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Erschienen in: Empirical Economics 4/2019

26.06.2018

The impact of commodity price shocks in a copper-rich economy: the case of Chile

verfasst von: Michael Pedersen

Erschienen in: Empirical Economics | Ausgabe 4/2019

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Abstract

The present paper analyzes how shocks to the copper price affect the economy of Chile, the world’s largest producer of this metal. Chile is an interesting case study, it being an emerging commodity producing economy that has a fiscal rule, which explicitly takes into account the expected future copper price, and has adopted an inflation targeting monetary policy with a fully floating exchange rate. The empirical analysis consists in estimating structural vector autoregressive models where the shocks are identified by sign restrictions in order to make the distinctions of those caused by increasing world demand, decreasing copper supply, and specific copper demand, e.g., speculation in future price increases. Positive copper price shocks, independently of the source, result in an appreciated currency, whereas the effects on the other macroeconomic variables do depend on the source of the shock. While demand shocks affect the Chilean gross domestic product positively, this is not the case when the copper price increases because of a supply-side event or a specific copper demand. Particularly, the activity in the mining sector is affected, while the non-mining sector expands only when the shock is caused by increasing world demand. One explanation could be the stabilizing effect of the fiscal rule. The impacts on inflation and the interest rate are correlated because of the inflation targeting monetary policy.

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Fußnoten
1
Recent examples of the effect of oil price shocks on oil exporting economies are those of El Anshasy and Bradley (2012) and Allegret and Benkhodja (2015). Akanbi (2015) argues that the oil exporting country, Nigeria, relies a great deal on the oil-revenue.
 
2
For oil price shocks this has been documented by e.g. Kilian (2009).
 
3
For an early example, see Hotelling (1931).
 
4
Van der Ploeg (2011) provides a discussion of why the presence of natural resource is a benefit for some countries, while this is not the case for others.
 
5
Chen and Rogoff (2003) argue that the commodity’s price has a strong influence on the real exchange rate in Australia, Canada and New Zealand. Cashin et al. (2004) find a long-run relation between the real exchange rate and real commodity prices in one-third of the countries in their sample of 58 commodity-dependent countries. Chile, however, is not one of them. Chen et al. (2010) provide evidence that real exchange rates in commodity exporting countries are higher in periods where commodity prices are high.
 
6
Chapter 4: Commodity Price Swings and Commodity Exporters.
 
7
See e.g. Rodríguez et al. (2007) or Pedersen (2008) for a description of the fiscal rule.
 
8
These numbers exclude eastern bloc countries.
 
10
These figures are only for primary supply. An important part of the supply is secondary, i.e. recycling of copper.
 
11
The main purpose of copper (almost 70% of total use in 2012) is in the manufacturing of electric and electronic products and the construction of buildings. In the present analysis, the copper price used is that of refined copper and world demand is measured by the gross domestic product (GDP), which includes the construction sector, rather than industrial production, which is the indicator frequently applied in oil price studies.
 
12
Numbers cited are from Meller (2013).
 
13
With observations from 1948 to 1968, Fisher et al. (1972) argue that the Chilean mining sector is hardly sensitive to price changes. Vial (2004) employs 30 years of data and finds for Chile a small and negative short-run price elasticity for the copper production, while it is positive in the long run.
 
14
The importance of the copper price for the exchange rate in Chile has been documented by Cowan et al. (2007) and the International Monetary Fund (2013).
 
15
Estimations of supply and demand elasticities can be found in Fisher et al. (1972), Taylor (1979), Adams and Behrman (1982), Lord (1986), Thurman (1988) and, more recently, Vial (2004).
 
16
Examples of recent studies that account for possible speculative effects in commodity prices are Kilian and Murphy (2013) and Knittel and Pindyck (2016). Lehecka (2015) casts doubt about the explicative power of hedging and speculative positions on commodity prices.
 
17
Yuan et al. (2014) estimate that almost one-third of China’s short-term foreign exchange loans are backed by commodities as collateral. See Tang and Zhu (2016) for a description of the process of commodity-based financing.
 
18
These four variables have often been employed in the oil-price literature to characterize the domestic economy. See e.g. Peersman and Van Robays (2009, 2011).
 
19
Even though the monetary policy in an inflation targeting economy should, per se, not react to exchange rate movements, it does react to the impact these changes have on inflation and, particularly, second-round effects. A recent study on the exchange rate pass-through in Chile is that of Justel and Sansone (2015).
 
20
This methodology was introduced in the late 1990s by Faust (1998), Canova and De Nicoló (2002, 2003), and Uhlig (2005), and by now several empirical applications with this approach have been published in academic journals. Fry and Pagan (2011) present a critical review of the sign restriction method.
 
21
A similar approach is employed by Peersman (2011), and Peersman and Van Robays (2011). The prior and posterior are obtained from Zellner (1971) and RATS is utilized for estimation.
 
22
Identification should be improved if the all restrictions are satisfied at the same time (Paustian 2007).
 
23
It should be noted that the fiscal rule in Chile has been operative since 2000, though with some changes across time, and the exchange rate has been freely floating since 1999, where inflation targeting was fully implemented. Hence, the sample analyzed contains only few observations before the introductions of these regimes, which should not affect the results.
 
25
The GDP level series extracted from the June 2013 CD-issue of IFS contains observations till 2012Q1, but is discontinued. The growth rate series utilized is from the December 2016 issue. Before 2008Q1 the revisions of the growth rates are minor (less than 0.2 basis points), hence the series is updated with changes from that quarter onward.
 
27
Own translations: “Total employment”, “Employment by economic sector. Exploration of mining and quarrying” and “Table 3—spliced series”.
 
28
Furthermore, if the real interest rate and the inflation rate are stationary, the nominal interest rate must be stationary as well. A similar specification is applied by Peersman and Van Robays (2009) for studying the impact of oil price shocks.
 
29
Kilian (2001) argues that more precise impulse-response functions may be estimated if including more lags than what is suggested by the relatively conservative Schwarz criterion. Robustness is analyzed with respect to the chosen lag length in subsection 4.2.
 
30
See Table 5 in “Appendix B”. Juselius (2006) notes that estimated VAR models are more sensitive to non-normal residuals caused by skewness than by excess kurtosis.
 
31
To defend the exchange rate, the Central Bank of Chile raised the policy rate from 8.5 to 14% in September 1998.
 
32
The hypothesis of excluding the deterministic trend could not be rejected with a p value of 0.14. On the other hand, the hypotheses of excluding the seasonal dummies and the blip dummy were both rejected with p values of 0.
 
33
Fornero and Kirchner (2014) examine a DSGE model for Chile where agents learn about the persistence of commodity price shocks. They find that investment increases in a gradual way driven by investment in the commodity sector. In the same line, Fornero et al. (2014) consider six major commodity exporting countries and argue that in most of them the expansionary effects of positive commodity price shocks are driven by investment in the commodity sector and there is spillover to the other sectors.
 
34
The nominal copper price is deflated with the US producer price index extracted from the IMF’s IFS database. The correlation between nominal and real prices is 0.99 for the period considered.
 
35
The MTs presented do not change if the variability measure is calculated as the average variance instead of the maximum.
 
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Metadaten
Titel
The impact of commodity price shocks in a copper-rich economy: the case of Chile
verfasst von
Michael Pedersen
Publikationsdatum
26.06.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Empirical Economics / Ausgabe 4/2019
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI
https://doi.org/10.1007/s00181-018-1485-9

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