• Korea journal of population studies
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• v.21 no.2
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• pp.58-82
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• 1998

It is usually proposed that job security of old age workers is hampered by the structure of wage increasing with age. This paper sets forth a model to comprehend the characteristic of the old age workers' labor market and policy implications derived from it. In order to stimulate demand for old age workers, policy initiatives should be taken as follows : the wage criteria should be simplified which apply differently from one institution to other; incentives relatively favorable for employing old age workers' in manufacturing sector should be also given to service sectors; employment subsidy or other tax incentives should be given for labor contract after the retirement age; licensing and evaluation system for job ability should be introduced based on occupation & job analysis. To lower the reservation wage of workers, mortgage loan for house and long-term low interest loan for tuition fees should be developed together with stabilization of housing cost. Wedding culture which requires high expense should be amended. Above all, it is necessary to install reasonable social security system. Policy orientation should also pay attention to reduce labor supply of the old aged via aiding old age workers' firm opening and voluntary civil service together with developing various honor programs for members of civil corps.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.