Brazil: from boom to bust?

Post by Facundo Abraham ’16 and Alberto González de Aledo Pérez ’16, current master’s students in the Barcelona GSE International Trade, Finance, and Development Program.

brazil

In 2001 it was widely predicted that in a decade’s time, Brazil, Russia, India and China (dubbed the BRICs) would become leading economies in the world, reshaping the global economy and international institutions. Now, more than a decade later, with the BRICs economies slowing down, these countries have lost momentum and there is doubt whether the BRICs’ will actually take over the world economy. The most paradoxical case is perhaps Brazil. Once seen as the country of the future and put forward as an example of economic success, it has now sunk into recession, high inflation and corruption scandals. So, what happened to Brazil? How did the country go from being the pampered child of international investors to a pariah in just a decade? What we argue is that when the figures are examined, they reveal that since 2001 the economic performance of Brazil was far from spectacular and, in fact, rather disappointing. Thus, the negative shift in expectations towards Brazil should come as no surprise.

We will focus on a simple growth accounting exercise. Simply stated, we assume that output in an economy is produced under a Cobb-Douglas production function in which capital and labour are used as inputs together under a certain technology/productivity. Mathematically:

equation

Then, to eliminate the effect of country size on output, we can define output per worker as:

equation

In this way, we can see that growth can be derived from two sources: technological progress and capital accumulation such that:

equation

Did Brazil keep up pace with the other BRICs?

The first question we need to answer is whether Brazil was experiencing high growth rates like the rest of the BRICs. The data shows that after 2001, the economy of Brazil lagged behind the other BRIC countries. Between 2002 and 2011 output per worker grew on average only 1.2%, far behind the 8.5% in Russia, 7.3% in China and 6.9% in India. Comparing the growth rates year by year clearly shows the sluggish performance of Brazil among the BRICs.

figure

Going further, we can ask ourselves how did Brazil perform related to capital accumulation and productivity growth. In the period 2002-2011 capital accumulation in Brazil was low with the capital per worker ratio growing on average by 2.3%. This figure is far less than the 11.4% in China and 8.6% in India. Yes, Brazil did better than Russia where the ratio increased by 1.9% yearly. However, Russia beat Brazil by far in productivity growth. While in Russia productivity grew on average 7.7% per year, in Brazil it grew by only 0.2%. Brazil was the BRIC country with lowest productivity growth being also behind India (2.5%) and China (0.4%).

figure 2

But was Brazil doing better than before?

Even though Brazil could have been doing worse than the rest of the BRICs, maybe Brazil was experiencing an economic boom compared to the previous years, which motivated the positive change in investor sentiment. However, again the data shows that Brazil performed worse since 2001 than in the 90s. Between 1990 and 2001 the Brazilian output per worker grew at 3.9% on average per year, with capital per worker growing at 4.5% and productivity at 1.8%. As shown below, these figures are better than the ones from 2002 onwards.

table

An interesting observation comes from analysing the capital intensity ratio, measured as capital stock over output. In the growth literature, as an economy moves towards its steady state, the growth rate of the capital to output ratio diminishes and eventually becomes zero in the steady state. Thus, the growth rate of capital to output is called “transitional growth” while the growth rate of productivity is the long-term growth. Looking at the data, the growth of the capital intensity ratio in Brazil dropped over recent years, being near to zero. This behaviour is more consistent with an economy that is exhausting its growth rather than with an economy entering a period of high growth.

figure 3

More Latin American, less BRIC

A final analysis consists in comparing the economic performance of Brazil to the other two big Latin American economies: Argentina and Mexico. Brazil’s output per worker growth of 1.2% per year was less than the 4.2% in Argentina and 1.5% in Mexico. In addition, comparing the growth rates for each year shows that the behaviour of the three economies was very similar and, moreover, Brazil performed worse than Argentina.

figure 4

This simple comparison could support the view that Brazil does not seem to have behaved like the other BRICs, being closer in performance to its Latin American neighbours. This observation is important considering that while Brazil was a star in the international markets, Mexico and Argentina were viewed with far more pessimism.

Concluding remarks

This growth accounting exercise is useful in providing simple insights that help us understand more clearly what has happened to Brazil over the last decade. There are many reasons behind the rise and fall of the Brazilian economy and it is not the aim of this article to account for them all. The results are enlightening because they show that, after being included in the BRICs and brought into the spotlight of financial markets, Brazil’s economic performance was modest compared to the other BRIC countries and even to its own past performance. Thus, even before the start of the crisis, the Brazilian economy showed some weaknesses that should have raised red flags early on.

About the authors

FacundoFacundo is a current student at the International Trade, Finance and Development program. Previously he worked in consulting projects on financial regulation and supervision in Latin America. He graduated in Economics from Universidad Torcuato di Tella. Connect with Facundo on Linkedin.

Alberto Alberto is a current student at the International Trade, Finance and Development program. He is a former Economist in BBVA’s Economic Research Department. He holds a BSc in Economics from Universidad Carlos III de Madrid. Connect with Alberto on Linkedin or follow him on Twitter.

The link between export diversification and economic growth

This empirical exercise examines how export diversification is related with higher GDP per capita growth.

