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Factors affecting Human Development Index (HDI) in Vietnam
Lecturer: Dao Thanh Binh Student: Tran Thi Thanh Loan
Tran Vu Tien Pham Tra My
Vu Tuyet Nhung Ho THi Minh Phuong
Ngo Thi Bich Hanh Vu Thai Duy
Bui Hai Ngoc Dinh Duy Quang
Tutorial: 4 BA-06
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Overview
Introduction
Model specification
Data graph
Results
Conclusions and suggestions
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Introduction
HDI: a summary measure of human development
HDI measures three basic dimensions of human development:
- a long and healthy life, as measured by life expectancy at birth
- knowledge, as measured by the adult literacy rate
- standard of living, as measured by GDP per capita
HDI has had a significant impact on drawing the attention of governments, corporations and international organizations to aspects of development that focus on the expansion of choices and freedoms, not just income.
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Model specificationHDI = β1 + β2* GDP + β3*EDU + β4*LIE + u
HDI: dependent variable
GDP (Gross Domestic Product): dependent variable
- Y = C + I + G + NX
- GDP per capita = Y/ Population
Educational attainment (EDU): dependent variable
- represented by the literacy rate
- considered as a crucial measure of a region's human capital since literacy increases job opportunities and access to higher education.
Life expectancy (LIE): dependent variable
- the average number of years a human has before death
- an important measure in its own right and can be a proxy measure for many other issues such as healthcare, wealth, opportunities, and education
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Data graph
HDI
Vietnam HDI through 1995-2006
0.709
0.704
0.539
0.540
0.557
0.560
0.664
0.671
0.682
0.688
0.688
0.691
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
HDI
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Data graph
GDP per capital
Vietnam GDP index during 1995-2006
0.550
0.540
0.520
0.510
0.500
0.490
0.4700.470
0.1800.180
0.110
0.170
0.000
0.100
0.200
0.300
0.400
0.500
0.600
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
Bil
lion
of
doll
ars
GDP
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Data graph
Educational attainment
Vietnam Education index 1995-2006
0.810
0.8200.820
0.830
0.8400.840
0.830
0.820
0.810
0.800
0.790
0.780
0.750
0.760
0.770
0.780
0.790
0.800
0.810
0.820
0.830
0.840
0.850
199519961997199819992000200120022003200420052006
Year
Per
cen
t
literacy index
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Data graph
Life expectancy
Vietnam Life Expectancy index 1995-2006
0.760
0.760
0.730
0.730
0.7200.710
0.710
0.710
0.6900.680
0.6800.670
0.620
0.640
0.660
0.680
0.700
0.720
0.740
0.760
0.780
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
yea
rs
LE
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Data graph
0.760.820.540.7042005
0.730.820.520.6912004
0.690.810.180.5601998
0.680.800.180.5571997
0.730.830.510.6682003
0.710.840.490.6822001
0.710.830.470.6712000
0.720.840.500.6682002
0.710.820.470.6641999
0.760.810.550.7092006
0.680.790.110.5401996
0.670.780.170.5391995
Life expectancy
LiteracyGDPHDIYear
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Results
Eview result table
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Results
Functional test
- Log – linear function:
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Results
- Lin – log function:
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Results
- Log – log function:
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Results
C.V1 (linear-linear) = 0.0044/0.6410= 0.0068
C.V2 (log-linear) =0.0069 /0.4502= 0.0153
C.V2 (linear- log) = 0.01/0.6410=0.0156
C.V2 (log- log) = 0.0158/0.4502 = 0.035
=>best model linear-linear:
HDI =-0.021 + 0.3214* GDP + 0.384* EDU + 0.3144* LIE
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Results
Errors in the model:
- Multicolinearity:
+ happens when there is a functional relationship among independent variables.
+ all coefficients are significant
=> don’t need to worry about this error
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Results- Heteroscedasticity: to check this error, Park-test is used
lnu^2 = β1 + β2*EDU
^Lne_sp= 22.0571 -42.157*EDU
H0 : β2 = 0 Heteroscedascity does not exist.
H1 : β2 ≠ 0 Heteroscedascity exists
From the table, P-value of β2> 0.05
=> β2 is not significant at 0.05
=> do not reject H0
=> there is no heteroscedascity
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Results
- Autocorrelation: to check this error, use The Breusch-Godfrey (BG) test
H0 : no autocorrelation
H1: autocorrelation exists
BG-statistic: 1.204509 < X 2p=1 = 3.84146
(α=0.05).
=> do not reject H0
=> no autocorrelation
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Results
Discussion
^HDI= - 0.021 + 0.3214 * GDP + 0.384 * EDU + 0.3144 * LIE
+ β2, β3 and β4 at 5% level of significance, their P-value are all < 0.05
=> GDP, EDU, LIE have significant impact on HDI Index
+ R2 = 0.996975 => more than 99% of the variation of HDI index can be explained by GDP, LIE, EDI indexes
=> very high level of fit between the regression line and the data provided.
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Conclusions and suggestions
Conclusions:
With the lowest CV, linear_linear was the best model
All the coefficients are significant shows a significant impact of EDU, LIE, and GDP indexes on HDI index
With positive sign of coefficients, there is a positive relationship between HDI indexes and the other three listed indexes.
Suggestions:
Invest more in education
Provide more health care service with good quality
Apply suitable economic development strategy to increase GDP
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