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

Can Africa DeveIop without Smokestacks? Jaime de Melo FERDI Sao Paulo, Roundtable at a Workshop «Trade and Labor Markets in July 6, 2016 Developing Countries»

Can Africa Develop without Factories? 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%) 2

Reforms, Growth and poverty (1) • 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) 3

Trade and Industrialization Patterns (2 ) • 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) 4

De-industrialization: Convergence via services ?(3) • 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) 5

Summary and some open questions • 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 6

Figures 7

Macroeconomic Distorsions and Reforms in SSA 1960-2010 Reform Index : Giuliano et al. (2013) Black Market Premium (%) Black Market Reform Index Premium (left axis) (right axis) (back) Source: Cadot et al. (2015). Figure 4 from UNECA (2014) based on Giuliano, Mishra and Spilimbergo (2013) 8

GDP Growth and Poverty GDP per capita growth by region (1950-2010) 60 1700 58 1600 56 Real GDP per capita Poverty Headcount 1500 54 Source: Cadot et al. (2015). Figure 2(a) from Rodrik (2011). 52 1400 50 1300 GDP per capita and 48 46 1200 poverty headcount ratio 44 1100 42 in SSA 40 1000 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Poverty Headcount Ratio at $1.25 a day (PPP) GDP per capita (constant 2011$) (back) 9 Source: Cadot et al. (2015). Figure 2(b) from PovcalNet and WDI.

Poverty Headcount Ratio by Region, 1981-2011 80 70 60 Poverty Headcount 50 40 30 20 10 0 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 East Asia & Pacific Europe & Central Asia Latin America & the Caribbean Middle East & North Africa South Asia Sub-Saharan Africa (back) Note : Poverty headcount ratio at 1.25$ per day (2005 PPP) Source: Cadot et al. (2015) Figure 5 from PovecalNet. 10

Poverty Reduction (  HC) vs. GDP per capita Growth 20% 0% 0% 2% 4% 6% 8% SSA -10% 15% y = -3,5631x - 0,0406 y = -4,8361x - 0,4157 -20% SSA 10% -30% 5% -40% -50% 0% ECA SA -2% -1% 0% 1% 2% 3% LAC -60% -5% EAP MENA ECA -70% -10% MENA -80% SA LAC EAP -15% -90% GDP per capita growth GDP per capita growth (back) 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. 11

Resource Abundance and Growth South is Africa excluded.  RP have had a relatively stable growth ≈ 5% p.a.  Running out of steam is attributable to RR group (back) Note : Resource-rich = Resource rents > 15% of GDP Source: Cadot et al. (2015). Figure 7(b) from WDI. 12

Export Concentration in SSA is driven by RR Countries 8 6 4 2 4 6 8 10 12 GDP per capita (log), PPP Other Countries Resource-Poor (SSA) Resource-Rich (SSA) Fitted values (back) 13 Source: Cadot et al. (2015). Figure 9 from IMF, Diversification Toolkit.

Export Surges in SSA (log of exports sector surges around event) (event analysis à la Freund-Pierola) 12 Export surges have a ratchet effect on the 11 level of exports… 10 9 4.68 8 4.66 7 4.64 -5 -4 -3 -2 -1 0 1 2 3 4 5 Sub-Saharan Africa Other Countries 4.62 Source: Cadot et al.(2015). Figure 11 from Woldemichael (2015) 4.6 … and seem to be 4.58 associated with a -5 -4 -3 -2 -1 0 1 2 3 4 5 temporary REER Sub-Saharan Africa Other Countries depreciation (back) Source: Cadot et al. (2015). Figure 13 from Woldemichael (2015) 14

In SSA, industrialization is poverty-reducing mostly in countries with high initial poverty rates (back) Source: Cadot et al. (2015). Figure 15 from PovcalNet and WDI. 15

Premature de-Industrialization in SSA .5 .4 SWZ .3 .2 MUS .1 0 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) (back) Source: Cadot et al. (2015). Figure 16 from WDI. 16

Mauritius and Ethiopia trajectories confirm premature de-industrialization (14 more in paper) Mauritius Ethiopia 10.0 12.0 14.0 16.0 18.0 20.0 22.0 24.0 26.0 8.0 1987 1988 1997 1990 1998 1999 1992 1986 1989 1996 1991 2000 1997 1995 1993 2001 1994 7.0 2002 2003 1985 2004 2002 2005 1999 . . 2006 6.0 1984 2000 2008 2004 1998 2009 1996 2007 1987 2011 2013 1983 2010 1988 1981 . 1982 2005 2006 2007 2012 1995 1980 1976 5.0 1979 1977 1978 1989 1990 1986 1981 1994 1982 1983 1985 2008 1993 2011 2010 4.0 2013 2009 2012 1991 8.0 3.0 1992 2000 3000 4000 5000 6000 7000 100 150 200 250 300 GDP per capita GDP per capita (back) Source: Cadot et al. (2015). Figure 18 from WDI. 17

Decomposition of productivity growth in SSA 1960-2010 4 3 2 Static labor reallocation effect Within-sector effect 1 Dynamic labor reallocation effect 0 1960-1975 1975-1990 1990-2010 -1 (back) -2 Source: Cadot et al. (2015). Figure 22 adapted from Timmer et al (2014 ). 18

SSA countries are latecomers in industrialization. They exhibit lower levels of manufacturing VA and employment at peak share in GDP Manufacturing VA (% GDP) Employment in manufacturing 50 .5 40 .4 ZMB MUS 30 ZWE .3 MUS ZAF LSO CMR GNB MWI SYC 20 .2 SEN RWA BFA MOZ GHA CIV NAM GHA TCD BDI KEN CPV MRT KEN BEN AGO ERI TZA CAF MDG NGA BWA 10 .1 SOM SLE SEN COG UGA GAB TGO SDN LBR STP ETH NGA GIN BWA ETH MLI NER MWI COM ZMB TZA 0 0 1940 1960 1980 2000 2020 1960 1970 1980 1990 2000 2010 Peak Year Peak Year Other Countries Sub-Saharan Africa, uncensored Other Countries Sub-Saharan Africa Sub-Saharan Africa, censored Fitted values Trend, Sub-Saharan Africa Trend, Other Countries (back) 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

High labor costs in Sub-Saharan Africa seem to explain the lack of employment creation in manufacturing Country comparisons : high mfg. labor costs in selected SSA countries relative to India … … a pattern confirmed by regression analysis 2500 8000 GDP per capita (2005 $) 2000 Labor cost, annual 6000 1500 4000 1000 AGO 2000 KEN ZMB TZA SEN UGA 500 NGA MLI MOZ ETH GHA 0 5 6 7 8 9 0 GDP per capita (log) Zambia Tanzania Kenya Nigeria Bangladesh India Other Countries Sub-Saharan Africa Source: Cadot et al. (2015). Figure 25 from Fitted values Fitted values Gelb et al. (2013) Source: Cadot et al. (2015). Figure 26 adapted (back) from Gelb et al. (2013) 20

Lack of Conditional Convergence in SSA (positive slope) 10 GNQ ETH 5 TCD CPV MOZ GHA MUS LSO BFA NAM BWA SYC KEN NER GIN MRT SWZ GNB 0 MWI TGO ERI ZWE -5 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) 21

Convergence in services, a possible structural transformation paradigm for Sub-Saharan Africa? 22 (back) Source: Cadot et al. (2015) Figure 31