Higher Education and the Labor Market in Turkey

April 2007 This study was prepared by a TEPAV team which was led by Emre Deliveli and comprised of Ozan Acar, İpek Aydın, Özhan Bozkurt, Esen Çağlar, Can Çetin, Asuman Erdem, Ömer Fazlıoğlu, Çetin Haşar, Sarp Kalkan, Emre Koyuncu, Savaş Kuş, Haki Pamuk, Zeynep Songülen, Çınar Tan and Utku Tuncay.

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This report is a part of Higher Education Sector Study of the World Bank and prepared by a Team of Economic Research Foundation of Turkey. Please send questions and comments to ozan.acar@epri.org.tr

Table of Contents

Acknowledgements........................................................................................................6 Executive Summary .......................................................................................................8 1 Introduction..........................................................................................................12 2 Data Description ..................................................................................................14

2.1 Interviews and Accompanying Questionnaires (IAQ).................................15 2.1.1 Firms and employees ...........................................................................16 2.1.2 Unemployed people .............................................................................17 2.1.3 Universities ..........................................................................................17 2.1.4 Other Interviews...................................................................................18

2.2 TOBB Survey...............................................................................................19 2.3 Outside Sources ...........................................................................................19

2.3.1 World Bank Investment Climate Survey .............................................20 2.3.2 MEGEP/SVET Survey.........................................................................20 2.3.3 Household Labor Force Survey (HLFS)..............................................20

3 Descriptive Statistics............................................................................................21 3.1 General data description ..............................................................................21

3.1.1 TOBB survey .......................................................................................21 3.2 Some general characteristics of the labor force ...........................................22 3.3 Skills: What is lacking, what is asked for? ..................................................25 3.4 A first look at higher education ...................................................................28 3.5 University-private sector relationship..........................................................31 3.6 A unifying framework..................................................................................33

4 Analysis of the Findings: towards a second version of Higher Education Sector35 4.1 A labor force suited to Turkey’s Technological Change .............................38

4.1.1 Language skills ....................................................................................38 4.1.2 Computer skills ....................................................................................39 4.1.3 The need for generic skills ...................................................................40 4.1.4 Practical Experience.............................................................................44

4.2 Higher Education as a Catalyzing Force for Turkey’s Technological Change 48

4.2.1 Private sector-higher education partnerships .......................................48 4.2.2 Information and coordination mechanism ...........................................49

4.3 Higher Education and Regional Needs ........................................................51 4.3.1 Universities ..........................................................................................52 4.3.2 MYOs...................................................................................................54

5 Conclusions..........................................................................................................57 Tables and Figures .......................................................................................................58 References....................................................................................................................74

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Figures, tables and boxes

Figures

Figure 1: Intermediate Workers by School of Education ............................................24 Figure 2 .......................................................................................................................34 Figure A.1: Size of Manufacturing Firms in TOBB survey as a Share of The TURKSTAT database in terms of number of establishments and number of employees......................................................................................................................................71 Figure A.2: Among the college-educated applicants, experience is more important than education ..............................................................................................................71 Figure A. 3: ..................................................................................................................71 Figure A. 4: ..................................................................................................................72 Figure A. 5: ..................................................................................................................72 Figure A. 6: ..................................................................................................................72 Figure A.7: Competitiveness of Turkish Industries .....................................................73 Figure A.8: Percent of students that report on existence of extracurricular activities .73

Tables

Table 1: General Information about IAQ.....................................................................18 Table 2: What does private sector expect from the graduates of university, MYO and VTHS? (According to Modernity)...............................................................................26 Table 3: How do employers rate new university, MYO and VTHS graduates?..........28 Table 4: Wage Premium of Tertiary Education Graduates..........................................30 Table 5: Distribution of firm Level Premiums ............................................................31 Table 6: Private Sector - Universtiy relationship.........................................................32 Table 7: Firms’ View on Different Statements about Universities..............................32 Table 8: What Private Sector Look for in Blue and White Collar Workers? ..............42 Table A. 1: Comparison of Sectoral Composition of Dataset with TURKSTAT .......58 Table A. 2 Wage Offers and Firm Modernity-Internal Training .................................59 Table A. 3: Wages Paid to Graduates of Selected Universities’ and Departments’ Graduates .....................................................................................................................59 Table A. 4 Determinants of Wage Offered to University Graduates...........................60 Table A.5: Determinants of University Premium........................................................60 Table A. 6 Determinants Wage Offered to MYO Graduates.......................................61 Table A. 7 Determinants of MYO premium................................................................61 Table A. 8 Firms’ Views on Different Statements about Universities (According to Firm Modernity) ...................................................................................62 Table A. 9: Determinants of Internal Training ............................................................62 Table A. 10: Determinants of Turnover Rate of White Collar Workers .....................63 Table A. 11: Determinants of the turnover rate among university graduates in traditional firms............................................................................................................64 Table A. 12 Determinants of the turnover rate among MYO graduates in traditional firms .............................................................................................................................65 Table A. 13 Share of Different Sectors in Manufacturing Industry, Innovation and Growth Rates ...............................................................................................................66

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Table A.14: Private Sector’s Skills Needs (Regional and Sectoral Composition) ......67 Table A.15: Dispersion of University Entrants According to the City Groups that They Go .......................................................................................................................69 Table A. 16: Dispersion of University Entrants According to the City Groups that They Go .......................................................................................................................70

Boxes

BOX 1: How about a trip to Antalya? An unusual case of low turnover ....................23 BOX 2: Antalya: A usual case of high turnover in the tourism sector ........................23 BOX 3 A small disclaimer: The dual Structure of the private sector is reflected in human capital assessment ............................................................................................37 BOX 4: A tale of two export-oriented clusters (and two universities) ........................38 BOX 5: A good program without the good tools to accompany .................................39 BOX 6: Why are VTHS graduates preferred for intermediate positions? ...................43 BOX 7: Entering without examination to MYOs: Is anyone happy? ..........................44 BOX 8: Internships as a tool to reduce job mismatch .................................................45 BOX 9 TOBB Economics and Technology University: First Example of a Co-Op Program........................................................................................................................46 BOX 10: Two clusters speak: Practical experience in the textiles sector....................47 BOX 11: Ege University Textile Engineering Department .........................................48 BOX 12: Two professors establishing private sector-university partnerships.............51 BOX 13: Location and quality: The usual chicken and egg problem..........................53 BOX 14: No upholsterer, how about I give you electricians? .....................................55 BOX 15: An apple of a vision......................................................................................55 BOX 16: Cappadocia MYO: A successful partnership with the region and more? ....56

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Acknowledgements This study was prepared by a TEPAV team which was led by Emre Deliveli and comprised of Ozan Acar, İpek Aydın, Özhan Bozkurt, Esen Çağlar, Can Çetin, Asuman Erdem, Ömer Fazlıoğlu, Çetin Haşar, Sarp Kalkan, Emre Koyuncu, Savaş Kuş, Haki Pamuk, Zeynep Songülen, Çınar Tan and Utku Tuncay. Significant contributions were provided by Emin Dedeoğlu, Sachi Hatakenaka, Robin S. Horn, Maureen McLaughlin, Ferda Şahmalı, and Güven Sak. Contributions were also received by Bilgi Aslankurt, David Barchard, and Yasin Dalgıç. The World Bank Ankara office contributed a lot in providing office support- their support was especially crucial in providing access to key stakeholders. In this respect, we would like to thank Aysu Serpen. We would like to thank the Higher Education Board (YOK) for supporting our project and helping us with the university process involved in the interviews. Their support made sure that we were able to reach the university administration quickly. The State Employment Office (ISKUR) provided extensive help for interviews with the unemployed by organizing panels and accepting to distribute the unemployed questionnaires. Last but not least, we would like to thank all the businessmen/managers, students, university professors and all the other stakeholders of the higher education system for devoting time to our interviews and going out of their ways to helps us. This report is dedicated to them.

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List of Abbreviations

EU European Union

GDP Gross Domestic Product

HLFS Household Labor Force Survey

HR Human Resources

HS High School

IAQ Interviews and Accompanying Questionnaires

ICS Investment Climate Survey

ISKUR State Employment Office

ITU Istanbul Technical University

KTU Karadeniz Technical University

MEGEP/SVET Strengthening The Vocational Education and Training System in Turkey

METU Middle East Technical University

MONE Ministry of National Education

MYO Vocational College

OECD Organization of Economic Cooperation and Development

OSYM The Center of Student Selection and Placement

PAU Pamukkale University

PISA Program for International Student Assessment

SPO State Planning Organization

TOBB Unions of Chamber and Commodity Exchanges, Turkey

TURKSTAT Turkey Statistics Institute

VTHL Vocational Technical High School

YOK Higher Education Board

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Executive Summary

This study demonstrates the extent and nature of the skill mismatches between higher education and the private sector. Unlike previous studies that consider only one of the stakeholders, this one uses inputs from several stakeholders. People from private sector, higher education sector and regional ISKUR offices were interviewed. University students, employees of firms, and unemployed people from fourteen cities were addressed questionnaires. Panels were organized in universities and regional ISKUR offices, which students and unemployed people participated, respectively. One of the main sources of data was a survey prepared in optical format. Another source which this study uses inputs is a questionnaire in optical format. It was prepared and distributed to chambers in the network of the Union of Chambers and Commodity Exchanges of Turkey (TOBB) to be addressed to chamber members during regular assembly meetings. Finally, questionnaires which are used in prior studies are also utilized in this study to form an accurate picture of the relationship between higher education and the labor market. Expectations of firms before hiring new graduates of universities, MYOs and vocational/technical high schools vary across different sectors and regions. However, for the three groups, completing military service, ability to work as a part of a team, and loyalty are important for private sector in Turkey. In the firms’ view, education turns out to be less important than work experience. This finding is not valid for those modern firms which both export and engage in R&D activities. Computer and language skills are deemed important for all the three groups. School of graduation is a more important factor in recruiting from university graduates relative to MYO and VTHS graduates. Whereas, the match between the job and graduated department is a more important factor in recruiting from MYO and VTHS graduates relative to university graduates. When it comes to evaluation of the graduates of the three groups; the most significant pattern to note is that university graduates are better off than MYO graduates, who in turn are perceived better than VTHS graduates in social, vocational, behavioral, analytical thinking and communication skills as well as foreign language and computer skills. In fact it was reported to our interviewers by many firms that students go to MYOs because they can not secure a place in any university and that if they were smart and hard-working, they could have won a place in a university. MYO graduates perceived at the bottom in practical experience, where VTHS graduates fare better than university graduates. Internship programs may help students to improve their practical experience. However, it came out of the interviews as a key problem area. Neither students nor employers are happy about their internship experience. The analysis also pointed out that successful internships tend to be the result of the efforts of individual professors rather than university placement offices. In addition to internship programs, practical experience of students could be improved by increasing practical application content of education. However, interview findings show that especially MYOs and universities are perceived as insufficient in equipping graduates with practical experience. Throughout the interviews we encountered complaints that MYOs have suffer from lack of up to date equipment machinery. Therefore in MYOs students can not become familiar with the new machinery.

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Private sector considers that graduates of each member of the three groups are lacking some of the above mentioned skills. In particular social and communication skills were mentioned during the interviews as an area where graduates of higher education are lacking. In line with these findings analysis of the questionnaires addressed to employees, students, and unemployed people reveal that they would like to improve themselves in foreign language, computer and vocational skills. A noteworthy finding is that only 48 and 44 percent of students think that their university has contributed to their foreign language and computer skills, respectively. However, language skills are not viewed as a deficiency by all firms –firms located in areas with higher concentration of foreign language speakers and non-exporting firms are less in need of workers who can speak foreign languages. Nevertheless, in general not only university graduates but also MYO and VTHS graduates who do not have language skills are at a big disadvantage since the employers would like their workers to easily operate imported machinery, which have manuals in English. Despite these skill deficiencies a low percentage of the firms in Turkey provide their workers with formal training. Fifty-four percent of the respondents of TOBB survey indicated that they allocate less than 1 percent of their budget to training and 80 percent reporting that they allocate less than 5 percent to training. Fifty-eight percent of workers who addressed employee questionnaires reported that they have received some training and of those that have, 73 percent were satisfied with the training they got. It is important to note that employees and employers both tend to ignore on the job training. In terms of wages paid to employees there is a significant dispersion between universities. Graduates of some specific departments and universities are paid more relative to others. In addition to this, although there is a high return to university education, returns to MYO and open-university education are low in Turkey (Okten, 2006). A survey distributed to TOBB’s member chambers supports this finding as well. In employers view, university and MYO graduates expect to promote rapidly and receive high salaries at the start of their career. This complaint is prevalent especially for university graduates and absent in VTHS graduates. In fact this trait was cited by many firms as why they preferred VTHS graduates to MYO graduates for intermediate level positions. There is quite a bit of intra-sectoral variation in the skill needs of private sector. In particular, modern firms are demanding much more from graduates than traditional ones do, Moreover, the types of skills needed vary between firms. Traditional firms mentioned behavioral skills more than modern firms, whereas modern firms mentioned analytical skills more than traditional firms. Types of social skills that a traditional firm expects from a MYO graduate are quite different from the social skills a modern firm expects from a university graduate. In contrast to their moderate ratings of graduates, firms are much more critical of universities. Again, the criticisms are wide-ranging: First of all, there is not much information flow going on between higher education institutions and the private sector. Similarly, good working partnerships are the exception rather than the norm, with all good cases coming as the result of individual efforts of one or two key people. Thirdly, there is a lot of demand/supply mismatch regionally; with some areas in demand undersupplied and some areas not needed oversupplied. The interviews also

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provide further proof on the importance of taking regional needs into account with numerous case studies. In addition, the interviews illustrate the heterogeneous nature of the universities as well; certain schools and programs are particularly preferred for providing the particular skills desired by employers. In fact, interviews illustrate numerous good cases that could be repeated elsewhere as well. The higher education system needs to play a leading role in the economic transformation of Turkey. One of the main roles that higher education institutions would take is the partnerships formed with the private sector. However, one of the most striking findings of the interviews is that most of the firms (55 percent of firms in TOBB survey) have not even attempted to form a partnership with the higher education institutions. These findings indicate that current institutional setting should be changed in a way to remove barriers on forming effective partnerships between higher education institutions and private sector. High consulting fees of professors are turned out to be one of the most important reasons that impede partnerships to be formed. This is due to YOK Law Article 37 which leaves the tax on consulting for revolving funds to the executive board of each university. Relaxing revolving fund requirements is a necessary but not a sufficient condition to promote partnerships to be formed. Experiences of other countries show that there needs to a mechanism for supporting innovation, which will act as an intermediary for innovative activities between higher education institutions and the private sector. In the interviews several firms mentioned that TOBB may assume the role of coordinator or intermediary. Creating incentives for higher education institutions is also needed. Current promotion criteria of assistant and associate professors do not serve for this purpose. For all of them publication is the only criterion for promotion, making it more difficult for professors to engage in projects that will not result in a research paper. Therefore, it might be useful to consider diversifying promotion criteria for some fields to include non-research projects. Higher education institutions should include responding regional needs in their future strategies. However, interview results indicate that most of the time it is not the case. Firms in the TOBB survey report that university in their region trains people neither having the skills they demand not in the occupation their province is in need. During interviews most of the university administrators said that a university’s primary goals are not to respond to the region’s needs, but to provide students with a uniform quality education. They saw the region where their university exists as a curse they had been forced to live with because of factors beyond their control. Students also consider region as a curse and they plan to leave the region immediately after graduation. Firms in the region also suffer from this. The first step in assuring universities train students suited to the region’s needs is to make sure creating an environment suited to university graduates working in local firms for long haul. MYOs are also responding unsatisfactorily to the region’s needs. Many MYOs have departments that the region does not need or do not have some departments that are vital for the region. The decision made to open new departments should be prioritized in line with the regions need and in collaboration with the private sector as well. Given the heterogeneous nature of firms’ needs and expectations from higher education institutions, it is fair to ask whether some general conclusions from the

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study emerge. However, the rapid inter and intra-sectoral transformation of the private sector, a process that is likely to accelerate during the EU accession, is causing traditional sectors and firms to converge to their modern counterparts. As the traditional firms shift out of informality, adopt modern production methods, and become increasingly a part of global supply chains, they are more likely to shift-up their demands and expectations from their employees as well. In this respect, education-related constraints are likely to become stricter constraints to private sector development in the near future.

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1 Introduction The skills education of the workforce has always been an important issue on the agendas of academics and policymakers. It has become even more significant with the increasing integration of global economy, in which human capital is among the core elements that constitute the differentials for propelling private investment. One of the main themes of the discussion is the relationship between higher education and the private sector. However, it is surprising that there is not much work on education, still less work on higher education and even less work on the higher education-private sector relationship. The several studies we have encountered that discuss this relationship do so at a relatively macro level, ignoring sectoral and regional differences.1 While the skills and education of the labor force have always been an important factor for Turkey, its inadequacy as a constraint for private sector development is likely to exacerbate in the near future for several reasons. The reforms and the relative stability of the country in recent years have altered the importance of bottlenecks in doing business. Economic and political stability, once the biggest concern for the private sector, is not seen as a major bottleneck today. Education, particularly higher education, on the other hand, is one of the areas in which there has not been major improvement relative to the progress in the other constraints. As a result, many firms may see education as a severe constraint, for which there is evidence for in the World Bank’s Investment Climate Survey (ICS): Percentage of companies that see skills and education as a severe constraint is higher in Turkey than comparable countries. Moreover, as will be detailed in Section 4, the Turkish economy is undergoing a transformation in which innovative capabilities play an increasingly important role.2 The ongoing transformation is changing the skill needs of the private sector and their expectations from higher education graduates. A different justification for assessing higher education and the labor market comes from more macro factors. While the Turkish economy has had a successful growth performance after the 2001 crisis, it has not been accompanied by a decline in unemployment rates. Several competing or complementary explanations have been offered for the problems in job creation such as increased competition from China, transformation of the economy from agriculture, informal sector dynamics or statistical artifacts. However, more micro factors like skill mismatches have only recently been mentioned.3 Although showing the effect of these mismatches on unemployment is beyond the scope of this paper, we hope to provide evidence on the extent of these mismatches and how different skills and qualifications of higher educations graduates affect the firms’ hiring decisions. Still another justification for the importance of the higher education system is related to Turkey’s demographic transformation. Just like Ireland 30 years ago Turkey is now undergoing its demographic window of opportunity, i.e. the share of working age population in total population increases.4 However, to make use of this, Turkey needs to invest in the skills of the labor force. Ireland’s economic miracle is partly 1 The few works that adopt a regional approach limit themselves to a particular region and therefore are not very useful for national policymaking. 2 We explain our definition in a separate footnote in section 4 3 See the World Bank’s Labor Market Study and Country Economic Memorandum (2006) as well as the OECD Turkey report (2006) for discussion of such mismatches. 4 For a detailed analysis, see Mumcu and Çağlar (2006), and World Development Report (2006).

