韓國開發硏究제 32 권제 2 호 ( 통권제 107 호 ) 지방대학졸업자의노동시장성과와지역별교육격차 김희삼 ( 한국개발연구원부연구위원 ) Analysis on the Labor Market Performance of Local University Graduates and
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1 韓國開發硏究제 32 권제 2 호 ( 통권제 107 호 ) 지방대학졸업자의노동시장성과와지역별교육격차 김희삼 ( 한국개발연구원부연구위원 ) Analysis on the Labor Market Performance of Local University Graduates and Regional Education Gap Hisam Kim (Associate Research Fellow, Korea Development Institute) * 본고는고영선편, 지역개발정책의방향과전략 ( 연구보고서 , 한국개발연구원, ) 중제 8 장 ( 김희삼, 지방대학문제의분석과정책방향 ) 의일부내용을바탕으로작성한것임. ** 김희삼 : ( ) hisamkim@kdi.re.kr, (address) Korea Development Institute, 49 Hoegiro, Dongdaemun-gu, Seoul, Korea Key Word: (Local University), (Labor Market Performance), (SAT Score), (Regional Education Gap) JEL Code: I21, J24, J31 Received: Referee Process Started: Referee Reports Completed:
2 ABSTRACT In terms of labor market accomplishments, such as income, size of the company, and the matching quality between one s job and college major (specialization), a very large discrepancy is observed between the graduates from colleges located in Seoul and those outside Seoul. But, when the department average score of the Scholastic Aptitude Test (SAT) at the time of college entrance is controlled for, the discrepancy is found to be reduced to a considerable degree. In the case of wage gap, at least two third can be explained by the SAT score gap. The remaining wage gap seems to reflect the characteristics of workplace. In other words, graduates with high SAT scores enter colleges located in Seoul and thus tend to find better jobs leading to earning differences. This result that confirms the importance of aptitude test scores suggests that in the labor market, one of the major reasons behind a lower accomplishment of the graduate from local colleges is due to a lower competitiveness of local colleges in attracting the brightest students. But, this should not be viewed as only an internal problem of local colleges. This is because the growth of local economies tends to haul the advancement of local colleges in that area rather than being the other way around. The agglomeration effect in Seoul where headquarters of large corporations and financial institutions gather is the factor that has elevated the status of colleges located in Seoul since this provides highly preferred job choices of graduates. When the competitiveness of college is significantly influenced by exogenous factors, such as the vicinity to Seoul, the effort being made by colleges alone would not be enough to improve the situation. However, the central government, too, is not in the position to carry out countermeasure policies for such problems. The regional development strategy boosted through supportive policies for local colleges, such as financial support, is not based on the persuasive and empirical grounds. It is true that college education is universal and that the government 's intervention in assisting local colleges to secure basic conditions, such as tenure faculty and adequate facilities is necessary. However, the way of intervention should not be a support-only type. In order to improve the efficiency and effect of financial support, restructuring programs, including the merger and integration of insolvent colleges, should be underway prior to providing support. In addition, when the policy is focused on education recipients local college students, and not on education providers local colleges, the importance of regional gap in compulsory education (elementary and junior high schools) turns out to be much important as the gap between metropolitan area colleges and local colleges. Considering the educational gap before college entrance shown from the discrepancies of aptitude test scores among different regions, the
3 ABSTRACT imbalance between regions in terms of human resources is apparently derived from compulsory education, and not from college education. Therefore, there is a need to double the policy efforts to reduce the educational gap among different regions. In addition, given the current situation where it is difficult to find appropriate ex post facto policy measures to solve the problem of income gap between the graduates from metropolitan colleges and local colleges, it can be said that improving the environment for compulsory education in local areas is a growing necessity for bridging the educational gap among different regions.
4 58 韓國開發硏究 / Ⅱ Ⅰ. 서론,.,..,,.,.,,., ,.,,...,..
