PERU: 2050
November 15th, 2021 David Bohl
INTS 4579
Original Maps from: http://d-maps.com/pays.php?num_pay=150&lang=en The Frederick S. Pardee Center for International Futures
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Table of Contents EXECUTIVE SUMMARY 4
THE IFS MODEL: INTERNAL STRUCTURES 5
1 POPULATION 7 1.1 IMPORTANT VARIABLES 7 1.2 DATA SOURCES 7 1.3 EQUATIONS 8 1.4 CAUSAL DIAGRAM 9 2 ECONOMY 11 2.1 IMPORTANT VARIABLES 11 2.2 DATA SOURCES 12 2.3 EQUATIONS 13 2.4 CAUSAL DIAGRAM 14 3 EDUCATION 15 3.1 IMPORTANT VARIABLES 15 3.2 DATA SERIES 15 3.3 EQUATIONS 16 3.4 CAUSAL DIAGRAM 17 4 AGRICULTURE 18 4.1 IMPORTANT VARIABLES 18 4.2 DATA SERIES 18 4.3 EQUATIONS 19 4.4 CAUSAL DIAGRAM 20 5 ENERGY 21 5.1 IMPORTANT VARIABLES 21 5.2 DATA SERIES 21 5.3 EQUATIONS 22 5.4 CAUSAL DIAGRAM 23 6 DOMESTIC SOCIO-POLITICAL AND INTERNATIONAL POLITICAL 24 6.1 IMPORTANT VARIABLES 24 6.2 DATA SERIES 24 6.3 EQUATIONS 25 6.4 CAUSAL DIAGRAM 26 7 ENVIRONMENT 27 7.1 IMPORTANT VARIABLES 27 7.2 DATA SERIES 27 7.3 EQUATIONS 27 7.4 CAUSAL DIAGRAM 28 8 INFRASTRUCTURE 29 8.1 IMPORTANT VARIABLES 29 8.2 DATA SERIES 29 8.3 EQUATIONS 30 9 HEALTH 31 9.1 IMPORTANT VARIABLES 31 9.2 DATA SERIES 31
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9.3 EQUATIONS 32
FORECASTS 33
1 POPULATION 34 2 GDP AND AVERAGE GROWTH 35 3 GDP PER CAPITA 36 4 EXPORTS 37 5 INVESTMENT 38 6 TAX REVENUE 39 COMMENTS ON DATA FROM INEI AND BCRP 41 CONCLUSION 42 APPENDIX A: PREPROCESSOR VARIABLES 43 APPENDIX B: VARIABLE DEFINITIONS 73 APPENDIX C: ALTERNATIVE SOURCES 80 APPENDIX D: INDICATORS CURRENTLY FORECASTED BY IFS 81
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Executive Summary
The following pages are the first outline of what will be a report to be delivered to the Peruvian National Center for Strategic Planning (CEPLAN) as part of their ongoing “Bicentenary Plan”. The plan defines 31 specific goals across six strategic axes aimed at increasing growth, eliminating poverty, improving quality and access to education, healthcare, and infrastructure, as well as a strengthening of cultural and governmental values. Guidelines used by the Bicentenary Plan draw from the UN Declaration of Human Rights, the UN Millennium Development Goals, Amartya Sen’s concept of human development, and Peru’s National Agreement signed in 2002. 1,2 CEPLAN has asked the Pardee Center at the University of Denver to assess the IFs model’s capacity to forecast systems in Peru of interest to the Bicentenary study. This outline begins to address some of the preliminary requests made by CEPLAN. It pulls out some of the more important or relevant variables and series for the potential forecasting of Peru and does a surface comparison of which series could potentially be augmented by data provided by the Peruvian Central Reserve Bank or the Peruvian National Institute for Information and Statistics (INEI). Also included are equations (mostly quoted verbatim from the IFs Help system3) used in the model, as well as flow charts and causal diagrams for general overviews of the modules. Each of the following module sections contains two tables. The first lists the relevant variables by colloquial name, IFs system forecast variable name, and related historic series already existing in the model. The subsequent table again lists the colloquial name followed by the name and year of primary contributing source for both the datasets found in the IFs database and their Peruvian counterparts. To save space this report truncates some names; however, a complete alphabetized list with definitions can be found in Appendix B. The final section addresses a few of the targets set by CEPLAN in their Plan Bicentenario: El Perú hacia el 2021. Within the plan there are a number of specialized targets that can be explored later in greater detail, but the six listed below have been featured in the brief. These targets have been compared to a base case scenario computed by the IFs system.
1 Plan Bicentenario: El Perú hacia el 2021. Pages 1-7. 2 Bicentenary Plan: Peru in 2021 – Executive Summary. Pages 11, 13-14. 3 The IFs Help System can be found online at: http://www.ifs.du.edu/assets/help/WebHelp/ifshelp.htm
The IFs Model: Internal Structures
The International Futures model is a large-scale, long-term modeling system, integrating models across many human and environmental systems. This section explores many of the dominant relations and variables that comprise these modules by indicating the important variables and historical series utilized in the forecast. The Important Variables subsection indicates key variables that would be used in the analysis and forecast of the Peruvian situation. Forecast variables, listed in all capital letters, are evolved over time beginning at the year 2010, whereas the historical series are used in the longitudinal analysis of states, and to initialize the forecasts. The Data Sources subsection lists many of these variables, and indicates the original sources that have been brought together under the IFs database. The series listed below are a small subset of the more than 2,500 data series included in the IFs database. Next to the IFs sources are alternative sources that may be found on Peru’s Instituto Nacional de Estadística e Informática (INEI), or the Banco Central de Reserva del Perú websites. Appendix C offers further information for how to find these specific datasets. The Equations subsection indicates a few important equations for each module. Due to the vast interconnected nature of the model and its algorithms the equations listed may be shown in a simplified form. The Casual Diagrams subsection presents a flow chart of specific elements of each module in question. The links shown are examples from much larger sets. For further elaboration on the model’s equations and relationships please visit the IFs Help System.4
4 http://www.ifs.du.edu/assets/help/WebHelp/ifshelp.htm
Figure 1: Block diagram of major elements and links in the IFs Model. Links shown are examples from a much larger set.
1 Population
1.1 Important Variables
Name Variable Historical
Population POP Population
Urban Population POPURBAN PopulationUrban
Population In Rural Areas POPRURAL PopulationRural
Total Fertility Rate TFR TFR
Crude Birth Rate CBR CBR
Life Expectancy At Birth LIFEXP LifExpect
Crude Death Rate CDR CDR
Annual Net Migration MIGRATE PopMigration
1.2 Data Sources5
5 See Appendix C for links to INEI and BCR sources.
Name IFs Source IFs Years Alternate Source Years
Population WDI 2011
Online 1960-2010
INEI - Censos Nacionales de Población y Vivienda
1940,1961,1972,1981, 1993, 2007
Urban Population
WDI 2011 Online
1960-2010 INEI - Censos Nacionales de
Población y Vivienda 1940,1961,1972,1981,
1993, 2007 Population In Rural Areas
WDI 2011 Online
1960-2010 INEI - Censos Nacionales de
Población y Vivienda 1940,1961,1972,1981,
1993, 2007
Total Fertility Rate
WDI online 2011
1960-2009 INEI - Encuesta Demográfica y de Salud Familiar, (ENDES)
1996, 2000, 2004/2006,
2007/2008, 2009-2010 Crude Birth
Rate WDI online
2011 1960-2009
Life
Expectancy At Birth
WDI CD 2009
Mostly 2-3 years from 1960-2007
Crude Death Rate
WDI online 2011
1960-2009
Annual Net Migration
UN Population
Division
Every 5 years from
1950
INEI - Ministerio del Interior - Dirección General de
Migraciones y Naturalización 2000-2010
1.3 Equations Total Fertility Rate (TFR) To evolve TFR from the initial condition, the IFs model considers the influence of GDP per capita, changing income distribution, contraception use, an exogenous multiplier, and cultural or technological change.
In this equation TFRGDP is computed as a function of TFR and GDP per capita. INCSHR and EINCSHR are the income share and expected income share, and ENCONTRUSE is the expected level of contraception use as a function of GDP per capita. The most recent equation for total fertility rate differs from the earlier version presented above. Currently the equation also incorporates infant mortality, years of adult education, and income share. Crude Birth Rate (CBR), Crude Death Rate (CDR), and Population Growth Rate (POPR) Crude Birth Rate (CBR) and Crude Death Rate (CDR) determine the population growth rate of a state, where:6
and,
.
6 Summarized from IFs Help System.
1.4 Causal Diagram
Figure 2: Flow chart of overview of Population Model
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1.4.1 Stocks and Flows
The above flow chart7 illustrates the drivers of population change. The stock of population is affected by three primary factors: births, deaths, and migration. If considering global population, migration has zero net effect. Births increase the population, while at the same time higher population will increase the raw number of births. Similarly, deaths will decrease the stock of population, and decreasing the population will decrease the number of deaths. These recurrent relationships illustrate positive and negative feedback loops respectively.
