Higher Education, Employment and Economic Growth: Mexico and Peru

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Higher Education, Employment and Economic Growth: Mexico and Peru

Elsa-Sofia Morote, Ed.D.

Associate professor, Education Administration, Leadership and Technology

Dowling College

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


Growth Rate


(Until 1970s)


Growth Rate


(Until 1970s)


North America






United States


United Kingdom




Latin America












South Korea





















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.

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

where C is an ( N x ( K2p + K)) matrix of a rank N and c is an (N x 1) vector.

Assuming that


in a LS/ML estimation, we get


and hence

This statistic is the Wald statistic.

Replacing and CMu by their usual estimators and the resulting statistic


still has an asymptotic -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 is a consistent estimator of . Hence the following result is obtained.

Asymptotic Distribution of the Wald Statistic

Suppose (21) holds. Furthermore, 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)

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 is unknown and has been replaced by an estimator. Working in that direction we note that

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 (N)-distribution divided by N, it seems reasonable to consider the test statistic


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).


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 non-stationary; 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 ADF Test Statistics Mckinnon Critical Values

MEXICO MGDP -1.255009 1% -3.6576

MHE -0.425010 5% -2.9591

MEM -1.969826

PERU PGDP -2.442325

PHE 1.388312

PEM -2.122202

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 ADF Test Statistics Mckinnon Critical Values

MEXICO MGDP -4.777844 1% -3.6852

MHE -3.798076 5% -2.9705

MEM -4.477712

PERU PGDP -4.678298

PHE -3.171842

PEM -4.412620

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)






















Mim: p = 6



Table 5

Peru – Estimation of the VAR Order of the Higher Education and Economic Growth, and Employment System

VAR (m)

AIC (m)

FPE (m)






















Mim: p = 6



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):

, (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.


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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