đề tài FACTORS AFFECTING GROSS REGIONAL DOMESTIC PER CAPITA PRODUCT OF VIET NAM IN 2018

FOREIGN TRADE UNIVERSITY FALCUTY OF INTERNATIONAL ECONOMICS -*** ECONOMETRICS MID-TERM REPORT FACTORS AFFECTING GROSS REGIONAL DOMESTIC PER CAPITA PRODUCT OF VIET NAM IN 2018 Instructor: Ph.D Đinh Thị Thanh Bình Class ID: KTEE 309.2 Group number: 10 Group members: 04 – Đỗ Quang Anh – 1911150007 23 – Nguyễn Khắc Đức – 1911150019 16 – Nguyễn Tuấn Cường – 1911150015 24 – Tạ Văn Đức – 1911150020 Ha Noi – December, 2020 TABLE OF CONTENTS INTRODUCTION I LITERATURE REVIEW Question of interest Procedure and program II DATA COLLECTION Data type Data collection III STATISTICAL DESCRIPTION OF VARIABLES Running DES function Running SUM function Running TAB function IV QUANTITATIVE ANALYSIS 13 OLS method and assumptions 13 Regression and correlation 14 V TESTING PROBLEM 16 Test omit variable 16 Multicollinearity testing 17 Heteroskedasticity testing 18 Normality (of u) testing 21 VI SATISTICAL HYPOTHESIS TESTING 23 Critical value method 23 Confidence interval method 23 VII CONCLUSIONS AND POLICY IMPLICATION 25 Conclusions 25 Policy implication 25 VIII REFERENCE 26 IX APPENDIX 27 INTRODUCTION Economics is a science which determines social development and national growth With the development in economics research, econometrics is an important subject which helps people study many economics issues to find the way to develop the economy Econometrics is based on the development of statistical methods for estimating economic relationships, testing economic relationships, testing economic theories, evaluating government and business policies It is an useful and indispensable tool for economists to measure economic relationships Therefore, we realized the importance of understanding econometrics and successfully applying this knowledge to logically analyze statistical problems.  Thanks to econometrics, humans will have a clear view about economic policies, theories and phenomena.    Similar to GDP, GRDP per capita is an index which reveals the development of the economy but in small regions such as cities, towns or provinces Many people wonder what factors affect this index and their impact on it In this report, we will try our best to clarify for the readers about “Factors affecting gross regional domestic product of Viet Nam” by using the methodology of econometrics and the STATA program   We sincerely appreciate our econometrics instructor – PhD Dinh Thi Thanh Binh on helping us to complete this report During our working process, mistakes are inevitable but we hope that you can comment on our work and give us some advice to help us develop ourselves.  I LITERATURE REVIEW Question of interest   Gross domestic product (GDP) is a statistic that measures the size of a region's economy The GDP per capita is useful in capturing real output per person growth since inflationary effects have been removed It is, therefore, the most widely used measure of real income However, we believe that the income of people in each region of a country is relative different, hence, so we chose GRDP per capita (gross regional domestic product per capita) as the main object of our research The GRDP per capita is one of the most important indexes to rate the growth of the economy of a region Therefore, our group raised a question:” What are the factors and their impact on the GRDP per capita”   Even though there are many factors that impact on the GRDP per capita, we focus mainly on factors They are population density, high school graduate rate, participation labor rate and FDI We will focus on the factor to find out what impact or statistical impact of them on GRDP per capita of Viet Nam These factors have their own ways to affect the economic growth, and can be shown by some significant indexes like GDP, CPI, etc And that’s why we consider that they can affect the GRDP, and GRDP per capita, too Based on Anna Ek's study in 2007, the theoretical framework shows that FDI has a positive impact on economic growth because it serves as a channel through which new technology is transferred from one country to another, and thereby it increases output and GDP/GRDP in the recipient country.  