Electricity consumption and economic growth at the state and sectoral level in India: Evidence using heterogeneous panel data methods

2021 ◽  
Vol 94 ◽  
pp. 105064
Author(s):  
Aviral Kumar Tiwari ◽  
Leena Mary Eapen ◽  
Sthanu R Nair
2021 ◽  
pp. 84-107
Author(s):  
Julia Payson

This chapter uses a variety of panel data methods to estimate the returns to lobbying for individual municipalities. When cities start lobbying, they receive significantly more revenue from the state in the following year compared to other cities. But not all localities benefit equally. In particular, wealthy communities with higher median incomes tend to receive substantially more revenue after lobbying than less affluent municipalities. The chapter concludes by discussing some of the mechanisms that might be driving these results. While higher-income cities don’t spend more money on lobbying, they do spread their efforts across a greater number of bills, and they appear to be particularly savvy at using their lobbyists to advocate for shovel-ready projects that make attractive funding targets for state officials.


2013 ◽  
Vol 805-806 ◽  
pp. 591-594
Author(s):  
Dong Heng Hao ◽  
Guo Zhu Li ◽  
Dian Ru Wang

we analyzed the relationship between energy conservation and economic using panel data. the reduction of energy consumption per unit of GDP and energy consumption per unit of industrial value-added will promote economic growth, however, lower electricity consumption per unit of GDP may inhibit economic growth. Finally, this article puts forward corresponding suggestions, including improving the relevant laws and regulations, speeding up the energy saving information disclosure, improving public participation mechanisms, speeding up the adjustment of industrial structure and technological innovations, and promoting the reform of energy prices.


2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Guilherme Mendes Resende

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.


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