Urban and. Regional Report No. 81 10 RESIDENTIAL LOCATION DECISIONS OF MULTIPLE WORKER HOUSEHOLDS IN BOGOTA, COLOMBIA By Jose Fernando Pineda July, 1981 This report was prepared under the auspices of the City Study Research Project (RPO 671-47) as City Study Project Paper No. 22. The views reported here are those of the author, and they should not be interpreted as reflecting the views of the World Bank or its affiliated organizations. This report is Deing circulated to stimulate discussion and comment. Urban and Regional Economics Division Development Economics Department Development Policy Staff The World Bank Washington, D.C. Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized
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RESIDENTIAL LOCATION DECISIONS OF - World Bank · 1. Rafael Stevenson, "Housing Programs and Policies in Bogota: An Historical/Descriptive Analysis," Washington, D.C., The World Bank,
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Urban and. Regional Report No. 81 10
RESIDENTIAL LOCATION DECISIONS OF
MULTIPLE WORKER HOUSEHOLDS IN BOGOTA, COLOMBIA
By
Jose Fernando Pineda
July, 1981
This report was prepared under the auspices of the City StudyResearch Project (RPO 671-47) as City Study Project Paper No. 22. The viewsreported here are those of the author, and they should not be interpretedas reflecting the views of the World Bank or its affiliated organizations.This report is Deing circulated to stimulate discussion and comment.
Urban and Regional Economics DivisionDevelopment Economics Department
Development Policy StaffThe World Bank
Washington, D.C.
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ABSTRACT
In Bogota, as in many other cities of the world, the number ofworkers per household is increasing,as women and other household membersjoing the labor force in greater numbers. The number of workers per householdin Bogota has increased,from 1.42 in 1972 to 1.70 in 1978, for example.Preliminary analyses in other countries has suggested that increasing thenumber of workers per household has virtually no effect on residentiallocation on housing consumption of households, and this results is confirmedin Bogota when all workers other than the household head are treated as asingle group of secondary workers. However, when secondary workers are stratifiedby their level of qualification (adult and educated vs. others) or by theirworkplace (central business district vs. other) significant and offsettingeffects are observed that tend to cancel each other out when all secondaryworkers are pooled. In terms-of residential location effects, qualifiedsecondary workers tend to move the residential location of the householdtoward the center of the city and to shorten the head's commuting distancewhile other secondary workers move the residential location of the house-hold away from the center of the city and lengthen the head's commutingdistance. These-effects seem to reflect some joint optimisation acrossworkers on the part of the household because qualified secondary workersare likely to work in the city center while other secondary workers tend towork at more peripheral worksites. The presesnce of secondary workers alsohas some effect on housing consumption: secondary workers decrease housingconsumption by their presence but increase it through their added income.Taking these two effects together, secondary workers increase housingconsumption slightly. The empirical results in the paper are based on asample of renter households in Bogota collected in 1978.
PREFACE
This paper forms part of a large program of research grouped underthe rubric of the "City Study" of Bogota, Colombia, being conducted at theWorld Bank in collaboration with Corporacion Centro Regional de Poblacion.The goal of the City Study is to increase our understanding of the workingsof five major urban sectors -- housing, transport, employment location, labormarkets, and the public sector -- in order that the impact of policies andprojects can be assessed more accurately.
The author thanks Gregory K. Ingram and Alvaro Pachon for helpfulcomments and discussions. He also thanks Sonia Rodriguez and Elsa de Cranefor valuable research assistance, and Leticia de Noriega and Maria ElenaEdwards for manuscript preparation.
Other City Study Papers dealing with housing and residentiallocation include:
1. Rafael Stevenson, "Housing Programs and Policies in Bogota:An Historical/Descriptive Analysis," Washington, D.C.,The World Bank, Urban and Regional Report No. 79-8,June 1978 (City Study Project Paper No. 3).
2. Alan Carroll, "Pirate Subdivisions and the Market forResidential Lots in Bogota," Washington, D.C., WorldBank Staff Working Paper No. 435, October, 1980.
3. Gregory K. Ingram, "Housing Demand in the DevelopingMetropolis: Estimates from Bogota and Cali, Colombia"Washington, D.C., World Bank, Urban and RegionalReport No. 81-11, June, 1981 (City Study ProjectPaper No. 20).
L ..... .
