Population (1988) and Per Capita Income (1987) Estimates [United States]: Governmental Units

Author(s):  
2004 ◽  
Vol 30 (2) ◽  
Author(s):  
Daniel Almeida Fonseca ◽  
José Luís Oreiro

O artigo pretende analisar em que medida os modelos neoclássicos de crescimento econômico – mais especificamente, o modelo de Solow (1956, 1957), o modelo de Mankiw, Romer e Weill (1992) e o modelo de Romer (1990) – são capazes de explicar a divergência global nos níveis de renda per capita nos últimos dois séculos e a convergência nos níveis de renda per capita e o catch-up ocorridos entre Europa e Estados Unidos no período do Pós Segunda Guerra Mundial. Com efeito, trata-se de uma confrontação entre teoria e prática, de modo a analisar de que forma tais modelos explicam (ou não) os fatos supramencionados. No trabalho, demonstra-se que a ocorrência dos fatos anteriormente mencionados deveu-se fundamentalmente às diferenças do progresso técnico existente entre as economias (no caso da divergência) e à redução de tais disparidades entre os Estados Unidos e a Europa no período de tempo imediatamente após a 2.a Guerra Mundial (no caso da convergência e do catch-up). Na verdade, tenta-se demonstrar que os modelos apresentados não conseguem explicar satisfatoriamente os fatos ocorridos, sendo válidos apenas em casos específicos. O que o artigo se propõe a expor é que a realidade do crescimento econômico mundial é bastante diferente das conclusões dos modelos neoclássicos considerados. Abstract This work intends to analyze in which way the neoclassical growth models – more specifically, Solow (1956, 1957), Mankiw, Romer and Weill (1992) and Romer (1990) – are capable to explain the global divergence on the levels of per capita income over the last two centuries and the convergence on the levels of per capita income and the catch-up occurred between Europe and the United States after World War II. In fact, it is a confrontation between theory and practice, in order to view in which way these models explain (or not) the above-mentioned facts. During the present work, we demonstrate that the occurrence of these facts were mainly caused by differences on technological progress between economies (case of divergence) and the reduction of such disparities between the United States and Europe on the period of time immediately after World War II (case of convergence and catch-up). In fact, we try to demonstrate that these models are incapable to give a satisfactory explanation to the occurred facts, being only valid on specific cases. The work tries to propose that the reality of global economic growth differs considerably from the conclusions of the considered neoclassical growth models.


2005 ◽  
Vol 37 (9) ◽  
pp. 1613-1636 ◽  
Author(s):  
Peter B Nelson

Many advanced economies have an aging population that relies heavily on government pensions, social security, and privately held investment-based income. In the United States the geography of social security and investment income (collectively called nonearnings income) is uneven. Furthermore, the ways in which migration serves to redistribute such income across space remain unstudied. This paper highlights regions in the United States that are becoming increasingly attractive to nonearnings income through migration. Overall, there is a consistent Rustbelt-to-Sunbelt shift in nonearnings income due to migration. These income shifts, however, are quite distinct between metropolitan and nonmetropolitan areas. Starting in the late 1980s, nonmetropolitan portions of the Rustbelt enjoyed net gains in nonearnings income through migration processes. Therefore, it appears that the migration systems which drew income away from the nonmetropolitan north during the 1970s are now shifting to some degree. Analysis further indicates that migration contributes to greater levels of economic disparity across space. Whereas flows of social security income are highly influenced by the aggregate level of migration, flows of investment income are more influenced by differentials in migrants' per capita income levels. Regions such as the Plains are attracting migrants with relatively low per capita nonearnings income whereas the Rocky Mountain and New England regions are attracting individuals with high per capita income. Destinations such as the Rocky Mountains and New England are likely to enjoy significant economic benefits as new sources of income arrive which are tied to migration, but the Plains region is left with less-well-off populations, which pose significant social and economic problems in such sending regions. As the population in the United States and other advanced economies ages, these processes of nonearnings income migration become increasingly important in shaping local and regional economic conditions.


2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e21039-e21039
Author(s):  
Alan Geller ◽  
Juliana Berk-Krauss ◽  
David Polsky ◽  
Jennifer Stein

e21039 Background: To our knowledge, no study has looked at U.S. melanoma mortality trends by state. We sought to determine the ten states with the highest melanoma mortality rates (per white population) and those with the lowest, as well as any state-wide demographics that could account for these trends. Methods: State melanoma mortality rates were collected from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program and National Program for Cancer Registries. Data on state characteristics were collectied from the Area Health Resource File (AHRF) and the US Census Bureau. We used a regression model to determine associations between melanoma mortality and the state demographic context, such as median income, per capita income, unemployment rate, education level, and rural versus nonrural. We also examined the effect of access to health care resources by looking at density of dermatologists, density of primary care providers, and total number of hospitals. Results: We identified ten states concentrated across the central United States with the highest melanoma mortality rates. Per capita income was the only significant association for melanoma mortality rates (p = 0.0016, 95% CI 6.88 to 18.09). Median income, unemployment rate, education level, rural versus non-rural, health professional density, and unemployment rate were not associated with melanoma mortality rates by state. Conclusions: There exists a ‘melanoma mortality belt’ across the central United States made up of the ten states with the highest melanoma mortality rates. This trend could not be consistently accounted for by state demographics, even socioeconomic status traditionally thought to correlate with mortality. Only one significant association was seen between melanoma mortality rate and per capita income. Our preliminary findings highlight the multifactorial picture of geographic melanoma mortality inequalities in the U.S.


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