International remittances and household welfare in vietnam from VHLSS 2006 to VHLSS 2008

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HUGE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROJECT FOR M.A IN DEVELOPMENT ECONOMICS INTERNATIONAL REMITTANCES AND HOUSEHOLD WELFARE IN VIETNAM FROM VHLSS 2006 AND VHLSS 2008 “This paper was submitted in partial fulfillment of the requirements for the Masters of Development Economics (MDE) degree at the Vietnam - The Netherlands Programme (VNP), August/2012” BY NGUYEN VAN PHUC Ho Chi Minh City, August 2012 ABSTRACT International remittances has more important role in progress of economy and society in Vietnam With US$ billions in 2011, Vietnam is one of 10 top countries received the remittances in the world This paper examines the impact of international remittances, which increases over time, on the household welfare of receiving households in Vietnam The thesis has combined the propensity score matching and difference-in-differences methods with panel data taken from Vietnam Household Living Standard Surveys 2006 and 2008 It is found that international remittances increases income and expenditures for recipients, but the effect of remittances on expenses of healthcare and education is not statistically significant although the expenses also rise over time When studying separately for urban and rural areas, the thesis found that the impact of international remittance on income and expenditures are positive and statistically significant for rural areas and positive and insignificant for urban areas; meanwhile, the paper has not detected the affects of foreign remittances on education and healthcare in urban or rural areas Key words: international remittances, household welfare, household surveys, Vietnam TABLE OF CONTENTS ABSTRACT i TABLE OF CONTENTS ii CHAPTER 1: INTRODUCTION 04 1.1 Problem statement 1.2 Research objectives 1.3 Research question 1.4 Thesis structure ………………………………………………………………… CHAPTER 2: LITERATURE REVIEW .08 2.1 The key concepts 2.2 Empirical literature 2.3 Empirical framework 2.4 Overview of theory on impact evaluation 2.5 Summary of PSM and DD methods 2.5.1 Propensity Score Matching (PSM) 2.5.2 Difference-in-difference (DD) CHAPTER 3: METHODOLOGY AND DATA 22 3.1 The research model 3.2 Variable introduction 3.3 Data … 3.4 Estimation Strategy CHAPTER 4: INTERNATIONAL REMITTANCES IN VIETNAM .33 4.1 General view of migration remittances in Vietnam ………….………………… 4.2 Role of international remittances on economy…………………………………… 4.3 International remittances and household welfare in Vietnam…………………… CHAPTER 5: EMPIRICAL ANALYSIS………………… 42 5.1 Data description 5.2 Estimation results 5.3 Interpretation of results CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 55 6.1 Conclusions 6.2 Recommendations 6.3 Limitations of the paper REFERENCES APPENDIX CHAPTER 1: INTRODUCTION 1.1 Problem statement After foreign direct investment (FDI), remittances by international migrants to their home countries contribute the largest source of external finance to developing countries, around $300 billion/year in stage of 1995-2005 The funds are used for consumption and investment in migrants’ home countries Remittances are found to give significant impacts on receiving households, especially on low-income families It may help households to establish or expand their small business (Woodruff and Zenteno, 2007 and Amuedo-Dorantes and Pozo, 2006), increase of expenditures in family (Ahmed and et al 2010), or reduction on poverty (Adams, 1991 and Lopez-Cordova, 2005) Some other studies examine the impact of remittances on household welfare, such as savings, consumption, health care and education However, the empirical studies exposed the different results about the impact of remittances on household welfare For instance, Adams (2005), Adams and Cuecuecha (2010) found that the remittances has positive impacts on health-care and educational expenditures in Guatemala Ahmed and et al (2010) showed the significant impacts of remittances on food, education, clothing and recreation in Pakistan Conversely, in the study of McKenzie and Rapoport (2006), the negative impact of migration detected on schooling ratio of children; or Hildebrandt and McKenzie (2005) uncovered that the preventative health-care of children in receipt households lower than in non-receipt households in Mexico According to Vietnamese Oversea Committee (2009), there are about millions Vietnamese living, working and studying permanently in 102 nations and territories in the world The international remittances have been rising over time in Vietnam The average of remittances from oversea Vietnamese sent to the home country in 2008-2010 by formal channel is bigger than $7 billion/year, which accounts more 7% of GDP in that period So the fact indicates the important of international remittances on economic progress in general and improve the living standard of receiving households in particular There are many studies on the impact of migration in Vietnam, such as Dang (2001), Djamba and et al (1999), Andrew T Pham (2010) Nonetheless, there are only a few of studies that examined the impact of international remittances on household welfare in Vietnam, such as the studies of Pfau and Long (2008) and Nguyen (2009) Pfau and Long (2008) studied the impact of international remittances on household welfare in term of economic inequality and poverty By using the Vietnam (Household) Living Standards Survey in 1992/93, 1997/98, 2002 and 2004, this study found that foreign remittances reduce inequality and poverty in Vietnam with regards to per-capita household expenditures and income Nguyen (2009) examined the impact of international and internal remittances on welfare of receiving households By using the panel data from VHLSS 2002 and VHLSS 2004, he concluded that the international remittances have positive and significant impacts on income, expenditures of non-food consumption (excluding healthcare and educational spending); but not significant impacts on expenditures of food, healthcare and education He also showed the effect of remittances in urban area is much more than in rural area Differently from the paper of Nguyen (2009), the thesis of Toan (2010) and Ha (2010) showed that the foreign remittances have positive and significant impacts on healthcare and educational expenditures, but not significant between urban and rural areas