PERU: 2050 David_Peru2050.pdfINEI - Censos Nacionales de Población y Vivienda 1940,1961,1972,1981, 1993, 2007 Total Fertility Rate WDI online 2011 1960-2009 INEI - Encuesta Demográfica

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

2

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

3

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

4

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

10

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


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%

12

2.2 Data Sources


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


Value Added In Industry


Value Added In Services


Value Added In ICT OECD Measuring ICT

Sector 1997 INEI and BCR. 1950-2011

13

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


3.2 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


Secondary, Enrollment Rate, Net,

Male

UNESCO Institute for Statistics

1998-2011 Total (not by

gender)

Secondary, Enrollment Rate, Net,

Female


1998-2011 Total (not by

gender)

Tertiary, Gross Enrollment Rate,

Male

UNESCO Institute for Statistics; WDI

1960, 1965, 1970, 1975, 1980-2010


Tertiary, Gross Enrollment Rate,

Female


1960, 1965, 1970, 1975, 1980-2010



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

16

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.

17

3.4 Causal Diagram

18

4 Agriculture



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.

19

Root And Tuber Production

FAOSTAT 1961-2010 Ministerio de Agricultura 1983-2012

Production Of Fruit, Excluding Melons


Vegetable, Melon Production


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.

20

4.4 Causal Diagram

21

5 Energy



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


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


Production Of Hydro Power

Beyond 20/20 Browser CD Release 7.0.2491 (32)

1960-2009

Oil Consumption BP's Statistical Review of


Gas (Natural) Consumption


1965-2010

Coal Consumption BP's Statistical Review of


Hydroelectricity Consumption


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.

24

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



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.


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



% 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



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


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


2002

32

Years Of Life Lost To Injuries


2002

Years Of Life Lost To Non-Communicable

Diseases


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

64

19

68

19

72

19

76

19

80

19

84

19

88

19

92

19

96

20

00

20

04

20

08

20

12

20

16

20

20

20

24

20

28

20

32

20

36

20

40

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

19

84

19

87

19

90

19

93

19

96

19

99

20

02

20

05

20

08

20

11

20

14

20

17

20

20

20

23

20

26

20

29

20

32

20

35

20

38

20

41

20

44

20

47

20

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


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


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


Land 1990-2008 FAOSTAT

LandIrPotential Irrigation potential (1000 ha)\r\n


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


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


AgMeatIm Meat imports Agriculture, Trade


AgPulsesEx Pulse exports Agriculture, Trade


AgPulsesIm Pulseimports Agriculture, Trade


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


45

HouseCon%GDP

Household final consumption expenditure as percent of GDP


InvestGrCapForm%GDP

Gross capital formation (Investment), percent of GDP


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)


Xdebt

External long-term (more than 1 year) debt: public, publically guaranteed and priv nonguaranteed


XDebtPNG%GDP

External debt, private non-guaranteed, as percentage of gross domestic product


XDebtPPG%GDP

External debt, public and publicly guaranteed, as percentage of gross domestic product


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


XFlowsIDA%GDP

Net flows from IDA as % of GDP


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


XIMFCredit%GDP

IMF credits as % of GDP Economic Finance 1970-2008 WDI CD 2010

46

XIncPayments%GDP

Income payments as % of GDP


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


XPortEquity%GDP

Portfolio investment in equity as % of GDP


XReserves%GDP

Gross international reserves as % of GDP


XWBLoans%GDP

IBRD loans and IDA credits as % of GDP


XWorkerRemit%GDP

Worker remittances by home country as % of GDP


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)


ImportGoodSer%

Imports of goods and services as % of GDP

Economic Trade 1960-2010 WDI Web 2012

47

ImportsMerchandise

Imports of merchandise (current currency)


OresMetsEx%MerchEx

Ores and Metals exports as % of merchandise exports


OresMetsIm%MerchIm

Ores and Metals imports as % of merchandise imports


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


Attainment

1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010


EdYearsAge15Total

Education, Average years of schooling for those 15 or older, total, Barro-Lee estimation


Attainment

1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010


EdYearsAge25

Education, Average years of schooling for those 25 or older, total, Barro-Lee estimation


Attainment

1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010


EdYearsAge25Female

Education, Average years of schooling for those 25 or older, female, Barro-Lee estimation


Attainment

1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010


48

EdYearsAge25Male

Education, Average years of schooling for those 25 or older, males, Barro-Lee estimation


Attainment

1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010


EdExpSecLowr%GDPPC

Education, Expenditure per student as % of GDPPC, Lower Secondary


Education 1999-2005 UNESCO Institute for Statistics

EdExpSecUppr%GDPPC

Education, Expenditure per student as % of GDPPC, Upper Secondary


Education 1999-2005 UNESCO Institute for Statistics

EdPriAdultGrads15Female%

Adult population (15 and over) with primary (or more) education, female %


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

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

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 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 1970, 1975, 1980-2010


