台灣地區高等教育學生「職業與其教育不相稱」之探討

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(1)Education and Employment Mismatch among Bachelor’s and Master’s Graduates in Taiwan. by Hui-Hsia Yang. A Thesis Submitted to the Graduate Faculty in Partial Fulfillment of the Requirements for the Degree of MASTER OF EDUCATION Major: International Workforce Education and Development. Advisor: Wei-Wen Chang, Ph. D.. National Taiwan Normal University Taipei, Taiwan June, 2009.

(2) ACKNOWLEDGEMENT 首先誠摯的感謝指導教授張媁雯老師，老師悉心的教導使我得以順利完成論文，不時的討論並指點我正確的方向，從中獲益匪淺。另外，本論文的完成亦得感謝兩位口試委員－本所的蔡錫濤教授及中興大學企業管理系所的陳心懿教授，因為有你們的指導，使得本論文能夠更完整而嚴謹。轉眼間，兩年的日子匆匆的過去。在人力所的這兩年，謝謝所上老師們的教導，讓我在知識上有所精進。在這裡也結交許多不同背景的朋友，讓我在語言能力及國際觀上也有所增進。此外，感謝所上的助教－Lynn、Abby、Katie 的協助，使我能夠順利口試及畢業，這兩年也麻煩你們許多，謝謝您們。還有感謝一路上有慧萍(Claire)、意婷(Nini)、德倫(Sophie)、瓊姿(Emily)、玉雲(Mina)的支持及陪伴，宇熙學長和 Laura 學姐協助解決論文上的困惑，還有謝謝 Ella 及元太在論文上提供大力協助，也感謝所上其他同學的幫忙，恭喜我們順利走過這兩年。最後，謝謝我摯愛的家人，謝謝您們對我的包容。.

(3) ABSTRACT The number of students who majored in the field of science and technology has been gradually growing in recent years in Taiwan. However, a report revealed by Manpower (2008) suggested that Taiwanese employers had difficulty in recruiting information technology (IT) staff. This seems to indicate an existing gap between the needs in industry and the higher education systems. And this gap might result from the mismatch between education and employment. Thus, the purpose of the study is to examine the phenomenon of mismatch between employment and education in terms of bachelor’s and master’s graduates who majored in the engineering and technology. In this study, the data collected by Taiwan Higher Education Database was utilized, and the sample was restricted to the graduates majored mathematics and computation. According to the statistical results, it was confirmed that a phenomenon of mismatch between employment and education exists. Additionally, the bachelor’s graduates were found to be more likely to be mismatched between job and major, whereas the master’s graduates were found to be more likely to be overeducated in terms of the additional years of overeducation. Therefore, the reason why the Taiwanese companies had difficulty in recruiting IT staff could be inferred. Because of the distrust of the ability of bachelor’s graduates, the companies would rather recruit the candidate with master degree if a job requires a bachelor degree. When the companies raise the qualification standard, they face the problem to recruit people who can match their positions. And the bachelor’s graduates might turn to seek other employment position, if they could not gain the jobs related to their majors.. Keywords: Education and employment mismatch, tertiary education graduates, technology industry. I.

(4) TABLE OF CONTENTS ABSTRACT....................................................................................................................I TABLE OF CONTENTS .............................................................................................. II LIST OF FIGURES .....................................................................................................IV LIST OF TABLES ........................................................................................................ V. CHAPTER I.. INTRODUCTION ............................................................1. Background of the Study....................................................................................... 1 Purposes of the Study............................................................................................ 4 Questions of the Study .......................................................................................... 5 Significance of the Study ...................................................................................... 6 Definition of Terms ............................................................................................... 7. CHAPTER II. LITERATURE REVIEW..................................................9 Education-Employment Mismatch ....................................................................... 9 Jobsearch Duration.............................................................................................. 16 Work Values ........................................................................................................ 18. CHAPTER III. METHODOLOGY........................................................24 Research Framework .......................................................................................... 24 Research Hypotheses .......................................................................................... 26 Research Methods ............................................................................................... 30. CHAPTER IV. RESULTS AND DISCUSSIONS..................................38 Demographic and Descriptive Statistics ............................................................. 38 Hierarchical Regression Analysis of Employment-education Mismatch on Factors of Demographic, Work values, and Jobsearch Duration ........................ 46 Logistic Regression Analysis of Employment-education Mismatch on Factors of Demographic, Work values, and Jobsearch duration .......................................... 50. II.

(5) Hierarchical Regression Analysis of Jobsearch Duration on Factors of Demographic Variables, and Work Values .......................................................... 54 Discussions ......................................................................................................... 57. CHAPTER V. CONCLUSIONS AND SUGGESTIONS.......................60 Conclusions......................................................................................................... 60 Suggestions ......................................................................................................... 65. REFERENCES.. .......................................................................................70 APPENDIX A. QUESTIONNAIRE FOR BACHELOR’S GRADUATES................................................................78 APPENDIX B. QUESTIONNAIRE FOR MASTER’S GRADUATES ..91. III.

(6) LIST OF FIGURES Figure 3.1 Research framework ...................................................................................25 Figure 3.2 Research procedure ....................................................................................29. IV.

(7) LIST OF TABLES Table 1.1 Taiwan students graduating from higher education by field ..........................2 Table 1.2 Unemployment rate by education level..........................................................3 Table 2.1 Review of the definition of work values ......................................................18 Table 2.2 Review of the types of work values .............................................................21 Table 3.1 The schooling years of each level of education ...........................................33 Table 3.2 Variables coding system ...............................................................................35 Table 4.1 The profile of the respondents......................................................................40 Table 4.2 Overeducated: Frequency of the sample ......................................................42 Table 4.3 Analysis of educational attainment to the additional years of overeducation…. ......................................................................................................................42 Table 4.4 Percentage of respondents working outside their majors.............................43 Table 4.5 Analysis of educational attainment to match between work and majors .....43 Table 4.6 Analysis of educational attainment to jobsearch duration............................44 Table 4.7 Analysis of educational attainment to work values......................................44 Table 4.8 Hierarchical regression analysis for variables predicting the additional years of overeducation...........................................................................................47 Table 4.9 Hierarchical regression analysis for variables predicting the relatedness between work and major ..............................................................................48 Table 4.10 Summary of the possible work values predicting employment-education mismatch ....................................................................................................49 Table 4.11 Logistic regression analysis for variables predicting overeducation .........51 Table 4.12 Logistic regression analysis for variables predicting mismatch ................52 Table 4.13 Summary of the possible work values predicting employment-education mismatch ....................................................................................................53. V.

(8) Table 4.14 Hierarchical regression analysis for variables predicting jobsearch duration ....................................................................................................................55 Table 4.15 Statistic results of research hypotheses ......................................................56 Table 5.1 Summary of results of research hypotheses .................................................64. VI.

(9) CHAPTER I. INTRODUCTION This chapter introduces the background of the study, statement of the problem, purpose of the study, questions of the study, significance of the study, as well as the definition of terms.. Background of the Study The technology is rapidly evolving due to globalization. In order to compete with other countries, nations around the world emphasize the development of the technology. The technology industry is a key industry in Taiwan. Likewise, Taiwan’s government put more emphases on scientific and technological (S&T) development such as adding more resources on science and technology or being devoted to developing S&T workforce (National Science Council, 2007). Hence, there is a demand for technical human resources. However, according to a report revealed by Manpower (2008), Taiwanese employers have a difficulty in recruiting information technology (IT) staff. While the technology companies meet difficulty in finding enough employees, however, according to a report of the Ministry of Education, the number of higher educated people was growing. The number of undergraduates had soared by 79.5%; the number of postgraduates had soared by 104% from 2001 to 2005. As shown in Table 1.1, both in master’s and bachelor’s programs, there are the most students majoring in the field of science and technology.. 1.