About density, too high population density decreases the natural endowment per capita, but eases the development of infrastructure, leading to existence of an optimal population density for economic growth (Yegorov, 2009).  The Alliance for Excellent Education (2015) released data outlining the economic benefits of a high school diploma The “Graduation Effect” data shows how increasing the high school graduation rate to 90 percent creates new jobs, increases consumer spending, boosts tax revenue, and increases the GDP/GRDP According to research published by the Federal Reserve Bank of Philadelphia in 2017, a falling participant labor rate can slow the growth of GRDP at a region, since fewer people are contributing to the region’s output of goods and services Additionally, a lower participation rate can lead to higher tax rates, since the government has a narrower tax base from which to draw revenue, the authors noted   In the following parts, models and data are going to be utilized in order to run the regression model and the result will be analyzed in order to answer the question of interest Procedure and program    Econometrics refers to a branch of business analytics, modeling, and forecasting techniques for modeling the behavior or forecasting certain business, financial, economic, physical science, and other variables The Stata program is primarily used to analyze the data and run the regression model    A basic tool for econometrics is the multiple linear regression model Econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency   There are steps to conduct an empirical analysis:  Step 1: Question of interest based on economic theories   In principle, econometric methods can be used to answer a wide range of questions, such as: testing some aspects of an economic theory and effects of a government policy In cases when we need to test an economic theory, a formal economic model is constructed An economic model consists of mathematical equations that describe various relationships For example, individual consumption decisions, subject to a budget constraint, are described by mathematical methods  Step 2: Set up mathematical model   The mathematical model reflects the exact relationship between variables  Step 3: Set up econometric model   An econometric model can be derived from a mathematical model by allowing for uncertainty   The error term of disturbances in econometric models represents factors that are not included in the model but can affect the dependent variable  Step 4: Data collection   Data can be divided into types: Primary and Secondary data   The structure of economic data: Cross-sectional data, time-series data and pooled data Pooled data can be furthermore categorized into pool cross sectional data and panel data  Step 5: Estimate parameters of the model   Parameter estimates (also called coefficients) are the change in the response associated with a one-unit change of the predictor, all other predictors being held constant The unknown model parameters are estimated using least-squares estimation  Step 6: Test mistakes of the model   The assumptions of the model can be violated when there are high multicollinearity, heteroskedasticity and autocorrelation  Step 7: Test hypothesis  Fisher, Durbin-Watson, Lagrange, Hausman test can be used to test the appropriation of the model and estimated parameters  Step 8: Analyze the estimated results and forecasting/ policy implication II DATA COLLECTION Data type - The estimation of the model is in the form of a Cross Sectional Data - A cross-section data set consists of a sample of individuals, households, firms, cities taken at a given point of time The analysis might also have no regard to differences in time Analysis of cross-sectional data usually consists of comparing the differences among selected subjects The data collected in this report are obtained from the data collected by each provinces/cities of Vietnam Data collection - Data in this report is secondary data, as they are collected from a given source - Collected in 2018, from 62 provinces of Vietnam Source of data: General Statistics Office of Vietnam (link: gso.gov.vn) The meanings of each