through using data from VHLSS 2006 Therefore, the objective of the paper is to re-examine the impact of international remittances on the welfare of receiving household in Vietnam by some reasons The first, the last studies used the series of data only until 2004, for instant in the period 2002-2004 the average of international remittances is about more US$ 2.8 billion per year However, in our paper, we use the data from VHLSS 2006 and 2008 with the average of foreign remittances is double more to US$ 5.8 billon per year in 2006-2008 compared to the period of 2002-2004 The increasing trend of remittances over time, which may be change the relationship between remittances and household welfare, compared the last studies The second reason is the results of Toan (2010) and Ha (2010), based on VHLSS 2006, about the impact of international remittances, which is difference from Nguyen (2009)’s results The last reason, the result of empirical studies is important referential sources for policy-makers on building suitable policies with different stages, because of the implementation of non-suitable policy may lead to waste social resources Relying on that, the thesis is to contribute some new findings to the debate about relationships between international remittances and household welfare in Vietnam and suggest some suitable policies for the government to explore efficiently international remittances on improving the household welfare in Vietnam 1.2 Research objectives The thesis has three objectives: 1) To determine whether international remittances significantly influence the household welfare in Vietnam through total income, total expenditures, healthcare expenditures and education expenditures 2) To examine whether there is the differential impact of remittances on household welfare between urban and rural area 3) To make recommendations to policy-makers for efficiently exploiting remittances, this improves the household welfare 1.3 Research question To meet the objectives, the study has three research questions: 1) Does international remittance significantly influence on the household welfare in Vietnam? 2) Is there the differential impact of international remittances on household welfare between urban and rural area? 3) What are the recommendations could help the policy-makers to explore efficiently remittances on improving the household welfare 1.4 Thesis structure The thesis consists of six chapters The first chapter presents the research issues, research objectives and research questions, the thesis structure The second chapter describes the basic concepts, the results of empirical studies, and summarizes the methods of impact evaluation The third chapter presents the research model, describes the variables and the dataset, and estimation strategy The fourth chapter illustrates an overview of international remittances in Vietnam, the role of the remittance for the economy in general and statistical descriptions of the relationship between the remittance and household welfare in particular The fifth chapter shows statistical description of the variables, the results of difference-in-difference (DD) regression with propensity score matching (PSM), and interpretation of researching results The last chapter presents a summary of main findings, policy recommendations and the limitation of the thesis CHAPTER 2: LITERATURE REVIEW 2.1 The key concepts International Monetary Fund defined the economic concept of remittances as follows: “Remittances represent household income from foreign economies arising mainly from the temporary or permanent movement of people to those economies Remittances include cash and non-cash items that flow through formal channels, such as across electric wire, or through informal channels, such money or goods carried across borders They largely consist of funds and non-cash items sent or given by individuals who have migrated to a new economy and become residents there, and the net compensation of border, seasonal, or other short-term workers who are temporarily employed in an economy in which they are not resident” (Appendix on remittances to the “Balance of Payments and Investment Position Manual” (2008) In this research, we only examine international remittances by formal channels For informal channels, it is very difficult to define how flows in amount of informal remittances and what the effects on the economy are It needs further deeply studies, which is excluded in the research Welfare is physical and mental health and happiness, especially of a person It relates to the income and consumption of a person Household is a group of people, often a family, who live together (Cambridge Advanced Learner's Dictionary) However, the economic concept of the household-welfare is very complex and wide In this paper, we only to analyze the impacts of international remittances on household welfare by examining the impact on indicators of household welfare: income and expenditures of the receipt households 2.2 Empirical literature There are many researches, which describe the role of remittances in the economic progress of the nations The motivations of overseas migrants are altruism (Lucas and Stark, 1985), helping family members to improve their house’s infrastructure (Duryea et al 2005), financing for household business (Woodruff and Zenteno 2007, and Amuedo Dorantes and Pozo 2006), covering medical expenses (Amuedo Dorantes and Pozo, 2006), and contributing in education investment of children (Edward and Ureta, 2003) Many empirical studies explore the impact of international remittances on welfare of receiving households, such as Quartey (2006) in Ghana, Soraya (2007) in Philippines, Subedi (2009) in Nepal, Adams and Cuecuecha (2010) in Guatemala, Ahmed and et al in Pakistan (2010) and Raihan (2009) in Bangladesh Quartey (2006) used Ghana Living Standards Survey (GLSS) to examine the impact of international remittances on household welfare in Ghana He concluded that international remittances is the important sources of income for consumption smoothing, improving household welfare and decreasing negative effects of economic shocks, and households which own land can withstand economic shocks and have better welfare than those without land Soraya (2009) found the positive and significant impact of international remittances on education and recreation expenditures in Philippines By using Nepal Living Standards Survey (NLSS) for analyzing in Nepal, Subedi (2009) realized that remittances from India increase income and decrease inequality on receiving households At the micro-level, Nepal reached a significant reduction in poverty over the period of 1996-2006 (from 42% in 1995-1996 to 2003-2204), despite a low economic growth and political instability in that period Therefore, the remittance is one of key factors for declining the poverty in Nepal Meanwhile, Raihan et al (2009) carried out the examining effects of international remittances on household consumption and poverty at macro and household levels in Bangladesh, where the remittances accounted 10% GDP in 2008 At macro level, Raihan found the positive impact of foreign remittances on economy and reducing poverty At micro level, he discovered the different effects of remittances on indicators of household welfare: positive and significant impact on food and housing, positive but insignificant on education and health-care, and negative and significant on durable good The positive and statistically significant impacts on health-care of remittances also were found in Guatemala (Adams and Cuecuecha, A 2010) In Pakistan, Ahmed and et al (2010) showed the significant impacts of remittances on food, education, clothing and recreation In Vietnam, by using the Vietnam (Household) Living Standards Survey in 1992/93, 1997/98, 2002 and 2004, Pfau and Long (2008) included that foreign remittances come from throughout the world, but the United State is a main source The destinations of international remittances have become more diversification when they move away from Ho Chi Minh City and other urban areas to other regions and rural areas over time However, the percentage of household receiving external remittances held at around to percent of population The elderly, female-headed and the head not working households received disproportionately foreign remittances As a result, these remittances help improving equality in Vietnam in term of per-capita household expenditures, although the improvements are small Furthermore, international remittances are also to help to reduce poverty in Vietnam In the paper of Nguyen (2009), it was examined the impact of international and internal remittances on welfare of receiving households Namely, the paper focuses on direct welfare indicators: income, consumption expenditure, food and non-food expenditures, education and healthcare By using the panel data from VHLSS 2002 and VHLSS 2004, he concluded that the international remittances have positive and significant impacts on income, expenditures of non-food consumption (excluding health-care and educational spending); but not significant impacts on expenditures of food, health-care and education 2.3 Empirical framework As stated above there are various empirical studies analyzing the impact of international remittances on household welfare The most relevant to research questions mentioned in this paper including the researches of Raihan (2009) in Bangladesh, Adams and Cuecuecha (2010) in Guatemala, Nguyen (2009) in Vietnam and Quartey (2006) in Ghana Raihan and et al (2009) examined the relationship between remittances and expenditures in Bangladesh by using the multivariate regression model, which summarized as follows: Householdexp = ß0 + ∑ ßjXij + ε Where Householdexp is the household expenditures as dependent variable (i.e., housing, medication, education, durable good and food) Xij is the explanatory variables including international remittances, household and geographic characteristic, for instant household size, education level of household head, religion, marital status, urban or rural etc The paper revealed that an international remittance has different impact on indicators of household welfare: positive and significant impact on food and housing, positive but insignificant on education and health-care, and negative and significant on durable good 10 M.A in Development Economics Woodruff, Christopher and Rene Zenteno (2007), “Migration Networks and MicroEnterprises in Mexico”, Journal of Development Economics, Vol 82(2), 509-528 World Bank, (2012) “Developing Countries to Receive Over $350 Billion in Remittances in 2011, Says World Bank Report”, Press Release No:2012/175/DEC www.worldbank.org/migration CPI (1995-2010) from General Statistic Office http://www.gso.gov.vn/default.aspx? tabid=393&idmid=3&ItemID=11599 Ministry of Agriculture & Rural Development http://agro.gov.vn/news/id187_Bao-cao-thuong-nien-nganh-Nong-nghiep-Viet-Nam2011-va-Trien-vong-2012-TV.htm APPENDIX Appendix 4.1 International Remittances, Gross Domestic Product, Foreign Direct Investment, Official Development Assistance and Trade Balance of Vietnam in 2001-2010 (Unit: million USD) Year IR GDP (USD) FDI ODA Trade Balance Export 2,000 32,685 1,300 1,432 15,029 483 2,714 35,058 1,400 1,280 16,706 -1,054 2,700 39,553 1,450 1,772 20,149 -2,581 3,200 45,428 1,610 1,846 26,485 -2,287 4,000 52,917 1,954 1,913 32,442 -2,444 4,800 60,913 2,400 1,845 39,826 -2,776 5,500 71,016 6,700 2,511 48,561 -10,438 7,200 91,094 9,579 2,552 62,685 -12,783 6,840 97,180 7,600 3,744 57,096 -7,607 (*) 8,000 106,427 8,000 72,192 3,541 2010 -5,147 Source: World Bank (2012), (*) from Ministry of Planning and Investment (MPI, 2012) 2001 2002 2003 2004 2005 2006 2007 2008 2009 Import 14,546 17,760 22,730 28,772 34,886 42,602 58,999 75,468 64,703 77,339 Appendix 4.2 T-test with equal variances of welfare indicators for Table 4.6 2006 Indicators pcincome pctexpen pceducation pchealthcare Between Urban and Rural receiving households Between Urban and Rural non-receiving households Between receiving and non-receiving households -11.61*** -1.27 -10.52*** -2.03** -13.85*** -8.63*** -0.21 -10.70*** -4.49*** -0.47 -2.89*** -4.03*** 2008 pcincome pctexpen pceducation pchealthcare -7.08*** -22.55*** -10.65*** -7.43*** -26.48*** -10.12*** -3.86*** -19.00*** -5.24*** -4.09*** -7.05*** -4.05*** (Note: At the prices of 2008 *** significant at 1% level; ** significant at 5% level; * significant at 10% level) Source: Author's calculation from VHLSS 2006, VHLSS 2008 Appendix 5.1 Construction the control and treatment groups by PSM **************************************************** Algorithm to estimate the propensity score **************************************************** The treatment is inremit inremit Freq Percent 2,754 135 95.33 4.67 Total 2,889 100.00 Cum 95.33 100.00 Estimation