NSF-AERC-IGC Workshop on Agriculture and Development December 3, 2010 • Mombasa, Kenya

1. New Technology in Agriculture: Data and Methods to Overcome Asymmetric InformationWill MastersFriedman School of Nutrition, Tufts Universityhttp://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. New Technology in Agriculture: Data and Methods to Overcome Asymmetric Information 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

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 Here is some modern data on a somewhat similar technology lag: 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.

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

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

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

20. How proportional prizes would workto accelerate innovation • 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

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

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

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

24. Implementing Proportional Prizes: Hypothetical results of a West African contest • Example results using case study data

25. Implementing Proportional Prizes: Opportunity for a single-country trial in Ethiopia New technology adoption is stalled: 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.

26. Implementing Proportional Prizes: Opportunity for a single-country trial in Ethiopia 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