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AN AFRICAN MIRACLE? Richard H. Sabot Lecture, CGD Dani Rodrik April 24, 2014 A remarkable growth turnaround in Africa (and the rest of the developing world) Growth performance of country groups since 1980 10.0% 1980-1990 1990-2000 8.0% 2000-2012 6.0% 4.0% 2.0% 0.0% World Low income Middle income East Asia & Pacific (developing only) South Asia -2.0% annual average per-capita GDP growth Sub-Saharan Latin America & Africa (developing Caribbean only) (developing only) TFP growth rates are back to 1960s levels Source: UNECA (2014) But many countries still have to catch up to income levels of some decades ago Economic performance in Sub-Saharan Africa, 1960-2012 (GDP per capita, constant 2005 $) 10000 1000 100 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 SSA CAF ZAR CIV ETH GHA KEN LBR MDG MOZ NER NGA RWA SEN TZA UGA ZMB ZWE MUS Is growth temporary or permanent? Reasons to be wary Last two decades have been particularly favorable to developing countries • high commodity prices • low interest rates • plenty of foreign capital • recovery (from civil wars and macro instability) • the Chinese impact So future may not look like recent past Need to understand drivers of economic growth Convergence is historically the exception rather than the norm 1870-2008 .03 1965-2005, decadal WBG NPL PRK -.1 .02 .01 GRC PRT 0 FIN NOR SWE ESP IRL MYS CAN THA ITA AUTDNK CHN FRA CHE USA DEU YUG MEX CZE POL BGR CHL SYR TUN BRA IRN TUR ALB NLD BEL HUN ARG JOR AUS IDN GBR JAM MMR MAR EGY NZL VNM LKA IND ZAFROM LBN PHL DZA URY GHA VEN -.05 JPN .05 .1 HKG SGP orthogonal component of growth TWN KOR 0 IRQ 6 6.5 7 7.5 log GDP per worker, 1870 8 4 6 8 10 log GDP per worker, at start of decade 12 Notes: For RHS chart, variable on the vertical axis is growth of GDP per worker over four separate decades (1965-1975, 1975-1985, 1985-1995, 1995-2005), controlling for decadal fixed effects. Source: Rodrik (2013), using data from Maddison (2010) and PWT 7.0 (2011). Unconditional versus conditional convergence Latecomers have access to • technology • capital • markets But face other headwinds, specific to each country • bad policies • weak institutions • geographical disadvantages • poverty traps So conventional theory: convergence is conditional: π¦π = π½ ln π¦ ∗ (π£π ) − ln π¦π + ππ The growth “fundamentals” Long-term convergence is conditional on: • Institutional quality • governance • rule of law • “business environment” • Human capital • education, skills, training Need not take a position on debate as to which is more fundamental than other Africa’s fundamentals: better policies Source: UNECA (2014) Africa’s fundamentals: democratization Source: UNECA (2014) Africa’s fundamentals: fewer civil wars Source: Straus (2012) The empirical disconnect between fundamentals and growth • Empirical relationship between fundamentals and growth strong in levels (i.e., in long-run), but not so much in growth rates • there is only weak relationship between growth and • improvements in institutional quality, • standard measures of economic reform (except in the extremes), • increases in educational attainment • High-performing Asian countries have been weak on many of the fundamentals during much of their growth • Latin American growth post-1990 has been subpar despite significant improvements in governance and policy • e.g. Mexico The policy disconnect between fundamentals and growth • Institutions: measured as “rule of law,” “expropriation risk” • broadly defined, these have large effects on long-run levels of income • but no clear, easily exploitable mapping from institutions as “rules of the game” to institutions as “policy” • Democracy, as example • recent paper by Acemoglu et al. (2014) finds full democratization produces ≈20% increase in GDP per capita over 30 years • growth effect is 0.6 percent per year -- not insignificant, but it’s temporary and phased out over time • typical cross-country findings (in levels) with “expropriation risk,” “rule of law” suggest much larger magnitudes • “as much as 75% of the [income] gap between high and low institutions countries” (Acemoglu, Gallego, Robinson, 2014, p. 3) Another look at convergence So standard growth equation does not do a very good job of describing growth miracles π¦π = π½ ln π¦ ∗ (π£π ) − ln π¦π + ππ A complementary perspective: structural change • economic dualism • sectors that have different productive trajectories • unconditional convergence in modern industries There is unconditional convergence -- in (formal) manufacturing industries Notes: Vertical axis represents growth in labor productivity over