EdPriEntranceAge

Education Primary Entrance Age


Education 1999-2008 UIS

EdPriNIRFemale%

Net (adjusted) intake rate for primary grade 1, % of school-aged females


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 1989-1997, 1999-2006

WDI CD 04; WDI 08

EdPriNIRTotal%

Net (adjusted) intake rate for primary grade 1, % of school-aged total population


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

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

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

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 1999 OECD, Education at a Glance 2001:146

EdSecGradRateFem

Percent of age group graduate with upper secondary (ISCED 3) education, female



EdSecGradRateMale

Percent of age group graduate with upper secondary (ISCED 3) education, male



EdSecLower2UpperFemale%

Percentage of female lower secondary last graders starting higher secondary


Education 1999-2005 Calculated by IFs Team from Enrollment and Repeater Data

50

EdSecLower2UpperMale%

Percentage of male lower secondary last graders starting higher secondary



EdSecLower2UpperTotal%

Percentage of total lower secondary last graders starting higher secondary



EdSecLowerSurvivalFemale%

Percentage of female entering students reaching last grade of lower secondary (persistence)



EdSecLowerSurvivalMale%

Percentage of male entering students reaching last grade of lower secondary (persistence)



EdSecLowerSurvivalTotal%

Percentage of total entering students reaching last grade of lower secondary (persistence)



EdSecUpperSurvivalFemale%

Percentage of female entering students reaching last grade of Upper secondary (persistence)



EdSecUpperSurvivalMale%

Percentage of male entering students reaching last grade of Upper secondary (persistence)



EdSecUpperSurvivalTotal%

Percentage of total entering students reaching last grade of Upper secondary (persistence)



EdTerAdultGrads15Female%

Adult population (15 and over) with tertiary (or more) education, female %


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

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

1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010

Barro-Lee

EdTerIntakeGrossFemale%

Education Tertiary Intake Rate, Gross, female


Education 1999-2001 IFs Calculation

EdTerIntakeGrossMale%

Education Tertiary Intake Rate, Gross, male



EdTerIntakeGrossTotal%

Education Tertiary Intake Rate, Gross, total



EdYearsAge15-24Female

Average years of schooling for those 15-24, females


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 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000


EdYearsAge15-24Total

Average years of schooling for those 15-24, total population


Education 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000


EdExpPri%GDPPC

Expenditure per student in primary education (% of GDP/capita)


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)


Expenditure

1970, 1975, 1980, 1985, 1990-2007

World Bank; UNESCO Institute for Statistics

EdExpTer%GDPPC

Expenditure per student in tertiary education (% of GDP/capita)


Expenditure

1970, 1975, 1980, 1985, 1990-2007

World Bank; UNESCO Institute for Statistics

EdSecEnrollNet Education, Secondary, net enrollment rate, total


Participation

1998-2011 UNESCO Institute for Statistics

EdSecEnrollNetFemale

Education, Secondary, Enrollment Rate, Net, Female


Participation

1998-2011 UNESCO Insitute for Statistics

52

EdSecEnrollNetMale

Education, Secondary, Enrollment Rate, Net, Male


Participation

1998-2011 UNESCO Insitute for Statistics

EdPriAIRFemale%

Education, Primary, Apparent (gross) intake rate in grade 1, female (% of relevant age group)


Primary 1970, 1975, 1980-2010


EdPriCompletionFemale%

Education, Primary, completion rate, gross, Female


Primary 1988-2011 UNESCO Institute for Statistics; World Bank WDI 2005

EdPriCompletionMale%

Education, Primary, completion rate, gross, Male



EdPriCompletionTotal%

Education, Primary, completion rate, gross, total



EdPriDuration Education, Primary, Cycle Length


Primary 1999-2010 UIS

EdPriEnrollGrossFemalePcnt

Education, Primary, Enrollment Rate Gross, female


Primary 1960, 1970, 1975, 1980-2010


EdPriEnrollGrossMalePcnt

Education, Primary, Enrollment Rate Gross, male


Primary 1960, 1970, 1975, 1980-2010


EdPriEnrollGrossTotalPcnt

Education, Primary, Enrollment Rate Gross (% Total)