variable:      GRDP: GRDP per capita (Mil VND/ Capita/ year) Grad: Highschool graduation rate (%) Inv: Foreign Direct Investment (Mil USD) Dens: Population density (people/km2) Rate: Labor participation rate (%) III STATISTICAL DESCRIPTION OF VARIABLES Running DES fuction The most important information after using the DES function is the variables’ label des grdp grad inv dens rate storage display value variable name type format label variable label -grdp double %10.0g Gross regional domestic product per capita grad double %10.0g High school graduation rate inv double %10.0g Foreign direct investment dens int %8.0g Population density rate double %10.0g Labor participation rate DES function provides the meaning and the measurement of the variables below:  Grdp: stands for Gross regional domestic product per capita (unit: mil VND/capita/year) Grdp is a quantitative variable  Grad: stands for High school graduation rate (unit: percent) Grad is a quantitative variable  Inv: stands for Foreign direct investment (unit: mil USD) Inv is a quantitative variable  Dens: stands for Population density (unit: people/km2) Dens is a quantitative variable  Rate: stands for Participation labour rate (unit: percent) Rate is a quantitative variable Running SUM function SUM function lets us know about observations, mean, standard deviation, max and value of the variables sum grdp grad inv dens rate Variable | Obs Mean Std Dev Min Max -+ grdp | 62 55.39306 27.99608 20.7 154.84 grad | 62 94.59387 3.3962 85.36 99.4 inv | 62 678.3661 1571.956 8669.7 dens | 62 516.0645 667.7978 51 4363 rate | 62 58.16613 3.807755 50.4 68.8 Where:  Obs is the number of observations  Std Dev is the standard deviation of the variable  Min/ Max is the minimum/ maximum value of the variable By using SUM function, we have:  Grdp: With 62 observations, the mean value is 55.393, Std Dev is 27.996 The minimum value is 20.7, the maximum value is 154.84  Grad: With 62 observations, the mean value is 94.593, Std Dev is 2.396 The minimum value is 85.36, the maximum value is 99.4  Inv: With 62 observations, the mean value is 678.366, Std Dev is 1571.956 The minimum value is 0.1, the maximum value is 8669.7  Dens: With 62 observations, the mean value is 516.0645, Std Dev is 667.7978 The minimum value is 51, the maximum value is 4363  Rate: With 62 observations, the mean value is 58.166, Std Dev is 3.808 The minimum value is 50.4, the maximum value is 68.8 Running TAB function Using TAB function respectively allows us to describe more than variable coincidently with frequency and percent of the variables Tab Grdp tab grdp Gross | regional | domestic | product | Freq Percent Cum + 20.7 | 1.61 1.61 26.7 | 1.61 3.23 27.31 | 1.61 4.84 30 | 1.61 6.45 33 | 3.23 9.68 33.6 | 1.61 11.29 34.33 | 1.61 12.90 36 | 1.61 14.52 36.64 | 1.61 16.13 37.49 | 1.61 17.74 37.5 | 3.23 20.97 ……… … …… …… 80.5 | 1.61 85.48 83.16 | 1.61 87.10 86.5 | 1.61 88.71 93.94 | 1.61 90.32 97.1 | 1.61 91.94 97.3 | 1.61 93.55 117.66 | 1.61 95.16 130.8 | 1.61 96.77 150.1 | 1.61 98.39 154.84 | 1.61 100.00 + Total | 62 100.00 Analyzing information from the table above:  Gross regional domestic product ranges from 20.7 to 154.84 (mil VND/capita/year)  93.56% of the observations have the gross regional domestic product that is less than 100 mil VND/capita/year Tab Grad tab grad Graduation | from high | school rate | Freq Percent Cum + 85.36 | 1.61 1.61 86.01 | 1.61 3.23 86.74 | 1.61 4.84 87.07 | 1.61 6.45 89.81 | 1.61 8.06 90.45 | 1.61 9.68 90.77 | 1.61 11.29 90.86 | 1.61 12.90 91.1 | 1.61 14.52 91.45 | 1.61 16.13 91.51 | 1.61 17.74 ……… … …… …… 97.83 | 1.61 82.26 97.92 | 1.61 83.87 97.97 | 1.61 85.48 98.22 | 1.61 87.10 98.29 | 1.61 88.71 98.43 | 1.61 90.32 98.87 | 1.61 91.94 99 | 1.61 93.55 99.22 | 1.61 95.16 99.24 | 1.61 96.77 99.39 | 1.61 98.39 99.4 | 1.61 100.00 + Total | 62 100.00 Analyzing information from the table above:  High school graduation rate ranges from 85.36% to 99.4% Tab Inv tab inv Foreign | direct | investment | Freq Percent Cum + .1 | 4.84 4.84 | 1.61 6.45 10  Dens & GRDP: The higher the population density is, the higher ross regional domestic product is  Rate & GRDP: The lower the Labor participation rate is, the