Slums in Brazil: Where are They Located, Who Lives in Them,and Do They ‘Squeeze’ the Formal Housing Market?

by

Jan K. Brueckner

Department of Economics

University of California, Irvine

Lucas Mation

Instituto de Pesquisa Economica Aplicada (IPEA), Brazil

University of Chicago

Vanessa G. Nadalin

Instituto de Pesquisa Economica Aplicada (IPEA), Brazil

August 2018

Abstract

Making use of data from the Brazilian Census, this paper presents descriptive evidence onBrazilian slums while attempting to test the squeezing hypothesis from the squatting literature.A comparison of mean values for a host of household and neighborhood variables often showswide, and usually predictable, differences in values between neighborhoods designated as slumsby the Census and nonslum neighborhoods that lack this designation. The paper presents avariety of descriptive regressions making use of these rich data, while providing evidence infavor of the squeezing hypothesis in city-level regressions showing that a higher populationshare or land share in slums leads to higher formal rents.

Slums in Brazil: Where are They Located, Who Lives in Them,and Do They ‘Squeeze’ the Formal Housing Market?

by

Jan K. Brueckner, Lucan Mation, and Vanessa G. Nadalin∗

1. Introduction

Slums represent a major social challenge for poor and emerging economies. It is estimated

that, in the developing regions, 872 million people lived in slums in 2010, with Brazil accounting

for 11 million of this total slum population (see UN-Habitat (2016)). Slums are pockets of

poverty inside urban areas, mostly deprived of public services and associated with informal

land tenure. Informality often involves squatting, where households illegally occupy a parcel of

land while paying no compensation to its owner.1 Since the legal owner of the land has eviction

rights, informality is often accompanied by tenure insecurity. Slums and insecurity are, in turn,

closely linked because the eviction threat may retard investment in housing improvements by

the slum household. This missing incentive to invest helps (along with poverty) to explain

the minimal nature of much slum housing, which often consists shacks constructed out of

abandoned materials. For a graphic description of slum conditions in developing countries, see

Marx, Stoker and Suri (2003).

During the second half of the 20th century, Brazil experienced significant migration flows

of poor rural emigrants to the largest metropolitan areas. These migrants settled either on

the periphery or in empty areas within cities, often on steep terrain with landslide risk or in

flood-prone areas near rivers. Slums thus emerged mostly without state oversight, not following

urban planning guidelines and deprived of public services such as sanitation or policing. Slum

proliferation became one of the endemic socioeconomic problems in Brazil, helping to engender

extreme levels of inequality accompanied by violence.2 Despite the seriousness of the slum

problem, most research on the topic is based on case studies and anecdotal evidence.

The purpose of this paper is to provide a body of descriptive evidence on Brazilian slums

(known as favelas), relying on the wealth of information available in the Demographic Census

of the Brazilian Bureau of Statistics (IBGE). We rely on data at the level of enumeration

1

district, each of which contains around 250–350 households who respond to a detailed survey

asking for information about the household (race and sewer connection, for example), which

is supplemented by block-level information on neighborhood characteristics associated with

slums (street garbage and sewage, for example), which is tabulated by Census enumerators.

Aggregation to the enumeration-district level yields average values across households or blocks.

Census workers also assign a slum designation to enumeration districts that meet a number

of criteria, including lack of public services, absence of land titling, and terrain steepness, and

this designation plays a central role in the analysis.3 To capture the common hillside locations

of slums, the Census data are supplemented by satellite information on the average steepness

of the terrain slope within the enumeration district.4 Other sources yield additional data on

the district’s distance to the CBD and proximity to a riverbank, a non-river body of water,

or a railway. The resulting data set contains 7 household characteristics, 13 neighborhood

characteristics associated with slums, 5 locational characteristics, and the slum dummy.

We start by discussing the data in section 2, providing a revealing comparison of the

variables’ mean values between slum and nonslum enumeration districts. In section 3, we

start by asking which physical features of an enumeration district (lack of electricity or sewer

connections, for example) lead to slum designation by the Census enumerators, running a logit

regression that makes use of the detailed neighborhood, terrain, and locational characteristics.

This exercise is carried out in section 3.1 of the paper. In section 3.2, we ask how each of

the 13 slum-related neighborhood characteristics individually varies with location, regressing

each characteristic (lack of electricity, for example) on the slope, riverbank, water, and railway

dummies and on distance to the CBD. In section 4, we ask how income and population density

vary with distance to the metropolitan area’s CBD and how they differ between slum and

non-slum districts. In addition to documenting slum effects, these regressions show for the

first time that income in Brazilian cities declines with distance to the CBD, reversing the

US pattern. Our next question concerns who lives in slums, and it is answered in section 5

by individually regressing the slum dummy and each of the 13 neighborhood-characteristics

variables on the set of 7 household characteristics. The regressions show, for example, how

a lack of electricity is related to the district’s average household income. These regressions

2

are similar to ones presented by Brueckner (2013b) in a study of slum housing in Indonesia.

Together, the regressions just described provide a wealth of descriptive evidence on Brazilian

slums.

The last step in the empirical analysis is designed to test a hypothesis suggested by the-

oretical analysis of urban squatting presented by Brueckner and Selod (2009) and Brueckner

(2013a).5 The models in these papers view squatter or informal housing as “squeezing” the

formal housing market by reducing the amount of a city’s land area available to formal res-

idents, thus raising the price of land in the formal area. This price increase, by raising the

rental income lost to squatting, increases the incentive of landowners to evict the squatters,

an outcome that the model’s squatter organizer avoids by limiting expansion of his informal

community. The squeezing hypothesis is tested by regressing, at level of individual cities, a

measure of average rent in the formal sector on the average characteristics of formal dwellings

and on the size of the city’s slum sector, measured either by land area or population. This

exercise is carried out in section 6, and the paper’s conclusions are presented in section 7.

While providing a rare picture of slums in an important country, the paper adds to a

long literature on housing in the developing world. Some of these studies estimate standard

hedonic price functions using LDC data (Follain, Lim and Renaud (1980, 1982), Follain and

Jimenez (1985)), while others constrast estimate hedonic functions for informal and formal

areas to measure the price discount associated with tenure insecurity (Jimenez (1984), Fried-

man, Jimenez and Mayo (1988), Kapoor and le Blanc (2008)). Other studies investigate how

land titling in informal areas increases the incentive for housing investment (Field (2005),

Galiani and Schargrodsky (2010)), while some papers appraise the effect of land-use regulation

in LDCs (Hannah, Kim and Mills (1993), Malpezzi and Mayo (1997), Bertaud and Malpezzi

(2001), Monkkennon (2013)). Other studies analyze the effects of slum-improvement programs

with the goal of guiding the design of such programs (Takeuchi, Cropper and Bento (2008),

Lall, Lundberg and Shalizi (2008)). In studies focusing on Brazilian cities, Hidalgo, Naidu,

Nichter and Richardson (2010) explore the determinants of “land invasions,” where newly ar-

rived squatters occupy urban land, and Feler and Henderson (2011) study under-servicing of

slum areas as a way some Brazilian cities attempt to reduce rural-urban migration. See Smolka

3

and Biderman (2011) and Brueckner and Lall (2015) for surveys of this literature.

2. Variables and Summary Statistics

As explained above, most of the empirical work uses data at the enumeration-district level.

