Impact of Covid-19 on households in the Northern States of Malaysia and the potential policy recommendations
Bibliographic record
Abstract
The purpose of this study is the determine the impact of COVID-19 on households in the Northern States of Malaysia namely Perlis, Kedah, Pulau Pinang and Perak. The first main objective of this study was to analyse the impact from the perspective of socioeconomy and sectoral performance as well as the impact on salaries and wages based on available secondary data in 2020 and up to Quarter 1, 2021. From the analysis, the impact of COVID-19 was significant as Gross Domestic Product (GDP) for Malaysia and all the four States contracted in 2020 albeit all the northern States with exception of Perlis fared much better than the national average. The second main objective of the study was to determine the current impact of COVID-19, the author conducted an online survey for a period between week 2 and week 3 of July 2021, at a time where Malaysia was in the mode of full lockdown and no inter-district and interstate travels were allowed. For the online survey, 19% of respondents reported their incomes decreased during the time of pandemic in comparison to before the pandemic, 73.2% of respondents reported no change in their incomes and 2.4% reported an increase in their incomes. Unfortunately, 3.4% of respondents reported that they have no income during the current pandemic. Based on the data analysis from the primary data collection, gender was a significant predictor in the model used by the author and while educational attainment, income before the pandemic and marital status were other significant predictors. This showed that female was less likely to suffer a decrease in income compared to male during the pandemic. While the higher education attainment group was less likely to suffer a decrease in income during the pandemic. The same probability was for the group with higher income before the pandemic and the group which was married compared to the single group. Some of these factors – long-standing status markers, which have been exacerbated by COVID-19, may lead towards increased inequalities within the society and create further disparities between income groups. The third objective of this study is to recommend additional policies that can be implemented to alleviate the impact of COVID-19 including by helping the vulnerable groups, realising the approved investment, dealing with the impact on tax incentives caused by Pillar 2 solutions proposed by OECD, and expediting the recovery of the tourism sector.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".