Campo Grande: demographics, income, education, and connectivity in the IBGE 2022 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 scenario 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 so we can work in the right direction.

This is not the first time. In 2024, the Bytes of Changes research, conducted with Instituto Semear, listened to 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 tracks.

What that research demonstrated was a method: conducting research, analyzing data, and recognizing the daily life of the territory guides action better than intuition. This research continues this 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 August 31, 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

There is a real obstacle between IBGE data and the question that interests those working in a territory. The microdata — the individual and anonymous record of each household and person interviewed, which allows variables to be crossed — does not go down to the neighborhood level. It stops at the weighting area, a grouping of tracts drawn by IBGE using statistical criteria, with a minimum sample size, rather than a border that anyone recognizes 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 in it. Campinas makes this worse: there is no neighborhood breakdown in the IBGE database for the municipality. The 2022 Census neighborhood aggregate layer covers 58 municipalities in São Paulo state and Campinas is not among them, and the neighborhood field is empty in 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 solves 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 border errors.
2. Returning names to the tracts. CNEFE — National Address Register 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; matching each point with the tract that contains it, 254 of the 256 tracts receive a name, forming 43 localities. The match 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 borders coincide, both describe the same territory: the universe provides spatial detail, while the sample provides income, occupation, education, and disability. On top of this, variable cross-references were performed, each tested for sample size before becoming a number.

An important caveat about neighborhoods. The 43 names in this study come from the official address register: they are the 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 recognize borders, neighborhoods, and belongings that no registry captures, and which often 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 — and not because it is the true reading of the territory. For that reading, there is another method, which is that of Bytes of Changes: listening to those who live there.

the territory, indicator by indicator

The tracts in gray fall outside the color scale, for two distinct reasons. Twelve have no residents at all and another two have fewer than five — these are what IBGE classifies as low household threshold areas: non-residential areas that exist in the grid to tile the territory without gaps, such as industry, parks, and institutional strips. In the income, color or race, and literacy indicators, three additional tracts appear in gray 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, which is why they have their own category.

where neighborhood names come from

IBGE does not publish neighborhoods for Campinas: the municipality is not part of the 2022 Census neighborhood aggregate layer, and the NM_BAIRRO field is empty in all 2,592 tracts. However, CNEFE — National Address Register for Statistical Purposes, the Census address registry, provides the locality name of each address with coordinates.

There were 52,958 addresses within the Campo Grande district, matched by position to the 256 tracts. This names 254 of them and produces 43 localities. The match is spatial rather than by code because CNEFE uses a previous district division — it does not even have the Campo Grande district, which was created later; only 1,886 of the 2,592 tracts in Campinas 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 straddles two localities and the label is the most frequent one, not the only one. CNEFE neighborhoods serve to guide and compare, not as legal borders.

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 that IBGE classifies as Favelas and Urban Communities, compared to 12.4% in Campinas. This is the main internal inequality of the territory, and it disappears 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 level. The sample, which is the long questionnaire and provides individual income, occupation, and detailed education, does not record which tract a person lives in: it stops at the weighting area. Therefore, the indicators below are those of the universe, which has fewer variables but knows where each household is located.

The median income of the head of household is 24% lower in favela tracts. Literacy differs little — 94.1% against 96.5%.

Campo Grande and Campinas

The greatest distance is not income. It is higher education: 8.4% versus 29.8%, a ratio of 3.5 times. And it is mirrored precisely in work — science and intellectual professionals make up 5.3% here versus 19.6% in the municipality.

The smallest distance is the most surprising: internet access, 90.0% versus 92.0%. Connection has arrived. What failed to keep pace is the position in the knowledge economy.