I. INTRODUCTION
The changing labor force composition of households in developing
countries has stirred some interest on the impact of additional family
workers on housing consumption and residential location. For Bogota in
1972 the average number of workers per family was 1.42. In 1978 this
numwber had risen to a comparable figure of 1.70, equivalent to an annual
increment of 3%. The distribution of households by the number of workers
can be obserVed in Table 1. Households with more than one worker represented
half of the total number of households interviewed in 1978, and 20% of
the interviewed families had three or more family memberF who declared
some form of gainful occupation. From 1972 to 1978 the proportion
of households with jus't one worker had been"reduced"by 1'0%.
One of the major obstacles to the measurement of the consequences
of this phenomenon is the multiplicity of effects generated by the
presence of additional workers in the household. This difficulty is
further aggravated by the untenable nature of some of the traditional
assumptions of residential location models a la Alonso, such as the
monocentricity assumption. In moving from the monocentric to the
multicentric city, space loses its one dimensional quality, and makes
the analytical treatment of the subject somewhat more complex. In this
brief essay, part of a larger work on residential location patterns, we
present some estimates of the impact of additional workers on housing
consumption and residential location in Bogota. The first part of the
-2-
essay reviews briefly the residential location models and some previous
findings. The second part provides a brief summary of Bogota and a
description of the data base. The third section presents some of the
results obtained, and the last part is a summary of conclusions.
II. RESIDENTIAL LOCATION MODELS
In the traditional residential location models households have
two elements in their utility function: housing, Q, and other goods, Z.
The price of housing is made up of a series of attributes like age of the
dwelling unit, quality, amenities in the neighborhoodl and distance to
the workzone. One of the major contributions of these models is their
demonstration that a differential accessibility rent is reflected in the
price of housing when the other housing attributes are controlled. As
shown by Montesano, having an additive utility function and transport
costs that are positively associated with distance to the workplace
results in a housing price that declines with distance from the
1/workzone-- In addition the household incurs some transport expenditures
that decline with proximity to the employment center. In an equilibrium
condition the marginal costs of transport with regard to distance should
be equal to the absolute value of the marginal decrement in the expenditure
on housing while holding its quantity and quality constant.
For each quantity of housing consumed we can then find a
minimum expenditure location as shown in figure 1. The actual amount
of expenditure on housing and the remaining portion of income destined
-3-
TABLE 1
BOGOTA 1972- 1978
% DISTRIBUTION OF HOUSEHOLDS ACCORDING TO NUMBER
OF WORKERS PER HOUSEHOLD. 1/
YEAR
Number of. 1972 1978
worker in the
household
1 60 50
2 23 30
3 9 12
4 and more 8 8
TOTAL 100.0 100.0.
1/ Sources 1972: Phase II household survey
1978 : World Bank- DANE household survey (EH-21)
to other goods consumption at the i-Lnimum expenditure point indicates
not only the housing expenditure but also the optimal location for
that given amount of housing and the expenditures on Z. This is
graphically shown in Figure 2. This very simple description allows
us to understand the impact of having additional workers. Still holding
the monocentricity assumption, an extra worker implies an incremental
expend .:ure on transport and, ceteris paribus, a more central location.
However, it also means an increase in the household income which might
be represented by an increase in housing consumption, forcing a more
peripheral location. But in addition it also represents a decrease
in the leisure time available to the household. If there is some degree
of complementary between housing and leisure time, an extra worker
implies, ceteris paribus, a decrease in the quantity of housing services
purchased. Again this effect would point to a more central location.
Michelle J. White seeked to derive a bid-price function for two earner
households where the husband works at the center and the wife at a
2/peripheral location.- The housing price offer curve depends on the
value of the wife's leisure time (relative to her husbands) in the
household's utility function. Since the slopes of the bid-rent
functions is known but not their intercepts, White attempted to show by
trial and error, that households with two workers would seek to locate
closer to the wife's work place. However, this depends OI the assumption
that the bid rent function of two worker households is flatter between
THE RESIDENTIAL EQUILIBRIUM OF THE HOUSEHOLD - 5 -
Total Residential\ | Costs (Q1
Total ResidenCosts (QO )
(Housing + Trans.Cost'
,Transport Costs
\Housing Costs
Housing Costs (Q )
C.B.D. t t Distance to CBD.
FIGURE 1
z
II (Q,Z)
I QO Q, (quantity of housing)I0
Distance to CBD
t t
Q Q, (quantity of housing)
FIGURE 2
-6-
the two job centers than the one worker households. This is only true
if the diffzrence between the husband's and wife's wage rates are smaller
than the commuting outlays, a fact that does not necessarily hold. So,
two earner households might well stay closer to t:ie husband's job then
one worker households.
