Impact of China on commodity exporters

China’s meteoric rise over the past three decades is one of the most remarkable economic stories of our times. This rapid development has been accompanied by extensive industrialisation and urbanisation and, as a result, has been highly commodity-intensive. Gauvin and Rebillard  document that in 2011 China represented around 11 per cent of global oil consumption, 41 per cent of global copper consumption and 54 per cent of global iron ore consumption. Between 2003 to 2011 alone, China’s demand for iron ore increased by 213 per cent and copper by 157 per cent. 

For commodity-exporting nations, the rise of China and its voracious appetite for commodities has been an economic blessing. However, China’s growth model has been decelerating since the beginning of 2011, raising concerns about its possible global ramifications, especially for commodity exporters. 

I have compared the impact of a slowdown in China’s growth to that of the US on major commodity-exporting nations, based on my research with Richa Saraf (University at Albany, SUNY).  

Our main conclusions are:

• In response to a negative shock to economic growth in either China or the US, financial variables of all commodity exporters — relevant commodity price, exchange rate and stock price — are affected negatively. 
• In terms of real impact on aggregate output, emerging economies — like Brazil and Russia — are more vulnerable to a negative shock to China’s growth than advanced economies such as Australia and Canada. Even the emerging economies display a remarkable pattern of heterogeneity. 
• The main channel of transmission of the external shock to China on real economy is the associated decline in exports. 
• Finally, for the Australian economy, a negative China shock is more important than a negative US shock. 

In our empirical approach, we first carefully estimated a common economic activity factor from a range of real indicators of Chinese economy following Fernald, Spiegel and Swanson.  In terms of the Chinese data, the quality of the reported output figures has long been under scrutiny and so we used a range of economic indicators for China including industrial production index, electricity production, real retail sales, consumer expectation index, rail freight traffic, real estate investment —residential building, total loans, fixed asset investment and floor space started — and commodity building to estimate the common economic activity factor.  Similarly, we estimated an economic activity factor for the US. 

In the second step of our empirical framework, we assessed dynamic effects of an unanticipated one standard deviation negative shock to the economic activity factor of China and the US on 12 commodity-exporting nations: Argentina, Australia, Brazil, Canada, Chile, Colombia, Indonesia, Malaysia, Mexico, Peru, Russia and South Africa. We separately estimated a seven-variable domestic dynamic vector auto-regressive (VAR) system for each of these countries to assess the impact on output measured by industrial production, country-specific commodity price index, exchange rate and net export to China (or the US) while also controlling for domestic prices and short-term monetary policy instruments over the period January 2000 to September 2015. In each of these country-specific VAR, we use the China and the US economic activity factors as exogenous shocks. 

The main conclusion of our paper is summarised in Figure 1 where we rank the countries in terms of aggregate output impact of an unexpected negative shock to China over the horizon of 12 months after the shock. The top three countries with most negative real effect of a China shock are Brazil, Russia and Argentina. Australia ranks fifth out of the 12 nations, evidently the only advanced economy to suffer a severe implication. Still the overall magnitude of the impact is relatively small but long lasting for Australia. The countries to be least impacted include Canada, Mexico and somewhat surprisingly, Indonesia.  

Financial variables of all the countries suffer, commodity prices and stock prices decline and the exchange rate depreciates. The countries that suffer more in terms of aggregate output are the ones whose exports to China are hit harder in comparison. Thus, the transmission of the China shock to the real economy is via the direct trade channel. 

For comparison, we present our results for the US shock for all the countries in Figure 2. The Australian economy shows no significant real impact for the US shock mirroring the Canadian economy for the China shock. On the other hand, both Canada and Mexico, little affected by China, show strong vulnerability to the US shock. In fact, the effect of a negative US shock on the Canadian economy is much larger in magnitude compared to the impact of a negative China shock on the Australian economy. 

Our overall results strongly echo Gauvin and Rebillard who show that emerging economies are on average more vulnerable to a slowdown in China than advanced economies. The magnitude of the real effects on Brazil and Russia, for example, far outweighs that on Australia. This possibly reflects a more diversified economy with a sizeable service sector among the advanced countries. 

Our results also strongly suggest that emerging economies are quite diverse in terms of their possible vulnerability to China. Even within Latin America, Brazil and Argentina are a lot more exposed to a China shock than Colombia and Chile. What explains such heterogeneity among emerging economies is subject to further research. 

Finally, we find that some countries are more sensitive to developments in China than to shocks in the United States. Importantly for policymakers, Australia is a prominent example of such a country.

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1. Gauvin, L. and Rebillard, C. 2015, “Towards Recoupling? Assessing the Global Impact of Chinese Hard Landing through Trade and Commodity Price Channels”, Banque de France Working Paper No. 562.

2. Please see all the detailed empirical results in Chatterjee, A and Saraf, R. “Impact of China on world commodity prices and commodity exporters,” UNSW Business School Research Paper No. 2017-13, 2016.  

3. Fernald, J. G.; Spiegel, M. M. and Swanson, E. T. 2014, “Monetary Policy Effectiveness in China: Evidence from a FAVAR Model”, Journal of International Money and Finance, 49, 83-103.

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