Research Article |
Corresponding author: Rumi Azim ( azim.rumi05@gmail.com ) Academic editor: Marina Sheresheva
© 2022 Manmohan Agarwal, Rumi Azim, Sushil Kumar.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits to copy and distribute the article for non-commercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Agarwal M, Azim R, Kumar S (2022) BRICS: The 2008 financial crisis and economic performance. BRICS Journal of Economics 3(2): 21-49. https://doi.org/10.3897/brics-econ.3.e86488
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The 2008 global financial crisis (GFC) resulted in a deterioration of the economic condition in developing countries with lower growth of per capita GDP, a decline in the share of exports of goods and services in GDP, and a worsening of the external balance. After a limited initial impact, growth rates declined in all the economies and were substantially lower in all the BRICS countries in the period 2015–2019 than before the crisis. Two of them, Brazil and South Africa, experienced a drop in per capita GDP during 2015–1019. Export performance suffered and the external balance worsened for all BRICS countries. The BRICS share of world GDP increased mainly because of the rapid growth in China and to a lesser extent in India. The relative size of per capita GDP in Brazil, Russia and South Africa decreased between 2001–2007 and 2015–2019. Furthermore, the average per capita GDP in Brazil and South Africa decreased compared to that of the world. BRICS, however, fared better in trade. Both their share of world trade and the share of trade in their GDP increased. The BRICS countries have strong trade links with other developing countries and have become more stable after the GFC, thereby contributing to the performance of the global economy. There are strong growth linkages among the member countries. Trade relations are dominated by China. BRICS, however, failed to comply with G20 commitment made at the 2014 Brisbane summit to raise the rate of growth by 2% by 2018. The authors undertook a time series analysis to investigate the relationship between growth of per capita income, the share of gross fixed investment in GDP, the share of exports of goods and services in GDP, and the share of external balance in GDP. We found out that usually, but not for all BRICS countries, capital formation had a positive effect on growth, while the external deficit had a negative effect.
BRICS, financial crisis, economic performance.
The BRICS countries are significant members of the world economy with four of them among the ten largest economies. BRICS countries account for 41% of the world’s population and 23% of global GDP (
The role of trade in economic development has been a controversial issue, with the dominant view changing over time. In the fifties and sixties, when many developing countries became independent and their governments sought to accelerate growth in order to improve the living conditions of their people, the general opinion of development economists was that these countries should adopt a strategy of import-substituting industrialization (ISI). Accelerating growth required increasing investment and more imports of capital goods as developing countries had limited capacity to produce them. The question was how to pay for imported capital goods.
Experience has shown that growth has stalled after an initial spurt and balance of payments problems have re-emerged.
Now the consensus has shifted to the assertion that protection worsened performance and that countries should adopt freer trade policies. Scitovsky, Little and Scott (1970) and
One of the methods of estimating the effect of trade was to regress the rate of growth on trade performance, in which different variables were used to measure trade performance.
Empirical analyses of the relationship between GDP and trade openness for the BRICS countries show a variety of results.
Given the variety of results, we undertake a re-examination of the relationship between exports, imports and GDP growth for the BRICS countries.
The 2008 GFC had a long-term negative impact on performance in developing countries. Growth of per capita GDP was negative in the period 2015–2019 in Latin America and the Caribbean (LAC) and Sub-Saharan Africa (SSA) regions (Table
Growth of per capita GDP in Europe and Central Asia (ECA) was only one fourth of what it was before the crisis. The effect on Asia was much smaller. Growth in East Asia and the Pacific (EAP) fell by a third, whereas it actually increased in South Asia (SA) (see Table
Region | 2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | 2015-2019 as ratio of 2001-2007 |
EAP | 8.4 | 7.9 | 6.7 | 5.7 | 0.68 |
ECA | 6.5 | 0.8 | 3.4 | 1.6 | 0.25 |
LAC | 2.1 | 1.5 | 1.6 | -0.2* | |
MNA | 2.8 | 2.0 | -0.4 | 0.4 | 0.15 |
SA | 4.7 | 4.5 | 4.5 | 5.2 | 1.1 |
SSA | 3.0 | 1.9 | 1.8 | -0.2* |
Region | 2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | Difference between 2015-2019 and 2001-2007 |
EAP | 4.5 | 4.8 | 2.2 | 1.8 | -2.8 |
ECA | 4.9 | 3.1 | 2.0 | 2.9 | -2.0 |
LAC | 1.5 | -0.4 | -1.6 | -1.3 | -2.7 |
MNA | 3.1 | 1.6 | -1.1 | -5.8 | -8.9 |
SA | -2.5 | -5.7 | -5.4 | -3.9 | -1.4 |
SSA | 1.1 | 0.0 | -0.4 | -2.9 | -4.0 |
Exports of goods and services (XGS) as a percentage of GDP fell in all regions and were lower in the period 2015–2019 compared to 2001–2007, except in LAC and SA (Appendix, Table
In brief, the GFC resulted in a deterioration of the economic condition of developing countries with lower growth of per capita GDP, a decline in the share of XGS in GDP, and a worsening of external balance.
