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The Impact on Labour Productivity in Malaysia Industries
Zahariah Sahudin, Saffiah Mohammad Nor
University Technology MARA, Shah Alam, Malaysia
DOI: https://dx.doi.org/10.47772/IJRISS.2025.915EC00759
Received: 20 October 2025; Accepted: 28 October 2025; Published: 17 November 2025
ABSTRACT
Labour productivity is a crucial determinant of economic growth, reflecting how efficiently a nation utilises its
inputs and workforce. In Malaysia, persistent productivity gaps between primary and secondary industries
raised concerns about the effects of capital intensity, intermediate input efficiency, and labour composition.
This study examined the long-run relationship between intermediate input, capital, labour composition (local
and foreign), wages, and labour productivity across Malaysia’s major industries. Using the annual panel data
from 2001 to 2021, the analysis adopted Fully Modified Ordinary Least Squares (FMOLS) estimation
technique to correct endogeneity and serial correlation in cointegrated panels, providing robust long-run
elasticities. The results revealed substantial heterogeneity across industries. In the agriculture industry,
intermediate input of (0.9132, p < 0.01) and capital (0.3454, p = 0.0227) significantly enhanced productivity,
while wages have a negative effect (-0.6978, p = 0.0195), reflecting inefficiencies from low-skilled
employment. The mining and quarrying industry showed productivity driven mainly by intermediate input
(0.9203, p = 0.0249), whereas capital (-0.4169, p = 0.0836) has a negative effect, indicating overcapitalisation
and resource depletion. In the manufacturing industry, the intermediate input of (0.9012, p < 0.01), salary
(0.1313, p < 0.01), and foreign labour (0.0482, p = 0.0403) exhibited significant positive effects,
demonstrating strong complementarities between skilled migrant labour and technology adoption. The
construction industry showed significant positive effects of intermediate input (0.7556, p < 0.01) and capital
(0.2520, p < 0.01), confirming increasing returns to scale through capital deepening and efficient material
utilisation. Overall, the findings confirmed that Malaysia’s long-run productivity growth is driven primarily by
capital accumulation and intermediate input efficiency, consistent with the Solow Growth Model. Policies
promoting technological upgrading, mechanization, digitalization, and skill development are essential to
sustain productivity towards achieving Malaysia’s Vision 2030 aspirations for a high-income, innovation-
based economy.
Keywords: Labour Productivity, MOLS, Intermediate Input, Capital, Foreign Labour, Malaysia
INTRODUCTION
Malaysia, a developing nation, has undergone a remarkable economic transformation from an agriculture-
based economy to a manufacturing- and service-oriented economy since the 1970s. This structural shift has
been central to the country’s development strategy, allowing it to harness comparative advantages, expand
industrial capacity, and diversify sources of growth. Today, the services sector has emerged as the main engine
of national economic growth, contributing over 50% of Malaysia’s gross domestic product (GDP) in 2022,
followed by manufacturing at 22.1%, mining at 7.1%, and agriculture at 6.6% (Department of Statistics
Malaysia, 2022). Sub-sectors of the services industry include transportation and storage; food, beverage, and
accommodation; wholesale and retail trade including motor vehicle repairs; administration and support
services; personal services; and private health and social work. These sectors collectively generate substantial
employment opportunities, ranging from unskilled to highly skilled labour, and simultaneously support
domestic consumption and downstream industrial activities (Azer et al., 2018; Setiawan, Wakhyuni, & Siregar,
2020).
Employment generation in Malaysia had consistently risen across all industries. By 2021, there were
approximately 16 million employees, including 2.4 million foreign workers (Department of Statistics
Malaysia, 2022). Table 1 illustrates the steady growth in local employment across decades and the rising
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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XV October 2025 | Special Issue on Economics
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participation of foreign workers since 1985. It highlighted the upward trend, emphasising the significant role
of foreign labour in supporting various industries. The presence of foreign workers has been instrumental in
supplementing domestic labour shortages, particularly in labour-intensive sectors such as construction,
agriculture, and selected manufacturing segments. However, this growing reliance on foreign labour also
introduced uncertainties in productivity dynamics, as differences in skill levels, experience, and integration
with domestic labour can affect efficiency and output per worker.
Table 1: Increasing Trend of Employment in all sector and Foreign Labour participation, from year 1986 to
2021
Years
1986
1996
2006
2016
2021
Local Labour
Employment
5,558.7
7,499.1
9,255.6
11,958.4
13,414.8
Foreign Workers
202.0
900.2
1,019.8
2,205.3
2,673.1
Source: Department of Statistics Malaysia, 2022
Despite Malaysia’s industrial growth, the pressures of globalisation, liberalisation, and rapid technological
change have created challenges for sustaining labour productivity across sectors (Zhao et al., 2023; Ayhan &
Elal, 2023). Researchers have identified several productivity-related constraints, including limited skills in
exploring new markets, inefficiencies in production processes, and challenges in improving quality and
operational efficiency (Tadesse et al., 2021; Birhanu et al., 2021; Bold et al., 2022; Sahudin et al., 2014; Nor et
al., 2023). Additional barriers include rising raw material costs, high initial capital requirements, limited
government support, labour shortages, and intense global competition. Industries also faced operational
inefficiencies, such as project delays, fragmentation, hazards, and waste generation (Smol et al., 2021). Rapid
industrialisation has further intensified environmental concerns, with increased waste and pollution affecting
sustainability in Malaysia and other developing nations (Aziman et al., 2023; Ehigiamusoe et al., 2021).
While the manufacturing and services sectors have shown relatively high productivity, sectors such as
construction and agriculture remained less productive. In 2021, construction labour productivity was
RM36,669 per worker; lower than agriculture (RM52,786), services (RM84,716), and manufacturing
(RM128,807) (Department of Statistics Malaysia, 2022). Table 2 illustrates these sectoral productivity trends,
highlighting the declining trajectory of construction productivity between 2019 and 2021. These disparities
underscored the importance of understanding how input factors such as, capital, intermediate input, local
labour, foreign labour, and wage, affecting productivity differently across industries.
Table 2 Sector Labour Productivity, 1985-2021 (‘000)
Table 2: Decreasing Trend of Labour Productivity for each Sector, from year 2019 to 2021
Source: Department of Statistics Malaysia, 2022
Years
2019
2020
2021
Construction
45,421
38,322
36,669
Agriculture
54,225
53,114
52,786
Manufacturing
123,881
120,645
128,807
Services
89,568
84,336
84,716
Within the framework of the Solow Growth Model, long-term economic growth is driven by the accumulation
of capital, expansion of labour, and improvements in production efficiency. In this context, the variation in
labour productivity across Malaysian industries can be interpreted through differences in capital intensity,
workforce composition, and the effective utilisation of intermediate inputs. Capital deepening is essential to
enhance the marginal productivity of labour, while an optimal mix of local and foreign labour ensured that
labour shortages are mitigated without diminishing overall efficiency. The Solow framework thus provided a
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theoretical lens to evaluate the long-run determinants of labour productivity and the potential impact of
structural and compositional factors in the Malaysian context.
Given the growing presence of foreign labour in Malaysia’s primary and secondary industries, the relationship
between workforce composition and productivity has become a central concern for policymakers and
researchers. While foreign workers alleviated labour shortages, excessive reliance on low-skilled migrants