5 지방대학졸업자의노동시장성과와지역별교육격차 59 Ⅱ. 선행연구와분석자료 1. 지방대졸업생의노동시장성과에관한선행연구 (Youth Panel), ( ) ~29 8,296., 1,000. ( ) ,026 (4 7,543, 6,483 ) ( ,320, 12,721 ).., 1 2 (2003) 13%. (2005) 2,,. 1 4 (2005) 4.,.,
6 60 韓國開發硏究 / Ⅱ, (2005) 2003,,,. (2007) (2007) 11.5%..,.., (2007) (2007). (2007), 4.. (, ). 2. 분석자료 가. 대졸자직업이동경로조사 (GOMS) 2006 (Graduates Occupational Mobility Survey, GOMS). GOMS ,. ( ) ,764. (158 ), 4 (173 ), (11
7 지방대학졸업자의노동시장성과와지역별교육격차 61 ) GOMS 150 5% (multi-stage stratified sampling) ,544. GOMS (, 14,,, ).,,. (cohort),.,,., GOMS 22. 나. GOMS 표본의기초통계 <Table 1> GOMS., % , %, 4 60%, 38%, 2.5%. 1). 1) GOMS 5% 10% (over-sampling).
8 62 韓國開發硏究 / Ⅱ <Table 1> Descriptive Statistics of the GOMS College Graduates Sample(n=26,544) Year of college entrance and graduation Percent College categories and majors Percent Entrance before Branch 5.3 Entrance in National institution 13.2 Entrance in Public institution 1.9 Entrance in Private institution 84.9 Entrance in year university 59.9 Entrance in University of education 2.5 Entrance in year junior college 37.6 Entrance in Humanities major 9.6 Entrance in Social sciences major 24.7 Entrance in Education major 7.4 Entrance in Engineering major 30.7 Entrance in Natural sciences major 11.3 Graduation in August Medical and pharmacy major 5.9 Graduation in February Arts and physical major 10.4 다. 학과별취업률및평균수능점수 GOMS. 2), GOMS 2005 ( ) ( ).,., ( ) ) GOMS,.
9 지방대학졸업자의노동시장성과와지역별교육격차 63 ( ). 3). Ⅲ. 대졸자의노동시장성과에대한지역적요인의영향,, 임금 (Mincer[1970])., (,,,,,, ) (,,,,,,,,,, ). 가. 대졸취업자의출신대학지역별임금격차. <Table 2> (1) 3).
10 64 韓國開發硏究 / Ⅱ 15.3%. (, ),., ( [2007] 11.5%).,, ( 2 ).,,.,,,,, 4),,.,. ( ). <Table 2> (2) 15., 20%, 10%, 10%, 6%, 5%. 5) 4) , ,. 5),, (GRDP)
11 지방대학졸업자의노동시장성과와지역별교육격차 65 <Table 2> College Graduates Wage Gaps by Region of College Dependent variable: ln(monthly average wage) (1) (2) Coefficient Std. dev. Coefficient Std. dev. Intercept *** *** College outside Seoul *** Busan *** Daegu *** Daejeon *** Incheon *** Gwangju *** Ulsan * Gyeonggi *** Gangwon *** Chungbuk *** Chungnam *** Jeonbuk *** Jeonnam *** Gyeongbuk *** Gyeongnam *** Jeju *** Branch *** ** Private institution *** *** College of education *** *** Junior college *** *** Employment rate of the department *** *** Major (omitted: Arts & Physical) Humanities *** *** Social sciences *** *** Education *** ***
12 66 韓國開發硏究 / Ⅱ <Table 2> Continued Dependent variable: ln(monthly average wage) (1) (2) Coefficient Std. dev. Coefficient Std. dev. Engineering *** *** Natural science *** *** Medical and pharmacy *** *** GPA *** *** Number of licences * *** Weekly working hours *** *** Months of experience *** *** Months of experience squared *** *** Age *** *** Female *** *** Married *** *** Living with parents * Household head *** *** Number of household members Mother's years of schooling *** *** Family income at the time of college entrance (omitted: under 1 million won) 1 million - 2 million won *** ** 2 million - 3 million won *** *** 3 million - 4 million won *** *** 4 million - 5 million won *** *** 5 million - 10 million won *** *** 10 million won and over *** *** Number of observations 17,046 17,046 Adjusted R F ratio 239.0*** 182.1***
13 지방대학졸업자의노동시장성과와지역별교육격차 67 나. 수능점수를고려했을때출신대학교지역이임금에미치는효과, ),., ( [2007], p.51) , ,. (=100 ) <Table 3> 8,215. <Table 3> (1). 16.4%. (2) 5.2% 6) ( ) %, % % %, %, % OECD ( %, 47.3%).
