Population and housing

Population Research and Policy Review 15:419-435 (December 1996) © 1996 Kluwer Academic Publishers. Printed in the Netherlands.

Population and housing

ANDREW MASON East-West Center, Honolulu, Hawaii, USA

Abstract. The analysis described here was carried out in response to a political crisis in Australia. In 1994, a Member of Parliament who opposed the use of foreign aid funds for family planning programs blocked the passage of the national budget. The impasse was resolved through a compromise. The use of foreign assistance for population activities was frozen pending an independent inquiry into the impact of population on economic development. A team of nine researchers prepared background papers on population and economic development, health, education, food supply, housing, poverty, the environment, family planning, and human rights. The overall conclusion of the inquiry was that slower population growth will yield more rapid development in most countries, especially in relatively poor, agricultural nations. The purpose of this contribution to the inquiry was to assess how population growth was affecting the housing sector and, in turn, economic development. Among other questions, does population growth increase the demand for residential land, housing, and urban infrastructure? Demographic methods were critical to answering the questions, especially assessing the impact of population growth on the demand for housing.

Key words: Applied demography, Family planning programs, Housing, Population characteristics

The problem

The analysis described here was carried out in response to a political crisis in Australia. In 1994, a Member of Parliament who opposed the use of foreign aid funds for family planning programs blocked the passage of the national budget. The impasse was resolved through a compromise. The budget was passed and the use of foreign assistance for population activities was frozen pending an independent inquiry into the impact of population on economic development. Dennis Ahlburg of the University of Minnesota headed the inquiry. He and a team of eight other researchers prepared background papers on population and economic development, health, education, food supply, housing, poverty, the environment, family planning, and human rights. I was asked to write the background paper on housing.

The overall conclusion of the inquiry was that slower populat ion growth will yield more rapid development in most countries, especially in relatively poor, agricultural nations. The report was presented to the Minister o f For-

420

eign Affairs and the Minister for Development Cooperation and funding for population programs was restored.

The purpose of the inquiry was broader than those of the typical applied demography problem. The team was not asked to assess a specific policy or project, but rather to address a much more general issue, the impact of population on economic development. No estimates of the costs, the benefits, nor the internal rate of return from family planning programs were requested, nor were they provided.

The team was mindful that investment decisions would be based, in part, on the conclusions of the report. Had the enquiry concluded that slowing population growth had no beneficial development impact, the decision to terminate development assistance for family planning programs would have been easily made. However, concluding that slowing population growth has a benefit development impact is insufficient, by itself, to justify development assistance for family planning. In addition, one would need to know the magnitude of the benefits, how fast they would accrue, whether the benefits would accrue to broad segments of society or more narrow interest groups, and the costs of achieving slower population growth. 1

The purpose of my contribution to the inquiry was to assess how population growth was affecting the housing sector and, in turn, economic development. This required that I answer three questions. First, does population growth increase the demand for residential land, housing, and urban infrastructure? Second, are there adverse effects associated with increased demand? For example, will price rise, shortages emerge, or quality deteriorate? Third, will accommodating the demand for housing and urban infrastructure divert economic resources from more growth oriented uses, e.g., investment in manufacturing?

Demographic methods were critical to answering the first of these three questions, especially assessing the impact of population growth on the demand for housing. The remainder of the paper will examine this particular issue.

The context. The study of housing draws heavily on the Asian experience. The diversity of Asia makes it a natural laboratory for studying demographic issues. A number of countries in the region have rates of childbearing that are comparable to or even lower than those prevailing in the USA and Western Europe. In other countries in the region, rates of childbearing and population growth are verY high. Many other countries are in transition from high to low fertility and from rapid to slow population growth. The study of housing is focused on Asia for a second reason - a major share of Australia's foreign aid goes to the neighboring countries of Asia.

421

Asia is a very large, diverse, and dynamic region. The countries of East Asia are the most demographically advanced. Most have total fertility rates (TFRs) that are near or below replacement level. 2 In Japan, for example, the TFR reached 2 births per woman around 1960 and is currently below 1.5. China recently reached near replacement fertility with a speed unprecedented in human history. As a consequence, China's population growth has dropped almost tQ one percent per year, contributing significantly to the slowdown in world population growth.

