Socio-Econ Phn Sri, Vol. I?. pp. 303.31 I 0 Pergamon Press Ltd.. 1978. Printed in Great Britm

PLANNING PRE-SCHOOL SERVICES: A SOCIO-DEMOGRAPHIC ANALYSISt

IAN PLEWIS Thomas Coram Research Unit, University of London, Institute of Education, 41 Brunswick Square,

London WCIN lAZ, England

(Received 11 February 1978; received for publication 18 May 1978)

Abstract-Data were collected from mothers with a child under 5 living in 3 small areas of Inner London. Information was provided by them about various aspects of pre-school provision and these were related to a number of socio-demographic factors. The concepts of demand, need, desire and take-up were discussed in the context of preschool services and it was shown by using log-linear models that desire for pre-school provision could be efficiently predicted by a combination of these socio-demographic variables. Some emphasis was placed on the use of log-linear models with sparse data and the models were replicated on fresh data.

1. INTRODUCTION

In recent years, there has been a considerable growth of interest in the care and education of pre-school children and the circumstances of their families, partly stimulated by the Plowden Report[l] and partly, it may be hypo- thesized, by pressure from young mothers allied to the growing economic and political importance of women in general. A recent book[2] looks in detail at historical trends in the provision of services for the under-5’s and in the number of mothers going out to work and also considers what mix of pre-school services might be provided to meet their needs and wishes. This article examines the extent to which mothers want their chil- dren to go to some form of pre-school provision and whether this is correlated with demographic and social variables. Log linear models were used for much of the statistical analysis and their appropriateness for the data is discussed.

2. DATA SOURCE

The data come from a study of families with pre- school children undertaken by the Thomas Coram Research Unit in which a sample of women were inter- viewed in their own homes. The sample consisted of all women with a child under 5 living in 3 small areas of Inner London. Following a door-to-door census, 350 women with 454 children under 5 were interviewed be- tween June 1974 and May 1975. This represents a response rate of 86%. Further details of the sample can be found in[3].

3. CONCEPTUAL FRAhiEWORK

It is important to discuss at an early stage the aims of the analysis and to consider, if only cursorily, the concepts involved. The major parameters determining the level of pre-school services could be regarded as demand, need, desire and take-up. Economists define demand as a function of prices, incomes and tastes; when considering pre-school provision (a blanket term covering all forms such as nursery schools, day nurseries, playgroups, childminders etc), the mother’s or family’s demand for the service for their child would

tThis research was sponsored by DHSS with support from the University of London, Institute of Education and the Thomas Coram Foundation.

depend on the relative price of a place, their income and their feelings about the suitability of such services for young children. A generally agreed definition of need is hard to find but it usually implies decisions made on behalf of individuals by a third party (often government or government agencies and sometimes social scientists). The Department of Health and Social Security (DHSS) includes children of single working parents among the priorities it recommends to local authorities for allocat- ing day nursery places and also suggests environmental measures, psychiatric disorders and handicap as in- dicators of need. These groups are defined, by indicators which are objectively measured but subjectively selec- ted, to be “in need”, The concepts of demand and need as they are applied in the related context of health and the National Health Service are considered in more detail in[4].

A preliminary analysis of the data reported here[5] was made in terms of the “demand” for pre-school provision where “demand” was defined as the number of children attending some form of provision plus those children whose mothers wanted them to attend at the time of the interview. However, this definition of demand is unsatisfactory as it does not take into account the effects of prices and income and so this variable, which is a central part of the analysis, is re-named desire for pre-school provision. A recent national Government survey of pre-school families[6] also used this term. Desire is related to the economist’s definition of tastes but whereas tastes are independent of prices, desire is measured by questioning mothers who are presented, albeit implicitly, with the possibility of a free, flexible service. An alternative definition of desire might there- fore be “demand at zero price”. Certainly it would be possible to reduce the level of desire by the imposition of a price system and alIowing the market to determine demand; to some extent, this is what happens at present with regard to childminders and private nurseries. The present analysis considers the way in which desire changes with differing social and demographic charac- teristics; it is thus an indication of how tastes would cause demand for pre-school provision to vary across these groups.

