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An introduction to creating a State of the Future Index. An index to forecast the trend of a country or regions future.
Citation preview
World Federation of United Nations AssociationsThe Millennium ProjectOctober, 2007
http://www.weforum.org/en/media/Latest%20Press%20Releases/voiceofthepeoplesurvey
http://www.worldpublicopinion.org/pipa/articles/brmiddleeastnafricara/165.php?nid=&id=&pnt=165&lb=brme
.
http://www.gallupworldpoll.com/content/?CI=28483
Is the future improving? Are people getting smarter? Will terrorism diminish? Will people have jobs? Will corruption abate? Will democracy spread? Will people have enough water and
food? Will women get fair treatment?
Human Development Index (UNDP) Corruption Perception Index
(Transparency International) Environmental Sustainability Index
(Center for International Earth Science Information Network (CIESIN))
Peace Index (The Tami Steinmetz Center for Peace Research ,Tel Aviv University)
Dow Jones Industrial Average (Dow Jones & Company)
What variables should be included? How can the variables be combined? How can the variables be forecast? How can the variables be weighted? How can double accounting be avoided?
What variables should be included? A Delphi study asking experts for advice on
important variables How can the variables be combined?
The variables are “normalized on a scale of 1 – 100
How can the variables be forecast? By using standard “best fit” curves
How can the variables be weighted? Using the Delphi judgments
How can double accounting be avoided? Careful scrutiny
Combining variables leads to loss of detail.
Judgments about what variables to include
Variable weightsCan mask variations among regions,
nations, or groups. Unwarranted apparent precisionSO…keep track of the variables
Please obtain the following Excel spreadsheet titled “2007 National SOFI
SpreadsheetB” Report: A Standardized Approach to Building
National SOFIs” The following reports are also
available: Report: Millennium Project Study of State of the
Future Index Variables and Their Use in Country to Country Comparisons (the Real Time Delphi)
Building the 2007 SOFI
Global
National Comparison
National Focus
1. Choosing the Variables2. Obtaining the Historical Data3. Extrapolating the Data4. Non-dimensionalizing the Variables5. Weighting the variables6. Best and Worst Values7. Surprise Free SOFI Computation8. Inputs to the Trend Impact Analysis9. Running a TIA10. Final SOFI Calculation
SHEET 1: HISTORY AND EXTRAPOLATIONS. THIS IS THE WORKSHEET THAT RECEIVES ALL NATIONAL HISTORICAL DATA AND FORECASTS OF THE VARIABLES.
THE DATA SHOWN HERE IS FOR EXAMPLE ONLY; THEY APPLY TO NO COUNTRY. PLEASE SUBSTITUTE YOUR DATA FOR THAT PRESENTED HERE.
Notes on the use of this spreadsheet: On this spreadsheet, you will enter the historical data for all your variables. You should obtain the equation for the best fit curve using other software
It is good practice to show all "hard" data in bold print. You can use this sheet to calculate future values and (interpolate) missing data points using the best fit equations which should be entered on rows 45-60.
Also please enter data sources for later reference on rows 44-45.
Variable Number >>>>
>>>>>>> 1 2
CO2 emissions (percent of global emissions)Energy produced from non fission, non fossil sources (percent of total primary national energy supply)
1985 1.700 13.122
1986 1.720 13.134
1987 1.740 13.146
1988 1.750 13.158
1989 2.000 13.170
Sheet Title
General noteson this sheet
Specific instructions
Operational portion
1 Infant mortality (deaths per 1,000 births) 2 Food availability (Calories/capita) 3 GDP per capita (constant 2000 US) 4 Improved water source (percent of population without access) 5 Carbon dioxide emissions (Metric tons per capita) 6 Population growth rate (percent per year) 7 Percent unemployment 8 Literacy rate, (percent of people aged 15 and above) 9 Prevalence of HIV (percent of population ages 15-49) 10 Life expectancy at birth (years)
11 Armed conflicts {number involving >1,000 deaths /yr) 12 Total Debt (percent of GDP: developing countries) 13 Forest Lands (% of land area) 14 People Living on Less than $1 per day) (% population) 15 People killed or injured in terrorist attacks (number) 16 Homicides (49 countries, per 100,000 population) 17 People in Free/ Partially Free Countries (% population) 18 School Enrollment, secondary (% school age) 19 Healthcare workers (per 1,000 population) 20 Countries having nuclear weapons or plans (number)
21 Energy produced from non fission, non fossil sources (percent of all energy produced) 22 R&D expenditures (percent of GDP) 23 Global Surface Temperature Anomalies (degrees C) 24 People voting in free elections (% voting age pop) 25 Internet Users (users/1000 population) 26 Number of refugees, asylum seekers, and internally displaced persons (millions) 27 Energy consumption per GDP (metric tons oil equivalent/million $) 28 Seats held by women in national parliaments (%)29 Corruption (% of world's people living in countries rated as having low levels of corruption)
All of the global variables except: Global Surface Temperature Anomalies Nuclear Proliferation Number of armed conflicts
Two changes: People in Countries that are Free becomes the
country’s freedom rating Corruption (% of world's people living in
countries rated as having low levels of corruption) becomes the country’s corruption .
