1. Knowledge Innovation Growth UNIK Chapter511f EDIT

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DRAFT

May 2011

National Innovation Strategy

Knowledge, Innovation and Long-Run Growth

Dr. Albert G. Zeufack, Lim King Yoong and Devendran Nadaraja.

Khazanah Research and Investment Strategy Macro Modeling Project

1. Introduction

1.1 Background

In its sustained effort to achieve Vision 2020, Malaysia has embarked on a new economic model “to

transform Malaysia into a knowledge-based and innovation-rich nation” 1. This strategic direction is reflected in

the New Economic Model (NEM) reports and the 10 th five year Development Plan (RMK10, 2011-

2015). The NEM highlights the need for growth to be “Innovation Driven” in its Strategic Reform

Initiative 7 (SRI 7), while the 4th “Big Idea” of the 10th Plan points to increased innovation as a

necessary condition to achieve Vision 2020. The focus on knowledge and innovation cannot be

overemphasized. There is ample empirical evidence that innovation is positively associated with firm

performance, as measured by revenue growth, irrespective of the industry in which the innovative

firm operates. Also, firms’ knowledge assets are positively associated with firm-level innovation.Firms with higher levels of capabilities are more likely to introduce innovations (see Thornbill,

2006). Moving to High-Income status would therefore require growth in Malaysia to result

increasingly from knowledge, innovation, combined with a deeper stock of physical and human

capital. For this painful transformation to take place, a number of policies requiring significant effort

and resources from the Government would need to be implemented. For example, a serious

upgrading of skills across a broad spectrum and a sharp increase in the quality of education would

have to happen. Also, the Government may need to launch a much more aggressive and effective

promotion of research and development (R&D) activities in the private sector and support the

emergence of a strong venture capital industry. However, in a post 2008 global recession

environment where resources are scarce and sovereign defaults are spreading to an increasing

number of countries, including advanced ones, the Malaysian Government may need to make some

trade-offs. Making the right trade-offs in the allocation of public resources requires an

understanding of the growth effects of these various policies.

1 PM’s Interview to the Star, March 13th, 2010.



1.2 Can Innovation Extricate Malaysia from the Middle Income Trap (MIC)?

Knowledge and innovation are key to escaping the MIC Trap. Indeed, there is a natural limit to

growth based on accumulation of capital stock. Increased competition from low cost producers in

neighboring Asia and increased in Malaysian labor costs overtime inevitably lead to a decline in

industrial competitiveness. Increasing productivity is therefore crucial. Yet productivity levels andgrowth both labor productivity and Total Factor Productivity (TFP) have been rather modest in

Malaysia. TFP growth in Malaysia has been considerably lower than that in selected Asian countries

in recent years. TFP target of 2.3% for 2011-15 is achievable only if full potential of innovation can

be unleashed – skills development and enhancing innovation capabilities are been given greater

focus. A proper resources allocation mechanism able to redeploy resources away from inefficient

uses and innovation and provides benchmarks for Malaysia.

Figure 1: Malaysia caught in the middle income trap.

Source: World Development Indicators, Khazanah Research & Investment Strategy (“KRIS”)

Figure 2: Sources of growth in Malaysia

Source: 10th Malaysia Plan (EPU)



2. Benchmarking Malaysia’s Innovation Capacity

An innovation is the implementation of a new or significantly improved product (good or service),

or process, a new marketing method, or a new organizational method in business practices,

workplace organization or external relations. There are many types of innovation such as productinnovation, process innovation, marketing innovation and organizational innovation. The

relationship between innovation and economic development is widely acknowledged. Hence, good

measurement of innovation is essential for policymaking. Measuring innovation is complex. There

are enormous numbers of macro-indicators to measure and benchmark innovation capacity such as

innovation indicators in OECD statistical database. However due to inadequate and missing data for

Malaysia, we only could look at limited number of indicators to benchmark Malaysia’s innovation

capacity against other countries. Mentioned indicators are as follows:

• Expenditure on R&D.

• Number of researchers in R&D.

• Number of patents.

• High technology exports.

• Global Innovation Index.

• ICT infrastructure indicators.

• ICT expenditure.

The world’s most innovative economies are also the world’s fastest growing economies such as

Korea, India and China. Is Malaysia spending enough on research and development (R&D) and is

this spending efficient? The level of spending on R&D seems to be determined by the volume of

research. The limited supply of researchers may itself reflect weak demand for R&D spending.

Indeed it is a two-way causation. R&D expenditure as a percentage of GDP in Malaysia is relatively

low compared to Japan, South Korea, Singapore and Hong Kong. The trend shows R&D

expenditure over the years in Malaysia did not increase much. (Refer Figure 3). Malaysia’s gross

expenditure on R&D as a proportion of GDP in 2006 was 0.64 percent although it targeted to

increase gross expenditure to 1.5 percent by 2010, according to the Malaysian Economic Planning Unit (EPU). Singapore, on the other hand, spent 2.31 percent of its GDP on R&D in 2006, and it

targeted 3.0 percent of its GDP on R&D by 2010. The figures for Japan in 2006 were 3.32, South

Korea (3.22), Taiwan (2.58) and China (1.42). Since the late 1990s, mainland China has boosted its

R&D spending by 50 percent. Now, China wants to increase that spending to 2.5 percent of GDP

annually. Finland, the Nokia country with only 5.2 million people, invested 3.4 percent of its GDP

in R&D, one of the highest percentages in the world.



Figure 3: R&D expenditure as % of GDP.

Source: World Development Indicators, KRIS.

If we further breakdown the R&D expenditure in Malaysia, it is evident that the public sectorappears to be driving the R&D activities after year 2006 (Refer Figure 4). Whereas, in South Koreathe private sector contribution is more prevalent (Refer Figure 5). Malaysia should strive to boostmore private sector involvement in the R&D activities to create a healthy competitive andinnovative business environment.

Figure 4: Malaysia’s expenditure in R&D by sector

Source: MASTIC’s National Survey of Research and Development 2008.



Figure 5: Korea’s expenditure in R&D by sector

Source: OECD Reviews of Innovation Policy of Korea.

The same trend of R&D expenditure applies to the number of R&D researchers in Malaysia (Refer

Figure 6). The ratio of researchers to the total population has increased rapidly in Singapore over the

years. Increasing the number of R&D researchers is a big challenge and tougher than pumping more

in R&D spending since developing research skills and capabilities takes much longer time.

Increasing R&D expenditure with inadequate number of R&D researchers will lead to ineffective

consumption of the spending and low value product development.

Figure 6: Number of researchers in R&D.




An economy’s capability to innovate is continuously translated into patenting activity. Developed

nations have the most number of patents. Malaysian firms do very little patenting (Refer Table 1).

More than 90% of patents granted and applied to the Malaysian Patent Office are to foreign

residents (Refer Figure 7 and 8). Only a small number of patents are granted to local residents.

Patenting activity in Malaysia is relatively low compared to Taiwan, South Korea, Hong Kong and

Singapore. Patents granted by USPTO to Malaysian residents are mostly for semiconductor and

electronics industries only.

Table 1: Patents Granted by USPTO to foreign residents

Source: USPTO

Figure 7: Number of patents applied in Malaysia.

Source: MyIPo, KRIS.



Figure 8: Number of patents granted in Malaysia.

Source: MyIPo, KRIS.