Until very recently, the countries of South Asia had made relatively little progress in reducing rates of population growth. Recent surveys in Bangaldesh and India, however, document that rates of childbearing have declined rapidly in recent years. In both countries, women are now averaging 3.4 births each. Of the major countries in the sub-continent, only Pakistan, with a TFR exceeding six births per woman, has experienced no significant fertility decline.

The demographic situation in Southeast Asia is also quite varied. In Sin- gapore and Thailand fertility rates are below replacement level. In Indonesia, fertility has dropped significantly and women are now averaging about 3 births each. In the Philippines and most of Indochina fertility rates are higher, averaging about four or more births per woman.

Methodology

Demographic studies of housing typically rely on household projections as a starting point. Given income and other social and demographic characteristics of the household, the demand for new housing units should rise and fall with the number of households being established each year. Housing needs analysis is used to calculate the number of new housing units required to maintain the current ratio of housing units to households.

Using household projections and housing needs analysis neglects a second and potentially important aspect of population and housing - the decline in average household size or, more specifically, the number of children per household. The decline in average household size is potentially much more important than changes in the number of households because famly size is an immediate consequence of family planning programs. By contrast, the number of households is directly affected by family planning programs after a delay of twenty to thirty years. To achieve any reasonable rate of return, the implications of average household size will loom much larger in investment decisions.

Consequently, the analysis of the housing sector assessed the impact both of changes in the number of households and the demographic characteristics of households. To do so requires a household projection model that projects

422

both the number and demographic characteristics of households. One such model is the HOMES model (Mason t 987). 3

Household projections

The HOMES model, applied to standard population projections, yields projections of the number of demographic characteristics of households. The number of households are projected by the sex and age of the household head or marker. 4 In addition, four type of households are projected: (1) intact households, i.e., those in which the head and the head's spouse are both present; (2) single head households, i.e., family households in which the head's spouse is absent; (3) one-person households; and, (4) multi-person primary individual households that consist of unrelated individuals. Intact households are projected by the age of both the head and the marker.

HOMES uses a variation of the headship rate method to project the number of households. The headship rate is calculated in a base year (t = 0) as the ratio of the number of households heads (or markers) aged a (Hao) to the number of persons aged a (Na0):

hao = Hao/Nao. (1)

The number of households is projected as the product of the age-specific headship rate and the projected population in each age group.

Hat = haoNat. (2)

The HOMES model uses headship rates that are calculated separately for male and female heads and for different types of households to project the number of single head, one person, and primary individual households headed by males and females. Projecting the number of intact households poses special technical problems related to matching husbands and wives. The details of the methodology used are provided in Mason & Racelis (1992).

One of the most serious problems with the simple headship rate method is that headship rates may change substantially over the projection period. In some countries, the rules governing living arrangements are relatively stable and headship rates have not changed very rapidly. In Indonesia, for example, overall headship rates changed very little between 1980 and 1990. The rates were especially stable for men (Figure 1), but also changed only modestly for women. In Japan, by contrast, headship rates have dropped precipitously for young men and women and have increased substantially for older adults.

A number of variations on the headship rate method have been proposed to deal with secular change in headship rates (UN 1973). However, given the

423

unproven reliability of these methods, the results presented here are based on constant headship rate methods. 5

Projecting household membership

HOMES projects the number of household members and their age and sex distributions for each household group (type of household and age and sex of the household marker). For example, the model can be used to project the number of pre-school-age children living in households headed by single women who are teenagers; or, the number of elderly living in households headed by their adult children.

The number of household members is projected using kinship based methods. Members other than the head or spouse of the head are projected, first, by projecting the numbers of surviving children, grandchildren, and parents and, second, by determining the numbers of each kinship group who are co-resident. A residual category captures other household members who are assigned to households using probabilities estimated from a base year census.

In the Asian countries to which the model has been applied, around 90 to 95% of all family members are typically captured in the five categories of relatives explicitly modeled and about 5 to 10% in the residual category, other household members.

A detailed, formal description of the methods is provided in Mason, 1987; only a brief description focusing on projecting children and grandchildren is provided here.

