Modeling as a Tool for Planning

robertoBy Roberto Telleria

What are the determinants of wellbeing, and how can they be influenced by policies? As pointed out by experts such as John Helliwell, Co-Director of the Canadian Institute for Advanced Research (CIFAR) (Helliwell, 2002), Professor Richard Easterlin of the University of Southern California (Easterlin, 2001) and Christian Grootaert of the World Bank (Grootaert, 1999), the determinants vary according to geographical location, age, gender, time, and other economic, social and psychological variables. Thus the question of what determines wellbeing is complex. The level of wellbeing of a household seems to be a question that every member of that household can answer themselves, but this answer relies on their perceptions of what constitutes a high quality of living, family life, health, education, and the environment.

In this short article the complexities involved in modeling the interactions between the variables that determine wellbeing, and those that determine policies, are presented. Two examples illustrating the interactions between health and deforestation, that in turn affect wellbeing, are discussed.

Example 1: Differences in health, determined by differences in socioeconomic position, have been identified as key determinants of household wellbeing (Lantz, 1998). But the wellbeing of a population also depends upon many other factors such as income, education, culture, social control, the climate, natural resources, and the quality of the environment. Easterlin (2001) considers household wellbeing to be explained by internal factors (family, health, education, and psychological factors) and external factors (income, institutions, and the environment). Material circumstances (i.e. income), an external factor, are identified as being the main determinant of wellbeing. This is followed by two internal factors: family concerns (a happy family life and good relationships with children and immediate relatives), and one’s personal and family health. They are followed by work satisfaction (external) and issues related to personal character such self-esteem and confidence (internal). However, this ranking is questionable because isolating internal and external factors from one another is not necessarily appropriate. For example, education and employment affect wellbeing through their income effect. Also, it turns out that many factors including health and many socio-economic circumstances have some sort of output that is manifested as income.

Example 2: One of the reasons that people deforest is in order to obtain wood and leaves that provide energy for cooking and heating. Nowadays, almost no one in developed countries uses old-fashioned open fires or traditional stoves because technology has made modern life much simpler by providing safe and convenient cooking and heating machines. But this technology is not accessible to everyone. A study by researchers from the Abdul Latif Jameel Poverty Action Lab (JPAL) and Harvard Kennedy School (Hanna et al., 2008) reports that about half of the world population, of which about 95% are in the least developed and developing countries, still rely on solid fuels (such as wood, leaves, dung, agricultural residues, and coal) to meet their energy needs. One problem with these fuels is that they generate high levels of particulates and carbon monoxide which are well-known to cause respiratory problems, pneumonia, and eventually death (Bruce et al, 2006). But health is also affected by many other factors; for example, Aradhyula et al. (2008) investigated the determinants of children’s health and wellbeing, and found that income inequality has a statistically significant effect on their emotional wellbeing (mental health). Diverse factors such as socioeconomic status, income distribution, household behavior, neighborhood characteristics are all determinants of child health.

These two examples show how predicting the outcomes which result from the decisions of many actors and the effects of multiple variables can be very complex. Policies, market forces, environmental considerations, and other issues all influence deforestation, health, and many other factors which affect wellbeing. Policy makers are challenged by these complexities and frequently face trade-offs when devising policies. This is where SimPachamama can help: it is a simulation tool that allows us to model multiple agents and their behaviors and preferences that result from policy shocks. This thus provides a more comprehensive picture of the possible outcomes, allowing us to acquire much-needed intuition and knowledge of the actual situation in order to design more effective policies that affect the environment and human wellbeing.

Roberto Telleria is an Agricultural Policy Specialist at the International Center for Agricultural Research in the Dry Areas (ICARDA), Jordan.

For your reference:

Aradhyula S., & Rahman T. (2008). Socioeconomic Status, Neighborhood, Household Behavior, and Children’s Health in the United States: Evidence from Children’s Health Survey Data. Selected Paper presented at the American Agricultural Economics Association Annual Meeting, Orlando, July 27-29, 2008.

Bruce N., Rehfuess E., Mehta S., Hutton G., & Smith K. (2006). Indoor Air Pollution. In D. T. Jamison (Eds.), Disease Control Priorities in Developing Countries (2nd ed.). Oxford University Press.;

Diener E. (1984). Subjective well-being. Psychological Bulletin, 95, 542-575.

Easterlin R.A. (2001). Income and Happiness: Towards a Unified Theory. The Economic Journal, 111(473), 465-484.

Grootaert C. (1999). Social Capital, Household Welfare, and Poverty in Indonesia. World Bank – Social Development, July 1999. World Bank Policy Research Working Paper No. 2148.

Hanna R., Duflo E., & Greenstone M. (2008). Indoor Air Pollution, Health and Economic Well-being. New York University Wagner Graduate School of Public Service and J-PAL.

Helliwell J. F. (2002). How’s Life? Combining Individual and National Variables to Explain Subjective Well-Being. National Bureau of Economic Research. Working Paper 9065.

Lantz P., House J., Lepkowski J., Williams D., Mero R., & Chen J. (1998). Socioeconomic Factors, Health Behaviors, and Mortality: Results From a Nationally Representative Prospective Study of US Adults. JAMA, 279(21), 1703-1708.


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