7 Verbatim from “Forecasting Change and Development with IFs” Session 1 Power Point.
Stocks and Flows7
Stocks are stores, accumulation over time Flows are specific to time point – may add to or decrement stocks Example stocks and associated flows:
o Population with births, deaths, migration o Capital with investment, depreciation o Energy resources with production o Knowledge with discovery/learning and forgetting o Culture with adoption/invention and discarding
2 Economy
2.1 Important Variables
Name Variable Historic
Gross Domestic Product GDP GDP2005
GDP Per Capita GDPPC (MER)
GDPPCP (PPP)
Government Expenditures GOVEXP FORMULA
Government Consumption GOVCON GovCon%GDP
Military Expenditures GDS GovtMil%GDPWDI
Health Expenditures
GovtHl%GDP
Educational Expenditures
GovtEdPub%GDP
R&D Expenditures
R&Dgovt%GNP
Total Infrastructure Expenditures
GovtInfraTotEx%GDP
Government Revenue GOVREV FORMULA
Multifactor Productivity MFP
MFP From Human Capital MFPHC
MFP From Social Capital MFPSC
MFP From Physical Capital MFPPC
MFP From Knowledge MFPKN
Labor Participation Rate LAPOPR
Portion Of Labor Force Made Up By Women
FEMSHRLAB LaborFemale%
Household Final Consumption C HouseCon%GDP
Investment, Global Capital Formation
IGCF%GDP
Imports By Sector MS
Exports By Sector XS
Net Foreign Aid AID FORMULA
Exchange Rate Index EXRATE
Foreign Direct Investment XFDISTOCK
Value Added In Agriculture VADD VAddAg%
Value Added In Manufacturing
VAddMan%
Value Added In Industry
VAddInd%
Value Added In Services
VAddSer%
Value Added In ICT
VAddICT%
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2.2 Data Sources
Name IFs Source IFs Years Alternate Source Years
Gross Domestic Product
WDI 2011 Online 1960-2012 INEI and BCR. 1950-2011
Government Expenditures
MEF, Banco de la Nación and BCRP.
1970-2011
Government Consumption
WDI CD 2010 1960-2008 MEF, Banco de la
Nación, Multiple Sources 1970-2011
Military Expenditures WDI 2011 Online 1988-2010
Health Expenditures WDI 2011 Online
Database 1991-2009
Educational Expenditures
WDI 2011 Online Database
1960, 1965, 1970-1996, 1998-2010
R&D Expenditures R&D OECD 2000 Basic Science and Tech Stats
1981-2000
Total Infrastructure Expenditures
OECD STAN Database, Multiple other sources
1985-2006
Government Revenue
MEF, Banco de la Nación, BCRP, Sunat,
Aduanas, Enci, Ecasa and Petroperú.
1970-2011
Portion Of Labor Force Made Up By
Women WDI CD 2010 1960-2008
Household Final Consumption
WDI CD 2010 1960-2008
Imports By Sector
BCRP, SUNAT, Zofratacna and Banco de
la Nación. 1950-2011
Exports By Sector
BCRP, SUNAT and Customs.
1950-2011
Value Added In Agriculture
WDI CD 2010 1960-2008 INEI and BCR. 1950-2011
Value Added In Manufacturing
WDI CD 2010 1960-2008 INEI and BCR. 1950-2011
Value Added In Industry
WDI CD 2010 1960-2008 INEI and BCR. 1950-2011
Value Added In Services
WDI CD 2010 1960-2008 INEI and BCR. 1950-2011
Value Added In ICT OECD Measuring ICT
Sector 1997 INEI and BCR. 1950-2011
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2.3 Equations The Production Function The IFs model used a Cobb-Douglass production function:
where Y is total production, K is capital, L is labor, and are the output elasticities of capital and labor respectively, and MFP is multifactor productivity. The multifactor productivity term is comprised of the following:
The component terms are the contributions to multifactor productivity from Human Capital, Social Capital, Physical Capital, and Knowledge.8
8 Summarized from “Forecasting Change and Development with IFs” session 3 Power Point.
2.4 Causal Diagram
3 Education
3.1 Important Variables
3.2 Data Series
Name IFs Source IFs Years Alternate
Source Years
Primary, Net Enrollment Rate,
Male
UNESCO Institute for Statistics, WDI for
previous years 1970, 1975, 1980-2010
Total (not by gender)
Primary, Net Enrollment Rate,
Female
UNESCO Institute for Statistics, WDI for
previous years 1970, 1975, 1980-2010
Total (not by gender)
Secondary, Enrollment Rate, Net,
Male
UNESCO Institute for Statistics
1998-2011 Total (not by
gender)
Secondary, Enrollment Rate, Net,
Female
UNESCO Institute for Statistics
1998-2011 Total (not by
gender)
Tertiary, Gross Enrollment Rate,
Male
UNESCO Institute for Statistics; WDI
1960, 1965, 1970, 1975, 1980-2010
Total (not by gender)
Tertiary, Gross Enrollment Rate,
Female
UNESCO Institute for Statistics; WDI
1960, 1965, 1970, 1975, 1980-2010
Total (not by gender)
Name Variable Historic
Primary, Net Enrollment Rate, Male
EDPRIENRN EdPriEnrollNetMalePcnt
Primary, Net Enrollment Rate, Female
EdPriEnrollNetFemalePcnt
Secondary, Enrollment Rate, Net, Male
EDSECENRN EdSecEnrollNetMale
Secondary, Enrollment Rate, Net, Female
EdSecEnrollNetFemale
Tertiary, Gross Enrollment Rate, Male
EDTERENRG EdTerEnrollGross%Male
Tertiary, Gross Enrollment Rate, Female
EdTerEnrollGross%Female
Average Adult Years Of Schooling, Male
EDYRSAG15 EdYearsAge15Male
Average Adult Years Of Schooling, Female
EdYearsAge15Female
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Average Adult Years Of Schooling, Male
http://www.barrolee.com 1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985,
1990, 1995, 2000, 2005, 2010
Total or Female by Region
Average Adult Years Of Schooling, Female
http://www.barrolee.com 1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985,
1990, 1995, 2000, 2005, 2010 By Region
3.3 Equations Average Years of Education The average years of education (EDYRSAG25) is an average of all the accumulated years of schooling in the system for a population older than 25. Currently the equation is:
EdPriPerAg25, EdSecPerAg25, and EdTerPerAg25 is the percent of the population over the age of 25 who have achieved primary, secondary, and tertiary levels of education respectively. The term PartialYearsTotal corrects for students who dropped out before completing a level of education, and is calculated by totaling the partial years from each level:9
9 Summarized from IFs Help System.
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3.4 Causal Diagram
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4 Agriculture
4.1 Important Variables
Name Variable Historical
Land Area LANDAREA LandTotal
Land, Crop LD (Crop) LandCrop
Land, Grazing LD
(Grazing) LandGrazing
Land, Forest LD (Forest) LandForest
Land, Other LD (Other) LandOther
Land, Urban And Built-Up Areas
LandUrban&Built
Agricultural Demand AGDEM Crop Production AGP (Crop) AgProd10
Meat Production AGP (Meat) AgProdMeat
Root And Tuber Production
AgProdRootsTub
Production Of Fruit, Excluding Melons
AgProdFruitsExclMelons
Vegetable, Melon Production
AgProdVegMel
Cereal Imports AGM AgCerealsIm
Fruit, Vegetable Imports
AgFruVegIm
Cereal Exports AGX AgCerealsEx
4.2 Data Series Name IFs Source IFs Years Alternate Source Years
Land Area FAO Stat 1961-2009
Land, Crop FAOSTAT 1961-2008 Ministerio de Agricultura -
Dirección General Forestal y de Fauna
1975, 1995, 2000
Land, Grazing FAOSTAT 1961-2008
Land, Forest FAO, WDI
2005 1961-2009
Ministerio de Agricultura - Instituto Nacional de Recursos Naturales
1975, 1995, 2000
Land, Other FAOSTAT 1961-2008
Land, Urban And Built-Up Areas
WRI Earthtrends
1992
Cereal Production FAOSTAT 1961-2010 Ministerio de Agricultura 1983-2012
10 Historical crop production can be further broken down into Cereals, Fruits, Pulses, Roots and Tubers, and Vegetables.
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Root And Tuber Production
FAOSTAT 1961-2010 Ministerio de Agricultura 1983-2012
Production Of Fruit, Excluding Melons
FAOSTAT 1961-2010 Ministerio de Agricultura 1983-2012
Vegetable, Melon Production
FAOSTAT 1961-2010 Ministerio de Agricultura 1983-2012
Cereal Imports FAOSTAT 1961-2009
Fruit, Vegetable Imports
FAO STAT 1961-2009
Cereal Exports FAOSTAT 1961-2009
Fruit, Vegetable Exports
FAO Stat 1961-2009
4.3 Equations Agricultural Production Agricultural Production (AP) is determined by Agricultural Yield (YD) and land devoted to crops (LD) by the equation:
Yield is the product of a basic yield (BYL), representing a long-term tendency in agricultural production levels, and an adjustment term (ADJSTR) which is a function of agricultural demand (AGDEM) and changes in stock (FSTOCK).
