scholarly journals CONDIÇÕES SOCIAIS E COMPETITIVIDADE

2000 ◽  
Vol 24 ◽  
Author(s):  
Mariano De Matos Macedo

O objetivo desse artigo é mostrar que as idéias e as propostas de políticas vinculadas ao conceito de competitividade sistêmica e à “moderna teoria do crescimento econômico” são muito semelhantes e convergentes. Tanto esse conceito quanto essa teoria afirmam que condições sociais precárias (grandes desigualdades na distribuição de renda, atraso educacional, etc.) constituem fatores que podem limitar o crescimento econômico, a expansão da produtividade (PIB per capita) e, portanto, as possibilidades de competitividade internacional de um País. A explicação do “resíduo de Solow” pela “moderna teoria do desenvolvimento” - endogeneizando na função de produção todos aqueles fatores acumuláveis e potencializadores de riqueza (estritamente econômicos ou não), antes considerados exógenos ou residuais por Solow – leva, como nas concepções relativas à competitividade sistêmica, a um amplo leque de variáveis econômicas e sociais como determinante de fundamental importância na explicação da taxa de crescimento per capita do PIB. Essas concepções teóricas também escapam da armadilha dos rendimentos decrescente, presentes nos “antigos modelos”, e explicam porque as taxas de crescimento de alguns países podem crescer, ao longo do tempo, mais do que a de outros países, ampliando - ao invés de fazer convergir, pela liberdade dos mercados e mobilidade dos fatores - as diferenças de níveis de desenvolvimento econômico e de competitividade entre as Nações. Abstract The objective of this article is to show that the ideas and the political proposals tied to the concept of sistemic competitiveness and the “modern theory of the economic growth” are very similar and convergent. Both the concept and the theory affirm that precarious social conditions (great inequalities in the income distribution, educational delay, etc.) constitute factors that can limit the economic growth, the expansion of productivity (the GDP per capita) and, therefore, the possibilities of international competitiveness of a Country. The explanation of the “Solow’s residue” for the “modern theory of the development” – internalized in the production function all those factors that improve the wealth (strictly economic or not), before considered external or vestigial for Solow - leads, as in the conceptions related to the sistemic competitiveness, to an ample fan of economic and social variables as determinants of basic importance in the explanation of the per capita tax growth of the GIP per capita. These theoretical conceptions also escape of the the incomes decreasing trap, found in the “old models”, and explain why the growth rates of some countries can grow, along the time, more than other countries, extending - instead of making to it converge, through the freedom of the markets and mobility of the factors - differences of levels of economic development and competitiveness between the Nations.

2019 ◽  
Author(s):  
cut jussara mufda

The cause of economic growth but not followed by the improvement of the income distribution system is because economic growth is measured by an increase in GDP (Gross Gross Domestic Product), namely the number of products in the form of goods and services produced within a country's territory in one year.Gross Domestic Product is always considered to be an indicator or determinant of living standards in a country. Therefore it is necessary to calculate GDP per capita. The calculation of Indonesia's GDP is carried out every year and always changes. The amount of GDP in Indonesia in 2016 is approximately 3,604 per capita and in 2018 it has decreased to 3,788 per capita after 2017 has increased to 3,875 per capita.Economic growth in Indonesia continues to increase along with the 4 components above which continue to be improved. Because GDP is a standard that has become a benchmark for economic growth, the 4 components that are continually being improved also encourage economic growth in Indonesia. This can be seen from 2019 Indonesia's GDP which increased compared to 2018. Investment that continues to increase then also increases GDP per capita in Indonesia in 2019.


Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 890
Author(s):  
Jakub Bartak ◽  
Łukasz Jabłoński ◽  
Agnieszka Jastrzębska

In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes characterized by different means and volatility of growth rates. We further investigate whether a volatile economy is likely to grow more slowly and analyze the determinants of high/low growth with high/low volatility patterns. The main results indicate a negative relationship between volatility and growth. Moreover, the results suggest that international trade simultaneously promotes growth and increases volatility, human capital promotes growth and stability, and financial development reduces volatility and negatively correlates with growth.