Attention is restricted to enumeration districts in Brazil’s 12 largest metropolitan areas: Man-

aus, Belem, Fortaleza, Recife, Salvador, Belo Horizonte, Rio de Janeiro, Sao Paulo, Curitiba,

Porto Alegre, Goiania, and Brasilia. These metro areas contain 91,521 enumeration districts,

of which 11,690 (or 12.8%) are designated as slums. Missing values for some variables reduce

the number of observations for the district-level regressions, but the regressions never have

fewer than 86,061 observations.

The first two panels of Table 1 show the variables used in the district-level regressions,

along with their means and definitions (the city-level variables are discussed later). The first

panel contains variables measured through enumerator observations or individual household

responses to the Census survey (the short questionnaire), which are aggregated by the Census

to the enumeration-district level. The second panel contains variables measured at the level

of the enumeration district (rather than the household) using non-Census sources. The table

shows separate variable means for slum and nonslum districts.

The first line of Table 1 shows a dramatic income difference between slum and nonslum

districts, with the nonslum value of 3.49 thousand Brazilian real per month more than double

the slum value of 1.61. The share of households with black or brown members is less than half

in nonslum districts but around two-thirds in slum districts. The shares of non-married and

female household heads show only small differences between slum and nonslum districts (all

are near 40%), while household size is only slight larger in slum districts (3.51 vs. 3.16 persons

in nonslum districts). Illiteracy, however, is dramatically higher in slum districts, with the

shares of both illiterate household heads and illiterate residents about double those in nonslum

districts. However, even the slum shares, both about 10%, are not strikingly large.

Households in slum districts are only somewhat more likely than nonslum districts to lack

a public water system (9.5% vs. 7.9%), but are about twice as likely to lack a public sewer

system (37.4% vs. 21.8%) or a public garbage collection system (3.7% vs. 1.6%; both shares

4

are low). Electricity is twice as likely to be absent in slum districts, but this absence is rare in

both types of districts (well below 1%).

The remaining variables in the upper panel of Table 1 use direct observation by Census

enumerators to capture neighborhood characteristics, which are recorded at the block level

and then averaged across blocks to produce an enumeration-district value. Lack of street

lights is more than twice as likely in slum districts (6.9% vs. 2.8% in nonslum districts), but

the presence of accumulated garbage and sewage on the streets show much more dramatic

differences between slum and nonslum districts. The likelihood of accumulated garbage in the

streets is 54.0% in slum districts (vs. 8.7% in nonslum districts), and the likelihood of sewage

in the streets is 60.4% in slum districts (vs. 9.8% in nonslum districts).

Lack of desirable street features is somewhat more likely in slum districts, with the absence

of sidewalks (29.2% vs. 19.7%), paved streets (15.2% vs. 9.9%)), and curbs (25.4% vs. 14.6%)

more likely in slum than in nonslum districts. Curiously, absence of street manholes is more

likely in nonslum districts (43.9% vs. 34.5% in slum districts), although the absence of trees

on streets is more likely in slum districts (36.8% vs. 30.7% in nonslum districts). Interestingly,

the share of owned dwellings is slightly higher in slum districts (78.4% vs. 71.8% in nonslum

districts). While the absence of a land title is undoubtedly more common in slums than in

nonslum districts, ownership of a parcel need not imply that the resident has title to the

land. As a result, the larger ownership share in slums need not imply greater tenure security.

Another point to note is that an owner who lacks title tends not to rent out the house.6

The variables in second panel of Table 1 are measured at the enumeration-district rather

than household or block level. The terrain slope variable comes from satellite data, as described

earlier, and the variables measuring 200m proximity to a riverbank, a non-river water body, or a

railway are generated through the OpenStreetMap project (www.openstreetmap.org). These

variables, along with distance to the metro-area CBD, use the centroid of the enumeration

district as the reference point. Note that locations near riverbanks or with steep terrain are

areas where the construction of formal dwellings is not permitted, because of the risk of flooding

and landslides.

The share of slum enumeration districts within 200m of a riverbank is more than double

5

the nonslum share (5.0% vs. 2.2%), while the share within 200m of a non-river body of water is

slightly higher for nonslum districts (3.9% vs. 3.5% for slum districts). Notably, a greater share

of nonslum than slum districts are within 200m of a railway (12.0% vs. 9.5%). Brazilian slums

are well known for their hillside locations, and this pattern can be seen in the terrain slope

averages, which are 12.64 degrees for slum districts and 9.73 degrees for nonslum districts.

Finally, slum districts tend to be closer to the metro-area CBD, with an average distance

of 15.36 km vs. 17.85 km for nonslum districts, and slum districts are much denser, with

population density (people per sq km) of 29,854 vs. 18,905 for nonslum districts.

3. Determinants of Slum Designation and Slum Characteristics

3.1. Slum designation

Slums and formal neighborhoods should be quite distinctive in their physical neighborhood

characteristics. The land occupied by slums may public or private, occupied with or without

the permission of the land owner, and it may or may not be suitable for formal development

(because of natural disaster risk, for example). But the occupation of the land always flouts

urban standards and regulations, generating high densities and lacking amenities such as proper

streets, drainage systems and green space.

Census enumerators take such factors into account in assigning a slum designation to an

enumeration district, and it is useful to estimate a model that shows which neighborhood

characteristics actually matter in determining slum designation. Accordingly, we estimate a

logit model with the 0-1 slum designation as the dependent variable and the 13 neighborhood

characteristics and 4 locational characteristics as explanatory variables. In effect, by relating

slum designation to a group of covariates, the logit model serves as way of recovering the

procedure used by the Census in designating enumeration districts as slums. Note that distance

to the CBD is not included as a covariate since Census enumerators do not use it as a criterion

for slum designation.

The logit results are shown in Table 2, which reports the marginal effects from the logit

estimation. As can be seen, some of the marginal effects are opposite in direction to the

slum/nonslum differences in means seen in Table 1. For example, marginal effects of the

6

no sewer, no water and no garbage variables, are negative, indicating that the absence of

public sewer, water or garbage systems reduces the probability of a slum designation, in contrast

to the difference in means. The estimates show that individually increasing these variables from

0 to 1 (going from absence to presence of a public sewer, water or garbage system) reduces the

slum-designation probability by 0.6, 6.9, or 7.7 percentage points, respectively. The marginal

effect of no paved str is also negative rather than having expected positive sign suggested by

Table 1 (a unit 0-to-1 increase reduces the slum probability by 11.5 percentage points).

The other estimates in Table 2 conform to expectations based on Table 1. The marginal

effects of str garbage and str sewer are both strongly positive, matching the large difference

in means for these variables seen in Table 1. Individual unit increases in the variables lead,

respectively, to 15.7 and 16.0 percentage-point increases in the probability of slum designation.

Similarly, the designation probability rises in response to a unit increase in no str light (by

11.3 percentage points), no sidewalk (by 3.5 points), no curb (by 10.5 points), no trees (by

(10.2 points), or 200m riverbank (by 4.4 points). A ten degree increase in terrain slope raises

the slum-designation probability by 1.4 percentage points, while individual unit increases in

200m water and 200m railway reduce the designation probability by 4.0 and 1.4 percentage

points, respectively. A unit increase in owned raises the designation probability by 14.4%,

while the marginal effect of no electr is not significantly different from zero.

It is important to note that the logit estimation in Table 2 includes only physical neigh-

borhood characteristics as explanatory variables, with the household characteristics shown in

Table 1 not included as covariates. This specification corresponds to the usual notion of slums,

which pertains to the physical attributes of an area rather than to the attributes of people

living there. This focus also reflects the Census criteria used in the process of designating

slums.

