Forecasting the MDGs

Which countries will successfully achieve the Millennium Development Goals?  What policy decisions can we make today to increase the number of countries that will make the 2015 targets? What are reasonable benchmarks for UN development goals after 2015?  The software application presented here, International Futures, or IFs, for short, is a tool designed to help people think critically about these kinds of questions. IFs is an online software application housed at the Pardee Center for International Futures at the University of Denver’s Josef Korbel School of International Studies. IFs is designed to help users critically think about complex relationships between systems in order to inform policy decisions, and has been used in analysis conducted for the European Union, African Union, United Nations Environment Programme, and others. As a model, IFs is designed to help users do three things: 1) analyze historic patterns and trends, 2) better understand where key systems and variables are likely to develop over time and 3) frame uncertainty by modeling different kinds of policy choices and their impact. The utility of our model is directly connected to the data-gathering work of the World Bank for the purposes of evaluating the United Nations Millennium Development Goals (MDGs) to 2015 and beyond. We collected hundreds of World Bank variables, and several hundred from other sources (we have nearly 2,500 historic data series). We used these data to explore trends cross-sectionally (between countries, regions, and groups) and longitudinally (over-time). These analysis tools then inform the structure of the forecast model. You can access all of these historic variables through the Data Analysis feature of the software, and forecasts through the Display feature. The structure of IFs rests on the assertion that all things are interconnected.  As such, IFs is comprised of sub models from the following key global systems: demographics, economics, energy, agriculture, socio-political, government/finance and the environment. The demographic model forecasts changes in fertility and mortality rates across time for males and females. The economic model endogenizes productivity changes, represents six capital sectors and agents as representing governments, households or firms. In energy, IFs calculates overall available levels of fossil fuel reserves and production for six types of energy along with secondary production and access to electricity. The agricultural model calculates yields based on a land-use model, changing levels of technology, and production of grains and meat for import and export. Environmental systems are driven by fossil fuel use impacting carbon in the atmosphere. Democracy, economic freedom, governance effectiveness and regulatory quality are all taken up in the socio-political model. Finally, government finances are considered through revenues (taxes of different types on households and firms) as well as expenditures (education, health, military, R&D, infrastructure and a residual category). The descriptions in this paragraph are not exhaustive of the capabilities of each sub-model, but are illustrative. The systems mentioned above are operationally linked to one another. For example, increases in female education decreases fertility. This change impacts variables such as government spending on primary education, which changes the makeup of the labor supply. This then drives changes in economic activities such as production, imports, and exports which further impacts the global economy. We can explore MDGs out to 2015 using this integrated modeling approach.  For this example, let's evaluate MDG 1.A—the halving of the number of people living on less than 1$ per day relative to 1990 levels— by exploring Zambia. In 1990 this southern African country experienced nearly 70% of the population living below 1$ per day.  The base case forecast of IFs—considering the full range of systems discussed above—calculates that Zambia will not be able to meet goal 1.A if the current development path continues. Instead of less than 35% of the population living on less than 1$ per day, it is much more likely that this number will be closer to 55% by 2015. Scenario analysis can help to broaden insights provided by the base case. Further exploring Zambia and MDG 1.A, different policy choices embedded in scenarios produce a range of results. If we take four scenarios that were created for the United Nations Environment Programme by IFs, the output in the percentage of people living on less than 1$/day in 2015 ranges from 55% to 60%. By 2040, this distribution is much larger, and extends from 15% to 35%. This may argue for future goals being set in a similar way to current goals: halving the number of people living on less than 1$/day relative to 2015 levels by 2040. However, global development has progressed in many places to the point where new goals will be necessary. The percentage of people living on less than 1$/day should be augmented with the percentage living on less than 2$/day, and 5$/day. Primary education enrollment rates should be added to by also exploring lower-secondary enrollment rates. Health concerns around malnutrition should remain targets, but concerns about under nutrition resulting from obesity should now be a part of the discussion. IFs provides the software platform to think critically about these development planning issues. IFs is the most user-friendly, large-scale integrated modeling application that we are aware of. This software application is both complex and powerful. It leverages World Bank data series and produces forecasts that directly pertain to the evaluation of the MDGs for different countries and time horizons. While its use is non-trivial, its payoff as a tool for informing pedagogy, theory and policy can be important. Resources for users: Website- http://www.ifs.du.edu Online Help System- http://www.ifs.du.edu/assets/help/helpoverview.aspx (not viewable in Google Chrome) Forum- www.ifsforums.du.edu Further Documentation- http://www.ifs.du.edu/documents/reports.aspx         

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