The Financial Evaluation of Population Growth in Relation to Development Aid: China Versus India (Two Nation Brands’ Stories)

Rostislav Mansfeld () and Barbora Růžičková ()
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Rostislav Mansfeld: IP100040, University of Economics in Prague, Faculty of Finance and Accounting
Barbora Růžičková: University of Economics in Prague, Faculty of International Relations

A chapter in The Impact of Globalization on International Finance and Accounting, 2018, pp 53-60 from Springer

Abstract: Abstract For the past 40 years, the most dynamic economies in the world were China and India. From approx. 5% of US GDP level, China grows to 26% and India to 11% of current US levels. The ratio of foreign trade to GDP is similar for both countries as well as capital flows to GDP. So why did China outperform India twice in terms of GDP growth and why is India first now catching up? This is of extreme relevance in relation to implementation of Sustainable Development Goals (SDGs) of Agenda 2030 in which the objective is to eradicate poverty and to enhance economic growth in developing countries. The reduction of poverty in shortest time was best mastered by China over the last 30 years; hence, to put developing countries on the same track is the beginning of the path of their development. A theoretical comparison analysis, backed by observations of China, India, Uganda, and Chad GDP as well as their population growths, indicates that population growth is the major negative enabler of GDP growth per capita of developing countries. Using our drafted methodology, we can assess if certain levels of development aid will be sufficient to overcome the negative GDP per capita pull driven by overpopulation and calculate how much development aid is needed to get countries on sustainable GDP per capita growth path. We ask ourselves, are current SDGs (especially SDG 1, 2, and 8) in developing countries really financially achievable?

Keywords: Growth; Developing countries; Economic development; Development policy; Population (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-68762-9_6

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