Presentation: The role of agriculture in the development process. Maputo, Mozambique
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The document discusses the role of agriculture in development processes based on perspectives from development literature and international experiences over the past 25-30 years. It notes that while agriculture traditionally played a passive role, more recent evidence shows it can play an active role in supporting industrialization and structural transformation if agricultural growth induces urban-industrial growth. However, many countries in Africa failed to successfully transform due to various challenges. The global context today faces three major crises - finance, food, and climate change - which impact the role of agriculture. In Mozambique, agriculture's contribution to GDP decreased from 1997-2001 but has since stabilized.
Presentation: The role of agriculture in the development process. Maputo, Mozambique
- 1. OMR Conference, 4 September 2013 Maputo, Mozambique The Role of Agriculture in the Development Process
- 2. Overview • Perspectives from the development literature • International experiences: the last 25‐30 years • Current global context: three crises • Mozambique – experiences over the past decade • Concluding remarks
- 3. Traditional views (from theory and empirics) • Ricardo and colleagues • The Lewis two‐sector model • The linkage literature • Falling relative share of agriculture • All this tended to suggest a passive and at best a supportive role
- 4. The basis for a positive role • Classical paradigm of positive role of ag in development (1960s) • Agricultural growth in support of industrialization through the agricultural transformation (ADLI) (Asian examples) • How the structural transformation works: – Agricultural growth induces urban‐industrial growth through capital, labor, foreign exchange, and market contributions • Industry (starting with agribusiness) grows faster than agriculture • As a consequence, the shares of agriculture in aggregate employment and GDP decline due to success in triggering GDP growth, not due to failure to grow.
- 5. A powerful cross-country regularity (1990-2005 average) (de Janvry and Sadoulet 2008)
- 6. Successful transformation in Asia
- 8. But Africa experienced many challenges • Many implementation failures (1970s) • Import Substitution Industrialization failed • Many failures in agriculture‐based projects • Too complex, insufficient support • Integrated rural development to meet broadened development objectives (McNamara 1973) ineffective: – Underestimate emerging private sector roles – Overestimate state capacity to coordinate – Undermine cooperative producer organizations
- 9. And we also saw • 20 years of neglect under structural adjustment and Washington Consensus (1985‐2005) • Adjust the macro‐fundamentals but no sectoral policy • Industrialize through open economy not agriculture • Plus for example: – Descale the role of the state in agriculture, despite pervasive market failures – Reduce rural poverty through transfers instead of autonomous incomes – Investment in agriculture discouraged by low international commodity prices (OECD) & adverse environmental effects – Sharp decline in public expenditures on agriculture – Sharp decline in overseas development assistance to agriculture • So what was the outcome?
- 10. So failed transformation in Africa in general: Labour displaced from agriculture without associated growth in GDP per capita
- 11. Plus another type of failed transformation: Growth without transformation
- 12. In effect there are (at least) three types of economies (with associated roles of agriculture in development) AZE BDI BEN BFA BGD BGR BLR BOLBRA CHL CHN CIV CMR COL DOM ECU EGY ETH GHA GTM HND HUN IDN IND IRN KEN KHM LAO LKA MAR MDG MEX MLI MOZ MWI NER NGA NPL PAK PER PHL POL PRY ROM RUS RWA SEN SLV THA TJK TUN TUR TZA UGA UKRVEN VNM YEM ZAF ZMB AGO ARG CZE DZA GIN MYS PNG SDN SVK SYR TCD TGO ZAR ZWE -0.2 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 1.0 Rural poor/total poor, 2002 Poverty data from Ravallion et al. 2007 Other predicted poverty data Dynamic analysis Urbanized countries Agriculture-based countries Transforming countries 70-75 90-96 Indonesia (1970-96) Brazil (1970-96) India (1965-94) China (1981-2001) Source: World Bank, World Development Report 2008. Y-axis: ag contribution to growth: 1990-2005