(10) Table 1.1. Taiwan students graduating from higher education by field Academic year (AY) Program Master’s program. Bachelor’s program. Field 2001. 2002. 2003. 2004. 2005. Humanities. 2,788. 3,986. 4,949. 6,034. 7,002. Social sciences. 4,912. 6,472. 8,225. 9,850. 12,149. Science and technology. 13,052. 15,442. 17,682. 20,097. 23,183. Persons subtotal. 20,752. 25,900. 30,856. 35,981. 42,334. Humanities. 21,285. 23,600. 26,595. 29,440. 31,729. Social sciences. 38,750. 67,363. 62,614. 70,378. 77,932. Science and technology. 57,395. 72,308. 86,835. 93,036. 101,102. Persons subtotal. 117,430 146,166 176,044 192,854 210,763. Total persons. 138,182 172,066 206,900 228,835 253,097. Source: National Science Council, 2007. Even though schools developed considerable amounts of students for workforces, companies had problems of recruiting sufficient IT staff. This indicates an existing gap between the needs in industry and the higher education systems. The phenomenon of mismatch between employment and education is getting worse in Taiwan (Yen & Yeh, 1997; Wang, 2000). Many literatures stated that high educated people had higher risk in underutilization (Yen & Yeh, 1997; Yeh, 2001), while few studies further discussed whether graduates with bachelor degree were more likely to be underutilized than those with master degree. And according to Yen & Yeh (1997), they suggested that high educated people would face the dilemma of being unemployed or being inadequately utilized because they could not easily find jobs equal to their education level (Mai & Tsai, 2003). In addition, Wang (2000) argued that if a jobseeker’s unemployment time was too long to tolerate, one might find a job below one’s education level. There are a lot of research 2.

(11) discussed that people with higher education level had shorter unemployment time (Kettunen, 1997; Mills, 2001; Audas, Berde & Dolton, 2005), but limited literature discussed whether graduates with bachelor degree or master degree both could quickly find jobs. Table 1.2 showed that the unemployment rate for people with a college education or higher degree was high, around four percent. Moreover, the unemployment rate for people with a university education or higher degree was not only increasing, but also larger than the total unemployment rate in past years. The results implied that people with higher education would be likely to be unemployed. Table 1.2. Unemployment rate by education level Year. Junior college or higher degree. Total (%) Total (%). Junior college (%). University or higher degree (%). 2001. 4.57. 3.72. 4.03. 3.32. 2002 2003 2004 2005. 5.17 4.99 4.44 4.13. 4.28 4.09 4.06 4.01. 4.6 4.32 4.02 3.78. 3.89 3.82 4.11 4.23. 2006 2007. 3.91 3.91. 3.98 4. 3.55 3.36. 4.36 4.51. Source: Department of Statistics of the Ministry of Education, 2008.. In Taiwan, after the educational reform, the number of undergraduates is increasing (Table 1.1). With the high unemployment rate, more bachelor’s graduates would like to study further (Huang, 2008). A Taiwanese magazine, “Global view”, revealed that most master’s graduates believed that after gaining the master degree, they could easily find jobs. This thought encouraged more bachelor’s graduates to pursue master degree in order to enhance their competitiveness of employment. Also, the survey revealed that most master’s graduates believed that educational background was more critical than work experience.. 3.

(12) In addition, although the quantity of undergraduates is increasing, about 90 percent of companies considered that the quality of them is not increasing as well (Mai & Tsai, 2003). Hence, while people with bachelor degree and people with master degree contended for a job, many companies would hire people with master degree (Mai & Tsai, 2003). Therefore, it seems that people with master degree could easily and quickly find jobs, and might be more likely to be well utilized. Further, work values might be related to either jobsearch duration or overeducation. For higher educated people, most of them would rather take time to find a satisfied job, where the job choice is influenced by work values (Chen & Liu, 1995; Lin, 1996). Therefore, in order to get a satisfied job, they might either spend more time on searching a job, or be overeducated. However, ample literatures discussed about the relationship between work values and job satisfaction, while seldom research examined whether the perceived work value was significantly different between people with bachelor degree and people with master degree and whether jobseekers with certain work values would be more likely to be overeducated or take more time to look for a job.. Purposes of the Study The purpose of this study is to examine the phenomenon of mismatch between employment and education in terms of bachelor’s and master’s graduates who majored in the engineering and technology. Further purposes are demonstrated as follows. 1.. To investigate whether the phenomenon of education-employment mismatch exists among bachelor’s and master’s graduates.. 2.. To examine whether there exist a significant difference in jobsearch duration among bachelor’s and master’s graduates.. 3.. To identity if there is a relationship between the work values and education-employment mismatch among jobseekers with bachelor degree and master degree.. 4.

(13) Questions of the Study There are questions that generated from the study: 1.. Is there a relationship between education attainment and education-employment mismatch?. 2.. Is the jobsearch duration of master’s graduates shorter than that of bachelor’s graduates?. 3.. Are the perceived work values significantly different between bachelor’s and master’s graduates?. 4.. Is there a positive relationship between jobsearch duration and education-employment mismatch?. 5.. Is there a positive relationship between work values and education-employment mismatch?. 6.. Is there a relationship between work values and jobsearch duration? This research focused on the phenomenon of education-employment mismatch in the. technology industry, so the sample of the study is delimited to graduates with bachelor degree and master degree and whose majors were in the field of science and technology (engineering and technology in particular). Therefore, the phenomenon of education-employment mismatch among graduates majoring in the field of humanities and social sciences cannot be identified through this study.. 5.

(14) Significance of the Study The technology industry is one of important industries in Taiwan, so the government has been actively devoted to cultivating the technical workforces. Through this study, the utilization of IT human resources will be explored. It can be indicated whether a phenomenon of education-employment mismatch exists among technical human resources with bachelor degree and master degree, namely, whether human resources developed by schools for the IT industry were inadequately utilized. As a result, the findings of the study might help explain why companies encountered difficulty in recruiting IT staff. Furthermore, the findings could help examine the stereotype of master’s graduates having the advantage of obtaining a job. In addition, the reason why technology companies can be competitive is partly because that they could recruit and retain talents. And understanding the work values of jobseekers could help companies recruit and retain good employees (Tsai, 2004). Also, through this study, the perspective of bachelor’s and master’s graduates on work values can be identified, so employers might take into account while recruiting.. 6.

(15) Definition of Terms Educational Attainment Educational attainment refers to the highest degree an individual has received (US Census Bureau). In the study, the levels of educational attainment are restricted to bachelor degree and master degree. Field of Science and Technology According to Ministry of Education, it classified the students into three fields (humanities, social sciences, and science and technology) by program. Further, the field of science and technology can be categorized into four groups: natural sciences, engineering and technology, medical sciences, and agricultural sciences (National Science Council, 2004), and the researcher focused on engineering and technology in the study. Mismatch between Employment and Education In this research, there are two perspectives of mismatch between employment and education. One perspective is overeducation, and the other one is mismatch. First, overeducation is that the education level required for the job is lower than one’s (Wang, 2000; Wirz & Erdal, 2005). For instance, if a college graduate obtains a job which requires a junior college degree or below, s/he is overeducated. On the other hand, mismatch is that one’s work in an occupational field is unrelated to one’s educational backgrounds (Wang, 2000; Wirz & Erdal, 2005). If an individual’s work is not or somewhat related to their degree field, s/he is classified as mismatched, while if one’s work is mostly or closely related to one’s degree field, one is matched. Jobsearch Duration Ghukasyan (2008) described jobsearch duration as “the time period between job search and job placement” (p.15). In the study, jobsearch duration, and unemployment duration are interchangeably utilized.. 7.

