Income and Employment Multiplier Effects of the Malaysian Higher Education Sector

Abstract

The aim of this article is to examine the income and employment multiplier effects of the higher education sector in Malaysia based on conventional input–output methodology. We examined simple, total, Type I and Type II income and employment multiplier effects of private and public higher education institutions (HEIs) in Malaysia. We found that private HEIs have larger direct and indirect income impacts than public HEIs. With the presence of household spending, both public and private HEIs have greater induced income impacts than direct and indirect income generation effects. We also found that Type I multipliers for private and public HEIs lead to additional income of 1.34 and 1.32 for every initial Ringgit of labour income, respectively, while Type II income multipliers for private and public HEIs account for additional income of 3.09 and 3.05, respectively. Higher education creates 1.21 workers per RM 10,000 investment. The overall results show that private higher education has a relatively greater income effect on the economy.compared to public higher education. The higher education sector is also found to be ineffective in creating new employment in the economy.

JEL Classification: I23, I25

Keywords

Input–Output Analysis Income Multiplier Employment Multiplier Higher Education

1. INTRODUCTION

The role of higher education in the economy is attracting growing attention in many nations across the globe. Higher education has the potential to contribute to economic prosperity through innovation and knowledge exchange in the wider society, and the development of new ideas, products and services from research, besides continuing to raise the education levels of citizens. Its role has increasingly been recognised as a core part of the economic infrastructure of a country and the region in generating employment and output, attracting export earnings and contributing to gross domestic product.

The overall economic impact of higher education institutions (HEIs) can be divided into three categories: the direct, indirect and induced effects. The direct effects are related to local expenditures of the university, staff and students of the university. The indirect effects are estimated from the definitions of the income multiplier and employment multiplier; for example, each ringgit spent at the location by the university community (university, employees and students) generates indirect transactions in the location linked to businesses that do not have a direct relation to the university. The induced effects involve, for example, expenditures of people who visit the university, the effects upon financial institutions, the effects upon property values and the impact on the location of new companies and so on (Parsons and Griffiths, 2003). Inter-industry linkages and multiplier effects have been used to identify key industries that are central for economic development since the 1950s. Hirschman (1958) is the classical linkage literature which can be viewed as the first attempt to measure the pattern of industrial interdependence.

One way to examine how much HEIs contribute to a country’s economy is to estimate the amount of additional incomes and jobs created across different industries. HEIs tend to be labour-intensive enterprises. Usually their importance as employers is well recognised at the regional level, since they are frequently among the largest employers in their regions (UUK, 2009). HEIs pay wages and salaries to their employees, who then spend their incomes on goods and services. This creates incomes for employees in related sectors, who also spend their incomes and so on. The spillover effects on other related industries further spin off economic activities throughout the whole economy and create additional employment.

In Malaysia, the education sector has experienced an encouraging average annual growth rate of 6.8 per cent from 2000 to 2009. It contributed about 4 per cent to the gross domestic product in 2009, an increase of about 1 per cent from the previous decade (Economic Transformation Programme, 2010). The education sector has been identified as one of 12 potential national key economic areas (NKEAs) to be elevated in the Economic Transformation Programme (ETP). The target is to increase the contribution of private education by 1.5 times to 2 per cent of GDP in 2015.

Today there are 20 public universities in Malaysia and around 450 private higher learning institutions including 25 universities, 22 college universities and 5 branch campuses (Ministry of Higher Education Malaysia, 2011a). Private colleges started to mushroom in the country after the Private Higher Educational Institutional Act (PHEIA) was enforced in 1996. In 2010, total enrolment in HEIs was 1,134,134 students, of which about 52 per cent were enrolled in the private HEIs. Total student enrolment in both private and public HEIs has increased by 56 per cent since 2003. Over the same period, the number of international students in Malaysia (89,923 in 2010) increased at a whopping 185 per cent (Ministry of Higher Education Malaysia, 2011b). Higher education spending was at 2.17 per cent of GDP in Malaysia in 2009, the highest compared to other countries, such as, the UK (0.81 per cent), the USA (1.25 per cent), Japan (0.63 per cent, Indonesia (0.43 per cent) and Thailand (0.79 per cent) (UNESCO Institute for Statistics, 2011).

1.1 Objective of the Study

Similar to HEIs in other countries, employee emoluments have been the single-largest component of HEI expenditure in Malaysia. The sample expenditure pattern of a public university (University A) that we used in this study indicated about 60 per cent of operating expenses was for employee emoluments. The expenditure of universities’ employees generates additional income and employment across the economy. The objective of this study is to examine the Type I and Type II income and employment multiplier effects of public and private HEIs in Malaysia. Using the input–ouput approach, the Type I income multiplier or the simple income multiplier is based on the direct and indirect effects, while the Type II income multiplier or the total income multiplier effect includes all three categories, direct, indirect and induced effects. The same explanation goes for the Type I and Type II employment multiplier effects. In addition to the analysis of the income and employment multiplier effects of the higher education sector, we will compare the income and employment effects in the higher education sector with other industries.

The article is organised as follows. Section 2 presents insights from related literature, and a description of the data and methodology are given in Section 3. Analyses of income and employment multiplier effects in HEIs and a comparison of the income and employment multipliers between HEIs and other industries are presented in Section 4. Discussion and conclusion of the study are given in Section 5.

2. INSIGHTS FROM THE LITERATURE

A number of methods have been widely used in previous studies to examine the impact of higher education on an economy. Using cointegration and Granger-causality tests, Meulemeester and Rochat (1995) found causality effects of national higher educational efforts on economic development in Sweden, United Kingdom, Japan and France. The same study indicated that such a relationship did not manifest in Italy and Australia. Using least-square methods, studies by Petrakis and Stamatakis (2002) indicated that growth in the OECD economies relied mainly on higher education. The measurements of economic growth included private capital investment and GDP per capita while the measurement of education levels was the completion rate of the education level. Their findings suggested that the contribution of higher education increases as the level of economic development increases. Higher education in Pakistan was also found to have a very significant causal link with its economic growth (Muhammad et al., 2011). In Taiwan, Lin (2004) used the Cobb-Douglas production function to investigate the impacts of the curricular structure of higher education on Taiwan’s labour force and economic development. The results showed that overall higher education had a positive and significant effect on economic development, with engineering and the natural sciences playing a major role in the process. The empirical research of Siegfried et al. (2007), on the other hand, indicated that studies of the economic impact of colleges and universities could be further enhanced if local spillover benefits from human capital produced by the higher education were examined. Other studies on the economic impact of higher education include those in the US which referred to the economic impact of a group of universities. For example, Appleseed (2003) documented the roles of Boston’s eight research universities in the growth and continued vitality of the Boston area economy. Hodges et al. (2012) assessed economic contributions to the state of Florida by the State University System (SUS) of Florida (one of the largest public university systems in the US, consisting of 11 separate institutions and 30 campus branch locations across the state).

