ABSTRACT
The COVID-19 pandemic is revealing itself to be much more than a health crisis: it is becoming an economic and social one as well. Some segments of the population are more affected than others from the detrimental economic troubles brought about by COVID-19, which are likely going to become worse, and last longer, than the pandemic itself. Inequalities are going to rise, due to loss of wellbeing caused by the measures taken to contrast the spread of the virus. Such measures were directed towards everyone, despite the most vulnerable to the health consequences were also the ones with the smallest role on the economy. However, the economic consequences of the pandemic are especially affecting high-risk groups such as older adults. Making use of the SHARE Corona Survey, we examine the impact of COVID-19 among the older European population, focusing on their ability to make ends meet, loss of employment, and financial support received. Our results show that the ability to get through the month and the likelihood of job loss is positively correlated with increasing age, while aged individuals are less likely to receive financial support. Moreover, we show that such support mostly goes to those who really need it. We also reveal the existence of a social component of poverty. Finally, we highlight some interesting country group differences.
ABSTRACT
Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007-08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015-16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid-19 shock of 2020-21.
ABSTRACT
Sustainable development goals (SDGs) are intended to be attained as a balanced whole. However, significant interactions (the synergies and trade-offs) between the SDGs have caused the need, especially in developing economies, to identify and pursue them in line with their particular developmental needs. The research intends to empirically investigate the relationship between selected UN SDGs and GDP growth rate as a proxy for economic well-being in Saudi Arabia. We also investigate the role of education and training in achieving SDGs in accordance with the Saudi Vision 2030, which places emphasis on the knowledge economy. This research employs multiple regression analysis to explore the relationship between the SDG variables and the GDP. The results show that education and training, gender equity/women's empowerment, greenhouse gas emissions, and decent employment are positively and significantly related to the GDP growth, whereas poverty, hunger, and health appear to be negatively related. The research indicates that education and training can promote economic, socioeconomic, and health goals without compromising environmental goals. Consequently, the Saudi government should invest more in education and training to maximize synergies and minimize tradeoffs between the SDGs. This will help to promote sustainable employment generation, build human capital, improve socioeconomic empowerment through technology, and boost economic growth.
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This study explores the relationship between the business environment, economic growth, and funding sources of Chinese small- and medium-sized enterprises (SMEs) to determine the relevance of the business environment for technological SMEs. The agency theory was used as a theoretical framework to describe how asymmetric information among SMEs and borrowers affects SMEs’ financial decisions as well as China’s investment climate and GDP growth. A binary logistic test was used to assess the financing of SMEs and business development for economic recovery after the pandemic in China. Data from the World Economic Forum and Development Bank were examined. According to the results, funding (e.g., formal and informal) under the banking structure and tax regulation may potentially boost standard credit choices and lower casual credit choices. Consequently, it has demonstrated a considerable impact on GDP growth for technologically small and medium-sized enterprises. This study is the first to examine the asymmetric information and institutional theory regarding funding a café. These findings are essential for business leaders and policymakers concerned with the financial health of small and medium-sized enterprises. Policy implications for important stakeholders are also included in this study.
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This paper examines the effect of policy measures adopted by governments worldwide since the onset of the COVID-19-led pandemic on health and social sectors and the global economy. Taking the case of the United States, India, the Republic of Korea, China, Brazil, Saudi Arabia, and Germany, it comparatively studies the effect of pandemic and related policy measures: on the unemployment rate, GDP growth, per capita income, inequality (measured using the Gini coefficient), accumulated real income, interest rates of Central Bank of the respective countries, Relief packages as per the percentage of GDP, and Cash transfer by number of people covered of the said countries. Developing countries preferred to use direct cash transfers to the beneficiary account rather than providing food baskets as most developed countries provided to their population of the lower economic strata. Except for China, there is a contraction in the GDP of all the countries witnessed in 2020 and is set to rebound in 2021 and 2022. It is witnessed that the impact of the pandemic is uneven in different sectors, regions, and socio-economic groups;in some cases, the repercussions of these measures are likely to continue. © 2022 Universidad Tecnica de Manabi. All Rights Reserved.
