Adaptation to land constraints: Is Africa different?
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International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI) Seminar Series. April 05, 2013. Addis Ababa University

International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI) Seminar Series. April 05, 2013. Addis Ababa University

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  • (e.g. Egypt’s 8000 years of experience with irrigation has surely influenced migration and fertility decisions)

Adaptation to land constraints: Is Africa different? Presentation Transcript

  • 1. 1Adaptation to land constraints:Is Africa different?Derek HeadeyInternational Food Policy Research Institute (IFPRI)Thom JayneMichigan State University (MSU)
  • 2. Outline1. About the project2. Introduction (background on existing theory &evidence)3. Expanding land use (extensification)4. Intensifying agriculture5. Reducing fertility rates6. Diversifying out of agriculture7. Conclusions
  • 3. This paper is part of a Bill & Melinda Gates Foundationproject on emerging land issues in African agricultureThe motivation for the project was the observation ofvarious puzzles of Africa agriculture: apparent landabundance in Africa, but much of Africa has majorland constraints, and very, very small farmsIn addition to five African case studies (Ethiopiaincluded), we decided to look at the cross-countryevidence on agricultural intensificationThat is what I am presenting todayAbout the project
  • 4. Some 215 years ago, Malthus argued that pop. growthcyclically outstrips agricultural productivityStrong assumptions: high exogenous fertility rates,land constraints, zero ag. productivity growthIn much of the world, economic history has not beenkind to Malthus, because of “induced innovations”Whilst “induced innovation” is associated with Hayamiand Ruttan, plenty of prior research looked atparticular elements of induced innovationMore generally, “responding to incentives” is at theheart of economic theories1. Introduction
  • 5. Land expansionMalthus’ theory depends on land constraints, butpeople have been adept at expanding the land frontierthrough 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 landexpansion in 1990s and 2000s (opening the cerrado)Of course, for specific countries, land expansion maynot be an option1. Introduction – Land constraints
  • 6. Agricultural intensificationBoserup (1964): as land to labor ratios shrink, peopleintensify agricultural production – use more inputsper hectare to get more output per hectareBoserup described transition from land-abundanttechnologies (slash-and- burn, long fallow) to land-scarce technologies (short fallow, adoption of plow,increased fertilizer use, irrigation)She also emphasized increased labor inputs, andtransition from communal to private property rightsBinswanger et al generalized the theory in 1980s1. Introduction - intensification
  • 7. 1980s saw substantial empirical literatureBroadly supports Boserup’s theory, but lots ofcomplexityBinswanger emphasizes that land constraints interactwith access to markets, and agroecological factorsFor example, irrigation and high rainfall allow multiplecropping – not possible in all agroecologies, howeverMarket access can be an driver of intensification, butmight also interact with land constraintsAnd institutions matter – e.g. literature in 1990sunfavorably compared Ethiopia to Kenya1. Introduction - intensification
