The Dynamic Effect of Unemployment Rate on Per Capita Real GDP in Iran

Meidani, Ali Akbar Naji; Zabihi, Maryam

doi:10.5539/ijef.v3n5p170

Cited by 14 publications

(9 citation statements)

References 22 publications

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“… Gross domestic product, GDP (GDP per capita; gdp). As Meidani & Zabihi (2011) emphasize, the link between GDP and unemployment is usually negative. According to the Okun's law, in order to reach a one percentage point decrease in unemployment rate per year, real GDP should increase about two percentage points faster than the rate of potential GDP growth over that year (Misini & Badivuku-Pantina, 2017).…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Effect of Technological Innovations on Unemployment in the European Union Countries

Lydeka

Karaliūtė

2021

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Innovation and unemployment are two economic elements related to each other that have been constantly analyzed in the economic debates from the beginning of the 21st century. A classical question is whether innovation creates or destroys jobs. The conventional approach contemplates innovation as a transformation instrument of an economy, resulting in economic growth and jobs creation. Another approach points out to various mechanisms which can compensate the primary effect of innovations and cause an ultimate effect of innovations on labour demand to be unclear. In view of the fact that there are many different explanations about the impact of innovations on labour demand, this paper, after the analysis of theoretical and empirical scientific literature in this field, provides an empirical analysis with unemployment as the dependent variable. The authors use data from 28 European Union countries for the period of 1992–2016 and pursue to research how technological innovations affect unemployment rate. There are two core independent variables – expenditure on R&D (research and development) and number of patent applications – as the main proxies for technological innovations. Control variables that affect unemployment are included to the model as well. The model was estimated using a dynamic two-step System Generalized Method of Moments (GMM-SYS) of a panel data system. After the composition of 12 different estimations of the model, the results suggest that, in some cases, technological innovations affect unemployment.

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Section: Methodsmentioning

confidence: 99%

Assessment of the Effect of Technological Innovations on Unemployment in the European Union Countries

Lydeka

Karaliūtė

2021

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“…Empirical studies (e.g., Meidani and Zabihi 2011;Malley and Molana 2007) reveal the relationship between unemployment and GDP-the unemployment rate is most often negatively related (i.e., GDP is increasing, unemployment is decreasing) with GDP per capita, however, in some research a positive correlation exists between these variables.…”

Section: Empirical Research On the Quantitative Assessment Of Technolmentioning

confidence: 99%

Do Technological Innovations Affect Unemployment? Some Empirical Evidence from European Countries

2017

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This paper analyses theoretical and empirical scientific literature about the impact of technological innovations on unemployment, considering the former as a key driver of long-term productivity and economic growth. Using panel data from 25 European countries for the period of 2000-2012, we aim to examine whether technological innovations affect unemployment. We used triadic patent families per million inhabitants as our main proxy for technological innovations, as well as other unemployment controls, in our model, which were estimated using System Generalized Method of Moments (SGMM). Finding no significant relationship between technological innovations and unemployment in our base estimation, we re-estimated it testing the impact with a time lag as well as using alternative proxies for technological innovations. Overall, the research estimations do not suggest that technological innovations have an effect on unemployment.

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“…Then, the results from Fernandes and Reddy (2020) show that energy consumption and economic growth are integrated into the long term in China and India. Research by Meidani and Zabihi (2014) found a strong one-way causality from energy consumption in the industrial sector to real GDP. Increasing energy consumption in the industrial sector can encourage economic growth.…”

Section: Research Bymentioning

confidence: 99%

The Influence of Natural Resources, Energy Consumption, and Renewable Energy on Economic Growth in ASEAN Region Countries

Rusiadi,

Hidayat,

Rangkuty

et al. 2024

IJEEP

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This research aims to identify and analyze the role of natural resources, CO2 emissions, and renewable energy on the economic growth of countries in the ASEAN region. In this study, we tested whether a dynamic process occurred based on a dynamic panel data model while comparing it with static panel data. The dataset used is ASEAN region countries with time series data from 2000-2021. The analysis tools used are static and dynamic panel data. The research results found that natural resources, FDI, and renewable energy significantly positively affect economic growth in the ASEAN region. Conversely, CO2 has a negative and insignificant effect. These results prove that the ASEAN region's economic development has been clean energy-oriented.

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The Dynamic Effect of Unemployment Rate on Per Capita Real GDP in Iran

Cited by 14 publications

References 22 publications

Assessment of the Effect of Technological Innovations on Unemployment in the European Union Countries

Assessment of the Effect of Technological Innovations on Unemployment in the European Union Countries

Do Technological Innovations Affect Unemployment? Some Empirical Evidence from European Countries

The Influence of Natural Resources, Energy Consumption, and Renewable Energy on Economic Growth in ASEAN Region Countries

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