What this number measures, and what it does not measure. The Census asks only if there is internet access in the household — a single variable, yes or no. It does not ask about connection quality, device type, or what people manage to do with it. A household with a prepaid cell phone and one with fiber and a computer count equally. The 90% measures the gateway, not usage capacity, and it is entirely possible that the real difference compared to Campinas lies precisely there — in what the Census does not reach.

income and color or race

In Campinas, the racial income gap is large: the median per capita household income for white individuals is R$ 1,925, compared to R$ 1,212 among Black (preta) individuals and R$ 1,160 among brown (parda) individuals. Within the Campo Grande district, this distance 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 one lives. 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 downward, and compresses those who would be privileged outside of it even more.

The same appears in degrees: in Campinas, 38.3% of white people aged 25 or older have a college degree, compared to 17.0% of Black people. In Campo Grande, they are 9.2% and 9.5% — practically equal, and equal at a low level.

Yellow (amarela) and Indigenous individuals total 291 people in Campo Grande — too small a sample for a reliable estimate, which is why they are left out of the chart.

education and occupation

Among the 54,142 employed workers in Campo Grande, education level is a strong predictor of position. Those with complete higher education move into 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 high school, 42% of all employed workers — has 51.0% in low-qualification occupations. This is part of the territory’s bottleneck: high school alone does not move anyone up in position.

Where estimates cannot be pinned down. The high-qualification column is rare outside of complete higher education: in the “no instruction”, “complete elementary”, and “incomplete high school” rows, it relies on 1 to 3 observations. Percentages marked with ° on the chart rest on fewer than 30 cases and serve only to indicate orders 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 to 37 in the municipality. The aging index is less than half of the municipal one: 36 elderly people for every 100 children, compared to 79. The dependency ratio, however, is almost the same — the same load, with an inverted composition. Here it comes from children; in Campinas, from the elderly.

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 kilometers from the center of Campo Grande. Sirius, at CNPEM, the largest and most complex scientific equipment ever built in the country, is 20 kilometers away. CPQD is 18 kilometers away. Add to that PUC-Campinas, Embrapa Informática, 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 a one-hour bus ride — away 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 kilometers. One hundred and twenty-five thousand people live next to one of Latin America’s largest research centers and participate in it in a proportion nearly four times lower than the municipality’s own average.

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

what the data validate

Some points cease to be mere perception and become measured evidence. They serve to ground public policy, projects, and fundraising:

• The demand is for children and youth, not the elderly. Aging index of 35.8 compared to 79 in the municipality. The dependency ratio is practically the same as Campinas’s, but inverted in composition. Daycare centers, full-time schools, after-school programs, and youth training have a demographic basis here — and the window is open now.
• Household connectivity is not the bottleneck. 90.0% compared to 92.0%. A digital inclusion program designed to “bring internet” to Campo Grande would be solving a problem that has already been solved.
• The bottleneck is the transition from high school to higher education and qualified employment. 22,878 people — 42% of all employed individuals — stopped at complete high school, and only 3.5% of them are in high-qualification jobs. Those who reached complete 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 slum and urban community sectors, with the head-of-household’s income 24% lower. Policies designed for “Campo Grande” without this breakdown miss the target within the target.
• The territory can now be addressed and measured. 43 named localities, 256 sectors with indicators. A program can be designed for Jardim Bassoli — median income of R$ 1,318 — rather than for an average that lumps it together with Residencial Cosmos, at R$ 2,646.

what is discrepant

Three results contradict common sense about the periphery and deserve to be stated plainly:

• Infrastructure has arrived, but opportunity has not. It is unusual to find 90% household internet access coexisting with 8.4% higher education attainment. 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 to 63.9% in the municipality — practically the same. People work in the same proportion. What changes is what they do: elementary occupations account for 22.1% here compared to 13.2% in Campinas, and domestic workers are double. The difference is not in 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 to R$ 1,212 for Black people. In Campo Grande, R$ 1,000 compared to 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 ZIP code did the work that skin color would have.