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attributed to its demographic window of opportunity coupled with a rapid improvement in the quality of its education system. For Turkey to be able to follow a similar path, outlining possible areas for improvement would be a useful first step. In that sense, this paper, along with the other World Bank studies that are part of the same project, can be of use by taking an accurate picture of the higher education sector and suggesting avenues for improvement. This study aims to contribute to the understanding of the relationship between higher education and the private sector by utilizing a variety of approaches. We conduct interviews with higher education and private sector representatives as well as employment agency of Turkey. We prepared questionnaires to be addressed to students, employees, and unemployed people. In addition, we have made use of the existing data to check for the robustness of the collected data and to discover additional evidence for the findings. Therefore, the paper uses many different methodologies and sometimes resorts to rough calculations that, while not rigorous, test or support different points. As the reader will see throughout the text, they are far more than a guess but far less than a rigorous proof or econometric test. This approach allows for a first attempt in establishing relationships and provides motivation for future work. The underlying assumptions and weaknesses of specific analyses and how to expand on them are elaborated in the footnotes. The paper is organized as follows: In the next section, we describe the data that will be used in the study. In Section 3, we provide summary statistics that give a snapshot of the higher education sector in relation to the labor market. Section 4 provides the main body of the report: Results of the interviews, supported by data collected by TEPAV and outside sources. The last section concludes.

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2 Data Description Since available data was limited and not fully appropriate for the purposes of this study, a lot of resources were devoted for new data collection. In order to ensure that the main questions are answered with enough detail and that the study goes a step further than the previous ones, in-depth interviews were made with representatives of higher education and private sector in several provinces of Turkey. In each province interview team also asked students to fill out questionnaires. Unemployed people were also included the scope of this study and they filled out the questionnaires prepared specifically for them. Regional ISKUR offices served as an intermediary between our team and unemployed people. With the help of ISKUR officials we organized panels that unemployed people participate. These interviews are the main data collection tools which are supplemented by different questionnaires. This was an initial but an important first step which also provides us with the opportunity to get acquaintance with the subject. As a second step we developed the questionnaire in optical format with the help of experience we acquired during in-depth surveys. In this questionnaire we asked all the questions which were asked to private sector representatives during the firm interviews. However, we did not constrained ourselves with the interview questions, several others were also added to questionnaire in the optical format. Finally it was sent to all chambers of industry and commerce in Turkey which are within the network of the Union of Chambers and Commodity Exchanges of Turkey (TOBB). The interviews and the chambers of industry and commerce survey (“TOBB survey” hereafter) methodology are explained in detail below. Although our unique data allow us to shed light on many previously unanswered issues, it should be accepted with certain disclaimers. Selection bias, a general problem in all surveys, is prevalent in both questionnaires based on interviews as well as the TOBB survey.5 In addition, all subjective survey and questionnaire questions are subject to what we call perception bias.6 Although various recent methodologies exist for taking care of such survey problems, we do not attempt to solve them in this study because of time constraints and leave them to further work. Possible empirical improvements to the study are discussed in the conclusion section. However, we do check the robustness of our data with various cross-check questions in the questionnaires. We also check for validity and robustness using newly outside sources exploring similar topics. The specific issues with each dataset will be described in the subsections below. In addition to the above data sources we used several others. They are; Household Labor Force Survey (HLFS) of Turkish Statistics Institute (TURKSTAT), Investment Climate Survey (ICS) of the World Bank and TEPAV, MEGEP/SVET survey data from European Community (EC), Ministry of National Education (MONE) and Turkish Employment Agency (ISKUR). Detailed explanations of all are presented in 5 It is possible to correct for the selection bias and other econometric problems. Perhaps the most well-known methodology in dealing with selection bias is Heckman (1979), now integrated into many statistical packages. Intuitively, the methodology integrates the determinants of the decision to complete the survey into the econometric analysis. 6 Deliveli and Gozpinar (2006) show, in a small note based on Turkish Investment Climate Survey (ICS) data that such survey bias is very common: Although high electric and energy costs put Turkish firms at a serious disadvantage, they rank at the bottom when firms are asked to rank how different factors impede their business performance. Surprisingly, the results do not change even for firms using energy and electricity intensively (measured as a share of their costs or by looking at energy/electricity-intensive sectors)

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the following sections. We will discuss the consistency of our findings and how they fit other data extensively in the next two sections. 2.1 Interviews and Accompanying Questionnaires (IAQ) As discussed in the Introduction, there is not much detailed knowledge about the experiences of higher education stakeholders. Moreover, the little knowledge that exists is usually limited in scope. To fill in this gap, we decided to collect data in two stages. Firstly, we interviewed people from higher education (rector, vice rector, academicians) and private sector institutions (owners, general managers, human resources managers, experienced workers) in addition to them we also interviewed university students and unemployed people. Employees of firms and students in higher education institutions, and unemployed people are addressed questionnaires prepared by the TEPAV team, Secondly we complement the IAQ with the HLFS, ICS, MEGEP/SVET and TOBB survey. The interviews and questionnaires form the core of the analysis as well as the design of the TOBB survey. The first stage of the IAQ was choosing the cities and firms. Since we were constrained to interview about 120 firms in fourteen cities, choosing the cities became a very important process. We developed our own criteria in selecting the cities and number of firms in each city, given that we constrain ourselves with such a small sample, the usual sample selection criteria would not work. We paid special attention to the followings;

Broad coverage of Turkey: We have two seemingly contradictory goals in determination of the coverage of Turkey: One of them is representing different regions with different characteristics and the other is capturing a significant part of the country that has a significant share in GDP. To realize these objectives, we made use of both objectives and also subjective data such as expert opinion.7 Consequently, provinces like Izmir, Adana or Bursa could not be included even though they are quite large in terms of their share in the GDP.8

University Presence: The existence of a local university was a major factor

in our city choice to conduct interviews and questionnaires. The reason is obvious; a significant part of our analytical framework was based on the interaction of the local higher education institutions with the private sector.

Compatibility with other data: As described above, a valid concern was to

be able to compare the collected subjective (interviews) and objective (questionnaires and TOBB survey) data with other sources. Therefore, although this was not a major selection criterion, we tried to choose cities for which different data were available.

As a result, we decided that to be able to satisfy all these factors, we conducted interviews and questionnaires in Ankara, Antalya, Denizli, Diyarbakir, Erzurum,

7 SPO provincial statistics were used to be able to compare different development indicators. 8 However, we were still able to capture about half of Turkish GDP because of our inclusion of Istanbul and Ankara.

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Eskisehir, Gaziantep, Istanbul, Kayseri, Kocaeli, Nevsehir9, and Trabzon. In addition, we conducted informal interviews in Mugla with four hotels.10 The second stage of the IAQ was forming the questions to be used in the interviews and the questionnaires. The questions to be used in the interviews and questionnaires were formed after discussion with the TEPAV team, the representatives of The World Bank, and other project consultants. Furthermore we derive benefit from the existing literature and formal/informal consultations with experts. The first drafts of the interview questions and questionnaires were tested in pilots and then finalized. After deciding on the questions to be asked in questionnaires specifically to students, employees, and unemployed people we put in practice these questionnaires in universities, firms, and regional ISKUR offices to students, employees, and unemployed people respectively. To capture all the relevant stakeholders’ extent of their interaction in each city, we decided to hold interviews with all the major stakeholders. We conducted interviews with the board members, human resources managers, and an experienced employee of the firms. Moreover, as previously mentioned we addressed questionnaires to employees in most of the firms. In universities we interviewed their rector and/or vice-rector together with academics. In the meantime we held panels with the participation of university students. On the unemployed side, we interviewed with managers of the regional ISKUR offices and leave them with questionnaires which are addressed to unemployed people. We paid special attention to interviewing more than one person in each institution. Therefore the probability of reaching biased conclusions due to potentially subjective judgment was decreased. 2.1.1 Firms and employees Once the locations were chosen, the issue became selecting the number of firms to be interviewed in each city and the firms themselves. The total number of firms interviewed was 120. The allocation of these firms to each city was based on our concern for broad coverage. The final distribution of each city can be seen in Table 1. The firm in Mardin was part of the Diyarbakir sample. The firms in Kocaeli are from the Gebze Organized Industrial Zone only - because of the proximity to Istanbul, they can be considered in Istanbul as well. In each firm, the interviewer spoke to a manager. Depending on the size of the firm, this person was either the owner/manager (for small firms), general manager (for mid size and large firms) or a specific department manager (for very large firms). In addition, we interviewed Human Resource Department managers where available. To gain additional insights on each firm, when available, we also interviewed some experienced employees who have been working in the firm for a long time. In addition to the interviews, we asked managers to have their employees fill out the questionnaires. Overall, 1654 employees from 88 firms filled out these forms; the distribution of the number of employees who filled out the questionnaire in each city is in Table 1. The questionnaire included objective variables like salary, education and experience as well as subjective variables such as job satisfaction, job/education

9 We have not written a city report for Nevsehir because of the small sample size. 10 The findings of these interviews are integrated into the report, although we have not written a city report for Mugla because of the small sample size. There are no questionnaires for Mugla either.

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mismatch, the skills needs and other control variables. The questionnaire is included in Appendix 2.1. 2.1.2 Unemployed people Interviews with firms and questionnaires for workers overlook the significant amount of workers that are unemployed; involving the unemployed segment of the workforce would definitely help in understanding those factors that impede finding work. Data from the unemployed was collected in two formats. Questionnaires were filled out using ISKUR offices- overall 512 unemployed filled out the surveys. The surveys included questions on objective variables such as education and duration of unemployment as well as subjective variables such as reasons for being unemployed and skills deficiencies. The questionnaire is included in Appendix 2.2. In addition to the questionnaires, panel discussions with the unemployed were held in most cities. The organization of the panels was undertaken by ISKUR. Table 1 indicates whether a panel was conducted in a city as well as the number of employees who filled out the questionnaires. 2.1.3 Universities Since universities provide the supply side of higher education, special attention was given to interviewing actors at different levels in each university. Interviews were conducted with the president of the university (rector) and/or the vice president (vice rector) responsible for placement. In addition, the interviewers spoke with at least two university professors in each university- one experienced, tenured professor and another younger assistant/associate professor who had earned his/her Ph.D. abroad. During earlier discussions with the World Bank and experts, we decided that having the opinions of a younger professor educated abroad would give additional insight on the university-private sector relationship. In fact, as will be discussed below, some of the most interesting insights came from these professors. We also spoke with directors in the office of career services where available. In addition to the universities, we talked to the Vocational College (Meslek Yüksek Okulu, MYO) director associated with each university. Overall, we visited 24 universities in 14 cities. We also visited the Cappodocia MYO, which is not affiliated with any university since it is a foundation MYO. The universities visited are listed in Table 1. In each university, we also asked the rector/ vice rector whether we could hold a panel discussion with their students and have them fill out questionnaires. Out of these 24 universities, 20 accepted our request and 985 students filled out the questionnaires; the distribution of student questionnaires in each city is also provided in Table 1. The student questionnaires, like their firm counterparts, were designed to get a balanced and diverse view of the universities. They included objective variables like the universities of their choice as well as subjective variables such as their decision to enroll in university, their evaluation of their universities and their expectations on finding a job after graduation.

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2.1.4 Other Interviews To supplement the findings of the interviews, we also interviewed other key actors. These included interviews at the Istanbul Chamber of Industry Foundation, which was involved in various MYO programs during the last decade; Alev Alatli, founder of the Cappodocia MYO; various MYO directors during the MYO Directors’ Conference, and visits to best-practice MYOs in Cappodocia and Cankiri, among others. Although these interviews are not the foundations of our study, they give important supporting evidence to our findings and provide illustrative case studies for certain situations.

Table 1: General Information about IAQ Firms Universities ISKUR

Number of Firms

Number of Firm

Questionnaires

University Names Number of University

Questionnaires

ISKUR Panel

Number of ISKUR

Questionnaires

Ankara 9 58 Ankara, Bilkent, Gazi, Hacattepe, METU 276 No -

Antalya 12 134 Akdeniz 104 Yes 9 Denizli 12 206 Pamukkale 16 Yes 85 Diyarbakır 8 127 Dicle 89 Yes 23 Erzurum 6 92 Atatürk 88 Yes 49 Eskişehir 5 44 Anadolu, Osmangazi 34 No - Gaziantep 14 249 Gaziantep 62 Yes 53

İstanbul 16 240

Bilgi, Galatasaray, Koç, Sabancı, İstanbul, ITU,

Yeditepe

140 Yes 158

Kayseri 11 135 Erciyes 48 Yes 66 Kocaeli 4 0 No - Mardin 1 50 Mardin MYO 0 No - Muğla 4 0 Muğla 0 No - Nevşehir 6 55 Cappodocia MYO 53 No - Trabzon 13 264 KTU 75 Yes 69 TOTAL 121 1654 985 512 Source: IAQ

19

2.2 TOBB Survey After having done the interviews and accompanying questionnaires with the students, employees and unemployed people we prepared the TOBB survey, which is addressed to private sector representatives. This survey is a complement to the others. The firm visits provided the interview team with a solid understanding about firms’ human resources issues, their expectations from employees as well as the university- private sector relationship. We kept in mind our previous understanding in the preparation of the questions in TOBB survey. Moreover, we analyzed similar surveys conducted in several other countries and referred to outside opinion. They included the areas mentioned above as well as control variables like quality standards and incidence of exports/imports. The interviews showed us that firms often differentiated between university, MYO, vocational/technical high school (VTHL) and high school graduates in terms of their expectations and evaluations. Therefore, the questions in the TOBB survey we asked firms to differentiate between university, MYO, and VTHL graduates where appropriate In addition we also observed a similar differentiation between white and blue collar workers, we asked firms to specify these workers where appropriate as well.11 The survey was prepared in optical format and then sent to all chambers of industry and trade in Turkey, to be completed by the members of the each chamber assembly. The survey is included in Appendix 3. Out of 364 chambers and 9000 firms throughout Turkey, we received responses from about 1900 firms in 57 provinces.12 Given the experience of TEPAV with conducting these types of surveys, we attribute the less-than-normal response rate to the effect of the summer holiday season as well as the length of the surveys.13 However, the data are more than sufficient to conduct statistical analyses.14 2.3 Outside Sources As mentioned above, we also made use of outside data sources to supplement and complement our analysis. Since these sources have not been collected for this project and have been utilized before, we do not describe in detail their methodology; instead, we briefly describe each dataset and refer the reader to more detailed descriptions.

11 An early concern in designing the survey was whether respondents would know which types of workers white and blue collar were being referred to. However, we found out in pilot studies as well as firm interviews that almost all owners/managers were familiar with these terms. Nevertheless, we did define the terms at the beginning of the survey. 12 The analysis is conducted with 1845 firms because the last few surveys arrived too late to be included in the analysis. 13 In other surveys to the chambers TEPAV | EPRI has conducted, the response rate has usually been 25-30%, slightly more than our response rate of 21%. 14 Again, we do not correct for survey nonrespondent bias, leaving that to future work. Deaton (1997) summarizes recent research in this area.

20

2.3.1 World Bank Investment Climate Survey The World Bank Investment Climate Survey was implemented by the World Bank and its country partners TOBB and TEPAV. The survey was designed by the World Bank and TEPAV and was carried out in 2005, covering a sample of 1,300 firms in eight industries15 selected from five regions and seventeen cities.16 The sample represents more than 80 percent of Turkish industry, 82 percent of the total number of firms in sampled sectors, 84 percent of total value added in the manufacturing industry, and 82 percent of total manufacturing employment. The firm population distribution and sample were drawn from TOBB’s database.17 The questionnaire includes both objective and perception based questions on investment climate related issues, which can be grouped into six areas: (1) business-government relationships (2) access to finance and corporate governance, (3) access to infrastructure, (4) firms’ use of quality standards; (5) firms’ use of technology and innovation; and (6) labor regulations and labor skills. 2.3.2 MEGEP/SVET Survey The MEGEP/SVET survey is part of the MONE, ISKUR and EC program on strengthening the vocational education and training system in Turkey. The survey is conducted among 5650 firms in 6 cities: 1192 in Ankara, 395 in Antalya, 1226 in Gaziantep, 1405 in Istanbul, 936 in Izmir, and 496 in Trabzon. Although we do not know about the sample selection criteria (city, number of firms in each city and the firms themselves), the cities chosen and the distribution of the number of firms in each city suggest that these surveys were not designed to represent the entire population. This survey is designed to analyze the technical and vocational high schools in Turkey. However, they do ask questions to firms on their current and future skill needs. Therefore, we use the survey as a complement to our own questions (from the firm questionnaires and TOBB survey) regarding this data. The survey questions are included in Appendix 4. 2.3.3 Household Labor Force Survey (HLFS) This is a standard HLFS conducted quarterly by the Turkish Statistics Institute (TURKSTAT). The details of the survey can be found at the TURKSTAT web site.18

15 These are grouped into eight main categories: food and beverages (21.32 percent of the sample), textiles and apparels (27.89 percent of the sample), chemicals (13.3 percent of the sample), non-metallic mineral products (6.27 percent of the sample), metal products (10.73 percent of the sample), machinery and equipment (9.67 percent of the sample), electrical machinery (5.06 percent of the sample) and transport equipment (5.74 percent of the sample). See World Bank and TEPAV (2005) for more detail. 16 The five regions are Marmara, Ege, Ic Anadolu, Akdeniz and Karadeniz. 17 Please see World Bank (2006) for a detailed description of the survey and results. 18 www.tuik.gov.tr

21

3 Descriptive Statistics In this section, we provide some general characteristics of the main datasets described in the previous section. Our aim is not to provide all cross tabulations of data in this section, we would rather like to examine how accurately the data represents Turkey as well as provide summary statistics, which will also motivate the main results of the interviews. The data will be used in an analytical format in the next section to complement the interviews as well as to test the robustness of the findings from the interviews. 3.1 General data description 3.1.1 TOBB survey We use the TOBB survey for two main purposes: (1) to check the robustness of our results we obtain during IAQ, (2) to see how much the findings can be generalized to Turkey. We can try to extend our interview findings to the whole country only if we know that they are not biased from small sample size and interviewee bias. However, as previously mentioned we conducted interviews in fourteen cities. Because of that it is possible that we fall into trap of small sample size and obtain biased results. To check whether this is actually the case we compare interview findings with the results derived from the analysis of TOBB survey. However, to be able to reach conclusions, we need to know the extent to which the TOBB survey represents Turkey. The representation power of the dataset is confounded by two distinct factors: the surveys were not sent randomly, but to a selected list of business representatives, who are all members of the local chamber assemblies. Our sample will present a clear image of Turkey to the extent that assembly members reflect the composition of the private sector on their own. Moreover, with a response rate of 21%, non-respondent bias becomes a potential problem. Instead of using econometric techniques that can correct for these two biases, we will first see whether our sample is an accurate representation of the Turkish economy. By using the available data on all manufacturing industry, we can see how well the dataset is presented. The manufacturing surveys conducted by TURKSTAT are supposed to include all manufacturing firms except informal firms that are not registered.19 In Figure A.1 in the Appendix, we report the size of the manufacturing firms in our dataset as a share of the TURKSTAT database in terms of number of establishments and number of employees. In terms of employees, the coverage of the TOBB dataset was about 5.02% of the TURKSTAT dataset in 2001 (the last year the TURKSTAT dataset is available), whereas in terms of number of establishments, it was about 2.69%. Although the coverage is acceptable for a dataset of this sort, the discrepancy in coverage between number of establishments and number of employees brings to mind whether the data could represent the average firm in Turkey.Table A. 1 in the Appendix shows that the average number of employees per firm is around 181 for our dataset, whereas the average size of a firm in the TURKSTAT database for the period 1994-2001 is 100. Another issue is whether the dataset gets a good 19 However, it is widely accepted that although the TURKSTAT database is a good representation of Turkish manufacturing in terms of sales or value added; it is less of a perfect sample in terms of number of employees and still even a less perfect sample in terms of number of firms. For example, the Chamber of Commerce and Industry (TOBB) Database, described in detail in Acar et. al (2006), has about 60,000 firms for 2003.