14 68 韓國開發硏究 / Ⅱ <Table 3> SAT Scores - Good Predictor of Wage Gaps by Region of University (1) (2) Dependent variable: Not controling for SAT scores Controling for SAT scores ln(monthly average wage) Coefficient Std. dev. Coefficient Std. dev. Intercept *** *** University outside Seoul *** *** Branch ** Private institution *** College of education *** *** Employment rate of the department *** *** Major (omitted: arts & physical) Humanities ** *** Social sciences *** Education *** Engineering *** Natural science *** Medical and pharmacy *** GPA *** *** Number of licences * *** Weekly working hours *** *** Months of experience *** *** Months of experience squared *** *** Age *** *** Female Married ** *** Living with parents Household head *** *** Number of household members Mother's years of schooling *** ** Family income at the time of college entrance (omitted: under 1 million won) 1 million - 2 million won million - 3 million won ** ** 3 million - 4 million won *** *** 4 million - 5 million won *** *** 5 million - 10 million won *** *** 10 million won and over *** *** Department average SAT percentile score *** Number of observations 8,215 8,215 Adjusted R F ratio 110.0*** 115.3***
15 지방대학졸업자의노동시장성과와지역별교육격차 69, ), 10 6%. 8).,,,,,,.,.. 9) 다. 수능점수와직장특성을고려했을때출신대학교지역이임금에미치는효과. (16 ), ( ),, ( ), 4 ( / ), ( / ),. 7),., 14.9%, 6.0%. ( ). 8) (2007) 10 5%. 9) 7,642 <Table 3>. ( ) (0.012), (0.014). <Table 3> (0.683=( )/0.164) (0.735=( )/ 0.162),.
16 70 韓國開發硏究 / Ⅱ. (endogeneity) (,, ) (specification). <Table 4>. <Table 4> 5,689. <Table 4> (1), (2). (1) 8.7%.,,., 1,000,,,. (2). 10), ) 10) ( 5,310 ) <Table 4>. ( ) (0.014), (0.017),. 11) , 94.3% 54.6%, 63.9%, (, 914, ).
17 지방대학졸업자의노동시장성과와지역별교육격차 71 <Table 4> Wage Gaps by Region of University Conditioned on Job Characteristics Dependent variable: (1) Not controling for SAT scores (2) Controling for SAT scores ln(monthly average wage) Coefficient Std. dev. Coefficient Std. dev. Intercept *** *** University outside Seoul *** Branch ** Private institution ** College of education Employment rate of the department *** ** Major (omitted: Arts & Physical) Humanities *** *** Social sciences *** Education *** Engineering *** Natural science *** *** Medical and pharmacy *** GPA *** *** Number of licences Weekly working hours *** *** Months of experience *** *** Months of experience squared *** *** Age *** *** Female Married ** ** Living with parents Household head *** *** Number of household members Mother's years of schooling * Family income at the time of college entrance (omitted: under 1 million won) 1 million - 2 million won * * 2 million - 3 million won ** ** 3 million - 4 million won *** *** 4 million - 5 million won *** *** 5 million - 10 million won *** *** 10 million won and over *** *** Department average SAT percentile score ***