How useful are the concepts of demand, need and desire to planners of pre-school services? If the decision to provide services is based solely on their economic

303

304 I. PLEWIS

costs and benefits, then data on demand (i.e. “willingness to pay”) would be the most valuable and, as explained, desire is only part of this picture. However, cost-benefit analyses of preschool provision have yet to be done and most planning has been on the basis of need or desire. Indicators and definitions of need are examined in some detail in[6] and need will not be considered further here. The value of data on desire is greater once a decision has been taken to provide a free pre-school service available to all families in a defined catchment area who wish to use it. The recommendations of the Plowden report (see e.g. para 328) were based on this premise and part of the research programme of the Thomas Coram Research Unit is linked to the existence of two Children’s Centres set up with the aim of providing just such a service. These Centres became fully operational some months after the first period of data collection reported here.

It is important to know whether mothers’ responses to questions about desire for pre-school provision are reflected in the use made of a service by their children. An ideal comparison requires a flexible service which will take all those children whose mothers want them to attend and will take them for as many hours and days of the week as the mothers want. It also requires data on the take-up of the service, i.e. the actual daily atten- dance by each child. The first of these conditions-a comprehensive service-was approximated for two of the areas where the Children’s Centres had been set up although, in fact, they were unable to achieve this parti- cularly for the under-2’s. It could be argued that if the Centres were providing this “ideal” service, the concept of desire becomes redundant and should be replaced by take-up. In fact, data are presented in Section 5.3 which suggest that it is reasonable to assume an equivalence between expressed desire in an interview and actual take-up. Thus, the data presented in this article could help a local authority, for example, to predict the level of take-up of free comprehensive services and how it would vary according to the children’s and mothers’ charac- teristics.

The criteria for selecting the independent variables were that they were socio-demographic in nature and easily and recognisably defined. It was thought that most local authorities would have at least some indication of the distributions of these variables for small areas and thus they would be available for planning purposes. For this reason, other potential explanatory variables such as children’s behaviour and mother’s mental state were omitted.

4. hDXTHOD OF ANALYSIS

The dependent variable, desire (in hours per week) for pre-school provision (DES), was divided into 4 cate- gories-0, l-19, 20-34, >35-the last 3 corresponding roughly to “morning or afternoon”, “school day”, “all day” on the assumption that mothers want their children to attend for 5 days a week. This assumption held for 90% of the mothers in this sample. Given the aim of relating this categorised dependent variable to a combination of socio-demographic independent variables which were also categoric, a log-linear modelling ap- proach was adopted.

The log linear model can be written as

log, [E(Y)1 = xs (1)

where E(y) is the expected value of a m x 1 vector of the

cell frequencies yi from a s-way (s 2 2) contingency table, /3 is a u x 1 (u s m) vector of coefficients to be estimated, X is a m x u design matrix and it is usually assumed that the error distribution is Poisson.

Where both the dependent variable (response) and an independent variable (factor) have a natural ordering, their association can be partitioned into linear and non- linear components by attaching scores (ri, i = 1,2 )..... a) to the response and cfi, j = 1,2.. . . . b) to the factor which are, in all cases reported here, assumed to be equally spaced. If the non-linear component of this association is omitted from the model, the number of parameters to be estimated is reduced by (ab - b - a) and the corresponding section of the design matrix becomes a column vector with elements ri .fh Otherwise, the partitioning amounts to re-parameterizing the model. The distinction between “factor” and “response” is important in that the margins of all the factor combinations are fixed and this defines the “minimal” model[7]. A satisfactory fit is sought after considering associations (interactions) between the response and various combinations of the factors.