Malaysia has high technology exports as % of manufactured products and the trend was increasing

for some time (Refer Figure 9). However, it’s mainly contributed by electrical and electronics

industry. In order to become an innovation led economy, Malaysia has to come out from its

conventional “semiconductor and electronics” trap box. Malaysia needs to expand and diversify its

patent activity to many other fields like engineering, biotechnology, automotive, agricultural

technology and many more.

Figure 9: High technology exports (as % of manufactured exports).




The Global Innovation Index 2008-2009 is the most comprehensive report to assess innovation in

130 countries. Lower ranking denotes better innovation capability of a nation. When we analyze the

sub components of global innovation index, we found that performance of Malaysia in general and

ICT infrastructure seems not satisfactory relatively to Japan, South Korea and Singapore (Refer

Figure 10). We collected the data that make up “General and ICT Infrastructure” and tabulated it.

Malaysia has lower internet users and broadband subscribers than South Korea (Refer Figure 11 &

Table 2).

Figure 10: Global Innovation Index (GII) of Malaysia.

Source: Global Innovation Index, 2008-2009Note: Ranking over 130 countries, lower ranking denotes better performance.

Figure 11: GII of Malaysia, Japan, South Korea, Singapore.

Source : Global Innovation Index, 2008-2009



Table 2: ICT infrastructure in Malaysia and Korea.

Source: World Development Indicators, ITU World Telecommunication Indicators

ICT expenditure as % of GDP in Malaysia is relatively low compared with Japan, Hong, Singapore

and showing decreasing trend from year 2000 to 2006. However, ICT expenditure in South Korea is

slightly lower than Malaysia (Refer Figure 12).

Figure 12: ICT expenditure (as % of GDP)


Malaysia has 5 broadband internet subscribers per 100 people whereas South Korea 32, Singapore

21 and Hong Kong 28. Malaysia was parallel with other East Asian economies in broadband

penetration in 1996 when Multimedia Super Corridor (MSC) launched. But currently several years

after that launch, we have not moved far forward either in terms of broadband penetration or



innovation. Number of broadband subscribers in Malaysia is significantly lower than South Korea,

Japan, Singapore and Hong Kong. South Korea, Singapore, Hong Kong and Japan had a huge rise

in number of broadband subscribers from year 1998 to 2005. We can see the dynamics of increase in

broadband subscribers per 100 people for the mentioned countries in the scatter plot below (Refer

Figure 13). Malaysia shows little movement. Korea has made good use of broadband penetration to

promote multimedia-based activities and web services especially in Seoul.

Figure 13: Broadband subscribers per 100 people.


The IT infrastructure in particular is becoming a vital aspect influencing the efficiency of research

efforts. Better IT infrastructure permits researchers to connect globally, to exchange ideas, to search

for ideas, to access papers and so on. We expect broadband penetration rate in Malaysia will be

higher in coming years, since Prime Minister launched the National Broadband Initiative (NIB) in

March 2010. NIB is expected to provide next generation high-speed broadband (HSBB) access.

Government has allocated RM1bil to provide schoolchildren from poor households with Internet-

enabled notebook computers, as part of the National Broadband Initiative. Another RM60mil would

be invested to set up community broadband centres that would benefit 615,000 households in 246

locations in the country. Government will also set up 138 Internet centres at state information

department offices that will benefit 400,000 people. The Government will also set up 1,105 e-kiosks

at community centres nationwide, at the cost of RM40mil in total (Refer Figure 14).

Figure14:



National Broadband Initiative (NIB).

Source: Malaysian Communications and Multimedia Commission, 2010

In spite of many policy initiatives and institutional support to move Malaysia towards an innovation-

led economy, comparative analyses showed that the incidence of innovation is low in Malaysia,

compared to what it should be based on its level of development. While the knowledge content at

the industry level showed improvements, knowledge constraints such as the lack of financial sources

and soft skills remained a stumbling block for firms to move up the value added chain. Hence, there

is a continued role for the government in subsidizing training and knowledge-based upgrading activities to propel industries and the economy at large to the desired goal of a truly knowledge-

based economy.



3. An Analytical framework: The Malaysia Vision 2020 Model (“MV20 model”)

3.1 The MV20 Model: Innovation, Knowledge and Human Capital in one framework

The analytical framework adopted in this Chapter, the MV20 model, is an Overlapping Generations(OLG), endogenous growth model that studies the links between innovation and long-term growth

of the country. Consistent with most endogenous growth literature2, it adopts a dynamic general

equilibrium framework that captures interactions between individuals, firms, and the Government in

determining long-term growth path of an economy. At the same time, it is also in coherent with the

ideologies and strategies advanced by the World Bank and OECD in developing a knowledge

economy 3, by stressing on the importance of knowledge and human capital in driving innovation-led

growth. More specifically, the framework emphasizes on the inter-linkages between human capital

accumulation, knowledge creation and transmission, the talent pool distribution, the labour market

allocation, and their resulting implications on growth.

The term OLG comes from the division of the population into three generations at any one time:

children, working adults and senior citizens. Each generation has a duration of 25 years, which

makes it appropriate in analysing long-term growth. Given that different generations are involved,

decisions made at the household level would naturally have dynamic implication on the next

generation, and therefore need to be taken into account in determining the outcomes of the next

period. For example, the savings of working adults are calculated as consumption in old age, i.e.

when they move into the senior citizens category. Similarly, the decision to acquire higher education

before entering the workforce is a determinant of the level of wages earned as working adults.

In addition to household level decisions, the MV20 model is also complemented by four additional

broadly defined sectors, which include the final production sector, intermediate goods sector,

education sector, and innovation sector. All five sectors of the economy interact with each other in

determining the long-run balanced growth equilibrium of the economy. Apart from the endogenous

nature of the five sectors, the MV20 model also emphasizes and measures the impact of the public

sector in spurring innovation, both through investments and public expenditure allocations, to

create an enabling environment for innovation and growth.

Another distinctive, key feature of the MV20 model is that it also explicitly attempts to analyze the

talent pool distribution of the economy, despite the practical difficulties involved due to lack of 2 For surveys of the technical literature on economic growth, see the Handbook of Economic Growth (edited by

Aghion and Durlauf, 2005).3 For details, see World Bank (2007) and OECD (2007, 2010b).



official statistical data and indicators of such nature. It introduces a generic measure of cognitive

skills, which would play a very important role in analyzing labour market incentives, and the

resulting distribution of workers across different sectors. Given that strategic human capital is

fundamental to growth and innovation, this attribute of the model further outlines the importance in

understanding and therefore creating a broad labour market environment that is conducive to

innovation-led growth. The inter-linkages of the various sectors in the analytical framework are

illustrated in Figure 15, as follows:

Figure 15: A stylized depiction of the overall feature of the MV20 model framework

As a result of the uncertainty in human capital and cognitive skills distribution, an economy’s long-

run growth may be characterized by two different labour market environments. In the first scenario,



there is a general misallocation of talents across the economy. A lot of talented graduates with high

abilities, who could perform well in a specialist role, are not willing to work in the innovation sector

due to unattractive relative wages differentials, when compared to remain in the final output sector

as generalists. In the second environment, growth is constrained by a lack of labour quality across

the broad human capital of the economy. Under such environment, despite the fact that higher

education may have produced high number of graduates, the graduates in general lack the necessary

cognitive skills required to work in the innovation sector. Since the impact of any policy action

undertaken would naturally differ between the two different labour market environments, the need

to understand the different labour market conditions of the economy is essential in determining the

best policy prescription to be administered in fostering an innovation-led growth in the economy.