Economic performance of the BRICS countries mirrors that of developing countries. The rate of growth of per capita GDP in 2015–2019 was negative for Brazil and South Africa and almost zero for Russia, but declined only marginally in China and India (Table
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 2.3 | 3.2 | 1.4 | -1.2 |
China | 10.2 | 9.4 | 7.6 | 6.2 |
India | 5.2 | 5.0 | 4.8 | 5.6 |
Russia | 7.2 | 4.2 | 2.1 | 0.9 |
South Africa | 3.0 | 1.9 | 0.9 | -0.6 |
The relatively good growth performance of China and India is deceptive as the analysis of annual growth rates shows a considerable decline in growth rates in these two countries (Figure
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 1.2 | -0.5 | -1.8 | 0 |
China | 4.4 | 5.2 | 2.4 | 1.8 |
India | -2.1 | -5.0 | -4.8 | -2.7 |
Russia | 11.9 | 8.2 | 6.6 | 7.2 |
South Africa | 1.0 | 0 | -1.1 | 0.3 |
The BRICS countries have, however, maintained high levels of investment (Appendix, Table
Despite the poor overall export performance (Appendix, Table
There is a strong correlation between rates of growth in the BRICS countries. The correlation for most countries is significant at the 99% level of significance, with the exception of India, whose growth does not correlate with growth in any of the other BRICS countries (Table
The share of intra-BRICS exports in their total exports increased from 4.3% in 2000 to 7.2% in 2008 and slightly more slowly to 10.0% in 2019 (Table
China | India | Russia | South Africa | |
Brazil | 0.64 | -0.18. | 0.63 | 0 .65 |
China | 0.19 | 0.59 | 0.77 | |
India | -0.19 | -0.04 | ||
Russia | 0.88 |
Brazil | China | India | Russia | South Africa | BRICS Total | |||||||
USD, billion | Share (%) | USD, billion | Share (%) | USD, billion | Share (%) | USD, billion | Share (%) | USD billion | Share (%) | USD billion | Share (%) | |
2001 | 3.7 | 6.4 | 7 | 2.6 | 2.3 | 5.3 | 6.9 | 6.9 | 1.1 | 4.4 | 21.1 | 4.3 |
2008 | 24.0 | 12.3 | 92.1 | 6,4 | 16.9 | 9.3 | 28.5 | 6.1 | 7.5 | 10.1 | 169.0 | 7.2 |
2014 | 50.4 | 22.8 | 158.5 | 6.8 | 28,5 | 9.0 | 44.4 | 8.9 | 13.5 | 14.7 | 295.4 | 8.5 |
2019 | 68.9 | 31.2 | 176.4 | 7.1 | 28.2 | 8.7 | 67.4 | 15.8 | 14.4 | 16.5 | 355.3 | 10.0 |
BRICS were expected to play an increasingly important role in the world economy as their share of both world GDP and trade was increasing. In the immediate aftermath of the crisis of 2008–2010, the share of each of the BRICS countries in world GDP increased leading some analysts to expectations that BRICS might lift the world economy out of the recession that accompanied the crisis (Table
Even though over the longer term, namely between 2001–2007 and 2015–2019, their share of world GDP increased by almost 60% (Table
The BRICS countries, however, fared better in trade. Their share of world trade has increased and the share of trade in their GDP has also increased. BRICS substantially increased their share of world exports of goods and services in the periods 2001–2007 and 2015–2019; China’s and India’s share of world exports almost doubled (Appendix, Table
The share of BRICS countries in world merchandise trade increased by almost 70%, more than their share of exports of goods and services (Appendix, Table
However, the share of exports of goods and services in GDP behaved differently for some of the BRICS countries than for the world in general. The share increased for the world, despite a dip in the period 2015–2019 (Appendix, Table
In the initial period of 2001–2007, the share of merchandise exports in GDP was higher for China, Russia, and South Africa than for the world (Appendix, Table
One of the objectives of BRICS is to foster South-South economic cooperation in addition to expanding economic cooperation among themselves.
Four of the BRICS countries send more of their exports to developing countries than the average for all G20 developing country members, and three of them send more of their exports to developing countries than the average for all developing countries (Appendix, Table
Here we analyze whether the BRICS economies have achieved the goals enunciated at various G20 summits. The Philadelphia summit of 2009 declared the goal to achieve strong, stable and balanced global growth (SSBGG). The Brisbane summit of 2014 declared that G20 countries would raise growth of GDP by 2%. We measured the economic volatility of the BRICS member countries by calculating the standard deviation of their annual growth of per capita GDP from 2001 to 2007 and separately from 2011 to 2019 to examine whether economies have become more stable. The picture here is mixed; the standard deviation increased for Brazil and Russia and decreased substantially for China, while remaining almost constant for India and South Africa (Appendix, Table
Annual data on macroeconomic variables are obtained from World Bank indicators for the sample period from 1992 to 2019. We investigate the relationship between growth of per capita GDP, the share of GFCF in GDP, and the share of external balance in GDP. In Model 1, we estimate the causal relationship between the variables of GDP per capita, external balance, and GFCF for each of the BRICS countries for the period from 1992 to 2019. Further, we also analyze this relationship by considering exports and imports separately in Model 2. These two models are specified as follows:
Model 1: PCI𝑡−1 = 𝛼0 + 𝛼1𝐺𝐹𝐶𝐹𝑡−𝑚 + 𝛼1𝐸𝐵𝑡−𝑚 + 𝜆1𝐸𝐶𝑇𝑡−1 + 𝜀𝑡 (1),
Model 2: 𝑃𝐶𝐼𝑡−1 = 𝛽0 + 𝛽1𝐺𝐹𝐶𝐹𝑡−𝑚 + 𝛽1𝑋𝑡−𝑚 + 𝛽1𝑀𝑡−𝑚 + 𝜆2𝐸𝐶𝑇𝑡−1 + 𝜇𝑡 (2),
where PCI – log of per capita GDP, GFCF – log of gross fixed capital formation, EB – log of (Imports/Exports), i.e. EB > 1 for current account deficit and EB < 1 for current account surplus, X – log of exports of goods and services, M = log of imports of goods and services, ECT = error correction term εt, µt = the stochastic error term and α0, β0 = constant or intercept term (t-m) = (year t – lag order), where m is the order of lag length.
