Campo Grande: demographics, income, education and connectivity in the 2022 IBGE Census

why this research

Casa Hacker was born in this territory. Our movement emerged in Campo Grande from a concrete and urgent need for technological emancipation, and the political landscape of 2018 made this work even more necessary by bringing visibility to the problem of media literacy and the lack of digital skills. Researching our territory means appropriating these data to work in the right direction.

This is not the first time. In 2024, the Bytes of Change research, conducted with Instituto Semear, consulted Parque Via Norte and Vila Olímpia through generative sessions, questionnaires and interviews — all with the participation of educators and researchers from our city. It mapped access, skills already developed and desired, fears regarding internet use, interests and aspirations, and became the evidence base for Inclusão Tech’s learning pathways.

What that research demonstrated was a method: conducting research, analysing data and recognising the everyday reality of the territory directs action better than intuition. This research continues that path through another route — instead of data produced in the field, public data read with care.

Why now: IBGE released the microdata from the 2022 Demographic Census on 31 August 2026. It was the first time it became possible to read the 2022 Census in the detail of our territories, rather than just in the municipality’s ready-made tables.

the problem that needed to be solved

A real obstacle exists between IBGE data and the question that matters to those working in a territory. Microdata — the individual, anonymous record of each household and person interviewed, which allows variables to be cross-referenced — does not go down to the neighbourhood level. It stops at the weighting area, a grouping of tracts drawn by IBGE based on statistical criteria, with a minimum sample size, rather than on boundaries that anyone recognises on the ground.

In practice: asking “what is the income in Jardim Bassoli?” to the microdata does not work, because Jardim Bassoli does not exist within it. Campinas makes this worse: there is no neighbourhood breakdown in the IBGE database for the municipality. The 2022 Census neighbourhood aggregate layer covers 58 municipalities in São Paulo state and Campinas is not among them, and the neighbourhood field is empty across the city’s 2,592 tracts. IBGE does not document, in the published files, the inclusion criteria for municipalities in this layer — this research notes the absence without asserting its cause.

This research resolves this in three movements, all documented and reproducible:

1. Proving that the district and weighting areas coincide. By cross-referencing IBGE’s Composition of Weighting Areas with the district code of each tract, it was verified that the Campo Grande district is exactly equivalent to areas 3509502023, 024 and 025 — 256 tracts, with none left over and none missing in either direction. This was not guaranteed, and it is what allows the two Census databases to be summed without boundary errors.
2. Returning names to the tracts. CNEFE — National Register of Addresses for Statistical Purposes — records the locality name of each address visited by the Census, along with latitude and longitude. 52,958 addresses fall within Campo Grande; by matching each point with the tract containing it, 249 of the 256 tracts receive a name, forming 43 localities. The joining is spatial, not by code, because CNEFE uses a previous district division that does not even contain the Campo Grande district.
3. Combining the two databases and cross-referencing variables. The universe — the questionnaire applied to all households — has total coverage and goes down to the tract level, but asks little. The sample asks much more, but does not go below the weighting area. Since the boundaries coincide, both describe the same territory: the universe provides spatial detail, while the sample provides income, occupation, education and disability. Building on this, variable cross-references were each tested for sample size before becoming a number.

An important caveat regarding neighbourhoods. The 43 names in this study come from the official address register: they represent administrative nomenclature — for allotments, correspondence, and what municipal governments and registries record. It is not the division that the population makes of their own territory. Those who live here recognise boundaries, neighbourhoods and belongings that no register captures, and which frequently do not coincide with the name on the envelope. This research uses the administrative breakdown because it is the only one that ties precisely to Census data — not because it represents the true reading of the territory. For that reading, another method exists, which is that of Bytes of Change: listening to those who live there.

the territory, indicator by indicator

The tracts in grey fall outside the colour scale, for two distinct reasons. Twelve have no residents at all and another two have fewer than five — these are classified by IBGE as low household threshold: non-residential areas that exist within the grid to tile the territory without gaps, such as industry, parks and institutional strips. In the income, colour or race, and literacy indicators, three additional tracts appear in grey because IBGE suppresses that specific value to prevent the identification of families. Painting any of them as a low value would be a reading error, and therefore they have their own category.

where neighbourhood names come from

IBGE does not publish neighbourhoods for Campinas: the municipality is not part of the 2022 Census neighbourhood aggregate layer, and the NM_BAIRRO field is empty across all 2,592 tracts. However, CNEFE — National Register of Addresses for Statistical Purposes, the Census address register, provides the locality name for each address, along with coordinates.

