HANOI UNIVERSITY Faculty of Management and Tourism ECONMETRICS GROUP PROJECT UNEMPLOYMENT RATE IN VIETNAM (1986-2015) Tutor: Mr Pham Van Hung Tut number: Group: Students: Đặng Thị Thanh Hương – 1304000034 Ngô Thị Hải Yến - 1204010122 Văn Thúy Diễm – 1304010011 Vũ Thị Thu Liên - 1204010050 TABLE OF CONTENTS I II INTRODUCTION Unemployment is one of the major problems of developed as well as developing countries today Unemployment affects not only person who has lost job but also affects many aspect of the society (family, living standard, economic growth…) It can make individual suffer bad emotion such as stress, sadness, confusion…All these emotions can break them down Moreover, it can go as far as affecting the society Without income, firstly, their family will directly hurt from this situation because of education of children, food, condition of living Secondly, when unemployment increase, it means that the society did not use efficiently source of labor, lead to reducing in productivity Besides, unemployment also influences government budget Government has to pay more for allowance, social welfare and no income as well as decreasing income tax Therefore, government always tends to decrease unemployment rate That is reason why our group choose the topic of unemployment in Vietnam with three main factors: GDP, inflation and population The period we chose from 2000-2015 to see how Vietnamese economy has changed before and after crisis 2008-2009 up to now The model has used available data of GDP, inflation and population to reflect more truthful about estimated value of unemployment rate in Vietnam METHOLODOGY Variables According to the theory of macroeconomic, GDP and unemployment rate are used to measure the living standard and they have negative relationship It means that an increase in GDP lead to decrease in unemployment rate In addition,in economic, Okun’lawis an empirically observed relationship between unemployment and GDP The "gap version" states that for every 1% increase in the unemployment rate, a country's GDP will be roughly an additional 2% lower than its potential GDP Therefore, we decide to test whether there is an actual existence between unemployment rate and GDP Population is also one of significant factors that affect to the change in unemployment rate Many reports indicate that when the number of population of a country increases significantly, it will create the pressure about employment problem It means that number of people not being find out a job will rise directly, as a result, unemployment rate also grow up significantly The final variable which we selected to support for this report, is inflation variable According to Phillip’s curve, in the long run period, a change in inflation has not affected to the change in unemployment rate and it was a line However, in the short run period, unemployment rate and inflation have related because of the change in demand Sample size In the report, we choose 16 years from 2000 to 2015 as sample size 3 Collection data We choose the time series data from 2000 to 2015 to illustrate the changes in unemployment rate are explained by changes in other economic variables, namely, GDP, population, and Inflation rate All of the data are recorded annually All data are collected from General Statistic Office Table Unemployment rate and other factors Year 200 200 200 200 200 200 200 200 200 200 201 201 201 201 201 201 III Unemployment rate (%) GDP(mil USD) Population (mil) Inflation rate (%) 6.42 31,200 77.63 -1.768 6.28 32,487 78.62 -0.31 6.01 35,081 79.54 4.079 5.78 39,798 80.47 3.303 5.6 45,359 81.44 7.895 5.31 52,899 82.39 8.394 4.82 60,819 83.31 7.503 4.64 71,003 84.22 8.349 2.38 89,553 85.1187 23.12 2.9 91,533 86.025 6.71 2.88 101,623 86.9474 9.2 2.22 135,540 87.8604 18.67 1.96 155,820 88.8093 9.1 2.18 171,220 89.7595 6.59 2.1 186,200 90.7289 4.1 2.31 198,638.16 91.903961 0.63 Sources: General Statistic Office and IMF data (2000-2015) EQUATION REGRESSION Functional form It is very necessary to test for the best model illustrating the relationship between Unemployment and the explanatory variables We regress all the functional models, as well as linear model to choose a least coefficient of variance (CV) Before establishing the model, we assume that our model response to the ten classical assumptions (The Gaussian, standard or classical linear regression model (CLRM)): (1) Linear regression model (2) X values are fixed in repeated sampling (3) Zero mean values of disturbance : E(ui\Xi) = (4) Homoscedasticity or equal variance of ui: var(ui\Xi) = σ2 (5) No autocorrelation between the disturbances: cov(ui, uj\ Xi, Xj) = (6) Zero covariance between ui and Xi: cov (ui,Xi) = (7) The number of observations n must be greater than the number of parameters to be estimated (8) Variability in X values (9) The regression model is correctly specified (10) There is no perfect multicollinearity Our group run OLS regression on Eviews with different functional forms and then compute coefficient of variation (C.V.) to find the most suitable functional form with lowest C.V Equation semi-Log model has the smallest CV, as a result this equation will be used to indicate the relationship between the UN and GDP, POP, INF The final result is : Ln(UE^)= 7.559700- 1.637896* ln(GDP)+ 0.144371* POP- 0.008729* INF • • • UE^: Expected unemployment rate GPD: Gross domestic product POP: Population rate INF: Inflation rate Model interpretation  