This study investigates the implications of systemic problems on policy-making and the associated financial burdens placed on specific consumer groups, particularly within the low- to middle-income brackets and small to medium-sized businesses. By examining the policies passed ostensibly to address societal issues, this analysis highlights a recurring pattern: well-intentioned or performative policy decisions often impose additional costs on society without adequately measurable standards for accountability or effectiveness. For instance, increased sales and property taxes, along with rising rent costs, create additional financial strain for consumers, small business owners, and renters, ultimately diminishing their economic resilience and heightening their dependency on governmental aid. The lack of clear Key Performance Indicators (KPIs) or Objectives and Key Results (OKRs) to measure success contributes to a feedback loop where unproductive programs remain unchecked, furthering the original problem and creating a costly cycle of dependency and program expansion. The analysis outlines how, due to financial strain, businesses close or relocate, resulting in job market contraction and increased competition, leaving vulnerable populations with fewer employment opportunities. For populations already economically disadvantaged, especially those lacking adequate resources or education, this dynamic disproportionately impacts mental health and escalates reliance on maladaptive coping mechanisms. Rising financial and emotional distress manifest through heightened anxiety, substance misuse, and deteriorating family stability, adding pressure to public health systems and amplifying the original issues. This cycle emphasizes the significance of comprehensively evaluating the socioeconomic impacts of policies, particularly when metrics of success are absent or poorly defined.

A societal issue has arisen, creating a perceived need for government intervention. In response, politicians—often presenting their actions as compassion-driven—enact policies intended to address this issue. However, as the political environment evolves, additional policies are frequently introduced, sometimes beyond what is necessary. This can lead to an excessive policy landscape that, while well-intentioned, may inadvertently exacerbate the problem.

Welcome to the culmination of a journey that has been an academic endeavor and a voyage into the heart of one of our society's most pressing issue: economic inequality. I am Joseph Wankelman, and today, through my Data Visualization project, I invite you to explore a landscape that has transformed drastically since 1980. My hypothesis is both bold and unsettling. I propose that there is a tangible, direct correlation between the changes in U.S. public policy and the intensifying landscape of financial inequality. This isn't just about numbers; it's about lives. It's about the ever-widening gap in median household incomes, the disparities in tax rates, that essential are collectively funnel wealth into the hands of a select few. Armed with comprehensive datasets from revered institutions like the Federal Reserve Economic Data, the U.S. Census Bureau, and the World Bank, I have embarked on a mission to validate or challenge this hypothesis. Each dataset is a piece of a larger puzzle, providing clarity on how wealth is not just earned but also distributed and controlled. As we delve into the heart of this research, every graph and every visualization will be a testament to the data that shaped it, ensuring a journey that is as transparent as it is enlightening. This study is more than an academic pursuit; it's a call for action, exploring how policy reforms can lead us toward balanced growth and equitable wealth distribution.

The project undertaken was an ambitious and insightful endeavor to harness the power of the U.S. Census Bureau’s API, creating an R Shiny application capable of visualizing changes in the Gini Index and median household income across U.S. counties from 2015 to 2020. The goal was to present a dynamic, data-driven narrative that could illuminate socio-economic trends over this period. However, the journey was not without its hurdles and learning moments. Though not fully unraveled, the challenges encountered pointed to the complexity and nuances inherent in working with large-scale, real-world data sets. While impeding the final realization of the project’s full potential, these challenges provided valuable insights into the intricacies of data handling and application development in R. Initially, the project started with extracting data from CSV files from the Census Bureau. While successful in creating a foundational Shiny application, this preliminary approach revealed limitations in terms of data comprehensiveness. Many counties were missing from the dataset, prompting a pivot to directly accessing the Census Bureau’s API for a more exhaustive dataset. This shift marked a significant step forward, enabling the creation of static visualizations that more accurately represented the socio-economic landscape of the United States at a county level. The process involved meticulously joining multiple datasets to form a cohesive and comprehensive view of the Gini Index and median household income trends. The resulting visualizations offered a static but insightful glimpse into the economic disparities and shifts across the nation. However, transitioning from static visualizations to a dynamic Shiny application introduced a new set of challenges. The development was frequently hindered by unexpected errors, particularly in rendering the maps, which became a recurring issue. Efforts to troubleshoot included extensive debugging and strategically removing NA values from the dataset to stabilize the application. Despite these efforts, the application fell short of its full interactive potential, with issues in map display persisting. This project phase underscored the complexities involved in developing a fully functional Shiny application, particularly when handling large and intricate datasets. The experience, though marked by setbacks, was a profound learning journey in data manipulation, API utilization, and interactive application development in R.

My recent endeavor has been to investigate the hypothesis that the middle class is shrinking in the United States. My primary tool in this analysis has been an extensive examination of census data sets from 1969 to 2022. This data includes crucial information such as the monetary boundaries defining each quantile and the corresponding percentage of wealth each quantile possesses. By meticulously dissecting these data sets, I aimed to uncover trends and patterns that could confirm or refute the notion of a contracting middle class. This longitudinal data is critical, as it allows for observing changes over a significant period, offering a comprehensive view of the economic shifts affecting various income groups. To illustrate these trends effectively, I utilized line graphs and treemaps in my analysis. Line graphs are handy for showcasing the evolution of income distribution over time, allowing us to observe any significant shifts in the wealth of the middle class relative to other income groups. On the other hand, treemaps offer a more nuanced visual representation of wealth distribution across different quantiles. They provide an intuitive understanding of how the economic landscape has transformed, highlighting the disparities in wealth accumulation among these groups. My ultimate goal is to develop a dynamic treemap within a Shiny application. This interactive platform will enable users to explore the data in real-time, observing how the percentage of wealth held by each quantile has evolved year by year, thus offering a compelling and user-friendly way to engage with the data and understand the economic changes impacting the middle class.