1. New Technology in Agriculture: Data and Methods to Overcome Asymmetric Information Will Masters Friedman School of Nutrition, Tufts University http://sites.tufts.edu/willmasters NSF-AERC-IGC Workshop on Agriculture and Development December 3, 2010 Mombasa, Kenya

2. New Technology in Agriculture: What can explain these huge differences in yield (and TFP?)? USDA estimates of average cereal grain yields (mt/ha), 1960-2010 Source: Calculated from USDA, PS&D data (www.fas.usda.gov/psdonline), downloaded 7 Nov 2010. Results shown are each region’s total production per harvested area in barley, corn, millet, mixed grains, oats, rice, rye, sorghum and wheat.

3. New Technology in Agriculture: What can explain these huge differences in yield (and TFP?)? The old literature is still relevant! –Induced innovation and collective action in response to factor scarcity –Political economy of support for agriculture, commitment to R&D etc. –Rates of return, incidence of benefits and market structure –Adoption and behavior (commitment, learning, discounting, risk etc.) Something new to consider: –Asymmetric information between funders and R&D agencies –The resulting insights could help explain other rates of innovation

4. A one-slide summary: Motivation (stylized facts about agricultural innovation) –technologies are location-specific, tailored to agroecological conditions –benefits are largely non-excludable, spread among consumers & users –benefits are difficult to distinguish from other trends or shocks –benefits remain consistently very large, with persistent underinvestment Diagnosis (one of many potentially relevant models) –an Akerlof (1970) ‘market for lemons’ –R&D is a credence good, difficult for investors/funders to buy Remedies (interventions to be tested) –procurement only from trusted brand (e.g. CGIAR, universities), or… –third-party certification to reveal performance data impact assessments and case studies technology contests and prizes for disclosure New Technology in Agriculture: Data and Methods to Overcome Asymmetric Information

5. Motivation: Technologies must be tailored to local agro-ecologies Regions differ in their technology lags; a classic example is:

6. Motivation: Technologies must be tailored to local agro-ecologies Source: Reprinted from W.A. Masters, “Paying for Prosperity: How and Why to Invest in Agricultural Research and Development in Africa” (2005), Journal of International Affairs, 58(2): 35-64. Here is some modern data on a somewhat similar technology lag:

7. Motivation: Benefits are diffuse and hard to attribute, but very large Source: J.M. Alston, M.C. Marra, P.G. Pardey & T.J. Wyatt (2000). Research returns redux: A meta-analysis of the returns to agricultural R&D. Australian Journal of Agricultural and Resource Economics, 44(2), 185-215.

8. Motivation: Investment rates stable and falling, despite high estimated rates of return Reprinted from Philip G. Pardey, Nienke Beintema, Steven Dehmer, and Stanley Wood (2006), “Agricultural Research: A Growing Global Divide?” Food Policy Report No. 17. Washington, DC: IFPRI.

9. Diagnosis: Why is there persistent underinvestment? Why need public R&D at all – why not just IPRs ? –enforcement is prohibitively expensive for many technologies –e.g. in genetic improvement, contrast maize vs. soy vs. wheat & rice Why would public R&D be unresponsive to impact data? –this could be a generic collective-action failure, but also specifically… –ag. technology performance data are private and location-specific; R&D project selection and supervision is particularly difficult One aspect of this problem is Akerlof’s ‘market for lemons’ –Investment is constrained by trust (R&D is a credence good) –Without trust, investment level would be zero The investments we see occur via only the most trusted institutions

10. Remedies: How can funders target their R&D investments? What are the (more or less) trusted R&D agencies we see? –IARCs: core funding through CGIAR, plus donor-funded projects –NARIs: core funding from host govts, plus donor-funded projects –Donor-country institutions: core funding varies, plus projects Can third-party certification overcome info. asymmetry? –Who does evaluation and impact assessments? –What do they find?

11. Slide 11 Selected results from Alston et al. (2000) meta-analysis for rate of return estimates (n=1,128)

12. Remedies: How can funders target their R&D investments? Trusted brands –IARCs: core funding through CGIAR, plus donor-funded projects –NARIs: core funding from host govts, plus WB loans and projects –Donor-country universities: core funding varies, plus projects Third-party certification –Who does evaluation and impact assessments? –What do they find? Consistently high payoffs, self-evaluations actually show lower returns Can the new wave of evaluation research help? –Are RCTs appropriate? Yes, but… Not for R&D itself [national-scale programs, non-excludable impacts] –For this, we have pull mechanisms... A long history with important new twists

13. (shown here: 1700-1930) Pull mechanisms: the long history of philanthropic prizes

14. (shown here: 1930-2009) Pull mechanisms: an explosion of new interest

15. Pull mechanisms are prize contests; can offer very high-powered incentives Successful prize contests offer: –an achievable target, an impartial judge, credible commitment to pay Such prizes elicit a high degree of effort: –Typically, entrants collectively invest much more than the prize payout –Sometimes, individual entrants invest more than the prize e.g. the Ansari X Prize for civilian space travel offered to pay $10 million the winners, Paul Allen and Burt Rutan, invested about $25 million Why do prizes attract so much investment? –contest provides a potentially valuable signal of success –value of the signal depends on degree of previous market failure the X Prize winners licensed designs to Richard Branson for $15 million and eventually sold the company to Northrop Grumman for $??? million total public + private investment in prize-winning technologies ~ $1 billion

16. …but traditional prize contests have serious limitations! Traditional prize contests are winner-take-all (or rank-order) –this is inevitable when only one (or a few) winners are needed, but... Where multiple successes could coexist, imposing winner-take-all payoffs introduces inefficiencies –strong entrants discourage others (paper forthcoming in J.Pub. E.) potentially promising candidates will not enter –pre-specified target misses other goals more (or less) ambitious goals are not pursued –focusing on few winners misses other successes characteristics of every successful entrant might be informative New incentives can overcome these limitations with more market-like mechanisms, that have many winners

