Delayed Employment Among College Students in Guangdong – Taking Shenzhen University as an Example

Changes in social structure and macro-environment have gradually made “delayed employment” a key area of social concern. This paper assumes the college students from Shenzhen University as research subjects, conducts a special research on delayed employment, explores the employment needs and status quo of college students, analyzes the causes and results of this phenomenon, as well as provides students with a more comprehensive and accurate understanding of employment. This paper also aims to increase the employment rate of graduates, enable reasonable allocation and management of human resources, as well as improve the quality of employment in the society.

A Snapshot of the Crisis of COVID-19: Estimate of the Employment Impact Due to Lockdowns in Cagayan de Oro City, Philippines

When coronavirus disease 2019 (COVID-19) became a national health crisis, the local government of the Cagayan de Oro City (CDOC) did not implement total lockdown. The COVID-19 Adjustment Measure Program adopted by the local government probably affected the April 2020 Labor Force Survey that showed that Region 10 posted an employment rate of 88.9%, which is higher than the national average of 82.3% (Department of Labor and Employment, Region Office No. X (DOLE-X). NorMin secures highest employment rate amid COVID 19. 2020. Available from: https://pia.gov.ph/news/articles/1044898 [Accessed 9th May 2021]). Despite the regional figure being 6.6 percentage points higher than the national one, there is a decrease in employed persons by around 400,000 from 2.302 million persons employed in April 2019 to 1.883 million in April 2020 (Department of Labor and Employment, Region Office No. X (DOLE-X). NorMin secures highest employment rate amid COVID 19. 2020. Available from: https://pia.gov.ph/news/articles/1044898 [Accessed 9th May 2021]). Hence, the study determines the effect of COVID-19 protective measures implemented by the government on the economy of CDOC. Using the barangay-level and selected sectoral-level data on business registration, and employment data between 2010 and 2019, the study estimates that one-week lockdown means a [Formula: see text] 1,825 loss of income for a minimum-wage employee. One-month lockdown costs [Formula: see text]7,300 foregone income, while one-quarter lockdown (or a half of six months) is equivalent to [Formula: see text]21,900 income loss. We recommend 10 policy interventions, but the government should also think big and invest its resources into programs that create a multiplier effect on the economy. Multipliers are interventions that create ripples or positive impacts on other sectors and/or economic participants.

Forecasting Automobile Sales in Turkey with Artificial Neural Networks

This study aims to reveal significant factors which affect automobile sales and estimate the automobile sales in Turkey by using Artificial Neural Network (ANN), ARIMA, and time series decomposition techniques. The forecasting model includes automobile sales, automobile price, Euro and Dollar exchange rate, employment rate, consumer confidence index, oil prices and industrial production confidence index, the probability of buying an automobile, female employment rate, general economic situation, the expectation of general economic situation, financial status of households, expectation of financial status of households. According to the regression results, changes in Dollar exchange rate, the expectation of financial status of households, seasonally adjusted industrial production index, logarithmic form of automobile sales before-one-month which have a significant effect on automobile sales, are found to be the significant variables. The results show that ANN has a better estimation performance with MAPE=1.18% and RMSE=782 values than ARIMA and time series decomposition techniques.

Outsourcing and the strategy of organizations in Mexico

Outsourcing has been a very commonly used strategy to increase productivity in a variety of organizations. In Mexico, it can be said that it has had a positive impact on employment rate. Nevertheless, there is a “dark side” in this strategy, since it has been used as a way to avoid direct hiring of employees and to evade fiscal and social security regulations. This document aims to analyze outsourcing from a regulatory and contractual approach, as well as its role as a way to increase the employment rate, and from a managerial perspective. As a result of this analysis, it was found that in our country outsourcing has been a successfully used strategy by many organizations of different sizes, but at the same time, the lack of an appropriate legislation has originated a series of unethical practices. In Mexico, in 2021 a new legal framework that tries to balance the relationship between firms and employees was approved. This paper ends with a reflection of the possible impacts of this new legislation.

Estimating Vat Refunds the Case of South Africa

This study intends to estimate VAT refund levels in South Africa in an ideal situation where there are well-equipped, incorruptible officials and a proper VAT system is in place. Understanding the dynamics behind the behaviour of VAT and its main drivers is crucial and could have a huge benefit to the country’s economy with regards to closing the tax gap related to this tax type. Using the data from various sources (VAT refunds and some macroeconomic variables), a Vector Autoregression (VAR) model was used to estimate the level of VAT refunds in South Africa. The model estimates VAT refunds for the period 2021/22 to be R242.7 billion, while the VAT refunds forecast for the period 2022/23 and 2023/24 amounts to R254.6 billion and R267.3 billion, respectively. Furthermore, VAT refunds contribute on average 17.5% to the total tax for the forecast period of 2021/22-2023/24. The study also indicates that the growth in VAT refunds is influenced by the growth in domestic VAT collections, increasing employment rate and the growth in both agriculture and construction GDP. The estimated level of VAT refunds can serve as an important consideration in the national budgeting processes in South Africa. Adequate provisions can be made to enable proper planning and distributions to government departments. To our knowledge, this study is the first of its kind for South Africa. In summary, the South African tax authority should not deviate from the primary goal of building sound VAT systems based on improved voluntary compliance through effective systems of self-assessment