of the propensity score (sum of wgt is 5.9836e+06) note: reg_6 dropped because of collinearity Iteration 0: log pseudolikelihood = -564.83794 Iteration 1: log pseudolikelihood = -548.45093 Iteration 2: log pseudolikelihood = -547.32861 Iteration 3: log pseudolikelihood = -547.26013 Iteration 4: log pseudolikelihood = -547.25896 Iteration 5: log pseudolikelihood = -547.25896 Probit regression Number of obs Wald chi2(23) Prob > chi2 Pseudo R2 Log pseudolikelihood = -547.25896 inremit loremit hhsize hhsize2 headage gender child16 elder technic secondary highschool college pcannuland pcpereland pcforexland pcwatersuf~e reg_1 reg_2 reg_3 reg_4 reg_5 reg_7 reg_8 urban _cons Coef -.2556712 219806 -.0210414 0068283 -.062847 0638238 -.0030868 0366876 3807539 4391909 -.0030856 -.0000467 -.0000308 0000122 -.0000791 1446135 2742696 -.7115154 1957889 2702996 3011096 373007 0385163 -2.584993 Robust Std Err .1245396 1216828 0114795 0048635 1119446 302483 2738044 3435754 2326915 2881381 2061954 0000451 0000272 0000214 0000597 2192806 2216005 4125128 2307529 2401949 2483024 2239582 1432872 4777591 z -2.05 1.81 -1.83 1.40 -0.56 0.21 -0.01 0.11 1.64 1.52 -0.01 -1.04 -1.13 0.57 -1.32 0.66 1.24 -1.72 0.85 1.13 1.21 1.67 0.27 -5.41 P>|z| -.4997643 -.018688 -.0435407 -.0027039 -.2822545 -.529032 -.5397336 -.6367078 -.0753131 -.1255493 -.4072213 -.000135 -.0000842 -.0000298 -.0001961 -.2851685 -.1600594 -1.520026 -.2564786 -.2004737 -.1855541 -.065943 -.2423214 -3.521383 Description of the estimated propensity score in region of common support Estimated propensity score Percentiles 0073945 0174035 0234129 0335875 50% 047083 75% 90% 95% 99% 0619873 0780505 0887223 1113898 Smallest 0051048 0051328 0055492 0055823 Largest 1217247 1220738 1230806 1256593 Obs Sum of Wgt 2889 36.26 0.0388 0.0311 [95% Conf Interval] 0.040 0.071 0.067 0.160 0.575 0.833 0.991 0.915 0.102 0.127 0.988 0.301 0.259 0.570 0.186 0.510 0.216 0.085 0.396 0.260 0.225 0.096 0.788 0.000 Note: the common support option has been selected The region of common support is [.00510475, 12565926] 1% 5% 10% 25% = = = = 2738 2738 Mean Std Dev .0491768 0216516 Variance Skewness Kurtosis 0004688 5895527 3.345945 -.011578 1.00393 1.648602 ****************************************************** Step 1: Identification of the optimal number of blocks Use option detail if you want more detailed output ****************************************************** The final number of blocks is This number of blocks ensures that the mean propensity score is not different for treated and controls in each blocks ********************************************************** Step 2: Test of balancing property of the propensity score Use option detail if you want more detailed output ********************************************************** The balancing property is satisfied This table shows the inferior bound, the number of treated and the number of controls for each block Inferior of block of pscore 0051048 05 Total inremi t Total 1,479 1,064 60 50 75 10 1,529 1,139 70 2,603 135 2,738 Note: the common support option has been selected ******************************************* End of the algorithm to estimate the pscore ******************************************* Source: Author's estimation from panel data of VHLSS 2006-2008 Appendix 5.2 Results of DD regression and Tests (1.1) Equation (5.1) – Full model Linear regression Number of obs = 5476 F( 26, 5449) = 91.15 Prob > F = 0.0000 R-squared = 0.3408 Root MSE = 53787 ln_income inremit year interact loremit hhsize hhsize2 headage gender child16 elder technic secondary highschool college pcannuland pcpereland pcforexland pcwatersuf~e reg_1 reg_2 reg_3 reg_4 reg_5 reg_6 reg_7 reg_8 urban _cons Coef .2332633 1160908 2106704 -.0096275 -.0750653 0021019 -.002829 0247143 -.3051435 -.0536805 718232 1602168 4825907 -.2141716 0000639 0000659 1.14e-06 0000715 3760398 1963703 (omitted) 0638655 3011125 282132 6862894 5640638 2711991 8.797506 Robust Std Err t P>|t| [95% Conf Interval] 0495858 0156398 0735732 0248171 0218467 0020717 0008319 0209317 0519111 047568 0583331 039063 0527742 0393627 5.35e-06 8.03e-06 2.91e-06 0000195 0602246 0608393 4.70 7.42 2.86 -0.39 -3.44 1.01 -3.40 1.18 -5.88 -1.13 12.31 4.10 9.14 -5.44 11.96 8.22 0.39 3.66 6.24 3.23 0.000 0.000 0.004 0.698 0.001 0.310 0.001 0.238 0.000 0.259 0.000 0.000 0.000 0.000 0.000 0.000 0.696 0.000 0.000 0.001 1360554 0854306 0664376 -.0582788 -.1178937 -.0019595 -.0044598 -.0163202 -.4069099 -.1469328 6038759 0836377 3791323 -.2913382 0000535 0000502 -4.57e-06 0000332 2579755 077101 0620649 0624585 0679986 0676755 0618539 0283249 0919706 1.03 4.82 4.15 10.14 9.12 9.57 95.66 0.304 0.000 0.000 0.000 0.000 0.000 0.000 -.0578066 178669 1488277 5536184 4428054 215671 8.617206 -.03223 -.001198 -.20337 -.137004 6.84e 8.97780 * To test for dropping out variables: hhsize2,loremit,gender,elder,pcforexland,reg_3,reg_4 test hhsize2 loremit gender elder pcforexland reg_3 reg_4 ( ( ( ( ( ( ( 1) 2) 3) 4) 5) 6) 7) hhsize2 = loremit = gender = elder = pcforexland = o.reg_3 = reg_4 = Constraint dropped F( 6, 5449) = Test: Ho: 0.72 ß1 = ß2 = ß3 = ß4 = ß5 = ß6 = ß7 = H1: at least one ß =0 At significant level 5%, P-value (Prob) = 63.45%, so it can not reject Ho (1.2) Equation (5.1) – Restricted model Linear regression Number of F( 20, 5455) Prob > F R-squared Root MSE ln_income inremit year interact hhsize headage child16 technic secondary highschool college pcannuland pcpereland pcwatersuf~e reg_1 reg_2 reg_5 reg_6 reg_7 reg_8 urban _cons Coef .2332048 1168918 2124158 -.0511768 -.0034149 -.3098952 7148465 1652192 4831579 -.2231954 0000644 0000664 0000719 3150969 1381856 2393156 2236803 6247664 5057284 2676984 8.833331 Robust Std Err .0494506 0156408 0736958 0054987 0006955 0501064 0580904 0385393 051991 037976 5.34e-06 8.03e-06 0000195 0249774 0278718 0297822 0409135 0394624 0283776 0281476 056322 t 4.72 7.47 2.88 -9.31 -4.91 -6.18 12.31 4.29 9.29 -5.88 12.06 8.27 3.70 12.62 4.96 8.04 5.47 15.83 17.82 9.51 156.84 P>|t| 0.000 0.000 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 = = = = = 5476 118.11 0.0000 0.3402 53779 [95% Conf Interval] 136262 0862297 0679426 -.0619565 -.0047783 -.4081238 6009662 0896668 3812347 -.2976435 0000539 0000507 0000338 2661312 0835458 1809306 1434735 5474044 450097 2125178 8.722918 -.040397 -.002051 -.211666 -.148747 8.94374 ** To Test for multicollinearity vif Variable VIF reg_8 interact inremit reg_1 