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.

Spatial Autocorrelation Test on the Average Unemployment in Indonesia

According to World Bank statistics in 2019, Indonesia ranked two in the average unemployment rate with 5.28% in South East Asia. Although the unemployment rate can be reduced by an equitable distribution of human resource empowerment and national development, the global pandemic COVID-19 made a major impact on increasing the rate of unemployment. This paper tests the spatial autocorrelation on the average unemployment in Indonesia using Ordinary Least Squares (OLS) and Moran's I. The OLS method was used to examine the effects that affect the unemployment rate using an independent variable. In contrast, the Moran's I used to prove the existence of spatial effect on the level of movement in Indonesia. From the experiment, there are four variables that influence the unemployment rate by using the OLS modeling method. The Moran's I test showed a p-value = 0.006 with α = 0.05. Therefore, there is a spatial autocorrelation between provinces in Indonesia. In addition, the model is tested using the Variance Inflation Factor. The model showed a VIF score ¡10, therefore there is no collinearity and the assumption is fulfilled. The model is also being tested using dwtest, bptest, and Lilliefors test. The result showed p-value = 0.6231 for dwtest, p-value = 0.932 for bptest, and p-value = 0.08438 for Lilliefors test.. © 2022 IEEE.

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.

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.

Decoding COVID-19 Vaccine Hesitancy Using Multiple Regression Analysis with Socioeconomic Values

With the growth and development of COVID-19 and its variants, reaching a level of herd immunity is critically important for national security in public health. To deal with COVID-19, the United States has implemented phased plans to distribute COVID-19 vaccines. As of November 2022, over 80% of Americans had received their first shot to guard against COVID-19, and 68.6% were considered fully vaccinated, according to the dataset provided by CDC. However, a significant number of American people still hesitate to receive a shot of the COVID-19 vaccine. This paper aims to demystify COVID-19 vaccine hesitancy by analyzing various socioeconomic characteristics among individuals and communities, including unemployment rate, age groups, median household income, and education level. A multiple regression modeling and data visualization analysis show patterns with an increasing trend of vaccine hesitancy associated with a lower median household income, a younger age group, and a lower education level, which would help policymakers to make policies accordingly to target vaccine support information and remove this hurdle to end the COVID-19 pandemic effectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Nowcasting the output gap

We propose a way to directly nowcast the output gap using the Beveridge–Nelson decomposition based on a mixed-frequency Bayesian VAR. The mixed-frequency approach produces similar but more timely estimates of the U.S. output gap compared to those based on a quarterly model, the CBO measure of potential, or the HP filter. We find that within-quarter nowcasts for the output gap are more reliable than for output growth, with monthly indicators for a credit risk spread, consumer sentiment, and the unemployment rate providing particularly useful new information about the final estimate of the output gap. An out-of-sample analysis of the COVID-19 crisis anticipates the exceptionally large negative output gap of −8.3% in 2020Q2 before the release of real GDP data for the quarter, with both conditional and scenario nowcasts tracking a dramatic decline in the output gap given the April data. © 2022 The Authors

Note: ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

Policymakers often make decisions based on GDP, unemployment rate, industrial output, etc. The primary methods to obtain or estimate such information are resource-intensive. In order to make timely and well-informed decisions, it is imperative to come up with proxies for these parameters, which can be sampled quickly and efficiently, especially during disruptive events like the COVID-19 pandemic. We explore the use of remotely sensed data for this task. The data has become cheaper to collect than surveys and can be available in real-time. In this work, we present Regional GDP-NightLight (ReGNL), a neural network trained to predict GDP given the nightlights data and geographical coordinates. Taking the case of 50 US states, we find that ReGNL is disruption-agnostic and can predict the GDP for both normal years (2019) and years with a disruptive event (2020). ReGNL outperforms time-series ARIMA methods for prediction, even during the pandemic. © 2022 ACM.

COVID-19 Influence: A General Analysis using Machine Learning Methods

In this paper, we mainly investigate how COVID-19 affects society in multiple ways, such as infected, death, and recovered population, air and ground transportation, and unemployment rate in specific countries. This is important to study since it can help the government create a specific policy to minimize the negative influence of COVID-19 on our society as a whole. Recent works focus on the prevention of COVID-19 and the efficiency of specific immunization. However, the influence of COVID-19 in a specific area of society, such as transportation, has not been paid enough attention to. We first used K-means to find similarities between countries, then we grouped all nations by their continents and used bar plots to visualize how each continent performs. Then, we used matplotlib to visualize the influence of pandemics on air transportation and ground transportation of the U.S. Finally. We create an interactive visualization that investigates the unemployment rate in specific countries, such as China, the United States, Japan, the United Kingdom, and Canada, during the pandemic period. Our methods show all those continents, including Africa, Asia, Australia/Oceania, Europe, North America, and South America, can be divided into five clusters according to the test percentage, and South America represents the highest death percentage. Europe has both the highest test and infected percentage. For both air transportation and inbound ground transportation, revenue and numbers of transportation dropped dramatically at the beginning of April 2020, which is the outbreak of COVID-19 in the U.S. Similarly, the unemployment rate for North America suddenly boost. © 2021 IEEE.