higher ross regional domestic product is c Running regression function Using function: reg Grdp grad inv dens rate We have the following result: reg grdp grad inv dens rate Source | SS df MS -+ -Model | 25049.3279 6262.33198 Residual | 22761.2936 57 399.32094 -+ -Total | 47810.6215 61 783.780681 Number of obs F(4, 57) Prob > F R-squared Adj R-squared Root MSE = = = = = = 62 15.68 0.0000 0.5239 0.4905 19.983 -grdp | Coef Std Err t P>|t| [95% Conf Interval] -+ -grad | 1927351 8116529 0.24 0.813 -1.432572 1.818042 inv | 0030894 0028439 1.09 0.282 -.0026054 0087841 dens | 0165378 0070495 2.35 0.022 0024214 0306541 rate | -2.245402 7477628 -3.00 0.004 -3.742771 -.7480329 _cons | 157.1376 79.52838 1.98 0.053 -2.115253 316.3904 It can be inferred from the above result: Variables Coefficient s Coeffcient values T p-values β0 157.1376 1.98 0.053 Grad β1 0.193 0.24 0.813 Inv β2 0.003 1.09 0.282 Dens Rate β3 β4 0.017 -2.246 2.35 -3.00 0.022 0.004 We can have the following regression model: ^ GRDP=157.1376+ 0.193× Grad+0.003 × Inv + 0.017 × Dens−2.246 × Rate 15 V PROBLEM TESTING Test omit variable We have to run Ramsey’s test to check the functional form of the model Apply ovtest reg grdp grad inv dens rate       Source |       SS           df       MS      Number of obs   =        62 -+   F(4, 57)        =     15.68        Model |  25049.3279         4  6262.33198   Prob > F        =    0.0000     Residual |  22761.2936        57   399.32094   R-squared       =    0.5239 -+   Adj R-squared   =    0.4905        Total |  47810.6215        61  783.780681   Root MSE        =    19.983 -        grdp |      Coef   Std Err.      t    P>|t|     [95% Conf Interval] -+ -        grad |   1927351   8116529     0.24   0.813    -1.432572    1.818042          inv |   0030894   0028439     1.09   0.282    -.0026054    0087841         dens |   0165378   0070495     2.35   0.022     0024214    0306541         rate |  -2.245402   7477628    -3.00   0.004    -3.742771   -.7480329        _cons |   157.1376   79.52838     1.98   0.053    -2.115253    316.3904 - ovtest Ramsey RESET test using powers of the fitted values of grdp        Ho:  model has no omitted variables                   F(3, 54) =      3.60                   Prob > F =      0.0190 From the result, we can see that (Prob > F) = 0.0190 < 0.05 => reject H => The model has omitted variable => The model has misspecification of functional form: We have to change the functional form from lin – lin to lin – log model by changing variable “Inv” into “ log (Inv)” (linv) 16  Apply gen linv = log(inv)   Apply reg grdp grad linv dens rate  Apply ovtest reg grdp grad linv dens rate       Source |       SS           df       MS      Number of obs   =        62 -+   F(4, 57)        =     16.41        Model |  25586.7408         4  6396.68521   Prob > F        =    0.0000     Residual |  22223.8807        57  389.892643   R-squared       =    0.5352 -+   Adj R-squared   =    0.5025        Total |  47810.6215        61  783.780681   Root MSE        =    19.746 -        grdp |      Coef   Std Err.      t    P>|t|     [95% Conf Interval] -+ -        grad |   0867559   7864118     0.11   0.913    -1.488007    1.661518         linv |    1.77309    1.10238     1.61   0.113    -.4343888    3.980569         dens |   0197201    004646     4.24   0.000     0104167    0290235         rate |  -1.810615    793251    -2.28   0.026    -3.399073   -.2221578        _cons |   134.4399   80.91612     1.66   0.102    -27.59186    296.4716 - ovtest Ramsey RESET test using powers of the fitted values of grdp        Ho:  model has no omitted variables                   F(3, 54) =      2.14                   Prob > F =      0.1054 As (Prob > F) = 0.1054 > 0.05 => Accept H ,the functional form is no longer misspecification Multicollinearity testing Multicollinearity is the high degree of correlation amongst the explanatory variables, which may make it difficult to separate out the effects of the individual regressors, standard errors may be overestimated and t-value depressed The problem of Multicollinearity can be detected by examining the correlation matrix of regressors and carry out auxiliary regressions amongst them.   17 In