might reduce incentives for firms to invest in technological upgrades, automation, and skills development.
Consequently, productivity gains from capital and intermediate inputs might not be fully realised if labour
composition is suboptimal. This uncertainty highlighted the need to examine the nuanced interplay between
capital, intermediate inputs, local labour, and foreign labour in shaping long-run productivity outcomes.
The importance of this issue extended beyond economic growth alone. Enhancing labour productivity
contributed directly to higher income levels, reduced unemployment, and improved socio-economic well-
being. By identifying the factors that drive or constrain productivity, policymakers can implement targeted
interventions to optimise labour utilisation, capital investment, and input efficiency. Moreover, understanding
industry-specific productivity dynamics supports Malaysia’s national development agenda, including Vision
2030 and the Twelfth Malaysia Plan, which emphasised sustainable growth, industrial competitiveness, and
employment creation.
This study addressed these critical issues by investigating the long-run effects of five key determinants such as
intermediate input, capital, local labour, foreign labour, and wages on labour productivity across Malaysia’s
primary and secondary industries. Employing the Fully Modified Ordinary Least Squares (FMOLS) approach,
the analysis utilised panel data from 2001 to 2021, combining cross-sectional and time-series observations to
generate robust estimates of long-run relationships. By focusing on industries with a higher proportion of
foreign labour, this study explicitly examined whether workforce composition significantly affects labour
productivity and how other input factors interact with this effect.
In detailing the research methodology, this study departed from prior literature that predominantly examined
single industries or short-run relationships. By applying FMOLS to the industry-specific panel data, this study
captured the long-run elasticities of labour productivity with respect to each input factor. The results are
expected to show differential impacts of local and foreign labour, highlighting which industries gain or lose
productivity advantages from workforce composition. Table 1 and Table 2 provide empirical context for these
analyses by illustrating employment trends and historical productivity patterns across sectors.
The novelty of this research lies in its comprehensive industry-level examination of labour productivity
determinants in Malaysia using a long-run estimation framework. While previous studies often focused on
aggregate productivity trends or single-sector analyses, this study quantified the contributions of capital,
intermediate input, and labour composition across multiple industries simultaneously. By doing so, it bridged
gaps in the literature regarding the role of foreign labour in shaping productivity dynamics within primary and
secondary sectors. The findings are anticipated to offer actionable insights for policymakers, enabling them to
formulate productivity-enhancing strategies tailored to each sector, including targeted investment in capital,
skills development for local labour, and optimal management of foreign workforce participation.
Finally, the roadmap of the paper is as follows: Section 2 presents the review of relevant literature on labour
productivity determinants in Malaysia, contextualised within the Solow Growth Model framework. Section 3
outlines the methodology, data sources, and FMOLS estimation technique. Section 4 provides empirical results
and discussion, followed by Section 5 which concludes the study, highlighting the policy implications and
recommendations for Malaysia’s industrial development and workforce management.
LITERATURE REVIEW
Labour productivity has long been recognised as a central driver of economic growth, efficiency, and
competitiveness in both developed and developing economies. In Malaysia, the performance of labour
productivity has become a critical policy concern, especially within the primary and secondary industries
where capital accumulation, intermediate inputs, and the composition of labour jointly determined the rate of
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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output growth. The productivity performance of Malaysian industries has been uneven across sectors, with
agriculture, manufacturing, and construction lagging behind service-based activities in recent years. The Solow
Growth Model provides a relevant theoretical framework to understand these variations, positing that capital
deepening and labour quality improvements are essential for long-term output growth. However, productivity
in Malaysia remains heavily influenced by the structure of its inputs; particularly capital, intermediate inputs,
foreign labour dependence, and wage disparities which collectively shaped efficiency, competitiveness, and
sustainable industrial transformation.
Intermediate inputs have become an increasingly important component in explaining variations of productivity,
especially in industries where technological upgrading and resource intensity play a key role. They include
goods and services consumed during production, such as raw materials, energy, and services that are not part
of fixed capital assets. In the Malaysian context, the value-added contribution of intermediate inputs has been
shown to vary significantly across industries, reflecting different technological intensities and production
linkages. Recent studies highlighted that the effective utilisation of intermediate inputs can enhance
productivity by improving resource allocation, enabling economies of scale, and fostering inter-industry
spillovers (Gilles et al., 2022).
In agriculture, for example, productivity has been increasingly driven by the efficient use of fertilizers,
machinery services, and technological inputs that function as intermediate goods (Priyanto et al., 2023). The
Malaysian Ministry of Agriculture reported that improvements in agricultural input management, such as seed
quality and irrigation systems, contributed directly to rising yields per worker between 2020 and 2023.
Similarly, manufacturing sectors that rely on high-quality imported inputs, particularly in electronics and
petrochemicals have achieved faster productivity growth than those dependent on locally sourced, low-value
materials (Khanna & Sharma, 2025). However, industries with weaker linkages to upstream suppliers faced
input bottlenecks that constrained output efficiency.
The relationship between intermediate inputs and output growth also embodies the Solowian mechanism of
capital deepening through technological absorption. As intermediate goods often embody new technologies,
their use contributes indirectly to the accumulation of productive capital and labour efficiency. In Malaysia,
this dynamic is most apparent in the manufacturing sector, where intermediate imports served as channels for
technology diffusion (Lee et al., 2021). Nevertheless, the increasing reliance on imported intermediate inputs
raises concerns about domestic value retention and long-term competitiveness. The literature increasingly calls
for a balance between import dependence and local input development to ensure sustainable productivity gains
across industries (Pane & Patunru, 2022).
Malaysia’s industrial productivity landscape is deeply intertwined with its labour composition, particularly the
balance between local and foreign workers. Foreign labour played a significant role in key industries such as
agriculture, construction, and manufacturing, yet its impact on productivity had been both positive and
negative across studies. The influx of foreign workers had been justified to address domestic labour shortages
and sustain production, especially in low-skilled, labour-intensive sectors (Brucker Juricic et al., 2021).
Nevertheless, the long-term implications for productivity depend on skill levels, technological adaptation, and
management practices within firms.
Empirical evidence suggested that while foreign workers contributed to maintaining output levels, their
concentration in low-skill tasks tends to suppress productivity growth when they were not accompanied by
technological upgrading or human capital development (Tajuddin et al., 2023). For instance, Malaysia’s
construction sector that relied heavily on foreign labour had been stagnated in labour productivity despite the