All variables must be stationary to avoid the problem of spurious regression. The Augmented Dickey Fuller (ADF) reveals that all the variables are non-stationary at levels and stationary after taking the first difference (Tables
Since the variables are non-stationary at levels and stationary at the first differences, the variables can be co-integrated. The Johansen test of cointegration suggests the existence of cointegrating equations in both Model 1 and Model 2 for all BRICS countries (Appendix, Table
Therefore, we use the Vector Error Correction Model (VECM) to estimate the short-run and long-run relationship between the variables, as defined above in equations (1) and (2).
Short-run model looks like:
Model 1: ∆ 𝑃𝐶𝐼𝑡−1 = 𝛼0 + 𝛼1 ∆ 𝐺𝐹𝐶𝐹𝑡−𝑚 + 𝛼1 ∆ 𝐸𝐵𝑡−𝑚 + 𝜆1𝐸𝐶𝑇𝑡−1 + 𝜀𝑡 (3),
Model 2: ∆ 𝑃𝐶𝐼𝑡−1 = 𝛽0 + 𝛽1 ∆ 𝐺𝐹𝐶𝐹𝑡−𝑚 + 𝛽1 ∆ 𝑋𝑡−𝑚 + 𝛽1 ∆ 𝑀𝑡−𝑚 + 𝜆2𝐸𝐶𝑇𝑡−1 + 𝜇𝑡 (4).
The cointegrating equations (long-run model) when PCI is normalized to unity looks like:
Model 1: 𝐸𝐶𝑇𝑡−1 = 𝑃𝐶𝐼𝑡−1 − 𝜑1𝐺𝐹𝐶𝐹𝑡−1 − 𝜑2𝐸𝐵𝑡−1 + 𝜑0 (5),
Model 2: 𝐸𝐶𝑇𝑡−1 = 𝑃𝐶𝐼𝑡−1 − 𝛾1𝐺𝐹𝐶𝐹𝑡−1 − 𝛾2𝑋𝑡−1 − 𝛾3𝑀𝑡−1 + 𝛾0 (6),
where 𝛼0, 𝛽0, 𝜑0, 𝛾0 = constant or intercept term.
The VEC residual autocorrelation test confirms that there is no autocorrelation, i.e. the errors are not serially correlated (Table
The results of the short-run regression are presented in Appendix (Table
Now we summarize the short-run causality (Table
Since the coefficient of the error correction term is negative and significant for Brazil, India and China (Models 1 and 2), this indicates that there is a convergence towards the long-run dynamics compared to short-run. Thus, long-run relationship exists between per capita GDP and other variables in these countries. However, there are no long-run relationships in Russia and South Africa.
Brazil | China | India | |
Model 1 | GFCF <--> PCI (+,+) | GFCF --> PCI (+) | GFCF --> PCI (+) |
EB --> PCI (-) | PCI --> EB (-) | ||
EB --> GFCF (+) | GFCF --> EB (+) | ||
Model 2 | GFCF <--> PCI (+,+) | GFCF --> PCI (+) | GFCF --> PCI (+) |
X <--> PCI (+,+) | X --> PCI (+) | X <--> PCI (+) | |
M --> PCI (-) | M <--> PCI (-,-) | M --> PCI (-) | |
X <--> GFCF (-,-) | GFCF --> X (-) | ||
M --> GFCF (+) | GFCF --> M (+) | ||
M --> X (+) | X --> M (+) | M --> X (+) |
In the long run, Brazil, China and India show a classical relationship between GFCF and growth. In addition, in Brazil, we find that the external balance adversely affects GDP but positively affects GFCF, as the government seeks to re-ignite growth when the external balance is favorable. While exports have a positive effect on per capita GDP as the external constraint is relaxed, imports have a negative effect on per capita GDP in Brazil; this supports the findings of
To summarize, we find that GFCF has a positive effect on per capita GDP in Brazil, India and China in the long run. In all three countries, exports have a positive effect on per capita GDP but imports have a negative effect, a typical import multiplier effect. Per capita GDP, in turn, has a positive and significant effect on exports in Brazil. The external balance adversely affects GDP and positively affects GFCF in Brazil, while it has little effect on either GDP or GFCF in China and India. Imports trigger exports in Brazil and India, whereas in China, exports trigger imports. GFCF has a negative effect on exports in Brazil and India. Domestic output does not respond rapidly to GFCF demand, and output is diverted to meet investment demand. GFCF crowds out exports.
The GFC has had a deep and lasting effect on growth in the BRICS countries. In two of them, Brazil and South Africa, growth turned negative. Russia experienced a steadily declining growth rate, now approaching almost zero. Both China and India, after seemingly successfully weathering the crisis, faced a slowdown in growth. The growth experience of the BRICS countries means that only China and India increased their share of world GDP, in contrast to the original prediction in the Goldman Sachs study. The share of exports in GDP and their share in world exports tend to decrease. They are, however, a positive force for South-South trade as four BRICS countries export more to developing countries than the average G20 developing country and three BRICS countries – more than the average developing country. The volatility of GDP growth and GFCF ratio decreased for most of the BRICS countries; but this suggests that they are stuck at a low level on both these important indicators. Also, BRICS failed to achieve the goal of raising growth rate by 2% set at the Brisbane G20 summit.
