Returns to education a case study in the mekong delta vietnam (2)

INSTITUTE OF SOCIAL STUDY THE HAGUE THE UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM THENETHERLANDS THE VIETNAM-NETHERLANDS PROJECT FOR MA PROGRAM IN DEVELOPMENT ECONOMICS RETURNS TO EDUCATION A CASE STUDY IN THE MEKONG DELTA - VIETNAM By TRAN NAM QUOC IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER 0F ARTS IN DEVELOPMENT ECONOMICS SUPERVISOR: Prof NGUYEN TRONG HOAI HO CHI MINH CITY, DECEMBER 2009 RETURNS TO EDUCATION: A CASE STUDY IN THE MEKONG DELTA - WETNAM By TRAN NAM QUOC SUPERVISOR: Prof NGUYEN TRONG HOAI HO CHI MfNH CITY, DECEMBER 2009 CERTIFICATION I certify that the substance of this study has not already been submitted for any degree and is not being currently submitted for any other degree I certify that to the best of my knowledge, any help receiving in preparing this thesis, and all sources used, have been acknowledged in this thesis He Chi Minh City, December 2009 TRAN NAM QUOC ACKNOWLEDGEMENTS The thesis, titled “2?eterns to Education.• A case study in the Mek:eng Delta- Vietnam”, is a fulfilled research of Vietnam Netherlands Program (VNP) This thesis is the quantitative analysis mainly based on data from the VHLSS 2004 and VHLSS 2006 which surveyed by General Statistical Office (GSO) In order to have an accomplishment of this research, the author had received a lot of precious supports from professors and office staffs of VNP Firstly, the author would like to express special thanks to Prof Nguyen Trong Hoai, the author’s supervisor, who advised many scientific instructions in during process to conduct the thesis Particularly, thanks to these worthy instructions and kindly help from Professor, the author completed the research and released scientifically convincible results I would like to thank Prof Karel Jansen, for worthy comments on Thesis Research Design (TRD) formation of this thesis I would like to express sincere thanks to Dr Nguyen Huu Dung who has provided valuable comments for completion of this thesis I would like to thank sincerely Mr Truong Thanh Vu, the visiting lecturer of VNP, for valuable comments and instructions in filtering the appropriate data set I am indebted to all teachers and staffs of the Vietnam — Netherlands Program at University of Economics HCM Finally, I am indebted to my family and others who give me great encouragement and support for my study ABSTRACT The purpose of this research is to examine the return to education in Mekong Delta The author has applied descriptive statistics, econometric model to examine return to education classified by return to schooling (RTS) and rate of return to education (RORE) within each different level of education The return to education is also investigated separately for each of controlling variables in sub-groups The data sources are selected from VHLSS 2004 and VHLSS 2006 of GSO This study found out meaningful findings Firstly, on average, RTS is increased overtime Thus, it increased from 3.46 percent in 2004 to 4.13 percent in 2006 RTS is higher for female than for male and higher for persons living in urban than those living in rural, at 