Within-group estimators for unbalanced-panel data regression model of the open unemployment rate data in east kalimantan province

The COVID-19 pandemic has hit hard the Indonesian economy. Many businesses had to close because they could not cover operational costs, and many workers were laid off creating an unemployment crisis. Unemployment causes people's productivity and income to decrease, leading to poverty and other social problems, making it a crucial problem and great concern for the nation. Economic conditions during this pandemic have also provided an unusual pattern in economic data, in which outliers may occur, leading to biased parameter estimation results. For that reason, it is necessary to deal with outliers in research data appropriately. This study aims to find within-group estimators for unbalanced panel data regression model of the Open Unemployment Rate (OUR) in East Kalimantan Province and the factors that influence it. The method used is the within transformation with mean centering and median centering processing methods. The results of this study may provide advice on factors that can increase and decrease the OUR of East Kalimantan Province. The results show that the best model for estimating OUR data in East Kalimantan Province is the within-transformation estimation method using median centering. According to the best model, the Human Development Index (HDI) and Gross Regional Domestic Product (GRDP) are two factors that influence the OUR of East Kalimantan Province (GRDP). © 2023, International Association of Engineers. All rights reserved.

Digital Banking through the Uncertain COVID Period: A Panel Data Study

This research investigates how the uncertainty caused by the COVID-19 pandemic has affected digital banking usage in India. The study is made by utilizing a panel of data consisting of 108 firm-month observations during covid period from 2020 to 2022, with data mainly collected to analyze the impact of COVID-19 uncertainty. Most of the determinants were collected from the RBI data website. The main emphasis of this study is on the utilization of digital banking services in the context of the pandemic, and the research assesses the factors that have influenced this trend, including the number of physical bank branches, the utilization of debit and credit cards at automated teller machines (ATMs) and points of sale (PoS), as well as the level of economic policy uncertainty (EPU). The analysis was conducted using panel regression analysis, a suitable method for handling the error components in the model that are either fixed or random. The findings indicate that the uncertainty caused by the pandemic has had a negative impact on the use of digital banking services. Additionally, the study highlights that the usage of debit and credit cards at PoS has significantly contributed to promoting the progress of digital banking services during the pandemic. Overall, this study provides valuable insights into how digital banking services have evolved during a period of significant uncertainty and disruption. © 2023 by the authors.

Economic Uncertainty and Firms' Capital Structure: Evidence from China

This article assesses the effects of economic uncertainty on the corporate capital structure of Chinese-listed firms using a panel dataset of 1138 firms with A-shares traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange for the period 2006–2020 and fixed-effect regression analysis. Economic uncertainty had a negative influence on Chinese firms' debt ratios, especially for non-state-owned enterprises. Furthermore, firms' leverage decreased on average during the 2008 Great Recession, whereas it increased during the 2018–2019 US–China Trade War and the 2020 COVID-19 pandemic. The findings provide quantitative evidence of the effects of economic uncertainty on the capital structure of firms in a transition economy.

The COVID-19 Pandemic and Chinese Insurance Firms: A Panel Predictability Analysis

This paper examines whether the COVID-19 pandemic predicts Chinese insurance firms' stock excess returns. COVID-19 is proxied using three indices: the stringency index, containment and health indices, and the government support index. We use monthly data from January 2020 to September 2020 on 64 insurance firms. Using a newly developed factor-augmented panel predictability model, we find that COVID-19 is a statistically insignificant predictor of excess returns. Our results are robust to the use of different control predictors such as macro variables, financial indicators and Fama-French factors.

REDUCTION OF POVERTY AND MATERIAL DEPRIVATION IN THE EU COUNTRIES: WHAT MATTERS THE MOST?

