Corelations Quality of Eldery and Regional Socio-Economic Indicators in Indonesia

Risky Primastuti; Sukamdi; Umi Listyaningsih

doi:10.55927/mudima.v3i2.2386

Authors

Risky Primastuti Universitas Gadjah Mada
Sukamdi Universitas Gadjah Mada
Umi Listyaningsih Universitas Gadjah Mada

DOI:

https://doi.org/10.55927/mudima.v3i2.2386

Keywords:

Quality of Eldery, Clustering, Regional Socio-Economy

Abstract

Every region has various features, one of which is the condition of the elderly in Indonesia where there are have gaps between regions. In addition to the features of the elderly, the socio-economic conditions of each province also diverse. So that policies of older peolpe can be right on target, his further research aims to assess the quality categories of the elderly and test how they relate to regional socio-economic indicators. The unit of analysis for this research is 34 provinces in Indonesia, with data sources from BPS publications (Susenas and Sakernas 2021). Examination of the quality of the elderly was carried out by means of non-hierarchical K-means clustering, classification of socio-economic conditions is done by scoring HDI, per capita income, and poverty in an area ,while the correlation of the quality of the elderly with socio-economic indicators was carried out by Spearman rank analysis. The results of this study indicate that in 2021 there will be 19 provinces with poor elderly quality, six provinces with good elderly quality, and 19 provinces with good elderly quality. There is a significant, quite strong, and one-way relationship between socio-economic conditions and the quality of provincial elderly in Indonesia. The HDI and GRDP Per capita have a significant, fairly strong, and in-line relationship with the quality of the provincial elderly. Meanwhile, the proportion of poor people has a significant, quite strong, but contradictory relationship with the quality of the provincial elderly

References

Badan Pusat Statistik. (2021). Statistik Penduduk Lanjut Usia 2021. Badan Pusat Statistik.

Br Ginting, L. A. U., Mulyani, W. P., & Muta"ali, L. (2019). Pemetaan Lansia di Indonesia Ditinjau dari Karakteristik Sosial, Ekonomi, dan Status Kesehatan. Sosio Informa, 5(1), 51–63.

Dobrokhleb, V. G., & Barsukov, V. N. (2017). Demographic theories and the regional aspect of population ageing, . Economic and Social Changes: Facts, Trends, Forecast, 10(6), 89–103.

Garrett D, M., & Poulain, M. (2013). Geography and The eldery. Oxford University Press.

HelpAge International Global Network. (2013). Global Age Watch Index 2013: Purpose, Methodology, and Result.

Jamalludin. (2021). Keputusan Pekerja Lansia tetap Bekerja Pascapensiun dan Kaitannya dengan Kebahagiaan. Jurnal Samudra Ekonomi dan Bisnis, 12(1), 89–101.

Khabazi, M. (2018). Regional Geography and Quantitative Geography: Compare and Contrast. OSF Preprints.

Mares, J., Cigler, hynek, & Vachkoova, E. (2016). Czech version of OPQOL-35 questionnaire: the evaluation of the psychometric properties. Health Qual Life Outcomes, 14(93).

Prayitno, S. (1999). Penduduk Lanjut Usia: Tinjauan Teori, Masalah, dan Implikasi Kebijakan. Jurnal Masyarakat, Kebudayaan Dan Politik, 7(4), 45–50.

Raharjo, S. (2020). Tutorial Analisis Korelasi Rank Spearman dengan SPSS. SPSS Indonesia.

Survey Meter. (2012). Memanusiakan Lanjut Usia: Penuaan Penduduk dan Pembangunan di Indonesia.

Theou, O., Brothers, T. D., Rockwood, M. R., Haardt, D., Mitnitski, A., & Rockwood, K. (2013). Eksploring the Relationship Between National Economic Indicators and Relative Fitness and Frailty In Middle-Aged and Older Europeans. Age and Aging, 42, 614–619.

Todaro, M. P., & Smith, S. C. (2009). Economic Development (11 ed.). Pearson.

United Nations Economic Commission for Europe. (2012). Active Ageing Index 2012: Concept, Methodology and Final Results.

United Nations Population Funds and HelpAge International. (2012). Aging in Twenty-First Century :A Cellebration and Chalenge.