Tale of two cities Lessons from the recent commodity price cycle - - PowerPoint PPT Presentation

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Tale of two cities Lessons from the recent commodity price cycle - - PowerPoint PPT Presentation

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Tale of two cities Lessons from the recent commodity price cycle Guillermo Perry Inaugural Lecture UNIFI November 8, 2017 All happy families are alike; each unhappy family is unhappy in its own way Ana Karenina, Leon Tolstoi. Recent

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´Tale of two cities´

Lessons from the recent commodity price cycle

Guillermo Perry Inaugural Lecture UNIFI November 8, 2017

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´All happy families are alike; each unhappy family is unhappy in its own way´ Ana Karenina, Leon Tolstoi.

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Recent economic slowdown in all developing regions, especially in South America and, less so, in Mexico

*Also includes Afghanistan and Pakistan Source: World Economic Outlook database, IMF projections, WEO October 2017

GDP growth rate (%)

6,5 1,2 2,2 2,6 6,8

  • 4,0
  • 2,0

0,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0 16,0 Emerging and developing Asia Latin America and the Caribbean Middle East and North Africa* Sub-Saharan Africa China 2,1

3,7 0,9

  • 6
  • 4
  • 2

2 4 6 8 Mexico Central America South America

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20 40 60 80 100 120 140 160 180 Sep-00 Mar-01 Sep-01 Mar-02 Sep-02 Mar-03 Sep-03 Mar-04 Sep-04 Mar-05 Sep-05 Mar-06 Sep-06 Mar-07 Sep-07 Mar-08 Sep-08 Mar-09 Sep-09 Mar-10 Sep-10 Mar-11 Sep-11 Mar-12 Sep-12 Mar-13 Sep-13 Mar-14 Sep-14 Mar-15 Sep-15 Mar-16 Sep-16 Mar-17 Sep-17 Monthly indices based on nominal US dollars 2010=100

Commodity Price Index

Energy Agriculture Other metals and minerals

Mainly explained by the slump in commodity prices, after a long boom since 2003

Source: World Bank

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50 100 150 200 250 300 Venezuela

That led to a boom and bust in Terms of Trade

Source: Citi Bank.

Terms of Trade Index (2002=100)

80 100 120 140 160 180 200 220 2000 2003 2006 2009 2012 2015 Chile Colombia Mexico Peru 80 90 100 110 120 130 140 2000 2003 2006 2009 2012 2015 Argentina Brazil

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We see today quite unhappy and less unhappy countries: differences are not fully explained by TOT

GDP growth rates (simple averages, %)*

*Less unhappy: Chile, Colombia, Mexico and Peru; Quite unhappy: Argentina, Brazil and Venezuela Source: World Economic Outlook database, IMF projections, WEO October 2017

  • 10
  • 5

5 10 15 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 Less unhappy countries Quite unhappy countries

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GDP growth: The less unhappy ones look quite alike

GDP growth rate (%)

Source: World Economic Outlook database, IMF projections, WEO October 2017

  • 15
  • 10
  • 5

5 10 15 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Chile Colombia Mexico Peru

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GDP growth: The more unhappy ones look much less alike

Source: World Economic Outlook database, IMF projections, WEO October 2017

GDP growth rate (%)

  • 20
  • 15
  • 10
  • 5

5 10 15 20 25 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Argentina Brazil Venezuela

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An early symptom of unhappiness in Venezuela and Argentina: loss of reserves

International Reserves (includes gold, % of GDP)

Source: World Development indicators, World Bank, Ministry of Finance of Chile

5 10 15 20 25 30 35 40 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Chile (includes Gov's Funds) Colombia Brazil Mexico Peru 5 10 15 20 25 30 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Argentina Venezuela, RB

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And sovereign risk hikes

EMBI stripped spreads; end of period (simple averages, basis points)

Source: Bloomberg, JP Morgan

500 1.000 1.500 2.000 2.500 3.000 3.500 2000 2002 2004 2006 2008 2010 2012 2014 2016 Less unhappy countries Quite unhappy countries

