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    Degree of socio-economic marginality in Ethiopia, using the following conditional indicators of the health sector: 1. Illiteracy rate (% of population (>5years) not being able to read and write in their native language) 2. Net not enrolment ratio primary school (total no. of students (age 7-14) being not enroled, expressed as % of total pop. in primary school age) 3. Net not enrolment ratio secondary school (total no. of students (age 15-18) being not enroled, expressed as % of total pop. in secondary school age) Data source: 1. Central Statistical Agency(CSA), ICF International (2012): Ethiopia Demographic and Health Survey 2011. Addis Ababa, Ethiopia; Calverton, USA 2./3. Central Statistical Agency (CSA) (2007): Population and Housing Census. Atlas of Ethiopia 2007. Washington DC, USA

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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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    This data was collected through field surveys conducted in all four provinces of Pakistan (Balochistan, Khyber Pakhtunkhwa (KPK), Punjab and Sindh). Within the KPK province Federally Administered Tribal Areas (FATA) could not be covered because of political insurgency in those areas at the time when survey was conducted. A questionnaire was structured, tested in pilot areas and revised for improvements based on feedback. With a team of 40 students from Balochistan University of Information Technology Engineering and Management Science (BUITEMS), Quetta, a survey was conducted in 43 urban and rural settings during the months of August to December, 2009. Using multistage stratified random sampling design, 963 heads-of-household were interviewed and data on a total of 2,496 children were collected.

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    Primary data from a village survey in China 2011 (Jiangxi Province, Yangyi village). The data is mainly about migration, education, demography, and agriculture in China's rural areas.

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    Degree of socio-economic marginality in Ethiopia, using the following positional indicators of the education sector: 1. Access to education (no. of primary schools related to population density) 2. Access to information (% of households without radio) 3. Access to information (% of households without television) Data source: 1. Central Statistical Agency (CSA), Ethiopian Development Research Institute (EDRI), International Food Policy Research Institute (IFPRI) (2006): Atlas of the Ethiopian Rural Economy. Washington DC, USA. 2./3. Central Statistical Agency (CSA) (2007): Population and Housing Census. Atlas of Ethiopia 2007. Washington DC, USA

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    This dissertation focuses on the impacts of rural-urban migration on the rural areas in China. It consists of five chapters. The first chapter introduces the research problems and presents the framework for studying three selected impacts of migration. These impacts are on the demographic change of the rural population, human capital investment, and agricultural productivity, and are all respectively discussed in the middle three chapters. The last chapter is a case study of a typical Chinese village with massive rural-urban migration. The second chapter first estimates the scale and age-structure of the rural-urban migrants, and then separates the effects of migration on the rural demography by performing simulations with the Cohort Component Method and using data from China’s latest censuses in 2000 and 2010. In addition, it uses household data to confirm the huge effect of rural-urban migration on the demographic structure. The third chapter develops a theoretical framework to investigate the relationship between migration and education. Empirical research reveals a robustly positive effect of migration on educational attainment among the stayers by proposing a novel instrument of the availability of local train stations to deal with the endogeneity. The fourth chapter sets up a theoretical model to study the impacts of migration on agricultural productivity and empirically employs a Simultaneous Equations Model estimated by two-step-least-square method. Empirical results show that migration of the labor force reduces agricultural productivity and households with migration do not invest more in agriculture unless the land size reaches an optimal level. Migration along with land transfer can improve agricultural productivity. The fifth chapter presents a case study of a Chinese village which is transforming its labor-intensive agriculture into a capital-intensive one based on changes in relative scarcity of production factors triggered by the rural-urban migration. It indicates that migration as an external force has broken equilibrium of the traditional agriculture and leads modern agriculture to take off by inducing capital to substitute for labor in agriculture.

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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.