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

Migration in Campo Belo do Sul/SC (2017–2022)

Campo Belo do Sul/SC had a reference population (aged 5 or over in 2022) of 6,810 people. In the 2017–2022 five-year period, the municipality received 505 in-migrants and lost 460 out-migrants to other municipalities in Brazil, resulting in a net migration of +45 people (6.46 per 1,000 population) and a migration effectiveness index (IEM) of 0.046. This classifies the municipality, for the period, as one of migratory attraction.

Indicators

Migration indicators for Campo Belo do Sul/SC, 2017–2022
IndicatorEstimate95% CIPrecision
In-migrants505384 to 626good
Out-migrants460305 to 615use with caution
Net migration+45−151 to 241not classified
Net migration rate, TLM (per 1,000 pop.)6.46--
Migration effectiveness index (IEM)0.046--

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 Campo Belo do Sul/SC
OriginPeoplePrecision
Florianópolis/SC · view pair in the atlas135use with caution
Lages/SC · view pair in the atlas85use with caution
Capão Alto/SC · view pair in the atlas35low precision
Cerro Negro/SC · view pair in the atlas25low precision
Anita Garibaldi/SC · view pair in the atlas20low precision
Caxias do Sul/RS · view pair in the atlas15low precision
Painel/SC · view pair in the atlas15low precision
São José/SC · view pair in the atlas10low precision
Top 10 destinations of out-migrants from Campo Belo do Sul/SC
DestinationPeoplePrecision
Lages/SC · view pair in the atlas125low precision
Capão Alto/SC · view pair in the atlas50use with caution
Cerro Negro/SC · view pair in the atlas15low precision

Profile of migrants and residents

In-migrants (arrivals)

Migrant type
CategoryEstimateShareSample
First move from municipality of birth18537.0%50-99
Return to municipality of birth16533.0%50-99
Multi-step migration15030.0%20-49
Educational attainment (people aged 25 and over)
CategoryEstimateShareSample
No schooling or incomplete elementary18053.7%50-99
Completed high school, incomplete higher education7020.9%20-49
Completed higher education4513.4%5-19
Completed elementary, incomplete high school4011.9%5-19
Household income per capita (in 2022 minimum wages)
CategoryEstimateShareSample
½ to 1 minimum wage18035.6%50-99
¼ to ½ minimum wage13526.7%20-49
1 to 2 minimum wages12023.8%20-49
Up to ¼ of a minimum wage356.9%5-19
More than 2 minimum wages356.9%5-19
Age and sex
CategoryEstimateShareSample
Ages 40–59, male8516.8%20-49
Ages 25–39, male7013.9%20-49
Ages 25–39, female7013.9%20-49
Ages 40–59, female6512.9%20-49
Ages 5–14, male5510.9%5-19
Ages 5–14, female458.9%5-19
Ages 15–24, male407.9%5-19
Ages 60 and over, male305.9%5-19
Ages 15–24, female305.9%5-19
Ages 60 and over, female153.0%5-19

Out-migrants (departures)

Migrant type
CategoryEstimateShareSample
First move from municipality of birth20545.1%20-49
Multi-step migration19041.8%20-49
Return to municipality of birth6013.2%5-19
Educational attainment (people aged 25 and over)
CategoryEstimateShareSample
No schooling or incomplete elementary13040.0%20-49
Completed high school, incomplete higher education8526.2%5-19
Completed higher education6018.5%5-19
Completed elementary, incomplete high school5015.4%5-19
Household income per capita (in 2022 minimum wages)
CategoryEstimateShareSample
½ to 1 minimum wage12527.2%20-49
1 to 2 minimum wages12527.2%20-49
More than 2 minimum wages11023.9%5-19
¼ to ½ minimum wage7015.2%20-49
Other / suppressed306.5%5-19
Age and sex
CategoryEstimateShareSample
Ages 25–39, female9019.8%5-19
Ages 25–39, male7516.5%5-19
Ages 40–59, female5512.1%5-19
Ages 5–14, male408.8%5-19
Ages 40–59, male408.8%5-19
Other / suppressed408.8%<5
Ages 60 and over, male357.7%5-19
Ages 15–24, female357.7%5-19
Ages 60 and over, female255.5%5-19
Ages 5–14, female204.4%5-19

Non-migrant residents

Educational attainment (people aged 25 and over)
CategoryEstimateShareSample
No schooling or incomplete elementary2,42053.5%>=500
Completed high school, incomplete higher education1,08524.0%100-499
Completed elementary, incomplete high school53511.8%100-499
Completed higher education48010.6%100-499
Household income per capita (in 2022 minimum wages)
CategoryEstimateShareSample
½ to 1 minimum wage2,55037.7%>=500
1 to 2 minimum wages1,60023.7%>=500
¼ to ½ minimum wage1,30019.2%100-499
More than 2 minimum wages6559.7%100-499
Up to ¼ of a minimum wage6459.5%100-499
Other / suppressed50.1%<5
Age and sex
CategoryEstimateShareSample
Ages 40–59, male98015.6%100-499
Ages 40–59, female93014.8%100-499
Ages 60 and over, male68510.9%100-499
Ages 25–39, male66510.6%100-499
Ages 60 and over, female65510.4%100-499
Ages 25–39, female5959.4%100-499
Ages 15–24, female4857.7%100-499
Ages 15–24, male4807.6%100-499
Ages 5–14, male4206.7%100-499
Ages 5–14, female4056.4%100-499

Commuting (work and study)

Commuting indicators for Campo Belo do Sul/SC
Employed persons (aged 10 and over)2,805
Commute out to work in another municipality190
Commute in from another municipality to work here145
Net commuting−45
Out-commuting rate (%)6.74
Commuting attraction index0.969
Median commuting time50 min

This municipality is part of Região Metropolitana de Lages → see the metropolitan region page

Open Campo Belo do Sul/SC 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]: Campo Belo do Sul/SC. Data from the 2022 Demographic Census (IBGE). Data version: 2026-10-01. Available at: https://atlas-da-migracao.github.io/en/municipio/campo-belo-do-sul-sc-4203402/. Dataset DOI: https://doi.org/10.5281/zenodo.22469791.