extfieldsofstudy: Geography Medicine

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Regional inequalities in child malnutrition in Egypt, Jordan, and Yemen: a Blinder-Oaxaca decomposition analysis

There is substantial evidence that on average, urban children have better health outcomes than rural children. This paper investigates the underlying factors that account for the regional disparities in child malnutrition in three Arab countries, namely; Egypt, Jordan, and Yemen. We use data on a nationally representative sample from the most recent rounds of the Demographic and Health Survey. A Blinder-Oaxaca decomposition analysis is conducted to decompose the rural-urban differences in child nutrition outcomes into two components; one that is explained by regional differences in the level of the determinants (covariate effects), and another component that is explained by differences in the effect of the determinants on the child nutritional status (coefficient effects). Results show that the under-five stunting rates are 20 % in Egypt, 46.5 % in Yemen, and 7.7 % in Jordan. The rural- urban gap in child malnutrition was minor in the case of Egypt (2.3 %) and Jordan (1.5 %), while the regional gap was significant in the case of Yemen (17.7 %). Results of the Blinder-Oaxaca decomposition show that the covariate effect is dominant in the case of Yemen while the coefficients effect dominates in the case of Jordan. Income inequality between urban and rural households explains most of the malnutrition gap. Results were robust to the different decomposition weighting schemes. By identifying the underlying factors behind the rural- urban health disparities, the findings of this paper help in designing effective intervention measures aimed at reducing regional inequalities and improving population health outcomes.

Background
Chronic malnutrition in early childhood, including the pregnancy period, impairs brain development and has inescapable effects on child growth, such as having an inadequate height for age (stunting) for the rest of their life. Child stunting arises from a combination of poor food consumption, unhealthy environment, and poor health care. 1 The United Nations Children's Fund (UNICEF) reported an association between low school performance and nutritional status, which in turn lower employment opportunities and income generation [24].
There is substantial evidence that on average, urban regions have better health outcomes than rural regions in developing countries [25]. Understanding the nature and the underlying factors behind the urban-rural health disparities would help in designing effective intervention measures to improve population health outcomes.
Egypt has the largest number of children under five years old who are stunted in the Middle East and North Africa, and the twelfth worldwide. According to 2008 Egypt Demographic and Health Survey (EDHS), child stunting rate reached 29 %, with 14 % are severely stunted. The 2012 and 2013 Jordan and Yemen Demographic and Health Surveys show that the under-five years stunting rate is 7.7 % in Jordan and 46.5 % in Yemen, the second highest rate in the world. Studies reveal that there are significant disparities in child health outcomes between urban and rural regions in the three countries [12,21].
The main objective of this paper is to analyze the sources of the malnutrition gap between urban and rural children, in the three countries. A Blinder-Oaxaca Decomposition analysis is used to decompose this gap into two components; one that is explained by differences in the level of the determinants, and another component that is explained by differences in the effect of the determinant on the child nutritional status. The current study aims at informing policy measures in the three countries that combat child malnutrition in rural regions.
The Oaxaca decomposition is a technique that decomposes inequities between any two groups and has been used extensively in explaining wage differentials between males and females, immigrants and natives, blacks and whites workers. The intuition behind the Oaxaca decomposition is that it quantifies the gap in the outcome between the two groups into two parts, a part that is explained by the gap in the level of the determinants, such as income or education level, and a part that is explained by the gap in the effect of the determinants on the outcome variable. For instance, rural children could be less healthy not only because they visit health care providers less frequently but also because health care providers at rural region are less effective. The Oaxaca decomposition quantifies the contribution of each factor to the gap in the outcome, thus identifying which factors contribute most to generating inequality between the two groups [18]. To the best of our knowledge, the current study is the first to examine the disparity in child malnutrition using Oaxaca decomposition in Egypt, Jordan, and Yemen.
The paper is organized as follows: Literature review presents a brief review of the related empirical literature. Methods provides a description of the data and the empirical methodology. Results discusses the results, and Discussion concludes the paper.