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Spreads: The less unhappy bunch look again quite alike

Source: Bloomberg, JP Morgan

EMBI stripped spreads; end of period (basis points)

200 400 600 800 2000 2002 2004 2006 2008 2010 2012 2014 2016 Chile Colombia Mexico Peru

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Spreads: The very unhappy ones again look less alike

Source: Bloomberg, JP Morgan

EMBI stripped spreads; end of period (basis points)

1.000 2.000 3.000 4.000 5.000 6.000 7.000 2000 2002 2004 2006 2008 2010 2012 2014 2016 Argentina Brazil Venezuela

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A closer look to the less unhappy bunch and Brazil:

Credit Default Swaps

Source: Bloomberg

5-Year Credit Default Swaps (basis points)

111,9 170,2 105,7 54,2 77,0 30 80 130 180 230 280 330 380 430 480 530 dic 11 mar 12 giu 12 set 12 dic 12 mar 13 giu 13 set 13 dic 13 mar 14 giu 14 set 14 dic 14 mar 15 giu 15 set 15 dic 15 mar 16 giu 16 set 16 dic 16 mar 17 giu 17 set 17 Colombia Brasil México Chile Perú

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Two factors behind differences in unhappiness (in addition to micro-policies and politics):

Fiscal deficits and exchange rate regimes and interventions

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Behind deep unhappiness: fiscal deficits

General Government Net Lending/Borrowing (% of GDP)

Source: World Economic Outlook database, IMF projections, WEO October 2017

  • 14
  • 12
  • 10
  • 8
  • 6
  • 4
  • 2

2 4 6 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Less unhappy countries Quite unhappy countries

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Some differences in fiscal deficits among the less unhappy

General Government Net Lending/Borrowing (% of GDP)

Source: World Economic Outlook database, IMF projections, WEO October 2017

  • 8
  • 6
  • 4
  • 2

2 4 6 8 10 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Chile Colombia Mexico Peru

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Wider fiscal deficits and differences among the very unhappy

General Government Net Lending/Borrowing (% of GDP)

Source: World Economic Outlook database, IMF projections, WEO October 2017

  • 20
  • 15
  • 10
  • 5

5 10 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Argentina Brazil Venezuela

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Sovereign debt levels are also a concern in Brazil

Source: World Economic Outlook database, IMF projections, WEO October 2017

20 40 60 80 100 120 140 160 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016p 2017p

General Government Gross Debt (% of GDP)

Argentina Brazil Chile Colombia Mexico Peru Venezuela

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Argentina and Venezuela attempted to keep nominal exchange rates constant but had sharp recent devaluations

Source: Banco de Bogotá, author’s calculations

Nominal Exchange Rate (year-on-year variation, %)

  • 200

200 400 600 800 1.000 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Argentina Venezuela

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Both had inflationary pressures since the beginning of the boom; Venezuela has hyperinflation now

Source: Bloomberg. *Data for Argentina is extracted from independent sources

Inflation, end of period (year-on-year variation, %)

200 400 600 800 1.000 1.200 5 10 15 20 25 30 35 40 45

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Argentina* Venezuela (right axis)

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Their Real Exchange Rates did not appreciate much during the early boom but showed significant appreciation latter, even during the bust.

Source: BIS, Central Bank of Argentina, author’s calculations.

