Matin Qaim, University of Gottingen "How to Evaluate Nutrition and Health Impacts of Agricultural Innovations"

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Science Forum 2013 (www.scienceforum13.org)
Plenary session: Evaluating nutrition and health outcomes of agriculture
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Matin Qaim, University of Gottingen "How to Evaluate Nutrition and Health Impacts of Agricultural Innovations"

  1. 1. How to evaluate nutrition and health impacts of agricultural innovations Matin Qaim Agricultural Economics and Rural Development CGIAR Science Forum, 23-25 September 2013, Bonn “Nutrition and Health Outcomes: Targets for Agricultural Research”
  2. 2. Department of Agricultural Economics and Rural Development Introduction… Many undernourished people depend on agriculture as a source of food, income, and employment Agriculture is an important entry point to improve these people’s nutrition and health Agricultural innovations can have important impacts on nutrition and health, but relatively little is known about the types and magnitudes of these effects at the micro level Impact studies primarily look at productivity; some look at income, very few explicitly at nutrition and health (surprising in a CGIAR context) PAS Study Week 2009 2
  3. 3. Department of Agricultural Economics and Rural Development …Introduction Can we always conclude that higher yields lead to better nutrition? What exactly and how much? PAS Study Week 2009 3 Future impact analysis of agricultural innovations should look at nutrition and health outcomes more explicitly. How can we do this? Are standard approaches available? No, this is the focus of this presentation. Intention not to provide blueprint, but discuss possible approaches and issues that need to be considered.
  4. 4. Department of Agricultural Economics and Rural Development Overview Conceptual framework of impact pathways Metrics of nutrition Metrics of health Design of impact studies Selected empirical examples PAS Study Week 2009 4
  5. 5. Department of Agricultural Economics and Rural Development Conceptual framework (impact pathways for farm households) 5 Agricultural innovation Food quantity produced Food consumption/ nutrition Food quality produced Food diversity produced Household income Health Intra-household distribution
  6. 6. Department of Agricultural Economics and Rural Development Metrics of nutrition 1. Subjective food security assessment 2. Food consumption based measures 3. Anthropometric measures 4. Clinical assessment (e.g., blood) PAS Study Week 2009 6 If we want to evaluate nutrition impacts of agricultural innovations, we need to measure nutrition.
  7. 7. Department of Agricultural Economics and Rural Development Criteria to choose most suitable nutrition metric Type of agricultural innovation Expected impact pathways Target group (children, women, or more general) Intended sample size and regional coverage Financial and human resources available Etc. PAS Study Week 2009 7
  8. 8. Department of Agricultural Economics and Rural Development Metrics of health Incidence rates of adverse health outcomes (diseases and premature deaths) PAS Study Week 2009 8 How to measure health outcomes? For better comparison and economic evaluation: Cost-of-illness (value of lost work days, physician treatment, travel cost to physician etc.) Disability-adjusted life years (DALYs) lost
  9. 9. Department of Agricultural Economics and Rural Development Design of impact studies Basic idea: Collect data on nutrition/health variables for adopters and non-adopters of innovation and compare. PAS Study Week 2009 9 Attribution problem: Are observed differences only due to the innovation? Possible solutions: Assign innovation randomly (RCT) Differencing techniques with panel data Instrumental variable (IV) approaches or propensity score matching (PSM), possible with cross-section data
  10. 10. Department of Agricultural Economics and Rural Development Selected empirical examples Tissue culture (TC) bananas in Kenya In Kenya, banana is grown for home consumption and local markets TC is a technology where clean planting material produced in the lab is used instead of suckers from old plantations Together with improved management techniques, TC technology can increase banana yields significantly PAS Study Week 2009 10 We collected data from 385 farms in 2009 to assess impacts on household income and food security
  11. 11. Department of Agricultural Economics and Rural Development Impacts of TC bananas in Kenya 11 TC adoption by social network was used as instrument. Other covariates not shown for brevity. *** p<0.01. Source: Kabunga, Dubois, Qaim (2013) -0,2 -0,1 0 0,1 0,2 0,3 Non-adopters Adopters Index Food insecurity (FI) Severe food insecurity (SFI) We find large positive income effects of TC adoption Food security was captured with HFIAS tool (9 questions) Factor analysis used to construct two food insecurity indices Food insecurity for TC adopters and non-adopters FI index SFI index TC adoption -0.437*** -0.316*** Net treatment effects of TC adoption on food insecurity (IV models)
  12. 12. Department of Agricultural Economics and Rural Development 12 Host plant resistance to major cotton pest (bollworms). In India, cotton is grown by smallholder farmers. Bt cotton was commercialized in 2002; by 2012, over 7 million farmers had adopted (93%) Bt cotton in India We have collected panel data of over 500 farmers in four rounds between 2002 and 2008 (in four states). Panel fixed effects estimates show that Bt adoption entails: Chemical pesticide reductions of 40-50% Yield increases of 20-30% Profit increases of 50%
