Introduction
The SWTS is a multi-component survey instrument developed by the International Labour Organization (ILO) to gather education and labour market information of youth. It has been tested in more than 30 countries. This is the first time that the SWTS has been conducted in Malaysia.
The SWTS supplements the official employment national estimates (via labour force surveys) by providing information on:
- determinants of labour market advantage or disadvantage
- aspirations and behavioural choices of youth
- the quality of school-to-work transition
- youth labour demand from the perspective of employers
Before downloading and utilizing the SWTS data, please read and adhere to the Data Usage Guidelines. For a detailed Frequently Asked Questions, please refer to the FAQs. For full report, please refer to the Main Book.
Metadata
| Data Source(s) | Online Survey and Face-to-face data collection |
| Last Updated | 2021 |
| Frequency | One-off |
| Format | CSV |
| Note | Original data were in Stata (.dta) format and have been converted to .csv for accessibility. Minor differences (e.g., variable labels or formatting) may occur. |
Datasets
1. SWTS_Employer
Dataset Brief Description
This module represents feedback from employers, among the SMEs that captures their hiring practices, skill requirements, and perceptions of youth employability.
Methodology
A probability sampling design was employed using business registers, with stratification by firm characteristics and location, and weighting applied to ensure national representativeness.
Each category of respondents in SWTS employs distinct methodologies and sample selection criteria. For detailed explanations, please consult Appendix 1 in SWTS report.
Caveats
The module relies on employer-reported perceptions, which may not fully reflect actual hiring behaviour, and may underrepresent informal enterprises or smaller firms outside formal registries.
Download Dataset
Dataset Preview
| state | module | Category | Subsector | Group | A1 | A1_11other | A2 | A2_19other | A3 | A4_1 | A4_2 | B1 | B2_1 | B2_2 | B2_3 | B2_4other | B3_1 | B3_2 | B4 | B4_4other | B5 | B6 | B7 | B8 | B9 | B10 | B11 | B12 | B13 | C1_employees_male | C1_employees_female | C1_employees_total | C1_below30_male | C1_below30_female | C1_below30_total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 5 | 3 | 12 | A | 1 | 4 | kilang memproses buah kelapa sawit | 3 | 3 | 2 | 1 | 0 | 0 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 46 | 9 | 55 | 20 | 7 | 27 | |||||||||
| 1 | 5 | 4 | 26 | J | 1 | 18 | barang perkahwinan | 3 | 3 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 3 | 5 | 1 | 3 | 4 | |||||||||||
| 1 | 5 | 3 | 4 | C | 1 | 1 | fertigasi lada | 3 | 3 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 1 | 2 | 3 | 0 | 3 | 3 | 0 | 0 | |||||||||||
| 1 | 5 | 4 | 12 | E | 1 | 4 | cabinet daripada aluminium | 3 | 3 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 2 | 2 | 3 | 0 | 3 | 3 | 0 | 0 | |||||||||||
| 1 | 5 | 3 | 12 | D | 1 | 4 | pile joint | 3 | 3 | 2 | 1 | 0 | 0 | 1 | 3 | 1 | 1 | 2 | 2 | 2 | 13 | 0 | 13 | 0 | 0 | 0 |
This preview shows selected columns only. To access the complete set of variables, please download the full dataset.