18 72 韓國開發硏究 / Ⅱ <Table 4> Continue (1) (2) Dependent variable: Not controling for SAT scores Controling for SAT scores ln(monthly average wage) Coefficient Std. dev. Coefficient Std. dev. Region of job (omitted: Seoul) Busan * ** Daegu Daejeon ** ** Incheon Gwangju ** * Ulsan Gyeonggi Gangwon *** *** Chungbuk Chungnam Jeonbuk * Jeonnam Gyeongbuk ** ** Gyeongnam Jeju *** ** Regular employee *** *** Union member *** *** Firm size (omitted: 4 persons or under) 5-9 persons * persons persons persons persons ** ** persons * * persons persons or over *** *** Social insurance provided by job National or occupational pension *** *** Health insurance *** *** Employment insurance Industrial accident compensation insurance Benefits provided by job Severance pay ** ** Overtime pay *** *** Bonus *** *** Annual/monthly paid vacation *** *** Paid maternity leave * Paid sick leave ** * Number of observations 5,689 5,689 Adjusted R F ratio 84.6*** 85.6***
19 지방대학졸업자의노동시장성과와지역별교육격차 73 라. 출신대학교지역이임금에미치는효과의임금분위별추정 (quantile regression model).,, ( ),. (OLS) 0, 0.,. arg. 0.1, 0.25, 0.5, 0.75, <Table 5>. 100 (bootstrapping)., 10%( ), 25%( ), 50%( ), 75%( ), 90% ( ) 5.1%, 5.0%, 5.1%, 5.4%., 5%, 5.2%,
20 74 韓國開發硏究 / Ⅱ Dependent variable: ln (monthly average wage) (1) 10% (2) 25% (3) 50% (4) 75% (5) 90% Coeff Std. dev. Coeff Std. dev. Coeff Std. dev. Coeff Std. dev. Coeff Std. dev. Intercept (0.429) *** (0.305) *** (0.171) *** (0.174) *** (0.219) *** University outside Seoul (0.028) (0.014) *** (0.010) *** (0.014) *** (0.013) *** Branch (0.062) (0.025) * (0.017) ** (0.023) (0.022) ** Private institution (0.030) (0.018) (0.013) ** (0.012) (0.014) College of education (0.060) *** (0.038) *** (0.020) ** (0.019) (0.026) * Employment rate of the department (0.075) * (0.033) *** (0.022) *** (0.025) *** (0.031) *** Major (omitted: Arts & Physical) Humanities (0.051) * (0.032) * (0.026) ** (0.027) *** (0.047) ** Social sciences (0.047) (0.028) * (0.024) (0.026) (0.045) Education (0.064) (0.038) ** (0.028) ** (0.032) (0.047) ** Engineering (0.048) (0.030) *** (0.026) ** (0.028) (0.044) Natural science (0.062) *** (0.035) ** (0.029) (0.029) ** (0.043) *** Medical and pharmacy (0.149) ** (0.043) (0.041) (0.039) (0.058) GPA (0.013) (0.007) *** (0.005) *** (0.006) *** (0.006) *** Number of licences (0.007) *** (0.004) *** (0.003) ** (0.003) (0.004) Weekly working hours (0.001) *** (0.001) *** (0.001) *** (0.001) *** (0.001) *** Months of experience (0.002) *** (0.001) *** (0.001) *** (0.001) *** (0.001) *** Months of experience squared (0.000) * (0.000) ** (0.000) *** (0.000) ** (0.000) *** Age (0.005) * (0.003) *** (0.003) *** (0.003) *** (0.006) *** Female (0.031) (0.016) ** (0.015) *** (0.013) *** (0.023) * Married (0.032) *** (0.018) * (0.015) (0.015) * (0.019) Living with parents (0.033) (0.020) (0.013) (0.013) (0.017) Household