Studies on Interdisciplinary Economics and Business - Volume V
Summary
Economics, Public Finance and Business written by researchers from several different
universities. The studies include a wide range of topics from issues in Economics
and Business. The book is aimed at educators, researchers, and students interested
in Business and Economics.
Excerpt
Table Of Contents
- Cover
- Title
- Copyright
- About the editor
- About the book
- This eBook can be cited
- Foreword
- Table of Contents
- List of Contributors
- Income Level and Education Level by Gender: An Ampirical Analysis for Turkey at the Provincial Level (Mehmet Şengür & Esra Doğan)
- Does Ramadan Harm Child Health in Turkey? (Ömer Limanlı)
- Reflections of the Changes in Social Capital on Economic Growth: The Case of Turkey (Melahat Batu Ağırkaya)
- The Impact of Financial Development on Economic Complexity in Turkey (Salih Türedi, Mustafa Şit & Haydar Karadağ)
- Positioning the Economy of Public Relations and Corporate Communications (Müge Bekman)
- Chaotic Financial Systems (İnci Merve Altan & Metin Kılıç)
- Economic Consequences of 6–7 September 1955 Events in Turkey (Meral Balcı & Muhammed Alkış)
- A Critic of Incumbent Political Theories of State-Society Relations in Turkey (Hayrettin Özler & Aykut Acar)
- The Use of Psychological Variables in Organisational Behaviour (Sevcan Fırın)
- Effect of Emotional Contagion on the Team Performance (Güler Yanık)
- The Impact of the Covid-19 Pandemic on the Financial Performance of Turkish Participation Banks (Berke Koç)
- In the Context of International Marketing; Pricing in Terms of Payment Methods (Filiz Çayırağası)
- Green Growth and Economy Research on Eco-Innovation within the Framework of Corporate Social Responsibility Understanding of Businesses (Ahmet Gümüş)
- Recognition of Agricultural Activities in Accordance with POA’s Draft Chart of Accounts Within the Scope of TMS 41 Standard (Ahmet Yanık & Arzu Yaroğlu)
- Security for the Internet of Things (Cemalettin Hatipoğlu)
- Understanding the New Media Economy from a Critical Perspective (Eyüp Al)
- Detection of Outlier Values in Univariate Data (Selim Tüzüntürk)
List of Contributors
Aykut Acar
Ph.D., Assistant Professor, Kütahya Dumlupinar University, Faculty of Economics and Administrative Sciences, Department of Public Administration, aykut.acar@dpu.edu.tr, ORCID: 0000-0001-7859-8979
Eyüp Al
Ph.D., Research Assistant, Marmara University, Faculty of Communication, Department of Radio, Television and Cinema, Istanbul, Turkey, eyup.al@marmara.edu.tr, ORCID: 0000-0003-1201-6299
Muhammed Alkış
Research Assistant, Marmara University, Faculty of Political Science, Department of International Relations, muhammed.alkis@marmara.edu.tr, ORCID: 0000-0003-0991-2164
Meral Balcı
Ph.D., Associate Professor, Marmara University, Faculty of Political Science, Department of International Relations, mbalci@marmara.edu.tr, ORCID: 0000-0003-3638-5339
Melahat Batu Ağırkaya
Ph.D., Iğdır University, Vocational School, Fianas Banking and Insurance Department, melahatagirkaya@hotmail.com, ORCID: 0000-0002-8703-5622
Müge Bekman
Ph.D., Assistant Professor, Istanbul University-Cerrahpasa, Technical Sciences Vocational School, Department of Civil Aviation Cabin Services, mugebekman@iuc.edu.tr, ORCID: 0000-0002-8400-0993
Filiz Çayırağası
Ph.D., Assistant Professor, Gaziantep University, Faculty of Economics and Administrative Sciences, Department of Business Administration, cayiragasi@gantep.edu.tr, ORCID: 0000-0001-5941-1320
Ph.D., Associate Professor, Eskisehir Osmangazi University, Faculty of Economics and Administrative Sciences, Department of Public Finance, edogan@ogu.edu.tr
Sevcan Fırın
Lecturer, Ph.D., Kütahya Dumlupinar University, Altıntaş Vocational School, Department of Management and Organization, sevcan.firin@dpu.edu.tr, ORCID: 0000-0001-8914-100X
Ahmet Gümüş
Ph.D., Assistant Professor, Ağrı İbrahim Çeçen University, Faculty of Economics and Administrative Sciences, Department of Public Relations and Advertising, agumus@agri.edu.tr, ORCİD ID: http://orcid.org/0000-0001-7089-5936