- 13. Role of agriculture fundamental but it differs … • Agriculture based economies: growth – Agriculture essential for growth: large – Importance for food security and poverty • Transforming countries: equality – Rapidly growing non‐ag – Agriculture key to reduce imbabalance + marginalization • Urbanized economies: inclusion – Sub‐sectors with comparative advantage – Include small holdes as suppliers
- 14. Losses due to global trade policies
- 15. Agriculture exports highly taxed
- 17. Subsidies and public investment in Indian agriculture
- 18. Disaggregated project aid, 2002-2009
- 19. Foreign aid and poverty
- 20. Ag growth and the link to poverty
- 21. Examples of impressive successes
- 22. Agricultural productivity: two types of farming (1) Highly efficient agriculture of developed countries and high output per worker (2) Inefficient and low‐productivity agriculture of developing countries • Between the extremes: developing regions e.g. regions in India, Brazil, export‐oriented sectors in Latin‐America and Asia : reach higher agricultural productivity levels and growth! • Agricultural productivity and productivity growth low especially in Sub‐ Saharan Africa
- 23. Agriculture value added ($ per agricultural worker) 2003-05 Cereal production Yields (in kilo per hectare) 2003-05 Thailand 554 3,044 Vietnam 182 4,641 Indonesia 421 4,278 Ethiopia 64 1,213 Tanzania 167 1,403 Mozambique 83 925 Uganda 101 1,559 Kenya 169 1,682 Denmark 22,260 6,088 Source: World Development Report 2008, selected indicators Productivity examples x 348 x 40 labour productivity land productivity
- 24. The risk averse peasant and technology choice Not necessarily: uncertainties (e.g. weather, price), imperfect information, transactions costs, lack of access to credit and insurance The peasant tries to maximise not income but the chance of the family’s survival => rational Min. desirable consump. Minimum consump. Production/consum. Time Technology A Technology A: Low yield, little variation Technology B Technology B: High yield, big variation Yield Probability Technology A Technology B Is resistance to technological innovation due to lack of rationality or incompetence?
- 25. What else causes low output growth in low- productivity subsistence farming 1) Large amounts of land sometimes available, only small parts can be cultivated: traditional tools (e.g. hoe, ax, knife, …). Use of animals sometimes impossible because of tsetse fly, lack of fodder in dry season => agriculture depends on applying human labour only to small plots of land. 2) Due to limited amount of land cultivated and tools used, small areas tend to be cultivated intensely => rapidly diminishing returns to labour. Best farming method is shifting cultivation (i.e. once minerals drawn from soil, new land cleared and cultivated while old land can recover and be used again later). If fallow time long enough, manure and chemical fertiliser would be unnecessary. 3) Seasonality: scarcity of labour in busy parts of the season (planting, weeding) while underemployed at other times. Net result: constant level of agricultural output and labour productivity…as long as population size stable …
- 26. The global context: three crises • Finance • Food • Climate change
- 28. IMFForecast -4-202468 RealGDPAnnualGrowth(%) 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year World Advanced Economies Emerging and Developing Economies Source:IMF World Economic Outlook Database October (2009) Real GDP Growth in World and Major Economic Groupings (1970-2014) Present economic downturn deepest in 60 years, and no region untouched + a lot speculation as to recovery28
- 29. IMFForecast -11.89 -2.66 -0.93 0.24 First Oil Crisis Second Oil Crisis Dot Com Bubble Financial Crisis -15-10-5051015 TradeVolume(AnnualPercentageChange) 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Year World Trade Volume Source:IMF World Economic Outlook Database October (2009) World Trade Volume (1970-2014) World trade has experienced its sharpest decline in decades + uncertain future
- 30. 2.98 -0.71 IMFForecast -10123 NetPrivateCapitalFlows(%ofGDP) 1990 1995 2000 2005 2010 2015 Year Net Private Capital Flows Source: IMF World Economic Outlook Data Base April (2009) Net Private Capital Flows to Emerging and Developing Economies (1990-2014) Net private capital flows to the South have fallen dramatically
- 31. CRISIS 2: FOOD
- 32. 050100150200250 IndicesofMarketPrices(2005=100) 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year Rice Wheat Maize Soybeans Source : IMF Primary Commodity Price Data Base Note: Rice:Thailand(Bangkok); Wheat:US Gulf ; Maize:US ; Soybeans: US Cereal Prices in Indices of Market Prices (1957-2008) Food prices soared in 2007‐2008 and then fell back: prospects?
