International Futures

Transcription

1 International Futures (IFs) Dr. Julius Gatune Kariuki Frederick S. Pardee Centre for International Futures University of Denver November 2007

2 IFs Modeling System Topics to be covered I. IFs Background and History II. III. IV. IFs Design Approach and Economic Model IFs as a Data Analysis Tool IFs as a Simulation and Policy Analysis Tool

3 IFs Background and History Deep roots in the world models of 1970s Inspired by the World Integrated Model (Mesarovic and Pastel 1974) in which IFs founder Prof. Barry Hughes was involved Also draws on Leontif World Model (1977), the Bariloche Foundation World Model (1976) and Systems Analysis Research Unit Model (SARU 1977) First Generation released in Fortran in 1980 and used by the US Foreign Service Institute for mid-career training program 2 nd Generation on microcomputer platform released in 1985 and is now in 5 th generation

4 IFs Modeling System Design Concerns Global transformations- their uncertainties and their timing and the human leverage on them Data driven but deeply rooted in theory Key Agents: Households, Firms, Governments Five key elements: organizing structures, stocks, flows, key aggregate relationships, and key agent-class behavior relationships IFs draws upon techniques found in both econometric and systems dynamics traditions

5 IFs Structure Modules (Technology module distributed throughout)

6 IFs: Structure Economic Model A Computable General Equilibrium (CGE) Model Equilibrium chasing as full equilibrium is never achieved Inventories are used as buffers that allow partial equilibrium at any time and also as signals to suppliers/producers Sectors: Agriculture, energy, manufactures, services, Materials and ICT. Collapsed from GTAPs data, other sectors could be configured as desired Wrapped around in a Social Accounting Matrix (SAM) that tracks financial flows across agents

7 IFs: Structure Economic Model Uses a Cobb Douglass formulation with endogenous growth enhancements Y = Α K α L β The power of IFs derives from its efforts to breakdown components of Α (the MFP) to its constituents and forecast them Literature used as the basis for this breakdown

8 IFs: Structure Unpacking MFP components provides the policy handles that makes IFs a policy analysis tool

9 IFs: Structure Systems Dynamics Investment in Human and Social Capital + Multi-Factor Productivity (MFP) + + R&D and Knowledge Diffusion Labor + Gross Domestic Product (GDP) + Capital Investment Depreciation - Capital +

10 IFs: Model Verification, Validation Verification Documentation and Inspection- Provides transparency I. Block diagrams II. Equations III. Computer code These are all available within the IFs help system and code can be provided on request

11 IFs: Model Verification, Validation

12 IFs: Model Verification, Validation

13 IFs: Model Verification, Validation

14 IFs: Model Verification, Validation Historical Forecasting with IFs IFs database: Data in a variety of areas for 182 countries stretching as afar as 1960 where possible Some data holes and also some countries did not exist in the earlier periods e.g. Eritrea, IFs data preprocessor used to fill the holes (see documentation) Over 800 data series from many reputable sources is available. IFs allows comparison of historical and forecasted trends

15 IFs: Model Verification, Validation History Vs Forecasting (Kenya s GDP)- Very Good Fit!

16 IFs: Model Verification, Validation But not always that good as the forecast for Exports Shows

17 IFs: Model Verification, Validation But also hard to forecast shocks (e.g. SAPs, AIDS epidemic)

18 IFs: Model Verification, Validation Comparison with Other Forecasts Most forecasts are rooted in single issue while IFs is an integrated model Population: The UN median forecast for world population in 2050 has been adjusted from a 9.8 billion (in 1994) to 9.4 billion (in 1996) to 9.3 billion (in 2000) due to failure to foresee reduction in fertility while IFs had forseen this and as such UN forecast is now closer to IFs (9.06 billion)

19 IFs: Model Verification, Validation Comparison with Other Forecasts Country IFs Emerging Africa 2020 (2001) 2020 Growth Avg Growth 2020 Growth Avg. Growth Burkina Faso % 2.10% % 2.40% Mali % 2.80% % 2.00% Tanzania % 3.00% % 1.70% Uganda % 3.80% % 2.60% Cote divoire % 1.60% % 2.50% Ghana % 0.90% % 1.50%

20 IFs: Model Verification, Validation Comparison with Other Forecasts Country Ethiopia Mozambique Uganda Zimbabwe Cote d Ivoire Burkina Faso OECD-Africa 2010 (1999) BASE OECD -Africa 2010 (1999) Scenario IFs Base Ghana Mali Mauritania Kenya Togo Senegal Benin Gabon Average

21 IFs: Model Verification, Validation Comparison with Other Forecasts Year Average Growth (SSA) (GDP) PPP 2 World Bank (WEP 2007) 3.30% IFs 3.80%

22 IFs: Model Verification, Validation Comparison with Other Forecasts Year Calories Consumption (SSA) Malnourished Children SSA (Millions) IFPRI (2005) IFs IFPRI (2005) IFs ,377 2, ,444 2, ,526 2,

23 IFs: Model Verification, Validation Comparison with Other Forecasts Year Calories Consumption Malnourished People (%) FAO (2006) IFs FAO (2006) IFs ,420 2, % 26.65% ,600 2, % 18.69% ,830 2, % 8.75%

24 IFs: Model Verification, Validation Comparison with Other Forecasts Most forecasts are rooted in single issue while IFs is an integrated global model Note that the comparisons shown are on a region and also focused on an issue IFs has been as good as the other forecasts despite the fact that is it neither issue oriented nor region oriented But proper comparison will require a good understanding of the assumption behind the forecasts

25 IFs: Model Verification, Validation Accrual Validity When a number of institutions adopt the systems and use it then credibility increasingly occurs. Institutions that have deployed IFs I. European Union 3 yr Terra Project for moving Europe and the world towards sustainability II. CIA: Strategic Assessments Group (SAG) III. US National Intelligence council (NIC) Project 2020 IV. UNEP Global Environmental Outlook V. IBM Global Futures Group Many copies of software have been downloaded and thousand of Copies of IFs book sold and feedback from many users received.

