Can Africa DeveIop without Smokestacks? Jaime de Melo FERDI Sao - - PowerPoint PPT Presentation

Jaime de Melo FERDI

Roundtable at a Workshop «Trade and Labor Markets in Developing Countries»

Can Africa DeveIop without Smokestacks?

Sao Paulo, July 6, 2016

Can Africa Develop without Factories?

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challenges

• Inclusive growth: Get sufficiently inclusive growth so as to get

a middle class ($10-$50 a day) that will be willing to pay for public goods and will have a stake in functioning governments

• Employment: 122 million jobs need to be created by 2020

(McKinsey (2012). Size of labor force expected to exceed China’s by 2035 Broad Cross-country evidence (since Chenery-Syrquin)

• Within-sector increases in labor productivity insufficient: need

resource shift to manufacturing and Services sectors (especially today to hook up to value chains

• So far resource shift has not happened in spite of FDI (X4 in

2000-10 relative to 1990-2000, civil wars cut by half, sharp reduction in HC (57% to 41%)

Reforms, Growth and poverty (1)

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• End of lost generation (70-95); reforms picked up

and macroeconomic distortions fell (here)

• … growth picked up; poverty down sharply (here)
• … but the poverty gap with other regions persists

(here)

• The elasticity of poverty reduction to growth is

varied across regions but lower in SSA (here)

• Are we witnessing another resource-driven

boom-bust cycle? (here)

Trade and Industrialization Patterns (2)

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• SSA export basket diversified «as expected» (here)
• Export surges have ratchet effect and associated

with real exchange rate depreciation (here)

• Industrialization is poverty reducing mostly in

initally high-poverty countries (here)

• Premature de-industrialization confirmed (here)
• …as in Ethiopia and Mauritius (here)
• Labor has not shifted to high productivity growth

sectors (here)

• SSA has high labor costs relative to Bangladesh

and India… (here)

De-industrialization: Convergence via services ?(3)

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• As latecomers, SSA have lower levels of mfg. VA

and employment at mfg. peak (here)

• Lack of conditional convergence (here)
• Convergence in services, a possible structural

transformation paradigm for SSA? (here)

Summary and some open questions

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• Reforms + favorable external environment  growth ↑ and

poverty ↓ (although low elasticity of poverty reduction to growth)

• Resources have not shifted towards high productivity

growth sectors. Moving costs, lack of human capital?

• SSA has not taken up labor intensive activities: Some

possible causes: Labor costs too high because of lack of appropriate skills? Contribution of : (i) soft infrastructure— inadequate contracting institutions in labor and goods markets; (ii) of hard infrastructure to high trade costs.

• Can the service sector (now increasingly tradable) help

convergence? Labor market implications

Figures

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Macroeconomic Distorsions and Reforms in SSA 1960-2010

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Reform Index : Giuliano et al. (2013)

Black Market Premium (%)

Black Market Premium (left axis) Reform Index (right axis)

Source: Cadot et al. (2015). Figure 4 from UNECA (2014) based on Giuliano, Mishra and Spilimbergo (2013)

(back)

GDP Growth and Poverty

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1000 1100 1200 1300 1400 1500 1600 1700 40 42 44 46 48 50 52 54 56 58 60 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Real GDP per capita Poverty Headcount Poverty Headcount Ratio at $1.25 a day (PPP) GDP per capita (constant 2011$)

GDP per capita growth by region (1950-2010) GDP per capita and poverty headcount ratio in SSA

Source: Cadot et al. (2015). Figure 2(a) from Rodrik (2011). Source: Cadot et al. (2015). Figure 2(b) from PovcalNet and WDI.

(back)

Poverty Headcount Ratio by Region, 1981-2011

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Note: Poverty headcount ratio at 1.25$ per day (2005 PPP) Source: Cadot et al. (2015) Figure 5 from PovecalNet.

10 20 30 40 50 60 70 80 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Poverty Headcount East Asia & Pacific Europe & Central Asia Latin America & the Caribbean Middle East & North Africa South Asia Sub-Saharan Africa

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Poverty Reduction (HC) vs. GDP per capita Growth

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EAP ECA LAC MENA SA SSA y = -3,5631x - 0,0406

• 15%
• 10%
• 5%

0% 5% 10% 15% 20%

• 2%
• 1%

0% 1% 2% 3% EAP ECA LAC MENA SA SSA y = -4,8361x - 0,4157

• 90%
• 80%
• 70%
• 60%
• 50%
• 40%
• 30%
• 20%
• 10%

0% 0% 2% 4% 6% 8%

GDP per capita growth GDP per capita growth

Note: Poverty line at 1.25$ per day (PPP). 101 countries ( 43 SSA). HC= head count Source: Cadot et al. (2015). Figure 6 from PovcalNet.

(back)

Resource Abundance and Growth

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Note: Resource-rich = Resource rents > 15% of GDP Source: Cadot et al. (2015). Figure 7(b) from WDI.

South is Africa excluded.