ABSTRACT
The Pandemic Covid-19 outbreak cause a negative shock to the world economy, throwing many countries into economic uncertainty, facing an economic recession and if Covid-19 continuously actively spread possibly many countries face an economic depression. This study assessing the economic impact of Covid-19 by analyzing on the three main economic indicators which are GDP growth rate, inflation, and unemployment. This study using estimation proposed by Aditya and Acharyya (Aditya and Acharyya J. Int. Trade Econ. Dev. 22:959–992, 2013), applies generalized methods of moments (GMM) estimators. Data consist of 171 countries of the quarterly data set. The results of the study indicate that the most significant effect of the Covid-19 outbreak is on the GDP growth rate. However, the effect of the Covid-19 outbreak on inflation and unemployment is no exception. The findings suggest that the world economy can recovery or expand if policymakers and government focusing to stimulate investment through fiscal intervention which is likely to give a positive multiplier effect on economic activity. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
It has been two years since the outbreak of Covid 19, and we still live with the pandemic. No one knows when the pandemic will finish and how long it will take until all is back to normal and the global economy fully recovers from the pandemic. Undoubtedly, the Corona crisis has had devastating effects on the global economy. The global GDP decreased by 3.5%, the global FDIs were below $ 1 trillion for the first time in the last 20 years in 2020, falling 35% compared to 2019, and the global trade dropped 5.3% at the same time. The main objective of this paper is to discover the impact of a pandemic on the Turkish economy, or more precisely, on the Turkish GDP growth rate, FDI inflows, and export. To get the most reliable results, a multiple regression analysis has been conducted, using the contemporary economic software EViews 12. World Pandemic Uncertainty Index (WPUI) that was introduced by the International Monetary Fund (IMF) in 2020 was used as a measure of the uncertainty caused by the pandemic. For the robustness of the analysis, World Uncertainty Index (WUI), which measures the overall uncertainty caused by the economic and political factors, was also included. The data about GDP growth rate and export were provided from the World Bank Database, while the data for the FDI inflows from UNCTAD. Different independent variables were used in the research for each dependent variable (GDP growth rate, FDI inflows, and export). The research has shown that Covid 19 pandemic has a significant negative impact on the Turkish GDP growth rate (if we consider a 5% level of significance) and on the Turkish export (if we consider a 10% marginal level of significance). However, the regression analysis has shown that the pandemic has a positive but insignificant impact on FDI inflows to Turkey. The study will be a great benefit for further analysis of the impact of Covid 19 on economies as it is one of the first empirical studies that analyzes the effect of Covid 19 on a specific economy. © 2022 Veljanoska et al.
ABSTRACT
In the efforts to ensure the health of the Australian population during the COVID pandemic, social, economic, and environmental aspects of people's life were impacted. In addressing the pandemic risks, a number of governments prioritized people's health and well-being over GDP growth. The Genuine Progress Indicator (GPI) is used to account for factors that influence well-being. We used the GPI to assess the pandemic's impact on well-being and we examined our results in relation to the GDP. We estimated the GPI for the first 6 months of 2019 and the same period in 2020, during which the first stages of the COVID pandemic and the first nationwide lockdown in Australia took place. We examined two scenarios, in the first we found that in Q1 the GDP growth (1.4%) was accompanied by a significant GPI growth (5.3%), showing a positive relation to the GDP;but in Q2 the significant drop (-6.3%) in the GDP was not followed by the GPI, instead the GPI growth remained almost steady with even a relatively small increase (0.33%), indicating a negative relation to the GDP growth. Whereas in the second scenario, the GPI growths (7.12%) in Q1 and (-2.60%) Q2 were positively related to the GDP growths (4.6%) in Q1 and (−0.25%) Q2.We discuss the reasons for the divergence between the two indicators and one of the limitations of the GPI as a measure of well-being. Lastly, we discuss the behavioural and policy lessons of the lockdown and their relevance to what is proposed by degrowth economists. © 2022 The Author(s)
ABSTRACT
Based on the content of the novel World Uncertainty Index (WUI), this paper examines whether the World Uncertainty Index has the predictability for the U.S. gross domestic product growth rate. Empirical results show that the information of WUI indices is able to predict GDP growth rate, especially the U.S. WUI indices such as USA WUI (frequency) and USA WUI (total number) indices. During the COVID-19 period, we find that all the WUI models can generate stronger forecasting ability for the GDP growth rate. Our paper tries to provide new evidence for predicting the GDP growth rate.
ABSTRACT
Output gap is an important index to analyze the macroeconomic operation situation. In the macroeconomic policy framework, the formulation of many policies depends on evaluating the output gap. According to the impact of COVID-19 on China's economic growth in 2020, this paper aims to explore the future change law of China's output gap. Firstly, China's real GDP growth rate data is calculated according to the original GDP data. Secondly, the potential output and output gap are estimated by H-P filtering method. Finally, the output gap series is brought into the ARMA model for fitting and prediction. To sum up, under the influence of COVID-19, China's actual economic growth level was significantly lower than the potential economic growth in 2020, forming a higher negative output gap. The epidemic's impact on China's actual economic growth will last for four years, and China's output gap will return to a stable state slightly less than zero in 2025. © COPYRIGHT SPIE.
