Interdisciplinary Public Finance, Business and Economics Studies—Volume V

by Adil Akinci (Volume editor) ÖZER ÖZCELIK (Volume editor)
©2022 Edited Collection 276 Pages


This volume is a collection of empirical and theoretical research papers regarding
Economics, Public Finance and Business written by researchers from several different
universities. The studies include a wide range of topics from issues in Economics,
Public Finance and Business. The book is aimed at educators, researchers, and
students interested in Public Finance, Business and Economics.

Table Of Contents

  • Cover
  • Title
  • Copyright
  • About the editor
  • About the book
  • This eBook can be cited
  • Table of Contents
  • List of Contributors
  • Economic Complexity and the Ecological Footprint: Testing the EKC Hypothesis in Countries with the Highest Economic Complexity (Tunahan Hacıimamoğlu & Salih Türedi)
  • Female-Male Wage Differentials in Turkey: Evidence from Matching and Distributional Approaches (Ömer Limanlı)
  • Industry 4.0 and Employment: Development of Innovation on Employment in Turkey (Ezgi Kopuk & Özer Özçelik)
  • Tax Revenues by Subsectors of General Government: A Comparison of OECD Countries (1995–2018) (Esra Doğan & Mehmet Şengür)
  • A Study on Government Expenditures-Revenues Nexus in Turkey: Evidence from Fourier Causality Approach (Süleyman Kasal)
  • An Application for Convergence Analysis of Selected Financial Indicators in Banks: An Example of BIST Bank Index (Fatih Akbaş)
  • The Effects of Covid-19 Pandemic on the Financial Performance of Chain Markets Quoted on the Stock Exchange (Ahmet Yanık & Mustafa Genç)
  • Analysis of the Financial Performance of BIST Information Technology Companies in the Covid-19 Process Using the Gray Relational Analysis Method (İnci Merve Altan & Cemalettin Hatipoğlu)
  • The Effect of CAMELS Ratios on Share Prices of Deposit Banks Traded in Borsa Istanbul (Melike Aktaş Bozkurt, Ozan Gülhan & Serhan Karaarslan)
  • The Development of Islamıc Thought in Turkey (1908–1945) (Selami Erdoğan & Mustafa Kaplan)
  • Mass Customization and Industry 4.0 (Arzu AL & Oğuzhan İrgüren)
  • Determining University Students’ Industry 4.0 Awareness Levels: A Sample of Aydın ADU (Ahmet Unlu & Ibrahim Isik)
  • The Relationship between Internal Audit, Internal Control and Earnings Manipulation: A Study in the BIST 100 Index (Meryem Uslu)
  • On the Transformation Methods for Non-Normal Univariate Data (Selim Tüzüntürk)
  • The Interplay between Brand Names and Sound Symbolism and Its Influence on Size and Width Perception of Consumers (İlkin Yaran Ögel & Ayşe Gül Bayraktaroğlu)
  • Freedom of Choice, Restrictedness, and Information Load: A Perspective for Online Retailers (Çağla Tuğberk Arıker)
  • The Effect of COVID-19 on the Employment in Turkey (Ezgi Demir and Melike Torun)

←8 | 9→

List of Contributors

Fatih Akbaş

Ph.D., Lecture, Ordu University, Ikizce Vocational School, Department of Finance-Banking and Insurance, fatihpalba@gmail.com, ORCID:0000-0003-2474-8913

Melike Aktaş Bozkurt

Ph.D., Assistant Professor, OSTIM Technical University, Faculty of Economics and Administrative Sciences, Department of Marketing, melike.aktasbozkurt@ostimteknik.edu.tr, ORCID:0000-0001-5207-0615

Arzu AL

Ph.D., Associate Professor, Marmara University, Faculty of Political Sciences, Department of International Relations, arzu.al@marmara.edu.tr, ORCID:0000-0002-3287-3661

Ezgi Demir

PhD, Sumer Robotics Engineering & Consultancy Ltd. London, UK, dr.ezgidemir34@gmail.com, ORCID: 0000-0002-7335-5094

Esra Doğan

Ph.D.Associate Professor,Eskisehir Osmangazi University, Faculty of Economics and Administrative Sciences, Department of Public Finance, edogan@ogu.edu.tr

Selami Erdoğan

Ph.D., Assistant Professor, Kutahya Dumlupınar University, Faculty of Administrative and Economics Sciences, Department of Public Administration, Kutahya-Turkey, selami.erdogan@dpu.edu.tr; ORCID: 0000-0002-2245-4105