2019 ◽  
Author(s):  
Dhina Vadyza

Economic growth is a process of increasing per capita output that occurs continuously in the long run. Economic growth is one indicator of the success of development. Increasingly increasing economic growth usually increases people's welfare. While economic development is an effort to increase per capita income by processing potential economic forces into the real economy through investment, increasing knowledge, increasing skills, using technology, adding management skills and organizing.Economic growth is also related to the increase in "per capita output". The theory must include theories about GDP growth and theories about population growth. Then the third aspect is economic growth in a long-term perspective, that is, if for a long period of time the per capita output shows an increasing tendency.The distribution of income distribution in Indonesia is increasingly uneven. This can be seen from the increasing Indonesian Gini Index. As is known, the Gini index measures the income distribution of a country. The size of the Gini index Between 0 (zero) to 1 (one), the Gini index Equal to 0 (zero) indicates the index that the income distribution is perfectly equal, while the Gini index is 1 (one ) shows that the income distribution is totally uneven. Based on the data, the Indonesian Gini index continues to increase from year to year.The state of income distribution in Indonesia since 1970 can be said not to improve, this is caused by many factors, including the First production factor market (input market) which is the increase in labor supply which results in excess labor, low labor wages and limited employment opportunities in urban areas resulting in unemployment and urban slums.Second, land ownership. Land distribution is the main determinant of the extent of poverty and income distribution.


2016 ◽  
Vol 12 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Zenonas Norkus

AbstractThis paper contributes to cliometric research on the economic output of Finland, Estonia, Lithuania and Latvia between 1913 and 1938. For Finland, gross domestic product (GDP) values from Maddison project dataset are accepted. For Estonia, Arno Köörna’s and Jaak Valge’s estimates are endorsed with reservations for 1923–1924. According to an optimistic estimate, Lithuania’s GDP per capita was below all-Russian mean in 1913, but was not less than USSR level in 1938, while Gediminas Vaskela’s pessimistic estimate of the 1938 Lithuanian GDP implies its GDP growth underperformance. Using new sources, the first estimates of Latvia’s output for the 1913–1938 period in cross-country and cross-temporally comparable measurement units (1990 Geary Khamis international $) are substantiated. Under optimistic estimates of Lithuanian GDP growth, this country was on par with Finland in terms of annual growth rates, with Latvia following next and Estonia displaying the weakest growth performance.


2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Saleh Nagiyev

Demographic factors have sometimes occupied center-stage in the discussion of the sources of economic growth. In the 18th century, Thomas Malthus made the pessimistic forecast that GDP growth per capita would fall due to a continued rapid increase in world population. There is a straightforward accounting relationship when identifying the sources of economic growth: Growth Rate of GDP = Growth Rate of Population + Growth Rate of GDP per capita, where GDP per capita is simply GDP divided by population. This article examines the interconnection between economic development and the demographic policy of Azerbaijan. The article analyzes various approaches of the impact of demographic factors on the economic development of a country. The following demographic factors have been identified and described as significant for the economic development: fertility dynamics, mortality dynamics, population size and gender and age structure.


2021 ◽  
Author(s):  
Mengmeng Hu ◽  
Yafei Wang ◽  
Beicheng Xia ◽  
Guohe Huang

Abstract Analysing the relationship between energy consumption and economic growth is essential to achieve the goal of sustainable development. We employ hot spot analysis to discover the spatial agglomeration of GDP per capita and energy intensity in Guangdong, China, from 2005–2018. Furthermore, panel vector autoregression coupled with a system generalized method of moments is performed to examine the dynamic causal relationship between energy consumption and economic growth under the framework of the Cobb-Douglas production function. Using a multivariate model and grouped studies based on the differences in regional economic development, we show that the GDP per capita of the Pearl River Delta (PRD) is significantly higher than that of the peripheral municipalities. However, energy intensity shows an entirely different spatial distribution. The development of the regional economy depends on its own “assembling effect”. GDP explains approximately 68.3% of the total variation in energy consumption in the PRD and only approximately 34.5% of that in the peripheral municipalities. We do not confirm Granger causality between energy consumption and economic development. Guangdong can decrease its energy consumption growth without substantially sacrificing its economic growth. The analysis framework of this paper has significant implications for regions in balancing economic development and energy consumption.


Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 289
Author(s):  
Vladimir Balash ◽  
Olga Balash ◽  
Alexey Faizliev ◽  
Elena Chistopolskaya