- 33. Underlying factors • Food price developments reflect: – Low priority to agriculture/food production – Shifting demand patterns – Biofuels (+ lack of research in alternative energy sources) • Underlying structural drivers behind 2007‐2008 spike remain in place – if growth resumes food prices likely to increase again • Global food architecture not geared to deal with supply shortages – governments may intensify protection to try to satisfy domestic consumers
- 34. Responses • National responses to food crisis have varied • Africa: Macro‐policies main tool to limit impact of world price shocks • Elsewhere: Greater focus on social protection • But too much social protection ad hoc, stop‐go, high cost – needs to be systematic • A double bind: – If recovery stalls: new trade and financial shocks – If recovery is sustained: food and energy prices will climb and hit energy and food importers • Need for public action – but fiscal space limited in the smaller and poorer economies
- 36. Climate change (1) • Present global growth model clearly unsustainable – the challenges are unprecedented • To respond, the world must transform existing energy systems (mitigation) and simultaneously adapt to the climate change that is already built into global climate (adaptation) • Failure in shifting from fossil‐fuel dependence evident in run‐up in oil price prior to the financial crisis (due to lack of investment in energy research)
- 37. 020406080100120140 US$PerBarrel 01jan1998 01jan2000 01jan2002 01jan2004 01jan2006 01jan2008 01jan2010 Daily Note:Oil prices refer to Brent; US dollars per barrel Source:US Department of Energy Oil Prices ( January 1998 to October 2009) Huge run‐up, then a fall as recession set in – but ...?
- 38. Climate change (2) • If growth resumes energy prices will move back up • Places huge burdens on poor countries – a range of fiscal effects, which make states more aid‐dependent, not less • Costs far exceed current level of aid: – Per annum mitigation in developing countries by 2030: USD 140‐175 billion – Per annum adaptation costs by 2050: USD 30‐109 billion – Aid is presently around USD 100 billion in total • Climate change finance is as fragmented as traditional aid, will funding be additional, and who takes control of supply (how much voice for the South?) • Climate change financing seen as compensation – but aid processes remain conditional
- 40. The Role of Agriculture in Mozambique • The contribution of agriculture to GDP decreased significantly from 1997 to 2001, before immediately increasing again. Since recovery from the 2000 floods, the contribution of all sectors has stayed quite stable. 35 35 35 31 29 24 23 28 28 27 27 28 28 30 31 32 32 15 16 18 22 23 25 26 23 26 27 25 26 26 24 24 23 24 51 48 47 47 49 51 52 49 46 45 48 46 46 46 45 45 44 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Sectoral Contributions to GDP Agriculture, value added (% of GDP) Industry, value added (% of GDP) Services, etc., value added (% of GDP) Source: World Bank Mozambique Metadata
- 41. Public expenditure in agriculture • State support to agriculture has not achieved the 10% Maputo Declaration target – the average empirical value attained by ‘transforming’ economies over the last 30 years. • Actual expenditure of budget increases have tended to remain unfulfilled • The large budgets in 2003 and 2004 can be attributed to the rehabilitation of the Massingir Dam, and the Chokwe Irrigation Scheme in 2006 and 2007 – accounting for a large part of the budget 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 2001 2002 2003 2004 2005 2006 2007 2008 2009 Share of Agriculture in the Total Budget Share of Actual Agricultural Expenditure in Total Maputo Declaration Target ‐ 10% Source: Public expenditure review ‐ MozSAKSS (2011)