rising output volumes, which largely due to the persistence of manual work and limited mechanisation (Nor et
al., 2023). Similarly, in the manufacturing industry, overreliance on foreign low-skilled workers had been
linked to the declining capital-to-labour ratios, indicating a slowdown in productivity-enhancing investment
(Tajuddin et al., 2023).
Conversely, sectors that had effectively integrated foreign workers through skill-based employment and
training recorded productivity improvements. For instance, in export-oriented manufacturing industries such as
electronics and electrical products, selective recruitment of semi-skilled foreign workers had supported the
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scaling of production and improved labour utilisation efficiency (Goldar, 2023). Moreover, the diversification
of Malaysia’s workforce had spurred the transfer of technical knowledge and international work practices,
though this remained concentrated in high-performing firms. A study by Manoharan et al. (2024) found that
firms investing in joint skill training programmes for both local and foreign employees reported higher labour
productivity compared to firms relying on segmented labour structures.
The Solow Growth Model underlined the importance of labour quality as a driver of output per worker.
Malaysia’s policy challenge, therefore, lies not merely in balancing foreign labour inflows but in enhancing the
productivity capacity of its workforce through human capital investments. The Twelfth Malaysia Plan (2021–
2025) emphasised skill upgrading, mechanisation, and productivity-linked wages to reduce the structural
dependence on low-skilled migrant labour. However, implementation gaps persisted across industries,
especially in agriculture and construction, where technological diffusion and training remain limited.
Capital accumulation had traditionally been regarded as the central determinant of productivity growth is
consistent with the Solowian theory, which emphasised on capital deepening. In Malaysia’s primary and
secondary industries, capital formation continued to influence output through the acquisition of machinery,
automation, and infrastructure that enhanced production efficiency. Recent research indicated that the
manufacturing and construction sectors experienced notable capital deepening between 2020 and 2024,
primarily driven by digitalisation initiatives and public investment programmes (World Bank Malaysia, 2024).
Investment in capital equipment, particularly automation and digital tools, had significantly boosted
productivity in manufacturing industries such as automotive, electrical and electronics, along with food
processing (Li et al., 2022). For example, automation has enabled firms to increase production capacity while
maintaining workforce stability, leading to productivity gains, which is consistent with the Solow framework.
In the construction sector, however, capital intensity remained relatively low due to slow adoption of
mechanisation and Building Information Modelling (BIM) technologies (Gharaibeh et al., 2024).
The role of capital in agriculture had also evolved. Mechanisation and modern irrigation systems had replaced
traditional labour-intensive practices, contributing to higher yield and efficiency per hectare (Gebiso et al.,
2024). These developments demonstrated how capital investment complemented labour, enhancing its
marginal productivity. Nevertheless, disparities persisted between industries: capital formation is heavily
concentrated in urban-based manufacturing and infrastructure projects, while rural-based industries lagged in
capital accumulation (Goodfellow, 2020). Such imbalances hindered balanced productivity growth across the
economy and contributed to structural dualism between high- and low-productivity sectors.
The relationship between wages and productivity had been a recurring theme in productivity literature and
economic policy. In Solowian sense, wages are the reflection of marginal labour productivity under
competitive conditions. However, in practice, wage dynamics are influenced by institutional settings, labour
market segmentation, and bargaining power asymmetries. Malaysia’s wage–productivity relationship has
evolved amid rising living costs, automation pressures, and foreign labour inflows.
Empirical studies indicated a mixed linkage between wage growth and productivity improvement. Recent
research by Ibrahim and Putit (2020) revealed that wage increases in Malaysia have lagged productivity gains
in several manufacturing subsectors, suggesting a decoupling effect that undermined labour motivation and
innovation. Similarly, wage disparities between foreign and local workers persisted, with local workers
generally earned higher wages even in similar occupations (Storm, 2022). Such wage segmentation could
weaken overall workforce morale and impede productivity enhancement.
On the other hand, productivity-linked wage systems (PLWS), promoted by the Malaysian Productivity
Corporation (MPC), had been increasingly implemented across firms to align remuneration with performance
outcomes. Studies showed that firms adopted PLWS have experienced productivity improvements averaging
8–10% over two years (MPC, 2023). This demonstrated that appropriate wage structures can strengthen the
incentive mechanism for efficiency improvements. However, wage growth unaccompanied by productivity
increases can lead to inflationary pressures and loss of international competitiveness, especially in export-
oriented industries (Nadoveza, 2025). Therefore, wage policies must be balanced to sustain both equity and
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efficiency within the broader growth framework.
The reviewed literature underscored the complex interdependencies between intermediate inputs, labour
composition, capital investment, and wages in determining productivity within Malaysia’s primary and
secondary industries. The Solow Growth Model provides a coherent framework for understanding these
relationships by highlighting capital accumulation and labour efficiency as the primary engines of long-term
growth. However, Malaysia’s case illustrated that productivity performance did not solely dependent on the
quantity of inputs, but also its quality, allocation efficiency, and technological integration.
The post-pandemic era had further highlighted the urgency of addressing productivity challenges through
strategic rebalancing of input structures. Overreliance on foreign labour had constrained technological
upgrading in several sectors, while insufficient domestic input linkages had weakened supply chain resilience.
Moving forward, policy interventions should focus on enhancing capital formation, promoting intermediate
input innovation, and strengthening labour quality through targeted training and digital skill development.
Furthermore, there is a pressing need to align industrial and human capital policies to support inclusive
productivity growth. Increasing automation and digitalisation must be complemented with reskilling
programmes to prevent technological displacement. Similarly, wage reforms are linked to productivity
outcomes, promoting more equitable income distribution while preserving competitiveness. Collectively, these
measures would enable Malaysia to progress toward its aspiration of becoming a high-income, innovation-
driven economy by 2030.
METHODOLOGY
THEORETICAL FRAMEWORK
This study adopted the Solow growth model as the fundamental theoretical framework to examine the
determinants of labour productivity across Malaysia’s primary and secondary industries. The Solow model,
introduced by Solow (1956), established that economic growth is primarily driven by capital accumulation,
labour input, and technological progress. In this context, labour productivity reflects the efficiency, in which
labour transforms inputs into output. Within the Solow framework, productivity growth is not solely dependent
on labour or capital, but also on how efficiently these inputs are combined with other factors such as
intermediate inputs and wages.
The model assumed a Cobb-Douglas production function, which is widely applied in productivity and growth
studies for its simplicity, flexibility, and empirical robustness. This functional form allowed the estimation of
output elasticities with respect to different production inputs, thereby enabling the identification of the returns
to scale and relative contribution of each input to productivity. Following Noor et al. (2011) and Nor et al.
(2023), the basic production function is specified as:
=A