The analytical framework is calibrated based on most recent data for Malaysia. However, against the

backdrop of a lack of broad innovation statistics and indicators, some parameter values for countries

at similar stage of development are used. It provides a rich policy simulation tool to analyze andaddress long-run macro issues associated with innovation-led growth. Consistent with the initiative

to drive Malaysia’s transition to a high-income economy, the model provides a macro-framework

that not only outlines the broad economic environment in an innovation context, but also allows for

the analysis of certain policy actions, their respective tradeoff and transmission mechanism in

stimulating growth.

The basic features of the model are discussed below, with the various sectors being further

structured into four thematic blocs to facilitate readability:

3.2 Production structure – Final Output Production and Intermediate Goods sector

The key feature of the model is that growth is driven by “horizontal” innovations, that is, through

the introduction of new varieties of intermediate goods. This implies that new varieties of

intermediate goods are assumed to be of the same quality as previously invented goods. Firms in the

final production sector, in addition to the standard inputs of capital and labour, use the intermediate

goods produced in the intermediate goods (IG) sector to produce final output for the economy.

Meanwhile, firms in the IG sector use the blueprints designed (or trademarks and patents)purchased from the innovation sector to produce the intermediate inputs that are made available to

final output firms in each period.

While such simplified production approach is commonly found in most dynamic growth models,

recognizing the shortfall that firm innovations extend beyond that of the introduction of new



products and technologies, the final production function also implicitly incorporates the element of

process innovation and quality-improvements on a broad aggregate context. This is done through

the introduction of two elasticity parameters with respect to intermediate goods – shares of

intermediate goods in final output production and elasticity of substitution between pairs of

intermediate goods. The former is an elasticity parameter in the final production function while the

latter affects the net price mark-up for IG producers.

In the context of the model, any growth that results from incremental innovation, such as

improvement in production routine and organisational efficiency, would be reflected in a larger

elasticity parameter with respect to the shares of intermediate goods in the final production function.

On the other hand, a smaller elasticity of substitution between pairs of intermediate goods would

naturally reflect the outcome of access to specialised technology and experts. When IG producers

gain greater access to specialised technologies through value innovation over time, the elasticity of

substitution parameter would be lowered, and therefore allows IG firms greater net price mark-upsover its marginal cost.

3.3 Education Sector, Human Capital, and Labour Allocation

For simplicity, the labour in the economy is broadly categorized into two different categories,

qualified skilled workers and non-qualified low-skilled workers. The resulting classification is

modelled as a direct result from households’ inter-temporal decisions with respect to pursuing

advanced education. At early adulthood, individuals decide on whether to pursue advanced

education, depending on wage incentives of the labour market. Obtaining advanced skills qualify

individuals to potentially work in either the innovation or final goods sector. For individuals who

choose to acquire advanced skills, human capital then depends on factors such as education in

childhood, income shares and time allocated to education, public capital spent on education, as well

as existing stock of ideas or knowledge (as a result of spillover effect of innovation on learning). On

the other hand, the general human capital of individuals who choose not to acquire advanced

education (and therefore restrict themselves to only able to work in the final production sector)

would be modelled as function of childhood education, as well as positive externality benefited from

working with the qualified skilled workers. This proximity effect fittingly captures the concept of

workplace and organisational learning, where higher degree of spillovers and proximity through co-operation and collaboration would improve the broad human capital of the economy. The assumed

flows of human capital accumulation modelled are illustrated in Figure 16, as follows:



Figure 16: Graphical depiction of the education and human capital sector of the MV20 modelframework

While the analytical framework assume all individuals as receiving the same advanced education,

graduates nonetheless, leave with different distribution of cognitive skills (or in short, abilities). The

different cognitive skill levels, coupled with the relative wage differentials across sectors, would

determine the allocation of graduates across sector. The aggregate uncertainty introduced in the

distribution of abilities would therefore result in two distinct labour market environments. As

explained earlier, there is a general misallocation of talents across the economy in the first case.

There are adequate talented graduates with high cognitive skills that are capable to work in the

innovation sector, but some of these talents opted not to do so due to the relative wages differential

across the final output and innovation sector. Matching the model context to a real world

perspective, this simply means that jobs in the innovation sector are simply not compensated

enough, be it salaries, non-remuneration benefits, and other non-observable incentives. The

presence of labour market distortions across both sectors could have contributed to this

misallocation too, which are explicitly modelled in the framework.



On the other hand, in the second case, growth is constrained by a lack of labour quality across the

working age population in the economy. Under such environment, despite the fact that higher

education may have produced high number of graduates, the graduates in general lack the necessary

abilities and cognitive skills required to work in the innovation sector. This results in an undesirable

low quality talent pool, therefore making educational and curriculum reform a top priority in order

to escape this growth trap. Hence, while the former calls for better labour mobility and allocation,

the latter is clearly a quantity issue (better known as brain drain) where most policy tools would

likely to be less effective, in the absence of a long-term reformation of the education system and

learning culture. A diagrammatic interpretation of the two possible labour market environments is

presented in Figure 17, as follows:

Figure 17: The two possible labour market environments under MV20 model framework

As will be shown in the Strategy Simulation section later, the ability to diagnose the current state of the

labour market environment is essential in order for any policy prescription to be effective. In reality,

the true labour market conditions are likely to differ across industry, where in some cases, an

industry suffers from both misallocation of talents and low labour quality traps. Furthermore, in

practice, the true distribution of cognitive skills of the working age population is unobservable in the

absence of official indicators on intellectual assessments. Perhaps, this shortfall highlights the dire

needs of establishing a competencies assessment of international standards, such as that of theOECD’s Programme for International Student Assessment (PISA).



3.4 Innovation Sector and Knowledge Capital

Within the framework of the model, innovation is conveniently measured through the intensity of

research & development (R&D) activities, where firms generate blueprints and designs to be used in

the production of intermediate inputs. In return, the successful researches are compensated with

monopoly rents extracted from the patent price charged on IG sector. The production of theblueprints or designs is specified as a function of the existing stock of designs, effective high-skilled

labour with the minimum cognitive skills required to work in the innovation sector, access to public

capital, as well as shares of public expenditure on R&D support. Technical knowledge enters

production in two distinct ways. First, new designs or blueprints enable the production of new

intermediate inputs; second, the new designs contribute to the accumulation of total designs, thereby

increasing the productivity and intensity of research and innovation in the future. In similar spirit to

other production functions, various elasticity parameters are also introduced to better capture the

non-linearity of these effects.

While the product of innovation is conveniently labeled as designs or blueprints on a non-

differentiated basis, one can easily interpret the innovation function in a more intuitive manner, i.e.

production of knowledge. In such perspective, the innovation sector can then be viewed as a

collection of various professionals, inclusive of firms’ in-house designers, research consortia,

scientists, technopreneurs and those knowledge networks that are collectively known as knowledge

brokers (OECD, 2010). Given that not all researches are entitled to be filed for patents and

trademarks, the existence of knowledge brokers, such as international organizations, consultants,

applied researchers, advisory firms, and science journalists, helps to package information and bridge

the knowledge gaps between academicians and applied practitioners. Their roles in facilitating creation, transmission and accumulation of knowledge would help fostering greater innovation and

by extension, growth.

