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Africa's Adaptation to Land Constraints: Is the Continent Different
- 1. 1 Adaptation to land constraints: Is Africa different? Derek Headey International Food Policy Research Institute (IFPRI) Thom Jayne Michigan State University (MSU)
- 2. Outline 1. About the project 2. Introduction (background on existing theory & evidence) 3. Expanding land use (extensification) 4. Intensifying agriculture 5. Reducing fertility rates 6. Diversifying out of agriculture 7. Conclusions
- 3. This paper is part of a Bill & Melinda Gates Foundation project on emerging land issues in African agriculture The motivation for the project was the observation of various puzzles of Africa agriculture: apparent land abundance in Africa, but much of Africa has major land constraints, and very, very small farms In addition to five African case studies (Ethiopia included), we decided to look at the cross-country evidence on agricultural intensification That is what I am presenting today About the project
- 4. Some 215 years ago, Malthus argued that pop. growth cyclically outstrips agricultural productivity Strong assumptions: high exogenous fertility rates, land constraints, zero ag. productivity growth In much of the world, economic history has not been kind to Malthus, because of “induced innovations” Whilst “induced innovation” is associated with Hayami and Ruttan, plenty of prior research looked at particular elements of induced innovation More generally, “responding to incentives” is at the heart of economic theories 1. Introduction
- 5. Land expansion Malthus’ theory depends on land constraints, but people have been adept at expanding the land frontier through colonialization, tech. and infrastructure e.g. recent surge in global food prices has prompted “land grabs” in Africa & land expansion more generally Agricultural research in Brazil led to massive land expansion in 1990s and 2000s (opening the cerrado) Of course, for specific countries, land expansion may not be an option 1. Introduction – Land constraints
- 6. Agricultural intensification Boserup (1964): as land to labor ratios shrink, people intensify agricultural production – use more inputs per hectare to get more output per hectare Boserup described transition from land-abundant technologies (slash-and- burn, long fallow) to land- scarce technologies (short fallow, adoption of plow, increased fertilizer use, irrigation) She also emphasized increased labor inputs, and transition from communal to private property rights Binswanger et al generalized the theory in 1980s 1. Introduction - intensification
- 7. 1980s saw substantial empirical literature Broadly supports Boserup’s theory, but lots of complexity Binswanger emphasizes that land constraints interact with access to markets, and agroecological factors For example, irrigation and high rainfall allow multiple cropping – not possible in all agroecologies, however Market access can be an driver of intensification, but might also interact with land constraints And institutions matter – e.g. literature in 1990s unfavorably compared Ethiopia to Kenya 1. Introduction - intensification
- 8. Policy-induced intensification One weakness of Boserup’s theory is that endogenous intensification takes place over the long run But Africa’s population has doubled in last 40 years Hence, much of the ag-economics literature focuses on policy-induced intensification - e.g. Green Revolution Of course, many scientific successes in agriculture But Binswanger emphasized that adoption of technologies is typically a function of land-labor ratios, agroecology and market access (“Boserup matters!”) 1. Introduction - intensification
