4.4 Further commodity price analysis

The analysis of subsections 4.2 and 4.3 illustrates the importance of world commodity prices for the New Zealand economy, particularly for movements in the real exchange rate. However, it is plausible that different sub-categories of commodity prices may affect the New Zealand economy in different ways. In particular, Bowman and Conway (2013a, 2013b) highlight changes associated with China's demand for dairy and forestry products from New Zealand. Commodity prices, particularly those relating to mineral products, are important also for the Australian economy, which in turn affects New Zealand. The present subsection therefore analyses sub-groups of the ANZ commodity price index, specifically dairy, forestry, meat and wool, and aluminium.

Table 1 shows the accumulated impact of shocks to the growth rates of both China and US on these commodity prices; those shown for the aggregate index in Table 1 are accumulated from the responses shown in Figures 7 and 8, with those for commodity categories obtained in the same way, but using the respective sub-group world price index in the SVAR model of subsection 4.2. The effects of demand shocks originating in both countries have statistically significant impacts on all commodity price series. On the other hand, the impact varies across sectors, with the biggest responses being for aluminium and dairy products. A one percentage point shock to growth rates in the US and China leads to accumulated increases in the real price of aluminium within four quarters of 12.6 and 5.5 percentage points respectively. The corresponding increases in dairy products are 6.7 and 5.6 percentage points respectively. The relatively high elasticity for aluminium products may be due to the rapid growth of China's manufacturing sector during the period studied here. While the response of forestry product prices is also sizeable in both cases, the impacts on the meat and wool category are relatively muted. As discussed in Bowman and Conway (2013a), wool would have been influenced by China in late 1980s but more stable since then even though they are the dominant market. China, on the other hand, is becoming more important for meat exports but the effect might not have shown up yet.

Overall, the results in Table 1 show that US demand is generally more important than China in driving global commodity prices, whether these prices are examined through the aggregate ANZ index or through sub-indices relevant to New Zealand. This partly reflects the larger international spillovers generated by US growth to other major economies, indicated in Figure 8 for China, and is in line with the findings of Roache (2012). It is important to note here that our analysis is silent about the supply side responses that may have an impact on the movements in commodity prices.

Nevertheless, the effects on commodity prices of growth from these major economies are only part of the story, since the focus of interest of this study is the effects on the New Zealand economy itself. Therefore, Table 2 provides the comparable accumulated responses of domestic GDP growth to US and China shocks in the SVAR model with commodity prices, both when the aggregate commodity price series is used (as in subsection 4.2) and when sub-group indices are employed in place of the aggregate. However, the overall pattern is unaffected by which index is used. To be specific, the US shocks are found to have a substantially greater impact on New Zealand GDP than those originating from China. Although the confidence intervals are relatively wide, the response to China peaks one quarter after the shock while the response to the US is longer-lived, with a one percentage point rise in US and China's GDP estimated to result in accumulated increases of approximately 0.5 and 0.2 percentage points, respectively, in that of New Zealand within four quarters.

Notes: Estimated responses are shown in percentage points, for a GDP shock of one percent, for the quarter of the shock (Q1) and three subsequent quarters (Q2, Q3, Q4). The 90 percent confidence intervals shown in parentheses are based on 2000 Monte Carlo replications. The model used is the SVAR of subsection 4.2, with the aggregate ANZ commodity price index replaced by the dairy, forestry, meat and wool, or aluminium sub-group index, as appropriate. Source: Authors' calculations.

Notes: See Table 1. Source: Authors’ calculations.