11688245-15a9-4e12-929b-84cc547daf21 a602768a-c07c-48c0-81f6-346f02832fdf Michael Simon 2020-09-22T16:33:08 ISO 19115:2003/19139 1.0 Secondary Data on Social Indicators and Public Expenditure on District and Regional Level in Tanzania (1996-2010) 2015-01-03T11:00:00 2010-12-22 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. The uploaded data were the basis for the following PhD-thesis: 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. Michael Simon Center for Development Research, Department Economy and Technological Change (ZEF B), University of Bonn PhD Student Germany michel-simon[at]gmx.net National Bureau of Statistics, Tanzania +255 (0) 22-2122722/3 +255 (0)22-2112352 Dar es Salaam 18 Kivukoni Road 11992 Tanzania, United Rep of dg[at]nbs.go.tz http://www.nbs.go.tz Ministry of Health and Social Welfare, Tanzania +255-22-2342000/5 +255-22-2137951 Dar es Salaam 6 Samora Machel Avenue 11478 Tanzania, United Rep of ps[at]moh.go.tz www.moh.go.tz https://daten.zef.de:/geonetwork/srv/api/records/11688245-15a9-4e12-929b-84cc547daf21/attachments/statistic-2.PNG large_thumbnail PNG public expenditure health education agriculture gross domestic product GEMET - Concepts, version 2.4 2010-01-13 geonetwork.thesaurus.external.theme.gemet Tanzania Continents, countries, sea regions of the world. 2015-07-17 geonetwork.thesaurus.external.place.regions Free accessible. Data sources have to be cited. National Bureau of Statistics, Tanzania & Ministry of Health and Social Welfare, Tanzania: Secondary Data on Social Indicators and Public Expenditure on District and Regional Level in Tanzania (1996-2010). economy 28.916027838063034 41.835949711264824 -12.153925915991802 -1.3475524122842806 1996-01-01 2010-12-31 1 Microsoft Excel Binary File (.xlsb) 2007 Center for Development Research WWW:LINK-1.0-http--link OGC:WMS-1.1.1-http-get-map https://daten.zef.de:/geonetwork/srv/api/records/11688245-15a9-4e12-929b-84cc547daf21/attachments/Tanzania_PhD_Database.xlsb WWW:DOWNLOAD-1.0-http--download Tanzania_PhD_Database.xlsb Data