(Equation 1)
where
denotes total output,
represents capital,
is labour, and 
refers to intermediate inputs used in
the production process. A represents total factor productivity, which is assumed to be captured indirectly
through the efficiency of the inputs used in this study.
To focus the analysis on labour productivity, each variable is divided by the total number of workers,
transforming the function into a labour productivity equation. Moreover, to better capture the structural
composition of Malaysia’s labour market, labour (
) is disaggregated into local labour and foreign labour.
This decomposition allows an assessment of the distinct roles played by domestic and migrant labour forces,
which is particularly relevant for Malaysia’s economic structure, where foreign workers form a significant
proportion of the workforce in agriculture, manufacturing, and construction (Hii & Lau, 2025).
The augmented functional form, expressed in logarithmic form to linearise the relationships and stabilise the
variance, is as follows:
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Ln 

t=α+


+
ln 

+
ln 

+
 

+
ln

+

(Equation 2)
where:
i=1, …..,N represents the industry (agriculture, mining, manufacturing, construction, etc.),
t=1,…, T denotes the time period (2001–2021),


= labour productivity, measured as the ratio of GDP to total employment,

= capital intensity, calculated as fixed assets per worker,


= local labour share (number of local workers divided by total workers),


= foreign labour share (number of foreign workers divided by total workers),


= intermediate input per worker,

= wage rate per worker,

= error term capturing unobserved factors.
The transformation of all variables into natural logarithms enables the coefficients to be interpreted as
elasticities, indicating the percentage change in labour productivity in response to a one percent change in each
determinant. This specification is consistent with the Solow growth framework, where changes in output per
worker are determined by proportional changes in input factors and technological efficiency (Ross, McGregor
& Swales, 2024).
Data Description and Sources
The study employed panel data consisting of four major industries in Malaysia’s primary and secondary
sectors: the agriculture, mining and quarrying, manufacturing, and construction, covering the period of 2001 to
2021.These sectors were selected because they represented the key productive pillars of Malaysia’s economy
and collectively account for a substantial share of employment, foreign labour absorption, and capital
formation. The data were obtained from the Department of Statistics Malaysia (DOSM), primarily from the
Annual Economic Survey, Labour Force Survey, and the National Accounts Database and compiled in annual
basis, ensuring consistency across industries and time. The data were further transformed into logarithmic form
to facilitate econometric estimation and mitigate issues of heteroscedasticity.
A brief description of each variable is provided below:
Labour Productivity (LPR): Calculated as the ratio of industry value added (in constant prices) to total
employment in that industry. This measure reflects the efficiency of labour input in generating output
and is consistent with productivity metrics adopted by OECD and DOSM.
Capital (K): Represented by fixed assets divided by total employment. This ratio serves as a proxy for
capital intensity and reflects the extent to which physical capital contributes to output generation.
Local Labour (LL): Measured as the proportion of local workers to total employment, capturing the
relative share of Malaysian nationals in each industry’s workforce.
Foreign Labour (FL): Defined as the proportion of foreign workers to total employment. This variable
captured the impact of migrant labour on productivity and reflects Malaysia’s dependence on foreign
manpower.
Intermediate Input (INT): Includes materials, energy, and services used in production, divided by total
employment. It reflects the role of supply chain efficiency and resource utilisation in productivity
performance.
Wages (W): Represented by total compensation paid to employees divided by the total number of
workers. This variable captures the wage–productivity linkage and labour market efficiency.
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Estimation Technique: FMOLS Approach
The study applied the Fully Modified Ordinary Least Squares (FMOLS) estimation technique to assess the
long-run relationships among the variables. The FMOLS estimator, developed by Phillips and Hansen (1990),
is particularly suitable for panel data involving non-stationary series that are cointegrated, in which variables
that move together in the long run despite being individually non-stationary.
FMOLS corrected both serial correlation and endogeneity bias, which are the common problems in
conventional OLS estimation when dealing with integrated variables. This made it superior for long-run
estimations in productivity and growth studies (Ahmed et al., 2016; Aziz & Othman, 2019).
The panel FMOLS estimation is conducted in two forms:
1. Group-Mean FMOLS, which estimated an average long-run relationship across all industries.
2. Individual FMOLS, which separately estimated the long-run elasticities for each industry, thereby
allowing for industry-specific heterogeneity in the productivity determinants.
Before applying the FMOLS estimator, several preliminary tests were conducted to ensure data suitability and
robustness:
Unit Root Test
The first step involved testing the stationarity of each variable using the ADF-Fisher Chi-square test for panel
unit roots. This test combined the individual Augmented Dickey-Fuller (ADF) tests for each industry to
determine whether the variables are stationary in the level or become stationary after first differencing.
Stationarity ensures that the time series properties of the variables do not produce spurious regression results.
Cointegration Test
Once stationarity is confirmed, the Kao panel cointegration test was applied to verify the existence of a long-
run equilibrium relationship among the variables. The presence of cointegration implied that the variables
shared a stable long-term association, which is an essential condition for applying FMOLS.
The Kao test, based on residual analysis, tested the null hypothesis of no cointegration against the alternative
of cointegration. A rejection of the null supports the validity of a long-run relationship between labour
productivity, capital, intermediate input, local and foreign labour, and wages.
Estimation and Interpretation
Upon confirming cointegration, the FMOLS estimation was carried out using both pooled and individual
industry-specific estimators. The coefficients obtained represent long-run elasticities, capturing the percentage
change in labour productivity resulting from a one percent change in each explanatory variable.
Positive coefficients for capital, intermediate input, and wages indicated that increases in these factors would
enhance labour productivity, which is consistent with the Solow growth framework. Conversely, negative
coefficients for foreign labour might suggest diminishing returns to productivity due to over-reliance on low-
skilled migrant workers, a pattern documented in previous studies (Huerta de Soto, 2024).
Analytical Scope and Justification
This methodological framework offers several advantages. First, by employing industry-level panel data, the
analysis captured both cross-sectional differences among industries and temporal dynamics over the two-
decade period. This dual dimension enhanced the statistical efficiency and explanatory power of the estimates
(Baltagi, 2021).
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Second, the inclusion of foreign labour and local labour as distinct variables reflected the dual labour market
structure in Malaysia. Given that industries such as agriculture and construction are heavily dependent on
migrant workers, separating this labour types provided critical policy insights, whether such dependence
promotes or hinders long-term productivity growth.
Third, the FMOLS estimator effectively corrected the potential endogeneity arises when inputs such as capital