Children and grandchildren

The HOMES model relies on a few simplifying assumptions to indirectly estimate the age, sex, and number of surviving offspring to women of each age. The number of surviving offspring of sex s, age a, in year t with mothers aged x (Osazt) is equal to the number of births to women aged x - a in year t - a (Bx-a , t -a ) , times the proportion of the birth cohort surviving to year t, (qat).6

Osaxt = qatBz-a, t -a . (3)

Summing over the age of the mother x yields the total number of offspring in year t:

Osat = qatBt-a. (4)

Dividing Equation (3) by Equation (4) yields the distribution of offspring by age of mother and rearranging terms yields:

424

1.2

0.8

0.6

o .-I-

0.4

0.2

._~_ I I i I i I I I I I i I 1

15-t9 25-29 35-39 45-49 ,55-59 65-6g 75-79 85+ 20-24 30-34 40-44 50-54 60-64 70-74 80.84

Age Group

1111980.1990 i

Figure la. H e a d s h i p r a t e s , m a l e s , I n d o n e s i a , 1 9 8 0 a n d 1 9 9 0 .

O . = t = O at (5)

In a closed population all members are offspring. Thus, Osat is equal to the population of sex s aged a. The second term on the right-hand side is the lagged distribution of births by age of mother. For many countries this information is readily available from vital statistics. Altematively, the distribution can be estimated using historical data or estimates of the female population by age and model schedules of age-specific fertility rates (Mason 1987).

The HOMES model uses the number of surviving offspring in two ways. First, for the base year (1990) the number of surviving offspring provides an estimate of the number of offspring by age and sex who are candidates for membership in households with a female householder aged x. From the 1990 census we know the average number of sons and daughters of each age who

425

¢-

m

-I-

0.8

0.6

04

02 l

0 I 5-t9

1 I I I I I I I I 25-29 35-39 4549 55-59

20-24 30-34 40-44 50-54 60-64 A g e Group

~111980 . 1990/

I L I

65-69 75-79 70-74 80-84

Figure lb. Headsh ip rates, females* , 1980 and 1990. * Inc ludes w o m e n who are the spouse o f a head.

I 85+

are living in households with a female householder aged x. Dividing one by the other yields an estimate of the probability that a son or daughter aged a with a mother aged x will be living in the household of which she or her husband is the head.

The second way in which the information is used is for projections. The number of surviving offspring by own age and age of mother is easily projected given the projected population and the underlying age-specific fertility rates employed to make the projection. The projected number of offspring is then combined with the estimated co-residence probabilities to calculate

426

the projected number of sons and daughters living in the household of their parents.

The information employed to project children of the head can be readily used to project the number of grandchildren (who are children of children).

Housing needs assesment

The housing needs assessment forecasts the number of housing units required to accommodate the projected number of hosueholds at some specified standard determined by a vacancy rate and the extent to which households share housing units. 7 The construction of new units is required to accommodate the additional household and to replace any housing units that have been withdrawn. The number of units to be constructed, Ct, during period t is calculated by:

Ct = /3AHt + wUt_~ , (6)

where AHt is the change in number of households during year t and Ut-~ is the number of housing units at the beginning of the period. The parameter /3 depends on the assumed vacancy rate and the extent to which housing units are shared; 8 w is the withdrawal rate, i.e., the proportion of the units withdrawn from the market each year.

Forecast of consumer spending on housing

Consumer expenditures are forecast with the HOMES model using a system of expenditure equations estimated from a consumer expenditure survey. A detailed discussion of the statistical procedures used are provided in Mason et al. (1992). Briefly, the expenditure system consists of a set of equations with one equation estimated for each of the major expenditure categories. The dependent variables in the system are the share of household expenditure devoted to each category. The independent variables include household income, the age and sex of the household head, the type of household, the number, age, and sex of household members, and other social, demographic, and economic characteristics of the household. Typically, prices are not available. Thus, any forecasts using the expenditure system assume that relative prices will not change in the future.

Each of the independent variables in the expenditure system must be projected. They are then combined with the set of estimated expenditure equations to project or forecast household expenditures, including expenditures on housing.

Table 1. Household projections (in 1000s), Indonesia, 1980-2005

427

1980 1990 1995 2000 2005

Number of households 30 ,377 40,677 46,696 53,365 60,312 Household population 146,692 182,451 199,519 216,510 232,435 Average household size 4.83 4.49 4.27 4.06 3.85 Annual change 995 1,204 1,334 1,389 -

Growth rates* Number of household 0.029 0.028 0.027 0.024 Household population 0.022 0.018 0.016 0.014

* Growth rates are reported in the first year for the interval for which they are calculated.