On average, if GDP increases by unit, UE will decrease by about 1.637896 million, other things remain constant  On average, if POPULATION increases by unit (million people), UE will rise by about 0.144371 million, other things remain constant  On average, if INF increases by unit (million USD), UE will decrease by about 0.008729 million, other things remain constant  Confidence interval: α = 5% => tα/2, n-k = t0.025, 12 = 2.179    IV RESULT ANALYSIS Overall significant test Step 1: H0: β2 = β3 = β4 = H1: β22+β32 β42 ≠ Step 2: (k: Number of parameters) = 107.1504 Step 3: With α = 5%, Fc = Fα,df,df2 = F0.05,3,12 = 3.49 Step 4: Reject H0 if F-value > Fc Step 5: Compare 107.1504 > 3.49 => Reject H0 Step 6: There is enough statistically evidence to conclude that at least one of these variables: Log (GDP), Population, Inflation has been significant Individual significant test Step 1: H0: βi = H1: βi ≠ Step 2: Step 3: α = 5%, tc = t α/2, n-k = t0.025, 12 = 2.179 Reject H0 if |t-value| > tc Step 4: parameter Log(GDP) s |t-value| 4.164961 decision Reject We have table Population Inflation 2.538879 Reject 1.962671 Not reject There is enough information about the significant effect of log(GDP) and Population or log(Un) otherwise Inflation Because inflation has negative relationship with unemployment in short term Hence, in long term, economist should estimate based on log(GDP) and population Chow test During period 2000 – 2015, there is an economic crisis over the world so this sample is divided two periods: period (2000 -2007) and period (2008 – 2015) Each period has observations Two new equations will be presented below: Step 1: Ho: no structural change H1: yes • Restricted model: • Unrestricted model: Step2: (k: number of period) = Step 3: Fα,df1,df2 = F0.05,2,12 = 3.89 Step 4: Reject Ho if F – value > Fc Step 5: Since 61.11873 > 3.89 => Reject Ho There is enough statistically evidence to infer that there should have a structural change between Log unemployment, Log GDP, Population and Inflation It can be explained that in 2008, Vietnam applied the first stimulus package to encourage the demand to overcome this depression period Errors a.Multicollinearity  Because R2 and individual significant test Since R2 = 96.1013% > 90% and inflation rate variable is not significant, multicollinearity might exist  VIF From the table, centered VIF of log(GDP) and pop are greater than 10, which result in existence of multicollinearity Nevertheless, two above variables have less likely collinearity with inflation rate because VIF of the inflation is only 1.245108  Correlation Matrix The result from above table might be conflicted with the prediction of VIF, which dedicates that there is no multicollinearity among independent variables  Auxiliary Regression Step 1: Step 2: Run OLS on auxiliary Regression (k is number of parameters in original model) Step 3: Fc = Fα,df1,df2 = F0.05, 2,13 = 3.81 Step 4: Reject H0: Xi is collinearity with Xf & Xm if F- value > Fc We have independent variables corresponding with auxiliary regressions Auxiliary regression Log(GDP) POP INF F-value Fc Decision 652.128 3.81 Reject 637.192 3.81 Reject 1.5932 3.81 Not reject In conclusion, there is enough evidence to infer that there is multicolinearity between log(GDP) & Pop, otherwise Inf b Heteroscedasity Step1: H0: homoscedasticity,Var (ui) = σ2 H1: heteroscedasticity, Var (ui) = σi2 Step2: Run OLS estimation on original model => obtain Step3: Obtain R2 from Auxiliary Regression R2 = 0.872554 We have W = n.R2 = 16 x 0.872554 = 13.960864 Step4: Look at χ2α,df (df: number of regressors to auxiliary regression) χ20.05,9 = 16.919 Step 5: If W >χ2α,df= Reject H0 Since 13.960864 < 16.919 => Not reject H0 There is not enough evidence to infer that heteroscedasticity exists c.Autocorrelation  First-order correlation Step 1: H0: ρ = 0, no autocorrelation H1: ρ> 0, yes, positive autocorrelation Step 2: Durbin-Watson test : DW = 1.455488 Step 3: Critical value α = 5%, n = 16, k’ = – = dL= 1.728 du= 0.857 Step 4: Since du< DW there is not enough information that first-order autocorrelation happens or not  Higher-order correlation Step 1: H0: no higher-order autocorrelation H1: yes Step 2: Run OLS estimation, obtain R2 R2 = 0.129469 Step 3: Breusch-Godfrey test = n.R2 = 16 x 0.129469 = 2.071504 Step 4: Critical value: χ2α,ρ = χ20.05,2 = 5.99147 Step 5: Reject H0 if BG test >χ2α,ρ Since 2.071504 < 5.99147 => Not reject H0 There is no existence of higher-order autocorrelation (with the number of lag years is 2) We continue the number of lag years from to 10 but the results are still the same Finally, there is only first-order autocorrelation Limitation In our process, the data is collected from trustworthy website, however, other sources also provided the same period but difference data Besides, the data is estimated in specific sample, it’s also challenging when GDP, Inflation and population are measured by the general Department of Statistic Therefore, the data may not reflect all exactly data of economy In addition, our sample size is still small(16 years) Furthermore, we used EViews software and knowledge which we are learnt in econometrics to perform test, interpret results to get V conclusion So the report might have some errors, nevertheless, we had done our best to finish this project REFERENCE An Ngoc, 2015, “GDP năm 2015 tăng 6,68%, caonhấttrong5năm”, available at 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