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SCRIP Workshop, Kampala—October 14-15, 2004 Alternative growth potentials and poverty reduction in Uganda An economy-wide dynamic modeling approach. What’s new?. Updated social accounting matrix Dynamic economy-wide (CGE) model Calibration of baseline scenario (1999-2015)
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SCRIP Workshop, Kampala—October 14-15, 2004Alternative growth potentials andpoverty reduction in UgandaAn economy-wide dynamic modeling approach
What’s new? • Updated social accounting matrix • Dynamic economy-wide (CGE) model • Calibration of baseline scenario (1999-2015) • Growth potentials of various sector aggregates • Integration of modeling and poverty analysis • Poverty profiles by location, region, and strata • Comparison of differentiated poverty impacts across alternative growth scenarios Page 2
Introduction • CGE models are widely used for policy analysis in developing economies • They are particularly useful to analyze • Links across different production sectors • Links b/w the macro, mezzo, and micro levels • The disaggregated impact of changes in policies and exogenous shocks on production, trade, and welfare • Dynamics • Simulation period 1999-2015 • Recursive dynamic • Updating of factor quantities and productivities Page 3
Policy and poverty analysis Integrated hhld and labor force survey Base year poverty profile Constructing SAM Compare poverty impact of altern. scenarios Calibrate baseline growth path in CGE Recompile poverty profiles Implement alternative policy scenarios Page 4
Alternative growth scenarios • BASE: Projected dynamic path without shock • BASELOW: Base with low growth expectations • AGR-HIG: ... plus productivity growth in agric. • NAG-HIG: ... plus productivity growth in non-agric. • EXP-HIG: ... plus resurgence of the coffee sector and productivity growth in other export agric. • OIL-COF: ... plus increase in world price for oil • STA-HIG: ... plus productivity growth in staples • STA-PRO: ... plus productivity growth in food processing • “Targeted” scenarios 2a, b, c, and d replicate average real annual GDP growth in the BASE Page 5
Some baseline scenario assumptions • Population growth 2.9% (WDI 2004) • Labor force growth 2.75% (+300’ jobs p.a.) • 69.1% engaged in agriculture (02/03 LFS) • Land expansion 2.5% (“area under cereal cultivation”) • Capital depreciation rate 7.5% • Different productivity growth rates (WDI 2004 and FAO yield data) • Calibrated to match past and estimated total GDP growth rates (IMF, Sept. 2004) • Coffee production decline by 50% in 2015 Page 6
Alternative growth scenarios Page 8
Alternative growth paths Page 9
Diverse agricultural growth Page 10
Production by sector aggregate Page 11
Household types Page 12
Impact on households Page 13
Vulnerability of poor households Page 14
Composition of factor incomes Page 15
Poverty results for the poorest Page 16
Winners and “losers” Page 17
“Pro-poor “ growth scenarios? Page 20
Growth incidence seems pro-poor Page 21
... diverse across provinces ... Page 23
Some broad conclusions • There is a variety of growth opportunities • Agriculture is likely to (have to) contribute to future growth to a large extent • Agriculture in connection with processing sectors seems particular promising • Poor farmers and non-farmers (esp. in the Northern province) will not reach “their” MDG goal without broad-based agricultural growth • Pro-poor growth will require sector and region-specific targeted policy interventions • Pro-poor policies need to consider the vulnerable and (relative) losers, esp. in (remote) rural areas Page 25
Extension for future research • Update SAM database using 2002/03 UNHS • More “space” • More sector detail • Production sectors • Labor markets (migration issues) • Constraints and limitations • Related to Jordan’s DD development strategies (in the context of dynamics) • Provincial poverty lines • Calories link and nutrition? • Regional and international trade (EAC issue) • How can it be done? Sector policies and financing/investment mechanisms Page 26
Thank you very much Webare nnyo
Policy and poverty analysis • Household and labor force survey • Constructing SAM from survey (and other) data • Creating base year poverty profiles from survey data • Calibrating baseline growth path • Implementing alternative policy scenarios • Feeding household-specific changes in impact variable (e.g. real per capita consumption expenditures) back into survey • Recompiling poverty profiles for terminal year of baseline and policy scenarios • Comparing poverty impact across households Page 32