Kaichun Zhou (p), Chunla Liu, Binggeng Xie, Xiaoqing Li, Liwen YI, Jiancheng Zheng, Xiaofei Pang

Anastasia Sherubneva

The 2020 COVID-19 epidemic significantly impacted economic development, leading to reduced recruitment by small and medium-sized enterprises amid a growing number of college graduates, thereby creating an unprecedented employment situation for these graduates. This study, based on data from universities’ graduates from 2018 to 2021 in Hunan, China, examines the impact of the COVID-19 epidemic on graduate employment. Key findings include: (1) a post-epidemic increase in graduates returning to hometowns for employment, with eastern region cities remaining popular work locations; (2) significant influence of individual characteristics, family background, and social environment on employment paths; and (3) notable employment heterogeneity among graduates, based on gender, urban/rural background, and major, both pre- and post-epidemic. This research sheds light on the profound effects of the pandemic on graduate employment trends and provides valuable insights for policymakers and educators seeking to adapt and improve talent training and employment strategies in response to significant global events.

Giacomo Pignataro (p), Francesco Vidoli, Calogero Guccio, Stefania Fontana

Kaichun Zhou

Objectives
The resilience of healthcare systems in managing and responding to crises, particularly pandemics, is of paramount importance. This study employs an interrupted time series analysis coupled with a geographic approach to assess the territorial heterogeneity regarding the resilience of hospital supply during pandemic events.
Methods
By integrating geographic disaggregation and temporal data analysis techniques, this research examines the abnormal supply peaks and the time to return to normality in the supply experienced by hospitals in the wake of a pandemic. The interrupted time series methodology enables the identification and evaluation of temporal patterns in the availability of critical medical resources through parametric approaches (Arima methods) or non-parametrical approaches such as the Extreme Gradient Boosting (XGBoost) algorithm for time series. The geographic approach provides spatial insights into the disparities and vulnerabilities of hospital supply, exploring variations in resource allocation and access across different regions.
Results
Through the analysis of historical data encompassing pandemic periods, this study aims to offer a comprehensive evaluation framework for assessing the resilience of hospital supply systems in Italy highlighting clear territorial patterns linked both to the administrative dimension of the healthcare system and to factors related to the supply and efficiency.
Discussion
The findings from this research are anticipated to inform strategies to enhance the preparedness and responsiveness of healthcare institutions during future pandemics, ultimately contributing to the development of more robust and adaptive healthcare systems.

Anastasia Sherubneva (p)

Giacomo Pignataro

The impact of the COVID-19 pandemic has varied widely across Russia. This work examines the impact of COVID-19 on changes in the performance of Russian firms in spatial and regional contexts. The following hypotheses were empirically tested: 1) firms located in the regional centre have, on average, larger size; 2) market mechanisms work more perfectly in large cities; 3) the greater the population growth rate in a region, the more revenue is generated by companies there. To test the mentioned hypotheses, we estimated two types of regression models explaining the financial performance of the firm by its individual, spatial and regional characteristics. The first is geographically weighted regression (GWR) model to obtain local regression coefficients for each firm. The second one is a multilevel model to account for two levels of factors (individual and regional). Both models were estimated separately for the pre-crisis period (2019) and the crisis period (2020). In total, the sample included 791 439 firms from the 82 regions of Russia approved by the international community. As the dependent variable the logarithm of revenue (normalised by industry) is used. This normalisation is necessary to take account of sectoral differences: it is clear that the steel industry has, on average, larger enterprises than the catering industry. We distinguish three groups of independent variables: 1) non-spatial variables for firms: logarithms of assets and number of employees (according to the Cobb-Douglas production function), age and financial leverage; 2) spatial variable for each firm: distance of the firm from the nearest large agglomeration (i.e. agglomeration effect); 3) characteristics of the region where the firm is located: severity of quarantine restrictions; level of digitalisation of the region; presence of state borders; level of urbanisation; population growth in the region; median per capita income. Hypotheses 1-2 were confirmed: the firm's revenue does indeed decrease with the distance from the regional centre, with this effect becoming weaker during the crisis; both before and during the crisis, the revenue of firms in densely populated areas was more predictable, suggesting better market mechanisms. Hypothesis 3 was partially confirmed: in 2019, rapid population growth favoured business development, while in 2020 this factor had no effect.