In the VECM regression, we found that a long-run relationship existed between per capita GDP and other variables in Brazil, India and China. In Russia and South Africa, there is no long-run relationships. In the long run, the external balance has a negative effect on the growth of per capita GDP in Brazil, but the effect is insignificant for China and India. GFCF has a positive impact on per capita GDP in Brazil, India and China, as one would expect from traditional growth models. While this effect is unidirectional in China and India, it is bidirectional in Brazil. In all three countries (Brazil, China and India), exports have a positive effect, exports are growth inducing, as most analysts currently believe, and imports have a negative effect on GDP per capita in the long run. In Brazil and India, there is bidirectional causality between per capita GDP and exports (positive). In China, there is a bidirectional causality between per capita GDP and imports (negative). Imports trigger exports in Brazil and India, whereas in China, exports trigger imports. Based on Granger causality test, we fail to reject the null hypothesis that there is no causal relationship between variables in the short run in Brazil. In China, GFCF causes per capita GDP in the short run. In India, imports cause per capita GDP and exports in the short run. In Russia, GFCF causes exports, but does not cause per capita GDP in the short run. In South Africa, there is bidirectional causality between per capita GDP and GFCF, and both per capita GDP and GFCF cause imports in the short run. Some political implications of this research are to encourage an increase in domestic investment, which is found to be a key determinant of growth, to implement export-promotion strategies to control the current account deficit, as well as to enhance growth and stimulate imports of foreign inputs and capital goods, which, in turn, can boost exports and economic growth.
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | 2015-2019 as ratio of 2001-2007 | |
EAP | 34.6 | 32.3 | 29.5 | 24.8 | 0.72 |
ECA | 33.9 | 30.8 | 30.3 | 31.4 | 0.92 |
LAC | 20.7 | 19.5 | 19.8 | 20.8 | 1.01 |
MNA | 35.3 | 33.9 | 31.2 | 27.8 | 0.79 |
SA | 17.2 | 21.0 | 22.8 | 18.1 | 1.05 |
SSA | 30.2 | 31.3 | 30.0 | 24.1 | 0.80 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | 2015-2019 as ratio of 2001-2007 | |
EAP | 34.3 | 38.7 | 40.5 | 39.1 | 1.14 |
ECA | 21.3 | 23.4 | 23.5 | 23.2 | 1.09 |
LAC | 18.5 | 20.4 | 20.5 | 18.0 | 0.97 |
MNA | 23.5 | 26.9 | 24.9 | 22.9 | 0.98 |
SA | 29.2 | 31.4 | 30.0 | 27.0 | 0.92 |
SSA | 21.1 | 22.1 | 21.0 | 21.0 | 1.0 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 17.5 | 19.7 | 26.3 | 15.7 |
China | 37.5 | 42.3 | 37.5 | 42.2 |
India | 31.4 | 34 | 31.5 | 28.6 |
Russia | 18.7 | 22.0 | 21.0 | 21.2 |
South Africa | 17.1 | 21.4 | 21.9 | 18.9 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 14.5 | 11.8 | 11.6 | 13.3 |
China | 29.5 | 28.2 | 25.0 | 19.6 |
India | 17.3 | 22.5 | 24.4 | 19.2 |
Russia | 34.4 | 29.5 | 27.0 | 28.0 |
South Africa | 28.6 | 30.7 | 30.7 | 30.0 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 2.9 | 1.4 | 0.5 | 0.4 |
China | 27.5 | 28.8 | 26.8 | 26.8 |
India | 1.4 | 2.1 | 1.7 | 1.2 |
Russia | 0.2 | 0.2 | 0.4 | 0.6 |
South Africa | 1.5 | 1.2 | 1.2 | 1.2 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 2.5 | 2.1 | 2.2 | 5.5 |
China | 2.7 | 5.3 | 8.1 | 12.0 |
India | 45.9 | 48.6 | 46.8 | 46.2 |
Russia | 3.9 | 5.4 | 5.9 | 7.8 |
South Africa | 2.6 | 2.0 | 3.4 | 4.0 |
Exports of goods and services (% of world exports of goods and services)
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 1.0 | 1.2 | 1.2 | 1.1 |
