The Mean May Not Mean What You Think It Means: The Use and Misuse of Measures of Central Tendency

Analysis of business studies often involves the quantification of qualitative data to derive meaningful insights and making informed decisions. One such challenge is the inappropriate use of the arithmetic mean in economic and financial modeling. The arithmetic mean is a widely used statistical measure of central tendency that sums up a set of values and divides it by the total number of observations. While the arithmetic mean is simple and intuitive, its appropriateness in financial and economic modeling highly depends upon the nature of the data and the specific research question being addressed. This creates a dilemma. Despite the business community traditionally emphasizing quantitative research modeling, the growth of artificial intelligence and big data make qualitative research more desirable, particularly in areas such as ESG scorecards and financial literacy surveys. This paper discusses the challenges presented with analyzing studies after quantifying qualitative data and provides examples of how ordinal regression and other techniques could be used to analyze qualitative variables. This is especially applicable in undergraduate education

Religion and Interfaith Dialogue: The Forgotten Pedagogical DEI Initiative in Business Education and Strategic Planning

Diversity, equity, and inclusion (DEI) initiatives are considered “mission critical” objectives within higher education strategic planning processes and by accrediting bodies, particularly at private liberal arts-based institutions. A research gap currently exists with respect to how organized faith, religion, and interfaith dialogue play a pedagogical role in business, particularly when preparing undergraduate students for technical business careers in accounting, finance, and decision sciences. Viewed as the “forgotten DEI” initiative, interfaith dialogue is critical to success at multinational corporations and at the center of employee resource group (ERG) creation. The infusion of interfaith classroom examples, articles, and pedagogy can serve a dual purpose, both preparing undergraduates for careers in technical business disciplines and exposing them to cross-cultural dialogue necessary within multinational companies. This paper describes how religion and interfaith dialogue can be incorporated within the pedagogical design of technical business disciplines to help support DEI initiatives and students preparing for careers in a global world

When the Rising Tide Lifts All Boats Differently: Income Distribution Matters

In 2018, Alonzi, Drougas, and Condon (“ADC”) developed a simple macroeconomic model to analyze the effect of a less equal income distribution. This paper builds upon that paper by constructing a model incorporating a rising absolute income in both the high and low groups while at the same time allowing a higher proportion of income to go to the high-income group, but a lower proportion go to the low-income group. Notably, we find that the qualitative results of the “Reverse Robin Hood” case remain in the “Rising Tide” case but there are quantitative differences

Ownership Structure and Stock Price Crash Risk: Evidence from China

This paper examines how corporate ownership concentration affects stock price crash risk in Chinese listed firms. Results show that ownership concentration is negatively associated with firm-specific crash risk and this negative relation is robust against difference-in-difference test. Further evidence demonstrates that the negative relation between ownership concentration and stock price crash risk is more pronounced in privately held firms than in state-owned firms

Enterprise zones and employment in Illinois.

Enterprise zones and employment in Illinois

Distribution Matters: The Reverse Robin-Hood Macroeconomic Effects

This paper examines income distribution’s impact on aggregate demand. Income distribution is introduced by replacing consumption as a function of aggregate income with consumption as the sum of two consumptions: a high income group’s and low income group’s. Notably the high income group’s MPC is assumed to be less than the lower income group’s. Analysis reveals two key results. As the high income group’s proportion of income rises: (1) aggregate demand falls and (2) autonomous spending changes cause smaller aggregate demand shifts. A reduced multiplier is the key. Empirical work supports the assumption that the high income group’s MPC is less than the low income group’s