A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S

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1 A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S Luc Christiaensen, Presentation at Managing African Agriculture: Markets, Linkages and Rural Economic Development, International Conference, Maastricht School of Management, 4 September 2015, Maastricht

2 A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S

3 Agricultural picture often imprecise & possibly outdated Making movies when the pictures are blurred Kg/ha 2500 Maize yields your many averages Source 1 Source 2 Source 3 Source 4 Source 5 0 Country 1 Country 2 Country 3 Page 3

4 An opportunity exists! Nationally representative (rural and urban) w/ focus on agriculture ts with strong focus on agriculture Plot, household, and community-level survey data from 8 SSA countries Burkina Faso Ethiopia Malawi Mali Niger Nigeria Tanzania Uganda Page 4

5 Features 4 I s Integrated multi-topic & geo-referenced Individual - gender/plot Intertemporal - panels cum tracking Information technology - concurrent data entry (CAPI, GPS) Open data access policy

6 Survey Schedule On the web Completed/in field Funded Pending Country Baseline Follow-Up Waves Tanzania 2008/ / / / / /19 Uganda 2009/ / / / / /17 Malawi Nigeria 2010/ / / /18 Ethiopia 2011/ / / /18 Niger 2011/ /15 WAEMU? Mali 2014/ /17 WAEMU? Burkina Faso 2014/ / /19 WAEMU?

7 OBJECTIVES Myths and Facts Updated picture of Africa s agriculture & its farmers livelihoods Harmonized, easy to use database of core agricultural variables for tabulation and regional cross-country benchmarking Community of practice. Mentorship program. Abidoye Babatunde, Fuje Habtamu, Gandonou Esaie, Kasirye Ibrahim, Kinuthia Bethuel, Liverpool-Tasie Lenis, Ndiaye Moctar, Ngenzebuke Rama Lionel, Ogunyemi Oluwole, Owoo Nkechi Page 7

8 Partnering institutions African Development Bank Alliance for a Green Revolution in Africa Cornell University Food and Agriculture Organization Maastricht School of Management Trento University Université Libre de Bruxelles University of Pretoria University of Rome Tor Vergata London School of Economics AFRCE and LSMS, World Bank Page 8

9 COMMON WISDOM REVISITED 1) Modern use of modern inputs remains dismally low 2) Modern input use is profitable, but low 3) Land, labor and capital markets remain largely incomplete 11) The majority of rural households are net food buyers 4) Land is abundant and land markets Introductory are remarks 12) Post harvest losses are large poorly developed 5) Labor productivity in agriculture is low 6) Trees are negligible on farms 7) African agriculture is intensifying 8) Women perform the bulk of Africa s agricultural tasks 9) Youth is leaving agriculture en mass 10) Seasonality continues to permeate rural livelihoods 13) Droughts dominate Africa s risk environment 14) African farmers are increasingly diversifying their income 15) Household enterprises operate mainly in survival mode 16) Agricultural commercialization improves nutritional outcomes Page 9

10 COMMON WISDOM REVISITED 1) Modern use of modern inputs remains dismally low 2) Modern input use is profitable, but low 3) Land, labor and capital markets remain largely incomplete 4) Land is abundant and land markets are poorly developed 5) Labor productivity in agriculture is low 6) Trees are negligible on farms 7) African agriculture is intensifying 8) Women perform the bulk of Africa s agricultural tasks 9) Youth is leaving agriculture en mass 10) Seasonality continues to permeate rural livelihoods 11) The majority of rural households are net food buyers 12) Post harvest losses are large 13) Droughts dominate Africa s risk environment 14) African farmers are increasingly diversifying their income 15) Household enterprises operate mainly in survival mode 16) Agricultural commercialization improves nutritional outcomes Page 10

11 COMMON WISDOMS REVISITED OUTPUT OUTPUT 1 Published paper, 12 working papers, 5 draft papers Special Issue for Food Policy (under review, late 2015) Book with overview and 17 6-page summaries (in production, late 2015) Blog series on Africa Can End Poverty (6 blogs) Page 11