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, simply connecting people would suffice. If it were basic digital literacy, teaching how to use it would be enough. But the territory is already connected and yet participates in the knowledge economy in 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 fancy text, why Casa Hacker embraced social innovation through training and leadership development. A trained individual who leaves the territory solves their own life and does not change the indicator. A trained leader who stays, organizes, and transforms changes the structure that produces the indicator. The gap between 8.4% and 29.8% in higher education cannot be closed one person at a time — it closes when the territory gains the independent capacity to demand, 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 — long tracks, continuous projects, productive use — instead of spending the budget on connectivity.
• Target the high school transition. The 22,878 people with complete high school education and low-qualification jobs are the territory’s largest measured demographic, and the one with the highest potential for upward movement. This is where a qualification track yields the highest return per person.
• Participate and occupy institutional spaces. Unicamp and CNPEM are less than twenty kilometers away. The distance is geographical and, often, institutional as well. Bridge programs, internships, visits, mentoring, and collaborative research have data here that justify priority.
• Neighborhood-level analysis. With 43 localities and 256 measured sectors, it is possible to choose where to start based on criteria rather than convenience — and later prove whether it worked.
• Establish a baseline. This is a 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, hardworking, connected, and systematically kept at a distance from the economy its own city built. The deprivation is not Campo Grande’s — it belongs to 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 combining the universe, which goes down to the sector level, and the sample, which brings income and education, without boundary errors.
• Everything is weighted. The Census sample is not self-weighting. The validation: the weighted population totals 125,044, identical to the universe; occupied households total 42,481 compared to 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) of Campinas’s Master Plan has not yet been cross-checked against the IBGE district boundary: until then, these numbers apply to the district.

Neighborhoods do not exist for Campinas in the IBGE database. The neighborhood aggregate layer covers 58 municipalities in São Paulo state, and Campinas is not among them; the NM_BAIRRO field is empty across all 2,592 sectors of the municipality. The inclusion criteria for this layer are not specified in the published files, and this research makes no assertion regarding the reason for the absence. Intra-municipal breakdowns here are only possible by district or through custom sector aggregation.

glossary

Census sources and breakdowns

• IBGE — Brazilian Institute of Geography and Statistics. Federal agency responsible for the Demographic Census.
• Demographic Census — The complete count of the Brazilian population, conducted 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. Total coverage that drills down to the census tract level, but asks fewer questions.
• sample — The part of the Census applied to a fraction of households, using the long questionnaire — income, occupation, detailed education, disability, migration. Much richer, but does not go below the weighting area level.
• microdata — The individual and anonymous record of each interviewed household and person. This allows variables to be crossed freely, rather than relying on pre-made tables.
• census tract — The smallest collection and publication unit of the Census: the area covered by a census taker, typically 250 to 350 households in urban areas. Campo Grande has 256.
• weighting area — A grouping of tracts used to calculate sample weights. This is the smallest geography that exists in the microdata. Campo Grande corresponds to exactly three.
• district — Official administrative subdivision of the municipality, recognized by IBGE. Campinas has seven; Campo Grande is one of them.
• CNEFE — National Address Register for Statistical Purposes. The list of all addresses visited by the Census, with locality names and coordinates. This is the source for the neighborhood names in this research.
• APG — Planning and Management Area. Planning unit of 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 register. A dominance of 100% means that all addresses in that tract state the same neighborhood; below 60%, the tract spans two localities and the adopted name is simply the most frequent one.
• Slum and Urban Community — Official IBGE classification for tracts with specific urbanization and service provision patterns. In Campo Grande there are 47 tracts, representing 16.3% of the population.
• low household threshold — IBGE classification for tracts with very few or no households. These are the gray tracts on the map, outside the color scale.

