The Impacts of COVID-19 Shock on Intergenerational Income Mobility: Evidence from China.

The COVID-19 crisis has caused a huge negative shock to economic activities worldwide, leading to a reduction in income and changes in income distribution. Intergenerational mobility is an important indicator of sustainable social development. This paper explores the short-term impacts of the sudden COVID-19 pandemic on intergenerational income mobility and personal income in China. Using the variation in the number of confirmed cases across provinces, we construct a province-level pandemic intensity index and combine it with individual data from the China Family Panel Studies (CFPS). We apply a general difference-in-difference strategy to identify the causal effect of the pandemic on intergenerational income mobility. We find that personal income is positively related to parental income, and that the COVID-19 crisis has caused a decline in individual income and exacerbated intergenerational income persistence. A more intense COVID-19 pandemic shock is associated with a larger increase in intergenerational income elasticity and intergenerational income rank-rank slope. We found that with one standard deviation increase in local pandemic intensity, the intergenerational income elasticity increases by 0.315 and the intergenerational income rank-rank slope increases by 0.198 on average. The mechanism testing suggests that heterogeneous effects among different groups are the force underlying the results. Low-income, low-skilled, and low-parental-income individuals have suffered a more severe impact from the pandemic shock.

Effects of Social Mobility and Stringency Measures on the COVID-19 Outcomes: Evidence From the United States.

This paper examines the effects of stringency measures (provided by the Oxford Coronavirus Government Response Tracker) and total time spent away from home (provided by the Google COVID-19 Community Mobility Reports) on the COVID-19 outcomes (measured by total COVID-19 cases and total deaths related to the COVID-19) in the United States. The paper focuses on the daily data from March 11, 2020 to August 13, 2021. The ordinary least squares and the machine learning estimators show that stringency measures are negatively related to the COVID-19 outcomes. A higher time spent away from home is positively associated with the COVID-19 outcomes. The paper also discusses the potential economic implications for the United States.

National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil.

In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate model with the number of infected people as input data to forecast the number of deaths. This simple model is compared with a more robust deep learning multi-variate model that uses mobility and transmission rates (R0, Re) from a SEIRD model as input data. A principal components model of community mobility, generated by the principal component analysis (PCA) method, is added to improve the input features for the multi-variate model. The deep learning model architecture is an LSTM stacked layer combined with a dense layer to regress daily deaths caused by COVID-19. The multi-variate model incremented with engineered input features can enhance the forecast performance by up to 18.99% compared to the standard one-variate data-driven model.

Social mobility and gonorrhoea in Germany during 2020.

OBJECTIVES: The incidence of STIs is likely to be related to levels of social activity and mobility. Novel datasets detailing levels of social activity were made widely available during the COVID-19 pandemic. These allow the relationship between activity and STI incidence to be examined. METHODS: The correlation between social activities and the reported number of gonorrhoea cases between March and December 2020 in Germany was studied. Regression through Autoregressive Integrated Moving Average (ARIMA) time series modelling identified those activities associated with case numbers. RESULTS: ARIMA regression identified a significant association with 'transit' activity within the Apple data and 'parks' within Google. CONCLUSIONS: This study illustrates the potential newly available measures of social activity provided for STI research. Reductions in STI incidence are likely to have occurred due to COVID-19 social restrictions. Although other studies report reductions in infectious diseases during this period, few examine the potential social factors mediating this. The results illustrate the continual need for sexual health services throughout the pandemic.

Use of wearable sensors to assess compliance of asthmatic children in response to lockdown measures for the COVID-19 epidemic.

Between March and April 2020, Cyprus and Greece health authorities enforced three escalated levels of public health interventions to control the COVID-19 pandemic. We quantified compliance of 108 asthmatic schoolchildren (53 from Cyprus, 55 from Greece, mean age 9.7 years) from both countries to intervention levels, using wearable sensors to continuously track personal location and physical activity. Changes in 'fraction time spent at home' and 'total steps/day' were assessed with a mixed-effects model adjusting for confounders. We observed significant mean increases in 'fraction time spent at home' in Cyprus and Greece, during each intervention level by 41.4% and 14.3% (level 1), 48.7% and 23.1% (level 2) and 45.2% and 32.0% (level 3), respectively. Physical activity in Cyprus and Greece demonstrated significant mean decreases by - 2,531 and - 1,191 (level 1), - 3,638 and - 2,337 (level 2) and - 3,644 and - 1,961 (level 3) total steps/day, respectively. Significant independent effects of weekends and age were found on 'fraction time spent at home'. Similarly, weekends, age, humidity and gender had an independent effect on physical activity. We suggest that wearable technology provides objective, continuous, real-time location and activity data making possible to inform in a timely manner public health officials on compliance to various tiers of public health interventions during a pandemic.

Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis.

Mobile phones have been used to monitor mobility changes during the COVID-19 pandemic but surprisingly few studies addressed in detail the implementation of practical applications involving whole populations. We report a method of generating a "mobility-index" and a "stay-at-home/resting-index" based on aggregated anonymous Call Detail Records of almost all subscribers in Hungary, which tracks all phones, examining their strengths and weaknesses, comparing it with Community Mobility Reports from Google, limited to smartphone data. The impact of policy changes, such as school closures, could be identified with sufficient granularity to capture a rush to shops prior to imposition of restrictions. Anecdotal reports of large scale movement of Hungarians to holiday homes were confirmed. At the national level, our results correlated well with Google mobility data, but there were some differences at weekends and national holidays, which can be explained by methodological differences. Mobile phones offer a means to analyse population movement but there are several technical and privacy issues. Overcoming these, our method is a practical and inexpensive way forward, achieving high levels of accuracy and resolution, especially where uptake of smartphones is modest, although it is not an alternative to smartphone-based solutions used for contact tracing and quarantine monitoring.

Partisan pandemic: How partisanship and public health concerns affect individuals' social mobility during COVID-19.

Rampant partisanship in the United States may be the largest obstacle to the reduced social mobility most experts see as critical to limiting the spread of the COVID-19 pandemic. Analyzing a total of just over 1.1 million responses collected daily between 4 April and 10 September reveals not only that partisanship is more important than public health concerns for explaining individuals' willingness to stay at home and reduce social mobility but also that the effect of partisanship has grown over time-especially among Republicans. All else equal, the relative importance of partisanship for the increasing (un)willingness of Republicans to stay at home highlights the challenge that politics poses for public health.

The dual effect of social ties on COVID-19 spread in Japan.

We investigate why some communities experience worse COVID-19 outcomes than others. Past studies have linked the resilience of communities against crisis to social vulnerability and the capacity of local governments to provide public goods and services like health care. Disaster studies, which frequently examine the effect of social ties and mobility, may better help illuminate the current spread of COVID-19. We analyze Japan's 47 prefectures from February 12 to August 31 using 62,722 individual confirmed cases of COVID-19, paired with daily tallies of aggregate Facebook user movement among neighborhoods. Controlling for mobility levels, health care systems, government finance, gender balance, age, income, and education levels of communities, our analysis indicates that areas with strong linking social ties see no or far lower levels of COVID-19 case rates initially. However, case fatality rates rise in such communities once the disease enters as they lack horizontal (bonding) ties which can mitigate its health impacts. We anticipate this study to be a starting point for broader studies of how social ties and mobility influence COVID-19 outcomes worldwide along with shining a light on how different types of social relationships play different roles as a crisis or disaster progresses.