7.45 percent and 3.26 percent, respectively It is also higher for individuals working in service sector and industry-construction compared to whose working in agriculture-forestry sector Secondly, RORE at high - school level is highest, at 11.49 percent in 2004 and 11.89 percent in 2006 In addition, RORE of workers working in public sector is higher compared to private sector at all below education levels; however, RORE of individuals with university and working in private sector is 14.19 percent, higher than those in public sector (8.29 percent) Finally, in particular, RORE of workers with university and working in industry — construction sector obtained highest rate, at 24.81 percent in 2006 This study also argued mainly significant policy implications Firstly, the policies attracting high educated labor force to work in rural areas are worth to be considered Secondly, the process of rural urbanization should be strengthened more quickly to develop private sector and create more employment in rural areas Thirdly, high school, college and university education should be prioritized and more improved to increase more high-skilled labor force serving for economic development strategy in this region Favorable conditions and subsidies to make more opportunities for women increasing enrollment should be more considered Finally, advocacy programs should be strengthened for all organizations, families and individuals to change their perceptions and share roles and responsibilities for education and human development CONTENTS Certification i Acknowledgements ii Abstract iii Content table iv List of Tables viii List of Figures ix List of Abbreviations x CHAPTER 1: INTRODUCTION 1 Introduction 1.2 Research objectives 1.3 Research hypotheses 1.4 Research methodology 1.5 Data 1.6 Thesis structure CHAPTER 2: LITERATURE REVIE 2.1 Theoretical background 2.1.1 Economics of education 1.2 Human capital theory 2.1.3 Education and economic development 10 2.2 Theoretical methods and conceptual models 12 2.2.1 Theoretical methods 12 2.2 1.1 Cost Benefit Analysis methods 12 2.2.1.2 The Short-cut method 2.2.1.3 The Reverse Cost — Benefit method .13 2.2 1.4 The Earning function methods .14 2.2.2 Conceptual models 14 2.2.2 Utility function 14 2.2.2.2 Mincerian model .15 2.2.2.3 Other conceptual models 16 2.3 Empirical approaches with determinants of rate of return to education 17 2.3.1 Empirical models 18 2.3.2 Empirical evidences 21 2.3.2.1 Schooling year and education level .21 2.3.2.2 Experience .24 2.3.2.3 Gender 24 2.3.2.4 Household characteristics 27 2.3.2.5 Private and public sector 27 2.3.2.6 Rural and urban area 28 2.3.2.7 Agricultural and Non — Agricultural sector .29 2.3.2.8 Ethnic group 29 2.4Chaptersu a 30 CHAPTER 3: RESEARCH METHODOLOGY 31 3.1Keyconcepts 31 3.2 Model specification 31 3.3 Identification bias ability of missing an innate variable 35 3.4 Data set and variable measurement 36 3.5 Steps to estimate parameters in regression models 38 3.6 Chapter summary 39 CHAPTER 4: RESEARCH CONTEXT: THE MEKONG DELTA OVERVIEW 40 4.1 Geographic 40 4.2 Demographics .41 4.3 Education and Earning in Mekong Delta 41 4.3.1 Enrolment