## 1. Chile: Output Impact of Copper Price, Shocks

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### Introduction
- Research question: Do copper price shocks generate positive spillovers from mining to manufacturing and construction in Chile, or evidence of “Dutch disease”?
- Economy characterized by three stylized sectors: a tradable sector highly exposed to copper price shocks (copper mining), a non-copper tradable sector (manufacturing), and a non-tradable sector (construction).
- Main empirical approaches:
  - VAR estimated using data spanning 1995−2016 for short- to medium-run dynamics.
  - Long-run analysis via an extension of Blanchard and Quah (1989) to two production sectors.
- Key contextual facts and sectoral exposure (in percent):
  - Mining: Export share of production 83.42; Imported input share 14.13; Import penetration 8.12.
  - Manufacturing: Export share of production 25.45; Imported input share 71.02; Import penetration 20.46.
  - Construction: Export share of production 0.01; Imported input share 19.66; Import penetration 8.38.
  - Farming, fishing and forestry: Export share 14.55; Imported input share 20.79; Import penetration 8.57.
  - Trade, hotels and restaurants: Export share 6.63; Imported input share 9.21; Import penetration 4.52.
  - Transport and communications: Export share 13.06; Imported input share 43.36; Import penetration 17.47.
  - Financial intermediation services: Export share 2.23; Imported input share 10.92; Import penetration 6.71.
  - Real estate activities: Export share 0.33; Imported input share 0.41; Import penetration 0.32.
  - Business related activities: Export share 2.60; Imported input share 3.68; Import penetration 2.50.
  - Personal services: Export share 5.55; Imported input share 3.00; Import penetration 2.11.
  - Public administration: Export share 0.40; Imported input share 9.32; Import penetration 6.39.
- Main purpose: quantify short-, medium-, and long-run responses of mining, manufacturing, and construction to copper price shocks.

### Short- and Medium-Run Dynamics
- Theoretical channels considered:
  - Global demand effects: higher copper prices benefit copper-exporting industry.
  - Expenditure switching effects: REER appreciation makes domestic goods more expensive relative to foreign goods; ambiguous effect on non-copper industries depending on import penetration and imported input share.
  - Domestic income effects: higher mining incomes generate positive second-round effects on employment, wages, and demand, especially for domestic-oriented industries.
- VAR specification details:
  - Endogenous vector Yt = {copt, va-it, vat, pt, it, reert}, where:
    - copt = real copper prices
    - va-it = real GVA excluding GVA of industry i
    - vat = real GVA of industry i
    - pt = consumer price index
    - it = monetary policy rate
    - reert = real effective exchange rate
  - Exogeneity block Xt = {wgdpt, pft, iUSt}, where:
    - wgdpt = world real GDP
    - pft = U.S. consumer price index
    - iUSt = Fed funds target rate
  - Data: quarterly seasonally-adjusted series spanning 1995−2016 for mining, manufacturing, and construction. Most variables entered as log first differences; VAR with two lags.
  - Identification: Cholesky recursive ordering with commodity prices first, va-it before vat, followed by pt, it, and reert.
- Empirical impulse-response findings to a positive copper price shock:
  - Aggregate:
    - Positive copper price shock generates near- to medium-term expansion of total output and REER appreciation; inflation falls on impact then rises, prompting monetary tightening.
  - Mining output:
    - A one percent point shock to copper prices results in a negative response of mining GVA on impact, reaching about 0.1 percentage points below baseline two to three quarters after the shock, and remains negative for about three years.
    - Interpreted drivers: increased intermediate input costs, operation near full capacity, higher average costs for marginal deposits, and long investment lead times.
  - Nominal GVA and wealth effects:
    - Strong positive income effects visible in nominal mining GVA over a 1½ year horizon (increase in mining profits and wages).
    - These benefits spread to other sectors via demand for intermediate inputs, final goods and services, and higher copper-related tax collection.
  - Construction:
    - Real and nominal GVA for construction respond positively (at 95 percent confidence band), peaking at 0.05 percentage points relative to baseline after 1½ years, and remaining positive for a couple of years.
    - Likely drivers: mining-related construction and second-round income effects; mining booms tighten low-skilled labor market affecting mining, construction, and some services.
  - Manufacturing:
    - Real and nominal GVA for manufacturing respond positively (at 95 percent confidence band), peaking at almost 0.1 percentage points relative to baseline after 1½ years.
    - Drivers: high share of imported investment goods and intermediate inputs means REER appreciation lowers cost of imports, improving profits and investment; increased demand from construction also supports manufacturing.
  - Overall interpretation:
    - Evidence points to positive, but modest, linkages (spillovers) from mining to manufacturing and construction.
    - Comparative note: a similar VAR exercise for Australia finds negative spillovers to manufacturing and construction at a one-year horizon.
- Caveats:
  - Uncertainty in magnitudes and timing of responses.
  - Current relationships may evolve; estimates reflect historical 1995−2016 dynamics.