or wages are simultaneously determined with output, ensuring unbiased and consistent long-run parameter
estimates.
Finally, this methodological approach is aligned with Malaysia’s developmental context under Vision 2030,
which emphasised productivity enhancement through efficient resource allocation, technological upgrading,
and labour market reform. The study’s empirical design thus provided evidence-based insights to formulate
policy aimed for achieving sustainable industrial productivity growth.
Summary of Methodological Steps
1. Model Specification: The study commenced with the Solow-based Cobb-Douglas production function,
reformulated into a labour productivity model by incorporating key inputs: capital, intermediate input,
local and foreign labour, and wages.
2. Data Collection: Annual industry-level data (2001–2021) were obtained from the Department of
Statistics Malaysia.
3. Transformation: All variables converted to logarithmic form to interpret coefficients as elasticities.
4. Unit Root Testing: ADF-Fisher test applied to verify stationarity.
5. Cointegration Testing: Kao panel cointegration test was conducted to confirm long-run relationships.
6. Estimation: FMOLS was used to estimate long-run elasticities both for pooled and industry-specific
models.
7. Interpretation: Results were analysed to identify the most influential factors affecting labour
productivity across industries.
8. This step-by-step framework ensures the methodological rigor and robustness of the analysis, enabling a
comprehensive assessment of the long-run determinants of labour productivity in Malaysia’s primary and
secondary industries.
RESULTS AND DISCUSSION
Unit Root Test
Table 1 presents the results of the Fisher-ADF unit root tests for all variables at both levels and first
differences.
Table 1: Unit Root Test at Levels and at First Difference
Variables
Fisher-ADF at Levels
Fisher-ADF at First Difference
Intercepts
Intercepts and
Trends
Intercepts
Intercepts and
Trends
Labour Productivity
9.8129
16.8939**
38.4121***
27.3998***
(0.2784)
(0.0312)
(0.0000)
(0.0006)
Intermediate Input
7.6804
13.1131
33.3120***
21.7412***
(0.4653)
(0.1080)
(0.0001)
(0.0054)
Local Labour
13.9691
25.0375***
35.8673***
27.4603***
(0.0826)
(0.0015)
(0.0000)
(0.0006)
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Foreign Labour
14.1169
25.4895***
38.8877***
31.9749***
(0.0788)
(0.0013)
(0.0000)
(0.0001)
Fixed Assets
4.8625
5.4571
47.6097***
32.3919***
(0.7722)
(0.7078)
(0.0000)
(0.0001)
Salary
4.1273
14.0272
57.3539***
45.1104***
(0.8455)
(0.0811)
(0.0000)
(0.0000)
Note: statistics are given in parenthetic
Notes: *** significant at level 1%, ** significant at level 5%, and * significant at level 10%
Source: Author’s computation based on EViews9
The results in Table 1 revealed that most variables: labour productivity, intermediate input, local labour,
foreign labour, fixed assets (capital), and salary are non-stationary at levels but become stationary after first
differencing. This pattern indicated that each series was integrated of order one, I(1). Labour productivity,
local labour, and foreign labour appeared to show some stationarity at the level with intercept and trend terms;
however, their statistical significance is not consistently strong enough to reject the null hypothesis of a unit
root across all model specifications.
This finding satisfied the precondition for panel cointegration analysis, which required that variables exhibit
non-stationarity in levels but stationarity in first differences. The result indicated that these macroeconomic
and industrial variables evolved over time but shared stable long-run relationships, aligning with the logic of
the Solow Growth Model. According to Solow (1956), economies converged to a steady-state equilibrium
where growth in output per worker depends on capital accumulation, labour inputs, and technological progress.
Hence, the existence of unit roots in levels but stationarity in first differences implied that productivity growth
and factor accumulation evolved dynamically but maintained a long-run equilibrium path.
These findings are consistent with earlier Malaysian productivity studies, such as Noor et al. (2011), who
identified similar integration orders for labour productivity and input factors. More recent empirical work also
confirmed that macroeconomic productivity indicators in Malaysia tend to follow the I(1) process (Nor et al.,
2023), suggesting structural persistence and adjustment mechanisms within industrial sectors. The presence of
I(1) variables reflected the persistence of sectoral shocks such as wage changes, labour migration, or capital
investments, which influenced productivity trajectories in both primary and secondary industries.
Cointegration Result
To establish whether these variables share a long-run equilibrium relationship, the Kao (1999) residual-based
cointegration test was conducted.
Table 2: Kao Test Regression
Series
ADF-Statistics
Statistics
Prob
LLPR = LIN + LLOC + LFOR + LSAL+ LCAP
-3.0389***
0.0012
Notes: ***, ** and * imply significance level at 1%, 5% and 10% respectively.
Source: Author’s computation based on EViews9
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The test result in Table 2 indicated that the null hypothesis of no cointegration is rejected at the 1%
significance level, confirming that labour productivity, intermediate inputs, local labour, foreign labour, salary,
and capital are cointegrated in the long run. This finding signified that changes in factor inputs and wages were
not independent of productivity growth in Malaysia’s industrial sectors over time.
In the context of the Solow Growth Model, this outcome implied that Malaysia’s industries exhibited steady-
state convergence, where output per worker is jointly determined by capital intensity and effective labour. The
long-run relationship further supported the hypothesis that capital accumulation, factor efficiency, and labour
composition jointly determined productivity growth paths. Over time, industries adjusted toward equilibrium
levels of productivity, consistent with their structural factor endowments and technological capabilities.
Empirical evidence from Malaysian studies corroborated these findings. For instance, (Dehdasht et al.,2022)
found significant cointegration between capital and productivity in Malaysia’s construction industry, while
Shahzad & Miao (2025) highlighted the long-run link between the capital–labour ratio and manufacturing
productivity. The Kao test result therefore, confirmed that a long-run equilibrium mechanism governed the
dynamics of productivity across Malaysia’s primary and secondary industries, reinforcing the theoretical
validity of applying the Solow framework in this study.
Fully Modified OLS (FMOLS) Estimation
The Fully Modified Ordinary Least Squares (FMOLS) estimator was applied to examine the long-run
coefficients of the relationship between labour productivity and its determinants for each industry, as shown in
Table 3.
Table 3: Individual Fully Modified OLS Result
Variable
Intermediate Input
Capital
Salary
Agriculture Industries
0.9132***
(0.0008)
0.3454
(0.0227)
-0.6978
(0.0195)
Mining And Quarrying
Industries
0.9203** (0.0249)
-0.4169
(0.0836)
-0.3499
(0.3798)
Manufacturing Industries
0.9012***
(0.0000)
-0.0199
(0.0860)
0.1313
(0.0000)
Construction Industries
0.7556***
(0.0000)
0.2520
(0.0000)
0.0344
(0.5308)
Notes: ***, ** and * imply significance level at 1%, 5% and 10% respectively.
Source: Author’s computation based on EViews9 (2024)
The FMOLS estimator, proposed by Pedroni (2001), corrected serial correlation and endogeneity inherent in
cointegrated systems, providing reliable long-run parameter estimates even in small samples. This approach is
particularly suitable for Malaysia’s industrial data, which exhibited cross-sectoral heterogeneity. The results in
Table 3 revealed interesting and sector-specific dynamics between productivity and its determinants.
Agriculture Industry
In the agriculture industry, intermediate input (0.9132) showed a strong and statistically significant positive
effect on labour productivity at 1% level. This suggested that improvements in agricultural inputs such as
