Title of article :
Spatial data modeling in disposable income per capita in china using nationwide spatial autoregressive (SAR
Author/Authors :
Purwaningsih , Tuti Department of Statistics - Faculty of Mathematics and Natural Science - Universitas Islam Indonesia , Indonesia , Chumairoh, Chumairoh Department of Statistics - Faculty of Mathematics and Natural Science - Universitas Islam Indonesia , Indonesia , Ghosh, Anusua University of South Australia , Australia
Pages :
9
From page :
98
To page :
106
Abstract :
China as a country became the economic center of the world. However, with a population of 1.3 billion, China's per capita income is still at number 80 in the world. In the world, considering the imbalance between town and country with 100 million people still living in poverty. Thus, to address this imbalance, it is necessary to study the condition in depth, because income per capita is often used as a benchmark to measure the prosperity of a country. With greater and equitable income per capita, the country will be judged increasingly affluent. Two factors, mainly industry and tourism, play an important role in the economic progress in China. These are include Per capita Disposable Income Nationwide (yuan), Total Value of Exports of operating units (1,000 USD), Registered Unemployed Person in Urban Area (10000 person), Foreign Exchange Earning from International tourism(in millions USD) and Number of Overseas Visitor Arrivals (million person/time). Thus, it is necessary to investigate the influence of these factors to increase per capita income. Since the economic development of a region usually affect the surrounding area, this study aims to include spatial effects, using Spatial Autoregressive (SAR) Model. The results suggest that the per capita income affected by the Tourism factor is about 58.65% (R-squared).
Keywords :
Spatial Autoregressive Model (SAR) , industry tourism , Per Capita Disposable Income Nationwide
Journal title :
International Journal of Advances in Intelligent Informatics
Serial Year :
2017
Full Text URL :
Record number :
2601758
Link To Document :
بازگشت