Literature review
It is well documented that children malnutrition is particularly high in developing countries, especially among the least wealthy households, and rural area residents. There is substantial empirical evidence that, on average, rural children have poor health outcomes compared to their urban counterparts [1,9,22,25]. For example, Van de Poel et al. [25] examined children's nutritional status in 47 countries and found a significant difference between urban and rural children in 43 of the examined countries. Garrett and Ruel [8] and Smith et al. [22] examined whether the socio-economic determinants of children nutritional status differ between urban and rural regions and found that such difference arises from the difference in the nature of characteristics that shapes urban and rural living. They found that rural regions are characterized by more dependence on agriculture activities, less involvement of women in outdoors income generation activities, less female-headed household, and larger household size. Furthermore, rural regions are characterized by lower access to electricity, sanitation and health care. Accordingly, the determinants of children malnutrition could differ due to this difference in the living environment. However, they found no support for the difference in the main socio-economic determinants of child nutritional status between urban and rural regions. They concluded that health disparities between urban-rural children exist because of the gap in the level of the key determinants, as urban regions have more favorable living environment.
Using data from Demographic and Health Surveys (DHS) of 15 sub-Saharan African countries, Fotso [6] found substantial though declining over time, urban-rural differentials in child malnutrition rates. The urban-rural gaps in child malnutrition disappeared once the socio-economic status of the households and communities are controlled for. In another cross-country study of 36 countries from South Asia, Sub-Saharan Africa, and Latin America and the Caribbean, Smith et al. [22] investigated whether the socioeconomic determinants of child malnutrition differ between urban and rural regions. They found little evidence for the urban-rural differences in key socio-economic determinants of child nutritional status, including women's education, access to safe water and sanitation, and household economic status, or in the strength of their association between urban and rural areas.
Using micro-data from the DHS for 47 developing countries, Van de Poel et al. [25] documented the magnitude of the urban-rural disparities in child nutritional status and under-5 mortality. They then adjusted these disparities for differences in the socio-economic characteristics between urban and rural regions. They found considerable urbanrural differences in the average child health outcomes in the entire sample. Controlling for household wealth and socio-demographic factors reduced the urban-rural risk ratios of stunting and under-5 mortality by 75 % and 84 %, respectively. In a considerable number of the covered countries, the urban poor had higher rates of stunting and mortality than their rural counterparts. Using DHS data from 35 developing countries, Fox and Heaton [7] utilized multilevel regression to examine rural-urban differentials in the nutritional status net of the individual, community and national determinants of health status. They found rural children to have a substantially higher risk of poor nutrition, which was mostly attributable to socioeconomic status, access to medical care, and reproductive norms. Srinivasan et al. [23] utilized quantile regression-based counterfactual decomposition methods, to quantify the contribution of different socio-economic determinants to rural-urban gaps in child nutrition outcomes in Bangladesh and Nepal. They found that the urban-rural disparity in child nutrition outcomes was mainly due to differences in the levels of socio-economic characteristics, such as maternal education, spouse's education, and the wealth index, access to drinking water and sanitation, while differences in the strength of the association between socio-economic characteristics and child nutrition outcomes accounted for less than a quarter of the rural-urban disparities.
Though extant literature is mostly dominated by crosscountry studies covering a wide range of heterogeneous countries, a growing number of country-specific studies have investigated the urban-rural disparities in child malnutrition. For example, using a multi-level logistic regression and micro-data from Thailand, Firestone et al. [4] investigated whether the urban-rural disparities in undernutrition and obesity are due to child socioeconomic conditions, or if aspects of the social and physical environment accounted for these disparities, after adjusting for child characteristics. In an unadjusted model, they found statistically significant urban-rural differentials; with urban communities have a lower risk for stunting and underweight. However, the urban-rural differences in the odds of stunting disappeared after controlling for child characteristics, while differences in the odds of underweight remained. Under-nutrition was associated with household poverty. In a recent country-specific study, Kumar and Kumari [13] examined the pattern of rural-urban gap in childhood malnutrition, identified its underlying factors and quantified their contribution, in India during the period 1992 to 2006. They found a considerable widening gap in childhood malnutrition across most rural-urban regions over the study period. They also found the economic status of the household and parental education as the most significant contributors to the rural-urban gap in childhood malnutrition in India.
Few studies have recently explored the socioeconomic disparities in child health in the Middle East. For example, in a recent cross-country study, Krafft and El-Kogali [12] examined the inequality of opportunity in early childhood development in twelve countries in the Middle East and North Africa along several development dimensions. They then quantified the contribution of a set of socioeconomic circumstances to these inequalities. They found substantial inequality of opportunities in early childhood and geographical differences was a key contributor to these inequalities. In another study, Sharaf and Rashad [21] suggested that while Egypt has managed to improve child health, socioeconomic disparities remain substantial. Additionally, they found that the rapid growth in GDP per capita did not result in lower malnutrition rates in Egypt [19].
While a substantial number of studies have examined the urban-rural differentials in nutritional outcomes in a wide range of countries, mainly low-income countries, there is limited evidence, at the population level, in the Arab countries, namely in Egypt, Jordan, and Yemen. Since countries are likely to be heterogeneous with respect to their socio-economic conditions, and level of development, it is to be expected that the factors associated with child malnutrition could be country or regionally-specific and may differ from one country to another. Understanding the nature and the underlying factors behind the urban-rural health disparities would help in designing effective intervention measures to improve population health outcomes.

Data
This paper uses data from the most recent rounds of the Demographic and Health Survey (DHS) for, Jordan, Yemen, and Egypt conducted in the years 2012, 2013, and 2014 respectively. The DHS is an international survey conducted in 85 countries. The surveys present data that allows monitoring and impact assessment indicators in the areas of population, health, and nutrition in the developing countries. The surveys gather information from 12,997 children in Egypt, 5,672 children from Jordan and 12,348 children from Yemen. The DHS samples are nationally-representative household surveys. They have complex survey design that involves stratification based on the level of urbanization and clustering where villages are the primary sampling unit for rural, and districts/ towns are the primary sampling unit for urban areas [3]. All the analyses are population weighted using the sampling weights provided in the DHS surveys, and the survey design has been taken into account in the analysis.
The multivariate analyses include a set of Height for Age Z-score (HAZ) determinants that are widely used in the literature. These include child characteristics such as child age, sex; and parental and household-level factors along with other socioeconomic determinants related to the affordability of purchasing nutrition rich food, and living in healthy environment. In particular, the analyses included child sex, age, age squared, mother's age and level of education, father's education, household economic status measured by the wealth index, access to piped water, having an independent toilet facility, delivery in a health facility, and parental healthcare visits.
Previous studies suggested that educated mothers could feed their children better, as they are more skilled and could benefit more effectively from health care providers and health care information, and they are more aware of nutrition rich food and the importance of the hygienic living environment. The lack of satisfactory sanitation and safe water supply expose children to the risk of diseases and infections. Satisfactory sanitation is represented by a dichotomous variable indicating whether the household has a toilet, and safe water supply is measured by dummy variable indicating whether a household has access to piped water.
The wealth status, a key socioeconomic determinant, has been consistently linked to health outcome such as malnutrition, as it reflects the household ability to purchase nutrition rich food, living in a healthy environment, and to access health care services. The economic status is measured by wealth index, which is developed by the DHS team. The wealth index is produced by statistical methodology known as the principal components analysis. The value of wealth index is based on household's ownership of selected assets such as car and TV. Each asset is assigned a weight based on the principle component analysis. Studies have shown that antenatal care is negatively associated with child malnutrition, as it could treat and protect mothers and babies from iron deficiency, anemia, and other diseases. Also, it helps to identify whether the mother needs special care during delivery. Delivery at a health facility helps prevent harmful health consequences, like infections and pregnancy complications. In addition, it provides the necessary information for adequate food intake for the baby and the mother.

