The impact of political instability on foreign direct investment in Malaysia: a sectoral level analysis
Bibliographic record
Abstract
Foreign Direct Investment (FDI) is a critical driver of economic growth and development, particularly in developing nations such as Malaysia. However, the country's recent episodes of political instability have introduced significant uncertainties regarding its ability to attract and sustain FDI. This study rigorously examines the impact of political instability on FDI inflows across Malaysia's primary, secondary, and tertiary sectors from the first quarter of 2008 to the fourth quarter of 2023. This study examines the impact of political instability on FDI in Malaysia, focusing on sectoral variations across the primary, secondary, and tertiary sectors. Using quarterly data from 2008 to 2023, the research employs the Autoregressive Distributed Lag (ARDL) model to investigate both the short-run and long-run effects of political instability on FDI inflows. Political instability, a recurring issue in Malaysia's recent history, is analysed alongside key macroeconomic variables such as GDP growth, interest rates, exchange rates, and trade openness. The findings reveal that political instability exerts a significant negative impact on FDI across all sectors, with the primary sector being the most adversely affected. In contrast, the secondary and tertiary sectors, though also impacted, exhibit less sensitivity to political volatility. The study further identifies that macroeconomic factor, particularly GDP growth and interest rates, play crucial roles in shaping FDI inflows. GDP growth is shown to positively correlate with FDI, reinforcing the notion that a robust economy attracts foreign investment. Conversely, higher interest rates are found to deter FDI by increasing the cost of capital, particularly in the primary and secondary sectors. Trade openness has a positive but varied impact across sectors, while exchange rate fluctuations show limited influence on FDI. This research contributes to the existing literature by offering a sectoral analysis of FDI determinants in Malaysia, highlighting the significance of political stability and macroeconomic management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".