Empirical Analysis and the Countermeasure on the Determination of Needy Students in Colleges and Universities with Non-income Indicators

WANG Li-min; LIU Jing

Volume 29 Issue 1

Jun. 2021

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Article Navigation > Journal of University of Science and Technology Beijing ( Social Sciences Edition) > 2013 > 29(1): 89-93

WANG Li-min, LIU Jing. Empirical Analysis and the Countermeasure on the Determination of Needy Students in Colleges and Universities with Non-income Indicators[J]. Journal of University of Science and Technology Beijing ( Social Sciences Edition), 2013, 29(1): 89-93.

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WANG Li-min, LIU Jing. Empirical Analysis and the Countermeasure on the Determination of Needy Students in Colleges and Universities with Non-income Indicators[J]. Journal of University of Science and Technology Beijing ( Social Sciences Edition), 2013, 29(1): 89-93.

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Empirical Analysis and the Countermeasure on the Determination of Needy Students in Colleges and Universities with Non-income Indicators

WANG Li-min¹,
LIU Jing²

1. Management Center of China Education Economy Information Net, University of Science and Technology Beijing, Beijing 100083, China;
2. Institute of Education Economics and Management, University of Science and Technology Beijing, Beijing 100083, China

Received Date: 2012-06-23
Available Online: 2021-06-24

Abstract

Abstract

As China's tax system is not perfect, the accurate information of household income and expenditure is more difficult to get.Thus, the identification of poor students in colleges and universities has become difficult to quantitative research.In this paper, nine non-income variables, as follows, the main domestic source of income, registered residence, poverty-stricken counties, single parents, major disease, grandparents income, the population of the schooling, family indebtedness, major incident, are usedto estimate the family's income and expenditure, and to identify poor students.The results show that the model is effective and feasible.
- needy students,
- non-income variables,
- binary logistic regression

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