Interdisciplinary Public Finance, Business and Economics Studies - Volume I
Summary
Excerpt
Table Of Contents
- Cover
- Title Page
- Copyright Page
- Foreword
- About the author
- About the book
- Citability of the eBook
- Contents
- List of Contributors
- The Classification of Cities’ Employment Rate with Cluster and Exploratory Spatial Data Analysis
- 1 Introduction
- 2 Method
- 2.1 Cluster Analysis
- 2.2 Exploratory Spatial Data Analysis
- 3 Findings
- 4 Conclusion
- Tax Competition: Evaluation in Terms of Tax Havens
- 1 Introduction
- 2 Tax Competition and Its Features
- 2.1 Tax Competition and OECD
- 2.2 Harmful Tax Competition
- 3 Tax Havens
- 3.1 Countries in Tax Havens
- 4 Legal Regulations Concerning Tax Havens in Turkey
- 5 Result
- Ethnic Economies and the Case of Syrian Refugees in Turkey1
- 1 Introduction
- 2 Framework of Ethnic Economies
- 3 Syrian Refugees in Turkey and Ethnic Economies
- 4 Conclusion
- Situation Assessment on Development Performance of TR90 Region
- 1 Introduction
- 2 Social Case Analysis
- 2.1 Demographic Structure
- 2.2 Labor and Employment
- 2.3 Healthcare
- 2.4 Education
- 2.5 Income Distribution
- 3 Economic Conditions
- 3.1 Income (GDP)
- 3.2 Export
- 3.3 Importation
- 4. Result
- Investigating the Relationship between Inflation and Economic Growth in the MIST Countries: Evidence from Combined Cointegration Tests
- 1 Introduction
- 2 Literature
- 3 Data and Econometric Methodology
- 4 Empirical Results
- 5 Conclusion
- A Theoretical Approach to the Effect of Exchange Rate Volatility on Exports: Example of Turkey1
- 1 Introduction
- 2 Exchange Rate Volatility
- 2.1 Measuring the Exchange Rate Volatility
- 3 Exchange Rate Volatility and Foreign Trade Relationship
- 3.1 Exchange Rate Volatility and Turkey
- 4 Conclusion
- The Relatıonship between Consumer Confıdence and Real Exchange Rate: The Case of Turkey
- 1 Introduction
- 2 Literature
- 3 Data Set and Methodology
- 4 Empirical Findings
- 5 Conclusion
- Asymmetric Stochastic Volatility in Nordic Stock Markets1
- 1 Introduction
- 2 Literature Review
- 3 Econometric Methodology
- 4 Data and Empirical Results
- 5 Conclusions
- Notes
- Testing Life-Cycle Hypothesis for Emerging Markets
- 1 Introduction
- 2 Empirical Literature
- 3 Methodology
- 4 Model and Data
- 5 Conclusion
- Analysis of the Relationship between Interest Rates and Non-Performing Loans Ratio in the Framework of Adverse Selection Problem; Turkey Case1
- 1 Introduction
- 2 A Brief Review of Banking Sector in Turkey
- 3 Literature Review
- 4 Econometric Analysis
- 4.1 Data and Methodology
- 4.2 Analysis and Results
- 5 Conclusion
- Russia’s Macroeconomic Performance during the Transition Process
- 1 Introduction
- 2 Literature Review
- 3 Macroeconomic Performance
- 4 Conclusion
- Effect of the Culture on Internal Audit: Application for Participation Banks
- 1 Introduction
- 2 Literature
- 3 Internal Audit Concept, Goal, and Scope of Internal Audit
- 4 Hofstede’s Cultural Values
- 5 Application
- 5.1 Goal and Importance of the Research
- 5.2 Population and Sample Selection
- 5.3 Obtaining Data and Creating Hypotheses
- 5.4 Analyzing and Commenting the Data
- 5.5 Research Findings
- 6 Conclusion
- Financial Consumer Protection and Party Autonomy: Is Laissez-Fare Really Dead?