In this article, we analyze the σ - and β -convergence, using the data of the socio-economic development of Russian areas, and discover the role of spatial autocorrelation in regional economic development. We are considering 80 areas of the Russian Federation for the period of 2010–2017. Moran coefficients were used to estimate spatial autocorrelation. We compare the Moran scatterplots for GDP per capita and GDP growth rates per capita in 2017 and in 2014. We study the impact on raising investment in leading capital and the costs of technological innovation. We evaluate a wide range of specifications of spatial econometric models for all kinds of weight matrices. We combine standard geographical proximity with specialization proximity to assess whether they are substitutes or additions to converging economic growth rates. The weight matrix of the neighborhood and specialization similarities are used. The weight matrix of specialization similarities of the regional economies is based on data on the structure of tax payments in 82 industries. The specialization structure of the region’s economy is related to its location. Clusters obtained by matrices of specialization proximity are well separable from each other in space. The connectivity within clusters and the boundaries between them become more apparent over time. It is shown that according to the results of estimation of conditional β -convergence models, the models of 2010–2014 and 2014–2017 differ significantly. There is a statistically significant β -convergence for the period 2010–2014. There is also the presence of spatial autocorrelation. Based on the results of valuation models constructed from data after 2014, it can be concluded that the coefficient estimates for the explanatory variables are not significantly different from zero, and accordingly there is no tendency towards regional convergence in terms of economic development. The results obtained in the work are stable for the proposed models and spatial weight matrices. Territorial proximity is a more important factor than the similarity of specialization for explanation the economic growth rates of Russian regions.


2021 ◽  
Vol 10 (4) ◽  
pp. 114
Author(s):  
Myslym Osmani ◽  
Kledi Kodra ◽  
Drini Salko

This study focuses on the institutional factors of Albania's economic development, from a comparative, dynamic, and regional European perspective. We use longitudinal data for the years 2002, 2014, and 2019 and a small selection of 13 countries in the region and some EU member states. Descriptive statistics, graphical representation, and econometric modeling are used for data analysis. The purpose of the study is to discuss, in real and comparative terms with the region and beyond, the economic growth of Albania based on the GDP per capita indicator, as well as to identify and evaluate dynamically the role of institutions in the country's development through important institutional factors, such as the effectiveness of government, rule of law, corruption, etc. The analysis shows that Albania's economic performance is weakover the last two decades. This is reflected in the insufficient relative growth of GDP per capita, the small increase in per capita income, and especially in the low increase in income for every 1% of relative growth. In these indicators, Albania continues to be consistently in the lowest positions in the region and beyond. The study highlights the strong link between economic growth and the effectiveness of government, the rule of law, and weak control over corruption. Improving corruption control by one unit in the range (-2.5 to 2.5) is expected to improve GDP per capita by an average of about 2.2 times. Improving the rule of law by one point is expected to improve GDP per capita on average by about 2.4 times. The country's sluggish economic performance is mainly attributed to weak institutions.   Received: 4 March 2021 / Accepted: 6 May 2021 / Published: 8 July 2021


2021 ◽  
Vol 25 (111) ◽  
pp. 165-173
Author(s):  
Victor Quinde Rosales ◽  
Rina Bucaram Leverone ◽  
Martha Bueno Quinonez ◽  
Michelle Saldana Vargas