Column Definitions
| Column Name | Data Type | Description |
|---|---|---|
| state | int8 | state |
| module | int8 | questionnaire module |
| Category | float64 | Company category |
| Subsector | int8 | Subsector category |
| Group | object | Group A–J MSIC |
| A1 | float64 | Type of company |
| A1_11other | object | Other (specify) |
| A2 | int8 | Main operating sector |
| A2_19other | object | Other (specify) |
| A3 | object | Main product or service |
| A4_1 | int8 | Restrictive regulations |
| A4_2 | int8 | High taxation |
| A4_3 | int8 | Poor economic conditions |
| A4_4 | int8 | Too much competition |
| A4_5 | int8 | Lack of skilled workers |
| A4_6 | int8 | Shortage of unskilled workers |
| A4_7 | int8 | High labour cost |
| A4_8 | int8 | Lack of funding |
| A4_9 | int8 | Unsatisfactory utilities |
| A4_10 | int8 | Poor internet service |
| A4_11 | int8 | Lack of new technology access |
| A4_12 | int8 | Harassment by authorities |
| A4_13 | int8 | Currency exchange issues |
| B1 | int8 | Provides training for new employees |
| B2_1 | float64 | In-house training |
| B2_2 | float64 | External training |
| B2_3 | float64 | Secondment/attachment |
| B2_4other | object | Other training |
| B3_1 | int8 | 1Malaysia Training Scheme |
| B3_2 | int8 | Graduate Employability Scheme |
| B4 | float64 | Training programme funding |
| B4_4other | object | Other funding |
| B5 | float64 | Total participants (last 2 years) |
| B6 | float64 | Participants hired |
| B7 | float64 | Has training budget |
| B8 | int8 | Registered with recruitment agency |
| B9 | float64 | Usefulness of employment service |
| B10 | float64 | Provides work experience |
| B11 | float64 | Advises institutions |
| B12 | float64 | Participates in job fairs |
| B13 | float64 | Invited for career talks |
| C1_employees_male | float64 | Male employees |
| C1_employees_female | float64 | Female employees |
| C1_employees_total | float64 | Total employees |
| C1_below30_male | float64 | Male employees below 30 |
| C1_below30_female | float64 | Female employees below 30 |
| C1_below30_total | float64 | Total employees below 30 |
| C2 | float64 | Workforce trend (past 2 years) |
| C3 | float64 | Expected workforce trend |
| C4 | float64 | Main hiring method |
| C5a_undergraduate_minimum | object | Min salary (undergrad) |
| C5a_undergraduate_maximum | object | Max salary (undergrad) |
| C6 | float64 | Recruitment difficulties |
| C6_2specify | object | Specify recruitment issue |
| C7a_age_skilledprofessional | float64 | Age preference (skilled) |
| C7a_age_lowskilledmanual | float64 | Age preference (low-skilled) |
| C8_1 | float64 | Academic qualifications |
| C8_2 | float64 | Technical skills |
| C8_3 | float64 | Communication skills |
| C8_4 | float64 | Writing skills |
| C8_5 | float64 | IT skills |
| C8_6 | float64 | Teamwork |
| C8_7 | float64 | Discipline |
| C8_8 | float64 | Creativity |
| C8_9 | float64 | Work readiness |
| C9 | float64 | Most important hiring factor |
| C10_1 | float64 | Soft skills importance |
| C10_2 | float64 | Hard skills importance |
| C10_3 | float64 | Work experience importance |
| C11 | float64 | What youth look for in jobs |
| C11_12other | object | Other (specify) |
2. SWTS_InSchool
Dataset Brief Description
This module captures the earliest stage of the school-to-work transition by focusing on youth in upper secondary education. It examines students’ aspirations, educational pathways (academic versus TVET), and expectations for future employment, as well as how school environments shape their preparedness for work.
Methodology
The sample comprises students aged 15 and above enrolled in upper secondary levels (Form 5, Upper 6, or equivalent) across national, technical/vocational, religious, and private schools. A probability sampling approach was used, drawing on official sampling frames and stratified by school type and geography, with weighting applied to ensure national representativeness.
Each category of respondents in SWTS employs distinct methodologies and sample selection criteria. For detailed explanations, please consult Appendix 1 in SWTS report.
Caveats
The module reflects stated aspirations rather than realised outcomes, and excludes school dropouts or youth outside formal education. Findings may understate vulnerabilities among disengaged groups.
Download Dataset
Dataset Preview
| state | module | strata | form | form_others | gender | ethnic | age | SchoolType1 | A5_father | A5_mother | B2 | B4 | B5 | B7 | C2_men | C2_women | C6 | C9 | C14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 1 | 1 | 3 | DIPLOMA TAHUN 2 | 2 | 1 | 19 | 7 | PENGURUS JURUTERA | DURI RUMAH | 2 | 5 | 1 | 1 | 2 | 3 | WEB DESIGNER | 3500 | IBU BAPA |
| 6 | 1 | 1 | 1 | 2 | 1 | 17 | 4 | MENIAGA | SURI RUMAH | 1 | 5 | 2 | 1 | 2 | 2 | BERNIAGA | 2500 | MY PARENTS | |
| 10 | 1 | 1 | 1 | 2 | 1 | 17 | 8 | PEGAWAI BOMBA | SURI RUMAH | 3 | 2 | 1 | 1 | 2 | 3 | PERUNDING | 3000 | ORANG YANG GAGAL DAN TELAH BERJAYA BANGKIT DAN MENCAPAI KEJAYAAN DI DUNIA DAN AKHIRAT. | |
| 15 | 1 | 1 | 1 | 2 | 4 | 17 | 4 | TENTERA | SURI RUMAH | 1 | 1 | 2 | 6 | 2 | 2 | DOKTOR PAKAR BEDAH | 3000 | YUNALIS MAT ZARA'AI | |
| 16 | 1 | 1 | 1 | 1 | 1 | 17 | 4 | PEGAWAI | JABATAN PERKIDMATAN AWAM | 3 | 1 | 6 | 3 | 3 | PENJAWAT AWAM | 5000 | BAPA |
Note: This preview shows selected columns only. To access the complete set of variables, please download the full dataset.