The basic yield is a product of capital in agriculture (KAG), labor (LABS), technological advance (AGTECH), and scaling parameter (CD), and an exponent (CDALF).
where SATK is a saturation coefficient intended to produce decreasing marginal returns.11
11 Summarized from IFs Help system.
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4.4 Causal Diagram
21
5 Energy
5.1 Important Variables
Name Variable Historic
Production Of Oil ENP EnProdOilBP
Natural Gas Production
EnProdGasBP
Coal Production
EnProdCoalBP
Production Of Hydro Power
EnProdHydroCDIEA
Oil Consumption ENDEM EnConOilBP
Natural Gas Consumption
EnConGasBP
Coal Consumption
EnConCoalBP
Hydroelectricity Consumption
EnConHydroBP
Oil Reserves RESER EnReserOil
Gas Reserves
EnReserGas
Coal Reserves
EnReserCoal
Hydro Reserves
EnReserHyd
Undiscovered Oil Resources
EnREsorOilUSGS
Undiscovered Liquid Gas Reserves
EnREsorNGLUSGS
Coal Resources
EnResorCoal
5.2 Data Series Name IFs Source IFs Years Alternate Source Years
Production Of Oil BP's Statistical Review of
World Energy 2011 1965-2010
Ministerio de Energia y Minas - PERUPETRO
1989-2012
Gas (Natural) Production
BP's Statistical Review of World Energy 2011
1970-2010 Ministerio de Energia y Minas - PERUPETRO
1989-2012
Coal Production BP's Statistical Review of
World Energy 2011 1981-2010
Production Of Hydro Power
Beyond 20/20 Browser CD Release 7.0.2491 (32)
1960-2009
Oil Consumption BP's Statistical Review of
World Energy 2011 1965-2010
Gas (Natural) Consumption
BP's Statistical Review of World Energy 2011
1965-2010
Coal Consumption BP's Statistical Review of
World Energy 2011 1965-2010
Hydroelectricity Consumption
BP's Statistical Review of World Energy 2011
1965-2010
Energy Reserve, Oil, In Billion Barrels
WEC; Oil and Gas Journal; 1960 estimated
1952-2012 Ministerio de Energía
y Minas - Dirección General de Minería
2000-2008
22
Energy Reserves, Gas
WEC; Oil and Gas Journal; 1960 estimated
1960, 1967-2012
Ministerio de Energía y Minas - Dirección General de Minería
2000-2008
Energy Reserves, Coal
WEC 1960, 1999,
2005
Energy Reserves, Hydro
WRI Annual 1960, 1999
Undiscovered Energy Resources,
Oil
U.S. Geological Survey World Petroleum Assessment 2000
2000
Undiscovered Energy Resources,
Natural Gas Liquids
U.S. Geological Survey World Petroleum Assessment 2000
2000
Energy Resources, Coal
WEC 1999
5.3 Equations Energy Production (ENP) Energy production is the quotient of capital in each energy category (KEN) and the appropriate capital-to-output ratio (QE). The model user can modify a multiplier to this ratio (QEM) to represent changes in technology. The capital-to-output ratio is itself a function of resource availability. Known reserves (RESER) pose a direct constraint on production, however. Specifically, the reserve-to-production ratio may not fall below a specified factor (PRODTF). In the case of oil and gas, for example, no more than about 10% of known reserves can be produced in a given year. (This is similar to the assumption of the Stanford Pilot Model, Stanford University, 1978). Within the reserve constraint, the user can force increases or decreases in production via an energy production multiplier (ENPM). A capacity utilization factor (CPUTF) also affects the production level.
The real dynamics of supply in IFs occur in energy investment, to be discussed below. In
representing investment dynamics IFs differs from most energy models; the approach here
is similar to that of Naill (1977).
23
Once production is computed it is possible to compute a world average price (WEP),
weighted by energy production (ENP) in each category and each region.12
5.4 Causal Diagram
12 Verbatim from IFs Help system.
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6 Domestic Socio-Political and International Political
6.1 Important Variables Name Variable Historical
Freedom House Index FREEDOM Freedom
Economic Freedom FREEDOMECON FreedomEcon
Polity Democracy Index DEMOCPOLITY PolityDemoc
Polity Project'S Combined Measure
PolityCombined
Gender Empowerment GEM GEM
Government Effectiveness GOVEFFECT GovernanceEffect
Government Corruption Perception GOVCORRUPT Corruption
Governance Quality GOVREQUAL GovernanceRegQual
Military Expenditures GDS GovtMil%GDPWDI
Risk Index GOVRISK
Consolidated Event Occurrence SFINTWARAL SFPITFConsolidatedEv
Consolidated Event, Maximum Magnitude
SFINTLWARMAG SFPITFConsolidatedMag
Governance Security Index GOVINDSECUR
Governance Capacity Index GOVINDCAPAC
Governance Inclusiveness Index GOVINDINCLUS
Relative Material Power POWER RelativeMaterialPower
Threat Index As Probability of Militarized Dispute
THREAT
6.2 Data Series Name IFs Source
Freedom House Index Freedom House
Economic Freedom Fraser International
Polity Democracy Index Polity Project
Polity Project's Combined Measure Polity Project
Gender Empowerment Measure Of The UNDP
UNDP HDR
Government Effectiveness World Bank
Government Corruption Perception Transparency International
Governance Quality World Bank
Military Expenditures As Percent Of GDP
WDI 2011 Online
Consolidated Event Occurrence State Failure Project
Consolidated Event, Maximum Magnitude
State Failure Project
25
Relative Material Power Jonathan Moyer from Herman and Hillebrand
6.3 Equations State Instability Intrastate conflict used by the IFs system is modeled as a function of social, economic, and political drivers.
where
and
SFINTLWAR = Internal war or state failure INFMOR = Infant mortality, normed globally X = Exports in billions of dollars M = Imports in billions of dollars GDP = Gross domestic product in billions of dollars POLITYDEMOC = Polity’s scale of democracy YTHBULGE = Population aged 15-25 as portion of adults GDPRMA = GDP moving average carrying forward 60% past year’s value SFINTLWARMA = State failure, moving average sfintlwarm = Exogenous multiplier for model user13
13 Verbatim from “Forecasting Change and Development with IFs” Session 6 Power Point.
26
6.4 Causal Diagram
27
7 Environment
7.1 Important Variables
7.2 Data Series Name IFs Source IFs Years Alternate Source Years
Annual Carbon Emissions
Carbon Dioxide Information
Analysis Center 1960-2000
Annual Average Precipitation Change
UCAR 1999
Annual Average Temperature Change
UCAR 1999 Provincial - Servicio Nacional de Meteorología e Hidrología
1995-2010
Cereal Yield FAOSTAT 1961-2010 Absolute by crop - Ministerio
de Agricultura 2001-2009
Annually Renewable Water Resources
WRI Earthtrends 1962-2009 River and Lake - SEDAPAL 1991-2010
Annual Water Withdrawals
WRI Earthtrends 1990-2000
7.3 Equations Water Use (WATUSE) IFs calculates the water use per capita (WATUSEPC) and the total water use (WATUSE) for each model region. The biggest water use for most countries is agricultural (on a global basis 65% of freshwater use, according to Postel, 1996: 13). IFs uses a table function that relates change in per capita use to change in agricultural production per capita.14
14 Verbatim from IFs Help System.
Name Variable Historical
Annual Carbon Emissions
CARANN EmissionsCarbonCDIAC
Annual Average Precipitation Change
ENVPRCHG EnvPrecipitationChg
Annual Average Temperature Change
ENVTPCHG EnvAvgAnnTempChg
Agricultural Annual Yield Change
ENVYLCHG
Annually Renewable Water Resources
WaterAnRenResources
Annual Water Withdrawals
WaterAnWithdrawals
28
7.4 Causal Diagram
29
8 Infrastructure
8.1 Important Variables
Name Variable Historical
% Of Urban Population With Access To Electricity
INFRAELECACC Enelecaccess%Urban
% Of Rural Population With Access To Electricity
Enelecaccess%Rural
Electricity Generation Capacity INFRAELECGENCAP FORMULA
Electricity Transmission Loss INFRAELECTRANLOSS EnElecTransLoss%
Road % Paved INFRAROADPAVEDPCNT RoadsPaved%
Road Rural Access Index INFRAROADRAI RoadRuralAccessIndex
Road Density INFRAROAD FORMULA
Mobile Broadband Usage ICTBROADMOBIL ICTBroadbandMobileSubsPer100
Broadband Usage ICTBROAD ICTBroadbandSubscribersPer100ITU
Mobile Phone Usage ICTMOBIL ICTTelephoneCellSubscribersPer100
Fixed Telephone Lines Per 100 Inhabitants
INFRATELE ICTTelephoneLinesPer100
Sanitation SANITATION WSSJMPSanitationTotal%Improved
Water Safety WATSAFE WSSJMPWaterTotal%OtherImproved
8.2 Data Series
Name IFs Source
% Of Urban Population With Access To Electricity
IEA
% Of Rural Population With Access To Electricity
IEA
Electricity Generation Capacity
Electricity Transmission Loss
WDI 2012 Online
Road % Paved WDI CD 2012
Road Rural Access Index The World Bank Rural
Access Index
Road Density
Mobile Broadband Usage ITU 2010 Database; 2010
are estimates
Broadband Usage ITU 2011
Mobile Phone Usage ITU 2011
Fixed Telephone Lines Per 100 Inhabitants
ITU 2011
30
Sanitation WHO/UNICEF JMP
Water Safety WHO/UNICEF JMP
8.3 Equations The IFs model forecasts the demand for total road density as a function of income density (GDP per unit land area), population density, and land area using the following equation:
ln INFRAR AD = 2. 3 0. 3 ln income density 0.1 3 ln population density 0.102 ln ANDAR A
where
INFRAROAD is total road density in kilometers per thousand hectares, income density is measured at PPP in year 2000 dollars per hectare, population density is measure in persons per hectare, and LANDAREA is total land area in million hectares.