Another point to note is that, since the short questionnaire that generates the enumeration-

district-level data lacks questions about dwelling characteristics, such variables cannot be in-

cluded in the district-level empirical analysis, even though they might help to explain the

genesis of slum designations. Dwelling characteristics are only reported in the long Census

questionnaire, whose results are aggregated to the level of “weighting areas,” which contain

7

groups of enumeration districts (see section 6 below for more detail).

The description of the distinctive features in slums brings to mind the process of slum emer-

gence. The usual urban-development stages (purchasing an empty parcel, equipping it with

urban infrastructure, building housing units and occupying them) are inverted when slums are

generated. The land is first occupied by very precarious structures that are gradually replaced

by sounder housing. Then, after some lobbying, water and electric service are provided. Even-

tually land titles may be offered and neighborhood amenities such as paved streets, sidewalks,

drainage systems will be built, mainly through slum upgrading policies.

The results in Table 2 are consistent with this process. The unexpected negative and

insignificant coefficients for no water and no elect, for example, suggest that slums already

have these services (see also Table 1), which were provided as the inverted development process

unfolded. But the process has not yet eliminated disamenities such as street garbage and

sewage, which awaits investment in slum upgrading.

3.2. The Locational Determinants of Slum Characteristics

While Table 2 showed the determinants of slum designation, Table 3 explores locational

variation in the 13 slum-related neighborhood characteristics that serve as explanatory vari-

ables in Table 2. The regressions relate these characteristics to CBD distance, slope, and

proximity to a riverbank, a non-river water body, or a railway. The regressions also include

metro-area dummy variables, with Manaus being the default area.7

The first row of Table 3 shows that every one of slum-related neighborhood characteris-

tics (including homeownership) increases with distance to the CBD, suggesting worse slum

characteristics tend to be found far from the city center. In addition, all of the slum-related

characteristics increase with terrain slope. With the exception of two variables (no water

and no manhole), the slum-related characteristics also increase with proximity to a river-

bank. The latter two effects align with the logit results in Table 2, which show that steep

terrain and riverbank proximity are associated with slums.

Occupation of land with steep slopes and near riverbanks is forbidden by urban regulations

because of the risk of land slides or inundation, so the correlation between these characteristics

and slum features is expected. But high-risk locations that characterize slums in LDCs also

8

describe formal areas on the urban periphery, mainly occupied by poor residents. Thus, as

distance to the CBD increases, the more similar slums and formal areas become.

The slum-related neighborhood characteristics are affected in a more mixed fashion by

proximity to a non-river body of water, with most coefficients positive (indicating a greater

incidence of slum-related characteristics near these water bodies) while three are insignifi-

cant or negative. Although these mostly positive values are inconsistent with the negative

slum-designation effect of 200m water from Table 2, the relationships between the slum-

related characteristics and proximity to a railway more closely match the negative effect of

200m railway in Table 2. The 200m railway coefficient is negative for 8 of the 12 slum-

related characteristics, indicating a lower incidence of these characteristics near railways.

The metro-area dummy coefficients indicate how the incidence of the slum-related char-

acteristics varies across metro areas, holding the locational determinants constant. Most of

the Belem coefficients are positive, showing a greater incidence of slum-related characteristics

than in Manaus (the default metro area), while the coefficients in Fortaleza and Salvador show

a more mixed pattern of positive and negative signs. Most coefficient signs for the remaining

metro areas, however, are negative, showing a lower incidence of slum-related characteristics

than in Manaus.

Summarizing, the results in Table 2 show that slum-related characteristics are more likely

to be found in enumeration districts farther from the metro-area CBD. These characteristics

are also more prevalent in districts with steep terrain or near riverbanks.

4. Spatial Behavior of Income and Population Density

The spatial behaviors of income and population density are traditional concerns of urban

economists (see Brueckner (2011)). In the context of Brazilian cities, slum residence is a way

of occupying good locations within the city, with favorable access to jobs and a better chance

of upward mobility that may compensate for a slum’s disadvantages. This locational incentive

may lead to concentrations of poor households close to the CBD, with implications for the

spatial pattern of incomes. Accordingly, this section presents regressions that relate household

income and population density measured at the enumeration-district level to CBD distance.

9

Additional regressions include a slum dummy and a slum-distance interaction term, which allow

the level of income and the income pattern across space to differ for slums (equivalently for

density). The regressions also include metro-area dummies, and the two dependent variables

as well as distance are measured in natural logs.

Table 4, which presents the income regression results, first shows an income regression

without the slum and slum-distance interaction variables. The results show that income falls

with distance to the CBD in Brazilian cities, the opposite of the well-known tendency in US

cities for income to rise moving away from the center (see Glaeser, Kahn and Rappaport

(2008)). This pattern, however, matches those often seen in Europe and other Latin American

countries, where high-income households tend to live near the city center (see Ingram and

Carroll (1981) and Hohenberg and Lees (1986) along with Brueckner, Thisse and Zenou (1999)

for a theoretical explanation). The negative income coefficient also shows that any tendency

of poor job-seeking households to locate in central slums is not enough to reverse an overall

declining trend of incomes moving away from the CBD.

Quantitatively, the −0.418 log distance coefficient (which is an elasticity), indicates that

a 1% increase in distance reduces income by 0.42%. Even though the regression in Table 4

just captures the average pattern across metropolitan areas, a separate unreported regression

where distance coefficients vary by city shows that incomes fall with distance in each of Brazil’s

12 largest metropolitan areas. These results appear to be the first evidence yet presented on

spatial income patterns in Brazil.

The second regression in Table 4, which adds the slum and slum-distance interaction

variables, shows that income in nonslum districts declines with distance slightly faster than in

all districts, with an elasticity of −0.443. In addition, the positive slum-distance interaction

coefficient of 0.243 implies that slum incomes also decline with distance, with an elasticity

equal to the coefficient difference, or −0.200. Thus, centrally located slum residents have higher

incomes than those living far from the center, perhaps because of better job access. Relative

to peripheral slums, central slums are also older and frequently occupy areas not eligible for

formal development. Newer pheripheral slums compete for land with formal development, but

being more profitable, they often win the competition (Abramo, 2012). Note that the slower

10

decline of slum incomes with distance means that the largest gap between nonslum and slum

incomes is found close to the CBD.

The slum coefficient in the second regression is negative, suggesting lower incomes in slum

districts. But the full effect of slum designation at a given CBD distance z equals the slum

coefficient plus the interaction coefficient times log(z). Calculation shows that the resulting

income effect of slums is negative over the entire range of z values in the sample, as expected.

Finally, the metro-area dummy coefficients show that all metro areas aside from Fortaleza and

Recife have incomes at least as large those in Manaus, the default area (Salvador’s positive

coefficient is insignificant).

Table 5 presents the population-density regression results. The first regression does not

include the slum or slum-distance interaction variables, and it shows the usual decline in pop-

ulation density with distance seen in cities everywhere in the world (the elasticity is −0.626).

The second regression shows that density in both nonslum and slum enumeration districts

declines with distance. While a 1% increase in distance reduces nonslum density by 0.63%,

the decline of density in slum districts is more gradual, as in the case of income (the decline

is 0.51%). Note that since both the slum and interaction coefficients are positive, the effect

of slum designation on density is positive regardless of the magnitude of distance. The signs

of the metro-area dummy coefficients are fairly evenly split between positive and negative,

showing that densities in Fortaleza, Recife, Salvador, Rio de Janeiro, Sao Paulo and Brasilia

are higher than in Manaus, while densities in Belem, Curitiba, Porto Alegre, and Goiania are

lower than in Manaus (Belo Horizonte’s coefficient is insignificant).