There were 52,958 addresses within the Campo Grande district, matched by position with the 256 tracts. This names 249 of them and produces 43 localities. The joining is spatial rather than by code because CNEFE uses an earlier district division — it does not even have the Campo Grande district, which was created later; only 1,886 of Campinas’ 2,592 tracts would match by code.

The attribution is good, but it is not official: the median dominance of the main name in a tract is 100%, and 17 tracts fall below 60% — in these, the tract spans two localities and the label represents the most frequent one, not the only one. CNEFE neighbourhoods serve to guide and compare, not as legal boundaries.

inequality within the district

47 of the 256 tracts are favelas or urban communities. Twenty thousand people — 16.3% of the district — live in tracts classified by IBGE as Favelas and Urban Communities, compared to 12.4% in Campinas. This represents the primary internal inequality of the territory, and it vanishes when looking only at the district aggregate.

This breakdown can only be made using the universe — the part of the Census applied to all households, which goes down to the census tract. The sample, which is the long questionnaire providing individual income, occupation and detailed schooling, does not record which tract a person lives in: it stops at the weighting area. Therefore, the indicators below are from the universe, which has fewer variables but knows where each household is located.

The median head-of-household income is 24% lower in favela tracts. Literacy differs very little — 94.1% compared to 96.5%.

Campo Grande and Campinas

The greatest distance is not income. It is higher education: 8.4% compared to 29.8%, a ratio of 3.5 times. And this is reflected precisely in employment — science and intellectual professionals make up 5.3% here compared to 19.6% in the municipality.

The smallest distance is the one that surprises most: internet access, 90.0% compared to 92.0%. Connectivity has arrived. What has not kept pace is the position within the knowledge economy.

What this figure measures, and what it does not measure. The Census only asks whether there is internet access at home — a single variable, yes or no. It does not ask about connection quality, device type, or what people are able to do with it. A household with a pay-as-you-go mobile phone and one with fibre broadband and a computer count equally. The 90% measures the gateway, not the capacity for use, and it is quite possible that the real difference compared to Campinas lies precisely there — in what the Census fails to capture.

income and colour or race

In Campinas, the racial income gap is significant: the median per capita household income for white people is R$ 1,925, compared to R$ 1,212 among black (preta) people and R$ 1,160 among mixed-race (parda) people. Within the Campo Grande district, this gap almost disappears — R$ 1,000, R$ 837 and R$ 906.

It is not that racial inequality does not exist here. It is that it has already operated beforehand, starting from where people live. A white person from the Campo Grande district has half the income of a white person from Campinas; a black person from the Campo Grande district has 69% of the income of a black person from Campinas. The territory compresses everyone downwards, and compresses those who would be privileged outside of it even more.

The same pattern appears in educational attainment: in Campinas, 38.3% of white people aged 25 or older have complete higher education, compared to 17.0% of black people. In Campo Grande, the figures are 9.2% and 9.5% — practically identical, and identical at a low level.

Yellow (Amarela) and indigenous people total 291 individuals in Campo Grande — a sample too small for a reliable estimate, which is why they are excluded from the chart.

education and occupation

Among the 54,142 employed individuals in Campo Grande, education level is a strong predictor of position. Those with complete higher education enter high-qualification occupations in 53.1% of cases — the most solid estimate in the chart, supported by 115 of the 225 observations in that row.

And the most numerous group — 22,878 people with complete secondary education, 42% of all employed individuals — has 51.0% in low-qualification occupations. This is part of the territory’s bottleneck: secondary education, on its own, moves no one upwards in position.