22

approximation of the sectoral composition of the Turkish manufacturing sector. In Table A. 1 we report the number of firms, average number of employees and share of sectors for both the TURKSTAT and TOBB datasets: The sectoral compositions of both datasets turn out to be surprisingly close: they are very similar in all the sectors and as a result the correlation between sectoral shares is 63%. As detailed in the previous paragraph, the average firm size in the sample of TOBB Survey is not exactly the same as an average Turkish manufacturing firm: It is bigger, most probably because its owner is one of the bigger businessmen of the region. Moreover our dataset does not cover a significant part of Turkish manufacturing. Therefore it may be misleading to reach conclusions about the overall Turkish manufacturing industry. However, the sectoral similarity of the TOBB Survey and TURKSTAT dataset leads us to believe that our snapshot of the data is not an inaccurate picture at all, at least at the sectoral level. 3.2 Some general characteristics of the labor force 3.2.1 Turnover Given recent work demonstrating labor market inflexibility and its effects in Turkey, it is not surprising to see that the firms in the TOBB survey do not have a lot of turnover.20 66% and 79% of the firms report that less than 5% of their blue collar and white collar employees have, respectively, left in the past few years. Interestingly, these figures are lower than the 2005 World Bank ICS, which finds average yearly turnover of 17%.21 It is noteworthy that blue collar workers have higher turnover than white collar workers. Although explaining low turnover and the difference between blue/white collar workers are beyond the scope of this study, it is important to note that there are several competing or complementary explanations based on either frictions of labor supply or demand. While high turnover is traditionally explained by flexibility in labor markets, firm interviews point to a more micro cause. We have observed that turnover is lower in modern firms22. These firms train their workers and pay them a higher premium than traditional firms in the same region and sector. As a result, the workers would like to stay in the firm as much as the firm would like to keep them; hence there is low turnover (see BOX 1). There might be other explanations as well. For example, in regions with a high rate of unemployment, workers would be less likely to quit as their chance of finding a new job is smaller.23 There might also be sector specific reasons affecting turnover (see BOX 2). However, the framework we suggest here is in line with other observations we discuss below.

20 The World Bank’s Labor Market Study demonstrates the extent of as well as causes for and consequences of labor market inflexibility in Turkey. 21 Of course, we do not observe average yearly turnover, as the TOBB survey questions are categorical variables, but it is very unlikely for the average number to be close to 17% yearly rate. The TOBB survey figures are much more in line with the inflexible labor markets in Turkey, see World Bank Labor Market Study for more on this issue. The high numbers in the ICA might be because of temporary workers, as it specifically asks respondents to include those workers as well. 22 We classify firms in the TOBB Survey as traditional, modern, and very modern. If a firm exports goods and at the same time engages in R&D activities then we classify it as a very modern firm. Whereas if a firm either exports or engage in R&D, but not both, we classify it as modern. Finally, firm which neither exports nor engage in R&D is classified as traditional. 23 A related theory is whether turnover is higher in areas with clusters. The existence of more job opportunities would mean workers are more likely to find work if they leave their current jobs. However, a supply effect might be going on rather than a demand effect: It might also be the case that firms fire workers rather than workers leaving on their own will. However, evidence points to contrary.

23

In Section 3.6, we test this argument in an analytic way by making use of TOBB survey data. BOX 1: How about a trip to Antalya? An unusual case of low turnover

BOX 2: Antalya: A usual case of high turnover in the tourism sector

Positions 22% of the firms in the TOBB survey report that they employ university graduates for jobs that could be performed by a MYO or a high school graduate. However, it would not be accurate to infer from this figure that 22% of the university graduates are working in jobs where their skills are not put to optimal use. It is possible for a survey respondent to have marked “yes” even if 1 of her 100 workers is working in a job filled by a MYO graduate. However, the sectoral and provincial differences point to some interesting observations from the interviews as we discuss below. A very important position, especially in manufacturing, is that of the intermediate worker, “ara eleman”. Since MYOs are expected to produce intermediate workers, it is interesting to see that only 8.9% of the firms do actually use MYOs as intermediate workers, as shown in Figure 1 We can also observe that as the level of education decreases, percentage of firms designating graduates of that degree as intermediate workers increases. This puzzling piece of data is in fact in line with firm interviews, which show that many companies prefer to hire VTHS or HS graduates for basic positions and train them as intermediate workers in a few years.24 This finding,

24 A simpler explanation might be that respondents did not mean what we meant by intermediary workers. However, both firm interviews and pilot surveys indicated that almost all employers/managers were familiar with the concept of intermediary workers.

A weaving/carpet firm in Gaziantep, a large and modern company with one of the lowest turnover rates among the firms we interviewed, told us that most of their workers had been with the firm for more than ten years. Interestingly enough, the HR manager complained a lot about the skill deficiencies they observe in recent graduates, partly reflecting their high expectations. At the same time, she revealed they were paying more than their competitors, provided a fair amount of training to their employees (she noted they specifically prefer new graduates because they can adapt them to their corporate culture easily, a fact noted by many large and modern firms in different sectors and regions) and gave many examples of different incentives the firm was providing to induce their workers to stay in for the long haul such as support to buy a flat for employees working in the firm for more than five years. The most interesting incentive was probably that the firm had taken all workers who had spent more than six years with them to a week-long holiday in Antalya.

While the firm that took its employees to holiday had very low turnover, the holiday village where the workers went probably had a very high one: Among the firms we visited, the hotels in Antalya had more turnover than average. This result reflects the characteristics of the tourism sector and the types of workers it demands rather than macroeconomic environment or any specific characteristic of the region. First of all, the percentage of university and MYO graduates were very low in the firms in the region. Most occupations do not require any training, and hotels prefer to hire seasonal workers with little or no skill to these manual jobs and pay them very low wages. Even positions that might better be filled by MYO or university graduates are often filled with high school graduates; in general there is a tendency in the sector to employ people who have learned the trade on the job (“alayli”) rather than at school. While this tendency was present in other sectors as well, it was extremely strong in the tourism sector. These results were in line with the interviewers’ findings that many tourism graduates (MYO or university) do not work in the tourism sector. We will investigate these issues in detail in Section 4.

24

consistent with our explanation of low turnover above, will come into play, along with sectoral and provincial differences, when we discuss interview results in the next section.25

Figure 1: Intermediate Workers by School of Education

0%

10%

20%

30%

40%

50%

60%

Univers ity MYO Vocational/TechnicalHigh School

High School or Prim arySchool

% o

f res

pond

ents

Training 54% of the firms in the TOBB survey report that they allocate less than 1% of their budget to training and 80% report that they allocate less than 5% to training. Lack of training is one of the puzzles of the ICS results as well. Although firms in the ICS sample report skills and education of workforce as "major" or "very severe" constraint on operations and growth more than comparable countries do, 45% of the firms in the survey report that they do not provide training for their employees- significantly less than comparable countries.26 IAQ firm questionnaires give similar results: 58% of the workers from the sample report that they have received some training and of those that have, 73% were satisfied with the training they received.27 Although explaining training in detail is beyond the scope of this paper, we can offer two reasons why many firms do not offer training28: firms may not have the resources or skills to conduct training, an issue we provide some evidence for in Section 3.6. Alternatively, it is also possible that employees lack the fundamental skills that are hard to acquire by training only.29 Note that both explanations are consistent with the fact that modern firms offer more training since they have more resources and are able

25 It is conceivable that firms are less likely to fire workers they have invested, we test this hypothesis in Section 3.6. 26 For a more detailed discussion of training decisions of firms, see World Bank – TEPAV (2006). 27 Note employees and employers alike are likely to ignore on the job training when responding to training questions, a common method of training in many manufacturing companies as evidenced in the firm interviews. In this sense, data on training most probably underestimates actual training that takes place. Earlier versions of the TOBB survey differentiated between formal and on the job training; however, since the question did not work well in pilots, it was dropped from the final version. 28 These results are consistent with the ICA results as well. 29 This is the major explanation offered on why company-based training was successful in Japan, whereas it did not work in Singapore.

Source: TOBB survey

25

to hire more highly skilled workers through higher wages and more screening. However, we do not attempt to test the second hypothesis in this paper.30 An important question is who pays for the training. As suggested by Becker, firms and employees might share the cost of training, implying that firms offering training would also offer lower wages (see Table A. 2). However, other factors might be playing a role such as firms’ desire to keep workers for the long haul or their resources. When we look at the TOBB survey data, we get consistent results across university, MYO and VTHS graduates. We do not have evidence for the Becker framework in our data, at least not for traditional firms: Traditional firms that spend more on training offer higher wages.31 Modern firms pay significantly higher wages as well, in line with the framework we introduce in Section 3.6.32 However, there is some evidence that Becker’s model is working for modern firms: For modern firms, there is a consistent (but insignificant except for VTHS graduates) negative relationship between money spent on training and wages.33 3.3 Skills: What is lacking, what is asked for? Data on skills comes from the TOBB survey as well the IAQ questionnaires. We have data on what skills employers are looking for in university, MYO and VTHS graduates, how they rate these graduates as well as students’ own evaluations of themselves and their higher education institutions in providing these skills. Expectations before hiring: The TOBB survey asks the firms which criteria are important when they hire recent graduates from universities, MYOs and vocational/technical high schools. Table 2 shows the percentage of firms that checked each category. For the three groups, completing military service, teamwork and loyalty (being likely to stay in the firm long-term) come out to be the critical factors. Computer skills are important for all the three groups, whereas language skills matter only for university graduates. For university graduates, private sector thinks that school of graduation is more important than the match between the job and the department graduated from. However, the reverse is true for MYO and VTHS graduates. It is important to note that only 20% and 35% of the respondents consider the school of graduation and the match between the job and the department graduated from as an important factor recruiting from MYO graduates, respectively.

30 We thank Sachi Hatakenaka for offering the second explanation in the context of the differences in training between Japan and Singapore. 31 Of course, we can not control for the resources of the firm, its profits and the productivity of workers, all of which might be an important determinant of wages. 32 We do not define formally the “modern” firm in this paper, although we do refer to some characteristics of modern firms in Box 3. We use size as a proxy for modernity, although we have encountered small but modern firms as well as large and traditional firms. For an index on modernity in the corporate sector, see Deliveli and Demir (2006). Two proxies that are part of the index constructed in that study are whether the firm has a quality certificate and whether it exports. These proxies came up as important characteristics of modern firms in the interviews as well. We use these two as proxies for modernity in the empirical analysis. 33 We would like these findings to be seen as suggestive evidence rather than definitive findings for many reasons. For one thing, our measure of training excludes internal training, to which firms could be devoting significant resources as well. We also implicitly assume that all the training resources are devoted to all the workers in the firms, although it could be targeted to only certain graduates. Although such detailed analysis between wages and training is beyond the scope of this work, we hope that these initial findings will motivate further research on this topic.

26

Table 2: What does private sector expect from the graduates of university, MYO and VTHS? (According to Modernity)

% of respondents who think that the following expressions are important for the graduates of

University MYO VTHS

Ability of the applicant to use foreign languages 40% 15% 12%

Willingness to work in your firm in the long run 38% 31% 39%

Specialization in the degree earned 32% 14% 11%

Extracurricular activities participated in college 25% 11% 9%

Commitment to teamwork 37% 32% 37%

Social skills matching the position applied for 30% 21% 24%

School of graduation 39% 17% 25%

The degree earned matching the area of job 32% 29% 28%

Career goals of the applicant 32% 14% 13%

The cost of the applicant to the firm 32% 23% 31%

The wage demanded by the applicant 34% 24% 30%

Internships related to the job 24% 25% 30%

Certificates related to the job 22% 23% 33%

References from the employees working in the firm 22% 23% 42%

Previous internship in your firm 20% 21% 28%

Gender of the applicant 16% 14% 27%

Computer literacy of the applicant 42% 33% 27%

Having completed military service 41% 34% 41%

Source: TOBB Survey Another important issue is whether firms give more importance to education or experience for different types of workers with different education levels. Figure A.2, Figure A. 3 and Figure A. 4 show the results of the TOBB survey: Only very modern firms think, on average, that education is more important than experience34- for all the other combinations, experience always comes out as more important than experience. However, we consistently find that the importance of education relative to experience is higher for more modern firms when different types of workers and education levels are considered. This result is consistent with our interviews as well, as we discuss in Section 4. We integrate these findings into a common framework in Section 3.6. Evaluating graduates: Firms’ views: It is also important to see how employers rate new university, MYO and VTHS graduates after they have been hired. Table 3 reports the percentage of respondents that agree with each statement for the three groups. The first pattern to note is that university graduates are better off than MYO graduates in each category, who in turn appear better than VTHS graduates. The only exception to this intuitive rule is practical experience, where VTHS graduates fare better than university graduates (44% versus 39%). However, MYO graduates are perceived at the bottom, with 36% of the respondents thinking they have sufficient practical experience- a seemingly 34 Here, we use our two proxies of modernity that we described before to come up with a simple subjective index of modernity. We define a firm as traditional if it neither exports nor has a quality certificate. A firm is classified as modern if it exports or has a quality certificate. Finally, a firm is classified very modern if it exports as well as has a quality certificate.

27

puzzling fact that will make more sense when interview results are discussed in the next section. Lack of foreign language skills comes out as an important weakness for all three groups with the MYO and VTHS graduates faring significantly worse than university graduates. According to TOBB survey results, both MYO and university graduates have sufficient computer skills, whereas VTHS graduates do not have sufficient computer skills.35 When we look at IAQ questionnaires, we can see similar patterns that emerge. Workers would like to improve themselves in foreign language and vocational skills (58%), followed by computer skills (41%). Similarly, the unemployed people would like to improve themselves most in foreign languages (71%), followed by computer skills (61%) and vocational skills (56%). However, significantly less than half of the students see themselves proficient enough in a foreign language (43%) and computer skills (34%) to find jobs. The percentage of students who think that their university has contributed to their foreign language and computer skills is larger, but not very high (48% and 44% respectively).

35 However, lack of computer skills comes out as an important deficiency in firm interviews; we will discuss this point further in the next section.

28

Table 3: How do employers rate new university, MYO and VTHS graduates?

University Graduate

MYO Graduate

Profession/Technical Collage

They are inclined to do research and to ask questions. 61% 41% 28% Their computer skills are sufficient for conducting business. 74% 57% 33% They aspire to be promoted quickly. 80% 66% 46% They are able to motivate other employees 58% 46% 37% They are adequately equipped in the area of their education and related technical and theoretical subjects. 53% 53% 52% They have information in areas they have not been educated on 36% 27% 25% They are able to establish authority on other employees when they need to. 66% 44% 27% They can take initiative/responsibility 66% 53% 40% They are able to question the ideas and actions of others and themselves. 66% 45% 33% They are able to communicate easily. 75% 62% 47% They are sufficient in terms of field experience. 39% 36% 44% They have the analytical thinking and problem solving skills 68% 54% 33% They are able to convey their ideas to other employees openly and clearly. 69% 54% 42% They are committed to teamwork. 63% 59% 56% They are able to present an idea, a product or an application to an audience. 68% 48% 32% They learn the job easily/They are able to assimilate the information given. 69% 63% 59% Their foreign language skills are sufficient for conducting business 50% 24% 14% They are able to create new ideas and solutions. 68% 51% 40% They are able to manage time well. 62% 51% 45%

Source: TOBB Survey 3.4 A first look at higher education The main hard data on universities comes from the IAQ student questionnaires. The most important result here is that there is the significant variance between universities, much more than sectoral and provincial results in IAQ firm questionnaires and TOBB surveys. Rather than reporting those results here, we will refer to them as we analyze the firm interviews in the next section. The variance between universities is confirmed when we look at higher education premiums as well. There are two sources we can deduce higher education premiums, employee-data and data from employers. We can use IAQ questionnaire data to estimate Mincerian regressions similar to studies that usually undertake this type of analysis using HLFS data. Although this approach can control for individual characteristics, it can not control for differences across firms. An advantage of the TOBB survey is that it enables us to compare university, MYO and VTHS graduates in the same firms so that we do not have to worry about firm-specific effects. Bircan (2004), using HLFS data for 2002, has found a significant premium for university education.36 In a paper complementary to this report, Okten (2006), similarly finds high returns to university education, although returns to MYO and 36 She finds annual rate of return to higher education 13% over general high school education and 11% for VTHS education. However, she does not differentiate between different types of higher education such as open-university and MYOs.

