scholarly journals Spatial Dimensions of Economic Growth in Brazil

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Guilherme Mendes Resende
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Spatial Autocorrelation ◽  
Spatial Scales ◽  
Statistical Significance ◽  
State Level ◽  
The State ◽  
Omitted Variables ◽  
Spatially Correlated ◽  
Determinants Of Economic Growth

The contribution of this paper is to explore time and spatial scale dimensions of economic growth in Brazil using alternative panel data techniques to provide a measure of the extent of spatial autocorrelation (in kilometres) over three decades (1970–2000) as well as discussing the determinants of economic growth at a variety of geographic scales (minimum comparable areas, micro-regions, meso-regions, and states). The magnitude and statistical significance of growth determinants such as schooling, population density, population growth, and transportation costs are dependent on the scale of analysis. Moreover, the extent of residual spatial autocorrelation showed that it seems to vary across spatial scales. Indeed, spatial autocorrelation seems to be bounded at the state level and it shows positive and statistically significant values across distances of more than 1,500 kilometres at the other three spatial scales. Among other results, the study suggests that the nonspatial panel data techniques are not able to deal with spatially correlated omitted variables across different spatial scales, except for the state level where nonspatial panel data models seem to be appropriate to investigate growth determinants and convergence process in the Brazilian states case.

Poverty under Planning

2021 ◽  
pp. 254-295
Author(s):  
K. L. Datta
Keyword(s):  
Economic Growth ◽  
Policy Making ◽  
Poverty Reduction ◽  
State Level ◽  
The State ◽  
High Rate ◽  
Methodological Issues ◽  
Poor Households ◽  
The Impact ◽  
Over Time

Describing the manner in which poverty is incorporated as a parameter in planning, this chapter delineates the use of poverty estimates in policy-making, and in tracking progress of development over time and space. It dwells on the methodological issues related to measurement of poverty, and identification of poor households, comprehensively summarizing the debates surrounding it. Viewing the pace of poverty reduction as the ultimate test of planning, it quantifies the level and change in poverty since the 1970s. It analyses the state of poverty at national and state level, and assesses the impact of economic growth and income redistributive measures on poverty reduction. It brings out that the phenomenal decline in poverty in the reforms-era took place exclusively due to increase in income, eventuated by high rate of economic growth. Finally, it states that despite the decline, poverty remains a major concern.

Peak Car Travel in the United States: Two-Decade-Long Phenomenon at the State Level

2015 ◽  
Vol 2531 (1) ◽  
pp. 36-44 ◽  
Author(s):  
Timothy J. Garceau ◽  
Carol Atkinson-Palombo ◽  
Norman Garrick
Keyword(s):  
United States ◽  
Economic Growth ◽  
State Level ◽  
Developed Countries ◽  
The United States ◽  
The State ◽  
Economic Consequences ◽  
National Scale ◽  
Peak Car ◽  
Car Travel

Peak car travel is an international phenomenon that became evident in the United States on a national scale in 2004. Potentially related to peak car travel is the decoupling of economic growth from driving levels. A wealth of research has addressed these phenomena on a national scale in the United States and other developed countries. Yet few studies have been undertaken on other geographic scales, especially the statewide scale in the United States. This study investigated U.S. state-level driving and economic patterns from 1980 to 2011 to understand occurring changes. The research results showed that peak car travel first occurred at the state level as early as 1992 in Washington State, whereas another 10 states peaked in 2000. By 2011, 48 of the 50 states had peaked. The longevity of this phenomenon at the state level provided evidence that peak car travel in the United States was a more permanent phenomenon than previously thought. In addition, the decoupling of economic growth from driving was evident at the state level. In the 1980s, these indicators were positively correlated at the state level. A significant change occurred by the 2000s, however, when any significant connection ceased for most states. For four of the earliest peak car travel states, the relationship between economic growth and driving turned negative. This finding showed that decreases in driving were not associated with negative economic consequences. Rather, in several states, driving reductions were now associated with increased, rather than decreased, economic growth.

scholarly journals Income Inequality: Do Different Inequality Measures Show Different Impacts on Economic Growth, at the State Level? An Analysis of the State of New Jersey

2016 ◽  
Vol 5 (12) ◽  
pp. 40
Author(s):  
Bertram Chukwudum Ifeanyi Okpokwasili
Keyword(s):  
Economic Growth ◽  
New Jersey ◽  
Income Inequality ◽  
Income Distribution ◽  
State Level ◽  
The State ◽  
Inequality Measure ◽  
Inequality Measures ◽  
Real Gdp ◽  
Different Levels

