) is the initial number of infected. y(t) is the average growth rate of infection cases over an incubation period. As Eq. (A2) and (A3) demonstrate, besides the probability of infection that we expect to vary daily during the communicability period, y(t) also accounts for the duration of communicability and the fact that not everyone develops symptoms despite being contagious.

As it is now, y(t) represents an average growth rate of infection cases from the beginning of the epidemic until day t. However, in our study we want to follow evolution of contagiousness under the influence of particular factors, such as changes in policy. Thus, we focus on weighted 14-days intervals – incubation periods – and follow evolution of cases not from the first day of the epidemic, but from the first day of the incubation period of infections diagnosed by a doctor on day t. To avoid breaks in the data on days, when there were no infections registered, we calculate our average exponential growth rate of new infections over the incubation period by adding one to the number of infections. Putting both sides of the Eq. (A3) in natural logarithms and rearranging it yields us:

(A4) y ̂ ( t ) = ln ( I ( t ) + 1 ) − ln ( I p ( t ) + 1 ) ,

where ln(I ^p(t)) is an average number of infections diagnosed and recorded by RKI between t − 1 and t − 14 weighted using the distribution derived by Backer, Klinkenberg, and Wallinga (2020). Now y ̂ ( t ) is the estimated average growth rate of the number of infection cases diagnosed on day t over the incubation period. However, for an easier interpretation of our results, we can refer to Eq. (A3) and use the fact that e ^y(t) can be read as the number of new infections generated from the initial number of infections until day t. Given that we fix the time frame equal to one incubation period, we can obtain the effective reproductive number:

(A5) R ( t ) = e y ̂ ( t ) .

In other words, R(t) tells us how many new infections an average infected person diagnosed with COVID-19 and isolated from the population on day t was generating while being contagious.

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Received: 2022-12-20

Accepted: 2023-07-16

Published Online: 2023-08-15

Published in Print: 2023-08-26