Where estimates cannot be pinned down. The high-qualification column is rare outside of complete higher education: in the rows for “no instruction”, “complete primary” and “incomplete secondary”, it relies on 1 to 3 observations. Percentages marked with ° on the chart rest on fewer than 30 cases and serve only to indicate an order of magnitude — they are not estimates. Reliable reading is found in the medium and low-qualification columns, and in the complete higher education row.

High = directors, managers, and science professionals. Medium = technicians, administrative support, operators, and construction. Low = services, sales, and elementary occupations.

a young population

Median age of 32 years, compared with 37 in the municipality. The ageing index is less than half of the municipal one: 36 older adults for every 100 children, compared with 79. The dependency ratio, however, is almost equal — the same burden, with an inverted composition. Here it comes from children; in Campinas, from older adults.

the numbers

critical reading

the distortion of a science and technology city

Campinas is one of Brazil’s science and technology capitals, and this is not municipal rhetoric. Unicamp is located 17 kilometres from the centre of Campo Grande. Sirius, at CNPEM, the largest and most complex scientific equipment ever built in the country, is 20 kilometres away. CPQD is 18 kilometres away. Add to that PUC-Campinas, Embrapa Informatics, Instituto Eldorado, the technology park, and hundreds of technology-based companies. The municipality closed 2023 with a GDP of R$ 92 billion.

Within the same municipality, less than a twenty-minute drive — and about an hour by bus — from these institutions:

This is not about a poor city that failed to generate wealth. It is about a city that generated wealth, knowledge, and international-standard scientific infrastructure — and whose economy did not cross seventeen kilometres. One hundred and twenty-five thousand people live next to one of Latin America’s largest research centres and participate in it at a proportion nearly four times lower than the municipality’s own average.

This is the distortion that these data expose, and it is not a statistical accident: it appears across all analysed axes, in the same direction, with the same size and proportion.

what the data validate

Some points cease to be perceptions and become measured evidence. They serve to substantiate public policy, projects, and fundraising:

• The demand is for children and youth, not older adults. An ageing index of 35.8 compared with 79 in the municipality. The dependency ratio is practically equal to that of Campinas, but composed in reverse. Nurseries, full-time schools, after-school activities, and youth training have a demographic basis here — and the window is open now.
• Household connectivity is not the bottleneck. 90.0% compared with 92.0%. A digital inclusion programme designed to “bring internet” to Campo Grande would be solving a problem that has already been solved.
• The bottleneck is the transition from secondary to higher education and qualified employment. 22,878 people — 42% of all employed individuals — stopped at completed secondary education, and only 3.5% of them are in highly qualified occupations. Those who reached completed higher education achieve this position in 53.1% of cases. The diploma works; the problem is how many reach it.
• There is inequality within the district that the average conceals. 20,351 people live in favela and urban community sectors, with the head of household’s income being 24% lower. Policy designed for “Campo Grande” without this breakdown misses the target within the target.
• The territory can now be addressed and measured. 43 named localities, 256 sectors with indicators. A programme can be designed for Jardim Bassoli — median income of R$ 1,318 — and not for an average that lumps it together with Residencial Cosmos, at R$ 2,646.

what is discrepant

Three findings contradict common sense regarding the periphery, and deserve to be stated plainly:

• Infrastructure arrived, but opportunity did not. It is unusual to find 90% household internet access coexisting with 8.4% higher education. The easy reading — “lack of access” — is wrong here. What is lacking is what comes after access. It is worth remembering that the Census only measures the existence of a connection: stating that the connection has arrived is safe; stating that usage is equivalent to the rest of the city would go beyond the data.
• It is not a lack of willingness to work. The participation rate is 61.5% compared with 63.9% in the municipality — practically equal. People work in the same proportion. What changes is what they do: elementary occupations are 22.1% here compared with 13.2% in Campinas, and domestic workers are twice as many. The difference lies not in the effort, but in the door that opens.
• Racial income inequality almost vanishes within the territory — and this is proof of segregation, not its absence. In Campinas, the median per capita income for white people is R$ 1,925 compared with R$ 1,212 for Black people. In Campo Grande, R$ 1,000 compared with R$ 837. The distance compresses because inequality has already operated beforehand, deciding who lives where. A white person from Campo Grande has half the income of a white person from Campinas. The postcode did the work that skin colour would have done.

why Casa Hacker does what it does

These data explain, better than any institutional text, why Casa Hacker did not stop at technology education.