Column Definitions
| Column Name | Data Type | Description |
|---|---|---|
| state | int8 | state |
| module | int8 | questionnaire module |
| strata | int8 | area |
| form | int8 | form |
| form_others | object | form (other, specify) |
| gender | int8 | gender |
| ethnic | int8 | ethnicity |
| ethnic_a | object | ethnicity (other) |
| age | int8 | age |
| SchoolType1 | int8 | school type |
| A11 | int8 | current residence |
| A12_year | float64 | year (if applicable) |
| A3 | float64 | reason for moving |
| A3_a | object | reason (other) |
| A4_oldbro | int8 | older brothers |
| A4_oldsis | int8 | older sisters |
| A4_ybro | int8 | younger brothers |
| A4_ysis | int8 | younger sisters |
| A5_father | float64 | father highest education |
| A5_father_others | object | father education (other) |
| A5_mother | float64 | mother highest education |
| A5_mother_other | object | mother education (other) |
| A6_1 | object | father occupation |
| A6_2 | object | mother occupation |
| A7_father | float64 | father sector |
| A7_mother | float64 | mother sector |
| A8 | float64 | family status |
| B1_1 | int8 | Biology |
| B1_2 | float64 | Chemistry |
| B1_3 | int8 | Physics |
| B1_4 | int8 | Additional Mathematics |
| B1_5 | int8 | Computer Science |
| B1_6 | int8 | Economics |
| B2 | float64 | who chose field of study |
| B2_4other | object | other (specify) |
| B3 | float64 | received career advice |
| B4 | float64 | main source of advice |
| B4_8other | object | other (specify) |
| B5 | float64 | plan after education |
| B5_8other | object | other (specify) |
| B6 | float64 | expected job |
| B6_13other | object | other (specify) |
| B7 | float64 | willing to wait for job |
| B7_depends | object | depends on |
| C1 | float64 | life goal |
| C1_12_other | object | other (specify) |
| C2_men | float64 | working age (men) |
| C2_women | float64 | working age (women) |
| C3 | float64 | useful education |
| C3_12other | object | other (specify) |
| C4 | float64 | useful job quality |
| C4_14_other | object | other (specify) |
| C5 | int8 | preferred work type |
| C5_12other | object | other (specify) |
| C6 | object | preferred occupation |
| C7 | int8 | sector preference |
| C7_19other | object | other (specify) |
| C8 | float64 | important job characteristics |
| C8_12other | object | other (specify) |
| C9 | object | minimum salary |
| C10 | int8 | internet job opportunities |
| C11_migrant | float64 | migrant worker perception |
| C11_migrant_3agree | object | agree (other) |
| C11_migrant_7disagree | object | disagree (other) |
| C11_expatriate | float64 | expatriate perception |
| C11_expatriate_3agree | object | agree (other) |
| C11_expatriate_7disagree | object | disagree (other) |
| C12_migrant | float64 | migrant threat perception |
| C12_expatriate | float64 | expatriate threat perception |
| C13_migrant | float64 | reason hire migrant |
| C13_expatriate | float64 | reason hire expatriate |
| C14 | object | inspiration |
3. SWTS_JobSeeker
Dataset Brief Description
The dataset captures youth actively navigating the labour market transition. The information collected focuses on job search processes, barriers to employment, reservation wages, and expectations. It distinguishes between first-time job seekers, those previously employed but currently unemployed, and those employed but seeking better opportunities.