- 42. Levels of staple crop production • Maize continues to be most produced staple crop • Production levels have shown no signs of increase over the last 10 years • Rainfall and weather patterns continue to heavily influence staple crop production (2005 and 2006) 0 20 40 60 80 100 120 140 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2002 2003 2005 2006 2007 2008 2012 Kg Per Capita '000 Tonnes Millet Sorghum Rice Maize Staple Production p/c Staple Production p/c (Rural) Source: TIA/IAI
- 43. Staple crop productivity (kg/ha) • Maize productivity levels still fluctuating and much below potential • Rice productivity stagnation • Sorghum and millet productivity levels fluctuate with no trend of improvement 0 100 200 300 400 500 600 700 800 900 2002 2005 2006 2007 2008 2012 Crop Yield (Kg/Ha) Maize Rice Sorghum Millet Source: TIA/IAI
- 44. Input use • Extremely low utilisation of agricultural inputs • No evidence of improvement • Not uniformly distributed throughout the country... 0% 2% 4% 6% 8% 10% 12% 14% 2002 2003 2005 2006 2007 2008 2012 % of Input Use Improved Seed Fertilisers Pesticides Animal traction Irrigation Source: TIA/IAI
- 45. Who uses inputs (%)? IMPROVED SEEDS FERTILISER PESTICIDE ANIMAL TRACTION IRRIGATION 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 Niassa 8.8 5.5 13.1 8.5 7.5 6.0 0.0 0.0 5.5 6.3 Cabo Delgado 1.6 5.0 1.8 1.4 11.7 14.4 0.0 0.1 2.0 2.4 Nampula 6.4 4.9 2.3 2.3 9.5 5.6 0.1 0.1 3.7 5.4 Zambézia 7.3 7.2 0.8 0.5 1.2 0.8 0.1 0.1 2.4 3.2 Tete 16.5 23.4 15.4 15.2 7.3 6.9 30.7 21.2 18.0 16.3 Manica 17.4 22.1 2.2 2.5 1.9 2.7 12.4 14.6 9.9 17.2 Sofala 8.4 11.4 1.1 1.3 7.0 3.4 1.8 3.2 4.8 9.0 Inhambane 7.9 6.1 1.7 3.4 1.8 2.6 47.4 42.8 18.7 20.5 Gaza 8.6 11.2 3.4 2.4 2.8 2.5 45.4 44.9 19.9 12.9 Maputo 13.7 10.1 4.7 6.2 4.1 5.0 13.5 14.0 22.8 22.4 Total 8.4 9.7 3.7 3.6 5.7 4.7 11.0 9.8 7.8 8.9 • Large use of improved seeds, fertilisers, pesticides and animal traction in Tete • Evidence of increased adoption of improved seeds varieties in Tete, Manica, Sofala and Cabo Delgado • Fertiliser, pesticide and animal traction has generally not changed much • Extremely low use of animal traction above the Zambezi River Increase > 5 % Increase 3 ‐ 5% Decrease 3 ‐ 5 % Decrease > 5 % BOLD values show above 10% usage
- 46. Land use • Average farm size falling in the South – labour shortages, rural to urban migration • Increasing average farm size in Zambézia and Nampula • Low levels of land registration nationally. Improved vastly in Maputo Province (25% in 2012) time, greater competition over land? 0 0.5 1 1.5 2 2.5 2002 2003 2005 2006 2007 2008 2012 Average size of area cultivated (Ha) Average Cultivated Area per Household North Centre South National 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 2002 2005 2008 2012 % of Farmers with Registered Land DUAT Registration Source: TIA/IAI Source: TIA/IAI
- 47. Services (%) • Large fall in farmers receiving extension advice throughout the country • Farmers receiving price information decreased in the north, but rose in the centre and south • Association membership increased slightly nationally, yet fell drastically in Maputo province • Emergency seed reception fell in the centre and south, and was already initially very low in all other areas of the country EXTENSION ADVICE RECEIVED PRICE INFO ASSOCIATION MEMBER RECEIVED CREDIT EMERGENCY SEEDS 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 Niassa 14.5 9.4 40.7 34.3 6.7 5.9 7.5 3.3 3.0 3.7 Cabo Delgado 14.9 6.3 43.4 35.8 4.5 4.7 2.9 2.5 2.2 2.0 Nampula 15.2 9.1 62.7 43.5 6.4 7.8 3.6 3.8 2.4 1.2 Zambézia 9.5 7.4 26.1 37.7 3.8 7.6 0.9 1.6 4.1 3.2 Tete 16.3 11.9 37.5 46.3 5.0 4.9 7.7 6.6 4.8 5.5 Manica 12.6 7.2 43.3 46.1 4.7 5.8 1.3 3.4 5.4 2.3 Sofala 20.4 11.2 46.2 50.4 3.2 4.8 4.5 3.1 9.8 6.3 Inhambane 7.2 6.5 23.3 29.9 2.6 6.2 1.1 3.1 3.7 2.4 Gaza 16.7 6.5 24.8 31.3 9.3 8.0 2.5 2.4 12.0 5.7 Maputo 11.6 7.2 21.5 32.8 15.2 8.0 2.9 2.0 18.5 2.7 Total 13.4 8.3 39.6 39.6 5.3 6.6 3.1 3.1 4.8 3.2 Increase > 5 % Increase 3 ‐ 5% Decrease 3 ‐ 5 % Decrease > 5 % BOLD values show usage above national average Source: TIA/IAI
