African Development Bank Group T THE ROLE OF HUMAN CAPITAL IN MANUFACTURING VALUE ADDED DEVELOPMENT IN AFRICA PROF. JOHN C. ANYANWU* LEAD RESEARCH ECONOMIST DEVELOPMENT RESEARCH DEPARTMENT AFRICAN DEVELOPMENT BANK TEMPORARY RELOCATION AGENCY BP 323, 1002 TUNIS, TUNISIA E-Mail: J.ANYANWU@AFDB.ORG KEYNOTE ADDRESS AT THE PhD PRIZE AWARD DINNER OF THE 57 TH ANNUAL CONFERENCE OF THE NIGERIAN ECONOMIC SOCIETY, NICON LUXURY HOTEL, ABUJA, NIGERIA, 27 SEPTEMBER 2016 * The views expressed here are those of the authors and in no way reflect those of the AfDB and its Executive Directors.
Outline of Presentation I. INTRODUCTION & MOTIVATION II. III. IV. WHY DOES MANUFACTURING MATTER? SOME STYLIZED FACTS ON MANUFACTURING VALUE ADDED IN AFRICA THE MODEL V. EMPIRICAL RESULTS VI. POLICY IMPLICATIONS 2
INTRODUCTION & MOTIVATION 3
Introduction & Motivation Diversification is needed to overcome economic vulnerabilities that African economies are facing. Diversification exposes producers to a wider range of information, including about foreign markets, and so raises the number of points for potential self-discovery. In essence, as Gelb (2010) puts it, capability in one sector can open the way to others, especially those that use related knowledge. And manufacturing is a key element of economic diversification. 4
Forms of Diversification 5
Objectives of the Paper This paper extends and contributes to the literature on substantially increasing manufacturing value added development in Africa in four ways: Firstly, we show why manufacturing development matters and hence why policymakers need to put high and increased focus on it so as to scale it up substantially. Secondly, we document stylized facts on recent manufacturing development in Africa. Thirdly, the paper empirically assesses the key determinants of manufacturing value added in the continent using a time series crosssectional data set of African countries (1970 to 2013) with a view to drawing key lessons for African countries. Fourthly, we offer policy suggestions in light of the evidence that would help African countries, including Nigeria, to effectively tackle the problems hindering manufacturing development in the continent with a view to scaling up and breaking into substantial manufacturing development across the continent, providing a strong product diversification vehicle. 6
WHY DOES MANUFACTURING MATTER? 7
Why Does Manufacturing Matter? Very few countries have been able to grow and accumulate wealth without investing in their manufacturing industries, and a strong and thriving manufacturing sector usually precipitates industrialization as a key diversification component: Manufacturing is labor-intensive & export-focused. By increasingly adding value to commodities before they are sold, revenues are boosted, thus raising average earnings per input. Manufacturing development enables dynamic learning-by-doing gains that raise productivity and income. The manufacturing sector is more sustainable and less vulnerable to external shocks than primary commodities, for example. Recently, African countries have been buffeted by four very serious and interrelated external shocks, namely hikes in food prices, increases in energy prices, the global financial and economic crisis, and the ongoing collapse in commodity (especially oil) prices that started in 2014, whose economic and social costs in Nigeria have been quite substantial. These quadruple crises have refocused attention on Africa s high vulnerability to external shocks and the need for African policymakers to take urgent action to diversify their production and export structure to build resilience to external shocks. 8