Methodology
The sample comprises youth aged 15–29 across these job-seeking categories. Due to the absence of a comprehensive sampling frame, non-probability sampling methods were used.
Each category of respondents in SWTS employs distinct methodologies and sample selection criteria. For detailed explanations, please consult Appendix 1 in SWTS report.
Caveats
Due to sampling methodology restrictions, the findings are not fully representative at the national level and should be interpreted as indicative. Potential biases include self-selection (particularly among more active or digitally connected job seekers) and underrepresentation of less visible or informal job search populations.
Download Dataset
Dataset Preview
| state | module | strata | gender | ethnic | age | A5 | A9_1 | A9_2 | B1 | B2 | B3 | B5 | C1 | C3 | C6 | C9 | D1 | D6 | D14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 3 | 1 | 1 | 5 | 17 | 1 | BURUH | SURI RUMAH | 4 | 1 | 2 | 2 | 12 | SAMBUNG BELAJAR | 6 | 3 | KILANG | MY PARENTS | |
| 7 | 3 | 1 | 2 | 5 | 19 | 1 | PENOREH GETAH | SURI RUMAH | 5 | PERAKAUNAN | 3 | 2 | 2 | 4 | SAMBUNG BELAJAR | 13 | 7 | KAKAK | |
| 15 | 3 | 1 | 1 | 5 | 20 | 1 | PETANI | SURI RUMAH | 6 | TEKNOLOGI PENYEJUKBEKUAN DAN PENYAMANAN UDARA | 8 | 2 | 2 | 5 | MELANJUTKAN PELAJARAN | 11 | 3 | POLIS | TIADA |
| 8 | 3 | 1 | 2 | 5 | 19 | 1 | MENGAMBIL UPAH (RACUN SAWAH) | BURUH LADANG | 6 | PASTRY | 8 | 2 | 2 | 10 | KILANG | 18 | 1 | ||
| 5 | 3 | 2 | 2 | 5 | 20 | 1 | PEGAWAI | EKSEKUTIF | 8 | GEOGRAFI | 3 | 2 | 2 | 4 | 18 | 11 | DOKTOR | IBU |
Note: This preview shows selected columns only. To access the complete set of variables, please download the full dataset.
Column Definitions
| Column Name | Data Type | Description |
|---|---|---|
| state | int8 | state |
| module | int8 | questionnaire module |
| strata | int8 | area |
| gender | int8 | gender |
| ethnic | float64 | ethnicity |
| age | int8 | age |
| A11 | int8 | current residence |
| A12_year | float64 | year (if applicable) |
| A3 | float64 | previous residence |
| A3_4other | object | reason for moving (other) |
| A4_oldbro | int8 | older brothers |
| A4_oldsis | int8 | older sisters |
| A4_ybro | int8 | younger brothers |
| A4_ysis | int8 | younger sisters |
| A5 | int8 | marital status |
| A6 | float64 | age first married |
| A7 | float64 | number of children |
| A8_father | float64 | father education |
| A8_father_others | object | father education (other) |
| A8_mother | float64 | mother education |
| A8_mother_other | object | mother education (other) |
| A9_1 | object | father occupation |
| A9_2 | object | mother occupation |
| A10_father | float64 | father sector |
| A10_mother | float64 | mother sector |
| B1 | float64 | highest education |
| B1_12other | object | other (specify) |
| B2 | object | main field of study |
| B2_Main | float64 | main field code |
| B3 | float64 | reason stopped education |
| B3_8other | object | other (specify) |
| B4 | float64 | who chose field |
| B4_5other | object | other (specify) |
| B5 | float64 | received advice |
| B6 | float64 | source of advice |
| B6_8other | object | other (specify) |
| C1 | int8 | worked before |
| C2 | float64 | number of past jobs |
| C3 | float64 | current/last occupation |
| C3_13other | object | other (specify) |
| C4 | float64 | sector |
| C4_19other | object | other (specify) |
| C5 | float64 | job status |
| C6 | float64 | reason leaving job |
| C6_11 | object | other |
| C7 | float64 | unemployment duration |
| C8 | float64 | desired occupation |
| C8_13other | object | other (specify) |
| C9 | float64 | target sector |
| C9_19other | object | other (specify) |
| C10 | float64 | job search type |
| C10_12other | object | other (specify) |
| C11 | float64 | main job obstacle |
| C11_16other | object | other (specify) |
| C13 | float64 | activity while job searching |
| C13_9parttime | object | part-time specify |
| C13_10other | object | other |
| C14 | float64 | jobs applied |
| C15 | int8 | interviews attended |
| C16 | float64 | interview feedback |
| C18 | float64 | refused job |
| C19 | float64 | reason refused |
| C20 | float64 | financial support |
| D1 | float64 | life goal |
| D2_men | float64 | working age men |
| D2_women | float64 | working age women |
| D3 | float64 | useful education |
| D4 | float64 | useful job quality |
| D5 | float64 | preferred work type |
| D6 | object | preferred occupation |
| D7 | float64 | sector preference |
| D8 | float64 | job characteristics |
| D10 | float64 | internet job opportunities |
| D11_migrant | float64 | migrant perception |
| D11_expatriate | float64 | expatriate perception |
| D12_migrant | float64 | migrant threat |
| D12_expatriate | float64 | expatriate threat |
| D13_migrant | float64 | reason hire migrant |
| D13_expatriate | float64 | reason hire expatriate |
| D14 | object | inspiration |
4. SWTS_Tertiary
Dataset Brief Description
This module focuses on youth enrolled in higher education and examines how tertiary institutions contribute to employability.
