The literature indicates that flexibility strategies can mitigate supply chain risks and enhance resilience. This study, using manufacturing industry-level data, not only investigates the shares of different transportation modes in global supply chains during the pandemic but also provides practical insights into the impact of transportation modal flexibility on inventory efficiency. Specifically, we examine how manufacturing industries adjusted their use of ocean and air freight during significant disruptions, offering real-world implications for supply chain management. Our study, using empirical research methods and USA Trade manufacturing industry data, employs rigorous statistical methodologies to identify patterns among businesses with higher air freight usage. We delve into the decision-making processes behind switching transportation modes during supply chain disruptions through a comprehensive literature review, ensuring the credibility of our findings.Our findings suggest that companies dealing with higher-value products, such as electronics, machinery, and minerals, tend to use more air freight than those handling lower-value products, such as wood and paper. This research aims to provide a foundation for future studies on supply chain responses to disruptions, enabling continuous evaluation of the impacts of such events and offering insights for industry and academic supply chain analysts.

Predicting the direction that a stock will move is of principal importance to investors. This is especially true when it comes to short term stock trading, which is generally considered to be more risky due to the high volatility in the stock market. The purpose of this project is to compare the short term forecasting abilities of four models: Linear Regression, Logistic Regression, Artificial Neural Network ANN, and Support Vector Machine SVM. I tested the four models on a dataset containing the daily price variables and volume of every stock on the NYSE and NASDAQ from January 1st, 1962, to November 10th, 2017. I then compared their ability to make accurate predictions on the closing prices movements over 1, 3, and 5 business days. My findings show that the machine learning models performed vastly better when it comes to making short term predictions with 1 day prediction scores of 52.3 percent from both the SVM and the ANN when compared to 51.4 percent from the regressions. However, the regression models are more accurate when it comes to predicting closing price movement over 3 days and 5 days, with both having 5 day accuracies of 51.7 percent and 3 day accuracies of 51.3 percent. In comparison, the ANN achieved accuracies of 50.6 percent and 51.5 percent, while the SVM had accuracies of 50.7 percent and 51.3 percent over the 3 and 5 day periods.

The labor market is facing a crisis unlike anything we have ever seen before. The demand for labor is incredibly low, leading to low wages and job insecurity for many. Unemployment statistics fail to capture the true number of unemployed people, as they do not account for discouraged workers, those underemployed in terms of time, and those earning below their potential wage. The rise of automation and Artificial Intelligence is often blamed for this low labor demand, but this narrative is incomplete. Deindustrialization and economic stagnation have played significant roles in creating these challenging labor market conditions. Ultimately, however, the cause lies in the capitalist economic system itself. This research aims to analyze modern capitalism from a Marxist perspective to explore how capitalism’s inherent tendencies toward crises and technological dynamism have contributed to these labor market conditions. Furthermore, it will investigate how Artificial Intelligence and automation could potentially facilitate a transition toward a non-capitalist economic system designed to harness the abundance created by our productive capacity.

This study presents econometric analysis of socio-economic factors which influence wage inequalities in Kenya. This study tests the hypothesis that women experience cross sectoral gender wage disparities which are influenced by cultural and social expectations. Linear regressions models and factorial regressions were utilized to identify statistical determinants of the gender-wage gap through disproportionate educational outcomes, labor outcomes, and social outcomes. The outcomes reflect that education and other demographic factors play a pivotal role in the wage and employment outcomes for women in Kenya. The results suggest that cultural implications are the main influence to the socio-economic constraints on women in Kenya as opposed to men. The study recommends that government policies and investments in instruments for female autonomy are utilized to reduce the prevalence of gender-wage inequalities in Kenya.