- 9. Reducing fertility rates Massive economic & demographic lit. on fertility Economics sees fertility as a choice variable If land is becoming constraint (and labor is not), then farmers will have less children . . . all else equal But children serve other purposes (consumption goods, old age security), so fertility response to land constraints may be low Moreover, demographic literature emphasizes “supply” constraints: family planning, female education, etc Not obvious there is a strong endogenous mechanism 1. Introduction - intensification
- 10. Diversifying out of agriculture Major omission from 1980s literature was discussion of nonfarm economy, which is large in many countries If land is a constraint, why not migrate? Of course, farmers do migrate, but viability of migration in domestic economy is a general equilibrium issue: are there nonfarm jobs? Rural nonfarm economy (RNFE) often felt to be driven by agric. productivity, infrastructure, education Policies matter: RNFE does not spontaneously emerge 1. Introduction - intensification
- 11. The African context What about international migration? Has boomed in last 20 years: remittances to LDCs grown by 1600% from 1990 to 2010. Moreover, not just small islands: Philippines, Pakistan and Bangladesh hugely dependent on remittances, and they are all much larger than most African countries Are land constraints driving rural people to explore international migration as a way out of farming? 1. Introduction - intensification
- 12. So we have 4 adaptations to land constraints In this paper we focus on international evidence, and on whether and how Africa adapts to land constraints Why be especially concerned about Malthus in SSA? Many reasons: 1. Very poor, and poverty still heavily rural: history of famine & drought; progress might be deceptive 2. Rural poverty closely associated with small farms; most Africa farms have a few hectares or less 3. Low inherent agric. potential (incl. low irrigation) 1. Introduction
- 13. 5. Rapid population growth (double by 2050); suggests that farm sizes will only get smaller 6. Climate change: secular changes in climate, but also likelihood of more shocks 7. Very limited success with industrialization; urban jobs mostly in low-wage informal services sector 1. Introduction
- 14. 1. Introduction
- 15. Our overarching objective is to assess international experience in these 4 adaptations to land pressures There is a large literature exploring Boserup’s hypothesis, as well as policy-induced intensification There is much smaller literature on land expansion There is essentially no literature on farm sizes & fertility rates And there is some indirect literature on farms sizes, rural nonfarm activity and migration For each of these adaptations, we also ask whether Africa is different, and why? 1. Introduction
- 16. In terms of data and methods, we make use of: 1. FAOSTAT ag production and land data; 2. Census (FAO) and survey data on farm size distributions 3. DHS data on rural fertility rates & occupations 4. Some WB data on remittances We combine these data in an unusually rich data set on agricultural and rural development (though we also acknowledge that some of the numbers are fairly speculative) 1. Introduction
