Download Higher Education, Employment and Economic Growth: Mexico and

Higher Education, Employment and Economic Growth: Mexico and Peru
Elsa-Sofia Morote, Ed.D.
Associate professor, Education Administration, Leadership and Technology
Dowling College
[email protected]
The emerging markets continue to growth in importance to the world economy;
however, the academic literature in this area is highly fragmented, and lies mainly in the
finance field. For that reason, it is important to develop an understanding of some of the
key elements in the development and growth of these emerging markets. This study
illuminates two important variables for these markets: higher education and economic
growth.
The central premise of this study was that increasing the educational attainment of
the population can help to increase the economic growth; however, this relationship is not
always direct, and there are some key variables, such as employment, that can affect this
relationship. To support this hypothesis, an empirical analysis using a Granger causality
approach was applied to Mexico and Peru.
The following question is answered: Considering the presence of the employment
rate, is there a causal relationship between higher education enrollment per capita and per
capita GDP rates?
Theoretical Underpinnings
The interrelation between education and economic development has been
discussed since ancient Greece. Adam Smith (1776, 1976) and the classical economists
emphasized the importance of investment in human skills. Early attempts to measure the
contribution of education to economic development were based either on the growth
accounting approach or on the rate of return to human capital.
However, it was not until late in the twentieth century that researchers undertook
formal and scientific analysis of this relationship. Several studies have investigated the
relationship between economic growth and education such as Psaharoupolous, 1988;
Pencavel, 1993; De Meulmester and Rochet, 1995; Jorgenson and Fraumeni, 1998. Their
starting point was always the root of the economic growth itself. The pioneer theorists
hypothesized that economic development depended on the increase of capital and the
labor factor in the productive processes. A fundamental reason for economic growth was
found to be the increase of productivity in these factors of production. Whereas
researchers such as Pencavel (1993) affirmed that correlations exist across countries
between economic growth rates and schooling enrollment rates including enrollment in
higher education, another group of researchers such as De Meulmester and Rochet
(1995), using more sophisticated econometric techniques, found that this relationship is
not always a direct one.
Psacharopoulos’ data and research have been considered a starting point by several
researchers (Table 1).
Table 1
Percent of the Economic Growth Rate (by Country) Explained by Education
Country
Growth Rate
Country
Growth Rate
Explained
Explained
(Until 1970s)
(Until 1970s)
Europe
North America
Belgium
Denmark
14
4
Canada
25
15
United States
12
United Kingdom
Germany
Latin America
Argentina
Brazil
Chile
Colombia
Ecuador
Honduras
Peru
Mexico
Venezuela
2
16.5
3.3
4.5
4.1
4.9
6.5
2.5
0.8
2.4
Asia
Japan
Malaysia
South Korea
3.3
14.7
15.9
Africa
Ghana
Kenya
Nigeria
23.2
12.4
16.0
Source: Psacharopoulos (1988), pp. 893-921.
Based on the results in Table 1, it can be concluded that education is one of the
factors that explain economic growth, but the explanation varies depending upon the
level of development of a country. For instance, on Table 1, except for the United States’
case, there seems to be an inverse relationship between per capita production and
education. The higher the economic level of the country, the smaller the contribution of
education to economic development.
The question of the connections between higher education and the labor market
are again among the key issues of debate whenever challenges for innovation in higher
education are at stake (Teichler, 1999). For example, in its 1994 report entitled “Higher
Education: Lessons of Experience”, the World Bank cited the tensions between higher
education and employment as one of the key elements of the higher education crisis
related to mismatch of supply and demand of graduates and lack of contact with the
market. In the same way, The Organisation for Economic Co-operation and Development
(OECD) addressed the transition from higher education to employment in one of its
largest projects in the early 1990s. Further, The United Nations Educational Scientific,
and Cultural Organization’s (UNESCO) World Conference on Higher Education stated
that the demands of the labor market are changing dramatically. Indeed, the patterns of
employment are also changing. College courses which once met national needs are now
irrelevant.
