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 a series of relationships in global systems and to generate its forecasts. The model is therefore hybrid in structure that includes the use of 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. 

IFs forecasts development for 186 countries and their interaction, including 54 countries and territories in Africa. It blends different modelling techniques to form a series of relationships based on academic literature to generate its forecasts. IFs is therefore a dynamic, recursive system with annual time-steps. It use historical data from 1960 (where available) to identify trends and produce a 'Current Path' scenario from 2017 (the current base year) to 2100. The Current Path scenario (or Base Case) is, therefore, a result of dynamic interactions across key systems based on prevailing policies and environmental conditions rather than a linear extrapolation of trends.

Currently, 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 analysis of African development. Collectively, the IFs platform projects more than 700 variables of which more than 100 represent goals and targets of the SDGs.

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. Although international organizations commit significant resources towards updating data, it often lags behind the current year. Because IFs produces forecasts that move beyond a linear extrapolation, its forecasts have historically been comparable to the data that is ultimately released by international organizations. Where possible and where data is available and comparable, we replace the international 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.

The IFs model integrates forecasts for different sub-modules, including population, economy, agriculture, education, energy, governance, international politics, environment, technology, infrastructure, and 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 endogenizes more relationships from a wider range of key global systems than any other model in the world. Below is a visual representation of the major sub-modules in IFs followed an explanation of some.

Link to interactive bubble diagram of International Futures model

The Economic module in IFs 

The economic representation within IFs is a global, annually recursive dynamic general equilibrium model, integrated extensively with all other modules. It combines country-specific and globally-linked Social Accounting Matrices (SAM) with structures that generate their dynamics. Three significant features differentiate IFs from most other models with these characteristics. First, the representation in IFs of economic growth is responsive to an unusually extensive set of productivity drivers, as well as to capital and labour. Second, the social accounting system of IFs has an especially extensive representation of government revenues and expenditures. Third, IFs elaborates general equilibrium treatment of the economy with extended partial equilibrium representations of agriculture and energy, with representation of the informal economy, and with elaborated formulations of income inequality and poverty.

On the input side, variables from almost all other modules within IFs affect production levels while on the output side, the levels of GDP and GDP per capita are crucial for almost all other modules. The module also includes the informal economy. The economic model draws on both the classical tradition’s focus on economic growth (with great attention to the newer work on endogenous growth theory) and the neoclassical perspective's general equilibrium approach. The supply side of the economic model is based on the Cobb-Douglas production function which uses labour, capital, and multifactor or total factor productivity (MFP) as the primary drivers of economic growth. Capital stock (K) is a function of investment and depreciation rates, labour supply (LABS) is determined from population and endogenously derived labour force participation rates. Multifactor or total factor productivity in the economic module is determined by human capital (education and health), social capital (governance effectiveness, corruption, and economic freedom), physical capital (infrastructure) and knowledge capital (R&D). The production function is embedded in a six-sector model of the economy featuring agriculture, raw materials, energy, manufactures, services, and information and communication technology (ICT) that balances domestic demand and trade in a general equilibrium-seeking structure. Production and consumption of goods and services are in turn incorporated into a larger SAM, which represents the behaviour and financial interaction of households, firms, and government. As in many computable general equilibrium models, the behaviour of agents within this system is not fixed. For example, variations in the levels of government debt trigger different patterns of government expenditures and revenues in the model, with effects on spending on education and infrastructure, etc. that in turn affect economic growth. 

Poverty in IFs rely upon a) economic production/consumption models; b) resource distribution; and c) demographic dynamics. Economic production is driven by a dynamic-recursive computable general equilibrium model with Cobb- Douglas production function and Solow residual. The model assumes a log-normal income distribution and uses a measure of Gini-coefficient for income inequality to drive changes in distribution across time. Poverty values are initialized from PovCalNet at the World Bank.

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

IFs distinguish between ‘core’ and ‘other’ infrastructure. Core infrastructure refers to roads, electricity generation, improved water and sanitation, and ICT. Other infrastructure refers to railroads, ports, airports.  The IFs infrastructure model forecasts mainly developments in physical infrastructure such as road density, electricity access, irrigation systems and access to water and sanitation.  Access to water is divided into unimproved, improved (other) and piped water access. Similarly, access to sanitation is divided into three categories: unimproved, shared and improved. Projections over time for access to water and sanitation are determined by an expected demand for access to safe water and sanitation, which is driven by general levels of development approximated by GDP per capita and average education years of the adult population, and further informed by information on government spending (percent of GDP going to health), and the share of the population living in urban areas. Together this constitutes the expected level of safe access to water and sanitation, which informs the government spending required to invest in infrastructure to improve access to safe water and sanitation. Not in all cases will the expected demand be met by the required financial government resources, limiting the overall rise in access to safe water and sanitation. Access to electricity over time is a function of changes in population and household size, changes in urbanization rates and changes in government effectiveness and poverty levels.  Access to electricity is separately forecasted for rural and urban communities, but given historic patterns in development, the model assumes that urban electricity is always equal or higher than rural electricity access. Changes in infrastructure access affect economic growth through its effect on productivity. In addition, changes in some infrastructure indicators are also linked to health and food security, with access to water and sanitation being an important driver of child health and child 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 agricultural model tracks the supply and demand, including imports, exports, and prices, of three agricultural commodities: crops, meat, and fish. Crops, meat and fish have direct food, animal feed, industrial and food manufacturing uses. The agricultural model is also where land use dynamics and water use are tracked in IFs, as these are key resources for the agricultural sector. Like in the economic model, the agriculture model combines a growth process with a partial economic equilibrium process using stocks and prices to seek a balance between the demand and supply sides. As in the economic model, no effort is made in the standard adjustment mechanism to obtain a precise equilibrium in any time step. Instead stocks serve as a temporary buffer and the model chases equilibrium over time. In the IFs model, agricultural production is a function of the availability of resources such as land, livestock, capital, and labour, as well as climate factors and technology. Technology is most directly seen in the changing productivity of land in terms of crop yields, and in the production of meat relative to the input level of feed grain. The model also accounts for lost production (such as spoilage in the fields or in the first stages of the food supply chain), distribution and transformation losses and consumption losses (which account for food lost at the household levels) which are all determined by average income. Agricultural demand depends on average incomes, prices, and a number of other factors. For example, changing diets can affect the demand for meat, which in turn affects the demand for feed crops. The industrial demand for crops, some of which is directed to the production of biofuels, is also affected by energy prices. 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 module forecasts three governance dimensions around capacity, inclusiveness and security. Government security is driven by state fragility, and the risk of internal conflict. Effects of government security operate primarily through economic and demographic pathways through the contribution of social capital to economic growth and patterns of mortality and migration. In the model, corruption is one of the most powerful determinants of government capacity and governance effectiveness as well as accountability.

