The Determining Factors of Elderly People to Continue their Work Activities in Palembang

• Published in: International journal of recent technology and engineering Vol. 8; no. 4; pp. 2008 - 2014
• Main Authors: Wijaya, Hendry, Marnisah, Luis, Halin, Hamid, Zamzam, Fakhry, Handayani, Susi, Purnamasari, Endah Dewi
• Format: Journal Article
• Language: English
• Published: 30.11.2019
• ISSN: 2277-3878; 2277-3878
• DOI: 10.35940/ijrte.D6879.118419

This research study focuses on the determination factors of elderly that still continue their work activities in the city of Palembang. The purpose of this study is to find out how the influence of Social Security, Education and Health on the decision of the elderly people to continue their work activities in Palembang. The method of data analysis in this study uses the binary logistic regression method in which all variables (independent and dependent) use 2 categories (binary). The results of the estimated data, it is known that the variable Social Security, Education and Health has a significant influence on the decision of the elderly people to continue their work activities in Palembang. Odds Ratio value of 0.046 means that the elderly population who have an elderly social security program have a probability of choosing not to work by 0.046 times when compared to the elderly population who does not have an elderly social security program. Odds Ratio value of 12.298 means that the elderly population completing tertiary education has a probability of choosing to continue working 12.298 times compared to the elderly population completing primary and secondary education. Odds Ratio value of 20.153 means that the elderly population who goes to the doctor/clinic for treatment at least once per 3 months has a chance of continuing to work 20,153 times compared to the elderly population who goes to the doctor/clinic for treatment more than once every 3 months. Nagelkerke's R-Square value is 0.524. This implies that 52.4% of the variation in the independent variable can explain the dependent variable, while the remaining 47.6% is explained by non-model factors or by other variables not examined.