An iterative solution is generally required for maxi- mum likelihood (ML) estimation of the parameters in (1) and two computationally different but statistically equivalent procedures have been proposed. The first uses the iterative scaling procedure (ISP) which is described later in this section and is the major algorithm in L. A. Goodman’s computer program ECTA. The other uses an iterative weighted least squares procedure with the Newton-Raphson technique and this is built into the package GLIM[8]. GLIM was used to estimate the models in this paper although ECTA was used in the model-testing process. Only hierarchical models-those which may contain a tth order interaction only if the corresponding (t - 1)th order interactions are included- were considered.

An alternative approach to the analysis of categorical data has been put forward in[9]. Their general formula- tion includes both linear and log-linear models as special cases and is solved by standard, non-iterative weighted least squares. A disadvantage of their approach is that it deals with zero cells less satisfactorily than maximum likelihood and this was one reason for not using it with these data.

The use of log linear models poses an immediate problem less often apparent with multiple regression- how many factors can be included given the number of observations? The situation of a response with, say, 4 categories and 3 factors with 5, 3 and 2 categories respectively creates 120 cells; the introduction of another 3-level factor raises this to 360 etc.. With sample sizes similar to this study, these additional factors soon result in zero cells and zero n-way “margins” which need to be handled with care. There were up to 10 potential factors for any model and it was not possible to deal with all of these at the same time. These factors were therefore passed through an initial screening procedure and only those with an association with DES having a chi-square p-value less than 0.05 were considered for inclusion in the model. This procedure is not entirely satisfactory as a simple factor-response association can be “suppres- sed” by an association in the opposite direction between this factor and another and also because the choice of 0.05 is clearly arbitrary. However, alternative strategies based on prior knowledge are also arbitrary and, it is argued, can be less informative than the one adopted

Dlnrnkn “PP rnI.I\nI “,*.,;..a.. 1 raru1rug yr~-?JL,lv”l 3-1 “Lb&J a socio-demographic analysis

here. Some prior expectations were used in a limited search for “suppressions” but none were found.

After reducing the problem to a more manageable size, the aim of the next stage of the analysis was to build a sensible model involving combinations of factors. This also provides fitted cell estimates for the random zeros of the observed contingency table-a process referred to in[lO] as “smoothing”. Some helpful suggestions for deciding on a final model given the problem of the large number of different models which could be fitted to the response (the analogue of the stepwise problem of mul- tiple regression) are provided by Brown[ll]. This method uses simultaneous test procedures to determine the highest order terms allowed in the model and then to include with certainty only those individual terms whose “marginal” and “partial” associations are both statistic- ally significant. (Given a response, A and 2 factors, B and C, the “marginal” association of AB is obtained by subtracting the “deviance” (see below) of the model A.B + B*C from that of A + B*C. The “partial” asso- ciation comes from (A.B t A*C t B*C) - (A*C + B*C). A*C is defined as A + C + A.C.) In fact, the examples Brown gives suggest that all terms with significant partial associations should be included given a significant simultaneous test while some uncertainty is attached to those terms whose marginal associations are significant but whose partial associations are not. The occurrence of a non-significant marginal association with a significant partial association corresponds to the “suppression” of an association mentioned previously.

deviance from the value obtained excluding this term and this difference also has an asymptotic ,$ distribution. The degrees of freedom for ,$ values is the difference between the number of parameters fitted for the two models.

Having constructed models for the dependent variable (Section 5.1) it was necessary to test them on some fresh data (Section 5.2). Part of the testing involved the use of the iterative scaling procedure. This shows that given the cross-product ratios &,hb of an r x s table where

&,b = E (2) a=1,2...r-1

6=1,2...s-I

and given the marginal distributions (niO, i = 1,2, . . r) and (nor,j=1,2 ,..... s) it is possible to determine uniquely the cell frequencies (nij). More details can be found in[12].