Primary 1960, 1970, 1975, 1980-2010


EdPriEnrollNetFemalePcnt

Education, Primary, Net Enrollment Rate, Female


Primary 1970, 1975, 1980-2010

UNESCO Institute for Statistics, WDI for previous years

EdPriEnrollNetMalePcnt

Education, Primary, Net Enrollment Rate, Male


Primary 1970, 1975, 1980-2010


EdPriEnrollNetTotalPcnt

Education, Primary, Net Enrollment Rate, Total


Primary 1970, 1975, 1980-2010


EdPriSurvivalFemale%

Education, Primary, percentage of entrants persisting to last grade, female


Primary 1970, 1975, 1980-2008

UNESCO Institute for Statistics; WDI CD 2009

EdPriSurvivalMale%

Education, Primary, percentage of entrants persisting to last grade, male


Primary 1970, 1975, 1980-2009

UNESCO Institute for Statistics; UIS Database

EdPriSurvivalTotal%

Education, Primary, percentage of entrants persisting to last grade, total


Primary 1970, 1975, 1980-2009

UNESCO Institute for Statistics; WDI CD 04

53

EdSecEnrollGross%Female

Education, Secondary, gross enrollment rate, female


Secondary 1960, 1970-2009 WDI 2009; UIS Website

EdSecEnrollGross%Male

Education, Secondary, gross enrollment rate, male


Secondary 1960, 1970-2009 WDI 2009; UIS Website

EdSecEnrollGross%Total

Education, Secondary, gross enrollment rate, total


Secondary 1960, 1970-2010 UNESCO Institute for Statistics

EdSecEnrollNetFemaleOlder

Education, Secondary, net enrollment rate, female


Secondary 1970, 1975, 1980-1997

WDI CD 02; UIS Website; WDI 2008

EdSecEnrollNetMaleOlder

Education, Secondary, net enrollment rate, male


Secondary 1970, 1975, 1980-1997

WDI CD 02; UIS Website; WDI 2008

EdSecEnrollNetOlder

Education, Secondary, net enrollment rate, total


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


Secondary Lower


EdPriTransition2Sec%Male

Education, Percentage Transition from Primary to Lower Secondary General, Male


Secondary Lower


EdPriTransition2Sec%Total

Education, Percentage Transition from Primary to Lower Secondary General, Total


Secondary Lower


EdSecLowerDuration

Education Duration of Lower Secondary


Secondary Lower


EdSecLowerEnrollGross%Female

Education, Secondary, Lower, Gross Enrollment Rate All Programs, Female


Secondary Lower

1970, 1975, 1980-1997, 1999-2010


EdSecLowerEnrollGross%Male

Education, Secondary, Lower, Gross Enrollment Rate All Programs, Male


Secondary Lower

1970, 1975, 1980-1997, 1999-2010


EdSecLowerEnrollGross%Total

Education, Secondary, Lower, Gross Enrollment Rate, Total


Secondary Lower

1970, 1975, 1980-1997, 1999-2010


EdSecUpperDuration

Education, Secondary, Upper, duration of upper secondary


Secondary Upper

1999-2010 UIS

54

EdSecUpperEnrollGross%Female

Education, Secondary, Upper, Gross Enrollment Rate All Programs, Female


Secondary Upper

1991, 1999-2010 UNESCO Institute for Statistics

EdSecUpperEnrollGross%Male

Education, Secondary, Upper, Gross Enrollment Rate All Programs, Male


Secondary Upper


EdSecUpperEnrollGross%Total

Education, Secondary, Upper, Gross Enrollment Rate All Programs, Total


Secondary Upper


EdTerEnrollGross%Female

Education, Tertiary, gross enrollment rate, female


Tertiary 1960, 1965, 1970, 1975, 1980-2010


EdTerEnrollGross%Male

Education, Tertiary, gross enrollment rate, male


Tertiary 1960, 1965, 1970, 1975, 1980-2010


EdTerEnrollGross%Total

Education, Tertiary, gross enrollment rate, total


Tertiary 1960, 1965, 1970, 1975, 1980-2010


EdTerGradRate1stDegreeFemale%

Education, Tertiary, Gross Graduation Ratio, ISCED 5A, first degree, female


Tertiary 1998-2011 UNESCO Institute for Statistics

EdTerGradRate1stDegreeMale%

Education, Tertiary, Gross Graduation Ratio, ISCED 5A, first degree, male



EdTerGradRate1stDegreeTotal%

Education, Tertiary, Gross Graduation Ratio, ISCED 5A, first degree, total



EdSecLowerVoc%AllFemale

Education, Secondary, Lower, Vocational as % of All Program Enrollments, Female


1999-2005 UNESCO

EdSecLowerVoc%AllMale

Education, Secondary, Lower, Vocational as % of All Program Enrollments, Male


1999-2005 UNESCO

EdSecLowerVoc%AllTotal

Education, Secondary, Lower, Vocational as % of All Program Enrollments, Total


1999-2005 UNESCO

EdSecUpperVoc%AllFemale

Education, Secondary, Upper, Vocational as % of All Program Enrollments, Female


1999-2005 UNESCO

EdSecUpperVoc%AllMale

Education, Secondary, Upper, Vocational as % of All Program Enrollments, Male


1999-2005 UNESCO

55

EdSecUpperVoc%AllTotal

Education, Secondary, Upper, Vocational as % of All Program Enrollments, Total


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


EnConElec Electricity consumption total in BBOE