college child16 secondary highschool reg_2 reg_7 reg_6 headage reg_5 hhsize technic pcpereland pcannuland year urban pcwatersuf~e 2.15 2.12 2.08 1.92 1.90 1.90 1.83 1.70 1.61 1.49 1.47 1.47 1.43 1.27 1.24 1.17 1.11 1.06 1.05 1.04 Mean VIF 1.55 1/VIF 0.465941 0.470778 0.481446 0.520381 0.527507 0.527533 0.545410 0.587242 0.621545 0.672635 0.678539 0.681727 0.699084 0.786313 0.806848 0.853633 0.900419 0.942140 0.953785 0.957470 Since VIF (Variance inflation factor) < 10, so there is not multicollinearity in the model (2.1) Equation (5.2) – Full model Linear regression ln_texpen inremit year interact loremit hhsize hhsize2 headage gender child16 elder technic secondary highschool college pcannuland pcpereland pcforexland pcwatersuf~e reg_1 reg_2 reg_3 reg_4 reg_5 reg_6 reg_7 reg_8 urban _cons Number of obs = 5476 F( 26, 5449) = 110.98 Prob > F = 0.0000 R-squared = 0.3864 Root MSE = 42896 Coef .1061509 2374887 1182877 0700131 -.1018957 0043493 -.0017322 009872 -.3166402 -.0192326 4227388 0889356 4859525 -.2329716 0000246 0000525 -8.26e-08 0000332 159452 0627712 (omitted) -.063128 126105 1586043 4851911 3397214 2201151 8.759913 Robust Std Err t P>|t| [95% Conf Interval] 0391173 0124716 0600193 0196293 0184518 0017659 0007034 0171928 0424157 03883 0488773 0313904 0423909 0315882 4.17e-06 6.80e-06 1.99e-06 0000104 0519685 0520821 2.71 19.04 1.97 3.57 -5.52 2.46 -2.46 0.57 -7.47 -0.50 8.65 2.83 11.46 -7.38 5.90 7.73 -0.04 3.18 3.07 1.21 0.007 0.000 0.049 0.000 0.000 0.014 0.014 0.566 0.000 0.620 0.000 0.005 0.000 0.000 0.000 0.000 0.967 0.001 0.002 0.228 0294654 2130395 0006258 0315318 -.1380685 0008875 -.0031111 -.0238327 -.399792 -.095355 3269199 0273979 4028495 -.2948972 0000164 0000392 -3.99e-06 0000127 0575729 -.0393305 0532446 0537786 0577247 0570452 0530567 0229753 0786821 -1.19 2.34 2.75 8.51 6.40 9.58 111.33 0.236 0.019 0.006 0.000 0.000 0.000 0.000 -.1675087 0206775 0454409 3733597 2357091 1750744 8.605664 -.065722 -.000353 -.233488 -.171046 3.82e 8.91416 * To test for dropping out variables: gender, elder, pcforexland, reg_2, reg_3 test hhsize2 gender elder pcforexland reg_2 reg_3 ( ( ( ( ( ( 1) 2) 3) 4) 5) 6) hhsize2 = gender = elder = pcforexland = reg_2 = o.reg_3 = Constraint dropped F( 5, 5449) = Prob > F = Test: Ho: 1.53 0.1758 ß1 = ß2 = ß3 = ß4 = ß5 = ß6 = H1: at least one ß =0 At significant level 5%, P-value (Prob) = 17.58%, so it can not reject Ho (2.2) Equation (5.2) – Restricted model Linear regression ln_texpen inremit year interact loremit hhsize headage child16 technic secondary highschool college pcannuland pcpereland pcwatersuf~e reg_1 reg_2 reg_5 reg_6 reg_7 reg_8 urban _cons Number of obs = F( 21, 5454) = Prob > F = R-squared = Root MSE = Coef .1032228 2382191 1195105 0703888 -.0585845 -.0018125 -.3406618 4322046 0860889 4737728 -.2286429 000025 0000525 0000335 2175475 1209002 1817074 2184809 5427775 3960802 219203 8.621797 Robust Std Err .0390928 0124844 0601811 0196414 0045881 0005711 0411567 0483711 0310818 0418037 0308342 4.14e-06 6.77e-06 0000103 019966 0220588 0243012 0324687 0306332 0226348 0229515 0492454 t 2.64 19.08 1.99 3.58 -12.77 -3.17 -8.28 8.94 2.77 11.33 -7.42 6.04 7.75 3.24 10.90 5.48 7.48 6.73 17.72 17.50 9.55 175.08 P>|t| 0.008 0.000 0.047 0.000 0.000 0.002 0.000 0.000 0.006 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 [95% Conf Interval] 0265854 2137448 0015316 0318837 -.067579 -.002932 -.4213453 337378 0251562 3918208 -.2890902 0000169 0000392 0000132 1784061 0776561 1340673 1548292 4827242 3517069 1742089 8.525257 ** To Test for multicollinearity vif Variable VIF reg_8 interact inremit reg_1 child16 college secondary highschool reg_2 reg_7 reg_6 headage reg_5 hhsize technic pcpereland pcannuland year urban pcwatersuf~e loremit 2.16 2.13 2.08 1.92 1.90 1.90 1.83 1.71 1.61 1.49 1.48 1.47 1.44 1.27 1.24 1.17 1.11 1.06 1.05 1.04 1.04 Mean VIF 1.53 5476 137.53 0.0000 0.3853 42914 1/VIF 0.463421 0.469083 0.481094 0.519783 0.527148 0.527245 0.545179 0.586005 0.621108 0.669674 0.675899 0.681707 0.696732 0.786308 0.806395 0.853329 0.900160 0.940684 0.953754 0.957306 0.959692 Since VIF < 10, so there is not multicollinearity in the model -.04959 -.000692 -.259978 -.168195 8.71833 (3.1) Equation (5.3) – Full model Number of F( 26, 5449) Prob > F R-squared Root MSE Linear regression ln_education inremit year interact loremit hhsize hhsize2 headage gender child16 elder technic secondary highschool college pcannuland pcpereland pcforexland pcwatersuf~e reg_1 reg_2 reg_3 reg_4 reg_5 reg_6 reg_7 reg_8 urban _cons Coef .2919119 1064261 -.1816808 2365747 1.331848 -.1052021 -.0059111 -.1430546 5.147411 -.3156927 -1.590647 1.332629 4.135642 -1.231294 -.0000152 0000598 -.0000292 -.0000485 3750097 0092737 (omitted) 2353785 6362804 3666346 6947778 3373333 2732272 -1.506851 Robust Std Err t P>|t| = = = = = 5476 301.64 0.0000 0.4373 2.0939 [95% Conf Interval] 1899634 0614341 2745474 0964781 1006738 0102276 003206 0847487 1979326 1674489 2990068 1429724 2235283 1226714 0000207 0000243 0000187 0000463 2900552 2896754 1.54 1.73 -0.66 2.45 13.23 -10.29 -1.84 -1.69 26.01 -1.89 -5.32 9.32 18.50 -10.04 -0.73 2.46 -1.56 -1.05 1.29 0.03 0.124 0.083 0.508 0.014 0.000 0.000 0.065 0.091 0.000 0.059 0.000 0.000 0.000 0.000 0.463 0.014 0.119 0.295 0.196 0.974 -.0804923 -.0140094 -.7199034 047439 1.134487 -.1252523 -.0121961 -.309196 4.759384 -.6439595 -2.17682 1.052346 3.697437 -1.471779 -.0000558 0000122 -.0000658 -.0001394 -.1936143 -.5586059 1.52920 -.085151 5.53543 1.61291 4.57384 -.990809 7.47e .2944982 2990484 3049254 3064271 2929909 1115658 3989569 0.80 2.13 1.20 2.27 1.15 2.45 -3.78 0.424 0.033 0.229 0.023 0.250 0.014 0.000 -.3419557 0500261 -.231141 0940584 -.2370458 0545137 -2.288966 1.22253 1.29549 -.724736 * To test for dropping out variables: gender, pcannuland, pcwatersuface, reg_2, reg_3 test pcannuland pcforexland pcwatersuface reg_2 reg_3 ( ( ( ( ( 1) 2) 3) 4) 5) pcannuland = pcforexland = pcwatersuface = reg_2 = o.reg_3 = Constraint dropped Test: Ho: F( 4, 5449) = Prob > F = 1.01 0.3983 ß1 = ß2 = ß3 = ß4 = ß5 = H1: at least one ß =0 At significant level 5%, P-value (Prob) = 39.83%, so it can not reject Ho (3.2) Equation (5.3) – Restricted model Number of F( 22, 5453) Prob > F R-squared Root MSE Linear regression ln_education inremit year interact loremit hhsize hhsize2 headage gender child16 elder technic secondary highschool college pcpereland reg_1 reg_4 reg_5 reg_6 reg_7 reg_8 urban _cons Coef .2941515 1022529 -.1776371 2371439 1.332567 -.1052766 -.0057832 -.1505232 5.167226 -.309366 -1.582201 1.337169 4.138421 -1.23199 0000625 4016128 2570714 6576991 3791997 7122269 3327739 2886682 -1.554266 Robust Std Err .1895393 0614149 274452 0959533 1008377 0102534 0032059 0846741 1979115 1672666 2972932 1429079 2235804 1228598 0000241 095683 1076087 1200868 1317098 1357918 099845 1107046 2963833 t 1.55 1.66 -0.65 2.47 13.21 -10.27 -1.80 -1.78 26.11 -1.85 -5.32 9.36 18.51 -10.03 2.59 4.20 2.39 5.48 2.88 5.24 3.33 2.61 -5.24 P>|t| 0.121 0.096 0.518 0.013 0.000 0.000 0.071 0.076 0.000 0.064 0.000 0.000 0.000 0.000 0.010 0.000 0.017 0.000 0.004 0.000 0.001 0.009 0.000 = = = = = 5476 356.80 0.0000 0.4366 2.0944 [95% Conf Interval] -.0774212 -.0181449 -.7156725 0490372 1.134885 -.1253774 -.0120682 -.3165182 4.779241 -.6372752 -2.165015 1.057013 3.700114 -1.472844 0000151 214036 0461153 4222811 1209959 4460207 1370379 071643 -2.135296 1.53024 -.085175 5.55521 -.99938 1.61732 4.57672 -.991135 -.973236 ** To Test for multicollinearity vif Variable VIF hhsize hhsize2 elder reg_1 headage interact reg_8 inremit child16 college secondary reg_4 highschool reg_7 reg_5 reg_6 technic gender pcpereland year loremit urban 18.29 16.02 2.45 2.20 2.16 2.13 2.09 2.08 2.03 1.99 1.89 1.84 1.77 1.52 1.49 1.48 1.25 1.16 1.16 1.06 1.05 1.04 Mean VIF 3.10 1/VIF 0.054669 0.062414 0.407377 0.454201 0.463655 0.469058 0.478905 0.481012 0.493276 0.503351 0.530447 0.542034 0.565987 0.660003 0.673346 0.674457 0.802739 0.860546 0.861563 0.940651 0.955706 0.960798 *** Test for dropping out of variables: hhsize and hhsiz2 test hhsize ( 1) hhsize = F( 1, 5453) = 174.64 Prob > F = 0.0000 test hhsize2 ( 1) hhsize2 = At significant level 5%, P-value (Prob) = 0.00 %, so it can reject Ho or can not reject variables of hhsize and hhsiz2 (4.1) Equation (5.4) – Full model Linear regression ln_healthc~e inremit year interact loremit hhsize hhsize2 headage gender child16 elder technic secondary highschool college pcannuland pcpereland pcforexland pcwatersuf~e reg_1 reg_2 reg_3 reg_4 reg_5 reg_6 reg_7 reg_8 urban _cons Number of obs = 5476 F( 26, 5449) = 19.95 Prob > F = 0.0000 R-squared = 0.0941 Root MSE = 1.5353 Coef .1191241 2825305 2070746 1617597 -.0542357 0019894 0014143 -.1129233 -.3242208 9628061 2125277 1547017 0800778 -.2902534 -3.88e-06 0000396 -9.45e-06 0000267 5576647 0408262 (omitted) 2220246 4378316 6130141 8562668 8278447 1656807 4.201827 Robust Std Err t P>|t| [95% Conf Interval] 1314311 0451969 1815137 0726265 0611675 0056324 0024166 0575717 1459692 1332477 1830291 1126663 1556656 114449 0000123 0000169 7.51e-06 0000342 1832835 1852595 0.91 6.25 1.14 2.23 -0.89 0.35 0.59 -1.96 -2.22 7.23 1.16 1.37 0.51 -2.54 -0.31 2.35 -1.26 0.78 3.04 0.22 0.365 0.000 0.254 0.026 0.375 0.724 0.558 0.050 0.026 0.000 0.246 0.170 0.607 0.011 0.753 0.019 0.208 0.435 0.002 0.826 -.1385333 1939264 -.1487648 0193828 -.1741484 -.0090524 -.0033232 -.225787 -.6103788 7015873 -.1462824 -.0661692 -.225089 -.5146191 -.0000281 6.60e-06 -.0000242 -.0000403 1983557 -.3223565 1910313 1940258 1982889 1995125 1877273 0704409 2674949 1.16 2.26 3.09 4.29 4.41 2.35 15.71 0.245 0.024 0.002 0.000 0.000 0.019 0.000 -.152473 0574636 2242887 4651427 4598241 0275884 3.67743 -.000059 -.038062 1.22402 -.065887 5.27e-06 1.0017 1.24739 1.19586 4.72622 * To test for dropping out variables: hhsize2 ,headage ,highschool, secondary, technic, pcannuland, pcwatersuface, pcforexland, reg_2, reg_3 70 test hhsize2 headage technic highschool secondary pcannuland pcforexland pcwatersuface reg_2 reg_3 ( 1) ( 2) ( 3) ( 4) ( 5) ( 6) ( 7) ( 8) ( 9) (10) hhsize2 = headage = technic = highschool = secondary = pcannuland = pcforexland = pcwatersuface = reg_2 = o.reg_3 = Constraint 10 dropped Test: Ho: F( 9, 5449) = Prob > F = 0.73 ß1 = ß2 = ß3 = ß0.6774 = ß5 = ß6 = ß7 = ß8 = ß9 = ß10 = H1: at least one ß =0 At significant level 5%, P-value (Prob) = 67.74%, so it can not reject Ho (4.2) Equation (5.4) – Restricted model Linear regression ln_healthc~e inremit year interact loremit hhsize gender elder child16 college pcpereland reg_1 reg_4 reg_5 reg_6 reg_7 reg_8 urban _cons Number of obs = 5476 F( 17, 5458) = 29.45 Prob > F = 0.0000 R-squared = 0.0929 Root MSE = 1.535 Coef .1279332 2846712 2096983 1656316 -.0322502 -.1250595 9866056 -.4231229 -.3803445 0000408 5445099 1998493 393182 5772869 812608 783254 1779429 4.363923 Robust Std Err .130419 0450406 1809295 0721059 0167672 0558315 1078052 1272306 0951485 0000167 0679105 0859841 091289 1009069 1010484 0721276 0695364 1200684 t 0.98 6.32 1.16 2.30 -1.92 -2.24 9.15 -3.33 -4.00 2.44 8.02 2.32 4.31 5.72 8.04 10.86 2.56 36.35 P>|t| 0.327 0.000 0.247 0.022 0.054 0.025 0.000 0.001 0.000 0.015 0.000 0.020 0.000 0.000 0.000 0.000 0.011 0.000 [95% Conf Interval] -.12774 1963737 -.1449956 0242752 -.0651206 -.2345116 7752644 -.6725456 -.5668735 8.00e-06 4113782 0312861 2142193 3794691 6145129 6418551 0416239 4.128541 -.015607 1.19794 -.173700 -.193815 1.01070 4.59930 ** To Test for multicolinearity Variable VIF reg_1 interact inremit reg_8 reg_4 elder reg_7 child16 reg_6 reg_5 college hhsize pcpereland gender year loremit urban 2.15 2.13 2.07 1.99 1.80 1.57 1.50 1.49 1.48 1.46 1.43 1.36 1.16 1.08 1.06 1.04 1.03 Mean VIF 1.52 1/VIF 0.465622 0.469243 0.481935 0.501654 0.556888 0.638290 0.668082 0.670967 0.677469 0.683916 0.698421 0.734224 0.862199 0.922162 0.943778 0.960874 0.969490 Since Mean VIF < 10, so there is not multicolinearity in the model 71 Appendix 5.3 Summary of results of DD regression for the whole country Variables (1) Ln_income (2) (3) (1) Ln_texpen (2) (3) International remittances (inremit) 0.272*** [0.059] 0.233*** [0.050] 0.233*** [0.049] 0.137*** [0.048] 0.106*** [0.039] 0.105*** [0.039] Time effect (dummy 2008) (year) 0.150*** [0.019] 0.116*** [0.016] 0.117*** [0.016] 0.269*** [0.015] 0.237*** [0.012] 0.238*** [0.012] Interaction term (interact=inremit*year) 0.222*** [0.087] 0.211*** [0.074] 0.212*** [0.074] 0.112 [0.070] 0.118** [0.060] 0.120** [0.060] Constant 8.827*** [0.013] 8.833*** [0.056] 8.570*** [0.011] Local remittances (loremit) 8.798*** [0.092] -0.010 [0.025] 8.760*** [0.079] 0.070*** [0.020] 8.734*** [0.046] 0.074*** [0.020] Household size (hhsize) -0.075*** [0.022] -0.051*** [0.005] -0.102*** [0.018] -0.059*** [0.005] Household size squared (hhsize2) 0.002 [0.002] 0.004** [0.002] Age of household head (headage) Gender of household head (gender) -0.003*** [0.001] 