Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/329407 
Year of Publication: 
2025
Citation: 
[Journal:] Economies [ISSN:] 2227-7099 [Volume:] 13 [Issue:] 5 [Article No.:] 127 [Year:] 2025 [Pages:] 1-21
Publisher: 
MDPI, Basel
Abstract: 
This study examines the asymmetric effects of foreign worker employment and low educational attainment on labor productivity across Malaysia's three main economic sectors - agriculture, industry, and services - from 1991 to 2019 using the nonlinear autoregressive distributed lag (NARDL) model. Three sectoral models are estimated to capture how overdependence on foreign workers and low-skilled local labor influences productivity. Model 1 for agriculture underscores positive variations vis-à-vis how foreign worker employment boosts agricultural productivity in both the short and long term. However, negative variations lead to diminished productivity in the long run. Primary education negatively affects long-term agricultural productivity. In Model 2 for industry, neither foreign worker employment nor low educational attainment significantly affects productivity. Model 3 for services reveals a short-term boost in productivity with increased foreign workers' employment, whereas reduced employment enhances long-term productivity. The absence of formal education is detrimental to long-term service productivity, while primary education affects it negatively in the short term. NARDL multiplier graphs and Wald tests confirm significant long-run asymmetric effects of foreign labor in the agriculture and services sectors. The findings highlight the need for Malaysia to reduce reliance on low-skilled labor and accelerate its transition toward a high-skilled workforce to sustain productivity growth and economic competitiveness.
Subjects: 
foreign worker employment
low educational attainment
NARDL model
sectoral labor productivity
JEL: 
E24
I25
J08
J21
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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