29

open-university education are lower.37 However, with the IAQ surveys, we are able to glimpse, for the first time for Turkey, to the differences between different universities and/or fields of study: Although the average HS, VTHS, MYO, open-university and university graduates earn 734, 772, 818, 1109 and 1450 YTL respectively38, there are significant differences between universities and departments. Although our data does not permit us to compute university premiums by university and department, Table A 3 shows the wages/in some universities departments for which data was available.39 The differences in wages between different departments and universities are more than a factor of two for some fields (mechanical engineering) and universities (Dokuz Eylül University). The TOBB survey, weighing wages by the number of employees working in the firms, finds that new VTHS, MYO and university graduates are paid 482, 639 and 974 YTL respectively.40 While the wages are less than those of experienced workers, some common trends do emerge: First, wage premiums are much lower for MYO graduates than university graduates. According to IAQ questionnaire results, if we take the wages of a VTHS graduate to be 100, MYO graduates earn 106 and university graduates 185. A comparison to selected countries reveals that while MYO premiums are the lowest, university premiums are among the highest Table 4.41 Highest MYO and university premiums are in Norway and Finland, respectively. TOBB survey results attribute greater premiums to both MYO and university graduates (100, 133, 202), although these figures are for new graduates.

37 She finds annual rate of returns to university, MYO and open-university education of 16%, 8% and 4% respectively over high school education (general of VTHS). In general, these results are consistent with Bircan’s results. 38 Wages are calculated using mid-point values of ranges; someone who has declared she is earning in the 500-999 YTL range is assumed earning 750 YTL. Since the same ranges are used in the TOBB survey, the same methodology is applied there for wage calculations as well. 39 Unfortunately, most Istanbul and Ankara universities are absent from this sample, as there were not many employees from these universities in the dataset. To capture graduates of these universities, the survey needs to be extended, with more weight on big cities. See Okten (2006) for some more illustrative examples. 40 Since these are firms’ declarations on how much they pay their employees, it is necessary to weigh workers by the number of employees to make sure results are not biased. We weigh the university wages by number of white collar employees in the firm and the MYO/VTHS wages by the number of blue collar worlers. The lower unweighted numbers (817, 576, 440 YTL for university, MYO and VTHS graduates) suggest that smaller firms pay lower wages, which we attempt to explain in Section 3.6. 41 Table 4 is from Sam Mikhail’s The Alternative Sector of Tertiary Education (ASTE) in Turkey: Options for Reform of MYOs, part of the same project as this paper.

30

Table 4: Wage Premium of Tertiary Education Graduates

ASTE Graduates* USTE Graduates*

Australia 116 144

Canada 112 162

Finland 120 190

France 125 169

Germany 130 165

Ireland 111 157

Norway 136 141

Spain 128 138

UK 128 181

USA 114 180

Source: Mikhail (2006) Note: ASTE graduates are similar to MYO graduates and USTE graduates are similar to university graduates To be able to measure premiums at the intra-firm level, we look at the difference in wages each firm pays to VTHS, MYO and university graduates. Table 5 shows the distribution of the firm-level premiums by brackets used in the TOBB survey. The table shows that while there is an intra-firm university premium, the MYO is much smaller.42 Forty percent of the firms have indicated they pay university graduates the same wages as MYO graduates, whereas 54% pay one bracket higher (the same figures are 56% and 41% respectively for MYO graduates). However, the university premium is higher in firms where respondents care more about which university their employee graduated from.

42 Of course, the MYO premium is underestimated because it compares the MYO graduates to VTHS graduates, who are expected to earn higher than HS graduates.

31

Table 5: Distribution of firm Level Premiums

University premium

(univ-myo) # of

observations Relative

frequency MYO prem (myo-mte)

# of observations

Relative frequency

Lower than 350 YTL -6 1 0.1 -4 1 0.1 350-499 YTL -3 1 0.1 -2 3 0.31 500-999 YTL -2 1 0.1 -1 10 1.04 1000-1499 YTL -1 12 1.25 0 537 55.82 1500-1999 YTL 0 385 40.02 1 395 41.06 2000-2499 YTL 1 522 54.26 2 15 1.56 2500-3000 YTL 2 35 3.64 4 1 0.1 Higher than 3000 YTL 3 5 0.52 - - - Source: Own calculations from TOBB Survey

Table A. 4, Table A.5, Table A. 6 and Table A. 7 show some simple regressions that illustrate this fact. It is interesting that when we look at premiums in the same firm; both university and MYO premium are much lower than traditional calculations using a cross-section of workers from different firms. 3.5 University-private sector relationship The TOBB survey has questions on the extent of partnership with universities. Table 6 reports firms working with the universities in different formats. The only significant university-private sector relationship turns out to be internships, which is less than 41%. Innovation-related projects fare at the bottom, at around 10%. However, these objective data do not tell us why there is no partnership going on. The next question asks for the respondents’ views on different statements about universities in their area (see Table 7). More than half of the respondents note that academic consultant fees are prohibitively high, that academics can not follow recent developments and they are far from the problems of the private sector (57% and 56% respectively).43 However, results also suggest that modern firms have better perceptions of or relationships with universities. The percentage of respondents which think that academics are alienated from the problems of the private sector decrease to 46% for very modern firms, whereas only 28% of modern firms find consulting fees too high.44 The same question also addressed respondents to evaluate their regional university. 57% of the respondents, as Table 7 reports, are not satisfied with the quality of university graduates. Moreover, they think that their regional universities train people neither in the skills they need nor in the jobs their regions need (61% and 63% respectively). Although these earlier results point to a potential problem of higher

43 Respondents also report that more than half of them have not tried in establishing a relationship with the university (55%), so these numbers are percentages of those that have established a relationship. 44 As Table A.8 illustrates, although there is not much difference in the responses of traditional firms in terms of these two questions, very modern firms are significantly different between the two groups. We discuss the different perceptions and experiences of traditional and modern firms with universities in Section 4 in line with interview results.

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education not responding to the region’s needs, regional results will be used along with the interview findings for a more detailed analysis in the next section.45

Table 6: Private Sector - University relationship

Yes No

No, but maybe in the future

We accept university students as interns 41 44 15

We sponsor our employees to attend other courses and trainings 25 58 16

We attend career days at universities 14 70 16

We are taking advantage of the courses adapted to our needs offered by universities

13 58 30

We are taking the advantage of the short term courses offered by universities 11 57 32

We sponsor our employees to attend certificate programs in universities and to earn MYO/graduate/undergraduate degrees

11 70 20

We hire researchers and academicians from universities as consultants for training/R&D projects

11 67 22

We use the equipment and test control services of universities 11 72 18

We conduct R&D projects with universities 9 70 22

WSe conduct non R&D projects with universities 7 75 19

Source:TOBB Survey

Table 7: Firms’ View on Different Statements about Universities

% of respondents

who strongly agree

We cannot use the consultancy services of academics because the fees are too high. 57

Academics are far from the problems of the business environment, they fail keep up with the latest developments.

56

We haven’t attempted to cooperate with the universities 55

Universities are not very enthusiastic about cooperating with us 48

We are pleased with the quality of college educated workforce in our town. 43

The university in our town educates people with the qualities we need 39

The university in our town educates people in the areas we need 37

Source: TOBB survey The results presented here seem to be in contradiction with firms’ ratings of university students: How come firms find most of the skills of university graduates sufficient if they do not think universities do not train graduates in the skills they need or they are not pleased with the quality of university graduates. The most plausible explanation is that having them compared to MYO and VTHS graduates have made university graduates look better

45 This question might have been misinterpreted by firms in Istanbul and Ankara, where the concept of local university does not make much sense. However, excluding firms in Istanbul, Ankara kept the percentage of respondents satisfied with the quality of university graduates and those who think universities train people in the skills they need unchanged (43% and 39%). However, 46% of the respondents now thought the university trains people in line with the region’s needs, up from 37%, suggesting firms in Istanbul and Ankara have answered this last question more negatively.

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in the eyes of the survey respondents.46 However, as we implicitly report in Section 3.3 and other parts, the ratings of graduates are consistent if we compare them across different education levels or different skills for each educational group. Therefore, we believe that the results of Table 7 are internally consistent even though looking at the absolute numbers by themselves might be deceiving. 3.6 A unifying framework The previous sections have highlighted some key findings from the interviews, which are also supported by survey and questionnaire data. Modern and large firms would like to hire workers for the long haul. Therefore, they pay their workers higher wages, while hiring inexperienced workers straight from school and training them. As a result, we observe lower turnover in modern firms: Firms have incentives to keep the workers that they have invested in; at the same time, workers would like to stay since they are getting paid more.47 This simple story is shown in Figure 2 which also shows the analytical framework that could be used to test the model. Table A. 9 and Table A. 10 show a simple, back-of-the-envelope test of the model using TOBB survey data. In Table A. 9 using export dummies as proxy for modernity, we see that modern firms spend more on education. In line with the framework above, firms that give more importance to education (rather than experience) educate workers. (Since we do not have questions on whether firms prefer to hire experienced workers or workers straight out of school and train them, we have to test this leg of the framework indirectly). Table A. 10, which is the final step of the framework, shows that both demand and supply factors affect turnover; firms that pay a higher university premium and spend more on education train workers more.48 We have encountered a similar framework for small, traditional firms during the interviews as well. More traditional firms reported that they pay lower wages due to their limited resources. As a result, they have to offer lower wages. Moreover since they can not provide formal training with their limited resources they prefer to hire workers with experience rather than a sound education and then do not spend much on education. These two factors (low wages and low training), using the same arguments of the previous paragraph for modern firms, increase turnover. However, we have also found out during the interviews that traditional firms depend more on references of their employees (especially for MYO and VTHS graduates). In Figure 2, we summarize these findings with a simple arrow chart.

46 Of course, a simpler explanation may be that this very long question was not filled out carefully by the respondents. 47 This story is one which we have encountered time and time again during the interviews. The textile company described in Box 1 is a good example of this story, although we have encountered many more in different sectors in all the cities. 48 Because of data limitations, this is the most unifying test we can do at this point. Note that the same regressions yield insignificant results when wages are used instead of premiums. Although this might be an artifact of data issues, it might also be reflecting a “keeping up with the Jones” phenomenon as well as a reflection of unobserved firm-based work conditions and other factors.

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Figure 2

In Table A 11 and Table A. 12, as well as Figure A. 3 and Figure A. 4, we test these findings. Figure A. 5 and Figure A. 6 show that experience becomes relatively more important for more traditional firms for both blue and white collar workers. Table A 11 and Table A. 12 test the framework of the previous paragraph. Using firms we classify as traditional in the TOBB survey data, we see the predicted negative effect of wages and references from employees as well the positive effect of giving more weight to experience.49 We will come back to this framework when we discuss regional needs and higher education sector in the next section.

49 We also see that firms that would give importance to loyalty have higher turnover. However, this variable is not part of our framework and firms might be giving importance to staying in the firm for the long haul just simply because they have higher turnover, the usual case of reverse causality. Although the insignificance of the results except this final variable

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4 Analysis of the Findings: towards a second version of Higher Education Sector The previous section has shown some introductory hard data on labor force characteristics, skills of the young labor force, and the higher education system. In this section, we report and analyze the findings from interviews and support them from hard data collected for this project as well as data from other sources. When investigating the relationship between higher education and the labor market, it is vital to have an accurate snapshot of the private sector. As mentioned in the Introduction, Turkey is undergoing a remarkable economic transformation50, which raises the importance of innovative capabilities at the firm level.51 Table A. 13 shows the growth rate and share in value added of different sectors; traditional sectors in Turkey seem to have stagnated, while new sectors are emerging. Moreover, Table A. 13 also shows that these newly emerging sectors are undertaking more innovative activities. More evidence on the economic transformation can be seen by taking export performance as proxy of competitiveness. Figure A.7 maps out the competitive position of GDP using export and growth figures. The same pattern of emergence of certain sectors can be seen as well. The structural transformation of the Turkish economy has important consequences for the higher education system as a whole. The newly emerging sectors will increasingly shift demand for skills to suit their innovative capabilities. Moreover, as in other countries that have successfully gone through this process before, the universities could themselves catalyze the innovation process by working in close partnership with the private sector. As the transformation process goes on, it will be important for an EU-oriented Turkey to close regional disparities. In addition to working directly with the private sector, the universities could also play an important role in regional development by providing qualified workforce that will suit the long-term regional needs. This line of argument raises the question whether the higher education sector can adapt to the challenges outlined above as well as in the Introduction. Although one can not overlook the many “good cases” dispersed across the country, the main result of the firm interviews, supported by the hard data, is straightforwardly negative. This argument does not justify the important outcomes reached and externalities generated with the current system, which are beyond the scope of this work.52 However, we hope that we have provided a convincing case in the previous section to show that some important constraints need to be overcome for the sector to be able to respond to the needs of the private sector. We outline the basics of this overhaul in this section, as well as investigate results of the previous section deeper using firm interviews.

50 For a more detailed analysis of this argument, see Industrial Policy Report (2006), TEPAV and State Planning Organization 51 Among a wide variety of definitions of the concept of innovation, we prefer to use a broad definition, which is similar to that of the OECD: “the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations 52 Such benefits include (but are not limited to) wider access to higher education and benefits of universities to quality of life.

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A successful higher education system should be based on three legs to be in line with the transformation of the private sector. First, the higher education system should train workers suited to Turkey’s technological change. Second, the education system should be able to contribute to Turkey’s innovation process and support it. Finally, the higher education system should provide workers serving long-term regional needs.

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BOX 3: A small disclaimer: The dual Structure of the private sector is reflected in human capital assessment

The concept of the dual economy is well-known in Development Economics. Attributed to J. H. Boeke from study of postwar Indonesia, (1) dual economy theory, in its original form, refers to an economy in which rich, capital-intensive modern sectors exist in the same model as comparatively poor, traditional, labor intensive sectors. Recently, the theory has been expanded to explain other existing dual structures such as traditional/modern firms or formal/informal firms. Recent work by McKinsey and TEPAV | EPRI (2) has shown that this dual structure is very prevalent in Turkey as well, not only at the firm level but also at the more micro or macro stages. For example, traditional sectors are part of the same value chain as modern sectors that are a part of the global value chain and export to world markets. Moreover, the same division may be within each sector, as the McKinsey study hints at for five sectors. Such a dual structure can explain many puzzles such as the coexistence of large, formal productive firms with small, informal, unproductive firms in the same sectors or why Turkey scores at the bottom of PISA tests, while at the same time ranking near the top of multinational companies’ mid-level manager rankings We have encountered such a dual structure during our interviews as well. While some firms were fully aware of the skill deficiencies and had a clear business plan and therefore knew their future human capital needs fairly well, others were “just trying the make end’s day meet”, many firms have noted (the traditional Turkish expression managers use is “fry in our own butter”, an expression our interviewers came across many times). To the causal observer, this observation might be obvious; after all, one would expect the general/HR manager of a big group in Istanbul to be more knowledgeable about industry trends/skill needs than a small dairy producer in Erzurum. However, what we have observed during the interviews is that the dual structure exists within sectors and regions as well. Even in clusters in Gaziantep, Denizli (textiles) and Kayseri (furniture), some firms were much more knowledgeable on their skill needs from higher education and their deficiencies. Perhaps the most intriguing example is Nevsehir, where we interviewed a truly globally integrated farmer with a “We are potato guys” mug on his desk in the chicest office building in town, a nationally integrated wine producer that is hoping to go into world markets as well as a hotel and a pottery producer that are not even integrated with the local education market in the sense that they were not making use of the area’s very capable MYO. Although definitely more work needs to be done on these initial findings, it useful to discuss the policy implications of this dual structure on human capital needs. For one thing, the existence of the dual structure makes the case for an intermediary between higher education and the private sector even stronger, as many traditional firms do not complain about the lack of ties with higher education because they are not even aware such ties could enhance their productivity and enable them to hire a more skilled workforce. Moreover, there is an increasing body of evidence that the private sector is under a rapid transformation from traditional to modern and from informal to formal, not to mention the sectoral shift of the economy from traditionally strong sectors to new areas that are more integrated with the world economy. (3) Moreover, other recent work shows that the competitive environment and balance sheet structure of the firms are changing as well, resulting in a more competitive environment that will force firms to be more efficient in all areas to stay in business. (4) Because of this fast-track transformation, it is natural that firms will be converging towards their modern counterparts in terms of their education needs. (1) J H Boeke, Economics and Economic Policy of Dual Societies as Exemplified by Indonesia (New York, 1953) (2) McKinsey Global Institute (2003), Deliveli and Demir (2006), (3) TEPAV – DPT (2006) (4) World Bank (2006a)

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4.1 A labor force suited to Turkey’s Technological Change The technological transformation summarized above is surely changing the needs of the private sector. Companies increasingly need workers with new and different skills. During the city interviews, we have asked the firms to discuss the skills they need from new graduates of higher education institutions and what they expect from them. 4.1.1 Language skills As discussed in the previous section, language skills are cited as lacking by firms and are among the skills that the students, employees and the unemployed desire to improve the most. Firm interviews reveal why language skills are important. First of all, English emerges as language of choice by firms, although there are a few regional/sectoral exceptions (firms in Antalya require Russian as well, whereas firms in Diyarbakir and Gaziantep that trade with Syria require Arabic). As expected, exporting/importing firms give importance to language skills so that their workers can communicate effectively with their partners/clients. More interestingly, language skills come out as important for MYO graduates, although for a different reason. Firms would like their workers to operate imported machinery. Given the manuals and operating buttons are in English, they are looking for workers who will not have difficulty in operating these machines. To find support for these findings, we turn to the TOBB survey. Similarly, language skills are not in demand everywhere; there is almost no demand for foreign language skills in a city that does not get involved in international trade such as Erzurum. In Istanbul and Ankara, language did not prove to be a significant deficiency out of the interviews, most probably because firms here have access to workers with decent language skills. Likewise, the interviewees did not report significant deficiency in language skills in cities where English was taught, as the following box illustrates. BOX 4: A tale of two export-oriented clusters (and two universities)

Denizli and Gaziantep are two export-oriented textile clusters, each with a local university. In the

firm interviews, we noticed that many of the Denizli firms complain about the lack of language

skills in university graduates, whereas the same issue was not mentioned at all in Gaziantep. Later

we found that the Gaziantep University taught engineering classes in English, whereas the language

of instruction in Pamukkale University (PAU) in Denizli was Turkish. In fact, both university

officials and managers in Gaziantep proudly mentioned the language abilities of the university’s

graduates. On the other hand, firms in Denizli noted that they preferred Ege University graduates

over PAU graduates, although in the former the language of instruction is Turkish, intensive

technical English instruction is provided. While textiles firms especially like the specialization

integrated into modular education and practical training during studies in Ege University (Ege

University textile engineering department is described in detail in BOX.