China | 5.4 | 8.1 | 9.7 | 10.6 |
India | 1.1 | 1.7 | 2.0 | 2.1 |
Russia | 1.8 | 2.4 | 2.5 | 1.8 |
South Africa | 0.5 | 0.5 | 0.5 | 0.4 |
Total | 9.8 | 13.9 | 15.9 | 16.1 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 1.0 | 1.2 | 1.3 | 1.2 |
China | 6.4 | 9.5 | 11.3 | 13.0 |
India | 0.9 | 1.3 | 1.6 | 1.6 |
Russia | 2.0 | 2.6 | 2.8 | 2.0 |
South Africa | 0.5 | 0.5 | 0.5 | 0.5 |
Total | 10.9 | 15.2 | 17.5 | 18.4 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 0.7 | 0.9 | 1.1 | 0.8 |
China | 0.7 | 2.0 | 2.9 | 2.5 |
India | 2.3 | 3.4 | 3.4 | 3.6 |
Russia | 1.0 | 1.3 | 1.4 | 1.1 |
South Africa | 0.6 | 0.6 | 0.4 | 0.3 |
Total | 5.3 | 8.1 | 9.2 | 8.3 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 48.2 | 60.2 | 63.7 | 66.2 |
China | 45.0 | 50.2 | 55.7 | 55.6 |
India | 54.6 | 64.5 | 66.5 | 62.5 |
Russia | 36.8 | 39.6 | 46.8 | 46.6 |
South Africa | 36.4 | 49.1 | 64.5 | 62.7 |
Average BRICS | 44.2 | 52.7 | 59.4 | 58.7 |
Average G2016 |
42.0 | 49.8 | 55.2 | 54.2 |
Average LDCs17 |
49.5 | 57.3 | 62.5 | 61.1 |
Standard deviation | ||
2001-2007 | 2011-2019 | |
Brazil | 2.0 | 2.5 |
China | 2.1 | 1.0 |
India | 1.8 | 1.4 |
Russia | 1.4 | 2.2 |
South Africa | 1.2 | 0.9 |
Standard deviation | |||
2001-2007 | 2011-2019 | ||
Brazil | 1.4 | 1.2 | |
China | 6.3 | 3 | |
India | 3.4 | 2.8 | |
Russia | 2.1 | 1.6 | |
South Africa | 2.4 | 0.6 |
External balance on goods and services of the BRICS Countries (% of GDP)
Standard deviation | ||
2001-2007 | 2011-2019 | |
Brazil | 2.0 | 1.2 |
China | 2.8 | 0.8 |
India | 1.3 | 1.8 |
Russia | 1.7 | 1.6 |
South Africa | 2.4 | 1.3 |
Standard | deviation | |
2001-2007 | 2011-2019 | |
Brazil | 0.6 | 2.7 |
China | 2.3 | 1.1 |
India | 2.8 | 2.3 |
Russia | 1.1 | 0.5 |
South Africa | 2.0 | 0.9 |
2001-2007 | 2008-2010 | 2011-2014 | 2015-2019 | |
Brazil | 21.8 | 19.2 | 19.2 | 20.2 |
China | 54.0 | 49.3 | 44.5 | 33.4 |
India | 25.6 | 36.3 | 41.4 | 29.3 |
Russia | 48.0 | 43.0 | 39.3 | 39.1 |
South Africa | 47.6 | 53.2 | 58.8 | 56.5 |
Variables | Brazil | China | India | Russia | South Africa |
PCI | -1.43 | -1.17 | 0.02 | -0.52 | -0.97 |
(0.56) | (0.68) | (0.96) | (0.88) | (0.76) | |
GFCF | -1.57 | -0.82 | -0.66 | -0.51 | -0.82 |
(0.49) | (0.81) | (0.85) | (0.88) | (0.81) | |
EB | 0.30 | 0.21 | -0.82 | -1.01 | -2.47 |
(0.26) | (0.1) | (0.81) | (0.74) | (0.12) | |
X | -0.69 | -1.97 | -1.95 | -2.48 | -1.65 |
(0.84) | (0.30) | (0.30) | (0.12) | (0.45) | |
M | -1.74 | -2.15 | -1.37 | -0.64 | (-2.00) |
(0.41) | (0.22) | (0.59) | (0.86) | (0.28) |
Variables | Brazil | China | India | Russia | South Africa |
PCI | -3.4 | -2.83 | -5.04 | -3.39 | -3.44 |
(0.01) | (0.04) | (0.00) | (0.01) | (0.00) | |
GFCF | -3.44 | -6.98 | -5.22 | -3.5 | -3.33 |
(0.00) | (0.00) | (0.00) | (0.00) | (0.01) | |
EB | -2.67 | -6.84 | -5.00 | -3.41 | -4.13 |
(0.04) | (0.00) | (0.00) | (0.01) | (0.00) | |
X | -4.13 | -4.36 | -4.76 | -4.45 | -5.26 |
(0.00) | (0.00) | (0.00) | (0.00) | (0.00) | |
M | -5.47 | -6.84 | -3.05 | -4.09 | -4.98 |
(0.00) | (0.00) | (0.03) | (0.00) | (0.00) |
Model 1 | Model 2 | |||||
Eigenvalue | Trace Statistic | 5% critical value | Eigenvalue | Trace Statistic | 5% critical value | |
Brazil | ||||||
None | 30.2151 | 29.68 | 95.8336 | 47.21 | ||
At most 1 | 0.50661 | 11.8471* | 15.41 | 0.89658 | 39.1092 | 29.68 |
At most 2 | 0.3234 | 1.6897 | 3.76 | 0.66706 | 11.6144* | 15.41 |
At most 3 | 0.06292 | 0.34834 | 0.9087 | 3.76 | ||
At most 4 | 0.0357 | |||||
China | ||||||
None | 42.7901 | 29.68 | 69.3598 | 47.21 | ||
At most 1 | 0.67842 | 14.4272* | 15.41 | 0.71994 | 36.2679 | 29.68 |
At most 2 | 0.37961 | 2.492 | 3.76 | 0.55671 | 15.1161* | 15.41 |
