<p><font size="2" face="Arial"><font size="2" face="Arial"> <p align="left">This research paper is a review of the assertion by some commentators that the regulation of the labour market is a cause of the high unemployment rate in South Africa. It starts by providing a historical background of statutory industrial relations in South Africa leading to the current labour dispensation. The discussion includes a review of the current labour legislation and assessment of its compliance with international law. The rating of the South African labour market by the Doing Business study is discussed. This study seeks to ascertain whether there is a causal relation between labour market regulation and the unemployment rate. The conclusion reached is that South African labour legislation complies with international law as espoused in International Labour Organisation (ILO) Conventions, is not excessively rigid and, most importantly, that there is no convincing evidence of a causal relation between labour market regulation and the unemployment rate.</p> </font></font><font size="2" face="Arial"><font size="2" face="Arial"> <p>&nbsp / </p> </font></font></p>

<p>This research paper examines the different forms of employment patterns that have emerged as a result of globalisation as well as the mechanisms that have been used by the legislator to accommodate those in non-standard employment relationships.</p>

Thesis (B. Ec.(Hons.))--University of Adelaide, 1972. / Includes bibliographical references (p. 97-98).

Thesis (Ph. D.)--University of Texas at Austin, 2009. / Title from PDF title page (University of Texas Digital Repository, viewed on Sept. 9, 2009). Vita. Includes bibliographical references.

Magister Legum - LLM / This research paper examines the different forms of employment patterns that have emerged as a result of globalisation as well as the mechanisms that have been used by the legislator to accommodate those in non-standard employment relationships. / South Africa

Local employment opportunities are hypothesized to influence educational attainment decisions made by high students, measured by the dropout percentage and the post-secondary education percentage. Data from 1990, 1980 and 1970 are used to estimate these relationships in Virginia's 133 school districts. Secretary of Labor Robert Reich's framework, developed in the Work of Nations: Preparing Ourselves for 21st Century Capitalism, is adopted to incorporate changes in the global-labor market. / Master of Science

by Chan, Yuk Fai Weslie. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 67-70). / Abstracts in English and Chinese. / Acknowledgments --- p.ii / English Abstract --- p.iv / Chinese Abstract --- p.v / Table of Contents --- p.vi / List of Tables --- p.viii / List of Figures --- p.ix / List of Appendices --- p.x / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Theoretical Background --- p.6 / Chapter 2.1 --- Concepts of Beveridge Curve --- p.7 / Chapter 2.2 --- Beveridge Curve Derived from Labori Market Stock-Flow Identities --- p.15 / Chapter 2.2.1 --- Some Basic Labor Market Stork-Flow Identities --- p.15 / Chapter 2.2.2 --- Steady State Properties of Beveridge Curve --- p.19 / Chapter 2.2.3 --- Comparative Static Analysis of Beveridge Curve --- p.20 / Chapter 2.2.4 --- Short Run Dynamics along Beveridge Curve --- p.24 / Chapter 2.3 --- Beveridge Curve Derived from Matching Function Approach --- p.25 / Chapter Chapter 3. --- Empirical Evidences --- p.28 / Chapter 3.1 --- Decomposition of Total Unemployment of Hong Kong --- p.28 / Chapter 3.2 --- Beveridge Curve of Hong Kong --- p.31 / Chapter 3.2.1 --- Time Series Estimation of Hong Kong's Beveridge Curve --- p.32 / Chapter 3.2.2 --- Cross Sectorial Estimation of Hong Kong's Beveridge Curve --- p.33 / Chapter 3.3 --- Natural Unemployment Rate --- p.34 / Chapter 3.4 --- Unemployment-Vacancy Ratio --- p.36 / Chapter 3.4.1 --- Relation between U-V Ratio and K-L ratio --- p.38 / Chapter Chapter 4. --- Conclusion --- p.41 / Tables --- p.42 / Figures --- p.46 / Appendices --- p.56 / Bibliography --- p.64

This research investigates the specific labour market dynamics that underline the shortage of engineers in the Petrochemical Industry in South Africa. The central argument of this dissertation is that an understanding of a skills shortage requires a distinct knowledge of the internal and external nature of each labour market in which the shortage is being experienced. This dissertation develops a critique of the neoclassical perspective which dominates current analysis of skill shortages. While it is important to understand the external labour market, it is equally important to consider the internal labour market to better identify and understand the specific dynamics that underline a skills shortage in an organisation and industry. Using Engen Refinery as a case study, this dissertation focuses on an in depth examination of the experiences of engineers working at the Refinery. The findings reveal that the dynamics that underline the skill shortage of engineers in the Petrochemical Industry in the South African context are manifold. Skill shortages are a consequence of dynamics in both the external and internal labour market; these I argue are interrelated. / Thesis (M.Soc.Sc.)-University of KwaZulu-Natal, Durban, 2009.