(16) Work Values Work values are any entity (object, behavior, situation) on which an individual place a high importance in the work context (Elizur, Borg, Hunt, & Beck, 1991).. 8.

(17) CHAPTER II.. LITERATURE REVIEW. In this chapter, a review of the literature regarding education-employment mismatch, jobsearch duration, and work values would be provided.. Education-Employment Mismatch Hauser (1974) developed a labor underutilization framework (LUF), where the labor force is classified into two groups, adequately utilized workers and inadequately utilized workers. Inadequately utilization could be further classified by its cause of underutilization, namely, unemployment, inadequate work hours, inadequate income, and education and employment mismatch, while the adequately utilized workers are the rest of the total work force. And this section focuses on the studies of education and employment mismatch. Education and employment mismatch has attracted substantial attention in recent years, since waste resulting from workers being in the wrong jobs might be more critical than that associated with unemployment in the economy (Hutt, 1939). Overeducation and Overqualification Most literature regarded overeducation and overqualification as the same phenomenon (Battu, Belfield & Sloane, 1999; Frenette, 2004; Ng, 2001; Hersch, 1991); however, Badillo Amador, López Nicolás and Vila Lladosa (2008) stated that they were different phenomena. First, they defined qualification as the abilities, skills, attitudes and knowledge, and therefore overqualification is that one’s qualification is higher than those necessary for the job. They also explained the reason why overeducation is often used as overqualification. Since qualification is difficult to identify and measure, one’s educational attainment is often utilized as a proxy for one’s qualifications. All in all, overschooling, overeducation, overqualification, overskilled, underemployment, occupational mismatch, and so forth could be used interchangeably (Borghans & Grip, 1999).. 9.

(18) Measures of Overeducation There are various methods to measure overeducation. All these methods have its advantages and disadvantages; it depends on availability of data and the researchers’ preferences (Wirz & Atukeren, 2005). The first method is the worker self-assessment method or the direct-self-response method, which is a subjective measure because participants themselves compare their educational attainment to the requirements of the job currently performed (Cohn & Ng, 2000; Dolton & Vignoles, 2000; McGoldrick & Robst, 1996; Robst, 1995; Wirz & Atukeren, 2005). The advantage of this measure is that it is job-specific; however, it may suffer from a self-response bias (Bauer, 1999; Wirz & Erdal, 2005). But Wirz & Erdal (2005) suggested that this problem can be solved, if there is no uncontrolled systematic bias or correlation of the responses across individuals (Dolton & Vignoles, 2000). On the other hand, there are the other three objective measures, which these methods can avoid suffering from a self-response bias. The first method is standard deviation method, which is one of the realized matches methods. This method is to compare the number of years of individual’s schooling completed (SCHOOL) with the average years necessary to perform the tasks in an occupational field (Wirz & Erdal, 2005). According to Verdugo and Verdugo (1989), when one’s SCHOOL is higher than one standard deviation above the mean value for the occupation, one is considered to be overeducated (Cohn & Ng, 2000; Dolton & Vignoles, 2000). This method, however, is also criticized because using one standard deviation as the cut off point might be arbitrary (Dolton & Vignoles, 2000). Moreover, Kiker, Santos and Oliveira (1997) mentioned that this measure is more sensitive to technological changes and changes in workplace organization than the others. Therefore, the second method, one of realized matches methods, was developed by Kiker et al. (1997). They suggested using the modal value of SCHOOL for each occupation 10.

(19) instead. Those with SCHOOL greater than the modal level of schooling for their specific occupation are overeducated (Cohn & Ng, 2000). The third method is the job analysis method (Hartog, 2000) or the expert opinion method, which is considered the optimal method to measure whether one is overeducated. The experts specify the number of years of schooling necessary to perform the job in an occupational field. The most elaborate example is the United States Dictionary of Occupation Titles (DOT). While this measure is more optimal and objective than methods mentioned above, the available sources may be outdated and not capture the changes in job characteristics overtime. Moreover, it is costly to measure overeducation by this method (Hartog, 2000; Tsai, Chuang & Yeh, 2005; Wirz & Erdal, 2005). The Determinants of Mismatch between Employment and Education Following is a review of the literature discussed that some variables used in the analysis of likelihood of being overeducated (in terms of the additional years of overeducaiton) or mismatched (between job and major), and these variables are gender, age/cohort, experience, education background, marital status and ethnic. Gender There is no consistent conclusion that whether gender would have significant impact on the possibility of being overeducated or mismatched. The results showed that either females were less likely to be inadequately matched and to be overeducated than males (Cohn and Ng, 2000; Kiker et al., 1997), or females were no more likely than males to be overeducated in their jobs (Dolton & Vignoles, 2000), whereas Bender and Heywood (2006) suggested that females were more likely to be mismatched (Groot & Maassen van den Brink, 2000). Furthermore, Chevalier (2000) indicated that women with master degree were more likely than men to accept non-graduate jobs and that married women were more likely to be overeducated due to the fact that they might be more constrained in their job search by family preferences. Additionally, Groot and Maassen van den Brink (1996) indicated that people 11.

(20) who had experienced a career interruption were more likely to be in jobs for which they were overeducated. Thus, these results implied that women with children might have higher probability of being overeducated. On the other hand, there was a finding that gender has no significant effects on the risk of overeducation (Battu, Belfield, & Sloane, 2000; Wirz & Atukeren 2005). Education background The likelihood of being mismatched decreases with the level of the most recent degree; in other words, individuals with master degree or doctoral degree were less likely to be mismatched than those with bachelor degree (Robst, 2007). Also, Wirz and Atukeren (2005) suggested that university education had a positive and significant relationship with overeducation in the Swiss labor market. However, Wang (2000) illustrated that people with master degree were significantly overeducated than people with bachelor degree were. Further, Dolton & Vignoles (2000, p.18) expressed that “arts/humanities or languages graduates are more likely to be overeducated than graduates of other faculties”. Additionally, Robst (1995) stressed that US graduates from more prestigious institutions were less likely to be overeducated. Also, public-school students were less likely to be overeducated than private-school students (Wang, 2000). Likewise, graduates with good grades were less likely to be overeducated than those with bad grades (Dolton & Vignoles, 2000). Therefore, as mentioned. above,. educational. background. difference. could. have. impact. on. education-employment mismatch. And there were two sub-hypotheses were proposed: (a) educational attainment is positively and significantly related to overeducation; (b) educational attainment is negatively and significantly related to mismatch. Marital status In Wirz and Atukeren’s study (2005), the result showed that for the overall sample or the sample restricted to female, there was no statistically positive relationship between marital status and overeducation, whereas for the sample restricted to male, there is a statistically 12.

