Going the Last Mile: Accelerating Progress in Food Security and Nutrition workshop (June 14, 2016)

1. Pierre Boulanger
Hasan Dudu
Emanuele Ferrari (team leader)
Alfredo Mainar Causape
Modelling the Impact of National
Policies on SSA Countries
IFPRI-CTA-EC-HarvestPlus-JRC Event, Brussels, June 14, 2016
European Commission, Joint Research Centre
Institute for Prospective Technological Studies (JRC-IPTS)
AGRILIFE Unit

2. 2
● Need for ex-ante policy impact analysis of Partner Countries
sectorial measures, as well as EU policies (development and
cooperation, agricultural, trade, etc.) on food security and rural
poverty alleviation, especially in SSA.
● Focusing on the food and agricultural sector JRC has
undertaken major macroeconomic modelling exercises, using
CGE modelling tools.
● Ability to model policies which affect all sectors/people/economy
● A CGE model is a system of nonlinear simultaneous equations
representing the constrained optimising behaviour of all agents
within the economy (e.g. producers, consumers, factor suppliers,
exporters, importers, taxpayers, savers, investors, or
government)
 Complete interrelations within an economy
Motivation Modelling Simulation Conclusion

3. Example of policy analysis (EU and
national):
1.Agricultural inputs, investments
programs, etc.
2.Infrastructure
3.Education & Health
4.Social protection
5.Fiscal
6.Trade, food aid
7.Others (national specific)
+ Impact of EU policies on African
regions (e.g., CAP)
Five countries plus one region over 5
years: Kenya (2015-), Ethiopia
(2016-), Senegal (2016-), Cote
d'Ivoire (2017-), Rwanda (2017-)
Motivation Modelling Simulation Conclusion

4. 4
 Use of Computable General Equilibrium (CGE) models, based
on Social Accounting Matrices (SAM) for ex ante analysis of
domestic/regional/EU policies' effects on food and nutrition security
P
O
L
I
C
Y
I
M
P
A
C
T
MAGNET GLOBE STAGE-DEV
Motivation Modelling Simulation Conclusion

5. • STAGE is a ‘standard’ single country CGE model
• The model is designed for calibration using a reduced form
of a SAM that broadly conforms to the UN System of
National Accounts (SNA).
• STAGE-DEV model is enhanced with the following
extensions:
1. Nested consumption/utility functions
2. Endogenous functional distribution of income
3. Home Production Home Consumption (HPHC)
4. Endogenous labour supply
5. Household internal migration
6. Factor market segmentation
7. Fully flexible Production Function
Motivation Modelling Simulation Conclusion
• Open source model. Documentation:
http://www.cgemod.org.uk/stage.html

6. 6
SAM (Social Accounting Matrix) constr./improvement
incl. agricultural/regional details, including updated national
Accounts (IOTs, SUTs) with disaggregation of :
Household Types (e.g. urban/rural), macroeconomic
aggregates (e.g. production, consumption), functional
distribution of income, supply of labour by type/household, etc.
Motivation Modelling Simulation Conclusion
Informal
cooperation
with
IFPRI
**
FAO-MAFAP
**
AGRODEP
**
others

7. 7
Example of macro analysis: Input Policies in Kenya
A new SAM for Kenya (2014) based on official data from KNBS
•9 agro-ecological areas: (i) Nairobi, (ii) Mombasa, (iii) High
Rainfall, (iv) Semi-Arid North, (v) Semi-Arid South, (vi) Coast, (vii)
Arid North, (viii) Arid South, and (ix) Turkana
•54 activities (12 of them accounts of households as producers)
•52 marketed and 18 HPHC commodities
•3 types of labour (skilled, unskilled and semi-skilled) in 10 regions
(30 labour accounts in total)
•3 types of capital (agricultural, non-agricultural and livestock) and 2
types of land (irrigated and non-irrigated)
•5 types of taxes: direct, indirect, sales, factors and imports taxes.
•24 regionalized Representative Households Groups (RHG)
Motivation Modelling Simulation Conclusion

8. 8
• Key policy background: Agricultural Sector Development Strategy
(ASDS), which sets the vision to achieve an average growth
rate of 7% per year; the Comprehensive African Agricultural
Development Programme (CAADP); the National Food and
Nutrition Security Policy (FNSP); recognises as key issues the
increased funding to the food and agriculture sectors to 10%
• 3 scenarios:
• Fertiliser scenario: New plant in Uasin Gishu that will
increase both fertiliser production and agricultural
productivity
• Seed scenario: increased in commercial seed productivity +
hypothetical seed subsidy
• Irrigation scenario: higher usage of irrigation water that
will increase agricultural productivity
Motivation Modelling Simulation Conclusion

9. First results
•Decrease in CPI, i.e. relatively
better access to food, higher for
rural household and area
•Improvement of self-suficiency
Table1. Consumer Price Index
Motivation Modelling Simulation Conclusion
Table 2. Share of imports in domestic
+ Results on macro aggregates; share of
HPHC in food consumption; calorie, protein and
fat intake per capita; internal migration, etc.
Workshop in Nairobi; April 26, 2016
5th Conference of the African Association of
Agricultural Economists in Addis Ababa; Sept.
2016
 forthcoming JRC Report