Poverty reduction belongs to the long-term priorities of public policy actions in most countries. In 2010, the European Union and its member states aimed to reduce the number of people living at risk of poverty by 2020. However, most EU countries failed to achieve their targets concerning poverty reduction, partly because of the challenges they had to cope with (slow economic recovery after the crisis, migration, COVID-19). In 2022, poverty risks were increasing in the EU countries once again. Therefore, research focused on determinants of poverty can help policymakers to identify the areas in which policy measures will be useful for poverty reduction or at least its stabilisation in the EU countries. The paper introduces an analysis examining five determinants of poverty (related to employment, incomes, education, and social protection), when poverty was understood in terms of incomes as well as material deprivation. The panel regression analysis was done for cross-sectional data covering EU 26 countries and the period 2010–2019. Statistical results revealed the statistically significant relationships between poverty risks (measured with the use of at-risk-of-poverty rate and rate of material deprivation), and employment, work intensity, and income inequality (representing the determinants of poverty). Findings indicated that particularly the policy measures adopted within the employment and labour market policies must be used in the fight against poverty in EU countries. © 2023, Bulgarska Akademiya na Naukite. All rights reserved.

Does active transport lead to improved mood and performance? A panel study of travel changes during the Covid-19 lockdown in Norway

Everyday commuting is seen as a burden and an unwanted necessity for people. Recent studies have challenged this notion and have found that certain aspects of commuting can be positive. In particular, research has shown that active commuting can be an important source of everyday physical activity and a pause between arenas for daily routine. The current study uses the Covid-19 lockdown situation in Norway, and the associated travel restrictions, as a backdrop to study the relationship between active travel and self-reported mood and work performance. In a situation where people are strongly encouraged to take up active mobility forms in place of more passive forms, the often-encountered challenge of self-selection is reduced. A convenience sample was recruited via social media (N = 1319) in May 2020 and completed a total of six follow-up surveys over a period of four months, thus allowing for a panel design as well as a within-subjects comparison. The survey covered topics related to commute mode, experience of travel, current mood, and work performance. Background variables related to personality, general wellbeing as well as sociodemographic measures were also captured. Multivariate models show that those who during this period commute with active modes (walking and cycling) report a higher degree of travel satisfaction than users of passive modes (driving and public transport). Further, active modes are associated with being in a better mood, and with reporting higher work performance. Finally, looking at individuals who over time change travel mode (N = 151), we find that they report improved mood and work performance when travelling with active vs passive modes. The results have implications for policy makers and for employers looking for justification to spend company money on measures to increase active travel. © 2023 The Authors

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China.

The human social and behavioral activities play significant roles in the spread of COVID-19. Social-distancing centered non-pharmaceutical interventions (NPIs) are the best strategies to curb the spread of COVID-19 prior to an effective pharmaceutical or vaccine solution. This study investigates various social-distancing measures' impact on the spread of COVID-19 using advanced global and novel local geospatial techniques. Social distancing measures are acquired through website analysis, document text analysis, and other big data extraction strategies. A spatial panel regression model and a newly proposed geographically weighted panel regression model are applied to investigate the global and local relationships between the spread of COVID-19 and the various social distancing measures. Results from the combined global and local analyses confirm the effectiveness of NPI strategies to curb the spread of COVID-19. While global level strategies allow a nation to implement social distancing measures immediately at the beginning to minimize the impact of the disease, local level strategies fine tune such measures based on different times and places to provide targeted implementation to balance conflicting demands during the pandemic. The local level analysis further suggests that implementing different NPI strategies in different locations might allow us to battle unknown global pandemic more efficiently.

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments.

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 âˆ¼ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020-20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the "post-COVID" era.

Spatiotemporal non-stationarity in green space and stress relationships: Recent evidence from South Korea.

