Vehicle Ownership Analysis Based on GDP per Capita in China: 1963–2050

The growth of vehicle ownership not only brings opportunity for the economy, but also brings environment and transport problems, which is not good for sustainable transportation. It is of great significance to build supporting infrastructure and other services based on accurate forecasts of vehicle ownership in various provinces because of the variance of economic development stages, the carrying capacity of resources, and different degrees of transport planning in each province. We used the Gompertz model in order to predict China’s provincial vehicle ownership from 2018 to 2050. Considering the impact of the population structure, we summed up the growth rate of GDP per labor, the growth rate of population and the growth rate of employment rate to get the growth rate of GDP and then the GDP per capita of each province. We found that the vehicle ownership in each province will grow rapidly in the next 30 years; however, the change in the ranking of vehicle ownership among provinces varies. The ranking of some provinces with high or middle ranking now will decline in the following years, especially Beijing, Tianjin, Shanghai and Xinjiang. While the ranking of some provinces that locates in the middle and low ranking now will increase, such as Chongqing, Hubei, Anhui, Sichuan, Heilongjiang, Jiangxi, Hunan and Guangxi. We also investigate the reasons that affect the trend in each province and we find that the suitable vehicle growth pattern of each province, the stage of economic development and government policy, which are related to the growth rate of GDP per labor, employment rate, and GDP per capita, can affect vehicle ownership in the future.

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Searching for new Sources of growth. Are the Developing Countries Catching up with the Developed ones?

Voprosy Ekonomiki ◽

10.32609/0042-8736-2015-10-30-53 ◽

2015 ◽

pp. 30-53

Author(s):

V. Popov

Keyword(s):

Developing Countries ◽

Western Europe ◽

Gdp Per Capita ◽

The West ◽

Catching Up ◽

The Us ◽

The World ◽

Economic Divergence ◽

Per Capita ◽

Relative Standing

This paper examines the trajectory of growth in the Global South. Before the 1500s all countries were roughly at the same level of development, but from the 1500s Western countries started to grow faster than the rest of the world and PPP GDP per capita by 1950 in the US, the richest Western nation, was nearly 5 times higher than the world average and 2 times higher than in Western Europe. Since 1950 this ratio stabilized - not only Western Europe and Japan improved their relative standing in per capita income versus the US, but also East Asia, South Asia and some developing countries in other regions started to bridge the gap with the West. After nearly half of the millennium of growing economic divergence, the world seems to have entered the era of convergence. The factors behind these trends are analyzed; implications for the future and possible scenarios are considered.

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Atlas of economic complexity, Russian regional pages

Voprosy Ekonomiki ◽

10.32609/0042-8736-2018-6-71-91 ◽

2018 ◽

pp. 71-91 ◽

Cited By ~ 2

Author(s):

I. L. Lyubimov ◽

M. V. Lysyuk ◽

M. A. Gvozdeva

Keyword(s):

Value Added ◽

Growth Strategy ◽

Gdp Per Capita ◽

Export Diversification ◽

Russian Regions ◽

Economic Complexity ◽

Diversification Strategies ◽

Central Regions ◽

Per Capita ◽

Regions Of Russia

Well-established results indicate that export diversification might be a better growth strategy for an emerging economy as long as its GDP per capita level is smaller than an empirically defined threshold. As average incomes in Russian regions are likely to be far below the threshold, it might be important to estimate their diversification potential. The paper discusses the Atlas of economic complexity for Russian regions created to visualize regional export baskets, to estimate their complexity and evaluate regional export potential. The paper’s results are consistent with previous findings: the complexity of export is substantially higher and diversification potential is larger in western and central regions of Russia. Their export potential might become larger if western and central regions, first, try to join global value added chains and second, cooperate and develop joint diversification strategies. Northern and eastern regions are by contrast much less complex and their diversification potential is small.

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The Reproduction Vector of the Russian Economy: 1999-2007

Voprosy Ekonomiki ◽

10.32609/0042-8736-2008-4-94-109 ◽

2008 ◽

pp. 94-109 ◽

Cited By ~ 1

Author(s):

D. Sorokin

Keyword(s):

Economic Policy ◽

Human Capital ◽

Growth Rates ◽

Developed Countries ◽

Gdp Per Capita ◽

Real Sector ◽

Russian Economy ◽

Per Capita ◽

The Developed Countries

The problem of the Russian economy’s growth rates is considered in the article in the context of Russia’s backwardness regarding GDP per capita in comparison with the developed countries. The author stresses the urgency of modernization of the real sector of the economy and the recovery of the country’s human capital. For reaching these goals short- or mid-term programs are not sufficient. Economic policy needs a long-term (15-20 years) strategy, otherwise Russia will be condemned to economic inertia and multiplying structural disproportions.

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The monetary value of disability-adjusted-life-years lost in Kenya in 2017.pdf

10.31124/advance.9754874 ◽

2019 ◽

Author(s):

Joses Kirigia ◽

Rose Nabi Deborah Karimi Muthuri

Keyword(s):

New York ◽

International Monetary Fund ◽

Health Expenditure ◽

Analytical Framework ◽

World Health ◽

Gdp Per Capita ◽

Current Health ◽

Monetary Value ◽

Life Years ◽

Per Capita

<div>A variant of human capital (or net output) analytical framework was applied to monetarily value DALYs lost from 166 diseases and injuries. The monetary value of each of the 166 diseases (or injuries) was obtained through multiplication of the net 2019 GDP per capita for Kenya by the number of DALYs lost from each specific cause. Where net GDP per capita was calculated by subtracting current health expenditure from the GDP per capita. </div><div> </div><p>The DALYs data for the 166 causes were from IHME (Global Burden of Disease Collaborative Network, 2018), GDP per capita data from the International Monetary Fund world economic outlook database (International Monetary Fund, 2019), and the current health expenditure per person data from the WHO Global Health Expenditure Database (World Health Organization, 2019b). A model consisting of fourteen equations was calculated with Excel Software developed by Microsoft (New York).</p><p> </p>

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