The demand for paved road percentage is forecasted as a function of per capita income, population, land area, and road density:
where
INFRAROADPAVEDPCNT is the percentage of total roads that are paved, GDPPCP is average income at PPP in thousands of year 2000 dollars, POP is total population in million persons, LANDAREA is total land area in million hectares, and INFRAROAD is total road density in kilometers per thousand hectares
Finally, rural road access is forecasted as a function of income density (GDP per unit land area) and paved road density (paved roads per person):
where
INFRAROADRAI is the rural road access index, income density is measured at PPP in year 2000 dollars per hectare, and paved roads per person are measured in kilometers per millions persons.15
15 Verbatim from PPHP Volume 4 Manuscript.
31
9 Health
9.1 Important Variables
Name Variable Historic
Deaths DEATHCAT HealthDiarrhoeaDth*
Malarial Deaths Per Year
HealthMalar*
Diabetes Deaths Per Year
HealthDiabetes*
Aids Realated Deaths Per Year
HealthHIV*
Smoking Prevalence
HealthSmoking*
Childhood Obesity
HealthObesity*
Infant Mortality Rate INFMOR InfMort
Years Of Life Lost To Communicable Diseases
HLYLL HealthYLLComDis%
Years Of Life Lost To Injuries
HealthYLLInjuries%
Years Of Life Lost To Non-Communicable Diseases
HealthYLLNonComDis%
Years Of Living With Disability
HLYLD
9.2 Data Series
Name IFs Source IFs Years Alternate Source Years
Deaths WHO Department of
Public Health and Environment
1960-2009
Infant Mortality Rate WDI 2011 Online
Database 1960-2010
INEI - Encuesta Demográfica y de Salud
Familiar, (ENDES) 1995-2010
Malarial Deaths Per Year
United Nations Statistics Division
1990-2007
Diabetes Deaths Per Year
International Diabetes Federation's Diabetes
Atlas 2003-2012
Aids Related Deaths Per Year
UNAIDS 1970-2012
Tobacco Smoking Prevalence
WHO Statistical Information System
1977-2008
Childhood Obesity WHO Statistical
Information System 1976-2003
Years Of Life Lost To Communicable
Diseases
WHO Statistical Information System
2002
32
Years Of Life Lost To Injuries
WHO Statistical Information System
2002
Years Of Life Lost To Non-Communicable
Diseases
WHO Statistical Information System
2002
9.3 Equations Global Burden of Disease (GBD) The GBD is a quantitative approach to look at the impact of disease as compared to an ideal level of global health. The unit of measure for the GBD is the Disability Adjusted Life Year (DALY), and is defined as:
where YLL is years of life lost relative to the globally oldest population, and years of life lost to disability (YLD). From the GBD project, mortality level (M) for a given age group (a), sex (k) cause (i), and country or region (R), can be calculated by the following equation:
where Y is GDP per capita, HC is total years of adult education (25 years or older), T is time (year – 1900), and SI is smoking impact.16
16 Summarized from “Forecasting Change and Development with IFs” session Power Point.
33
Forecasts
We start from the idea that Peru is a partially developed country enjoying rapid economic growth. Based on this premise, in quantitative terms achievement of the national strategic objectives of the Bicentenary Plan must be translated into the following indices by 2021:
- CEPLAN: Bicentenary Plan Executive Summary (page 19)
Our understanding of the dynamics of human systems is increasing rapidly, and this increasing sophistication is reflected in IFs, which endogenizes more variables than any other global forecasting model. This allows us to better consider the relationships between agent classes and structures, and explore how our policy decisions can affect their coevolution. Forecasting in IFs enables us to explore global trends to examine where they appear to be leading us. The model also offers us tools to clarify goals and priorities and to develop alternative scenarios about the future.
The following sections compare C P AN’s Bicentenary Plan core targets with the IFs, business-as-usual, base case. To avoid currency conversions and recalculating base years, the 2010 CEPLAN figures have been pegged to IFs data and the targets have been adjusted appropriately, unless otherwise noted.
1 Population “A population of 33 million Peruvians without extreme poverty, unemployment, poor nutrition, illiteracy or infant mortality…” (19)
A base case population forecast for Peru puts the 2021 population at 34.3 million. This is nearly % higher than C P AN’s goal. While C P AN’s figure might be incidental and perhaps not even a goal it serves as an important reference to consider with future forecasts.
25
27
29
31
33
35
37
39
41
43
Mil
lio
ns
of
Pe
op
le
Year
Population Forcast
IFs Base Case CEPLAN Goal
Figure 3: Population of Peru, IFs base case forecast to 2050 with CEPLAN's Bicenterary Plan objective of 33 million.
2 GDP and Average Growth “Gross domestic product that has doubled between 2010 and 2021…” “Average annual growth of around 6%...” (19)
IFs data lists Peru’s GDP (at M R) in 2010 at $112.2 million. C P AN’s goal requires an increase of the same amount over 11 years. At 2012 IFs forecasts Peru’s GDP to be $20 . million. This is 6.6% lower than the goal. However, over the last decade Peru has experienced substantial growth. While IF’s forecasts a decline in annual growth into the future, if the country can sustain these higher levels the target may be achieved with little intervention.
-15
-10
-5
0
5
10
15
0
100
200
300
400
500
600
700
800
19
60
19
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76
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80
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84
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88
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92
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96
20
00
20
04
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08
20
12
20
16
20
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20
24
20
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20
32
20
36
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20
44
20
48
Gro
wth
Ra
te
GD
P a
t M
ER
(B
illi
on
$2
00
5)
Year
GDP Forecast IFs Base Case CEPLAN Goal Growth Rate Forecast CPLAN Avg Growth Goal
Figure 4: Gross Domestic Product forecast.
3 GDP per Capita “Per capita income between US$8000 and US$10000…” (19)
IFs base case shows a steady increase in GDP per capita beginning with the initializing year. However, by 2021 the forecasted GDP per capita falls $1, below C P AN’s lower goal of $8,000. It should be noted that the IFs forecast and CEPLAN target are in 2005 and 2008 US dollars respectively, however correcting for inflation only brings the forecasted GDP per capita to $6,793.
0
5
10
15
20
19
60
19
63
19
66
19
69
19
72
19
75
19
78
19
81
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84
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87
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90
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93
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96
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99
20
02
20
05
20
08
20
11
20
14
20
17
20
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20
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50
GD
P p
er
Ca
pit
a a
t M
ER
(T
ho
usa
nd
s
$2
00
5)
Year
GDP per Capita Forecast CEPLAN Goal IFs Base Case
Figure 5: GDP per capita Forecast
4 Exports “Exports that have quadrupled between 2010 and 2021…” (19)
Quadrupling exports by 2012 requires an increase from $27.93 billion to $111.72 billion over 11 years. IFs does not forecast the accelerated output necessary to accomplish this increase for another twenty years.
0
50
100
150
200
250
Ex
po
rts
(Bil
lio
ns
US
D)
Year
Total Export Forecast
IFs Base Case CEPLAN Goal
Figure 6: Total Export Forecast
5 Investment “Average annual investment rate of 2 %...” (19)
The base case scenario forecasts that without intervention Peru should be able to sustain investments of at least 25 percent of GDP for the next few decades.
0
5
10
15
20
25
30
35
Inv
est
me
nt
as
pe
rce
nt
of
GD
P
Year
Investment Forecast
CEPLAN Goal IFs Base Case
Figure 7: Investment forecast
6 Tax Revenue “Annual average tax revenue 5 points higher, with respect to GDP…” (19)
Figure 8: Government revenue as percent of GDP. Historical figures from are BCRP and IFs database. Forecast is a calculation from revenue and GDP.
40
Data from IFs for government revenues as percent of GDP for Peru is consistently higher than that of the Peruvian Central Reserve Bank. This discrepancy could possibly arise from the fact that the IFs historical series takes into account central government revenue, whereas the IFs forecast also includes local revenues.
Comments on data from INEI and BCRP Most of the immediately accessible datasets from INEI and BCR Peru cover a limited time-span or are broken down into units that cannot be aggregated in a fashion useful to an IFs analysis. A strong point of the data found from the INEI website, though again not for this project, is that it offers quite a bit of data on regional levels. The Central Reserve Bank does have extensive records on trade accounts, which may be of particular use for an analysis of the country’s resource dependency on mining exports. Migration may also be another point to follow up on, since as of 2005 over 3% of the country’s GDP came from workers remittances, and to look further into the question of the brain drain.
Conclusion
The Bicentenary Plan defines 263 strategic actions over six primary strategic axes.17 A strategic plan of this magnitude inherently recognizes the interconnected nature of the systems and issues facing growth and development. All policy must be informed by forecast of one form or another; the IFs model is uniquely situated to aid in the analysis of C P AN’s goals for Peru, 2021 and beyond. Exploring where Peru has been and where it appears to be going is an essential element in thinking about how it may achieve its goals. As indicated throughout this review, the IFs database already includes extensive historical data on Peru, the region, and the world. As substantial as this database is, there are a few areas in which it may be augmented by data from Peru’s National Institute for Statistics and Informatics and the National Reserve Bank of Peru, such as migration and trade accounts. The IN I and BCR’s regional breakdowns of various datasets could also be of use for a department based analysis of the country. The future of Peruvian citizens, firms, industry, and government will only become more tightly intertwined, and as Peru continues on the trend of increasing trade and integration, consideration of its position in global systems also becomes of greater significance. The IFs model strives to represent these relationships as part of a larger system, more complicated and interconnected than most conventional models can capture, so that we may begin to understand them on a more fundamental level. What impact will growing mineral exports have on malnourished children by 2021? Exploring these questions can be used to inform a set of policies whose coordinated implementation can offer greater benefits. With an eye to the future, a widespread approach to managing these issue areas is the best approach to shaping the country’s development. C P AN’s numerous actions and indicators illustrate an understanding and deliberation of the importance of this fact. The IFs model is well suited to forecast many of the specific indicators in each of the six strategic axes.18 Comparing the IFs base case with some of the core quantitative aims of the Bicentenary Plan may reveal issue areas that require more or less attention in order to meet the specific goals by 2021. For example, IFs forecasts indicate that Peru is on track with C P AN’s 2021 targets for GDP, growth rate, and investment rate, whereas the goals for increased exports and GDP per capita will require more intervention. While there are many specific targets and indicators included in the Bicentenary Plan that are not supported in the model, as a tool, the IFs system can augment an analysis of the broader goals, to help shape realistic expectations for a reasonable time horizon.