  • 8. Policy-induced intensificationOne weakness of Boserup’s theory is that endogenousintensification takes place over the long runBut Africa’s population has doubled in last 40 yearsHence, much of the ag-economics literature focuses onpolicy-induced intensification - e.g. Green RevolutionOf course, many scientific successes in agricultureBut Binswanger emphasized that adoption oftechnologies 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), thenfarmers will have less children . . . all else equalBut children serve other purposes (consumptiongoods, old age security), so fertility response to landconstraints may be lowMoreover, demographic literature emphasizes “supply”constraints: family planning, female education, etcNot obvious there is a strong endogenous mechanism1. Introduction - intensification
  • 10. Diversifying out of agricultureMajor omission from 1980s literature was discussionof nonfarm economy, which is large in many countriesIf land is a constraint, why not migrate?Of course, farmers do migrate, but viability ofmigration in domestic economy is a generalequilibrium issue: are there nonfarm jobs?Rural nonfarm economy (RNFE) often felt to be drivenby agric. productivity, infrastructure, educationPolicies matter: RNFE does not spontaneously emerge1. Introduction - intensification
  • 11. The African contextWhat about international migration?Has boomed in last 20 years: remittances to LDCsgrown by 1600% from 1990 to 2010.Moreover, not just small islands: Philippines, Pakistanand Bangladesh hugely dependent on remittances, andthey are all much larger than most African countriesAre land constraints driving rural people to exploreinternational 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, andon 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 offamine & drought; progress might be deceptive2. Rural poverty closely associated with small farms;most Africa farms have a few hectares or less3. Low inherent agric. potential (incl. low irrigation)1. Introduction
  • 13. 5. Rapid population growth (double by 2050); suggeststhat farm sizes will only get smaller6. Climate change: secular changes in climate, but alsolikelihood of more shocks7. Very limited success with industrialization; urbanjobs mostly in low-wage informal services sector1. Introduction
  • 14. 1. Introduction
  • 15. Our overarching objective is to assess internationalexperience in these 4 adaptations to land pressuresThere is a large literature exploring Boserup’shypothesis, 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 whetherAfrica 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 sizedistributions3. DHS data on rural fertility rates & occupations4. Some WB data on remittances We combine these data in an unusually rich data seton agricultural and rural development (though we also acknowledge that some of thenumbers are fairly speculative)1. Introduction
  • 17. On methods, our approach is necessarily exploratoryEstablishing causation is an under-recognizedproblem with Boserup’s theoryProblems of simultaneity, omitted variables, selectionbiases, parameter heterogeneity. Some examples:1. Agroecological (AE) factors & market access jointly determinesettlement patterns and intensification2. Boserupian intensification depends on AE potential3. Unsuccessful intensification encourages out-migration4. Policies promote intensification, discourages out-migrationIV rarely plausible in cross-country setting, but we domake 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, butthis neglects the heterogeneity within Africa2. Land expansionRegion PeriodHectares peragric. worker(FAO)Hectares perholding(censuses)Used land as %of potentiallycultivable landAfrica - highdensityb (n=5)1970s 0.84 1.99 32.72000s 0.58 1.23 43.8Africa - low densityb(n=11)1970s 1.65 2.65 17.22000s 1.37 2.82 24.7South Asia 1970s 0.78 2.01 129.5(n=5) 2000s 0.55 1.19 135.9China & 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 fromcensus, 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 thatland (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, infrastructure4. Farm sizes are unchanged (on average) in lowdensity Africa, but still very small on average2. Land expansion