(21) negative relationship between them. Also, Bender and Heywood (2006) suggested that the married were less likely to be overeducated. Ethnic The result showed that whites and blacks were equally likely to be mismatched; nevertheless, Robst (2007) found that if ethnic groups face discrimination, they will be less likely to find a related job. Also, the result showed that Whites and Asians were more mismatched than Blacks and Hispanics (Robst, 2007). Age/Cohort Bender and Heywood (2006) illustrated that older workers were more likely to be mismatched (Robst, 2007) while Groot and Maassen van den Brink (1996, 2000) found that younger workers were more likely to be overeducated than older workers (Chevalier, 2000; Kiker et al., 1997). Chevalier (2000) explained why younger workers were more likely to be overeducated. First, older workers have had more time to prospect the labor market so that their skills could be improved through on-the-job training. Second, workers would consider that it is inevitable to change their career expectations over time. Third, graduates from the younger cohort were less likely to acquire graduate skills while studying due to over-crowding or changes in the curriculum. Experience Workers with less work experience were more likely to be overeducated (Cohn & Ng, 2000; Daly, Bu¨chel & Duncan, 2000), because overeducated workers may substitute education for the lack of previous job experience (Kiker et al., 1997). The Reasons for Being Willing to Be Overeducated or Mismatched According to Bender and Heywood (2006), the result indicated that people who were willing to be overeducated because of these three factors, better pay and promotion, the lack of jobs or changed interests. Also they found that the reason why those were mismatched was 13.

(22) because they lack jobs. As mentioned before, if jobseekers still could not find jobs for a long time, they would rather be overeducated (Wang, 2000). Likewise, people might be willing to be overeducated, if what they desired is satisfied. For example, if they were satisfied with the pay which companies offered, they would be willing to be overeducated. Thus, there might be a relationship between work values and overeducation. As a result, the researcher hypothesized that the certain perceived work values are significantly and positively associated to education-employment mismatch. In addition, jobsearch duration could be positively related to education-employment mismatch, and the hypothesis was developed: jobsearch duration is significantly and positively related to education-employment mismatch. The Consequences of Mismatch between Employment and Education The consequences could be divided into two groups: the monetary consequences and the non-monetary consequences for workers. Monetary consequences Considerable studies investigated the relationship between education-employment mismatch and wages, and the results were consistent among these researches. In most study, there was a positive relationship between overeducation and wages. Overeducated workers earned more than their coworkers with exactly the adequate years of schooling and identical other characteristics. And Oosterbeek (2000) mentioned that only when males had a bachelor degree, males who were overeducated could earn more than males who were properly matched. However, overeducated workers earned lower wages than workers with the same level of educational attainment, but work in occupations which fully utilize their education (Allen & Van der Velden, 2001; Borghans, Bruinshoofd & Grip, 2000; Cohn & Ng, 2000; Daly, Bu¨chel & Duncan, 2000; Dolton & Vignoles, 2000).. 14.

(23) Non-Monetary consequences There were also several studies investigated the relationship between overeducation and non-monetary consequences such as job satisfaction. Most studies had similar results showed that overeducation was positively related to diminished job satisfaction. Those who were overeducated were less satisfied than those who work in occupations which fully utilize their education (Belfield & Harris, 2002; Hersch, 1991; Moshavi & Terborg, 2002), whereas the adequately educated workers have a premium on job satisfaction (Battu et al., 2000). Vieira (2005) further divided job satisfaction into four dimensions, which were pay, job security, type of work and number of hours of work, and found that overeducation had negative effect on these four dimensions. As mentioned, some authors regarded overeducation and overqualification as different. Thus, they investigated whether both overeducation and overqualification affect the job satisfaction. According to Badillo-Amador and Vila-Lladosa (2006), they argued that the qualification mismatched workers were more likely to be disappointed than those who were accurately match in terms of qualification, while the effects of education mismatch situations (overeducation) on job satisfaction were not significant (Allen & Van der Velden, 2001; Green & McIntosh, 2002). Yet, Buchel (2002) suggested that overqualified employees had the same job satisfaction as those properly matched. In addition, overeducation resulted in not only dissatisfied workers (Tsang, 1987), but also higher turnover rate (McGoldrick & Robst, 1996; Wolbers, 2003). Further, people who were overqualified would have difficulties in professional / private life and poor health status (Wirz & Atukeren 2005).. 15.

(24) Jobsearch duration Following is a review of literature studied on the jobsearch duration or unemployment duration. According to Ghukasyan (2008, p.15), the author defined the duration of unemployment or jobsearch duration as “the time when the citizen is searching for a job irrespective of the way of searching it”. The duration of unemployment has two characteristics: 1.. Duration of completed unemployment, which is the time period between job search and job placement.. 2.. Duration of incomplete unemployment, which is the time period between job search and the moment of official registration of unemployment. Educational Background and Jobsearch Duration. According to Zhang (1994), he found that school, major and, educational attainment have significantly impact on jobsearch duration. For example, lower educated people continued to face greater barriers to the exit from unemployment (Kletzer, 1998; Mills, 2001). In most cases, a significantly negative relationship between educational attainment and jobsearch duration was found; in other words, the higher the educational level, the shorter the jobsearch duration (Chen, 2008; Tao & Li, 2006). However, in Lin’s study (2007), there was no significant difference in jobsearch duration between bachelors and masters. And in this study, the hypothesis was proposed: the jobsearch duration of master’s graduates is significantly shorter than that of bachelor’s graduates. Other Factors and Jobsearch Duration  Tao and Li (2006) indicated that personal characteristics such as age, gender and marital status significantly impact on the unemployment duration. Take age for example, if one is under the age of 34, as one grows older, one has shorter unemployment duration (Lin, 1991). However, if one is over the age of 34, as one grows older, one has longer unemployment duration (Chen, 2002). Additionally, the unemployment duration of males was significantly 16.

(25) longer than that of females (Chen, 2002; Chen, 2008). Chen (2002) explained that because men have taken on more economic responsibilities, they spent more time on searching a high salary job. Nevertheless, the result also showed that gender had no significant impact on jobsearch duration (Lin, 2007). Moreover, Lin (2007) suggested that first-time jobseekers had lower probability of employment due to the lack of work experience. And the average unemployment duration was also found to be longer in non-metropolitan than in metropolitan areas (Mills, 2001; Swaim, 1990). Also, the welfare policy had positively and significantly impact on the jobsearch duration (Bloemen, 1997; Paul & Ian, 2001; Stancanelli, 1999). As mentioned above, several factors were found to be related to jobsearch duration. In Chen’s study (2002), the researcher further stressed that males had longer jobsearch duration because of the preference for the high salary. Based on the explanation, the hypothesis was developed: work values are significantly associated with the length of jobsearch duration.. 17.

(26) Work values The definition of work values, the types of work values, and literature about work values would be included in this section. Definition of Work Value With regard to work values, there remains disagreement on the definition or dimension in spite of the considerable literature discussed about work values (Dose, 1997). For example, Super (1970) defined that work values are the part of individuals’ values that work can satisfy (Li, Liu & Wan, 2008, p876), while Kalleberg (1977) defined them as the conceptions of what is desirable that individuals hold with respect to their work activity (p140). According to Hazer and Alvares (1981), however, they argued that work values are shaped by the socialization. As shown in Table 2.1, the various definitions of work values could be identified. Table 2.1. Review of the definition of work values Authors. Definitions. England (1967, p.54). Work values are ideologies or philosophies that enable the understanding of individuals' behaviors at work.. Super (1970) Cited Li, Liu, Wan (2008). Work values are the part of individuals’ values that work can satisfy.. Kalleberg (1977, p.140). The conceptions of what is desirable that individuals hold with respect to their work activity.. Pryor (1981) Cited Zhang, Wang,. The author used the term preferences to define values, so work values are what individuals like or prefer in a job rather than what. Yang, & Teng (2007). they think is good or should be done.. Cook, Hepworth, Wall, & Warr (1981, p.132). Work values focus on the more enduring aspects of people's orientations towards employment in general rather than on their reactions to particular jobs or occupations. (table continues) 18.

