UNEMPLOYMENT
AUTHOR – OMKAR MAVKAR
UNEMPLOYMENT AND ONLINE LABOR: EVIDENCE FROM MICROTASKING
Laitenberger, U et al (2023) states that the relationship between unemployment and the adoption of Online Labor Markets (OLM) for micro tasks is complex. High unemployment rates make micro tasking more attractive to users in regions with specific demographics, such as older, predominantly male, white workers with lower levels of education. However, this effect on participation was temporary and did not significantly impact the overall number of active users. Workers experimented with micro tasking platforms after the Great Recession, using them as a substitute for traditional offline work, especially during normal working hours. The online labor supply became more elastic with higher unemployment rates, although workers were not highly sensitive to wage changes. Overall, micro tasking offered a temporary online option for low-skilled workers but did not permanently replace traditional employment.
AN HISTORICAL BACKGROUND OF ANDALUSIA’S UNEMPLOYMENT: AN INSTITUTIONAL PERSPECTIVE
Muriel-ramírez, M. J (2023) states that the history of Andalusia is marked by state violence and the eradication of minorities, particularly Muslims, during the creation of Spain as a nation-state. Landownership in Andalusia was highly concentrated in the hands of the nobility, Church, and municipalities, leading to inefficient agriculture and mass unemployment. The Agrarian Reform of 1932 attempted to address these issues but was hindered by institutional complementarities and ultimately disrupted by the Civil War. Emigration and state violence under Franco’s regime temporarily alleviated unemployment in Andalusia, but it resurfaced as a significant social problem in the late 20th century. The region’s unemployment rate remains high, reflecting the difficulty in transitioning from a limited access order to an open access order. Prioritizing long-term soft policies aimed at informal constraints and organic institutions is seen as essential in addressing these challenges.
PREDICTING THE CONTRIBUTION OF ARTIFCIAL INTELLIGENCE TO UNEMPLOYMENT RATES: AN ARTIFCIAL NEURAL NETWORK APPROACH
Mutascu, M and Hegerty, S. W(2023) states that a new approach to forecasting unemployment rates using artificial intelligence as an additional explanatory variable. An artificial neural network model was used with a set of socio-economic determinants to predict unemployment rates in 23 countries from 1998 to 2016. The findings suggest that artificial intelligence can be successfully used in predicting unemployment rates, particularly in high-tech and developed countries where it is widely adopted. Additionally, foreign direct investment, population growth rate, and labor productivity were found to have significant impacts on unemployment rates. Policymakers are advised to attract foreign direct investments and carefully consider demographic policies to address unemployment. Automation and artificial intelligence, along with other factors, should be taken into account in designing balanced labor market policies to accurately assess the causes of increasing unemployment.
UNEMPLOYMENT RATE RETURNED TO ITS PREPANDEMIC LEVEL IN 2022
In 2022, the labor market in the United States showed signs of recovery from the recession caused by the COVID-19 pandemic. The national unemployment rate declined to 3.6 percent, total employment continued to grow, and the employment-population ratio increased. The labor force participation rate remained steady. The jobless rate decreased for all major race and ethnicity groups, and the number of people working part-time for economic reasons also went down. Median usual weekly earnings increased to $1,059, a 6.1 percent rise from the previous year, although it did not keep up with inflation. These trends suggest an improving labor market, but there may still be challenges in terms of wage growth and keeping up with rising costs.
TIME-VARYING GRANGER CAUSALITY BETWEEN THE STOCK MARKET AND UNEMPLOYMENT IN THE UNITED STATES
Fromentin, V (2023) states that the relationship between the stock market and unemployment in the United States from 1960 to 2020, using a time-varying causality framework. The results show a bidirectional causality, especially during crash periods, with a transient effect. Similar conclusions have been drawn in previous studies, suggesting that these findings may apply to other countries, including European nations. Policymakers could use stock prices to predict unemployment in developed countries and focus on improving market psychology to positively impact the economy. Understanding the relationship between stock market fluctuations and unemployment is crucial, especially during the COVID-19 pandemic and its aftermath.
HOW SENSITIVE ARE SPORTS FANS TO UNEMPLOYMENT?
Reade, J. J and Van Ours, J. C (2023) states that the analysis explores the relationship between attendances at social events, football matches, and unemployment conditions. Different competitions within a hierarchy of quality show varying levels of responsiveness to changes in unemployment. While a model with only national unemployment rates cannot fully explain fluctuations in stadium attendance, it does demonstrate the sensitivity of sports fans to economic conditions. The study suggests that a simple model can provide valuable insight into the behavior of sports fans during changing economic circumstances.
