About

Modelling platform

The International Futures (IFs) modelling platform is developed and hosted by the Frederick S. Pardee Center for International Futures at the Josef Korbel School of International Studies, University of Denver. IFs is an integrated forecasting platform for thinking about long-term, country-specific, regional, and global development.

The IFs tool draws from multiple modelling methods and integrates them to form relationships in global systems and generate forecasts. The model is, therefore, hybrid in structure that includes stock-and-flow and equilibrating feedback. The scenario analysis capabilities of IFs allow users to explore the potential impact of simulated policy interventions or frame long-term uncertainty within and across development systems.

Modelling Techniques

IFs forecasts development for 186 countries and their interaction, including 54 countries and territories in Africa. It blends different modelling techniques to form relationships based on academic literature to generate forecasts. IFs is, therefore, a dynamic, recursive system with annual time-steps. It uses historical data from 1960 (where available) to identify trends and produce a 'Current Path' scenario from 2019 (the current base year) to 2100. The Current Path scenario (or Base Case) is, therefore, a result of dynamic interactions across critical systems based on prevailing policies and environmental conditions rather than a linear extrapolation of trends.

IFs is one of the few global modelling platforms capable of projecting SDG achievement across all SDGs at the country level and has been widely used in the analysis of African development. Collectively, the IFs platform projects more than 700 variables, of which more than 100 represent the goals and targets of the SDGs. IFs still uses several traditional measures of economic size, growth and distribution, such as GDP, GDP per capita, Gini and GEM (gender empowerment measure). New indices and data are phased in as global data sets become available, often only several years after the launch. Examples include using the UNCTAD Inclusive Growth Index and the UNDP Gender Development Index (GDI).

Data Challenges

Data in Africa is a big problem. For instance, in 12 African countries, the last census was conducted before 2010, and no census has been completed after the cessation of South Sudan from Sudan, meaning that basic statistics for these two countries should be treated with caution. The latest census in Somalia was conducted in 1987. Poor data inevitably translates into poor forecasts, including for the SDGs, where indicators such as secondary school enrolment and completion have notable gaps.

With that caveat, the data series within IFs (more than 5 500 series integrated within it) come from a range of international sources like the World Bank, International Monetary Fund (IMF), World Health Organization (WHO), various United Nations (UN) bodies like Food and Agricultural Organization (FAO) and United Nations Population Fund (UNPF), etc. These organisations invest considerable resources in harmonising data for comparisons between countries and sometimes use alternative sources to estimate missing data. Still, the available data often needs to catch up to the current year.

Forecasting requires that IFs be able to initialise from this incomplete historical data. To fill the associated 'data gaps', IFs has a powerful pre-processing function that estimates current data and allows for forecasts.  Because IFs produce forecasts that move beyond a linear extrapolation, their projections have historically been comparable to the data that international organisations and national governments ultimately release.

Where possible and where data is available and comparable, we update the data in IFs with national statistics sourced from national data providers. This is, however, labour-intensive and only possible when doing extended analysis on a specific country or region. National governments may also use different standards and definitions, such as national poverty instead of the international poverty line, meaning that updates must be validated and done with care.

Sub-Modules

The IFs model integrates forecasts for different sub-modules: population, economy, agriculture, education, energy, governance, international politics, environment, technology, infrastructure, health, etc. These sub-modules are dynamically connected, so the model simulates how changes in one system lead to changes in all other systems. As a result, IFs endogenises more relationships from a broader range of critical global systems than any other model. Below is a visual representation of the significant sub-modules in IFs, followed by an explanation of some.

Link to Understanding the IFs model.

The Economic Module in IFs 

The IFs economic module is a complex, global system that predicts economic changes every year. It links detailed country data and global information to understand how economies interact. Here are its key features:

• Economic Growth: The model predicts economic growth by considering factors like productivity, capital (like machinery and buildings), and labour (workers).
• Government Role: It has a detailed way of showing how governments earn and spend money.
• Balanced Economy: It balances different parts of the economy, like farming, energy, and technology, and includes informal (unofficial) economic activities. It also looks at income inequality and poverty.
• Inputs and Outputs: Information from other areas affects how much is produced, and the model's results (like GDP) are important for other areas.
• Model Foundations: The model uses established economic theories and a production formula (Cobb-Douglas) that includes labour, capital, and multifactor productivity (how effectively resources are used). Multifactor productivity depends on factors like education, government effectiveness, infrastructure, and research.
• Economy Sectors: The economy is divided into six areas: agriculture, raw materials, energy, manufacturing, services, and technology. The model makes sure that production and trade are balanced.
• Government Spending and Debt: Changes in government debt influence spending patterns that then affect areas like education and infrastructure.
• Poverty Analysis: It looks at poverty by considering production, resource distribution, and population changes. It uses a complex formula (Cobb-Douglas with Solow residual) and considers income distribution and inequality to predict changes in poverty. Poverty data is initially taken from the World Bank's PovCalNet.