- 17. On methods, our approach is necessarily exploratory Establishing causation is an under-recognized problem with Boserup’s theory Problems of simultaneity, omitted variables, selection biases, parameter heterogeneity. Some examples: 1. Agroecological (AE) factors & market access jointly determine settlement patterns and intensification 2. Boserupian intensification depends on AE potential 3. Unsuccessful intensification encourages out-migration 4. Policies promote intensification, discourages out-migration IV rarely plausible in cross-country setting, but we do make an effort to add as many controls as possible
- 18. If farm sizes are shrinking, why not expand land use? Africa is typically thought of as land abundant, but this neglects the heterogeneity within Africa 2. Land expansion Region Period Hectares per agric. worker (FAO) Hectares per holding (censuses) Used land as % of potentially cultivable land Africa - high densityb (n=5) 1970s 0.84 1.99 32.7 2000s 0.58 1.23 43.8 Africa - low densityb (n=11) 1970s 1.65 2.65 17.2 2000s 1.37 2.82 24.7 South Asia 1970s 0.78 2.01 129.5 (n=5) 2000s 0.55 1.19 135.9 China & S.E. Asia 1970s 0.80 2.08 71.2 (n=4) 2000s 0.68 1.58 83.0
- 19. Several important facts & mysteries emerge from census, FAO and FAO-IIASA data: 1. Farm sizes are shrinking in high-density Africa. 2. Some high-density countries still have unused land, but smallholders face major constraints to using that land (e.g. Ethiopia, Madagascar). 3. Even in countries with unused land (e.g. Ethiopia), there are major constraints to using new lands: different agronomics, disease burdens, infrastructure 4. Farm sizes are unchanged (on average) in low density Africa, but still very small on average 2. Land expansion
- 20. 3. Agricultural intensification In the framework above, the most welfare-relevant indicator of intensification is just output per hectare Boserup focused more on cropping intensity, and the ag-econ profession & CGIAR looks a lot at yields But switching to high value crops is obviously also a potentially important adaptation, especially in SSA. So I’m going to show you a series of graphs, and then some more formal econometric tests. Note that I also decompose agricultural output per hectare into cereal yields, cereal cropping intensity and high value non-cereals
- 21. 3. Agricultural intensification AFG ALB DZAAGO ARG ARM AZE BGD BLR BEN BTN BOLBIHBWA BRA BGR BFA BDIKHM CMR CAF TCD CHL CHNCOL COMZAR COG CRI CIV DOM ECU EGY SLV ERI ETH FJI GAB GMB GEO GHA GTM GINGNB GUY HTI HND INDIDN IRN IRQ JAM JOR KAZ KEN PRK KGZ LAO LVA LBN LSO LBR LBY LTU MKD MDG MWI MYS MLI MRT MEX MDA MNGMNE MAR MOZ MMR NAM NPL NIC NER NGA PAK PAN PRY PER PHL ROM RUS RWA SEN SRB SLE SOM ZAF LKA SDN SWZ SYR TJK TZA THA TMP TGO TUN TUR TKM UGAUKR URY UZB VEN VNM ZMBZWE 0 200040006000 0 200 400 600 800 Agricultural population density (person per sq km)
- 22. 3. Agricultural intensification AFG ALB AGO ARG ARMAZE BGD BLR BEN BTN BOL BIH BRA BGR BFA BDI KHM CMR CAF CHL CHN COL COM ZARCOG CRI CIV DOM ECU EGY SLV ETH FJI GAB GMB GEO GHA GTMGIN GNB GUY HTI HND IND IDN IRN IRQ JAM JOR KEN PRK KGZ LAO LVA LBN LSO LBRLTU MKD MDG MWI MYS MEX MDA MAR MOZ MMR NPL NIC NGA PAKPAN PRY PER PHL ROM RUS RWA SEN SRB SLE ZAF LKA SWZ SYR TJK TZA THA TMP TGO TUR TKM UGA UKR URY UZB VEN VNM ZMB ZWE 0 500 10001500 0 200 400 600 800 Agricultural population density (person per sq km)