Stata, to test the multicollinearity, the VIF command is used VIF (Variance Inflation Factor) is defined as A measure of the amount of multicollinearity in a set of regression variables The presence of multicollinearity within the set of independent variables can cause a number of problems in the understanding the significance of individual independent variables in the regression model When severe multicollinearity issues exist, the variance inflation factor exceeds the acceptable value of 10 or proves to be very large for the variables involved The VIF is given by:   VIF = 1/(1-Rj^2)   If at least one of the variables has VIF greater than 10, we can define that the model is multicollinearity.  vif     Variable |       VIF       1/VIF   -+ -        linv |      1.56    0.639765         dens |      1.51    0.664010         rate |      1.43    0.700577         grad |      1.12    0.896042 -+ -    Mean VIF |      1.40    As the result, all variables and the mean VIF of variables is smaller than 10 (1.4 < 10), we can jump to the conclusion that there is no multicollinearity Heteroskedasticity testing    Heteroskedasticity indicates that the variance of the error term is not constant, which makes the least squares results no longer efficient and t tests and F tests results may be misleading The problem of Heteroskedasticity can be detected by plotting the residuals against each of the regressors, most popularly the White’s test It can be remedied by respecifying the model – look for other missing variables In Stata, the imtest, white command is used, which stands for information matric test.    { H 0: Homoscedasticity     H 1: Heteroskedasticity  } If P – Value is smaller than 0.05, we will reject H0 and accept H1 Apply the White Test or Breusch - Pagan to test the model’s error.  - Firstly, we use command rvfplot, yline (0) to see whether the model has heteroskedasticity   18 As the distribution of the residual doesn’t converge in anydirection, we can predict that the model has heteroskedasticity => We will use the White Test and Breusch-Pagan / Cook-Weisberg test for further conclusion:  Apply command imtest, white: imtest, white White's test for Ho: homoskedasticity          against Ha: unrestricted heteroskedasticity          chi2(14)     =     23.64 Prob > chi2  =    0.0506 Cameron & Trivedi's decomposition of IM-test               Source |       chi2     df      p 19 -+   Heteroskedasticity |      23.64     14    0.0506             Skewness |      14.92      4    0.0049             Kurtosis |       3.96      1    0.0467 -+                Total |      42.51     19    0.0015 - As we can see (Prob > chi2) = 0.0506 > 0.05, so we not have enough evidence to reject the null hypothesis at the significant level of 5%.  We also need to check again with Breusch-Pagan / Cook-Weisberg test for heteroskedasticity: hettest Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of grdp chi2(1) = 8.16 Prob > chi2 = 0.0043 As we can see : Prob > chi2= 0.0043 we reject the null hypothesis => the model has heteroskedasticity => In conclusion, the model has heteroskedasticity To alter the problem, we will run the model again with robust standard errors to fix the heteroskedasticity, change into the real value of these errors reg grdp grad linv dens rate, robust Linear regression 20 Number of obs = 62 F(4, 57) = 23.81 Prob > F = 0.0000 R-squared = 0.5352 Root MSE = 19.746 -| Robust grdp | Coef Std Err t P>|t| [95% Conf Interval] -+ -grad | 0867559 74026 0.12 0.907 -1.395589 1.569101 linv | 1.77309 7925958 2.24 0.029 1859443 3.360236 dens | 0197201 0043407 4.54 0.000 011028 0284122 rate | -1.810615 7342411 -2.47 0.017 -3.280908 -.3403229 _cons | 134.4399 55.52095 2.42 0.019 23.2611 245.6187 The value of these errors have been changed, giving the real value for variables in the regression model Normality (of u) testing H 0: u has normal distribution    Using the demand:   predict u, residuals                                  histogram u, normal  We have the following result:  21   22 As can be seen from the graph, u does not have normal distribution The Jacque - Bera test is then executed with the demand:  sktest u                     Skewness/Kurtosis tests