  • 20. 3. Agricultural intensification In the framework above, the most welfare-relevantindicator of intensification is just output per hectare Boserup focused more on cropping intensity, and theag-econ profession & CGIAR looks a lot at yields But switching to high value crops is obviously also apotentially important adaptation, especially in SSA. So I’m going to show you a series of graphs, and thensome more formal econometric tests. Note that I also decompose agricultural output perhectare into cereal yields, cereal cropping intensityand high value non-cereals
  • 21. 3. Agricultural intensificationAFGALBDZAAGOARGARMAZEBGDBLRBENBTNBOLBIHBWABRABGRBFABDIKHMCMRCAFTCDCHLCHNCOLCOMZARCOGCRICIVDOMECUEGYSLVERI ETHFJIGABGMBGEOGHAGTMGINGNBGUY HTIHND INDIDNIRNIRQJAMJORKAZKENPRKKGZ LAOLVALBNLSOLBRLBYLTUMKDMDG MWIMYSMLIMRTMEXMDAMNGMNEMARMOZMMRNAMNPLNICNERNGAPAKPANPRYPERPHLROMRUSRWASENSRBSLESOMZAFLKASDNSWZSYRTJKTZATHATMPTGOTUNTURTKMUGAUKRURYUZBVENVNMZMBZWE02000400060000 200 400 600 800Agricultural population density (person per sq km)
  • 22. 3. Agricultural intensificationAFGALBAGOARGARMAZEBGDBLRBENBTNBOLBIHBRABGRBFABDIKHMCMRCAFCHLCHNCOLCOMZARCOGCRICIVDOMECUEGYSLVETHFJIGABGMBGEOGHAGTMGINGNBGUYHTIHNDINDIDNIRNIRQJAMJORKENPRKKGZLAOLVALBNLSOLBRLTUMKDMDGMWIMYSMEXMDAMARMOZMMRNPLNICNGAPAKPANPRYPER PHLROMRUSRWASENSRBSLEZAFLKASWZSYRTJKTZATHATMPTGOTURTKMUGAUKRURYUZBVENVNMZMBZWE0500100015000 200 400 600 800Agricultural population density (person per sq km)
  • 23. 3. Agricultural intensificationAFGALBDZAAGOARGARMAZEBGDBLRBENBTNBOLBIHBWABRABGRBFABDIKHMCMRCAFTCDCHLCHNCOLCOMZARCOGCRICIVDOMECUEGYSLVERIETHFJIGABGMBGEOGHAGTMGINGNBGUYHTIHNDINDIDNIRNIRQJAMJORKAZKENPRKKGZLAOLVALBNLSOLBRLBYLTUMKDMDGMWIMYSMLIMRTMEXMDAMNGMNEMARMOZMMRNAMNPLNICNERNGAPAKPANPRYPERPHLROMRUSRWASENSRBSLESOMZAFLKASDNSWZSYRTJKTZATHATMPTGOTUNTURTKMUGAUKRURYUZBVENVNMZMBZWE0501001500 200 400 600 800Agricultural population density (person per sq km)Cropping intensity in non-Africa sample is heavilyexplained by irrigation:R-sq = 0.56
  • 24. 3. Agricultural intensificationAFGALBAGOARGARMAZEBGDBLRBENBTNBOLBIHBRABGRBFABDIKHMCMRCAFCHLCHNCOLCOMZARCOGCRICIVDOMECUEGYSLVETHFJIGABGMBGEOGHAGTMGINGNBGUYHTIHNDINDIDNIRNIRQJAMJORKEN PRKKGZLAOLVALBNLSOLBRLTUMKDMDG MWIMYSMEXMDAMARMOZMMRNPLNICNGAPAKPANPRYPER PHLROMRUSRWASENSRBSLEZAFLKASWZSYRTJKTZATHATMPTGOTURTKMUGAUKRURYUZBVENVNMZMBZWE0100020003000400050000 200 400 600 800Agricultural population density (person per sq km)
  • 25. Regression No. R1 R2 R3 R4Dep. var.Agric. outputper haCereal outputper haCereal cropintensityNon-cerealoutput per haPopulation density 0.33*** 0.18*** 0.20*** 0.28***Density*Africa -0.11** -0.23*** -0.01 -0.01Road 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 YesSign of SSA dummies? + in E.Africa Zero Neg. + in E.AfricaAE controls Yes Yes Yes YesNo. Obs 243 243 243 243R-square 0.8 0.74 0.67 0.79Table 4. Log-log estimates of agricultural value per hectareand its three components