Cemalettin Hatipoğlu
Ph.D., Associate Professor, Bilecik Şeyh Edebali University, Faculty of Economics and Administrative Sciences, Department of Management Information Systems, cemalettin.hatipoglu@bilecik.edu.tr, ORCID ID: 0000-0002-3129-9725
Haydar Karadağ
Ph.D., Associate Professor, Recep Tayyip Erdogan University, Faculty of Economics and Administrative Sciences, Department of Economics, haydar.karadag@erdogan.edu.tr, ORCID: 0000-0003-2398-7314
Metin Kılıç
Ph.D., Associate Professor, Bandırma Onyedi Eylül University, Faculty of Economics and Administrative Sciences, mkilic@bandirma.edu.tr, ORCID: 0000-0002-5025-6384
Berke Koç
Research Assistant, Kirklareli University, Faculty of Applied Sciences, Department of Accounting and Finance Management, berkekoc05@gmail.com, ORCID: 0000-0003-4889-3816
Ömer Limanlı
Ph.D., Assistant Professor, Düzce University, Akçakoca Bey Faculty of Political Sciences, Department of Economics, omerlimanli@duzce.edu.tr, ORCID: 0000-0002-6897-4253
Ph.D., Assistant Professor, Bandırma Onyedi Eylül University, Faculty of Health Sciences, ialtan@bandirma.edu.tr, ORCID:0000-0002-6269-7726
Hayrettin Özler
Ph.D., Professor, Kütahya Dumlupinar University, Faculty of Economics and Administrative Sciences, Department of Public Administration, hayrettin.ozler@dpu.edu.tr, ORCID: 0000-0001-7056-4061
Mehmet Şengür
Ph.D., Associate Professor, Eskisehir Osmangazi University, Faculty of Economics and Administrative Sciences, Department of Economics, msengur@ogu.edu.tr
Mustafa Şit
Ph.D., Associate Professor, Harran University, School of Tourism and Hotel Management, Department of Tourism Management, msit@harran.edu.tr, ORCID: 0000-0001-9694-0547
Salih Türedi
Ph.D., Associate Professor, Recep Tayyip Erdogan University, Faculty of Economics and Administrative Sciences, Department of Economics, salih.turedi@erdogan.edu.tr, ORCID: 0000-0001-6294-1007
Selim Tüzüntürk
Ph.D.,Associate Professor, Bursa Uludağ University, Department of Econometrics, selimtuzunturk@uludag.edu.tr
Ahmet Yanık
Ph.D., Associate Professor, Recep Tayyip Erdoğan Üniversity, Faculty of Economics and Administrative Sciences, Department of Business Administration, ahmet.yanik@erdogan.edu.tr; ORCID: 0000-0002-7283-2557
Güler Yanık
Ph.D., Assistant Professor, Recep Tayyip Erdoğan University, Faculty of Economics and Administrative Sciences, Department of Business Admistration, guler.yanik@erdogan.edu.tr, ORCID: 0000-0002-5228-2981
Arzu Yaroğlu
CPA, Ph.D. Student, arzu-yaroglu@hotmail.com, ORCID:0000-0003-2203-2970
Income Level and Education Level by Gender: An Ampirical Analysis for Turkey at the Provincial Level
1 Introduction
Human Development Index (HDI) is not only concerned with the gross domestic product of countries, but also with various other factors such as abilities, education level, and living standards of the individuals. While calculating the index, a real and expected time period of education, life expectancy at birth and national income are taken into account (UNDP, 2021). Income is the most important factor facilitating an individual’s participation in economic and social life. Income is a fluctuating variable, which is defined as the share from the revenue generated through production. Education level or access to education is generally in parallel with income level.
Education is a fundamental right of each individual in society and the right to receive education should be fulfilled by the governments. Education policies that aim to provide each person with an equal opportunity in education will result in a positive development in income distribution in the medium and long term (Dansuk, 1997). The relationship between education and income has been investigated and empirically analyzed for many years. Either in the direction from education to income or in the direction from income to education, a positive and significant relationship has been found between the two factors in most studies.