Though econometric tools have been extensively applied in examining the economic impact of the higher education sector, no study has been conducted using the input–output approach to investigate the multiplier effects of public and private higher education in Malaysia. Most previous studies have focused on structural changes in the production sector and the impact on the Malaysian economy (Bekhet, 2011, 2013; Bekhet and Abdullah, 2013; Bekhet and Harun, 2012).

The theoretical basis of input–output analysis has gone through many stages of development since it was first proposed by Leontief (1936). Its advantage is that it provides greater detail on buying and selling in the higher education sector across different production sectors. This method can be used for predictive purposes by providing estimates of the multipliers. Multiplier analysis generally focuses on the effects of exogenous changes on the output of sectors in the economy, income earned by households because of the new outputs and also employment that is expected to be generated because of the new outputs.

The input–output approach has been widely applied in the analysis of the higher education sector of many developed economies. Most of these studies were carried out by their research institutions. For example, UK universities were commissioned to study the economic impact of universities based on multiplier effects in the country since 1997; since then higher education’s contribution to national and regional economic development had attracted the attention of policy-makers (UUK, 2009). The study reported that HEIs in UK spent £22.9 billion on labour costs which accounted for about 60 per cent of total costs and directly created over 314,632 full-time equivalent jobs in 2007–08. It also stated that for every 100 full-time jobs created in HEIs in the UK, a further 100 full-time jobs were generated through Type II employment multiplier effects in other sectors in the economy. Another study by Universities UK (UUK, 2010) looked at the impact of universities and colleges in the nine regions of England, Scotland, Wales and Northern Ireland, estimating their economic impact on those regions and on the UK economy.

The Australian Council for Private Education and Training (2009) used the input–output approach to study the economic impact of international students in Australia. The Western New York Consortium of Higher Education (2008) and Northern Ireland Assembly’s Committee for Employment and Learning (2010) employed a similar approach to examine the regional impact of higher education in Western New York and Northern Ireland, respectively, with the latter focusing on the multiplier effects. Garrido-Yserte and Gallo-Rivera (2010), on the other hand, analysed the demand side or backward linkages of the University of Alcala (Madrid, Spain), showing a positive economic impact of the university on income, output and employment, particularly in the surrounding area. Abbas (2003) classified the sectoral potentials for the creation of jobs based on input–output analysis. His study identified education as a sector which could play a crucial role in generating employment, as it was not only one of the fastest-growing areas, but also possessed a relatively high employment elasticity.

3. DATA AND MODEL DEVELOPMENT

Input–output tables 2000 published by the Department Statistics of Malaysia were used to examine the income and employment multiplier effects of higher education on the economy. (Despite having over 120 production sectors in the economy, the input–output tables of 2005 merged private and public education into a single sector, so they could not be used.) The input–output tables of 2000 listed 94 production sectors of the economy, with details on public and private institutions, but were not further segregated into school and tertiary education. To observe the impact of higher education on economic growth, some modifications were made to the input–output tables 2000 of private and public education. They were scaled using the expenditure details of a public higher institution (University A) to reflect the production structure of private and public higher education. Note that the expenditure details of private HEIs are highly confidential and were not accessible for the purpose of this study. The spending pattern of HEIs was, therefore, assumed to be its representative contributions to the economy. Data on the number of workers hired in different industries were obtained from the Department of Statistics in Malaysia and used to calculate the employment multiplier effects.

The number of sectors in the expenditure sheets of universities did not coincide with the production structure in the input–output tables 2000. Given that only 16 main activities were found in the universities’ expenditure sheets, these were correspondingly designated across the 94 production sectors of the economy and the input coefficients of each corresponding sector in the private and public education were summed accordingly in the input–output tables 2000. The two column vectors of public and private higher education can be obtained using the coefficients of University A’s expenditure as follows:

x_{i} \frac{X_{i}}{_{i 1}^{16} X_{i}}

(1)

where $x_{i}$ is the input coefficients for each activity i of University A; and $X_{i}$ represents the university’s expenditure on activity i.

The scaling factor for each activity ( $_{i})$ was obtained by dividing the university’s input coefficient $(x_{ij})$ by the total input coefficient $(a_{i})$ of each corresponding sector in the private education.

_{i} \frac{x_{i}}{a_{i}}

(2)

Hence, the scaled input coefficient of activity i for private higher education $({\overset{}{a}}_{i})$ is simply the product of each input coefficient $(a_{i})$ and scaled factor ( $_{i})$ . The input coefficients of public higher education $({\overset{}{a}}_{i})$ were obtained in the same way. An example of the scaling calculation is provided in Table 1.

In the example given above, the scaled input coefficients of the two items were found to be greater than those of the actual input–output 2000 tables by a factor of approximately 0.82. We thus assumed that private higher education spends less in those items than private education as a whole. The same process was applied to public higher education and the two columns were then substituted into the original input–output table for further adjustments. The substitution occurred separately for both, because our aim was to trace the relationships between private higher education and the other industries (inclusive of public education in this case) and vice versa.

There was also an issue in accurately collapsing the 94 industries listed in the input–output table 2000 into the 16 categories (fewer) according to University A’s spending data. Here, uncertainty persists in the less obvious cases. For example, University A’s spending on delivery and travelling individually would contribute to the transport industry in Malaysia. How can we decide then, the exact proportion of contribution to the transport industry each made? Although this problem could be solved by merging the delivery and travelling expenses into a single category (as shown above), this was done at the cost of achieving greater precision, because then the scale factor for both delivery and travelling expenses would be identical and not according to their respective weights.

3.1 Model Development

Type I and Type II income and employment multipliers were calculated after treating the household sector as an additional, that is, the 95th, sector with inter-industry connections with the other industries. Income and employment multipliers show the increase in employment income and increase in numbers employed induced by the increase in industry sales that occurred because of the initial RM 1 investment made in the higher education sector.

Table 1

Scaling of Input Coefficients of Private Higher Institutions

Sector Listed in the I/O 2000 Tables	Input Coefficient of Private Education	Total Coefficient $(a_{i})$	Sector Listed in University A’s Spending Sheet	University A’s Spending Amount $(X_{i})$	University A’s Input Coefficient $(x_{i})$	Scale Factor $(_{i})$	Scale Input Coefficient $({\overset{}{a}}_{i})$
Hotels and Restaurants (a)	0.0118777		Travelling expenses and delivery				0.009796812
Transport (b)	0.0104356			17,198,062	0.01840417	0.82480715	0.008607357
(a) + (b)		0.0223133	Total spending	934,465,523

Source: Authors’ computations.