Real Exchange Rate Index (average Jan. 2001 – Dec. 2002=100)

50 100 150 200 Aug-01 Apr-02 Dec-02 Aug-03 Apr-04 Dec-04 Aug-05 Apr-06 Dec-06 Aug-07 Apr-08 Dec-08 Aug-09 Apr-10 Dec-10 Aug-11 Apr-12 Dec-12 Aug-13 Apr-14 Dec-14 Aug-15 Apr-16 Dec-16 Aug-17 Brazil Argentina 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 Aug-01 Apr-02 Dec-02 Aug-03 Apr-04 Dec-04 Aug-05 Apr-06 Dec-06 Aug-07 Apr-08 Dec-08 Aug-09 Apr-10 Dec-10 Aug-11 Apr-12 Dec-12 Aug-13 Apr-14 Dec-14 Aug-15 Apr-16 Dec-16 Aug-17 Venezuela

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Major differences in Real Exchange Rate performance between Inflation Targetting and non-IT countries

50 75 100 125 150 175 200 225 250 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

*Less unhappy: Brazil, Chile, Colombia, Mexico and Peru; Quite unhappy: Argentina and Venezuela Source: Banco de Bogotá, author’s calculations

Real Exchange Rate Index

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Some differences in Real Exchange Rate performance among IT countries: Chile and Peru appreciated less than Brazil and Colombia during the boom, in spite of higher TOT gains.

Source: BIS, author’s calculations.

Real Exchange Rate Index (average Jan. 2001 – Dec. 2002=100)

50 100 150 200 Brazil Chile Colombia Mexico Peru

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Due to the fact that Perú and Chile had both fiscal surpluses and higher accumulation of reserves

5 10 15 20 25 30 35 40 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Chile (includes Gov's Funds) Colombia Brazil Peru

International Reserves as % of GDP

General Government Net Lending (% of GDP)

  • 12
  • 10
  • 8
  • 6
  • 4
  • 2

2 4 6 8 10 2000 2002 2004 2006 2008 2010 2012 2014 2016 Chile Colombia Peru Brazil

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As a consequence, there were sharper recent compensatory nominal devaluations in Brazil and Colombia

Source: Banco de Bogotá, author’s calculations

Nominal Exchange Rate (year-on-year variation, %)

  • 30
  • 20
  • 10

10 20 30 40 50 60 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Chile Colombia Mexico Peru Brazil

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That contributed to recent inflationary pressures

Inflation, end of period (year-on-year variation, %)

Source: Bloomberg

  • 2

2 4 6 8 10 12 14 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Brasil Chile Colombia Mexico Peru

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And led Brazil’s and (less so) Colombia’s Central Banks to adopt pro cyclical interest rate hikes (Mexico in 2016: Trump effect)

Monetary Policy Rate, end of period (%)

Source: Bloomberg

5 10 15 20 25 30 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Brasil Chile Colombia Mexico Peru

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Not surprisingly DUTCH DISEASE symptoms were higher in COLOMBIA and BRAZIL than in PERU and CHILE: Exports

Fuente: WTO; cálculos propios

500 1000 1500 2000 2500 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

ExportsIndex (1992=100)

Colombia (1992-2015)

Agricultura Combustiblesy Minas Manufacturas

200 400 600 800 1000 1200 1400 1600 1800 1992 199319941995 19961997 1998 199920002001 200220032004 200520062007 2008 20092010 201120122013 20142015

ExportsIndex (1992=100)

Brasil (1992-2015)

Agricultura Combustiblesy Minas Manufacturas

200 400 600 800 1000 1200 199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015

ExportsIndex (1992=100)

Chile (1992-2015)

Agricultura Combustiblesy Minas Manufacturas

100 200 300 400 500 600 700 800 900 1000 1992 199319941995 19961997 1998 199920002001 200220032004 200520062007 2008 20092010 201120122013 20142015

ExportsIndex (1992=100)

México (1992-2015)

Agricultura Combustiblesy Minas Manufacturas

200 400 600 800 1000 1200 1400 1600 199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015

ExportsIndex (1992=100)

Perú (1992-2015)

Agricultura Combustiblesy Minas Manufacturas

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SLIDE 29 100 200 300 400 500 600 700 800 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Index (2010=100)

México (1992-2016)

Agricultura Manufactura Industria Servicios 50 100 150 200 250 300 350 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Index (200=100)

Colombia (2000-2017)

Agricultura Minas y Canteras Manufacturas Construcción Financiero

Fuente: WDI, World Bank; cálculos propios y Banco de la República de Colombia

Not surprisingly DUTCH DISEASE symptoms were higher in COLOMBIA and BRAZIL than in PERU and CHILE : Production

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The key lessons

  • As expected, flexible exchange rate regimes operated as

important shock absorbers. Countries with fixed exchange rate regimes (Argentina and Venezuela) had higher variability of growth and inflation

  • But significant Real Exchange Rate appreciations and

depreciations created serious Dutch Disease, adjustment and inflationary costs during the commodity price cycle in Brazil and Colombia.