11)

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The only consistent result on language skills from the interviews is that language skills are deemed important for many firms, and therefore, students graduating without any language skills are at a disadvantage. However, formal instruction in English did not come out as an important. Although graduates of universities providing instruction in English such as Bilkent and Sabanci have sufficient English skills, other models exist as well. For example, Ege University, whose textiles engineering graduates are deemed to have sufficient English skills (see Box above), provides general instruction in Turkish, while at the same time teaching students technical English. 4.1.2 Computer skills Computer skills emerge as one of the critical factors in firm interviews as well. Like language skills, computer skills are demanded of university and MYO graduates alike, albeit for different reasons. For university graduates, computer skills are needed for the usual reason, to be able to use computers. However, for MYO graduates in technical areas, firms demand computer skills to make sure that their employees would be at ease at using the machinery and equipment appropriately, i.e. as a proxy for being able to operate more complicated machinery. This result, robust of regions or sectors, might be pointing to a deeper issue. A few firms told us that they demand computer skills partially for their signaling effect: They noted that since MYOs do not have up-to-date equipment (or any equipment at all), they do not feel at ease leaving expensive machinery with a new MYO graduate without much practical experience.53 The incidence of practical training in MYOs is investigated below. BOX 5: A good program without the good tools to accompany

For non-technical MYOs, computer skills were needed as well, although not as frequent as graduates of technical MYOs and for the same reasons for university graduates. For example, it was expected that an accounting MYO graduate would be familiar with accounting software.

53 If MYO graduates were using machinery and equipment during their training at a significant level, computer skills would not have been demanded as signaling. However, to be able to test this argument, we would have to look at whether computer skills are required from graduates of MYOs with modern equipment. Although there are examples of such MYOs, we did not have such richness of data to attempt this differentiation.

As discussed in the text, firms often complain that higher education institutions’ tools and teaching methods are extremely outdated. Although lack of resources normally applies to technical MYOs and engineering programs, the following example illustrates the need for modern equipment. Students at the MYO in Diyarbakir, when asked about teaching practices, noted that they were being asked to do their accounting projects using software adopted by accounting firms, with one small catch: The school did not have this program. Therefore, the students were being forced to pay accounting firms up to 250 YTL to have their projects done. The result: The program not learned with students with the equivalent of a month of minimum wages less in their pockets.

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4.1.3 The need for generic skills In interviews, managers have consistently emphasized the need for generic skills. In fact, it is possible to group language and computer skills as generic skills as well. These are the two skills that were consistently mentioned, independent of the characteristics of the firms or the type of worker firms were looking for, as long as their business required these skills.54 Other than these two universal (or rather national) skills, social skills, behavior skills and analytical thinking as well as familiarity with machinery were cited skills firms gave importance to.55 However, the extent these skills were required and which one was deemed more important depended a lot on the characteristics of the firm as well as the worker/job. For example, as we explain in detail below, analytical/problem solving skills were cited in the interviews more with more modern firms for university graduates, whereas familiarity with machinery turned out to be important for MYO and VTHS graduates in manufacturing sectors. In general, these results make sense intuitively and are supported by TOBB surveys. Social and communication skills were mentioned during the interviews as an area where many students of higher education are lacking. Although one would expect such a general word to be mentioned by managers, social and communication skills came up as the top problem skill (a skill where survey respondents felt was lacking) in almost all the sectors, cities and occupations in the MEGEP/SVET survey, which asks survey respondents to rate types of skill deficiencies in different occupations.56 Moreover, in-depth interviews have allowed us to go beyond the basics and investigate why such skills are important for the firms and why firms think students are weak in these skills. When asked about why social and communication skills are important, firms small and large, as well as traditional and modern have provided many colorful examples. For example, a medium-sized traditional family firm’s manager in Diyarbakir complained that the new university graduate she hired could not talk to a client on the phone for a simple business and noted that she had to fire him as a result. The manager of a modern software company in Istanbul, on the other hand, told our interviewers that it was very important for their software engineers and programmers to be able to communicate with their clients and understand what their needs were and noted communication skills to be the only weak point of the several highly proficient engineers. When asked about why they thought students did not have the social and communication skills they desired, local universities were usually criticized for producing “timid” graduates, as many firms in different cities noted. The consensus among managers was the lack of extracurricular activities in local universities. While our sample size is too small to verify correlation, let alone causality, we can see if students in local universities do indeed report fewer extracurricular activities. Figure A.8 from the IAQ student questionnaires, shows that 54 Naturally, a traditional dairy producer in Erzurum mentioned neither of these skills. 55 Note that this result of interviews is not in line with the TOBB survey, where these skills did not come out to be insufficient. However, as we have discussed earlier, we think those particular results should be compared relative to each other rather than trying to make a sense of the overall figures. 56 As Table 2 and Table 3 show, those social and communication skills did not come out at the significant problem areas, although firms do give importance to extracurricular activities for university graduates they are considering hiring. As for communication skills, ¾ of respondents were satisfied with communication skills of university graduates, whereas this number decreased to 62% for MYO graduates less than ½ for VTHS graduates. However, we think that lack of very strong results for social and communication skills is because of the surveyor perception bias we have discussed before.

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this is indeed the case; students in local universities report less than universities in big cities that student clubs are active. In general, behavioral and analytical skills were mentioned a lot; however behavioral skills were mentioned more by traditional firms, whereas modern firms tended to emphasize analytical skills. Enthusiastic, trustable, loyal, reliable, good-mannered were the traits that were emphasized by traditional firms.57 The firms that mention these traits also mention that they rely a lot on personal references (from a friend, employee or someone they can trust), a result that we mentioned and tested in Section 3.6.58 The one trait that was consistently mentioned by more modern firms as well as traditional ones was long-term commitment. Big, modern firms tended to emphasize analytical skills such as problem-solving, ability to react to different situations quickly and ability to learn quickly/being open to education and training.59 Behavioral skills that were important for more modern firms were teamwork, enthusiasm, decision-making (ability to take initiative) and ability to take responsibility. Just as different types of firms looked for different skills in potential employees, they also looked for different skills for different types of positions/jobs. Familiarity with machinery was mentioned almost all the time in firms traditional and modern alike in manufacturing sectors, especially for MYO and VTHS graduates. This skill included having used lathes as well as being able to use computerized machinery (and therefore linked with familiarity with computers). Although it was more frequently cited for non-university graduates, having used the tools of the trade was an important factor for new university graduates in textiles and forestry engineering graduates. Analytical thinking and decision-making came out as more important for university graduates. Table 8 illustrates the differences based on worker and firms characteristics in a two-by-two matrix. Finally, it is important to note that there were important sectoral and city-level differences in terms of the generic skills firms were looking for. The sectoral differences are the natural result of the different needs of different sectors. The city results, on the other hand, might be a by-product of sectoral differences. However, they can be an indicator of how the local higher education institutions fare in satisfying the generic skill needs of the private sector and how much they can give these generic skills to their students. Table A.14, from the MEGEP/SVET survey, lists the skills firms have difficulty finding for each-city sector pair.60 The matrix can be used in two ways: Using the rows, one can see the skill problems in a sector in different cities. In this way, it is possible to see sectoral skills needs. At same time, it is possible to deduce certain skills that are problematic in a given study. Another way to come up with a skill assessment of each city is to look at which skill areas seem problematic in a city across sectors. Although the MEGEP/SVET survey’s coverage is less than our IAQ coverage,61 its firm coverage is much wider since it is a

57 It is not that modern firms do not care about these traits; however, it is not at the top of their priority risk. When specifically asked about behavioral skills, these are the skills they bring up as well. However, they do not usually bring these skills up right when they are asked about which skills they look for or only bring them up when specifically asked by the interviewers. 58 Relying on references is much more prevalent for VTHS graduates, as TOBB survey results confirm as well. 59 This last trait makes sense, since these firms prefer to train workers from scratch, as we found out on the interviews and tested empirically in Section 3.6. 60 Although MEGEP/SVET surveys are intended to assess VTHSs and graduates of these schools, the questions on skills are intended as general skill assessment for the firms. 61 Izmir is the only city covered in MEGEP/SVET not covered in IAQ. On the other hand, IAQ cities, Denizli, Diyarbakir Erzurum, Eskisehir, Kayseri, Kocaeli, Nevsehir.

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traditional survey.62 Therefore, we have not interviewed firms in all the sectors in each city; the sectors we have interviews on each firm are highlighted in the matrix’s specific cells. Although we can not conduct a thorough comparison, the problem areas that are consistent with the interviews include basic and elementary skills in the tourism sector in Antalya, social and communication skills in Gaziantep and computer skills in Trabzon.

Table 8: What Private Sector Look for in Blue and White Collar Workers?

WORKER

Blue Collar White Collar

Traditional Loyalty Loyalty

FIRM

Modern Computer, language, leadership, social skills

Source:TOBB Survey & IAQ

A characteristic of graduates of higher education institutions that was criticized frequently in the interviews was their desire for climbing the job ladder quickly. Both firms and private career placement officers complained that especially university graduates would like to start at high positions and be promoted rapidly. Career placement officers noted that many candidates do not accept positions because they find the wages or the positions unsatisfactory.63 This is supported by TOBB surveys as well: Although the desire for rapid promotion is high for both university and MYO graduates, it is perceived to be much higher for university graduates. It is notable that survey respondents do not think VTHS graduates desire rapid promotion; in fact, this trait was cited by many firms as why they preferred VTHS graduates to MYO graduates for intermediate level positions. Interviewees noted that an intermediate level position is a position where there is not much chance for promotion and that VTHS graduates were more likely to be satisfied in the job for the long-run.64 The desire for promotion was notably a stronger factor in the tourism sector, where the interviewees noted that almost all MYO and university graduates would like to start off at the top of their trade; for example, they noted that tourism university graduates wanted to be hotel managers, whereas culinary MYO graduates wanted start off as head cooks.65 The problem seemed to be more severe for university graduates, as lower level, intermediate positions do not exit at hotels for these graduates; the graduates would like to start as front desk managers if not as hotel managers, whereas hotel managers are not inclined to give fresh university graduates a high position until

62 See Section 2.3.2 for a detailed description of the survey. 63 Of course, the experiences of the career placement agencies are based mostly on the graduates of top universities. On the other hand, their clients are usually bigger firms that can offer higher wages relative to the rest of Turkey. Although the net effect of these factors is uncertain, firm interviews suggest that he issue is important in other parts of Turkey as well. 64 This finding is also consistent with the finding from interviews and surveys that firms, especially large, modern companies, would like to hire inexperienced workers, train them, and keep them for a long time. 65 Although most of the hotels interviewed were in Antalya, hotel interviews in other cities yielded similar findings.

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they have learned the tools of the trade by working in different departments of the hotel.66 In general, even firms that mainly hire graduates from universities with low scores on the OSYM exam as well as firms producing high-tech products requiring significant amount of human capital are satisfied with the knowledge of the students in their fields of study. This might be due to sufficient theoretical knowledge provided in the universities, which was reported to us by firm managers, but it might also reflect the fact that many firms do not require their new hires to know the cutting edge research in their field. As a manager of a firm in a high-tech sector noted, “even the most innovative companies do not do rocket science in Turkey”. However, interviews also expect new graduates to have some knowledge of other fields. However, as Table 3 indicates; firms did not find satisfactory the level of knowledge new graduates have other than areas they educated on. BOX 6: Why are VTHS graduates preferred for intermediate positions?

66 It is noteworthy that there might be some path dependency going on as well: Most of the managers interviewed have themselves learned hotel management on the job, starting off at the bottom of the ladder and slowly climbing up- this trend partly reflects the lack of tourism programs until a couple of decades ago, when the tourism sector boomed in Turkey. As a result, managers might be expecting new graduates to follow a similar path as them.

With regard to generic skills, interviewees noted that university graduates fare better than MYO and VTHS graduates. In Table 4, university graduates are fared better than MYO and VTHS graduates all areas except practical experience. However, there is not a big difference between MYO and VTHS graduates in some generic areas. Many interviewees noted that they did not observe any difference between MYO and VTHS graduates and preferred them because they were more motivated and more likely to stay with the firm longer. In the TOBB survey, although there are some differences between the two groups for certain skills like analytical thinking, the difference is insignificant for other skills like their knowledge in their area of education and other areas. Moreover, according to the survey respondents, VTHS graduates have more practical experience than MYO graduates, mainly because of compulsory, long-term internships, which we investigate further below. When we think of the skill set needed for intermediary workers, interview and survey results point to skills where the difference between MYO and VTHS graduates is miniscule. The skills where MYO graduates are better off than VTHS graduates do not place them at an advantage over MYO graduates, but their lack of practical experience as well as their relatively higher job expectations put them at a disadvantage. Therefore, the result of the previous section that VTHS graduates and HS graduates are used as intermediary workers more than MYO graduates suddenly seems not so surprising at all once we compare the skill sets of the different groups. Many firms also noted that they would have loved to place MYO graduates in intermediate positions if those workers had been able to rise to those positions. Firms in Eskisehir, Erzrurum and Gebze emphasized that they hired MYO graduates as workers, with the expectation that they would become intermediary workers (ara eleman) eventually. However, these workers did not succeed in the production line sufficiently to fill intermediary positions, even though there are notable exceptions. When firms can find a suitable MYO graduate, they use her in an intermediary position and can even pay her more than what they pay to university graduates.(1) “For me, MYO graduates are indispensable, they make the production line running. That’s why I pay them higher wages”, noted one manager. (1)Note that in the TOBB survey, about 1.5% of the firms pay MYO graduates more than university graduates, so this is indeed a small sample, but one from which we can infer valuable lessons.

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Another coherent result of the interviews is that employers find MYO and VTHS graduates, with few exceptions, quite insufficient in many skills compared to university graduates. We encounter this result in the TOBB survey as well, as Table 3 illustrates. Interviews suggest that this is partly because of signaling as well as the general perception of MYOs in the society: It was reported to our interviewers by many firms that students go to MYOs because they can not secure a place in any university and that if they were smart and hard-working, they would have won a place in a university. Similar comments were made by many other firms, reflecting on these firms’ and the society’s general perception of MYOs.67 A services firm in Istanbul said they could not send MYO graduates to their customers. However, interviewees also note that a specific policy change of the government has decreased the quality of MYOs a lot, as the following box illustrates. BOX 7: Entering without examination to MYOs: Is anyone happy?

4.1.4 Practical Experience The lack of practical experience has been raised as an important issue during interviews. When asked about practical training, firms mention internships as well as programs with hands-on training. The interviewers asked the interviewees to differentiate these two nodes of practical training and tried to differentiate between them to make sure we understood which areas were particularly problematic for which reasons. Internships came out of the interviews as a key problem area. In general, neither students nor employers are happy about the internship experience, each side blaming the other: Students note that they are often used in internships as office boys and firms do not want to show them anything related to their actual work done in the firm. On the other hand, firms note that most students are only there to fulfill their required internship requirement and are not interested in contributing to the firm at all. However, firms do believe in the benefits of internships because they note that they make the students familiar with the sector so that they have a better idea on whether they would like to work in that sector (see BOX 8). In addition, they think students could be useful to them. The interviewees agree on some common pointers for an internship to be successful: firstly, short-time internships are seen as not useful for both firms and students. Firms mention that the students need at least a month to get

67 The general perception is of MYOs is discussed in detail in an accompanying World Bank report. The Alternative Sector of Tertiary Education (ASTE) in Turkey: Options for Reform of MYOs, by Sam Mikhail.

During the interviews, MYO directors as well as managers complained a lot that Law No. 4702, which allows VYHS graduates to enroll in MYO programs in their fields without any examination, has considerably decreased the quality of MYO graduates. MYO directors cite various examples to illustrate the decrease in quality.1 Directors at Denizli told our interviewer that although they are aware that the OSS (student placement exam) has serious deficiencies, it is necessary to have this exam to be selective. For their side, firms in Gaziantep, Kayseri, Denizli and Diyarbakir tied the low quality of MYO graduates to this new law. They specifically noted that the new law affected them adversely because of the low general quality of education in VTHSs. More interestingly, the manager of a firm in Gaziantep said that he had stopped hiring MYO graduates two years ago (in 2004) after he had observed a significant drop in the quality of the local MYO graduates. However, this manager was not aware of this new law and even more remarkably, 2004 was the year students who first made use of the new law graduated.

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to know their firm and only after that they can be useful. Moreover, they say they are disinclined to train their interns if they are there only for a couple of months as they will not reap the benefits of their training. Similarly, firms prefer interns to come year-around rather than just for the summer; this way, they could save on labor, as the interns could be performing some jobs continuously. Interestingly enough, firms would also like universities to keep track of their students, which is usually not the norm in Turkish universities. Firms in different regions and sectors complain that there is no official they can turn to if there are problems with the intern. Employees also complain that what universities expect from obligatory interns is quite standard: Students usually come with a form that has questions on all aspects of the firms. Employees suggest that if students were coming to their internships with specific projects, they could be more useful to the firms as well as get more from their internships. The desire for project-oriented work was repeatedly mentioned to the interviewers in small and large or traditional and modern firms alike. Still another problem with internships is that the cost of insurance premiums on the interns falls on the employees. A new law aims to tackle this problem by partially shifting the cost of obligatory internships from firms to universities for obligatory internships. However, we feel that this policy change might decrease the overall number of new official, obligatory internship programs as well as make it less likely for firms to hire interns (at least officially) from programs without internships.68 BOX 8: Internships as a tool to reduce job mismatch

The results of the interviews note that firms would like internships to be long and preferably continuous, coordinated from the university in practice (not on paper) as well as project-based (rather than getting to know the firm) and cost-free. All successful internship experiences we have encountered carry at least some of the traits we mention above. For example, a small but modern firm in Eskisehir noted that they involve the interns at specific projects- this is not only beneficial for the students, but it also allows them to be have the opinion of someone from outside, as they can sometimes “can’t see the wood for the trees”. Sabanci University’s career placement office has implemented a team-based program where students undertake projects that

68 Social Insurance and General Health Insurance Law (no. 5510 official gazette dated 16.06.2006), which will be in force in 2007, is regulating the internship of students in higher education. According to the Law, an internship bears no cost to companies as long as it is obliged by the education program. The Law also requires universities to pay the insurance premiums of interns covering occupational accidents and illness as long as the internships are obligatory; the premiums of all other interns are to be paid by the firms. Although the new law might be considered as an incentive for companies to recruit more students for compulsory internships, universities might be reluctant to expand the scope of internship and make it obligatory for all programs because of financial concerns. In the same sense, companies might hesitate to recruit interns whose programs do not require internships since they will not benefit from the advantages of compulsory internship and considered as a normal employee.