As a result of analyses on Latin America, Asia, and Africa, Fields (1980) found that education level leads to a significant difference in rural and urban poverty. O’Neill (1995) conducted a cross-section analysis on developed and underdeveloped countries for the period of 1967–1995. According to the analysis results, convergence was found in the income distribution along with the convergence in the education level among developed country groups. Gregorio and Lee (2002) carried out an analysis of Africa, Asia, Latin America, and OECD countries with data for the period of 1960–1990. The findings show that the increase in education level leads to a decrease in income inequality. The analysis results of the G7 Countries in Rehme’s (2007) revealed a direct relationship between education, income distribution, and economic growth. Tomul (2007) analyzed the relationship between income and education level in Turkey with data from 2003. It was ←13 | 14→found that an increase in income level is positively related to receiving education. It has been observed that an increase in income level in Turkey is in parallel with increases in the rate of receiving education, and it especially differs by gender.
As seen in the studies in the literature, there is a significant correlation between education level, income, and gender. This relationship is mostly in the form of an increase in the rate of receiving education, and therefore the education level, with an increase in income. On the other hand, another finding is that increased education level also has a positive effect on income after a certain point. As their education level increases, individuals get access to more qualified jobs and higher wages. The direction and extent of the relationship between education, income, and gender are important for the determination of the current situation in countries and the policy strategies for future development. In this study, the status of education and income level in Turkey was analyzed by gender using data from the Turkish Statistical Institute (TSI). The Complex Proportional Assessment (COPRAS) method, which has seven stages, was used in the study. In the study, 37 provinces with a high level of female population were selected in order to determine the differentiation of the variables by gender.
2 Overview of Gender and Education in Turkey
A proportional balance is seen in terms of gender when the general structure of the population in Turkey is examined, and according to the data from the Address-Based Population Registration System by the Turkish Statistical Institute (TSI), 49.9 % of the total population is female and 50.1 % of the population is male as of 2020. When these rates are analyzed at the provincial level, although the proportional difference is not significant, it is seen that the female population is higher in 34 provinces, while the male population is higher in the remaining 47 provinces. The province-based details of this distribution are given in Fig. 1.
Fig. 1:Distribution of Female Population. Source: The graph was generated by the authors using by GEODA.
←14 | 15→When the proportional balance of population in Turkey is analyzed in terms of education level, an opposite situation is observed. While there is no clear difference between men and women at any education level, the difference has been decreasing over the past years. According to data from Women with Statistics, 2020 News Bulletin by TSI, the proportion of women who completed at least one level of education was 72.6 % and that of men was 89.8 % as of 2008, while these rates increased to 85.7 % and 96.4 % as of 2019, respectively.
On the other hand, there is still a segment of the population in Turkey that has not completed even one level of education, and a significant portion of this segment is female. These rates correspond to 3.44 % of the total male population and to 13.71 % of the total female population in Turkey. At the same time, illiterate people are also included in this population; the rate of illiterate people constitutes 1.10 % of the total male population and 6.75 % of the total female population in Turkey. Although the rates differ by province, they are given in Figs. 2 and 3 based on provinces in Turkey with a high female population.
Fig. 2:Adult Illiteracy Rate by Gender. Source: The graph was generated by the authors using Turkish Statistical Instute Database.
←15 | 16→When Fig. 2 is examined, the difference between the male and female populations can be seen clearly. At the same time, it is seen that the rate of illiterate population, both male and female, is higher compared to the general population of Turkey in certain provinces (Çorum, Zonguldak, Kastamonu, Malatya, Giresun, Ordu, Yozgat, Erzurum).
The data in Fig. 3 also reveals a great deal of similarity despite the proportional difference compared to the data in Fig. 2. Although there is a clear difference between male and female populations here, especially in certain provinces (Samsun, Bartın, Çorum, Tokat, Sinop, Kastamonu, Giresun, Erzurum), the rate of population that have not completed even one education level, both male and female, despite being literate is higher compared to the general population of Turkey.
Fig. 3:Literate but Uneducated Rate by Gender. Source: The graph was generated by the authors using Turkish Statistical Instute Database.