For more technical information on the IFs economic module, including poverty, see: 

https://pardeewiki.du.edu/index.php?title=Economics

The Infrastructure Module in IFs

The IFs infrastructure model looks at two types:

1. Core Infrastructure: This includes roads, electricity generation, water and sanitation facilities, and Information and Communication Technology (ICT).

2. Other Infrastructure: This covers railroads, ports, and airports.

The model mainly predicts changes in physical infrastructure, like road density, electricity availability, irrigation systems, and access to water and sanitation. Here's how it works:

• Water and Sanitation Access: Access is divided into categories: unimproved, improved, and piped water; and unimproved, shared, and improved sanitation. Predictions for future access are based on factors like GDP per capita, average education years of adults, government health spending, and urban population share. However, the actual increase in safe water and sanitation access depends on available government funding.
• Electricity Access: This is predicted based on population changes, urbanisation, government effectiveness, and poverty levels. The model differentiates between rural and urban areas, usually assuming better urban access.
• Impact on Growth and Health: Infrastructure improvements affect economic growth by increasing productivity. Moreover, access to water and sanitation plays a crucial role in child health, including reducing undernourishment and stunting.

For more technical information on the IFs infrastructure model, see:

https://pardeewiki.du.edu/index.php?title=Infrastructure

The Agriculture Module in IFs

The IFs agriculture model focuses on three main areas: crops, meat, and fish. It examines their supply and demand, including imports, exports, and prices. These agricultural products are used for direct consumption, animal feed, industry, and food manufacturing. Key elements of the model include:

• Land and Water Use: It tracks how land and water are used, which is crucial for farming.
• Balancing Supply and Demand: Like in its economic model, IFs tries to balance agricultural supply and demand using a system that includes stocks and prices. It doesn't aim for perfect balance at every moment but tries to achieve it over time.
• Factors Affecting Production: Agricultural output depends on resources like land, livestock, capital, labour, climate, and technology. Technology improves how much can be produced, like higher crop yields or more meat from less feed grain.
• Losses and Wastage: The model considers losses at various stages – in the fields, during distribution, and at homes. These losses are influenced by average income.
• Demand Factors: Income, prices, and other factors shape demand for agricultural products. For instance, changes in what people eat can affect meat demand, impacting the need for crops used as animal feed. The use of crops for biofuels, influenced by energy prices, is also factored in.

For more technical information on the IFs agriculture model, see:

https://pardeewiki.du.edu/index.php?title=Agriculture

The Governance Module in IFs

The IFs governance model predicts how governments will perform in three areas:

1. Capacity: How well a government can function and serve its people.

2. Inclusiveness: How much it includes different groups in decision-making.

3. Security: The government's ability to maintain peace and prevent conflicts.

Key aspects of the module include:

• Government Security: This is influenced by how stable a country is and the likelihood of internal conflicts. Security affects the country's economy and population, including how people live and move.
• Corruption's Impact: Corruption is a crucial factor that affects government capacity. It impacts how effectively and responsibly a government operates.
• Determinants of Corruption/Transparency: Corruption levels are linked to several factors:
• Economic development (measured by GDP per capita).
• Gender empowerment and equality.
• The level of democracy in the country.
• Dependence on energy exports, although this is a less strong influence.

These factors explain about 80% of the differences in cross-country corruption, based on the latest data from Transparency International's Corruption Perceptions Index (CPI).

For more technical information on the IFs governance model, see:

https://pardeewiki.du.edu/index.php?title=Governance

The Education Module in IFs 

The IFs education model predicts how education will develop in each country, focusing on:

1. Adult Educational Attainment: The education levels achieved by adults.

2. Children's Graduation Rates: How many children of the right age complete their education.

The model tracks students' journey from primary to tertiary education (like college), considering both boys and girls. It looks at factors like demographic changes and economic factors (the country's economic situation and how much the government invests in education).

