Abstract
<p><em>This research aimed to analyse the the social-economis factors influencing the working decisions of elderly in Yogyakarta Province.<strong> </strong>The sample used was the elderly population of Special Region of Yogjakarta province aged 60 years amounted to 1.906 people following the number of respondents of Susenas in 2016. The analytical tool used is Binary Logit Regression using SPSS 21.The results show that the number of productive elderly people was 57,50% from the whole number of elderly people. The elderly workers dominantly work in the agriculture sector. Their profession is as an entrepreneur and mostly helped by their workers and others are as full time force workers. Binary regression test showed that duration of illness, hospitalization, dependent burden, living location, and pension guarantee significantly negative influence to their decision to go working. Meanwhile, level of primary education, marital status, and their status in the household positively significant influence to work. In addition, gender, level of secondary and higher education, and their household expenses do not significantly influence to their decision to work in the Special Region of Yogjakarta province in 2016.</em></p>
Highlights
One of the factors that promote aging is decreased fertility rates, decreased life expectancy and declining facts mortality makes up for decreased fertility (Beard et al, 2012)
The binary Logit Regression Model is used because the dependent variable is a dummy which consists of two categories, namely the value 1 for the elderly who decide to work and the value 0 for those who do not work
In the Special Region of Yogyakarta who decided to work consisted of male elderly by 51.73% and female elderly by 48.27%. These results indicate that the participation of elderly people in the Special Region of Yogyakarta does not occur inequality between men or elderly women
Summary
One of the factors that promote aging is decreased fertility rates, decreased life expectancy and declining facts mortality makes up for decreased fertility (Beard et al, 2012). This aging roblem causing impact to workplace (Hennekam, 2015). Based on population data from UNFPA and Bappenas in 2016, the proportion of the elderly population at the beginning of the project year was 1971 at 4.5%. This number continued to increase until 2015 to 8.5%. In contrast to the proportion of the population under 15 years of age and 15-59 years of age who experienced a significant decline
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