0.025 [0.021] -0.003*** [0.001] -0.002** [0.001] 0.010 [0.017] -0.002*** [0.001] Ratio of members younger than 16 (child16) -0.305*** [0.052] -0.310*** [0.050] -0.317*** [0.042] -0.338*** [0.041] Ratio of the old -male>60, female>55 (elder) -0.054 [0.048] -0.019 [0.039] Ratio of members with technical degree (technic) Ratio of members with secondary (secondary) Ratio of members with highschool (highschool) 0.718*** [0.058] 0.160*** [0.039] 0.483*** [0.053] 0.715*** [0.058] 0.165*** [0.039] 0.483*** [0.052] 0.423*** [0.049] 0.089*** [0.031] 0.486*** [0.042] 0.426*** [0.048] 0.085*** [0.031] 0.474*** [0.042] Ratio of members with college (college) -0.214*** [0.039] -0.223*** [0.038] -0.233*** [0.032] -0.230*** [0.031] Annual crop land per capita (m2) (pcannuland) 0.000*** [5.35e-06] 0.000*** [5.34e-06] 0.000*** [4.17e-06] 0.000*** [4.14e-06] Perennial crop land per capita (m2) (pcpereland) 0.000*** [8.03e-06] 0.000*** [8.03e-06] 0.000*** [6.80e-06] 0.000*** [6.76e-06] Forestry land per capita (m2) (pcforexland) 1.14e-06 [2.91e-06] Aquaculture water surface per capita (m2) (pcwatersuface) Red River Delta (reg_1) 0.000*** [0.000] 0.376*** [0.060] 0.000*** [0.000] 0.315*** [0.025] 0.000*** [0.000] 0.159*** [0.052] North East (reg_2) 0.196*** [0.061] 0.138*** [0.028] 0.063 [0.052] North West (reg_3) (base) -8.26e-08 [1.99e-06] (base) 0.000*** [0.000] 0.101*** [0.019] North Central Coast (reg_4) 0.064 [0.062] South Central Coast (reg_5) 0.301*** [0.062] Central Highlands (reg_6) -0.063 [0.053] -0.121*** [0.022] 0.239*** [0.030] 0.126** [0.054] 0.065*** [0.23] 0.282*** [0.068] 0.224*** [0.041] 0.159*** [0.058] 0.102*** [0.031] South East (reg_7) 0.686*** [0.068] 0.625*** [0.039] 0.485*** [0.057] 0.426*** [0.029] Mekong River Delta (reg_8) 0.564*** [0.062] 0.506*** [0.028] 0.340*** [0.053] 0.279*** [0.021] Urban (dummy) (urban) 0.271*** [0.028] 0.268*** [0.028] 0.220*** [0.023] 0.217*** [0.023] 5,476 0.341 5,476 0.340 5,476 0.386 5,476 0.385 Observations R-squared 5,476 0.033 5,476 0.070 Note: (1) Basis model, (2) Full model, (2) Restricted model Robust standard errors in parentheses *** significant at 1% level; ** significant at 5% level; * significant at 10% level) Source: Author's estimation from panel data of VHLSS 2006-2008 Variables (1) International remittances (inremit) Time effect (dummy 2008) (year) Interaction term (interact=inremit*year) Constant Local remittances (loremit) Household size (hhsize) Household size squared (hhsize2) Age of household head (headage) Gender of household head (gender) Ratio of members younger than 16 (child16) Ratio of the old -male>60, female>55 (elder) Ratio of members with technical degree (technic) Ratio of members with secondary (secondary) Ratio of members with highschool (highschool) Ratio of members with college (college) Annual crop land per capita (m2) (pcannuland) Perennial crop land per capita (m2) (pcpereland) Forestry land per capita (m2) (pcforexland) Aquaculture water surface per capita (m2) (pcwatersuface) Red River Delta (reg_1) North East (reg_2) North West (reg_3) North Central Coast (reg_4) South Central Coast (reg_5) Central Highlands (reg_6) South East (reg_7) Mekong River Delta (reg_8) Urban (dummy) (urban) Observations R-squared Ln_education (2) (3) (1) Ln_healthcare (2) (3) 0.500** [0.249] -0.036 [0.082] -0.338 0.292 [0.190] 0.106*** [0.061] -0.182 0.294 [0.190] 0.102*** [0.061] -0.178 0.129 [0.141] 0.323*** [0.047] 0.179 0.119 [0.131] 0.283*** [0.045] 0.207 0.128 [0.130] 0.285*** [0.045] 0.210 [0.365] 3.767*** [0.056] [0.275] -1.507*** [0.399] 0.237** [0.096] 1.332*** [0.100] -0.105*** [0.010] -0.006* [0.003] -0.143* [0.085] 5.147*** [0.198] -0.316* [0.167] -1.591*** [0.299] 1.333*** [0.143] 4.136*** [0.224] -1.231*** [0.123] -0.000 [0.000] 0.000** [0.000] -0.000 [0.000] -0.000 [0.000] 0.375 [0.290] 0.009 [0.290] (base) [0.274] -1.554*** [0.296] 0.237** [0.096] 1.333*** [0.101] -0.105*** [0.010] -0.006* [0.003] -0.151* [0.085] 5.167*** [0.198] -0.309* [0.167] -1.580*** [0.297] 1.337*** [0.143] 4.138*** [0.224] -1.232*** [0.123] [0.192] [0.182] 4.202*** [0.267] 0.162** [0.073] -0.054 [0.061] 0.002 [0.006] 0.001 [0.002] -0.113** [0.058] -0.324** [0.146] 0.963*** [0.133] 0.213 [0.183] 0.155 [0.113] 0.080 [0.156] -0.290** [0.114] -3.88e-06 [0.000] 0.000** [0.000] -9.45e-06 [7.51e-06] 0.000 [0.000] 0.558*** [0.183] 0.041 [0.185] (base) [0.181] 4.364*** [0.120] 0.166** [0.072] -0.032* [0.017] 0.000*** [0.000] 0.402*** [0.096] 0.235 0.257** 0.222 [0.108] [0.294] [0.191] 0.636** 0.658*** 0.438** [0.299] [0.120] [0.194] 0.367 0.379*** 0.613*** [0.132] [0.198] [0.305] 0.695** 0.712*** 0.856*** [0.306] [0.136] [0.200] 0.337 0.333*** 0.828*** [0.100] [0.188] [0.293] 0.273** 0.289*** 0.166** [0.112] [0.111] [0.070] 5,476 5.476 5.476 5.476 0.033 0.437 0.437 0.094 Note: (1) Basis model, (2) Full model, (2) Restricted model Robust standard errors in parentheses *** significant at 1% level; ** significant at 5% level; * significant at 10% level) Source: Author's estimation from panel data of VHLSS 2006-2008 -0.125** [0.056] -0.423*** [0.127] 0.987*** [0.108] -0.380*** [0.095] 0.000** [0.000] 0.545*** [0.068] 0.200** [0.086] 0.393*** [0.091] 0.577*** [0.101] 0.813*** [0.101] 0.783*** [0.072] 0.178** [0.070] 5.476 0.093 Appendix 5.4A Results of DD Regression with PSM for Urban only Variables Inremit Year Interact=inremit*year Loremit Hhsize Hhsize2 Headage Gender Child16 Elder Technic Secondary Highschool College Pcannuland Pcpereland Pcforexland Pcwatersuface Reg_1 Reg_2 Reg_3 Reg_4 Reg_5 Ln_income (1) (2) 0.292** [0.128] 0.092* [0.054] 0.263 [0.225] -0.030 [0.088] -0.154* [0.079] 0.009 [0.008] -0.003 [0.003] 0.068 [0.062] -0.282 [0.182] -0.058 [0.149] 0.516*** [0.162] -0.088 [0.135] 0.275* [0.166] -0.351** [0.140] 0.000 [0.000] 0.000** [0.000] 0.000*** [8.21e06] 0.000 [0.000] 0.245*** [0.087] -0.084 [0.091] 0.183 [0.141] 0.185 [0.122] base 0.284** [0.122] 0.096* [0.053] 0.278 [0.221] -0.073*** [0.019] 0.499*** [0.152] 0.368*** [0.131] -0.341*** [0.115] 0.000*** [0.000] 0.000*** [6.90e-06] 0.285*** [0.072] 0.227* [0.125] 0.202* [0.113] Ln_texpen (1) (2) 0.110 [0.120] 0.199*** [0.043] 0.149 [0.177] 0.134* [0.074] -0.250*** [0.060] 0.019*** [0.006] -0.004* [0.003] -0.016 [0.052] -0.191 [0.161] 0.022 [0.136] 0.281* [0.155] -0.091 [0.105] 0.413*** [0.141] -0.250** [0.118] -0.000 [0.000] 0.000** [0.000] 0.000*** 0.114 [0.122] 0.203*** [0.043] 0.164 [0.179] 0.155** [0.070] -0.283*** [0.058] 