### The Long Run
- Theoretical framework:
  - Draws on Johansson (1998), Fischer (1977), Blanchard and Quah (1989).
  - Variables: logs of output yt,i, money supply mt, prices pt,i, nominal wages wt,i, employment nt,i, labor force lt, sectoral productivity θt,i.
  - Labor force, sector-specific productivity, and nominal money follow random walks with drift; short-run demand effects from money only.
  - In the long run: employment driven by labor trend; sectoral production driven by sector-specific productivity trend and labor trend.
  - Baseline assumption: no long-run spillovers from manufacturing or construction to mining; focus tests on spillovers from mining to manufacturing and construction.
- Empirical specification and identification:
  - Structural VAR in differences: Δxt = B(L)xt + δ + ηt.
  - Identification via long-run zero restrictions on D(1) matrix, imposing three zero restrictions so that:
    - Employment long-run driven only by labor force trend (d(1)12 = 0, d(1)13 = 0).
    - Sector-specific trend in manufacturing (construction) has no long-run effect on production in mining (d(1)23 = 0).
    - Tests whether d(1)32 ≠ 0 (long-run spillovers from mining to manufacturing/construction).
  - Data: quarterly seasonally-adjusted series spanning 1990−2016 for mining and manufacturing, and 1996−2016 for construction; value added and employment from National Accounts; labor force from Labor Force Survey; variables in natural logs; VAR with two lags.
- Long-run empirical findings:
  - Positive long-run spillovers from mining to manufacturing and from mining to construction.
  - Growth in manufacturing and construction depends positively on growth in mining in the long run.
  - Shocks to the labor trend are a common source of sectoral production fluctuations.
  - All other coefficients significantly different from zero with expected signs.

### Conclusion and Policy Implications
- Empirical summary (1995−2016):
  - A 1 percent increase in real copper prices increases mining output over the medium term and generates positive spillovers into manufacturing and construction.
  - Permanent shocks originating in mining have positive long-run effects on manufacturing and construction.
  - Results overall reject the “Dutch disease” hypothesis for Chile over the sample period and indicate positive linkages among mining, manufacturing, and construction, though spillovers are modest in size.
- Policy implications and context (as of 2016):
  - The mining sector:
    - Accounts for around 10 percent of GDP,
    - Originates half of the exports,
    - Represents 30 percent of investment.
  - Modest estimated spillovers imply room for strengthening mining linkages with the rest of the economy, especially manufacturing (processing of copper and other high-value added mining products).
  - Continued importance of mining suggests policy focus on promoting competitiveness in other tradable sectors (manufacturing, agriculture, tourism) to help avoid or mitigate potential Dutch disease effects.

*IMF Staff calculations and analysis contained in the supplied content.*

### 1. Chile: Output Impact of Copper Price, Shocks ....................................................................12

### 1. Chile: Output Impact of Copper Price, Shocks ....................................................................12

### Contained sections
- "Chile: Output Impact of Copper Price, Shocks" — page 12
- "Chile: Sectoral Production, Employment, and Productivity" — page 16

*Source: wp17177 - 1. Chile: Output Impact of Copper Price, Shocks ....................................................................12*

### References  ............................................................................................................

### wp17177 - References

### I. Introduction
- Research question: Do copper price shocks generate positive spillovers from mining to manufacturing and construction in Chile, or evidence of “Dutch disease”?
- Economy characterized by three stylized sectors: a tradable sector highly exposed to copper price shocks (copper mining), a non-copper tradable sector (manufacturing), and a non-tradable sector (construction).
- Main empirical approaches:
  - VAR estimated using data spanning 1995−2016 for short- to medium-run dynamics.
  - Long-run analysis via an extension of Blanchard and Quah (1989) to two production sectors.
- Key contextual facts and sectoral exposure (Table 1, in percent):
  - Mining: Export share of production 83.42; Imported input share 14.13; Import penetration 8.12.
  - Manufacturing: Export share of production 25.45; Imported input share 71.02; Import penetration 20.46.
  - Construction: Export share of production 0.01; Imported input share 19.66; Import penetration 8.38.
  - Farming, fishing and forestry: Export share 14.55; Imported input share 20.79; Import penetration 8.57.
  - Trade, hotels and restaurants: Export share 6.63; Imported input share 9.21; Import penetration 4.52.
  - Transport and communications: Export share 13.06; Imported input share 43.36; Import penetration 17.47.
  - Financial intermediation services: Export share 2.23; Imported input share 10.92; Import penetration 6.71.
  - Real estate activities: Export share 0.33; Imported input share 0.41; Import penetration 0.32.
  - Business related activities: Export share 2.60; Imported input share 3.68; Import penetration 2.50.
  - Personal services: Export share 5.55; Imported input share 3.00; Import penetration 2.11.
  - Public administration: Export share 0.40; Imported input share 9.32; Import penetration 6.39.
- Main purpose: quantify short-, medium-, and long-run responses of mining, manufacturing, and construction to copper price shocks.