for Normality                                                            joint -    Variable |        Obs  Pr(Skewness)  Pr(Kurtosis) adj chi2(2)   Prob>chi2 -+            u |         62     0.0000        0.0041       18.95         0.0001 As we can see from the table, because (Prob > chi2) = 0.0001 < 0.05, we reject the null hypothesis Thus, u has no normal distribution However, in this model, we still assume that u has normal distribution 23 VI STATISTICAL HYPOTHESIS TESTING reg grdp grad linv dens rate, robust Linear regression Number of obs = 62 F(4, 57) = 23.81 Prob > F = 0.0000 R-squared = 0.5352 Root MSE = 19.746 -| grdp | Robust Coef Std Err t P>|t| [95% Conf Interval] -+ -grad | 0867559 74026 0.12 0.907 -1.395589 1.569101 linv | 1.77309 7925958 2.24 0.029 1859443 3.360236 dens | 0197201 0043407 4.54 0.000 011028 0284122 rate | -1.810615 7342411 -2.47 0.017 -3.280908 -.3403229 _cons | 134.4399 55.52095 2.42 0.019 23.2611 245.6187 Critical value method As we can see, grad has the absolute values of t=0.12 smaller than the critical value of 2.00 Consequently, we not have enough evidence to reject the null hypothesis, meaning that high school graduation rate does not have statistically significant effect on GRDP.  The absolute value of t of linv, dens and rate (2.24, 4.24, 2.28) is higher than the critical value of 2.00 As a result, we reject the null hypothesis, meaning that Foreign direct investment (FDI); Population density and labour participation rate has statistically significant effect on GRDP.  Confidence interval method According to the statistics, β j = is included in the confidence interval of grad so we accept the null hypothesis and jump to a conclusion that that high school graduation rate does not have statistically significant effect on GRDP.  Whereas, β j = does not fall in the range of the confidence interval of linv (0.1859443, 3.360236); dens (0.011028, 0.0284122), and rate (-3.280908, -0.3403229), as a result, we could 24 reject the null hypothesis, which means that FDI; Population density and Labor participation rate has statistically significant effect on GRDP 25 VII CONCLUSIONS AND POLICY IMPLICATION Conclusions After the whole analytic and testing process, we have raised an overview of the data set given in terms of the statistical indication about how different determinants affect the Gross regional domestic product in Vietnam in 2018 As mentioned above, we carry out a research how four factors - Grad (Graduation from high school rate); Inv (Foreign direct investment), Dens (Population density) and Rate (Labour participation rate) – are involved in changing Gross regional domestic product per capita in Vietnam The model illustrates that these four independent variables can explain 53,52% of the total variation in the dependent one; the 46,48% remaining depends on other variables that are not mentioned in our research In addition, most of independent variables have the same positive effect on the dependent variable except for Rate (Labour participation rate) based on the coefficient testing result The data analysis, regression model and hypothesis testing have shown Foreign direct investment (FDI), Population density and Labour participation rate has statistically significant effect on GRDP per capita, while high school graduation rate not have statistically significant effect on GRDP per capita Policy implications About policy implication, through the analysis, we notice that labor participation rate (of people from 15 years old) have a significant but negative impact on the GRDP per capita We believe that due to the low education level and harsh conditions in rural cities, People have to go to work from early age with low income And as a consequence, the GRDP per capita in these areas are lower while labor participation rate is higher when comparing with urban cities Therefore, in order to increase GRDP per capita in Vietnam, the government must decrease labor participation rate in each city, which can be done indirectly by improving our education system, especially in rural area Also, the government should create more subsidies for students with poor condition to prevent them from dropping out