Methodology: Blinder-Oaxaca decomposition
The outcome variable of interest in this study is the HAZ (stunting) in comparison with the World Health Organization's reference population. To explain the urban-rural disparities in chronic malnutrition among children, we use the Blinder-Oaxaca decomposition. This technique decomposes the gap in HAZ between urban and rural regions into two parts; a part that is due to difference in the distribution of the determinants of HAZ (covariates effect) between the two regions, and another part that is due to the difference in the effect of these determinants (coefficients effect) between the two regions. For example, if y i , our outcome variable, is affected by a single variable, x, and we have two groups, urban and rural, then HAZ for the rural, and urban children are given by Eqs. (1) and (2) respectively.
Thus the urban-rural gap in the mean HAZ (y urban − y rural ), is given as in Eq. (3). 2 Where x urban and x rural are the explanatory variable at their means for the urban and rural. The overall urbanrural gap could be decomposed into a gap that is attributable to difference in the level of the covariates, X's, and a gap that is attributable to difference in coefficients, β ' s as in Eqs. (4) and (5): Where Δx = x urban − x rural and Δβ = β urban − β rural . The decomposition equation could be re-written as in Eq. (6): Where the overall urban-rural gap in child malnutrition is comprised of the gap in endowment (E), and the gap between coefficients(C), and the interactions (CE). Additionally, y urban − y rural = Δxβ rural − Δβx urban can be equal to (E+(C + CE)) and y urban − y rural = Δxβ urban − Δβx rural is equal to ((E + CE) + C).
The Blinder-Oaxaca decomposition could be considered a special case of a more comprehensive decomposition Eq. (7).
Where D is a matrix of weights, and I is an identity matrix. When x is a scalar, the identity matrix would be equal to one. In such case, if the weight (D) equals zero, Eq. (7) would yield Eq. (4), and if D equals one, then we will get Eq. (5) [18]. However, other economists suggest different weights.
For example Reimers [20] suggests weighting the gap in x by using the average mean, β Ã ¼ 1 2 β urban þ 1 2 β rural , while Cotton [2] suggests weighting the β by the relative groups sizes, β Ã ¼ n urban n urban þn rural β urban þ n rural n urban þn rural β rural . Furthermore, Neumark [16] suggests using the pooled regression coefficients β p in weighting the difference in x, y urban − y rural = Δxβ p + (x urban (β urban − β p )) + (x rural (β p − β rural )). Figure 1 depicts the aggregate, as well as the urban and rural, child stunting rates in Egypt, Jordan, and Yemen. In Egypt, about one in every five children under five years old is stunted. However, the difference in stunting rates between rural and urban regions is not stark (2.3 %), with rural children have better nutritional status than their urban counterpart. In Yemen, the situation is catastrophic, and the country has substantial high malnutrition rates, with more than half of the Yemen under-5-years children are stunted. Figure 1 shows a significant difference, of about 17 %, in stunting rates between urban and rural children. Jordan is performing much better compared to its neighbors. The overall prevalence of child stunting is quite low (less than 8 %), and the difference in stunting rates between the urban and rural regions is also small (1.5 %) with rural children are performing worse than urban ones. Table 1 shows the differences in selected background characteristics of households in the urban and rural regions in the three countries. The rural households, are on average, less educated, have lower access to satisfactory sanitation and improved drinking water, and lower access to healthcare than urban households. While the urbanrural differentials are negligible in the case of Jordan, the differentials in the level of the determinants of HAZ between urban and rural regions are apparent in Egypt and are more significant in Yemen. For example, in Yemen, almost half of the urban households in Yemen deliver in a health facility compared to 22.6 % for rural households. The percentage of urban women with secondary or higher education is more than four times the rate of rural women. Substantial difference also exists in the living conditions where 27.2 % of the rural households have improved and non-shared toilet facilities compared to 83.4 % for urban households. Similarly, 49.7 % and 65.2 % of rural households have access to improved water sources and electricity compared to 78.7 % and 98.5 % for urban households respectively. A similar pattern exists in Egypt but with less severity. For example, 59.6 % of the rural women have secondary or higher education compared to 77 % for urban women. 64.4 % and 83.5 % of rural households have access to improved water sources and deliver in a health facility compared to 94.3 % and 93.7 % for urban households respectively. Table 2 presents the decomposition of the urbanrural malnutrition gap into three components; a gap due to the difference in the level of determinants, a gap due to the difference in the effect of the coefficients and a gap due to the interaction. The first section of the table gives the mean HAZ score in the urban and rural regions, and the HAZ gap between them. Consistent with the results in Figure 1, the HAZ gap in Egypt is insignificant. On the other hand, the difference in HAZ between urban and rural regions in Jordan and Yemen are large and highly significant, particularly in the case of Yemen. Accordingly, the subsequent analysis will focus only on Yemen and Jordan as the HAZ means do not differ significantly between urban and rural regions in Egypt. The second panel of Table 1 divides the HAZ gap into three parts. The endowment part reflects the average increase in rural children HAZ if they had the same level of determinants of the HAZ. The coefficient part shows the change in rural children's HAZ when using the urban children's coefficients with the current level of rural children determinants. The interaction component indicates the simultaneous effect of the disparities in the magnitude of the determinants and coefficients [11]. The results show that the gap in child z-score is higher in Jordan than in Yemen. The threefold decomposition analysis in Yemen suggests that the gaps in the means of HAZ are primarily accounted for by the difference in the magnitude of the determinants of child stunting (p < 0.001) rather than by differences in their effect. In  Jordan, the difference in determinants is important and significant as in Yemen. However, the difference in the effect of coefficient accounts for a large bulk of the z-score gap and is significant at 10 % level of significance. Table 3 presents results of the Oaxaca decomposition, using different weights, for both Jordan and Yemen. The first and second columns reflect the decomposition in Eqs. 1 and 2, in which the matrix of weights (D) has a diagonal of weights equals 0, and equals one respectively. The Reimers [20], and Cotton [2] decomposition are in the third and fourth columns respectively. The last column, named *, refers to Neumark [16] decomposition, which uses pooled regression coefficients. The results of Tables 3 suggest that irrespective of the decomposition weight adopted difference in the level of covariates account for the majority of the nutrition gap in Yemen. In addition to the gap in the level of determinants, the difference in the effect of HAZ predictors has a substantial contribution to the malnutrition inequalities in Jordan.

Results
Tables 4 and 5 present the contribution of each determinant in the overall explained rural-urban gap in Yemen and Jordan at different weighting scheme, thus identify which factor explains most of the overall gap. In Yemen, Table 4 shows that, regardless of the adopted weight, income inequality between urban and rural households, measured by the wealth index based on the level of assets ownership, accounts for the majority of the overall gap. The remaining factors have a tiny effect in explaining the HAZ gap in the case of Yemen. For instance, focusing on the Neumark's decomposition at the last column of Table 4, we find that the gap in the wealth level between urban and rural constitutes 84 % of the explained gap while the remaining correlates explain only 16 % of the overall explained gap. A similar pattern is observed for different weights.
In Jordan, Table 5 shows that the wealth gap comprises the largest bulk of the gap in endowments regardless of the applied weight. The difference in the coefficients comprises a large part of the nutrition status gap. The effect of the socio-demographic variables (child's age, mother's age, and child sex) disfavors the rural children. Interestingly, the difference in the wealth index effect under the coefficient column favoring the rural residents, indicating that rural households use their wealth more efficiently in improving their child health. Table 5 shows that mother's education has a higher return in urban regions compared to rural regions. This could to the difference in the quality of graduates between urban and rural areas. More importantly, the analysis shows the difference in the effect of receiving prenatal care between urban and rural regions has a considerable contribution to the coefficient gap. This has an important policy implication, as it might be that the quality of health care in the urban areas largely outperforms the rural regions driving this significant difference in child  health between the regions. Wealth index has a trivial contribution to the coefficient, which implies rural household are not efficient in using their assets. Thus, the conclusion here is that children in rural areas are less healthy mainly due to the lower return of education and health care service in the rural regions in Jordan, which may reflect some discrimination.