Analysis of the Impact of COVID-19 on the U.S. Economy Based on Big Data Processing and Experimental Analyses

With the outbreak of COVID-19, the world has experienced unprecedented crises especially in economy. The United States is more seriously affected. In order to more clearly show the current situation of the U.S. economy affected by the epidemic from the data level, the author completes the paper by using the research method of big data processing and experimental analyses to show that how Coronavirus influences economy, that is, the impact on GDP and the exchange of volume of stock shares and the impact on unemployment rate which can be shown in specific data. The author also discusses the degree of influence on different industries. The result shows that COVID-19 has seriously affected the overall economy of America. The specific data performance is the decline of GDP (about 5%) and the rise of unemployment (about 15%). The stock price has dropped significantly, even affecting the overall stock trading volume (declined by 55%). The purpose of this paper is to clearly show the specific influence on the US economy from the data level. The result can provide a specific data reference for the formulation of American economic policy in the next few years and provide a data basis for the study of the economic situation after the epidemic in the United States. © 2021 IEEE.

The Sixty-Two Counties of New York State

Well over half the population of New York State (NYS) lives in New York City (NYC) and three abutting counties (Westchester, Nassau, Suffolk). Socio-economic (SE) measures such as median household income, percent adults with college or higher degrees, and percent of population with various “race”/ethnic labels (white, Black, Latinx) differ significantly between the 31 rural and 31 urban counties. NYS has a functional SE system. For example: counties with high median income have high percent of adults with college degrees, lower percent white population, higher poverty rate, higher population, and higher population density. The COVID-19 pandemic led to much higher unemployment rates in 2020 in all counties than in 2019, with the NYC counties more than doubling their rates. The SE profile of counties that voted for Trump (Donald Trump) in the 2020 election was consistent with the national pattern: low educational attainment, high percent white population, high percent vote in rural counties. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The impact of the covid-19 pandemic on the unemployment rate in latvia

The economic and labour market consequences of Covid-19 were immediate, significant and lasting, with long-term negative effects on global economic development and activity in general. The global virus pandemic is having a profound effect on the labour market with a sharp rise in the number of unemployed, which are at high risk of becoming long-term unemployed as the pandemic drags on, some of whom will not return to the labour market. The outbreak of Covid-19 and the measures taken to combat it are leading to a rapid demand for unemployment benefits, but a large proportion of the unemployed registered with employment agencies are not actively involved in job search or do not want to establish an employment relationship at all. It is important to track and describe the major changes in the labour market around the world, but the restrictions that are being put in place to combat Covid-19 pose a huge obstacle to conventional data collection approaches and activities. Within the framework of this study, the unemployed in Latvia were interviewed in order to find out and evaluate the impact of the Covid-19 pandemic on the unemployment rate and the unemployed in Latvia. The aim of the article is to find out problems caused by the Covid-19 pandemic, which are related to the unemployed and which need to be solved in Latvia, and possibly also in other European and other countries of the world. © 2021, Latvia University of Life Sciences and Technologies. All rights reserved.

COVID-19 and homeless people

Extreme poverty and homelessness are not novel topics in the United States. Homelessness appeared to be misstated as merely a problem of being without s shelter, while it is more properly viewed as the most aggravated state of a more prevalent problem. This study discusses the relationship between political partisanships as well as U.S. economic policies among people experiencing homelessness during the COVID-19 pandemic. The statistics and U.S. census are used to analyze the research. During the process, it is found that political policy or partisanship changes would give rise to an adjustment to public attitude toward homeless people;changes in economic policy would directly affect the number of homeless people;the medical system is also one crucial factor that influences the homelessness. More than 80% of the homeless population gathers in the urban area, and the number is still increasing. Due to the outbreak of the COVID-19, the economy has been in a downturn, bringing a higher unemployment rate that causes the increase of vagabonds in the country. Furthermore, the US Healthcare System is lacking preparation to deal with the pandemic. The result of this study is monumental and can be seen as a reference for the U.S. government to improve their policy, eventually decrease and well manage the number of infected homeless number. Moreover, further detailed suggestions on the topic are included in the study. © 2021 ACM.