A raw input–output table portrays only the direct linkages among industries. In reality, the interactions among industries are more complicated than those suggested by the direct flows of output. For example, where an industry does not have a direct linkage with another industry in the economy, the second industry may still benefit from the expansion of the first industry if a third industry linked the two together. That could occur if the second industry sold to the third industry, which in turn, sold to the first industry. We call this an indirect linkage. Direct and indirect linkages can be numerically shown by making X, the final demand, the subject of the equation is as follows: AX + Y= X(1)Y= X AXYIAX X(2)IA^–1Y∆XI – A^–1∆Y

Where A is the square matrix of the inter-industry flow expressed in terms of the coefficient per dollar of output; X is the gross industry output; Y is the final demand; and I is an identity matrix.

Simple Income Multiplier

HEIs do not restrict their purchases to other industries; they also purchase labour from the economy. Rather than translating changes in final demand into the total value of output only, it is also important to translate changes in final demand into the creation of income. An approach to calculate this would be to convert each of the elements in any column of (I – A)^–1, which measures the value of direct and indirect output, into a ringgit’s worth of household income via household input coefficients. These coefficients that made up the (n + 1) row, previously used to close the model with respect to households, represented income paid to workers per ringgit’s worth of industrial output.

We represented the elements of (I – A)^–1 as a_ij, where i and j refer to the row and column of an element a in a matrix, respectively, with the simple household income multiplier for sector j as H_j, then:

H_{j}_{i 1}^{n} a_{n 1, i} ._{ij}

Total Income Multiplier

The above picture, however, is not complete because the matrix has not taken into account wages and salaries received by employees that would be spent purchasing more goods and services, thereby generating demand for additional output, and by extension, creating additional income. To calculate the induced income effects, we have to do the same for the elements in (I – $\overset{}{A}$ )^–1. As before, using an over bar to denote the multiplier derived from $\overset{}{A}$ , in which the household is endogenous to the matrix, the parallel of H_j is:

{\overset{}{H}}_{j}_{i 1}^{n 1} a_{n 1, i} . {\overset{}{a}}_{ij}

Note that these total income multipliers are equal to the first two elements of the last row of (I – $\overset{}{A}$ )^–1. This is a mathematical property resulting from the inverse of matrix $\overset{}{A}$ . But recall too that any element in (I – $\overset{}{A}$ )^–1 measures the direct, indirect and induced effects on sector i’s output of a ringgit’s worth of demand for industry j’s output. Thus, ${\overset{}{a}}_{n 1, j}$ measures the direct, indirect and induced effects on the output of the household sector when there is a ringgit’s worth of demand for industry j’s output. Therefore alternatively, the formula for the total income multiplier is:

{\overset{}{H}}_{j} {\overset{}{a}}_{n 1, j}

Type I and Type II Income Multipliers

With output multipliers it was fairly clear that the initial effect of a ringgit’s worth of final demand for the output of industry j is that industry j’s production must increase by a ringgit (and subsequently, of course, more than a ringgit). But for income, there was another option of what should be logically termed the initial effect of new demand, because a ringgit’s worth of new output from sector j also means an additional income payment of $a_{n 1, j}$ to workers in industry j. Therefore, $a_{n 1, j}$ could be viewed as the initial income effect of demand for industry j’s output. If this was the case, then we have two more multipliers to adopt for analysis: Type I and Type II.

The Type I income multiplier had direct and indirect income effects, or the simple income multiplier, as a numerator and the initial income effect, $a_{n 1, j}$ as the denominator. If we denoted Y_j as Type I income multiplier for industry j, then:

Y_{j}_{i 1}^{n} \frac{a_{n 1, i} . a_{ij}}{a_{n 1, j}}

If instead, the direct, indirect and induced income effects, or the total income multiplier, was used as a numerator, then the Type II income multiplier could be found. If we denote $\overset{}{Y}$ _j as Type II income multiplier, then:

{\overset{}{Y}}_{j}_{i 1}^{n} \frac{{\overset{}{a}}_{n 1, i} . a_{ij}}{a_{n 1, j}}

Employment Multipliers

To calculate the employment multipliers, it was necessary to estimate the number of workers employed in an industry relative to that industry’s value of output. Note that unlike the (n + 1) (household) row, these labour coefficients were computed in physical, not monetary, terms. Details on the number of workers hired in different industries were obtained from the Department of Statistics in Malaysia. However, they were not segregated into the 94 industries of the input–output table for 2009 but were classified into 16 industries following the Malaysian Standard Industrial Classification (MSIC) 2000. In light of this, the 94 industries of out input–output table had to be reclassified to fit into the 16 MSIC codes.

Calculations of simple, total, Type I and Type II employment multipliers paralleled those of the income multipliers described earlier. The only difference was that physical labour input coefficients, w_n_+1,j, were used instead of the monetary labour input coefficients, a_n_+1,j. We denoted E_j, $\overset{}{E}$ _j, W_j, $\overset{}{W}$ _j as the simple employment multiplier, total employment multiplier, Type I employment multiplier and Type II employment multiplier, respectively; so the formulae are as follows:

E_{j}_{i 1}^{n} w_{n 1, i} ._{ij}

{\overset{}{E}}_{j}_{i 1}^{n 1} w_{n 1, i} . {\overset{}{a}}_{ij}

W_{j}_{i 1}^{n} \frac{w_{n 1, i} . a_{ij}}{w_{n 1, j}}

{\overset{}{W}}_{j}_{i 1}^{n} \frac{{\overset{}{w}}_{n 1, i} . a_{ij}}{a_{n 1, j}}

Where i and j are indices for rows and columns of the input–output matrix contains n number of sectors or industries;

$_{ij}$ are elements of the $(I A)^{1}$ inverse matrix and $\overset{}{_{ij}}$ are elements of the augmented inverse matrix that includes the household sector as an additional industry.

The coefficient of the household sector is $a_{n 1, i}$ .

These few pieces of information are all that are needed to calculate all 10 different combinations according to their formulae, to look at the direct and indirect impacts of output resulting from investments, or the induced impacts caused by additional household spending as well as the creation of additional employment.

4. ANALYSIS OF INCOME AND EMPLOYMENT MULTIPLIER EFFECTS

We examine the contribution of private and public higher education to the Malaysian economy by estimating the amount of additional incomes and jobs created as a result of a ringgit’s worth of output produced. Based on the simple and total multipliers for household income and employment, the findings could help policy makers decide on how much investment should to be made in public and private higher institutions in order to observe the desired income and employment impacts.

