Internationnal remittances and the education of yuong generations , the case of vietnam , luận văn thạc sĩ

.\ UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THEHUGUE VIETNAM THE NETHERLANDS VIETNAM- THE NETHERLANDS PROJECT OF M.A ON DEVELOPMENT ECONOMICS INTERNATIONAL REMITTANCES AND THE EDUCATION OF YOUNG GENERATIONS: THE CASE OF VIETNAM A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS BY NGUYEN HAl NGAN HA Academic Supervisor: DR PETER CALKINS HO CHI MINH CITY, OCTOBER 2009 ACKNOWLEDGEMENT I would like to express my sincere gratitude to my supervisor Prof Peter Calkins for his kind support of my Master study and research from initial to final level, for his patience, enthusiasm, encouragement and immense knowledge His assistance has helped me a lot during the time of designing and writing this research I am heartily thankful to Dr Nguyen Trang Hoai, Dr Nguyen Minh Due and Dr Tran Tien Khai for their comments and evaluation of my initial research proposal I also send my gratefulness to Mr Truong Thanh Vu and Mr Luong Vinh Quae Duy who help me to deal with VHLSS 2006 dataset and Stata software Special thanks go to my friends for their support and motivation during my study at the Vietnam -Netherlands Program for M.A in Development Economics Last but not the least, I would like to thank my family: my parents for giving birth to me, my parents-in-law, my husband and my older brother for supporting me spiritually throughout my life Finally, I offer my regards and blessings to all of those who supported me in any respect during the completion of the thesis CERTIFICATION I certify that the substance of this thesis has not been submitted for any degree and is not being current submitted for any other degree I certify that to the best of my knowledge, any help received in preparing this thesis, and all sources used have been acknowledged in this thesis NGUYEN HAl NGAN HA Date: 23 October, 2009 ii ABSTRACT Over the last decades, Vietnam has experienced a sharp increase in international remittances inflows from overseas migrants The total volume of remittances accounts for approximately 6-8% of GDP Many Vietnamese people have migrated not only with the hope of getting more income and sending more remittances to support their poor families in hometown, but also with the desire to pay school fee for their young generations By lifting liquidity constraints, remittances raise the educational attainment of young people left behind in receipt countries The impact of remittances on the education of young generations has been studied in many papers However, this relationship has not been tested empirically in Vietnam Therefore, the contribution of this paper is to investigate empirical evidences on the link between international remittances and probability of being enrolled in schools of young individuals in Vietnamese households through exploring data of the Vietnam Household Living Standard Survey 2006 by applying Logit econometric model Our results show that remittance receipts statistically significantly increase the probability of school enrollment, particular for girls and in rural areas In addition, this research also finds that the young who have to work tend to gain lower chances to go to schools than their friends without any job Based on the findings, the author suggests many ways to improve the school enrollment rate of young individuals Methods aiming at stimulation of remittance income are appropriate such as encouragement of labor exportation programs since Viet Kieu has been getting older and less altruistic as well as lowering cross border money transfer fees Moreover, when reserved financial budget for young people increases via the effect of attracting more remittance receipts, number of school aged individuals participating in labor market at early ages tends to diminish further iii CHAPTER 1: INTRODUCTION 1.1 PROBLEM STATEMENT 1.2 RESEARCH OBJECTIVES 1.3 RESEARCH QUESTIONS 1.4 RESEARCH HYPOTHESES 1.5 METHODOLOGY 1.6 RESEARCH SCOPE 1.7 THESIS STRUCTURE CHAPTER 2: LITERATURE REVIEW 2.1 INTRODUCTION 2.2 THE CONCEPT OF INTERNATION 2.3 THEORETICAL FRAMEWORK AN IMPACT OF INTERNATIONAL REMITTANCES ON THE EDUCATION 2.3 Theoretical literat 2.3 Empirical literatur 2.4 THE ANALYTICAL FRAMEWORK 2.4.1 Empirical model 2.4.2 Variables introduc 2.5 SUMMARY CHAPTER 3: OVERVIEW OF INTERNATIONAL REMITTANCES AND EDUCATIONAL ATTAINMENT OF THE YOUNG IN VIETNAM iv 3.1 INTRODUCTION 3.2 OVERVIEW OF INTERNATIONAL 3.3 OVERVIEW OF EDUCATION OF T 3.4 SUMMARY CHAPTER 4: METHODOLOGY 4.1 INTRODUCTION 4.2 DATA DESCRIPTION 4.2.1 Sampling method and sample size 4.2.2 Description of 4.2.3 Descriptive sta 4.3 STRENGTH AND WEAKNESS OF DATASET 4.4 MODEL SPECIFICATION 4.5 ESTIMATION STRATEGY CHAPTER 5: EMPIRICAL ANALYSIS 5.1 ESTIMATION RESULTS 5.2 INTERPRETATION OF THE RESULTS CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 6.1 CONCLUSIONS 6.2 RECOMMENDATIONS REFERENCES APPENDICES APPENDIX APPENDIX APPENDIX v LIST OF FIGURES Figure 2.1 Home Investments in Young generations Figure 3.1 Number of exported workers during 1992-2007 .23 Figure 3.2 Percentage of Vietnamese migrant workers by countries 1992- 2006 24 Figure 3.3 Inward remittances from Vietnamese migrants during 2001-2009 25 Figure 3.4 Remittances as percentage of GDP during 1999-2008 26 Figure 3.5 Uses of international remittances(%) 28 Figure 4.1 Distribution of independent variable "remittances per capita" 65 Figure 4.2 Distribution of independent variable "logarithm of remittances per capita" 65 Figure 4.3 Distribution of independent variable "income excluding remittances per capita" 66 Figure 4.4 Distribution of independent variable "logarithm of income excluding remittances per capita" 66 Figure 4.5 Distribution of independent variable "age of young generations" 67 Figure 4.6 Distribution of independent variable "percentage of school-aged children in the household" 67 Figure 4.7 Distribution of independent variable "age of household head" 68 Figure 4.8 Scatter plot of predicted value and standardized Peason residuals 74 Figure 4.9 Scatter plot of predicted value and deviance residuals 74 Figure 4.10 Scatter plot of predicted value and leverage 75 Figure 5.1 Predicted probability of school enrollment by remittances per capita 46 Figure 5.2 Predicted probabilities of school enrollment by remittances per capita and urban-rural areas 48 Figure 5.3 Predicted probabilities of school enrollment by remittances per capita and gender 49 Figure 5.4 Predicted probabilities of school enrollment by remittances per capita and employment status of young generations 50 vi LIST OF TABLES Table 3.1 Flow of international remittances by origin(%) 26 Table 3.2 Percentage of households receiving international remittances 27 Table 3.3 Share of total remittances in urban and rural area 27 Table 3.4 Net enrollment rate in year 2006 by urban rural areas and sex 29 Table 3.5 Average expense on education and training per person in the past 12 months in year 2006 by expense item, urban rural, sex, age group and type of school 31 Table 4.1 Investigated objects 34 Table 4.2 Descriptive statistics of independent variables 37 Table 4.3 Descriptive statistics of dependent variable "Enrollment status" 68 Table 4.4 School enrollment status of young generations by amount of international remittances 38 Table 4.5 School enrollment status of young generations by age group 38 Table 4.6 School enrollment status of young generations by employment 39 Table Description and measurement of variables 40 Table 4.8 Logistic regression results of Model I (full) 70 Table 4.9 Logistic regression results of Model (restricted) 70 Table 4.10 Diagnostic test to compare Model and Model 70 Table 4.11 Hosmer and Lemeshow's goodness-of-fit test 70 Table 4.12 Diagnostic test for model specification error 71 Table 4.13 Diagnostic test for Multicollinearity 72 Table 4.14 Logistic regression results of Model (after excluding influential observations) 76 Table 5.1 Estimation results of Logit Model 45 Table 5.2 Marginal effects of explanatory variables on the probabilities of enrollment 76 vii ECV EHPM EMP ENIGH ENROLL FDI GDP GSO HDI HHAGE HHEMP HHGENDER HHSCHOOL IMF INER PC LAMP-DR7 LFS LN INER PC LN RE PC viii ML Maximum Likelihood MMP107 Mexican Migration Project MOLISA Ministry ofLabor-Invalids and Social Affairs NLSS Nepal Living Standards Survey ODA Official Development Assistant OECD Organization for Economic Cooperation and Development PER SA CHILD Percentage of school-aged children in household PSLSD Project for Statistics on Living Standards and Development RE PC Remittances per capita SBV State Bank ofVietnam UNDP United Nations Development Program USD United States Dollar VHLSS Vietnam Household Living Standard Survey VND Vietnam Dong WB World Bank ix Figure 4.5 Distribution of independent variable "age of young generations" 1.