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

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* 9.9*9 .* . 4. . . . . . . * . * * . * * PAGE 2 Of 2

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TABLE 3DI 1)02 .13 CROiS%iAIJLI.AON0~S Of OISItANCiES (A'E:;-WORK) BOGOTA

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COLtIJMN 411 91 103 318 106 18 717 62' 58 170 13410

(CN1 N (E)TAl. 35.1 7.2 7.1 8.8 7.9 5.8 5-7 4.6 4.3 12.7 100.0

D1.02.t3. C1ROSSI'ALl1IAIIANS or DiSrANCES (RE1S-WOUK) a

* * + + + * ' . .* * .* * . * .4 C R l S S T A n U L A I I O n * . * * * 4 *

tJicWI) IIllEAAD.olST OEIWEEil RES. AND WJORK BY NEWID3 1111 AtlD S.W0RKEfn.O1YS BETWEEN WORKKZONES

* ' * 4 4 * * * * ' - 0 * # * 0 . ' ' * * * * ' * * * * . *. * * * PAGE 2 Of 2

NEW03COJNI I

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coI PCI I TOlAI.

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a 1 30 1 14 1 7 1 10 1 10 1 10 1 5 1 3 I II 1 16 1 123

1 24.4 1 11.4 1 5.7 1 8.1 I 8.1 1 8.1 1 4.1 1 2.4 1 14.6 1 13.0 1 9.2

1 6.4 1 14.4 1 6.8 1 8.5 1 9.4 1 12.8 1 6.5 1 4.8 1 31.0 1 9.4 1

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COL.UiMN 471 97 103 1la 106 78 77' 62 58 170 1340 H

lofAl, 35.1 7.2 7.- 8.8. 7.9 5.8 5.7 4.6 4.3 12.7 100.0

RAW Cill S0UIAIRV = 549.66573 WITII 8l DEGREES OF FREEDOM. SIGNIFICANCE 0.0000

TABLE4

DIf2L) IO&A~J iUI FDSACS~BOGOTA Page 1

D2 x D3

* . .. **~* ~~.** * * . C R~ O3S Si A 13 L -A I IO0N aOf * ******* * *****

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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

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1 22 71 6 1 9 1 7 1 261 1 3 0 O

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22-

and secondary workers travel long distances but to the same workzone.

The last location pattern is made up of combinations of cases (see

Graph No. 1 for the graphic illustration of cases). Table 5 suimmarizes

the percentage distribution of cases for Bogota.in 1972 and 1978 among the

five patterns illustrated. Notice the decline in the number of short

commute cases in moving from one year to the other. What factors

explain the commuting patterns observed? First, age of the worker.

Very young workers and very old ones commute very short distances.

Seondly, education: illiterates or workers with primary education

have very short commutes. And thirdly occupational status: self

employed workers or family workers exhibit short journeys to work. On

the basis of these findings we classify additional workers into two

categories: workers type 1, made up of workers with high education,

within 15 and 50 years of age, and not being self employed. The

remaining workers are labelled workers type 2. For each category

of workers, household heads, secondary workers type 1 an\- secondary

workers type 2, we estimated employment density gradients for both

years. The results can be observed in Table 6. As seen in this

table the jobs of secondary workers type 2 are more decentralized

than either the jobs of household heads or secondary workers type 1.

There is then some correlation between the type of skills required

on the demand side of employment and its location. In short more

qualified workers are more likely to have central job locations, while

GRAPH 11

(;CP4 'PIIIC 1IE' lIESEN'TAT'ION O*

CONNhIUTING PATTElJINS

S.Wl. Workzone

lilt /1ork Zon-e

Residence " - UU Irk -or--

S.1V. W,ork Zone -

Residetice

15-0

DI' D 2 D OTIIER 1111 CObUTES

2 3 C)34 1

Resiudence Residence 2-11. it .Work Zone S.W. lork Zoie illt Work Zone

S.W. Work Zone

- S.l. CO:CIUTES | 3- JOIhIT COMlrurr

- 24 -

TABLE 5

BOGOTA 1972 - 1978

PERCENTAGE DISTRIBUTION OF RESIDENTIAL LOCATION PATTERNS

Percentage of cases PercentageType of commute pattern 1972 1978 change per

year

Short-commute 14.5 6.4 -1.01

Household head commutes 11.8 7.3 -056

Secondary worker c.ommutes 12.0 9.0 -0.37

Joint commute 27.0 28.1 0.13

Other cases 34.7 49.2 1.81

TOTAL 100.0 100.0

TABLE 6

BOGOTA 1972- 1978

EMPLOYMENT DENSITY GRADIENTS BY TYPE OF WORKER

YEAR

TYPE OF WORKER 1. 9 7 2 1. 9 7 8

b R2 F D0 b R2 F

Household heads. 166.0 0.25 .65 68.8 146.3 0.23 .66 71.9

Secundary Workers I 128.5 0.283 .55 47.7 106.1 0.45 .43 29.5

Secundary Workers II 110.0 0.22 .73 97.6 95.6 0.18 .65 72.7

En

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

1 9.3 19.5 2.1 12.7 185.1 2.3 66.2 9.4 33.2 2.2 9.5 192.5 4.7 57.3 14.3 234.6 2.2 7.0 127.0 7.9 68.4 2.8 25.3 2.4 7.3 149.4 4.7 70.5 7.0 25.1 2.8 4.5 214.9 8.1 62.

6 1.3 16.0 3 2.3 163.2 5.3

7 1.7 23.4 2.7 0..7 132.9 6.8 53.

8 4.4 30.0 2.4 2.5 275.8 7.0 70.9 10.0 21.0 2.5 3.3 141.8 2.7 70.

10 16.7 39.5 2.2 2.6 251.1 2.8 75.