17. New pull mechanisms allow for many winners From health and education, two examples: –pilot Advance Market Commitment for pneumococcal disease vaccine launched 12 June 2009, with up to $1.5 billion, initially $7 per dose –proposed “cash-on-delivery” (COD) payments for school completion would offer $200 per additional student who completes end-of-school exams What new incentive would work for agriculture? –what is the desired outcome? unlike health, we have no silver bullets like vaccines unlike schooling, we have no milestones like graduation instead, we have on-going adoption of diverse innovations in local niches –what is the underlying market failure? for AMC and COD, the main market failure is commitment failure for agricultural R&D, the main market failure is asymmetric information

18. What new incentives could best reward new agricultural technologies? New techniques from elsewhere did not work well in Africa –local adaptation has been needed to fit diverse niches –new technologies developed in Africa are now spreading Asymmetric information limits scale-up of successes –local innovators can see only their own results –donors and investors try to overcome the information gap with project selection, monitoring & evaluation, partnerships, impact assessments… –but outcome data are rarely independently audited or publically shared The value created by ag. technologies is highly measureable –gains shown in controlled experiments and farm surveys –data are location-specific, could be subject to on-side audits So donors could pay for value creation, per dollar of impact –a fixed sum, divided among winners in proportion to measured gains –like a prize contest, but all successes win a proportional payment

19. Achievement awards (e.g. Nobel Prizes, etc.) Most technology prizes (e.g. X Prizes) Proportional prizes (fixed sum divided in proportion to impact) Success is ordinal (yes/no, or rank order) AMC for medicines, COD for schooling (fixed price per unit) Target is pre-specified Target is to be discovered Success is cardinal (increments can be measured) Proportional prizes complement other types of contest design Main role is as commitment device Main role is informational

20. Donors offer a given sum (e.g. $1 m./year), to be divided among all successful new technologies Innovators assemble data on their technologies –controlled experiments for output/input change –adoption surveys for extent of use –input and output prices Secretariat audits the data and computes awards Donors disburse payments to the winning portfolio of techniques, in proportion to each one’s impact Investors, innovators and adopters use prize information to scale up spread of winning techniques How proportional prizes would work to accelerate innovation

21. Data needed to compute each year’s economic gain from technology adoption Implementing Proportional Prizes: Data requirements DSS’S” Price Quantity J (output gain) I (input change) QQ’ K (cost reduction) Variables and data sources Market data P,Q Nationalag. stats. Field data J Yieldchange×adoptionrate I Input change per unit Economic parameters K Supply elasticity(=1 to omit) Δ Q Demand elasticity (=0 to omit) ΔQ P

22. Data needed to impute each year’s adoption rate Fraction of surveyed domain Year First survey Other survey (if any) Linear interpolations First release Projection (max. 3 yrs.) Application date Implementing Proportional Prizes: Data requirements

23. Discounted Value (US$) First release Calculation of NPV over past and future years NPV at application date, given fixed discount rate Projection period (max. 3 yrs.?) “Statute of limitations” (max. 5 yrs.?) Implementing Proportional Prizes: Data requirements Year

24. Implementing Proportional Prizes: Hypothetical results of a West African contest Example technology Measured Social Gains (NPV in US$) Measured Social Gains (Pct. of total) Reward Payment (US$) 1. Cotton in Senegal14,109,52839.2%392,087 2. Cotton in Chad6,676,42118.6%185,530 3. Rice in Sierra Leone6,564,25518.2%182,413 4. Rice in Guinea Bissau4,399,64412.2%122,261 5. “Zai” in Burkina Faso2,695,4897.5%74,904 6. Cowpea storage in Benin1,308,5583.6%36,363 7. Fish processing in Senegal231,8100.6%6,442 Total$35.99 m.100%$1 m. Note: With payment of $1 m. for measured gains of about $36 m., the implied royalty rate is approximately 1/36 = 2.78% of measured gains. Example results using case study data

25. Implementing Proportional Prizes: Opportunity for a single-country trial in Ethiopia Share of cropped area under new seeds for major cereal grains, 1996-2008 Source: Ethiopian Central Statistical Agency data, reprinted from D.J. Spielman, D. Kelemework and D. Alemu (forthcoming), “Seed, Fertilizer, and Agricultural Extension in Ethiopia.” Draft chapter for P. Dorosh, S. Rashid, and E.Z. Gabre-Madhin, eds., Food Policy in Ethiopia. New technology adoption is stalled:

26. Implementing Proportional Prizes: Opportunity for a single-country trial in Ethiopia Number and proportion of farm holders applying new inputs, by education Proportion of farms using new inputs: No. of farms Fert.Impr. SeedPesticideIrrigation All farm holders12,916,120 44%12%24%8% Of whom: Illiterate8,239,61541%10%22%8% Informally educated1,016,28448%13%23%12% Some formal education3,660,222 51%16%30%8% Source: Author's calculations, from CSA (2010), “Agricultural Sample Survey 2009-2010 (2002 E.C), Meher Season.” Version 1.0, 21 July 2010. Addis Ababa: Central Statistical Authority of Ethiopia. Available online at http://www.csa.gov.et/index.php?&id=59. Adoption is especially slow for seeds:

27. In conclusion…. Back to the intro: The old literature is still relevant! –Induced innovation and collective action in response to factor scarcity –Political economy of support for agriculture, commitment to R&D etc. –Rates of return, incidence of benefits and market structure –Adoption and behavior (commitment, learning, discounting, risk etc.) Something new to consider: –Asymmetric information between funders and R&D agencies –The resulting insights could help explain other rates of innovation