In this context, higher education is being challenged to reconsider its fundamental
objectives. For example, to strike a balance between supply and demand of graduates,
between responding to the demands directly expressed by the employment system and
influencing the labor market, and between its relationship with business and industry, and
government. Most definitively, there will need to be an adjustment between higher
education policies and the employment sector.
Not only is higher education being challenged, but government’s policies toward
unemployment are also being questioned. Unemployment can be discussed by the match
between supply and demand of graduates to the market; and the level of the economy and
economic policies toward unemployment and education. There are three important
stakeholders in this scenario (Figure 1): the higher education institutions, the private
economic sector (business and industry) and governments. These actors interrelate and
affect important variables that are the focus of this study (Figure 1): higher education,
economic growth and employment.
Employment
Higher Ed.
Enrollments
Economic Growth
Higher Education
Institutions
Economic Sectors
Government
s
Figure 1: Economic Growth, Employment and Higher Education.
Figure 1 illustrates that most of the higher education enrollment is affected by the
incentives to attend college by the governments, higher education institutions and
business and industry. In the same way, economic growth and employment will be
affected for these three stakeholders.
Employment is an influential factor. For example, in a thorough analysis of
almost 23,000 seniors from the national longitudinal study of the high school classes of
the USA in 1972, Maski and Wise (1983) found that students are very responsive to
tuition, scholarship and alternative employment opportunities in deciding which college
to attend. Salaries given by industry are also highly related. This sensitivity to the issue of
monetary considerations is important: Willis and Rosen (1979) estimated that a 10 %
increase in starting salaries induced almost a 20 % increase in college enrollments. In
general, college enrollments respond to the pecuniary net returns from investing in higher
education.
Data Collection
Data on the countries presented in this study was obtained from several sources.
Economic figures are obtained mainly from the Inter-American Development Bank
reference materials, Statistical Abstract of Latin America, and also abstracted from World
tables of the World Bank Report as well as and those sources listed in the references.
Higher education figures were obtained mainly from the International Historical
Statistics: The Americas. Data for the variables examined were obtained for the period
1970 through 2000.
A Wald Test for Granger-Causality
In an estimated VAR (p) system if we want to test for Granger Causality we need to test
zero constraints for the coefficients (Lutkepohl, 1991, p. 93). More generally we consider
testing
H 0 : Cβ  c,
H 1 : Cβ  c.
where C is an ( N x ( K2p + K)) matrix of a rank N and c is an (N x 1) vector.
Assuming that
d
T (βˆ  β) 

N(0, Γ 1  CM u ) ,
in a LS/ML estimation, we get
d
T (Cβˆ  Cβ) 

N [0, C (Γ 1  CM u ) C' ] ,
and hence
d
T(C βˆ  c)' [C (Γ 1  CM u ) C' ]-1 (Cβˆ  c) 

χ 2 (N).
This statistic is the Wald statistic.
Replacing  and
CMu by their usual estimators Γ̂  ZZ' /T and CM̂ u the resulting
statistic
λ w  (Cβˆ  c)' [C ((ZZ' ) 1  CM̂ u ) C' ]-1 (Cβˆ  c) ,
still has an asymptotic  2 -distribution with N degrees of freedom provided y, satisfies
the conditions of asymptotic properties of the white noise covariance matrix estimators
because under these conditions [C ((ZZ' ) 1  CM̂ u ) C' ]-1 / T is a consistent estimator of
[C(Γ 1  CM u )C' ] 1 . Hence the following result is obtained.
Asymptotic Distribution of the Wald Statistic
Suppose (21) holds. Furthermore,
plim (ZZ' /T)  Γ, plim CM̂ u  CMu are both
nonsingular and Ho: Cβ = c is true, with C being an (N x (K2 p + K)) matrix of rank N.
Then (Lutkepohl, 1991, p. 94)
d
λ w  (Cβˆ  c)' [C ((ZZ' ) 1  CM̂ u ) C' ]-1 (Cβˆ  c) 

χ 2 (N).