The manager of a furniture firm in Trabzon recounted the experience of a recent engineering intern, who, on the first day of work, showed up with make-up and her hair made up. Once the sawdust got to her hair and make-up, she started crying and left her internship a couple of days later. This student had two internships on her CV at furniture companies, obviously “on-paper” internships, as she would otherwise have known how to dress up. Although the manager was not pleased about not getting an intern, we think it was positive for the intern to have found out that a job at a furniture factory was not for her, at least with her make-up on.

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have been determined by the firms participating in the program. The successful internship programs at Gaziantep University Department of Textile Engineering, Dicle University Department of Mining Engineering both have long-term internship programs that have the students work part-time at local clusters (textiles and marble respectively). Not surprisingly, the placement rate of these students after graduation is quite high despite being in areas with limited job opportunities. Although none of the local universities interviewed has a career placement office that facilitates internships, all successful internship experiences are the results of the efforts of individual professors who use their own connections to set up the internship program, usually as a part of these individuals’ partnership efforts with the private sector, as we explain below. In certain cases, the professor has replaced the position of the career placement office and firms can directly go to the professor if they are not happy with the intern. BOX 9: TOBB Economics and Technology University: First Example of a Co-Op Program

Practical experience during (as part of) education is as important as internships. During interviews, firms in various sectors and cities have constantly mentioned that higher education should be based more on practical applications. Notably, the complaint is addressed to MYOs and firms universities. It is surprising to see that both in the interviews and surveys firms rate MYOs as having less practical experience than even universities; after all, practical, hands-on experience is integrated into the MYOs programs, at least in theory69. Although a thorough analysis of the MYOs is being addressed at a separate report that will be part of the same project as this paper, we would like to highlight some findings of the interviews that shed light on why MYOs do not provide practical experience, at least not in the way firms would like. 69 This could of course be the result of the response of the firms to failed expectations: It might as well be that since firms do not expect university graduates to have significant practical experience.

TOBB Economics and Technology University appears to suit the critical demand for practical experience, by responding to the tasks that the firms have recommended us during the interviews. The university has an obligatory co-op program which lasts a semester (1) rather than summer internships. The internships are arranged by a co-op office, which is independent of the career placement office. The firms are found by the university and rather than allocating them directly to the universities, the university acts as an intermediary, matching students’ interests with the firms’ preferences. In addition, the co-op office is in contact with the firms and the students before and during the student’s internships as well as after they have completed the program. Since students are sent to participating firms for two quarters, the program is also a first step for developing long-term relationships between the firms and the university. The program has only been in place for a year, so it is early to assess whether it is increasing the students’ employability or creating positive externalities towards the partnerships between the university and the private sector, but interviews indicate students and firms, barring some expected glitches, are in general quite satisfied with the program. Moreover, the university ranks at the top for prospects of finding a job and practical experience in university questionnaires. To what can we attribute the similarity between the firms’ description of the “ideal internship experience and TOBB ETU’s co-op program needs to be analyzed further. We suspect that the university’s co-op program is a direct reflection of the private sector’s demands, as the university’s board of trustees is heavily geared towards the private sector. (1) TOBB-ETU follows a system of three semesters rather than two; and the students do their internships in one of the three semesters.

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One reason firms often cite is lack of resources of the MYOs. They note that MYOs do not have sufficient equipment and the equipment they have is usually outdated. Even if they have relatively new equipment, there is a shortage of instructors able to teach the relevant machinery and equipment.70 Other problems in the MYO system will be discussed when we place MYOs in the context of regional development below. For universities, lack of practical experience is cited as an important concern for engineering departments.71 However, the issues are more or less the same as MYOs: Lack of new equipment and professors that can provide practical experience was brought up by firms in Denizli, among others. On the other hand, programs that can provide successful practical training are praised by the private sector, as the following example illustrates. BOX 10: Two clusters speak: Practical experience in the textiles sector

70 For that reason, Istanbul Chamber of Industry Foundation ran a program that provided training to MYO instructors. 71 In this sense, practical experience for universities is similar to MYOs. It involves using machinery similar to those actually used in the private sector rather than more scientific (lab work) or abstract (case studies) experience. Although a few firms mentioned the lack of labs as the reason for lack of practical experience, practical experience was equivalent to using machinery for most firms.

Box 3 has summarized the needs of the Denizli and Gaziantep textiles clusters for language skills. These clusters seem to have similar needs in terms of practical skills as well. However, whereas the firms in Gaziantep note that the textile engineering department provides ample practical training, firms in Denizli cite the lack of practical experience of PAU graduates as the principal reason they tend to recruit from Ege University textile engineering department. Incidentally, Ege University came out as the top preference of Gaziantep firms along with Gaziantep University and ITU. We therefore summarize key characteristics of Ege University in BOX 11 below. Of course, there is always the possibility, in this one or any other university comparison, that a looks better because it attracts better students and the outcomes we observe are due to the quality of its students rather than quality of its education?. However, in the Turkish system, the universities’ ability to attract better students (at least better-scoring ones in the OSS exam) is entirely dependent on the preferences of the students. Therefore, for this argument to have some validity, factors other than the quality of a university/program should be playing a role. This is indeed a claim echoed by university professors and university officials of smaller city universities, as we discuss below. However, if such factors were at play, they would presumably have an affect across the university rather than in certain departments/fields. Therefore, we would expect universities to have more or less the same rankings across departments. In other words, we would not see cases like Hacettepe Medicine and Ege University Textiles Engineering, where these particular departments are ranked better than the university’s other departments. Although we do not know of a study that looks at the determinants of students’ preferences on the OSS exam, the mere existence of such “star departments” suggests that quality plays at least some role in the students’ decisions. Nevertheless, if better departments attract better students, the effect of a department may be overestimated (empirically or by employers’ assessment)

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BOX 11: Ege University Textile Engineering Department

4.2 Higher Education as a Catalyzing Force for Turkey’s Technological Change Other than training workers suited to Turkey’s economic transformation, the higher education system needs to play a leading role in the process as well. To do so, the effective private sector- higher education partnerships need to be developed, which requires changes in the current institutional setting as well. However, even if effective partnerships are achieved, there needs to be a mechanism for securing fast and accurate information flow between higher education and the private sector to ensure these partnerships are sustainable and diffuse to the whole economy rather than a few individual firms. 4.2.1 Private sector-higher education partnerships In a small number of successful economic miracles of the last few decades, partnerships between higher education institutions and the private sector have played an important role. However, such partnerships are very rare in Turkey for a number of reasons. Firms consistently complain about the high cost of working with the higher education sector. Since a significant portion of the consulting fees of the professors need to go

The diverse production range in the textiles industry is too broad to be covered during a 4 year standard license curriculum. Ege University Department of Textile Engineering is offering a unique textile engineering program, where education is carried out within optional programs in order to enable students to better adapt to professional life. In the department, students complete the fundamental science and engineering classes and the internship program during the first four semesters. The next four semesters, three different optional programs are followed: 1) Textile technology (spinning, weaving and knitting), 2) Textile chemistry and finishing 3) Apparel manufacturing. Students have the opportunity to specialize on one of these branches during undergraduate education. The language of instruction in the department is Turkish. Nevertheless, an intensive language training program for English and German is offered to students prior to the first year of education. Besides, technical language courses are offered in every semester. In this way, students become capable of keeping up with the latest developments in textiles literature and communicate with foreign specialists or customers, avoiding the risk of being isolated from their own language in the professional context. In terms of practical education, the department has its own enterprises (companies) where students can find the opportunity of “learning by doing.” Applied R&D activities and test productions are carried out in these enterprises considering the demands of the industry. A characteristic of the program that needs some further comments is its specialization. A very consistent result of the interviews was that firms, especially traditional ones, insisted on specialized programs suited to their needs. At the extreme, a very small chair manufacturer told the interviewers that she wanted an MYO program specializing in straw chairs, although she probably would not need more than a couple of graduates of the program. It is important to notice that specialization, especially in university education, is a two-edged support. While specialization may make it more likely for graduates to be hired in certain sub-sectors/firms, it can also lock them in to narrow professions. The advantage of Ege University’s program is that it does provide some specialization, while it does not produce overlok-sewing machine experts, as one firm in Denizli would like.

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into the revolving fund of the university, consulting fees end up either being unaffordable for most firms or too low for professors. Although YOK Law Article 37 leaves the tax on consulting for revolving funds to the executive board of each university, those fees are traditionally very high. METU, a university undertaking lots of projects both with the private and public sector, has allocated 55% of consulting fees to the revolving funds. Although relaxing revolving fund requirements is a necessary condition for university- private sector partnerships, it is in no way a sufficient one, at least from a “national innovation system” perspective. Experiences of other countries show that there needs to be a mechanism for supporting innovation, which will act as an intermediary for innovative activities between universities and the private sector.72 Of course, one needs micro approaches as well as macro ones: A particular problem is that higher education institutions do not have the incentives to engage in partnerships with the private sector. Part of the problem lies in the criteria for promotion: For all assistant and associate professors, publication is the only criterion for promotion, making it more difficult for professors to engage in projects that will not result in a research paper.73 Even when a project can produce a research paper, there is no guarantee that paper fills a void in the literature and therefore can be published at a leading journal.74 Therefore, it might be useful to consider diversifying promotion criteria for some fields to include non-research projects.75 Interestingly, MYO instructors are subject to the same criteria as university professors. As a general point, although a macro approach like a national innovation system might be needed for universities, a micro approach would be more suited to MYOs, as they should be following up closely and contributing to regional needs. We will look at MYOs’ partnership with the private sector in the context as part of our analysis of higher education and regional needs in the next section. 4.2.2 Information and coordination mechanism The previous section has some guidelines for effective higher education-private sectors partnerships. However, it does not explain how such partnerships are started in the first place, how they can evolve rapidly and are then sustained independent of the personal relationships of the key stakeholders. One of the stronger results of the interviews is that many firms have not actually attempted any relations with higher education institutions. Although 55% of the firms in TOBB survey said they have not attempted to form a partnership with the universities until now, the number came out to be even higher in the firm interviews. Interestingly enough, many interviewees, when asked about their lack of relations with the private sector, started by accusing the higher education institutions of not responding to their needs and not initiating any contact with them. However, they 72 Such a mechanism is being proposed by both YOK’s Higher Education Strategy Report and EU’s Higher Education whitepaper. 73 Some projects can not result in research papers because certain projects in defense are clandestine, as professors at METU and Eskisehir University have told our graduates. Still others do not end up in research papers because they are more practice-oriented, such as helping a firm with a specific solution. 74 A metallurgy professor noted that although she wanted to specialize in aluminum, where there were opportunities to work with the private sector, she had to work on other areas because she could not find a gap in the aluminum literature. Such publication bias is common in many fields in the academia. 75 Although it is beyond the cope of this paper to define such a mechanism, TEPAV | EPRI Industrial Policy Ad Hoc Commission Report for the Ninth Development Plan has more details on this issue.

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then also accepted that they did not ask for help from the universities either: “To tell the truth, we have been ignorant of the university as well”, added one interviewee from Diyarbakir after criticizing the University for being arrogant and ignoring the local private sector. In firm interviews, there were two main reasons cited for not initiating contact with the universities. Some interviewees, although they had a good idea on what they would need from the universities, just could not find the time to initiate contact and said they expected the university to come to them. However, a significant number of interviewees were not sure on what they could expect from universities and how universities could help them. Whatever the reason may be there does not seem to be an efficient information flow between higher education and the private sector because of the lack of action on both sides. The private sector, in all the cities interviewed, is not the only stakeholder with imperfect / inadequate information flow. The universities did not impress our interviewers as particularly knowledgeable about the needs of the private sector. Although we were able find individual professors with a good knowledge of the region’s employment needs, administrators did not answer our questions on regional labor needs in detail. When asked about needs of the private sector, our interviewers often got generic replies.76 However, workers had their share if inadequate information as well. We met with unfulfilled demand in certain occupations in Erzurum and Kayseri. Whereas it might be impossible for an unemployed worker to respond to an opening as upholsterer at a furniture factory, even professions requiring a short-amount of training were unmet. The ISKUR offices and firms in Kayseri and Erzurum noted that training courses with job guarantee after graduation were often not filled, whereas there was huge demand for other positions.77 Although the interviewers could not pursue this issue in detail, it seems that there is a lot of word-of-mouth action and herding going on and workers as well as young people would certainly benefit from more reliable information. A common trend in all the provinces is that although there are mechanisms for higher education- private sector partnerships, those mechanisms exist on paper and are not utilized at all. The few successful private sector-higher education partnerships have been initiated by the efforts of one or two individuals and therefore usually have a lifespan limited to those individuals’ involvement in the partnership. We encountered the most institutional of these partnerships in Denizli, where a textile firm had undertaken several projects with the Ege University textile engineering department. Although this partnership started through the efforts of a former employee, who was also a research assistant at the university, it had continued to grow after he left the firm and moved full time to the university. However, the manager of the firm was confident that if the previous worker had left the university, their partnership would still continue. Other examples of partnerships we encountered were similar to the one described in the preceding paragraph, except that they had recently started and therefore had weaker foundations. Two of them, in Diyarbakir and Trabzon (incidentally the

76 This might be because of both lack of knowledge, as we will claim in this section and lack of care, as we discuss in the next section. 77 An often-cited example was the huge demand for private security officer positions. The interviewees tied this demand to a recent change in the law that has allowed private security firms to protect public offices and required public offices to buy such services. However, workers cite the low wages offered in these guaranteed positions as a reason why they do not prefer these positions. In certain cases, it has been claimed that poverty support from local towns can exceed the wages offered in these jobs.

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provinces with the weakest private sector links in the region) were the result of the solitary efforts of the professors, who has some similar characteristics despite having different backgrounds. BOX 12: Two professors establishing private sector-university partnerships

A great number of firms as well as some university professors pointed out to this fact before stating that a new mechanism needed to be in place. Among the new mechanisms offered, the most common one, by far, was for TOBB to assume the role of coordinator or intermediary. The interviewees, several firms as well as a university professor, offered several justifications for this mechanism: As the professor in KTU noted, it is important for the firms and universities alike to know where the economy is going to as well as relay that information as quickly as possible. TOBB, with its access to the chamber network as well as its university and research institutions, is in a good position to assess the information and relay it to the regional stakeholders. 4.3 Higher Education and Regional Needs Even if all the ingredients for an effective partnership are in place, including appropriate mechanisms ensuring coordination and rapid information flow, such a partnership may never materialize if the higher education sector does not develop the capacity to take particular regional needs into consideration.78 As discussed in Section 3, firms in the TOBB survey report that their regional university trains people who have neither the skills they demand nor in the occupations their province need. However, to understand why this is the case, we need to examine the interviews.

78 One might not agree on a sharp dichotomy between mechanisms for partnerships and capacity of higher education to take into account regional needs. In many international cases, they imply each other and are virtually the same thing. However, what we mean by capacity includes the will and ability to play a regional role. Otherwise, instituted partnership mechanisms will not go beyond being on-paper arrangements. A good example is the existing higher education-private sector partnership councils. These are mechanisms that exist on paper, but our interviewers have yet to see one that is actually functioning.

Two of the most effective partnerships were developed by a mechanical engineering professor at KTU and a mining engineering professor at Dicle University. Although the two professor’s partnership arrangements differ, they had similar characteristics. Specifically, both were young, had their Ph.D. abroad, were enthusiastic on partnerships with the private sector (they had personal projects going on with the private sector as well as the partnerships they initiated) and were eager instructors, keen to apply teaching practices that involved generic skills like preparing presentations. Whereas the KTU professor was doing projects for the private sector with his students, the Dicle University professor had set up an internship/co-op program with a local marble mining company. However, their strongest similarity was their enthusiasm for working with the private sector, strengthening existing relationships as well as establishing new ones, and neither of them was shy about sharing her enthusiasm with our interviewers. Of course, there were some differences between the two professors as well. The KTU professor had an informal bridge function between the university and the private sector: He was the industry consultant of the rector as well as being a consultant to the chamber of industry. The Dicle professor, on the other hand, noted the absence of such mechanisms and was critical of deciding on the region’s needs by a select group of university and public officials.

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4.3.1 Universities It is now widely accepted that there is no “one size fits all” formula for different higher education institutions. Other than the differences between MYOs and universities, universities should be able to differentiate among themselves. However, one factor emphasized by local university administrators is their desire to follow the example of top research universities. The university administrators mention that they take the example of Middle East Technical University (METU) or Istanbul Technical University (ITU) for engineering and Hacettepe University for medicine. While all university administrators note “they are sensitive to the region’s needs”, when asked detailed questions, they say that a university’s primary goals are not to respond to the region’s needs, but to give a quality uniform education.79 During the interviews, we were under the impression numerous times that the university administrators saw their province not as a place where they could establish fruitful partnerships with regional actors, but rather as a curse they had been forced to live with because of factors beyond their control. One common trend in the interviews was the tendency of university administrators and professors to relate the low scores they get in the OSYM exam to locational factors rather than quality issues, as the following box illustrates.

79 EU whitepaper A New Vision of Higher Education in Turkey has a historical explanation on the formation of this uniform view of education.