Summarizing, both income and population density in enumeration districts decline as

distance to the CBD increases, with the declines being more gradual in slum districts. As

expected, slum districts have lower incomes and higher densities than nonslum districts. The

negative effect of distance on income is noteworthy, being the first evidence of such an effect

for Brazilian cities.

5. Who Lives in Slums?

Although the historic roots of slums date back to the abolition of slavery in 1888, the

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major growth of the slum population in Brazil is strongly linked to a rapid urbanization

process between the years 1940 and 1980, which was driven by rural-urban migration (Harris

and Todaro, 1970). In this historical process, socio-cultural groups exhibited disparate reasons

for choosing slum living as a means of accessing the benefits of cities. The analysis is this

section gives insights into who lives in Brazilian slums.

While Table 3 showed regressions of the slum-related neighborhood characteristics on loca-

tional determinants, a separate question is how these characteristics are related to the charac-

teristics of an enumeration district’s households. The current section answers this question by

presenting regressions that show the connection between slum-related neighborhood charac-

teristics and the 7 household characteristics listed in Table 1. An additional regression relates

an enumeration district’s slum designation to the characteristics of its households, which were

omitted in Table 2’s logit regression. The regressions provide insight into the choices of neigh-

borhood characteristics by different types of households. For example, if low-income households

tend to choose, for affordability reasons, neighborhoods with inferior characteristics, this choice

will be manifested in the regression results.

Households that choose to live in neighborhoods with slum-related characteristics will be

found at locations associated with these characteristics, namely, in districts far from the CBD,

with steep terrain or near riverbanks. But since the link between slum-related neighborhood

features and household characteristics is our focus, locational variables (which are a byproduct

of this choice) do not belong in these regressions.

The regression results are shown in Table 6. Consider first the regression in the first column

of the table, which relates slum designation to the household characteristics. Surprisingly,

the hh inc coefficient is positive despite the results of Table 4, which showed a negative

association between income and slum designation (also reflected in the means in Table 1).

This unexpected result is explained by the presence of many other household characteristics

in the regression, some of which are highly correlated with income, tending to obscure the

unconditional effect of income. The coefficients of all the remaining variables, black brown,

not married, fem head hh, illit head hh, and illit nonhead, are positive, indicating that

households that are non-white, with unmarried or female heads, or with illiterate heads or non-

12

head members are more likely to live in slums.

Looking horizontally across the Table 6 shows which household characteristics are asso-

ciated with slum-related neighborhood characteristics. The coefficients of household income

again show an anomalous pattern, being unexpectedly positive in the no sewer, no water,

no garbage, no electr, str garbage, and str sewage regressions, while having the expected

negative signs in five of the remaining regressions. The coefficient in the owned regression

is also positive as expected. Again, these anomalies are presumably due to other variables

capturing much of the effect of income.

In contrast, the coefficients of black brown and illit nonhead show a striking pattern,

being positive in the regressions for each and every slum-related characteristic. The powerful

message is that choice of slum-related neighborhood characteristics is strongly associated with

a household’s non-white status or the illiteracy of its members. The illiteracy of the household

head has less consistent effects, with the regressions exhibiting both positive and negative

coefficients for illit head hh, possibly indicating that most of the effects of illiteracy are

captured by illit nonhead. Note that, while a non-white race or illiteracy of the head makes

a household more likely to be an owner, illiteracy of the non-head members makes ownership

less likely.

The choices of slum-related neighborhood characteristics by households with an unmarried

or female head do not show a consistent pattern, with the coefficients of not married and

fem head hh showing both positive and negative signs across the regressions. The effects of

household size show the same pattern, with a mixture of signs for hh size. Note that female

headed or large households are more like to own, while a non-married head makes ownership

less likely.

Despite mixed effects for some variables, the lesson of Table 6 is that non-white status or

illiteracy are the main forces leading households to neighborhoods with slum-related character-

istics. This racial effect may arise because non-white households value living in high-density

communities with very close relationships, relying on the neighborhood social network as a

survival strategy. Such community life is less feasible in lower-density formal areas, but the

cost of enjoying it in slums is loss of privacy. In addition, the illiteracy that characterizes slums

13

may generate a poverty trap, with each household surrounded by others with little education.

6. Do Slums Squeeze the Formal Housing Market?

6.1. Data and variables

Slum areas consume land that in some cases could be used for formal housing, “squeezing”

the formal market and potentially raising formal rents. This squeezing phenomenon plays a

central role in the squatting models of Brueckner and Selod (2009) and Brueckner (2013a),

where the threat of eviction hangs over squatter settlements (who pay no land rent) and guides

the behavior of squatter organizers, who are assumed to manage such settlements. Knowing

that eviction is worthwhile for landowners when the rent that their land commands in the

formal market is high relative to the cost of carrying out evictions, squatter organizers limit

the size of their settlements and thus the degree of squeezing of the formal market. As a result,

formal land rent does not rise to the level that would prompt evictions.

While this picture of squatting and the behavior of squatter organizers is highly stylized,

the squeezing effect of slum expansion on formal rents may be relevant more generally, and

the purpose of this section of the paper is to conduct a test for its existence. Causality issues,

which are not present in the previous descriptive regressions, must then be considered. The

empirical strategy is to regress formal rent, measured at the city level, on the share of the city’s

population (or, alternatively, land area) contained in slum-designated enumeration districts.

A positive coefficient for the slum share is evidence in favor of the squeezing hypothesis.

The city-level data are generated using household responses to the long Census ques-

tionnaire, which asks about rent and housing characteristics. The Census aggregates these

responses to the level of the “weighting area,” which is a collection of enumeration districts.

The 90,000+ metro-area enumeration districts used in the previous regressions are contained

within 2100 weighting areas. These areas contain an average 44 enumeration districts, with an

average of 6 out of these 44 representing slum districts.

Average rent is reported by weighting area, but since this rent is computed across a mixture

of formal and slum enumeration districts within the area, a restriction must be imposed to

produce a formal rent measure. In particular, our measure of a city’s formal rent is the average

14

rent in weighting areas that contain no slum enumeration districts. The resulting average rent

value is thus based on reported rents from only a subset of the city’s formal enumeration

districts, not making use of rent information from formal districts in weighting areas that also

contain slum districts. While this rent-measurement method could produce an unpredictable

type of selection bias, there is no alternative way of measuring formal rents using readily

available data. It should be noted that rent is the only housing price measure collected by the

Census, with the long questionnaire not asking about house values.

The bottom panel of Table 1 shows mean values for the city-level variables used in the

rent regressions, which come from a sample of 145 cities contained in Brazil’s 12 largest metro

areas. Average formal rent is 348 real per month, while the average city population is just over

375,000. The average slum population share is 3.6% and the average slum land share is 0.9%.

It should be noted that 71 out of the 145 cities have zero values for these variables (indicating

the complete absence of slums). In cities that contain slums, the average land and population

shares take the larger values of 7.0% and 1.9%, with the high density of slums accounting

for the greater relative magnitude of the population share. The maximum share values are

considerably larger, equal to 54.5% for the slum population share and 10.3% for the slum land

share.

City-level values of the remaining variables in Table 1 are average values across the same

weighting areas used to generate the formal rent variable, and their purpose is to capture

housing and neighborhood quality in the neighborhoods that generate the rent measure. The

rooms variable, representing the city’s average number of rooms in formal houses, has an

average value across cities slightly below 5. The no brickwork variable, which represents the

average share of formal houses in the city that lack external brick walls (thus being an inverse

quality measure), has an average value across cities of 13.8%.

