New Technology in Agriculture: Data and Methods to Overcome

Asymmetric Information

Will MastersFriedman 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

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

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

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

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:

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?

Slide 11

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

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

1700 1930

British Longitude prize for determining longitude at sea

French government prize for producing alkali soda

1800 1750

French government prize for food preservation techniques

1900

French Academy of Sciences Montyon prizes for medical challenges

French government prize for large scale hydraulic turbine

Chicago Times-Herald prize for motors for self-propelling road carriage

Deutsch Prize for flight between the Aero-Club de France and Eiffel Tower

Scientific American prize for first plane in US to fly 1 km

Wolfskehl prize for proof of Fermat’s Last Theorem

The Daily Mail prize for flight across the English Channel

Milan Committee prize for flight across Alps

The Daily Mail prize for transatlantic flight

Hearst prize for crossing continental US in 30 days

Orteig prize for solo flight NY to Paris

$3,364,544

$421,370

$1,045,208

$51,118,231

$644,203

$123,833

$12,600,000

$56,502

$31,690

$5,997,097

$618,956

$515,770

$582,689

$289,655

Net present value of prizes paid

(2006 US dollars, not to scale)

1850

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

1940 1950 1960 1970 1980 1990 2000

Kremer Prize for Human Powered Flight (Figure 8)

$290,153

Kremer Prize for Human Powered Flight Across the English Channel

$588,092

Fredkin Prize for Chess Computer Program $128,489

1930

Polytechnische Gesellscaft Prize for Human Powered Flight

$59,240

Soviet Incentive Awards For Innovative Research

$165,755,396

Loebner Prize for Computer that can pass the Turing Test

$100,000

$1,210,084 Budweiser Challenge for first non-stop balloon flight around the globe

$250,000

CATS Prize for inexpensive commer-cial launch of payload into space

International Computer Go Championship

$100,000

Beal’s Conjecture Prize

$654,545

Electronic Frontier Foundation Cooperative Com-puting Challenge for new large prime numbers

Goldcorp Challenge for best gold prospecting methods or estimates

$50,000-250,000

$7,000,000 Millennium Math Prizes for seven unsolved problems

$250,000

Feynman Prizes for nano tech robot technology

$37,682,243 Super Efficient Refrigerator Program for highly efficient CFC free refrigerator

$1,210,084 Rockefeller Foundation Prize for Rapid STD Diagnostic Test

$ 10,917,192 European Information and Communication Technology Prize

$6,000,000 Lemelson-MIT Prize for invention of a patented product useful to society

$ 10,717,703 Ansari X Prize for private manned space flight

$1,600,000

$1,882,290 Schweighofer Prize for Europe’s forest industry competitiveness

$6,660,406 DARPA Grand Challenge for robotics in vehicles

$4,300,000 Methuselah Mouse Prize for demonstration of slowing of ageing process on mouse

$2,000,000 NASA Centennial Challenges for Improvements in space exploration

$1,210,084

Grainger Challenges for development of economical filtration devices for the removal of arsenic from well water in developing countries

Net present value of prizes paid (2006 US dollars, not to scale)

$ 10,000,000 Archon X Prize for sequencing the human genome

$ 25,000,000 Virgin Earth Challenge for removal of greenhouse gases

up to $1.5 billion Advance market Commitment for pneumococcal disease vaccine

$ 50,000,000 Bigelow Space Prize for crew transport into orbit

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

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

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 deviceMain role is

informational

• 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 workto accelerate innovation

Data needed to compute each year’s economic gain from technology adoption

Implementing Proportional Prizes: Data requirements

D S S’ S”Price

Quantity

J (output gain)

I(input change)

Q Q’

K(cost reduction)

Variables and data sources

Market dataP,Q National ag . stats.

Field dataJ Yield change × adoption rateI Input change per unit

Economic parametersK Supply elasticity (=1 to omit)Δ Q Demand elasticity (=0 to omit)

Δ Q

P

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

DiscountedValue(US$)

First release

Calculation of NPV over past and future years

NPV at application date, given fixed discount rate

Projectionperiod(max. 3 yrs.?)

“Statute of limitations”

(max. 5 yrs.?)

Implementing Proportional Prizes: Data requirements

Year

Implementing Proportional Prizes: Hypothetical results of a West African contest

Example technology

Measured Social Gains(NPV in US$)

MeasuredSocial Gains (Pct. of total)

RewardPayment

(US$)

1. Cotton in Senegal 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

7. Fish processing in Senegal 231,810 0.6% 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

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:

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. Seed Pesticide Irrigation

All farm holders 12,916,120 44% 12% 24% 8%

Of whom:

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%

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:

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