Ex Ante Evaluation of Investment in Research and Development for Major Food Commodities: Case Study of the Philippines Using the Welfare Impact Simulator for Evaluating Research (WISER) Model

Roehlano M. BrionesResearch Fellow, Philippine Institute for Development StudiesPresented at the 52nd Meeting of the Philippine Economic Society, 14 November 2014, Hotel Intercontinental, Makati City

Background

• Research intensity ratio = 1.00 (public R&D spending on agriculture divided by agricultural GDP) has been cited as a benchmark for public expenditure on agricultural R&D • For the Philippines, the 1997 AFMA elevates benchmark to a legal

obligation: annual budgeted expenditure on agricultural R&D should reach 1% of the agricultural GVA two years earlier (starting 2001)• However no annual budget since 2001 has reached this target. • The current budget proposal for 2015 is nearest this benchmark; =

5.26 billion, vs AFMA benchmark of 12.96 billion

Aims and scope

• This study: ex ante impact evaluation for meeting the AFMA benchmark (counter-factual scenario analysis)• Applied to major food commodities starting 2013; these account fo

62% of agricultural GVA that year• Method: economic surplus analysis, using Welfare Impact Simulator

for Evaluating Research (WISER)• Based on standard linear model and techniques in Alston, Norton, and

Pardey (1995)• Generates measures of project worth: NPV, BCR, IRR

Background: Shares in Agriculture GVA

Paddy rice23%

Maize6%

Sugarcane2%

Banana7%

Mango2%

Livestock13%

Poultry9%

Others38%

Background: Output (million tons)

15.8

6.4

17.9

9.1

0.8

1.9

1.4

18.4

7.4

24.6

8.6

0.8

2.0

1.6

0.0 5.0 10.0 15.0 20.0 25.0 30.0

Paddy rice

Maize

Sugarcane

Banana

Mango

Livestock

Poultry

2013 2010

Background: Public outlays for agri R&D

2013 2015

Total 3,144,939 5,261,917

DA – Office of the Secretary (OSec) 455,400* 2,113,509**

DA - Bureau of Plant Industry (DA - BPI) 31,128

DA - Bureau of Fisheries and Aquatic Resources (DA - BFAR) 69,015 417,518

DA - Fiber Industry Development Authority (DA - FIDA) 60,694

DA - Cotton Development Authority (DA - CoDA) 23,372 129,121

DA - Agricultural Training Institute (DA - ATI) 2,290

DA - Philippine Carabao Center (DA - PCC) 27,827 117,084

DA - Philippine Coconut Authority (DA - PCA) 54,000 108,500

DA - Philippine Rice Research Institute (DA - PhilRice) 532,200 397,007

PCAARRD 786,727 842,305

Forest Products Research and Development Institute (FPRDI) 70,888 53,008

UPLB*** 272,107 360,128

State Universities and Colleges 759,291 802,401

Background: Public outlays for agri R&D

2013 2015

Total 3,144,939 5,261,917

DA – Office of the Secretary (OSec) 455,400* 2,113,509**

DA - Bureau of Plant Industry (DA - BPI) 31,128

DA - Bureau of Fisheries and Aquatic Resources (DA - BFAR) 69,015 417,518

DA - Fiber Industry Development Authority (DA - FIDA) 60,694

DA - Cotton Development Authority (DA - CoDA) 23,372 129,121

DA - Agricultural Training Institute (DA - ATI) 2,290

DA - Philippine Carabao Center (DA - PCC) 27,827 117,084

DA - Philippine Coconut Authority (DA - PCA) 54,000 108,500

DA - Philippine Rice Research Institute (DA - PhilRice) 532,200 397,007

PCAARRD 786,727 842,305

Forest Products Research and Development Institute (FPRDI) 70,888 53,008

UPLB*** 272,107 360,128

State Universities and Colleges 759,291 802,401

Similar to allocation in Southeast Asia:• Southeast Asian countries ranked among the last in a list of 60

countries: • Myanmar (59)• Vietnam (57)• Indonesia (54)• Laos (50).

• Even the Philippines, which ranks 36, has a research intensity ratio of 0.44, short of the median ratio of 0.50. • The highest ranking country in Southeast Asia is Malaysia at a ratio of

1.01 (ranked 16th).

Method

• Economic surplus analysis (linear model; implemented in GAMS)• Thirty-year scenario starting 2013• 40% reduction in average cost during the first ten years after the

research lag (i.e. years 5 – 15 from the baseline), then 20% reduction until the end of the scenario. Adoption follows a logistic process based on the following: adoption rate at baseline is 0.5%; in adoption in ten years is 60%; and ceiling adoption is 80%.