The remaining variables in Table 1 are familiar from the previous analysis. Comparison

of their city-level mean values to the nonslum enumeration-district means in the top panel

of the table reveals some differences. The city-level averages are higher for all the variables

other than no sewer, indicating that the formal neighborhoods used to generate the city-level

variables have worse features than those in the average formal enumeration district. This

15

difference is puzzling given that the weighting areas used for city-level data contain no slums

and thus would presumably contain formal enumeration areas of above-average quality. The

city-level averages for the location and terrain slope variables also differ somewhat from the

enumeration-district averages, but a large difference is evident in the CBD distance values.

The much larger city-level value is presumably due to the aggregation required to generate a

measure at this level.

6.2. Results

Table 7 presents the results of the formal-rent regressions. The first two columns of the

table present linear regressions using the slum pop share and slum land share variables,

respectively. The columns 3 and 4 present the same regressions with rent and the city popu-

lation variable pop expressed in log form.

As can be seen, the coefficient of slum pop share is positive and significant in both the

linear and log versions of the regression containing this share variable. The slum land share

coefficient is positive and significant at the 10% level in the linear regression, but the variable’s

coefficient in the log regression, while positive, is not significant. While these results provide

moderately favorable evidence in support of the squeezing hypothesis, further discussion is

presented below.

A sense of the quantitative magnitude of the squeezing effect can be gained from the log

population-share regression. An increase of 0.02 in the slum population share (which has a

mean of 0.07 in cities that contain slums) leads to an increase in log rent equal to 0.02 × 0.818

= 0.016. Thus, a moderate increase in the slum population share raises formal rent by 1.6%,

indicating that the squeezing effect is fairly mild. The results are largely unchanged if the

cities with zero slum shares are deleted from the sample.

Appraisal of the remaining coefficients in Table 7 provides further insight. The pop coef-

ficient is positive and significant in all the regressions, confirming the standard urban model’s

prediction that rents are higher on average in larger cities (see Brueckner (2011)). The rooms

coefficient is also positive in all regressions, with the log versions indicating that an extra room

raises rents by 16-17%. Proximity to a non-river body of water raises rent by around 65%,

while railway proximity reduces rent by around 25%.8 An extra degree of terrain slope raises

16

rent by 1%, while the absence of street manholes reduces rent by 45%. The coefficients of all

of the remaining variables, including the CBD distance measure, are insignificant.

Although the regressions in Table 7 provide some support for the squeezing hypothesis, the

estimated coefficients of slum pop share and slum land share may be subject to simul-

taneity bias. The reason is that, while one direction of causation may run from the slum-share

variables to rents (as suggested by the squeezing hypothesis), causation may also run in the

opposite direction. In particular, high formal rents in a city may lead households to seek

housing in cheaper slum areas, leading to a high slum share. Thus, the both the squeezing hy-

pothesis and this alternative “affordability” hypothesis predict a positive association between

formal rents and slum shares. Since the affordability effect means that cities with large values

of the error term in the rent regression (and thus high rents) will also have high slum shares,

the implication is that the slum-share variables are positively correlated with the error term.

This correlation in turn leads to upward bias in the coefficients of the slum-share variables,

potentially leading to spurious evidence in favor of the squeezing hypothesis.

The remedy for this simultaneity problem is use of instrumental variables, but such vari-

ables (which affect the slum shares without directly affecting rent) are hard to find. While

Hidalgo et al. (2018) use rural incomes and rainfall as instruments for the extent of new land in-

vasions in Brazil, the fact that the current slums in Brazilian cities are the result of rural-urban

migration over many past decades means that contemporaneous rural variables are unlikely

to function as good instruments for our slum-share variables. We tried using the metro-area

dummies as instruments in 2SLS estimation of the column-3 regression in Table 7, but the

test of overidentifying restrictions indicated that these dummies are not valid instruments.

Another possible instrument is the Gini coefficient for the city’s income distribution, on the

belief that greater income inequality increases the slum share without directly affecting rents.

However, in a first-stage regression relating the slum pop share to the Gini variable and

the other covariates, the Gini variable’s coefficient is insignificant, making it unsuitable as an

instrument.

With our attempts to address the simultaneity issue in Table 7 thus unsuccessful, these

regressions represent the best we can do in testing the squeezing hypothesis with the available

17

data. The results are at best suggestive, indicating that slum expansion may raise formal rents

by restricting housing supply in the formal sector. But the regressions may also indicate the

presence of an affordability effect operating on its own or in conjunction with the squeezing

hypothesis.

7. Summary and Conclusion

Making use of data from the Brazilian Census, this paper has presented descriptive evi-

dence on Brazilian slums while attempting to test the squeezing hypothesis from the squatting

literature. A comparison of mean values for a host of household and neighborhood variables

often shows wide, and usually predictable, differences in values between neighborhoods desig-

nated as slums by the Census and nonslum neighborhoods that lack this designation. However,

the results of a logit regression designed to reveal the determinants of the Census slum des-

ignation does not always match the patterns suggested by the comparison of means. Strong

predictors of slum designation, however, are the presence of accumulated garbage and sewage

in streets.

Additional regressions show how the individual slum-related neighborhood characteristics

(lack of electricity, street garbage, etc.) vary with location. The lesson is that almost all slum-

related characteristics are more prevalent farther from the metro-area CBD, in areas with steep

terrain, and in areas near riverbanks.

Other spatial regressions show that income and population density are, respectively, lower

and higher in slums than in nonslum areas, and that both variables decrease with CBD distance,

but at a more gradual rate in slum than in nonslum areas. Answering the question of who

lives in slums, regressions relating the slum-related neighborhood characteristics to household

characteristics show that exposure to each and every slum characteristic is more likely for non-

white households, who may value social networks in dense neighborhoods, and for illiterate

households.

Finally, the paper provides evidence in favor of the squeezing hypothesis by showing in

city-level regressions that a higher population share or land share in slums leads to higher

formal rents. The estimates, however, may be affected by simultaneity bias.

18

The paper makes a significant contribution to the literature on informal housing in de-

veloping countries by exploiting the wealth of evidence available in the Brazilian Census. A

similar exercise would be useful in any country that provides census data with the quality and

breadth of Brazil’s data.

19

Table 1: Variable Definitions and Means

level of variable nonslum slum definitionmeasurement mean mean

household inc hh 3.49 1.61 household income (1000 real/month)black brown 0.448 0.675 % black and brown residentsnot married 0.396 0.387 % household heads not marriedfem head hh 0.429 0.463 % female household headssize hh 3.16 3.51 residents per householdillit head hh 0.044 0.099 % illiterate household headillit nonhead 0.049 0.096 % illiterate non-head household membersno sewer 0.218 0.374 % no public sewer systemno water 0.079 0.095 % no public water systemno garbage 0.016 0.037 % no public garbage collectionno electr 0.001 0.002 % no electricityowned 0.718 0.784 % dwellings owned by occupierno str light 0.028 0.069 % no street lightingstr garbage 0.087 0.540 % accumulated street garbagestr sewage 0.098 0.604 % street sewageno sidewalk 0.197 0.292 % no sidewalksno paved str 0.099 0.152 % no paved streetsno curb 0.146 0.254 % no street curbsno manhole 0.439 0.345 % no street manholesno trees 0.307 0.368 % no trees on streets

enumeration 200m water 0.039 0.035 distance to non-river water body < 200mdistrict 200m riverbank 0.022 0.050 distance to river bank < 200m