A growing body of literature has linked exposure to "green space" (vegetation-rich areas) and other forms of nature to mental health. Exposure-outcome associations at regional or national scales can overlook local associations that define how geographically distinct populations may experience nature differently. Large-scale results might downplay the importance of lived experiences and heterogeneity of human-nature relationships at local scales. The current study examines three types of vegetative cover and identifies how they are associated with perceived stress in South Korea during and before the COVID-19 pandemic. We find forest cover is consistently negatively associated with perceived stress at nationwide scales. In contrast, grass cover and the normalized difference vegetation index (NDVI) show mixed associations with perceived stress at nationwide scales. Models accounting for spatial and temporal variability demonstrate that associations of forest cover, grass cover, and NDVI with perceived stress varies across the country and the study's four-year time horizon. Local governments may need divergent urban greening strategies for health promotion that respond to their specific sociodemographic and pre-existing environmental conditions.

A sectoral-level analysis of the short- and long-term impacts of the COVID-19 pandemic on China's stock market volatility.

Sampling China's Shenyin & Wanguo Sectoral Indices for 28 industries and 3272 listed firms included in those indices, and using industry- and firm-level daily data up to December 31, 2020, this paper empirically examined the short- and long-run impacts of the COVID-19 pandemic on stock return volatility. The results of the event study and univariate graphic analysis suggested that the market volatilities of the 28 industries were affected by COVID-19 events at various levels and that the events increased the volatility continuously for up to 6 days. The results of the panel data regression models revealed that the COVID-19-related daily new confirmed cases, daily new deaths, and cumulative cured cases were associated with higher volatility for all industries, although the impact levels were small; the daily deaths impacted volatility more than confirmed and cured cases. Finally, positive and significant effects of firm-specific variables such as total assets, turnover ratio and trading volume were recorded, indicating that fundamental aspects of the company and investors' behaviour also made great sense.

Short- and long-term effects of COVID-19 on bicycle sharing usage.

Using panel regression methods, this paper investigates how the COVID-19 pandemic impacted bicycle sharing system (BSS) ridership in Budapest. In particular, the paper aims to separate the effects of mobility and government restrictions on BSS ridership and analyse whether long-term positive effects are observable in this city. Results indicate that both mobility and government stringency measures significantly and positively affected BSS usage, particularly in residential areas and close to public parks. However, after the first wave of the pandemic passed and government measures were partially lifted, BSS ridership declined in line with the elimination of the restrictions. New users often churned after their first trial, and usage frequency dropped to lower levels than before the pandemic. This indicates that BSS was a valuable transportation mode during a pandemic, but a permanent increase in usage was not observed in Budapest despite a considerable price decrease in bicycle fares. The unsatisfactory experiences with this BSS, primarily due to heavy bike frames and solid rubber tires may be the cause of this. Our results prove the benefits of BSS in mitigating a pandemic but call the attention to the need to improve particular system characteristics that may undermine long-term ridership. These characteristics can be different for every BSS; hence, local market research is required. This limits the generalizability of the results.

The Moderating Effect of the Covid 19 on Credit Risk of Islamic and Conventional Bank in Emerging Markets: Evidence from Palestine

Credit risk is one of the largest and most significant risk exposures facing banks. This study aims to empirically measure the impact of credit risk on the profitability of Islamic banks and conventional banks operating in Palestine. The study also aims to show if there is a significant difference in the impact of credit risk on the profitability of Islamic and conventional banks. The interactive effect of the Covid 19 pandemic with the credit risk factors is studied to prove whether the pandemic affects the profitability of both types of banks. The study analyzed the data of 13 banks (11 conventional and two Islamic banks). The sample period extends from 2011 to 2020. Banks’ profitability is measured using return on assets (ROA) and return on equity (ROE). Credit risk variables are measured using the non-performing loan ratio, loan provision to gross loans, and capital adequacy ratio. In addition, a set of macroeconomic and micro-control variables are investigated. Using panel regression analysis, the study finds that credit risk significantly impacts Islamic and conventional banks’ profitability. However, this effect is sensitive to the measure of profitability. While credit risk significantly impacts the ROA, it has no significant impact on the ROE. In addition, the study finds that the impact of credit risk on the profitability of Islamic banks is different from that of conventional banks. In addition, the credit risk that rises during the Covid 19 pandemic has an insignificant impact on the profitability of both types of banks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Modeling And Monitoring Covid-19 Monthly Infected Cases and Deaths