- 1 Consumer Law and It’s Effect to the Party Autonomy
- 2 Financial Consumer and Scope of the Financial Consumer Protection
- 3 Recent Developments About the Topic in the Positive Law Field
- 3.1 Some of International Documents
- 3.2 Financial Consumer Revolution in the USA
- 3.3 Financial Consumer Protection and It’s Effect to Party Autonomy in Turkish Law
- 4 Conclusion: Is Laissez-Fare Really Dead?
- Evaluation of Orchards Within the Scope of Tms 41: Apricot Garden Sample
- 1 Introductıon
- 2 Production of Apricot in Turkey and Malatya
- 3 TMS 41 Standards of Agricultural Activities
- 3.1 The Aim and Scope of Standards
- 3.2 Recognition of Agricultural Products and Living Assets
- 3.3 Evaluation of Agricultural Products and Living Properties
- 4 Evaluation and Recognition of Fruit Orchards According to TMS 41
- 4.1 Costs Paid at the Stage of Preparation
- 4.1.1 Preparation Stage Accounting Records (Establishment Year, 2014)
- 4.2 Maturing Stage
- 4.2.1 2015 Year Records
- 4.2.2 2016 Year Records
- 4.2.3 2017 Year Records
- 4.3 Product Stage
- 4.3.1 2018 Year Records
- 5 Conclusion
- The Independent Auditing Profession in Turkey: Theoretical an Investigation
- 1 Introduction
- 2 Development of Independent Auditing Profession in Turkey (The Period Until the New Turkish Commercial Code)
- 3 Development of Independent Auditing Profession in Turkey (The Next Period from the Entry into Force of the New Turkish Commercial Code)
- 4 Features Required for Those Who Want to Become Independent Auditor in Turkey and Independent Auditor Examination
- 5 Conclusion
- Belated Innovative Financial Instrument for Turkish Capital Markets: Mortgage-Backed Securities
- 1 Introduction
- 2 Mortgage-Backed Securities and Securitization
- 3 Historical Development of Mortgage-Backed Securities in Turkey
- 4 Definition and Basic Legal Features of Mortgage-Backed Securities
- 4.1 Definition
- 4.2 Basic Legal Features
- 5 Issuance of Mortgage-Backed Securities
- 5.1 Related Legal Framework
- 5.2 Issuance and Redeem Process in General
- 5.3 Risk Retention Liability or Skin-in the Game Regulation
- 6 Issuers of Mortgage-Backed Securities
- 6.1 Housing Finance Funds
- 6.1.1 Definition
- 6.1.2 Fund Statute
- 6.1.3 Fund Portfolio (Pool) Coverage
- 6.1.4 Segregation and Protection of Fund Assets
- 6.2 Mortgage Finance Corporations
- 6.2.1 Definition and Legal Framework
- 6.2.2 Special Provisions Regarding to MBS Issuances by MFI
- 6.2.3 Protection of MFI Assets
- Performance Measurement of the Enterprises Traded at the BIST 50 Index Using the Moora Method
- 1 Introduction
- 2 Literature Review
- 3 Methodology
- 4 Dataset and Findings
- 5 Conclusion
- Changing Risk Management and Updated Risk Frameworks/Standards: Coso & ISO 31.000
- 1 Introduction
- 2 History of Risk Management and Transition from Traditional Risk Management to Enterprise Risk Management
- 3 Updated COSO and ISO 31.000
- 3.1 COSO Updates (2013–2017)
- 3.1.1 COSO 2013 Update
- What has been changed with COSO 2013 IC?
- 3.1.2 COSO 2017 Update
- What has been changed with COSO 2017 ERM?