in general education 42 4.3.2 The important role of primary 42 4.3.3 Vocational and tertiary education 43 4.4 Regional and Sector .44 4.5 Human resource development of the region 45 4.6 Distribution of education attainment as a policy tools 46 CHAPTER 5: EDUCATION AND RETURNS TO EDUCATION IN THE MEKONG DELTA 47 5.1 Education - human capital and earnings of workers in the Mekong Delta 47 5.1.1 Education and human capital 47 5.1.1.1 Educational level structure 47 5.1.1.2 Labor structure classified by sectors 48 5.1.1.3 Schooling year classified by gender and region characteristics .49 1.1.4 Schooling year classified by sectors 50 5.1.2 Earnings of workers 51 5.1.2.2 Per capital income classified by sectors .52 5.1.2.3 Per capita income classified by education levels 53 5.2 Empirical findings 54 5.2.1 Examine the effect of parental education on RTS of workers 54 5.2.2 Estimation of RTS in 2004 and 2006, Model 55 5.2.3 Estimation of RTS in 2004 and 2006 associated with Sub-group variables, Model 56 5.2.4 Estimation of RORE within each education level in 2004 and 2006, Model 57 5.2.5 Calculation RORE within each education level for sub- group variables in 2oo‹i, riodel :: 5:› 5.3 Charter remarks 61 CHAPTER 6: CONCLUSION AND POLICY IMPLICATION 63 6.1 Conclusion 63 6.2 Policy implications 65 6.2 Economic policy .65 6.2.2 Education policy .66 LIMITATION OF THE RESEARCH 67 SUGGESSION FOR FURTHER RESEARCH 67 REFERENCES APPENDICES LIST OF TABLES Table 2.1: Return to education in the world .22 Table 3.1: Definitions and notations of model variables 33 Table 4.1: Gini coefficients in the Mekong Delta 46 Table 5.1: Mean of schooling year classified by gender and region characteristics 49 Table 5.2: Mean of schooling year classified by sectors 50 Table 5.3: RTS for workers having parental education (2006) 54 Table 5.4: Estimation results of Model 55 Table 5.5: RTS associated with Sub-group variables, Model 56 Table 5.6: Estimation results of Model 58 Table 5.8: Estimation results for sub- group variables, Model 59 Table 5.9: RORE within each education level 60 5.2.20 Return to schooling of individuals working in service sector in 2006 reg lnEARN SCHOOL EXP EXPSQR REGION GEN if SERVICE 1, robust Linear regression Number of obs F( 5, 533) - Prob > F R-squared Root MSE Coef Err lnEARN | SCHOOL EXP EXPSQR REGION GEN Constant (C) | 0924436 | 0627206 | -.0012483 | 1929701 | 0981636 | 7.591343 Robust Std .0056849 0093578 0002056 0555888 0556063 1l02971 - = 539 79.52 0.0000 0.3677 6529 [95% Conf Interval] 16.26 6.70 -6.07 3.47 1.77 68.83 0.000 0.000 0.000 0.001 0.078 0.000 0812761 0443378 -.0016522 0837701 -.0110708 7.374673 1036112 0811034 -.0008445 3021702 207398 7.808013 5.2.21 parameters of education levels in 2004 reg lnEARN PRIMA SECON HISCHO UNIV EXP EXPSQR REGION GEN STATE INDU_CON SERVICE, robust Linear regression Number of obs = 1933 F( 11, 1921) - 179.70 Prob > F = 0.0000 R-squared Root MSE Coef Err lnEARN | PRIMA | 0301975 SECON | 0301248 HISCHO | 4594454 UNIV | 7551682 EXP | 0396027 EXPSQR | -.0008688 REGION | 1121098 GEN | 284175 STATE | 2873528 INDU CON | 9113488 SERVICE | 686474 Constant(C) | 7.250438 Robust Std .0422252 0617291 076387 0889899 0054978 0001217 043852 0345283 0724208 0446582 0571386 0657296 = 0.4341 72372 [95% Conf IntervalJ 0.72 0.49 6.01 8.49 7.20 -7.14 2.56 8.23 3.97 20.41 12.01 110.31 0.475 0.626 0.000 0.000 0.000 0.000 0.011 0.000 0.000 0.000 0.000 0.000 -.0526146 -.0909382 3096354 5806413 0288203 -.0011075 0261072 2164581 145321 8237652 5744139 7.121529 1130096 1511878 6092555 9296951 0503851 -.0006301 1981123 3518919 4293845 9989324 7985341 7.379346 5.2.22.parameters of education levels in 2006 reg lnEARN SERVICE, robust PRIMA SECON HISCHO UNIV EXP EXPSQR REGION GEN STATE INDU_CON Number of obs F( 11, 1921) - Linear regression Prob > F R-squared Root MSE lnEARN | Coef Err Robust - 1933 179.28 - 0.0000 0.4419 70018 [95% Conf Interval] Std PRIMA | 0375155 0404533 0.93 0.354 -.0418215 1168525 SECON | HISCHO | 1626987 5192856 058066 079142 2.80 6.56 0.005 0.000 0488196 3640724 2765779 6744987 UNIV | 899681 0864901 10.40 0.000 7300566 1.069305 EXP | 0426069 EXPSQR | -.0009184 0052053 0001081 8.19 -8.50 0.000 0.000 0323983 -.0011303 0528156 -.0007064 REGION GEN STATE INDU_CON SERVICE Constant (C) | | | | | | 1032837 267712 1905496 9008625 7351158 7.473517 0437334 0334932 0702085 0409216 0549607 065504 2.36 7.99 2.71 22.01 13.38 114.09 0.018 0.000 0.007 0.000 0.000 0.000 0175137 2020251 0528567 8206072 6273269 7.34505 1890537 3333989 3282425 9811179 8429048 7.601983 5.2.23 parameters of education levels of individuals in urban in 2004 reg lnEARN PRIMA SECON HISCHO UNIV EXP EXPSQR GEN STATE INDU_CON SERVICE ETHINIC if REGION , robust Linear regression Number of obs = F( 11, Prob > F R-squared 392) - Root MSE Coef Err lnEARN | PRIMA | -.0207703 SECON | 0719075 HISCHO | 4860703 UNIV | 6960283 EXP | 0643258 EXPSQR | -.0013451 GEN | 2127305 STATE | 2400156 INDU_CON | 7141936 SERVICE | 6383284 ETHINIC -.3657812 | Constant (C)| 7.295757 Robust Std .1129158 1208429 1212887 1600868 0123764 0002652 0722146 1109023 1404229 1429347 2168651 182708 404 29.00 = 0.0000 0.4205 - 68922 [95% Conf Interval] -0.18 0.60 4.01 4.35 5.20 -5.07 2.95 2.16 5.09 4.47 -1.69 39.93 0.854 0.552 0.000 0.000 0.000 0.000 0.003 0.031 0.000 0.000 0.092 0.000 -.2427666 -.1656738 2476125 3812922 0399934 -.0018665 0707542 0219778 4381174 357314 -.7921453 6.936547 2012259 3094887 7245281 1.010764 0886581 -.0008237 3547069 4580533 9902699 9193429 0605829 7.654968 5.2.24 parameters of education levels of individuals in rural in 2004 reg lnEARN PRIMA SECON HISCHO UNIV EXP EXPSQR GEN STATE INDU CON SERVICE if REGION 0, robust Linear regression Number of obs F( 10, 1518) - Root MSE lnEARN | 73065 [95% Conf Interval] Coef Err PRIMA | 1529 141.09 Robust Std .0222897 0458861 0.49 0.627 -.0677172 1122966 SECON | -.0094149 HISCHO | 4113935 0722639 1001272 -0.13 4.11 0.896 0.000 -.1511625 2149912 1323327 6077958 EXP | EXPSQR | GEN | 0334763 -.000756 3030588 0061394 0001365 0393421 5.45 -5.54 7.70 0.000 0.000 0.000 0214337 -.0010238 2258882 0455189 -.0004882 3802294 INDU CON | SERVICE | 9530855 6465684 0483123 0700855 19.73 9.23 0.000 0.000 8583196 5090937 1.047851 7840431 UNIV | STATE | Constant (C) | 7484558 3191199 7.305946 0998105 0982321 0728059 7.50 