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CSV Singapore Department of Statistics

Resident Households by Monthly Household Income from Work Per Household Member, Ethnic Group and Sex of Household Reference Person (Census of Population 2020)

Description

Census of Population 2020 counts of resident households in Singapore by monthly household income from work per household member, cross-tabulated by ethnic group and sex of the household reference person. Columns cover Chinese, Malay, Indian and Other groups with male, female and total splits. Income from work has been updated to include employers' Additional MediSave contributions following the Key Household Income Trends 2025 report. Researchers use it to compare per-capita household income across ethnic groups. Adapted from SingStat TableBuilder CT/17515.

Official description

Source: SINGAPORE DEPARTMENT OF STATISTICS Data Last Updated: 30/06/2026 Update Frequency: 10 years Survey period: Census of Population 2020 Footnotes: Note: Data on income from work have been updated to include Additional MediSave Contributions from employers with the release of the Key Household Income Trends 2025 report in February 2026. For more information, you may refer to the Technical Note on Household Income (https://go.gov.sg/khit2025-tech-note) which explains the concepts and components of our household income coverage. Adapted from: https://tablebuilder.singstat.gov.sg/table/CT/17515

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Last updated
Rows
18
Columns
16

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Values are summed where multiple rows share the same category.

Data

Number (Total) Total (Total) Males (Total) Females (Chinese) Total (Chinese) Males (Chinese) Females (Malays) Total (Malays) Males (Malays) Females (Indians) Total (Indians) Males (Indians) Females (Others) Total (Others) Males (Others) Females
Total 1372559.000000 994133.000000 378427.000000 1062046.000000 761631.000000 300415.000000 151741.000000 111723.000000 40017.000000 121222.000000 93080.000000 28142.000000 37551.000000 27698.000000 9853.000000
No Employed Person 182445.000000 91635.000000 90809.000000 149384.000000 75261.000000 74124.000000 16977.000000 8042.000000 8935.000000 12774.000000 6608.000000 6167.000000 3309.000000 1725.000000 1584.000000
Below 500 41752.000000 28165.000000 13587.000000 28987.000000 19603.000000 9384.000000 8815.000000 6163.000000 2652.000000 3197.000000 2030.000000 1166.000000 754.000000 369.000000 385.000000
500 - 999 101299.000000 74426.000000 26873.000000 70225.000000 52106.000000 18119.000000 21263.000000 15918.000000 5345.000000 8337.000000 5535.000000 2802.000000 1474.000000 867.000000 607.000000
1,000 - 1,499 123039.000000 92420.000000 30619.000000 85846.000000 63931.000000 21916.000000 23796.000000 18552.000000 5244.000000 11557.000000 8742.000000 2815.000000 1839.000000 1195.000000 644.000000
1,500 - 1,999 128481.000000 97698.000000 30783.000000 91405.000000 69532.000000 21873.000000 22378.000000 17066.000000 5312.000000 12318.000000 9343.000000 2975.000000 2380.000000 1757.000000 623.000000
2,000 - 2,499 116362.000000 90820.000000 25542.000000 85659.000000 65986.000000 19673.000000 16019.000000 13019.000000 3000.000000 11645.000000 9592.000000 2053.000000 3040.000000 2224.000000 816.000000
2,500 - 2,999 104615.000000 82494.000000 22121.000000 79451.000000 62086.000000 17366.000000 12074.000000 9698.000000 2377.000000 10135.000000 8368.000000 1767.000000 2954.000000 2343.000000 611.000000
3,000 - 3,499 89418.000000 69215.000000 20203.000000 69252.000000 53147.000000 16104.000000 8736.000000 6894.000000 1843.000000 8365.000000 6797.000000 1567.000000 3065.000000 2377.000000 688.000000
3,500 - 3,999 76639.000000 58847.000000 17792.000000 61070.000000 46352.000000 14719.000000 6101.000000 4849.000000 1252.000000 7178.000000 5918.000000 1259.000000 2290.000000 1728.000000 561.000000
4,000 - 4,499 62743.000000 48317.000000 14426.000000 51426.000000 39187.000000 12238.000000 3957.000000 3151.000000 805.000000 5851.000000 4703.000000 1148.000000 1509.000000 1275.000000 234.000000
4,500 - 4,999 50961.000000 39122.000000 11840.000000 41818.000000 31768.000000 10050.000000 2949.000000 2238.000000 711.000000 4602.000000 3903.000000 700.000000 1592.000000 1213.000000 379.000000
5,000 - 5,499 44895.000000 34286.000000 10609.000000 36895.000000 27820.000000 9075.000000 2005.000000 1540.000000 466.000000 4431.000000 3805.000000 626.000000 1564.000000 1121.000000 443.000000
5,500 - 5,999 34813.000000 27315.000000 7498.000000 29259.000000 22959.000000 6301.000000 1519.000000 1090.000000 429.000000 2835.000000 2374.000000 461.000000 1200.000000 892.000000 308.000000
6,000 - 6,999 53195.000000 40460.000000 12735.000000 45806.000000 34546.000000 11260.000000 1591.000000 1150.000000 441.000000 4113.000000 3419.000000 694.000000 1685.000000 1345.000000 339.000000
7,000 - 7,999 37969.000000 29149.000000 8821.000000 31960.000000 24280.000000 7680.000000 1234.000000 879.000000 355.000000 3201.000000 2819.000000 382.000000 1574.000000 1170.000000 404.000000
8,000 - 8,999 27872.000000 21450.000000 6422.000000 23749.000000 18008.000000 5741.000000 820.000000 570.000000 251.000000 2266.000000 2031.000000 235.000000 1037.000000 842.000000 195.000000
9,000 & Over 96061.000000 68314.000000 27747.000000 79853.000000 55061.000000 24792.000000 1505.000000 906.000000 599.000000 8418.000000 7094.000000 1324.000000 6285.000000 5253.000000 1032.000000

Columns

Column Type Categorical
Number Text No
(Total) Total Numeric No
(Total) Males Numeric No
(Total) Females Numeric No
(Chinese) Total Numeric No
(Chinese) Males Numeric No
(Chinese) Females Numeric No
(Malays) Total Numeric No
(Malays) Males Numeric No
(Malays) Females Numeric No
(Indians) Total Numeric No
(Indians) Males Numeric No
(Indians) Females Numeric No
(Others) Total Numeric No
(Others) Males Numeric No
(Others) Females Numeric No