  • Perú and Chile that mitigated them through a combination
  • f counter cyclical fiscal and monetary policies and ´against

the wind´ exchange market interventions by central banks (´dirty´ floating) encountered less problems

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Vulnerabilities to potential external shocks

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FED interest rate hikes will impose threats to capital flows to Emerging Markets, with high external and fiscal vulnerabilities

Source: IMF, REO April 2017

Capital Flows in Emerging Markets (percent of trend GDP; median)

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Though gross capital inflows are highly correlated with commodity prices in South America : a China hard landing would impose huge risks.

Source: IMF, REO April 2017

Gross Inflows and Commodity Prices (percent of trend GDP; median)

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Protectionist policies in the US would affect countries with high trade links: Mexico and Central A merica

Source: IMF, REO April 2017

South America has lower exposure to the US –mostly through commodities-, compared with Central America and Mexico Brazil, Argentina and Chile export more manufactured goods to the US, compared to Peru and Colombia

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Remittances and Direct Investment from the US are also quite high, especially to Mexico

Source: IMF, REO April 2017

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A matrix of global risks for Latin American Countries

Sources: Deutsche Bank, Bank of América, own estimates. 36

Potential impact Likely medium term impact Likely short run impact

A China hard landing and an additional fall in commodity prices A showdown with Catalonia Market turbulence due to FED hikes and balance sheet contraction Terrorist Attacks Confrontation with North Corea US protectionism Market turbulence due to a bank crisis in Italy European slowdown due to Brexit US stock market Price collapse Budget stalemate in US Elections in Latin America 2018 Polítical Risks Economic Risks

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The key challenge going forward: productivity growth

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The key long term challenge: closing the productivity gap

Source: Grazzi, M. & Pietrobelli, C., “Firm Innovation and Productivity in Latin America and the Caribbean”, IDB, June 2016

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Growthin LatinAmericahas beendrivenbycapital and labor growth, not by total factor productivity growth

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This will have to change: as investment rates are already high in several countries..

5,0 10,0 15,0 20,0 25,0 30,0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017p

Investment (% of GDP)

Argentina Brazil Chile Colombia Mexico Peru

Source: World Economic Outlook database, IMF projections, WEO October 2017

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And the demographic bonus will soon be

  • ver

10 20 30 40 50 60 70 Brazil Chile Colombia Mexico Peru Venezuela, RB Argentina

Population ages 15-64 (% of total population, averages)

1970-1979 1980-1989 1990-1999 2000-2009 2010-2016

Source: World Development Indicators, World Bank

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Summing Up

1. The growth boom and posterior slowdown in most Latin American countries is basically explained by the cycle of commodity prices (plus high international liquidity and low international interest rates). 2. Countries that saved more in the boom (fiscal surplus and reserve accumulation), like Chile and Peru, had lower symptoms of Dutch Disease, have had to engage in less painful fiscal and monetary pro cyclical adjustments in the bust and have now lower vulnerabilities to additional shocks. 3. Venezuela and Argentina engaged in unsustainable macro policies (and anti private sector micro policies) and lost access to international capital markets (and had sharp reserve losses) well before the fall in commodity prices. Venezuela is in full implosion while the new regime in Argentina is trying to cope. 4. Brazil problems began after 2013 (fiscal relaxation and temper tantrum) and were then aggravated by the political crisis. 5. The key going forward are increases in productivity: no tale winds in the horizon and lower capital and labour growth!