In this paper, the relationships, and trends in the number of COVID-19 infected new cases and the number of deaths due to COVID-19 in all 37 districts of Tamil Nadu state, India, during the period, 3rd July, 2020 to 31st March, 2021 are studied based on a panel regression model. The interesting results obtained in this paper are that even though the data is Panel type, none of the panel regression models are found suitable whereas the Constants Coefficient Model (Pooled Regression Model) is found suitable to study the relationships between number of covid infects and deaths. The average death due to COVID-19 was about 1.6%. © 2022. NSP Natural Sciences Publishing Cor.

DETERMINANTS OF PUBLIC DEBT IN EU COUNTRIES

Due to the Covid-19 pandemic, governments must support their economy to prevent a possible recession which will lead to an increase in public debt. Therefore, it is necessary to know important determinants of public debt. This paper provides an analysis of public debt determinants. The main aim of the article is to identify the impact of specific variables on the level of public debt in EU countries by using econometric methods. The article analyses studies that focus on determinants of public debt, and it defines ten fundamental independent (explanatory) variables. Panel data regression model is used to monitor the impact of these variables on an independent variable – public debt, while it uses data from 1999 to 2019. The model’s results show that the growth of variables, such as current account balance of payments, budget balance, public administration investments, inflation rate, and GDP growth, lead to reducing public debt in EU countries. On the other hand, the increase in variables, such as annual population density change and budget expenditure, leads to public debt growth. The impact of both, unemployment rate and purchasing power parity, on public debt is insignificant based on the study results. © 2022, Czestochowa University of Technology. All rights reserved.

The association between health expenditure, institutions, and economic growth in MENA countries.

Background: Expenditure on health is vital in the development of a country. Furthermore, the current COVID-19 pandemic emphasises the importance of health investments in maintaining a healthier economy worldwide. A substantial amount of empirical research on the relationship between health expenditure and economic growth yields conflicting results. The study intends to investigate the relationship between health spending and economic growth and institutions' role in causing health spending to promote growth. Methods: The study uses longitudinal data to examine the relationship between health spending and economic growth in seven MENA countries from 2000 to 2017. The study uses the Phillips Perron (PP) Fisher chi-square stationarity test, indicating that the data series is not stationary. Following this, we used the Pedroni test for cointegration, and the results show long-run relationships between the variables. Next, Granger causality determines the direction of causality. Finally, panel data methods of panel ordinary least squares (Panel OLS), fully modified OLS (FMOLS), and dynamic OLS (DLOS) supplement the findings. Results: The Pedroni cointegration test (P value<0.0001) indicates that the variables have a long-run cointegrating relationship. On the other hand, the Granger causality test finds no causal relationships between health spending and economic growth. Furthermore, the panel data models show that expenditure on health does not directly contribute to higher economic growth in MENA countries. Conclusion: The findings of this study indicate that health spending does not lead to increased economic growth; this could be due to poor institutional quality. However, for health spending to positively impact economic growth, these investments in health care must be supplemented by other factors, particularly institutions.

“Modelling the effects of capital adequacy, credit losses, and efficiency ratio on return on assets and return on equity of banks during COVID-19 pandemic”

The study aims to determine the impact of Capital Adequacy Ratio, Credit Losses Ratio and Efficiency Ratio on the two significant profitability ratios, namely Return on Assets (ROA) and Return on Equity (ROE), during the pandemic. Panel Data Regression is used to model the effects of Capital Adequacy, Credit Losses and Efficiency Ratio on Return on Assets and Return on Equity of Indian banks. A suitable model has been developed by analyzing the results of the Hausman test and the p-values. It has been found that Capital Adequacy Ratio (CAR) with coefficient value of –0.664, CET1 with coefficient value of 1.83 and efficiency ratio with coefficient value of 1.825 have significantly affected the return on assets as their p-values are less than 0.05. However, the accepted relationship between CAR and ROA, efficiency ratio and ROA were inverse, but their coefficients were significant. The provision for credit losses (PCL) was not affecting the ROA significantly during the pandemic and hence was not considered while framing the model. Again, the dependent variable is the return on equity, except CAR. Other ratios, i.e., CET1, efficiency ratio, and PCL ratio have unacceptable correlations and are even non-significant as their p-values are less than 0.05. © The author(s) 2022. This publication is an open access article.