Discussion
In this study, we comprehensively investigated the underlying proximate, demographic and socioeconomic factors that account for the rural-urban inequalities in child malnutrition in Egypt, Jordan, and Yemen, on which limited research has been conducted, using a Blinder-Oaxaca decomposition analysis. Data from the DHS show that the overall under-five stunting rate is 20 % in Egypt, 46.5 % in Yemen, and 7.7 % in Jordan. The rural-urban gap in child malnutrition was minor in the case of Egypt (2.3 percentage points) and Jordan (1.5 percentage points) while the regional gap was substantial in the case of Yemen (17.7 percentage points).
We found marked rural-urban disparities, though not uniform, across the three countries, in the level of socioeconomic determinants of children's nutritional status. In general, rural households, are less educated, have lower access to satisfactory sanitation and improved drinking water, and lower access to healthcare than urban households. The urban-rural differentials in the level of the socioeconomic determinants of HAZ were substantial in Yemen and to a lesser degree in Egypt and are negligible in the case of Jordan. While the current study has documented considerable differences, on average, between urban and rural regions, it is essential not to ignore the heterogeneity of urban and rural communities when designing intervention measures. Several studies have reported large intraurban and intra-rural differences in socioeconomic conditions. For example, in an earlier cross-country study, Menon et al. [15] found that for some of the examined countries, intra-rural differences in stunting rates were much greater than intra-urban differences. Urban children from the lowest income quintiles were ten times more likely to be stunted compared to children from the highest income quintiles while the intra-rural differential in stunting risk by income quintiles was much less.
Identifying whether the rural-urban gap in child malnutrition is more depending on differences in the level of the determinants (covariates effects), or on differences in the effects of the determinants (coefficients effects) is crucial for designing the appropriate intervention measures and policies aiming at reducing health inequalities. For example, if the rural-urban health gap is due to differences in the effect of the determinants, then, the redistribution of wealth and improving access to healthcare, clean water and sanitation would not be sufficient to improve child health in rural areas, since the impact of these interventions are weaker in rural areas. Behavioral and awareness programs would be necessary interventions to close the gap between the two groups. However, if the rural-urban health gap is due to differences in the level of the determinants, then redistribution of wealth and improvement in the level of the determinants in rural areas would be an effective policy to reduce regional health inequalities. In an earlier study, Jalan and Ravallion [10] examined the impact of access to piped water on child health in rural India. They suggest that expanding piped water does not necessarily guarantee child health improvement, as the outcome of public investment also depends on the parental behavioral responses. Though public investments have been favoring the poor, children from the low socio-economic status may benefit less from access to medical care or piped water because their parents are less knowledgeable about how to reap the maximum benefits from medical care or piped water. They will have lower coefficients in comparison to parents from high socio-economic status who for example can read child drug instructions and take full advantage of it, or know better how to use piped water. The merit of using the Blinder-Oaxaca decomposition is that it accounts for group differences in the effects of the determinants (coefficient effects), as well as differences in the effect of the determinants across urban and rural families, which would unmask the source of the regional inequalities and help design effective intervention measures.
In the current study, results of the Blinder-Oaxaca decomposition suggest that the rural-urban gap in child malnutrition in Yemen is primarily explained by the difference in the magnitude of the determinants of child nutritional status (covariate effect is dominant), rather than by differences in their effect. In particular, income inequality between urban and rural households, measured by the wealth index, accounts for most of the overall gap. This is consistent with the study of Larrea and Kawachi [14] on economic inequality and child stunting in Ecuador. They suggested that economic inequality has significant negative impact on child stunting. This implies that in Yemen, redistribution of wealth, and improving access to healthcare, clean water and sanitation in rural areas would be effective to reduce the regional inequalities in child malnutrition and improve child health in rural areas. However, in Jordan, though the difference in the determinants is important and significant as in Yemen, the difference in the effect of determinants accounts for a large bulk of the z-score gap and is significant (coefficient effects dominate). These results are robust to the different decomposition weighting schemes. Accordingly, the redistribution of wealth and improvement in the level of the determinants in rural areas in Jordan may not be sufficient to reduce regional health inequalities. Behavioral and awareness programs that would increase the effect of the determinants would be necessary interventions to close the rural-urban gap in child malnutrition in Jordan.
Since the seminal work of Oaxaca and Blinder in the early 1970s, several decomposition methods, including those going beyond the mean, have been developed to decompose the difference in a distributional statistic between two groups, or its change over time. For a comprehensive review of the various decomposition methods in economics, see Fortin et al. [5]. In general, each decomposition method has its own underlying assumptions, advantages and limitations. One limitation that several decomposition methods face is path dependence, that is, the decomposition results depend on the order in which the decomposition is conducted. In addition, some methods such as, quantile regression methods, are computational intensive and may fail to provide a detailed decomposition for distributional parameters other than the mean.
An alternative approach to the Oaxaca-Blinder approach for analyzing rural-urban differences in the impact of child nutritional determinants would be the use of dummy variable interaction effects in a conventional multiple regression framework. The disadvantage of this approach is that it may not be possible to include as many interactions as there are covariates while still retaining a meaningful interpretation of the coefficients. Several other studies use stepwise regression type approaches, however, this is difficult to use if we are interested in quantifying the relative contributions of all the determinants to the outcome gap. In addition, results of this approach are sensitive to the order in which we control for the covariates, which is not the case in the Oaxaca-Blinder approach.
The Oaxaca-Blinder decomposition technique has several limitations. One limitation is the "index number problem" in which the choice of the reference group could affect the ratio of explained to unexplained portions of the rural-urban gap in child malnutrition. Another problem is the "indicator variable" problem, where results related to dummy variables in the model might be sensitive to which group is the reference or the omitted group. For the unexplained portion of the gap, we may not differentiate the part attributed to the group membership (true "unexplained" captured by the difference in intercepts) from the part attributed to differences in the coefficient of the omitted group [5]. Another limitation of the Oaxaca-Blinder decomposition is that it might be difficult to make inference about the main cause of the unexplained part of the rural-urban gap in child malnutrition.
The Oaxaca-Blinder approach that is used in the current study decomposes the difference in the mean HAZ scores between two groups (rural versus urban children). Group differences in other parameters of the HAZ distribution could also be of interest. One extension to the current study would be to use quantile regression to decompose the regional differences in the full distribution of the HAZ score into the contribution of differences in the distributions of the covariates, and differences in the effects of these covariates. Among the studies that used that approach see for example O'Donnell et al. [17]. The merit of decomposing the full HAZ distribution is that it allows the effect of the covariates to differ over the conditional distribution of the HAZ score which would be more informative for policy guidance.
The current study is not free from limitations. One limitation is its cross-sectional design which limits the ability to make causal inferences and examine how the regional inequalities evolve over time. The rural-urban disparities in health could be evolving over time as a result of urbanization and other areas of socioeconomic development. Another limitation is that there could be other confounding factors, whether observed or unobserved, that could explain the rural-urban differentials and that we did not control for.
One problem that is common in the analysis of ruralurban differentials involves the way localities are classified as urban or rural. There are several urban and rural classification criteria such as administrative boundaries, population size, population density, the percentage of the labor force in non-agricultural activities. The lack of a uniform classification for urban and rural regions could potentially hinder making cross-country comparisons.