If the problem were access, connecting would suffice. If it were basic digital literacy, teaching how to use it would suffice. But the territory is already connected and yet participates in the knowledge economy at a proportion nearly four times lower than its own city’s average. Technology education alone cannot bridge this distance — because the distance is not one of skill, but of position.

This justifies, more than any polished text, why Casa Hacker embraced social innovation through leadership training and development. A trained person who leaves the territory resolves their own life and does not change the indicator. A trained leader who stays, organises, and transforms changes the structure that produces the indicator. The gap between 8.4% and 29.8% higher education is not closed one person at a time — it is closed when the territory gains its own capacity to claim, design, and execute.

This is also why this research exists and is public. A territory without data is a territory without arguments. Anyone negotiating with public authorities, funders, or universities goes further with 256 measured sectors than with the certainty of who lives there — even when that certainty is correct, as it almost always is.

what can be built from here

Critical reading is only useful if it clears a path. What these data enable, starting today:

• Start with digital, not infrastructure. With 90% connectivity, it is possible to design training that presupposes access — a long track, a continuous project, productive use — instead of spending the budget on connectivity.
• Target the secondary education transition. The 22,878 people with completed secondary education and low-skilled employment are the largest measured public in the territory, and the one with the greatest potential for movement. This is where a qualification track yields the highest return per person.
• Participate in and occupy institutional spaces. Unicamp and CNPEM are less than twenty kilometres away. The distance is geographical and, often, institutional as well. Bridge, internship, visit, mentorship, and collaborative research programmes have, here, data that justify priority.
• Neighbourhood-level analyses. With 43 localities and 256 measured sectors, it is possible to choose where to start based on criteria rather than convenience — and to prove afterwards whether it worked.
• Establish the baseline. This is the portrait of 2022. The next Census will show what has changed, and comparison will only exist if today’s portrait is recorded methodically. This research is that baseline.

A note on tone. Nothing here describes a deprived territory. It describes a territory that is young, hard-working, connected, and systematically kept at a distance from the economy that its own city built. The deprivation does not lie in Campo Grande — it lies in the distribution. And distribution is a choice, which means it can be changed.

how to read these numbers

• Two bases, one territory. The Campo Grande district corresponds exactly to weighting areas 3509502023, 024, and 025 — 256 sectors, a perfect fit in both directions. This allows the universe, which goes down to the sector level, to be combined with the sample, which brings income and education, without boundary errors.
• Everything is weighted. The Census sample is not self-weighted. Validation: the weighted population gives 125,044, identical to the universe; occupied households give 42,481 compared with 42,448, a difference of 0.08%.
• What is missing. Sampling error was not calculated — estimates for small subgroups have wide confidence intervals. And the boundary of the APG (Planning and Management Area) from the Campinas Master Plan has not yet been checked against that of the IBGE district: until then, these numbers apply to the district.

Neighbourhoods do not exist for Campinas in the IBGE database. The neighbourhood aggregates layer covers 58 municipalities in São Paulo and Campinas is not among them; the NM_BAIRRO field is empty across the municipality’s 2,592 sectors. The inclusion criteria for this layer are not stated in the published files, and this research does not claim the reason for the absence. Intra-municipal breakdowns here are only possible by district or by own sector aggregation.