- 3.2 ISO 31.000–2018 Update
- What has been changed with ISO 31.000:2018
- 4 Conclusion
- Reactions of Football Investors: Analysis of Turkish 3 Bigs Derbies
- 1 Introduction
- 2 Literature Review
- 3 Data
- 4 Methodology
- 5 Results
- 6 Conclusion
- Electronic Commerce (E-Commerce)
- 1 Definition of Electronic Commerce
- 2 Scope of Electronic Commerce
- 3 Stages of Electronic Commerce
- 4 Products and Services Appropriate for E-Commerce
- 5 Electronic Trading Tools
- 6 Types of Electronic Commerce
- 7 Payment Methods in Electronic Commerce
- 8 Advantages of Electronic Trade
- 9 Disadvantages of Electronic Commerce
- 10 Effects of Electronic Commerce on International Trade
- 11 Concluding Remark
- Green Consumption: Barriers and Drivers in Kırklareli
- 1 Introduction
- 2 Literature Review
- 3 Research Design
- 3.1 Sample
- 3.2 Scales
- 3.3 Hypothesis of the Research
- 3.4 Analysis Method
- 3.5 Findings
- 4 Result
- The Effect of Success Factors of New Product Development on Product Development Performance: An Application on White Goods Sector
- 1 Introduction
- 2 Methodology
- 2.1 Research Objective and Importance
- 2.2 Population and Sample
- 2.3 Data Collection
- 3 Findings
- 4 Conclusion
- An Application of Multi-Criteria Decision Making Method in the Selection of Knowledge Management Strategies
- 1 Introduction
- 2 Knowledge Management Strategies
- 3 Research Methodology: Analytic Network Process (ANP)
- 4 Application: Selecting KM Strategies for Group Companies
- 5 Conclusion
- Modern Production Approaches
- 1 Introduction
- 2 Six Sigma (6σ) Approach
- 2.1 Definition of Process Adequacy
- 3 Reengineering
- 3.1 Implementation Phases of Reengineering
- 4 Agile Manufacturing
- 5 Theory of Constraints (TOC)
- 5.1 Basic Principles of the Theory of Constraints
- 5.2 Evaluation of the Theory of Constraints in the Context of Continuous Improvements
- 5.2.1 Determination of the Constraints in the System
- 5.2.2 Deciding How to Fix the Constraints in the System
- 5.2.3 Management of the Corrected Constraints in the System
- 5.2.4 Removal of Constraints in the System
- 5.2.5 Returning to the First Step Once the Constraint is Eliminated
- 6 Conclusion
- Total Quality Management in Public Administration: A Return from Weber, Fayol and Taylor or Not?
- 1 Introduction
- 2 Weberian Bureaucratic Management and Fayolism
- 3 “New” Management Approach: Total Quality Management
- 4 Human Resources Management Instead of (Public) Personnel Administration
- 5 Application of TQM in the Public Sector
- 6 Conclusion
- Socio-Politic Analysis of Constitutional Movements in Turkish History
- 1 Introduction
- 2 Why Were Constitutions Needed? The Constitutionalism
- 3 In Which Way and by Whom Should Be Prepared a Constitution?
- 4 Making Methods of the Turkish Constitutions
- 4.1 The Constitution of 1876
- 4.2 The Constitution of 1921
- 4.3 The Constitution of 1924
- 4.4 The Constitution of 1961
- 4.5 The Constitution of 1982
- 5 Conclusion
Neşe Aral
Department of Econometrics, Faculty of Economics and Administrative Sciences, Uludağ University, Bursa. nesearal@uludag.edu.tr
Nuran Bayram Arli
Department of Econometrics, Faculty of Economics and Administrative Sciences, Uludağ University, Bursa. nuranb@uludag.edu.tr
Barış Yildiz
Asst. Prof., Gümüşhane University, Faculty of Economics And Administrative Sciences, Department Of Public Finance, barisyildiz61@gmail.com.