head (0.030) *** (0.021) *** (0.013) *** (0.013) *** (0.015) * Number of household members (0.009) (0.005) (0.003) ** (0.004) (0.005) Mother's years of schooling (0.004) (0.002) (0.002) ** (0.002) *** (0.002) *** Family income at the time of college entrance (omitted: under 1 million won) 1 million - 2 million won (0.079) (0.033) (0.025) (0.021) * (0.041) 2 million - 3 million won (0.079) (0.035) (0.023) (0.018) (0.038) 3 million - 4 million won (0.086) (0.035) * (0.023) (0.020) (0.039) 4 million - 5 million won (0.083) (0.035) * (0.023) ** (0.022) (0.040) 5 million - 10 million won (0.081) (0.036) *** (0.023) *** (0.025) (0.041) 10 million won and over (0.096) (0.047) (0.033) (0.034) (0.056) Department average SAT percentile score (0.001) *** (0.000) *** (0.000) *** (0.000) *** (0.000) *** Number of observations 8,215 8,215 8,215 8,215 8,215 Pseudo R <Table 5> Quantile Regression Result of Wage Gaps by Region of University
21 지방대학졸업자의노동시장성과와지역별교육격차 ),. 10% 50%,. 10%,,., %. 2. 사업체규모.,,.,. 가. 대졸취업자의출신대학지역별사업체규모 9 ( ). 1 4 (13.1%), 5 9 (11.8%), (18.7%), (9.4%), (11.5%), (11.7%), (5.0%), (5.9%), 1,000 (13.0%). 1 9 (ordered logistic regression model) <Table 6>. (1) (-0.651), 12) (2007) %.,.
22 76 韓國開發硏究 / Ⅱ <Table 6> College Graduates Firm Size by Region of College Dependent variable: firm size (9 categories) (1) (2) (Ordered logit model) Coefficient Std. dev. Coefficient Std. dev. College outside Seoul *** Busan *** Daegu *** Daejeon *** Incheon *** Gwangju *** Ulsan Gyeonggi *** Gangwon *** Chungbuk *** Chungnam *** Jeonbuk *** Jeonnam *** Gyeongbuk *** Gyeongnam *** Jeju *** Branch *** *** Private institution *** *** College of education ** Junior college *** *** Employment rate of the department *** *** Major (omitted: arts & physical) Humanities *** *** Social sciences *** *** Education *** *** Engineering *** *** Natural science *** *** Medical and pharmacy *** *** GPA (omitted: lowest) Lower * ** Median *** *** Higher *** *** Highest *** *** Number of licences Age *** *** Female *** *** Married ** ** Living with parents *** *** Household head *** *** Number of household members ** *** Mother's years of schooling *** *** Family income at the time of college entrance (omitted: under 1 million won) 1 million - 2 million won million - 3 million won *** *** 3 million - 4 million won *** *** 4 million - 5 million won ** * 5 million - 10 million won ** ** 10 million won and over ** * Number of observations 17,710 17,710 Log likelihood 2, ,770.3 Pseudo R
23 지방대학졸업자의노동시장성과와지역별교육격차 77 (, odds ratio) 0.521(=exp(-0.651)). <Table 6> (2) 15..,.,,. 나. 수능점수를고려했을때출신대학교지역이사업체규모에미치는효과 <Table 7> 4. (1) (=exp(-0.705)). (2) 0.763(=exp(-0.270)). 13) 9 (parallel regression assumption). (proportional odds test Brant test), ( 13) (7,924 ) <Table 7>, ( ) (0.0481), (0.0577) <Table 7>.