<p>This paper investigates whether the use of different inequality measures is instrumental in determining impact on economic growth at the State level. We find that different measures show different levels of significance with respect to economic health. We study New Jersey income distribution and shares from 1964 to 2014, using graphs and statistics. The dual analyses approach and the use of different inequality measures enabled conclusions to be reached, that only one view and one inequality measure would have made difficult, if not misleading. New Jersey Real GDP/Capita (RGC) was going up, whether or not the inequality measure was getting better. Inequality had little or no effect on the direction of the RGC. Economic Growth is not a good measure of the effects of inequality.</p>

scholarly journals The Determinants of Economic Growth in CEMAC through a Panel Data Approach

2016 ◽  
Vol 4 (2) ◽  
pp. 483
Author(s):  
Nzingoula Gildas Crepin
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Fixed Effects ◽  
Good Governance ◽  
Generalized Method Of Moments ◽  
Central Africa ◽  
Ordinary Least Squares ◽  
Fixed Effects Model ◽  
Main Determinants ◽  
Determinants Of Economic Growth

<div><p><em>This article highlights through a panel data approach the determinants of economic growth; observed over the last decade in the Economic and Monetary Community of Central Africa (CEMAC) and necessary to reach emerging economies stage. To do this, we essentially used Stata 12 software to come up with the results, and a panel data sample comprising six CEMAC member states, namely Congo, Cameroon, Gabon, Equatorial Guinea, Central African Republic and Chad, for the period ranging from 2000 to 2013. The results obtained after estimating ordinary least squares, fixed effects model, random effects model, generalized method of moments (GMM) and specification tests show that the best model to estimate these types of data is the fixed effects model. Besides, the main determinants of economic growth in CEMAC over that period are Foreign Direct Investment (FDI) and loans lending to the economy (LOAN). After estimation, FDI is found positive and significant on economic growth, while LOAN is significant and found negative maybe due to lack of good governance.</em></p></div>

scholarly journals Northern Bobwhite Occupancy Patterns on Multiple Spatial Scales Across Arkansas

2021 ◽  
Author(s):  
Ellery V. Lassiter ◽  
Marcus Asher ◽  
Grace Christie ◽  
Connor Gale ◽  
Andrhea Massey ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Scales ◽  
Bird Species ◽  
State Level ◽  
Northern Bobwhite ◽  
Habitat Associations ◽  
The State ◽  
Ouachita Mountains ◽  
Priority Areas ◽  
Human Land Use

Northern bobwhite Colinus virginianus populations have been rapidly declining in the eastern, central, and southern United States for decades. Declines have been driven by land use change and an incompatibility between northern bobwhite resource needs and human land use practices. Here, we applied occupancy analyses on two spatial scales (state-level and ecoregion-level) to more than 5,000 northern bobwhite surveys conducted over six years across the entire state of Arkansas to explore patterns in occupancy and land use variables, and to identify priority areas for management and conservation. At the state level, northern bobwhite occupied 29% of sites and northern bobwhite were most likely to occur in areas with a high percentage of early successional habitat (grassland, pasture, and shrubland). The statewide model predicted that northern bobwhite were likely to occur (≥75% predicted occupancy) in &lt;20% of the state. Arkansas is comprised of five distinct ecoregions, and analyses at the ecoregion spatial scale showed that habitat associations of northern bobwhite could vary between ecoregions. For example, northern bobwhite occupancy in both the Arkansas River Valley and Ozark Mountains ecoregions was best predicted by early successional habitat, but was further refined by other habitat associations such as the proportion of herbaceous habitat and hay-pasture habitat, respectively. Contrastingly, northern bobwhite occupancy in the Ouachita Mountains ecoregion was best predicted by richness of landcover classes alone. Ecoregion-level models were thus more discerning than the state level model and should be more helpful to managers in identifying priority conservation areas. However, in 2 of 5 ecoregions, northern bobwhite were too rarely encountered to accurately predict their occurrence. We found that likely occupied northern bobwhite habitat lay primarily on private properties (95%), but that numerous public entities own and manage land identified as suitable or likely occupied. We conclude that management of northern bobwhite in Arkansas could benefit from cooperation among state, federal, and military partners, as well as surrounding private landowners and that ecoregion-specific models may be more useful in identifying priority areas for management. Our approach incorporates multiple landscape scales when using remote sensing technology in conjunction with monitoring data and could have important application for the management of northern bobwhite and other grassland bird species.

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