Guner Tuncer
Asst.Prof.Dr., Dumlupınar University, Department of Public Finance, guner.tuncer@dpu.edu.tr
Ozer Ozcelik
Asst.Prof.Dr., Dumlupınar University, Department of Economics, ozer.ozcelik@dpu.edu.tr
Gizem Yildiz
Asst. Prof., Gümüşhane University, Faculty of Economics And Administrative Sciences, Department Of Economics, gizem-akbulut@hotmail.com
Muhammed Tıraşoğlu
Faculty of Economics and Administrative Sciences, Department of Econometrics, Kırklareli University, muhammedtirasoglu@klu.edu.tr
Yavuz Odabaşi
Asst. Prof. Dr., Dumlupınar University, Faculty of Economics and Administrative Sciences, Department of Economics, yavuz.odabasi@dpu.edu.tr
Mesut Alper Gezer
Research Fellow Mesut Alper Gezer, Kütahya Dumlupınar University, Department of Economics, e-mail: alper.gezer@dpu.edu.tr
Tuba Gezer
Research Fellow Tuba Gezer, Kütahya Dumlupınar University, Department of Public Finance, e-mail: tuba.yildirim@dpu.edu.tr
Aycan Hepsag
Istanbul University, Faculty of Economics, Department of Econometrics, Beyazit, 34452, Istanbul, Tel.: +90 212 440 01 60, E-mail: hepsag@istanbul.edu.tr
Ayşegül Çimen
Dokuz Eylül University, Faculty of Economics and Administrative Sciences, aysegul.cimen@deu.edu.tr
Yağmur Sağlam
Sinop University, Faculty of Economics and Administrative Sciences, yagmur.saglam@sinop.edu.tr
Esra N. Kilci
Asst. Prof. Dr., Istanbul Arel University, International Trade and Finance Department, esrakilci@arel.edu.tr
←11 | 12→Esra Balli
Asst.Prof.Dr., Erzincan Binali Yıldırım University, Faculty of Economics and Administrative Sciences, esra.balli@erzincan.edu.trNiyazi Kurnaz
Assoc.Prof.Dr., Kütahya Dumlupınar University, Faculty of Economics and Administrative Sciences, niyazi.kurnaz@dpu.edu.tr
Ali Kestane
Research Assistanat, Kilis 7 Aralık University, , Faculty of Economics and Administrative Sciences, alikestane@kilis.edu.tr
Altan Fahri Gülerci
PhD. Afyon Kocatepe University Faculty of Law, Commercial Law Department, afgulerci@aku.edu.tr
Ahmet Fethi Durmuş
PhD. Inönü University Department of Business administration ahmet.durmus@inonu.edu.tr
Figen Canbay Çiğdem
PhD. The Central Union of the Agricultural Credit Cooperativesi Malatya Regional Union, figencanbay@tarimkredi.org.tr
Tansel Çetinoğlu
Asst.Prof., Kütahya Dumlupinar University, School of Applied Sciences, tansel.cetinoglu@dpu.edu.tr
M. Murat, Aktaş
Full-time Faculty staff of the T.R. Kütahya Dumlupınar University Faculty Of Economics and Administrative Sciences; LL. M. in Commercial Law and Ph. D. in Commercial Law candidate at Ankara University. Mmurat.aktas@dpu.edu.tr
Arif Saldanlı
Asst. Prof., Istanbul University, Faculty of Economics, Department of Business Administration, saldanli@istanbul.edu.tr
Hakan Bektaş
Asst. Prof., Istanbul University, Faculty of Economics, Department of Econometrics, hbektas@istanbul.edu.tr
Yusuf Kaya
Asst. Prof., Pamukkale University, Buldan Vocational High School, ykaya@pau.edu.tr
Ferit Karahan
Kütahya Dumlupinar University, Faculty of Economics and Administrative Sciences, ferit.karahan@dpu.edu.tr
Ali Konak
Karabuk University, Faculty of Economics and Administrative Sciences, Economics Department, alikonak@karabuk.edu.tr
İsmail Dülgeroğlu
PhD, Kırklareli University, Faculty of Economics and Administrative Sciences, Business Administration Department, email: ismail.dulgeroglu@klu.edu.tr
Derya Öztürk
PhD. Faculty Member, Ordu University, Ünye Faculty of Economics and Administrative Sciences, Department of Business, Ordu, Turkey. E-mail: deryaozturk@odu.edu.tr
A.Cansu Gök-Kisa
Asst. Prof. Hitit University, Faculty Of Economics And Administrative Sciences, Department of International Trade And Logistics Management, cansugok@hitit.edu.tr←12 | 13→
Mihriban Cindiloğlu Demirer
Asst. Prof. Hitit University, Faculty Of Economics And Administrative Sciences, Department of International Trade And Logistics Management, mihribancindiloglu@hitit.edu.tr
Aysel Çetindere Filiz
Asst.Prof., Ondokuz Mayıs University, Faculty of Economics and Administrative Sciences, aysel.cetindere@omu.edu.tr
Cengiz Duran
Assoc.Prof., Dumlupınar University, Faculty of Economics and Administrative Sciences, cengiz.duran@dpu.edu.tr
Sema Behdioğlu
