1. “Myths and Facts” in African Agriculture: What We Now Know Christopher B. Barrett Cornell University African Development Bank workshop on Structural Transformation in African Agriculture and Rural Spaces (STAARS) Nairobi, Kenya, April 8, 2015

2. The world in which African agriculture operates has changed… High and volatile food prices Urbanization Income growth Soil erosion Cell phones Increased investment … but our evidence base remains often inadequate or rooted in the past.

3. 1.Represents 40 percent of Sub-Saharan Africa’s population 2.8 countries with cross-country comparable information 3.Strong focus on agricultural data collection, but also consumption, expenditures, etc. 4.Plot, household, and community level information 5.Nationally-representative statistics as well as within-country (and even within-household) analysis 6.Statistics derived from farmers’ accounts 7.Coupled with growing collection of geo- referenced data sets 8.Repeated visits to households (panel data) An opportunity to update our understanding of African agriculture!

4. Provide a solid, updated, and bottom-up picture of Africa’s agriculture and farmers’ livelihoods using cross-sectional data Create a harmonized and easy-to-use database of core agricultural variables for tabulation and regional cross-country benchmarking Build a community of practice Partnering institutions: World Bank, African Development Bank, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, Yale University Mentorship program for young African scholars from US and African institutions AGRICULTURE IN AFRICA TELLING FACTS FROM MYTHS “Myths and Facts” project objectives:

5. 1)Use of modern inputs remains dismally low 2)Land, labor and capital markets remain largely incomplete 3)Agricultural labor productivity is low 4)Land is abundant and land markets are poorly developed 5)Rural entrepreneurs largely operate in survival mode. 6)Extension services are poor 7)Agroforestry is gaining traction 8)African agriculture is intensifying 9)Women perform the bulk of Africa’s agricultural tasks 10)Seasonality continues to permeate rural livelihoods 11)Smallholder market participation remains limited 12)Post harvest losses are large 13)Droughts dominate Africa’s risk environment 14)African farmers are increasingly diversifying their incomes 15)Agricultural commercialization and diversification improves nutritional outcomes Common wisdoms revisited…

6. 1)Use of modern inputs remains dismally low 2)Land, labor and capital markets remain largely incomplete 3)Agricultural labor productivity is low 4)Land is abundant and land markets are poorly developed 5)Rural entrepreneurs largely operate in survival mode. 6)Extension services are poor 7)Agroforestry is gaining traction 8)African agriculture is intensifying 9)Women perform the bulk of Africa’s agricultural tasks 10)Seasonality continues to permeate rural livelihoods 11)Smallholder market participation remains limited 12)Post harvest losses are large 13)Droughts dominate Africa’s risk environment 14)African farmers are increasingly diversifying their incomes 15)Agricultural commercialization and diversification improves nutritional outcomes Common wisdoms revisited…

7. 1. Agricultural Inputs Sheahan and Barrett Ten new (or newly confirmed) facts about agricultural input use in Africa … a preview of five 1. Modern input use may be relatively low in aggregate, but is not uniformly low across (and within) these countries, especially for inorganic fertilizer and agro-chemicals (although not for irrigation and mechanization).

8. 2. There is surprisingly low correlation between the use of commonly “paired” modern inputs at the household- and, especially, the plot-level. This raises questions about untapped productivity gains. Ethiopia: household levelEthiopia: plot level 1. Agricultural Inputs Sheahan and Barrett 3. Farmers in East Africa do not significantly vary input application rates according to self-perceived soil quality and erosion status.

9. 1. Agricultural Inputs Sheahan and Barrett 4. An inverse relationship consistently exists between farm or plot size and input use intensity. Nigeria: farm levelNigeria: plot level In most cases, this relationship is more pronounced at the plot level, therefore inter- household variation cannot explain relationship. Suggests need to better understand intra-household agricultural input allocation decisions.