Why Does Manufacturing Matter? A strong manufacturing industry contributes to the development of the private sector, which further increases the economy s resilience to external shocks. Manufacturing development enables dynamic learning-by-doing gains that raise productivity and income. Manufacturing goods locally to supply the domestic market has a positive impact on the structure of the trade balance, improving external accounts. 9
SOME STYLIZED FACTS ON MANUFACTURING VALUE ADDED IN AFRICA 10
Africa has the lowest MVA (%GDP) (averaging just 11% against East Asia & Pacific s 25% between 1995 and 2014) among the world s regions. Africa s MVA is also been declining trend. 30 Figure 1: Regional Trends in MVA as % of GDP, 1995-2015 25 20 15 10 5 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Latin America & Caribbean North America South Asia East Asia & Pacific Europe & Central Asia Africa World Source: Author, using data from the WB (2016) 11
1970 1971 1972 1973 1974 1975 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 2014 2015 MVA (%GDP) in Sub-Saharan Africa is about one and half times less than that of North Africa with Nigeria s languishing at the bottom but recently trying to catch up, yet falling 20 Figure 2: Trend in Average MVA as % of GDP, 1970-2015 18 16 14 12 10 8 6 4 2 0 North Africa Sub-Saharan Africa Africa Nigeria Source: Author, using data from the WB (2016) 12
1970 1971 1972 1973 1974 1975 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 2014 2015 MVA in Net Oil Exporting Countries is trumped by that in Non- Oil Exporting Countries 14 Figure 3: Manufacturing Value Added (%GDP): Net Oil Exporters Versus Non-Oil Exporters, 1970-2015 12 10 8 6 4 2 0 Net Oil Exporters Non-Net Oil Exporters Source: Author, using data from WB (2016). 13
Regional and Oil-Non-Oil averages mask significant country differences in MVA And show that MVA is generally lower in Natural Resource-Rich Countries than non-resource rich ones Figure 4: MVA-Economic Development: A Kuznets Curve (Inverted-U-Shaped) Relationship Swaziland 10 20 30 South Mauritius Africa Zimbabwe Morocco Zambia Egypt, Tunisia Arab Rep. Senegal Cameroon Malawi Mozambique Congo, Lesotho Dem. Rep. Burkina Faso BeninCote d'ivoire Madagascar Kenya Namibia Algeria Burundi Rwanda Ghana Mauritania Cabo Verde Guinea-Bissau Chad Central Togo Eritrea Tanzania Uganda Mali Sudan African Republic Niger Gambia, The Congo, Rep. Botswana Ethiopia Guinea Sao Nigeria Sierra Tome and Principe Comoros Leone Djibouti Angola Seychelles Gabon Libya -10 0 0 5000 10000 15000 20000 25000 Real GDP Per Capita (mean) maanufvalgdp Fitted values Source: Author, using data from WB (2016). 14
MVA and Primary Education Enrolment in Africa A nonmonotonous relationship Figure 5: MVA-Primary Education: A Kuznets Curve (Inverted-U-Shaped) Relationship 10 20 30 Swaziland South Mauritius Africa Zimbabwe Morocco Zambia Egypt, Arab Rep. Tunisia Senegal Cameroon Burkina Faso Congo, Dem. Rep. Cote Mozambique d'ivoire Malawi Lesotho Benin Algeria Kenya Madagascar Namibia Mauritania Burundi Ghana Rwanda Seychelles Cabo Verde ChadGuinea-Bissau Central Tanzania African Republic Togo Mali Sudan Uganda Somalia Gambia, The Niger Nigeria Botswana Congo, Rep. EthiopiaGuinea Sao Tome and Principe Sierra Leone Comoros Libya Djibouti Angola Gabon 0 0 50 100 150 Primary School Enrolment Ratio (mean) maanufvalgdp Fitted values Source: Author, using data from WB (2016). 15
Human Capital (Education) and Manufacturing Development: The Linkages Source: Adapted from UNECA (2015). 16
THE MODEL 17