fertilizers, machinery, irrigation, and modern farming technology significantly enhanced productivity. This
finding is aligned with Kamaruddin & Abd. Rashid (2025), who found that mechanisation and modern input
adoption could drive productivity growth in Malaysian agriculture.
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However, capital also showed a positive and significant relationship (0.3454, p = 0.0227), implying that higher
levels of fixed asset investment contributed positively to long-run productivity gains. The capital-deepening
process is central to the Solow model, which postulated that increasing the capital-to-labour ratio enhanced
output per worker until diminishing returns set in. This evidence supported Malaysia’s ongoing agricultural
modernisation initiatives aimed at mechanising rural production to overcome labour shortages and improve
efficiency.
Conversely, local labour and foreign labour both showed insignificant effects, while salary has a significant
negative relationship (-0.6978, p = 0.0195). The negative wage coefficient could reflect rigid wage structures
and overemployment in lower-skilled agricultural jobs, leading to inefficiencies in productivity. Study by Feist
(2024) highlighted that sectors which heavily reliant on low-skilled labour often faced wage productivity
imbalances, reducing overall efficiency. Hence, while capital and intermediate inputs enhanced productivity,
labour composition and wage inefficiency remained as structural challenges in Malaysia’s agricultural sector.
Mining and Quarrying Industry
In the mining and quarrying sector, intermediate inputs also showed a significant positive impact (0.9203, p =
0.0249) on productivity, consistent with the global findings: higher input quality such as modern extraction
tools and refined energy inputs drives output efficiency (Kamaruddin & Abd. Rashid, 2025). However, both
local labour and foreign labour displayed insignificant relationships, and capital has a negative coefficient (-
0.4169, p = 0.0836), indicating possible overcapitalisation or inefficiency in asset utilisation.
This pattern might stem from the declining resource quality and rising extraction costs, which reduced the
productivity returns on capital investment. Moreover, salary exhibited a negative, though insignificant
relationship, suggesting that wage adjustments have limited impact on the output growth. Overall, the results
implied that productivity in Malaysia’s mining sector is primarily driven by technological efficiency in
intermediate inputs rather than labour or capital expansion, aligning with Solow’s notion that technological
progress is the key driver of long-run growth beyond capital accumulation.
Manufacturing Industry
In the manufacturing sector, both the intermediate input (0.9012, p = 0.0000) and salary (0.1313, p = 0.0000)
had strong positive and significant relationships with labour productivity, while foreign labour (0.0482, p =
0.0403) was also significant at the 5% level. The positive relationship between foreign labour and productivity
suggested that skilled or semi-skilled migrant workers played a complementary role in the manufacturing
process, supporting production continuity and reducing labour shortages. This finding supported Hii and Lau
(2025), who argued that foreign workers enhanced output efficiency in certain high-demand industries.
However, capital exhibited a slightly negative and insignificant relationship (-0.0199, p = 0.0860), implying
that the sector might already be operating near capital saturation, with diminishing returns on further fixed
asset investments. This result corresponded with the Solow model’s steady-state outcome, where increases in
capital have limited impact on productivity without technological advancement. The positive relationship
between salary and productivity might reflect higher labour motivation and retention in firms that aligned wage
growth with productivity gains (Ibrahim and Putit, 2020)
Construction Industry
In the construction sector, intermediate input (0.7556, p = 0.0000) and capital (0.2520, p = 0.0000) have
significant positive relationships with labour productivity, indicating that both material efficiency and
equipment investment played critical roles in output performance. These results are aligned with Gharaibeh et
al. (2024), who emphasised that efficient material usage and the adoption of modern technologies such as
Building Information Modelling (BIM) enhanced productivity in construction.
However, local labour and foreign labour showed negative but insignificant effects, implying that the sector
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remains labour-intensive yet struggles with skill mismatches and inefficiencies. Salary also had an
insignificant positive coefficient (0.0344), suggesting a weak wage with productivity alignment. Consistent
with the Solow framework, this sector demonstrated partial capital deepening but was constrained by human
capital quality and labour composition inefficiencies.
Panel Group FMOLS Estimation
To capture the overall long-run relationships across all industries, a Panel Group FMOLS estimation was
conducted, as shown in Table 4.
Table 4: Panel Group Fully Modified OLS Result
Variable
Intermediate Input
Local Labour
Foreign
Labour
Capital
Salary
Coefficient
0.4451*** (0.0000)
-0.9768
(0.0295)
-0.6289
(0.0000)
0.3287
(0.0000)
0.1883
(0.1317)
Notes: ***, ** and * imply significance level at 1%, 5% and 10% respectively.
Source: Author’s computation based on EViews9 (2024)
The results showed that intermediate input (0.4451, p = 0.0000) and capital (0.3287, p = 0.0000) have
significant positive effects on labour productivity across all industries. These findings affirmed that material
input efficiency and capital investment remained the most critical determinants of long-term productivity in
Malaysia’s industrial sectors. The results were consistent with the Solow Growth Model’s prediction where
increases in capital and technological efficiency (proxied by intermediate input usage) would drive higher
steady-state output per worker.
On the other hand, local labour (-0.9768, p = 0.0295) and foreign labour (-0.6289, p = 0.0000) exhibited
significant negative effects on productivity. This suggested that excessive reliance on both categories of labour
especially unskilled and low-productivity workers might hinder output growth. Nor et al. (2023) reported
similar findings in Malaysia’s construction sector, where foreign worker dependency reduced marginal
productivity of labour due to the limited skill transfer and over-saturation of low-cost labour. This result
emphasised the need for Malaysia to transform towards a more capital-intensive and skill-based production
structure.
Finally, salary (0.1883, p = 0.1317) showed a positive but insignificant effect, implying that wage adjustments
alone did not directly drive productivity unless accompanied by improvements in human capital and
technological adoption. This outcome reinforced Ibrahim and Putit (2020) argument that Malaysia’s wage
productivity decoupling limits the effectiveness of wage increases in enhancing competitiveness.
DISCUSSION AND THEORETICAL IMPLICATIONS
The overall findings provided robust evidence that intermediate input efficiency and capital accumulation are
the main long-run drivers of labour productivity in Malaysia’s primary and secondary industries. This outcome
aligned closely with the Solow Growth Model, which posited that economic growth depends on the
accumulation of physical capital and the efficiency of production inputs, moderated by diminishing returns.
Over time, total productivity growth is sustained by technological progress and improved factor utilisation.
The insignificant or negative coefficients on labour variables highlighted the structural weaknesses in
Malaysia’s labour market, particularly overdependence on low-skilled workers and limited human capital
formation. Such trends constrained technological absorption and productivity growth, reflecting the "middle-
income trap" dynamics as described by Simpasa (2024). The findings underscored the need for upskilling,
automation, and innovation-driven strategies to complement capital investment and material efficiency.