17 Bicentenary Plan: Peru in 2021 – Executive Summary. Page 11. 18 See Appendix D for a list of indicators the IFs model can forecast currently.
Appendix A: Preprocessor Variables
Variable Definition Group SubGroup Years Source
AgGrainLiv%GrainCon
Grain fed to livestock as % of total grain consumption
Agriculture Consumption
1960-2007 WRI online 2012
AgFishAquaInland
Aquaculture, inland Agriculture Production
1950-2005 WRI Earthtrends http://earthtrends.wri.org/
AgFishAquaMarine
Aquaculture, marine fish catch
Agriculture Production
1950-2005 WRI Earthtrends http://earthtrends.wri.org/
AgFishFreshwaterCatch
Freshwater fish catch Agriculture Production
1950-2005 http://earthtrends.wri.org/text/coastal-marine/variables.html
AgFishMarineCatch
Marine fish catch Agriculture Production
1950-2005 http://earthtrends.wri.org/text/coastal-marine/variables.html
AgProdCereals Cereal production Agriculture Production
1961-2010 FAOSTAT
AgProdFruitsExclMelons
Production of fruit, excluding melons
Agriculture Production
1961-2010 FAOSTAT
AgProdMeat Meat production Agriculture Production
1961-2009 WRI Earthtrends http://earthtrends.wri.org/
AgProdPulses Pulses production Agriculture Production
1961-2010 FAOSTAT
AgProdRootsTub
Root and tuber production Agriculture Production
1961-2010 FAOSTAT
AgProdVegMel Vegetable, melon production Agriculture Production
1961-2010 FAOSTAT
LandIrPotentialReached
Irrigation potential (1000 ha) for countries that reached the potential already
Agriculture, Infrastructure
Irrigation 2009 AQUASTAT, at http://www.fao.org/nr/water/aquastat/dbase/index.stm
LandPcntForest Proportion of Land Area Covered by Forest
Agriculture, Infrastructure
Land 1990-2008 FAOSTAT
LandIrPotential Irrigation potential (1000 ha)\r\n
Agriculture, Infrastructure
No Sub Category
1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002, 2005, 2007-2008
AQUASTAT, at http://www.fao.org/nr/water/aquastat/dbase/index.stm
44
LandArea Land Area Agriculture, Infrastructure, Environment
Land 1961-2010 WDI
LandAgri
Agricultural Land Area, sum of arable land, permanent cropland and permanent meadows and pastures
Agriculture, Infrastructure, Environment
1961-2008 FAOStat
LandIrAreaEquipFAO
Land Area Equipped for Irrigation
Agriculture, Infrastructure, Environment
1961-2009 FAOStat
AgCerealsEx Cereal exports Agriculture, Trade
Trade 1961-2009 FAOSTAT
AgCerealsIm Cereal imports Agriculture, Trade
Trade 1961-2009 FAOSTAT
AgFruVegEx Fruit, vegetable exports Agriculture, Trade
Trade 1961-2009 FAO Stat
AgFruVegIm Fruit, vegetable imports Agriculture, Trade
Trade 1961-2009 FAO Stat
AgMeatEx Meat exports Agriculture, Trade
Trade 1961-2009 FAOSTAT
AgMeatIm Meat imports Agriculture, Trade
Trade 1961-2009 FAOSTAT
AgPulsesEx Pulse exports Agriculture, Trade
Trade 1961-2009 FAOSTAT
AgPulsesIm Pulseimports Agriculture, Trade
Trade 1961-2009 FAOSTAT
GDP1995PPPWDIFilled
GDP at purchasing power parity in 1995 dollars
Economic Aggregate 1975-2002 WDI CD 04 filled with earlier IFs data from 2000PPP
GDP2000PCPPP GDP per capita (constant 2000 PPP International $)
Economic Aggregate 1960-2005 CIA and constructed (original mostly World Bank)
GDP2003PPP GDP (PPP) Economic Aggregate 1950, 1955, 1960-2025
CIA (original partly World Bank); extended by Evan Hillebrand
GDP95 Gross Domestic Product Economic Aggregate 1960-2002 Constructed, multiple sources including WDI
GDPCurDol Gross Domestic Product in Current US$
Economic Aggregate 1960-2008 WDI CD 2010
GovCon%GDP Government (general) final consumption as % of GDP
Economic Aggregate 1960-2008 WDI CD 2010
45
HouseCon%GDP
Household final consumption expenditure as percent of GDP
Economic Aggregate 1960-2008 WDI CD 2010
InvestGrCapForm%GDP
Gross capital formation (Investment), percent of GDP
Economic Aggregate 1960-2008 WDI CD 2010
AidRec%GNI Official development assistance and official aid, net, % of GNI
Economic Finance 1960-2010 WDI CD 2012 online
AidRecGrant%Total
Official development assistance and official aid, grants as % of ODA
Economic Finance 1960-2000 WDI CD 02
XCurActBal%GDP
Current account balance (% of GDP)
Economic Finance 1960-2008 WDI CD 2010
Xdebt
External long-term (more than 1 year) debt: public, publically guaranteed and priv nonguaranteed
Economic Finance 1970-2008 WDI CD 2010
XDebtPNG%GDP
External debt, private non-guaranteed, as percentage of gross domestic product
Economic Finance 1970-2008 WDI CD 2010
XDebtPPG%GDP
External debt, public and publicly guaranteed, as percentage of gross domestic product
Economic Finance 1970-2008 WDI CD 2010
XFDIInflows%GDP
Foreign direct investment net inflow as % of GDP
Economic Finance 1970-2010 WDI 2011 Download
XFDIOutflows%GDP
Foreign direct investment net outflow as % of GDP
Economic Finance 1960-2010 WDI 2011
XFlowsIBRD%GDP
Net flows from IBRD as % of GDP
Economic Finance 1970-2008 WDI CD 2010
XFlowsIDA%GDP
Net flows from IDA as % of GDP
Economic Finance 1970-2008 WDI CD 2010
XFlowsIMFCon%GDP
Net concessional flows from IMF as % of GDP
Economic Finance 1970-2009 WDI 2011 Online
XFlowsIMFNonCon%GDP
Net nonconcessional flows from IMF as % of GDP
Economic Finance 1970-2008 WDI CD 2010
XIMFCredit%GDP
IMF credits as % of GDP Economic Finance 1970-2008 WDI CD 2010
46
XIncPayments%GDP
Income payments as % of GDP
Economic Finance 1960-2008 WDI CD 2010
XIncReceipts%GDP
Income receipts as % of GDP Economic Finance 1960-2008 WDI CD 2010
XPortBonds%GDP
Portfolio investment in bonds (PPG and PNG) as % of GDP
Economic Finance 1970-2008 WDI CD 2010
XPortEquity%GDP
Portfolio investment in equity as % of GDP
Economic Finance 1970-2008 WDI CD 2010
XReserves%GDP
Gross international reserves as % of GDP
Economic Finance 1960-2008 WDI CD 2010
XWBLoans%GDP
IBRD loans and IDA credits as % of GDP
Economic Finance 1970-2008 WDI CD 2010
XWorkerRemit%GDP
Worker remittances by home country as % of GDP
Economic Finance 1970-2008 WDI CD 2010
GDP2005PCPPP GDP per capita (constant 2005 PPP International $)
Economic GDP per Capita
1960-2010 WDI 2011 Online (upto 2009); 2010 values are from from growth rates in GDP2000 and population
Labor Labor force size Economic Labor 1960-2008 WDI CD 2010
LaborFemale% Portion of labor force made up by women
Economic Labor 1960-2008 WDI CD 2010
LaborSecInd%Tot
Labor in industry as % of total
Economic Labor 1960, 1970, 1980-2008
WDI CD 2010
LaborSecSer%Tot
Labor in services as % of total Economic Labor 1960, 1970, 1980-2008
WDI CD 2010
VaddAg% Value added in agriculture as percent of GDP
Economic Production
1960-2008 WDI CD 2010
VaddInd% Value added in industry as percent of GDP
Economic Production
1960-2008 WDI CD 2010
VaddMan% Value added in manufacturing as percent of GDP
Economic Production
1960-2008 WDI CD 2010
VaddSer% Value added in services as percent of GDP
Economic Production
1960-2008 WDI CD 2010
ExportServices Exports of services (BoP, current currency)
Economic Trade 1960-2008 WDI CD 2010
ExportsMerchandise
Exports of merchandise (current currency)
Economic Trade 1960-2008 WDI CD 2010
ImportGoodSer%
Imports of goods and services as % of GDP
Economic Trade 1960-2010 WDI Web 2012
47
ImportsMerchandise
Imports of merchandise (current currency)
Economic Trade 1960-2008 WDI CD 2010
OresMetsEx%MerchEx
Ores and Metals exports as % of merchandise exports
Economic Trade 1962-2008 WDI CD 2010
OresMetsIm%MerchIm
Ores and Metals imports as % of merchandise imports
Economic Trade 1962-2008 WDI CD 2010
VaddICT%GDP Total ICT market share as % of GDP
Economic, Infrastructure
Production
1998-2000 Information Society Statistics Pocketbook 2001
ArmsImp%TotImp
Arms imports as % of total imports
Economic, Trade
Trade 1985-1999 WDI CD 04
ExportGoodSer%
Exports of goods and services as % of GDP
Economic, Trade
Trade 1960-2010 WDI online 2011
ImportServices Imports of services (current currency)
Economic, Trade
Trade 1960-2008 WDI CD 2010
EdYearsAge15Female
Education, Average years of schooling for those 15 or older, female, Barro-Lee estimation
Education, Knowledge
Attainment
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
http://www.barrolee.com/
EdYearsAge15Male
Education, Average years of schooling for those 15 or older, male, Barro-Lee estimation
Education, Knowledge
Attainment
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
http://www.barrolee.com/
EdYearsAge15Total
Education, Average years of schooling for those 15 or older, total, Barro-Lee estimation
Education, Knowledge
Attainment
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
http://www.barrolee.com/
EdYearsAge25
Education, Average years of schooling for those 25 or older, total, Barro-Lee estimation
Education, Knowledge
Attainment
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
http://www.barrolee.com/
EdYearsAge25Female
Education, Average years of schooling for those 25 or older, female, Barro-Lee estimation
Education, Knowledge
Attainment
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
http://www.barrolee.com/
48
EdYearsAge25Male