Mustafa Genç

Research Assistant, Recep Tayyip Erdoğan University, Faculty of Economics and Administrative Sciences, Department of Business Administration, mustafa.genc@erdogan.edu.tr; ORCID: 0000-0001-5897-9010

Ayşe Gül Bayraktaroğlu

Ph.D., Professor, Dokuz Eylül University, Faculty of Business, Department of Business, Division of Production Management and Marketing, gul.bayraktar@deu.edu.tr, ORCID: 0000-0002-5119-2853

←9 | 10→Ozan Gülhan

Ph.D., Assistant Professor, OSTIM Technical University, Faculty of Economics and Administrative Sciences, Department of MIS, ozan.gulhan@ostimteknik.edu.tr, ORCID: 0000-0002-1506-3982

Tunahan Hacıimamoğlu

Ph.D., Research Assistant, Recep Tayyip Erdogan University, Faculty of Economics and Administrative Sciences, Department of Economics, tunahan.haciimamoglu@erdogan.edu.tr, ORCID: 0000-0003-1474-8506

Cemalettin Hatipoğlu

Ph.D., Associate Professor, Bilecik Şeyh Edebali University, Faculty of Economics and Administrative Sciences, Department of Management Information Systems, cemalettin.hatipoglu@bileck.edu.tr, ORCID ID: 0000- 0002- 3129- 9725

Oğuzhan İrgüren

Research Assistant, İstanbul Medipol University, oirguren@medipol.edu.tr

Ibrahim Isik

Graduated with Master’s Degree, Aydin Adnan Menderes University, Institute of Social Sciences, Applied Econometrics Program, miharbi.0404@gmail.com, ORCID: 0000-0002-1998-1916

Mustafa Kaplan

Freelance Writer, Diyarbakır-Turkey, kaplanmustafa2143@hotmail.com; ORCID: 0000-0001-6997-1647

Serhan Karaarslan

Ph.D.c, Kütahya Dumlupinar University, Institute of Graduate Sciences, Department of Business Administration, serhankaraarslan@gmail.com, ORCID: 0000-0002-5020-6918

Süleyman Kasal

Ph. D., Research Assistant, Anadolu University, FEAS, Department of Public Finance, skasal@anadolu.edu.tr, ORCID: 0000-0001-8409-1090

Ezgi Kopuk

Ph.D.c., Eskişehir Osmangazi University, Social Sciences Institute, ezgiikopukk@gmail.com, ORCID: 0000-0001-7242-1160

←10 | 11→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

İnci Merve Altan

Ph.D., Assistant Professor, Bandırma Onyedi Eylül University, Faculty Of Health Sciences, ialtan@bandirma.edu.tr, ORCID:0000-0002-6269-7726

Özer Özçelik

Ph.D., Associate Professor, Kütahya Dumlupinar University, Faculty of Economics and Administrative Sciences, Department of Economics, ozer.ozcelik@dpu.edu.tr, ORCID:0000-0001-9164-5020

Mehmet Şengür

Ph.D.Associate Professor, Eskisehir Osmangazi University, Faculty of Economics and Administrative Sciences, Department of Economics, msengur@ogu.edu.tr

Melike Torun

PhD Research Assistant, Istanbul University-Cerrahpasa, Faculty of Health Science, Healthcare Managament, melike.torun@iuc.edu.tr, ORCID: 0000-0003-4828-9379

Çağla Tuğberk Arıker

Ph.D., Assistant Professor, Istanbul Gelisim University, Faculty of Economics Administrative and Social Sciences, Department of Business Administration, cariker@gelisim.edu.tr, ORCID: 0000-0002-2710-8455

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, ORCID: 0000-0002-8987-2280

Ahmet Unlu

Ph.D., Assistant Professor, Aydin Adnan Menderes University, Faculty of Economics and Administrative Sciences, Department of Economics, unlu100@gmail.com, ORCID: 0000-0003-4921-3157

←11 | 12→Meryem Uslu

Ph.D., Lecturer. Kütahya Dumlupınar University, Altintas Vocational School, Department of Accounting and Tax Applications, meryem.uslu@dpu.edu.tr, ORCID: 0000-0002-1953-3777

Ahmet Yanık

PhD, Associate Professor, Recep Tayyip Erdoğan University, Faculty of Economics and Administrative Sciences, Department of Business Administration, ahmet.yanik@erdogan.edu.tr; ORCID: 0000-0002-7283-2557

İlkin Yaran Ögel

Ph.D., Research Assistant, Afyon Kocatepe University, Faculty of Economics and Administrative Sciences, Department of Business Administration, ilkinyaran@aku.edu.tr, ORCID: 0000-0003-3414-753X