All variables are chosen specifically for the country.
Some examples not on the National Comparison list: Size of in-country nuclear stockpile Number of our soldiers killed or wounded Tax rates Tourism
Annual data for past 20 years for each variable
Interpolate for missing data points and extrapolate 10 years using a best fit algorithm
Data sources should be: Continuing Reliable Transparent Accurate Primary, if possible
Record sources and definitions
Freedom House Inter parliamentary Union International Energy Agency Transparency International UN organizations such as UNDP, UNFCR,
UNAIDS, UNESCO, WHO, FAO, UNICEF, ILO US Census Bureau US Department of Energy, Energy
Information Agency World Resources Institute
1991 51.811999 60.282000 62.062001 63.852002 66.592003 66.622004 65.06
1. The given data
Quadratic Fit: y=a+bx+cx^2
2. Were fit by a quadratic equation
a=
-48409.94b =47.371804c =-0.01156779
3. Yielding the full set of data
The non dimensionalizing formula is:
X = (actual value of the variable– MIN)/(MAX – MIN )X = (actual value of the variable– MIN)/(MAX – MIN )
X = (actual value of the variable– MIN)/(MAX – MIN )
The Max/ Min Problem in SOFI When a country wants to compute its
SOFI it is not likely to have the maximum and minimum values of all other countries
The SOFI involves a projection of the history of the variables into the future and thus the present maximum and minimum values may not represent extremes.
The maximum value is the greater of the “best” estimate or the highest value over the 30 year period.
The minimum value is the lesser of
the “worst” estimate or the lowest value over the 30 year period.
Year Variable V1(Increasing is good)
Variable V2(diminishing is good)
20 years ago 30 3010 years ago 35 35Ten Years hence 42 10Extreme data point in desirable direction 42 10
Extreme data point in undesirable direction 30 30
Expert Best 43 8
Expert Worst 40 20
Year Variable V1 (non-
dimensionalized)
Variable V2 (non-
dimensionalized)20 years ago 0.00 0.1910 years ago 0.38 0.00Present Year 0.77 0.74Ten Years hence 0.92 0.93
Not all variables are of equal importance Weights give emphasis to the more
important variables SOFI uses a convention that simply
multiplies the non dimensionalized values by the weights.
Weights are taken to be constant for all values of a variable although this is only an approximation
See next chart for weights
[1] The Freedom House scale runs from 1 which means completely free to 7 which is the other end of the spectrum. In the global panel, the “best” and “worst” were expressed in terms of percentage of the world population living in countries rated as free, so that the best and worst shown here represent high expectations as chosen by the staff. Similarly, in the cases of CO2 emissions, Refugees, and People killed or wounded in terrorists attacks, the “best” and “worst” targets represent the staff’s judgments, based on the global study.
Best 2017)
Worst 2017
Weight
1 CO2 emissions (percent of global emissions) 0 25 7.82
2Energy produced from non fission, non fossil sources (% national energy supply)
20.52 13.68 8.05
3 Food availability (Kcalories/cap/day) 3,006 2,205 7.084 Forest Lands (percent of national land area) 32.03 25.02 7.215 Freedom Level (Country Score) 1 3 7.526 GDP per capita (constant 2000 US$) 9,983 5,491 7.50
7GDP per unit of energy use (constant 2000 PPP $ per kg of oil equivalent)
5.29 4.86 8.00
8 Homicides, intentional (per 100,000 population) 4.89 14.66 6.929 Infant mortality (deaths per 1,000 live births) 42.09 89.00 7.01
10 Internet Users (per 1,000 population) 577.36 192.45 7.9011 Levels of Corruption (as measured by TI surveys) 4.23 3.31 8.5712 Life expectancy at birth (years) 75.06 65.05 7.1413 Literacy rate, adult total (% of people aged 15 and above) 90.42 78.87 7.45
Best 2017)
Worst 2017
Weight
14 Number of refugees displaced from the country (%) 0 10 6.9315 People killed or injured in terrorist attacks (%) 0 0.1 7.6616 People Voting in Elections (% voting age) 70.0 50.0 7.1917 Physicians (per 1,000 people) 2.55 1.46 7.5018 Population growth (annual %) 1.0 1.54 7.2719 Population lacking access to improved water sources (%) 10.0 30.0 8.3320 Poverty headcount ratio at $1 a day (PPP) (% pop) 12.72 26.49 7.8421 Prevalence of HIV (percent of national population) 0.64 1.91 5.9722 R&D Expenditures (percent of national budget) 4.0 2.0 8.6323 School enrollment, secondary (percent gross) 79.35 59.15 8.0924 Seats held by women in national parliament (%) 23.79 14.27 6.7825 Total Debt Service (percent of GNI) 7.58 8.68 6.7926 Unemployment, total (% of national labor force) 5.00 15.00 8.28
SOFI = sum (wt x ndv)/ SOFI ref
Where SOFI is the value of the SOFI in a given yearSOFI ref is the SOFI in the reference yearwt is the weight assigned to a given variablendv is the non dimensionalized value of the variable in that year