- 23. 3. Agricultural intensification AFG ALB DZA AGO ARG ARMAZE BGD BLR BEN BTN BOL BIH BWA BRA BGR BFA BDI KHM CMR CAF TCD CHL CHN COL COM ZAR COG CRICIV DOM ECU EGY SLV ERI ETH FJI GAB GMB GEO GHA GTM GIN GNBGUY HTI HND IND IDN IRNIRQ JAM JOR KAZ KEN PRK KGZ LAO LVA LBN LSO LBR LBY LTU MKD MDG MWI MYS MLI MRT MEX MDA MNG MNE MAR MOZ MMR NAM NPL NIC NER NGA PAK PAN PRY PER PHL ROM RUS RWA SEN SRB SLESOM ZAF LKA SDN SWZ SYR TJK TZA THA TMP TGO TUN TURTKM UGA UKR URY UZBVEN VNM ZMB ZWE 0 50 100150 0 200 400 600 800 Agricultural population density (person per sq km) Cropping intensity in non- Africa sample is heavily explained by irrigation: R-sq = 0.56
- 24. 3. Agricultural intensification AFG ALB AGO ARG ARM AZE BGD BLR BEN BTN BOLBIH BRA BGR BFA BDI KHM CMR CAF CHL CHN COL COMZAR COG CRI CIV DOM ECU EGY SLV ETH FJI GAB GMB GEO GHA GTM GINGNBGUY HTI HND INDIDN IRN IRQ JAM JOR KEN PRK KGZ LAOLVA LBN LSO LBR LTU MKD MDG MWI MYS MEX MDA MAR MOZ MMR NPL NIC NGA PAK PAN PRY PER PHL ROM RUS RWA SEN SRB SLE ZAF LKA SWZ SYR TJK TZA THA TMP TGO TUR TKM UGA UKR URY UZB VEN VNM ZMBZWE 0 10002000300040005000 0 200 400 600 800 Agricultural population density (person per sq km)
- 25. Regression No. R1 R2 R3 R4 Dep. var. Agric. output per ha Cereal output per ha Cereal crop intensity Non-cereal output per ha Population density 0.33*** 0.18*** 0.20*** 0.28*** Density*Africa -0.11** -0.23*** -0.01 -0.01 Road density 0.14*** 0.09** -0.03 0.19*** Number of ports 0.14*** 0.21*** 0.03 0.15*** Urban agglom (%) 0.29*** -0.09 0.31*** 0.31*** Regional fixed effects? Yes Yes Yes Yes Sign of SSA dummies? + in E.Africa Zero Neg. + in E.Africa AE controls Yes Yes Yes Yes No. Obs 243 243 243 243 R-square 0.8 0.74 0.67 0.79 Table 4. Log-log estimates of agricultural value per hectare and its three components
- 26. Regression No. R1 R2 R3 R4 Dep. var. Fertilizers per hectare Cattle/oxen per hectare Irrigation per hectare Capital per hectare Population density 0.76*** 0.42*** 0.59*** 0.24*** Density*Africa -0.32** 0.15* -0.47*** -0.10*** Road density -0.08 0.31*** 0.04 0.07** Number of ports 0.50*** 0.07 0.24*** 0.12*** Urban agglom (%) 0.38 0.03 0.24** -0.03 Regional fixed effects Yes Yes Yes Yes Sign of SSA dummies? Zero Neg. Zero Zero AE controls Yes Yes Yes Yes No. Obs 0.73 0.77 0.92 0.77 R-square 0.69 0.74 0.91 0.73 Table 5. Log-log estimates of specific agricultural inputs
- 27. Stylized facts Potential explanations Lowproductivityofcerealssector Low fertilizer application Agronomic constraints (e.g. low soil fertility) Poor management practices, low human capital High transport costs (see regression 1 in Table 4); Low rates of subsidization (structural adjustment) Low adoption of improved varieties More varied agroecological conditions and crop mix Lower returns because of limited use of other inputs (e.g. irrigation); Lower investment in R&D Low use of plough/ tractors Tsetse fly in humid tropics Feed/land constraints in some densely populated areas Low rates of irrigation Hydrological constraints; High costs of implementation and maintenance; Poor access to markets limits benefits Noncereals High non-cereal output per hectare Agroecological suitability; Colonial introduction of cash crops; Non-perishable cash crops (cotton, coffee, cocoa, tea, tobacco) not limited by poor infrastructure and isolation Table 7. Potential explanations of Africa’s agricultural intensification trajectory
- 28. 02468 0 500 1000 1500 Rural population density (person per sq km) Non-Africa gradient African gradient Figure 3. Rural fertility rates and rural population density 3. Reducing rural fertility rates