Conclusion
In this study, we decomposed, and identified the underlying factors, behind the rural-urban gap in child malnutrition in Egypt, Jordan, and Yemen. This is to inform and guide policies aimed at reducing health inequalities and improve population health in the Arab world. In Yemen, the regional gap in child malnutrition is mostly due to differences in the level of the determinants of child nutritional status, rather than by differences in their effect. Accordingly, intervention measures that entail redistribution of wealth, and improving access to healthcare, clean water and sanitation in rural areas would be effective to reduce the regional inequalities in child malnutrition. In Jordan, group difference in the effect of the determinants accounts for a significant fraction of the z-score gap. This implies that polices that improve the level of the determinants in rural areas in Jordan may not be sufficient to reduce regional health inequalities. Behavioral and awareness programs that would increase the effect of the determinants would be necessary interventions to close the rural-urban gap in child malnutrition in Jordan.


The Random Walk behind Volatility Clustering

Financial price changes obey two universal properties: they follow a power law and they tend to be clustered in time. The second regularity, known as volatility clustering, entails some predictability in the price changes: while their sign is uncorrelated in time, their amplitude (or volatility) is long-range correlated. Many models have been proposed to account for these regularities, notably agent-based models; but these models often invoke relatively complicated mechanisms. This paper identifies a basic reason behind volatility clustering: the impact of exogenous news on expectations. Indeed the expectations of financial agents clearly vary with the advent of news; the simplest way of modeling this idea is to assume the expectations follow a random walk. We show that this random walk implies volatility clustering in a generic way.

Background
Financial price changes obey two universal properties: they are distributed according to a power law, with an almost cubic exponent, and they tend to be clustered in time. The first regularity implies that extreme price changes are much more likely than would suggest the normal distribution, for instance. The second property, known as volatility clustering, implies that high-amplitude price changes tend to be followed by highamplitude price changes, and low-amplitude price changes, by low-amplitude price changes. This entails a nontrivial predictability in price changes: while their sign is uncorrelated, its amplitude (or volatility) is long-range dependent.
In formal terms, let t P be the price of a financial asset at the closing of period t, let its change during this period be 11 ( ) / . Many models have been proposed to account for these regularities, notably agent-based models, which often invoke relatively sophisticated mechanisms [1,2]. This literature has played an important preliminary role, at least by raising the important questions and by uncovering the central concepts and mechanisms (feedback, heterogeneity, trend following, etc.). But it is time we seek the essence of these phenomena, by reducing the important concepts and mechanisms to their simplest formulation.
In a previous paper we show that speculation and trend-following are the essence of the power law of financial price changes, as they imply this regularity almost by definition: the return follows a random-coefficient autoregressive process in a speculative market, as long as the speculators' use past price changes to predict the future price change, making their expectations endogenous [3]. Thus the return has a power law by Kesten's theorem [4][5][6]3]. But this model cannot explain volatility clustering, as another theorem by Basrak at al. implies [7][8][9]6]. This paper extends this model by including, as is usual, a second class of agents, value-investors (or 'fundamentalists'), who attach an (intrinsic) value to the asset and buy it when they think it is underpriced (or sell it, otherwise). When the agents' expectations are exogenously driven, in the sense of being entirely shaped by exogenous news, then they can be simply modelled as following a random walk. This basic random walk model of expectations explains volatility clustering in a natural way.

The model
The market is populated by speculators, who buy an asset for a purely speculative reason, that is, when they expect its price to rise (or sell it, otherwise), and value-investors (or 'investors' for short), who attach a value to the asset and buy it when they think it is worth more than it is currently priced (or sell it otherwise). Formally, we assume the demand of the ith speculator is , it it xd (1) where it d denotes the price change that the speculator expects to occur in period t, and 0 . We assume on the other hand that the demand of the jth investor is ( ), jt jt t x V P (2) where jt V denotes the value that the investor expects the security to be worth in the end of period t, and 0. As usual, supply is formally identified as a negative demand. We let t N and t M be respectively the numbers of speculators and investors who desire to buy or sell the security during period t. The overall excess demand can be written as V , namely the expected price change that speculators expect to occur on average, and the value that investors on average expect the asset to be worth, which we shall refer to simply as 'the value' of the security.
Finally, we assume that the price adjusts linearly to the overall supply-demand imbalance: , tt dx (4) where 0. The dynamics of price is then given by (3) and (4) combined, which can be written as ( ), So the dynamics of the price depends crucially on how expectations are formed. We distinguish two cases: endogenous versus exogenous expectations. We say that expectations are endogenous if they depends on information generated by the market dynamics itself, and we say they are exogenous, otherwise, that is, if they are determined by exogenous information.