12 A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S Enjoy the journey!

13 Use of modern inputs in Africa s agriculture is dismally low Megan Sheahan and Christopher B. Barrett IFPRI-World Bank Event, 15 June 2015 A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S

14 Use of inorganic fertilizer is low Myth-ish Fact: Inorganic fertilizer use taking off in some countries % Share of cultivating households using inorganic fertilizer in main season Average unconditional application rate = 26 kg/ha Conditional application rates > 40 kg/ha in 4 of 6 countries! Page 18

15 Inorganic fertilizer mainly for export crops Myth Fact: Inorganic fertilizer use is as high on maize dominated plots as on average on the farm Inorganic fertilizer use (kg/ha) Ethiopia Malawi Niger 1 5 Nigeria Tanzania Uganda 3 1 Maize plots* Household average *Niger: millet/sorghum/millet/cowpea instead of maize (too few) Page 19

16 Use of agro-chemicals is low Myth-ish Fact: At 30 percent, agro-chemical use is not negligible in some countries Share of cultivating households using any agro-chemicals (pesticide, herbicide, fungicide) Health hazards? 25 % Ethiopia Malawi Niger Nigeria Tanzania Uganda LSMS-ISA avg Page 20

17 Use of improved seed varieties is low Myth-ish The incidence of mechanization and irrigation are low Fact Fact: Improved maize seeds are used in major maize producing countries, but mechanization and irrigation remain very limited throughout Signs of improved maize seed use 24% (Ethiopia) to 56% (Malawi) Limited signs of mechanization Water control is not widespread ,4 0, % of all cultivated land under irrigation by smallholders % of households with at least some irrigation on farm Page 21

18 Where inputs are used, they are used together Myth Fact: Synergies from joint input use largely foregone on plots USE INORGANIC FERTILIZER Input use on plots in Ethiopia USE IRRIGATION <15 percent of plots with at least 1 of these inputs uses 2 or more of them together! 2% 0.2% 11% 0.6% USE IMPROVED SEED VARIETY Page 22

19 Local conditions are most important for input use Myth Fact: Most variation in binary input use comes at the country level Biophysical characteristics within country are the next most important. Great news for policy makers! Page 23

20 Modern input use in Africa is low MYTH OR FACT? Fertilizer, improved seed variety, and agro-chemical use are NOT always low Maize fields have relatively high input use intensity Irrigation and mechanization remain limited Country-level factors are most important to input use decision Scope for improvement remains! Page 26

21 Labor productivity in agriculture is low Ellen B. McCullough, Cornell University A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S

22 COMMON WISDOM: - SECTORAL DIFFERENCES IN LABOR PRODUCTIVITY ARE LARGE - HIGH GAINS FROM MOVING PEOPLE OUT OF AGRICULTURE TO NONAGRICULTURE (Lewis) - AFRICA? Page 42

23 CROSS-SECTOR PRODUCTIVITY DIFFERENTIALS ARE LARGE IN SUB-SAHARAN AFRICA though not as large as estimates based on National Accounts National Acct Gaps Micro gaps Page 43

24 PRODUCTIVITY DIFFERENTIALS DISAPPEAR AFTER ACCOUNTING FOR HOURS WORKED PER WORKER GAPS PER HOUR GAPS Page 44

25 PRODUCTIVITY DIFFERENTIALS DISAPPEAR AFTER ACCOUNTING FOR HOURS WORKED PER WORKER GAPS PER HOUR GAPS Page 45

26 CROSS-SECTOR EMPLOYMENT GAPS agricultural workers work fewer hours per year Page 46

27 Early stages of structural transformation, rural economies are ag-focused Employment gaps account for large share of productivity differentials Key challenges: 1. Improve access to higher-employment non-ag work 2. Understand what drives low agricultural labor demand Page 54

28 NON-FARM ENTREPRENEURSHIP IN RURAL AFRICA P. Nagler and W. Naudé Washington DC, 15 June 2015 A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S