Method

• sensitivity analysis is conducted with regard to model parameters and exogenous variables. The variations are as follows: • Parameters: 50% higher and 50% lower elasticities (respectively for

demand and supply, in absolute value);• Exogenous variables: • Zero exogenous growth in demand and in supply; • Research impact on processing and marketing efficiency.

Baseline data for simulatiosn

Rice White Corn

Cardaba Banana

Sugarcane Mango Hogs Chicken

Producer Price (pesos/ton) 17,070 13,930 8,350 1,630 23,520 95,660 83,830

Consumer Price (pesos/ton) 33,785 18,970 17,640 38,000 66,530 164,550 130,290

Loss due to processing, ratio 0.35 0 0 0.90 0.10 0 0

Quantity supplied, primary ('000 tons) 18,439 2,129 2,557 24,585 816 2,012 1,556

Elasticity of demand -0.50 -0.83 -1.01 -0.79 -1.55 -1.16 -0.40

Elasticity of supply 0.45 0.23 0.40 0.49 0.40 0.10 0.10

Growth of demand (%) 3.00 2.00 4.00 4.00 5.00 1.00 1.00

Growth of supply (%) 2.50 1.50 3.50 3.50 4.50 0.50 0.50

Counter-factual R&D expenditure per year

2,436

237

383

139

181

1,232

796

- 500 1,000 1,500 2,000 2,500 3,000

Rice

White corn

Cardaba banana

Sugarcane

Mango

Hogs

Chicken

Results: Prices and quantities by 2043 Reference R&D

Producer

price Consumer

price Output Producer

price Consumer

price Output

Rice 20 16 117 12 9 125

White corn 18 13 58 14 10 62

Cardava banana 16 7 188 7 3 201

Mango 14 5 278 6 2 296

Sugarcane 17 7 194 6 2 206

Hogs 18 10 18 15 9 20

Chicken 39 25 20 33 21 22

Note: All prices in real terms.

Results: Net Present Values

R&D Demand elasticity Supply elasticity Zero Growth

Post-harvest Lower Higher Lower Higher

Rice 108,395 117,310 103,598 53,625 158,443 45,644 208,475

White corn 3,597 4,223 3,343 1,291 5,780 3,058 12,233

Cardaba banana 11,793 12,423 11,446 6,064 17,098 4,520 35,483

Mango 12,481 13,163 12,127 6,226 18,317 3,829 33,046

Sugarcane 26,677 27,610 26,076 14,053 38,377 9,846 195,910

Hogs 10,871 13,515 9,890 2,547 18,881 6,233 86,682

Chicken 10,324 13,580 8,922 3,761 16,185 4,206 50,336

Results: Benefit-Cost Ratios

R&D Demand elasticity Supply elasticity Zero Growth

Post-harvest Lower Higher Lower Higher

Rice 9.9 10.6 9.5 5.4 14.0 4.7 18.1

White corn 4.0 4.6 3.8 2.1 5.9 3.6 11.3

Cardaba banana 17.9 18.9 17.4 9.7 25.6 7.5 52.0

Mango 14.8 15.5 14.4 7.9 21.2 5.2 37.5

Sugarcane 14.9 15.4 14.6 8.3 21.0 6.1 103.2

Hogs 2.8 3.2 2.6 1.4 4.1 2.0 15.1

Chicken 3.6 4.4 3.2 1.9 5.1 2.1 13.6

Results: Internal Rates of Return

R&D Demand elasticity Supply elasticity Zero Growth

Post-harvest Lower Higher Lower Higher

Rice 21.4 21.9 21.1 16.2 24.6 16.2 25.2

White corn 14.3 15.1 13.9 9.5 17.3 13.8 21.6

Cardaba banana 26.6 26.9 26.4 20.9 30.2 20.2 34.9

Mango 24.4 24.7 24.2 18.9 27.9 17.0 32.2

Sugarcane 24.8 25.0 24.7 19.5 28.3 18.4 43.7

Hogs 11.6 12.6 11.2 6.9 14.5 9.4 25.0

Chicken 13.4 14.8 12.7 9.0 16.1 9.6 24.0

Conclusion

• Numerous qualifications: • projections with and without the posited R&D investment• technical assumptions related to elasticities of demand and supply, functional

forms of supply, demand, and the adoption process; and single market competitive equilibrium under a closed economy. • every effort has been taken in this instance to avoid arbitrariness

• Bottom line: Except for hogs and chicken under conditions of implausibly low supply response and intrinsic market growth, the worthiness of R&D investment at the 1% benchmark is robust.