Trend following and power law
A common example of endogenous expectations is trend following (or chart reading). Assume a purely speculative market, that is, . Assume, to simplify, that these functions are linear, that is, assume where K corresponds to the furthest past that speculators overall consider relevant, jkt are coefficients, and jt capture other influences on expectations. Then the price change follows a random-coefficient autoregressive process:

Exogenous news and clustered volatility
Now let the agents' views be entirely driven by exogenous news. Formally, let t E and t F be the events associated with the arrival in period t of news relevant to speculators and investors, respectively; let the probabilities of these events be () We assume that, when they arrive, the news change the agents' views by t and , t respectively, which we assume to be normally distributed with zero mean and standard deviations and . In sum, we assume, . The implication of this simple random walk model of expectations is that the agents' views reflect all the information that came to them: This simple model exhibits volatility clustering, as FIG. 2 shows, which is a simulation of (10) using exponential distributions for

Discussion
The volatility clustering is generic in this model, in that it holds for different distributions of x beyond which the linear fit of the tail distribution is applied, which is 1.5 in this simulation, is determined throughout by an algorithm developed in paper [10].) But the underlying process being clearly nonstationary, given the random walks (8) and (9), the tail of return cannot be an exact and stationary power law as would be the case in a purely speculative market with trendfollowing speculators, where the process converges in distribution to a power law by a theorem [3]. But the distribution is always fat-tailed (on average over 10 realizations of the process with the above parameters, the kurtosis is 27, as in the real data of FIG.1). Also, the properties of t a and t b (namely their means in this particular choice of the exponential distribution) play only a quantitative role for the curvature of the distribution.
The key to volatility clustering in this model is the fact that the news have a permanent impact on the agents' views, as implied by the random walk model of expectations. This is a nontrivial result, however, as this persistence of the impact of news on expectations is reflected only in the amplitude of return, and not in its sign; indeed the return itself is essentially serially uncorrelated [FIG. 2 (d)].
But, actually, it is the speculators' expectations that matter for volatility clustering, as this regularity holds even when t V is constant ( ' 0), as FIG. 3 shows.
We choose 1 so that volatility clustering is not mistaken for a 'regime shift' in the return process, caused by a rare but consequential, regime-shifting, news (corresponding to a small ) , a conclusion that the 'occasional structural breaks' literature may have prepared one to jump to [11][12][13]. For instance, Granger and Hyung suggest to model absolute return directly as following a random walk with 'occasional structural breaks'. But (apart from its ad hoc nature), this model has the drawback of implying a divergent absolute return, an explosive volatility, as the authors themselves point out [13].
In sum, this paper suggests a simple explanation for excess and clustered volatility in financial markets. Excess volatility for an asset means that its price changes are too high given the underlying fundamentals: the price will fluctuate even when the underlying value of the asset is constant. Clustered volatility simply reflects, in this model, the flow of exogenous news affecting the expectations of speculators.


Assessing the Sustainability of Agricultural Technology Options for Poor Rural Farmers

This chapter presents an analytic framework to identify agricultural innovations that are sustainable and suitable for the poorest and most vulnerable parts of the population. The framework contains a set of tools to collect and evaluate information on appropriate innovations based on relevant criteria. It considers the dimensions of environmental resilience, economic viability, and social sustainability, as well as technical sustainability considering important properties of the innovation itself. Information on already available agricultural innovations was collected in ten countries in South and Southeast Asia, as well as from the national and international agricultural research communities. A composite sustainability indicator was constructed to compare the collected innovations and radar charts were computed to visualize their performance in each sustainability criterion.

income -75 % of the world's poor in developing countries live in rural areas, and in developing countries, the sector contributes 29 % of GDP and accounts for 65 % of all employment. And finally, agriculture uses resources that are becoming increasingly scarce -including land, soil, water, nutrients -and contributes to as well as suffers from the consequences of climate change (Godfray et al. 2010).
A transformation of the sector requires the adoption of new and innovative approaches that support sustainable outcomes. Many agricultural research organizations, both from the public as well as the private sector, and at national, regional and international levels, are involved in making solutions available for enhanced agricultural sustainability. Many of these have value beyond the particular local setting for which they were developed. However, decision takers at all levels, including farmers, extension workers and programme managers, require better tools to determine what innovations, i.e., what practices and technologies have relevance in certain settings. Traditional tools based on profit-maximization at the farm level, such as linear programming, do not take into consideration sustainability enhancing aspects and are therefore not sufficient in supporting the sustainability agenda.
The formulation of universal sustainable development goals that were agreed upon in the Rioþ20 conference (UN 2012) is based on the principles of economic profitability, social justice and environmental friendliness: "Sustainable development is the management and conservation of the natural resource base and the orientation of technological and institutional change in such a manner as to ensure the attainment and continued satisfaction of human needs for present and future generations. Such sustainable development (in the agriculture, forestry, and fisheries sectors) conserves land, water, plant and animal genetic resources, is environmentally non-degrading, technically appropriate, economically viable and socially acceptable". This definition was adopted in 1989 by FAO (1995).
A decision-making tool that aims for optimizing sustainability outcomes of the use of new technologies and innovations should, thus, take (at least) these three pillars of sustainable development into account. Yet, clearly, this is not an easy task. Sustainability does not have an intrinsic value unto itself, and different stakeholders and interest groups hold different assumptions about values, for instance, the relationship between economic development and human wellbeing, the relationship between present and future needs, the relationship between resource allocation and level of consumption, or views of what should be sustained. We therefore aim to present a tool that supports the decision-making process by making information on different aspects of sustainability available to decision-makers.