In practice it may be useful to make some adjustment to the statistic or the critical values
of the test in order to compensate for the fact that the matrix Γ 1  CM u is unknown
and has been replaced by an estimator. Working in that direction we note that
NF(N, T)
d
χ 2 (N),
T
where F(N, T) denotes an F random variable with N and T degrees of freedom (d.f.)
Since an F(N, T)-distribution has a fatter tail than the  2 (N)-distribution divided by N,
it seems reasonable to consider the test statistic
λF  λw / N .
The usual F-statistic for a regression model with nonstochastic regressors has
denominator d.f. equal to the sample size minus the number of estimated parameters.
Therefore this concept will be used to this research. Hence, the approximate distributions
are obtained (Lutkepohl, 1991, p.94).
λ F  F(N, KT  K 2 p  K)  F(N, T  Kp  1) .
To test the hypothesis a Wald test of Granger causality test between the variables
in question was applied. A short term (1970-2000) was considered for trivariate
relationships between higher education and economic growth in the presence of the
employment variable.
Human capital theorists agree to the existence of a correlation between higher
education and economic growth; however, their methodology has been questioned.
Correlational statistics have been the most widely used method by these theorists; but,
correlation does not imply causation. They are also questioned because usually the
economic growth rate is a dependent variable, and all other variables are independent.
This study tests per capita GDP and higher education enrollments per capita as dependent
variables and also as independent variables. This gives in depth information about these
relationships, adding new information to the theory. By presenting Granger causality test
analysis that relates economic growth and higher education, this study remains in the
same theoretical track that previous studies of Human Capital had established; and by
introducing employment as a third variable in the causality analysis and showing its
influence on higher education, this study challenges some points of the Labor Market
segmentation theory. The question of whether the system of higher education and
employment causes economic growth is addressed. Vector autoregressive (VAR) models
were used for forecasting and structural analysis, and also used to test for Granger
Causality. VAR (p) models (VAR model of order p) have become increasingly used in
this test. To test Granger Causality for VAR (p) system, zero constraints for the
coefficients must be tested.
The VAR system was constructed in Mexico and Peru using three variables:
Higher education enrollments per capita, GDP per capita and employment rates. The
VAR model was estimated using semi-annual data over the period 1970-2000. The size
of the VAR model requires six-monthly data rather than annual series to generate enough
degrees of freedom for estimation. Prior to testing for non-causality, it was necessary to
establish the order of integration. To this end, an Augmented Dickey-Fuller (ADF) test
was carried out on the time series in levels and differenced forms (as seen on Table 2 and
Table 3).
First, variables were tested for stationary (Table 2). The three variables are nonstationary; thus, are transformed taking first differences of logarithms. Employment is
represented by MEM in the case of Mexico and PEM on the case of Peru.
Table 2
Mexico and Peru: Unit Root Tests- Level (1970-2000)
Country
Variables
MEXICO
MGDP
-1.255009
1%
-3.6576
MHE
-0.425010
5%
-2.9591
MEM
-1.969826
PGDP
-2.442325
PHE
1.388312
PEM
-2.122202
PERU
ADF Test Statistics
Mckinnon Critical Values
Table 3 shows the transformed variables, which were stationary considering
critical values at 5%. The next step is testing Granger causality in the absence of the
variable employment on both countries.
Table 3
Mexico and Peru: Unit Root Tests - Transformed Series (1970-2000)
Country
Variables
MEXICO
MGDP
-4.777844
1%
-3.6852
MHE
-3.798076
5%
-2.9705
MEM
-4.477712
PGDP
-4.678298
PHE
-3.171842
PEM
-4.412620
PERU
ADF Test Statistics
Mckinnon Critical Values
Then, the number of lags included was derived using Akaike Information Criteria
(AIC) and FPE criterion.
Tables 4 and 5 show the optimum lag found for the Mexico and Peru systems.
The criteria for VAR order selection used were FPE criterion and the AIC (Akaike’s
information criterion). The first column shows the orders used to estimate AIC and FPE.