4.1 Analysis of Income Multiplier Effects

Table 2 shows the multiplier effects on income and employment of private and public higher education. As described earlier, the simple income multiplier measures additional income created from one ringgit’s worth of direct and indirect outputs produced by public and private higher education. The simple income multipliers of private and public higher education account for about 0.85 and 0.69 of additional income generated from one ringgit’s worth of direct and indirect outputs made by private and public higher education. These mean that after one ringgit’s worth of output has been invested in private and public higher education, additional income of 85 cents and 69 cents are generated, respectively. As an example, we estimate that the launch of a new graduate programme that would increase the demand for private higher institutions by RM 100,000, which would lead to an increase of about (RM 100,000)(0.85) = RM 85,000 in new income earned, while a new research grant that would increase the demand for public higher institutions by RM 100,000 would generate about (RM 100,000)(0.69) = RM 69,000 of new income earned. Hence, we deduce that private higher education has a stronger impact on income generation compared to public higher education.

Table 2
Income and Employment Multipliers of Private and Public Higher Education

Effect Multiplier Higher Education

Private Public

Income Simple 0.8513 0.6940

Total 1.9706 1.9435

Type I 1.3361 1.3208

Type II 3.0929 3.0504

Employment Simple 0.000121

Total 0.000121

Type I 1.01285

Type II 1.01287

Effect	Multiplier	Higher Education
Income	Simple	0.8513		0.6940
	Total	1.9706		1.9435
	Type I	1.3361		1.3208
	Type II	3.0929		3.0504
Employment	Simple		0.000121
	Total		0.000121
	Type I		1.01285
	Type II		1.01287

Source: Authors’ computations.

The total income multiplier, on the other hand, measures additional income to be generated from one ringgit’s worth of direct, indirect and induced transactions made by private and public higher education. This induced effect of total income multiplier takes into account income that is generated by the household sector, where the input–output model is closed with the household sector endogenously purchasing more goods and services. The total income multipliers of private and public higher education constitute about 1.97 and 1.94, respectively, of new income that results from the additional ringgit’s worth of final demand from private and public HEIs. These figures imply that 1.00 ringgit from the 1.97 and 1.94 ringgits are new incomes created to satisfy the direct and indirect demand of private and public higher institutions, respectively, whilst the remaining of 0.97 and 0.94 ringgits are induced incomes to meet the household consumption of goods and services produced by private and public higher institutions. It is thus important to note that the household sectors, such as students, lecturers, researchers, industrialists and policy makers, have a relatively larger income effects on other goods and services produced across industries.

We also examine the direct, indirect and induced income effects with respect to the initial labour income by estimating Type I and Type II income multipliers. As described in the methodology section, the Type I income multiplier takes the ratio of the simple income multiplier and initial labour income while the Type II income multiplier captures the ratio of the total income multiplier and initial labour income. These multipliers explain by how much the initial income effects are increased when direct, indirect and induced effects (due to household spending as a result of increased household income) are taken into account (Miller and Blair, 2009). As shown in Table 2, private higher education is found to have marginally higher Type I and Type II income effects than public higher education. For every ringgit increase in initial income payment to workers, the Type I income multiplier indicates that the direct and indirect income of private HEIs is multiplied by 1.34 (i.e., 1 plus 0.34), whereas public higher education is estimated to produce additional income of 1.32 times (i.e., 1 plus 0.32 times) more than the initial level of income payment to labours. Likewise, private higher education has a higher Type II income multiplier than public higher education. The values of the Type II income multipliers for private and public higher education are about 3.09 and 3.05, respectively, for every ringgit of initial additional income payment to workers. For instance, we estimate that a new research grant of RM 100,000 allocated to a public higher institution would generate about (RM 100,000)(3.05) = RM 305,000 for every initial additional ringgit of income payment. In addition, the values of the Type II income multipliers are substantially greater than the Type I income multipliers. This suggests that household spending as a result of increased household income has a sizeable income effect in the economy as a whole.

4.2 Analysis of Employment Multiplier Effects

The same types of multipliers are computed for jobs created, in physical rather than in monetary terms. As noted earlier, the number of jobs listed by the Department of Statistics Malaysia is not segregated by private and public higher education. We estimate the additional jobs created by the higher education. The simple employment multiplier indicates the additional jobs created as a result of a ringgit’s worth of direct and indirect outputs produced by higher institutions, while the total employment multiplier accounts for the number of new jobs created as a result of a ringgit’s worth of direct, indirect and induced (household spending) outputs from higher education. It is surprising to note that the values of the simple and total employment multipliers appear to be identical, but miniscule in terms of magnitude (Table 2). These imply that a new research grant of RM 100,000 made available in an institution would ultimately lead to an increase of (RM 100,000) (0.000121) = 12.1 new job opportunities in the economy. The results also show that the induced employment impact in higher institutions is extremely insignificant, implying that HEIs may not be effective in creating new employment in the economy as a result of household spending on other goods and services produced.

Similar to the income effects, we also gauge the Type I and Type II multipliers for employment in higher education. The Type I employment multiplier shows how many more jobs are created beyond the initial (direct) number of jobs, while the Type II employment multiplier accounts for additional jobs created, including household spending on goods and services produced by higher education beyond the initial (direct) number of jobs. It is noted that Type I and Type II multipliers simultaneously create approximately 1.013 additional jobs in all sectors throughout the economy for each initial jobs made available in higher institutions. This means that for every 100 new jobs created within the university, only 1.3 new jobs is generated outside the university. This is very different from the UK findings where 100 full-time jobs in the universities generated about 200 full-time jobs in the entire UK economy (UUK, 2009).

4.3 Income and Employment Effects between Higher Education and Other Industries

In this section, we show how multipliers are computed as we move from simple to total multipliers along with Type I and Type II. We also compare the multiplier effects across a selection of industries from one ringgit of investment in private and public higher education.

Table 3 exhibits simple, total, Type I and Type II income effects that result from one ringgit’s worth of investment in private higher education on selected industries while Table 4 refers to income effects from one ringgit’s investment in public higher education. Private higher education contributes a fairly high impact in total income generation. The total income multiplier of private higher education is about 1.97, of which the direct, indirect and induced incomes constitute about 0.64, 0.21 and 1.12, respectively (Table 3). These values are relatively close to the total income multiplier found in public higher education, where the total multiplier is 1.94, of which the direct, indirect and induced incomes are 0.64, 0.20 and 1.10, respectively (Table 4).