At most 3 | 0.09487 | 0.39443 | 2.0748 | 3.76 | ||
At most 4 | 0.0767 | |||||
India | ||||||
None | 38.6391 | 29.68 | 72.6165 | 47.21 | ||
At most 1 | 0.62125 | 14.3668* | 1.54E+01 | 0.82342 | 29.2671* | 29.68 |
At most 2 | 0.436 | 0.0491 | 3.76 | 0.54811 | 9.409 | 15.41 |
At most 3 | 0.00196 | 0.29925 | 0.5188 | 3.76 | ||
At most 4 | 0.02054 | |||||
Russia | ||||||
None | 31.1447 | 29.68 | 65.0525 | 47.21 | ||
At most 1 | 0.46239 | 15.0087* | 15.41 | 0.71 | 34.106 | 29.68 |
At most 2 | 0.39902 | 1.7697 | 3.76 | 0.5828 | 12.2514* | 15.41 |
At most 3 | 0.0658 | 0.28543 | 3.8495 | 3.76 | ||
At most 4 | 0.14271 | |||||
South Africa | ||||||
None | 43.8743 | 29.68 | 78.3307 | 53.12 | ||
At most 1 | 0.69644 | 14.0696* | 15.41 | 0.70035 | 48.2025 | 34.91 |
At most 2 | 0.38939 | 1.7372 | 3.76 | 0.61935 | 24.0558 | 19.96 |
At most 3 | 0.06713 | 0.4529 | 8.9778* | 9.42 | ||
At most 4 | 0.3017 |
Brazil | China | India | Russia | South Africa | ||||||
Lag | Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 |
0 | -1.3 | -1.79 | -1.43 | -2.78 | -1.53 | -5.25 | -0.66 | -4.43 | -3.93 | -5.99 |
1 | -5.24* | -8.44 | -9.47 | -11.43* | -8.81 | -11.46 | -5.55* | -9.61 | -8.14 | -11.44 |
2 | -5.02 | -8.45* | -9.76* | -10.99 | -8.81* | -11.52* | -5.21 | -10.08* | -8.27* | -11.96* |
VARIABLES | Brazil | China | India | Russia | South Africa | |||||
M1 | M2 | M1 | M2 | M1 | M2 | M1 | M2 | M1 | M2 | |
ECT | -0.38*** | -0.33*** | -0.27* | -0.25** | -0.17** | -0.63*** | 1.36** | 0.60 | 1.80 | 5.73*** |
(0.09) | (0.13) | (0.14) | (0.12) | (0.07) | (0.21) | (0.62) | (0.39) | (1.42) | (2.08) | |
PCI(L1) | 0.24 | -0.28 | 0.47* | 0.65*** | 0.18 | 0.18 | -0.49 | 2.38 | -0.06 | -2.70* |
(0.47) | (0.94) | (0.26) | (0.17) | (0.49) | (0.40) | (0.78) | (1.62) | (0.83) | (1.41) | |
PCI(L2) | -0.58 | 0.05 | -0.79 | -0.18 | -1.27 | -1.07 | -1.86** | |||
(0.74) | (0.23) | (0.50) | (0.39) | (1.27) | (0.85) | (0.89) | ||||
GFCF(L1) | -0.29 | -0.06 | -0.04 | 0.45** | -0.17 | -0.51 | 0.17 | -0.75 | -0.18 | 1.97 |
(0.42) | (0.82) | (0.37) | (0.21) | (0.32) | (0.34) | (0.65) | (1.17) | (0.57) | (1.26) | |
GFCF(L2) | 0.30 | 0.18 | 0.12 | -0.40 | 0.92 | 1.35* | 2.74*** | |||
(0.64) | (0.27) | (0.33) | (0.39) | (1.05) | (0.71) | (0.90) | ||||
EB(L1) | 0.57 | -0.05 | 0.15 | 0.49 | ||||||
(0.40) | (0.16) | (0.40) | (0.66) | |||||||
EB(L2) | -0.05 | 0.41 | -0.68 | |||||||
(0.15) | (0.35) | (0.65) | ||||||||
X(L1) | 0.11 | 0.24 | -0.73* | -1.05 | -0.75 | |||||
(0.59) | (0.16) | (0.43) | (1.08) | (0.61) | ||||||
X(L2) | -0.13 | -0.85** | 0.32 | 0.37 | ||||||
(0.54) | (0.41) | (0.60) | (0.60) | |||||||
M(L1) | 0.13 | 0.05 | 0.99** | -1.19 | 0.31 | |||||
(0.70) | (0.18) | (0.47) | (1.15) | (0.79) | ||||||
M(L2) | -0.02 | 0.78** | 0.55 | -0.17 | -1.50** | |||||
(0.72) | (0.38) | (0.56) | (0.90) | (0.72) | ||||||
Constant | 0.00 | 0.04 | -0.04 | -0.05 | 0.09* | -0.01 | 0.01 | 0.00 | 0.00 | -0.00 |
(0.03) | (0.05) | (0.03) | (0.03) | (0.04) | (0.04) | (0.04) | (0.06) | (0.03) | (0.03) | |
Observations | 26 | 25 | 25 | 26 | 25 | 25 | 26 | 25 | 25 | 25 |
Model 1 | Model 2 | |||||
Lags | chi2 | df | p-value | chi2 | df | p-value |
Brazil | ||||||
1 | 11.2128 | 9 | 0.2614 | 12.6268 | 16 | 0.69982 |
2 | 6.6794 | 9 | 0.67046 | 12.0504 | 16 | 0.7405 |
China | ||||||
1 | 12.8385 | 9 | 0.17005 | 20.6861 | 16 | 0.19089 |
2 | 10.5126 | 9 | 0.3106 | 11.6104 | 16 | 0.77033 |
India | ||||||
1 | 4.8409 | 9 | 0.84795 | 10.0495 | 16 | 0.86403 |
2 | 10.7523 | 9 | 0.29307 | 17.8176 | 16 | 0.33469 |
Russia | ||||||
1 | 12.2891 | 9 | 0.1975 | 10.6633 | 16 | 0.82978 |
2 | 17.1744 | 9 | 0.04605 | 18.1212 | 16 | 0.31685 |