Professor, Dumlupınar University, Faculty of Economics and Administrative Sciences, sema.behdioglu@dpu.edu.tr
Aykut Acar
Asst Prof., Ph.D., Kütahya Dumlupınar University, Faculty of Economics And Administrative Sciences, Public Administration Department, aykacar@yahoo.com
Selami Erdoğan
Asst.Prof., Kütahya Dumlupinar University, Faculty of Economics and Administrative Sciences, e-mail: selami.erdogan@dpu.edu.tr.←13 | 14→
Neşe Aral and Nuran Bayram Arli
The Classification of Cities’ Employment Rate with Cluster and Exploratory Spatial Data Analysis
1Introduction
An approach where economic development and regional development, essential dynamics of economic growth, are addressed individually and not in connection would lead to losses in the economy, let alone ensuring its growth. As advocated by experimental studies reported in the fields of economy, there must be emphasis placed on regional development policies if it is to ensure the development of a nation. Another important reason behind this need is the difficulties brought in by the varying regional development elements many developing countries have on their plate (Gül, 2014). There are significant interregional disparities in Turkey, especially with respect to population density, income distribution, distribution of workforce and employment, education level, etc. Such disparities, in turn, lead to important differences between the development level of regions and even between urban areas in a region. Although there are several measures in place to eliminate such disparities, it has not been possible to reduce interregional disparities to an acceptable level (Özdemir et al., 2006). In this context, the purpose of this study was to explore the regional differences in employment rate in Turkish cities using spatial analysis and cluster analysis.
2Method
2.1Cluster Analysis
There are many statistical analysis techniques that allow finding similarities or differences among datas. The reason behind the selection of cluster analysis, a multivariate analysis technique, was that this technique allows for grouping of highly similar data or variables. Cluster analysis allows for the identification of similarities and differences between the variables of a dataset and it helps unfold the structure of complex datasets. It is expected that each variable in a cluster to have high degree of “natural association” among themselves while clusters are “relatively distinct” from one another. To do so, many criteria have been described. (Sala ve Bragulat, 2004). In defining the groups in cluster analysis, two methods, namely, hierarchical cluster analysis and non-hierarchical cluster analysis, are used (Özdamar, 2002). If there is a predefined number of clusters to be formed in a study or if the researcher has defined the number of clusters ←15 | 16→needed, then, non-hierarchical agglomeration methods would be preferred over hierarchical ones, i.e. non-progressive grouping methods are used. Another reason behind the selection of the methods used in this study was the high theoretical validity they offer. Among these methods, K-means clustering developed by MacQueen and maximum likelihood estimation were used in a great extent (Tatlıdil, 1996).
2.2Exploratory Spatial Data Analysis
Spatial statistics and spatial econometrics have received interest of the academic community as the need for measuring the spatial effects related to the data becomes even more pressing (Zeren, 2010). The First Law of Geography, according to Waldo Tobler (1970), is “everything is related to everything else, but near things are more related than distant things.” Accordingly, similar results are observed in close proximity to each other which is called spatial cluster. For example, it is likely to find higher crime rates from neighboring cities of a city with a high crime rate. The assumption of independence does not apply in the presence of such spatial clusters (Anselin, 1992). Classical analyses methods are not preferred to process these data due to spatial dependence or spatial autocorrelation observed in the data (Eryılmaz, 2010). The use of classical statistics theory with these data proves problematic (Haining, 2003; Başar, 2009).
