Asocial capital: Civic culture and social distancing during COVID-19

Abstract

Social distancing can slow the spread of COVID-19 if citizens comply with it and internalize the cost of their mobility on others. We study how civic values mediate this process using data on mobility across Italian provinces between January and May 2020. We find that after the virus outbreak mobility declined, but significantly more in areas with higher civic capital, both before and after a mandatory national lockdown. The effect is not driven by differences in the risk of contagion, health-care capacity, geographic socioeconomic and demographic factors, or by a general North-South divide. Simulating a SIR model calibrated on Italy, we estimate that if all provinces had the same civic capital as those in top-quartile, COVID-related deaths would have been about 60% lower. We find consistent results for Germany where the incidence of the pandemic and restrictions to mobility were milder.

Keywords: COVID-19; Civic capital; Culture; Externalities; Social distancing.

Fig. 1

Daily movements during the spread of Covid-19 and civic capital. The left panel shows raw data average daily movements in high (above 75th percentile) and low (below 75th percentile) civic capital provinces in Italy over the different phases of the pandemic. The right panel shows the difference in daily movements between high and low civic capital provinces.

Fig. 2

Difference in mobility between high and low civic capital provinces. The figure plots the coefficients on the interaction terms between week fixed effects and a dummy variable for provinces in the top-quartile of the civic capital distribution. The coefficients in the left panel are estimated from the regression in column 3 of Table 1, using daily total movements as dependent variable. Those in the right panel are estimated from the regression in column 6 of Table 1 using daily local movements as dependent variable.

Fig. A.1

Newspaper front pages around the outbreak. The Figure shows the front pages of the three main Italian newspapers (Corriere della Sera, La Republica, and Il Sole 24 Ore) in the two days before and after the Covid-19 outbreak revealed on the night of February 21.

Fig. A.2

Time-line of Covid-19 pandemic in Italy and Germany. The table shows the timeline of the key events in the evolution of the Covid-19 pandemic in Italy and Germany.

Fig. A.3

Daily movements during the spread of the virus and civic capital. The figure depicts the evolution of average daily movements in high (above 75th percentile), middle (between the 75th and 25th percentile) and low (below 25th percentile) civic capital provinces in Italy over the different phases of the pandemic.

Fig. A.4

Geographic Distribution of Civic Capital in Italy. The figure illustrates the geographic distribution of our main civic capital measure across all the provinces in our sample (panel a), and separately for provinces in the Center-North (b) and Center-South (c). The red dots indicate provinces in the top quartile of the distribution of civic capital for the relevant sample.

Fig. A.5

Difference in mobility between provinces with high and low civic capital (including region $\times$ week fixed effects). The figures plots the coefficients on the interaction terms between week fixed effects and a dummy variable for provinces in the top-quartile of the civic capital distribution using as dependent variable total mobility (left panel) and local mobility (right panel), respectively. In both cases, the coefficients are estimated from an augmented version of our baseline specification which also includes region $\times$ week fixed effects.

Fig. A.6

Mobility and civic capital: interactions with daily dummies. The figure plots the coefficients of the interaction terms between a dummy for high-civic capital provinces (i.e., above 75th percentile) and daily dummies estimated in regressions equivalent to those in columns 3 and 6 of Table 1, respectively. The blue dots indicate coefficients that are not statistically significant, green dots coefficients that are statistically significant at the 10% level, and yellow dots coefficients that are statistically significant at the 5% level or less.

Fig. A.7

Difference in mobility between high and low civic capital districts (Germany). The figure plots the coefficients of the interactions between a dummy for German districts with high civic capital (above 75th percentile) and week fixed effects from a regression analogous to that reported in column 3 of Table A.8.

Fig. A.8

Evolution of cumulative and new daily deaths: actual vs. simulated high-civic capital scenario. The figure shows the daily evolution of cumulative deaths (panel a) and new deaths (panel a) for a benchmark economy (blue) and for an economy with high civic capital (red). It is based on simulations using a SIR model of optimal quarantine and testing calibrated to Italy by Piguillem and Shi (2020). The benchmark case is based on the daily mobility/social distancing patterns estimated in Table 1 for the entire country using the average civic capital across provinces. The high-civic-capital scenario is based on the assumption that all provinces in the country have the same level of civic capital as those in top quartile of the distribution.