Partially based on White's reasoning, Madden developed a
model of three simultaneous equations to analyze differences in housing
consumption between one and two earner households.-/ The first equation
explaina the separation between place of work and residence for a worker
in a multi-worker household as a function of each worker's wages, housing
prices, number of hours worked by each household worker, and the
household's unearned income. Wages are in turn a function of workplace
location and worker's attributes, and housing prices depend both on the
workplace-residence separation, and on a vector of market attributes.
The second equation is for housing size, in turn a function of the separation
between work and residence, each worker's income, household unearned
income, and household demographic characteristics. The third equation is
for quality of housing, also a function of the variables included in the
previous equation.
These equations are estimated across several cities simultaneously
using Survey Research Center Panel Data on income Dynamics. The prodedure
followed is to estimate wages first as a function of distance of job from
residence (instead of distance from job to city center) and then include
7
it to the estimate the value of the workplace to residence distance with all
the additional variables. Number of rooms in the househol's housing unit
represents housing size or quantity and price per room indicates quality.
The three equations are estimate separately for men and women workers
since sex is correlated with demographic labor force characteristics.
Madden concludes that differences in housing consumption
between one earner and two earner households are fully explained by their
differences in money income and fertility. In contrast to White's
conclusions, Madden finds that men from two earner households live closer
to their jobs than men with either non-employed wives or employed wives
and employed children. However none of these locational differences are
statistically significant. In short, one and two earner households
behave just the same when other relevant variables are controlled.
The methodology we use is somewhat different. First we
estimate housing prices by using the workzone stratification approach
employed by Ingram.4/ Briefly described, the theoretical approach
derives from the well known facts that workers commute down the rent
gradient from their workplace and do so in the steepest direction and
that workers facing the steepest gradient have the longest commute.
Hence different workplaces imply different residential areas and these
differences in location are derived From the trade offs between housing
prices and travel costs. The different workplace opportunity sets
available to workers provide the basis for workplace-based price variations
when using cross-section data. The hedonic price equations are obtained
by regressing che rent of a unit on its variables include d4.stance to
-8-
the workplace, distance from workplace to the city's CBD, type of building
structure and quality of the dwelling unit. Having defined an "standard"
housing unit we estimate workzone specific housing prices by multiplying
the estimated coefficients of each workzonie by the respective attribute
of the "standard":ihousing unit. If we consider rent expenditures as
price times quantity we obtain quantity of housing by dividing monthly
rent by price.
Once quantity is obtained we proceed to estimate the housing
demand equation introducing income, household size, sex of the household
head, price, and number of workers as independent variables. We also
seek to explain workplace-residence separation as a function of quantity
of housing consumed and household characteristics. The quality of housing
is taken care of in the hedonic price index. Since all workers in a
household live in the same residence, their impact on location is
estimated by looking at the length of the journey to work of just one
of them. We have chosen the cmmmuting distance of the household head as
our dependent variable in the equation that explains workplace-residence
separation.
III. BOGOTA AND THE DATA BASE
Bogota is a city of roughly 4.000.000 people located in the
highlands of the Colombian Andes. During the early 1960's its population
expanded at'annual geometric rates close to 7%. In the middle of the last
decade its population growth rate has declined to less than 4% as a result
-9-
of a decline in its crude birth rates (from 4.2% in 1960 to 2.6% in 1978)
and a reduction in the inflow of migrants. From 1972 to 1978 its real
per capita income grew at something close to 3% per year and auto owner-
ship expanded from 14% to 18% of all households.