South Africa | ||||||
1 | 4.977 | 9 | 0.83631 | 11.5649 | 16 | 0.77335 |
2 | 4.909 | 9 | 0.84216 | 18.6308 | 16 | 0.28827 |
Table |
||||||
---|---|---|---|---|---|---|
Model 1 | Model 2 | |||||
Equation | chi2 | df | p-value | chi2 | df | p-value |
Model 1 | Model 2 | |||||
Equation | chi2 | df | p-value | chi2 | df | p-value |
Brazil | ||||||
ΔPCI | 0.26 | 2 | 0.88 | 10.63 | 2 | 0.11 |
ΔGFCF | 1.64 | 2 | 0.44 | 0.13 | 2 | 0.94 |
ΔEB | 5.18 | 2 | 0.08 | |||
ΔX | 25.47 | 2 | 0.00 | |||
ΔM | 1.09 | 2 | 0.58 | |||
ALL | 7.08 | 6 | 0.31 | 37.32 | 8 | 0.00 |
China | ||||||
ΔPCI | 1.21 | 2 | 0.55 | 1.50 | 2 | 0.47 |
ΔGFCF | 2.92 | 2 | 0.23 | 0.50 | 2 | 0.78 |
ΔEB | 0.40 | 2 | 0.82 | |||
ΔX | 1.34 | 2 | 0.51 | |||
ΔM | 1.08 | 2 | 0.58 | |||
ALL | 4.53 | 6 | 0.61 | 4.42 | 8 | 0.82 |
India | ||||||
ΔPCI | 0.08 | 2 | 0.96 | 2.13 | 2 | 0.35 |
ΔGFCF | 5.27 | 2 | 0.07 | 0.89 | 2 | 0.64 |
ΔEB | 0.39 | 2 | 0.83 | |||
ΔX | 0.02 | 2 | 0.99 | |||
ΔM | 1.56 | 2 | 0.46 | |||
ALL | 5.73 | 6 | 0.45 | 4.60 | 8 | 0.80 |
Russia | ||||||
ΔPCI | 2.23 | 2 | 0.33 | 1.84 | 2 | 0.40 |
ΔGFCF | 0.04 | 2 | 0.98 | 2.31 | 2 | 0.31 |
ΔEB | 0.50 | 2 | 0.78 | |||
ΔX | 11.09 | 2 | 0.00 | |||
ΔM | 1.36 | 2 | 0.51 | |||
ALL | 2.78 | 6 | 0.84 | 16.59 | 8 | 0.03 |
South Africa | ||||||
ΔPCI | 2.83 | 2 | 0.24 | 3.59 | 2 | 0.17 |
ΔGFCF | 1.31 | 2 | 0.52 | 0.86 | 2 | 0.65 |
ΔEB | 1.23 | 2 | 0.54 | |||
ΔX | 0.20 | 2 | 0.91 | |||
ΔM | 0.25 | 2 | 0.88 | |||
ALL | 5.37 | 6 | 0.50 | 4.89 | 8 | 0.77 |
Table |
||||
---|---|---|---|---|
Country | Dependent variable | Source of causality | ||
Short run (Wald chi sq statistic) | ||||
ΔPCI | ΔGFCF | ΔEB | ||
Country | Dependent variable | Source of causality | ||
Short run (Wald chi sq statistic) | ||||
ΔPCI | ΔGFCF | ΔEB | ||
Brazil | ΔPCI | _ | 0.47 | 2.06 |
(0.49) | (0.15) | |||
ΔGFCF | 0.13 | _ | 3.23* | |
-0.71 | (0.07) | |||
ΔEB | 0.07 | 0.02 | _ | |
(0.78) | (0.87) | |||
China | ΔPCI | _ | 0.01 | 0.1 |
(0.91) | (0.75) | |||
ΔGFCF | 1.72 | _ | 0.25 | |
(0.18) | (0.61) | |||
ΔEB | 1.11 | 3.93** | _ | |
(0.29) | (0.04) | |||
India | ΔPCI | _ | 0.29 | 0.14 |
(0.59) | (0.70) | |||
ΔGFCF | 1.63 | _ | 0.17 | |
(0.20) | (0.68) | |||
ΔEB | 1.99 | 0.03 | _ | |
(0.15) | (0.86) | |||
Russia | ΔPCI | _ | 0.07 | 0.97 |
(0.79) | (0.32) | |||
ΔGFCF | 0.57 | _ | 0.89 | |
(0.44) | (0.34) | |||
ΔEB | 0.95 | 0.64 | _ | |
(0.32) | (0.42) | |||
South Africa | ΔPCI | _ | 3.79 | 1.39 |
(0.15) | (0.49) | |||
ΔGFCF | 4.43 | _ | 1.26 | |
(0.10) | (0.53) | |||
ΔEB | 1.61 | 3.82 | _ | |
(0.44) | (0.14) |
Table |
|||||
---|---|---|---|---|---|
Country | Dependent variable | Source of causality | |||
Short run (Wald chi sq statistic) | |||||
ΔPCI | ΔGFCF | ΔX | ΔM | ||
Country | Dependent variable | Source of causality | |||
Short run (Wald chi sq statistic) | |||||
ΔPCI | ΔGFCF | ΔX | ΔM | ||
Brazil | ΔPCI | _ | 0.28 | 0.07 | 0.04 |
(0.87) | (0.96) | (0.98) | |||
ΔGFCF | 0.1 | _ | 0.21 | 0.32 | |
(0.95) | (0.89) | (0.85) | |||
ΔX | 0.41 | 1.11 | _ | 4.47 | |
(0.81) | (0.57) | (0.10) | |||
ΔM | 0.63 | 0.68 | 0.78 | _ | |
(0.72) | (0.71) | (0.67) | |||
China | ΔPCI | _ | 4.44** | 2.17 | 0.06 |
(0.03) | (0.14) | (0.80) | |||
ΔGFCF | 1.33 | _ | 0.18 | 0.02 | |
(0.24) | (0.67) | (0.89) | |||
ΔX | 1.82 | 1.72 | _ | 0 | |
(0.17) | (0.19) | (0.99) | |||
ΔM | 0.11 | 0.05 | 0.07 | _ | |
(0.73) | (0.82) | (0.79) | |||
India | ΔPCI | _ | 2.33 | 2.89 | 4.47** |
(0.12) | (0.08) | (0.03) | |||
ΔGFCF | 0.61 | _ | 0.01 | 0.74 | |
(0.43) | (0.92) | (0.39) | |||
ΔX | 0.01 | 0.1 | _ | 4.19** | |
(0.93) | (0.75) | (0.04) | |||
ΔM | 0.18 | 0.01 | 0.14 | _ | |
(0.67) | (0.90) | (0.70) | |||