- 29. ALBARMARMARM AZE BGDBGDBGD BGD BGD BEN BEN BEN BOLBOL BOLBOLBOL BWA BRA BRA BFA BFABFA BDI BDI KHM KHM KHM CMR CMR CMR CAF TCD TCD COLCOL COLCOL COL COL COM ZAR COGCIV CIV DOM DOM DOM DOMDOM DOMECU ECU SLV SLV ERI ERI ETH ETH ETH GAB GHA GHA GHAGHA GHA GTM GTM GTMGTM GINGIN GUY HTI HTI HTIHND IND IND IND IDNIDNIDNIDN IDN IDN KAZ KAZ KENKENKEN KEN KEN KGZ LSO LBR LBR MDG MDG MDG MDG MWI MWI MWI MWI MLI MLI MLI MLI MRT MEX MOZ MOZ NAM NAM NAM NPL NPL NPL NPL NIC NIC NERNER NER NGA NGA NGA NGA PAK PAK PRY PRY PERPERPER PER PER PER PHL PHL PHL PHL RWA RWA RWARWA RWA SEN SEN SEN SEN SEN SLE LKA SDN SWZ TZA TZA TZA TZA TZA THA TMP TGO TGO TURTUR TKM UKR UZB VNM VNM ZMBZMB ZMB ZMB ZWE ZWE ZWE ZWE ZWE EGYEGYEGYEGYEGY EGY JOR JOR JOR JOR JOR MAR MAR MARTUN 02468 10 0 500 1000 1500 Rural population density (person per sq km) Full sample gradient African sample gradient Figure 4. Desired rural fertility & population density
- 30. Figure 5. Unmet contraception needs (%) and rural population density in Africa BEN BEN BEN BFA BFA CMR CMR CMR TCD COM ZAR COG CIV ERI ERI ETH ETH GAB GHA GHA GHA GHA GIN GIN KEN KEN KEN LSO LBR MDG MDG MWI MWI MWI MLI MLI MOZ MOZ NAMNAM NER NER NER NGA NGA NGA NGA RWA RWA RWA SEN SEN SLE TZA TZA TZA TZA TGO ZMB ZMB ZMB 152025303540 0 100 200 300 400 Rural population density (person per sq km) Sources
- 31. Regression number 1 2 3 4 Dependent variable Actual fertility Actual fertility Desired fertility Desired fertility Model Linear Log-log Linear Log-log b/se b/se b/se b/se Pop density (per 100 m2) -0.14*** -0.09*** -0.11*** 0.00 Density*Africa 0.05 0.09*** -0.34*** -0.07*** Female sec. education (%) -0.02*** -0.05*** -0.01** -0.08*** Ag. output per worker, log -0.58*** -0.13*** 0.01 0.06*** Africa dummy 1.25*** -0.15 2.13*** 0.67*** Number of observations 165 165 164 164 R-square 0.75 0.76 0.77 0.81 Table 8. Elasticities between rural fertility indicators & rural population density
- 32. 4. Nonfarm diversification Much neglected in 1980s literature on Boserup Subsequent literature on both RNFE and migration & remittances shows that RNF income is big But not much specific literature looking at pop density On RNF activity, often suggested there is a U-shaped relationship between farm size and RNFE: landless poor are pushed into RNFE, rich are pulled in Very difficult to look at rural-urban migration Int. remittances have boomed in last 10 years, particularly in densely population South Asia – now 22% of rural income in Bangladesh
- 33. High density Africa Low density Africa Other LDCs Country W M Country W M Country W M Benin 50.4 23.7 Burkina Faso 12.9 8.1 BGD 53.4 44.5 Congo (DRC) 14.0 23.5 Chad 13.7 9.6 Bolivia 71.4 25.9 Ethiopia 34.3 9.7 Cote d'Ivoire 31.7 22.1 Cambodia 36.0 Kenya 47.1 37.3 Ghana 50.1 26.6 Egypt 69.4 Madagascar 17.8 15.3 Mali 44.6 16.0 Guatemala 79.1 Malawi 41.5 36.0 Mozambique 5.2 23.0 Haiti 24.0 19.0 Nigeria 65.5 37.0 Niger 60.2 35.8 India 22.4 Rwanda 7.3 14.2 Senegal 63.7 37.1 Indonesia 59.2 39.5 Sierra Leone 25.2 20.1 Tanzania 7.2 10.5 Nepal 90.5 34.2 Uganda 15.5 20.3 Zambia 30.1 19.5 Philippines 16.2 42.6 Table 9. Speculative estimates of rural nonfarm employment shares for men and women in the 2000s
- 34. Regression No. R1 R2 R3 R4 R5 R6 Sample Women Women Women Men Men Men Population density 0.47 0.09 0.15 -0.33 -0.32 -0.31 Density*Africa -0.19** -0.22** -0.15* 0.03 -0.02 -0.02 Africa dummy -0.25 0.1 0.04 -0.43 0.09 0.09 Sec. educ. by gender 0.03 0.11 0.35*** 0.35*** Road density 0.14* 0.15** 0.17* 0.17* Electricity 0.20** -0.07 0.09 0.09 Ag. Output/worker, log 0.46*** 0.01 No. Obs. 162 122 95 74 74 74 R-square 0.2 0.53 0.24 0.55 0.55 0.55 Table 11. Elasticities between RNF employment indicators and rural population density for women and men