  13. 13. Department of Agricultural Economics and Rural Development Nutrition effects of Bt cotton adoption Household food consumption data through 30-day recall Converted to calorie consumption per adult equivalent (AE) PAS Study Week 2009 13 0 0,0001 0,0002 0,0003 0,0004 0,0005 0,0006 0,0007 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 Density kcal per AE and day Non-adopters of Bt Adopters of Bt Source: Qaim and Kouser (2013) Calorie consumption of Bt adopters and non- adopters
  14. 14. Department of Agricultural Economics and Rural Development Nutrition effects of Bt cotton adoption Treatment effects per AE (fixed-effects panel models) PAS Study Week 2009 14 Calories (kcal) Bt (per ha) 73.71*** Bt (average household) 145.19*** Increase +5.1% Non- staple calories 23.17** 45.70** +7.2% Iron (mg) Zinc (mg) Vitamin A (µµµµg) 0.57*** 0.30*** 15.54** 1.12*** 0.59*** 30.61** +4.6% +4.5% +9.6% *** p<0.01; ** p<0.05. Source: Qaim and Kouser (2013). Simulation analysis with these results suggests that Bt cotton has reduced food insecurity among Indian cotton growers by 15-20%.
  15. 15. Department of Agricultural Economics and Rural Development Health effects of Bt cotton adoption We have analyzed health effects of Bt adoption related to reduced exposure of farmers to chemical pesticides. Manual application of toxic pesticides regularly leads to poisoning symptoms (skin, eye, breathing, stomach etc.). PAS Study Week 2009 15 Poisoning incidence Bt (per ha) -0.26** Treatment effect of Bt adoption ** p<0.05. Poisson fixed effects panel regression. Other covariates not shown for space reasons. Source: Kouser and Qaim (2011). For total area under Bt cotton in India (per year): 2.6 million fewer cases of pesticide poisoning US$ 15 million lower cost-of-illness
  16. 16. Department of Agricultural Economics and Rural Development Impact pathways 16 Agricultural innovation Food quantity produced Food consumption/ nutrition Food quality produced Food diversity produced Household income Health Intra-household distribution
  17. 17. Department of Agricultural Economics and Rural Development EPSO Conference 2008 17 Assessing nutrition related health effects Malnutrition (nutrient deficiencies) entails adverse health outcomes, causing a health burden for individuals & society. The DALYs approach (disability-adjusted life years) can measure health burden by combining mortality and morbidity within a single index (Murray/Lopez 1996, Stein/Qaim 2007): DALYsLost = Years lost to mortality + Years with disability x Disability weight Without innovation With innovation Health benefit of innovation DALYs Lost
  18. 18. Department of Agricultural Economics and Rural Development Potential health benefits of biofortification (Ex ante analysis for India) PAS Study Week 2009 18 Wheat/rice (iron) Wheat/rice (zinc) Golden Rice (vitamin A) DALYs lost w/o biofortification 4.0 million 2.8 million 2.3 million DALYs saved through biofortification 2.3 million 1.6 million 1.4 million Reduction in health burden 58% 55% 59% Internal rate of return 168% 153% 77% Source: Qaim, Stein, Meenakshi (2007). Results refer to “optimistic” scenario assumptions.
  19. 19. Department of Agricultural Economics and Rural Development Conclusion 1. Most studies on impacts of agricultural innovations only look at productivity and/or income. 2. Nutrition and health effects should be analyzed more explicitly in future impact studies. 3. This is important to better understand what works. 4. Interesting methodological approaches are available, but more work is required: What type of data and metrics for what questions? Issues of intra-household distribution and gender Efficient survey design Etc. PAS Study Week 2009 19
  20. 20. Department of Agricultural Economics and Rural Development Backup slides PAS Study Week 2009 20
  21. 21. Department of Agricultural Economics and Rural Development Subjective food security assessment Food security self-assessment questions covering certain recall period (e.g., HFIAS tool) Construct subjective food security indices PAS Study Week 2009 21 Advantages Relatively easy to collect with standardized questionnaire Various aspects of diet quantity and quality captured Disadvantages How reliable and comparable are subjective measures? Intra-household distribution cannot be captured
  22. 22. Department of Agricultural Economics and Rural Development Food consumption based measures Collect detailed data on food consumption for specified recall period (e.g. 24 hours, 7 days, 30 days) Convert to calorie and nutrient consumption per capita PAS Study Week 2009 22 Advantages Relatively easy to collect as part of living standard module in survey questionnaire Diet quantity, quality, and diversity can be assessed Disadvantages Measurement error (e.g., food waste) Intra-household food distribution difficult to capture
  23. 23. Department of Agricultural Economics and Rural Development Anthropometric measures Data on age, weight, height etc. from individual household members Calculate Z-scores (or BMI) PAS Study Week 2009 23 Advantages More precise measures of nutrition status Individual based (no distribution assumptions required) Disadvantages Not easy to cover all household members in one visit (potential bias if only those at home covered) Diet quantity, quality, and diversity cannot be assessed More difficult to control for confounding factors

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