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In the agricultural and construction sectors, the positive capital coefficients indicated the strong capital
intensity and potential for modernisation, while manufacturing demonstrated foreign labour complementarity
and salary-driven motivation effects. The mining sector, however, revealed limited productivity response to
capital and labour inputs, suggesting structural exhaustion and declining resource-based growth potential.
From a policy standpoint, these results implied that Malaysia’s long-term productivity growth required a dual
focus on capital deepening and technological upgrading. Investments in digitalisation, mechanisation, and
process innovation especially in manufacturing and agriculture sectors would enhance intermediate input
efficiency and capital productivity. Simultaneously, labour market policies should prioritise skill upgrading,
targeted foreign labour management, and wage structures that incentivise productivity improvements.
CONCLUSION
Fundamentally, the main objective of this study is to determine the factors that affect labour productivity
across four major industries in Malaysia, namely the agriculture, mining and quarrying, manufacturing, and
construction. These sectors were selected because they represented the backbone of Malaysia’s primary and
secondary economic structure, each contributing differently to the nation’s output, employment, and long-term
development trajectory (Department of Statistics Malaysia [DOSM], 2023). This study considered five main
explanatory variables that theoretically and empirically influenced labour productivity: intermediate input,
local labour, foreign labour, capital, and salary. Using panel data from 2001 to 2021, this research sought to
capture the dynamic relationship between these variables and labour productivity over time, acknowledging
both cyclical and structural shifts in Malaysia’s industrial landscape.
To establish a robust theoretical and econometric foundation, this study adopted the Cobb–Douglas production
function framework, which is one of the most widely accepted models for understanding production and
productivity behaviour (Solow, 1957; Mankiw, 2019). Within this framework, the output (or productivity) is a
function of capital and labour inputs, allowing the researcher to examine the elasticity and returns associated
with each input. The econometric procedure began with the panel unit root test using the Augmented Dickey
Fuller (ADF) Fisher Chi-square method to determine the stationarity of each variable.
Establishing stationarity is crucial to ensure that the empirical relationships identified are not spurious
(Gujarati & Porter, 2009). Following this, the Kao panel cointegration test was employed to verify the
existence of long-run equilibrium relationships between the variables. The presence of cointegration implied
that although short-term fluctuations might occur, the variables move together in the long run, signifying stable
interdependencies among inputs and labour productivity (Pedroni, 2004).
The FMOLS estimation results indicated that, overall, the intermediate input and capital had a significant and
positive effect on labour productivity across the four industries studied. This suggested that greater utilisation
of quality intermediate inputs and investment in capital resources contributed to enhance the efficiency and
productivity of workers in the long term (Priyanto et al., 2023). Intermediate inputs such as materials, energy,
and services used in the production process reflected the technological and operational sophistication of an
industry. When these inputs were of higher quality and efficiently managed, they tend to complement human
labour, allowing workers to produce more output with the same or fewer resources. Similarly, capital
accumulation whether in the form of machinery, infrastructure, or equipment enhanced the productive capacity
of industries, allowing for more output per worker through mechanisation, automation, and improved
efficiency (Axunjonova, 2024).
On the other hand, the results demonstrated that both local labour and foreign labour tend to have a negative
relationship with labour productivity. This pattern implied that increases in labour input, without
corresponding improvements in efficiency or technology, might dilute productivity levels. In other words,
when more workers are added without proportionate gains in skills, capital, or intermediate inputs, the
marginal productivity of labour declined, a result consistent with the law of diminishing returns in classical
production theory (Huerta de Soto, 2024).
However, the industries’ specific findings revealed important nuances. For instance, both the agriculture and
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construction industries appeared to be more capital-intensive rather than labour-intensive. The positive and
significant relationship between capital and productivity in these sectors indicated that investments in
machinery, technology, and infrastructure have a direct impact on output per worker. In contrast, the negative
relationship between labour and productivity suggested that these sectors might still be over-reliant on low-
skilled or manual labour, which constrained efficiency gains (Lajom & Sibunruang, 2024). In agriculture,
mechanisation remained partial, with many smallholders depending on traditional farming practices
(Amponsah et al., 2025). Similarly, in construction, the heavy dependence on foreign manual workers had
contributed to a slow transition towards Industrialised Building Systems (IBS) and digitalisation (Yusof et al.,
2024).
In the manufacturing sector, the results portrayed a somewhat different dynamic. Here, foreign labour
contributed positively to productivity, while capital exhibited a negative relationship with productivity. This
finding might reflect the adaptive capacity of foreign workers in export-oriented manufacturing industries,
particularly in electronics and assembly lines, where they often possessed the technical discipline and
efficiency demanded by employers (Salimova, 2022). Meanwhile, the negative sign for capital might indicate
the presence of underutilised or outdated machinery, or capital stock that did not directly translate into labour
efficiency (Martin & Jones, 2022). Interestingly, salary in this sector showed a positive and significant
relationship with productivity, supporting the efficiency wage theory, which posited that higher wages could
enhance motivation, reduce turnover, and attract skilled labour, thereby improving productivity outcomes
(Ibrahim and Putit, 2020).
The mining and quarrying sector, however, presented a more complex scenario. The analysis revealed that
neither labour factors nor capital intensity showed a significant contribution to labour productivity. This could
be attributed to the high dependency of the mining sector on natural resource endowments rather than human
or capital inputs (World Bank, 2023). In such industries, productivity tends to be influenced more by
commodity prices, and extraction technologies than by conventional factor inputs. The result also suggested
that both capital and labour in this sector might not been efficiently allocated or technologically upgraded to
enhance productivity performance during the study period (Awan & Yaqoob, 2023).
Overall, these findings have several theoretical and policy implications. From the perspective of Classical
Production Theory, the observed variation in returns to scale across industries indicated that Malaysia’s
industrial structure is still in the process of transitioning towards higher efficiency and technological maturity
(Hashim & Fahmy-Abdullah, 2024). The Solow Growth Model also supported this interpretation, as
productivity improvements depend on the effective combination of capital accumulation and technological
progress (Solow, 1957). In sectors where capital and technology are underdeveloped, labour additions alone
could not sustain productivity growth. Similarly, from the Labour Market Theory standpoint, wage structures
and skill development played a critical role in aligning worker incentives with productivity outcomes (Becker,
1964).
Policy Implications
Labour Policy