  • 26. Regression No. R1 R2 R3 R4Dep. var.Fertilizersper hectareCattle/oxenper hectareIrrigation perhectareCapital perhectarePopulation 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** -0.03Regional fixed effects Yes Yes Yes YesSign of SSA dummies? Zero Neg. Zero ZeroAE controls Yes Yes Yes YesNo. Obs 0.73 0.77 0.92 0.77R-square 0.69 0.74 0.91 0.73Table 5. Log-log estimates of specific agricultural inputs
  • 27. Stylized facts Potential explanationsLowproductivityofcerealssectorLow fertilizerapplicationAgronomic constraints (e.g. low soil fertility) Poormanagement practices, low human capital High transportcosts (see regression 1 in Table 4); Low rates of subsidization(structural adjustment)Low adoptionof improvedvarietiesMore varied agroecological conditions and crop mixLower returns because of limited use of other inputs (e.g.irrigation); Lower investment in R&DLow use ofplough/ tractorsTsetse fly in humid tropics Feed/land constraints in somedensely populated areasLow rates ofirrigationHydrological constraints; High costs of implementation andmaintenance; Poor access to markets limits benefitsNoncerealsHigh non-cerealoutput perhectareAgroecological suitability; Colonial introduction of cash crops;Non-perishable cash crops (cotton, coffee, cocoa, tea,tobacco) not limited by poor infrastructure and isolationTable 7. Potential explanations of Africa’s agriculturalintensification trajectory
  • 28. 024680 500 1000 1500Rural population density (person per sq km)Non-Africa gradientAfrican gradientFigure 3. Rural fertility rates and rural population density3. Reducing rural fertility rates
  • 29. ALBARMARMARMAZE BGDBGDBGD BGDBGDBENBENBENBOLBOLBOLBOLBOLBWABRABRABFABFABFABDIBDIKHMKHMKHMCMRCMRCMRCAFTCDTCDCOLCOLCOLCOLCOLCOLCOMZARCOGCIVCIVDOMDOMDOMDOMDOMDOMECUECUSLVSLVERIERIETHETHETHGABGHAGHAGHAGHAGHAGTMGTMGTMGTMGINGINGUYHTIHTIHTIHNDINDINDINDIDNIDNIDNIDNIDNIDNKAZKAZKENKENKENKENKENKGZLSOLBRLBRMDGMDGMDGMDGMWIMWIMWIMWIMLIMLIMLIMLIMRTMEXMOZMOZNAMNAMNAMNPLNPLNPLNPLNICNICNERNERNERNGANGANGANGAPAK PAKPRYPRYPERPERPERPERPERPERPHLPHLPHLPHLRWARWARWARWARWASENSENSENSENSENSLELKASDNSWZTZATZATZATZATZATHATMPTGOTGOTURTURTKMUKRUZBVNM VNMZMBZMBZMBZMBZWEZWEZWEZWEZWEEGYEGYEGYEGYEGY EGYJORJORJORJORJORMARMARMARTUN02468100 500 1000 1500Rural population density (person per sq km)Full sample gradientAfrican sample gradientFigure 4. Desired rural fertility & population density
  • 30. Figure 5. Unmet contraception needs (%) and rural population density in AfricaBENBENBENBFABFACMRCMRCMRTCDCOMZARCOGCIVERIERIETHETHGABGHAGHAGHAGHAGINGINKENKENKENLSOLBRMDGMDGMWIMWIMWIMLIMLIMOZMOZNAMNAMNERNERNERNGANGANGANGARWARWARWASENSENSLETZATZATZATZATGOZMBZMBZMB1520253035400 100 200 300 400Rural population density (person per sq km)Sources
  • 31. Regression number 1 2 3 4Dependent variable Actual fertility Actual fertility DesiredfertilityDesiredfertilityModel Linear Log-log Linear Log-logb/se b/se b/se b/sePop density (per 100 m2) -0.14*** -0.09*** -0.11*** 0.00Density*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 164R-square 0.75 0.76 0.77 0.81Table 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-shapedrelationship between farm size and RNFE: landlesspoor 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 – now22% of rural income in Bangladesh
  • 33. High density Africa Low density Africa Other LDCsCountry W M Country W M Country W MBenin 50.4 23.7 Burkina Faso 12.9 8.1 BGD 53.4 44.5Congo (DRC) 14.0 23.5 Chad 13.7 9.6 Bolivia 71.4 25.9Ethiopia 34.3 9.7 Cote dIvoire 31.7 22.1 Cambodia 36.0Kenya 47.1 37.3 Ghana 50.1 26.6 Egypt 69.4Madagascar 17.8 15.3 Mali 44.6 16.0 Guatemala 79.1Malawi 41.5 36.0 Mozambique 5.2 23.0 Haiti 24.0 19.0Nigeria 65.5 37.0 Niger 60.2 35.8 India 22.4Rwanda 7.3 14.2 Senegal 63.7 37.1 Indonesia 59.2 39.5Sierra Leone 25.2 20.1 Tanzania 7.2 10.5 Nepal 90.5 34.2Uganda 15.5 20.3 Zambia 30.1 19.5 Philippines 16.2 42.6Table 9. Speculative estimates of rural nonfarmemployment shares for men and women in the 2000s