200m railway 0.120 0.095 distance to railway < 200mslope 9.73 12.64 average terrain slope (degrees)distance 17.85 15.36 straight-line distance to metro-area CBDdensity 18,905 29,854 population density (per sq km)

variable mean definition

city rent 347.61 average formal monthly rentpop 375,486 total populationslum pop share 0.036 % population in slumsslum land share 0.009 % land area in slumsrooms 4.77 average number of rooms in formal housingno brickwork 0.138 % formal houses with external walls not brick200m water 0.067 –200m riverbank 0.042 –200m railway 0.136 –slope 10.83 –distance 30.83 –no sewer 0.150 –no water 0.090 –str garbage 0.115 –str sewer 0.150 –no sidewalk 0.367 –no paved str 0.198 –no curb 0.241 –no manhole 0.559 –no trees 0.333 –

20

Table 2: Determinants of Slum

Designation

marginal

VARIABLES effect

no sewer -0.00696*

(-2.319)

no water -0.0692**

(-16.04)

no garbage -0.0766**

(-8.685)

no electr 0.0290

(0.510)

owned 0.144**

(23.91)

no str light 0.113**

(18.68)

str garbage 0.157**

(43.57)

str sewer 0.160**

(49.18)

no sidewalk 0.0354**

(8.646)

no paved st -0.115**

(-23.12)

no curb 0.105**

(22.96)

no manhole -0.0196**

(-6.284)

no trees 0.102**

(34.98)

200m water -0.0397**

(-8.417)

200m riverbank 0.0436**

(9.495)

200m railway -0.0142**

(-4.685)

slope 0.00137**

(12.57)

Observations 86,138

z-statistics in parentheses

** p<0.01, * p<0.05

21

Table

3:

Locatio

nalD

eterm

inants

ofSlu

mC

haracteristic

s

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

VA

RIA

BLE

Sno

sew

erno

wate

rno

garb

age

no

elec

trow

ned

no

str

light

str

garb

age

str

sew

erno

sidew

alk

no

paved

str

no

curb

no

manhole

no

tree

s

dis

tance

0.0

104**

0.0

0475**

0.0

00945**

7.1

2e-

05**

0.0

0190**

0.0

0128**

0.0

0133**

0.0

0229**

0.0

0834**

0.0

0550**

0.0

0559**

0.0

0670**

0.0

0484**

(104.0

)(6

4.7

1)

(30.0

7)

(12.0

8)

(41.1

4)

(31.6

7)

(14.4

4)

(24.3

1)

(78.5

6)

(65.3

5)

(60.5

6)

(60.3

0)

(45.6

0)

slope

0.0

0500**

0.0

0343**

0.0

0107**

4.5

3e-

05**

0.0

0146**

0.0

00920**

0.0

0855**

0.0

0858**

0.0

0559**

0.0

0173**

0.0

0367**

0.0

00430*

0.0

00131

(32.3

5)

(27.2

4)

(20.9

9)

(8.0

28)

(19.0

7)

(12.2

3)

(41.2

4)

(40.7

6)

(31.8

2)

(13.0

1)

(23.1

2)

(2.2

83)

(0.7

04)

200m

wate

r0.1

42**

0.0

595**

0.0

119**

0.0

00400

-0.0

0277

0.0

210**

0.0

234**

0.0

338**

0.0

554**

0.0

533**

0.0

483**

0.0

111

-0.0

593**

(22.6

9)

(13.4

1)

(5.8

89)

(1.2

29)

(-1.0

48)

(7.5

45)

(3.8

90)

(5.3

68)

(8.8

13)

(10.5

3)

(8.2

96)

(1.6

62)

(-10.1

9)

200m

river

bank

0.0

697**

-0.0

332**

0.0

267**

0.0

0166**

0.0

142**

0.0

171**

0.0

667**

0.0

607**

0.0

474**

0.0

343**

0.0

462**

-0.0

373**

0.0

162*

(9.8

41)

(-6.9

78)

(8.3

35)

(2.5

80)

(4.1

25)

(4.8

76)

(8.6

61)

(7.4

02)

(6.3

73)

(5.2

20)

(6.3

45)

(-4.5

42)

(2.0

16)

200m

railw

ay

-0.0

163**

-0.0

0733**

0.0

00415

0.0

00174*

-0.0

299**

0.0

00315

-0.0

127**

-0.0

210**

-0.0

248**

-0.0

237**

-0.0

277**

-0.0

422**

0.0

0363

(-5.8

48)

(-3.8

85)

(0.4

74)

(2.0

20)

(-19.0

2)

(0.2

45)

(-4.2

51)

(-6.9

82)

(-8.3

94)

(-10.5

5)

(-10.2

9)

(-11.5

6)

(0.9

94)

Bel

em0.1

09**

0.1

24**

0.0

123**

-0.0

0106

0.0

624**

0.0

196**

0.0

264**

0.2

58**

0.1

08**

0.2

59**

0.2

86**

0.0

0603

0.0

763**

(12.2

4)

(12.5

5)

(4.3

46)

(-1.3

29)

(18.6

2)

(5.0

85)

(3.3

45)

(21.6

0)

(9.0

27)

(29.9

3)

(26.3

1)

(0.5

34)

(7.5

39)

Fort

ale

za-0

.143**

-0.1

54**

0.0

169**

0.0

00812

-0.0

0732*

-0.0

158**

0.0

430**

0.0

143

-0.2

08**

0.0

551**

0.0

725**

0.2

96**

-0.4

27**

(-16.3

9)

(-22.9

7)

(7.3

93)

(1.3

08)

(-2.2

94)

(-5.9

65)

(6.2

27)

(1.5

45)

(-22.1

3)

(12.2

0)

(8.7

39)

(33.5

6)

(-49.4

2)

Salv

ador

-0.4

84**

-0.2

70**

0.0

125**

-0.0

00690

0.0

190**

-0.0

281**

0.1

63**

0.0

373**

-0.2

56**

-0.0

211**

-0.0

222**

-0.1

33**

-0.3

00**

(-66.9

4)

(-42.9

9)

(5.5

48)

(-1.0

77)

(6.5

52)

(-10.5

0)

(19.6

3)

(3.8

11)

(-26.5

2)

(-5.0

76)

(-2.7

40)

(-13.9

9)

(-31.4

0)

Bel

oH

ori

zonte

-0.5

44**

-0.2

76**

-0.0

116**

-0.0

0203**

-0.0

0449

-0.0

331**

-0.0

699**

-0.2

18**

-0.3

42**

-0.0

283**

-0.1

40**

0.0

622**

-0.4

80**

(-78.4

4)

(-43.7

8)

(-6.4

85)

(-3.0

82)

(-1.5

76)

(-12.7

6)

(-11.3

3)

(-26.9

7)

(-38.9

0)

(-7.4

49)

(-20.2

5)

(7.3

09)

(-58.4

1)

Rio

de

Janie

ro-0

.567**

-0.1

92**

-0.0

0668**

-0.0

0331**

-0.0

0458

-0.0

145**

0.0

152*

-0.1

16**

-0.3

40**

0.0

106**

-0.1

02**

-0.3

13**

-0.4

05**

(-84.8

4)

(-29.9

4)

(-3.8

27)

(-5.1

25)

(-1.6

99)

(-5.5

11)

(2.4

91)

(-14.3

9)

(-39.3

7)

(2.7

93)

(-14.9

5)

(-39.1

9)

(-50.4

5)

Sao

Paulo

-0.5

69**

-0.2

80**

-0.0

267**

-0.0

0294**

-0.0

526**

-0.0

269**

-0.0

271**

-0.1

63**

-0.4

39**

-0.0

734**

-0.2

02**

-0.1

02**

-0.5

16**

(-88.4

0)

(-45.4

2)