(27) Table 2.1. (continued) Authors Ravlian & Meglino (1989) Cited Zhang et al.. Definitions Work values are a kind of preference they viewed preference as various socially desirable modes of work behaviors, which consequently ought to be displayed. (2007) Elizur, Borg, Hunt, & Beck (1991, p.22). Value of a given social group is any entity (object, behavior, situation) on which that group place a high importance. Work values are such entities in the work context.. Dose (1997, p.227-228). Work values are evaluative standards relating to work or the work environment by which individuals discern what is “right” or assess the importance of preferences.. Ros, Schwartz & Surkiss (1999, p.54). Work goals or values are seen as specific expressions of general values in the work setting.. Schwartz (1999, p.41). Work values are the goals or rewards people seek through their work, and they are expressions of more general human values in the context of the work setting.. Taris & Feij (2001, p.3). Work values are enduring beliefs that a specific mode or conduct or end-state is preferable to its opposite, thereby guiding the individual’s attitudes, judgments and behaviors. Zhang, Wang, Yang, & Teng (2007,. Work values refer to broad tendencies to prefer certain job characteristics, outcomes or features of work environments.. p.1282). 19.

(28) Types of Work Value Notwithstanding there is also no agreement on the types of work values (Table 2.2); one of the most widely used approaches to work values classified them as either intrinsic or extrinsic. For instance, Work Values Questionnaire (WVQ; Mantech, 1983) comprises 37 items measuring intrinsic and extrinsic work values. According to Taris and Feij (2001, p.3), they illustrated that “intrinsic work values refer to immaterial aspects of their jobs that allow for self-expression, for example, job variety and autonomy, while extrinsic work values refer to material or instrumental work aspects, such as salary and opportunity for promotion”. In addition, George and Jones (1997) also proposed that intrinsic work values are relevant to the goals of work and dependent on the content of work, whereas extrinsic work values are independent of work content.. 20.

(29) Table 2.2. Review of the types of work values Authors Super(1970, 1973). Types of work values Super (1973) developed the Work Values Inventory, which contains 15 items. 1. Extrinsic values in the form of rewards: way of life, security, prestige, and economic returns. 2. Extrinsic social and environmental concomitants of work: surroundings, associates, supervisory relationships, and variety. 3. Intrinsic rewards derived from activity enjoyment (pleasure) and goal accomplishment: creativity, management, achievement, altruism, independence, intellectual stimulation, and aesthetics.. Zytowski (1970). There are 3 types of work values: extrinsic values, intrinsic values and concomitant values.. Rokeach (1973). Work values can be classified into two groups. 1. Instrumental values (having to do with a mode of conduct): they are further divided into the categories of moral such as honesty or competence such as intelligence. 2. Terminal values (having to do with an end-state of existence): they are described as either personal (an end state describing oneself) or social (an end state describing society).. Miller (1974). 1. Extrinsic values: way of life, security, prestige, economic returns, variety, independence, surroundings, associates, and supervisory relationships, 2. Intrinsic value: achievement, altruism, creativity, aesthetics, management, and intellectual stimulation.. Kalleberg (1977). There are six types of work values: intrinsic, convenience, financial, relationships with co-workers, career, and resource adequacy.. Pryor (1981). The author distinguished 12 factors in his Work Aspect Preference Scale (WAPS): security, self-development, altruism, lifestyle, physical activity, detachment, independence, prestige, management, coworkers, creativity, and money. (table continues). 21.

(30) Table 2.2. (continued) Authors Elizur (1984). Types of work values Two facets for classifying work values were proposed:. Elizur, Borg, Hunt, & 1. Modality of outcome: Beck (1991). (1) Instrumental outcomes: pay, hours of work, security, benefits, and work conditions. (2) Cognitive outcomes: relations with supervisor, coworkers, recognition, esteem, and opportunity to interact with people (3) Affective. outcomes:. responsibility,. advancement,. achievement,. influence, interest, feedback, meaningful work, use of abilities, independence, company, status, and contribution to society. 2. System–performance contingency: whether job rewards are contingent on task performance (reward such as pay, recognition, feedback, advancement, and status) or on membership in the organization (resource). Cornelius, Ullman,. They developed Comparative Emphasis Scale to assess four work values:. Meglino, Czajka, &. achievement, concen for others, honesty, and fairness.. McNeely (1985) Vaus & McAllister. 1. Extrinsic values. (1991). 2. Intrinsic values. England & Ruiz. They identified three categories of goals: social, expressive, and. Quintanilla (1994). instrumental. (parallel the social, intrinsic, and extrinsic types, respectively). Ros, Schwartz, &. 1. Intrinsic: personal growth, autonomy, interest, and creativity. Surkis (1999). 2. Extrinsic: pay and security 3. Social: contact with people and contribution to society 4. Power: prestige, authority, influence, power, and achievement in work are common in empirical research on work. These values have usually been classified as extrinsic.. Johnson (2001). There are four types of work values: extrinsic, intrinsic, altruistic, and social.. Sinisalo (2004). 1. Self-actualizing values: develop new ways of working, learn new things at work, use knowledge, develop skills, professional schooling, develop abilities, and responsibility. 2. Work hygiene values: safe and healthy, and work hours. 3. Extrinsic values: secure employment, wages, work environment, and possibilities for advancement. 4. Intrinsic values: variety, interesting work, and independence. 22.

(31) Age and Work Values There were many studies indicated that younger workers and older workers might have different work values (Li, Liu & Wan, 2008; Rhodes, 1983). The younger generations more focused on fulfilling their individual values when looking at career options and potential employers (Sullivan, Sullivan, & Buffton, 2002). For example, Hong (1993) found that the Taiwanese older labors, who were above 40 years old, emphasized more on the interpersonal relationship while the younger labors focused more on the advancement of intelligence and experiences. Chiu (1993), however, found that the younger labors more paid attention to interpersonal relationship than the older labors. Education and Work Values The research indicated that people with a lower level of education tended to value more on extrinsic work values such as salary, whereas those with a higher level of education had a strong expectation regarding the opportunity for promotion, interestingness and independence of work (Chiu, 1993; Sinisalo, 2004). High educated people work not only for more money, but also for their pursuit of self-fulfillment for a better life and recognition by others (Li et al., 2008). Thus, educational attainment difference could have impact on education-employment mismatch (overeducation and mismatch), and the hypothesis is that the perceived work values of bachelor’s graduates are significantly different from that of master’s graduates. Gender and Work Values There was also evidence about gender difference on work values. According to Lacy, Bokemeijer and Shepard (1983), they stated that male more concerned with income and advancement but female more concerned with helping people. On the other hand, Pu’s (1988) research indicated that female employees more focused on salary than male employees, while male employees focused more on intrinsic work values and prestige. Also, Chung’s (2000) study showed that women tended to more emphasize on self-advancement than men (Tsai, 2004). 23.

(32) CHAPTER III. METHODOLOGY This chapter is composed by three sections which are research framework, research procedure, research hypotheses and research methods.. Research Framework The research framework, as shown in Figure 3.1, is established based on the aim of the study and the previous reviewed literature. First, the levels of educational attainment could impact on the possibility of education-employment mismatch (overeducation and mismatch), the length of jobsearch duration, and perceived work values. In the study, the levels of educational attainment were restricted to bachelor degree and master degree. Second, people might be willing to be overeducated (receiving additional years of overeducation) or mismatched (between job and major), as their needs can be satisfied by that job, namely, their work values were met. Also, people might be overeducated or mismatched, if they could not get jobs for a long time. Thus, the length of jobsearch duration and work values might be associated with education-employment mismatch. Finally, work values might affect the length of the jobsearch duration as well. As mentioned before, due to the preference for the high salary, men would take time to find jobs with good pay, thus causing that men have longer jobsearch duration. Therefore, the preferences for certain work values might be correlated with jobsearch duration.. 24.