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Stock market is place where financial securities tradingtakes place of public companies. Public company first liststheir shares in the primary market. There are two primarymarket in context of India that is bombaystockexchnage (bse) and national stock exchange(nse). Investors can buy and sell their shares throughsecondary market. Covid-19 changed every event extraordinarily. Due to covid-19 caused "lockdown"everyeconomic sector went down due to no monetary transactionand shut down of every business sector. Indian economy hadnever experienced such a massive breakdown in both demandand supply in its past. The share market first hit the economy downturn at the beginning of the pandemic and the after some month the market increased in increasing order as thecovid-19 decreased.in this study we consider the aspects like gdp, indianeconomy, gold market, stock market-covid 19, stock return, investor behavior and economy downturn. © 2022 Author(s).
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This paper aims to analyze the impact of credit growth rate and political connection on commercial banks’ net interest return (NIM) in Vietnam in 2003–2020. We employ the Bayesian linear regression method through the Gibbs sampling algorithm to overcome the asymptotic, a property that can hinder when using frequentist methods in small-sample contexts. The empirical results indicate that the lending growth rate and political connection have a robust negative effect on NIM. In addition, we also figure out that higher bank funding diversity lowers the spread of commercial banks in Vietnam while higher bank liquidity creation pushes higher NIM. Moreover, the gross domestic product (GDP) growth, the growth of money supply M2 (M2GR) impact positively on NIM. Finally, our study figures out that Covid-19 has adverse impacts on NIM. Our findings are helpful for bank managers and market supervisors to maintain the sustainable growth of the financial market. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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India is suffering from an outbreak of COVID-19. Lockdown rules were enacted, but they were also a significant threat to the economy, mental and environmental health. Continual depreciation is occurring in the Indian rupee. The tourism, aviation, oil, capital and retail markets were seriously affected. Whereas, a unique opportunity has also been offered to India as multinational companies are losing trust with China. COVID-19 has also caused a severe threat to people's physical and mental health. World Health Organization also implemented compulsion of mask-wearing and awareness in local communities. The efforts to combat Coronavirus output a tremendous amount of masks, gloves and personal protective equipment kit waste. After the declaration of the lockdown, the quality of air and water has started to improve and wildlife has sprung back. But all these positive impacts were temporary one. Implementation of proper strategies has the potential to deal with all three aspects. © The Electrochemical Society
ABSTRACT
Internet of things is a progressive terminology as a future era of computing. The covid-19 outbreak that is drawn once in a hundred years’ experience to all the mankind and so as per the saying “necessity is the mother of invention”, nearly all scientific as well as technological fields are battled to develop various verticals like drugs discovery, health care, distant education and so the major challenge of managing industry with modest human resources. To maintain countries’ economic status high, as Indian industrial domain majorly contributes to GDP growth that further needs to be kept running in any predicament. The prolonged lockdown hampered the global economy and hence, this paper focuses on the Industrial IoT, where without human assistance, many key processes can be monitored and executed. As information technology (IT) employees can work from home, industrial processes can also execute various automation tasks using process scheduler. This paper presents the new CatchPro protocol request scheduling algorithm for IIoT which can be a great solution for unmanned process execution with the performance improvement of 2.56%. Paper also presents the methodology and framework for industrial automation using MQTT protocol, where manufacturing units can efficiently work with remote monitoring and automation of processes. This paper targets the three important roles as process execution without in-person process handling, algorithmic execution for scheduling of the industrial tasks, and more efficient execution of IoT protocols for machine-to-machine communication via sensor network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Korea has recorded negative real GDP growth only three times in the last 60 years: the oil crisis in 1980, the financial crisis in 1998, and the COVID-19 crisis in 2020. While the economic recession for the first time in 22 years may be attributed to COVID-19, it is more noteworthy is that the growth rate of the Korean economy has been continuously declining. To find counter measures for the decline in the economic growth rate, it is necessary to analyze the cause of the decline in the growth rate. The factors of economic growth can generally be divided into changes in input factors such as labor and capital and productivity. We analyzed the relative contribution of each input factor from 1982 to 2020. Our result suggests that the contributions of capital and labor to economic growth are decreasing over time, and the contribution of TFP is gradually increasing. This study is employing annual time series data to provide up-to-date estimates of TFP and exploring the determinants of TFP to help detect future growth engines for the long-run sustainable development in Korea. © 2022 IEEE.