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BOX 13: Location and quality: The usual chicken and egg problem

Maybe, the first step in assuring universities train students suited to the region’s needs is to make sure creating an environment suited to university graduates working in local firms for the long haul. This seems to be a serious issue in some cities/universities. For example, in Denizli, Trabzon and Diyarbakir, firms complained they could not find workers with certain university degrees, even though those programs existed. At the extreme, we encountered unmet demand for textile engineers in Denizli as well as forestry engineers in Trabzon. Although such unmet demand did not make sense at first, it became clear once we spoke to students: Many students had come to these cities because it was their only option and they intended to leave as soon as they graduated. Moreover, there was a supply effect in place: Firms in both cities were reluctant to hire graduates from the region, noting that these people could not adapt and would quit in the first opportunity. Although mechanism design issues of the university placement examination are beyond the scope of this paper, it

Regarding quality, a common argument mentioned was the importance of location of their university to our interviewers by almost all university administrators and some professors. University administrators and professors from local universities claimed that the quality of their university was first-tier by many standards, but because of their location in a non-major city, their scores in the OSYM exam turned out to be low. Some claimed that if they moved to Istanbul or Ankara, they would immediately be in the first-tier league in many departments in terms of scores. While such a natural experiment that would have enabled us to test these claims does not exist, we can look at some indirect evidence to analyze these claims. One interesting question is whether high school seniors are more likely to choose higher ranking universities from the province they live in and whether seniors from smaller cities are more likely to prefer local universities.(1) Table A.15 and Table A. 16 show the locational distribution of students that leave their home town to go to college and those that come to these towns. Although this evidence is far from being concrete, it does show that undeveloped cities that are expected to be unattractive for young people do not have higher percentage of seniors leaving to go to college. Similarly, the incoming student proportion of universities in these cities is not that different than big cities or attractive cities.(2) Another method to tackle this issue would be to look at the supply side rather than the demand side of providing higher education and see the campuses of major universities have smaller points, part of which could be attributed to locational factors, we leave this second method to further work. Regardless of whether being in a non-major city is a significant factor in affecting student choices or not, responding to regional needs will help. As discussed above, our interviews provide evidence that firms are more likely to hire and pay higher wages to workers that fit their needs, a fact for which we have provided some evidence in Section 3 as well. Making their students more employable is therefore a way to increase the universities’ demand. For regional universities, this means responding to the region’s needs as much as (and in conjunction) with increasing quality of education and university resources. (1) In fact, this is only weak indicator for the question we are tackling. Any difference can be attributed to the fact that the control group (students from Istanbul and Ankara) are less likely to choose local universities, which will support the claim we are testing or the fact that students from other cities are more likely to enroll in a local university, which will refute the claim. This question could only be properly assessed with a regression framework, which we do not attempt here, as we do not know about the decision-making of OSYM exam-takers. (2) Not to offend anyone, we will not attempt to label cities as unattractive, but one might use different proxies of attractiveness such as size, being in a warm climate or being by the sea. However, we doubt that a significant difference between attractive and unattractive cities will emerge whatever measure we use.

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should be noted that the current allocation mechanism seems to be enforcing regional mismatches rather than alleviating them. In this respect, systems giving incentives from students from the region to enroll in their regional universities should be investigated. Such a system, if properly designed, might even decrease university officials’ so called locational disadvantage effect if such an effect does indeed exist. Of course, just as we would not expect Bilkent or METU to give preferential access to Ankara residents, we do not expect all universities to try to recruit students from their regions. Such a system would not make sense for top-notch research universities. However, some universities, especially those that are well-placed to focus on the needs of the region more rather than engage only in traditional research could have a focus on recruiting students locally from the region, with expectations (and mechanisms to ensure these expectations like work opportunities with the region, programs tailored more towards the region’s needs80) that they would work in local/regional firms. Universities can only respond to regional needs if they are aware of them. Therefore, having a coordination mechanism that will keep them in touch with the regional economy is vital, in this case, for providing specific information on the regional economy to the regional university. 4.3.2 MYOs In theory, the MYO curricula and capacities should be in line with particular regional needs since their goal is to provide intermediate workers for the private sector. However, we heard even louder complaints that MYOs were not responding to regional needs.81 Although MYOs are being discussed at a separate report, it is useful to mention some factors that constrain MYOs from taking the region’s needs to consideration. We have already mentioned, while discussing practical experience, the lack of resources. At a more basic level, there are many institutional arrangements that need to be implemented. It became quite obvious during the interviews that except a few exceptions, small-town MYOs do not work well: They are extremely limited in terms of resources, even more so than MYOs in university campuses because they can not make use of the university’s resources.82 According to MYO directors, students at small-town MYOs can not develop their social skills. Moreover, as it has been brought up many times, MYO managers, at least those who would like to work closely with their region, note that evaluating MYO instructors with similar criteria as university instructors creates disincentives on working closely with the region’s private sector and responding to regional needs.83 An even more basic problem is related to the fact that regional needs have not been taken into account for many MYOs in the first place. Therefore, many MYOs have departments that the region does not need or do not have some departments that are

80 Without tying up students’ career opportunities by making them overly specialized in an area. 81 Of course, the fact that we got more complaints regarding MYOs in this issue could be reflecting the fact that firms expect MYOs to be localized rather than universities’ better ability to respond to regional needs. 82 Lack of equipment of small-town MYOs was especially prevalent in medicine-related fields such as nursing. 83 Creating opportunities for research was brought up as a recommendation at the latest MYO conference in Mugla as well.

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vital for the region.84 As a result, we encountered during our interviews an oversupply of certain occupations as well as unsatisfied demand for others, as BOX 14 illustrates. BOX 14: No upholsterer, how about I give you electricians?

Last but not the least, the political economy of MYOs should not be forgotten: The perception of the MYO as an externality-generating regional development institution needs to be eradicated. MYO managers have told us colorful stories how the public and local actors have withstood them when they tried to close down departments not in line with the region’s needs. Only when all the regional actors see the primary goal of MYOs as responding to the region’s needs rather than creating an artificial expansion, the MYOs will be able to serve the regional firms more efficiently. BOX 15: An apple of a vision

84 Interestingly, MYO directors complain that when they try to close down departments that do not fir the region’s needs, they encounter fierce opposition from the public, hinting that the public is more occupied with the externality effects of these schools rather than their main goal of responding to regional needs.

Kayseri is a well-known furniture cluster and the local MYO has an upholstery department. However, our interviewers found out that the local school’s capacity is well below the region’s needs. Therefore, although the graduates of the department are easily placed in a local furniture company, some companies would like to hire upholsterers but can not find any. The experience of a particular company is quite illustrating. The manager told our interviewers that when he asked the director of the local MYO for upholsterers, the director told him that there were none, but offered him electricians instead, who had not been placed. Although the importance of taking regional needs into account is the main lesson of the previous argument, it is not yet clear how those regional needs need to be assessed. Before pointing out to some guidelines on effective needs assessment, it is useful to illustrate a non-effective one (see Box 15)

The head of an MYO in Denizli told our interviewers proudly how he has envisaged the opening of a new foreign trade program: He noticed that the town has very nice apples that could be exported and decided to open up a foreign trade program to promote these apples. When asked by the interviewers about how he would find the faculty to teach in this program (after all, he had been complaining that he had difficulty recruiting new faculty because of locational reasons and low wages), he noted that he could easily use the faculty in Business and Accounting programs to teach these classes. This example highlights two important facts: First, this MYO principal seems to be one step ahead of the game; at least, he observes and tries to react to the region’s needs. Given that many MYO principals do not even live in the town of their MYO and are ignorant of the region’s needs, it is commendable that he is reacting to the region’s needs. However, this example also illustrates the pitfalls of forming a perception of the regional needs based on one’s own perception, without a well functioning information flow mechanism that involves communicating with the local stakeholders and experts who could provide feedback. Note that we do not suggest that opening up a foreign trade MYO in this town is a bad idea: On the contrary, it might be very useful for the region, given that Denizli is an export-oriented textiles cluster and firms have noted to us their need for qualified workers to boost their exports. However, we do question the reasons behind the principal’s decision and argue that he is not very likely to help this town export its apples and even less likely place the graduates of this program in textiles firms in Denizli, given that these firms are asking for computer and language skills, skills which we doubt this MYO’s Business and Accounting faculty could provide.

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Our interviews with the MYOs have shown that the well-functioning ones are unfortunately small in numbers. The MYO most in line with its region’s needs was Cappadocia MYO, which we illustrate in the box below. BOX 16: Cappadocia MYO: A successful partnership with the region and more?

The Cappadocia MYO is unique, not only because it is the first foundation MYO, but also because of its many practices that are examples of good performance. The MYO’s arguably most prominent characteristic is its partnership with the region. The programs of the school were decided after an economic needs assessment of Cappadocia. Even after the programs were designed, the curricula were tailored to fit the specific needs of the region’s economy. For example, the tourism department has a special program on educating well-informed guides on the region’s touristic attractions as well as the culture of the countries that mostly send tourists to the region. The school has supported its partnership with the region by establishing a private firm as the of sponsor its programs. The MYO, in line with its general policy explained below, does not expect financial contribution from its sponsors. It would rather like them to donate equipment and provide instructors from their staff and internship opportunities for its students. However, the MYO has extended its partnership to the whole region rather than having simple partnerships with a few private firms. For one thing, the school’s buildings were provided rent-free by the town; in return, the school renovated all the buildings it is using. Moreover, activities geared for the local people are organized every week, so the tensions between the locals and students mentioned in many small-town MYOs are not present here. Finally, the school would like to create incentives for the local students to enroll in the program, highlighting the importance a labor force that will stay in the region after graduation. Students from the region pay ½ of regular tuition. Other than a successful partnership with the region, the Cappadocia MYO’s policies are in line with the private sector’s needs. For one thing, the school creates ample opportunities for students to improve themselves in the generic skills many firms deem important. The school has a modern computer and language lab, where the training offered by proficient teachers that can provide practical experience (the language instructor is a native speaker of English living in the region and the computer instructor is a computer engineer who has a software firm in Kayseri. Communication skills and teamwork are honed in classes that give importance to team presentations of homeworks. Social skills are improved with the school’s offering of many clubs, who are run by the school’s full time instructors as well as instructors provided by firms in the region. (The music club is run by the sponsor hotel’s lead musician.) Equipment, a big problem in many MYOs, is not an issue here, partly due to the local sponsorships and partly due to the ability of the school’s founder’s to generate funds required to renew the equipment. Although the success of the Cappadocia MYO is exemplary, it seems hard to replicate because the school’s viability is dependent on the personal connections of its founders.

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5 Conclusions The innovative transformation of the Turkish economy has diverse consequences: It creates as many challenges as opportunities. One of the challenges that can be turned into an opportunity considering Turkey’s place in the demographic window of opportunity is the increasing importance of higher education in economic activities. This study has shown that the higher education sector can not produce graduates suited to the needs of the private sector, and the degree of mismatch is likely to increase in the future unless the higher education sector transforms itself rapidly. In short, the higher education sector must change if it is to support private sector transformation and economic development. Moreover, private sector needs to increase its collaboration with the higher education institutions. Although this study provides outlines on how the higher education sector should be transformed, it does not identify the exact measures to be taken, leaving those for future research. For example, while this paper has shown the importance of an education system providing generic skills, it has not attempted to come up with teaching practices geared towards providing these skills. Therefore, a natural follow-up to this study would be another one geared towards coming up teaching methodologies designed to improve deficiencies that have come out of this study. Other than natural extensions of the findings of this paper, its topics could be improved as well. Specifically, as described in the Introduction, we have not been able to cover many topics in much detail because of time constraints. We hope that the extensions of this study could take those factors into account. In particular, we have suggestions for improvements in empirical results, surveys, questionnaires and interviews. One obvious improvement to the study would be the increase the extent of cities and firms covered in future studies. Although we have demonstrated with hard data that our interview findings are robust, more coverage would definitely enrich the insights from the interviews. We have also gained quite a bit of insight from the interviews themselves. For one thing, both our experience as well as the experience of other companies that rely extensively on interviews show that it is better to conduct these types of interviews with two interviewees instead of one.85 Although we could only allocate one interviewee to each firm for most of the time (mainly because of time and resource constraints), we suggest any future interview work should be conducted with two interviewees.86 Suggested empirical improvements are mentioned in the specific sections of the report, so we do not report them here. However, more rigorous empirical techniques would also be a great way to check the robustness of the results in addition to increasing the coverage and improving the interview methodology. We hope that this study, which is in effect more of a diagnosis than anything else, will be the first among many that will help offer solutions to bottlenecks in the higher education sector and therefore contribute to the historically significant economic development and structural transformation through improving the climate for private sector.

85 For example, see The McKinsey Way for a concise description of the consulting interview process. 86 We were able to allocate two interviewers to university and ISKUR interviews as well as to some bigger firms. In all cases, having the additional interviewer helped a lot.

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Tables and Figures

Table A. 1: Comparison of Sectoral Composition of Dataset with TURKSTAT

TOBB Survey TURKSTAT

Number of Firms

Average Number of Employees

Share of Sectors

Number of firms

Average number of employees (1994-2001)

Share of Sectors

Manufacture of food products and beverages 230 78.4 38 846 81.9 16

Manufacture of tobacco products 3 51.0 0 18 219.9 1

Manufacture of textiles 83 244.9 14 667 136.9 20 Manufacture of wearing apparel; dressing and dyeing of fur 14 66.6 2 510 125.2 14 Tanning and dressing of leather; manufacture of luggage, handbags, saddler, harnets and foot 15 60.7 2 110 60.0 1 Manufacture of wood and of products of wood and cork, except furniture; manufacture of article 11 21.1 2 171 45.4 2 Manufacture of paper and paper products 9 119.5 1 105 101.6 2 Publishing, printing and reproduction of recorded media 12 17.3 2 87 46.3 1 Manufacture of chemicals and chemicals products 27 646.7 4 - - - Manufacture of rubber and plastics products 29 62.0 5 272 74.1 5 Manufacture of other non-metallic mineral products 10 507.3 2 309 105.0 7

Manufacture of basic metals 42 105.6 7 196 114.0 5

Manufacture of fabricated metal products, except machinery and equipment 25 878.9 4 250 85.0 5 Manufacture of machinery and equipment n.e.c 27 107.5 4 323 84.1 6

Electrical Machines and Apparatus 0 0.0 0 144 110.1 4

Manufacture of radio, television and communication equipment and apparatus 6 42.0 1 36 133.5 1

Manufacture of medical, precision and optical instruments, watches and clocks 6 1499.6 1 43 66.5 1 Manufacture of Motor Vehicles Trailers 10 66.0 2 205 138.4 6

Other Transport Equipment 4 265.4 1 32 83.3 1

Manufacture of Furniture n.e.c. 38 86.3 6 140 75.1 2

Source: TOBB Survey & TURKSTAT

59

Table A. 2 Wage Offers and Firm Modernity-Internal Training

Wage Offered to University

Graduates

Wage Offered to MYO

Graduates

Wage Offered to MYO

Graduates

Money spent on internal training as percent of total budget 0.174 0.126 0.31 (0.98) (0.55) (1.55) Modernity 1.093 0.936 1.06 (4.45)** (3.13**) (4.08)** (Money spent on internal training)*(Modernity) -0.094 -0.061 -0.222 (-0.86) (-0.04) (-1.68) Observations 746 724 951 Source: TOBB Survey

Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. Firms neither exporting nor engaging in R&D activities are classified as traditional firms and the variable modernity takes the value 1 for such firms. Firms either export or engage in R&D activities are classified as modern firms and the variable modernity takes the value 2. Firms which both export and engage in R&D activities are classified as very modern firms and the variable modernity takes the value 3 for such firms.

Table A. 3: Wages Paid to Graduates of Selected Universities’ and Departments’

Graduates Business Adm. Economics Mechanical Eng. Textile Eng.

Çukurova U. 1499 1821 1916 …… Dokuz Eylul U. 1332 1147 1641 724 Ege U. …… …… …… 1340 Erciyes U. 808 778 1249 …… KTU …… 731 896 …… Source IAQ

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Table A. 4 Determinants of Wage Offered to University Graduates Model 1 Model 2 Model 3 Model 4

School matters (Q3.1.1.UNIV) -0.517 -0.501 -0.442 -0.407 (4.37)** (4.11)** (3.60)** (3.11)** Size of the firm (Q1.5) 0.262 0.164 0.159 (5.26)** (3.14)** (2.86)** Quality certificate (Q1.7) -0.777 -0.724 (5.96)** (5.32)** White collar turnover (Q2.5) -0.007 (0.11) Observations 1092 1055 1033 902 Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. We used ordered logistic regression in order to figure out the determinants of the wages of the university graduates. We treat both endogenous and exogenous variables as categorical and ordinal. In the survey, if the respondent does care about the school of graduation then the variable Q3.1.1.UNIV takes the value 1 and it takes 2 otherwise. A negative sign indicates that if the school of graduation matters for the employer than the amount offered to newly graduated candidate increases. If the respondent has a quality certificate then the variable Q1.7 takes the value 1 and it takes 2 otherwise. A negative sign in all two specifications, which Q1.7 is present, indicates that firms having a quality certificate significantly offer higher wages to newly graduated candidates. Moreover, the positive sign in front of the variable measuring the firms size (Q1.5) indicates that bigger firms offer significantly higher wages relative to small firms. Finally, although not significant the negative sign in front of the variable measuring white collar workers' turnover indicates that higher the turnover lower the wage offered to newly graduated candidates.

Table A.5: Determinants of University Premium

Model 1 Model 2 Model 3 Model 4 School matters (Q3.1.1.UNIV) -0.251 -0.283 -0.264 -0.293 (1.97)* (2.15)* (1.98)* (2.04)* Size of the firm (Q1.5) -0.062 -0.094 -0.101 -1.18 -1.71 -1.72 Quality certificate (Q1.7) -0.215 -0.213 -1.51 -1.42 White collar turnover (Q2.5) 0.094 -1.29 Observations 985 953 932 818 Note: t statistics are in parenthesis. *significant at 5 Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. We used ordered logistic regression in order to figure out the determinants of the university premium. We measure the premium as the difference between the amounts offered to university and MYO graduates. Once more, we treat both endogenous and exogenous variables as categorical and ordinal. In the survey, if the respondent does care about the school of graduation then the variable Q3.1.1.UNIV takes the value 1 and it takes 2 otherwise. A negative sign indicates that if the school of graduation matters for the employer then the amount of university premium increases significantly at 5% significance level in all of the four specifications. If the respondent has a quality certificate then the variable Q1.7 takes the value 1 and it takes 2 otherwise. A negative coefficient in both third and fourth specifications indicates that firms having a quality certificate offer higher university premiums. Moreover, the positive sign in front of the variable measuring the firm size (Q1.7) indicates that bigger firms offer lower wages relative to bigger firms. Finally, the negative sign in front of the variable measuring white collar worker’ turnover indicates that higher the turnover higher the wage offered to newly graduated candidates. However, all coefficients other than the one measuring the effect of whether the school of graduation matters or not are lack of statistical significance.