The city has also become more decentralized. Estimates of the
5/populatiorn density gradient are -0.177 and -0.112 for 1972 and 1978 respectively,
The estimated dernsity at the center ,or che two yea7:s went down from 356.7
inhabitants per hectare to 262.6. Employment has also become more
decentralized. In 1972 the Central Business District had 22% of the total
number of jobs existing in the city and this percentage went down to 14%
six years later. Anpther indication of the same phenomenon is the change
in employmei%t density gradients. Using a negative expenential function,
the values of the gradient are -0.240 and -0.204 and the intercepts 135
and 116 jobs per hectare for 1972 and 1978 respectively. Bogota is
located in a plateau against the eastern range of mountains. The city
resembles a semicircle with the mountains tracing the division (see
Maps 1 and 2). The CBD is more or less at the center, and the wealthiest
neighborhoods are located along a corridor that goes from the city center
toward its northernmost portion. Low income hou3seholds are located almost
everywhere. One could say it is the upper income groups that are-
seggregated and not low income households, Growth has occured in the
periphery of the city as can be observed on map 3, which shows population
growth rates by distance from the canter for the 1924-1972 period. In
- 10 -
MAP No. 1
BOGOTA 1910
aw,~
- )7/
IAP 2
- 21 BOGOTA - 1975
IV (Shadowed area corresponds to city limits in 1910)
+%|{. e .. ,,t tg,t ^jlSt................Figlure 2. Bogota and vicinity
MCLCINTRO oi-N X
i, al-
BABR CHIC op.ado ; |%LdzAOT CENTE7 n O- ~CANDELAR4IA
IvonIa 2OdeJUL'- --
| SN FE"NArp E, II ZEtEPOTO
A.9
^ CSK ,- / /~~AEROPUEIT(v flsi
}t +- / t /2 /8 ELDORADO { >h> \
X/ . D \ jih-up ar.
-3 .- 4 1 fMadr/id y cIA I
t,. HG
r-M LA A
BOGOTA 1964e-1973-12 -A
30C-OTA
INTERCENSAL GROSITH RATE
OF POPULATION BY RING
1.29r
0\5\7&9 3 .43 ,' <X
5.65 2. . - /,
- 13 -
short Bogota shows a marked change in its spatial structure and all the
signs of the emergence of a sub-urbanization process. However, given
the growth of the city administrative boundaries most of the population
living and working in the city is covered by only one local administration.
This differentiates the city from large urban agglomerations in North-
America where there is much local government fragmentation and
different utility companies within the same metropolitan area. Our
data comes from two households surveys. The first one was carried out
in 1972 by a United Nations study on transportation. It includes roughly
4.000 households who were living within the city administrative boundaries.
The survey contains information on transportation, housing, labor force,
and employment location. In 1978 the World Bank City Study project
sponsored a household survey to update the 1972 information. This survey
covered 3.056 households and represented some marked improvement on the
quality of the information obtained, in particular with regard to income
estimates. Every worker in the household was interviewed about his or
her inco:,e from all sources including fringe benefits, something that was
not included in the 1972 household survey. The city was divided into 38
zones, called communas by the National Statistical Agency. Expansion
factors were calculated at the comuna level (see Map 4 for the comunas
division).
In the file created to carry out this analysis we excluded
households where some of the income information was missing. In addition,
households with no workzone data or with workers commuting outside tf the
MAP No. 4
BOGOTA - BCIJNDARIES OF COMUNAS
CFAA
ANf\ ,<1
' K..,, \,J( ,- t
-,- .'-f "f --- -
*J '-\ ,-I
(7, (v, )
- 15 -
city boundaries were also excluded. We also eliminated workers with no
fixed place of work: taxi-drivers, door to door, salesmen, etc. In the
analysis, maids or domestic servants were not considered to be part of
the household and are not included in the number of persons or number
of workers in a household. Finally, households with monthly earnings
inferior to 2.000 pesos (U.S.$50.00) were also excluded.