The world economy and industry in turn is driven by technology and innovation at a rapid rate. Worldwide, the information technology (IT) industry is volatile in terms of turnover intentions of IT professionals The unemployment level in South Africa is high according to global standards. The option of contracting as an IT consultant or self-employment is a form of an alternate employment arrangement. This arrangement will benefit the unemployed in South Africa if they choose to skill themselves and pursue a career in self-employment in the IT industry. There are internal and external employment factors that affect an IT professional’s employment arrangement. Moore’s (2000) information technology employee turnover model was adapted as a basis for this research. An external factor, entrepreneurship (self-employment) was introduced to Moore’s model. This research also tests Moore’s (2000) model for its internal factors. The following factors, role ambiguity, role conflict, autonomy, perceived workload, fairness of reward, work exhaustion and entrepreneurship were formulated in the hypotheses to determine which of these factors influences self-employment in IT professionals. Information technology professionals based in Durban were the target respondents in the City of Durban. The survey questionnaire was emailed to respondents using Questionpro. The sample data was based on 123 respondents who completed the survey. The data was then validated for internal consistency using Cronbach alpha ratio generated by the SPSS (version 19.0) software tool. The quantitative research design was chosen. Frequency tables and Pearson’s bivariate correlation coefficient statistics was used in the data analysis phase. The research objective was achieved successfully and the following factors were determined, they are role ambiguity, role conflict, autonomy, work exhaustion and entrepreneurship. The IT industry is volatile with IT professionals constantly re-skilling themselves to be on par with changing technology and innovation that make them very competent and competitive as a result, these IT professionals create a market for self-employment. / Thesis (MBA)-University of KwaZulu-Natal, Durban, 2012.

[pt] Este trabalho demonstra como web mining, processamento de linguagem natural e aprendizado de máquina podem ser combinados para melhorar a compreensão de vagas de emprego segmentando semanticamente os textos de suas descrições. Para atingir essa finalidade, foram coletados dados textuais de três grandes sites de vagas de emprego: Catho, LinkedIn e VAGAS.com.br. Baseado na literatura, este trabalho propôe uma estrutura semântica simplificada em que cada sentença da descrição da vaga de emprego pode pertencer a uma dessas classes: Responsabilidades, Requisitos, Benefícios e Outros. De posse dessa ideia, a tarefa de segmentação semântica pode ser repensada como uma segmentação de sentenças seguida de uma classificação. Usando o Python como ferramenta, são experimentadas algumas formas de construção de atributos a partir de textos, tanto léxicas quanto semânticas, e quatro algoritmos clássicos de aprendizado de máquina: Naive Bayes, Regressão Logística, Máquina de Vetores de Suporte e Floresta Aleatória. Como resultados, este trabalho traz um classificador (Regressão Logística com representação binária) com 95.58 porcento de acurácia, sem sobreajuste de modelo e sem degenerar as classificações por desbalanceio de classes, que é comparável ao estado da arte para Classificação de Texto. Esse classificador foi treinado e validado usando dados do Catho, mas foi testado também nos dados do VAGAS.com.br (88.60 porcento) e do LinkedIn (91.14 porcento), apresentando uma evidência de que seu aprendizado é generalizável para dados de outros sites. Além disso, o classificador foi usado para segmentação semântica das vagas de emprego e obteve uma métrica Pk de 3.67 porcento e uma métrica WindowDiff de 4.78 porcento, que é comparável ao estado da arte de Segmentação de Texto. Por fim, vale salientar duas contribuições indiretas deste trabalho: 1) uma estrutura para pensar e analisar vagas de emprego e 2) uma indicação de que algoritmos clássicos também podem alcançar o estado da arte e, portanto, sempre devem experimentados. / [en] This dissertation demonstrates how web mining, natural language processing, and machine learning can be combined to improve understanding of job openings by semantically segmenting the texts of their descriptions. To achieve this purpose, textual data were collected from three major job sites: Catho, LinkedIn and VAGAS.com.br. Based on the literature, this work proposes a simplified semantic structure in which each sentence of the job description can belong to one of these classes: Responsibilities, Requirements, Benefits and Others. With this idea, the semantic segmentation task can be rethought as a sentence segmentation followed by a classification. Using Python as a tool, some ways of constructing features from texts are tried out, both lexical and semantic, and four classic machine learning algorithms: Naïve Bayes, Logistic Regression, Support Vector Machine, and Random Forest. As a result, this work presents a classifier (Logistic Regression with binary representation) with 95.58 percent accuracy, without model overfitting and without degeneration by class unbalance, which is comparable to state-of-the-art for Text Classification. This classifier was trained and validated using Catho data, but was also tested on VAGAS.com.br (88.60 percent) and LinkedIn (91.14 percent) data, providing evidence that its learning is generalizable to data from other sites. In addition, the classifier was used for semantic segmentation of job openings and obtained a Pk metric equals to 3.67 percent and a WindowDiff metric equals to 4.78 percent, which is comparable to state-of-the-art for Text Segmentation. Finally, it is worth highlighting two indirect contributions of this work: 1) a structure for thinking and analyzing job openings and 2) an indication that classical algorithms can also reach the state of the art and therefore should always be tried.