Among the income effects of private higher education on the rest of industry in the economy, grain mills (20.12), manufacturing oils and fats (18.81), rubber process (18.80) and preservation of seafood (11.19) are recorded to have the highest values of direct, indirect and induced income effects for every initial ringgit of labour income (Appendix 1). In the public higher education model, the income effects are recorded to be high in grain mills (20.07), manufacture oils and fats (18.77), rubber process (18.75) and the preservation of seafood (11.17) (Appendix 2). However, it is worthwhile to note that the income effects of these industries in the private higher education model have marginally greater effects than those from public higher education, suggesting that more income is generated from every initial ringgit of labour income paid to the workers in private higher education.

It is also interesting to note that although many industries evidently have high total income multiplier effects, the additional direct and indirect incomes these industries create are, in fact, less than the induced incomes generated from household spending. Induced incomes are created by additional consumption by households after they receive additional direct and indirect incomes. For instance, based on numbers obtained in the Type II multipliers (Table 4), we find that for every initial ringgit of labour income paid to workers, manufacturing oils and fats (1.0133), preservation of seafood (0.9643), rubber processing (0.9202) and grain mills (0.7866) are indeed high income-generating industries that potentially drive the growth of household consumption in the economy and thus gross output as a whole.

Table 3
Comparison of the Income Multiplier Effects for Selected Industries with Reference to Private Higher Education

Mathematical Operations
Value Added Entries
Simple Income Multiplier – Direct Income
Total Income Multiplier – Simple Income Multiplier
Total Income Multiplier
Simple Income Multiplier/Direct Income
Total Income Multiplier/Direct Income

Industries Direct Indirect Induced Total Type I Type II

Dairy production 0.2306 0.3384 0.7480 1.3170 2.4676 5.7123

Manufacture oils and fats 0.0948 0.6758 1.0133 1.7840 8.1263 18.8113

Printing 0.3056 0.2528 0.7342 1.2927 1.8274 4.2301

Manufacture industries chemicals 0.2515 0.3560 0.7988 1.4063 2.4154 5.5914

Manufacture drugs & medicines 0.3565 0.2510 0.7988 1.4063 1.7043 3.9452

Petrol & coal industries 0.2245 0.4156 0.8416 1.4816 2.8510 6.5996

Manufacture industries machinery 0.5456 0.1188 0.8736 1.5380 1.2178 2.8190

Manufacture household machinery 0.1218 0.1154 0.3119 0.5492 1.9478 4.5090

Manufacture radio, TV, etc. 0.1842 0.1274 0.4097 0.7213 1.6917 3.9160

Electricity & gas 0.6343 0.1855 1.0780 1.8978 1.2925 2.9920

Waterworks 0.5011 0.2682 1.0116 1.7809 1.5352 3.5539

Building & construction 0.3180 0.2703 0.7735 1.3618 1.8501 4.2827

Wholesale & retail trade 0.7354 0.1111 1.1131 1.9596 1.1511 2.6646

Hotels & restaurants 0.3547 0.3071 0.8701 1.5319 1.8660 4.3195

Transport 0.3475 0.2451 0.7792 1.3719 1.7052 3.9474

Communications 0.5837 0.1239 0.9303 1.6379 1.2123 2.8062

Banks 0.7888 0.0929 1.1594 2.0412 1.1178 2.5876

Insurance 0.6369 0.1980 1.0978 1.9327 1.3109 3.0347

Real estate 0.5555 0.2618 1.0747 1.8920 1.4713 3.4058

Ownership dwellings 0.9849 0.0089 1.3067 2.3004 1.0090 2.3357

Business services 0.4902 0.1603 0.8552 1.5057 1.3270 3.0718

Private higher education 0.6371 0.2141 1.1193 1.9706 1.3361 3.0929

Health (private) 0.4752 0.1644 0.8411 1.4808 1.3460 3.1159

Health (public) 0.5452 0.1129 0.8652 1.5233 1.2070 2.7941

Private non-profit inst. 0.3847 0.3862 1.0136 1.7845 2.0040 4.6389

Radio & TV broadcasting 0.2221 0.5199 0.9756 1.7176 3.3403 7.7324

Rep. motor vehicles 0.3371 0.1985 0.7042 1.2398 1.5888 3.6778

Public administration 0.5165 0.2216 0.9705 1.7086 1.4289 3.3077

Mathematical Operations	Value Added Entries	Simple Income Multiplier – Direct Income	Total Income Multiplier – Simple Income Multiplier	Total Income Multiplier	Simple Income Multiplier/Direct Income	Total Income Multiplier/Direct Income
Dairy production	0.2306	0.3384	0.7480	1.3170	2.4676	5.7123
Manufacture oils and fats	0.0948	0.6758	1.0133	1.7840	8.1263	18.8113
Printing	0.3056	0.2528	0.7342	1.2927	1.8274	4.2301
Manufacture industries chemicals	0.2515	0.3560	0.7988	1.4063	2.4154	5.5914
Manufacture drugs & medicines	0.3565	0.2510	0.7988	1.4063	1.7043	3.9452
Petrol & coal industries	0.2245	0.4156	0.8416	1.4816	2.8510	6.5996
Manufacture industries machinery	0.5456	0.1188	0.8736	1.5380	1.2178	2.8190
Manufacture household machinery	0.1218	0.1154	0.3119	0.5492	1.9478	4.5090
Manufacture radio, TV, etc.	0.1842	0.1274	0.4097	0.7213	1.6917	3.9160
Electricity & gas	0.6343	0.1855	1.0780	1.8978	1.2925	2.9920
Waterworks	0.5011	0.2682	1.0116	1.7809	1.5352	3.5539
Building & construction	0.3180	0.2703	0.7735	1.3618	1.8501	4.2827
Wholesale & retail trade	0.7354	0.1111	1.1131	1.9596	1.1511	2.6646
Hotels & restaurants	0.3547	0.3071	0.8701	1.5319	1.8660	4.3195
Transport	0.3475	0.2451	0.7792	1.3719	1.7052	3.9474
Communications	0.5837	0.1239	0.9303	1.6379	1.2123	2.8062
Banks	0.7888	0.0929	1.1594	2.0412	1.1178	2.5876
Insurance	0.6369	0.1980	1.0978	1.9327	1.3109	3.0347
Real estate	0.5555	0.2618	1.0747	1.8920	1.4713	3.4058
Ownership dwellings	0.9849	0.0089	1.3067	2.3004	1.0090	2.3357
Business services	0.4902	0.1603	0.8552	1.5057	1.3270	3.0718
Private higher education	0.6371	0.2141	1.1193	1.9706	1.3361	3.0929
Health (private)	0.4752	0.1644	0.8411	1.4808	1.3460	3.1159
Health (public)	0.5452	0.1129	0.8652	1.5233	1.2070	2.7941
Private non-profit inst.	0.3847	0.3862	1.0136	1.7845	2.0040	4.6389
Radio & TV broadcasting	0.2221	0.5199	0.9756	1.7176	3.3403	7.7324
Rep. motor vehicles	0.3371	0.1985	0.7042	1.2398	1.5888	3.6778
Public administration	0.5165	0.2216	0.9705	1.7086	1.4289	3.3077

Source: Authors’ computations.