5. RESULTS

5.1 Model building stage The data relevant to the model building are presented

in Tables 24. It should be noted that the data for the 3 areas were combined and treated as one sample as there was no evidence that DES was associated with area once other social and demographic differences had been eli- minated.

For Poisson errors, the deviance is the likelihood ratio test statistic 2Cyi log, (yi/ji) where ji is the cell frequency fitted by the model, and is approximately distributed as ,$. Since this deviance is additive for a sequence of models, the explanatory power of an extra term can be measured by subtracting the associated

It can be seen from Table 1 that the screening pro- cedure was not very successful at eliminating variables. Housing index (associated with income), age of mother and household size (both associated with marital status) could reasonably be omitted but even so, it was not possible to build one model around the remaining 5 factors because of the zero margins shown by Table 2 (which does not include marital status). These zero

305

Table 1. Relationships between desire for pre-school provision and the selected factors

=m=K

WOREED

BY

MOTHER

,

SOCIAL

CLASS

(1)

T

INCOME

INDM

(2)

HOUSING

INDEX

(3)

COUNTBY OF

ORIGIN

OF MOTHEB

(4)

**

MARITAL AGE OF

STATUS CEILD

(5)

K-%X

KEY: *xx p< 0.001 ; *+ 0.001 i pc 0.01 ; * 0.01' p 4 0.05 ; - pa 0.05

NOTES: (1) Based on Registrar General's Classification of Occupations (1970)

(2) 0 = none of car, telephone, owner-occupier, 1 = at least one of these

(9) .1.5 persons per room = 2

(4)

(5)

.l butd 1.5 persons per room = 1

Lacking either B fixed bath or inside lavatory or hot water = 2

Sharing one of these amenities = 1

Living room on 2nd floor or above = 1

These scores were added and scores of 4 and 5 both treated as 4.

Inmigrant - any mother born outside U.K. and Eire.

Married - living with man, husband or otherwise.

SEX OF

CHILD

T

AGE OF

MOTHER

H0USEE0LD

SIZE

H *

306 I. PLEWIS

margins imply infinite parameter estimates and prevent the iterative process in GLIM from converging. It might be argued that more flexibility could be achieved by not fixing all the high-order factor margins and thus loosen- ing the strait-jacket of zero margins. However, it is not clear to what extent one might permit factor margins to vary without seriously affecting the analysis and there- fore this possibility was not allowed.

The two factors most strongly associated with DES- age of child (AC) and hours per week worked (HWW)- could not both be included in the same model because, by chance, there were no observations for any of the 3 categories of HWW for children under 1 yr and DES of 1-19 hr per week. Therefore, separate models were con- structed, one including AC and the other HWW. Al- though not entirely satisfactory, this approach can be justified on the grounds that HWW and the other poten- tial factors--country of origin of mother (CO), income index (CTO) and marital status (MS) (see Table 1 for

definitions)-are those which vary from area to area while the distribution of AC is likely to be constant. Thus, model 1 could be used to predict the overall level of desire and model 2 to apportion it between the chil- dren’s age groups (see Section 5.2). The approach can also be justified by the fact that AC is a “child” variable while HWW is a “mother” variable and there is some sense in keeping them separate. In fact, it was not possible to include both CT0 and MS in model 1 and a preliminary analysis (not presented here) showed that CT0 was more useful. CO was included in model 2 because of its potential importance in the organisation of a pre-school service (e.g. language programmes for the under 3’s).