CURRENT UNEMPLOYMENT VARIANCE DECOMPOSITION AND CONSEQUENCES OF USING PROXIES
Corseuil, C. H et al (2023) states that the decomposition framework proposed by Shimer (2012) to measure the contributions of labour market flows to fluctuations in the unemployment rate. Shimer’s framework uses the steady-state unemployment rate as a proxy for the current rate, which may not accurately represent the current rate in countries with slower labour market dynamics. This limitation has led to the development of alternative methodologies in the literature. The authors propose a new method to decompose the variance of the current unemployment rate as a function of labour market flows and the contribution of using proxies for the current rate. The proposed method aims to evaluate the adequacy of the chosen proxy for the variance decomposition of the actual rate. The decomposition method is applied to a three-state labour market (unemployment, employment, and inactivity) using data from the U.S. and Brazil. The results show that while there is no significant change in the U.S., in Brazil, the contributions of labour market flows vary substantially when the current rate decomposition is used, and the approximation error from using proxies is significant. This highlights the importance of using accurate proxies for the current rate in variance decomposition analysis.
ICT CAPITAL FORMATION, UNEMPLOYMENT, AND THE SOLOW PARADOX
Apergis, E et al (2023) states that the impact of ICT on employment, productivity, and the economy. It highlights that technological advancements lead to increased productivity, which in turn liberates resources to meet other economic needs. The adoption of ICT, especially 5G technology, has been accelerating, with the potential to create job opportunities and improve labour productivity. The study found an inverted U-shaped effect between ICT investments and unemployment over the period 1972-2020, as well as a positive influence of ICT on labour productivity. The research suggests that government policies to incentivize ICT investments can benefit the macroeconomy. In addition to investing in ICT infrastructure, such as internet exchange points and wireless systems, reforms in ICANN and decentralisation of internet structures are recommended. This will allow for higher data rates and fewer communication delays, promoting digital sovereignty and competition. The article also emphasizes the importance of rethinking education policy to enhance the overall abilities of the workforce and adapt to the changing digital economy. Future research should explore labour productivity and ICT impacts in different cultural contexts using more comprehensive data.
THE CHINA SHOCK, EMPLOYMENT PROTECTION, AND EUROPEAN JOBS
Aghelmaleki, H et al (2022) states that the effects of a large increase in Chinese exports on European workers following China’s accession to the WTO were analyzed using comparable microdata across 14 European countries. The study aimed to determine the effects on job security, job-finding rates, and the impact of differing levels of employment protection legislation (EPL). The results showed that Chinese exports significantly affected workers’ job security and job-finding rates in the EU. Increased exposure to Chinese imports led to higher worker flows from employment to unemployment and reduced the probability of unemployed workers finding a new job. Countries with high levels of EPL experienced a stronger reduction in worker flows from unemployment to employment in response to Chinese imports. The analysis also revealed differences in the effects of the China shock among worker groups based on age, skill level, and job tasks. The findings have important implications for welfare considerations and economic policy, as increased unemployment inflows and reduced outflows can lead to the loss of job-specific human capital and higher job search costs. Additionally, the study highlighted the impact of EPL on labor market adjustment. Future research should focus on the role of direct job-to-job transitions in adjusting to the China shock, as this study did not include retrospective information on occupation or sector in the analysis. Investigating this aspect using national data sets is recommended for a more comprehensive understanding of the effects of international trade on European workers.
CAUSAL RELATIONSHIPS BETWEEN ENTREPRENEURSHIP, UNEMPLOYMENT AND ECONOMIC GROWTH IN SELECTED COUNTRIES
Davari, A et al (2022) states that the relationship between entrepreneurship, economic growth, and unemployment in 39 countries using a new method and proxy for entrepreneurship. The findings suggest that economic growth and unemployment variables have a significant impact on entrepreneurship indicators, with economic growth causing changes in entrepreneurship and unemployment rates. However, there is no causal relationship found between entrepreneurship and unemployment. Policy decisions should focus on stimulating positive shocks in entrepreneurial indicators while minimizing negative shocks to achieve desired outcomes.
CONCLUSION
The relationship between unemployment and various factors such as online labor markets, historical institutional perspectives, artificial intelligence, stock market fluctuations, and sports attendance is complex and multifaceted. While high unemployment rates may lead to increased participation in online labor markets temporarily, they do not necessarily replace traditional employment. Historical factors, such as land ownership patterns and past policies, continue to affect current unemployment rates in regions like Andalusia. Artificial intelligence can be a valuable tool in predicting and understanding unemployment rates, especially in high-tech countries. The stock market and unemployment have a bidirectional relationship that policymakers should consider, particularly during economic downturns like the COVID-19 pandemic. Sports fans may be sensitive to changes in unemployment, reflecting broader economic conditions. Incorporating accurate proxies for current unemployment rates is crucial in analyzing variance decomposition and understanding the true state of unemployment. Investment in ICT infrastructure and education policies can influence unemployment rates positively, while external factors like Chinese exports can impact job security and job-finding rates in Europe. In-depth analysis of entrepreneurship, economic growth, and unemployment relationships in different countries is essential for policymakers to make informed decisions on stimulating economic growth and reducing unemployment rates.
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