29 MYTH or FACT? Rural Entrepreneurs Largely Operate in Survival Mode Page 56

30 A summary of current wisdoms The literature describes enterprises as survivalist type of businesses Non-farm enterprises are operated due to necessity Risky farming motives diversification Lack of alternative income sources, such as wage employment These enterprises are characterized by Small size, operating in the informal economy Low productivity Low probability to grow and survive Most research is based on one-period, single-country and limited survey data Focus on urban areas Page 57

31 Agriculture dominant source of income But non farm self employment > wage income Fact: Self employment important for nonfarm income Fact: Little ag and nonag wage income 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% SSA ROW Others Transfers Non-farm self empl Non-farm wage income Ag wage income On farm income Page 58

32 But variation across countries Fact: Self employment more important in Niger, Nigeria Fact: Ag wage income more important in Malawi (Ganju) Page 59

33 Non-farm enterprises are ubiquitous in rural areas But there is much country-level heterogeneity Country Number of households surveys Households with NFE In percent (weighted) Number of NFEs Average number of NFE per households Ethiopia 3, , Malawi 10,038 1, , Niger 2,430 1, , Nigeria 3,380 1, , Tanzania 2,629 1, , Uganda 2, , Total 24,551 8, , Page 60

34 Employment creation and business location Enterprises do not create jobs and are operated informally Enterprises with non-household employees Location of business operation Page 61

35 Rural enterprises are affected by seasonality Up to 60 percent do not operate throughout the year Months in operation Page 62

36 CW 4: Rural non-agricultural HH enterprises have low productivity Productivity varies widely across regions and distance to urban center northern central >100km 50-75km <10km Page 63

37 Productivity is lower in rural than in urban areas However, highly productive enterprises can also be found Page 64

38 CW 4: Rural non-agricultural HH enterprises have low productivity Gross productivity Dispersal Among Types of Businesses Ethiopia Agribusiness & sales Transport & bar/restaurant Substantial dispersal in the productivity levels of different types of enterprises Transport and restaurants services are more productive than sales or agribusiness More productive businesses are activities where credit and capital may be vital Source: Nagler and Naude, Page 65

39 Rural Enterprises Largely Operate on Survival Mode MYTH OR FACT? Most enterprises are small, informal enterprises that do not create jobs A large share operates for only a portion of the year However, there are also productive enterprises operated due to opportunities Not a myth, but a fact - with a small group of highly productive enterprises Page 67

40 How Much of the Labor in African Agriculture is Provided by women? Amparo Palacios-López, Luc Christiaensen, Talip Kilic A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S

41 RHETORIC.women are responsible for [percent] of the agricultural labour supplied on the continent of Africa. (UNECA, 1972; FAO, 1995) Women produce 60 to 80 percent of the food in developing countries and 50 percent of the world s food supply (Momsen, 1991) Page 69

42 REALITY Female Share in Agriculture Labor Total Average 40 Uganda 56 Tanzania 53 Malawi 52 Nigeria 37 Ethiopia 29 Niger 24 Page 70

43 REALITY Female Share in Agriculture Labor Total Average 40 Uganda 56 Tanzania 53 Malawi 52 Nigeria 37 Ethiopia 29 Niger 24 Northern Nigeria 32 Southern Nigeria 51 Page 71

44 PROCESSES UNDERLYING FEMALE LABOR INPUT INTO AGRICULTURE Factors that may explain women s contribution to agriculture: (i) (ii) Household labor availability and substitutes Culture-specific gender roles (iii) Economic reasons (iv) Methodological approach to data collection o Female Respondent: Malawi ( ) Nigeria ( ) o Most knowledgeable person Malawi ( ) Nigeria ( ) Page 78

45 RESPONDENT BIAS THE MYTH OF THE MYTH? Controlling for respondent characteristics does not overturn result Female share Malawi Nigeria Prediction on the whole 56% 32% Respondent knows and Respondent Female 61% 24% Respondent knows and Respondent Male 54% 27% Respondent does not know and Respondent Female 56% 36% Respondent does not know and Respondent Male 50% 38% Page 79

