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Atlas of internal migration in Brazil 2022 Census · 2017–2022

Migration in Maquiné/RS (2017–2022)

Maquiné/RS had a reference population (aged 5 or over in 2022) of 7,010 people. In the 2017–2022 five-year period, the municipality received 860 in-migrants and lost 550 out-migrants to other municipalities in Brazil, resulting in a net migration of +305 people (43.67 per 1,000 population) and a migration effectiveness index (IEM) of 0.217. This classifies the municipality, for the period, as one of migratory attraction. In addition, 5 arrived with an unreported origin and 15 came from abroad.

Indicators

Migration indicators for Maquiné/RS, 2017–2022
IndicatorEstimate95% CIPrecision
In-migrants860717 to 1,003good
Out-migrants550320 to 780use with caution
Net migration+30535 to 575not classified
Net migration rate, TLM (per 1,000 pop.)43.67--
Migration effectiveness index (IEM)0.217--

Sample of in-migrants: 100-499 observations. Sample of out-migrants: 50-99 observations. 95% confidence interval computed as estimate ± 1.96 × standard error.

Top origins and destinations

Top 10 origins of in-migrants to Maquiné/RS
OriginPeoplePrecision
Capão da Canoa/RS · view pair in the atlas115use with caution
Porto Alegre/RS · view pair in the atlas105use with caution
Xangri-lá/RS · view pair in the atlas95use with caution
Osório/RS · view pair in the atlas80use with caution
Terra de Areia/RS · view pair in the atlas50low precision
São Leopoldo/RS · view pair in the atlas40low precision
Tramandaí/RS · view pair in the atlas35low precision
Viamão/RS · view pair in the atlas25low precision
Canoas/RS · view pair in the atlas20low precision
Santa Maria/RS · view pair in the atlas15low precision
Top 10 destinations of out-migrants from Maquiné/RS
DestinationPeoplePrecision
Xangri-lá/RS · view pair in the atlas95low precision
Osório/RS · view pair in the atlas75low precision
Capão da Canoa/RS · view pair in the atlas60low precision

Profile of migrants and residents

In-migrants (arrivals)

Migrant type
CategoryEstimateShareSample
Multi-step migration48055.8%100-499
First move from municipality of birth24027.9%50-99
Return to municipality of birth14016.3%20-49
Educational attainment (people aged 25 and over)
CategoryEstimateShareSample
Completed high school, incomplete higher education21032.8%50-99
No schooling or incomplete elementary19530.5%50-99
Completed higher education13521.1%20-49
Completed elementary, incomplete high school10015.6%20-49
Household income per capita (in 2022 minimum wages)
CategoryEstimateShareSample
½ to 1 minimum wage30535.7%100-499
1 to 2 minimum wages26030.4%50-99
More than 2 minimum wages12014.0%20-49
¼ to ½ minimum wage11012.9%20-49
Up to ¼ of a minimum wage607.0%5-19
Age and sex
CategoryEstimateShareSample
Ages 25–39, female16519.3%50-99
Ages 25–39, male12514.6%20-49
Ages 40–59, male9511.1%20-49
Ages 60 and over, male9010.5%20-49
Ages 40–59, female9010.5%20-49
Ages 5–14, male708.2%20-49
Ages 60 and over, female708.2%20-49
Ages 15–24, female505.8%5-19
Ages 5–14, female505.8%5-19
Ages 15–24, male505.8%5-19

Out-migrants (departures)

Migrant type
CategoryEstimateShareSample
Multi-step migration35564.5%20-49
First move from municipality of birth16530.0%5-19
Return to municipality of birth305.5%5-19
Educational attainment (people aged 25 and over)
CategoryEstimateShareSample
No schooling or incomplete elementary20556.9%20-49
Completed high school, incomplete higher education6016.7%5-19
Completed elementary, incomplete high school6016.7%5-19
Completed higher education359.7%5-19
Household income per capita (in 2022 minimum wages)
CategoryEstimateShareSample
1 to 2 minimum wages21538.4%20-49
½ to 1 minimum wage14025.0%5-19
More than 2 minimum wages8515.2%5-19
¼ to ½ minimum wage7513.4%5-19
Other / suppressed458.0%<5
Age and sex
CategoryEstimateShareSample
Ages 25–39, male9517.1%5-19
Ages 25–39, female6511.7%5-19
Ages 15–24, female6010.8%5-19
Ages 40–59, female6010.8%5-19
Ages 40–59, male6010.8%5-19
Ages 15–24, male559.9%5-19
Ages 60 and over, female509.0%5-19
Ages 5–14, male509.0%5-19
Ages 60 and over, male305.4%5-19
Other / suppressed305.4%<5

Non-migrant residents

Educational attainment (people aged 25 and over)
CategoryEstimateShareSample
No schooling or incomplete elementary2,64558.0%>=500
Completed elementary, incomplete high school82018.0%100-499
Completed high school, incomplete higher education73016.0%100-499
Completed higher education3658.0%100-499
Household income per capita (in 2022 minimum wages)
CategoryEstimateShareSample
½ to 1 minimum wage2,46037.6%>=500
1 to 2 minimum wages2,10532.2%>=500
¼ to ½ minimum wage92014.1%100-499
More than 2 minimum wages68010.4%100-499
Up to ¼ of a minimum wage3755.7%100-499
Age and sex
CategoryEstimateShareSample
Ages 40–59, female90014.7%100-499
Ages 40–59, male85013.9%100-499
Ages 60 and over, male84513.8%100-499
Ages 60 and over, female79513.0%100-499
Ages 25–39, male61510.0%100-499
Ages 25–39, female5559.1%100-499
Ages 15–24, male4307.0%100-499
Ages 15–24, female4006.5%100-499
Ages 5–14, male3856.3%100-499
Ages 5–14, female3555.8%100-499

Commuting (work and study)

Commuting indicators for Maquiné/RS
Employed persons (aged 10 and over)3,430
Commute out to work in another municipality530
Commute in from another municipality to work here140
Net commuting−390
Out-commuting rate (%)15.46
Commuting attraction index0.866
Median commuting time30 min

Open Maquiné/RS in the interactive atlas →

Methodological note

Estimates from the sample microdata of the 2022 Demographic Census (IBGE, controlled access), fixed-date migration question (residence on July 31, 2017 compared with residence on July 31, 2022). Every estimate is rounded to multiples of 5 and has passed statistical disclosure control (no cell with fewer than 5 people or 3 sampled households). See the full methodology and the glossary.

How to cite this page

Daniel Pessini Sobreira. Atlas da migração interna no Brasil [Atlas of internal migration in Brazil]: Maquiné/RS. Data from the 2022 Demographic Census (IBGE). Data version: 2026-10-01. Available at: https://atlas-da-migracao.github.io/en/municipio/maquine-rs-4311775/. Dataset DOI: https://doi.org/10.5281/zenodo.22469791.