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Dynamic Modeling for Poverty Reduction: Growth Potentials in Uganda's Economy

This workshop explores the integration of a dynamic economy-wide modeling approach in analyzing growth potentials and poverty reduction strategies in Uganda. Utilizing an updated social accounting matrix and a recursive dynamic Computable General Equilibrium (CGE) model, the study projects economic scenarios from 1999 to 2015. It assesses the impacts of various agricultural and non-agricultural growth strategies on poverty profiles, emphasizing the need for targeted policy interventions to achieve pro-poor growth.

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Dynamic Modeling for Poverty Reduction: Growth Potentials in Uganda's Economy

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  1. SCRIP Workshop, Kampala—October 14-15, 2004Alternative growth potentials andpoverty reduction in UgandaAn economy-wide dynamic modeling approach

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. Baseline scenario calibration Page 7

  8. Alternative growth scenarios Page 8

  9. Alternative growth paths Page 9

  10. Diverse agricultural growth Page 10

  11. Production by sector aggregate Page 11

  12. Household types Page 12

  13. Impact on households Page 13

  14. Vulnerability of poor households Page 14

  15. Composition of factor incomes Page 15

  16. Poverty results for the poorest Page 16

  17. Winners and “losers” Page 17

  18. Poverty reduction for the poorest ... Page 18

  19. ... most of the poorest are in the North Page 19

  20. “Pro-poor “ growth scenarios? Page 20

  21. Growth incidence seems pro-poor Page 21

  22. National success in poverty reduction Page 22

  23. ... diverse across provinces ... Page 23

  24. ... the poorest relatively disadvantaged Page 24

  25. 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

  26. 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

  27. Thank you very much Webare nnyo

  28. Export potentials by sector aggregate Page 28

  29. Page 29

  30. Page 30

  31. Page 31

  32. 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

  33. National poverty reduction by sim Page 33

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