Table 3 gives the results for model 1. The estimated values of the linear regressions show that DES increases as HWW increases but the increase is less pronounced for immigrant mothers (the significant third order term) and that DES decreases as CT0 increases. DES. CO is

Table 2. Desire for pre-school provision X age of child (AC) X hrlweek worked mother (HWW) X country of origin of mother (CO) X income index (CTO): raw data

DESlRE FOR l'B.!Z-SCHOOL PFlOVISION I

Planning pre-school services: a socio-demographic analysis 307

KEX :-

WORKING P-T - WOREING PART-TIME (I.E.149 ER.S/WEXK)

WORKEVG F-T - WOREING FULLTIME (I.E. P 30 ERS/WEEK)

N.I. - NON-IMMIGRANT

I. - Irn(;RANT

R. - 'RICHER' (I.E. AT LEAST ONE OF CAR, TELEPHONE,

owNEROCcupIER)

P. - 'POORER

highly significant and the corresponding estimates show that this is mainly due to immigrant mothers wanting more all-day care. The partial non-linear relationships between DES and CT0 and DES and HWW are significant. Model 2 for DES is given in Table 4. Both the linear and non-linear components of DES. AC are highly significant (DES increasing with AC) as is DES. CO but there is no need for any 3rd order terms.

5.2 Model testing stage The validity of the models of the previous section

would obviously be improved by their being tested on a fresh set of data, preferably data from a different area so that predictions from the models could be shown to be independent of the rather restricted sample on which they were based. Unfortunately, such data were not available. However, it was possible to achieve a modest replication by attempting to fit the models to a second sample. This consisted of (i) mothers with a pre-school child who had moved into the survey areas during the 12 months following the initial round of interviews and (ii) women living in the survey areas at the time of the first sample and not having a pre-school child then but who

had a child in the next 12 months. There were 110 such mothers with 123 children.

It is well-known that values predicted from a model which is applied to a fresh set of data can show considerable differences from the observed values. The rather idiosyncratic nature of the cross-validation sample used here reinforces the difficulty of interpreting the goodness of fit of the second sample particularly as it was much smaller than the original and the majority of the predicted cell sizes were less than 5. These cell sizes suggested that the validity of the x2 approximation for any test of fit would be remote so that an alternative strategy of testing the observed distribution for each factor combination against the distribution predicted by the model using a Kolmogorov-Smirnov (K-S) one sample statistic was followed. Because the samples for each of the factor combinations (populations) were small and because good scientific practice in model testing should be to try and refute the models, all probability values co.10 were taken as suggestive of a bad fit. The choice of 0.10, although in some ways severe, does allow for the fact that published critical values of the K-S statistic are conservative when applied to discrete

308 I. PLEWlS

Table 3. Model 1 for desire for pre-school provision (DES): DES X hr/week worked by mother (HWW) X country of origin of mother (CO) X income index (CTO)

TYPE OF TERMS

TYPE OF TEST I

d.f. x2 PROB. TEST

x2 PROB.

ALL 2nd ORDER S 12 88.9 *** N/A

DES.HWW (LINEAR) M 1 39.4 *** P 30.0 ***

DES.~ (NON- M 5 16.1 * * * P 12.2 c

LINEAR)

DES.CO M 3 32.6 *** P 17.5 ***

DES.CTO (LINEAR) M 1 5.70 *

DES.CTO (NON-

MINIMAL MODEL HWW * CO * CT0

FITTED MODEL HWW * CO * CT0 + DES * HWW + DES.CO + DES.CTO + DES.HWW (LINEAR)

4 S = 'simultaneous' ; M = 'marginal' ; P = 'partial' (see text)

Table 4. Model 2 for desire for pre-school provision (DES): DES X age of child (AC) X country of origin of mother (CO)

TERMS

F ALL 2nd ORDER

DES.AC (LINFAH)

DES.AC (NON-

L_)

DES.CO

ALL 3rd ORDER

15

1

11

3

12

32.9

18.0

MINIMAL MODEL AC *CO

FITTED MODEL AC *CO + DES l AC + DES.CO

TYPE OF

TEST

d.f. K2 PROB. TYPE OF

258.0

66.5

154.0

***

xxx

x*x

l **

TEST

N/A

P

P

P

N/A

-

I

fi2 PROB.

t

83.4 ***

.42.0 * * l

36.2 * * *

distributions [ 131. If the fit was considered satisfactory, the two samples were combined and the parameters were re-estimated. However, because the interpretation of these parameters might be confusing for the user, the results have been presented instead as estimated cell proportions.