Prediction of College Employment Rate Based on Big Data Analysis

This paper uses big data technology to predict the employment rate of colleges and universities. In this paper, combined with the current rental price, daily life consumption, and college students’ personal interests and hobbies consumption and other indicators, the individual is simulated by big data, and the individual is associated by using the AI-driven edge fog computing service optimization algorithm to form a cluster, so as to realize the prediction from element to neural network cluster by using edge computing. In addition, this paper takes the employment data of colleges and universities in Hunan province from June 2020 to May 2021 as the research sample to test the prediction model and makes a comparative analysis with the CNN model and LSTM model. The edge fog computing model in this paper has more analytical indexes as tuples compared to the CNN model, so the results show that the prediction accuracy can reach 83.25%. In this case, there is little difference between the two models of data processing and predictive efficiency. Compared with the LSTM based classification prediction model, this model is edge computing, which greatly improves the data quality of model and data parameters, and the calculation efficiency can be increased by 45%–65%. Therefore, the use of big data technology can provide a reference for the research direction of higher education.

Prediction of College Students’ Employment Rate Based on Gray System

College students’ employment is affected by many factors such as economy and policy, which makes the prediction error of college students’ employment rate large. In order to solve this problem, a prediction method of college students’ employment rate based on the gray system is designed. Firstly, it analyzes the current research status of college students’ employment rate prediction, finds out the causes of errors, then collects the historical data of college students’ employment rate, fits the change characteristics of college students’ employment rate through the gray system, and establishes the prediction model of college students’ employment rate. Finally, the simulation test is realized by using the employment rate data of college students. The results show that the gray system can reflect the change characteristics of college students’ employment rate and obtain high-precision college students’ employment rate prediction results. The prediction error is less than that of other college students’ employment rate prediction methods. We achieved an average accuracy of 95.22% as compared to 92.3% and 87.7% of other proposed systems. The prediction results can provide some reference information for the university employment management department.

Exploring the Impact of Electronic Commerce on Employment Rate: Panel Data Evidence from European Union Countries

The objective of this study is to explore the impact of electronic commerce on employment rate for a sample covering the whole 27 Member States of the European Union (EU-27), from 2010 to 2019. Moreover, this research explores the clusters of nations with reference to electronic commerce adoption and employment rate dynamics. The outcomes of cluster analysis show that Western Europe reveals the most developed e-commerce marketplace in EU-27, shown by Internet accessibility and high penetration rate of digital tools, and the lowest figures are registered in the Eastern part of Europe. Furthermore, the empirical findings of the panel data fixed-effects and the generalized least squares regressions suggest that electronic commerce influences employment rate positively. By including country-level control variables (real GDP growth rate, research and development expenditure, employed ICT specialists, enterprises with Internet access), the outcomes reveal that one percentage change in enterprises’ total turnover from e-commerce sales, enterprises’ turnover from web sales, and enterprises with e-commerce sales of at least 1% turnover will increase employment rate by 0.205, 0.258, and 0.350 percentage points. Furthermore, the econometric evidence from the method of moments quantile regression models with fixed effects reinforces our findings. Enterprises’ total turnover from e-commerce sales and the percentage of enterprises with e-commerce sales of at least 1% turnover positively influence employment rate for all quintiles, but in the case of enterprises’ turnover from web sales, the effect is positive only for the quintiles ranging from 0.5–0.8.

Employment of patients with kidney failure treated with dialysis or kidney transplantation—a systematic review and meta-analysis

Background Patients with kidney failure treated with dialysis or kidney transplantation experience difficulties maintaining employment due to the condition itself and the treatment. We aimed to establish the rate of employment before and after initiation of dialysis and kidney transplantation and to identify predictors of employment during dialysis and posttransplant. Methods This systematic review and meta-analysis were carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for studies that included employment rate in adults receiving dialysis or a kidney transplant. The literature search included cross-sectional or cohort studies published in English between January 1966 and August 2020 in the PubMed, Embase, and Cochrane Library databases. Data on employment rate, study population, age, gender, educational level, dialysis duration, kidney donor, ethnicity, dialysis modality, waiting time for transplantation, diabetes, and depression were extracted. Quality assessment was performed using the Newcastle–Ottawa Scale. Meta-analysis for predictors for employment, with odds ratios and confidence intervals, and tests for heterogeneity, using chi-square and I2 statistics, were calculated. PROSPERO registration number: CRD42020188853. Results Thirty-three studies included 162,059 participants receiving dialysis, and 31 studies included 137,742 participants who received kidney transplantation. Dialysis patients were on average 52.6 years old (range: 16–79; 60.3% male), and kidney transplant patients were 46.7 years old (range: 18–78; 59.8% male). The employment rate (weighted mean) for dialysis patients was 26.3% (range: 10.5–59.7%); the employment rate was 36.9% pretransplant (range: 25–86%) and 38.2% posttransplant (range: 14.2–85%). Predictors for employment during dialysis and posttransplant were male, gender, age, being without diabetes, peritoneal dialysis, and higher educational level, and predictors of posttransplant: pretransplant employment included transplantation with a living donor kidney, and being without depression. Conclusions Patients with kidney failure had a low employment rate during dialysis and pre- and posttransplant. Kidney failure patients should be supported through a combination of clinical and social measures to ensure that they remain working.