(33) Figure 3.1. Research framework. 25.

(34) Research Hypotheses There are six hypotheses generated according to the research questions and the reviewed literature, and the research hypotheses were also demonstrated in Figure 3.1. Question 1: Is there a relationship between educational attainment and education-employment mismatch? Hypothesis 1: Educational attainment is significantly related to education-employment mismatch. Hypothesis 1.1: Master’s graduates are significantly overeducated than bachelor’s graduates. Hypothesis 1.2: Bachelor’s graduates are significantly mismatched than master’s graduates. Question 2: Is the jobsearch duration of master’s graduates shorter than that of bachelor’s graduates? Hypothesis 2: The jobsearch duration of master’s graduates is significantly shorter than that of bachelor’s graduates. Question 3: Are the perceived work values significantly different between bachelor’s and master’s graduates? Hypothesis 3: The perceived work values of bachelor’s graduates are significantly different from that of master’s graduates. Question. 4:. Is. there. a. positive. relationship. between. jobsearch. duration. and. education-employment mismatch? Hypothesis 4: Jobsearch duration is significantly and positively related to education-employment mismatch. Hypothesis 4.1: Jobsearch duration is significantly and positively related to overeducation. Hypothesis 4.2: Jobsearch duration is significantly and positively related to mismatch. 26.

(35) Question 5: Is there a positive relationship between work values and education-employment mismatch? Hypothesis 5: The certain perceived work values are significantly and positively associated with education-employment mismatch. Hypothesis 5.1: The certain perceived work values are significantly and positively associated with overeducation. Hypothesis 5.2: The certain perceived work values are significantly and positively associated with mismatch. Question 6: Is there a relationship between work values and jobsearch duration? Hypothesis 6: Work values are significantly associated with the length of jobsearch duration.. 27.

(36) Research Procedure The procedure of the study is showed in Figure 3.2 and the detail was described as following: 1. Identify the objectives of the research. The first step is to clarify the aim of the study. After discussing with the advisor, the researcher determined the objectives of the study, and headed toward next step. 2. Write down the research questions. Since the research questions are generated from the aim of the study, after answering these questions the purpose of the study can be achieved. 3. Review relevant literature. After searching relevant studies, literature review was conducted to understand the findings and discussion of other research regarding the topic the research. 4. Develop research hypotheses. Based on the reviewed literature and the aim of the study, the researcher could develop the hypotheses. 5. Collect the data. The researcher applied to Taiwan Higher Education Database for the survey results. 6. Analyze the data. After obtaining the data, the research could utilize statistical software to analyze the data, and to test the hypotheses. 7. Represent the results. Then the statistical results were presented in accordance with the hypotheses. 8. Draw the conclusion and provide suggestions. Consequently, the researcher drew the conclusion or implications from the results, and then proposed some suggestions based on the findings.. 28.

(37) Figure 3.2. Research procedure. 29.

(38) Research Methods This section includes data collection, and data analysis. The sample, instrument, and measures in the research would be illustrated in the subsection of data collection, whereas the used statistical methods would be demonstrated in the subsection of data analysis. Data collection The data was from a survey conducted by Taiwan Higher Education Database (THEDS). Due to the support from the Ministry of Education, THEDS could collect the information of students graduated from the junior colleges, colleges, universities and technological colleges and universities in Taiwan. Hence, THEDS could carry out a graduate census to investigate the flow of graduates one year after graduation. After obtaining the information about graduates, THEDS sent e-mail to inform them, irrespective of their majors, to complete the questionnaires on line. Consequently, the questionnaires were delivered to 262,743 bachelor’s graduates and 88,694 questionnaires were returned, thus the response rate was 33.76%. On the other hand, the questionnaires were delivered to 47,283 master’s graduates, and 11,912 questionnaires were returned, thus the response rate was 25.19% (Taiwan Higher Education Database, 2008). Sample The participants of the survey were bachelor’s and master’s graduates who graduated in 2006. In addition, the sample in the study was drawn as follows. First, the graduates who took in-service education program or in-service training were excluded. Second, THEDS divided the sample into 18 groups by their majors. However, due to the aim of the study, only graduates majored in the engineering and technology would be selected in the study. In order to extract the sample required for the study from these 18 groups, the principles established by Ministry of Education and National Science Council to classify the majors were followed. Further, the researcher specifically focused on mathematics and computation. Finally, graduates who had a part-time job or an intern job were excluded, that is only graduates who 30.

(39) had a full-time job were included. Moreover, an individual who had a full-time job but ran one’s own business would be eliminated. Instrument As mentioned, the questionnaire was used to survey the flow of graduates. The questionnaire covered four topics including current situation, employment situation, preparation for further study, and personal background. And since information concerning education-employment mismatch (overeducation and mismatch), jobsearch duration, and the perceived work values could be obtained in the employment situation section, the survey was suitable for the study. As a result, the survey to investigate the flow of graduates one year after graduation was employed in this study. Last, this survey was conducted from June 2007 to February 2008. Measures Employment-Education Mismatch. As discussed previously, employment-education mismatch was defined in two perspectives, one is overeducation (in terms of the additional years of overeducation), and the other one is mismatch (between job and major), so respondents were asked to answer the following questions. And self-assessment method was used to measure overeducation. (a) What extent is your current job related to your field of education? Four response alternatives were provided: . Not related. . Somewhat related. . Mostly related. . Closely related. (b) What extent educational level do you think is required to perform your current job? Six response alternatives were provided: . Below junior high school 31.

(40) . Senior High school. . Senior Vocational school. . Junior college. . University and college. . Master degree. Therefore, according to their answers, the respondents could be divided into two groups, matched people and mismatched people. Respondents who answered not related or somewhat related to the first question were described as mismatched people. Since the first question is scored on a four-point likert scale ranging from 1 (Not related) to 4 (Closely related), a lower score reflect the mismatch. Moreover, if the educational level required for the job was below one’s actual educational level, he or she was also called overeducated. Supposing that the respondent with university or college degree not answered university and college degree or master degree to the second question, s/he was overeducated. On the other hands, if the respondent with master degree not answered master degree to the second question, s/he was overeducated. Additionally, the extent of overeducation can be calculated by comparing the number of years of education required by one’s job (required schooling years) with the number of years of the education level one attained (schooling years). Providing that one’s schooling years was larger than required schooling years, one was overeducated. And Table 3.1 present the schooling years of each level of education (Ministry of Education, 2007).. 32.

(41) Table 3.1. The schooling years of each level of education Level of education. Schooling years. Below junior high school. 9. Senior High school. 12. Senior Vocational school. 12. Junior college. 14. University and college. 16. Master degree. 18. Doctorial degree. 21. Source: Ministry of Education, 2007. Jobsearch duration. Jobsearch duration was measured by this question. There are no options for respondents to choose, and they would write down the number on the space. . How many months did you spend on looking for this job?. Work values. The respondents were asked to evaluate the importance of the nine items of work values. These nine items are compensation, benefit, security, work location, advancement, work challenge, work responsibility, work independence, and contribution to the society. (a) While choosing an occupation, how important is each one to you? Each item has four options for respondents to check. . Very unimportant. . Somewhat unimportant. . Important. . Very important. Hence, a four-point Likert scale was used to calculate the score of the importance (1 = very unimportant, 2 = somewhat unimportant, 3 = important, 4 = very important).. 33.