←12 | 13→
Tunahan Hacıimamoğlu & Salih Türedi

Economic Complexity and the Ecological Footprint: Testing the EKC Hypothesis in Countries with the Highest Economic Complexity

1 Introduction

In the recent period, economies in the world focused more on the issues of economy, energy, and environment, which are briefly expressed as “EEE” (Rauf et al., 2018: 32066). In this process, while implementing the policies aiming to encourage economic development and growth, on the one hand, the policymakers also make an effort in order to minimize the negative effects of global warming and environmental pollution in parallel with the sustainable development objectives on the other hand (Dogan and Lotz, 2020: 12717). However, it can also be seen that these efforts do not always yield the desired outcomes because, even though industrial advancements, increases in energy consumption, and technological innovations bring economic growth and an increase in welfare, they also degrade the environmental quality and cause environmental problems, which are irreversible or very costly to reverse (Intergovernmental Panel on Climate Change–IPCC, 2014; United Nations Environment Programme–UNEP, 2019). The issues of global warming, climate change, and environmental pollution, which reached a level that worries societies, are closely followed by researchers, international organizations, and policymakers today.

Studies carried out on the environmental Kuznets curve (EKC) hypothesis in the literature are based on the hypothesis of Kuznets (1955) that there was an inverted U–shaped relationship between economic growth and income inequality. According to this hypothesis named the Kuznets curve, an increase in the income inequality would continue until a certain threshold income level and the increase in income after the peak point would decrease the income inequality (Kuznets, 1955). Upon the increases in problems arising from environmental pollution, global warming, and uncontrolled use of natural resources, the Kuznets curve has started to be called the EKC hypothesis addressing the relationship between income and environmental pollution or quality since the 1990s.

Making use of Kuznets curve, Grossman and Krueger (1991, 1995), Shafik and Bandyopadhyay (1992), Shafik (1994), Panayotou (1993, 1997), and Selden and Song (1994) ←13 | 14→concluded in their studies that there was an inverted U–shaped relationship between income level and various environmental pollution indicators. This inverted U–shaped relationship was defined as the EKC first time by Panayotou (1993). According to the EKC hypothesis, the production structure based on secondary technologies causing environmental damage during the first phase of economic growth would increase environmental pollution. At this phase, environmental pollution would increase as the income level increases until a turning point (Grossman and Krueger, 1991). In the second phase after the turning point, the pressure on the environment decreases as a result of the increase in environmental awareness, use of environment–friendly production technologies, and structural changes and, consequently, environmental pollution decreases as the level of income increases (Shafik, 1994; Vincent, 1997).

One of the indicators most frequently used in representing environmental pollution in literature is carbon dioxide (CO2) emission (see Shahbaz and Sinha, 2019; Wang et al., 2017). However, CO2 emission measuring the carbon concentration in the air ignores the pollution in the water and soil. Representing a limited dimension of environmental pollution, CO2 emission is criticized from this aspect (Solarin, 2019: 6167). For this reason, in recent studies, instead of CO2 emission, ecological footprint (EFP) which is a more comprehensive indicator has been used as an indicator of environmental pollution (Nathaniel, 2021; Peng et al., 2019; Rehman et al., 2021; Sarkodie, 2021). Introduced first by Rees (1992) and then improved by Wachernagel and Rees (1996), EFP measures the demand of human beings on nature and consists of six components, namely, croplands, grazing lands, forest lands, fishing grounds, built–up lands, and carbon footprint. (Global Footprint Network, 2021).

The relationship between environmental pollution and economic growth (or development) is investigated for different countries and country groups within the context of the EKC hypothesis. In these studies, the validity of the EKC hypothesis varies depending on the sample countries, analysis methods used, and variables included in the model. In recent studies testing the EKC hypothesis, some researchers use the economic complexity index (ECI) instead of GDP per capita (see Neagu, 2019; Yilanci and Pata, 2020; Pata, 2021; Chu and Le, 2022). ECI, from a general perspective, reflects all aspects of a country’s production, productivity, knowledge, and capability (Hidalgo, 2021). Higher ECI levels indicate the export of technologically more advanced or more complex goods and a better position in terms of economic growth rates and development level (Hidalgo and Hausmann, 2009). A lower ECI level indicates less diversification of exports and exports of qualified goods, and a lower level of economic development (Hausmann et al., 2014; Mealy et al., 2019: 1–2).