46 CONCLUSIONS No evidence supporting the view of women performing the bulk of labor in agriculture (range from 56 to 24%) The analysis illustrates shortcomings of generalizations from nonrepresentative samples and few countries only (publication bias). Gender and boosting ag supply no strong case to disproportionately focus on gender if total agricultural supply is the objective, also not based on gender gap in productivity (about 25% on average) b/c this mostly calculated for female headed households or female managed plots (at most 25% of population) there are other reasons for focusing on closing the gender gap in agricultural productivity such as empowerment Importance of using consistent metrics Page 80

47 Hope you enjoyed the remainder of the journey and much more on A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S /programs/africa-myths-andfacts#1

48 Post Harvest Loss in SSA - What do farmers say? Jonathan Kaminski and Luc Christiaensen A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S Source: Kaminski, Jonathan, and Luc, Christiaensen, 2014, Post-Harvest Loss in Sub-Saharan Africa What do Farmers Say?, World Bank Policy Research Paper 6831.

49 RHETORIC and REALITY Estimating post harvest loss Worldwide 32 % of all food produced is lost. In SSA, it amounts to 37 %. (FAO, 2011) Self-reported on-farm PHL adds up to % of total national maize harvest loss (UG, TZ, MWI) (Kaminski & Christiaensen, 2014) Page 99

50 DATA MANIPULATION Our focus: PHL for maize in UG, TZ, MWI onationally representative oself-reported losses following questions about amount harvested Did you incur any PHL due to rodents, pests, insects, flooding, rotting, theft, and other reasons? If yes, what proportion was lost? ocumulative loss at end of agricultural season has been imputed, exploiting cross-year implementation of household survey Page 100

51 1. Self reported on farm PHL is low, but concentrated Proportions (%) UG TZ TZ MWI PHL( 11 months adjusted) probability of reporting PHL PHL among losers only # maize producing hhs 1,853 1,520 1,301 10,331 Source: Kaminski and Christiaensen, 2014 Average on farm PHL: between 1 and 6 % Incidence of on farm PHL: between 7 and 22 % Average loss among losers: between 20 and 27 % Page 101

52 2. Use of improved storage technology is low Storage & crop protection maize farmers (national) (%) UG TZ TZ MWI Traditional storage Improved storage Spraying/ smoking Source: Kaminski and Christiaensen, 2014 Key findings: - Little use of better storage techniques - More use of spraying/smoking Page 102

53 3. Internal consistency provides confidence in estimates Tanzania 2008/9 Self reported maize PHL increases with Humidity and temperature Self reported maize PHL declines with Seasonal price gap Distance to market place Post primary education (not primary) Female headed households Self reported maize PHL not associated with Poverty Rural/urban areas Page 103

54 3. Self reported PHL only half as large at most Objective approach - FAO statistics/aphlis - 37 % PHL (weight) along the chain (harvesting, PHL handling & storage, processing, distribution/marketing, consumption/waste), weight) (SSA) - 23 % PHL (calories, SSA) % PHL (weight, cereals only) - 8% PH handling and storage (SSA); 6-11% (TZ-Uganda-Malawi) (APHLIS) Subjective approach - LSMS-ISA % (Malawi Uganda maize only) - Maize only (more perishable than millet/sorghum) Page 104

55 CONCLUSIONS Proper contextualization of widely quoted high PHL needed (along whole chain vs interventions focused on (on-farm) storage loss). Small (changing) proportion reports a loss incentive compatibility and targeting of interventions Importance of nationally representative information base (vs purposively sampled case studies) (updating APHLIS + fine-tuning underlying algorithms) Page 105

56 Hope you enjoyed the remainder of the journey and much more on A G R I C U L T U R E I N A F R I C A T E L L I N G F A C T S F R O M M Y T H S /programs/africa-myths-andfacts#1