glossary

Census sources and breakdowns

• IBGE — Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística). The federal body responsible for the Demographic Census.
• Demographic Census — The complete count of the Brazilian population, carried out every ten years. The 2022 edition is the most recent; the previous one was in 2010.
• universe — The part of the Census applied to all households, using the basic questionnaire. It has total coverage and drills down to the census tract level, but asks few questions.
• sample — The part of the Census applied to a fraction of households, using the long questionnaire — covering income, occupation, detailed education level, disability, and migration. It is much richer, but does not go below the weighting area level.
• microdata — The individual and anonymous record of each household and person interviewed. This is what allows variables to be cross-referenced freely, rather than relying on pre-compiled tables.
• census tract — The smallest collection and publication unit of the Census: the area covered by a census enumerator, typically 250 to 350 households in urban zones. Campo Grande has 256.
• weighting area — A grouping of tracts used to calculate sample weights. It is the smallest geography that exists in the microdata. Campo Grande corresponds to exactly three.
• district — The official administrative subdivision of the municipality, recognised by the IBGE. Campinas has seven; Campo Grande is one of them.
• CNEFE — National Address Register for Statistical Purposes (Cadastro Nacional de Endereços para Fins Estatísticos). The list of all addresses visited by the Census, including locality names and coordinates. This is the source for the neighbourhood names in this research.
• APG — Planning and Management Area (Área de Planejamento e Gestão). A planning unit within the Campinas Master Plan — a municipal breakdown, which should not be confused with the IBGE district.
• dominance — In this research, the percentage of addresses in a tract that declare the same locality in the registry. 100% dominance means that all addresses in that tract refer to the same neighbourhood; below 60%, the tract straddles two localities and the adopted name is simply the most frequent one.
• Slum and Urban Community (Favela e Comunidade Urbana) — The IBGE’s official classification for tracts with specific patterns of urbanisation and service provision. In Campo Grande, there are 47 tracts, accounting for 16.3% of the population.
• low household threshold — The IBGE classification for tracts with very few or no households. These are the grey tracts on the map, outside the colour scale.

how the calculations are made

• sample weight — How many real residents each interviewed person represents. Every sample estimate must be multiplied by this weight — counting rows gives the wrong number.
• weighting — Applying the sample weight. A weighted average gives each response a level of importance proportional to the number of people it represents, rather than treating all responses as equal.
• weighted average — An average where each value enters with a different weight. In the neighbourhood indicators for this research, the weight is the tract population: a tract with 1,200 residents influences the neighbourhood result ten times more than one with 120.
• median — The middle value: half the cases are below it, half above. For income, this is more reliable than the average, which can be skewed by a few very high values.
• percentile, P10 and P90 — The value below which a given fraction of cases falls. P10 is the value below which 10% fall — the floor; P90 leaves 90% below it — the ceiling. The ratio between the two measures how stretched the distribution is.
• quintile — One of five groups with the same number of cases, formed by ordering the values. This is how the colours on the map are divided.
• sample n — How many people were actually interviewed in a cell. Unlike the estimate: 225 interviews can represent 5,407 people. Below 30 cases, the percentage is not a reliable estimate.
• percentage point — The arithmetic difference between two percentages. From 8.4% to 29.8% is 21.4 percentage points — and, at the same time, 3.5 times greater. The two measures convey different things.
• per capita — Per person. Per capita household income divides total household income by the number of residents.
• reference minimum wage — R$ 1,212.00, the value in force on the reference date of the 2022 Census. All minimum wage cutoffs in this research use this value.
• ecological fallacy — The error of drawing conclusions about an individual based on the average of the place where they live. A low-income tract has high-income residents, and vice versa.

the indicators

• per capita household income — The sum of all household income divided by the number of residents. This is the income measure used in this research.
• Gini index — Measures income concentration, from 0 to 1. Zero would mean everyone has the same income; 1, that one person has everything. Campo Grande has 0.405 and Campinas 0.538 — the district is more homogenous and poorer.
• aging index — How many elderly people there are for every 100 children. Campo Grande has 35.8 and Campinas 78.9.
• dependency ratio — How many people outside of working age — under 15 and over 64 — exist for every 100 people of working age.
• labour force — People aged 14 or older who are working or looking for work. Those who are not looking are left out, even if unemployed.
• participation rate — How much of the population aged 14 or older is in the labour force. It measures willingness to work, not success in finding it.
• unemployment rate — How many people within the labour force are without work and actively looking. This is popularly known as being unemployed.
• employment status — The employment relationship of those who work: private sector employee, self-employed worker, domestic worker, employer, public servant.
• major occupational groups — The IBGE classification that groups occupations into families — from directors and science professionals to elementary occupations.
• education level — The level of schooling completed, ranging from no instruction to complete higher education. In this research, this is measured among people aged 25 or older, so as not to count those who are still studying.
• head of household income — The monthly income of the person responsible for the household. This is the only income measure published by the universe per census tract — which is why it appears on the map, rather than per capita household income, which only exists in the sample.
• colour or race — The five IBGE categories — white (branca), black (preta), brown (parda), yellow (amarela) and indigenous (indígena) — declared by the person themselves, not assigned by the census enumerator. This research sometimes adds black and brown together, following common practice in racial inequality studies.
• disability — In the 2022 Census, a person is considered to have a disability if they declare great difficulty or inability in at least one function: seeing, hearing, walking or climbing stairs, or mental or intellectual disability. The question applies to those aged 2 years and older.
• migration — In this research, having been born outside the municipality where one currently lives.
• residents per bedroom — How many people sleep, on average, in each room used as a bedroom. It measures crowding: the higher the figure, the more people per room.
• population density — Inhabitants per square kilometre.
• literacy — Knowing how to read and write a simple note, according to the Census definition. Measured among people aged 15 or older.