Education, Average years of schooling for those 25 or older, males, Barro-Lee estimation
Education, Knowledge
Attainment
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
http://www.barrolee.com/
EdExpSecLowr%GDPPC
Education, Expenditure per student as % of GDPPC, Lower Secondary
Education, Knowledge
Education 1999-2005 UNESCO Institute for Statistics
EdExpSecUppr%GDPPC
Education, Expenditure per student as % of GDPPC, Upper Secondary
Education, Knowledge
Education 1999-2005 UNESCO Institute for Statistics
EdPriAdultGrads15Female%
Adult population (15 and over) with primary (or more) education, female %
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
EdPriAdultGrads15Male%
Adult population (15 and over) with primary (or more) education, male %
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
EdPriAdultGrads15Total%
Adult population (15 and over) with primary (or more) education, total %
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
EdPriAIRMale%
Education, Primary, Apparent (gross) intake rate in grade 1, male (% of relevant age group)
Education, Knowledge
Education 1970, 1975, 1980-2010
UNESCO Institute for Statistics; WDI 2004; WDI 2008
EdPriAIRTotal%
Education, Primary, Apparent (gross) intake rate in grade 1, total (% of relevant age group)
Education, Knowledge
Education 1970, 1975, 1980-2010
UNESCO Institute for Statistics; WDI 2004; WDI 2008
EdPriEntranceAge
Education Primary Entrance Age
Education, Knowledge
Education 1999-2008 UIS
EdPriNIRFemale%
Net (adjusted) intake rate for primary grade 1, % of school-aged females
Education, Knowledge
Education 1989-1997, 1999-2006
WDI CD 04; WDI 08
49
EdPriNIRMale% Net (adjusted) intake rate for primary grade 1, % of school-aged males
Education, Knowledge
Education 1989-1997, 1999-2006
WDI CD 04; WDI 08
EdPriNIRTotal%
Net (adjusted) intake rate for primary grade 1, % of school-aged total population
Education, Knowledge
Education 1989-1997, 1999-2006
WDI CD 04; WDI 08
EDPriPTR Primary pupil-teacher ratio Education, Knowledge
Education 1970, 1975, 1980, 1982, 1985, 1990-2006
WB WDI 2004; WDI 2008
EdSecAdultGrads15Female%
Adult population (15 and over) with secondary (or more) education, female%
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
EdSecAdultGrads15Male%
Adult population (15 and over) with secondary (or more) education, male%
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
EdSecAdultGrads15Total%
Adult population (15 and over) with secondary (or more) education, total%
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
EdSecGradRate
Percent of age group graduate with upper secondary (ISCED 3) education
Education, Knowledge
Education 1999 OECD, Education at a Glance 2001:146
EdSecGradRateFem
Percent of age group graduate with upper secondary (ISCED 3) education, female
Education, Knowledge
Education 1999 OECD, Education at a Glance 2001:146
EdSecGradRateMale
Percent of age group graduate with upper secondary (ISCED 3) education, male
Education, Knowledge
Education 1999 OECD, Education at a Glance 2001:146
EdSecLower2UpperFemale%
Percentage of female lower secondary last graders starting higher secondary
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
50
EdSecLower2UpperMale%
Percentage of male lower secondary last graders starting higher secondary
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
EdSecLower2UpperTotal%
Percentage of total lower secondary last graders starting higher secondary
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
EdSecLowerSurvivalFemale%
Percentage of female entering students reaching last grade of lower secondary (persistence)
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
EdSecLowerSurvivalMale%
Percentage of male entering students reaching last grade of lower secondary (persistence)
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
EdSecLowerSurvivalTotal%
Percentage of total entering students reaching last grade of lower secondary (persistence)
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
EdSecUpperSurvivalFemale%
Percentage of female entering students reaching last grade of Upper secondary (persistence)
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
EdSecUpperSurvivalMale%
Percentage of male entering students reaching last grade of Upper secondary (persistence)
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
EdSecUpperSurvivalTotal%
Percentage of total entering students reaching last grade of Upper secondary (persistence)
Education, Knowledge
Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data
EdTerAdultGrads15Female%
Adult population (15 and over) with tertiary (or more) education, female %
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
51
EdTerAdultGrads15Male%
Adult population (15 and over) with tertiary (or more) education, female %
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
EdTerAdultGrads15Total%
Adult population (15 and over) with tertiary education, total %
Education, Knowledge
Education
1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
Barro-Lee
EdTerIntakeGrossFemale%
Education Tertiary Intake Rate, Gross, female
Education, Knowledge
Education 1999-2001 IFs Calculation
EdTerIntakeGrossMale%
Education Tertiary Intake Rate, Gross, male
Education, Knowledge
Education 1999-2001 IFs Calculation
EdTerIntakeGrossTotal%
Education Tertiary Intake Rate, Gross, total
Education, Knowledge
Education 1999-2001 IFs Calculation
EdYearsAge15-24Female
Average years of schooling for those 15-24, females
Education, Knowledge
Education 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000
Barro-Lee data set, Harvard CID
EdYearsAge15-24Male
Average years of schooling for those 15-24, males
Education, Knowledge
Education 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000
Barro-Lee data set, Harvard CID
EdYearsAge15-24Total
Average years of schooling for those 15-24, total population
Education, Knowledge
Education 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000
Barro-Lee data set, Harvard CID
EdExpPri%GDPPC
Expenditure per student in primary education (% of GDP/capita)
Education, Knowledge
Expenditure
1970, 1975, 1980, 1985, 1990-2007
World Bank; WDI 2005-6 augment; UNESCO Institute for Statistics
EdExpSec%GDPPC
Expenditure per student in secondary education (% of GDP/capita)
Education, Knowledge
Expenditure
1970, 1975, 1980, 1985, 1990-2007
World Bank; UNESCO Institute for Statistics
EdExpTer%GDPPC
Expenditure per student in tertiary education (% of GDP/capita)
Education, Knowledge
Expenditure
1970, 1975, 1980, 1985, 1990-2007
World Bank; UNESCO Institute for Statistics
EdSecEnrollNet Education, Secondary, net enrollment rate, total
Education, Knowledge
Participation
1998-2011 UNESCO Institute for Statistics
EdSecEnrollNetFemale
Education, Secondary, Enrollment Rate, Net, Female
Education, Knowledge
Participation
1998-2011 UNESCO Insitute for Statistics
52
EdSecEnrollNetMale
Education, Secondary, Enrollment Rate, Net, Male
Education, Knowledge
Participation
1998-2011 UNESCO Insitute for Statistics
EdPriAIRFemale%
Education, Primary, Apparent (gross) intake rate in grade 1, female (% of relevant age group)
Education, Knowledge
Primary 1970, 1975, 1980-2010
UNESCO Institute for Statistics; WDI 2004; WDI 2008
EdPriCompletionFemale%
Education, Primary, completion rate, gross, Female
Education, Knowledge
Primary 1988-2011 UNESCO Institute for Statistics; World Bank WDI 2005
EdPriCompletionMale%
Education, Primary, completion rate, gross, Male
Education, Knowledge
Primary 1988-2011 UNESCO Institute for Statistics; World Bank WDI 2005
EdPriCompletionTotal%
Education, Primary, completion rate, gross, total
Education, Knowledge
Primary 1988-2011 UNESCO Institute for Statistics; World Bank WDI 2005
EdPriDuration Education, Primary, Cycle Length
Education, Knowledge
Primary 1999-2010 UIS
EdPriEnrollGrossFemalePcnt
Education, Primary, Enrollment Rate Gross, female
Education, Knowledge
Primary 1960, 1970, 1975, 1980-2010
UNESCO Institute for Statistics
EdPriEnrollGrossMalePcnt
Education, Primary, Enrollment Rate Gross, male
Education, Knowledge
Primary 1960, 1970, 1975, 1980-2010
UNESCO Institute for Statistics
EdPriEnrollGrossTotalPcnt
Education, Primary, Enrollment Rate Gross (% Total)
Education, Knowledge
Primary 1960, 1970, 1975, 1980-2010
UNESCO Institute for Statistics
EdPriEnrollNetFemalePcnt
Education, Primary, Net Enrollment Rate, Female
Education, Knowledge
Primary 1970, 1975, 1980-2010
UNESCO Institute for Statistics, WDI for previous years
EdPriEnrollNetMalePcnt
Education, Primary, Net Enrollment Rate, Male
Education, Knowledge
Primary 1970, 1975, 1980-2010
UNESCO Institute for Statistics, WDI for previous years
EdPriEnrollNetTotalPcnt
Education, Primary, Net Enrollment Rate, Total
Education, Knowledge
Primary 1970, 1975, 1980-2010
UNESCO Institute for Statistics, WDI for previous years
EdPriSurvivalFemale%
Education, Primary, percentage of entrants persisting to last grade, female