Within the model context, their long term impacts to growth are implicitly captured by the

innovation elasticity parameters, particularly the elasticity with respect to public-private capital and

government’s spending on innovations. While policy-targeting involving elasticity coefficients are

hard due to the imprecise nature of parameters, the framework nonetheless introduces extra

potential innovation strategies that could be applied, i.e. one that involves nurturing and cultivating

the knowledge ecology of the economy. Hence, policies that stimulate collaboration and network

initiatives, and facilitate greater rates of ideas sharing and communication would be viewed as

favourable to growth within the framework of the innovation production function. The graphical

interpretations of the knowledge generation process flows within the context of the innovation

production function are illustrated in Figure 18, as follows:



Figure 18: Graphical depiction of the innovation sector of the MV20 model framework

3.5 Government as a facilitator in enabling innovation-led growth

The analytical framework assumes a simplified structure for the public sector, where the

Government in the economy is assumed to maintain a balanced budget with no external borrowing,

i.e. it finances its expenditure in any period using the taxes collected. Given the current federal

budget deficit position, this feature assumed may be subjected to much debate. Nevertheless, this is

highly consistent with the Strategic Reform Initiative 8 (SRI 8) outlined in the NEM, where the

Government is to promote sustainability of growth through improved public financial management

and fiscal discipline

4

. Given the long-term forward nature of the MV20 model, making anassumption of balanced budget in allocating future public expenditure will indeed be more

appropriate compared to a budget deficit assumption.

4 See the New Economic Model for Malaysia, Part I, page 108.



The total government expenditure at any period is classified into four separate components,

consisting of spending on public capital in infrastructure, education spending, spending to support

innovation and R&D activities, as well as all other non-directly productive spending, such as

subsidies and other wasteful public expenditure programmes. In line with other blocs of the

economy explained earlier, efficiency parameters are attached to the three main components of

public spending. While the expenditure shares are calibrated according to real fiscal data of Malaysia,

the efficiency parameters assumed in benchmark calibration are consistent with other developing

nations in similar stage of developments5. Serving as a facilitator in enabling innovation and

sustainable growth in the economy, the Government can directly intervene in the economy through

altering the composition of public expenditure permanently. More specifically, the Government may

increase the shares of spending on infrastructure, education, or innovation activities, at the trade-off

of other non-productive spending, to create an enabling environment for innovation and growth.

In addition to direct intervention through public expenditure programmes, the Government may also implement other indirect policy interventions to foster a conducive environment for

innovation-led growth. For instance, improving the efficiency of infrastructure spending through

fiscal reforms and more efficient procurement process would naturally be expected to have positive

implication on long-term growth. Similarly, improving governance and measurement system,

enhanced public sector research programme, as well as the adoption of international best practices

are measures that would likely lead to a permanent increase in efficiency of public expenditure on

education and innovation activities.

Other than capturing the aforementioned public policy interventions, the flexible analytical

framework also allows for other possible policy tools, with selected innovation strategies’ examples

explained in earlier sub-sections. Additionally, in order to create an enabling job market environment

that would foster innovations, the Government is also afforded the luxury to directly intervene in

the talent pool and broad labour market of the economy. More specifically, given the explicit

modelling of the talent pool (by cognitive skills distribution) and labour market imperfection (by the

relative cost mark-ups to labour market distortion across sectors), the Government may directly

influence the labour market and demographic structure to create an enabling environment for

innovation-led growth over time. The policy simulations of such interventions are explained further

in the next Section.

5 See Agénor and Neanidis (2010)



4. Strategy Simulations

4.1 Background

This section demonstrates the effects of various policy options in stimulating innovation activities

and by extension, sustainable long-run growth in Malaysia. More specifically, it explains the

transmission channels involved and the resulting impacts to growth of selected policy options

spanning over a period of 25 years, i.e. equivalent to five standard Malaysia Plan regimes. Given that

any successful and sustainable innovation-led growth strategies would inevitably involve a certain

degree of, or in some cases extensive, long-term structural transformation and addressing the

bottlenecks of the economic system, the adoptions of such long-term timeframe in policy thinking

and simulations are both necessary and well warranted. Again, the key word to be emphasized here

is “sustainable” growth, consistent with the nation’s long-term goals outlined in the NEM.

It is of this rationale in the needs to address long-term macro issues that a dynamic OLG model is

selected in anchoring the framework, where the model calibrated mirrors that of the current

economic system of Malaysia. This is accomplished through the baseline calibration of the model,

with relevant parameters selected based on official statistics of Malaysia. In the event where certain

parameters are not available, they were selected based on countries in similar stages of development

or empirical estimates from established literature. The benchmark calibration parameters and

respective sources are presented in the Appendix.

To illustrate the effectiveness of various policy options, the two different states or environments of

the Malaysian labour market, i.e. the low labour quality and talents’ misallocation environments, are

accounted for in the simulation studies conducted. Despite the wide range of possible innovation

strategies available within the model framework, this section nonetheless presents only those major

policy simulations that would most prepare an enabling background and environment for

innovation-led growth in Malaysia, and fittingly raise the curtain for specific micro-innovation

strategies documented in subsequent Chapters.



4.2 Labour market and innovation

4.2.1 Labour market reforms

Given the importance of the relative wages across sectors in serving as an incentive mechanism, as

well as in determining the economy-wide labour supply allocations, the second broad policy

simulation exercise involves that of broad labour market reforms. More specifically, it uses the

MV20 model to simulate the impact of labour market reforms in terms of reducing labour market

distortions in the innovation and final output production sector. The term labour market distortions

may be broadly defined as including all forms of rigidity that impede the proper functioning of the

market as an effective labour allocation mechanism, which encompasses that of rigid labour market

regulations, hiring and firing costs, and non-wage related costs. However, within the analytical

framework, these are collectively modelled as a proportional cost mark-ups with respect to labour

market distortions imposed by both the final output and innovation sector producers. The overallcost distortion to the broad labour market is then measured as the relative cost mark-ups of the

innovation sector over that of the final output producers, where the parameters are initially

calibrated as 1.10 and 1.07 respectively, reflecting a relatively larger existing distortion to cost in the

innovation sector.

From Figure 19, it is shown that the potential output growth effects are inversely related to changes

in the cost of labour market distortions in the innovation sector. Simply put, wages in the innovation

sector naturally increases with a reduction in the distortions of job market for the innovation and

knowledge generating sector. This would then intuitively induce more qualified and skilled labour to

work in the sector. However, such impact is only observed under the labour market environment of

talents’ misallocation due to weak incentives in the innovation sector. Reallocation of skilled labour

to engage in innovation activities is only possible under this labour market environment since the

economy is inherently populated with capable talents for the innovation sector. Under the

alternative labour market setting of low labour quality, this policy option is effectively useless since

no amount of job market reforms will result in higher innovation and growth, given that there is

innate low quality labour situation in the first place. Interested workers in other production sector

who are willing to move to innovation sector will not be able to work and succeed in the innovation

sector as they lack the necessary skills required.



Figure 19: Simulated growth impacts of a reduction in the cost of labour market distortions in the

innovation sector

The simulation results presented seem to suggest that the growth effects may depend on the relative

wage distortions across sectors, instead of merely a function of the cost mark-ups in the innovation

sector. Simulations are then conducted based on various combinations of labour market reform

across both sectors – first, a uniform reduction in the cost with respect to labour market distortions

across sector; second, greater focus of labour market distortions in the innovation sector. The

simulation results are presented in Figure 20 and 21.