Spatial autocorrelation measurements are about the covariance or correlation between nearby observations. In this context, autocorrelation compares two types of information, i.e. similarity of observations (value similarity) and similarity among locations. If nearby observation values are similar, then they show a positive spatial autocorrelation pattern as a whole (Fischer & Wang, 2011; Griffith, 2003). The lack of spatial autocorrelation, on the other hand, shows that there is no spatial relationship between observed values (Schabenberger & Gotway, 2005). Today, many methods are proposed to measure spatial autocorrelation. Moran’s (Spatial Statistics) I is one of the commonly used methods to measure spatial autocorrelation (Fischer & Wang, 2011). Possible values of Moran’s I are constrained to lie in the (-1, 1) range. The weight of positive spatial autocorrelation increases as the value approaches 1 (Çetin, 2012).
Exploratory Spatial Data Analysis (ESDA) is a useful tool for interregional spatial analysis. ESDA consists of techniques used to visualize and explain spatial distribution, to explore the pattern of spatial clustering and to identify the locations with contradictory values (Anselin, 1988, Dall’erba, 2005). In spatial data analysis, spatial weight matrix files are generated in order to show the interactions between regions. The reason behind the use of such matrix is to show that the event in question is in closer interaction in locations closer to each other when compared to distant regions (Başar, 2009). Spatial data analysis may be generated using two methods, distance-based spatial weights and contiguity-based spatial weights. Distance-based spatial weight matrix involves the measuring of the distance ←16 | 17→between locations, while contiguity-based spatial weight matrix considers similarities or shared borders between locations (Zeren, 2011).
3Findings
This study explored the employment rate data collected from the cities in Turkey for the period between 2008 and 2013 using spatial analysis and cluster analysis in a comparative perspective. The data used in this study were obtained from the official website of Turkish Statistical Institute.
The maps used to visualize the spatial distribution pattern play an important role in explanatory spatial data analysis. A quartile map was used in the applications dividing the dataset into four equal parts (Fischer & Wang, 2011). In this context, these quartile maps were evaluated first with respect to employment rates. Fig. 1 shows the maps generated for the spatial distribution of the data.
Fig. 1:Mapping the Distributions
The darkest color on the map represents the cities with the highest employment rates. Employment rate drops as the color becomes lighter. It can be observed that similar cities form a cluster and they are represented with the same color. Moreover, it can be observed that the cities located especially in the Southeastern Anatolia Region have a negative impact on each other and agglomerated in the low employment rate cluster.
The Moran scatter plot was used to explore the spatial interaction between the employment rates after defining the regional differences in a quartile map. Developed by Anselin (1995, 1996), this plot allows for the analysis of observations, y, made for a location and the means of neighboring observations, Wy (Anselin et al., 2007; LeSage & Pace, 2009). The Moran scatter plot generated for the employment rates in the period between 2008 and 2013 is illustrated below.
Fig. 2:Moran’s Scatter Plot for Employment Rates
The Moran scatterplot is divided into four quadrants corresponding to the four types of local spatial association between a region and its neighbors (Dall’erba, 2005):
•Quadrant I (This quadrant is usually noted HH) displays the regions with a high employment rate (above the average) surrounded by regions with high employment rate (above the average).
•Quadrant II (This quadrant is usually noted LH) shows the regions with low value surrounded by regions with high values.
•Quadrant III (This quadrant is usually noted LL) displays the regions with low value surrounded by regions with low values.
•Quadrant IV (This quadrant is usually noted HL) shows the regions with high value surrounded by regions with low values.
Regions located in quadrants I and III represent the spatial clustering of similar values (positive spatial autocorrelation), whereas quadrants II and IV refer to the spatial clustering of dissimilar values (negative spatial autocorrelation). A closer look into Fig. 2 showed that the values are not distributed randomly, that there was ←17 | 18→←18 | 19→a positive autocorrelation and that it was clustered in QI and QIII. Tab. 1 shows the names of the cities located in these regions.
