UNEMPLOYMENT BEHAVIOUR IN THE COVID-19 PANDEMIC: EVIDENCE FROM DEVELOPING COUNTRIES

This paper explores the impacts of the COVID-19 pandemic, corruption and other determinants on unemployment in developing countries using panel dataset for 89 developing countries from January to December 2020. The proposed unemployment model is estimated utilising a newly formulated conceptual framework to examine whether COVID-19 pandemic, corruption, and human capital, play a moderating role on unemployment determination in our selected developing countries. The model is estimated using the dynamic panel system generalised method of moments (GMM) estimator. Apart from output, inflation and human capital, our results show that the COVID-19 pandemic and corruption are major variables in explaining the unemployment rate for our sampled countries. Furthermore, and more notably, we find evidence that the COVID-19 pandemic and corruption appear to significantly restrain and alter the role of outputs and human capital in impacting unemployment. Therefore, the detrimental effects of the COVID-19 pandemic and corruption on the economies and labour markets of countries examined should not be under-estimated. Additionally, findings show that, while policy initiatives to combat the COVID-19 pandemic are critical, strengthening anti-corruption regulations would further improve the efficiency of any attempt to reduce unemployment rates associated with the COVID-19 period. © 2023, Universiti Malaysia Sarawak. All rights reserved.

Exploring outliers in global economic dataset having the impact of COVID-19 pandemic

Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. Similarly, in economic domain, it can be applied to analyse many unexpected events to harvest new knowledge like sudden crash of stock market, mismatch between country's per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest. These situations can arise due to several reasons, out of which the present COVID-19 pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them. Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently. Copyright © 2023 Inderscience Enterprises Ltd.

COVID-19 and Gender Equality: An Abnormal Normality

"A woman has a right to survival, protection, participation, and development." Although much emphasis is being laid worldwide on gender equality, yet deep-rooted discrimination still prevails in society. Forever since, women have been victims of social conventions and are constantly being pulled down in personal, social, economic and political scenarios. History has warned us numerous times that crisis in health and economy can create huge force to put women's solicitude to the back stage. Today, the COVID-19 pandemic is worsening the most pervasive and insidious inequities that women everywhere have been facing in their lives. This paper discusses the different cultural barriers to women's participation and success in a global scenario, and how the pandemic has aggravated the conditions. It begins by discussing the relationship between economic development and female employment and thereafter argues that the traditional cultural norms, which vary across societies, help explain the large differences in female employment universally. The paper examines several gender-based social norms and how they constrain women's development and participation. The arguments are compared and collated with the COVID-19 pandemic situation to efficiently explain the seriousness of the scenario. In conclusion, the paper examples change that must be incorporated into policies that are aimed at overcoming these cultural barriers to female employment and the volume of the impact that they will have in the future.

Analysis of the unemployment gender gap in Colombia 2001-2021: A SARIMA-GARCH model approach

Reducing the unemployment gender gap is seen as an indicator of women's empowerment capacity for the equitable growth of the country's economy. At the regional level, Colombia exhibits one of the highest unemployment gaps, despite the efforts made to close them. The objective of this study is to model the evolution of the unemployment gender gap in Colombia during the period 2001:01 to 2021:06, to forecast its behavior, and determine its volatility. For this purpose, a Seasonal Autoregressive Integrated Moving Averages (SARIMA) and Generalized AutoRegressive Conditional Heteroskedasticity model (GARCH) were fitted. The results indicate that, although the gender gap had been slightly declining in the last two decades, it was adversely affected by the Covid-19 pandemic, causing the gap to increase again. On the other hand, there is an increase in the volatility of the series, making it more vulnerable to economic and seasonal cycles. Finally, it is forecast that the gap will tend to decrease in the following months, however, it will increase again in December due to the seasonal component.

Epidemic–economic complexity of COVID-19 policies across skill groups and geographies

The COVID-19 pandemic has threatened public health and socio-economic activities across societal groups and geographies. We analyse the complex interplay between epidemic and economic factors using a structural panel vector autoregressive (PVAR) approach for Danish municipalities. Findings indicate that the pandemic shock and associated public health interventions led to significant increases in unemployment rates. Wage compensations reduce regional unemployment through both a direct local effect and indirect spatial spillovers. Decomposing the unemployment rate by skill, we find that the response to an increase in wage compensations is only significant for low-skilled persons and that it is larger in urban compared with rural settings. © 2023 Regional Studies Association.