Figure 20: Uniform reduction in the cost of labour market distortions in both sectors

Baseline



Figure 21: Reducing labour market distortions at higher rates in the innovation sector

From both sets of simulation results with respect to broad labour market reforms, it can beobserved that a uniform reduction of labour market distortions proportionately in all sectors would

have a slightly negative impact on growth. On the contrary, a greater focus in reducing labour

market distortions at the innovation sector relative to the final output production sector would bring

positive impact to growth over the long-run. These policy simulation results clearly outline the

roadmaps for any potential economy-wide labour market reform and liberalization policies. In order

to transform Malaysia into an innovation-led economy, the desirable labour market policies that

would best foster knowledge and innovation activities would be policies that would shift the focus

away from the traditional manufacturing sector towards the innovation and knowledge sector,

particularly to those industries that suffered from serious distortion in wage incentives and in direneeds of reducing labour market distortions.

4.2.2 Achieving a ‘critical mass’ by increasing the talent pool

Drawn to attention in the NEM and World Bank studies6, the traditional reliance of Malaysian firms

on low skilled foreign labour and by extension, the lack of demand for highly skilled talents, have

reduced the country’s ability in attracting talents required for the innovation and knowledge

generating sector. This traditional business model has left the existing talent pool of skilled workers

in Malaysia much to be desired. It is also reported that job vacancies arose because most workers didnot have the required skills looked for by firms. These evidences highlighted the country’s growing

concern of an increasingly depleted talent pool due to migration of skilled Malaysian Diaspora, as

well as its inability to attract high skilled talents. In order to achieve the necessary ‘critical mass’ from

6 See World Bank’s Doing Business Surveys.



a network of skilled researchers in preparing the country with the required capacity of a innovation-

led economic model, increasing the talent pool becomes an important policy agenda.

Besides, as observed from earlier policy simulations, a labour market characterized by insufficient

labour quality is totally irresponsive to any form of reduction in labour market distortions, while

showing lower positive growth effects towards any discretionary increase in public spending when

compared to a labour market environment with talents’ misallocation. However, given some of the

evidences cited earlier, there is a high possibility that some part of the country’s labour market, or

job market in some industries, does experience issues with respect to low labour quality. In such

instances, as Malaysia attempts to shift towards an innovation-led economic model, increasing the

talent pool of the economy, in addition to educational reform, becomes the most effective policy

tools in stimulating greater innovation and consequently, higher potential growth for the country.

The analytical framework attempts to explicitly model the talent pool distribution of the economy,

despite the lack of measured statistics or official indicators. More precisely, to facilitate policy

simulation experiments, the cognitive skills’ distribution of the graduates in the economy is modelled

as a uniform distribution normalized to unity, and indexed within the range of zero to one. The

minimum required cognitive skill level required for innovation workers is set arbitrarily at 0.6,

therefore resulting in a baseline average cognitive skill level of 0.8 for the knowledge sector’s labour

pool.

Consider the policy simulation that capturing policies of a direct intervention into altering the

distribution of the existing talent pool of the economy. In practical terms, such policy intervention

may be broadly defined as policies that include importing highly technical-skilled foreign talents,

attracting the Malaysian Diaspora in overseas, as well as reducing the reliance on low-skilled foreign

labour. These are all measures that directly enlarge the distribution of the talent pool and therefore

increase the average cognitive skills in the workforce, with the former two examples of raising the

ceiling of the population cognitive skill distribution, while the latter being a classic example of

addition by subtraction.

As expected, the simulation results show that there is a positive relationship between growth and the

size of the Malaysia’s talent pool, assuming there are no corresponding changes in other policy

variables. Consistent with other earlier simulation results, it is observed again that a labour market

setting with talents’ misallocation will be more responsive, but only slightly, to such policy

intervention when compared to one characterized by low labour quality. This is again due to the



additional ‘labour reallocation effect’ of existing misallocated labour to the innovation sector, on top

of the positive direct effect of a bigger talent pool available to the economy.

Figure 22: Simulated growth impacts of a direct intervention in changing the talent pool

From Figure 22, it is shown that increasing the current talent pool permanently by 10% for the next

quarter of a century is expected to push the potential growth rates of the country up to 7.5% (in the

case of talents’ misallocation) and 7.4% (in the case of low quality labour market) respectively, from

the benchmark rate of 6.0%. In the same manner, for a bullish upside scenario, permanently

increasing the current talent pool distribution by 30% would generate strong growth effects that may potentially deliver 10.2% and 10.0% respectively. Conversely, a continuously shrinking talent pool

would naturally result in lower growth rates, especially so if the country had shifted to an

innovation-led economic model.

Interestingly, besides its positive growth effects, policies to attract foreign talents and enlarge the

existing talent pool also have positive consequences for the education and innovation-led sectors.

Figure 23 shows that the completion rate for advanced education, in addition to the share of

graduates in innovation sector, is expected to increase naturally as a result of a positive changes in

the size of the talent pool, even in the absence of any extra educational policy intervention. Unlike in

other policy simulations where such outcomes are observed only in the framework with labour

market characterized by incentive-induced misallocation of talents, this actually true for both

environments of the labour market. This observation, coupled with the non-significant difference of

growth effects between both labour market settings, reaffirm the believes that directly altering the

existing talent pool represents one of the most effective policy tools in tackling a low quality labour

Baseline



market environment, in order to create the most enabling macro-environment for innovation-led

growth.

Figure 23: The completion rate of advanced education is endogenous to the overall talent pool

4.3 Direct policy interventions through discretionary public expenditure programmes

The simulations on the growth effects of a potential change in the composition of discretionary

government spending are also discussed. Discretionary public expenditure programmes, be it an

increase in the share of expenditure on innovation supports, an increase in the share of education

spending, or an increase in the share of core infrastructure spending, represent the most direct form

of policy interventions in fostering innovation activities. Given the importance of all three forms of

public expenditure programmes outlined, the policy simulations examined will involve budget-

neutral reallocation of government expenditure, i.e. each simulated increase in the share of spending

would be assumed to be financed by an equivalent-percentage cut in the other expenditure

component of public spending.

4.3.1 Increased share of spending on innovation supports

The first policy simulations examined involves that of a budget-neutral increase in the share of

government spending on innovation supports, i.e. the initial benchmark budget allocation of

approximately 0.03 percentage points is revised upwards by 1% share to 0.04. Note that within the

context of the model, this practically means the share of innovation spending is permanently

increased for a sustainable period of 25 years, not merely a one-off revision in an annual budget.

Consider first the environment where there is insufficient labour quality in the country’s labour

Baseline



force, i.e. the economy is “trapped” with low labour quality and therefore inherently lack of

resources in supporting innovation-led growth. In such instance, a permanent 1% share increase

would lead to an increase of 0.4 percentage points compared to the current baseline potential growth

rate of 6.0%. Thus, a direct increase in budget allocation of public expenditure to innovation

supports while spending less on other non directly-growth promoting expenditure will “pull” the

country’s potential growth rates up, despite an innate lack of labour quality. However, if the

expenditure increase is financed by a cut in other productive components, such as public

expenditure on core infrastructure and education, or at a trade-off of lowering the current efficiency

level of innovation spending by 30% (from 0.60 to 0.42), the resulting long-term growth impacts will

instead be negative, at lower potential growth rates of 5.6%, 4.7%, and 5.9% respectively. These

simulation experiments highlight the importance of always maintaining, if not improving the

efficiency and shares of spending on productive components, in the event discretionary reallocation

of public expenditure programmes are undertaken.