IV. RESULTS
Our analysis started with some very simple measures of residential
location. For each household with two or more workers we derived three
distance measures: D1, or distance between the residence and the place of
work of the household head; D2, or distance between the place of work
of the secondary worker and his or her residence' and D3, distance
between the two workzones. In Tables 2, 3, and 4 we present some cross
tabulations of the three distance measures. The entries of each table
represent rounded distance in kilometers. Entry 0, for exmaple, means
a distance between 0 kilometers andJl kilometers; entry 1, between 1
kilometer and 2 kilometers, etc. Looking at these tables we can observe
the existance of five commuting patterns. One is what we call the short
commute case where both the household head and the secondary worker labor
close to home. The second one is called household head commutes, where
the household head travels long distances while the secondary worker
stays close to home. The third pattern is the complement of the second
one: the head commutes less than one kilometer and the additional worker
travels longer distances. The four.h case is where both household heads
4 1 23 1 2 1 6 I 12 I 33 1 5 1 5 1 5 1 2 -1' 9 I 102
J 22.5 1 2.0 I 5.9 I 111.8 I 32.4 1 4.9 1 4.9 1 4.9 1 2.0 1 8.8 I 7.6
I 4.9 1 2.1 1 5.8 1 10.2 I 31.1 I 6.4 I 6.5 1 81.1 1 3.4 1 5.3 11 1.7 1 0.1 I 0.4 I 0.9 1 2.5 I 0.4 1 0.4 1 0.4 1 0.1 I 0.7 1
SI 38 1 4 1 71 1 a 91 15 1 0 1 5 1 31 5 1 9.1
I 40.4 1 4.3 1 7.4 1 8.5 1 9.6 1 16.0 I 0.0 1 5.3 1 3.2 1 5.3 1 7.0'I 8.1 1 4.1 I 6:8 I 6.8 1 8.5 1 19.2 I 0.01 8 .1 I.5.2 I 2.9 II 2.8 I 0.3 I 0.5 I 0.6 I 0.1 1 1.1 I 0.0 1 0.4 I 0.2 1 0.4 1
1 22 71 6 1 9 1 7 1 261 1 3 0 O
1 22.0 I 7.0 1 6.0 I 9.0 1 7.0 1 7.0 I 26.0 1 3.0 1 3.0 1 10.0 1 7.5I 4.7 I 7.2 1 5.8 1 7.6 1 6.6 1 9.01A33.81 I 4.8s1 5.21i 5.91I
1 1.6 I 0.5 I 0.4 1 0.7 I 0.5 1 0.5 I 1.9 I 0.2 1 0.2 I 0.7 1
Equations are of the form Density =Doe brwhere r is distance in kilometers.
- 26 -
workers type 2 certainly have more peripheral job opportunities. If
the household head's workplace is central, this impact that secondary
workers have on residential location will be different depending on
the combination of worker types existing in the household. The
classification of workers by type acts as a surrogate for job location
of workers.
The next step in our analysis is the calculation of the
hedonic price index. We divided the city into 12 workzones (see
Map No. 5) and concentrated our efforts on renters in 1978 for two
reasons. One, because the 1978 data allows the separation of the
Tnhome earned by the household head and by the secondary workers.
And two, because renters are more mobile and can adjust more readily
to changes in job location and family composition.
Our dependent variable is monthly rent. In our independent
variables we have included.
1) Distance from comuna of residence to comuna of work.
2) Distance from comuna of work to the CBD (comuna 31)
3) Use of dwelling unit: 1 if includes some use in addition to its
residential purpose. 0, if exclusively destined to residential uses.
4) Age of dwelling unit.
5) Number of rooms in the dwelling unit.
6) Number of rooms in the dwelling unit squared.
7) Meters from residence to closest bus stop.
8) Type of dwelling unit: (1) apartment, (0) otherwise.
s >.MAP No. 5O\s..: BOGOTA 1978
U0A! O City Workzone Stratification
A * a n d Hiedonic price Indeces
c.cg Ob,. 86 ijf 4 / O 'bs:v3
m~~b Xx>1 3sI,- /
LC s- 0'0 1
-28-
9) Type of dwelling unit: (1) row house (0) otherwise.
10) Condition of building: (1) dilapidated (0) otherwise.
11) Condition of building: (1) deteriorated (0) otherwise.
12) Has private telephone connection (1) yes (0) otherwise.
The mean value of all the independent var.ibles are presented
in Table No.6. Map 5 contains the number of observations for each
workzone as well as the estimated price index for each workzone.
We calculate Q, or the quantity of "housing services" purchased
by dividing the household monthly rent by the corresponding price index.
In Table No. 7 we present the demand equation for "'housing
services" corresponding to a Stone-Geary utility function of the house-
hold. The elasticity for household head's income is statistically
different from the one obtained from secondary workers income.
Secondary workers contribute less of their earnings to housing consumption
than do household heads. The number of secondary workers in the house-
hould also decrease housing consumption a fact that could be attributed
to the complementarity between household leisure time and quantity of
housing services desired. As the leisure time of the household declines
so does the consumption of housing. To measure the im1pact of additional
workers on residential location we take distance between the household
heads workzone and his or her place of residence as the dependent variable.
Q, enters now as an independent variable in combination with variables
related to household composition (number of workers by type, number of
TABLE 6 BOGOTA 1978
MEAN VALUIES FOR INlDE-PEN4DENT VARIABLE:S IN THIE HIEDO1]
Zone % of dwtelling Average Average Commuting Av. % of c cunxits with age of nurmber distance Dist. Apartment liouanother use building of rooms to CE3D to bus