Table 4

Comparison of the Income Multiplier Effects for Selected Industries with Reference to Public Higher Education

Mathematical Operations	Value Added Entries	Simple Income Multiplier – Direct Income	Total Income Multiplier – Simple Income Multiplier	Total Income Multiplier	Simple Income Multiplier/Direct Income	Total Income Multiplier/Direct Income
Industries	Direct	Indirect	Induced	Total	Type I	Type II
Dairy production	0.2306	0.3383	0.7450	1.3139	2.4676	5.6988
Manufacture oils and fats	0.0948	0.6758	1.0091	1.7798	8.1262	18.7672
Printing	0.3056	0.2528	0.7312	1.2896	1.8273	4.2201
Manufacture industries, chemicals	0.2515	0.3560	0.7955	1.4030	2.4153	5.5782
Manufacture drugs & medicines	0.3565	0.2510	0.7955	1.4030	1.7042	3.9359
Petrol & coal industries	0.2245	0.4155	0.8381	1.4782	2.8509	6.5841
Manufacture industry machinery	0.5456	0.1188	0.8700	1.5344	1.2178	2.8124
Manufacture household machinery	0.1218	0.1154	0.3107	0.5479	1.9477	4.4983
Manufacture radio, TV, etc.	0.1842	0.1274	0.4080	0.7196	1.6916	3.9068
Electricity & gas	0.6343	0.1854	1.0733	1.8930	1.2923	2.9844
Waterworks	0.5011	0.2678	1.0069	1.7759	1.5345	3.5438
Building, construction	0.3180	0.2703	0.7703	1.3585	1.8500	4.2724
Wholesale & retail trade	0.7354	0.1111	1.1085	1.9550	1.1510	2.6583
Hotels & restaurants	0.3547	0.3071	0.8666	1.5283	1.8659	4.3093
Transport	0.3475	0.2451	0.7760	1.3686	1.7052	3.9381
Communication	0.5837	0.1239	0.9265	1.6340	1.2122	2.7996
Banks	0.7888	0.0929	1.1546	2.0364	1.1178	2.5815
Insurance	0.6369	0.1980	1.0933	1.9282	1.3109	3.0275
Real estate	0.5555	0.2617	1.0702	1.8874	1.4711	3.3975
Business services	0.4902	0.1602	0.8517	1.5021	1.3269	3.0645
Public higher education	0.6371	0.2044	1.1020	1.9435	1.3208	3.0504
Health (private)	0.4752	0.1644	0.8376	1.4773	1.3460	3.1085
Health (public)	0.5452	0.1129	0.8617	1.5197	1.2070	2.7876
Private non-profit inst.	0.3847	0.3861	1.0093	1.7800	2.0037	4.6274
Radio & TV broadcasting	0.2221	0.5198	0.9715	1.7134	3.3398	7.7133
Rep. motor vehicles	0.3371	0.1985	0.7013	1.2369	1.5887	3.6692
Recycling	0.2880	0.2301	0.6785	1.1966	1.7989	4.1545
Public administration	0.5165	0.2215	0.9665	1.7045	1.4288	3.2999

Source: Authors’ computations.

Table 5

Employment Multiplier Effects of Higher Education

Industry	Direct	Indirect	Induced	Total	Type I	Type II
Agriculture, hunting and forestry	0.000031	0.000007	0.000000001	0.000038	1.2090	1.2090
Fishing	0.000022	0.000007	0.000000001	0.000029	1.2992	1.2992
Mining and quarrying	0.000001	0.000002	0.000000000	0.000003	4.0033	4.0034
Manufacturing	0.000004	0.000008	0.000000000	0.000012	2.9071	2.9071
Electricity, gas and water supply	0.000055	0.000007	0.000000001	0.000062	1.1272	1.1272
Construction	0.000001	0.000007	0.000000000	0.000008	6.4645	6.4646
Wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods	0.000034	0.000005	0.000000001	0.000039	1.1332	1.1332
Hotels and restaurants	0.000030	0.000011	0.000000001	0.000042	1.3647	1.3647
Transport, storage and communications	0.000011	0.000007	0.000000000	0.000018	1.6136	1.6136
Financial intermediation	0.000008	0.000004	0.000000000	0.000011	1.5184	1.5185
Real estate, renting and business activities	0.000016	0.000005	0.000000000	0.000020	1.2939	1.2939
Higher education	0.000119	0.000002	0.000000003	0.000121	1.0128	1.0129
Health and social work	0.000098	0.000007	0.000000002	0.000105	1.0687	1.0687
Other community, social and personal service activities	0.000857	0.000018	0.000000020	0.000876	1.0215	1.0215
Private households with employed persons	0.000171	0.000036	0.000000005	0.000207	1.2105	1.2106
Public administration and defence; compulsory social security	0.000013	0.000008	0.000000000	0.000021	1.6069	1.6069

Source: Authors’ computations.

Since the employment data follows the Malaysian Standard Industrial Classification (MSIC) 2000, we focus on the employment effects for 16 sectors (Table 5). The total employment multiplier of higher education ranks third in all the sectors. For every RM 10,000 worth of new output, colleges and universities in the economy are estimated to produce only 1.2 new and direct jobs in the economy. As shown in Table 5, higher education ranks lower than industries such as ‘other community, social & personal service activities’ and ‘private households with employed persons’, which are found to generate 8.8 and 2.1 total effects (direct, indirect and induced jobs) respectively.

With reference to new direct jobs created in the economy, higher education is found to result in lower new employment generation. For every new direct employment created in the economy, the Type II employment multipliers of construction and mining & quarrying indicated the creation of the highest number of employment opportunities, that is, about 6.5 and 4 vacancies, respectively. This is explained by the nature of these industries, whereby many labourers are required to build houses and execute mining activities. Meanwhile, higher education produces 1.01 new jobs for every new direct employment created, suggesting that higher education employees may be more productive where fewer workers are required to generate each RM 10,000 worth of output.