- 35. Figure 6. National remittances earnings (% GDP) and rural population density DZA ARG BGD BEN BOL BRA BFA BDI KHM CMR CHL CHN COL COG CRI CIV DOM ECU EGY SLV ETH GHA GTM GIN HTI HND IND IDN IRN IRQ JOR KEN LAO LBN LBR LBY MYS MLI MEX MAR MOZ NPL NIC NER NGA PAK PAN PRY PER PHL RWA SEN SLE ZAF LKA SDN SYR TZA THA TGO TUN UGA URY VEN VNM ZMB 05 10152025 0 500 1000 1500 Rural population density (person per sq km)
- 36. Estimator OLS Robust OLS Robust Structure Levels (logs) First difference Levels (logs) First difference Density variable Agricultural Agricultural Rural Rural Population density 0.25*** 0.97** 0.31*** 1.17*** Population density*Africa 0.05 -0.94 0.04 -1.22** Total population -0.24*** -1.31** -0.23*** -0.82 Lagged remittances -0.21*** -0.24*** Lagged population density 0.06 0.06 West Africa dummy -0.67* -0.49 Central Africa dummy -1.55*** -1.40*** East Africa dummy -0.90** -0.74* Southern Africa dummy 0.14 0.24 1977-87 dummy 0.15 0.12 1987-97 dummy 0.33* -0.09 0.28* -0.06 1997-2007 dummy 0.79*** 0.19 0.72*** 0.24* Number of observations 231 147 231 159 R-square 0.39 147 0.4 0.22 Table 11. Estimating elasticities between national remittance earnings (% GDP) and population density
- 37. 5. Conclusions Land pressures are severe in much of Africa, esp. high density SSA, where small farms are getting smaller, and will continue to get smaller as pop. grows Yet history shows that rural people are generally adept at adapting to mounting land pressures. Ag intensification is only part of the adaptation The question we posed is whether Africa is different In many ways, the answer is yes . . .
- 38. Adaptation 1 – Agricultural Intensification Africa has intensified agriculture, but largely through high value non-perishable crops (HVCs) Much less historical success with cereals, and much less potential given limited potential for irrigation Should we shift emphasis of research and development strategies from cereals to HVCs? CGIAR, for example, barely looks at cash crops like coffee, tea, cotton, cocoa, tobacco (even though cash buys food!) 5. Conclusions
- 39. Adaptation 2 – Reducing fertility rates Higher densities (smaller farms) apepar to lead to a desired reduction in fertility in Africa But desired reductions are not met by access to contraceptive technologies High-density East Africa now shows mixed policies Ethiopia & Rwanda are investing in family planning (*), but Museveni (Uganda) has resisted family planning (population growth is “a great resource”) Asian experience suggests FP yields high returns 5. Conclusions
- 40. Adaptation 3 – Nonfarm diversification Weak evidence, but evidence that is there suggests that nonfarm sector doesn’t just grow without engines like education, infrastructure, agriculture (also true for African cities?) Boom in overseas migration and remittances is new, and unexpected. 20 years ago, BGD and Pakistan were regarded as too big to benefit from remittances. Not true now. Why isn’t Africa getting more remittances? 5. Conclusions
- 41. Finally, we ask whether the results we find warrant a re-think in the way high density countries pursue rural development Are SSA countries thinking through the implications of rural pop. growth for farm sizes and rural welfare? Do SSA countries need rural development strategies that are more integrated with respect to smallholder intensification, commercial farms, family planning, migration and rural nonfarm development? What are the costs of not doing so? 5. Conclusions
Hinweis der Redaktion
- (e.g. Egypt’s 8000 years of experience with irrigation has surely influenced migration and fertility decisions)