Results

A complete analysis of housing needs and household expenditures on housing has been completed for three Asian countries: Indonesia, Thailand, and the Philippines. Projections of the number of households and their demographic characteristics have been prepared for 16 Asian countries. Space limitations preclude a complete description of these analyses. Consequently, presentation of results will concentrate on Indonesian projections to the year 2005 and draw on other results only as needed.

Household projections

The number of demographic characteristics of households were projected at five year intervals to the year 2005 using parameters based on the 1980 census. National and provincial population projections were provided by Biro Pusat Statistik, Indonesia's national statistical agency.

During a twenty-five year span, the number of hosueholds is projected to double, reaching 60 million in 2005 as compared with 30 million in 1980 (Table 1). During the same period, the household population is projected to increase by 60% to 232 million. A decline in average household size by a full member is anticipated by 2005, when households are projected to average under four members instead of nearly five, as was the case in 1980.

Over the twenty-five year period charted for Indonesia, slowing population growth is expressing itself primarily through a decline in average household size rather than through a decline in the rate of growth of the number of households. The annual average growth rate for households for the entire period is 2.7%, well above the projected population growth rate of 1.8%.

428

Some slowdown in the rate of growth of hosueholds is evident over the period analyzed, but between 2000 and 2005 the rate of growth is still 2.4%.

The same observation holds for most of the other countries of Asia. Of the 16 Asian countries for which projections have been prepared, only three are expected to experience less than a 40% increase in the number of households between 1990 and 2005: China, Japan, and South Korea. The number of households in both China and South Korea are projected to increase by over 30% (Table 2).

Projecting further out into the future shows that substantial growth in the number of households is likely throughout the region. By 2030, the number of households is projected to have doubled or more in every country but China, Japan, and South Korea. In several countries, the number of households is projected to triple by 2030.

Over the projection period, slowing population growth will have a significant impact on the number of households. The average growth rate of the number of households for 2025-30 for the 15 countries with data available is 1.6% per annum as compared with 2.8% for 1990-95. Regressing the growth in the number of households on the current total fertility rate (TFR) is a simple way to summarize the impact of reduced current fertility on future growth in the number of households: a reduction in the TFR by an additional birth in 1990 is associated with a 21% decline in the number of households projected for 2025.

The impact of fertility decline on average household size is much more immediate and apparent. Again with the exception of China, Japan, and South Korea, average household size in 1990 ranged from four and a half up to seven members per household. A steady decline is apparent in every country save Japan. By 2025, only Laos, Nepal, and Pakistan are projected to have households averaging more than four members each (Table 3).

The application of the housing needs analysis requires no demographic information beyond the projected number of households. However, forecasting changes in consumer spending on housing requires detailed projections of the demographic characteristics of households. Again, projections for Indone- sia illustrate the changes that are projected to occur there and elsewhere in the region. Among the most important findings are the following.

First, virtually all of the decline in average household size is accounted for by a decline in the number of children per household. The number of members under age 15 is projected to drop from 2.0 to 1.1 and the number 15-64 from 2.7 to 2.5. The average number of elderly is projected to increase from 0.16 to 0.21 members per households, a small absolute but a large percentage increase.

Tab

le 2

. Pr

ojec

ted

num

ber o

f ho

useh

olds

as

a pe

rcen

tage

of

1990

num

bers

Num

ber

in 1

990

Yea

r C

ount

ry

(tho

usan

ds)

1990

19

95

2000

20

05

2010

20

15

2020

20

25

2030

Ban

glad

esh

19,3

98"

100

117

138

162

187

214

240

266

292

Chi

na

290,

515"

10

0 11

4 12

7 13

8 14

8 15

8 16

7 17

4 18

0

Indi

a 15

2,00

9"

100

113

128

144

163

182

202

222

240

Indo

nesi

a 40

,679

10

0 11

4 13

0 14

7 16

4 18

1 19

7 2

ll

223

Japa

n 39

,t89

10

0 10

5 11

0 11

5 11

7 11

8 11

8 11

7 -

Kor

ea (

Rep

ublic

of)