Prediction Models and Testing of Resilience in RegionsCovid19 Economic Impact in USA Counties Case Study

A significant amount of research has been conducted regarding the resilience of the regions and the factors that contribute to allow them to face challenges, crises, or disasters. The rise of promising sectors like Machine learning (ML) and Artificial Intelligence (AI) can enhance this research using computing power in regional economic, social, and environmental data analysis to find patterns and create prediction models. Through Machine Learning, the following research introduces the use of models that can predict the performance of a region in disasters. A case study of the performance of USA Counties during the Covid19 first wave period of the pandemic and the related restrictions that were applied by the authorities was used in order to reveal the obvious or hidden parameters and factors that affected their resilience, in particular their economic response, and other interesting patterns between all the involved attributes. This paper aims to contribute to a methodology and to offer useful guidelines in how regional factors can be translated and processed by data and ML/AI tools and techniques. The proposed models were evaluated on their ability to predict the economic performance of each county and in particular the difference of its unemployment rate between March and June of 2020. The former is based on several economic, social, and environmental data-up to that point in time-using classifiers like neural networks and decision trees. A comparison of the different models' execution was performed, and the best models were further analyzed and presented. Further execution results that identified patterns and connections between regional data and attributes are also presented. The main results of this research are i) a methodological framework of how regional status can be translated into digital models and ii) related examples of predictive models in a real case. An effort was also made to decode the results in terms of regional science to produce useful and meaningful conclusions, thus a decision tree is also presented to demonstrate how these models can be interpreted. Finally, the connection between this work and the strong current trend of regional and urban digitalization towards sustainability is established. © 2023 by the authors. Licensee: REGION-The Journal of ERSA,.

Permanent layoff and consumer credit card loss forecasting

PurposeThe unemployment rate (UR) is the leading macroeconomic indicator used in the credit card loss forecasting. COVID-19 pandemic has caused an unprecedented level of volatility in the labor market variables, leading to new challenges to use UR in the credit risk modeling framework. This paper examines the dynamic relationship between the credit card charge-off rate and the unemployment rate over time.Design/methodology/approachThis study uses quarterly observations of charge-off rates on credit card loans of all commercial banks from Q1 1990 to Q4 2020. Univariate, multivariable, machine learning, and regime-switching time series modeling are employed in this research.FindingsThe authors decompose UR into two components – temporary and permanent UR. The authors find the spike in UR during COVID-19 is mainly attributed to the surge in temporary layoffs. More importantly, the authors find that the credit card charge-off rate is primarily driven by permanent UR while temporary UR has little predictive power. During recessions, permanent UR seems to be a stronger indicator than total UR. This research highlights the importance of using permanent UR for credit risk modeling.Originality/valueThe findings in the research can be applied to the credit card loss forecasting and CECL reserve models. In addition, this research also has implications for banks, macroeconomic data vendors, regulators, and policymakers.

LexDeep: Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions during COVID-19

The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product. Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media. However, limited research has been conducted in this domain using the LexDeep approach. This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic. First, tweet data were collected using Twint with relevant keywords before (9 March 2019 to 17 March 2020) and during (18 March 2020 to 21 August 2021) the pandemic. Subsequently, the tweets were annotated using VADER (lexicon-based) and fed into deep learning classifiers, and experiments were conducted using several embeddings, namely simple embedding, Global Vectors, and Word2Vec, to classify the sentiments expressed in the tweets. The performance of each LexDeep model was evaluated and compared with that of a support vector machine (SVM). Finally, the unemployment rates before and during COVID-19 were analysed to gain insights into the differences in unemployment percentages through social media input and analysis. The results demonstrated that all LexDeep models with simple embedding outperformed the SVM. This confirmed the superiority of the proposed LexDeep model over a classical machine learning classifier in performing sentiment analysis tasks for domain-specific sentiments. In terms of the risk of income loss, the unemployment issue is highly politicised on both the regional and global scales;thus, if a country cannot combat this issue, the global economy will also be affected. Future research should develop a utility maximisation algorithm for household welfare evaluation, given the percentage risk of income loss owing to COVID-19. © 2023 Tech Science Press. All rights reserved.

Japan's unemployment rate hike amid the COVID-19 pandemic – why was it so mild?

This essay is a flash report on the impact of the COVID-19 pandemic on Japan's labour market in the fiscal year 2020 wherein Japan's unemployment rate increase was much milder than in other G7 countries. How was such a favourable outcome achieved? To answer this question, this essay analyses primary statistics to show that the main contributing factors were the swift cut of labour hours and the rapid increase in coronavirus-related paid leave. The latter factor is due primarily to an expanded labour policy measure. Generous policy measures to support corporate finance were also effective in maintaining general financial stability and preventing an increase in failures in the fiscal year 2020.

COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for developed and developing countries of Asia.

