Migration in Aurora/SC (2017–2022)

Aurora/SC had a reference population (aged 5 or over in 2022) of 6,330 people. In the 2017–2022 five-year period, the municipality received 875 in-migrants and lost 500 out-migrants to other municipalities in Brazil, resulting in a net migration of +375 people (58.96 per 1,000 population) and a migration effectiveness index (IEM) of 0.272. This classifies the municipality, for the period, as one of migratory attraction. In addition, 5 arrived with an unreported origin and 5 came from abroad.

Indicators

Migration indicators for Aurora/SC, 2017–2022
Indicator	Estimate	95% CI	Precision
In-migrants	875	715 to 1,035	good
Out-migrants	500	266 to 734	use with caution
Net migration	+375	92 to 658	not classified
Net migration rate, TLM (per 1,000 pop.)	58.96	-	-
Migration effectiveness index (IEM)	0.272	-	-

Sample of in-migrants: 100-499 observations. Sample of out-migrants: 20-49 observations. 95% confidence interval computed as estimate ± 1.96 × standard error.

Top origins and destinations

Top 10 origins of in-migrants to Aurora/SC
Origin	People	Precision
Rio do Sul/SC · view pair in the atlas	240	use with caution
Ituporanga/SC · view pair in the atlas	185	use with caution
Gaspar/SC · view pair in the atlas	55	low precision
Joinville/SC · view pair in the atlas	35	low precision
Salvador/BA · view pair in the atlas	30	low precision
Blumenau/SC · view pair in the atlas	20	low precision
Curitiba/PR · view pair in the atlas	15	low precision
Pouso Redondo/SC · view pair in the atlas	10	low precision

Top 10 destinations of out-migrants from Aurora/SC
Destination	People	Precision
Ituporanga/SC · view pair in the atlas	330	low precision
Rio do Sul/SC · view pair in the atlas	90	low precision

Profile of migrants and residents

In-migrants (arrivals)

Migrant type
Category	Estimate	Share	Sample
First move from municipality of birth	475	54.6%	100-499
Multi-step migration	340	39.1%	100-499
Return to municipality of birth	55	6.3%	5-19

Educational attainment (people aged 25 and over)
Category	Estimate	Share	Sample
Completed high school, incomplete higher education	200	34.8%	50-99
No schooling or incomplete elementary	180	31.3%	50-99
Completed elementary, incomplete high school	100	17.4%	20-49
Completed higher education	95	16.5%	20-49

Household income per capita (in 2022 minimum wages)
Category	Estimate	Share	Sample
1 to 2 minimum wages	295	33.9%	50-99
More than 2 minimum wages	220	25.3%	50-99
½ to 1 minimum wage	200	23.0%	50-99
¼ to ½ minimum wage	130	14.9%	20-49
Up to ¼ of a minimum wage	25	2.9%	5-19

Age and sex
Category	Estimate	Share	Sample
Ages 25–39, female	165	18.9%	50-99
Ages 40–59, male	115	13.1%	20-49
Ages 25–39, male	115	13.1%	20-49
Ages 40–59, female	105	12.0%	20-49
Ages 5–14, female	80	9.1%	20-49
Ages 15–24, male	80	9.1%	20-49
Ages 5–14, male	70	8.0%	20-49
Ages 15–24, female	70	8.0%	20-49
Ages 60 and over, male	40	4.6%	5-19
Ages 60 and over, female	35	4.0%	5-19

Out-migrants (departures)

Migrant type
Category	Estimate	Share	Sample
Multi-step migration	280	56.6%	20-49
First move from municipality of birth	115	23.2%	5-19
Return to municipality of birth	100	20.2%	5-19

Educational attainment (people aged 25 and over)
Category	Estimate	Share	Sample
No schooling or incomplete elementary	145	41.4%	5-19
Completed high school, incomplete higher education	135	38.6%	5-19
Completed higher education	65	18.6%	5-19
Other / suppressed	5	1.4%	<5

Household income per capita (in 2022 minimum wages)
Category	Estimate	Share	Sample
More than 2 minimum wages	190	38.0%	20-49
1 to 2 minimum wages	145	29.0%	5-19
½ to 1 minimum wage	90	18.0%	5-19
Other / suppressed	75	15.0%	5-19

Age and sex
Category	Estimate	Share	Sample
Other / suppressed	125	25.0%	5-19
Ages 40–59, female	115	23.0%	5-19
Ages 25–39, female	80	16.0%	5-19
Ages 40–59, male	70	14.0%	5-19
Ages 15–24, male	55	11.0%	5-19
Ages 25–39, male	55	11.0%	5-19

Non-migrant residents

Educational attainment (people aged 25 and over)
Category	Estimate	Share	Sample
No schooling or incomplete elementary	2,040	51.3%	>=500
Completed high school, incomplete higher education	860	21.6%	100-499
Completed elementary, incomplete high school	670	16.8%	100-499
Completed higher education	410	10.3%	100-499

Household income per capita (in 2022 minimum wages)
Category	Estimate	Share	Sample
1 to 2 minimum wages	2,445	41.4%	>=500
More than 2 minimum wages	1,775	30.1%	>=500
½ to 1 minimum wage	1,065	18.0%	100-499
¼ to ½ minimum wage	310	5.2%	50-99
Up to ¼ of a minimum wage	300	5.1%	50-99
Other / suppressed	10	0.2%	<5

Age and sex
Category	Estimate	Share	Sample
Ages 40–59, male	805	14.8%	100-499
Ages 40–59, female	770	14.1%	100-499
Ages 25–39, male	695	12.8%	100-499
Ages 25–39, female	610	11.2%	100-499
Ages 60 and over, female	555	10.2%	100-499
Ages 60 and over, male	545	10.0%	100-499
Ages 5–14, male	395	7.2%	100-499
Ages 15–24, male	390	7.2%	100-499
Ages 15–24, female	345	6.3%	100-499
Ages 5–14, female	340	6.2%	100-499

Commuting (work and study)

Commuting indicators for Aurora/SC
Employed persons (aged 10 and over)	3,450
Commute out to work in another municipality	680
Commute in from another municipality to work here	600
Net commuting	−80
Out-commuting rate (%)	19.71
Commuting attraction index	0.890
Median commuting time	30 min

This municipality is part of Região Metropolitana do Alto Vale do Itajaí → see the metropolitan region page

Open Aurora/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]: Aurora/SC. Data from the 2022 Demographic Census (IBGE). Data version: 2026-10-01. Available at: https://atlas-da-migracao.github.io/en/municipio/aurora-sc-4201901/. Dataset DOI: https://doi.org/10.5281/zenodo.22469791.