Education, Knowledge
Primary 1970, 1975, 1980-2008
UNESCO Institute for Statistics; WDI CD 2009
EdPriSurvivalMale%
Education, Primary, percentage of entrants persisting to last grade, male
Education, Knowledge
Primary 1970, 1975, 1980-2009
UNESCO Institute for Statistics; UIS Database
EdPriSurvivalTotal%
Education, Primary, percentage of entrants persisting to last grade, total
Education, Knowledge
Primary 1970, 1975, 1980-2009
UNESCO Institute for Statistics; WDI CD 04
53
EdSecEnrollGross%Female
Education, Secondary, gross enrollment rate, female
Education, Knowledge
Secondary 1960, 1970-2009 WDI 2009; UIS Website
EdSecEnrollGross%Male
Education, Secondary, gross enrollment rate, male
Education, Knowledge
Secondary 1960, 1970-2009 WDI 2009; UIS Website
EdSecEnrollGross%Total
Education, Secondary, gross enrollment rate, total
Education, Knowledge
Secondary 1960, 1970-2010 UNESCO Institute for Statistics
EdSecEnrollNetFemaleOlder
Education, Secondary, net enrollment rate, female
Education, Knowledge
Secondary 1970, 1975, 1980-1997
WDI CD 02; UIS Website; WDI 2008
EdSecEnrollNetMaleOlder
Education, Secondary, net enrollment rate, male
Education, Knowledge
Secondary 1970, 1975, 1980-1997
WDI CD 02; UIS Website; WDI 2008
EdSecEnrollNetOlder
Education, Secondary, net enrollment rate, total
Education, Knowledge
Secondary 1970, 1975, 1980-1981, 1985, 1990-1997
WDI CD 04; UIS Website
EdPriTransition2Sec%Female
Education, Percentage Transition from Primary to Lower Secondary General, Female
Education, Knowledge
Secondary Lower
1999-2009 UNESCO Institute for Statistics
EdPriTransition2Sec%Male
Education, Percentage Transition from Primary to Lower Secondary General, Male
Education, Knowledge
Secondary Lower
1999-2009 UNESCO Institute for Statistics
EdPriTransition2Sec%Total
Education, Percentage Transition from Primary to Lower Secondary General, Total
Education, Knowledge
Secondary Lower
1999-2009 UNESCO Institute for Statistics
EdSecLowerDuration
Education Duration of Lower Secondary
Education, Knowledge
Secondary Lower
1999-2010 UNESCO Institute for Statistics
EdSecLowerEnrollGross%Female
Education, Secondary, Lower, Gross Enrollment Rate All Programs, Female
Education, Knowledge
Secondary Lower
1970, 1975, 1980-1997, 1999-2010
UNESCO Institute for Statistics
EdSecLowerEnrollGross%Male
Education, Secondary, Lower, Gross Enrollment Rate All Programs, Male
Education, Knowledge
Secondary Lower
1970, 1975, 1980-1997, 1999-2010
UNESCO Institute for Statistics
EdSecLowerEnrollGross%Total
Education, Secondary, Lower, Gross Enrollment Rate, Total
Education, Knowledge
Secondary Lower
1970, 1975, 1980-1997, 1999-2010
UNESCO Institute for Statistics
EdSecUpperDuration
Education, Secondary, Upper, duration of upper secondary
Education, Knowledge
Secondary Upper
1999-2010 UIS
54
EdSecUpperEnrollGross%Female
Education, Secondary, Upper, Gross Enrollment Rate All Programs, Female
Education, Knowledge
Secondary Upper
1991, 1999-2010 UNESCO Institute for Statistics
EdSecUpperEnrollGross%Male
Education, Secondary, Upper, Gross Enrollment Rate All Programs, Male
Education, Knowledge
Secondary Upper
1991, 1999-2010 UNESCO Institute for Statistics
EdSecUpperEnrollGross%Total
Education, Secondary, Upper, Gross Enrollment Rate All Programs, Total
Education, Knowledge
Secondary Upper
1991, 1999-2010 UNESCO Institute for Statistics
EdTerEnrollGross%Female
Education, Tertiary, gross enrollment rate, female
Education, Knowledge
Tertiary 1960, 1965, 1970, 1975, 1980-2010
UNESCO Institute for Statistics; WDI
EdTerEnrollGross%Male
Education, Tertiary, gross enrollment rate, male
Education, Knowledge
Tertiary 1960, 1965, 1970, 1975, 1980-2010
UNESCO Institute for Statistics; WDI
EdTerEnrollGross%Total
Education, Tertiary, gross enrollment rate, total
Education, Knowledge
Tertiary 1960, 1965, 1970, 1975, 1980-2010
UNESCO Institute for Statistics; WDI
EdTerGradRate1stDegreeFemale%
Education, Tertiary, Gross Graduation Ratio, ISCED 5A, first degree, female
Education, Knowledge
Tertiary 1998-2011 UNESCO Institute for Statistics
EdTerGradRate1stDegreeMale%
Education, Tertiary, Gross Graduation Ratio, ISCED 5A, first degree, male
Education, Knowledge
Tertiary 1998-2011 UNESCO Institute for Statistics
EdTerGradRate1stDegreeTotal%
Education, Tertiary, Gross Graduation Ratio, ISCED 5A, first degree, total
Education, Knowledge
Tertiary 1998-2011 UNESCO Institute for Statistics
EdSecLowerVoc%AllFemale
Education, Secondary, Lower, Vocational as % of All Program Enrollments, Female
Education, Knowledge
1999-2005 UNESCO
EdSecLowerVoc%AllMale
Education, Secondary, Lower, Vocational as % of All Program Enrollments, Male
Education, Knowledge
1999-2005 UNESCO
EdSecLowerVoc%AllTotal
Education, Secondary, Lower, Vocational as % of All Program Enrollments, Total
Education, Knowledge
1999-2005 UNESCO
EdSecUpperVoc%AllFemale
Education, Secondary, Upper, Vocational as % of All Program Enrollments, Female
Education, Knowledge
1999-2005 UNESCO
EdSecUpperVoc%AllMale
Education, Secondary, Upper, Vocational as % of All Program Enrollments, Male
Education, Knowledge
1999-2005 UNESCO
55
EdSecUpperVoc%AllTotal
Education, Secondary, Upper, Vocational as % of All Program Enrollments, Total
Education, Knowledge
1999-2005 UNESCO
EdTerGrads%SciEngg
Education, Tertiary, Science and Enginnering Graduates as % of total graduates
Education, Knowledge, Science Technology
Human Capital
1999-2009 UNESCO Institute for Statistics; http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx
EnElecConsPerCap
Electricity consumption per capita
Energy Consumption
1960-2008 WDI 2011 Online
EnExportsOilIEA
Crude oil exports Energy Exports 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EnImportsOilIEA
Crude Oil Imports Energy Imports 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdCoalIEA Production of coal products Energy Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdOilIEA Crude oil production Energy Production
1971-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnConElec Electricity consumption total in BBOE
Energy, Infrastructure
Consumption
1960-2007 WDI CD 2010
EnConHydroBP Hydroelectricity consumption
Energy, Infrastructure
Consumption
1965-2010 BP?s Statistical Review of World Energy 2011
EnConNucBP Nuclear electricity consumption
Energy, Infrastructure
Consumption
1965-2010 BP?s Statistical Review of World Energy 2011
EnConPhoto Energy consumption, photovoltaic solar
Energy, Infrastructure
Consumption
1960-1999 WRI Earthtrends http://earthtrends.wri.org/
EnConTotalWDI Energy consumption, use of primary energy from all sources
Energy, Infrastructure
Consumption
1960-2009 WDI 2011
EnConWind Energy consumption, wind Energy, Infrastructure
Consumption
1960-1999 WRI Earthtrends http://earthtrends.wri.org/
EnElecAccess%National
Percentage of national population with access to electricity
Energy, Infrastructure
Electricity 2000, 2002-2007,2009
IEA and various other sources quoted in a UNDP/WHO publication on energy access; 2010 estimated
EnElecAccess%Rural
% of rural population with access to electricity
Energy, Infrastructure
Electricity 2000, 2002-2008 IEA and various other sources quoted in a UNDP/WHO publication on energy access; 2010 estimated
EnElecAccess%Urban
% of urban population with access to electricity
Energy, Infrastructure
Electricity 2000, 2002-2008 IEA and various other sources quoted in a UNDP/WHO publication on energy access; 2010 estimated
56
EnElecShrEnDemOld
Electricity consumption as a percentage of total energy consumption
Energy, Infrastructure
Electricity 1960-2008 WDI 2011; IFs calculation using two WDI tables
EnElecTotalCapacityEIA
Total electricity installed capacity
Energy, Infrastructure
Electricity 1980-2009 EIA; US Energy Information Administration;
EnProdBiodieselIEA
Production of biodiesel. Energy, Infrastructure
Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdBiogasIEA
Production of biogas (derived from anaerobic fermentation of biomass and solid wastes and combusted to produce heat and/or power).
Energy, Infrastructure
Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdCoal Coal production Energy, Infrastructure
Production
1960-1995 WRI CD 98
EnProdCoalBP Coal production Energy, Infrastructure
Production
1981-2010 BP?s Statistical Review of World Energy 2011
EnProdElec Electricity production total in kilowatt-hours
Energy, Infrastructure
Production
1960-2008 WDI CD 2011; 2010 are estimates
EnProdGas Natural gas production Energy, Infrastructure
Production
1960-1997, 2000-2005
WRI CD 00-01
EnProdGasBP Gas (natural) production Energy, Infrastructure
Production
1970-2010 BP?s Statistical Review of World Energy 2011
EnProdGeoTherm
Energy production, geothermal
Energy, Infrastructure
Production
1960-1997 WRI Earthtrends http://earthtrends.wri.org/
EnProdGeothermIEA
Energy produced from heat emitted with earth's crust, usually in the form of hot water or steam.
