John Chambers College of Business and Economics

Does overlapping regulation of municipal electric utilities facilitate rent extraction at the expense of residential consumers? I study a setting in which two non-coordinating regulators, a city council setting prices and a state public utility commission approving investment, govern a single utility. Using Texas household data from 2005 to 2023, I exploit the 2010 acquisition of Xcel Energy’s distribution assets by the dual-regulated Lubbock Power Light (LP L) as a natural experiment. The acquisition eliminated competing service providers, consolidating Lubbock under a dual-regulated municipal monopoly. Leveraging a similarly-sized 2010 distribution asset acquisition in Midland, Texas and a dual-regulated utility environment in Denton, Texas absent acquisition, I apply a triple-differences estimation at the household level to measure additional acquisition rents attributable to dual-regulation. Synthetic control method specifications at the city level are additionally estimated to test for the effect of acquisitions on Lubbock and Midland billing independently of regulatory status. Under the most conservative specification, the regulatory environment in Lubbock increased the aggregate impact of acquisition on annual residential electric costs by an estimated 20.8 million for LP L customers alone, totaling270 million in additional rents from 2011 to 2023. This implies roughly 13.6

Significant advancements have been achieved in the field of artificial intelligence in recent years. OpenAI has emerged as a trailblazer in the domain, regularly spearheading research endevours. The ChatGPT language model has received much attention owing to its exceptional natural language processing capabilities. The potential of this technology to enhance education, particularly in emerging nations, necessitates a comprehensive analysis of its forthcoming influence. The primary objective of this research is to conduct a comprehensive evaluation of OpenAI, particularly ChatGPT, in relation to the educational environment of developing nations. Utilizing the critical document analysis methodology, this study examines the advantages of ChatGPT, including its capacity for flexible and personalized instruction, its ability to provide access to high-quality education, and its provision of language help, among other benefits. This study provides a complete examination of the advantages and drawbacks associated with this technology. Its objective is to enhance our understanding of the potential impact it may have on education in developing nations, achieved through an extensive evaluation of ChatGPT.

With the advancement of technology and social media usage, many firms accumulate a wealth of data left by (potential) consumers, such as user-generated posts in a firm's private Online Community (OC). In this paper, we show that these data can be leveraged to analyze consumers' key characteristics so firms can offer targeted deals to various consumer groups. In particular, we apply an integrated deep learning algorithm to a unique OC data set from an apparel and accessory e-retailer to extract consumers' regulatory foci information and classify consumers into two groups: those who are promotion-focused and those who are prevention-focused. Then, matching consumers' personality data with their purchasing data from the firm, we show the effectiveness of the firm’s various deals in increasing product checkout rates. We find that promotion-focused consumers are more likely to check out products in their shopping carts when free shipping or a price markdown is offered, while prevention-focused consumers are more likely to check out products in their shopping carts when a delivery guarantee or digital coupon is offered. These empirical findings are consistent with the theoretical arguments in the regulatory focus literature that promotion- (prevention-) focused consumers are more sensitive to positive (negative) messages and optimistic (pessimistic) in the face of uncertainties. These results illustrate the potential for an e-retailer to increase its revenue by fostering an online community and leveraging user-generated online content to offer targeted deals to different consumer segments.

The current pandemic of the COVID-19 virus will cause great consequences for the overall world economy. The global pandemic requires a global response and it is already clear that economic policy makers will need to implement significant targeted fiscal, monetary and social measures to help the affected population and economy. Within the framework of support measures for the economy, the focus should be on supporting the sectors most affected. The agri-food sector is one of the most endangered sectors of the economy, and for that very reason, the subject of this paper are the challenges and problems of food producers in the conditions of a pandemic.

Considering the role of life satisfaction of an employee’s as vital factor for employees' performance which directly affects the firms' success, this study explores the role of emotional labor as an antecedent of life satisfaction via mediating role of the job engagement of bank officers working in private and public banks of Pakistan. The sample of 314 employees of officer rank 314 employees of banking sector are selected randomly for data collection. Results of cross-sectional data of 314 employees working in both private and public sector banks of Pakistan predict that employees having higher emotional labor have lesser job engagements, reducing overall life satisfaction of an employee. For genializing the result this kind of research may include the broad sample i.e., Islamabad, Bahawalpur, Faisalabad, Multan, and Karachi. In future this type of research will also be conducted on the telecom, education, textile sector and education sector.