5. DISCUSSION AND CONCLUSION

The approach adopted in this study follows conventional economic impact assessment methodology by using the input–output model. This model forms part of the system of national accounts that most nations, including Malaysia, assemble in accordance with conventions prescribed by the Statistical Office of the United Nations. The input–output table not only makes a complete accounting of education’s share of the total economic production of the country, but also through the Leontief inverse equation, the direct, indirect and induced impacts (increased household consumption and employment creation) could be further analysed. The drawback, however, is the static nature of the input–output framework as well as the fixed coefficient and constant returns to scale production function that is not amenable to dynamics and technological changes. Another limitation of this analysis is its overstating of the economic importance of specific sectoral or regional activities. The analysis also fails to consider the opportunity cost of spending measures and alternate uses of resources (Gretton, 2013). Therefore, factors such as social benefits and costs could be missed out in policy prescriptions.

The simple income multipliers calculated were 0.85 and 0.69 for private and public higher education, respectively, and the total income multipliers were 1.97 and 1.94. The total multipliers differ from the simple multipliers in their use of different input–output tables: the simple multipliers uses tables containing 94 industry sectors and the total multiplier uses tables containing 95 sectors, the 95th sector being the household sector.

To understand better what these numbers mean, simple and total income multipliers can be expressed as Type I and Type II income multipliers, respectively, by weighing these numbers by the change of initial incomes received by workers. The Type I multipliers obtained were 1.34 and 1.32 for private and public higher education, respectively, implying that every ringgit of additional income will further induce an increase in income of 34 cents and 32 cents, respectively, when households undertake additional consumption. The Type II income multipliers that incorporate direct, indirect and induced linkages, with the inclusion of the household sector as an industry, will reveal additional impacts amounting to 3.09 and 3.05 in total for private and public higher education, respectively. Tables 3 and 4 (Section 4) show the decomposition of the total multiplier into the direct, indirect and induced components, beginning with the value added entries and adding up to the total.

It was not possible to separate private and public higher education in the analysis of employment multipliers as the employment data does not distinguish between these two and therefore the analysis conducted was limited to treating higher education as a whole. Employment multipliers have very low values because RM 1 of output will only need a small amount of labour input. For convenience, by scaling the multiplier value, interpretation is therefore made in terms of employment creation per RM 100,000 of investment. Specifically, the direct impact in terms of additional employment creation per ringgit of investment was 11.9 workers per RM 100,000 investment and indirect impacts of another 0.2 worker per RM 100,000 investment giving us a total of 12.1 workers. The Type I and Type II employment multipliers were almost identical in that for each additional person employed, there is potential to induce another 0.13 person per RM 10,000 worth of new output.

