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Aerodynamic coefficients. --- Aerodynamic stability. --- Airfoils. --- Time dependence. --- Vortices.
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Three dimensional models. --- Ice formation. --- Wind tunnel tests. --- Airfoil profiles. --- Aerodynamic coefficients.
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aerodynamic coefficients --- automated control systems --- bimetallic shell --- finite element method --- space vehicle --- bypass ratio --- Aerospace engineering --- Aeronautical engineering --- Aerospace engineering. --- Russia (Federation) --- Aeronautics --- Astronautics --- Engineering --- RF --- Rosja --- Rossii͡ --- Federation of Russia --- Federazione della Russia --- Federazione russa --- O-lo-ssu --- OKhU --- Orosyn Kholboony Uls --- Pravitelʹstvo RF --- Pravitelʹstvo Rossii --- Pravitelʹstvo Rossiĭskoĭ Federat͡sii --- Roshia Renp --- Rosiĭsʹka Federat͡sii͡ --- Rossiĭskai͡a Federat͡sii͡ --- Rossiya --- Rossiyskaya Federatsiya --- Russian Federation --- Russian S.F.S.R. --- Russische Föderation --- Urysye Federat͡sie --- O-lo-ssu (Federation) --- Rosja (Federation) --- Rossii͡a (Federation) --- Rossiya (Federation) --- Eluosi (Federation) --- Pravitelʹstvo Rossiĭskoĭ Federat︠s︡ii --- Roshia Renpō --- Rosiĭsʹka Federat︠s︡ii︠a︡ --- Rossii︠a︡ (Federation) --- Rossiĭskai︠a︡ Federat︠s︡ii︠a︡ --- Urysye Federat︠s︡ie --- Pravitelʹstvo RF --- Pravitelʹstvo Rossii --- Pravitelʹstvo RossiiÌskoiÌ Federatï¸ s︡ii --- Roshia RenpoÌ --- RosiiÌsʹka Federatï¸ s︡iiï¸ a︡ --- Rossiiï¸ a︡ (Federation) --- RossiiÌskaiï¸ a︡ Federatï¸ s︡iiï¸ a︡ --- Russische FoÌderation --- Urysye Federatï¸ s︡ie
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This paper uses the night lights (satellite imagery from outer space) approach to estimate growth in and levels of subnational 2013 gross domestic product for 47 counties in Kenya and 30 districts in Rwanda. Estimating subnational gross domestic product is consequential for three reasons. First, there is strong policy interest in how growth can occur in different parts of countries, so that communities can share in national prosperity and not get left behind. Second, subnational entities want to understand how they stack up against their neighbors and competitors, and how much they contribute to national gross domestic product. Third, such information could help private investors to assess where to undertake investments. Using night lights has the advantage of seeing a new and more accurate estimation of informal activity, and being independent of official data. However, the approach may underestimate economic activity in sectors that are largely unlit notably agriculture. For Kenya, the results of the analysis affirm that Nairobi County is the largest contributor to national gross domestic product. However, at 13 percent, this contribution is lower than commonly thought. For Rwanda, the three districts of Kigali account for 40 percent of national gross domestic product, underscoring the lower scale of economic activity in the rest of the country. To get a composite picture of subnational economic activity, especially in the context of rapidly improving official statistics in Kenya and Rwanda, it is important to estimate subnational gross domestic product using standard approaches (production, expenditure, income).
Agricultural output --- Agricultural performance --- Agricultural sector --- Agriculture --- Annual growth --- Annual growth rate --- Cities --- City --- Coefficients --- Consumption --- Criteria --- Development indicators --- Development policy --- Diseconomies of scale --- Distribution of income --- District --- District administrations --- District level --- District-level --- Economic activity --- Economic decline --- Economic downturns --- Economic growth --- Economic theory & research --- Economics --- Elasticity --- Empirical model --- Estimation method --- Financial crisis --- Fiscal management --- Fixed effects --- GDP --- GDP per capita --- Gross domestic product --- Growth --- Growth rate --- Growth rates --- Household surveys --- Incentives --- Incidence of poverty --- Indicators --- Informal economy --- Inputs --- Long-term growth --- Macroeconomics --- Macroeconomics and economic growth --- National poverty line --- Policy research --- Poverty --- Poverty impact evaluation --- Poverty levels --- Poverty line --- Poverty reduction --- Pro-poor growth --- Provinces --- Real GDP --- Resource allocation --- Revenue --- Revenue allocation --- Revenue sharing --- Revenue sharing formula --- Revenue-raising capacity --- Subnational entities --- Subnational governments --- Subnational unit --- Surveys --- Tax --- Underestimates --- Urban areas --- Wealth
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This paper uses the night lights (satellite imagery from outer space) approach to estimate growth in and levels of subnational 2013 gross domestic product for 47 counties in Kenya and 30 districts in Rwanda. Estimating subnational gross domestic product is consequential for three reasons. First, there is strong policy interest in how growth can occur in different parts of countries, so that communities can share in national prosperity and not get left behind. Second, subnational entities want to understand how they stack up against their neighbors and competitors, and how much they contribute to national gross domestic product. Third, such information could help private investors to assess where to undertake investments. Using night lights has the advantage of seeing a new and more accurate estimation of informal activity, and being independent of official data. However, the approach may underestimate economic activity in sectors that are largely unlit notably agriculture. For Kenya, the results of the analysis affirm that Nairobi County is the largest contributor to national gross domestic product. However, at 13 percent, this contribution is lower than commonly thought. For Rwanda, the three districts of Kigali account for 40 percent of national gross domestic product, underscoring the lower scale of economic activity in the rest of the country. To get a composite picture of subnational economic activity, especially in the context of rapidly improving official statistics in Kenya and Rwanda, it is important to estimate subnational gross domestic product using standard approaches (production, expenditure, income).
Agricultural output --- Agricultural performance --- Agricultural sector --- Agriculture --- Annual growth --- Annual growth rate --- Cities --- City --- Coefficients --- Consumption --- Criteria --- Development indicators --- Development policy --- Diseconomies of scale --- Distribution of income --- District --- District administrations --- District level --- District-level --- Economic activity --- Economic decline --- Economic downturns --- Economic growth --- Economic theory & research --- Economics --- Elasticity --- Empirical model --- Estimation method --- Financial crisis --- Fiscal management --- Fixed effects --- GDP --- GDP per capita --- Gross domestic product --- Growth --- Growth rate --- Growth rates --- Household surveys --- Incentives --- Incidence of poverty --- Indicators --- Informal economy --- Inputs --- Long-term growth --- Macroeconomics --- Macroeconomics and economic growth --- National poverty line --- Policy research --- Poverty --- Poverty impact evaluation --- Poverty levels --- Poverty line --- Poverty reduction --- Pro-poor growth --- Provinces --- Real GDP --- Resource allocation --- Revenue --- Revenue allocation --- Revenue sharing --- Revenue sharing formula --- Revenue-raising capacity --- Subnational entities --- Subnational governments --- Subnational unit --- Surveys --- Tax --- Underestimates --- Urban areas --- Wealth
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