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I am grateful to Stephan Danninger for support and guidance and to Martin Sasson for excellent research assistance.
Ideally, the exercise should distinguish between mining-related construction (construction of mines, transporting inputs to, and taking extracted resources away from mines) and the rest of the construction, most notably residential construction. Mining-related construction represents about half of total construction (Cámara Chilena de la Construcción, 2016).
In linking with the Dutch disease literature for resource-rich economies such as Chile, the concern is that commodity booms may result in currency appreciation (through rise in commodity prices and/or capital inflows directed towards mining investment), thus weakening or even shrinking the performance of other tradable sectors in the economy. Dutch disease was a term used in the 1970s to refer to the Netherlands’ uneven economy after natural gas deposits were discovered in the North Sea. The resulting rise in the country’s currency was blamed for the demise of Dutch manufacturing.
We include copper prices in the endogenous block to reflect the usage of commodities as financial assets that adjust instantaneously to news in the remaining foreign variables, including interest rates. Copper price shocks are Therefore interpreted as capturing signals of future changes in world demand for commodities. This interpretation is in line with Frankel (2006, 2008a, 2008b).
Series where seasonally-adjusted using the Census X-13 procedure where they were not available in seasonally adjusted form from the original source.
The modelling approach presumes that the variables are I(1) and not co-integrated. Co-integrating relations are formally tested for, using the trace and maximum eigenvalue texts, and suggest that all variables used in the VAR model are non-stationary in levels but first-difference stationary.
Lag exclusion and lag length criteria (Schwarz, Akaike, and Hannan-Quinn), point to two to three lags. Including too many lags risks over parameterizing the model, and so we retain two lags for the sake of parsimony.
Cholesky identification scheme attributes all the effect of any common component to the variable that comes first in the VAR system.
Although some of the higher spending goes to imports and to the remittance of dividends abroad (funds’ outflow for Chile), there is still a net appreciation.
Further analysis on the response of wages and profits is contingent on sectoral data from the Central Bank of Chile.
For Australia, Bishop and others (2013) find negative spillovers from the resources sector to the other tradable sector (manufacturing, agriculture, transport, wholesale trade and accommodation, and food services) and nontradable (mostly comprising retail and non-mining related construction), and strong positive spillovers to miningrelated construction and business services industries.
A system including only sectoral production would find spurious evidence of spillovers in both directions. Incorporating employment to the model allows to distinguish between sector-specific productivity shocks and common labor force shocks.
A VAR in differences is appropriate since all production and employment variables considered in the empirical analysis are found to be I(1) variables but not co-integrated
Series where seasonally-adjusted using the Census X-13 procedure where they were not available in seasonally adjusted form from the original source
Results are almost identical by the two alternative data sources.
The lag structure of the VAR is determined by means of lag exclusion and lag length criteria.