Footnotes

Appendix

Appendix 2

Direct, Indirect and Total Income Multiplier Effects of Public Higher Education

Industry	Direct	Indirect	Induced	Total	Type I	Type II
Agriculture other	0.7112	0.1406	1.1154	1.9673	1.1976	2.7659
Rubber planting	0.9243	0.0383	1.2605	2.2232	1.0414	2.4051
Oil palm estates	0.7436	0.1232	1.1350	2.0018	1.1657	2.6922
Coconut	0.9482	0.0182	1.2655	2.2319	1.0192	2.3539
Tea estates	0.7338	0.0993	1.0909	1.9240	1.1353	2.6220
Livestock breeding etc.	0.2608	0.3277	0.7707	1.3592	2.2567	5.2118
Forestry & logging	0.7374	0.1007	1.0975	1.9356	1.1365	2.6248
Fishing	0.5679	0.2827	1.1138	1.9643	1.4978	3.4592
Crude petrol, natural gas & coal	0.8340	0.0435	1.1491	2.0266	1.0522	2.4300
Metal ore mining	0.3085	0.3231	0.8271	1.4588	2.0474	4.7285
Stone, clay & sand quarrying	0.5779	0.1873	1.0020	1.7672	1.3242	3.0582
Meat & meat production	0.1641	0.4576	0.8140	1.4357	3.7885	8.7494
Dairy production	0.2306	0.3383	0.7450	1.3139	2.4676	5.6988
Preservation of fruits & veg.	0.1711	0.3038	0.6219	1.0968	2.7749	6.4086
Preservation of seafood	0.1516	0.5817	0.9603	1.6936	4.8357	11.1681
Manufacture oils and fats	0.0948	0.6758	1.0091	1.7798	8.1262	18.7672
Grain mills	0.0688	0.5294	0.7834	1.3817	8.6911	20.0719
Bakeries	0.3019	0.2687	0.7472	1.3179	1.8902	4.3654
Manufacture confect.	0.1805	0.3062	0.6373	1.1241	2.6967	6.2281
Manufacture of ice	0.5084	0.3038	1.0637	1.8760	1.5976	3.6896
Manufacture other food	0.2241	0.3930	0.8080	1.4250	2.7537	6.3595
Manufacture animal feeds	0.1201	0.1717	0.3821	0.6739	2.4296	5.6111
Prod. wine and spirits	0.4685	0.2715	0.9691	1.7092	1.5795	3.6479
Prod. of soft drinks	0.1703	0.2921	0.6055	1.0679	2.7160	6.2726
Manufacture tobacco	0.1777	0.2075	0.5044	0.8896	2.1672	5.0052
Manufacture yarns, cloth	0.2328	0.2614	0.6472	1.1414	2.1228	4.9025
Manufacture knitted fabrics	0.2916	0.1509	0.5796	1.0221	1.5175	3.5047
Manufacture other textiles	0.3229	0.1983	0.6825	1.2038	1.6141	3.7278
Manufacture wearing apparels	0.2576	0.2124	0.6154	1.0854	1.8246	4.2139
Leather industries	0.2163	0.3337	0.7202	1.2702	2.5427	5.8723
Manufacture footwear	0.2054	0.3010	0.6632	1.1696	2.4656	5.6942
Sawmills	0.2414	0.5294	1.0094	1.7802	3.1932	7.3747
Manufacture other wooden products	0.2444	0.4744	0.9414	1.6602	2.9410	6.7923
Manufacture of furniture	0.2720	0.3173	0.7716	1.3609	2.1668	5.0041
Paper & board industries	0.2636	0.2562	0.6807	1.2005	1.9722	4.5547
Printing	0.3056	0.2528	0.7312	1.2896	1.8273	4.2201
Manufacture industries chemic.	0.2515	0.3560	0.7955	1.4030	2.4153	5.5782
Manufacture paints & lacq.	0.2041	0.3268	0.6951	1.2260	2.6011	6.0071
Manufacture drugs & medicines	0.3565	0.2510	0.7955	1.4030	1.7042	3.9359
Manufacture soap etc.	0.2492	0.2593	0.6658	1.1743	2.0404	4.7122
Other chem. industries	0.5071	0.2345	0.9711	1.7126	1.4625	3.3776
Petrol & coal industries	0.2245	0.4155	0.8381	1.4782	2.8509	6.5841
Rubber proc.	0.0862	0.6137	0.9165	1.6163	8.1197	18.7524
Rubber industries	0.3701	0.2582	0.8227	1.4510	1.6976	3.9206
Manufacture plastic products	0.3241	0.1896	0.6726	1.1863	1.5850	3.6606
China & glass industries	0.4752	0.2394	0.9357	1.6502	1.5038	3.4730
Manufacture clay products	0.3779	0.3205	0.9145	1.6129	1.8481	4.2683
Manufacture cement etc.	0.3032	0.3385	0.8403	1.4821	2.1164	4.8879
Other non-metallic manufacture	0.2562	0.3856	0.8404	1.4822	2.5050	5.7852
Iron & steel industries	0.1161	0.2091	0.4259	0.7511	2.8003	6.4673
Manufacture non-ferrous metals	0.2014	0.1432	0.4513	0.7959	1.7107	3.9509
Manufacture of other fabricated metals & fixtures	0.2502	0.1744	0.5561	0.9808	1.6971	3.9195
Structural metal industries	0.2800	0.1835	0.6070	1.0705	1.6555	3.8234
Other metal industries	0.2384	0.2199	0.6002	1.0585	1.9222	4.4394
Manufacture industries mach.	0.5456	0.1188	0.8700	1.5344	1.2178	2.8124
Manufacture household machinery	0.1218	0.1154	0.3107	0.5479	1.9477	4.4983
Manufacture radio, TV etc.	0.1842	0.1274	0.4080	0.7196	1.6916	3.9068
Manufacture electrical appliances etc.	0.1977	0.1868	0.5034	0.8879	1.9449	4.4916
Manufacture other electrical machinery	0.2450	0.1637	0.5351	0.9437	1.6681	3.8525
Ship- & boat-building	0.3863	0.1155	0.6571	1.1588	1.2991	3.0001
Manufacture motor vehicle	0.1937	0.1907	0.5034	0.8878	1.9849	4.5840
Manufacture cycles, motorc.	0.1683	0.3190	0.6382	1.1256	2.8955	6.6870
Manufacture oth. transp. eq.	0.5654	0.1116	0.8866	1.5636	1.1974	2.7653
Manufacture instr. & clocks	0.2614	0.1624	0.5549	0.9787	1.6213	3.7443
Other manufacturing	0.2869	0.2165	0.6592	1.1625	1.7545	4.0520
Electricity & gas	0.6343	0.1854	1.0733	1.8930	1.2923	2.9844
Waterworks	0.5011	0.2678	1.0069	1.7759	1.5345	3.5438
Building, construction	0.3180	0.2703	0.7703	1.3585	1.8500	4.2724
Wholesale & retail trade	0.7354	0.1111	1.1085	1.9550	1.1510	2.6583
Hotels & restaurants	0.3547	0.3071	0.8666	1.5283	1.8659	4.3093
Transport	0.3475	0.2451	0.7760	1.3686	1.7052	3.9381
Communication	0.5837	0.1239	0.9265	1.6340	1.2122	2.7996
Banks	0.7888	0.0929	1.1546	2.0364	1.1178	2.5815
Oth. financial inst.	0.5892	0.2561	1.1070	1.9524	1.4347	3.3133
Insurance	0.6369	0.1980	1.0933	1.9282	1.3109	3.0275
Real estate	0.5555	0.2617	1.0702	1.8874	1.4711	3.3975
Ownership dwellings	0.9849	0.0089	1.3013	2.2951	1.0090	2.3303
Business services	0.4902	0.1602	0.8517	1.5021	1.3269	3.0645
Education - Private	0.4854	0.2086	0.9087	1.6027	1.4297	3.3019
Public Higher Education	0.6371	0.2044	1.1020	1.9435	1.3208	3.0504
Health - Private	0.4752	0.1644	0.8376	1.4773	1.3460	3.1085
Health - Public	0.5452	0.1129	0.8617	1.5197	1.2070	2.7876
Pr. non-profit inst.	0.3847	0.3861	1.0093	1.7800	2.0037	4.6274
Entertainment	0.4766	0.3369	1.0652	1.8786	1.7069	3.9420
Radio & TV broadcasting	0.2221	0.5198	0.9715	1.7134	3.3398	7.7133
Recreation	0.6135	0.2085	1.0764	1.8983	1.3398	3.0942
Rep. motor veh.	0.3371	0.1985	0.7013	1.2369	1.5887	3.6692
Other repair	0.5101	0.1785	0.9017	1.5903	1.3498	3.1175
Recycling	0.2880	0.2301	0.6785	1.1966	1.7989	4.1545
Other private services	0.4188	0.2564	0.8841	1.5593	1.6121	3.7231
Public administration	0.5165	0.2215	0.9665	1.7045	1.4288	3.2999
Public order	0.7455	0.1248	1.1396	2.0099	1.1674	2.6960
Defence	0.3943	0.1996	0.7776	1.3715	1.5061	3.4784
Other public administration	0.5162	0.1993	0.9369	1.6523	1.3861	3.2012

Source: Authors’ computations.

Acknowledgements

The authors are deeply grateful for the invaluable guidance, comments and suggestions from Dr Chan Huan Chiang, Senior Research Fellow at the Socio-Economic and Environmental Research Institute (SERI), Penang. We would like to thank the Malaysian National Higher Education Research Institution (NAHERI) for financial support. We thank Professor Fauziah Md. Taib, Director of NAHERI and project leader of Higher Education as a Catalyst to Economic Growth for her useful comments and suggestions in the preparation of this article. We would also like to thank the other co-researchers of the project, Dr Lim Hock Eam, Professor Osman Mohamad, Professor Rosni Bakar and Dr Eliza Nor, for their contributions.

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Income and Employment Multiplier Effects of the Malaysian Higher Education Sector

Abstract

Keywords

1. INTRODUCTION

1.1 Objective of the Study

2. INSIGHTS FROM THE LITERATURE

3. DATA AND MODEL DEVELOPMENT

3.1 Model Development

Simple Income Multiplier

Total Income Multiplier

Type I and Type II Income Multipliers

Employment Multipliers

4.1 Analysis of Income Multiplier Effects

Table 2 Income and Employment Multipliers of Private and Public Higher Education Effect Multiplier Higher Education Private Public Income Simple 0.8513 0.6940 Total 1.9706 1.9435 Type I 1.3361 1.3208 Type II 3.0929 3.0504 Employment Simple 0.000121 Total 0.000121 Type I 1.01285 Type II 1.01287

4.3 Income and Employment Effects between Higher Education and Other Industries

Footnotes

Appendix

Acknowledgements

References