The module measures education quantity (the number of students in school) and education quality (how well students perform in maths, science, and reading). Students’ progress through the education system from primary to higher levels based on transition rates (moving from one education level to the next), graduation rates (completing each education stage), and dropout and re-entry rates (leaving and rejoining the education system).

Education spending is linked to GDP per capita, demand for education (based on the number of children of school-going age), and government spending for education. The model also shows how education affects economic growth (through improving productivity) and population dynamics (influencing things like birth rates).

For more technical information on the IFs education model, see:

https://pardeewiki.du.edu/index.php?title=Education

The Demographic Module in IFs

The IFs demographic model predicts population changes in each country. It looks at the number of people of different ages and genders and how this changes over time due to births, deaths, and people moving in or out of a country (migration). Here's how it works:

• Population Calculation: The number of people each year is calculated by taking the population from the previous year, adding new births, and subtracting deaths.
• Fertility Rates: This is about how many children the average woman is likely to have during her lifetime. As countries develop economically (higher GDP per capita) and adults get more education, the average number of children per woman typically decreases. Other factors like infant mortality rates and the use of contraception also play a role.
• Mortality Rates: This is about how many people die each year, which the health module of IFs helps to determine.
• Migration: The model predicts people moving between countries using a "push-pull" logic, meaning it looks at reasons why people might leave a country (push factors) and reasons why they might move to a country (pull factors).

For more technical information on the IFs demographic and health models, see:

https://pardeewiki.du.edu/index.php?title=Population

https://pardeewiki.du.edu/index.php?title=Health

The Energy Module in IFs

The IFs energy model predicts how energy is used and produced in countries, focusing on:

1. Demand and Supply: It looks at how much energy is needed (demand) and how much is available (supply). The model balances these by considering prices, trade, and investment.

2. Factors Influencing Demand: Three main things shape energy demand:

• Economic Size: How big the economy is.
• Population: How many people are in the country.
• Energy Intensity: This measures how much energy is used for each unit of economic activity (GDP). It shows how efficiently energy is used and changes with:
• The country's development level.
• Advances in technology.
• Energy prices.

For more technical information on the IFs energy models, see:

https://pardeewiki.du.edu/index.php?title=Energy

Data and Classifications

Currency

Where required, US$ numbers from IFs have been converted from 2011 US$ to 2017 US$ values at a rate of 1:1.09 from the US Inflation Calculator. That was needed in IFs versions 7.84, 7.92 and 7.96 that used 2011 values. IFs version 8.06 and onward use 2017 US$ values.

Country Groupings and Regions

This website uses the 2021/22 World Bank country income group classification for all analyses done using IFs 7.84, 7.92 and 7.96. All analyses done using IFs 8.06 and higher use the 2023/24 country income groups. This is indicated in the text as appropriate. The World Bank changed the categories of Guinea and Zambia in mid-2023 from low- to low-middle-income. Countries are not removed from the groups they are being compared to. For example, Mozambique remains part of low-income Africa when it is compared to the group.

The user is advised to confirm the country composition of the African regions as used in the various geographies. In mid-2023 we moved to the use of the regions as defined by the AU and AUDA-NEPAD, available on the AU website here, and we are phasing this in across the site.

Initially, the regional composition that was used is listed below and was used in IFs versions 7.84 to 7.96:

• Central Africa: Angola, Cameroon, Central African Republic, Chad, DR Congo, Republic of the Congo, Equatorial Guinea, Gabon, São Tomé and Príncipe
• East Africa: Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan, South Sudan, Tanzania, Uganda
• North Africa: Algeria, Egypt, Libya, Mauritania, Morocco, Tunisia
• Southern Africa: Botswana, Eswatini, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Zambia, Zimbabwe
• West Africa: Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, Togo - equivalent to ECOWAS
• Sub-Saharan Africa consists of the countries of East, Southern, Central and West Africa
• Africa includes all 54 countries but excludes Western Sahara, for which data was unavailable. As from IFs version 8.06, Africa has Western Sahara as a 55th country.

