According to a survey conducted between July 9 and July 11, 2022, 45 percent of Americans thought that Joe Biden was highly responsible for the current trend in the inflation rate. This is compared to 26 percent of Americans who said President Biden did not have a lot of responsibility for the current inflation rate.

Inflation in the U.S. Global events in 2022 had a significant impact on the United States. Inflation rose from 1.4 percent in January 2021 to 9.1 percent in June 2022. Significantly higher prices of basic goods led to increased concern over the state of the economy, and the ability to cover increasing monthly costs with the same income. Low interest rates, COVID-19-related supply constraints, corporate profiteering, and strong consumer spending had already put pressure on prices before Russia’s invasion of Ukraine in February 2022. Despite rising wages on paper, the rapid growth of consumer prices resulted in an overall decline in real hourly earnings in the first half of 2022.

How much control does Joe Biden have over inflation? The bulk of economic performance and the inflation rate is determined by factors outside the President’s direct control, but U.S. presidents are often held accountable for it. Some of those factors are market forces, private business, productivity growth, the state of the global economy, and policies of the Federal Reserve. Although high-spending decisions such as the 2021 COVID-19 relief bill may have contributed to rising inflation rates, the bill has been seen by economists as a necessary intervention for preventing a recession at the time, as well as being of significant importance to low-income workers impacted by the pandemic.

The most important tool for curbing inflation and controlling the U.S. economy is the Federal Reserve. The Reserve has the ability to set, raise, and lower interest rates and determine the wider monetary policy for the United States – something out of the president’s control. In June 2022, the Reserve announced it would raise interest rates 0.75 percent for the second time that year – hoisting the rate to a target range of 2.25 to 2.5 percent – in an attempt to slow consumer demand and balance demand with supply. However, it can often take time before the impacts of interventions by the Federal Reserve are seen in the public’s day-to-day lives. Most economists expect this wave of inflation to pass in a year to 18 months.

Inflation increased rapidly around the world through 2022 and 2023, before it started falling in some countries in 2024. In a survey conducted in the spring of 2024, 70 percent blamed the state of the global economy for the rising cost of living in their country, whereas more than two thirds blamed the interest rate in their country as well as the policies of their national government.

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.

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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.

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.

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.

In the three months to April 2025, average weekly earnings in the United Kingdom grew by 5.2 percent, while pay including bonuses grew by 5.3 percent, when compared with the same period leading to April 2024. In the same month, the inflation rate for the Consumer Price Index was 3.5 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.

The aim of this study was to examine the effects of urbanization and land expropriation on the livelihoods of Peri-urban farmers in North Wollo Zone, Ethiopia. Data were collected from 378 peri-urban farmers using a structured questionnaire and multistage sampling. Qualitative data were obtained through focus group discussions and key informant interviews. A binary logistic regression model was used to identify factors influencing livelihood diversification among expropriated farm households. The results revealed a significant trend of expropriation without fair compensation, stemming from the disregard of legal procedures of expropriation and compensation in the study area. On average, 33.06% of respondents were fully expropriated and evicted, while the majority (66.94%) lost only part of their agricultural land. Expropriated farmers faced numerous challenges during their livelihood diversification, including high inflation, lack of urban living skills, informal land sales at low prices, misuse of compensation, displacement, family conflicts, social disruption, and skill gaps. They also blamed that they did not get any government support in properly utilizing compensation money for livelihood diversification. The Logit model results showed that livelihood diversification is positively influenced by factors such as the household head’s education, marital status, access to alternative land, fair compensation, off-farm employment, training, social networks, and livestock assets. It is negatively affected by farming as the primary occupation, household size, and expropriated land size. The study recommends that the government should help families of expropriated household’s secure sustainable livelihoods through fair compensation and proper support.