to work at young age and encourage them to search for higher education On the other hand, foreign direct investment (FDI) also have a significant impact on GRDP per capita (in a positive way) Hence, government need to take actions to raise foreign investor’s awareness about potential investments in Vietnam, especially in Tourism This can be done by running promotion campaigns for famous Tourist Attractions and culture heritage In addition, Government should create more policies and subsidies that encourage the development of Tourism in Vietnam, especially when Tourism are suffering a lot under the negative impact of Covid-19 26 VIII REFERENCE https://www.gso.gov.vn/so-lieu-thong-ke/ ERSA2015_00207.pdf (econstor.eu) Microsoft Word - thesis2.doc (diva-portal.org) https://www.philadelphiafed.org/error http://impact.all4ed.org 27 IX APPENDIX Province GRDP (Mil VND/ Capita/ year) Highschool graduation rate (%) Foreign Direct Investment (Mil USD) Population density (people/km2) Labour paticipation rate (%) GRDP Grad Inv Dens Rate Hà Nội Vĩnh Phúc Bắc Ninh Quảng Ninh Hải Dương Hải Phịng Hưng n Thái Bình Hà Nam Nam Định Ninh Bình Hà Giang Cao Bằng Bắc Kạn Tuyên Quang Lào Cai Yên Bái Thái Nguyên Lạng Sơn Bắc Giang Phú Thọ Điện Biên Lai Châu Sơn La Hịa Bình Thanh Hóa Nghệ An 93.94 86.50 150.10 117.66 56.30 97.10 55.30 38.00 55.20 52.00 48.50 20.70 26.70 30.00 36.00 61.84 33.60 77.70 38.40 52.10 38.50 27.31 33.00 38.00 48.30 41.10 36.64 95.83 99.39 99.40 97.34 99.00 91.88 97.76 98.29 96.70 99.24 98.22 91.10 95.24 96.65 98.87 96.27 96.07 92.98 91.51 99.22 97.97 94.53 97.92 98.43 97.36 97.34 97.83 8669.70 586.20 1695.20 242.10 691.40 1374.00 488.20 67.50 864.20 267.70 149.50 0.50 0.20 4.40 20.00 0.90 7.30 616.00 1.40 1163.30 348.40 1.20 0.10 0.40 0.10 350.40 315.10 2398.00 934.00 1664.00 214.00 1022.00 1176.00 1347.00 1185.00 991.00 1067.00 708.00 108.00 79.00 65.00 134.00 115.00 119.00 364.00 94.00 468.00 414.00 63.00 51.00 88.00 186.00 328.00 202.00 50.40 54.60 55.20 54.60 55.50 54.70 57.10 59.90 56.60 57.90 59.50 62.70 65.90 68.80 61.00 61.00 63.60 59.70 62.00 60.80 57.50 57.40 60.30 61.30 64.70 61.60 57.50 Hà Tĩnh Quảng Bình Quảng Trị Thừa Thiên Huế Đà Nẵng Quảng Nam Quảng Ngãi Bình Định Phú Yên Khánh Hòa Ninh Thuận 49.50 37.50 43.60 93.63 93.37 90.86 32.60 0.80 20.00 215.00 111.00 133.00 54.10 57.60 53.50 40.76 83.16 61.07 57.80 46.89 39.97 62.13 39.70 95.17 85.36 86.01 92.62 94.99 87.07 89.81 91.94 324.50 515.20 184.20 136.60 96.60 216.60 202.30 133.70 224.00 883.00 141.00 240.00 245.00 191.00 240.00 176.00 53.70 51.60 57.70 59.40 59.40 59.50 55.50 55.70 28 Bình Thuận Kon Tum Gia Lai Đắk Lắk Đắk Nơng Bình Phước Tây Ninh Bình Dương Đồng Nai Bà Rịa - Vũng Tàu TP Hồ Chí Minh Long An Tiền Giang Bến Tre Trà Vinh Vĩnh Long Đồng Tháp An Giang Kiên Giang Cần Thơ Hậu Giang Sóc Trăng Bạc Liêu Cà Mau 29 50.31 37.49 45.36 41.00 45.24 56.85 58.30 62.79 130.80 90.45 96.72 90.77 86.74 91.45 92.14 92.72 94.41 95.34 1809.00 507.00 0.10 206.00 708.00 465.90 1263.50 3508.60 2178.80 158.00 56.00 98.00 143.00 96.00 145.00 289.00 900.00 524.00 57.60 57.20 59.20 57.80 59.60 58.10 57.50 65.00 53.20 97.30 154.84 68.62 46.90 33.00 44.00 44.80 40.00 34.33 48.21 80.50 38.32 37.50 42.05 43.29 96.73 95.34 92.52 95.56 96.07 95.83 96.38 92.36 95.20 94.17 96.57 93.35 95.58 93.36 91.89 1085.40 8338.20 931.90 396.40 64.80 110.30 150.50 13.00 65.40 20.70 69.10 71.00 112.30 114.10 80.20 580.00 4363.00 376.00 703.00 538.00 428.00 693.00 473.00 540.00 272.00 858.00 452.00 362.00 340.00 226.00 52.40 51.70 58.80 63.10 63.20 57.00 58.80 64.20 54.80 53.50 58.50 60.30 53.70 55.30 56.30 ... rate) – are involved in changing Gross regional domestic product per capita in Vietnam The model illustrates that these four independent variables can explain 53,52% of the total variation in the... measure of real income However, we believe that the income of people in each region of a country is relative different, hence, so we chose GRDP per capita (gross regional domestic product per capita) ... testing process, we have raised an overview of the data set given in terms of the statistical indication about how different determinants affect the Gross regional domestic product in Vietnam in