 RP have had a relatively stable growth ≈ 5% p.a.  Running out of steam is attributable to RR group

(back)

Export Concentration in SSA is driven by RR Countries

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2 4 6 8 4 6 8 10 12 GDP per capita (log), PPP Other Countries Resource-Poor (SSA) Resource-Rich (SSA) Fitted values

Source: Cadot et al. (2015). Figure 9 from IMF, Diversification Toolkit. (back)

Export Surges in SSA (log of exports sector surges around event)

(event analysis à la Freund-Pierola)

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7 8 9 10 11 12

• 5
• 4
• 3
• 2
• 1

1 2 3 4 5 Sub-Saharan Africa Other Countries 4.58 4.6 4.62 4.64 4.66 4.68

• 5
• 4
• 3
• 2
• 1

1 2 3 4 5 Sub-Saharan Africa Other Countries

Export surges have a ratchet effect on the level of exports… … and seem to be associated with a temporary REER depreciation

Source: Cadot et al. (2015). Figure 13 from Woldemichael (2015) Source: Cadot et al.(2015). Figure 11 from Woldemichael (2015)

(back)

In SSA, industrialization is poverty-reducing mostly in countries with high initial poverty rates

Source: Cadot et al. (2015). Figure 15 from PovcalNet and WDI.

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(back)

Premature de-Industrialization in SSA

MUS SWZ

.1 .2 .3 .4 .5 4 6 8 10 12 GDP per capita (log) Other Countries Resource-Poor (SSA) Resource-Rich (SSA) Trend (Other Countries) Trend (Resource-Poor, SSA) Trend (Resource-Rich, SSA)

Source: Cadot et al. (2015). Figure 16 from WDI.

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(back)

Mauritius and Ethiopia trajectories confirm premature de-industrialization (14 more in paper)

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

8.0 10.0 12.0 14.0 16.0 18.0 20.0 22.0 24.0 26.0 2000 3000 4000 5000 6000 7000 GDP per capita

1981 1982 1983 . 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 . 2002 . 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

3.0 4.0 5.0 6.0 7.0 8.0 100 150 200 250 300 GDP per capita

Mauritius Ethiopia

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Source: Cadot et al. (2015). Figure 18 from WDI.

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Decomposition of productivity growth in SSA 1960-2010

• 2
• 1

1 2 3 4 1960-1975 1975-1990 1990-2010 Static labor reallocation effect Within-sector effect Dynamic labor reallocation effect

Source: Cadot et al. (2015). Figure 22 adapted from Timmer et al (2014).

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(back)

SSA countries are latecomers in industrialization. They exhibit lower levels of manufacturing VA and employment at peak share in GDP

AGO BDI BEN BFA BWA CAF CIV CMR COG COM CPV ERI ETH GAB GHA GIN GNB KEN LBR LSO MDG MLI MOZ MRT MUS MWI NAM NER NGA RWA SDN SEN SLE SOM STP SYC TCD TGO TZA UGA ZAF ZMB ZWE

10 20 30 40 50 1960 1970 1980 1990 2000 2010 Peak Year Other Countries Sub-Saharan Africa Trend, Sub-Saharan Africa Trend, Other Countries

BWA GHA KEN MUS NGA ZMB ETH MWI SEN TZA

.1 .2 .3 .4 .5 1940 1960 1980 2000 2020 Peak Year Other Countries Sub-Saharan Africa, uncensored Sub-Saharan Africa, censored Fitted values

Manufacturing VA (% GDP) Employment in manufacturing

Source: Cadot et al. (2015). Figure 23(a) from WDI Source: Cadot et al. (2015). Figure 23(b) from Groningen Growth and Development Center 19

(back)

High labor costs in Sub-Saharan Africa seem to explain the lack of employment creation in manufacturing

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500 1000 1500 2000 2500 Zambia Tanzania Kenya Nigeria Bangladesh India GDP per capita (2005 $) Labor cost, annual

Source: Cadot et al. (2015). Figure 25 from Gelb et al. (2013)

AGO ETH GHA KEN MLI MOZ NGA SEN TZA UGA ZMB

2000 4000 6000 8000 5 6 7 8 9 GDP per capita (log) Other Countries Sub-Saharan Africa Fitted values Fitted values

Source: Cadot et al. (2015). Figure 26 adapted from Gelb et al. (2013) … a pattern confirmed by regression analysis Country comparisons : high mfg. labor costs in selected SSA countries relative to India …

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Lack of Conditional Convergence in SSA

(positive slope)

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ETH GIN GNB GNQ MRT BFA BWA CPV ERI GHA KEN LSO MOZ MUS MWI NAM NER SWZ SYC TCD TGO ZWE

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5 10 6 8 10 12 GDP per capita PPP in 2000 Other countries Resource-Rich (SSA) Resource-Poor (SSA) Fitted values Fitted values, Resource-Rich (SSA)

Note: Slope of the line is the marginal effect of the initial level of GDP per capita (2000) on subsequent growth (2000-2012) after controlling for human capital

Source: Cadot et al. (2015). Figure 28(b) from WDI. (back)

Convergence in services, a possible structural transformation paradigm for Sub-Saharan Africa?

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Source: Cadot et al. (2015) Figure 31 (back)

AFD Report

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• Summmary of report commissioned by the Agence Française de

Développement (AFD)

• Olivier Cadot, Jaime de Melo, Patrick Plane, Laurent

Wagner, Martha Woldemichael (2016) “Industrialisation et Transformation Structurelle: L’Afrique sub-saharienne peut- elle se développer sans usines?”, AFD, no 2015-10 http://www.afd.fr/webdav/shared/PUBLICATIONS/RECHERCHE/Scientifiques/Pa piers%20de%20recherche/10-papiers-recherche.pdf English version here

• “Industrialiazation and Structural Change: Can Africa Develop without

Factories” http://www.ferdi.fr/en/publication/p143-industrialization-and-structural-change