occupation and work

• elementary occupations — Major group 9 of the IBGE occupation classification. It brings together jobs involving simple and routine tasks, with high physical demands and little requirement for formal qualification: cleaning and sanitation, construction labourer and helper, delivery worker, waste picker, street vendor, doorman, kitchen assistant. They account for 22.1% of employed people in Campo Grande, compared to 13.2% in Campinas.
• science and intellectual professionals — Major group 2. Occupations that generally require higher education: engineering, medicine, teaching, law, systems analysis, research, architecture. They account for 5.3% in Campo Grande compared to 19.5% in Campinas — the largest occupational gap between the district and the municipality.
• directors and managers — Major group 1. Those who direct an organisation or head an area. 2.9% in Campo Grande, 7.3% in Campinas.
• self-employed — Those who work for themselves, without employees. This includes both formalised independent workers, such as MEIs (Individual Microentrepreneurs), and informal workers.
• domestic worker — Those who work in third-party households — day cleaners, domestic maids, nannies, caregivers. They make up 6.0% of employed people in Campo Grande, double the 3.0% in Campinas.
• employer — Those who have at least one employee. 1.4% in Campo Grande, 3.4% in Campinas.

the charts

• choropleth — A map in which the colour of each area represents the value of an indicator in that area.
• dumbbell chart — Two dots connected by a bar. It compares two groups on the same indicator, and the length of the bar is the difference between them.
• normalized stacked bars — Bars where each equals 100%. It compares the internal composition of each category, not their size.
• grouped bars — Two or more bars side by side within each category. It compares the same groups repeatedly, category by category.
• diverging chart — Bars extending from a central line to both sides. The line is the reference point; the side indicates whether it is above or below it.
• population pyramid — Mirrored horizontal bars, with age groups from the youngest at the bottom to the oldest at the top, with each sex on opposite sides. The shape reveals whether the population is young or aging.

technical sheet

data sources

All from the IBGE — Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística), 2022 Demographic Census, unless otherwise indicated.

• Sample microdata · controlled access, Microdata Portal. Request CDNH3Z.
• Aggregates by Census Tracts · literacy, basic indicators, household characteristics, colour or race, demographics, deaths, and kinship.
• Aggregates by Census Tracts: Head of Household Income
• Census Tract Grid with attributes, São Paulo · map geometry.
• Composition of Weighting Areas
• CNEFE — National Address Register for Statistical Purposes (Cadastro Nacional de Endereços para Fins Estatísticos), Campinas · origin of neighbourhood names.
• Variable Dictionary and Microdata Layout, controlled access.
• GDP of Municipalities, 2023 · SIDRA table 5938.

references

GABRIELE, Layne; BARROS, Geraldo; JANSEN, Carolina. Bytes de Mudanças: pesquisa de território em inclusão digital. Campinas: Associação Casa Hacker; Instituto Semear, 2024. Available at casahacker.org/publicacoes/bytes-de-mudanca-livro-digital.

The analyses, interpretations, and conclusions are the sole responsibility of the author and do not represent the official position of the IBGE.