3.25 100.35 0.000 0.001 0.000 5526747 1264349 7.163135 9442368 5118048 7.448757 5.2.25 parameters of education levels of individuals in urban in 2006 reg lnEARN PRIMA SECON HISCHO UNIV EXP if REGION l, robust EXPSQR GEN INDU_CON SERVICE ETHINIC Linear regression Number of obs F( 10, 419) - Prob > F R-squared Root MSE Coef Err lnEARN | Robust Std .2043153 1073102 1.90 0.058 SECON | 403498 1305907 3.09 0.002 | 8429452 | 1.224998 | 0496828 | -.0009715 | 2767865 | 790657 | 8391263 | -.389771 | 7.319916 1247217 1222324 0112584 0002419 0701809 1363956 1322826 2184321 1795492 0.0000 0.4097 70967 [95% Conf Interval] PRIMA | HISCHO UNIV EXP EXPSQR GEN INDU CON SERVICE ETHINIC Constant (C) - = - 430 35.07 6.76 10.02 4.41 -4.02 3.94 5.80 6.34 -1.78 40.77 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.075 0.000 -.0066181 4152487 1468036 6601925 5977869 9847329 0275529 -.0014469 138836 5225521 579106 -.8191302 6.966987 1.088103 1.465263 0718128 -.000496 414737 1.058762 1.099146 0395882 7.672846 5.2.26 parameters of education levels of individuals in rural in 2006 reg lnEARN PRIMA SECON HISCHO UNIV EXP REGION 0, robust EXPSQR GEN STATE INDU_CON SERVICE if Linear regression Coef Err lnEARN | PRIMA SECON HISCHO UNIV EXP Number of obs = F( 10, 1492) Prob > F 0.0000 R-squared Root MSE Robust Std | | | | | 0017679 0932211 3937151 782144 0395489 0435396 0641166 1090627 1173887 005758 GEN | STATE | INDU CON | 2730159 2440665 9443596 Constant (C) | 7.549684 EXPSQR | -.0008932 SERVICE | 6724686 0.968 0.146 0.000 0.000 0.000 0380123 096843 0431342 7.18 2.52 21.89 0.000 0.012 0.000 0715111 105.57 0.000 0644821 0.4099 6934 [95% Conf Interval] 0.04 1.45 3.61 6.66 6.87 0001192 1503 134.27 - -7.49 10.43 0.000 0.000 -.0836373 -.0325472 1797825 5518796 0282543 -.0011271 1984527 0541036 8597495 0871732 2189894 6076476 1.012408 0508435 -.0006593 3475792 4340294 1.02897 5459835 7989537 7.409411 7.689957 5.2.27 parameters of education levels of male in 2004 reg lnEARN PRIMA SECON HISCHO UNIV EXP EXPSQR REGION STATE INDU_CON SERVICE if GEN l , robust Linear regression Number of obs - 1194 F( 10, 1183) 89.90 Prob > F = 0.0000 R-squared - 0.3703 Root MSE - 72449 lnEARN | [95% Conf IntervalJ Coef Err Robust Std PRIMA | SECON | 0407118 0050984 HISCHO | 0523002 0745094 0.78 0.07 0.436 0.945 -.0618998 -.1410869 1433234 1512837 5.41 0.000 4327504 9257535 3140303 0995411 3.15 EXP | 0451669 EXPSQR | -.0009598 0072146 0001628 6.26 -5.90 0512089 17.35 UNIV | 6792519 REGION | STATE | INDU CON | 1207926 2657828 0559449 108581 6238702 7.513852 0818288 08l6836 8885561 SERVICE | Constant (C) | 1256397 2.16 2.45 7.62 91.99 0.002 0.000 0.000 0.031 0.015 0.000 0.000 0.000 1187336 509327 031012 -.0012792 0593218 -.0006404 7880856 9890266 0110304 0527499 2305549 4788156 4633244 7.353592 7844159 7.674113 5.2.28 parameters of education levels of female in 2004 reg lnEARN robust PRIMA SECON HISCHO UNIV EXP EXPSQR STATE INDU_CON SERVICE if Linear regression Number of obs F( 9, 729) = Prob > F - 739 138.44 0.0000 Root MSE 71317 R-squared Coef Err lnEARN | PRIMA SECON HISCHO UNIV EXP EXPSQR STATE | -.0018428 | 0876439 | 6529107 | 8743112 | 0318337 | -.0007289 | 345713 