11

,355

" 10

0 11

2 12

4 13

3 14

0 14

5 14

9 15

1 15

1 L

ao P

DR

68

7 10

0 11

4 13

1 15

1 17

6 20

7 24

2 28

1 32

5 M

alay

sia

3,66

4 10

0 11

7 13

4 15

4 17

5 19

8 22

0 24

0 25

8 M

yanm

ar

8,35

2 10

0 11

5 13

2 15

1 17

1 19

2 21

4 23

5 25

4

Nep

al

3,32

9*

100

114

130

149

172

198

226

257

290

Paki

stan

15

,555

t0

0 11

8 14

0 16

5 19

6 23

4 27

7 32

5 37

7 Pa

pua

New

Gui

nea

725

100

113

128

145

166

190

216

243

270

Phi

lipp

ines

11

,407

" 10

0 11

7 13

7 15

8 18

1 20

6 23

1 25

7 28

1

Sri L

anka

3,

671

100

113

127

- 14

1 15

6 17

0 18

2 19

3 20

3 T

hail

and

12,4

10"

100

116

133

151

167

183

197

211

223

Vie

tnam

13

,715

10

0 11

7 13

6 15

8 18

2 20

8 23

5 26

0 28

3

Num

ber o

f hou

seho

lds

for

Ban

glad

esh,

Ind

ia, a

nd N

apal

are

for

199

1.

Sour

ces:

*M

ason

199

6; S

ourc

e fo

r Ja

pan:

Mas

on, O

gaw

a &

Fuk

ui 1

992;

all

oth

ers:

Mas

on &

Cho

e 19

91.

~D

430

Table 3. Projected average household size, Asian countries

Year Country 1990 1995 2000 2005 2010 2015 2020 2025 2030

Bangladesh 5.81 5 .51 5.17 4 .83 4 .53 4.26 4.02 3.84 3.69 China 3.99 3.74 3.56 3 .45 3.36 3 .28 3.22 3 .19 3.19 India 5.27 5 .11 4.89 4.65 4.40 4.16 3 .95 3.78 3.65 Indonesia 4.46 4 .25 4.02 3.80 3 .61 3.47 3.37 3.29 3.24 Japan* 3.11 2.99 2.92 2.86 2.80 2.76 2.72 2.71 - Korea (Republic of) 3.72 3.46 3.28 3.17 3 .11 3 .08 3.07 3.09 3.13 LaoPDR 6.09 6 .15 6.16 6.07 5.90 5.67 5.39 5.10 4.77 Malaysia 4.85 4.67 4.46 4.22 3 .98 3 .73 3.57 3.46 3.38 Myanmar 4.98 4 .79 4.57 4.32 4.06 3 .83 3.64 3.50 3.39 Nepal 6.09 6 .03 5 .91 5.73 5.49 5.20 4 .91 4.62 4.34 Pakistan 7.31 7.24 7.09 6.90 6 .65 6 .33 5.99 5.64 5.32 Papua New Guinea 5.39 5.34 5 .25 5.08 4 .83 4.54 4 .25 4 .01 3.83 Philippines 5.34 5.10 4.87 4 .63 4.40 4.16 3.94 3.74 3.58 SriLanka 4.62 4.32 4 .05 3.82 3.64 3.51 3 .41 3 .33 3.27 Thailand 4.63 4 .28 3.98 3 .75 3 .58 3.44 3.34 3 .25 3.19 Vietnam 4.91 4.69 4.44 4 .15 3.86 3.61 3.42 3.27 3.17

Sources: *Mason, Ogawa & Fukui 1992; all others: Mason 1996.

Second, the decline in average household size is not uniform across the household life-cycle. The impact o f fertility decline on average household size is confined to the households with middle-aged markers who are limiting their comple ted family size. For these households, average size was projected to decline quite rapidly. The decline in average family size at the peak o f the l ife-cycle is particularly significant to the housing sector to the extent that housholds make their housing decisions on the basis o f l ifet ime rather than current family needs.

Third, the great majori ty of households will be headed by adults who are relatively young. In 1980 about 45% of all household markers were under the age o f 35 and a lmost 80% under the age o f 50. Relat ively small percentages of households are headed by individuals who have comple ted their childrearing or reached ret i rement age. Little change is anticipated in this aspect o f Indonesian households. In 2005, 77% of all households markers are still under 50 years o f age.