Russia | ΔPCI | _ | 1.01 | 1.19 | 1.08 |
(1.60) | (0.55) | (0.58) | |||
ΔGFCF | 2.06 | _ | 1.06 | 1.54 | |
(0.35) | (0.58) | (0.46) | |||
ΔX | 0.68 | 6.74** | _ | 3.39 | |
(0.71) | (0.03) | (0.18) | |||
ΔM | 2.4 | 0.79 | 2.74 | _ | |
0.3 | 0.67 | 0.25 | |||
South Africa | ΔPCI | _ | 9.46*** | 1.71 | 4.48 |
(0.00) | (0.42) | (0.10) | |||
ΔGFCF | 9.62*** | _ | 1.45 | 3.8 | |
(0.00) | (0.48) | (0.14) | |||
ΔX | 2.02 | 0.56 | _ | 3.38 | |
(0.36) | (0.75) | (0.18) | |||
ΔM | 11.34*** | 10.48*** | 1.9 | _ | |
(0.00) | (0.00) | (0.38) |
Country | Normalized variable | |||
GDP (t-1) | GFCF (t-1) | EB (t-1) | ||
Brazil | PCI (t-1) | _ | 0.69*** | -2.01*** |
GFCF (t-1) | 1.44*** | _ | 2.91*** | |
EB (t-1) | -0.49 | 0.34 | _ | |
China | PCI (t-1) | _ | 1.23*** | 0.048 |
EB (t-1) | -20.43*** | 25.16*** | _ | |
India | PCI (t-1) | _ | 0.89*** | 0.17 |
Country | Normalized variable | ||||
GDP (t-1) | GFCF (t-1) | X (t-1) | M (t-1) | ||
Brazil | PCI (t-1) | _ | 1.89*** | 3.93*** | -6.06*** |
GFCF (t-1) | 0.52*** | _ | -2.07*** | 3.20*** | |
X (t-1) | 0.25* | -0.48*** | _ | 1.54*** | |
China | PCI (t-1) | _ | 2.01*** | 1.18*** | -1.92*** |
M (t-1) | -0.51** | 1.04*** | 0.61*** | _ | |
India | PCI (t-1) | _ | 0.53** | 0.79*** | -0.57*** |
X (t-1) | 1.25*** | -0.66*** | _ | 0.71*** |
1 The term BRIC was originally used in a report by Jim O’Neill of Goldman Sachs “Building better economic BRICs.” Beginning with meetings in 2006 among the foreign ministers of the 4 BRIC countries on the sidelines of the UN General Assembly, it was formally established in 2009. South Africa officially became a member in December 2010.
2 After adjusting various shortcomings of his analysis,
3 These countries could not adjust their policies to counter the effects of the GFC.
4 We do not find any substantial shifts in the share of major sectors, such as agriculture or manufacturing, in GDP.
5 Apart from infrastructure, investments are sought to be directed towards housing as this is expected to lead to higher employment. However, investments in construction rather than machinery and equipment can slow down production and growth (Agarwal et al., 2015).
6 See
7 Deterioration of the EB usually leads to lower growth.
8 The budget of President Obama in the US stressed the importance of shovel-ready projects that could be implemented quickly.
9 For instance, see
10 This is in contrast to the result of
11 For a discussion whether China was moving away from its investment and export dependent growth model, see
12 Partly for this purpose, the BRICS countries established the New Development Bank, open to membership of all countries, to provide development loans to developing countries. They also established the Contingent Reserve Arrangement to provide loans to countries with balance of payments deficits.
13 For further details, see Agarwal, (2013), South-South Economic Cooperation: Emerging Trends and Challenges, Background Research Paper submitted to the High-Level Panel on the post-2015 Development Agenda, May 2013, UN, New York.
14 Our data shows that growth of per capita GDP increased by less than 2%. Since growth of GDP would increase by less than the growth of per capita GDP unless the growth of population increased, which did not happen, we can infer that the Brisbane target was not met.
15 In Russia, the lack of a long-run relationship can be due to the high and rising dependence of the Russian economy on oil prices and negative oil price shocks (
16 Apart from the five BRICS countries, other developing countries of the G20 are Argentina, Indonesia, Mexico and Turkey.
17 This is the average for all developing countries.