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. Briones
Research Fellow, Philippine Institute for Development Studies
Presented 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 rice
23%
Others
38%
Maize
6%
Sugarcane
2%
Banana
7%
Livestock
13%
Poultry
9%
Mango
2%
Background: Output (million tons)
1.6
1.4
Poultry
2.0
1.9
Livestock
0.8
0.8
Mango
8.6
9.1
Banana
Sugarcane
17.9
24.6
7.4
6.4
Maize
Paddy rice
15.8
0.0
5.0
10.0
15.0
2013
2010
18.4
20.0
25.0
30.0
Background: Public outlays for agri R&D
2013
2015
3,144,939
455,400*
31,128
5,261,917
2,113,509**
DA - Bureau of Fisheries and Aquatic Resources (DA - BFAR)
DA - Fiber Industry Development Authority (DA - FIDA)
DA - Cotton Development Authority (DA - CoDA)
DA - Agricultural Training Institute (DA - ATI)
DA - Philippine Carabao Center (DA - PCC)
69,015
60,694
23,372
2,290
27,827
417,518
DA - Philippine Coconut Authority (DA - PCA)
DA - Philippine Rice Research Institute (DA - PhilRice)
PCAARRD
54,000
532,200
786,727
108,500
397,007
842,305
Forest Products Research and Development Institute (FPRDI)
UPLB***
70,888
272,107
53,008
360,128
State Universities and Colleges
759,291
802,401
Total
DA – Office of the Secretary (OSec)
DA - Bureau of Plant Industry (DA - BPI)
129,121
117,084
Background: Public outlays for agri R&D
2013
2015
3,144,939
455,400*
31,128
5,261,917
2,113,509**
DA - Bureau of Fisheries and Aquatic Resources (DA - BFAR)
DA - Fiber Industry Development Authority (DA - FIDA)
DA - Cotton Development Authority (DA - CoDA)
DA - Agricultural Training Institute (DA - ATI)
DA - Philippine Carabao Center (DA - PCC)
69,015
60,694
23,372
2,290
27,827
417,518
DA - Philippine Coconut Authority (DA - PCA)
DA - Philippine Rice Research Institute (DA - PhilRice)
PCAARRD
54,000
532,200
786,727
108,500
397,007
842,305
Forest Products Research and Development Institute (FPRDI)
UPLB***
70,888
272,107
53,008
360,128
State Universities and Colleges
759,291
802,401
Total
DA – Office of the Secretary (OSec)
DA - Bureau of Plant Industry (DA - BPI)
129,121
117,084
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
Producer Price (pesos/ton)
17,070
White
Corn
13,930
Consumer Price (pesos/ton)
33,785
18,970
17,640
38,000
66,530
164,550
130,290
0.35
0
0
0.90
0.10
0
0
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
Loss due to processing, ratio
Quantity supplied, primary ('000 tons)
Cardaba
Banana
8,350
Sugarcane
Mango
Hogs
Chicken
1,630
23,520
95,660
83,830
Counter-factual R&D expenditure per year
Chicken
796
Hogs
1,232
Mango
181
Sugarcane
139
Cardaba banana
383
White corn
237
Rice
2,436
-
500
1,000
1,500
2,000
2,500
3,000
Results: Prices and quantities by 2043
Rice
Reference
Producer Consumer
price
price
20
16
117
Producer
price
12
R&D
Consumer
price
9
Output
Output
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
Rice
108,395
Demand elasticity
Lower Higher
117,310 103,598
Supply elasticity
Lower Higher
Zero
Growth
Postharvest
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
Lower Higher
Supply elasticity
Lower Higher
Zero
Growth
Postharvest
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
Lower Higher
Supply elasticity
Lower Higher
Zero
Growth
Postharvest
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.