Consider now the alternative scenario of the labour market environment, i.e. one that suffers from

misallocation of talents and low incentives. In such instance, an equivalent one percentage point

budget neutral increase in innovation spending would result in a larger increase of 0.9 percentage

points in the country’s long-term potential growth rates, compared to the potential growth impact of

0.4 percentage point increase in growth rates under the earlier low labour quality labour market

environment. Assuming all other policy variables remain constant, this larger growth outcome is well

expected, given that the country is now stocked with talents with enough calibre to succeed in the

innovation sector, only to be kept away due to low incentives. The simulated policy will now

produce a labour reallocation effects and subsequently promotes greater human capital

accumulation, in addition to the earlier direct growth channel identified, i.e. higher intensity in

innovation activities. Given that higher spending on innovation sector will naturally lead to higher

marginal products and greater job incentives (wages and non-wages) in the innovation sector, greater

shares of the calibre talents will now opt to pursue an innovation career by engaging in research and

development (R&D) and other knowledge generating activities. This then, indirectly, would also

result in higher incentives for Malaysian to enroll for further advanced education, and by extent,

eventually embark on a career in the innovation sector. These extra job incentives and labour

reallocation effects therefore lead to relatively higher potential growth impacts.

Despite the higher potential upside to growth, one should nonetheless, exercise extra care in

managing innovation-led growth policies under an environment of talents’ misallocation due to

incentive issues. The situations are quite different when the expenditure increase is financed by a

same percentage point cut in other productive components, such as public expenditure on core

infrastructure and education, or at a trade-off of lowering the current efficiency level of innovation

spending by half. This time, under the labour market environment with misallocated talents, the



impacts to growth are magnified too, but negatively. A spending increase in the share of innovation

supports, at the trade-offs of the three simulated scenarios outlined above, would reduce the long-

term potential growth rates of Malaysia dramatically to 5.1%, 2.3%, and 5.8% respectively. These

reversed amplified effects are intuitively similar to the spending increase simulations, as higher

calibre talents of the economy are now further “driven away” from the innovation sector, opting to

stay on with the final output production sector, which has relatively lower value-adds and growth

impacts for a country that stuck in the middle income trap, such as in the case for Malaysia.

Figure 24: Simulated growth impacts of a one percentage point increase in share of governmentspending on innovation supports

4.3.2 Increased share of spending on advanced education

Consider now a one percentage point permanent increase in the share of advanced education

spending (from the baseline value of 0.05 to 0.06), financed by a cut in the share of other non-

productive government spending consistently for the next quarter of a century. Under both labour

market environments, the general equilibrium growth effects are quite powerful. In the innate low

labour quality market environment, the potential growth rate of final output increases from 6.0% to

7.7%, whereas potential growth rate is expected to jump to 10.1% under the talents’ misallocation

environment. As “ people are at the heart of the innovation process 7

”, spending more on advanced educationpromotes growth directly through greater advancement of average human capital of the labour

force, as well as indirectly through higher intensity of innovation activities. Under the misallocation

of talents environment, the long-term growth impact is naturally stronger, given the positive

“snowballing” incentive and reallocation effects documented earlier, which result in greater stock of

7 See OECD (2010b), Chapter 3, page 55.

Baseline

financedbycutin

otherspending

financedbycutin

infrastructurespending

financedbycutin

educationspending

financedbycutinother

spending,butatcostof30%

reductioninefficiencyof

innovationspending



knowledge accumulated, larger population share with advanced education, and larger share of skilled

labours’ participation in the innovation sector. Hence, to create, apply, and unleash knowledge and

innovation, a sustained efficient public spending on core human capital advancement, assuming

unchanged demographic factors, becomes a necessary policy tools in long-term innovation policies.

To examine the tradeoff of various policies, the same policy option of a one percentage share

increase in advanced education spending is repeatedly simulated, this time at the cost of an

equivalent percentage cut in the share of core infrastructure spending, share of spending on

innovation supports, and a 30% drop-off in efficiency of advanced education spending (from 0.60 to

0.42). For the tradeoffs involving other components of productive spending, the growth impacts

remain positive under both labour market environments, though at lower rates compared to the one

financed by a cut in other non-productive components of government spending. For the policy

tradeoff involving a reduction in spending efficiency, arbitrarily set at 30% reduction, the growth

impacts actually become negative under both labour market settings. In this case, the simulatedoutcomes for long-term potential output growth are merely 4.7% and 2.4% for the low labour

quality and misallocation of talents environment respectively. These simulation results deliver

significant policy implications on the uses of direct discretionary public spending as policy tools,

particularly with respect to public expenditure on advanced education. Simply pouring extra money

into advanced education sector, without thorough planning and cost-benefit assessments that would

ensure at least the same spending efficiency, could potentially result in adverse growth effects.

Figure 25: Simulated growth impacts of a one percentage point increase in share of government

spending on advanced education

Baseline

financedbycutin

otherspending financedbycutin

infrastructurespending financedbycutinspending

oninnovationsupports






4.3.3 Increased share of spending on core infrastructure

For completion, consider now a one percentage point increase in the share of public spending on

core infrastructure, from an initial value of 0.08 to 0.09. Public infrastructure, particularly modern

telecommunication facilities and ICT infrastructure, influences the growth dynamics through a

number of channels. First, it directly increases the public-private capital ratio, which in turnspromotes final output, intermediate goods, human capital and innovation activities production.

Second, the increase in the production of human capital indirectly helps to promote innovation-

related activities, while the creation and accumulation of knowledge exerts positive externality in the

production of human capital. These effects operate under both labour market settings, hence

directly contributing to a 0.7 percentage point increase in potential output growth for the economy

with inherently low labour quality.

In addition, under the labour market setting with talents’ misallocation, the increase in access to

infrastructure tends to also increase directly the productivity of labour, and therefore wages in both

sector. However, the additional effect on wages in the innovation sector turns out to be larger, as the

general expansion of the economy tends to increase also the demand and therefore profits of the

intermediate goods sector, resulting in higher patent prices, and consequently further raise wages in

the innovation sector. These leads to a net labour reallocation effect, where more skilled graduates

will now seek employment in the innovation and knowledge generating sector. These then induced

greater accumulation of both stocks of human capital and knowledge, and consequently higher

intensity in the knowledge sector, and by extension, growth. Under this labour market setting, the

simulation of a permanent 1% increase in the share of spending on public infrastructure over the

next 25 years, ceteris paribus, would be expected to result in a higher potential growth rate of 7.6%.

The same policy tradeoffs examined earlier are also assessed in this simulation scenario, i.e. a one

percentage share increase in public spending on infrastructure, financed by either an equivalent

percentage cut in the share of advanced education spending, innovation-related spending, and at an

opportunity cost of 30% drop-off in efficiency of public infrastructure expenditure, from initial

value of 0.650 to 0.455). In these instances, the net effects of policy shift are mixed under both

labour market settings. For the increased infrastructure spending financed by a cut in innovation-

related expenditure, the growth effects remain positive under both labour market settings, i.e. a 0.2%

(low labour quality) and a 0.4% (misallocation of talents) increase in potential growth rates,

compared to the baseline 6.0%. However, if the increase in infrastructure spending were to be

financed by a cut in advanced education spending, or came with the opportunity cost of 30%

reduction in efficiency, then adverse growth effects would be observed instead, under both labour

market environments.