Methodology
The sample includes youth aged 15–29 enrolled in public and private universities, polytechnics, and technical colleges. A probability sampling design was applied using institutional sampling frames, with stratification by institution type and field of study, and results were weighted to achieve national-level inference.
Each category of respondents in SWTS employs distinct methodologies and sample selection criteria. For detailed explanations, please consult Appendix 1 in SWTS report.
Caveats
The module is limited to formal tertiary pathways and does not capture youth outside higher education or those in informal training systems, and responses may reflect expectations that do not fully align with actual labour market conditions.
Download Dataset
Dataset Preview
| state | module | gender | ethnic | age | A9_1 | A9_2 | B2 | B3_1 | B3_3 | B5 | B7 | C1 | C3 | C6 | C10 | C14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 2 | 1 | 5 | 18 | PENOREH GETAH | SURI RUMAH | TEKNOLOGI DIGITAL | 0 | 1 | 1 | 2 | 3 | 8 | 1 | BEKERJA DALAM BIDANG IT / TEKNIKAL | |
| 2 | 2 | 2 | 5 | 21 | AHLI PERNIAGAAN | SURI RUMAH | MANAGEMENT | 0 | 1 | 1 | 7 | 9 | PENJAWAT AWAM | 2 | KELUARGA | |
| 9 | 2 | 1 | 5 | 21 | POLIS | SURI RUMAH | ENGLISH | 1 | 0 | 3 | 4 | 1 | 5 | AHLI POLITIK | 1 | SULTAN AL FATTAH |
| 10 | 2 | 2 | 5 | 18 | MANAGER | EDUCATION CONSULTANT | MEDICINE | 0 | 1 | 1 | 1 | 2 | 4 | SURGEON | 2 | PARENTS |
| 10 | 2 | 2 | 5 | 20 | PEMANDU LORI | PEKERJA SYARIKAT | COMMUNICATION | 1 | 0 | 2 | 1 | 2 | 10 | TIDAK PASTI | 1 | IBU BAPA |
Note: This preview shows selected columns only. To access the complete set of variables, please download the full dataset.