Unemployment remains a serious issue for both developed and developing countries and a driving force to lose their monetary and financial impact. The estimation of the unemployment rate has drawn researchers' attention in recent years. This investigation's key objective is to inquire about the impact of COVID-19 on the unemployment rate in selected, developed and developing countries of Asia. For experts and policymakers, effective prediction of the unemployment rate is an influential test that assumes an important role in planning the monetary and financial development of a country. Numerous researchers have recently utilized conventional analysis tools for unemployment rate prediction. Notably, unemployment data sets are nonstationary. Therefore, modeling these time series by conventional methods can produce an arbitrary mistake. To overcome the accuracy problem associated with conventional approaches, this investigation assumes intelligent-based prediction approaches to deal with the unemployment data and to predict the unemployment rate for the upcoming years more precisely. These intelligent-based unemployment rate strategies will force their implications by repeating diversity in the unemployment rate. For illustration purposes, unemployment data sets of five advanced and five developing countries of Asia, essentially Japan, South Korea, Malaysia, Singapore, Hong Kong, and five agricultural countries (i.e., Pakistan, China, India, Bangladesh and Indonesia) are selected. The hybrid ARIMA-ARNN model performed well among all hybrid models for advanced countries of Asia, while the hybrid ARIMA-ANN outperformed for developing countries aside from China, and hybrid ARIMA-SVM performed well for China. Furthermore, for future unemployment rate prediction, these selected models are utilized. The result displays that in developing countries of Asia, the unemployment rate will be three times higher as compared to advanced countries in the coming years, and it will take double the time to address the impacts of Coronavirus in developing countries than in developed countries of Asia.

The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe

Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.

IT shields: Technology adoption and economic resilience during the COVID-19 pandemic.

We study the labor market effects of information technology (IT) during the onset of the COVID-19 pandemic, using data on IT adoption covering almost three million establishments in the US. We find that in areas where firms had adopted more IT before the pandemic, the unemployment rate rose less in response to social distancing. IT shields all individuals, regardless of gender and race, except those with the lowest educational attainment. Instrumental variable estimates-leveraging historical routine employment share as a booster of IT adoption- confirm IT had a causal impact on fostering labor markets' resilience. Additional evidence suggests this shielding effect is due to the easiness of working-from-home and to stronger creation of digital jobs in high IT areas.

The long-term impact of the COVID-19 unemployment shock on life expectancy and mortality rates.

We adopt a time series approach to investigate the historical relation between unemployment, life expectancy, and mortality rates. We fit Vector-autoregressions for the overall US population and for groups identified based on gender and race. We use our results to assess the long-run effects of the COVID-19 economic recession on mortality and life expectancy. We estimate the size of the COVID-19-related unemployment shock to be between 2 and 5 times larger than the typical unemployment shock, depending on race and gender, resulting in a significant increase in mortality rates and drop in life expectancy. We also predict that the shock will disproportionately affect African-Americans and women, over a short horizon, while the effects for white men will unfold over longer horizons. These figures translate in more than 0.8 million additional deaths over the next 15 years.

Nowcasting unemployment rate during the COVID-19 pandemic using Twitter data: The case of South Africa.

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

Estimation of the unemployment rate in Turkey: A comparison of the ARIMA and machine learning models including Covid-19 pandemic periods.

The article focuses on analyzing the robustness of Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in unemployment rate estimation. In this context, a stochastic trend in the unemployment rate was determined by using monthly data in Turkey. The oil price, real exchange rate, interest rate and unemployment rate variables are imported into the ARIMA and ANN models with 176 data samples for the period of 01.01.2008-31.08.2022. The results of the conventional linear ARIMA and nonlinear ANN regressor models are compared. The comparison results show that the ARMA (2,1) model is the most suitable model for the unemployment rate estimation. This conclusion was reached based on ARMA (2,1) and ANN's RMSE, MAE, MAPE and R2 parameters. From the results of the specified criteria, it was found that both models gave results close to the actual unemployment rate however ARMA (2,1) was the more appropriate model for the current data set. The actual unemployment data and the estimated values are also given verifying the better modeling of the developed ARMA (2,1) model. In addition, there are meaningful relationships between month variables and the employment rate. This result supports that the unemployment possesses chronic reasons in Turkey. On the other side, the unemployment rate forecasting error of the ARMA (2,1) is higher than the ANN model for the 2020-2021 period during the intense pandemic. This result is important because it shows that during the times of the economic uncertainty caused by the Covid-19 pandemic, forecasts employing the neural network model is observed to have lower errors than the results of autoregressive moving average model. Therefore, under an economic uncertainty, it is shown that modeling the unemployment rate using artificial neural network provides novel insights for economic forecasting.

Single‐month unemployment rate estimates for the Brazilian Labour Force Survey using state‐space models

The Brazilian Labour Force Survey publishes monthly national indicators based on 3-month rolling data. This paper presents state-space models to produce state-level single-month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS-COV-2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state-space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation.

