- Количество слайдов: 27
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
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 4. 5 Rest-of-World Southeast Asia South Asia Sub-Saharan Africa 4. 0 3. 5 3. 0 2. 5 2. 0 1. 5 1. 0 0. 5 1 /2 10 20 00 /2 05 20 01 6 1 20 00 /2 00 6 /1 95 19 99 /1 90 19 99 1 6 19 85 /1 98 1 /1 80 19 97 /1 75 19 98 6 1 19 70 /1 97 6 96 /1 65 19 19 60 /1 96 1 0. 0 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.
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
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
Motivation: Technologies must be tailored to local agro-ecologies Regions differ in their technology lags; a classic example is:
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
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.
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.
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
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?
Selected results from Alston et al. (2000) meta-analysis for rate of return estimates (n=1, 128) Slide 11
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
Pull mechanisms: the long history of philanthropic prizes (shown here: 1700 -1930)
Pull mechanisms: an explosion of new interest (shown here: 1930 -2009)
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
…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
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
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
Proportional prizes complement other types of contest design Target is pre-specified Success is ordinal (yes/no, or rank order) Success is cardinal (increments can be measured) Target is to be discovered Most technology prizes (e. g. X Prizes) 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) s is a evice le n ro ent d Mai mitm com Ma info in role rm atio is nal
How proportional prizes would work to accelerate innovation • Donors offer a given sum (e. g. $1 m. /year), to be divided among all successful new technologies • Innovators assemble data on their technologies – controlled experiments for output/input change – adoption surveys for extent of use – input and output prices • Secretariat audits the data and computes awards • Donors disburse payments to the winning portfolio of techniques, in proportion to each one’s impact • Investors, innovators and adopters use prize information to scale up spread of winning techniques
Implementing Proportional Prizes: Data requirements Data needed to compute each year’s economic gain from technology adoption Price D S S’ S” Variables and data sources J (output gain) P K (cost reduction) ΔQ Field data Yield change × adoption rate J Input change per unit I I Economic parameters Supply elasticity (=1 to omit) K Δ Q Demand elasticity (=0 to omit) (input change) Q Market data P, Q National ag. stats. Q’ Quantity
Implementing Proportional Prizes: Data requirements Data needed to impute each year’s adoption rate Fraction of surveyed domain Other survey (if any) First survey Projection (max. 3 yrs. ) Linear interpolations First release Application date Year
Implementing Proportional Prizes: Data requirements Calculation of NPV over past and future years Discounted Value (US$) “Statute of limitations” (max. 5 yrs. ? ) First release Projection period (max. 3 yrs. ? ) Year NPV at application date, given fixed discount rate
Implementing Proportional Prizes: Hypothetical results of a West African contest Example results using case study data Example technology 1. Cotton in Senegal Measured Social Gains (NPV in US$) Measured Social Gains (Pct. of total) Reward Payment (US$) 14, 109, 528 39. 2% 392, 087 2. Cotton in Chad 6, 676, 421 18. 6% 185, 530 3. Rice in Sierra Leone 6, 564, 255 18. 2% 182, 413 4. Rice in Guinea Bissau 4, 399, 644 12. 2% 122, 261 5. “Zai” in Burkina Faso 2, 695, 489 7. 5% 74, 904 6. Cowpea storage in Benin 1, 308, 558 3. 6% 36, 363 231, 810 0. 6% 6, 442 $35. 99 m. 100% $1 m. 7. Fish processing in Senegal Total 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.
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.
Implementing Proportional Prizes: Opportunity for a single-country trial in Ethiopia Adoption is especially slow for seeds: Number and proportion of farm holders applying new inputs, by education Proportion of farms using new inputs: No. of farms Fert. Impr. Seed Pesticide Irrigation 12, 916, 120 44% 12% 24% 8% Illiterate 8, 239, 615 41% 10% 22% 8% Informally educated 1, 016, 284 48% 13% 23% 12% Some formal education 3, 660, 222 51% 16% 30% 8% All farm holders Of whom: 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.
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