This article is an inductive argumentation and an empirical-analytical paradigm that evaluates the actual relationship between Gross Domestic Product (GDP) per capita and the Carbon Dioxide (CO2) in country groups of the G8 considered as developed in a period of time from 1960 to 2011. It was developed an Augmented Dickey-Fuller unit root (ADF), a Granger Causality Test and a Johansen Cointegration test. The results evidence the non-stationary of constrains in both countries. It was obtained a VAR model with two variables with a number of lags of four - VAR2 (4) to which were tested for causality by demonstrating a unidirectionality of GDP per capita to CO2. Keywords: economic growth, economic development, income distribution, environmental economics. References [1]G. Brundtland, «Our Common Future,» de Report of the World Commission on Environment and Development , 1987. [2]R. Bermejo, Del desarrollo sostenible según Brundtland a la sostenibilidad como biomimesis, Bilbao: Hegoa, 2014. [3]R. B. and. P. C. Fander Falconí, «Flacso,» 16 03 2016. [Online]. Available: https://www.flacsoandes.edu.ec/agora/62767-la-discutible-curva-de-kuznets. [Last access: 15 01 2021]. [4]E. Urteaga, «Las teorías económicas del desarrollo sostenible,» Cuadernos de Economía, vol. 32, nº 89, pp. 113-162, 2009. [5]V. K. Smith, Scarcity and Growth Reconsidered, Baltimore: The Johns Hopkins Press, 1979. [6]J. y. A. Medina, «Ingreso y desigualdad: la Hipótesis de Kuznets en el caso boliviano,» Espacios, vol. 38, nº31, p. 23, 2017. [7]M. Ahluwalia, «Inequality, poverty and development, » Journal of Development Economics, nº 3, pp. 307-342, 1976. [8]A. and R. D. Alesina, «Distributive politics and economic growth,» Quarterly Journal of Economics, vol. 109, nº 2, pp. 465-490, 1994. [9]R. Barro, «Inequality and growth in a panel of countries, » Journal of Economic Growth, vol. 5, nº 1, pp. 5-32, 2000. [10]M. A. Galindo, «Distribución de la renta y crecimiento económico,» de Anuario jurídico y económico escurialense, 2002, pp. 473-502. [11]A. Álvarez, «Distribución de la renta y crecimiento económico, Información Comercial Española, ICE,» Revista de economía, nº 835, pp. 95-100, 2007. [12]J. C. Núñez, «Crecimiento económico y distribución del ingreso: una perspectiva del Paraguay,» Población y Desarrollo, nº 43, pp. 54-61, 2016. [13]S. Kuznets, «Economic Growth and Income Inequality, » American Economic Review, nº 45, pp. 1-28, 1955. [14]J. A. and. C. J. Araujo, «Relación entre la desigualdad de la renta y el crecimiento económico en Brasil: 1995-2012.,» Problemas del desarrollo, vol. 46, nº 180, pp.129-150, 2015. [15]F. V. A. and P. C. Correa, «La Curva Medioambiental de Kuznets: Evidencia Empírica para Colombia Grupo de Economía Ambiental (GEA),» Semestre Económico, vol. 8, nº 15, pp. 13-30, 2005. [16]W. Malenbaum, World Demand for Raw Materials in 1985 and 2000, McGraw-Hill: New York, 1978. [17]W. Beckerman, «Economists, scientists, and environmental catastrophe,» Oxford Economic Papers, vol. 24, nº 3, 1972. [18]G. y. K. A. Grossman, «Economic Growth and the Environment,» The Quarterly Journal of Economics, vol. 110, nº 2, pp. 353-377, 1995. [19]N. Stokey, «Are there Limits to Growth?,» International Economic Review, vol. 39, nº 1, 1998. [20]W. and. C. W. Jaeger, «A Theoretical Basis for the Environmental Inverted-U Curve and Implications for International Trade,» de Discussant: Clive Chapple, New York, 1998. [21]T. B. K. B. R. and. G. K. Cavlovic, «A Mets-Analysis of Environmental Kuznets Curve Studies,» Agricultural and Resource Economics, nº 29, pp. 32-42, 2000. [22]M. and. S. T. Heil, «Carbon emissions and economic development: future trajectories based on historical experience, » Environment and Development Economics, vol. 6, nº 1, pp. 63-83, 2001. [23]U. S. R. and E. B. Soytas, «Energy consumption, income, and carbon emissions in the United States,» Ecological Economics, vol. 62, nº 3, pp. 482-489, 2007.[24]C. W. J. Granger, «Investigating causal relations by econometrics models and cross spectral methods,» Econometrica, nº 37, pp. 424-438, 1969. [25]M. and U. R. Nasir, «Environmental Kuznets Curve for carbon emissions in Pakistan: An empirical investigation,» Energy Policy, vol. 39, nº 3, pp. 1857-1864,2011. [26]S. Johansen, «Statistical Analysis of Cointegration Vectors,» Journal of Economic Dynamics and Control, vol. 12, nº 2, pp. 231-254, 1988. [27]B. Goldman, «Meta-Analysis of Environmental Kuznets Curve Studies: Determining the Cause of the Curve’s Presence,» de Honors Projects, 2012. [28] M. B.  and T. T. Fosten, «Dynamic misspecification in the environmental Kuznets curve: Evidence from CO2 and SO2 emissions in the United Kingdom,» Ecological Economics, vol. 76, pp. 25-33, 2012.  


2018 ◽  
Vol 6 ◽  
pp. 247-252
Author(s):  
Łukasz Konopielko ◽  
Oleksandr Demchenko

This paper is focused on determining the effects of increase in the Internet usage on economic development of two groups of countries: OECD and NON-OECD countries. Two separate Vector Autoregression models were used. The hypotheses were inspired by claims that GDP per capita and trade, including trade in services, have a positive correlation with Internet usage growth. The hypotheses were tested on a set of 26 OECD and 21 NON-OECD countries for a period of 20 years, from 1995 to 2015. Results of the paper do not confirm the existence of a direct positive correlation between GDP per capita and Internet users. For all countries, a direct comparison of the chosen variables show a negative correlation. For OECD countries, trade in services has a positive correlation with Internet usage growth, while for NON-OECD countries both trade and trade in services showed a positive correlation with Internet usage growth.


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