Tab. 1:Moran Scatterplot
Employment Rate 2008 |
|
HH |
Çanakkale, Gümüşhane, Ordu, Düzce, Erzurum, Denizli, Tekirdağ, Samsun, Iğdır, Bursa, Burdur, Zonguldak, Kars, Çankırı, Sakarya, Artvin, Kastamonu, Muğla, Çorum, Tokat, Antalya, Bayburt, Ardahan, Karabük, Trabzon, Sinop, Giresun, Amasya, Bartın, Konya, Mersin, Rize, Isparta, Karaman, Erzincan, Kırklareli, Bilecik. |
LH |
Edirne, Yalova, Bolu, İstanbul, Afyonkarahisar, Aydın, Kocaeli, Sivas. |
LL |
Kütahya, Hakkari, Ankara, Elazığ, Kayseri, Tunceli, Şırnak, Siirt, Yozgat, Bingöl, Diyarbakır, Malatya, Gaziantep, Şanlıurfa, Niğde, İzmir, Aksaray, Batman, Kırşehir, Eskişehir, Kilis, Manisa, Nevşehir, Muş, Uşak, Balıkesir, Adıyaman, Kırıkkale, Adana, Kahramanmaraş, Hatay, Van, Osmaniye, Mardin, Bitlis. |
HL |
Ağrı. |
Employment Rate 2009 |
|
HH |
Edirne, Gümüşhane, Çanakkale, Ordu, Düzce, Erzurum, Denizli, Tekirdağ, Samsun, Burdur, Bolu, Zonguldak, Kars, Çankırı, Sakarya, Artvin, Kastamonu, Muğla, Çorum, Tokat, Aydın, Antalya, Bayburt, Ardahan, Karabük, Trabzon, Sinop, Giresun, Amasya, Bartın, Konya, Mersin, Rize, Isparta, Karaman, Erzincan, Kırklareli. |
LH |
Iğdır, İstanbul, Eskişehir, Afyonkarahisar, Sivas. |
LL |
Kütahya, Hakkari, Ankara, Yalova, Elazığ, Kayseri, Tunceli, Şırnak, Siirt, Yozgat, Bingöl, Diyarbakır, Malatya, Bursa, Gaziantep, Şanlıurfa, Niğde, İzmir, Aksaray, Batman, Kırşehir, Eskişehir, Kilis, Manisa, Kocaeli, Muş, Uşak, Adıyaman, Kırıkkale, Adana, Kahramanmaraş, Hatay, Van, Osmaniye, Mardin, Bitlis. |
HL |
Bilecik, Ağrı, Nevşehir, Balıkesir. |
Employment Rate 2010 |
|
HH |
Edirne, Gümüşhane, Çanakkale, Ordu, Düzce, Erzurum, Denizli, Tekirdağ, Samsun, Iğdır, Burdur, Bolu, Zonguldak, Kars, Çankırı, Sakarya, Artvin, Kastamonu, Muğla, Çorum, Tokat, Aydın, Antalya, Bayburt, Ardahan, Uşak, Karabük, Trabzon, Sinop, Amasya, Bartın, Konya, Giresun, Mersin, Rize, Isparta, Karaman, Erzincan, Kırklareli. |
LH |
İzmir, İstanbul, Afyonkarahisaar, Sivas. |
LL |
Kütahya, Hakkari, Ankara, Yalova, Elazığ, Kayseri, Tunceli, Şırnak, Siirt, Yozgat, Bingöl, Diyarbakır, Malatya, Bursa, Gaziantep, Şanlıurfa, Niğde, İzmir, Aksaray, Batman, Kırşehir, Kilis, Ağrı, Kocaeli, Muş, Adıyaman, Kırıkkale, Adana, Kahramanmaraş, Hatay, Van, Osmaniye, Mardin, Bitlis. |
HL |
Bilecik, Sakarya, Nevşehir. |
Employment Rate 2011 |
|
HH |