Column Definitions
| Column Name | Data Type | Description |
|---|---|---|
| state | int8 | state |
| module | int8 | questionnaire module |
| gender | int8 | gender |
| ethnic | int8 | ethnicity |
| age | float64 | age |
| A11 | int8 | current residence |
| A12_year | float64 | year (if applicable) |
| A3 | float64 | reason for moving |
| A3_3other | object | reason (other) |
| A4_oldbro | int8 | older brothers |
| A4_oldsis | int8 | older sisters |
| A4_ybro | int8 | younger brothers |
| A4_ysis | int8 | younger sisters |
| A5 | float64 | marital status |
| A6 | float64 | age first married |
| A7 | float64 | number of children |
| A8_father | float64 | father education |
| A8_father_others | object | father education (other) |
| A8_mother | float64 | mother education |
| A8_mother_other | object | mother education (other) |
| A9_1 | object | father occupation |
| A9_2 | object | mother occupation |
| A10_father | float64 | father sector |
| A10_mother | float64 | mother sector |
| B1_LEVEL | float64 | current education level |
| B1_7other | object | other (specify) |
| B2 | object | main field of study |
| B3_1 | int8 | funding: scholarship |
| B3_2 | int8 | funding: self |
| B3_3 | int8 | funding: parents |
| B3_4 | int8 | funding: relatives |
| B3_5 | int8 | funding: loan |
| funding2 | float64 | funding type |
| B3_6other | object | other (specify) |
| B4 | float64 | sufficient daily expenses |
| B4_2mainsupport | object | main support source |
| B5 | int8 | who chose field of study |
| B5_5other | object | other (specify) |
| B6 | int8 | received career advice |
| B7 | float64 | main source of advice |
| B7_8other | object | other (specify) |
| B8 | float64 | plan after education |
| B8_8other | object | other (specify) |
| B9 | float64 | expected job |
| B9_13other | object | other (specify) |
| B10 | float64 | willing to wait for job |
| B10_7depends | object | depends on |
| C1 | float64 | life goal |
| C1_12_other | object | other (specify) |
| C2_men | float64 | working age (men) |
| C2_women | float64 | working age (women) |
| C3 | float64 | useful education |
| C3_12other | object | other (specify) |
| C4 | float64 | useful job quality |
| C4_14other | object | other (specify) |
| C5 | float64 | preferred work type |
| C5_12other | object | other (specify) |
| C6 | object | preferred occupation |
| C7 | float64 | sector preference |
| C7_19other | object | other (specify) |
| C8 | float64 | job characteristics |
| C8_12other | object | other (specify) |
| C10 | float64 | internet job opportunities |
| C11_migrant | float64 | migrant perception |
| C11_migrant_3agree | object | agree (other) |
| C11_migrant_7disagree | object | disagree (other) |
| C11_expatriate | float64 | expatriate perception |
| C11_expatriate_3agree | object | agree (other) |
| C11_expatriate_7disagree | object | disagree (other) |
| C12_migrant | float64 | migrant threat |
| C12_expatriate | float64 | expatriate threat |
| C13_migrant | float64 | reason hire migrant |
| C13_expatriate | float64 | reason hire expatriate |
| C14 | object | inspiration |
5. SWTS_Worker
Dataset Brief Description
This module examines youth who have entered the labour market, focusing on employment status, job quality, working conditions, and satisfaction. It distinguishes between standard employment, non-standard arrangements (including gig and informal work), and self-employment, in order to assess whether youth transitions result in “decent work” and stable career trajectories.
Methodology
The sample includes youth aged 15–29 who are currently employed, including employees, self-employed individuals, and contributing family workers. Similar to the job seekers module, non-probability sampling was applied due to the lack of a complete sampling frame, with data collected through both field surveys and online platforms. Each category of respondents in SWTS employs distinct methodologies and sample selection criteria. For detailed explanations, please consult Appendix 1 in SWTS report.
Caveats
Findings are not nationally representative and may undercapture informal or highly mobile workers.
Download Dataset
Dataset Preview
| state | module | strata | gender | ethnic | age | A9_1 | A9_2 | B1 | B2 | C6 | C9 | D1 | D3 | D9 | D14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | 4 | 1 | 1 | 2 | 20 | SUPERVISOR | HOUSEWIFE | SIJIL KEMAHIRAN MALAYSIA (LEVEL 2&3) | AIR COND SITE | MECHANIC (CAR) | 1000 | 17 | LEARN NEW EXPERIENCE | SERVICING AIR COND, BASIC WIRING | YAAKUB (PROFESSIONAL WORLD FISHERMAN) |
| 7 | 4 | 1 | 1 | 2 | 23 | KILANG | SURI RUMAH | PENGURUS | 1800 | 22 | WIRING | INGIN BERJAYA | |||
| 7 | 4 | 1 | 1 | 2 | 26 | BERNIAGA | BERNIAGA | PENGURUSAN | 1200 | 22 | 5 | BENGKEL KERETA | BERJAYA | ||
| 7 | 4 | 1 | 1 | 2 | 28 | BERNIAGA | BERNIAGA | KERANI | PENGURUS | 1300 | 22 | REPAIRING | BERJAYA | ||
| 7 | 4 | 1 | 1 | 2 | 29 | BERNIAGA | BERNIAGA | PENGURUS | 1200 | 23 | TUKANG CAT | BERJAYA |
Note: This preview shows selected columns only. To access the complete set of variables, please download the full dataset.