- 48. Cash crop producers • Overall fall in farmers cultivating tobacco, whilst soybean is emerging as a viable cash crop • No major identifiable trends • National level statistics disguise important provincial level trends... 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 2002 2003 2005 2006 2007 2008 2012 % of Farmers Cultivating Crop Cotton Tobacco Sunflower Sesame Soybean Source: TIA/IAI
- 49. Cash crop producers (%) • Increase in cotton farmers in Niassa and Cabo Delgado, large fall in Nampula • Tobacco slightly falling throughout the country COTTON TOBACCO SUNFLOWER SESAME SOYBEAN 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 Niassa 0.6 3.9 15.8 12.4 7.3 2.2 5.7 3.4 0.3 0.2 Cabo Delgado 13.7 17.1 1.1 0.3 0.5 0.2 14.5 15.6 0.0 0.0 Nampula 12.0 6.3 1.8 0.8 0.6 0.1 11.3 10.4 0.1 0.1 Zambezia 3.2 2.0 2.5 2.0 1.8 1.6 2.6 3.8 0.1 0.4 Tete 8.5 7.0 11.7 8.1 0.8 1.1 3.7 5.0 5.7 7.7 Manica 2.2 3.5 3.8 0.6 3.5 3.9 12.9 10.5 0.0 0.2 Sofala 9.4 7.7 1.5 0.4 0.5 0.1 15.5 20.9 0.0 0.0 Inhambane 0.1 0.3 0.4 0.1 0.0 0.2 0.3 0.3 0.0 0.0 Gaza 0.0 0.0 0.4 0.0 0.2 0.0 0.0 0.0 0.0 0.0 Maputo 0.0 0.0 1.3 0.2 0.1 0.0 0.3 0.4 0.0 0.0 Total 6.3 5.1 3.4 2.2 1.4 0.9 7.1 7.4 0.5 0.9 Increase > 5 % Increase 3 ‐ 5% Decrease 3 ‐ 5 % Decrease > 5 % BOLD values show above 10% Source: TIA/IAI
- 50. Cash crop production • Soybean growth enormous between 2002 and 2012 ( >700%); 59% of the total production was in Tete in 2012, 24% in Zambézia • Sesame production has more than doubled; 25% of production in Sofala in 2012, 23% in Nampula • Large fall in tobacco production from 2006 peak; 49% of production in Tete in 2012, 38% in Niassa • Cotton has remained relatively stable; 27% of production in both Tete and Nampula in 2012 0 100 200 300 400 500 600 700 2002 2003 2005 2006 2007 2008 2012 Index = 100 Cotton Tobacco Sunflower Sesame Soybean Index = 100 Source: TIA/IAI
- 51. Characteristics of productive maize farmers (2002-12) • The top 20% productive maize farmers are much more productive than other maize farmers • Inverse relationship between maize productivity and the area cultivated (maize area and total area) • Farmers who use fertiliser and pesticides are more productive • Hired workers and cash crops are associated with higher maize productivity Maize Productivity Quintiles Maize Yield (Kg/Ha) Maize Area (Ha) Total Cultivated Area (Ha) Fertiliser Use (%) Pesticide Use (%) Hire Worker? (%) Cultivate Cash Crop? (%) 1 – Lowest Yield 73 0.8 1.8 1.8 3.3 16.1 42.2 2 272 0.8 1.8 3.1 4.6 19.2 45.6 3 514 0.7 1.7 3.9 6.7 20.6 48.8 4 912 0.6 1.6 5.9 7.0 24.5 51.4 5 – Highest Yield 2981 0.4 1.4 7.5 7.9 30.3 57.9 Source: TIA/IAI
- 52. Looking closer at the most productive 20% • The most productive farmers have generally become less concentrated in Niassa and Nampula, with Tete and Manica possessing a higher proportion. The share in Maputo province has increased dramatically. • The most productive farmers in Tete and Manica use more improved seeds and sell increasing amounts of maize. • The total area farmed by the most productive farmers decreased significantly nationwide, except in Nampula, Zambézia and Sofala. • The percentage of the most productive farmers selling maize has fallen in most parts of the country – significantly in Manica, yet increased in Nampula and Inhambane. This implies that those who do sell maize are