Difference between revisions of "Human development/Data uncertainties limitations"

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{{ComponentDataUncertaintyAndLimitationsTemplate
|Description=Due to the broad range of issues addressed in the model, there is also a range of uncertainties. Here, we discuss only the data uncertainties related to access to food, water and energy and the accompanied health risks.
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|Reference=FAO, 2012a; WHO/UNICEF, 2012; Hutton and Haller, 2004; Hutton et al., 2006; WHO, 2009; World Bank, 2009; Chen and Ravallion, 2008; Ackah et al., 2009; Lutz et al., 2007; FAO, 2001b; WHO/UNICEF, 2012;
Most data used in the model originate from the appropriate UN institution, or from the World Bank:
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|Description=<h2>Data, uncertainties and limitations</h2>
*Access to food: per capita food intake, coefficient of variation and region specific sex-age energy requirements (FAOSTAT, 2012)
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===Data===
*Access to water: access to safe drinking water and basic sanitation (WHO/UNICEF, 2012), (Hutton and Haller, 2004)
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*Access to energy: people using solid fuels and people using improved biomass stoves (Hutton et al., 2006)
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Most data used in GISMO originate from specific UN institutions or the [[World Bank database| World Bank]]:
*Health: Health risks, disease burden (WHO, 2009) health expenditures (World Bank, 2009),
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* Per-capita food intake and coefficient of variation ([[FAO, 2012a]]);
*Poverty: poverty data (Chen and Ravallion, 2008) and GINI coefficients (Ackah et al., 2009)
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* Region-specific sex-age energy requirements ([[FAO, 2001b]]);
*Education: enrolment ratios per education level (World Bank, 2009) and educational attainment (Lutz et al., 2007)
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* Access to safe drinking water and basic sanitation ([[WHO/UNICEF, 2012]]);
Per capita food intake is based on FAOSTAT data, applied as averages per region, in the startyear, and extended in the future by using the food consumption from [[MAGNET model|MAGNET]] (chapter 4.2.1). The trends in per capita food availability determined in MAGNET use income elasticities for broad groups of crops and animal products, while these trends are not necessary linked physical limitations in consumption levels. Also for water and energy, aggregations of different technologies to broad groups mask the underlying heterogeneity, and thus lead to uncertainties in model behavior. Access levels to improved water supply and sanitation encompasses a broad range of possible types of improved connection for which each is assumed to have the same health risk potential. The same holds for access to modern energy sources for cooking and heating that encompass a broad range of traditional fuel and fuel-stove combinations. Finally, health impacts are based on exposure-response relationships (described in the literature) and it is assumed that these relationships are the same across the globe, and stay constant over time. It should further be noted that many parameters are based on cross-sectional relationships with per capita GDP (PPP), making the outcomes rather dependent on this parameter.
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* People using solid fuels and improved biomass stoves ([[Hutton et al., 2006]]);
model includes some important limitations. A general limitation is the limited representation of the heterogeneous characteristics within populations. Mostly, a population average combined with a stylized distribution function is applied. This might not represent fully the distributional aspects in reality in which many of these issues might concentrate in particular populations, and, more importantly, these distribution functions are not changed in the model over time, while they probably do in reality. Furthermore, although health service efficacy and enrolment ratios are driven by investments in health and education services, these investments are not bound by a sound economic model. Similarly, the investments in drinking water and sanitation are not explicit in the model but derived from achieved coverage. Therefore, analysis on these topics can only be done by using pre-determined what-if scenarios instead of analyzing the effect of specific investments on health outcomes.
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* Health risks and disease burden ([[WHO, 2009]]);
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* Health and education expenditures ([[World Bank, 2009]]);
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* Poverty data ([[Chen and Ravallion, 2008]]) ;
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* GINI coefficients ([[Ackah et al., 2009]]);
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* Enrolment ratios per educational level ([[World Bank, 2009]]) ;
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* Educational attainment level ([[Lutz et al., 2007]]).
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===Uncertainties===
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The broad range of issues addressed leads to a range of uncertainties. Only the data uncertainties related to access to food, water and energy and the related health risks are considered.
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Per-capita food intake is based on [[FAOSTAT database|FAOSTAT data]], applied as averages per region, in the first year, and extended into the future by using food consumption data from the agro-economic model [[MAGNET model|MAGNET]] (Component [[Agricultural economy]]). The trends in per-capita food availability use income elasticities for broad groups of crops and animal products, but these trends are not necessarily linked to physical limitations on consumption levels. For water and energy, aggregations of different technologies to broad groups mask the underlying heterogeneity, and thus lead to uncertainties in model behaviour. Improved access to water supply and sanitation encompasses a broad range of forms of connection, each of which is assumed to carry the same potential health risk. The same is the case for access to modern energy sources for cooking and heating that encompass a broad range of traditional fuel and fuel-stove combinations. Health impacts are based on exposure-response relationships, and these are assumed to be the same worldwide and to remain constant over time. Many parameters are based on cross-sectional relationships with per-capita GDP ({{abbrTemplate|PPP}}), making the outcomes heavily dependent on this parameter.
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===Limitations===
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The model also has several limitations, and specifically limited representation of the heterogeneous characteristics within populations. In most cases, a population average combined with a stylised distribution function is applied. This may not fully represent the distributional aspects, as in reality many of these issues may be concentrated in particular populations, and, more importantly, these distribution functions do not change in the model over time, while they probably do in reality.  
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Furthermore, although health service efficacy and school enrolment ratios are driven in the model by investments in health and education services, these investments are not restricted by a limit on total investments. Similarly, the investments in drinking water and sanitation are not made explicit in the model but are derived from achieved coverage. Thus, instead of analysing the effect of specific investments in health outcomes, analysis can only be done by using pre-determined what-if scenarios.
 
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Revision as of 09:20, 20 May 2014