INDU_CON | SERVICE | Constant (C) | 9356934 7916139 7.283991 Robust GEN - - 0.5027 [95% Conf Interval] Std .0719391 1107117 1099419 1210845 0083501 0001809 0880206 -0.03 0.79 5.94 7.22 3.81 -4.03 3.93 0.980 0.429 0.000 0.000 0.000 0.000 0.000 -.1430753 -.1297079 4370701 6365952 0154405 -.0010839 1729088 1393898 3049957 8687512 1.112027 0482269 -.0003738 5185171 0803772 0999661 9.85 72.86 0.000 0.000 6338155 7.087735 9494122 7.480246 0833939 11.22 0.000 7719726 1.099414 5.2.29 parameters of education levels of male in 2006 reg lnEARN 1, robust PRIMA SECON HISCHO UNIV EXP Linear regression EXPSQR REGION INDU_CON SERVICE if GEN Number of obs - 1156 F( 9, 1146) - 97.55 Prob > F 0.0000 R-squared - 0.3955 Root MSE - 68669 lnEARN | Coef Err Robust Std [95% Conf IntervalJ PRIMA | -.0280156 SECON | HISCHO | UNIV EXP EXPSQR REGION 1218349 SERVICE | Constant (C) | -0.57 0.572 -.1252172 064482 1.89 0.059 -.0046813 1046383 0066947 0001433 054854 8.68 7.06 -6.85 2.20 0.000 0.000 0.000 0.028 7026901 0341111 -.0012632 012991 1.113298 0603817 -.0007008 2282422 0690112 0785352 10.79 98.70 0.000 0.000 6092133 7.597598 8800182 7.905776 4530509 0945129 8736258 0484352 | 9079942 | 0472464 | -.000982 | 1206166 INDU CON | 0495412 7446158 7.751687 4.79 18.04 0.000 0.000 2676133 7785942 0691859 248351 6384886 9686574 5.2.30 parameters of education levels of female in 2006 reg lnEARN PRIMA SECON HISCHO UNIV EXP 0, robust EXPSQR STATE INDU_CON SERVICE if GEN Number of obs F( 9, 767) - Linear regression Prob > F R-squared Root MSE Coef Err lnEARN | PRIMA SECON HISCHO UNIV EXP EXPSQR STATE INDU CON SERVICE Constant (C) | 1320938 | 2614299 | 6594951 | 9512718 | 0402917 | -.0008654 | 313526 | 9551942 | 7946468 | 7.405298 Robust - 0.0000 0.4809 7l537 [95# Conf Interval] Std .0681518 1133049 1246926 1318424 0082468 0001666 1078079 0745191 0720815 1036234 - 777 135.18 1.94 2.31 5.29 7.22 4.89 -5.19 2.91 12.82 11.02 71.46 0.053 0.021 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.000 -.0016925 0390055 4147158 692457 0241027 -.0011925 1018923 8089086 6531462 7.201879 2658801 4838544 9042744 1.210087 0564806 -.0005384 5251596 1.10148 9361473 7.608717 5.2.31 parameters of education levels of individuals in public sector in 2004 reg lnEARN PRIMA SECON HISCHO UNIV EXP EXPSQR REGION INDU_CON SERVICE if STATE 1, robust Linear regression Coef Err lnEARN | PRIMA SECON HISCHO UNIV EXP Number of obs F( 9, 309) Prob > F R-squared Root MSE | | | | | 2054246 3658719 8747516 1.156554 05177 EXPSQR | -.0011138 REGION INDU CON SERVICE Constant (C) | 1011156 | -.5248553 | -1.02559 | 8.881587 Robust Std .1773389 1740529 1693412 1719243 0094507 0002371 0591586 2461963 2407963 2842947 = - 319 22.28 0.0000 0.4010 48357 [95% Conf Interval] 1.16 2.10 5.17 6.73 5.48 0.248 0.036 0.000 0.000 0.000 1.71 -2.13 -4.26 31.24 0.088 0.034 0.000 0.000 -4.70 0.000 -.1435199 023393 5415439 8182636 0331741 -.0015804 -.015289 -1.009289 -1.499398 8.322188 5543691 7083508 1.207959 1.494845 0703659 -.0006472 2175202 -.0404221 -.5517819 9.440985 5.2.32 parameters of education levels of individuals in private sector in 2004 reg lnEARN -=0, robust PRIMA SECON HISCHO UNIV EXP EXPSQR