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Table A. 6 Determinants Wage Offered to MYO Graduates Model 1 Model 2 Model 3 Model 4

School Matters (Q3.1.1.MYO) -0.0276 -0.089 -0.096 -0.102 (-0.19) (-0.64) (-0.68) (-0.69)

Size of the firm (Q1.5) 0.379 0.264 0.245 (7.33)** (4.82)** (4.28)**

Quality certificate(Q1.7) -0.918 -0.916 (6.45)** (6.21)**

Blue collar turnover (Q2.4) -0.103 (-1.9)

Observations 1066 1031 1008 922 Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. We used ordered logistic regression in order to figure out the determinants of the wages of the MYO graduates. We treat both endogenous and exogenous variables as categorical and ordinal. In the survey, if the respondent does care about the school of graduation then the variable Q3.1.1.MYO takes the value 1 and it takes 2 otherwise. Although not significant, a negative sign indicates that if the school of graduation matters for the employer than the amount offered to newly graduated candidates increases. If the respondent has a quality certificate then the variable Q1.7 takes the value 1 and it takes 2 otherwise. A negative coefficient in both third and fourth specifications indicates that firms having a quality certificate offer significantly higher university premiums. Moreover, the positive sign in front of the variable measuring the firms size (Q1.5) indicates that bigger firms offer significantly higher wages relative to small firms. Finally, although not significant the negative sign in front of the variable measuring blue collar workers' turnover (Q2.4) indicates that higher the turnover lower the wage offered to newly graduated candidates.

Table A. 7 Determinants of MYO premium

Model 1 Model 2 Model 3 Model 4

School matters (Q3.1.1.UNIV) -0.027 0 0.007 -0.007 (-0.18) (0) (-0.04) (-0.04) Size of the firm (Q1.5) 0.159 0.146 0.126 (3.17)** (2.66)** (2.12)* Quality certificate (Q1.7) -0.174 -0.199 (-1.18) (-1.31) White collar turnover (Q2.5) -0.11 (-1.92) Observations 1008 976 956 883

Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. We used ordered logistic regression in order to figure out the determinants of the MYO premium. We measure the premium as the difference between the amounts offered to MYO and VTHS graduate. Once more, we treat both endogenous and exogenous variables as categorical and ordinal. In the survey, if the respondent does care about the school of graduation then the variable Q3.1.1.MYO takes the value 1 and it takes 2 otherwise. A negative sign indicates that if the school of graduation matters for the employer than the amount of MYO premium increases, vice versa. The sign of the coefficient changes depending upon the model specification. In the first and last specification the sign of the coefficient is negative. In the second and third specifications the coefficient takes the value zero and 0.007 respectively. If the respondent has a quality certificate then the variable Q1.7 takes the value 1 and it takes 2 otherwise. A negative coefficient in both third and fourth specifications indicates that firms having a quality certificate significantly offer higher MYO premiums. Moreover, the positive sign in front of the variable measuring the firms size (Q1.5) indicates that bigger firms offer higher MYO premiums relative to smaller firms. Finally, the negative sign in front of the variable measuring blue collar workers’ turnover indicates that higher the turnover higher the wage offered to newly graduated candidates. However, all coefficients other than the one measuring the size of the firm are insignificant.

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Table A. 8 Firms’ Views on Different Statements about Universities (According to Firm Modernity)

Traditional Modern Very Modern

Universities are not very enthusiastic about cooperating with us

50% 45% 46%

We haven't attempted to cooperate with the universities 60% 50% 24%

Academics are far from the problems of the business environment, they fail to keep up with latest developments

58% 58% 46%

We cannot use the consultancy services of academicians because the fees are too high

61% 55% 28%

We are pleased with the quality of college educated workforce in our town

43% 39% 51%

The university in our town educates people with the qualities we need

39% 36% 46%

The university in our town educates people in the areas we need

37% 33% 51%

Source: TOBB survey

Table A. 9: Determinants of Internal Training

Money spent on internal training as a

share of total budget (Q2.9)

Export Status(Q1.6.1) -0.303

(-2.49)*

Experience is considered as more important than education among university graduates (Q4.5.2)

-0.014

(-0.33)

Observations 1099

Source:TOBB Survey Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. We used ordered logistic regression in order to figure out the determinants of the amount of money spend on internal training as a share of total budget. We treat both endogenous and exogenous variables as categorical and ordinal. In the survey, in Q1.6.1 we ask the respondent whether her firm exports or not. If yes the variable takes the value 1, and if not it takes the value 2. A negative coefficient in front of the variable related with export indicates that exporters spend a greater percentage of their budget to internal training. One other question in the survey aims to determine whether experience is considered as more important than the education of the candidate (Q4.5.2). A negative sign indicates that as the firm gives more importance to experience relative to education the amount spend on internal training decreases.

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Table A. 10: Determinants of Turnover Rate of White Collar Workers Turnover rate of white collar

workers (Q2.4)

Money spent on internal training as percent of total budget (Q2.9)

-1.164

(-0.25) University premium -0.046 (-0.08) Constant 4.617 (-0.5) Observations 727 Source:TOBB Survey

Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. We used ordered logistic regression in order to figure out the determinants of the turnover rate of white collar workers. We treat both endogenous and exogenous variables as categorical and ordinal. On the right hand side we take money spend on internal training as percent of total budget (Q2.9) and the university premium, which is calculated as the difference between the wage given to university graduate and MYO graduate, as endogenous variables. We instrument the money spend on internal training with two other variables. They are; the one which respondent states whether she exports or not (Q1.6.1) and whether she considers experience more important than education (Q4.5.1). Both of the coefficients are negative. As the firm spends more on internal training it has less incentive to fire existing employees and as the firm pay more to university graduates they have less incentive to quit.

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Table A. 11: Determinants of the turnover rate among university graduates in traditional firms

Model 1 Model 2 Model 3 Model 4

Wage offered to university graduates (Q4.1) -0.035 -0.018 -0.014 -0.012 (-0.22) (-0.11) (-0.09) (-0.07)

Experience is considered as more important than education among university graduates (Q4.5.1)

0.018 0.025 0.03

(0.19) (0.27) (0.32)

References from the employees working in the firm (Q3.1.2) -0.193 -0.044

(-0.79) (-0.17)

Willingness to work in your firm in the long run (Q3.1.8) -0.504 (2.00)* Observations 525 489 489 489 Source: TOBB Survey

Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. We used ordered logistic regression in order to figure out the determinants of the turnover among university graduates. We treat both endogenous and exogenous variables as categorical and ordinal. In the survey, wage offered to university graduates is asked in Q4.1. This variable increases as the wage offered to university graduates increases. A negative sign indicates that as the wage rate increases, turnover rate decreases. One other question in the survey aims to determine whether experience is considered as more important than the education of the candidate (Q4.5.2). A positive sign indicates that as the firm gives more importance to experience relative to education the turnover rate declines among university graduates. References from the employees working in the firm (Q3.1.2) and willingness to work in your firm in the long-run (Q3.1.8) are yes no questions. If the respondent agrees, then the variables take the value 1 and 2 otherwise. Negative coefficient indicates that turnover is higher for the firms which give importance to reference of the candidates taken from the employees working in the firm. Likewise, in firms which give importance to the willingness of the candidates to work in the long-run, turnover is higher.

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Table A. 12 Determinants of the turnover rate among MYO graduates in traditional firms

Model 1 Model 2 Model 3 Model 4

Wage offered to MYO graduates (Q4.1) -0.058 -0.088 -0.087 -0.084 (-0.36) (-0.54) (-0.53) (-0.51)

Experience is considered as more important than education among MYO graduates (Q4.5.1)

0.032 0.037 0.037

(0.36) (0.41) (0.4) References from the employees working in the firm (Q3.1.2) 0.131 0.191

(0.63) (0.89) Willingness to work in your firm in the long run (Q3.1.8) -0.225

(-1.17) Observations 554 510 510 510 Source: TOBB Survey

Note: t statistics are in parenthesis. *significant at 5%; **significant at 1%. We used ordered logistic regression in order to figure out the determinants of the turnover among MYO graduates. We treat both endogenous and exogenous variables as categorical and ordinal. In the survey, wage offered to MYO graduates is asked in Q4.1. This variable increases as the wage offered to MYO graduates increases. A negative sign indicates that as the wage rate increases, turnover rate decreases. One other question in the survey aims to determine whether experience is considered as more important than the education of the candidate (Q4.5.2). A positive sign indicates that as the firm gives more importance to experience relative to education the turnover rate declines among MYO graduates. References from the employees working in the firm (Q3.1.2) and willingness to work in your firm in the long-run (Q3.1.8) are yes-no questions. If the respondent agrees, then the variables take the value 1 and 2 otherwise. Positive coefficient indicates that turnover is higher for the firms which give importance to reference of the candidates taken from the employees working in the firm. Conversely, in firms which give importance to the willingness of the candidates to work in the long-run, turnover is higher.

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Table A. 13 Share of Different Sectors in Manufacturing Industry, Innovation and

Growth Rates

Technological Innovation

2002-2004 After 2004 Average Growth Share in

Manufacturing Sector

Office equipment and IT equipment 52 34 36.9% 0.1%

Automotive 43 35 34.3% 8.7%

Radio TV and Communication 38 29 26.9% 2.6% Machinery and Equipment not classified elsewhere 40 27 18.7% 4.1%

Plastic and robber 53 41 15.2% 3.9%

Health and optical equipment 81 54 14.8% 0.1%

Printing and publishing 53 27 12.3% 0.5%

Wood products 18 6 12.3% 0.7%

Chemical 69 37 11.2% 9.2%

Non metallic minerals 35 21 10.4% 5.3%

Metal equipment 40 26 9.1% 10.4%

Paper and paper products 38 29 9.0% 1.9%

Electrical machinery not classified elsewhere 36 27 4.8% 1.6%

Food and Beverages 35 21 3.8% 14.9%

Furniture 23 23 3.0% 1.0%

Petroleum 23 24 2.0% 15.9%

Leather 22 9 0.5% 0.5%

Textiles 12 15 0.2% 9.3%

Tobacco 29 12 -0.2% 3.4%

Apparels 26 16 -1.0% 4.0%

Other transport vehicles 60 43 -6.2% 0.3%

Source: TURKSTAT

Table A.14: Private Sector’s Skills Needs (Regional and Sectoral Composition)

ANKARA ANTALYA GAZIANTEP ISTANBUL IZMIR TRABZON

Mining and quarrying Abilities for heavy physical work Management skills

Social and communication skills

Social and communication skills

Abilities for heavy physical work

Social and communication skills and elementary and basic skills

Manufacture of food product and beverages Social and communication skills

Management skills, Social and communication skills, Elementary and basic skills, Computer and information technology skills

Social and communication skills

Social and communication skills

Management skills Social and communication skills

Textile wearing apparel and leather industry

Social and communication skills

Social and communication skills

Social and communication skills

Elementary and basic skills

Elementary and basic skills Social and communication skills

Manufacture of wood products, paper and printing

Elementary and basic skills

Computer and information technology skills

Work planning skills

Social and communication skills

Willingness to teach and coach others, Work planning skills

Computer and information technology skills

Manufacture of petroleum and chemical products

Elementary and basic skills

Willingness to teach and coach others

Elementary and basic skills Work planning skills Elementary and

basic skills Social and communication skills

Manufacture of basic metals and non-metallic goods

Social and communication skills

Social and communication skills

Elementary and basic skills Work planning skills Elementary and

basic skills

Computer and information technology skills, Work planning skills, Commercial and selling skills

Manufacture of electrical and other machinery Social and communication skills

Social and communication skills

Elementary and basic skills Management skills Elementary and

basic skills Elementary and basic skills

Manufacture of transport equipment Social and communication skills, Elementary and basic skills

Social and communication skills, Elementary and basic skills

Social and communication skills, Elementary and basic skills, Willingness to teach and coach others

Social and communication skills

Work planning skills Computer and information technology skills

Electricity, gas and water Work planning skills Elementary and basic skills

Social and communication skills, Computer

Social and communication skills

Computer and information technology

Computer and information technology skills

68

ANKARA ANTALYA GAZIANTEP ISTANBUL IZMIR TRABZON and information technology skills

skills

Construction Social and communication skills

Elementary and basic skills

Elementary and basic skills Management skills

Social and communication skills

Social and communication skills

Wholesale and retail trade Social and communication skills

Social and communication skills

Social and communication skills

Social and communication skills

Social and communication skills

Social and communication skills

Hotels and restaurant Social and communication skills

Elementary and basic skills

Social and communication skills

Social and communication skills

Social and communication skills

Social and communication skills

Transport and communication Social and communication skills, Work planning skills

Elementary and basic skills

Social and communication skills

Social and communication skills

Elementary and basic skills, Computer and information technology skills

Management skills

Financial services and insurance Social and communication skills

Social and communication skills, Computer and information technology skills

Social and communication skills

Social and communication skills

Management skills, Social and communication skills, Willingness to teach and coach others

Social and communication skills, Computer and information technology skills, Financial and budgeting skills

Real estate and business Social and communication skills

Social and communication skills

Social and communication skills

Social and communication skills

Social and communication skills

Computer and information technology skills

Health, social work and other services Social and communication skills

Social and communication skills

Computer and information technology skills

Social and communication skills

Computer and information technology skills

Computer and information technology skills

Source: MEGEP/SVET

Table A.15: Dispersion of University Entrants According to the City Groups that They Go

Percentage of people who get into a university from the

city they live

Percentage of people who get into a university from İstanbul and Ankara

Percentage of people who get into a university from

other cities

Adana 23.80% 18.41% 57.79% Afyon 13.96% 23.02% 63.02% Antalya 7.31% 24.36% 68.33% Aydın 4.44% 20.13% 75.44% Bolu 20.00% 24.17% 55.83% Çanakkale 19.52% 19.92% 60.56% Denizli 41.23% 17.78% 40.99% Diyarbakır 45.82% 13.37% 40.81% Edirne 1.09% 25.09% 73.82% Erzurum 55.56% 13.70% 30.74% Gaziantep 13.11% 18.50% 68.38% Hatay 9.97% 20.08% 69.94% Mersin 9.69% 20.38% 69.93% İzmir 14.34% 15.87% 69.80% Kayseri 28.26% 22.07% 49.67% Kocaeli 23.28% 22.90% 53.82% Malatya 34.89% 12.18% 52.93% Muğla 14.04% 20.76% 65.20% Niğde 24.03% 20.93% 55.04% Samsun 23.99% 25.57% 50.44% Sivas 38.91% 16.00% 45.09% Tokat 9.82% 29.02% 61.16% Şanlıurfa 18.01% 18.48% 63.51% Source: OSYM

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Table A. 16: Dispersion of University Entrants According to the City Groups that They Go

Percentage of people who get into a university from

the city they live

Percentage of people who get into a university from İstanbul and

Ankara

Percentage of people who get into a university

from other cities

Adana 38.29% 6.06% 55.65% Afyon 7.64% 15.91% 76.45% Antalya 22.44% 18.11% 59.45% Aydın 14.21% 9.64% 76.14% Bolu 6.14% 33.50% 60.36% Çanakkale 8.83% 19.82% 71.35% Denizli 2.21% 32.07% 65.72% Diyarbakır 9.25% 11.71% 79.04% Edirne 0.51% 49.33% 50.17% Erzurum 6.26% 11.11% 82.63% Gaziantep 21.13% 6.04% 72.83% Hatay 25.27% 6.76% 67.97% Mersin 23.21% 6.70% 70.10% İzmir 40.69% 11.73% 47.58% Kayseri 18.53% 11.51% 69.96% Kocaeli 6.62% 43.76% 49.62% Malatya 34.25% 4.14% 61.61% Muğla 11.88% 12.13% 75.99% Niğde 7.79% 16.83% 75.38% Samsun 18.60% 9.30% 72.09% Sivas 24.77% 12.50% 62.73% Tokat 14.10% 14.10% 71.79% Şanlıurfa 43.18% 5.68% 51.14% Source: OSYM

71

LIST OF FIGURES

Figure A.1: Size of Manufacturing Firms in TOBB survey as a Share of The TURKSTAT database in terms of number of establishments and number of employees

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

Num ber of Establishm ent Num ber of Em ployees

Figure A.2: Among the college-educated applicants, experience is more important than

education

0

20

40

60

80

Traditional Modern Very modern

Sightly agree or don't agree

Strongly agree

Figure A. 3:

Among MYO-educated applicants, experience is more important than education

0

20

40

60

80

Traditional Modern Very modern

Slightly agree or don't agreeStrongly agree

72

Figure A. 4: Among vocational/technical school-educated applicants, experience is more important than education.

0

20

40

60

80

Traditional Modern Very modern

Slightly agree or don't agree

Strongly agree

Figure A. 5:

Among blue-collar employee applicants experience is more important than education.

0

20

40

60

80

100

Traditional Modern Very modern

Slightly agree or don't agree

Strongly agree

Figure A. 6:

Among white-collar employee applicants experience is more important than education

0

20

40

60

80

Traditional Modern Very modern

Slightly agree or don't agree

Strongly agree

73

Figure A.7: Competitiveness of Turkish Industries

Apparel

Automotiv e

Textiles

Iron&SteelFruit&Vegies

TVs and Telecom

Electrical Machinery

Non mettalic minerals

Metals

transportation equipment

General machinery

Petroleum Products

Güç kaynakları (makinalar)

Plastic Materials

0%

10%

20%

30%

40%

50%

60%

0% 1% 2% 3% 4% 5% 6%

World market share %

2000

-200

4 an

nual

gro

wth

rat

e of

exp

orts

(C

AG

R)

Figure A.8: Percent of students that report on existence of extracurricular activities

0.00%20.00%40.00%60.00%80.00%

100.00%120.00%

Osm

anga

ziÜ

nive

rsite

si

Dic

leÜ

nive

rsite

si

Pam

ukka

leÜ

nive

rsite

s

Gaz

iant

epÜ

nive

rsite

si

Atat

ürk

Üni

vers

itesi

Anad

olu

Üni

vers

itesi

KATÜ

TOBB

ETU

Yedi

tepe

Hac

ette

peÜ

nive

rsite

si

Koç

Akde

niz

Üni

vers

itesi

Cap

pado

cia

MYO

Gal

atas

aray

Üni

vers

itesi

Erci

yes

Üni

vers

itesi

İsta

nbul

Tekn

ik

Bilk

ent

Üni

vers

itesi

OD

TÜ

Boğa

ziçi

Üni

vers

itesi

İsta

nbul

Bilg

iÜ

nive

rsite

si

74

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