selling much more of it. PROVINCIAL DISTRIBUTION OF TOP 20% MAIZE FARMERS (%) IMPROVED SEEDS (%) TOTAL AREA (ha) FARMER SELLS MAIZE (%) MAIZE SOLD (KGS) 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 Niassa 32 24 11 7 1.70 1.61 35 34 175 447 Cabo Delgado 19 17 3 8 1.51 1.26 32 30 158 259 Nampula 25 17 10 10 1.17 1.33 34 40 150 474 Zambézia 20 22 11 11 1.39 1.43 52 49 227 442 Tete 22 27 22 26 2.07 1.64 27 21 471 1050 Manica 25 26 19 26 1.68 1.46 59 36 433 1119 Sofala 20 19 11 11 1.54 1.67 39 33 279 504 Inhambane 5 7 9 9 1.02 0.92 9 18 50 311 Gaza 9 13 12 16 1.17 0.93 13 5 85 169 Maputo 14 25 25 14 0.80 0.42 16 11 116 395 Total 20 20 13 15 1.48 1.39 38 33 239 586 Increase > 5 % Increase 3 ‐ 5% Decrease 3 ‐ 5 % Decrease > 5 % BOLD values show above average Source: TIA/IAI
- 53. Comparing the top 20% with the rest • In general, the most productive maize farmers produce and sell significantly more maize, using less land. • This is particularly apparent in Tete (where higher levels of fertiliser use is found) and in Manica. • The difference in the quantity of maize produced did not change much over time, yet the quantity of maize sold more than doubled, especially in Tete and Manica. • More farmers with higher maize yields sell their produce and cash crops than others DIFFERENCES BETWEEN THE TOP 20% PRODUCTIVE MAIZE FARMERS AND THE BOTTOM 80%, OVER TIME AVERAGE MAIZE PRODUCTION (KGS) AV. MAIZE SOLD (KGS) FARMER SOLD MAIZE (%) SELLS CASH CROP (%) USE FERTILISER (%) 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 2002‐06 2007‐12 Niassa + 1233 + 828 + 73 + 238 + 11 + 14 + 6 + 7 + 4 + 3 Cabo Delgado + 489 + 479 + 104 + 124 + 12 + 16 + 1 + 11 0 + 1 Nampula + 327 + 421 + 85 + 316 + 12 + 20 + 6 + 5 + 1 + 4 Zambézia + 511 + 515 + 136 + 278 + 17 + 15 + 9 + 4 + 2 + 1 Tete + 1340 + 1055 + 385 + 730 + 12 + 13 + 14 + 8 + 13 + 11 Manica + 1154 + 1195 + 299 + 851 + 32 + 19 + 7 + 5 + 1 ‐ 1 Sofala + 704 + 813 + 221 + 327 + 21 + 18 + 10 + 12 ‐ 1 + 2 Inhambane + 255 + 404 + 43 + 227 + 6 + 12 + 2 + 1 ‐ 1 + 3 Gaza + 976 + 673 + 73 + 16 + 9 + 2 0 + 1 + 4 + 6 Maputo + 910 + 319 + 97 + 125 + 12 + 8 + 3 + 7 0 + 4 Total + 777 + 744 + 167 + 406 + 18 + 16 + 9 + 7 + 3 + 4 BOLD values show above average differences Source: TIA/IAI
- 55. What can we say about jobs? • Diagnosis and analysis based on existing quantitative evidence • Four nationally representative micro-surveys: 1996/97, 2002/03, 2008/09 living standards household surveys 2004/05 labour force survey • National accounts: value added by sector
- 56. Informal jobs dominate 25 4 45 25 21 11 46 22 25 7 49 19 28 5 46 21 3 1 51 46 3 2 52 43 4 1 57 37 4 1 53 42 020406080100 96/97 02/03 04/05 08/09 96/97 02/03 04/05 08/09 Urban Rural Regular wage Irregular wage Informal / self employed Family worker %workingpop.
- 57. Why is the jobs and ag. agendas important? • Demographic dynamics unavoidable c. 300,000 new entrants/year to labour market, many unskilled • Growth of higher productivity firms (e.g., mining) good for aggregate macro/fiscal indicators BUT will have minimal direct positive effect on poverty reduction without employment growth • Raising productivity in household agriculture a powerful lever for reducing poverty BUT not so far transformative in itself on aggregate
- 58. Strategic policy priorities • Leverage natural resources to stimulate pro-jobs structural transformation (an opportunity) -> time to scale up • Specifically aim for: Employment growth in higher value secondary and tertiary industries Step-increase in agricultural productivity BOTH commercial and smallholder agriculture encouraging links between them Export push (outside of natural resources) • NOT an agenda of neutral or marginal improvements – decisive transformation required