ABSTRACT
Stock prices/indices have always played a very crucial role in any economy and in recent times it has also become an important parameter in defining the financial matrix of any country along with conventional measures such as interest rates, exchange rates, GDP growth rates etc. The effects of the COVID-19 pandemic on economies are an ongoing study which includes through various simulations and forecasts. The financial markets have obviously been impacted and its manifestation can be seen in data trends since the first lockdown from March 2020 to June 2021. The uncertainty created by the pandemic has caused fluctuations in macroeconomic variables and has increased volatility in the developed as well as emerging stock markets. In India some of these factors are even mutually dependent and the interaction among them is worth studying. Hence, this paper is an attempt to study the relationship between macroeconomic variables viz. GDP growth rate, exchange rate, etc. and volatility in stock exchange during the first lockdown period (24 May to 31 June 2020) in India. NIFTY 50 has been selected as the stock exchange under this study and the monthly data of all the variables as on 1 May to 31 June 2020 were collected from secondary sources. These data were analysed using linear regression analyses and it was found that there is a strong and significant relationship between the macro-economic variables and volatility of stocks of NIFTY 50 stock exchange even during the pandemic. The impact of stock volatility on Indian economy due to the pandemic was studied on other macro-economic variables based on data collected during the pandemic.
ABSTRACT
COVID-19 struck labor markets around the world, exposing and exacerbating the gender inequalities within the human capital structure. The last, in its turn, jeopardizes the return of the national economies to the growth trajectory undermined by pandemic impact. The authors assume that COVID-19 disproportionately affected the employment rates of women and men, which led to increased gender inequality in the labor market, which, in turn, affected GDP growth rates in the EU. To prove this hypothesis two research questions are discovered: 1) whether there was a different correlation between the number of COVID-19 cases in the EU and indicators of the labor market for women and men;and 2) whether there was a link between the growth of gender inequality in the EU labor market and the GDP dynamics in these countries. The analysis of the correlation between the number of cases of COVID-19 and indicators of the labor market in the EU revealed faster growth of women's unemployment rates compared to men's ones as the COVID-19 incidence unfolded. Multiple linear regression and factor analysis have been used to investigate the influence of gender inequality in the labor market on GDP dynamics. Despite the methodological limitations, the proposed model is both a sound argument and an analytical basis in favor of gender-responsive economic recovery backed by the systematic and consistent gender equality policy of a government.
ABSTRACT
All countries of the European Union (EU) have had their economies impacted by COVID-19 and should focus their efforts on managing the negative impacts on their GDP growth. Since EU countries vary considerably in many criteria, the same policy would not fit all EU countries. This paper analyzes how sustainable economic growth could be maintained in the long run while considering three criteria, including R&D investment, gross value added per employee and country size by population;and which factors could have the highest impacts on economic growth in the recovery process according to supply and demand. Countries were examined according to the mentioned criteria by applying the panel least squares method. The major estimation outputs show the stronger effect of the supply side on economic growth, the higher role of human capital in small EU countries where R&D investment exceeds 3% of GDP, and the critical effect of exports on GDP growth in the large EU countries with the lowest R&D investment. This segment depends the most on smooth exports of goods and service flows and could be the most vulnerable under COVID-19 conditions. Therefore, seeking to keep economic growth on track, EU countries should use different strategies and fiscal measures depending on the most vulnerable factors for their economic growth. In addition, this is the right time to revise values of economic growth, and governments should be more focused on the recovery of their economies on the sustainable development goals (SDGs) agenda.
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Social distancing policies have been criticized for their adverse effect on economies. However, we evidence that while they have a short-run adverse effect, they also have a long-run recovery effect on economic growth. Utilizing quarterly gross domestic product (GDP) growth rate data from OECD member states, we find that the medium-term recovery effect of stringent social distancing policies on economic growth is three times higher the short-term adverse effect. We additionally investigate social distancing measures with sub-components of GDP, as well as the conditioning roles of institutional factors.
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We analyse the economic impact of the economic recession caused by the unprecedented COVID-19 pandemic by estimating the amplitude, duration and scope of influence of the recession. We employ the turning point method to extract the characteristics of the historical recessions since 1980 in 153 countries and use the information to obtain the distribution characteristics of the GDP growth rate in these countries during the current pandemic-induced recession with Monte Carlo simulation. We then make judgment on the scope of influence of this pandemic-induced recession by investigating the co-movement relationship between the historical recessions in the 153 countries. The results show that this pandemic-induced recession is likely to be a severe global recession. The mean of the average simulated Delta GDP of the 153 countries will plunge into a trough at -1.16% in 2020 with a recession amplitude of approximately 4.50% and recover to the pre-crisis level of 3.29% in 2023.