Energy, Infrastructure

Consumption

1960-2007 WDI CD 2010

EnConHydroBP Hydroelectricity consumption


Consumption

1965-2010 BP?s Statistical Review of World Energy 2011

EnConNucBP Nuclear electricity consumption


Consumption


EnConPhoto Energy consumption, photovoltaic solar


Consumption


EnConTotalWDI Energy consumption, use of primary energy from all sources


Consumption

1960-2009 WDI 2011

EnConWind Energy consumption, wind Energy, Infrastructure

Consumption


EnElecAccess%National

Percentage of national population with access to electricity


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


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


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


Electricity 1960-2008 WDI 2011; IFs calculation using two WDI tables

EnElecTotalCapacityEIA

Total electricity installed capacity


Electricity 1980-2009 EIA; US Energy Information Administration;

EnProdBiodieselIEA

Production of biodiesel. Energy, Infrastructure

Production


EnProdBiogasIEA

Production of biogas (derived from anaerobic fermentation of biomass and solid wastes and combusted to produce heat and/or power).


Production


EnProdCoal Coal production Energy, Infrastructure

Production

1960-1995 WRI CD 98

EnProdCoalBP Coal production Energy, Infrastructure

Production


EnProdElec Electricity production total in kilowatt-hours


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


EnProdGeoTherm

Energy production, geothermal


Production


EnProdGeothermIEA

Energy produced from heat emitted with earth's crust, usually in the form of hot water or steam.


Production


EnProdHydroIEA

Potential and kinetic energy of water converted into electricity in hydroelectric plants.


Production


EnProdNuclearIEA

Energy produced by nuclear fission or nuclear fusion.


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


57

EnProdSolar Energy production, solar Energy, Infrastructure

Production


EnProdSolarPhotoIEA

Electricity production from photovoltaic cells.


Production


EnProdSolarThermIEA

Energy production from solar radiation used for hot water production and electricity generation (passive solar for direct heating, cooling, lighting not included).


Production


EnProdTideWave

Energy production, tide, wave, and water


Production


EnProdTideWaveOceanIEA

Electricity generation derived from tidal movement, wave motion, or ocean current.


Production


EnProdWindIEA Electricity generation by wind turbines.


Production


EnThermalElec Thermal electricity production


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


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


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


Resources 2000


EnResorOil Energy resources, oil Energy, Infrastructure

Resources 1999 WEC

EnResorOilUSGS

Undiscovered energy resources, oil


Resources 2000


EnResorSynthetic

Energy resources, synthetic fuels (oil shale, tar sands)


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)


Electricity 1960-2008 WDI CD 2011; 2010 are estimates using 2007 to 2008 growth rates

EnExportsCoalIEA

Exports of coal and coal products


Trade 1960-2009 IEA Energy Balances of OECD and non-OECD Countries 2008

EnExportsNatGasIEA

Exports of natural gas Energy, Infrastructure, Trade


EnExportsOilProductsIEA

Exports of Crude Natural Gas Liquids



EnExportsPeatIEA

Peat exports Energy, Infrastructure, Trade


59

EnExportsTotalIEA

Total energy exports Energy, Infrastructure, Trade


EnImportsCoalIEA

Imports of coal and coal products


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



EnImportsPeatIEA

Peat imports Energy, Infrastructure, Trade


EnImportsTotalIEA

Total energy imports Energy, Infrastructure, Trade


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


EnvAvgAnnTemp

Average annual temperature from 1980 to 1999

Environment Temperature

1999


60

EnvAvgAnnTempChg

Change in average annual temperature over time from 1980 to 1999

Environment Temperature

1999


LandCrop Land, crop Environment, Infrastructure


LandGrazing Land, grazing Environment, Infrastructure


LandOther Land, other Environment, Infrastructure


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


Water 1977-2001 WRI Earthtrends http://earthtrends.wri.org/

WaterAnWithdrawals

Annual water withdrawals/use (1990=70-99;2000=update, mostly 2000)


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

Documents

PERU: 2050 David_Peru2050.pdfINEI - Censos Nacionales de Población y Vivienda 1940,1961,1972,1981, 1993, 2007 Total Fertility Rate WDI online 2011 1960-2009 INEI - Encuesta Demográfica