10. 5. National-level factors explain nearly half of the farm-level variation in inorganic fertilizer and agro-chemical use. Categories of variablesShapley value Bio-physical variables: rain, soil, elevation, maximum greenness, agro- ecological zones 24 Socio-economic variables: consumption level, sex of household head, household size and dependency ratio 4 Farm operation characteristic variables: farm size, number of crops, type of crops 16 Market and accessibility variables: distance to market and road, prices of fertilizer and main grain 11 Country dummy variables45 Variation in household-level inorganic fertilizer use Ultimately interested to learn where most of the variation in input use comes from: biophysical, infrastructure, market, socio- economic, or policy-specific variables? Binary use at household level (avoids bias from survey design) 45 percent of variation in inorganic fertilizer use can be explained by country level (similar for agro-chem) Suggests the policy and operating environments facilitated by governments and regional processes (e.g., CAADP) are critically important for ushering in a Green Revolution in Sub-Saharan Africa. 1. Agricultural Inputs Sheahan and Barrett

11. 2. Factor Markets Dillon and Barrett Agricultural Factor Markets in Sub-Saharan Africa: An Updated View with Formal Tests for Market Failure … a preview 1.Provide a summary overview of land and labor market participation in Ethiopia, Malawi, Niger, Tanzania, and Uganda 2.Implement a simple test of market failures in data from five African countries (testing whether the separation hypothesis holds) Main findings: in spite of widespread participation in rural land and labor markets, we strongly reject the null hypothesis of complete and competitive markets in all study countries … both input market participation and failure are widespread

12. 2. Factor Markets Dillon and Barrett Labor markets exist and are active. Percent of agricultural households hiring labor CountryActivity Number of households Percent hiring workers EthiopiaCultivation309118.5% Harvest266620.9% Overall266630.2% MalawiNon-harvest260532.6% Harvest260516.0% Overall260542.0% Niger Preparation233919.5% Cultivation233937.4% Harvest233918.6% Overall233947.8% TanzaniaPlanting263018.5% Weeding263018.9% Fertilizing26302.6% Harvest263016.0% Overall263030.8% UgandaOverall210946.8%

13. 2. Factor Markets Dillon and Barrett Land markets exist and are active, too. Participation in land rental markets EthiopiaMalawiNigerTanzaniaUganda N30942666233926302135 Household rents land out 6.10%0.90%1.20%3.40%0.40% Household rents land in 19.50%13.10%7.30%6.20%18.10% Household rents or borrows land in 30.30%28.40%27.70%23.20%36.60% Clearly these markets have sufficient transactors to be competitive. But adequate transactional density is merely a necessary condition for the separation hypothesis to hold.

14. 2. Factor Markets Dillon and Barrett However, clear evidence of market failure across all countries and multiple specifications (Benjamin 1992, Udry 1999): But it’s not clear which markets are failing (next step), nor why (search, supervision or transactions costs? That’s the next phase of research … OLS regression results of farm labor use EthiopiaMalawiNigerTanzaniaUganda Log area (acres)0.489***0.528***0.343***0.444***0.379*** -0.04-0.048-0.026-0.027-0.033 Log median wage0.036-0.121**-0.155-0.0770.012 -0.051-0.052-0.107-0.065-0.043 Log HH size0.379***0.399***0.635***0.399***0.211*** -0.055-0.061 -0.043-0.044 R-squared0.330.2780.3010.3210.312 N24992556218323462047

15. 3. Women in Agriculture Christiaensen, Kilic, Palacios-López Common rhetoric: “….women are responsible for 60- 80 [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) Female share of agricultural labor documented in LSMS-ISA surveys: Uganda 56% Tanzania 53% Malawi 52% Nigeria 37% Ethiopia 29% Niger 24% Cross-country average 40% How Much Do Women in Africa Contribute to Agriculture? … a preview

16. 3. Women in Agriculture Christiaensen, Kilic, Palacios-López Agricultural activities appear to be gendered. Female share of agricultural labor by activity Women are relatively more involved in harvesting and less in land preparation in the countries in which men have the higher share of agricultural labor.