(-17.0

6)

(-4.6

34)

(-20.0

3)

(-10.8

7)

(-4.7

34)

(-21.0

0)

(-52.8

3)

(-22.6

7)

(-31.1

8)

(-13.1

6)

(-67.7

3)

Curi

tiba

-0.4

03**

-0.2

39**

-0.0

159**

-0.0

0227**

0.0

0207

-0.0

0320

0.0

0746

-0.1

25**

-0.0

0165

0.0

964**

0.0

997**

-0.2

56**

-0.3

83**

(-52.1

6)

(-38.3

8)

(-10.1

2)

(-3.6

95)

(0.6

79)

(-1.0

31)

(1.1

09)

(-14.6

6)

(-0.1

65)

(17.6

3)

(12.0

8)

(-29.0

5)

(-41.7

6)

Port

oA

legre

-0.3

65**

-0.2

04**

-0.0

138**

-0.0

0115

0.0

478**

-0.0

0214

0.0

0778

-0.1

31**

-0.1

09**

0.0

921**

-0.0

148

-0.2

87**

-0.5

56**

(-48.2

2)

(-31.7

0)

(-8.4

68)

(-1.8

00)

(15.5

6)

(-0.7

20)

(1.1

98)

(-15.7

4)

(-11.2

4)

(18.2

5)

(-1.9

08)

(-33.5

1)

(-67.1

8)

Goia

nia

-0.0

871**

-0.0

624**

-0.0

0910**

-0.0

0176**

-0.1

10**

-0.0

286**

-0.0

438**

-0.2

05**

-0.1

58**

0.0

513**

-0.0

791**

0.1

08**

-0.5

60**

(-8.2

38)

(-7.6

08)

(-5.1

95)

(-2.8

31)

(-29.9

4)

(-11.0

0)

(-6.8

43)

(-26.1

5)

(-14.8

0)

(8.4

09)

(-9.4

32)

(10.8

1)

(-65.1

7)

Bra

silia

-0.4

24**

-0.2

41**

-0.0

0354

-0.0

0253**

-0.1

47**

-0.0

302**

-0.0

373**

-0.1

85**

-0.2

36**

-0.0

166**

-0.1

36**

-0.1

67**

-0.1

76**

(-50.5

9)

(-35.5

6)

(-1.6

17)

(-3.9

14)

(-42.3

3)

(-10.1

4)

(-5.8

88)

(-22.3

1)

(-23.7

9)

(-3.3

81)

(-17.8

3)

(-17.5

2)

(-18.5

7)

Const

ant

0.4

50**

0.1

80**

0.0

00490

0.0

0181**

0.7

07**

0.0

200**

0.0

343**

0.1

55**

0.3

08**

-0.0

0185

0.1

27**

0.4

39**

0.6

50**

(69.5

2)

(28.9

5)

(0.3

18)

(3.0

38)

(277.6

)(8

.221)

(5.9

86)

(19.8

2)

(36.9

5)

(-0.5

57)

(19.2

4)

(57.5

1)

(85.6

5)

Obse

rvati

ons

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

86,1

38

R2

0.2

95

0.2

01

0.0

51

0.0

12

0.1

23

0.0

28

0.0

70

0.1

04

0.2

07

0.1

32

0.1

48

0.1

63

0.1

76

Robust

t-st

ati

stic

sin

pare

nth

eses

**

p<

0.0

1,*

p<

0.0

5

22

Table 4: Spatial Behavior of

Household Income

VARIABLES log hh inc

log distance -0.443**

(-154.4)

slum -1.170**

(-87.70)

slum × log distance 0.243**

(51.40)

Belem 0.231**

(17.67)

Fortaleza -0.144**

(-11.01)

Recife -0.0757**

(-5.585)

Salvador 0.0238

(1.699)

Belo Horizonte 0.249**

(21.08)

Rio de Janiero 0.450**

(39.15)

Sao Paulo 0.531**

(47.57)

Curitiba 0.319**

(26.50)

Porto Alegre 0.257**

(21.07)

Goiania 0.0379**

(2.979)

Brasilia 0.742**

(56.69)

Constant 1.797**

(145.6)

Observations 90,452

R2 0.414

Robust t-statistics in parentheses

** p<0.01, * p<0.05

23

Table 5: Spatial Behavior of

Population Density

VARIABLES log density

log distance -0.633**

(-96.31)

slum 0.448**

(13.52)

slum × log distance 0.123**

(9.450)

Belem -0.0735*

(-2.279)

Fortaleza 0.125**

(4.186)

Recife 0.243**

(8.402)

Salvador 0.482**

(16.49)

Belo Horizonte 0.00923

(0.342)

Rio de Janiero 0.794**

(30.65)

Sao Paulo 0.874**

(34.88)

Curitiba -0.345**

(-11.75)

Porto Alegre -0.107**

(-3.670)

Goiania -0.609**

(-19.33)

Brasilia 0.359**

(11.42)

Constant 10.27**

(390.7)

Observations 90,530

R2 0.220

Robust t-statistics in parentheses

** p<0.01, * p<0.05

24

Table

6:

Who

Liv

es

inSlu

ms?

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

VA

RIA

BLE

Ssl

um

no

sew

erno

wate

rno

garb

age

no

elec

trow

ned

no

str

str

str

no

side-

no

paved

no

curb

no

man-

no

tree

slight

garb

age

sew

age

walk

str

hole

hh

inc

0.0

0511**

0.0

0351**

0.0

0134**

0.0

0118**

3.6

4e-

05**

0.0

0545**

-0.0

00489**

7.7

5e-

05

0.0

0343**

-0.0

0834**

-0.0

0530**

-0.0

0572**

-0.0

0889**

-0.0

0783**

(16.4

7)

(8.7

94)

(4.8

31)

(13.9

7)

(2.8

10)

(21.1

0)

(-3.8

61)

(0.2

92)

(10.1

5)

(-18.1

2)

(-17.3

6)

(-12.7

7)

(-13.9

2)

(-17.7

8)

bla

ckbro

wn

0.2

17**

0.1

58**

0.1

15**

0.0

332**

0.0

00203

0.0

477**

0.0

0545*

0.1

48**

0.2

04**

0.0

982**

0.0

592**

0.1

28**

0.1

37**

0.4

44**

(33.3

9)

(24.9

2)

(25.6

6)

(16.3

2)

(0.6

87)

(14.6

0)

(2.1

31)

(22.4

1)

(30.3

9)

(13.4

3)

(11.1

5)

(19.1

7)

(16.5

8)

(60.3

2)

not

marr

ied

0.4

10**

-0.5

69**

-0.3

32**

-0.0

351**

0.0

0163*

-0.4

25**

-0.0

293**

0.0

115

0.0

911**

-0.5

33**

-0.4

13**

-0.2

94**

-0.3

39**

0.1

11**

(26.5

2)

(-35.6

2)

(-29.1

4)

(-6.7

24)

(2.1

23)

(-49.8

0)

(-4.1

22)

(0.7

47)

(5.7

66)

(-32.6

6)

(-33.4

4)

(-20.2

9)

(-17.0

8)

(6.1

77)

fem

hea

dhh

0.1

24**

-0.0

577**

-0.0

571**

-0.0

362**

-0.0

0276**

0.1

70**

-0.0

232**

0.0

213*

0.0

297**

-0.0

134

-0.0

206*

-0.0

260**

-0.1

43**

0.0

330**

(11.7

8)

(-5.3

91)

(-7.4

62)

(-10.1

5)

(-4.1

53)

(33.0

3)

(-5.0

62)

(2.0

60)