0.022*** [0.006] -0.003* [0.002] [6.24e-06] -0.000 [0.000] -0.010 [0.081] -0.047 [0.078] 0.166 [0.120] -0.007 [0.084] [5.66e-06] 0.265* [0.154] 0.496*** [0.117] -0.111** [0.096] 0.000** [0.000] 0.000*** 0.184* [0.109] Ln_education (1) (2) -0.283 [0.815] 0.162 [0.210] -0.515 [1.036] 0.913*** [0.273] 1.111*** [0.326] -0.087*** [0.032] -0.005 [0.011] -0.107 [0.242] 5.265*** [0.689] -1.424** [0.615] -1.506* [0.798] 1.855*** [0.516] 5.110*** [0.674] -0.849* [0.464] -8.60e-06 [0.000] 0.000** [0.000] -0.000 [0.000] 0.000 [0.000] -1.028** [0.474] -0.397 [0.445] -0.204 [0.691] -0.316 [0.493] -0.035 0.046 0.064 [0.102] [0.084] [0.421] 0.432*** 0.465*** 0.353*** 0.357*** 0.190 Reg_7 [0.119] [0.102] [0.093] [0.077] [0.452] 0.230*** 0.311*** 0.220*** 0.228*** -0.781* Reg_8 [0.085] [0.069] [0.074] [0.054] [0.436] 9.674*** 9.233*** 9.599*** 9.468*** -0.862 Constant [0.274] [0.105] [0.223] [0.188] [1.038] 498 498 498 498 498 Observations R-squared 0.276 0.263 0.310 0.304 0.488 Note: (1) Full model, (2) Restricted model Robust standard errors in parentheses -0.226 [0.833] 0.155 [0.205] -0.480 [1.040] 0.994*** [0.266] 1.080*** [0.309] -0.083*** [0.031] 5.319*** [0.601] -1.534*** [0.499] -1.592** [0.723] 1.798*** [0.490] 5.097*** [0.639] -0.842* [0.446] 0.000** [0.000] -0.920*** [0.306] Reg_6 *** significant at 1% level; ** significant at 5% level; * significant at 10% level) Source: Author's estimation from panel data of VHLSS 2006-2008 -0.728** [0.284] -1.264 [0.818] 498 0.485 Ln_healthcare (1) (2) 0.49 [0.489] 0.259* [0.133] -0.208 [0.574] 0.159 [0.251] 0.082 [0.201] -0.007 [0.019] -0.002 [0.007] 0.050 [0.141] -0.623 [0.447] 1.685*** [0.356] 0.062 [0.472] 0.264 [0.338] -0.011 [0.348] 0.213 [0.351] -0.000 [0.000] 0.000 [0.000] 0.000*** [0.000] -0.000** [0.000] -0.661*** [0.253] -0.393 [0.297] -0.749 [0.540] -0.126 [0.341] -0.092 [0.276] -0.269 [0.315] -0.007 [0.262] 4.725*** [0.689] 498 0.146 0.572 [0.497] 0.276** [0.132] -0.244 [0.570] 1.702*** [0.234] 0.000*** [0.000] -0.000* [0.000] -0.450*** [0.150] 4.778*** [0.111] 498 0.122 Appendix 5.4B Results of DD Regression with PSM for Rural only Variables Ln_income (1) (2) 0.360* [0.188] 0.103 [0.064] -0.097 [0.273] 0.189* [0.101] 1.402*** [0.101] -0.110*** [0.011] -0.009*** [0.003] -0.163* [0.089] 5.107*** [0.205] 0.005 0.034 -0.442*** [0.044] [0.036] [0.161] 0.381*** 0.379*** 0.359*** 0.331*** 0.041 Reg_7 [0.042] [0.039] [0 33] [0.029] [0.160] 0.268*** 0.269*** 0.212*** 0.185*** -0.249** Reg_8 [0.030] [0.027] [0.023] [0.019] [0.126] 9.070*** 9.060*** 8.838*** 8.812*** -0.863*** Constant [0.080] [0.056] [0.068] [0.049] [0.331] Observations 4,964 4,964 4,964 4,964 4,964 R-squared 0.338 0.338 0.383 0.382 0.435 Note: (1) Full model, (2) Restricted model Robust standard errors in parentheses -0.469*** [0.143] Year Interact=inremit*year Loremit Hhsize Hhsize2 Headage Gender Child16 Elder Technic Secondary Highschool College Pcannuland Pcpereland Pcforexland Pcwatersuface Reg_1 Reg_2 Reg_3 Reg_4 0.239*** [0.054] 0.119*** [0.016] 0.201** [0.078] -0.049*** [0.006] -0.003*** [0.001] -0.312*** [0.052] 0.728*** [0.063] 0.187*** [0.040] 0.507*** [0.056] -0.219*** [0.040] 0.000*** [5.43e-06] 0.000*** [7.89e-06] 0.000*** [0.000] 0.058** [0.027] -0.107*** [0.028] -0.389*** [0.068] -0.262*** [0.030] 0.107*** [0.042] 0.242*** [0.013] 0.114* [0.064] 0.065*** [0.020] -0.089*** [0.020] 0.003* [0.002] -0.001** [0.001] 0.017 [0.018] -0.332*** [0.044] -0.020 [0.041] 0.415*** [0.052] 0.109*** [0.033] 0.494*** [0.044] -0.229*** [0.033] 0.000*** [4.26e-06] 0.000*** [6.61e-06] -5.44e-07 [1.97e-06] 0.000*** [0.000] 0.035 [0.023] -0.065*** [0.025] -0.192*** [0.060] -0.200*** [0.026] 0.105** [0.042] 0.242*** [0.013] 0.115* [0.064] 0.066*** [0.020] -0.057*** [0.005] Ln_education (1) (2) 0.358* [0.189] 0.107* [0.064] -0.100 [0.273] 0.191* [0.102] 1.373*** [0.105] -0.108*** [0.011] -0.007** [0.003] -0.145 [0.090] 5.064*** [0.207] -0.202 [0.170] -1.649*** [0.324] 1.281*** [0.149] 3.969*** [0.238] -1.260*** [0.127] -0.000 [0.000] 0.000** [0.000] -0.000 [0.000] -0.000 [0.000] -0.199* [0.120] -0.662*** [0.127] -0.778** [0.340] -0.395*** [0.130] Inremit 0.239*** [0.054] 0.118*** [0.016] 0.199** [0.078] -0.008 [0.026] -0.069*** [0.023] 0.002 [0.002] -0.003*** [0.001] 0.026 [0.022] -0.309*** [0.054] -0.057 [0.050] 0.727*** [0.063] 0.181*** [0.041] 0.506*** [0.056] -0.209*** [0.041] 0.000*** [5.45e-06] 0.000*** [8.21e-06] 1.57e-07 [2.93e-06] 0.000*** [0.000] 0.059** [0.028] -0.390*** [0.030] -0.390*** [0.068] -0.261*** [0.032] Ln_texpen (1) (2) -0.002*** [0.001] -0.345*** [0.043] 0.420*** [0.052] 0.115*** [0.032] 0.491*** [0.043] -0.228*** [0.032] 0.000*** [4.20e-06] 0.000*** [6.18e-06] 0.000*** [0.000] -0.093*** [0.019] -0.224*** [0.057] -0.227*** [0.020] -1.645*** [0.323] 1.315*** [0.147] 3.997*** [0.237] -1.290*** [0.125] 0.000** [0.000] -0.219** [0.104] -0.711*** [0.110] -0.903*** [0.338] -0.420*** [0.116] Ln_healthcare (1) (2) 0.081 [0.135] 0.285*** [0.048] 0.243 [0.191] 0.175** [0.076] -0.068 [0.064] 0.003 [0.006] 0.001 [0.003] -0.135** [0.062] -0.322** [0.155] 0.903*** [0.142] 0.182 [0.199] 0.144 [0.120] 0.091 [0.169] -0.321*** [0.121] -2.29e-06 [0.000] 0.000** [0.000] -0.000 [7.80e-06] 0.000 [0.000] 0.198** [0.092] -0.377*** [0.098] -0.404* [0.215] -0.188* [0.107] 0.090 [0.135] 0.288*** [0.048] 0.250 [0.191] 0.193** [0.076] -0.034* [0.018] -0.153** [0.060] -0.429*** [0.133] 0.924*** [0.115] -0.387*** [0.100] 0.000** [0.000] 0.353*** [0.067] -0.250*** [0.078] Reg_5 Reg_6 *** significant at 1% level; ** significant at 5% level; * significant at 10% level) Source: Author's estimation from panel data of VHLSS 2006-2008 -0.286*** [0.105] -0.876*** [0.323] 4,964 0.435 0.207 [0.126] 0.505*** [0.121] 0.442*** [0.095] 4.662*** [0.237] 4,964 0.095 0.334*** [0.112] 0.626*** [0.106] 0.566*** [0.074] 4.606*** [0.121] 4,964 0.093 ... in Appendix 4.1.) 4.3 International remittances and household welfare in Vietnam In order to understand clearer the relationship between international remittances and the living standard of households... equals if households received foreign remittance and examined in 2008, and equals in remaining cases The information of householder’s characteristics is obtained from section in VHLSS 2006, 2008 The... section In order to eliminating the inflation effect, the data of income and expenditures in 2006 are adjusted to the price in 2008 (2) Independent variables Remittances specified in VHLSS surveys including