### II. Short- and Medium-Run Dynamics
- Theoretical priors and channels:
  - Global demand effects: higher copper prices benefit copper-exporting industry.
  - Expenditure switching effects: real effective exchange rate (REER) appreciation makes domestic goods more expensive relative to foreign goods; ambiguous effect on non-copper industries depending on import penetration and imported input share.
  - Domestic income effects: higher mining incomes generate positive second-round effects on employment, wages, and demand, especially for domestic-oriented industries.
- VAR specification:
  - Endogenous vector Yt = {copt, va-it, vat, pt, it, reert}, where:
    - copt = real copper prices
    - va-it = real GVA excluding GVA of industry i
    - vat = real GVA of industry i
    - pt = consumer price index
    - it = monetary policy rate
    - reert = real effective exchange rate
  - Exogeneity block Xt = {wgdpt, pft, iUSt}, where:
    - wgdpt = world real GDP
    - pft = U.S. consumer price index
    - iUSt = Fed funds target rate
  - Data: quarterly seasonally-adjusted series spanning 1995−2016 for mining, manufacturing, and construction. Most variables entered as log first differences; VAR with two lags.
  - Identification: Cholesky recursive ordering with commodity prices first, va-it before vat, followed by pt, it, and reert.
- Empirical results (impulse responses to a positive copper price shock):
  - Aggregate: positive copper price shock generates near- to medium-term expansion of total output and REER appreciation; inflation falls on impact then rises, prompting monetary tightening.
  - Mining output response:
    - A one percent point shock to copper prices results in a negative response of mining GVA on impact, reaching about 0.1 percentage points below baseline two to three quarters after the shock, and remains negative for about three years.
    - Explanation: increased intermediate input costs, operation near full capacity, higher average costs for marginal deposits, and long investment lead times.
  - Nominal GVA and wealth effects:
    - Strong positive income effects visible in nominal mining GVA over a 1½ year horizon (increase in mining profits and wages).
    - These benefits spread to other sectors via demand for intermediate inputs, final goods and services, and higher copper-related tax collection.
  - Construction response:
    - Real and nominal GVA for construction respond positively (at 95 percent confidence band), peaking at 0.05 percentage points relative to baseline after 1½ years, and remaining positive for a couple of years.
    - Likely drivers: mining-related construction and second-round income effects; mining booms tighten low-skilled labor market affecting mining, construction, and some services.
  - Manufacturing response:
    - Real and nominal GVA for manufacturing respond positively (at 95 percent confidence band), peaking at almost 0.1 percentage points relative to baseline after 1½ years.
    - Drivers: high share of imported investment goods and intermediate inputs means REER appreciation lowers cost of imports, improving profits and investment; increased demand from construction also supports manufacturing.
  - Overall interpretation:
    - Evidence points to positive, but modest, linkages (spillovers) from mining to manufacturing and construction.
    - Comparative note: similar VAR exercise for Australia finds negative spillovers to manufacturing and construction at a one-year horizon.
- Caveats:
  - Uncertainty in magnitudes and timing of responses.
  - Current relationships may evolve; estimates reflect historical 1995−2016 dynamics.