  • 34. Regression No. R1 R2 R3 R4 R5 R6Sample Women Women Women Men Men MenPopulation density 0.47 0.09 0.15 -0.33 -0.32 -0.31Density*Africa -0.19** -0.22** -0.15* 0.03 -0.02 -0.02Africa dummy -0.25 0.1 0.04 -0.43 0.09 0.09Sec. 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 0.09Ag. Output/worker, log 0.46*** 0.01No. Obs. 162 122 95 74 74 74R-square 0.2 0.53 0.24 0.55 0.55 0.55Table 11. Elasticities between RNF employment indicatorsand rural population density for women and men
  • 35. Figure 6. National remittances earnings (% GDP) andrural population densityDZAARGBGDBENBOLBRABFABDIKHMCMRCHLCHNCOLCOGCRICIVDOMECUEGYSLVETHGHAGTMGINHTIHNDINDIDNIRNIRQJORKENLAOLBNLBRLBYMYSMLIMEXMARMOZNPLNICNERNGAPAKPANPRYPERPHLRWASENSLEZAFLKASDNSYRTZATHATGOTUNUGAURYVENVNMZMB05101520250 500 1000 1500Rural population density (person per sq km)
  • 36. Estimator OLS Robust OLS RobustStructure Levels (logs) First difference Levels (logs) First differenceDensity variable Agricultural Agricultural Rural RuralPopulation 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*** -0.82Lagged remittances -0.21*** -0.24***Lagged population density 0.06 0.06West Africa dummy -0.67* -0.49Central Africa dummy -1.55*** -1.40***East Africa dummy -0.90** -0.74*Southern Africa dummy 0.14 0.241977-87 dummy 0.15 0.121987-97 dummy 0.33* -0.09 0.28* -0.061997-2007 dummy 0.79*** 0.19 0.72*** 0.24*Number of observations 231 147 231 159R-square 0.39 147 0.4 0.22Table 11. Estimating elasticities between nationalremittance earnings (% GDP) and population density
  • 37. 5. ConclusionsLand pressures are severe in much of Africa, esp. highdensity SSA, where small farms are getting smaller,and will continue to get smaller as pop. growsYet history shows that rural people are generally adeptat 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 largelythrough high value non-perishable crops (HVCs)Much less historical success with cereals, and muchless potential given limited potential for irrigationShould we shift emphasis of research and developmentstrategies from cereals to HVCs?CGIAR, for example, barely looks at cash crops likecoffee, tea, cotton, cocoa, tobacco (even though cashbuys food!)5. Conclusions
  • 39. Adaptation 2 – Reducing fertility ratesHigher densities (smaller farms) apepar to lead to adesired reduction in fertility in AfricaBut desired reductions are not met by access tocontraceptive technologiesHigh-density East Africa now shows mixed policiesEthiopia & Rwanda are investing in family planning(*), but Museveni (Uganda) has resisted familyplanning (population growth is “a great resource”)Asian experience suggests FP yields high returns5. Conclusions
  • 40. Adaptation 3 – Nonfarm diversificationWeak evidence, but evidence that is there suggeststhat nonfarm sector doesn’t just grow withoutengines 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 toobig 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 are-think in the way high density countries pursuerural developmentAre SSA countries thinking through the implicationsof rural pop. growth for farm sizes and rural welfare?Do SSA countries need rural development strategiesthat are more integrated with respect to smallholderintensification, commercial farms, family planning,migration and rural nonfarm development?What are the costs of not doing so?5. Conclusions