how calculations are made

• sample weight — How many real residents each interviewed person represents. Every sample estimate must be multiplied by it — counting rows gives the wrong number.
• weighting — Applying the sample weight. A weighted average gives each response a importance proportional to the number of people it represents, rather than treating all responses as equal.
• weighted average — The average where each value enters with a different weight. In the neighborhood indicators of this research, the weight is the tract population: a tract with 1,200 residents influences the neighborhood result ten times more than one with 120.
• median — The middle value: half of the cases are below, half are above. For income, it is more faithful 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 that leaves 10% below — the floor; P90 leaves 90% below — 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 map colors are divided.
• sample n — How many people were actually interviewed in a cell. Different from 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 more. The two measures say 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 effect on the reference date of the 2022 Census. All minimum wage cutoffs in this research use this value.
• ecological fallacy — The error of concluding something 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, one person has everything. Campo Grande has 0.405 and Campinas 0.538 — the district is more homogeneous and poorer.
• aging index — How many elderly people exist for every 100 children. Campo Grande has 35.8 and Campinas 78.9.
• dependency ratio — How many people outside working age — under 15 and over 64 — exist for every 100 of working age.
• labor force — People aged 14 or older who are working or looking for work. Those who do not look are left out, even if unemployed.
• participation rate — How much of the population aged 14 or older is in the labor force. Measures willingness to work, not success in finding it.
• unemployment rate — How many people within the labor force are without work and actively looking. This is popularly called unemployment.
• position in occupation — The employment bond of those who work: private sector employee, self-employed, domestic worker, employer, civil servant.
• broad occupational groups — IBGE classification that groups occupations into families — from directors and science professionals to elementary occupations.
• education level — The completed level of schooling, from no instruction to complete higher education. In this research, it is measured among people aged 25 or older, to avoid counting 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 that the universe publishes by census tract — which is why it appears on the map, rather than per capita household income, which only exists in the sample.
• color or race — The five IBGE categories — white, black, brown, yellow, and indigenous — declared by the person themselves, not assigned by the census taker. This research sometimes combines black and brown, following the usual 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 ages 2 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, per room used as a bedroom. Measures crowding: the higher the number, the more people per room.
• demographic density — Inhabitants per square kilometer.
• 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 — Broad group 9 of the IBGE occupation classification. Brings together jobs with simple and routine tasks, with high physical demand and little formal qualification requirement: cleaning staff, construction helper and assistant, delivery worker, waste picker, street vendor, doorman, kitchen assistant. They account for 22.1% of the employed in Campo Grande, compared to 13.2% in Campinas.
• science and intellectual professionals — Broad group 2. Occupations that generally require higher education: engineering, medicine, teaching, law, systems analysis, research, architecture. They represent 5.3% in Campo Grande versus 19.5% in Campinas — the largest occupational distance between the district and the municipality.
• directors and managers — Broad group 1. Those who direct an organization or head an area. 2.9% in Campo Grande, 7.3% in Campinas.
• self-employed — Those who work for themselves, without employees. Includes both formal self-employed workers, such as MEIs, and informal ones.
• domestic worker — Those who work in third-party households — daily cleaner, maid, nanny, caregiver. They represent 6.0% of the employed in Campo Grande, twice 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 where the color of each area represents the value of an indicator in that area.
• dumbbell chart — Two dots connected by a bar. Compares two groups on the same indicator, and the length of the bar is the difference between them.
• normalized stacked bars — Bars where each totals 100%. Compares the internal composition of each category, not their sizes.
• grouped bars — Two or more bars side by side within each category. Compares the same groups repeatedly, category by category.
• diverging chart — Bars extending to both sides from a central line. The line is the reference; the side indicates whether it is above or below it.
• population pyramid — Mirrored horizontal bars, age groups from youngest at the bottom to oldest at the top, one sex on each side. The shape reveals whether the population is young or aging.

technical sheet

data sources

All from IBGE — Brazilian Institute of Geography and Statistics, 2022 Demographic Census, except where indicated.

• Sample microdata · controlled access, Microdata Portal. Request CDNH3Z.
• Aggregates by Census Tracts · literacy, basic, household characteristics, color 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, Campinas · origin of neighborhood names.
• Variables Dictionary and Microdata Layout, controlled access.
• Municipal GDP, 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 IBGE.