subsequent decade (controlling for period fixed effects). Data are for the latest 10-year period available. Source: Rodrik (2013) --- regardless of period, sector, or aggregation π½ ≈ 2.9% (t-stat ≈ 7), implying a half-life for full convergence of 40-50 years! Notes: Data are for the latest 10-year period available. On LHS chart, each dot represents a 2-digit manufacturing industry in a specific country; vertical axis represents growth rate of labor productivity (controlling for period, industry, and period×industry fixed effects). Source: Rodrik (2013) African manufacturing seems no different (1) Full sample: 115 countries Sub-Saharan Africa: 20 countries Each observation represents a 2-digit manufacturing industry, for the latest 10 year period for which data are available. The horizontal axis is the log of VA per worker in base period, and the vertical axis is its growth rate over the subsequent decade. Period, industry, and period x industry controls are included. African manufacturing seems no different (2) Full sample Sub-Saharan Africa Each observation represents aggregate manufacturing industry in a specific country, for the latest 10 year period for which data are available. The horizontal axis is the log of VA per worker in base period, and the vertical axis is its growth rate over the subsequent decade. Period controls are included. Putting it together π¦ = π½ ln π¦ ∗ π£ − ln π¦ ∗ + πΌπ ππ π½π ln π¦π − ln π¦π + ππ − π π ππΌπ (A) (B) (C) Putting it together π¦ = π½ ln π¦ ∗ π£ − ln π¦ ∗ + πΌπ ππ π½π ln π¦π − ln π¦π + ππ − π π ππΌπ (A) (B) (C) (A) Conditional convergence, dependent on accumulation of fundamental capabilities (human capital and institutional quality) -- a slow process Putting it together π¦ = π½ ln π¦ ∗ π£ − ln π¦ ∗ + πΌπ ππ π½π ln π¦π − ln π¦π + ππ − π π ππΌπ (A) (B) (C) (B) Unconditional convergence in (formal) manufacturing -- rapid, but quantitatively small due to small initial share of manufacturing Putting it together π¦ = π½ ln π¦ ∗ π£ − ln π¦ ∗ + πΌπ ππ π½π ln π¦π − ln π¦π + ππ − π π ππΌπ (C) Structural change -- industrialization in particular (A) (B) (C) A typology of growth processes/outcomes Industrialization in Africa Source: de Vries, Timmer, and de Vries (2013) …is lagging behind, even controlling for incomes Manufacturing value added/GDP and GDP per capita 4 .4 6 8 log GDP per capita 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NGA ETH BWA ETH BWA ETH HKG ETH ETH KORNGA KOR BWA NGA ETH ETHKOR NGA ETH HKG ETH ETH ETH KOR NGA ETH NGA NGA BWA NGA HKG NGA NGA ETH NGA ETH NGA NGA NGANGA NGA ETH NGA NGA ETH NGA ETH NGA NGA NGA ETH KOR NGANGA NGA NGA NGA NGA ETH NGA NGA ETH ETH ETH NGA NGA NGA NGA N GA NGA NGA NGA NGA NGA NGA NGA NGA NGA NGA 6 8 log GDP per capita Africa Asia Africa: Botswana, Ethiopia, Ghana, Kenya, Mauritius, Malawi, Nigeria, Senegal, Tanzania, South Africa, and Zimbabwe. Asia: Hong Kong, Indonesia, India, Japan, Korea, Malaysia, the Philippines, Singapore, Thailand, Taiwan, and Vietnam. 10 Structural change in Vietnam versus… 1990-2008 Source: McCaig and Pavcnik (2013) … Africa Log of Sectoral Productivity/Total Productivity Correlation Between Sectoral Productivity and Change in Employment Shares in Kenya (1990-2005) -.1 ο’ = 0.0902; t-stat = 0.02 fire 2 pu pu fire min tsc 1 tsc con cspsgs 0 min con cspsgs wrt man man agr wrt -1 2 1 0 -1 agr 3 ο’ = 9.4098; t-stat = 0.91 3 4 Correlation Between Sectoral Productivity and Change in Employment Shares in Ethiopia (1990-2005) -.05 0 Change in Employment Share (οEmp. Share) .05 Fitted values *Note: Size of circle represents employment share in 1990 **Note:ο’ denotes coef f . of independent v ariable in regression equation: ln(p/P) = ο‘ + ο’οEmp. Share Source: Authors' calculations with data f rom National Bank of Ethiopia and Ethiopia's Ministry of Finance Source: McMillan and Rodrik (2008) -.3 -.2 -.1 0 Change in Employment Share (οEmp. Share) Fitted values *Note: Size of circle represents employment share in 1990 **Note:ο’ denotes coef f . of independent v ariable in regression equation: ln(p/P) = ο‘ + ο’οEmp. Share Source: Authors' calculations with data f rom Keny a National Bureau of Statistics, Central Bureau of Statistics, UN National Accounts Statistics and ILO's KILM .1 .2 Structural change in Africa has not been always conducive to growth 1990-1999 Sources: McMillan (2014) post-2000 Informality dominates in African manufacturing Manufacturing employment shares, GGDC and UNIDO datasets, 1990 (percent) year UNIDO GGDC ratio BWA 2008 3.6 6.4 56% ETH 2008 0.3 5.3 6% GHA 2003 1.0 11.2 9% KEN 2007 1.5 12.9 12% MUS 2008 16.3 21.5 76% MWI 2008 0.7 4.3 16% NGA 1996 