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    The optimal allocation of scarce resources for health improvement is a crucial factor to lower the burden of disease and to strengthen the productive capacities of people living in developing countries. This research project aims to devise tools in narrowing the gap between the actual allocation and a more efficient allocation of resources for health in the case of Tanzania. Firstly, the returns from alternative government spending across sectors such as agriculture, water etc. are analysed. Maximisation of the amount of Disability Adjusted Life Years (DALYs) averted per dollar invested is used as criteria. A Simultaneous Equation Model (SEM) is developed to estimate the required elasticities. The results of the quantitative analysis show that the highest returns on DALYs are obtained by investments in improved nutrition and access to safe water sources, followed by spending on sanitation. Secondly, focusing on the health sector itself, scarce resources for health improvement create the incentive to prioritise certain health interventions. Using the example of malaria, the objective of the second stage is to evaluate whether interventions are prioritized in such a way that the marginal dollar goes to where it has the highest effect on averting DALYs. PopMod, a longitudinal population model, is used to estimate the cost-effectiveness of six isolated and combined malaria intervention approaches. The results of the longitudinal population model show that preventive interventions such as insecticide–treated bed nets (ITNs) and intermittent presumptive treatment with Sulphadoxine-Pyrimethamine (SP) during pregnancy had the highest health returns (both US$ 41 per DALY averted). The third part of this dissertation focuses on the political economy aspect of the allocation of scarce resources for health improvement. The objective here is to positively assess how political party competition and the access to mass media directly affect the distribution of district resources for health improvement. Estimates of cross-sectional and panel data regression analysis imply that a one-percentage point smaller difference (the higher the competition is) between the winning party and the second-place party leads to a 0.151 percentage point increase in public health spending, which is significant at the five percent level. In conclusion, we can say that cross-sectoral effects, the cost-effectiveness of health interventions and the political environment are important factors at play in the country’s resource allocation decisions. In absolute terms, current financial resources to lower the burden of disease in Tanzania are substantial. However, there is a huge potential in optimizing the allocation of these resources for a better health return.

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    Secondary data on social indicators and public expenditure on district and regional level in Tanzania (1996-2010), as for example: THINV: Logarithm of deflated public per capita spending on health in the short- and long term (total spending of the current and the last five budget years) SANI: Latrines per 100 pupils INFRA: Percentage of women and men age 15-49 who reported serious problems in accessing health care due to the distance to the next health facility URB: Percentage of people living in urban areas TAINV: Logarithm of deflated public per capita spending on agriculture (current and previous budget year)* BREASTF: Percentage who started breastfeeding within 1 hour of birth, among the last children born in the five years preceding the survey IODINE: Percentage of households with adequate iodine content of salt (15+ ppm) MEDU: Percentage of women age 15-49 who completed grade 6 at the secondary level VACC: Percentage of children age 12-23 months with a vaccination card TWINV: Logarithm of deflated public per capita spending on water in the short- and long term (total spending of the current and the last five budget years)* TEINV: Logarithm of deflated public per capita spending on education in the short- and long term (total spending of the current and the last five budget years)* LABOUR: Percentage of women and men employed in the 12 months preceding the survey LAND: Per capita farmland in ha (including the area under temporary mono/mixed crops, permanent mono/mixed crops and the area under pasture) RAIN: Yearly rainfall in mm etc.

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    The child module questionnaire was used to collected information about child feeding practices such as exclusive breastfeeding during the first six months of life, current breastfeeding and complementary foods, prevalence of illness in the previous two weeks period (such as diarrhea, malaria, fever, pneumonia, and cough), immunization records and anthropometric measurements.

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    The follow-up surveys were administered between April and July 2014 after completing the baseline survey by the end of March 2014. The aim of the survey was to collect information on under-five children heath status—such as diarrhea, fever, vomiting, constant cough, stomach pain/cramps and skin infections in the preceding two weeks. This data collection process requires multiple successive visits to the households, and each household has been visited every fortnightly for a period of three months to record the health status of the children. For this purpose, one-page child health calendar questionnaire was developed to record information about symptoms of a particular illness; how long the symptoms stay; and what treatment they seek for if any. The data collection was done by health and agriculture extension workers who work in the selected kebeles. Training was given to the enumerators during the field organization for the follow-up household survey. In most cases, the data collectors ask the child’s primary caretaker—usually the mother or another adult woman household member.

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    The primary caregiver module questionnaire was used to collect information about the household primary caregiver's hygiene behavior and knowledge, community participation, handwashing demonstration, and food preparation, storage and handling.

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    The Household module questionnaire was used to list all the members of the selected households and basic characteristics of each listed person, such as age, sex, education, relationship to the household head and other household level information. The household questionnaire also collected information about total agricultural productions and sales, livestock ownership, holdings of various consumer and durable goods and other income sources, food and non-food expenditures, labor and time use, and household members’ health status in the last two months preceding the survey.

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    Overlapping Marginality Dimensions in Bangladesh. Methodology see also: Graw, V. and C. Ladenburger. 2012. Mapping Marginality Hotspots - Geographical Targeting for Poverty Reduction. (ZEF Working Papers 88). Indicators: Per capita income (HIES 2010, cut-off-point: least 3rd quantile) Under-five child mortality: DHS Survey 2008 (cut-off-point: least 3rd quantile) Accessibility: Nelson 2008; cut-off-point: more than 3 hours distance) Gender: Difference of men and women in eduction; secondary school complete and higher: DHS Survey 2008 (cut-off-point: least 3rd quantile) An overlay was created with the above mentioned indicators based on the respective thresholds. Those areas where most indicators - with low performance - overlap were ranked as those areas experiencing highest marginality.

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    This survey was carried out with around 400 households in the 5 districts of Uttar Pradesh India from March 2015 to June 2015. The survey deals with agriculture, energy use, consumption, livelihoods, education and health of the households.

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    The dataset includes 1332 observations on the household level in 3 regions with information from 6607 individuals. Subsets were used for the addressed research questions on agriculture, markets, economic preferences and resulting food and nutrition security. The precise methodology for research and data collection can be found in the doctoral thesis of the author.