DevelopmentProbability
by 2017
1 A nuclear accident such as Three Mile Island (causes many nuclear nations to de-nuclearize).
10
2 A very good, fast $150 laptop computer becomes available everywhere.
65
3 Advent of a “teachers without borders” movement (50,000 new teachers in the field)
30
4 A pandemic of the scale of HIV/AIDS 30
5 At least 10 countries introduce effective policies designed to increase birth rates
75
6 Automation and robotics increase productivity 25% to make “jobless" economic growth
50
A nuclear accident such as Three Mile Island (causes many nuclear nations to de-nuclearize).
Impact 3.00 3.00 -2.00Time 2.00 2.00 4.00
CO2%
Renew Food Forests Freedom GDP/C
2008 1.00 1 1.50 1.50 0.00 0.00 0.00 -0.502009 2.00 2 3.00 3.00 0.00 0.00 0.00 -1.002010 3.00 3 3.00 3.00 0.00 0.00 0.00 -1.502011 4.00 4 3.00 3.00 0.00 0.00 0.00 -2.002012 5.00 5 3.00 3.00 0.00 0.00 0.00 -2.002013 6.00 6 3.00 3.00 0.00 0.00 0.00 -2.002014 7.00 7 3.00 3.00 0.00 0.00 0.00 -2.002015 8.00 8 3.00 3.00 0.00 0.00 0.00 -2.00
Global SOFI National Comparison National Focus
Variables Standard setBased on global; same for all countries.
Newly chosen for the country
Historical dataGlobal data for last 2 decades
National data for last 2 decades
National data for last 2 decades
Best and Worst estimates
Chosen for global forecasts
Use global estimatesNew values for the new variables and the country
WeightsChosen for global forecasts
Use global estimatesNew values for the new variables and the country
TIA DevelopmentsChosen for global forecasts
Use global developmentsDevelopments important to the future of the country
TIA Development Probabilities
Estimated for global TIA developments
Use global TIA development probabilities
Global TIA values for global developments; new estimates for country specific developments
TIA Development Impacts
Estimated for global TIA developments and variables
Use TIA development impacts as they might affect the country
Use TIA development impacts as they might affect the country
The variable forecasts Domains of interest Good and bad trends Dynamic presentations
Intellectual Literacy, enrollments, R&D, Internet
Health Life expectancy, infant mortality, physicians, HIV, food
Wealth GDP/cap, unemployment, poverty, debt service
Security Terrorist attacks, nuc proliferation, refugees
Moral Corruption, freedom, voting, women in parliaments
Physical Water, CO2, forests, temperature, renewables
•Produce robust “enterprise level” software
•Review and utilize the "standard" for national SOFIs
•Construct and compare national SOFIs
•Conduct an analysis designed to find whether country SOFIs (weighted by population) add up to the global SOFI.
•Experiments with other applications (e.g. corporate SOFIs)
•Consider other dimensions (e.g. a measure of national innovativeness)
•Review and improve TIA judgments
•Construct on line data bases of variables and events to facilitate national and other applications.
Presented by Zhouying JIN, Chinese Academy of Social Sciences of the Beijing Academy of Soft Technology, July, 2006
Example of use of National SOFI for policy studies
Year 2000Year 2000
Long-term Strategy management and warning system
Zhouying JIN
Year 2020Year 2020
Long-term Strategy management and warning system
Zhouying JIN
Year 2050Year 2050
Long-term Strategy management and warning system
Zhouying JIN
Year 2050Year 2050
If we succeed in strategic, institutional change and corporate behavior transformation …………in China
optimistic
Zhouying JIN
The process is still in development and will benefit from your suggestions and feedback, so please send observations, questions, and descriptions of approaches you have tried.