(42) Data Analysis SPSS 15.0 for window was used for data analysis to test the research hypotheses. Following are the definitions of variables and statistical methods used in the study to test the hypotheses. First, variables used in the study were identified as following. Additionally, for regression purpose, some categorical variables must be converted to dummy variables (see Table 3.2). 1.. Independent variables The independent variables is educational attainment (bachelor = 1; master = 2), while. jobsearch duration, and the nine work values served as independent variables in hypothesis four and five respectively. 2.. Dependent variables The dependent variable was employment-education mismatch. As mentioned, there were. two items to evaluate employment-education mismatch, one item is mismatch, which is the relation between job and major, and the other one is overeducation, which is the additional schooling years of overeducation, namely, one’s actual schooling years (educational level) is higher than required schooling years (required educational level) for the job. And employment-education mismatch was dichotomized as 0 = matched/not overeducated and 1 = mismatched /overeducated. 3.. Control variables According to the previous reviewed literatures, gender, marital status, and school type. might be related to overeducation. Also, in Chen’s study (2006), the result showed that sector difference was associated with overeducation. Therefore, these four variables served as control variables in this study. And the codings of four control variables were as follow: gender (male= 1; female = 2), marital status (single = 1; married = 2), school type (public school = 1; private school = 2), and sector type (public sector = 1; private sector = 2; other = 34.

(43) 3; voluntary sector was the reference group). And the public sector consists of government, public enterprise, and public school, while private sector consists of private enterprise, and private school; voluntary sector consists of non-profit organizations (Wikipedia).. Table 3.2. Variables coding system Variables Educational attainment. Coding system 0 Bachelor degree 1 Master degree. Employment-education mismatch. 0 Matched / Not overeducated 1 Mismatched / Overeducated. Gender. 0 Male 1 Female. Marital status. 0 Single 1 Married. School type. 0 Public school 1 Private school. Sector type. 0 (100) Public sector 1 (010) Private sector 2 (001) Other sector 3 (000) Voluntary sector. 35.

(44) Then, here are the statistical methods used in the study. 1.. Descriptive statistics Description statistics provide the summaries concerning the measures. The information. concerning. number,. mean,. and. standard. deviation. of. the. variables. such. as. education-employment mismatch (overeducation and mismatch), jobsearch duration, and work values could be identified. Additionally, the rank of work values of bachelor’s graduates and master’s graduates would be demonstrated. 2.. Independent sample T test analysis Independent sample T test analysis was utilized to test the hypothesis one, two, and three.. Whether there. was. a significant. difference in. employment-education mismatch. (overeducation and mismatch), jobsearch duration, and work values among bachelor’s graduates and master’s graduates can be confirmed. 3.. Multiple regression model Hierarchical regression model. There are four levels in the study. The first level was. composed of demographic variables (educational attainment and four control variables, gender, marital status, school type and sector type). Then, the second level included the five intrinsic work values and control variables, and the third level included all work values and control variables. There were nine work values variables denoted as following: X1 = compensation, X 2 = benefit, X3= security, X4 = work location, X5 = advancement, X6 = work challenge, X7 = work responsibility, X8 = work independence and X9 = contribution to the society. And these nine items fall into two categories, one group contains intrinsic work values and the other one contains extrinsic work values based on the Minnesota Satisfaction Questionnaire (MSQ; Weiss, Dawis, England & Lofquist, 1967). As a consequence, intrinsic work values cover security, work challenge, work responsibility, work independence and contribution to the society while extrinsic work values cover compensation, benefit, work location, and advancement. Finally, the fourth level was composed of above variables, and 36.

(45) jobsearch duration. Thus, the relationship between employment-education mismatch (overeducation and mismatch) and educational attainment /jobsearch duration / these nine work values could be explored through this model, and employment-education mismatch could be predicted. Further, multiple regression model could be utilized to test the hypothesis six, the relationship between work values and jobsearch duration, so which work values was significantly related to jobsearch duration could be identified. In addition, the collinearity diagnostics would be conducted to examine whether there is a high correlation between independent variables. 4.. Logistic regression model There were two items to measure employment-education mismatch (overeducation and. mismatch). Firstly, according to the answer to the question (a), respondents who answered not related or somewhat related were mismatched, while people who answered mostly related or closely related were matched. Likewise, according to the answer to the question (b), people who were overeducated could be identified. For bachelor’s graduates, people whose answers were below junior high school, senior high school, senior vocational school or junior college were classified as overeducated, whereas master’s graduates whose answers were not master degree were classified as overeducated. While dependent variable employment-education mismatch is a categorical variable, the logistic. regression. model. was. utilized.. Therefore,. the. relationship. between. employment-education mismatch and educational attainment /jobsearch duration / the nine work values could be explored as well. Also, the hierarchical regression model is adopted.. 37.

(46) CHAPTER IV.. RESULTS AND DISCUSSIONS. This chapter presents the statistical results of the study, and the data were analyzed using SPSS 15.0. Additionally, in the discussion section, the results would be compared with previous studies to see whether there were differences between them.. Demographic and Descriptive Statistics As mentioned before, THEDS surveyed all the bachelor’s and master’s graduates, and there were a total of 100,606 questionnaires returned. However, not all the data were valid and required. First, the missing data were omitted, and 95,969 cases remained. Then, the sample required in the study was extracted as follows: 1. Not took in-service education program or in-service training 2. Majored in the engineering and technology, which included mathematics and computation 3. Worked full-time, and not run one’s own business Thus, a total of 2247 questionnaires were valid. Demographic The demographic of the sample can be found in Table 4.1. The following is the brief description. Gender The number of the female is much more than that of the male. The number of the male is 920 (40.9%), whereas the number of the female is 1327 (59.1%). Marital status Most of the respondents were single (N=2180; 97.0%), whereas only 66 respondents (2.9%) were married. And the respondents with other marital status were excluded while running regression.. 38.

(47) Educational background There were 1770 respondents with bachelor degree (78.8%), and 477 respondents with master degree (21.2%). Among those with bachelor degree, 26.9% of the respondents were university or college graduates, 25.7% of them were two-year technical college graduates, and 47.4% of them were four-year technical college graduates. In addition, respondents attended public schools were less than those attended private schools. Employment status Eighty percent of the respondents worked in the industry, 8.3% of them worked in the school, 3.2% of them obtained government employment, 1.1% of them got military employment, 2.0% of them worked in the non-profit organization, and 5.3% of them gained other types of employment. Of the 1798 respondents who worked in the industry, 2.1% of the respondents worked in the publicly owned corporations, and 97.9% of them worked in the private corporations. Further, among the respondents worked in the school, 55.9% of them worked in the public school, and 44.1% of them worked in the private school. Thus, the number of respondents worked in the public sector is 240 (10.7%), while that of respondents worked in the private sector is 1842 (82.0%).. 39.