17. 3. Women in Agriculture Christiaensen, Kilic, Palacios-López No systematic evidence of gender differences in youth’s engagement in agriculture. Female share of agricultural labor by age group

18. 3. Women in Agriculture Christiaensen, Kilic, Palacios-López No strong case to disproportionately focus on gender if total agricultural supply is the objective. Gender-based yield gaps range from 13% in Uganda to 25% in Malawi (World Bank, ONE Campaign 2014) due to lower use of improved technologies and lower returns to those technologies. If 40% of laborers (the female portion) increased their output by 13-25%, then closing this gap this would only contribute to a 5-10% increase in total agricultural production.

19. 4. Post-harvest Losses Kaminski and Christiaensen Common wisdoms about post harvest losses: “Worldwide 32 % of all food produced is lost. In SSA, it amounts to 37%.” (FAO, 2011) Post harvest losses for cereals alone is estimated at 20.5% (FAO, 2011). Goal of this analysis: Focus on farmer-reported post harvest losses of maize (more perishable than sorghum and millet) in East Africa. Post Harvest Loss in Sub-Saharan Africa: What do farmers say? … a preview

20. 4. Post-harvest Losses Kaminski and Christiaensen Between 7 (Malawi) and 22 (Uganda) percent of maize farmers report to incur on-farm PHL for maize, losing between 20 to 27 percent of their harvest. Adds up to between 1 and 6 percent of total national maize harvest. Insects and rodents/pests (biotic factors) as the most important reasons for reported losses. Proportions (%)UG 2009-10TZ 2010-11TZ 2008-09MW 2010-11 Post harvest losses, portion of harvest (unconditional) 5.92.94.41.4 Probability of reporting post harvest losses 21.514.719.06.8 Post harvest losses, portion of harvest (conditional) 27.419.723.120.6 # maize producing hhs1,8531,5201,30110,331

21. 4. Post-harvest Losses Kaminski and Christiaensen Increases with Humidity and temperature Declines with Seasonal price gap Proximity to market place Post primary education Female headed households Not associated with Poverty Rural/urban areas Correlates of self-reported post harvest maize losses:

22. 4. Post-harvest Losses Kaminski and Christiaensen Use of improved storage technology is low. UG 2009-10TZ 2010-11TZ 2008-09MWI 2010-11 Traditional storage1.424.819.217.9 Improved storage0.611.55.92.0 Spraying/ smoking63.149.037.210.8 Uptake of improved grain storage facilities (modern stores, improved local structures, air-tight drums) is minimal. Crop protection sprays and smoking, however, are widely used, further adding to the already higher than expected agro-chemical use on fields. Taken together, these facts suggest limited aggregate food supply gains to attempting reductions in on-farm loss or storage technology promotion.

23. 5. Labor and Productivity McCullough Labor allocation and productivity are key features of structural transformation because they describe the incentives households face when making decisions about time use Time use and labor productivity in Africa … a preview

24. 5. Labor and Productivity McCullough Micro-measures of labor shares are relatively similar to national accounts-based measures However, note that hours based measures offer lower agriculture shares than participation based measures do

25. 5. Labor and Productivity McCullough Agricultural workers supply far fewer hours of labor per year than do workers in other sectors Individuals participating in other sectors also work in agriculture; the reverse is much less true of primarily agriculture workers

26. 5. Labor and Productivity McCullough Labor productivity gaps are pronounced when only focusing on primary sector of participation But gaps virtually vanish or are reversed when accounting for hours worked (except Tanzania) What look like productivity gaps (left) could actually just be employment gaps Highlights the continued importance of agriculture in Africa’s structural transformation and the need to interrogate the data

27. Concluding remarks Solid descriptive statistics are key to guide policy debates We’ve learned a lot about the current status of African agriculture and livelihoods from this new descriptive evidence. Times are changing, and so is rural Africa. We must keep up to date Now, with emerging panel data, we have the opportunity to further contribute to evidence-based policy making by moving from descriptive work to research on the causes of change.

28. Thank you for your time, interest and comments! Thank you