Figure 26: Simulated growth impacts of a one percentage point change in share of publicinfrastructure spending

Overall, to create an enabling environment for sustainable innovation-led growth through a balanced

budget increase in government expenditure, the non-productive operating expenditure components

of the budget would need to be reallocated to either of the productive spending components of

advanced education, public infrastructure, and innovation supports. As illustrated in the simulation

results above, these three public expenditure components are ranked exactly in the same order, in

terms of their effectiveness in stimulating growth. The magnitudes and growth effects observed are

stronger, in both positive and negative directions, under the labour market environment with weak

innovation incentives compared to a labour market with low labour quality. From the perspectives

of policy tradeoffs, cares must be exercised when any discretionary increase in public expenditure is

implemented, in order to ensure that the sudden increase in discretionary spending is not met with

the opportunity costs of severe reduction in spending efficiency. Perhaps, this concern highlights the

needs for continuous public sector reform and services’ enhancement in order to improve and

ensure the efficiency of any public expenditure allocation.

4.4 Advanced education policies

In addition to a direct increase in discretionary public spending on advanced education, education

polices-related simulations within the context of the model include campaigns and scheme-driven

changes in households’ budget allocation to advanced education, as well as tertiary educational

curriculum reform-induced changes in time allocated for higher education.

Baseline

financedbycutin

otherspending

financedbycutin

educationspending

financedbycutinspending

oninnovationsupports






4.4.1 Increased households’ budget allocation to pursue advanced education

An increase in household spending on higher education may be interpreted in two ways. First, it may

be considered as a pure households’ decision. An increase in household spending on higher

education means increased allocation in the cost incurred in acquiring the advanced degree such as

tuition fees, academic books, etc. Second, it may be interpreted as special government programmes,such as mandatory EPF deduction or special incentive schemes for households to spend more on

pursuing higher education. While the former is intuitive given that voluntary pursue of higher

education by citizens would naturally lead to higher rates of human capital accumulation, it is

nonetheless an outcome of households’ choice that bears little policy implication, compared to the

latter.

For policy simulations, the proportion of household spending on higher education is assumed to be

permanently revised upwards from the baseline 10% to 20% of total expenditure. Notes that any

simulated shock introduced in the analytical framework represents that of a permanent shock for 25

years, this basically represents a cultural change in the Malaysian society to one that is more receptive

towards pursue of advanced education. When the labour market setting is one of low labour quality,

the potential growth rate of final output is expected to increase from 6.0% to 6.6%. On the other

hand, if the labour market environment is one of incentive-induced misallocation of talents, the

growth effects will be larger due to the extra “reallocation effects”, raising potential growth rates to

7.5%.

Figure 27: Simulated growth impacts of changes in household spending share on higher education

Baseline



4.4.2 Curriculum reform-induced increase in time allocated for higher education

Next, the growth effects of a curriculum reform-induced increase in time allocated for changes in

time allocated for advanced education are examined. Increasing the time allocated for higher

education may be perceived as a consequence of efforts to strengthen the curriculum of higher

learning institution. In a sense, graduates must now spend more time (either in advanced coursework or laboratory research) to acquire an advanced degree. For this particular simulation, given the

complexity involved in simulating shock of time allocation in an overlapping generation context, the

original direct linearity effect of time allocated for advanced education in the production of human

capital is scaled down to the power of 0.2, in order not to overestimate the growth effects of a

change in time allocated to studying.

More specifically, the particular simulation result examined involved increasing the time allocated to

advanced education from the baseline 18% to 26%. Given the definition of time in the MV20

model, this is equivalent to 24 months of extra postgraduate coursework or research in the highest

level. In the low labour quality labour market setting, the potential output is expected to increase

from the current 6.0% to 6.7%. In contrast, in an environment where incentives to engage in

knowledge and innovation are weak, the potential output growth rate is expected to jump from 6.0%

to 7.6%. In this instance, the extra labour “reallocation effect” is expected to result in a natural

increase in advanced education’s completion rate. The reason why the growth effects of this policy

simulation experiment can only be considered as moderate is that, when people spend more time to

pursue advanced education, they necessarily spend less time in market work, hence constraining the

equilibrium growth effects.

Figure 28: Simulated growth impacts of changes in time allocated to pursue higher education

Baseline



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National Innovation Strategy

Chapter 1: Knowledge, Innovation and Growth

Dr. Albert G. Zeufack, Lim King Yoong and Devendran Nadaraja

Khazanah Research and Investment Strategy Macro Modeling Project

Appendix 1 Executive Summary

In its sustained effort to achieve Vision 2020, Malaysia has embarked on a new economic model “to

transform Malaysia into a knowledge-based and innovation-rich nation” 8. While the definition of innovation

varies from the implementation of new and significantly improved product and practices, to the

incremental improvements of processes in terms of value creation, the key success factor of an

enabling environment for innovation-driven growth ultimately lies in the accumulation and efficient

allocation of both human and knowledge capital. Getting the broad macroeconomic settings right istherefore essential. This chapter conducted comprehensive analysis on Malaysia’s innovation

landscape, by first reviewing and benchmarking the innovation capacity of the nation, and

subsequently followed by robust macro-analysis and policy simulation on the nation’s long-term

growth implications using an analytical framework conveniently named as the Malaysian Vision 2020

model (“MV20 model”). The MV20 model examines the key inter-linkages between human capital

accumulation, knowledge creation and transmission, talent pool distribution, labour market

allocation, and their resulting implications on the nation’s potential growth capacity. More

importantly, it allows for the identification of key structural challenges to be faced and overcome by

Malaysia in order to create a fostering environment for innovation-led growth over the next decade.

From the studies, six (6) key messages and policy recommendations, broadly categorized into data

analysis-relevant and policy-relevant recommendations, are identified and put forward. The former

includes building and promoting new measurement agenda for innovation that is in line with

international best practice, while the latter involves capacity-building policies that will create the best

macroeconomic environment for innovation-led growth.

First, the benchmarking exercises conducted highlighted the needs for Malaysia to invest in a high

quality and comprehensive integrated innovation database. While it is acknowledged that there is

existing data infrastructure on science and technology (S&T) maintained by the Malaysia Science and Technology Information Centre9 (MASTIC), this recommendation focuses on the adoption of a

new measurement agenda for innovation, in line with those documented in the OECD Innovation

Strategy 10. This calls for the promotion of new statistical methods, the adoption of a wider and

8 PM’s Interview to the Star, March 13th, 2010. 9 For further references, please refer MASTIC official website, at www.mastic.gov.my 10 OECD (2010), Measuring Innovation: A New Perspective, OECD, Paris.



interdisciplinary approach in data collection, as well as the establishment and maintenance of a new

integrated innovation database that is both timely and publicly available. As the success of any

national policy and plan, inclusive of the NIP, needs to be measurable against well-designed Key

Performance Indicators (KPIs), the adoption of international best practice in tracking and

monitoring the nation’s performance in innovation-related indicators is therefore indispensible.

Second, in terms of data analysis-relevant recommendation, it is also proposed that independent

research institution, with possible tie-ups to domestic institutes of higher learning, be established to

facilitate improved empirical studies and econometric analysis in broad areas that are highly relevant

to the national innovation agenda. As evidenced in the development of the MV20 model framework,

well-established innovation policy research literature is in severe lacking which forbids any serious

analysis to be carried out to identify more précised innovation drivers for Malaysia.