Evaluating Technologies for Sustainable Agriculture
The following paragraphs present the most important scientific work and literature reviews with relevance for the task of assessing the sustainability of innovations in agriculture. Singh et al. (2012) provide an overview of sustainability assessment methodologies. They mention twelve approaches from four different fields that assess sustainability at the level of industries or technologies. The approaches are: (1) composite sustainability performance indices for industries; (2) product-based sustainability indices; (3) environment indices for industries; and (4) energy-based indices. But approaches based on life cycle assessments also play a role (Aistars 1999). Other reviews present international approaches to sustainability assessment (Grenz and Thalmann 2013) and provide an overview of sustainability assessment systems (Doluschitz and Hoffmann 2013).
Although the classical approach to sustainability comprises the three pillars of environment, economy and society, several authors suggest additional aspects of sustainability. Veleva and Ellenbecker (2001) suggest six main aspects of sustainable production: (1) energy and material use (resources); (2) natural environment (sinks); (3) social justice and community development; (4) economic performance; (5) workers; and (6) products. The authors adapt nine principles of sustainable production from the Lowell Center for Sustainable Production, provide recommendations for the development of indicators and suggest using a set of core and supplemental Indicators of Sustainable Production (ISP). Rigby et al. (2001) present three facets of agricultural sustainability: (1) improved farm-level social and economic sustainability (enhances farmers' quality of life, increases farmers' self-reliance, sustains the viability/profitability of the farm); (2) improved wider social and economic sustainability (improves equity/is 'socially supportive', meets society's needs for food and fiber); and (3) increased yields and reduced losses (while minimizing off-farm inputs, minimizing inputs from non-renewable sources, maximizing use of (knowledge of) natural biological processes, and promoting biological diversity/'environmental quality'). Dunmade (2002) suggests a framework of indices to assess the sustainability of a technology 1 for introduction into a developing country. Adaptability is the primary indicator of sustainability of a technology and is evaluated using four secondary indicators, namely technical, economic, environmental and socio-political sustainability ( Fig. 12.1). Dewulf and Van Langenhove (2005) describe a set of five sustainability indicators for the assessment of technologies based on industrial ecology principles. The indicators are: (1) renewability of resources; (2) toxicity of emissions; (3) input of used materials (reuse of materials); (4) recoverability of products at the end of their use (recoverability of waste materials); and (5) process efficiency. The indicators are based on the second law of thermodynamics. This allows for the quantification of all material and energy flows, exchange rates and conversion rates within a production system in exergy terms. Dantsis et al. (2010) combine 21 individual indicators that cover the three pillars of sustainability (environment, economy, and society) into a unique indicator using the Multiattribute Value Theory (MAVT).
Many authors agree that sustainability is difficult to define (Kemmler and Spreng 2007). Therefore, Smith et al. (2000) inverse the approach and look at features of a system that are unsustainable, rather than searching for those that are sustainable. In their threat identification model (TIM), they first identify and rate potential hazards to sustainability depending on location-specific conditions, in particular, considering soil conditions and the risks of identified hazards. The final results are locationspecific best management practice guidelines that can be developed for the farm  (Dunmade 2002, p. 464) level using GIS and that allow users to examine and understand the logic behind recommendations.

Analytical Framework
When analyzing the sustainability of an agricultural technology, the characteristics of the technology itself are of critical importance, because the speed and rate of adoption of an innovation depend on the personal characteristics of the potential adopter, the nature of the social system, the type of adoption decision, the extent of the change agent's promotion efforts and the specific attributes of the innovation itself that determine its usefulness for the potential adopter (Rogers 2003). Therefore, we consider four dimensions of sustainability in the analytical framework, namely the dimensions of environmental resilience, economic viability, social sustainability, and technical sustainability considering important properties of the innovation itself.

Criteria for Sustainable Agriculture
A literature search conducted at the end of 2012 resulted in the identification of 104 sustainability criteria relevant for agricultural technologies. These were reduced by merging similar indicators and deleting criteria with the same meaning but different terms, phrasing or unit of measurement, and eliminating indicators irrelevant for agriculture, or irrelevant in the context of developing countries. Criteria for which data collection would be too costly to collect were eliminated as well. As a result, 27 criteria were identified as highly relevant to the description of various aspects of technologies in the context of sustainability. Due to data limitations encountered during initial rounds of application of the framework, the criteria were further reduced, as shown in Fig. 12.2. The analysis aims to identify technologies that are sustainable, but also appropriate for the poor and vulnerable people, especially women and landless or land poor people. Such technologies are called suitable in this chapter. To address the special needs of poor and vulnerable people, two criteria are included that are not typically sustainability criteria, namely the minimum amount of land area required to adopt the technology and the percentage of female adopters as proxy for the suitability of the technology for vulnerable groups.
To decide on the relative importance of the criteria under consideration, experts were invited to provide weights for individual criteria using the Analytical Hierarchy Process (AHP) developed in the 1980s (Saaty 1990). This approach is a multicriteria decision-making process that is suitable for involving a group of experts. It was implemented via an online survey that asked experts to compare all criteria in a pairwise manner. For each pair of criteria within the same sub-objective, experts were first asked which criterion is more important or if they are of equal importance. If one was selected to be more important, experts were then asked how much more important the criterion is. Fifty-one experts were invited to participate in the online survey, out of which 12 took part (23.5 %). The results of this weighting exercise were ambiguous, probably due to the online survey format of the exercise. This format didn't allow for detailed personal oral presentation of the meaning of criteria to experts or questions and answers among survey participants. Although a written introduction was included at the beginning of the survey, the results revealed that respondents had a diverging understanding of some criteria. Further, the AHP method is prone to human error, especially with a large number of pairwise comparisons. For this reason, only five experts had consistency ratios below the recommended threshold. Based on these results, a criteria weight distribution was developed in consultation with a team of interdisciplinary scientists and project partners. where CSI is the composite sustainability indicator, w is the weight of criterion i, and cv is the criteria value of criterion i (compare Krajnc and Glavič 2005).

Application of the Tool
Detailed data was collected for 42 technologies, of which 30 3 were included in the data analysis presented here. The following questions were used for selection of technologies for analysis: (1) Can the technology be adopted by an individual or a single household? (2) Is the technology mature and has it been tested successfully in practice many times? (3) Is all information concerning the technology a public good and does the technology have no patent right attached to it? Technologies for which all questions could be answered "yes" were included and are listed in Table 12.2. Technologies for which no economic data was available were taken into account by using an average normalized net present value. 4 To make economic figures comparable, they were transformed into values per hectare of production. Conversion of monetary values from local currencies to US dollars was done using the OANDA online currency converter, 5 using the conversion rate of 31 March 2013. For criteria for which expert opinions were used, the data was compared among technologies and harmonized, where necessary. For instance, for the amount of water used, three answer options were available for experts to choose from: the technology uses (i) no water (0), (ii) little water (0.5), and (iii) large amounts of water (1). This left little room for distinctions between rainfed crops, intermittently irrigated, and flooded crops. Therefore, all technologies involving wet rice were assigned the score (1), all irrigated crops the score (0.5), and rainfed crops the score (0.2). Where necessary, data was normalized before analysis.
Three technologies will be presented in more detail, namely vermitechnology, broom grass farming on marginal lands, and the mini hatchery model for chickens.
Vermitechnology is a process which uses earthworms to produce good quality compost (vermicompost) through organic waste recycling. The commonly used earthworms include Eudrillus sp. Perionyx sp., Eisenia sp. or any locally-available earthworms living and feeding on the surface of the soil (epigeic worms). A tank of 5 Â 1 Â 1 m allows about 500 kg of waste to be composted through the activity of worms and microorganisms, producing about 250-300 kg of compost over approximately 1 month. Vermitechnology can either be practiced in tanks or in the ground. However, the major advantage of a tank is the efficiency of composting and keeping the worms captured. The technology requires little investment and technical knowhow.
Broomgrass (Thysanolaena maxima) is a perennial, high-value, non-perishable Non-Timber Forest Product (NTFP) that can be grown on degraded, steep, or marginal land. Broomgrass is a multipurpose crop: only its panicle is used for brooms. The stems are used by farmers as construction material, fuel, fodder, mulching, or staking crops, or sold to the pulp industry to manufacture paper. The leaves and tender shoots are used as fodder in times of scarcity. Broomgrass farming can generate additional income through cultivation on marginal lands unsuitable for food production. It can also be used as part of an agroforestry system to regenerate degraded land.
A sand-based mini-hatchery uses a simple wooden incubator to hatch chicken (and duck) eggs in rural areas to assure a regular supply of chickens (and ducks) for income and food security. The heat that is needed to brood the eggs comes from kerosene lamps: the sand helps to retain and distribute the heat evenly inside the insulated cabinet. Besides the wooden cabinet and wooden or metal trays and racks, it uses cheap local materials such as quilts, sand that can retain the heat, jute sacks, and kerosene. The incubator should be placed in a separate hatchery room. It can assure a regular supply of 1-day-old chickens (or ducks) for income and food security in rural areas.