(48) Table 4.1. The profile of the respondents (N=2247) Demographic. Number of Respondents. Percentage (%). 920. 40.9. 1327. 59.1. 2180 66. 97.0 2.9. Gender Male Female Marital status Single Married Other Educational attainment Bachelor Master. 1. .0. 1770 477. 78.8 21.2. Schools that respondents attended Public school. 486. 21.6. 1761. 78.4. Industry. 1798. 80.0. School. 186. 8.3. Government. 73. 3.2. Military. 25. 1.1. Non-profit organization. 46. 2.0. Other. 119. 5.3. Public organization. 38. 1.7. Private organization. 1760. 78.3. Public school. 104. 4.6. Private school. 82. 3.6. Public sector. 240. 10.7. Private sector. 1842. 82.0. Voluntary sector. 46. 2.0. Other sector. 119. 5.3. Private school Types of employment. Types of organization. School that respondents worked in. Types of economic sectors by ownership. 40.

(49) Descriptive statistics As mentioned above, there were two items to evaluate employment-education mismatch (overeducation and mismatch). Firstly, overeducation can be measure by the additional years of education, which is the difference between the respondent’s schooling years and the required schooling years for the job. Secondly, mismatch also can be assessed by the relatedness between work and major. If an individual’s additional year is lager (smaller) than zero, one is classified as overeducated (undereducated), whereas if one’s additional year is equal to zero, one is properly educated. Table 4.2 contains the distribution of respondents being overeducated. Master’s graduates were more likely to be overeducated. However, the mean additional years of overeducation of bachelor’s graduates are significantly larger than that of master’s graduates (see Table 4.3). On the other hand, Table 4.4 shows that 58.3% of participants obtained jobs which were not or somewhat related to their majors, while 41.7% of them gained jobs which were mostly or closely related to their majors. Most of them had jobs somewhat related to their majors. Additionally, the percentage of bachelor’s graduates having jobs not and somewhat related to their majors is more than 60%, and is larger than that of master’s graduates. As seen in Table 4.5, the mean of the relatedness between employment and education is shown, and the mean for master’s graduates is significantly larger than that for bachelor’s graduates. Hence, master’s graduates usually find jobs more related to their majors compared that of bachelor’s graduates. Overall, most bachelor’s graduates might find jobs somewhat related to their majors, while most master’s graduates might find jobs that require less education. However, the extent of overeducation for bachelor’s graduates is greater than that for master’s graduates.. 41.

(50) Table 4.2. Overeducated: Frequency of the sample (N=2247) Properly educated. Undereducated. Overeducated. n. Percentage (%). n. Percentage (%). n. Percentage (%). Total sample. 1286. 57.2. 34. 1.5. 927. 41.3. Males. 552. 60.0. 17. 1.8. 351. 38.2. Females. 734. 55.3. 17. 1.3. 576. 43.4. Single. 1250. 57.3. 34. 1.6. 896. 41.1. 35. 53.0. 0. 0. 31. 47.0. Bachelor’s graduates. 1056. 59.7. 33. 1.9. 681. 38.5. Master’s graduates. 230. 48.2. 1. .2. 246. 51.6. Public school. 301. 61.9. 8. 1.6. 177. 36.4. Private school. 985. 55.9. 26. 1.5. 750. 42.6. Public sector. 155. 64.6. 4. 1.7. 81. 33.8. Private sector. 1058. 57.4. 25. 1.4. 759. 41.2. Voluntary sector. 23. 50.0. 2. 4.3. 21. 45.7. Other sector. 50. 42.0. 3. 2.5. 66. 55.5. Married. Table 4.3. Analysis of educational attainment to the additional years of overeducation (N=927) M. SD. Total sample. 2.76. 1.10. Bachelor’s graduates. 2.97. 1.15. Master’s graduates. 2.16. 0.60. t. 10.511*. Note: The range of mean for bachelor’s graduates is from 2 to 7, whereas the range of mean for master’s graduates is from 2 to 9. *p<.05.. 42.

(51) Table 4.4. Percentage of respondents working outside their majors (N=2247) Not related. Somewhat related. Mostly related. Closely related. Total sample. 18.2%. 40.1%. 22.3%. 19.5%. Male Female. 13.7% 21.3%. 36.6% 42.5%. 27.0% 19.0%. 22.7% 17.3%. Single. 18.2%. 40.2%. 22.2%. 19.4%. Married. 16.7%. 36.4%. 25.8%. 21.2%. Bachelor’s graduates Master’s graduates. 22.0% 3.8%. 41.5% 34.8%. 19.7% 31.9%. 16.8% 29.6%. Public school Private school. 6.6% 21.4%. 33.7% 41.9%. 28.8% 20.4%. 30.9% 16.4%. Public sector. 15.0%. 31.7%. 25.4%. 27.9%. Private sector. 17.0%. 40.8%. 23.0%. 19.2%. Voluntary sector Other sector. 23.9% 40.3%. 37.0% 47.1%. 19.6% 5.9%. 19.6% 6.7%. Table 4.5. Analysis of educational attainment to match between work and majors (N=2247) M. SD. Total sample. 2.43. 0.999. Bachelor’s graduates. 2.31. 0.996. Master’s graduates. 2.87. 0.883. t. -11.162*. Note: The range of mean for bachelor’s graduates is from 1 to 4, and so is the range of mean for master’s graduates. *p<.05. As seen in Table 4.6, most respondents could find jobs within one and a half month. Further, bachelor’s graduates significantly have longer unemployment time than master’s graduates do. Thus, the hypothesis two is confirmed.. 43.

(52) Table 4.6. Analysis of educational attainment to jobsearch duration (N=2247) M. SD. Total sample. 1.46. 2.255. Bachelor’s graduates. 1.56. 2.350. Master’s graduates. 1.08. 1.819. t. 4.096*. Note: *p<.05.. The results demonstrated that most of the participants reported that these nine work values were important to them. As shown in Table 4.7, the rank of the mean scores of the nine work values is as follows: compensation, security, benefit, advancement, work challenge, work location, work responsibility, work independence, and contribution to the society. Except for work responsibility, work independence, and contribution to the society, the mean scores of other seven work values are above three; in other words, these three work values were somewhat unimportant to them. And the mean concern for security and work challenge were significant different between bachelor’s graduates and master’s graduates. Master’s graduates perceived that security and work challenge are more important to them. Table 4.7. Analysis of educational attainment to work values (N=2247) M Total sample Bachelor’s graduates Master’s graduates. Compensation 3.41 3.40 3.46. SD. t. 0.583 0.586 0.570. -1.923. Benefit Total sample Bachelor’s graduates. 3.39. 0.578. 3.38. 0.580. Master’s graduates. 3.42. 0.569. -1.235 (table continues). 44.

(53) Table 4.7. (continued) M Total sample Bachelor’s graduates Master’s graduates Total sample Bachelor’s graduates Master’s graduates Total sample Bachelor’s graduates Master’s graduates Total sample Bachelor’s graduates Master’s graduates Total sample Bachelor’s graduates Master’s graduates Total sample Bachelor’s graduates Master’s graduates. Security 3.41 3.40. SD. t. 0.586 0.595. -2.319*. 3.47 Work location 3.04 3.03. 0.551. 3.09 Advancement 3.16 3.15. 0.744. 3.18 Work challenge 3.04. 0.692. 3.02 3.10 Work responsibility 2.96. 0.659 0.618. 2.97 2.96 Work independence 2.94 2.94 2.94. 0.632 0.586. 0.720 0.713. 0.670 0.665. -1.478. -0.725. 0.651 -2.319*. 0.622 0.106. 0.680 0.681 0.676. -0.159. 0.775 0.769 0.797. -0.606. Contribution to the society Total sample Bachelor’s graduates Master’s graduates. 2.75 2.74 2.77. Note: The range of mean for bachelor’s graduates is from 1 to 4, and so is the range of mean for master’s graduates. *p<.05.. 45.