Spatial Panel Data Modelling of COVID-19 Spreading at District Level in Sarawak, Malaysia

The COVID-19 outbreak was well-controlled in the state of Sarawak, Malaysia in year 2020. A surge in positive cases started in January 2021 and affected all districts including the rural areas which have relatively limited health facilities. Hence, we investigated the spatial patterns of COVID-19 spreading at district level for the first 16 epidemiological weeks of 2021 by spatial autocorrelation analysis and spatial panel regression model. The results show that there exists weak positive spatial autocorrelation of COVID-19 confirmed cases. Having said that, the spatial cluster of high values in both weekly rate of confirmed cases and its spatial lag emerged in the center part of Sarawak in the seventh epidemiological week. Six other districts were identified as high potential for spill overing the disease to its neighbouring districts. Among the six spatial panel regression models constructed, the spatial autoregressive model which includes the spatial lag of COVID-19 confirmed cases, apart from the other two independent variables (recovered and death), is a better-fitting model. This implies that the COVID-19 spreading in the neighbouring districts has a significant effect on the rate of confirmed cases in a particular district of Sarawak. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Panel Cointegration Modelling of COVID-19 Monthly Infected Cases and Deaths

In this paper, the cointegration relationships between COVID-19 new infection cases and the number of deaths due to COVID-19 in all 37 districts of Tamil Nadu state, India, during the period from July 3, 2020 to March 31, 2021 are investigated based on a panel regression Fully Modified Least Squares method and the Granger causality test © 2022. Natural Sciences Publishing Cor

How does Covid-19 affect global equity markets?

This study applies OLS, panel regression and Granger causality test to investigate the impact of the Coronavirus disease 2019 (Covid-19) outbreak on the global equity markets during the early stage of the pandemic. We find that the Covid-19 outbreak has a significant negative impact on the overall equity index return of the eight economies even at 0.1% significance level. Furthermore, the pandemic has a more significant impact on the European countries than on the East Asian economies. The results have three main implications. Firstly, policy makers should react fast to mitigate the impact of a crisis. Secondly, investors should be aware of an outbreak of disease or other risks and adjust their investments accordingly. Furthermore, the Covid-19 outbreak results in a shift of power from the west to the east.

Does the augmentation of monetary and non-monetary factors prerequisite for the improvement of health outcomes? Evidence from the Indian states.

The present study was conducted in Indian states to examine the effect of monetary and non-monetary factors on Infant Mortality Rate (IMR) and Life Expectancy at Birth (LEB) by using the panel regression model. In addition, an attempt was also made to analysis the unequal pattern of health infrastructure and services across states over time with the help of a composite index on health infrastructure and services. It was found that the index value of the best performing state Chhattisgarh is more than fourth six times that of the worst performing state. The study also showed that, despite the higher level of average per capita public health expenditure and moderately better health infrastructure, the COVID 19 induced death rate was high in Punjab, Sikkim, Delhi and Goa. The panel regression results revealed that, an average increase of 1% in the monetary factor, public health expenditure to Gross State Domestic Product ratio (PHEGSDPR), would decrease the average of IMR by about 10%. Moreover, the elasticity of IMR with respect to non-monetary factor, health infrastructure and services per 0.1 million population (HISPLP), was negative and significant. Likewise, the explanatory variables, HISPLP and PHEGSDPR have a positive and significant effect on the LEB.