Fig. 1:Time-Paths of the ECI in countries being examined (1980–2017)

←14 | 15→The aim of this study is to test the EKC hypothesis in five countries, which have the highest average ECI value in the period of 1980–2017. The countries having the highest average ECI value in this period were Japan [2.338], Germany [2.119], Switzerland [2.098], Sweden [1.982], and Singapore [1.156]. Fig. 1 shows the time–paths of the ECI in these countries.

This study’s contributions to the literature can be listed as follows. First of all, CO2 emissions are mostly used as an indicator of environmental pollution in the literature. However, in this study, instead of CO2 emissions, EFP, which is a more inclusive indicator of environmental pollution and shows the pressure of humans on nature, is used. Second, unlike most studies in the literature, the validity of the EKC hypothesis was tested using ECI instead of GDP per capita. Third, a turning point was calculated for the countries, for which the EKC hypothesis was valid, and it was determined at which ECI level these countries should be in order to reduce the environmental pollution. And, finally, instead of conventional (first generation) estimation methods, second-generation estimation methods taking the conditions such as cross-sectional dependence and heterogeneity into account were used.

2 Literature on ECI – environmental pollution relationship

The reasons for environmental pollution, which has turned into a global threat, especially after the late 1970s, have been investigated in literature for a long time. Within this context, energy consumption, trade openness, economic growth, financial development, shadow economy, and globalization can be listed as the variables, effects of which on pollution have been examined. Besides them, the ←15 | 16→studies examining the effects of ECI on environmental pollution have drawn attention in recent years. The study carried out by Can and Gozgor (2017) for France, Lapatinas et al. (2019) for 88 developed and developing countries, Neagu and Teodoru (2019) for EU member countries, Yılancı and Pata (2020) for China, Romero and Gramkow (2021) for 67 countries, Shahzad et al. (2021) for the USA, Ikram et al. (2021) for Japan, Alvarado et al. (2021) for the Latin America countries, Leitão et al. (2021) for the BRICS countries, and Dogan et al. (2021) for 28 OECD member countries can be given as examples. However, while these studies focused on the linear effect of ECI on environmental pollution, they ignored its nonlinear effects. On the other hand, even though the number is limited, there also are studies investigating the nonlinear effects of ECI on environmental pollution within the context of the EKC hypothesis in the literature. In a panel data analysis on 25 EU member countries, Neagu (2019) used ECI instead of GDP per capita and tested the EKC hypothesis. The results showed that there was an inverted U-shaped relationship between ECI and CO2 emissions (indicating the validity EKC hypothesis) in only six EU member countries. Swart and Brinkmann (2020) examined the relationship between ECI and environmental variables (deforestation, solid waste generation, forest fires, and air pollution) for Brazil. For this purpose, the researchers using Fixed Effect (FE) panel data estimator couldn’t achieve any result supporting the EKC hypothesis. Emphasizing that there were few studies on the effect of ECI on the environment, Chu (2021) carried out a system – GMM analysis on 118 countries and determined that there was an inverted U-shaped relationship between ECI and CO2 emissions proving the EKC hypothesis. Such that, the increases in ECI initially increased the CO2 emissions and, after reaching a turning point, the increases in ECI decrease the emissions and, consequently, the environmental degradation. Finally, an inverted U-shaped relationship between ECI and CO2 emissions and between ECI and EFP, which shows the validity of the EKC hypothesis, was reported by Pata (2021) for the USA for the period of 1980–2016 and by Chu and Le (2022) for the Group of Seven (G7) countries for the period of 1997–2015.

3 Data, model, and method

3.1. Data

In the present study, the EKC hypothesis was tested for Japan, Germany, Switzerland, Sweden, and Singapore, which are at the top five in terms of ECI. A balanced panel data set covering the period of 1980–2017 was used. EFP per capita was used as the indicator of environmental pollution. The data of EFP ←16 | 17→per capita was achieved using the official webpage of Global Footprint Network (GFN, 2022). ECI data was obtained from the MIT Media Lab’s The Observatory of Economic Complexity (OEC, 2022). Included in the estimation model as control variables, renewable energy consumption (REN) data were obtained from Organization for Economic Co-operation and Development (OECD, 2022) and the trade openness (TRD) data from the World Development Indicators (WDI, 2022) database. In the analysis, all variables were used with their natural logarithmic values.

3.2. Model

Following Neagu (2019), the estimation model was established as follows:


where EFPit (global hectares per capita) is the dependent variable and refers to the ecological footprint per capita, whereas ECIit refers to the economic complexity index and ECIit2 to the square of the ECIit. RENit (tonne of oil equivalent) refers to renewable energy consumption and, finally, TRDit (total foreign trade volume, % of GDP) to the rate of trade openness.