Verification of Okun's Law on Russian Data

The purpose of the study is to assess the empirical relationship between economic growth and unemployment in the Russian economy. The research methodology is based on an econometric analysis of time series representing data on unemployment and economic growth to identify an empirical relationship between these variables. In the article author continued the work on identifying the relationship between the unemployment rate and GDP in Russia based on empirical data. Based on the results of the optimal length model, the long-term Okun coefficient describing the relationship between GDP and unemployment is calculated. As a result of the empirical assessment, the Okun coefficient was obtained equal to 0.87, which is consistent with the previous studies based on the data of the Russian economy. The discrepancies can be explained by the pandemic factor in 2020. It is concluded that the value of the long-term Okun coefficient confirms the stable relationship between the GDP and the unemployment rate. However, its value for Russia is somewhat inferior to estimates for most developed countries and is comparable to indicators for emerging market countries. The results of the study can be used in the construction of short-term forecasts of the response of unemployment changes to fluctations in GDP, as well as in the development of macroeconomic policy measures in Russia as a whole. © Zaitsev Yu.K., 2022.

Permanent layoff and consumer credit card loss forecasting

Purpose - The unemployment rate (UR) is the leading macroeconomic indicator used in the credit card loss forecasting. COVID-19 pandemic has caused an unprecedented level of volatility in the labor market variables, leading to new challenges to use UR in the credit risk modeling framework. This paper examines the dynamic relationship between the credit card charge-off rate and the unemployment rate over time. Design/methodology/approach - This study uses quarterly observations of charge-off rates on credit card loans of all commercial banks from Q1 1990 to Q4 2020. Univariate, multivariable, machine learning, and regime-switching time series modeling are employed in this research. Findings - The authors decompose UR into two components - temporary and permanent UR. The authors find the spike in UR during COVID-19 is mainly attributed to the surge in temporary layoffs. More importantly, the authors find that the credit card charge-off rate is primarily driven by permanent UR while temporary UR has little predictive power. During recessions, permanent UR seems to be a stronger indicator than total UR. This research highlights the importance of using permanent UR for credit risk modeling. Originality/value - The findings in the research can be applied to the credit card loss forecasting and CECL reserve models. In addition, this research also has implications for banks, macroeconomic data vendors, regulators, and policymakers.

Transitioning Economic Statuses of Appalachian Counties during COVID-19.

In the past decade, the Appalachian economy in the United States was scarcely discussed in the literature. No studies were devoted to local economic development after the outbreak of the Coronavirus Disease in 2019. This paper fills the literature gap by empirically examining how the Appalachian economy transitioned under the influence of the pandemic. Using county-level data from the Appalachian Regional Commission between 2019 and 2022, the study investigates how the Appalachian economy regressed during the pandemic. Transitioning economy indices were calculated for 420 local counties by comparing their composite index values before and after the outbreak of the pandemic. Regressions were run to estimate the influences of the unemployment rate, per capita income, and the poverty rate. During the pandemic, the unemployment rate consistently had the largest impact on the Appalachian counties' composite index value and the least effect on the poverty rate. The results suggest that the most effective strategy is for the government to reduce the local unemployment rate to improve the economic ranking. Supplementary Information: The online version contains supplementary material available at 10.1007/s11293-022-09749-2.

Analytical Toolkit in Terms of Increasing Competitiveness under Unstable Economic Conditions

The turbulence of the business environment creates special conditions for business. In particular, challenges in the context of business competitiveness are growing under unstable conditions at the micro and macro level. The aim of the paper is to determine the role of analytical tools in increasing the level of competitiveness of Ukrainian companies under unstable business conditions. It is emphasized that under the influence of agile global socio-economic processes, companies face the problem of maintaining the level of competitiveness through the use of outdated analytical tools. Attention is drawn to the fact that in Ukraine the problem of using analytical tools for that purpose is exacer-bated by protracted socio-economic and financial-economic crises, the aftermath of the COVID-19 pandemic, active hostilities, and humanitarian crisis caused by war. It is emphasized that Ukrainian companies are forced to look for the latest approaches to the development and application of analytical tools in terms of ensuring competitiveness. In particular, the special focus of Ukrainian business is aimed at building an integrated system of competitiveness management, covering all aspects of economic activity that are directly or indirectly related to the creation of competitive advantages. Analysis of macro-level indicators is performed in the context of determining the state of factors influencing Ukrainian business competitiveness. The dynamics of unemployment, consumer prices, and retail trade turnover in Ukraine are proposed as key indicators of the state of the national economy and prospects for its development, which in turn indicate the state of competitiveness. The analysis of the state of competitiveness in Ukraine on the basis of the Index of Economic Freedom was carried out separately. Emphasis is placed on urgent challenges for Ukrainian enterprises in the context of the difficult state of competitiveness in comparison with developed and developing countries in the European region. It is proposed to use the unemployment rate, consumer price index, and the dynamics of retail trade as the main drivers of competitiveness at the macro level. It is emphasized that there is a need to integrate macro-and micro-level indicators into the Balanced Scorecard analytical tool in order to make management decisions regarding business competitiveness.