Both policy recommendations outlined above share the same concern in terms of capacity

inadequacies, and therefore lead to similar institutional implications. These will likely involve theestablishment of a special unit within the Special Innovation Unit (“UNIK”) that will serve as the

custodian and one-stop centre for innovation policy-relevant data analysis and research. It is

envisioned that the unit would co-operate closely with various agencies, such as MASTIC,

Department of Statistics (“DOS”), Ministry of Higher Education (“MOHE”), Ministry of Finance

(“MOF”) and relevant agencies in the construction of the integrated innovation database, as well as

leading policy research initiatives in innovation issues, form S&T, education, human capital,

bibliometrics, firms-level innovation, to green technology.

The other four recommendations would focus on intervention in key policy areas. For instance, the

key findings in this chapter highlighted the need to recognise the different characteristics across

various job markets in Malaysia. To promote greater incentives for both firms and employees to

invest and engage in research and development (R&D) activities, it is necessary to recommend for a

broad reduction in labour market rigidities with a greater emphasis on R&D and knowledge-

intensive sector. More specifically, this will involve tackling existing labour market distortions in

terms of firing cost. Empirical literature shows that Malaysia has a high level of firing regulation

rigidities and redundancy cost, on average, above that of its peer economies in the East Asia Pacific

and OECD region11. Given the higher labour cost confronted by firms, these are likely to create a

detrimental impact on wages offered and therefore reduce the incentive in hiring highly-salaried

knowledge and R&D workers. In such instance, a reduction in redundancy cost, particularly withgreater emphasis on the R&D sector, will likely improve the wage incentive for knowledge workers.

From this recommendation, it is also worth noting that a policy of minimum wages will not be

helpful in this implication, since such policy will further equalize the wage differentials between

high-skilled and low-skilled workers, leading to lower incentive to participate in R&D activities. In

11 World Bank’s Doing Business Survey , reviewed in Choong et all (July 2010).



terms of institutional implications, it is therefore proposed for UNIK to engage with Ministry of

Human Resources in conducting comprehensive industry reviews on existing labour market

regulations, as well as conducting comprehensive job market studies in terms of firing and hiring

practices.

The fourth recommendation focuses on tackling another main job market issues highlighted in this

Chapter, i.e. industries that faced a severe skill shortage and depleting labour quality. The

recommendation therefore focuses on targeting a broad enlargement in the talent pool of Malaysia.

More specifically, on the demand side, it is proposed for UNIK to co-operate and work closer with

the Talent Corporation in identifying, targeting and attracting specific overseas talents and diaspora

needed for innovation and R&D activities. From the angle of the supply side, improvement in the

talent pool over the long-term will necessary require UNIK to engage and work with the Ministry of

Education, in terms of advising the adoption of best international practices in Science and

Technology syllabus, as well as promoting curriculum reform that is more tailored for innovation.

The fifth recommendation calls for the streamlining and sharpening of fiscal incentives in general

for innovation and R&D activities. The analysis conducted in this chapter highlighted the

importance of creating a flexible environment that provides the strongest incentive for fostering

innovation, in order to position Malaysia in successfully transforming to an innovation-led economy.

Given that the contribution of R&D workers are generally not-as-easily identifiable and measurable

compared to direct revenue-generating workers in the absence of a perfect intellectual property and

patent regime, this will require direct intervention in altering the overall incentive structure of the

economy, in promoting greater appreciation for innovation. Broadly, this involves incentivising the

knowledge and R&D workers through a series of tax savings incentives, both directly to the R&D

workers, as well as to firms. In terms of institutional implications, this will require greater co-

operations between UNIK and relevant agencies, such as Malaysia Investment and Development

Authority (MIDA), MOF and the various agencies under its umbrella, to formulate specific tax

incentives that are pro-R&D and S&T activities. One specific example may be to provide tax waiver

and exemptions to reward firms that increase the hiring of research workers, which will significantly

alter the current Malaysian business culture and landscape in terms of promoting the roles of

research and innovation.

Lastly, the final and sixth key messages will emphasize on the importance on promoting overall

infrastructure and capacity for innovation, in addition to standard direct R&D supports in the formof research grants. As shown in the chapter’s simulation exercises, direct government expenditure to

R&D supports is not as powerful as when comparing to spending the equal amount of money in

building pro-R&D and S&T infrastructure. Hence, this final recommendation will focus on getting

the “big idea” right, i.e. setting the environment right for innovation. These, broadly, will include

promoting the continued emphasis on investment in “soft” infrastructure, such as state-of-art

modern infrastructure in the form of telecommunication services and ICT equipments, which are



essential in fostering greater level of human capital and knowledge accumulation, as well as

promoting greater research practices across firms and institutions, such as simulating cross-

institutional project collaboration, practice of information sharing and resource mobilisation,

efficient knowledge management, and cross-research citations and alliances.

In short, the six recommendations proposed emphasize on the needs to intervene in terms of

improving resources allocation and fixing the misaligned incentives in innovation activities at the

macro level. This involves long-term commitment in attempting to create and cultivate an eventually

better national landscape and culture for research and innovation.

Matrix of Strategy Recommendations

Recommendations Rationale Institutional

Implications

Timeline

Data analysis-relevant

1 Invest in a high quality and

comprehensive integrated

innovation database

• The need to adopt a multidisciplinary

approach in measuring innovation, in line

with international best practice;

• Timely and transparent monitoring and

tracking mechanism for the NIP.

Establishment of a special

unit within UNIK to co-

operate with MASTIC,

DOSM, MOHE, MOF,

etc. to serve as one-stop

centre for innovation

database.

2 Establishment of

independent research

institution specifically for

innovation agenda

• To facilitate improved empirical studies and

analysis in broad areas that are highly

relevant to the national innovation agenda;

• To serve as leading example in facilitating

greater knowledge and research ecology for

innovation.

Establishment of a special

unit within UNIK with tie-

ups with local institute of

higher learning.

Key policy intervention-relevant

3 Reduces existing labour

market distortions, in terms

of firing cost, with emphasis

on R&D and knowledge-

intensive sector.

• Malaysia has a high level of firing regulation

rigidities and redundancy cost when

compared to its regional peers;• This indirectly increases labour cost for

firms, and therefore oppressing talent

mobility and wages in the R&D sector.

UNIK to engage with

Ministry of Human

Resources in conducting

comprehensive industry

reviews on existing labour

market regulations.



4 Broaden the talent pool

through both the demand

(attracting overseas talents)

and supply (S&T curriculum

reform) channels.

• To tackle the severe skill shortages and

depleting labour quality problems faced by

many industries in Malaysia;

• To create a domestic talent base that is well-

equipped for S&T and R&T developments

in Malaysia.

UNIK to collaborate with

Talent Corporation in

demand strategies; while

assisting MOE in adopting

international best

standards of S&T into

curriculum.

5 Streamline and sharpen fiscal

incentives for innovation and

R&D activities

• To improve overall incentive structure of

the economy, and in promoting greater

appreciation for research and innovation;

• To incentive knowledge and R&D workers,

as well as firms in conducting more R&D,

through a series of tax savings incentives.

UNIK to co-operate and

provide advisory studies to

MIDA and MOF with

respect to fiscal incentives

for R&D and S&T

workers.

6 To sustain commitment and

investments in improving the

overall modern infrastructure

and human capacity forinnovation, in addition to

standard direct R&D

supports in the form of

research grants.

• To set the right environment for

innovation;

• To promote greater collaboration, research

ideas, and information sharing practicesbetween organisations

UNIK to promote

sustained high level of

investments of ICT

infrastructure, greentechnology, as well as

education & trainings, to

both public and private

investment institutions.

1. Knowledge Innovation Growth UNIK Chapter511f EDIT

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