Housing needs forecast

In Indonesia, the 1980 census reported 34.8 mill ion households who resided in 33.1 mill ion housing units; thus, there were, on average, 1.05 households

Table 4. Forecasts of national housing variables, Indonesia, 1980-2005

431

1980-1990 1990-1995 1995-2000 2000-2005

29.8 39.8 45.6 52.1 Housing stock (at beginning of the period)

Required annual starts 1.61 1.92 2.15 2.33 Value of residential construction 5.92 8.59 11.31 14.51

share in real GDP (percent) 5.7 5.7 5.6 5.5 share in real GFCF (percent) 23.3 23.2 23.1 22.5

Housing stock and annual starts are in millions of housing units; the value of annual residential construction is in trillions of rupiah. Forecast housing stock for 2005 is 58.9 million. Value of residential construction assumes that per household real income grows at 3% annually and that the elasticity of housing expenditure is 1.0. GDP: gross domestic product; GFCF: gross fixed capital formation. Campbell & Pasay, undated.

per occupied housing unit. Reliable data on vacancy rates and withdrawal rates are not available for Indoensia, but based on the experience of other countries in the region, a vacancy rate of 3% and a withdrawal rate of 2% per year were used to project required additions as:

Ct = 0.98AHt + 0.02Ut_l. (7)

In order to keep pace with the growth in the number of households, the housing stock would be required to double over a twenty-five year period (Table 4). In 2005, 58.9 million units would be required as compared with 29.8 million units in 1980. Annual housing starts would increase from 1.6 million during the 1980s to 2.3 million during 2000-2005.

Forecasts of the real value of new residential construction shows a continued substantial macroeconomic impact from meeting Indonesia's housing needs. 9 At the current time, a high share of gross domestic product (GDP) and a high share of gross fixed capital formation are devoted to housing. Consequently, less capital is available for more growth-oriented types of investment. This is a direct consequence of the rapid growth in the number of households. However, Indonesia should begin to reap some of the benefits of slowing population growth as the demands associated with housing begin to ease. The value of residential construction grows more slowly than gross domestic product (GDP). The share in real GDP declines from 5.7% to 5.5%. Likewise, the share of residential construction in real grows fixed capital formation (GFCF) declines from 23.3% to 22.5% by the end of the period analyzed.

Similar analyses for Thailand (Campbell & Poapongsakorn 1993) and the Philippines (Campbell & Munsayac 1992) provide interesting comparisons with Indonesia. Despite divergent fertility paths over the last twenty years,

432

there was little difference in the housing needs of the three countries prior to 1990. Annual growth in the required housing stock varied from a high of 3.2% in Thailand to a low of 2.9% in Indonesia. The Philippines, where fertility is high, is now beginning to follow a divergent growth path. By 2005 the required housing stock will be 60% greater than the 1990 level; in Thailand and Indonesia growth of 50% is anticipated.

Following a lower population growth path is beginning to have a significant impact on the size of the housing sector in Indonesia and Thailand. Measured by annual housing starts required, the Philippines housing sector would be 53% larger during the 2005-10 quinquennia as compared with the 1990- 95 period. By contract, the Thailand and Indonesian housing sectors would increase by only 18% during the same period.

Consumer expenditure analysis and projections

In Indonesia, 12 expenditure categories were analyzed using the 1987 survey of family income and expenditures (SUSENAS). Housing was defined to include a relatively broad measure of housing services (rent or the rental value of owner occupied housing, maintenance and repair, utilities, etc.)

The independent variables included household income, demographic characteristics of the houshold, and other socio-economic factors. First, analysis of the impact of total expenditure indicates that housing expenditure will grow at about the same rate, perhaps a little more rapidly, than consumption in general as living standards improve.

Second, there are substantial economies of scale in housing. A decline in the number of household members, holding the age and sex distribution and per capita household income constant, results in a significant increase in the share of the budget devoted to housing services. The impact of changes in household membership vary with the age of the member. Raising one fewer child to age 16 increases expenditure on housing by about 5% on average.

Forecasts of housing expenditure

The greatest change is in the share of consumption devoted to food prepared at home, forecast to decline from 50.2% in 1990 to 41.3% in 2005. The greatest increase in the percentage share is forecast for housing and household operations, rising from 17.3 % of the household budget in 1990 to 21.8 % in 2005. A substantial increase is also forecast for transportation and communication, with implications for urban infrastructure needs (Table 5).