Corruption/transparency is a function of the level of development (GDP per capita), Gender Empowerment/gender equality, democracy, and the dependence of a country on exports of energy (less-powerful but still-significant). These variables collectively account for nearly 80% of the cross-country variation in corruption in the most recent year of data as measured by the Transparency International index of corruption perceptions (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 module forecasts development at the country level on educational attainment of adults, and educational graduation rates of of-age children. The module projects students’ progress from primary to tertiary education, disaggregated by sex, driven by changes in demographics, the economic module and government investment in education. The IFs model forecasts both education quantity (the number of students enrolled), and indicators of education quality (test scores on math, science and reading). From primary education, students move through the education system towards lower secondary, upper secondary and tertiary based on transition rates between education levels, graduation rates for each level, and dropout and re-entrance rates within each level. Spending on education is a function of changes in GDP per capita, changes in demand based on children of eligible age and government spending. In the IFs model, education affects economic growth through its effect on productivity, and the population through its effect on fertility 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 module uses an age-sex cohort distribution and forecasts demographic changes based on births, deaths and migration patterns. The IFs population projections are based on two dominant flows, fertility determining the inflow of newly born on a yearly basis, and mortality, determining the outflow of deaths on a yearly basis. As such the population in a single year is a function of last year’s population, plus the influx and outflux from births, and deaths. In addition, IFs forecasts migration using a push-pull logic. The total fertility rate, the number of children that the average woman will bear throughout her life, generally decreases in the long run as deep or distal drivers such as GDP per capita (from the economic model) or formal years of education of adults (from the education model) increase.  Other determinants of fertility rates in the model are infant mortality rates and contraception usage.  The health model of IFs determines infant mortality rates. 

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

Groups, US$ and maps

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

Countries are not removed from the groups they are being compared to. For example, Mozambique remains part of low-income Africa when compared to this group.

This website uses the 2021/22 World Bank country income group classification for all analysis done using IFs 7.84, 7.92 and 7.96. These are as follows:

• Low-income Africa consists of the following 23 countries: Burkina Faso, Burundi, Central African Republic, Chad, DR Congo, Eritrea, Ethiopia, The Gambia, Guinea, Guinea Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Sierra Leone, Somalia, Sudan, South Sudan, Togo, Uganda
• Low middle-income Africa consists of the following 23 countries: Algeria, Angola, Benin, Cameroon, Cape Verde, Comoros, Republic of the Congo, Côte d’Ivoire, Eswatini, Djibouti, Egypt, Ghana, Kenya, Lesotho, Mauritania, Morocco, Nigeria, São Tomé and Príncipe, Senegal, Tanzania, Tunisia, Zambia, Zimbabwe
• Upper middle-income Africa consists of the following seven countries: Botswana, Equatorial Guinea, Gabon, Libya, Mauritius, Namibia, South Africa
• High-income Africa consists of Seychelles

All analysis done using IFs 8.06 and higher uses the 2024 country income groups and this is indicated in the text as appropriate. In Africa Guinea and Zambia changed in their WB categories from 2023 to 2024.

Regions in Africa are as follows:

• 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-Sahara 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 not available. As from IFs version 8.06, Africa includes Western Sahara as a 55th country.

Regions in the rest of the world 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

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
• ECCAS: Angola, Burundi, Cameroon, Central African Republic, Chad, DR Congo, Republic of the Congo, Equatorial Guinea, Gabon, Rwanda, São Tomé and Príncipe
• ECOWAS (similar to West Africa): Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, Togo
• 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

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

Poverty lines: The analysis done using IFs version 7.84, 7.92 and 7.96 on this website uses 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 not yet announced the new poverty line for high-income countries, previously set at $22.70 in 2011 prices. We use the updated poverty lines for all analysis done using IFs 8.06 and more recent versions.

Carbon vs CO₂: Carbon emissions are presented in billion or million tons of carbon, not CO₂ equivalent. It, therefore, consists of the mass of the carbon component of each greenhouse gas.