Post by Facundo Abraham ’16 and Alberto González de Aledo Pérez ’16, current master’s students in the Barcelona GSE International Trade, Finance, and Development Program.

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The diversification of exports exemplifies the transition of economies towards higher levels of development with more complex economic structures. It can also facilitate risk reallocation and mitigate negative terms of trade shocks in a certain industry or geographical area. In addition, countries exposed to international competition can benefit from better ways of doing business.

This empirical exercise examines how export diversification is related with higher GDP per capita growth. For the most part it follows the dynamic panel data model proposed in Hesse (2008) for a sample of seven Asian emerging markets and developing economies. The author illustrates that these countries are considered to be a cluster characterised by both high degrees of export diversification and GDP per capita growth in the long run. The exercise updates the calculations made for this sample.

Model specification and data

The augmented version of the Solow growth model provides the necessary framework.

model

The dependent variable denotes the natural log difference of GDP per capita adjusted for PPP, retrieved from the World Bank. The independent variables are the initial income and a vector of growth determinants. Gamma captures the time-invariant unit-specific effects and eta the time effects.

The vector of growth determinants consists of human capital, the natural log difference of population, the share of investment in total GDP and a measure of export diversification. Population and investment are taken as proxies for employment and savings, respectively. Together with human capital, these were retrieved from the Penn World Table 8.1 release.

Export diversification is defined as the residual of a normalised Herfindahl-Hirschman index.

index

The equation exhibits reporter country i exports commodity x to partner j. The data was retrieved from the UN Comtrade database. To compute the indices, the chosen breakdown was the ninety-seven chapter disaggregation.

The sample period used as an input to the model runs from 1996 to 2011 on an annual frequency and covers Bangladesh, China, India, Indonesia, Malaysia, the Philippines and Thailand.

The model is estimated as a system generalised method of moments (GMM) similar to Arellano and Bover (1995) and Blundell and Bond (1998). This specification uses as instruments the first-differenced equations with up to four lag levels and equations in levels with up to four lag first-differences.

Estimation and robustness check

tableColumn 1 in Table 1 presents the estimation for the augmented Solow model. The computed coefficients are significant and have the expected sign. There is evidence from column 2 that export diversification has a positive and significant effect on GDP per capita growth as has already been predicted in previous studies. Columns 5 to 8 supports the robustness of export diversification with the inclusion of different control variables. If openness is entered as it is in column 8, initial income becomes not significant. The performance on this indicator varies across countries in the sample. In the case of the Philippines and Malaysia there is a downward trend. However, in the former the initial values were remarkably high. China has also experienced a decrease in its level of openness in the aftermath of the crisis.

Export diversification is not a linear process. It is better depicted as an inverted U-shaped pattern. On the one side, early stages of development are characterised by a concentration in production of a handful of items or extraction of natural resources. On the other side, advanced economies also specialise their exports in a number of items. The development of complex economic structures is a harbinger of increasing competitiveness and export diversification in emerging and developing economies.

Columns 3 and 4 test for the presence of nonlinearity in the relation between export diversification and GDP per capita growth. The squared term of export diversification has a negative effect on GDP per capita growth. However, it is not significant in this specification. On the contrary, the interaction term is significant and changes sign. These regressions show some evidence of a certain degree of nonlinearity.

Concluding remarks

The exercise has examined the link between export diversification and GDP per capita growth in a cluster of economies that have a particular intense relation among these indicators. The results illustrate that income could have benefited from the diversification of exports. These findings are robust and are consistent to the sample used in Hesse (2008) and previous literature on the topic.

Future research could include further variables such as partner diversification or trade in services statistics. However, the former is limited compared to trade in commodities. In addition, in order to evaluate shocks in price and cost competitiveness, real effective exchange rates could be introduced.

References

Arellano, M. & O. Bover (1995). “Another Look at the Instrumental-Variable Estimation of Error Component Models”. Journal of Econometrics Vol. 68(1), pp. 29-52.

Blundell, R. & S. Bond (1998). “Initial Conditions and Moment Restrictions in Dynamic Panel Data Model”. Journal of Econometrics, Vol. 87, pp. 115-43.

Hesse, H. (2008). “Export Diversification and Economic Growth”. Working Paper, No. 21. Commission on Growth and Development, World Bank.

Roodman, D. (2009). “How to do xtabond2: An Introduction to Difference and System GMM in Stata”. The Stata Journal, Vol. 9, No. 1, pp. 86-136.


† Trade data is reported in the Harmonised System international standardised nomenclature for traded commodities. This convention organises items into twenty-one sections, ninety-seven chapters and subsequent headings and subheadings. For example, section 15 breaks into 12 chapters such as iron and steel (72) and articles thereof (73).

About the authors

FacundoFacundo is a current student at the International Trade, Finance and Development program. Previously he worked in consulting projects on financial regulation and supervision in Latin America. He graduated in Economics from Universidad Torcuato di Tella. Connect with Facundo on Linkedin.

Alberto Alberto is a current student at the International Trade, Finance and Development program. He is a former Economist in BBVA’s Economic Research Department. He holds a BSc in Economics from Universidad Carlos III de Madrid. Connect with Alberto on Linkedin or follow him on Twitter.