Although forecasts of spending on housing and household operations are available for Thailand (Mason et al. 1993) and the Philippines (Figueroa & Bernal 1992), they cannot be easily compared to the results for Indonesia

Table 5. Forecast budget shares (percent), major expenditure groups, Indoensia

1990 1995 2000 2005

Food 50.2 47.8 44.6 41.3 Prepared food 6.6 6.9 7.2 7.4 Alcohol, tobacco & betelnut 5.1 5.1 5.0 4.8 Housing, water & energy 17.3 18.6 20.1 21.8 Personal care 1.5 1.4 1.5 1.4 Health care 1.0 1.1 1.2 1.3 Education 2.8 2.9 3.1 3.2 Transport & communication 4.0 4.6 5.6 6.5 Clothing 5.1 5.0 4.9 4.7 Durable goods 3.0 3.0 3.3 3.5 Paties & ceremonies 2.6 2.5 2.4 2.4 Other goods & services 0.9 1.0 1.3 1.6

433

because of differences in assumptions about economic growth. In all three countries, however, smaller families lead to more not less spending on housing by the average family.

Discussion and conclusions

The housing needs analysis and forecasts of consumer expenditure on housing apparently imply different short-term futures for Indonesia. The housing needs analysis implies that housing investment will decline as a percentage of income. However, this neglects the impact of changes in the quality of housing. This aspect is more fully captured in the consumer expenditures forecasts which anticipate a rise in the fraction of national income devoted to housing services during the next fifteen years. Part of this increase is due to the counter-intuitive finding that a decline in the number of children leads families to spend more, not less, on housing. This results is not peculiar to Indonesia, but was found to be true in the Philippines and Thailand, as well.

In the longer term, the decline in household size will become less important and changes in the number of households more important. Reducing fertility has a very substantial impact on the number of households and the demand for housing units and associated urban infrastructure thirty years hence. If growth in the demand for housing is met, capital will be diverted to the housing sector slowing long-term growth prospects. An alternative and probably more likely scenario is that serious housing shortages will emerge, leading to crowding,

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homelessness, and unsafe living conditions in many developing country urban centers.

Do the implications of population growth for the housing sector, by them- selves, justify investing in family planning programs? Given the long lags between reducing fertility and reducing the demand for housing, the answer surely depends on the costs of achieving fertility reduction. The conclusion of the inquiry was that the impact in any particular sector might not justify family planning programs. However, population growth has such a pervasive influence on the economy, affecting so many different sectors, that family planning programs were a worthwhile investment.

Acknowledgments

I would like to thank Dennis Ahlburg, Naohiro Ogawa, Mathana Phananira- mai and Prijono Tjiptoherijanto for their suggestions and comments. Noreen Tanouye provided technical assistance which is greatly appreciated.

Notes

1. To some extent the issues of costs were addressed in reports on family planning programs (Ahlburg & Diamond 1994) and population and human rights (K. Mason 1994).

2. The TFR is a common measure of childbearing that equals the average number of births per woman over her childbearing years if subject to current age-specific birth rates. Replacement level fertility, approximately two births per woman, is the level of fertility that will eventually result in zero population growth.

3. There are several microsimulation models that are used to simulate how changes in mor- tality, fertility, and living arrangements affect characteristics of families and households (Hammel et al. 1976; Ruggles 1987).

4. In general, a household marker is the individual within a household used to identify that household. Traditionally, the household head is used as the household markers. The HOMES model uses the female householder, i.e., the head, if female, or the wife of the head, if possible. Only in households headed by a male with no spouse present is the male householder used.

5. The one exception is that projections for Japan are based on a much more extensive modeling affort that forecasts changing headship rates (Mason, Ogawa & Fukui 1992).

6. For households headed by a male with an absent spouse the age of the spouse is estimated using the joint age distribution of husbands and wives in intact households.

7. Details of this work are reported in Campbell & Pasay, undated. 8. The current vacancy rate, v, and the current ratio of households per occupied housing

unit, d, are maintained if/3 is set equal to 1/(1 - v). 9. These forecasts assume that the relative price of residential construction will not increase,

that real income per household grows at 3% annually, and that the expenditure elasticity is 1.0. In other words, it is assumed that the value of residential construction per unit constructed increases at 3% annually.

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References

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