Analysis of Factors Influencing the Income of Women Informal Sector Workers in Indonesia (IFLS Data Analysis 5)

  • Suripto Suripto Universitas Ahmad Dahlan
  • Anandito Wicaksono Universitas Ahmad Dahlan

Abstract

This research was conducted with the aim of being able to find out the factors that influence the income of women informal sector workers in Indonesia in 2014 based on data from the 2014 batch 5 Indonesian family life survey (IFLS-5). IFLS 5 data is the latest data conducted by several institutions. such as rand meters, survey meters and the demographic institute of the Faculty of Economics and Business, University of Indonesia (LD FEB UI). Based on the existing problems, namely female workers contribute 38% of the total national workforce and the informal sector absorbs the highest workforce in Indonesia, which is equal to 69%, but it is ironic because the informal sector has the lowest income level compared to other sectors. This study took a sample of 2,294 respondents taken from 13 provinces in Indonesia. Respondents were filtered based on age with a range of 15-65 years. With the dependent variable income of women workers and the independent variables education level, age, marital status, working hours, work experience and job training. From the results of statistical tests conducted, it shows that 31% of the independent variables partially affect the dependent variable, and the rest are influenced by independent variables outside the research model. The results of multiple linear tests show that the independent variables of education level, age, working hours, work experience and job training are significantly independent variables that can explain the effect on the variable income of women informal sector workers in Indonesia.

Keywords: IFLS-5, Female Workforce, Income of Informal Sector Female Workers.

References

Amron, I. T. dan. (2009). Analisis Faktor-Faktor yang Berpengaruhi Terhadap Produktivitas Tenaga kerja Outlet Telekomunikasi Seluler Kota Makassar. Jurnal Sekolah Tinggi Ilmu Ekonomi Nobel Indonesia.
Baltagi, B. H. (Badi H. (2001). A companion to theoretical econometrics. 709. https://books.google.com/books?hl=en&lr=&id=xs55E7FsMHMC&oi=fnd&pg=PA310&dq=spatial+lag+model&ots=gnhmU1_AEq&sig=DL37Kg8kJvTOdfUCxK3eeRGqm8c#v=onepage&q=spatial lag model&f=false
BELZIL. (2008). Testing the Specification of the Mincer Wage Equation. Annales d’Économie et de Statistique, 91/92, 427. https://doi.org/10.2307/27917254
Breton, T. R., & Breton, A. S. (2021). Growth in a macro-Mincer model: Good results with schooling and experience interactions. Review of Development Economics, 25(2), 563–581. https://doi.org/10.1111/rode.12753
Chang, X., & Shi, Y. (2016). The Econometric Study on Effects of Chinese Economic Growth of Human Capital. Procedia Computer Science, 91(Itqm), 1096–1105. https://doi.org/10.1016/j.procs.2016.07.160
Chowdhury, S., Schulz, E., Milner, M., & Van De Voort, D. (2014). Core employee based human capital and revenue productivity in small firms: An empirical investigation. Journal of Business Research, 67(11), 2473–2479. https://doi.org/10.1016/j.jbusres.2014.03.007
Cremin, P., & Nakabugo, M. G. (2012). Education, development and poverty reduction: A literature critique. International Journal of Educational Development, 32(4), 499–506. https://doi.org/10.1016/j.ijedudev.2012.02.015
Davidson, R., & Flachaire, E. (2007). Asymptotic and bootstrap inference for inequality and poverty measures. Journal of Econometrics, 141(1), 141–166. https://doi.org/10.1016/j.jeconom.2007.01.009
Development, O. for E. C. and. (2010). Atlas of Gender and Development: How Social Norms Affect Gender Equality in Non-OECD Countries. https://doi.org/10.1787/9789264077478-en
DiBartolo, A. (1999). Modern Human Capital Analysis: Estimation of US, Canada and Italy Earning Functions. Maxwell School of Citizenship and Public Affairs Syracuse University Syracuse, Working Paper No.212, 212.
Durlauf, S. N., Johnson, P. A., & Temple, J. R. W. (2005). Chapter 8 Growth Econometrics. Handbook of Economic Growth, 1(SUPPL. PART A), 555–677. https://doi.org/10.1016/S1574-0684(05)01008-7
Floro, M. S., & Bali Swain, R. (2013). Food Security, Gender, and Occupational Choice among Urban Low-Income Households. World Development, 42(1), 89–99. https://doi.org/10.1016/j.worlddev.2012.08.005
Gujarat, D. N., & Porter, D. C. (2009). Basic Econometrics Fifth Edition Damodar (F. Edition & Damodar (eds.); Fifth Edit). The McGraw-Hill Series Economics ESSENTIALS.
Güris, S., & Aydın, G. K. (2022). Spatial Pseudo Panel Data Models with an Application to Mincer Wage Equations. Central European Journal of Economic Modelling and Econometrics, 2022(1), 37–56. https://doi.org/10.24425/cejeme.2022.140511
H.M. Antho, M. (2001). Wanita Dalam Masyarakat Indonesia. Sunan Kalijaga Press.
Kadek, A. N. (2013). Faktor-Faktor Yang Mempengaruhi Penduduk Lanjut Usia Masih Bekerja. Fakultas Ekonomi Dan Bisnis Universitas Udayana.
Lien, H. M., & Wang, P. (2016). The timing of childbearing: The role of human capital and personal preferences. Journal of Macroeconomics, 49, 247–264. https://doi.org/10.1016/j.jmacro.2016.07.004
Pratiwi, R. (2012). TANTANGAN DAN PELUANG SEKTOR INFORMAL. 2, 598–606.
Priyono, E. (2002). Mengapa Angka Pengangguran Rendah di Masa Krisis?: Menguak Peranan Sektor Informal Sebagai Buffer Perekonomian. 1 No.2 Jul.
Tsiboe, F., Zereyesus, Y. A., & Osei, E. (2016). Non-farm work, food poverty, and nutrient availability in northern Ghana. Journal of Rural Studies, 47, 97–107. https://doi.org/10.1016/j.jrurstud.2016.07.027
Published
2023-02-22
Abstract viewed = 152 times
pdf downloaded = 156 times