Energy, Infrastructure
Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdHydroIEA
Potential and kinetic energy of water converted into electricity in hydroelectric plants.
Energy, Infrastructure
Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdNuclearIEA
Energy produced by nuclear fission or nuclear fusion.
Energy, Infrastructure
Production
1960-2007 IEA Energy Balances for OECD and non-OECD Countries
EnProdOil Oil production Energy, Infrastructure
Production
1960-1995, 2000-2005
WRI CD 98
EnProdOilBP Production of Oil Energy, Infrastructure
Production
1965-2010 BP?s Statistical Review of World Energy 2011
57
EnProdSolar Energy production, solar Energy, Infrastructure
Production
1960-1999 WRI Earthtrends http://earthtrends.wri.org/
EnProdSolarPhotoIEA
Electricity production from photovoltaic cells.
Energy, Infrastructure
Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdSolarThermIEA
Energy production from solar radiation used for hot water production and electricity generation (passive solar for direct heating, cooling, lighting not included).
Energy, Infrastructure
Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdTideWave
Energy production, tide, wave, and water
Energy, Infrastructure
Production
1960-1997 WRI Earthtrends http://earthtrends.wri.org/
EnProdTideWaveOceanIEA
Electricity generation derived from tidal movement, wave motion, or ocean current.
Energy, Infrastructure
Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnProdWindIEA Electricity generation by wind turbines.
Energy, Infrastructure
Production
1960-2007 IEA Energy Balances of OECD and non-OECD Countries 2008
EnThermalElec Thermal electricity production
Energy, Infrastructure
Production
1984-1995
EnReserCoal Energy reserves, coal Energy, Infrastructure
Resources 1960, 1999, 2005 WEC
EnReserGas Energy reserves, gas Energy, Infrastructure
Resources 1960, 1967-2012 WEC; Oil and Gas Journal; 1960 estimated
EnReserGasBP Gas (natural) reserves Energy, Infrastructure
Resources 1980-2010 BP?s Statistical Review of World Energy 2011
EnReserHyd Energy reserves, hydro Energy, Infrastructure
Resources 1960, 1999 WRI Annual
EnReserOil Energy reserve, oil, in billion barrels
Energy, Infrastructure
Resources 1952-2012 WEC; Oil and Gas Journal; 1960 estimated
EnReserOilBP Oil reserves Energy, Infrastructure
Resources 1980-2010 BP?s Statistical Review of World Energy 2011
EnResorCoal Energy resources, coal Energy, Infrastructure
Resources 1999 WEC
EnResorGas Energy resources, gas Energy, Infrastructure
Resources 1999 WEC
58
EnResorGasUSGS
Undiscovered energy resources, gas
Energy, Infrastructure
Resources 2000
U.S. GEOLOGICAL SURVEY WORLD PETROLEUM ASSESSMENT 2000 available at: http://pubs.usgs.gov/dds/dds-060/index.html#TOP
EnResorNGLUSGS
Undiscovered energy resources, natural gas liquids
Energy, Infrastructure
Resources 2000
U.S. GEOLOGICAL SURVEY WORLD PETROLEUM ASSESSMENT 2000 available at: http://pubs.usgs.gov/dds/dds-060/index.html#TOP
EnResorOil Energy resources, oil Energy, Infrastructure
Resources 1999 WEC
EnResorOilUSGS
Undiscovered energy resources, oil
Energy, Infrastructure
Resources 2000
U.S. GEOLOGICAL SURVEY WORLD PETROLEUM ASSESSMENT 2000 available at: http://pubs.usgs.gov/dds/dds-060/index.html#TOP
EnResorSynthetic
Energy resources, synthetic fuels (oil shale, tar sands)
Energy, Infrastructure
Resources 1999 WEC
EnElecTransLoss%
Electric power transmission and distribution losses (% of output)
Energy, Infrastructure, Trade
Electricity 1960-2009 WDI 2012 Online
EnElecTransLoss%Old
Electric power transmission and distribution losses (% of output)
Energy, Infrastructure, Trade
Electricity 1960-2008 WDI CD 2011; 2010 are estimates using 2007 to 2008 growth rates
EnExportsCoalIEA
Exports of coal and coal products
Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EnExportsNatGasIEA
Exports of natural gas Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EnExportsOilProductsIEA
Exports of Crude Natural Gas Liquids
Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EnExportsPeatIEA
Peat exports Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
59
EnExportsTotalIEA
Total energy exports Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EnImportsCoalIEA
Imports of coal and coal products
Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD Countries and Energy Balances of non-OECD Countries 2008
EnImportsNatGasIEA
Imports of natural gas Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD Countries and non-OECD Countries 2008
EnImportsOilProductsIEA
Imports of Crude Natural Gas Liquids
Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EnImportsPeatIEA
Peat imports Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EnImportsTotalIEA
Total energy imports Energy, Infrastructure, Trade
Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008
EmissionsCarbonCDIAC
Total CO2 emissions Environment Atmosphere
1771-2006 Carbon Dioxide Information Analysis Center
EnvPM10 PM10 Country Level, micrograms per cm3\r\n
Environment Atmosphere
1990-2008 WDI Online
EnvSolidFuels % of population using solid fuels
Environment No Sub Category
1990-2004, 2007 http://mdgs.un.org/unsd/mdg/Data.aspx
EnvPrecipitation
Average annual precipitation from 1980 to 1999
Environment Precipitation
1999
http://www.cgd.ucar.edu/cas/wigley/magicc/ and http://na.unep.net/globalpop/1-degree/description.php
EnvPrecipitationChg
Change in average annual Precipitation over time from 1980 to 1999
Environment Precipitation
1999
http://www.cgd.ucar.edu/cas/wigley/magicc/ and http://na.unep.net/globalpop/1-degree/description.php
EnvAvgAnnTemp
Average annual temperature from 1980 to 1999
Environment Temperature
1999
http://www.cgd.ucar.edu/cas/wigley/magicc/ and http://na.unep.net/globalpop/1-degree/description.php
60
EnvAvgAnnTempChg
Change in average annual temperature over time from 1980 to 1999
Environment Temperature
1999
http://www.cgd.ucar.edu/cas/wigley/magicc/ and http://na.unep.net/globalpop/1-degree/description.php
LandCrop Land, crop Environment, Infrastructure
Land 1961-2008 FAOSTAT
LandGrazing Land, grazing Environment, Infrastructure
Land 1961-2008 FAOSTAT
LandOther Land, other Environment, Infrastructure
Land 1961-2008 FAOSTAT
LandTotal Land, total Environment, Infrastructure
Land 1961-2009 FAO Stat
LandUrban&Built
Land, urban and built-up areas
Environment, Infrastructure
Land 1992 WRI Earthtrends http://earthtrends.wri.org/
WaterAnRenResources
Annually renewable water resources
Environment, Infrastructure, Water
Water
19,621,967,197,219,700,000,000,000,000,000,000,0
00,000,000
WRI Earthtrends http://earthtrends.wri.org/
WaterAnRenResourcesOld
Annually renewable water resources
Environment, Infrastructure, Water
Water 1977-2001 WRI Earthtrends http://earthtrends.wri.org/
WaterAnWithdrawals
Annual water withdrawals/use (1990=70-99;2000=update, mostly 2000)
Environment, Infrastructure, Water
Water 1990, 2000
WRI Earthtrends http://earthtrends.wri.org/; Source: Pacific Institute, www.worldwater.org/data.html
Corruption Level of corruption, 10 to 0, Transparency Intl (10 most transparent)
Government Character 1995-2011 Transparency International www.transparency.org/documents/index.html. Various years
Freedom Civil and political freedom level on scale of 2 to 14 (lower is freer)
Government Character 1972-2012 Freedom House (Annual freedom in the world country scores 1972-2012); web updates
FreedomEcon Economic freedom level on scale of 1 to 10 (most free)
Government Character 1970, 1975, 1980, 1985, 1990, 1995, 1999-2007
Fraser International (http://www.freetheworld.com); replaces Gwartney, Lawson, Samida: 2000
GovernanceEffect
Governance quality, effectiveness (-2.5 to 2.5, higher is better)
Government Character 1996, 1998, 2000, 2002-2010
http://info.worldbank.org/governance/wgi2007 and http://info.worldbank.org/governance/wgi/index.asp
61
GovernanceRegQual
Governance quality, regulatory quality (-2.5 to 2.5, higher is better)
Government Character 1996, 1998, 2000, 2002-2010
http://info.worldbank.org/governance/wgi2007 and http://info.worldbank.org/governance/wgi/index.asp
PolityAutoc Polity project's measure of autocracy (0=low; 10=high)
Government Character 1800-2010 Polity Project; courtesy of Monty Marshall
PolityDemoc Polity project's measure of democracy (0=low; 10=high)
Government Character 1800-2010 Polity Project; courtesy of Monty Marshall
AidDon%GNI Aid donations as percent of GNI
Government Expenditure
1990-2011 United Nations Statistics Division
GovtEdPub%GDP
Educational expenditures (public) as percent of GDP
Government Expenditure
1960, 1965, 1970-1996, 1998-2010
WDI 2011 Online Database and existing IFs data
GovSSWelBen%Exp
Government Social Security and welfare expenditures as % of total expenditures
Government Finance 1990-2010 WDI 2011
GovtCurRev%GDP
Current government revenue as % of GDP
Government Finance 1970-2010 WDI CD 2012 online and old Ifs data
GovtDebt%GDP Central government debt as % of GDP
Government Finance 1970-2009 WDI 2011 Online Database and existing IFs data
GovtPen