If the β1 is significantly positive and the β2 is significantly negative in equation (1), it can be said that there is an inverted U-shaped relationship between ECI and EFP, that is, the EKC hypothesis is valid. Since REN reduces environmental pollution, β3 is expected to be negative. Depending on the development level, the production and export structure of countries β4 can be either positive or negative. Positive β1 indicates that environmental pollution will increase with increasing ECI level, whereas negative β2 indicates that there is a certain turning point and the increases in ECI after this point will reduce the environmental pollution.

The turning point, at which environmental pollution will start to decrease, is calculated using the formula ECI*=exp(-β12β2).

Considering Eq. (1), the relationships between ECI and EFP can be shown as follows within the framework of the EKC hypothesis: If;

β1 = β2 = 0 →There is no relationship between EFP and ECI.

β1 > 0, β2 = 0 →There is a linear and positive relationship between EFP and ECI.

β1 < 0, β2 = 0 →There is a linear and negative relationship between EFP and ECI.

←17 | 18→β1 > 0, β2 < 0 →There is an inverted U-shaped relationship between EFP and ECI.

β1 < 0, β2 > 0 →There is a U-shaped relationship between EFP and ECI.

3.3. Method

In the analysis, an estimation process consisting of three phases was conducted. Nowadays, the interaction and dependency between countries increase due to the factors such as globalization, trade openness, and financialization. Ignoring this situation that is named cross-sectional dependence (CSD) in analyses reduces the reliability of estimation findings. For this reason, in the first phase of the analysis, CDLM (Pesaran, 2004) and LMadj. (Pesaran et al., 2008) tests were used to investigate if there is a CSD between countries in the panel. The rejection of the null hypothesis at the end of tests indicates the existence of CSD. In the second phase, using the Cross-sectionally Augmented Dickey-Fuller (CADF) panel unit root test proposed by Pesaran (2007), the stationarity properties of variables are examined. The test considering CSD and heterogeneity offers reliable results for the panels with N>T and T>N (Pesaran, 2007: 266–267). In this test, the individual stationarity can be examined by calculating CADF statistics for each unit (country) in the panel and the stationarity can also be tested by using cross-sectionally augmented IPS (CIPS) statistics (mean of individual CADF test statistics) for the entire panel.

In the third phase, the long-term relationship between variables was estimated using Durbin–Hausman (DH) panel cointegration test introduced by Westerlund (2008). The DH test gave flexibility to the cointegration analyses by allowing the explanatory variables to be stationary at the level, provided that the dependent variable is stationary at the first difference (Westerlund, 2008: 194–195). In the DH test, two separate test statistics are calculated as group (DHgroup) and panel (DHpanel). DHgroup and DHpanel test statistics are shown in equations (2) and (3), respectively:



After determining a cointegration between the variables, the long-term coefficient estimation was performed using the augmented mean group (AMG) ←18 | 19→estimator introduced by Eberhardt and Teal (2010). Following a common dynamic process, the AMG estimator consists of two phases as shown in equations (4) and (5):

(4)AMGstage 1:Δyit=bΔxit+t=2TctΔDt+eit c^tμ^t

(5)AMGstage 2:b^AMG=1Nib^i

In Eq. (4), ΔDt refers to differenced series, T–1 to period dummies, and μ^t to estimation coefficients. In Equation (5), b^i refers to the mean of individual coefficient estimations and b^AMG to the panel AMG estimation.

4. Empirical results

The findings of CDLM and LMadj. used for determining CSD and delta (Δ˜) and adjusted delta (Δ˜adj.) tests employed for determining the slope homogeneity are shown in Tab. 1.

Tab. 1: CSD and slope homogeneity test results

CSD test













ISBN (Softcover)
Publication date
2022 (September)
Business Economics Panel Data Analysis Public Finance Time Series Analysis
Berlin, Bern, Bruxelles, New York, Oxford, Warszawa, Wien, 2022. 276 pp., 22 fig. b/w, 75 tables.

Biographical notes

Adil Akinci (Volume editor) ÖZER ÖZCELIK (Volume editor)

Adil Akıncı works at Bilecik Şeyh Edebali University as an associate professor. He currently teaches fiscal policy and public finance–related subjects in Turkey. His field of interest comprises public expenditure, public revenue and time series analysis.


Title: Interdisciplinary Public Finance, Business and Economics Studies—Volume V