1.4 6.6 21% SEN 2002 0.5 8.9 6% TZA 2007 0.5 2.3 22% ZAF 2008 7.0 13.1 53% ZMB 1994 1.5 2.9 52% Difference in coverage between two data sets: GGDC (which covers informal employment) and UNIDO (which is mostly formal, registered firms) Which may be why (aggregate) manufacturing in Africa is not converging Source: de Vries, Timmer, and de Vries (2013) Formal/wage employment very low and often declining across entire economy Source: Golub and Hayat (2014) Patterns of structural change agriculture informal organized manufacturing services Patterns of structural change: East Asia and advanced countries agriculture informal organized manufacturing services Patterns of structural change: Africa agriculture informal organized manufacturing services High-growth scenarios for Africa 1. Revive industrialization? 2. Agriculture-led growth through non-traditional agricultural products? 3. Raise productivity in services? 4. Growth based on natural resources? 1. Revive industrialization? • Is “poor business climate” the main culprit? • costs of power, transport, corruption, regulations, security, contract enforcement, uncertainty… (Gelb, Meyer, and Ramachandran 2014) • If so, remedy is clear-cut • for tradable industries, an undervalued exchange rate compensates for these costs • where culprit for slow industrialization is market failures, undervalued exchange rate also substitutes for industrial policy, • The obstacles that industrialization faces are more deep- seated • premature de-industrialization a common feature across developing world • driven by global competition, demand patterns, and technology With appropriate exchange rate, Africa can compete with China and Vietnam in certain industries Source: African Center for Economic Transformation (2014) Premature industrialization is a general problem for today’s developing countries Peak manufacturing levels GDP per capita when peak reached (1990 international $) 12,000 USA, 1953 GER, 1970 10,000 SWE, 1961 UK, 1961 8,000 KOR, 1989 MEX, 1990 6,000 BRA, 1986 4,000 COL, 1970 CHN, 1996 2,000 IND, 2002 0 5 10 15 20 25 peak share of manufacturing employment 30 35 40 .4 .5 De-industrialization in Africa .1 .2 .3 Mauritius 0 South Africa 6 7 Advanced GHA NGA 8 9 ln GPD per capita East Asia KEN TZA BWA MUS ZAF 10 11 ETH MWI ZMB Manufacturing employment share against per-capita GDP 2. Non-traditional agricultural products? • Agricultural diversification hindered by many of the same obstacles as manufacturing • “poor business climate” (e.g., Golub and Hayat 2014) • Plus, it requires extensive government effort in technology, land issues, standard setting, input provision, • Again, role for exchange rate policy as compensatory tool • Diversification and productivity growth in agriculture have played important role in Asia in early growth • China, Vietnam • But few successful cases of: • sustained growth based on agricultural exports • which is what agricultural diversification entails • slowing down of outmigration from rural to urban areas • so creation of high-productivity urban jobs will remain a challenge 3. Raise productivity in services? • Remember: services are not an escalator sector like manufacturing • Requires steady and broad-based accumulation of capabilities in human capital, institutions, and governance • “technologies” less tradable and more context-specific • complementarities across policy domains Structural transformation, industrialization (dα) slow rapid slow Investment in (1) (1) episodic growth no growth fundamentals (human capital, institutions) rapid (1) slow growth (1) rapid, sustained growth 4. Growth based on natural resources? • Downsides are well known: • resource sectors are capital intensive and absorb little labor • crowding out of other tradables (Dutch disease) • volatility of terms of trade • difficulty of managing/sharing resource rents • Very few countries have succeeded • A few small countries with atypical situations Sustained rapid growth based on natural resources has been exceedingly uncommon Industrializers in the European periphery and East Asia Is an African miracle possible? • Balance of evidence suggests caution • Much of recent high growth is due to temporary boosts: • highly advantageous external context • making up of lost ground • Main benefit of continent’s improved institutional/macro framework is to establish stability (rather than ignite takeoff) • Best we can expect is moderate, but steady growth • sustained 2% growth per annum is not bad! • If we do get growth miracles, they will look very different from those we have experienced to date, which have been based on rapid industrialization
