High Unemployment in the United States: Causes and Solutions
Bibliographic record
Abstract
EXECUTIVE SUMMARY One of today's biggest economic challenges facing the U.S. is the depressingly high unemployment rate. It is no wonder that the candidates running for President of the U.S. in 2012 (Barack Obama and Mitt Romney) constantly talk about the importance of job creation and national economic competitiveness. This article discusses the causes of the high unemployment rate in the U.S., and provides practical recommendations for creating jobs and strengthening American economic competitiveness. Keywords: Causes of the high unemployment problem, Solutions to the high unemployment problem, U.S. presidential election of 2012, President's Council on Jobs and Competitiveness, Globalization of markets and production INTRODUCTION 2012 is a presidential election year in the United States, and a May 2012 USA Today/Gallup poll indicates that American voters consider the economy to be the most important problem facing the nation. candidate (Barack Obama or Mitt Romney) who can convince the voters that he can handle the economy better than his opponent over the next four years will have a crucial competitive advantage in winning the election (Mendes, 2012). U.S. monthly unemployment rate moved within the range of 4.2 percent to 6.3 percent between January 2001 and September 2008, grew rapidly to 10 percent in October 2009, and has since dropped to 8.3 percent as of July 2012 (see Table 1). Diane Swonk (2012), chief economist at Mesirow Financial in Chicago, says, The labor market is healing, but we still have a long way to go to recoup the losses we have endured. main objectives of this article are twofold: (1) to discuss the causes of the high unemployment rate in the U.S.; and (2) to provide a list of recommendations to help the U.S. create jobs and lower its unemployment rate. CAUSES OF THE HIGH UNEMPLOYMENT RATE IN THE U.S. federal government collects labor statistics for the purpose of understanding the extent and nature of employment and unemployment in the nation. Government officials use these statistics, together with other relevant data, to formulate strategies and policies to help build a prosperous economy and to provide appropriate assistance to the unemployed. According to the Bureau of Labor Statistics (2012), which is a unit of the U.S. Department of Labor, persons (16 years old and over) are classified as unemployed if they do not have a job, have actively looked for work in the prior 4 weeks, and are currently available for work. U.S., Canada, Mexico, Japan, and several European Union (EU) member nations use the same system in counting the number of unemployed individuals. Some economists, public policymakers and journalistic commentators have been theorizing and debating on the causes of and solutions for unemployment for many years (Prybyla, 1961; Krugman, 1994; Palley, 2004; Gros, 2006; Sherk, 2010). While experts do not fully agree on the exact causes of high unemployment, the most common factors include economic recession, globalization of markets and production, and technological advances. single most important factor driving the unemployment rate in the U.S. so high during the past three and half years has been a severe global economic recession. recession was caused by a financial crisis involving a complex interplay of valuation and liquidity problems among several large U.S. financial institutions, the disastrous failures of U.S. credit rating agencies and regulators, the collapse of the U.S. housing market, and sharp declines in consumer wealth (The Financial Crisis Inquiry Commission, 2011). Even with the financial markets improving, the high unemployment rate is likely to remain for the near future due to many factors, including a weak housing market, rapidly rising federal debts, costly healthcare systems, the inflationary threat of high oil prices, economic competitive pressures from China, and the negative impact of Europe's severe and protracted financial crisis (Louis & Hopkins, 2012). …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".