Edirne, Kütahya, Gümüşhane, Ordu, Düzce, Denizli, Tekirdağ, Samsun, Iğdır, Bursa, Burdur, Bolu, Zonguldak, Bilecik, Kars, Çankırı, Sakarya, Artvin, Afyonkarahisar, Manisa, Kastamonu, Muğla, Çorum, Tokat, Aydın, Antalya, Bayburt, Ardahan, Uşak, Karabük, Trabzon, Sinop, Amasya, Bartın, Giresun, Mersin, Rize, Isparta, Karaman, Erzincan, Kırklareli. |
LH |
Çanakkale, Tunceli, Erzurum, İzmir, İstanbul, Eskişehir, Balıkesir, Kırıkkale, Sivas, Konya. |
LL |
Hakkari, Ankara, Yalova, Elazığ, Kayseri, Tunceli, Şırnak, Siirt, Yozgat, Bingöl, Diyarbakır, Malatya, Bursa, Gaziantep, Şanlıurfa, Niğde, İzmir, Aksaray, Batman, Kırşehir, Eskişehir, Kilis, Manisa, Kocaeli, Muş, Uşak, Adıyaman, Kırıkkale, Adana, Kahramanmaraş, Hatay, Van, Osmaniye, Mardin, Bitlis. |
HL |
Yozgat, Nevşehir, Ağrı. |
Employment Rate 2012 |
|
HH |
Edirne, Kütahya, Yalova, Gümüşhane, Çanakkale, Ordu, Düzce, Denizli, Tekirdağ, Iğdır, Bursa, Burdur, Bolu, Zonguldak, Bilecik, Kars, Çankırı, Sakarya, Artvin, Afyonkarahisar, Manisa, Kastamonu, Muğla, Çorum, Aydın, Antalya, Bayburt, Ardahan, Kocaeli, Uşak, Karabük, Sivas, Trabzon, Sinop, Bartın, Giresun, Mersin, Rize, Isparta, Karaman, Erzincan, Kırklareli. |
LH |
Yalova, Erzurum, Samsun İzmir, İstanbul, Çankırı, Eskişehir, Tokat, Balıkesir, Amasya, Konya. |
LL |
Hakkari, Ankara, Elazığ, Şırnak, Siirt, Diyarbakır, Gaziantep, Şanlıurfa, Niğde, Aksaray, Batman, Kırşehir, Kilis, Muş, Adıyaman, Kırıkkale, Adana, Kahramanmaraş, Hatay, Van, Osmaniye, Mardin, Bitlis. |
HL |
Yozgat, Bingöl, Malatya, Nevşehir, Ağrı. |
Employment Rate 2013 |
|
HH |
Edirne, Kütahya, Yalova, Gümüşhane, Tunceli, Ordu, Düzce, Tekirdağ, Samsun, Iğdır, Bursa, Burdur, İzmir, Bolu, Zonguldak, Bilecik, İstanbul, Kars, Sakarya, Artvin, Afyonkarahisar, Manisa, Muğla, Tokat, Ağrı, Aydın, Antalya, Bayburt, Ardahan, Kocaeli, Uşak, Karabük, Trabzon, Amasya, Bartın, Konya, Giresun, Rize, Isparta, Karaman, Erzincan, Kırklareli. |
LH |
Details
- Pages
- 332
- Publication Year
- 2018
- ISBN (PDF)
- 9783631782248
- ISBN (ePUB)
- 9783631782255
- ISBN (MOBI)
- 9783631782262
- ISBN (Softcover)
- 9783631771730
- DOI
- 10.3726/b15307
- Language
- English
- Publication date
- 2019 (February)
- Published
- Berlin, Bern, Bruxelles, New York, Oxford, Warszawa, Wien, 2018. 332 pp., 18 fig. b/w, 88 tables, 7 graphs
- Product Safety
- Peter Lang Group AG