The composition of the eight regional economic communities that are included in this site is as follows:

• AMU: Algeria, Libya, Mauritania, Morocco, Tunisia
• CEN-SAD: Benin, Burkina Faso, Cape Verde, Central African Republic, Chad, Comoros, Côte d’Ivoire, Djibouti, Egypt, Eritrea, The Gambia, Ghana, Guinea, Guinea Bissau, Kenya, Liberia, Libya, Mali, Mauritania, Morocco, Niger, Nigeria, São Tomé and Príncipe, Senegal, Sierra Leone, Somalia, Sudan, Togo, Tunisia
• COMESA: Burundi, Comoros, DR Congo, Djibouti, Egypt, Eritrea, Ethiopia, Eswatini, Kenya, Libya, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Somalia, Sudan, Tunisia, Uganda, Zambia, Zimbabwe.
• EAC: Burundi, DR Congo, Kenya, Rwanda, South Sudan, Tanzania, Uganda. Somalia joined the EAC in November 2023.
• ECCAS: Angola, Burundi, Cameroon, Central African Republic, Chad, DR Congo, Republic of the Congo, Equatorial Guinea, Gabon, Rwanda, São Tomé and Príncipe
• ECOWAS: Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, Togo. In February 2024, the military regimes in Burkina Faso, Mali and Niger announced that they would immediately leave ECOWAS, following the coups in the various countries.
• IGAD: Djibouti, Eritrea, Ethiopia, Kenya, Somalia, Sudan, South Sudan, Uganda
• SADC: Angola, Botswana, Comoros, DR Congo, Eswatini, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Tanzania, Zambia, Zimbabwe

Regions in the rest of the world that are used on the website are as follows:

• East Asia: China, Hong Kong, Japan, North Korea, South Korea, Mongolia and Taiwan
• South America: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay and Venezuela
• South Asia: Afghanistan, Bangladesh, Bhutan, India, Iran, Maldives, Nepal, Pakistan and Sri Lanka

Maps

Borders and delimitations follow international practice. Disputed borders are indicated in red.

Poverty Lines

The analyses done using IFs version 7.84, 7.92 and 7.96 on this website use the World Bank extreme poverty lines of US$1.90 for low-income countries (and to measure progress towards the 2030 Sustainable Development Goals), US$3.20 for low-middle-income countries, US$5.50 for upper-middle-income Africa and US$22.70 for high-income countries. All are defined in 2011 US$ prices.

In May 2022, the Bank announced that it would use US$2.15 in 2017 prices as the new international poverty line (to measure progress towards the Sustainable Development Goals) instead of US$1.90. Instead of the previous US$3.20 for low middle-income countries, the adjusted poverty line is now $3.65, and $6.85 for upper middle-income countries (instead of $5.50 in 2011 prices). The Bank has yet to announce the new poverty line for high-income countries, previously set at $22.70 in 2011 prices. We use the updated poverty lines for all analyses done using IFs 8.06 and more recent versions.

Carbon vs CO₂

Carbon emissions are presented in billions or millions of tons of carbon, not CO₂ (carbon dioxide) equivalents. A ton of carbon therefore, consists of the mass of the carbon component of each greenhouse gas and not only CO₂. A ton of carbon is roughly equivalent to 3.67 tons of CO₂.

Energy production

Data on energy production is in six types, namely oil, gas, coal, hydro, nuclear and other renewables. The data is converted into million or billion barrels of oil equivalent (BOE) to allow for comparisons between different sources. 

Informal sector

Estimations and data on the informal sector are often unreliable and must be treated carefully. Researchers generally distinguish between the shadow and informal economy. According to the ILO: ‘The informal economy refers to all economic activities by workers and economic units that are – in law or in practice – not covered or insufficiently covered by formal arrangements. Where data is not available, IFs estimate the size. Note that the ILO definition of employment in the informal economy excludes the agricultural sector

Agriculture production

The agricultural production and demand data in the IFs forecasting platform initialises from data provided on food balances by the Food and Agriculture Organization (FAO). IFs contains data on numerous types of agriculture but aggregate its forecast into crops, meat and fish, presented in million metric tons.

Economic sectors

The IFs platform uses data from the Global Trade and Analysis Project (GTAP) to classify economic activity into six sectors: agriculture, energy, materials (including mining), manufacturing, services and information and communication technologies (ICT). Most other sources use a threefold distinction between only agriculture, industry and services, with the result that data may differ. The differences in sectoral composition is often evident in the transition from the base year to the first forecast year.