10. 10
• Direct involvement of DG DEVCO,
EU delegations and SSA institutions
(Ministries, Research Centres, etc.)
in choice of relevant policies to
be modelled and analysed
• Development of a network of local
experts (individual and institutions)
• Capacity building in modelling for
delegations, DG DEVCO, local
partners (e.g., TEGEMEO)
• Relationships with recognised
players in development (e.g.,
IFPRI, Agrodep, FAO-MAFAP,
USAID, GIZ, OXFAM, Foodsecure
FP7)
Motivation Modelling Simulation Conclusion

11. 11
Thank you for your attention!
contacts:
pierre.boulanger@ec.europa.eu / emanuele.ferrai@ec.europa.eu

12. CGE – Visual representation
12
Appendix

13. • Standard Model Features of STAGE:
• Trade is modelled using nested functions with an
adaptation to mitigate the impacts of small trade shares.
• Production is modelled using nested CES functions; all
activities (industries) are modelled as multi product
activities, i.e., the SAM is configured using a Supply and
Use table format.
• There are multiple tax instruments on commodities,
activities, factors and institutions and multiple sources of
savings.
• The model is designed to include large numbers of factor
• (labour) and household accounts.
• Additional Model Features: Domestic trade and transport
margins; price responsive output mix by activities.
Appendix

14. Appendix

15. Commodities Margins Activities Factors Households Enterprises Government Savings-Investment Rest of the World Total
Commodities
Transaction cost
(trade and
transport)
Intermediate
consumption (inputs)
Household
consumption
Government
consumption
Fixed capital
formation and change
in stock (Investment)
Exports Total demand
Margins
Transaction cost
(trade and
transport)
Total margins
Activities
Marketed output /
Domestic sales
Activity income
Factors
Factor income from
activities
Factor income from
ROW
Factor income
Households
Labour and mixed
income
(Inter Households
transfers)
Distributed benefits
to Households / Other
transfers
Current transfers to
Households
Current transfers to
Households from
ROW
Household income
Enterprises
Operating surplus /
Capital income
Current transfers to
Enterprises
Current transfers to
Enterprises from
ROW
Enterprises income
Government
Net taxes on
products
Net taxes on
production
Factor income to
Government
Direct taxes
Surplus to
Government /
Enterprises taxes
Current transfers to
Government from
ROW
Government income
Savings-Investment Household savings Enterprises savings Government savings
(Capital accounts
transfers)
Capital transfers from
ROW
Savings
Rest of the World Imports
Factor income to
ROW
Households transfers
to ROW
Surplus to ROW
Government transfers
to ROW
Current external
balance
Foreign exchange
outflow
Total Total supply Total margins
Cost of production
activities
Factor income
payments
Households
expenditures
Enterprises
expenditures
Government
expenditures
Investment
Foreign exchange
inflow
Social Accounting Matrices
SAM structure and estimation
Appendix

16. Kenya SAM estimation
Kenya SAM 2014 (abbreviated version), Ksh
ch cm m ahf ahc a flab fland flivst fcap_ag fcap_na hh enter gov dirtax indtax saltax facttax imptax i_s row Total
HPHCcommodities(ch) 150.7 161.1 0.9 313
Marketedcommodities(cm) 292.5 293.9 50.1 3,158.5 4,162.0 750.4 1,144.2 954.0 10,806
Margins(m) 292.5 292
Householdsasactivitiesfood(ahf) 312.7 1,045.8 1,358
Householdsasactivitiescash-crops(ahc) 197.7 198
Activities(a) 7,087.1 7,087
Labourfactor(flab) 92.7 14.6 1,545.9 15.9 1,669
Landfactor(fland) 536.2 113.7 206.8 857
Livestock(flivst) 141.2 33.6 175
Capitalagricultural(fcap_ag) 98.7 19.3 77.3 195
Capitalnon-agricultural(fcap_na) 45.1 1,912.3 1,957
Households(hh) 1,600.2 856.1 174.7 195.2 455.4 1,048.5 41.6 324.3 4,696
Enterprises(enter) 0.3 1,501.0 505.4 2,007
Government(gov) 554.0 152.7 207.0 7.9 160.7 25.7 1,108
Directtaxes(dirtax) 311.6 242.4 554
Indirecttaxes(indtax) 152.7 153
Salestaxes(saltax) 207.0 207
Factortaxes(facttax) 6.6 0.3 0.1 0.1 0.9 8
Importstaxes(imptax) 160.7 161
Save/Investment(i_s) 51.3 715.8 -213.9 592.0 1,145
RestoftheWorld(row) 1,815 62 10 25 1,912
Total 313 10,806 292 1,358 198 7,087 1,669 857 175 195 1,957 4,696 2,007 1,108 554 153 207 8 161 1,145 1,912
16
Appendix