GEN Linear regression INDU_CON SERVICE if Number of obs - STATE 1614 F( 9, 1604) - 90.17 Prob > F - 0.0000 R-squared Root MSE lnEARN | Coef Err Robust Std - 0.3218 75l35 [95% Conf Interval] PRIMA | 022371 0434342 0.52 0.607 -.0628227 1075647 SECON | 0285379 0664276 0.43 0.668 -.1017561 1588318 HISCHO | UNIV | 3378581 9356615 1072347 2899598 3.15 3.23 0.002 0.001 1275232 3669216 5481931 1.504401 GEN | INDU_CON | 3513972 9293435 0404552 0452556 8.69 20.54 0.000 0.000 EXP | 037798 EXPSQR | -.0008196 SERVICE | 8081073 Constant (C) | 7.219461 0063212 0001361 5.98 -6.02 0595489 13.57 0761448 94.81 0.000 0.000 0.000 0.000 0253993 -.0010866 2720466 8405771 0501966 -.0005526 4307477 1.01811 6913054 9249091 7.070107 7.368814 5.2.33 parameters of education levels of individuals in public sector in 2006 reg lnEARN robust PRIMA SECON HISCHO UNIV EXP EXPSQR REGION SERVICE if STATE Linear regression Number of obs F( 8, 279) = Prob > F R-squared Root MSE lnEARN | 288 19.85 = 0.0000 - 56501 - 0.3426 [95% Conf Interval] Coef Err PRIMA SECON HISCHO UNIV EXP EXPSQR REGION SERVICE Constant (C) 1, | | 0579546 4363146 7846685 1.116409 0631776 -.0012765 2326498 | -.5094239 | 8.537919 Robust Std .2771874 275132 2838207 2777108 0149534 0003414 0732884 1718391 2817536 0.21 1.59 2.76 4.02 4.22 -3.74 3.17 -2.96 30.30 0.835 0.114 0.006 0.000 0.000 0.000 0.002 0.003 0.000 -.4876897 -.1052836 2259666 5697347 0337418 -.0019484 0883814 -.8476897 7.983286 603599 9779128 1.34337 1.663084 0926134 -.0006045 3769182 -.1711581 9.092552 5.2.34 parameters of education levels of individuals in private sector in 2006 reg lnEARN 0, robust PRIMA SECON HISCHO UNIV EXP Linear regression EXPSQR GEN INDU_CON SERVICE if STATE Number of obs = F( 9, 1635) 109.32 Prob > F 0.0000 1645 - R-squared Root MSE lnEARN | Coef Err Robust = - 0.3495 71523 [95‹ Conf Interval] Std PRIMA | SECOM | 0346577 1320909 0408076 0617112 0.85 2.14 0.396 0.032 -.045383 0110495 UNIV | 1.051492 2098075 5.01 0.000 6399725 1.463012 EXPSQR | -.000834 0001155 -7.22 0.000 -.0010605 -.0006074 8060171 0532152 0.000 7016399 HISCHO | EXP | GEN | INDU CON | SERVICE | Constant (C) | 4840029 0382734 3173234 9165424 7.493308 1025288 005618 4.72 0.000 6.81 0.000 0373318 0407435 8.50 22.50 0.000 0.000 0708199 105.81 15.15 0.000 2829013 0272542 2441001 8366274 7.3544 1146984 2531323 6851045 0492925 3905467 9964574 9103942 7.632215 5.2.35 parameters of education levels of individuals in industry and construction sector in 2004 reg lnEARN PRIMA SECON HISCHO UNIV EXP EXPSQR GEN STATE INDU COM I , robust Linear regression ETHINIC if Number of obs = F( 9, 428) - Prob > F R-squared Root MSE Coef Err lnEARN | PRIMA | SECON HISCHO UNIV EXP EXPSQR GEN STATE ETHINIC Constant (C) Robust 438 14.32 - 0.0000 - 0.2126 64924 [95% Conf Interval] Std .1388368 0870034 1.60 0.111 -.0321703 3098439 | 152737 | 4610668 | 1.319236 | 0446547 | -.0009061 | 3228269 | 4663744 | -.3398455 | 8.014582 1024447 1276405 1780983 010317 000247 0762217 0913568 1621581 1323393 1.49 3.61 7.41 4.33 -3.67 4.24 5.10 -2.10 60.56 0.137 0.000 0.000 0.000 0.000 0.000 0.000 0.037 0.000 -.0486203 2101865 9691796 0243764 -.0013915 1730114 2868107 -.6585708 7.754466 3540944 711947 1.669292 0649331 -.0004207 4726424 6459382 -.0211203 8.274698 5.2.36 parameters of education levels of individuals in service sector in 2004 reg lnEARN robust PRIMA SECON HISCHO UNIV EXP EXPSQR REGION GEN if SERVICE=-1 , Linear regression Number of obs F( 8, 502) = Prob > F R-squared Root MSE Coef Err lnEARN | PRIMA | SECON | 1003106 2437456 UNIV | 9778616 HISCHO | EXP | 770621 0553121 EXPSQR | -.0012598 REGION | 1920373 GEN | 0981129 Constant (C) | 7.877572 Robust 511 30.50 - 0.0000 - 0.3263 66398 [95% Conf Interval] Std .1092238 108843 0.92 2.24 0.359 0.026 -.1142815 0299017 3149027 4575894 0850791 11.49 0.000 8107065 1.145017 0002623 -4.80 0.000 -.0017752 -.0007444 0841348 0107819 0613453 059298 1207151 9.16 5.13 3.13 1.65 65.26 0.000 0.000 0.002 0.099 0.000 6053212 0341289 0715121 -.01839 7.640403 9359208 0764953 3125626 2146158 8.114741 5.2.37 parameters of education levels of individuals in industry and construction sector in 2006 reg lnEARN 1, robust PRIMA SECON HISCHO UNIV EXP Linear regression EXPSQR GEN STATE ETHINIC if INDU_CON Number of obs 539 F( 9, 529) - 12.42 Prob > F - 0.0000 R-squared Root MSE lnEARN | - 0.1550 69241 Robust Coef Std Err [95% Conf Interval] PRIMA SECON HISCHO UNIV EXP | | | | | 1753523 3682176 5230911 1.515573 0530469 EXPSQR | -.0011114 GEN STATE ETHINIC Constant (C) | 2161284 | 365534 | -.2802854 | 8.244672 0723525 0912361 1515985 2049575 0103909 0002415 0682051 1482819 1202419 1264945 2.42 4.04 3.45 7.39 5.11 0.016 0.000 0.001 0.000 0.000 3.17 2.47 -2.33 65.18 0.002 0.014 0.020 0.000 -4.60 0.000 0332188 1889881 2252821 1.112943 0326344 -.0015859 0821424 0742404 -.5164956 7.996178 3174859 547447 8209001 1.918204 0734594 -.0006369 3501144 6568277 -.0440752 8.493165 5.2.38 parameters of education levels of individuals in service sector in 2006 reg lnEARN robust PRIMA SECON HISCHO UNIV EXP Linear regression EXPSQR REGION GEN if Number of obs F( 8, 530) - Prob > F R-squared Root MSE nEAR SERVICE -=1, Coef Err Robust Std = - 539 52.74 0.0000 0.3816 6475 [95% Conf Interval] PRIMA SECON HISCHO UNIV EXP EXPSQR REGION GEN Constant (C) | 1066646 | 4366458 | 8581162 | 1.135782 | 0590422 | -.0012156 | 2282967 | 1519919 | 7.959062 0993735 1042086 0886685 0884913 0094085 000206 0550165 0554153 1173266 1.07 4.19 9.68 12.83 6.28 -5.90 4.15 2.74 67.84 0.284 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.000 -.0885496 2319332 6839313 9619451 0405595 -.0016202 1202196 0431313 7.728579 3018789 6413584 1.032301 1.309619 0775248 -.0008109 3363738 2608525 8.189544 ... in human capital, Other, mainly automatic increase of human Learning (from education and training Learning by doing Receiving tacit knowledge Inventing, innovating Loss of human capital: ageing... fundamental of the human capital theory is the analogy between physical and human capital that means individuals can invest in human capital by education and training with an expectation of higher returns. .. capital: conceptual schema of a company Flow of intellectual capital Investing in intellectual capital, Automatic increase ofStock intellectual capital of human Learning in personnel Organizational