(2.8

17)

(-1.2

01)

(-2.4

00)

(-2.6

46)

(-11.2

7)

(2.8

89)

hh

size

0.1

18**

0.0

262**

-0.0

516**

-0.0

237**

-0.0

00198

0.0

262**

-0.0

0395*

0.0

0387

0.0

562**

0.0

0622

-0.0

525**

-0.0

112**

0.0

763**

0.1

06**

(27.9

0)

(6.6

16)

(-18.4

9)

(-16.8

7)

(-0.7

38)

(13.1

4)

(-2.1

99)

(0.9

88)

(13.5

6)

(1.4

96)

(-16.8

1)

(-2.9

77)

(15.8

6)

(23.6

0)

illit

hea

dhh

0.1

43*

0.3

77**

0.0

933*

0.2

96**

0.0

0114

0.2

84**

-0.0

0602

0.4

71**

0.4

28**

-0.2

43**

-0.0

468

0.1

07

0.3

25**

-0.7

71**

(2.1

63)

(6.8

48)

(2.0

53)

(10.7

9)

(0.2

63)

(9.7

52)

(-0.1

89)

(7.5

25)

(6.6

94)

(-3.7

06)

(-0.8

95)

(1.8

23)

(4.9

44)

(-12.5

8)

illit

nonhea

d1.7

74**

2.4

72**

0.8

43**

0.3

56**

0.0

387**

-0.3

13**

0.5

72**

1.4

35**

1.7

15**

2.2

38**

1.6

66**

1.5

52**

0.2

22**

0.4

73**

(21.3

5)

(35.0

5)

(15.1

6)

(10.8

0)

(5.5

16)

(-8.1

99)

(14.2

3)

(18.6

1)

(21.8

7)

(26.9

3)

(25.0

3)

(20.9

8)

(2.7

12)

(6.1

57)

Const

ant

-0.6

87**

0.1

60**

0.2

90**

0.0

703**

0.0

00104

0.6

97**

0.0

367**

-0.0

510**

-0.2

86**

0.2

78**

0.3

48**

0.1

93**

0.3

16**

-0.2

54**

(-42.2

1)

(9.9

61)

(25.1

5)

(13.2

1)

(0.1

09)

(85.7

5)

(5.2

88)

(-3.3

63)

(-17.6

6)

(16.5

9)

(27.8

4)

(12.7

3)

(16.3

5)

(-14.2

5)

Obse

rvati

ons

90,4

52

90,4

52

90,4

52

90,4

52

90,4

52

90,4

52

86,0

61

86,0

61

86,0

61

86,0

61

86,0

61

86,0

61

86,0

61

86,0

61

R2

0.1

76

0.2

57

0.0

82

0.1

33

0.0

24

0.1

02

0.0

45

0.1

24

0.1

72

0.1

64

0.1

29

0.1

32

0.0

85

0.1

44

Robust

t-st

ati

stic

sin

pare

nth

eses

**

p<

0.0

1,*

p<

0.0

5

25

Table 7: Squeezing of the Formal Housing Market

(1) (2) (3) (4)VARIABLES rent rent log rent log rent

pop 4.40e-05** 4.73e-05** –(5.466) (5.695)

log pop – – 0.0835** 0.0956**(3.611) (4.069)

slum pop share 546.9** – 0.818* –(3.630) (2.030)

slum land share – 953.5† – 0.933(1.817) (0.674)

rooms 69.86** 77.51** 0.160** 0.176**(4.244) (4.555) (3.902) (4.260)

no brickwork -61.08 -110.8 -0.0427 -0.107(-0.598) (-1.053) (-0.164) (-0.409)

200m water 200.6* 211.8* 0.640** 0.667**(2.435) (2.477) (3.057) (3.151)

200m riverbank 28.93 30.38 0.193 0.216(0.427) (0.432) (1.095) (1.212)

200m railway -93.29† -104.2* -0.246* -0.255*(-1.971) (-2.128) (-2.070) (-2.111)

slope 3.284* 3.368* 0.0121** 0.0125**(2.087) (2.063) (3.044) (3.093)

distance -0.553 -0.850 0.000660 0.000453(-0.944) (-1.389) (0.398) (0.269)

no sewer -13.56 -38.33 -0.146 -0.181(-0.268) (-0.740) (-1.151) (-1.420)

no water 20.41 48.96 0.0656 0.103(0.325) (0.757) (0.417) (0.647)

str garbage -202.5† -292.3* -0.241 -0.381(-1.798) (-2.588) (-0.854) (-1.376)

str sewer 21.60 116.1 -0.344 -0.192(0.200) (1.079) (-1.267) (-0.729)

no sidewalk -78.77 -83.78 -0.139 -0.140(-1.082) (-1.104) (-0.749) (-0.744)

no paved str 151.9 182.3 0.224 0.266(1.302) (1.510) (0.763) (0.896)

no curb -118.7 -145.4 -0.152 -0.196(-0.947) (-1.120) (-0.482) (-0.616)

no manhole -195.5** -198.5** -0.459** -0.457**(-4.605) (-4.502) (-4.246) (-4.166)

no trees -30.20 -26.01 -0.167 -0.172(-0.656) (-0.545) (-1.430) (-1.445)

Constant 136.2 129.0 4.325** 4.142**(1.529) (1.396) (10.83) (10.26)

Observations 145 145 145 145R2 0.714 0.692 0.695 0.687

t-statistics in parentheses** p<0.01, * p<0.05, † p<0.10

26

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29

Footnotes

1For example, in the city of Dhaka, Bangladesh squatter settlements are estimated to provideas much as 15% of the housing stock (World Bank, 2007).

2During the period 2000–2010, the federal and local Brazilian governments attempted to ex-tend sanitation in slums. Between 2007 and 2010, through its “program of growth speeding”(“programa de acelerao do crescimento”, PAC) the Brazilian federal government investedUS$212 million in slum upgrading policies and US$580 million in improved sanitation.

3Assigning a slum designation to enumeration areas in a nationwide census is not a trivialtask since it requires a slum definition that applies to the entire territory and is sufficientlyprecise to be understood and implemented by Census staff nationwide. The Census in 1953introduced the concept of “subnormal clusters” as a guide to slum designation, and it hasremained the same since despite small changes in terminology. According to IBGE (2011),subnormal clusters are enumeration districts with some of the following characteristics: (i) atleast 51 households; (ii) occupation of land without formal titles; (iii) irregular urbanizationconsisting of narrow lanes of irregular alignment, uneven lots, or buildings not conformingto urban standards; (iv) lack of public services such as water, sewage or garbage collection;(v) poor topography due to steep slopes or a propensity to flooding.

4We use data from Topodata (Valeriano and Rossetti (2012)), which provides information onaverage slopes at 90 × 90 resolution based on Shuttle Radar Topography Mission (SRTM)data. Slope raster files are matched with enumeration district polygons, and the averageslope in each polygon is computed.

5For other theoretical models of squatting, see Jimenez (1984), Turnbull (2008), Hoy andJimenez (1991), and Shah (2014). Three more-recent studies by Heikkila and Lin (2014),Posasa (2018) and Cavalcanti, da Mata and Santos (2018) develop additional theoreticalmodels of slum formation (the latter paper also contains empirical work testing its model).

6Typically, rented units in slums are extensions of an owned property (so the landlord is theneighbor of the tenant).

7Because Recife’s values for some variables are missing, its enumeration districts are droppedin the regressions of Table 3.

8For simplicity, we do not carry out the usual exponential adjustment to the percentageimpacts of dummy variables with large coefficients.

30