The Empirical Model where MVA is manufacturing value added (%GDP) in country i at time t; with explanatory variables as human capital (primary, secondary and tertiary education); economic development (real per capita income in 2011 PPP and its square); and X it representing other variables - natural resources rents (oil, mineral, natural gas and coal); macroeconomic variables (domestic investment and government consumption expenditure); financial sector development (domestic credit to the private sector); globalization [economic globalization (trade openness, FDI stock (%GDP)); social globalization; political globalization]; institutionalized democracy; demographic variables (total population, age dependency (old and young); & science & technology (scientific & technical articles in journals, ICT mobile cellular subscriptions and fixed telephone subscriptions). Note that μit represents the sub-regionalfixed effects, while τt represents the time-fixed effects, and εit represents the error term. Due to possible endogeneity issues, we rely on the IV-2SLS estimation technique. 18
EMPIRICAL RESULTS 19
EMPIRICAL RESULTS Table 1: OLS and IV-2SLS Estimates of the Role of Human Capital in MVA (%GDP) in Africa (with sub-regional and time fixed effects), 1990-2014 Variable OLS IV-2SLS Primary school enrolment Primary school enrolment 2 Secondary school enrolment Tertiary school enrolment Real GDP Per Capita Real GDP Per Capita 2 Oil rent (%GDP) Mineral rent (%GDP) Natural gas rent (%GDP) Coal rent (%GDP) Domestic investment (%GDP) Govt. consumption expenditure (%GDP) Credit to private sector (%GDP) Trade Openness FDI Stock (%GDP) Social Globalization Political Globalization Democracy Democracy 2 Log of population Age dependency ratio (Old) Age dependency ratio (young) Scientific & technical journal articles Mobile cellular subscriptions Fixed telephone subscriptions Constant 0.289 (4.57***) -0.001 (-3.88***) -0.156 (-4.29***) 0.207 (2.70***) 18.968 (2.69***) -0.971 (2.18**) -0.347 (-8.13***) -0.052 (-0.81) -0.202 (-1.01) 10.387 (2.97***) -0.147 (-4.56***) -0.206 (-3.12***) 0.070 (2.44**) 0.104 (7.91***) -0.054(-3.15***) -0.181 (-2.83***) 0.022 (0.85) -2.220 (-8.13***) 0.101 (7.58***) 1.848 (4.50***) 0.783 (2.07**) 0.076 (2.20**) 0.001 (0.98) -0.069 (-2.80***) 0.383 (2.59***) -163.648 (-8.40***) 0.225 (3.75***) -0.001 (-3.18***) -0.175 (-5.21***) 0.264 (3.81***) 22.294 (3.29***) -1.185 (-2.76***) -0.400 (-10.07***) -0.137 (-2.34**) 0.033 (0.18) 6.885 (2.15**) -0.114 (-3.76***) -0.267 (-4.19***) 0.118 (3.90***) 0.135 (9.75***) -0.078 (-3.37***) -0.259 (-3.61***) 0.021 (0.78) -2.665 (-8.29***) 0.115 (7.58***) 1.043 (2.57***) 0.929 (2.19**) 0.088 (2.36**) 0.0002 (0.19) -0.012 (-0.51) 0.124 (0.75) -112.970 (-4.25***) Year Dummies Yes Yes Sub-Regional Dummies Yes Yes R-Squared F-stat/Wald chi 2 Prob > chi 2 N Tests of overidentifying restrictions - Sargan - Basmann Tests of endogeneity - Durbin - Wu-Hausman 0.6901 14.26 0.0000 423 Note: t-values are in parentheses; ***= 1% significant level; **=5% significant level; *=10% significant level. Source: Author s Estimations. 999.81 0.0000 308 2.43922 (p=0.2953) 2.04359 (p=0.3599) 1.56174 (p=0.4580) 0.652344 (p=0.5217) 20
EMPIRICAL RESULTS Table 2: Extension: IV-2SLS Estimates of the Role of Human Capital in MVA (%GDP) in Sub- Saharan Africa and North Africa (with sub-regional and time fixed effects), 1990-2014 Variable Sub-Saharan Africa North Africa Primary school enrolment Primary school enrolment 2 Secondary school enrolment Tertiary school enrolment Real GDP Per Capita Real GDP Per Capita 2 Oil rent (%GDP) Mineral rent (%GDP) Natural gas rent (%GDP) Coal rent (%GDP) Domestic investment (%GDP) Govt. consumption expenditure (%GDP) Credit to private sector (%GDP) Trade Openness FDI Stock (%GDP) Social Globalization Political Globalization Democracy Democracy 2 Log of population Age dependency ratio (Old) Age dependency ratio (young) Scientific & technical journal articles Mobile cellular subscriptions Fixed telephone subscriptions Constant 0.263 (4.23***) -0.001 (-3.61***) -0.201 (-5.70***) 0.514 (4.16***) 25.826 (3.35***) -1.401 (-2.86***) -0.379 (-8.94***) -0.019 (-0.29) -0.159 (-0.53) 9.300 (2.78***) -0.123 (-3.77***) -0.265 (-3.91***) 0.198 (4.24***) 0.127 (8.22***) -0.095 (-3.58***) -0.245 (-2.74***) -0.002 (-.06) -3.124 (-9.02***) 0.139 (8.49***) 1.462 (2.95***) 0.844 (1.66*) 0.082 (2.01**) -0.006 (-2.63***) -0.114 (-3.42***) -0.054 (-0.28) -127.973 (-4.19***) -1.146 (-6.97***) 0.006 (7.11***) -0.008 (-0.32) 0.110 (3.24***) -5.089 (-2.27**) -0.630 (-14.93***) -1.910 (-6.26***) -0.35 (-0.34) 0.361 (8.89***) -0.509 (-5.42***) 0.152 (8.68***) 0.056 (3.70***) 0.095 (5.44***) -0.147 (-3.17***) 0.066 (6.42***) -3.007 (-8.47***) 0.222 (6.94***) -7.826 (-6.69***) -4.093 (-12.91***) 0.829 (9.90***) 0.001 (2.99***) -0.220 (-5.68***) 0.043 (0.39) 275.056 (7.76***) Year Dummies Yes Yes Sub-Regional Dummies Yes No Wald chi 2 Prob > chi 2 N Tests of overidentifying restrictions 911.73 0.0000 261 62622.62 0.0000 47 - Sargan 2.12176 (p=0.3462) - Basmann 1.72116 (p=0.4229) Tests of endogeneity - Durbin 1.45025 (p=0.4843) - Wu-Hausman 0.586695 (p=0.5571) Note: t-values are in parentheses; ***= 1% significant level; **=5% significant level; *=10% significant level. Source: Author s Estimations. 1.8681 (p=0.1717) 0.124176 (p=0.7245) 0.063352 (p=0.8013) 0.004049 (p=0.9533) 21
EMPIRICAL RESULTS AFRICA AS A WHOLE Human Capital Not all human capital indicators are born equal with respect to MVA: Primary education has an inverted U-shaped relationship with MVA, inflection point being at about 108% given other factors; secondary education has a negative significant relationship with MVA; tertiary education is a significant positive relationship with MVA - for Africa, tertiary education is good for increasing MVA. Economic Development Inverted U-shaped relationship (Kuznet s curve) with MVA, inflection point being at about GDP per capita (2011 PPP) of US$2,030 given other factors. Natural Resource-dependence Not all resources are born equal as they relate to MVA: Oil- and mineraldependence are significantly negatively related to MVA while coal has the opposite relationship. Macroeconomic Variables Both domestic investment and government consumption expenditure are significantly negatively related to MVA. 22
EMPIRICAL RESULTS AFRICA AS A WHOLE Credit to the private Sector Increased credit to the private sector promotes increased MVA. Globalization Increased trade openness promotes MVA while FDI stock and social globalization are significant negatively correlated with MVA. Institutionalized democracy A U-shaped correlation exists between democracy and MVA. Demographic Variables Both total population and age dependency (old and young) are significantly and positively correlated with MVA. Science & Technology Scientific & technical publications have been unimportant for MVA; Mobile cellular subscription is significantly negatively related to MVA while fixed telephone subscriptions promote MVA. 23
EMPIRICAL RESULTS EXTENSION TO SUB-SAHARAN AFRICA AND NORTH AFRICA North Africa is different! Human Capital Primary education has an inverted U-shaped relationship with MVA in SSA but it is U-shaped in North Africa; secondary education has a negative significant relationship with MVA in SSA but insignificant in North Africa; tertiary education has a significant positive relationship with MVA in both SSA and North Africa as in the whole of Africa. Economic Development While there is an inverted U-shaped relationship (Kuznet s curve) between economic development and MVA in SSA as in the all-africa case, the level of economic development is negatively related to MVA in North Africa. Natural Resource-dependence Not all resources are born equal as they relate to MVA in SSA and North Africa: While oil-dependence is a drag on MVA in SSA and North Africa, mineral-dependence is significantly negatively related to MVA in North Africa. Macroeconomic Variables Domestic investment is bad for MVA in SSA but promotes it in North Africa but government consumption expenditure is significantly negatively 24 related to MVA in both SSA and North Africa.