26 IFs: As a Data Analysis Tool Data Display Capability If allows data analysis for historical data at both cross sectional and longitudinal dimensions. A choice of graphical displays Can display at country level or aggregate country groupings (standard or user created) Ability to export data to excel for further analysis (Trend analysis inbuilt in IFs)

27 IFs: As a Data Analysis Tool Cross section Data Analysis It is possible to analyze upto 3 data series for a particular entity (country or grouping) and fit trends

28 IFs: AS a Data Analysis Tool Cross section Data Analysis It is possible to also fit trends InfraPrivateInvTransport(MOSTRE CENT) y = x R 2 = InfraQualityScore(MOSTRECENT)

29 IFs: AS a Data Analysis Tool Longitudinal Data Analysis It is possible to analyze at country level

30 IFs: AS a Data Analysis Tool Longitudinal Data Analysis It is possible to analyze at regional level

31 IFs: As a Data Analysis Tool Longitudinal Data Analysis It is possible to get the data to excel and have in tabular format (Note that data is in 5 year intervals but also possible to get for every year). Also possible to decompose the regions to individual countries when exporting the data Region AfrCul-South AfrCul-East AfrCul-Horn

32 Simulation with IFs IFs Base Case It can be thought of as a central tendency scenario It allows system to unfold along a path that seems more or less to be the direction of its historic development Since IFs incorporates dynamics in its formulation the base case is not an extrapolation of the past It is the starting point for analysis as one then can simulate different ways events can unfold and the dynamics and constraints built in IFs then allows for different scenarios to emerge

33 Simulation with IFs Base Case: Selected Variables (SSA) VARAIABLE POPULATION URBAN POPULATION GDP (PPP) GDP PER CAPITA GDP GROWTH RATE LIFE EXPECTANCY YEARS OF SCHOOLING NO. ON $1/DAY IMPORTS EXPORTS

34 Simulation with IFs Simulate Three Strategic Approaches Policy 1: Self-Sustainment Focus (SSF) Policy 2: Export Focus (EF) Policy 3: Human Development (HD) Each requires different sets of assumptions and IFs allows one to incorporate these as deviations from base case

35 Simulation with IFs Assume that first we need a set of basic capabilities Say Governance, Knowledge and Entrepreneurship Assumptions that apply to all policy initiatives Improve governance variables by 20% over a period of 10 years (over the base case) Improve education expenditure by 20% over 10 years Improve economic freedom, Investment, and FDI by 20% over 10 years In IFs one then creates this Basic Capabilities Drivers (BCD) scenario and stores it for use by the three policy scenarios

36 Simulation with IFs Strategy 1: Self-Sustainment Objective is to achieve food self sufficiency, this will require actions to facilitate a green revolution, say I. Double R&D expenditures over the base case over a period of 10 years II. Increase roads density by a factor of 3 over the base case in 25 years III. Telephone density increases by a factor of 3 over the base case in 25 years IV. Agricultural yield double over the base case over a period of 25yrs Create this scenario and save it. Load the BCD scenario and run the combined scenario over the horizon of interest.

37 Simulation with IFs Simulation Results View the result of the simulation via the display menu To be able to view the results without having to rerun the scenario this run can be saved and always be loaded as need be The results can be downloaded to excel for further analysis It is possible to download result side by side with the base run to see the improvement over the base run (or any other run for that matter)

38 Simulation with IFs Policy 1: Self-Sustainment

39 Simulation with IFs Policy 2: Export Focus Main Assumptions Shift Exports by 4% per year over the forecast horizon Double economic freedom over a period of 15 years Increase investment levels by 50% over a period of 15 years Double FDI over a period of 15 years Increase number of networked people rise by a factor of three over a period of 2 years Roads and electricity density triple over a period of 25 years Internet and telephone density triple over 15 years

40 Simulation with IFs Policy 3: Human Development Focus Main Assumptions Improve health and education spending by 30% over 15 year period Improve education spending on girls at primary school by 20% over 15 years Increase R&D spending by 20% over 15 years Reduce military spending over 20% over 15 years Increase forest protection by 20% over 15 years Reduce HIV infection rate of advance by 50 over 15 years Reduce Aids deaths rate as % of infection by HIV by 50% over 15 years Improve agricultural yields by 20% over 15 years Reduce fertility by 20% over 25 years Increase number of networked people by 100% over 15 years Improve telephone density by 100 % over 15 years

41 Simulation with IFs Compare Strategies Variables Base BCD+SS 2050 % Change BCD+EFD 2050 % Change BCD +HDD 2050 % Change Population % % % Urban Population % % % GDP (PPP) % % % GDP/ Capita PPP % % % Life Expectancy % % % Years of Schooling % % % No. on $1/Day % % % Imports(Millions $) % % % Exports (Millions $) % % %

42 Simulation with IFs SAM Report

43 Simulation with IFs Financial Report

44 Simulation with IFs Country Report

45 Simulation with IFs MDG Report

46 Simulation with IFs Poverty Report

47 Simulation with IFs

48 IFs: Final Comments IFs is available for free, currently funded by Fredrick S. Pardee who has given a $7.5 million to create the International Futures Centre at the University of Denver A lite version can be downloaded from our website Otherwise a CD is available on request IFs now has capability to break countries down to units. Already this is being done for China and India. We are creating IFs user groups and in creating an Africa user group. Everyone here is Welcome to join IFs Contacts us: bhughes@du.edu, jkariuki@du.edu,