### III. The Long Run
- Theoretical model:
  - Framework draws on Johansson (1998), Fischer (1977), Blanchard and Quah (1989).
  - Variables: logs of output yt,i, money supply mt, prices pt,i, nominal wages wt,i, employment nt,i, labor force lt, sectoral productivity θt,i.
  - Labor force, sector-specific productivity, and nominal money follow random walks with drift; short-run demand effects from money only.
  - In the long run: employment driven by labor trend; sectoral production driven by sector-specific productivity trend and labor trend.
  - Baseline assumption: no long-run spillovers from manufacturing or construction to mining; tests focus on spillovers from mining to manufacturing and construction.
- Empirical specification and identification:
  - Structural VAR in differences: Δxt = B(L)xt + δ + ηt.
  - Identification via long-run zero restrictions on D(1) matrix, imposing three zero restrictions so that:
    - Employment long-run driven only by labor force trend (d(1)12 = 0, d(1)13 = 0).
    - Sector-specific trend in manufacturing (construction) has no long-run effect on production in mining (d(1)23 = 0).
    - Tests whether d(1)32 ≠ 0 (long-run spillovers from mining to manufacturing/construction).
  - Data: quarterly seasonally-adjusted series spanning 1990−2016 for mining and manufacturing, and 1996−2016 for construction; value added and employment from National Accounts; labor force from Labor Force Survey; variables in natural logs; VAR with two lags.
- Long-run results:
  - Positive long-run spillovers from mining to manufacturing and from mining to construction.
  - Growth in manufacturing and construction depends positively on growth in mining in the long run.
  - Shocks to the labor trend are a common source of sectoral production fluctuations.
  - All other coefficients significantly different from zero with expected signs.

### IV. Conclusion and Policy Implications
- Empirical summary (1995−2016):
  - A 1 percent increase in real copper prices increases mining output over the medium term and generates positive spillovers into manufacturing and construction.
  - Permanent shocks originating in mining have positive long-run effects on manufacturing and construction.
  - Results overall reject the “Dutch disease” hypothesis for Chile over the sample period and indicate positive linkages among mining, manufacturing, and construction, though spillovers are modest in size.
- Policy implications:
  - As of 2016, the mining sector:
    - Accounts for around 10 percent of GDP,
    - Originates half of the exports,
    - Represents 30 percent of investment.
  - Modest estimated spillovers imply room for strengthening mining linkages with the rest of the economy, especially manufacturing (processing of copper and other high-value added mining products).
  - Continued importance of mining suggests policy focus on promoting competitiveness in other tradable sectors (manufacturing, agriculture, tourism) to help avoid or mitigate potential Dutch disease effects.

*Source: IMF Staff calculations and analysis contained in the supplied content.*

### REFERENCES

### REFERENCES

### Cited works
- Blanchard, O.J., and Quah, D., 1989, “The Dynamic Effects of Aggregate Demand and Supply Disturbances”, The American Economic Review, Vol. 79, No. 4, pp. 654-673.  
- Aguirregabiria, V., and A. Luengo, 2015, “A Microeconometric Dynamic Structural Model of     Copper Mining decisions,” http://boston.eventful.com/events/microeconometric-dynamic-structural-model-copp-/E0-001-073746375-3  
- Blagrave, P., and M. Santoro, 2016, “Estimating Potential Output in Chile: A Multivariate Filter for Mining and Non-Mining Sectors,” IMF Working Paper WP No. 16/201            (Washington: International Monetary Fund).  
- Bishop, J., C. Kent, M. Plumb and V. Rayner, 2013, “The Resources Boom and Australian Economy: A Sectoral Analysis,” RBA Bulletin, (March), pp. 39−49.  
- Correa Mautz, F., 2016, “Encadenamientos productivos desde la mineria de Chile,” Serie Desarrollo Productivo, 203, CEPAL, Naciones Unidas.  
- Fornero, J., and M. Kirchner, 2014, “Learning About Commodity Cycles and Saving- Investment Dynamics in a Commodity-Exporting Economy,” Working Papers No. 727, Central Bank of Chile.  
- Fornero, J., M. Kirchner and A. Yany, 2016, “Terms of Trade Shocks and Investment in    Commodity-Exporting Economies,” Working Papers No. 773, Central Bank of Chile.  
- Frankel, J.A., 2006, “The Effect of Monetary Policy on Real Commodity Prices,” NBER      Working Papers 12713,( National Bureau of Economic Researh, Inc).  
- –––––––––––, 2008a, “An Explanation for Soaring Commodity Prices,” http://www.voxeu.org/index.php?q=node/1002  
- –––––––––––, 2008b, “Fed Modesty Regarding Its Role in High Commodity Prices,”                                                      http://content.ksg.harvard.edu/blog/jeff frankels weblog/category/commodities  
- García, P., and S. Olea, 2015, “Inversión Minera y Ajuste Macroeconómico en Australia y Chile,” Economic Policy Papers No. 56, Central Bank of Chile.  
- Johansson, K., 1999, “Permanent Shocks and Spillovers: A Sectoral Approach Using a Structural VAR,” National Institute of Economic Research, WP No. 63.  
- Knop, S. J., and J.L. Vespignani, “ The Sectorial Impact of Commodity Price Shocks in     Australia,” Discussion Paper Series N 2014−05, Tasmanian School of Business and Economics, Australia.

*Source: wp17177 - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17177.pdf_