AN INVESTIGATION OF INCOME AND SUBSTITUTION EFFECTS ON FEMALE LABOUR SUPPLY THROUGH A CHAID ANALYSIS: THE SERVICE SECTOR CASE SPECIFIC TO THE TR51 REGION

Öz

The aim of this study is to investigate factors affecting female labour supply and to study income effect and substitution effect which have a micro-economic significance for female labour supply in the factor market. To this end, the study used the data obtained from the TURKSTAT Household Labour Force Survey 2016 and performed a chi-squared automatic interaction detection CHAID analysis. The analysis results showed that working time in main job, working type, educational level, workplace, household income and social security are the most significant factors of the decision-making of single and married women who supply labour in the service sector

Anahtar Kelimeler

• Gelir Etkisi,İkame Etkisi,İşgücü Piyasası,CHAID Analizi

KADIN EMEK ARZI ÜZERİNDEKİ GELİR VE İKAME ETKİSİNİN CHAİD ANALİZİ İLE İNCELENMESİ: TR51 BÖLGESİ HİZMET SEKTÖRÜ ÖRNEĞİ

Öz

Bu çalışmanın amacı, kadın emek arzını etkileyen faktörleri araştırmak ve emek faktör piyasasında mikro boyutta önemli olan gelir ve ikame etkisinin kadın emek arzındaki yerini incelemektir. Bu amaçla 2016 yılına ait Hanehalkı İşgücü Anketlerinden elde edilen veriler kullanılmıştır. Uygulamada CHAID analizinden yararlanılmıştır. Analiz sonuçları; hizmet sektöründe emek arz eden bekâr ve evli kadınların emek arzı kararında esas işte çalışılan sürenin, çalışma şeklinin, eğitim düzeyinin, çalışılan yerin, hanehalkı gelirinin ve sosyal güvencenin en önemli faktör olduğunu göstermektedir.

Anahtar Kelimeler

• Income Effect,Substitution Effect,Labour Market,CHAID Analysis

Kaynakça

1. Akgeyik, T. (2017). Türkiye’de kadınların işgücü piyasasına katılımını etkileyen faktörler: TÜİK verileri üzerine bir analiz. Sosyal Siyaset Konferansları Dergisi, 70 – 2016/1, 31-53.
2. Argüden, Y., & Yılmaz, B. (2008). Veri madenciliği veriden bilgiye, masraftan değere. 1. Press. Istanbul: ARGE Consulting Publications.
3. Ashenfelter, O., & Heckman, J. (1974). The estimation of income and substitution effects in a model of family labour supply. Econometrica, 42(1), 73-85.
4. Assael, H. (1970). Segmenting markets by group purchasing behavior: An application of the AID technique. J. Marketing Research, 7(2), 153-158.
5. Attanasio, O., Low, H., & Snaches-Marcos, V. (2005). Female labour supply as insurance against idiosyncratic risk. Journal of the European Economic Association, 3(2/3), Papers and Proceedings of the Nineteenth Annual Congress of the European Economic Association (Apr. - May 2005), 755-764.
6. Ayvaz-Kızılgöl, Ö. (2012). Kadınların işgücüne katılımının belirleyicileri: Ekonometrik bir analiz. Doğuş Üniversitesi Dergisi, 13 (1), 88-101.
7. Blau, F. D., & Kahn, M. L. (2013). Female labour supply: Why is the United States falling behind?. American Economic Review: Papers & Proceedings, 103(3), 251–256.
8. Bloom, E. D., Canning, D., Fink, G., & Finlay J. E. (2009). Fertility, female labour force participation, and the demographic dividend. Journal of Economic Growth, 14(2), 79–101.

9. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. USA: CRS Press.
10. Buchmueller, T. C., & Valletta, R. G. 1999. The effect of health insurance on married female labour supply. The Journal of Human Resources, 34(1), 42-70.
11. Cahuc, P., & Zylbereg, A. (2001). Labour economics. London: The MIT Press, Cambridge.
12. Cerruti, M. (2000). Economic reform, structural adjustment and female labour force participation in Buenos Aires, Argentina. World Development, 28(5), 879-891.
13. Chamlou, N., Muzi, S., & Ahmed, H. (2011). Understanding the determinants of female labour force participation in the Middle East and North Africa region: The role of education and social norms in Amman. Almalaurea Working Papers, 31, September.
14. Chang, M. K., & Man Law, S. P. (2008). Factor structure for young’s internet addiction test: A confirmatory study. Computers in Human Behavior, 24(6), 2597-2619.
15. Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38, 11261-11272.
16. Cipollone, A., Patacchini, E., & Vallanti, G. (2012). Women’s labour market performance in Europe, trends and shaping factors. CEPS Special Report No: 66, Brussels.
17. Conover, W. J. (1971). Practical nonparametric statistics. New York: Wiley.
18. Cruces, G., & Galiani, S. (2007). Fertility and female labour supply in Latin America: New causal evidence. Labour Economics, 14 (2007), 565 – 573.
19. Dayıoğlu, M., & Kırdar, M. G. (2010). Determinants of and trends in labour force participation of women in Turkey. SPO&WB Working Paper No:5, Ankara.
20. Eckstein, Z., & Lifshitz, O. (2011). Dynamic female labour supply. Econometrica, 79(6), 1675–1726.
21. Er, Ş. (2013). Türkiye’de kadınların işgücüne katılım oranını etkileyen faktörlerin bölgesel analizi. Öneri, 10(40), 35-44.
22. Ettner, S. (1995). The impact of “parent care” on female labour supply decisions. Demography, 32(1).
23. Gift, M. (2013). Determinants of female labour force participation in Zimbabwe: 1980 To 2012 (Master Thesis). University of Zimbabwe.
24. Gorunescu, F. (2011). Data mining, concepts, models and techniques. Romania: Springer-Heidelberg, Intelligent Systems Reference Library.
25. İnce, M., & Demir, M. H. (2006). The determinants of female labour force: Empirical evidence from Turkey. Eskişehir Osman Gazi Üniversitesi İİBF Dergisi, 1(1), 71-91.
26. Jaumotte, F. (2003). Female labour force participation: past trends and main determinants in OECD countries. Economic Department Working Papers No.376, OECD.
27. Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119-127.
28. Kayri, M., & Boysan, M. (2007). Araştırmalarda CHAID analizinin kullanımı ve baş etme stratejileri ile ilgili bir uygulama. Ankara University Journal of Educational Sciences, 40(2), 135-151.
29. Killingsworth, M. R., & Heckman, J. J. (1986). Handbook of labour economics. Chapter 2 Female labour supply: A survey. 1, 103-204.
30. KILM. (2017). Key indicators of the labour market. 9th Ed. ILO.
31. Kimball, M. S., & Sphapiro, M. D. (2008). Labour supply: Are the income and substitution effects both large or both small?. NBER Working Paper No. 14208.
32. Magidson, J. (1993). The use of the new ordinal algorithm in CHAID to target profitable segments. The Journal of Database Marketing. 1, 29–48.
33. Mincer, J. (1962). Labour force participation of married women: A study of labour supply. Aspects of Labour Economics, Princeton University Press.
34. Nisbet, R., Elder, J. F., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Amsterdam: Elsevier.
35. OECD. (2017). https://stats.oecd.org/Index.aspx?DataSetCode=LFS_SEXAGE_I_R Retrieved Octorber 3, 2017.
36. Özekes, S. (2003). Veri madenciliği modelleri ve uygulama alanları. Istanbul Commerce University Journal. 3(3), 65-82.
37. Ritschard, G. (2013). CHAID and earlier supervised tree methods. In J. J. McArdle, G. Ritschard (Eds.), Contemporary issues in exploratory data mining in behavioral sciences. New York: Routeledge.
38. Seifert, J. W. (2004). Data mining: an overview. In D. D. Pegarkov (Eds.), National security issues. (pp. 201-217). New York: Nova Science Publishers Inc.
39. Shi, Y. (2016). What drives females’ labour force participation in China? A study comparing urban and rural area (Master Thesis). Georgetown University, Washington.
40. Sorsa, P., Mares, J., Didier, M., Guimaraes, C., Rabate, M., Tang, G., & Tuske, A. (2015). Determinants of the low female labour force participation in India. OECD Working Paper. No:1207, Paris, France.
41. Spierings, N., Smits, J., & Verloo, M. (2008). Micro and macro-level determinants of women’s employment in six MENA countries. NICE Working Paper 08-104, Netherlands.
42. Tansel, A. (2002). Economic development and female labor force participation in Turkey: Time-series evidence and cross-section estimate. METU/ERC Working Paper No. 02/3.
43. Taşseven, Ö., Altaş, D., & Ün, T. (2016). The determinants of female labour force participation for OECD countries. International Economic Research Journal, 2(2), 27-38.
44. Tunalı, H., & Göksu, Y. D. (2018). Türkiye’de kadınların işgücüne katılımının belirleyicileri üzerine ekonometrik bir analiz. Uluslararası Ekonomik Araştırmalar Dergisi, 4(1), 29-45.
45. TURKSTAT. (2018). İstatistiklerle kadın, 2017. News bulletin. Retrieved August 3, 2018 from http:// www.tuik.gov.tr/PreHaberBultenleri.do?id=27594
46. TURKSTAT. (2017). https://biruni.tuik.gov.tr/isgucuapp/isgucu.zul. Retrieved October 3, 2017.
47. Üngüren, E., & Doğan, H. (2010). Beş yıldızlı konaklama işletmelerinde çalışanların iş tatmin düzeylerinin CHAID analiz yöntemiyle değerlendirilmesi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11, 39-52.
48. Uysal, D., Keskin, R., & Sertkaya, Y. (2016). Türkiye’de kadınların işgücüne katılmını belirleyen faktörler üzerine ekonometrik bir analiz. Siirt Üniversitesi İktisadi ve İdari Bilimler Fakültesi İktisadi Yenilik Dergisi, 3(2), 73-92.
49. Wilkinson, L. (1979). Tests of significance in stepwise regression. Psychological bulletin. 86, 168-174.
50. Yakubu, Y. A. (2010). Factors influencing female labour force participation in South Africa in 2008. The African Statistical Journal. 11, 85-104.
51. Yıldırım, K., Karaman, D., & Taşdemir, M. (2010). Makro ekonomi. Ankara: Seçkin Yayıncılık.
52. Yohannes, Y., & Webb, P. (1999). Classification and regression trees, cart: A user manual for identifying indicators of vulnerability to famine and chronic food insecurity. Microcomputers in policy research 3, Washington: International Food Policy Research Institute.