EMPIRICAL RESULTS EXTENSION TO SUB-SAHARAN AFRICA AND NORTH AFRICA North Africa is different! Credit to the private Sector Increased credit to the private sector promotes increased MVA in North Africa but not in SSA. Globalization Increased trade openness promotes MVA in both SSA & North Africa while FDI stock is positively related to MVA in North Africa and not in SSA. Social globalization is significantly negatively correlated with MVA in both SSA & North Africa while political globalization promotes MVA in North Africa. Institutionalized democracy A U-shaped correlation exists between democracy and MVA in both SSA and North Africa. Demographic Variables Both total population and age dependency (old and young) are significantly and positively correlated with MVA in SSA; population and age dependency (old) are negatively related to MVA in North Africa while age dependency (young) has positive relationship. Science & Technology Scientific & technical publications are negatively related to MVA in SSA while the opposite is true in North Africa; Mobile cellular subscription is significantly negatively related to MVA in both SSA and North Africa. 25
POLICY IMPLICATIONS 26
POLICY IMPLICATIONS A Developmental State should Develop and implement policies that promote the up-skilling, better training and education for the low-skilled workforce. Address skills mismatch in the short-run through improved training programs and closer links between tertiary and vocational educational institutions on the one hand, and the private sector on the other. Fundamentally reform the educational curriculum/content (including the availability of school books, equipment/facilities, and other teaching materials) towards more emphasis on skill acquisition through technical and vocational education and training (TVET) as well for better quality education and requisite skills for industry. Promote mass education campaign on the importance of sending children to school. Promote industry-relevant research and technology development through foreign technology adaptation and increased expenditure on R&D. Quantitatively expand education through public-private partnerships & by increasing public spending on education at all levels. Assisting the poor with fees, including conditional cash transfers (safety nets) see illustration below. 27
Conditional Cash Transfer A Good Example of Appropriate Social Safety Net for Nigeria Conditional Transfers Targeted transfers conditional on school attendance Intended Beneficiaries Poor and vulnerable families with low level of human capital Targeting Methods Means or proxy means and/or Categorical Geographic and/or Community (together with one of above) Key Design Features Same as cash Efficient way to verify compliance
POLICY IMPLICATIONS To mobilize the needed funds, African countries need tax reforms for fair and efficient tax systems, improved tax administration, deepened tax-base, diversified tax mix and encouragement of investment by the private sector, including foreign ones by creating an enabling environment. To increase per capita income towards the necessary threshold, African countries must deepen macroeconomic and structural reforms to increase their competitiveness, dismantle existing structural bottlenecks to private and public investment, scale-up productive investments in hard and soft infrastructure, and increase productivity through creating incentives and opportunities for the private sector and increasing government support to SMES in terms of low-interest finance, among others. IFIs have a critical role to play in helping African countries acquire the much-needed capacity not only to negotiate beneficial contracts and earn higher natural resource rents but also for effective management of those rents. A new natural resources management framework is needed for better governance, sectoral linkages, human, capacity, infrastructure and manufacturing development with strong parliamentary legislation, oversight, and representation throughout the resources value chain. Promote increased regional trade, especially through the removal of crossborder barriers and infrastructure bottlenecks. 29
Thank you for your kind attention 30