Column Definitions
| Column Name | Data Type | Description |
|---|---|---|
| state | int8 | state |
| module | int8 | questionnaire module |
| strata | int8 | area |
| gender | int8 | gender |
| ethnic | int8 | ethnicity |
| age | int8 | age |
| A11 | float64 | current residence |
| A12_year | float64 | year (if applicable) |
| A3 | float64 | reason for moving |
| A3_3other | object | reason (other) |
| A4_oldbro | int8 | older brothers |
| A4_oldsis | int8 | older sisters |
| A4_ybro | int8 | younger brothers |
| A4_ysis | int8 | younger sisters |
| Siblings | int8 | total siblings |
| A5 | float64 | marital status |
| A6 | float64 | age first married |
| A7 | float64 | number of children |
| A8_father | float64 | father education |
| A8_father_others | object | father education (other) |
| A8_mother | float64 | mother education |
| A8_mother_other | object | mother education (other) |
| A9_1 | object | father occupation |
| A9_2 | object | mother occupation |
| A10_father | float64 | father sector |
| A10_mother | float64 | mother sector |
| A1.1 | float64 | family condition |
| B1 | float64 | highest education |
| B1_12other | object | other (specify) |
| B2 | object | field of study |
| B3 | float64 | reason stopped education |
| B3_8other | object | other (specify) |
| B4 | float64 | who chose field |
| B4_5other | object | other (specify) |
| B5 | float64 | received advice |
| B6 | float64 | source of advice |
| C1 | float64 | life goal |
| C2_men | float64 | working age (men) |
| C2_women | float64 | working age (women) |
| C3 | float64 | useful education |
| C4 | float64 | useful job quality |
| C5 | float64 | preferred work type |
| C6 | object | preferred occupation |
| C7 | float64 | sector preference |
| C8 | float64 | job characteristics |
| C9 | object | minimum salary |
| C10 | float64 | internet job opportunities |
| C11_migrant | float64 | migrant perception |
| C11_expatriate | float64 | expatriate perception |
| C12_migrant | float64 | migrant threat |
| C12_expatriate | float64 | expatriate threat |
| C13_migrant | float64 | reason hire migrant |
| C13_expatriate | float64 | reason hire expatriate |
| C14 | object | inspiration |
| D1 | float64 | age started working |
| D2 | float64 | number of past jobs |
| D3 | float64 | reason leaving job |
| D4 | float64 | refused job |
| D5 | float64 | reason refused |
| D6 | float64 | job search duration |
| D7_years | float64 | years in job |
| D7_months | float64 | months in job |
| D8 | float64 | how job obtained |
| D9 | object | current job description |
| D10 | float64 | current sector |
| D11 | float64 | education usefulness |
| D12 | float64 | secondary job |
| D13 | float64 | reason for second job |
| D14 | float64 | plan to change job |
| D15 | float64 | reason to change job |
| D16_2 | float64 | EPF participation |
| D16_3 | float64 | SOCSO participation |
| D17_2 | float64 | trade union |
| D17_6 | float64 | political party |
| D19 | float64 | other income |
| D20 | float64 | largest expenditure |
| D21 | int8 | employment status |
| E1 | float64 | business type |
| E2 | float64 | training received |
| F1 | float64 | reason self-employed |
| F2 | object | business description |
Additional Materials
Survey Instruments
Each dataset in the SWTS corresponds directly to a structured questionnaire module used during data collection. These questionnaires contain the full wording, response options, and definitions for every variable (column) in the datasets.
Users are strongly encouraged to consult the relevant questionnaire when working with the data, as it provides the most accurate and complete interpretation of variables beyond the abbreviated column descriptions provided above.
Questionnaires
- SWTS Employer Survey
- SWTS In-School Youth Survey
- SWTS Job Seekers Survey
- SWTS Tertiary Survey
- SWTS Young Workers Survey
Coding Manual
Credits
Author(s): Junaidi Mansor, Dr. Lim Lin Lean, Dr. Amirul Rafiq, Thuraya Sazali
This page was prepared and maintained by the Knowledge, Innovation & Data Hub (KID) team at Khazanah Research Institute. The team is responsible for the structuring, documentation, and ongoing maintenance of the dataset.