To address the structural weaknesses identified in the analysis, several policy recommendations were
proposed. First, consistent with Kvirchishvili (2023), it is crucial to enhance managerial, technical, and
operational skills within the workforce. The Malaysian industries should intensify training initiatives for
workers and professionals, focusing on both technical capabilities and managerial competencies to strengthen
competitiveness at both domestic and international levels. Without adequate skill formation, the productivity
gap between high-tech and traditional industries will persist, thereby limiting Malaysia’s ability to achieve
sustained productivity-led growth.
Furthermore, it is recommended that the government strengthen the industry-based skills training for local
workers by fostering collaboration with industry experts, training institutes, and universities (Varaprasad,
2022). These partnerships can facilitate the designing of curricula that are directly aligned with industrial
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needs, thereby producing a workforce that is more responsive to technological advancements and evolving
production systems. The aim is to create a virtuous cycle in which human capital development leads to
productivity growth, which in turn supports higher wages and better job quality (Ibrahim & Putit, 2020).
Equally important is the need to attract and retain more motivated local workers, especially in industries where
labour productivity showed a negative relationship with wages. Sectors such as agriculture, mining and
quarrying, and construction continue to struggle with low labour participation among locals due to unattractive
working conditions and limited career progression opportunities (Elsharnouby et al., 2024). To reverse this
trend, the government should implement targeted incentives such as performance-based bonuses, productivity-
linked allowances, and recognition programmes that reward excellence and innovation (Nikiforakis, Oechssler,
& Shah, 2019; Mohd Fateh et al., 2022). Such measures not only motivate employees to perform better but
also create a culture of productivity improvement at the workplace level.
Foreign Labour Management
In addition, it is necessary for the government to carefully manage the inflow of foreign labour. While foreign
workers play an important role in filling the gaps in Malaysia’s labour market, excessive dependence on them
can reduce incentives for firms to invest in automation, skill upgrading, and local talent development (Souto-
Otero, 2023). The findings from this study suggested that in industries such as agriculture, mining, and
construction, foreign labour has a negative impact on productivity, implying that the marginal contribution of
additional foreign workers is diminishing. Therefore, the policy direction should focus on reducing the intake
of low-skilled foreign workers while promoting the recruitment of higher-skilled migrant workers who can
complement rather than substitute local labour. Moreover, stricter monitoring mechanisms can ensure that
foreign labour recruitment aligns with national productivity objectives rather than short-term cost minimisation
goals (World Bank, 2023).
Capital Investment and Technological Advancement
Capital investment also requires strategic attention. This study recommended that the government and industry
stakeholders should prioritise upgrading capital stock, particularly in the mining, quarrying, and manufacturing
sectors, where the capital is currently exhibited a negative or insignificant effect on productivity. As
demonstrated by Cammeraat, Samek, and Squicciarini (2021), physical capital intensity is positively and
significantly associated with productivity in the construction industry. Similarly, Czubak & Pawłowski (2024)
found that increased investment in fixed assets in agriculture significantly contributed to labour and land
productivity growth, as well as investment efficiency. Applying these insights to the Malaysian context,
policymakers should encourage technological upgrading and capital modernisation through fiscal incentives,
low-interest financing schemes, and public–private partnerships.
At a broader level, the study’s findings reinforced the importance of a balanced approach to productivity policy
that integrated physical capital investment, skill development, technological adoption, and institutional reform.
The productivity challenges observed in Malaysia are not merely technical but also structural, rooted in
institutional arrangements, wage rigidities, and industry-specific constraints. Thus, reforms aimed at improving
governance, innovation systems, and business ecosystems are essential complements to sectoral interventions
(OECD, 2022).
From an academic standpoint, this study contributed to the literature by providing empirical evidence on the
long-run relationships between the key production inputs and labour productivity across four critical
Malaysian industries using the FMOLS estimation method. The analysis confirmed the heterogeneity of
productivity determinants across sectors, highlighting that one-size-fits-all policy solutions are ineffective.
Instead, productivity enhancement strategies should be industry-specific, reflecting the distinct technological,
organisational, and labour market realities of each sector.
Although, this study presented strong long-run empirical evidence on the structural drivers of labour
productivity across Malaysia’s industrial sectors, several limitations should be acknowledged. The analysis
primarily focused on quantitative inputs such as capital, labour composition, wages, intermediate inputs, and
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total factor productivity, without directly accounting for qualitative dimensions of human capital, such as
education level, experience, and skill intensity. Furthermore, the exclusion of research and development
(R&D), innovation capacity, and technology diffusion indicators might understate the true impact of
knowledge-based factors on productivity growth, particularly in manufacturing and service-oriented sectors.
Future research should therefore integrate more comprehensive measures of innovation, education quality, and
technological capability indices to capture the evolving dynamics of Malaysia’s transition towards a
knowledge-driven economy. Incorporating such variables, alongside regional and firm-level heterogeneity,
could provide a deeper understanding of productivity convergence and structural transformation. These
extensions would not only enrich the theoretical underpinnings of productivity studies but also offer more
granular policy insights for advancing industrial upgrading, human capital development, and sustainable
economic competitiveness.
Finally, this study opens avenues for future research. Future investigations could extend the analysis by
incorporating additional factors such as research and development (R&D), innovation, digitalisation, and
human capital quality indicators. Exploring these dimensions would provide a deeper understanding of the
mechanisms through which productivity improvements occur, especially in the context of Malaysia’s ongoing
industrial transformation and the global shift toward knowledge-based economies (Lammi, 2024). Moreover,
subsequent studies could employ more advanced econometric techniques, such as dynamic panel models or
causality frameworks, to explore the direction and causality of these relationships over time.
In summary, the findings of this research underlined the multifaceted nature of productivity growth in
Malaysia. While intermediate input and capital investment are crucial for enhancing productivity, their
effectiveness depends heavily on the quality of labour, the level of technology adoption, and the efficiency of
institutional frameworks. Therefore, improving productivity requires a coordinated effort involving
government policy, industry innovation, and labour market reforms. If Malaysia successfully strengthens these
interlinkages, the country will be well-positioned to sustain its productivity growth and competitiveness in an
increasingly challenging global economic environment.
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