The second column presents the AIC estimated that is based in quite different reasoning
than FPE. The last column shows estimated and corresponding FPE (m) of the VAR
models order m = 0, 1,…, 6. The order minimizing the FPE and AIC values is chosen as
estimated for p. The p chosen is highlighted.
Table 4
Mexico – Estimation of the VAR Order of the Higher Education and Economic Growth,
and Employment System
VAR (m)
AIC (m)
FPE (m)
0
-18.76043
7.12E-09
1
-18.65552
7.74E-09
2
-18.10262
1.28E-08
3
-18.27343
1.01E-08
4
-18.04943
1.17E-08
5
-18.34868
8.19E-09
6
-21.5799
3.24E-10
Mim: p = 6
-21.5799
3.24E-10
Table 5
Peru – Estimation of the VAR Order of the Higher Education and Economic Growth, and
Employment System
VAR (m)
AIC (m)
FPE (m)
0
-19.01289
5.53E-09
1
-18.69087
7.47E-09
2
-18.18603
1.17E-08
3
-17.69378
1.79E-08
4
-17.42466
2.18E-08
5
-18.04017
1.11E-08
6
-20.56743
8.93E-10
Mim: p = 6
-20.56743
8.93E-10
In both cases the order chosen is 6. Then, the Wald test for restrictions on the
parameters of VAR (6) is then applied. Thus the economic growth (GDP) and higher
education (HE) and an employment rate (EM) were related as in the following VAR(6)
model (a):
GDPt 
GDPt 1 
GDPt 2 
GDPt 6 
 HE   A  A  HE   A  HE
 ,..., A  HE

o
1
t 1 
2
t 2 
6
t 6  ,
 t 
 EM t 
 EM t 1 
 EM t 2 
 EM T 6 
(a)
where A1-A6 are three by three matrices of coefficients with A0 as an identity matrix.
The null hypothesis of no Granger causality from Higher Education/Employment
to Economic growth may be expressed in terms of the coefficients of the VAR (6)
process as:
Ho : 12, 1 = 13, 1,…,= 12, 3 = 13, 3,…,= 12, 6 = 13, 6 = 0
12, 1, 12, 2, …,12, 6 are coefficients of HEt-1, HEt-2,…,HEt-6 and
13, 1, 13, 2, …,13, 6 are coefficients of EMt-1, EMt-2,…,EMt-6 respectively in the
equation system (a) where the system is being estimated as VAR(6)
Using the formulas (26) and (27) of chapter four, and considering T = 60 (sample size); K
= 3 variables: higher education enrollments per capita, GDP per capita, and employment
rate; p = 6 (optimal order) and N = twelve restrictions.
The results were:
F (Mexico) = 0.977951,
F (Peru) = 1.730406.
In contrast, = F(N, T-Kp-1) =F(12, 60-3*6-1) = F( 12, 41) = 2.0
Thus, in a 5% level test, Granger non-causality was rejected from higher
education/employment to economic growth. The fact that causality runs from the system
higher education enrollments/employment to economic growth indicates that it is the
rapid higher education enrollments per capita and employment which proceeds the
changes in economic growth.
Conclusion
A causal relationship from the system of higher education / employment to
economic growth was found in both countries. This means that the system of higher
education enrollment per capita /employment rates does cause impact on the per capita
GDPs.
This study confirms the findings of the International Labor Office (ILO, 2000)
that education is one of the key indicators in the labor market. The connections between
higher education and the labor market are among the most frequently discussed issues of
higher education. Teichler (1999) showed his preoccupation with the need for systematic
knowledge and empirical evidence.
The results of this study confirm that the employment rate is a key factor in the
higher education and economic growth relationship. Employment was found to be highly
related to higher education. Using the employment variable, some evidence was found
confirming Blaug’s argument. In essence, he argued that explanations about the link
between education and development should include institutional and sociological factors
in addition to economic factors.
This study extends this theory specifically to higher education which provides
high skill and quality in labor. Nevertheless, investment would not contribute to
improvements in economic growth if policy makers did not also relate education to labor.
In other words, higher education must provide the education related to and needed by the
labor market.
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