Results
Technologies are grouped into more suitable and less suitable technologies based on a non-hierarchical cluster analysis (K means) of the Composite Sustainability Index (CSI) ( Table 12.2). Groups A to C represent the 13 most suitable technologies based on the data presently available and the analytical assumptions made. Looking at three exemplary technologies, the CSI ranks vermitechnology as the most suitable, followed by broom grass farming, and the mini hatchery for chickens.
The radar chart (Fig. 12.3) reveals more details on the performance of the three examples, vermitechnology (green line), broom grass farming (blue line), and mini hatchery (red line). If the line is close to the outer edge of the diagram, the technology is performing well in terms of the particular criterion. All technologies can be seen to be performing relatively well in terms of water consumption.
The hatchery hardly uses any water (for cleaning only), broom grass farming is a rainfed culture and vermitechnology needs little water to keep the substrate in which the earthworms live and upon which they feed moist. Only the hatchery uses a little energy, which comes, however, from a non-renewable source in the present state of technology design. It has no impact on biological processes, while broom grass prevents soil erosion and vermitechnology positively impacts nutrient cycling. Vermicompost has a better impact on biodiversity than the mini hatchery and broom grass farming. The latter performs not so well in terms of biodiversity, because land areas that were formerly covered with a diversity of wild plants are then cultivated with broom grass alone. All technologies require little input in terms of work; broom grass cultivation even creates local jobs in peak times. None of the compared technologies has a risk of disturbing the neighborhood or creating social conflicts. All technologies are suitable for female adopters, but broom grass farming involves some hard work to prepare the soil during the planting period. The hatchery and vermitechnology can be practiced on a few square meters of land, while only broom grass should be practiced over more extended areas. The composite sustainability indicator is calculated with an area of at least 200 m 2 . According to the vermitechnology expert, farmers can sell one kg of compost for 50¢ (US). With initial investment costs of about $50 (US) for a tank that covers 5 m 2 , and considering some additional space for charging and discharging the tank, the figures lead to a net present value per hectare of $4.78 million (US) for a 5 year period at 1.5 % interest. This is by far the highest NPV per hectare for the set of technologies included in the analysis. For comparison, the 5 year period NPV of Cage fish culture À0.07 -À 0.01 Improved Kharif paddy production system Improved cultivation of rainfed maize-based cropping systems Himalayan rainbow trout aquaculture technology Mean NPV Note: technologies with "example" in the right column are presented in more detail in this chapter broom grass farming and the mini hatchery is $1,881 and $50,016 (US), respectively. From the amount of knowledge and skills a person has to master for successful operation, the mini hatchery appears to be the most complex and broom grass farming the easiest of the three technologies.

Limitations of the Framework
Although the objective of identifying suitable technologies is soundly justified and well-grounded, there are several inherent issues and limitations that need to be kept in mind when interpreting results and formulating extension recommendations on the suitability of technologies. Firstly, the analytical framework presented in this brief should not be considered a tool for comparing the sustainability of different technology types against each other. Rather, it provides information on various aspects of sustainability for a given technology and can serve as a decision tool for comparing different but related technologies with each other.
Secondly, combining biophysical information with social and economic information into a single indicator carries the inherent problem of incommensurability between different dimensions of sustainability (Rigby et al. 2001). Another issue in respect to composite indicator calculation relates to compensation between the values of its components. For instance, low or no energy consumption cannot balance a high use of water or a low net present value. However, this issue can be overcome by looking at the underlying data that can be presented visually with radar charts.
Also, our assessment is based on inputs (e.g., energy and water), rather than actual sustainability outcomes. This is due to the limited availability of impact data. "It is commonly the case that assessments of sustainability operate by prediction rather than direct evaluation of impact. . . . One of the key issues is the extent to which one can map with confidence from inputs to environmental impact." However, we, as others, believe that the assumptions that we have made on impacts are, while crude, nevertheless robust (Rigby et al. 2001).

Future Research Needs
The research presented here is not final, results present the current state of knowledge, and data and efforts should go on to further increase the data set and refine the methodology. More sophisticated criteria, like the actual amount of water used, waste water produced, actual amounts of inputs and outputs, from recycled farm materials or from offsite, could be included if reliable data could be traced for all technologies. The effort by local experts to collect necessary data for sustainability analysis, and the exchange with the Food Security Center (FSC) to fill data gaps and validate expert data and opinions is ongoing. Furthermore, when additional technologies are included in the comparison, normalized variable values are likely to change, affecting the overall results. For this reason, the presented results are based on the best data available to date. Future findings might change the sustainability ranking and grouping of technologies. Newly emerging tools, like the SAFA small App tool (FAO forthcoming 6 ), will allow for cross-checking and validating the generated results when compared with each other.
Open Access This chapter is distributed under the terms of the Creative Commons Attribution-Noncommercial 2.5 License (http://creativecommons.org/licenses/by-nc/2.5/) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. The images or other third party material in this chapter are included in the work's Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work's Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.