3 Empirical Evaluation
←16 | 17→In this section, an empirical analysis is carried out in order to reveal the gender-based difference in terms of per capita income and education level in provinces with a high female population on a provincial basis. In this direction, first, education levels were categorized, and then the data set was created by carrying out population-based ratio calculations. Education levels were organized in a way that primary education is included under the primary school, high school is included under the secondary school, and undergraduate and above are included in the high school category.
In this direction, first of all, information is given about the scope of the research and the method used, and then an evaluation is made by presenting the findings.
3.1 Research Scope
The research scope consists of data on education levels and per capita income levels of female and male population based on 37 provinces in Turkey in which the female population is higher. In this context, in order to make an assessment in line with the purpose of the research, data from the Turkish Statistical Institute based on Regional and Provinces were used.
3.2 Research Method
←17 | 18→The Complex Proportional Assessment (COPRAS) method is a multi-criteria decision-making method developed in 1996 (Zavadskas & Kaklauskas, 1996). The method allows multi-criteria evaluation based on both maximizing and minimizing the criteria values. In this method, which is used widely since it enables an analysis of complex processes, the effect of the maximized and minimized criteria on the analysis results can also be examined separately. In this respect, it differs from other similar multi-criteria decision-making methods (Podvezko, 2011: 138).
There is a 7-stage process in the application of the COPRAS method. In the first step, a decision matrix consisting of values is created.
In the second step, the decision matrix is transformed into a normalized matrix through equation 1.
In the third step, the components of the normalized decision matrix are weighted according to the importance given to the criteria, and a weighted matrix is created. Here, the subjective opinions of the decision maker are included in the determination of the weights.
In the fourth stage, the values in the weighted matrix for maximizing and minimizing criteria are summed as defined by equations 3 and 4.
←18 | 19→In the fifth step, the relative importance value for each alternative is calculated using equation 5.
In the sixth step, the highest relative importance value is calculated with equation 6.
In the seventh step, the performance index is calculated with equation 7.
3.3 Empirical Findings
The decision matrix, the first step of the COPRAS method, obtained in the study is given in Tab. 1. Following the decision matrix, the first step of the method, the first formula and the normalized matrix were obtained as given in Tab. 2.
With the normalized matrix, the criteria were weighted, which is the third stage of the method. In the COPRAS method, the weights of the criteria are determined subjectively. Accordingly, the criteria in the normalized matrix were assumed to increase as the education level increases, and the weighted matrix in Tab. 3, the third stage of the method, was obtained by using equation 2.
←19 | 20→Tab. 1: Decision Matrix
|
|
GDP per Capita ($) |
Primary Scholl/Male |
Primary School/Female |
Secondary School/Male |
Secondary School/Female |
High School/Male |
High School/Female |
|
Kirsehir |
6092 |
0,261676468 |
0,333552542 |
0,494190729 |
0,374367165 |
0,20772142 |
0,16486602 |
|
Kütahya |
6798 |
0,302345033 |
0,42752861 |
0,473798623 |
0,319084466 |
0,190451712 |
0,14457132 |
|
Samsun |
5880 |
0,305406989 |
0,323450506 |
0,456318745 |
0,369778211 |
0,192052803 |
0,16890355 |
|
Bartin |
5493 |
0,363378572 |
0,366425756 |
0,431645946 |
0,341493326 |
0,156818069 |
0,12320954 |
|
Çorum |
5423 |
0,335619643 |
0,375440655 |
0,439202975 |
0,332564952 |
0,167571061 |
0,12438006 |
|
Trabzon |
6079 |
0,240686773 |
Details
- Pages
- 234
- Year
- 2022
- ISBN (PDF)
- 9783631883785
- ISBN (ePUB)
- 9783631883792
- ISBN (MOBI)
- 9783631883808
- ISBN (Softcover)
- 9783631872048
- DOI
- 10.3726/b20007
- Language
- English
- Publication date
- 2022 (September)
- Keywords
- Business Economics Panel Data Analysis Public Finance Time Series Analysis
- Published
- Berlin, Bern, Bruxelles, New York, Oxford, Warszawa, Wien, 2022. 234 pp., 27 fig. b/w, 31 tables.