24 78 韓國開發硏究 / Ⅱ <Table 7> SAT Scores - Good Predictor of Firm Size by Region of University Dependent variable: firm size (9 categories) (1) (2) (Ordered logit model) Coefficient Std. dev. Coefficient Std. dev. College outside Seoul *** *** Branch *** Private institution *** College of education Employment rate of the department *** *** Major (omitted: arts & physical) Humanities *** Social sciences *** *** Education *** Engineering *** *** Natural science *** *** Medical and pharmacy *** *** GPA (omitted: lowest) Lower * * Median ** ** Higher *** *** Highest *** *** Number of licences Age *** *** Female ** *** Married ** ** Living with parents *** *** Household head *** *** Number of household members * ** Mother's years of schooling *** *** Family income at the time of college entrance (omitted: under 1 million won) 1 million - 2 million won * 2 million - 3 million won million - 4 million won million - 5 million won * 5 million - 10 million won million won and over Department average SAT percentile score *** Number of observations 8,523 8,523 Log likelihood 1, ,618.8 Pseudo R
25 지방대학졸업자의노동시장성과와지역별교육격차 79 <Table 8> The Odds Ratio of Local University Graduates Getting Jobs by Firm Size Firm size 5 persons 10 persons 30 persons 50 persons 100 persons 300 persons 500 persons 1,000 persons (1) Not controling for SAT score (2) Controling for SAT score =3,777.4). 14) <Table 8>.. <Table 7>. (1), , 1, , 1,000. (2)., 5 5 ( ) , (1). 14) 9. 2~3 (multinomial logit).
26 80 韓國開發硏究 / Ⅱ. 3. 전공일치도. ( )?, (26.4%), (45.0%), (28.6%). 1, 2, 3. 가. 대졸취업자의출신대학지역별전공일치도 <Table 9>. (1) ( ) 0.863(=exp(-0.148))., ( =257.2). ( ) ( ) ( ) (A) (B). (A) 0.769, (B) ,,. (2),,. ( [2008]),
27 지방대학졸업자의노동시장성과와지역별교육격차 81 <Table 9> College Major and Job Matching Quality by Region of College Dependent variable: college major and job matching quality (3 categories) (Ordered logit model) (1) (2) Coefficient Std. dev. Coefficient Std. dev. College outside Seoul *** Busan *** Daegu Daejeon *** Incheon * Gwangju *** Ulsan *** Gyeonggi ** Gangwon *** Chungbuk ** Chungnam Jeonbuk *** Jeonnam Gyeongbuk *** Gyeongnam *** Jeju Branch Private institution College of education Junior college *** *** Employment rate of the department *** *** Major (omitted: arts & physical) Humanities *** *** Social sciences *** *** Education *** *** Engineering *** *** Natural science *** *** Medical and pharmacy *** ***
28 82 韓國開發硏究 / Ⅱ <Table 9> Continued Dependent variable: college major and job matching quality (3 categories) (Ordered logit model) GPA (omitted: lowest) (1) (2) Coefficient Std. dev. Coefficient Std. dev. Lower Median ** ** Higher *** *** Highest *** *** Number of licences ** *** Age *** *** Female Married Living with parents *** *** Household head ** ** Number of household members * Mother's years of schooling ** ** Family income at the time of college entrance (omitted: under 1 million won) 1 million - 2 million won million - 3 million won million - 4 million won million - 5 million won million - 10 million won million won and over Number of observations 17,733 17,733 Log likelihood 2, ,438.7 Pseudo R ,. 4
29 지방대학졸업자의노동시장성과와지역별교육격차 83., 2..,. (school to work).,. 나. 수능점수를고려했을때출신대학교지역이전공일치도에미치는효과 <Table 10> 4. (1), (=exp(-0.128)), 4. (2),. 15). 15) 1.015(=exp(0.015)),.
30 84 韓國開發硏究 / Ⅱ <Table 10> SAT Scores - Good Predictor of Major-Job Matching by Region of University Dependent variable: university major and job matching quality (3 categories) (1) (2) (Ordered logit model) Coefficient Std. dev. Coefficient Std. dev. College outside Seoul ** Branch Private institution College of education Employment rate of the department *** *** Major (omitted: arts & physical) Humanities *** *** Social sciences *** *** Education *** *** Engineering *** *** Natural science *** *** Medical and pharmacy *** *** GPA (omitted: lowest) Lower * * Median ** ** Higher *** *** Highest *** *** Number of licences * ** Age Female ** *** Married Living with parents Household head ** ** Number of household members Mother's years of schooling Family income at the time of college entrance (omitted: under 1 million won) 1 million - 2 million won million - 3 million won million - 4 million won million - 5 million won million - 10 million won million won and over Department average SAT percentile score *** Number of observations 8,536 8,536 Log likelihood 1, ,030.1 Pseudo R
31 지방대학졸업자의노동시장성과와지역별교육격차 ) Ⅳ. 고등교육이전단계의지역간교육격차 1. 지역별고교졸업자의대학진학지역선택행태..,. ( ). GOMS 4 <Table 11>.., z (= / )., (1),. (2) ( ).., 16) (7,936 ) <Table 10>, ( ) (0.0513), (0.0632) <Table 10>.