11 9.8 16.9 2.3 9.6 207.8 2.0 61.

12 18.3 20.5 1.9 6.7 142.0 64.

TOTAL 7.9 24.1 2.4 5.3 179.3 4.6 65..

- 30 -

TABLE 7

BOGOTA 1978

CONSUMPTION OF HOUSING SERVICES (Q) FOR RENTERS WITH

MONTHLY INCOME GREATER THAN $2. 000

(Housing consumption standarized to Unity )

Mean value Regression t Meanof vriables Coefficients Values Elasticities

Q 1.00

Rent 2.28

Price 3.36 1/ -. 2162 19.6 * -. 727

Household head Inco-me. 8.14 1/ .0810 9.5 * .660

Sec WorkerIncome 3.07 1/ .0540 5.3 * .166

No of persons 4.37 .0327 1.8 ** .143

Sex of H.Head 0.84 -. 1440 1.4

Age of H.Head 34.35 .0009 2.6 * .311

No of workers 1 0.25 -. 2792 3.1 * -. 069

No of workers 2 0.38 -. 1381 2.3 * -. 057

Constant 0.7391R2 .40

No of observations. 908

1/ in thousand pesos

* Signifficant at the 99% level

** Significant at the 95% level.

- 31 -

persons in the household) a series of attributes of the household head

(age, sex, occupational status, workzone) and the income of both house-

hold heads and secondary workers. Results are displayed on TAble No. 8

As might be seen from the results, as housing consumption increases,

6/travelled distance increases as predicted by residential location models.-

As income rises so does the length of the journey to work. Male house-

hold heads have longer journeys to work, older males travel shorter

distances, and those self employed reduce their journey to work.

Secondary workers type 1 having more central locations contribute to a

reduction in the commuting distance of household heads while, secandary

workers type 2 increase it. A separation of workers clearly improves

the understanding of their impact ot residential location. Running the

same regression with no discrimination of workers by type shows no secondary

workers effect. However, the coefficient of the secondary workers income

is not very clear. As their income rises distance travelled by the head

declines. The negative relationship might be due to the correlation

between number of workers type 1 and secondary workers income. Most of

the secondary workers income comes from the earnings of type 1 additional

workers.

V. CONCLUSIONS

We have tried to illustrate with this short essay the impact

of the number of workers in a household in terms of the selection of a

place to live and the consumption of housing services. In the selection

of a place to live the household takes into consideration the job locations

of all members in the labor force suggesting some form of joint

-32-

TABLE NO.8

BOGOTA 1978

CONMUTING DISTANCE OF HOUSEHOLD HEAD (D1 ) 1/ FOR RENTER

WITH HOUSEHOLD MONTHLY INCOE GREATER THAN $2.000

VARIABLE Mean Value Regression t Estimated

of Variable Coef'Cficient Values elasticity

D1 5.264

Q 0.74 .1100 2.50 0.64

Price 3.36

Head Income 8.14 0.040 3.60 .16

Sec.Worker income3. 0 7 -0.030 2.10 -. 57

NIo of.PerSOnS 4.37 0.3131 6.05 .03

Sex 0.84 0.1632 1.96

Age 34.35 -0.2065 5.33

No of Workers 1 0.25 -0.3614 2.01 -. 58

No of workers 2 0.38 0.93010 2.71 .15

Self employed 0.24 -0.1621 2.49

Works at CBD 0.17 1.1695 2.65

Age Squared 129. 89 0.0013 1.84

Constant 9. 884

R2 .24

No of cases 908

1/ Distance measured in kilometers

2/ in thousand pesos.

- 33 -

optimization. However, the impact of the additional workers is three

fold. First, the extra income earned by them increases housing

consumption, forcing a more peripheral location. Second, an increase

in the number of workers in the household reduces the amount of space

required by the household canceling somewhat their income effect. And,

thirdly, when employment is not monocentric the household head increases

or reduces his travelled distance depending on whether the additional

worker travels in the opposite or same direction to the head's

workzone. Since in our sample most of the household heads and secondary

workers type 1 have central job locations while secondary workers type 2

have more peripheral ones, we get opposite signs by type of workers.

Taking all these factors into consideration, for the case of Bogota's

renters, additional workers produce an increase of 4% in housing

consumption and a decline of roughly 6% in the length of the journey

to work of the household head. Their effect in the future will depend

on the wages paid to the additional workers and on the changing location

patterns of job opportunities available to them. This is not to say

that increasing participation rates might not also affect residential

location by alterning in the long-run the household socio-demographic

characteristics. But in the short run secondary workers are having

an influence in the patterns of residential location.

I''::C.7 '.1;.,.- a ;.>^, :.....,,- .. ,. -b.- ..:Y=-.

- 34 -

REFERENCES

1) Montesano, Aldo " A Restatement of Beckmenn's Model on the

Distribution of urban rent and residencial Density" Journal

of Economic Theory, 4.1972 pp 329-354

2) Michelle J. While. " A model of Residential Location Choice

and Conmuting by Men and Women workers" Journal of Regional

Science, Vol.17, No.1 1977 pp 41-52

3) Janice Fanning Madden, " Urban and use and the Growth in two-

Earner households" Paper presented to the American Economic

Association Meeting, Atlanta, 6a. XII/29/79.

4) Gregory K. Ingram." Housing demand in the Developing

Metropolis" Paper presented to the Annual Meeting of the

Econometric Society, Atlanta, 6a. CII/79.

5) Alvaro Pach6n" Auto-ownership and Model Choice in Bogota"

mimeo Washington, 1980

6) See for example WillialLt Alonso, Location and Land use.

Toward a General Theory of Land Rent. Harvard University

Press.Cambridge, Mass, 1964 pp 171-172