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This paper examines how cities and regions within countries are likely to adjust to trade openness and improved connectivity driven by large transport investments from China's Belt and Road Initiative. The paper presents a quantitative economic geography model alongside spatially detailed information on the location of people, economic activity, and transport costs to international gateways in Central Asia to identify which places are likely to gain and which places are likely to lose. The findings are that urban hubs near border crossings will disproportionately gain while farther out regions with little comparative advantage will be relative losers. Complementary investments in domestic transport networks and trade facilitation are complementary policies and can help in spatially spreading the benefits. However, barriers to domestic labor mobility exacerbate spatial inequalities whilst dampening overall welfare.
Connectivity --- Geospatial Economics --- International Economics and Trade --- Labor Mobility --- Macroeconomics and Economic Growth --- Territorial Development --- Trade Liberalization --- Transport --- Transport Corridors --- Urban Hubs
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Tanzania is home to the third highest population of stunted children in Sub-Saharan Africa, with about 2.7 million children under the age of five failing to reach their full potential of growth attainment compared with the reference population as per the World Health Organization standards. Several studies have shown that stunted growth during childhood entraps the future of children in a vicious circle of recurrent diseases, reduced human development, and lower earnings, thus increasing their likelihood of being poor when they grow up. To reduce stunting, the Government of Tanzania and development partners are introducing a convergence of multisectoral interventions adapted to local needs. However, the existing stunting data are representative only at higher administrative levels, thus making it difficult to implement these efforts. The paper uses the 2016 geo-referenced Demographic and Health Survey in conjunction with relevant spatially gridded covariate data, such as nighttime lights, water and sanitation access, vegetation index, travel time, and so on. Geospatial techniques, such as model-based statistics and Bayesian inference implemented using the INLA algorithm, along with appropriate model validation exercises are employed to develop high-resolution maps of stunting in Tanzania at 1x1-kilometer spatial resolution. The maps show that areas of consistently high stunting rates tend to be more common in rural parts of the country, especially throughout the western and southwestern border areas. There is high prevalence of low stunting in the urban areas around Dar es Salaam, Arusha, and Dodoma, as well as in the south of Lake Victoria.
Early child and children's health --- Geo-statistics --- Geospatial economics --- Health care services industry --- Health policy --- Health, nutrition and population --- Hydrology --- Inequality --- Nutrition --- Poverty reduction --- Reproductive health --- Stunting --- Water resources
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