32 86 韓國開發硏究 / Ⅱ <Table 11> Lower-Class Students Tend to Enter Local Universities with the Same SAT Score Dependent variable: whether the university is in local region or in Seoul (logit model) Going to a local university (1) Not controling for SAT score (2) Controling for SAT score Going to a university in Seoul (3) Not controling for SAT score (4) Controling for SAT score Odds ratio z-value Odds ratio z-value Odds ratio z-value Odds ratio z-value Department average SAT score Family income (omitted: under 3 million won) 3 million 4 million million 5 million million and over Mother's year of schooling Region of high school (omitted: Seoul) Busan Daegu Daejeon Incheon Gwangju Ulsan Gyeonggi Gangwon Chungbuk Chungnam Jeonbuk Jeonnam Gyeongbuk Gyeongnam Jeju Pseudo R Note: The year of university entrance, sex, and age are also controled in each estimation. Source: Calculated by the author using the 4-year university graduates sample of the GOMS data (n=12,837)..,,,
33 지방대학졸업자의노동시장성과와지역별교육격차 87., (3),. (4),. (z =41.0).,.. 17),. 2. 성장단계에따른수능점수의지역별격차,.,.. 17), Turley(2003).,.
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35 지방대학졸업자의노동시장성과와지역별교육격차 89 <Table 12> SAT Percentile Score Gap by Region (1) (2) (3) Region of birth SAT score gap (t-value) Region of 14-year old SAT score gap (t-value) Region of high school SAT score gap (t-value) Foreign 5.09 (1.73) Foreign 7.24 (3.44) Foreign - - Seoul 0.00 (reference) Seoul 0.00 (reference) Seoul 0.00 (reference) Incheon (-1.06) Incheon (-1.37) Incheon (-1.06) Daegu (-3.20) Daejeon (-3.77) Daejeon (-3.79) Daejeon (-2.65) Daegu (-4.80) Daegu (-4.87) Gangwon (-3.66) Gyeongbuk (-4.87) Gyeongbuk (-4.49) Gyeongbuk (-5.21) Gangwon (-4.25) Gangwon (-4.21) Gyeonggi (-5.48) Gyeongnam (-6.74) Gyeonggi (-8.09) Ulsan (-3.82) Gyeonggi (-7.91) Gyeongnam (-6.88) Busan (-7.41) Ulsan (-5.23) Ulsan (-5.72) Chungnam (-5.51) Chungnam (-6.04) Chungnam (-6.62) Gyeongnam (-6.94) Busan (-9.35) Busan (-9.85) Chungbuk (-5.84) Jeju (-4.12) Chungbuk (-7.53) Gwangju (-8.34) Chungbuk (-7.49) Jeju (-4.50) Jeonnam (-10.33) Gwangju (-11.30) Gwangju (-11.97) Jeju (-4.67) Jeonnam (-11.30) Jeonnam (-10.67) Jeonbuk (-16.54) Jeonbuk (-18.08) Jeonbuk (-17.87) Note: The respondent s sex, age, year of university entrance, and parents years of schooling are controled in each estimation. Source: Calculated by the author using the 2006 GOMS data and Jinhaksa SAT database.. <Table 12> <Table 2>,.,.,.
36 90 韓國開發硏究 / Ⅱ ( [2008] 51.8%),.,. <Table 2>, <Table 12> 4. Ⅴ. 결론 GOMS,. 16%..,, , 14,.., 20), 20) (2008) 3.
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