() ""-: ·c;; ""-: ~ c Q) 1.() '=! C) Figure 4.6 Distribution of independent variable "percentage of school-aged children in the household" percentage of school aged children in the household variable per_sa_child mean 39.92654 median 40 sd 15.66595 skewness 0.290074 kurtosis 2.10953 mm 12.5 max 75 67 Figure Distribution of independent variable "age of household head" lJ') ""! """ ""! ~M , "'c:: Q) N ""! ~ ""! 20 age of household head Table 4.3 Descriptive statistics of dependent variable "Enrollment status" Mean Median Std Deviation Skewness Kurtosis Minimum Maximum 68 APPENDIX3 Table 4.8 Logistic regression results of Model (full) (sum of w Logistic regression Log pseudolikelihood en ln- re ln- ine per sa c hh hhgen hhsch hhe ur gen e Table 4.9 Logistic regression results of Model (restricted) (sum of Logistic Log regression pseudolikelihood - 69 Table 4.10 Diagnostic test to compare Modell and Model Measures of Fit for legit of enroll (Efron's R2, Count R2, Model: N: Log-Lik Intercept Only Log-Lik Full Model D LR Prob McFadden's McFadden's ML (Cox-Snell) R2 Cragg-Uhler(Nagelkerke) McKelvey Efron's R2 Variance Variance Count R2 Adj AIC AIC*n BIC BIC' BIC AIC > LR R2 Adj & Zavoina's R2 of of Count R2 used by used by Difference of 18.355 in BIC' Note: p-value for difference provides in LR is very only strong valid support for current if models are nested model The calculation of BIC' is based on LR chi-square The difference in the BIC' from the two models indicates which model is more likely to have generated the observed data If BIC' y* err - BIC' < is negative, the first model is preferred In contrast, in case of obtaining positive difference of BIC', it is appropriate to choose the second model Absolute Difference 0-2 2-6 6-10 >I Source: Long & Freese (200 I, pp 82) Table 4.11 Hosmer and Lemeshow's goodness-of-fit test Sta Sta 70 Logistic (Table model collapsed for enroll, + goodness-of-fit on quantiles of test estimated probabilities) I Group - - I I I I I I I I I I 10 + -> With a very large p-value, we can say that our model fit the data well Table 4.12 Diagnostic test for model specification error 71 (sum of Logistic Log pseudolikelihood = -33.296117 It is necessary to check whether the suggested model in this paper has all relevant predictors and if the linear combination of them is sufficient In order to run specification error test, the author uses Pregibon' s link test C l applied in Stata In the specification error test illustrated above, the coefficient of indicator _ hatsq has p-value of z statistic very large Therefore, we can conclude that we should not be able to find additional predictors that are statistically significant except by chance Table 4.13 Diagnostic test for Multicollinearity (2) The idea behind link test is that if the model is correctly specified, one should not able to find any additional significant predictors The link test uses the linear predicted value (_hat) and linear predicted value squared (_hatsq) as the predictors to rebuild the model The variable _hat should be a statistically significant predictor as it is the predicted value from the model In addition, if our model is properly specified, the variable _hatsq should not be a significant predictor Therefore, in this case, if _hatsq is significant, then the linktest is significant That means we face the problems of omitting relevant variables or incorrect specified model (Bruin, 2006) 72 Collinearity Diagnostics gender Eigenval Eigenvalues Det(correlation & Cond Index matrix) computed 0.6189 from scaled raw sscp (w/ intercept) The table 4.13 above illustrates two common measures including tolerance (an indicator of how much collinearity that a regression analysis can tolerate) and VIF (Variance Inflation Factor -an indicator of how much of the inflation of the standard error could be caused by collinearity) Tolerance= 1- R2 and VIF =1 I Tolerance In the event that one variable are fully uncorrelated with others, both the tolerance and VIF indicator are equal to However, it is necessary to pay attention if the tolerance goes to 0, and the variance inflation gets very large In our case, the table shows 1