For a quick summary of the case study, please click "US Economy Powerpoint" and download the Powerpoint.

This dataset was inspired by rising prices for essential goods, the abnormally high inflation rate in March of 7.9 percent of this year, and the 30 trillion-dollar debt that we have. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

I labeled all of the datasets to be self-explanatory based off of the title of the datasets. The US Economy Notebook has most of the code that I used as well as the four of the six phases of data analysis. The last two phases are in the US Economy Powerpoint. The "US Historical Inflation Rates" dataset could have also been labeled "The Inflation Of The US Dollar Month By Month". Lastly, the Average Sales of Houses in Jan is just a filtered version of "Average Sales of Houses in the US" dataset.

Dataset and dofiles for replicating "Inflation, Blame Attribution, and the 2022 US Congressional Elections".

Iran’s inflation rate rose sharply to 34.79 percent in 2019 and was projected to rise another 14 percentage points before slowly starting to decline. Given the recent sanctions by the United States regarding the nuclear deal, this number has both political and economic implications. Political implications President Hassan Rouhani won the 2017 election based on economic promises, many stemming from the Joint Comprehensive Plan of Action (JCPOA), commonly known as the Iran Nuclear Deal. Lifting these sanctions opened the Iranian economy to many opportunities, including the chance to benefit from increased oil exports. The JCPOA was an integral part of the Rouhani campaign, so any economic hardship that is linked to the deal will likely be blamed on the president. Economic implications High inflation leads to high interest rates, which leads to less borrowing. Less borrowing means less investment, which slows economic growth. This slower growth often leads to higher inflation, which is what economists call an inflationary spiral. As such, Iran will have difficulty achieving substantial GDP growth until inflation returns to manageable rates.

In the three months to August 2025, average weekly earnings in the United Kingdom grew by 4.7 percent. In the same month, the inflation rate for the Consumer Price Index was 3.8 percent, indicating that wages were rising faster than prices that month. Average salaries in the UK In 2024, the average salary for full-time workers in the UK was 37,430 British pounds a year, up from 34,963 in the previous year. In London, the average annual salary was far higher than the rest of the country, at 47,455 pounds per year, compared with just 32,960 in North East England. There also still exists a noticeable gender pay gap in the UK, which was seven percent for full-time workers in 2024, down from 7.5 percent in 2023. Lastly, the monthly earnings of the top one percent in the UK was 15,887 pounds as of November 2024, far higher than even that of the average for the top five percent, who earned 7,641 pounds per month, while pay for the lowest 10 percent of earners was just 805 pounds per month. Waves of industrial action in the UK One of the main consequences of high inflation and low wage growth throughout 2022 and 2023 was an increase in industrial action in the UK. In December 2022, for example, there were approximately 830,000 working days lost due to labor disputes. Throughout this month, workers across various industry sectors were involved in industrial disputes, such as nurses, train drivers, and driving instructors. Many of the workers who took part in strikes were part of the UK's public sector, which saw far weaker wage growth than that of the private sector throughout 2022. Widespread industrial action continued into 2023, with approximately 303,000 workers involved in industrial disputes in March 2023. There was far less industrial action by 2024, however, due to settlements in many of the disputes, although some are ongoing as of 2025.

In August 2020, the UK government and regulation body Ofqual replaced school examinations with automatically computed A Level grades in England and Wales. This algorithm factored in school attainment in each subject over the previous three years. Government officials initially stated that the algorithm was used to combat grade inflation. After public outcry, teacher assessment grades used instead. Views concerning who was to blame for this scandal were expressed on the social media website Twitter. While previous work used NLP-based opinion mining computational linguistic tools to analyse this discourse, shortcomings included accuracy issues, difficulties in interpretation and limited conclusions on who authors blamed. Thus, we chose to complement this research by analysing 18,239 tweets relating to the A Level algorithm using Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), underpinned by social actor representation. We examined how blame was attributed to different entities who were presented as social actors or having social agency. Through analysing transitivity in this discourse, we found the algorithm itself, the UK government and Ofqual were all implicated as potentially responsible as social actors through active agency, agency metaphor possession and instances of passive constructions. According to our results, students were found to have limited blame through the same analysis. We discuss how this builds upon existing research where the algorithm is implicated and how such a wide range of constructions obscure blame. Methodologically, we demonstrated that CL and CDA complement existing NLP-based computational linguistic tools in researching the 2020 A Level algorithm; however, there is further scope for how these approaches can be used in an iterative manner.

Including search term, tweet ID, timestamp, number of favourites and number of retweets. (CSV)

In August 2020, the UK government and regulation body Ofqual replaced school examinations with automatically computed A Level grades in England and Wales. This algorithm factored in school attainment in each subject over the previous three years. Government officials initially stated that the algorithm was used to combat grade inflation. After public outcry, teacher assessment grades used instead. Views concerning who was to blame for this scandal were expressed on the social media website Twitter. While previous work used NLP-based opinion mining computational linguistic tools to analyse this discourse, shortcomings included accuracy issues, difficulties in interpretation and limited conclusions on who authors blamed. Thus, we chose to complement this research by analysing 18,239 tweets relating to the A Level algorithm using Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), underpinned by social actor representation. We examined how blame was attributed to different entities who were presented as social actors or having social agency. Through analysing transitivity in this discourse, we found the algorithm itself, the UK government and Ofqual were all implicated as potentially responsible as social actors through active agency, agency metaphor possession and instances of passive constructions. According to our results, students were found to have limited blame through the same analysis. We discuss how this builds upon existing research where the algorithm is implicated and how such a wide range of constructions obscure blame. Methodologically, we demonstrated that CL and CDA complement existing NLP-based computational linguistic tools in researching the 2020 A Level algorithm; however, there is further scope for how these approaches can be used in an iterative manner.