The existence of two models for DES raised another problem and suggested the need for a dual approach. It was unreasonable to expect the new data to fit model 2 without some adjustment because the distribution of the populations in the validation sample was different from the original sample and model 2 could not take account of as many of these differences as model 1. However it was reasonable to expect the cross-product ratios of model 2 would be replicated as they represent the rela-

tionships within the table and are independent of the marginal distributions.

The validation sample defined the factor margins (Q, the response margins (nio) were estimated by applying model 1 for DES to the validation sample and the esti- mates of #,,,, came from model 2; the iterative scaling procedure produced expected cell sizes satisfying these margins and cross-product ratios. Again, the observed and expected distributions for each population were compared with a K-S statistic. It is this dual procedure which could be followed by users of these models.

When model 1 was fitted to the validation sample, one K-S statistic had a p-value <O.Ol but all others were >O.lO. The difference arose for the “poorer”, immigrant mothers who were not working, where the level of desire

Planning pre-school services: a socio-demo~aphic anaiysis 309

was over-estimated. Mothers of very young children were over-represented in this group in the validation sample and this probably explains the discrepancy. In view of the satisfactory fit for the other populations, it was decided to combine the samples and re-estimate and the results are presented in Table 5. The number of observations on which this model is based (49s) is less than the total number of children (577) because of non- response to questions defining the populations (n = 28) and also because some mothers did not know how much pre-school provision they wanted for their children (n = 54). Both the final row of Table 5 and the percentages in the second column of Tables 5 and 6 refer to the original sample only as they give a truer picture of the areas. Application of ISP to model 2 gave a good fit for all populations and the cross-product ratios are presented in Table 6.

5.3 Desire for and use of pre-school provision As mentioned in Section 3, it is import~t to compare

desire and take-up. Attendance registers from the Chil- dren’s Centres and the other services in the areas were not available but it was possible to get some idea of

take-up from follow-up interviews when mothers were asked how many hours per week their children spent at any form of preschool provision. This does not, of course, allow for non-attendance because of illness etc. which may be considerable. Table 7 compares desire esti- mated from model 2 for all the original sample, with hours per week attended in each of the two areas with Chil- dren’s Centres from the follow-up sample. The follow-up sample consisted of the validation sample plus those mothers who had been interviewed a year earlier, and who were still living in the areas and still had a child under 5. There are differences at all ages with a tendency for desire to be greater than attendance p~ticul~ly for the younger children. However, these differences are not great and can, to some extent, be attributed to sampling error and to the admissions policies of the Centres. These data do not completely resolve the question of how well desire predicts take-up which has important implications for the organisation of services but they do suggest a reasonable level of co~espondence. This is reinforced when comparisons are made with Europe. Thus, over 90% of 3-5 yr olds in Belgium attend nursery schools while 23% of 2 yr olds and 70% of 3 yr olds in

Table 5. Estimated proportions for desire for pre-school provision arising from model 1

POTIONS % DESIRE FOR ~~OOL ~~SION

(N = 495) N=396) NONE i-19 BRs/wR 20-34 ERs/uK f&Y?HRs/wB

NOT WoRgJNG 'POORER' 20 0.47 0.21 0.17 0.15 NON IMMIGRANT

I ~~ / 8 1 0.33 / 0.08 1 0.16 1 0.43

NOT WOREING 'RICE?X' 25 NON IMMIGRANT

0.45 I I 0.29 0.19 0.08

NOT WOFXING 'RXCEER' I 9 ~~

WORKDIG PART-TIME 'POORER' I 5 NON IMMIGR4NT

WORKING PART-TIME 'PJORFX 2 IMMIGRANT

WORKING PART-TIME 'RICIBR' I 7 NON IMMIGRANT

WOTKING PART-TIME 'RICHER 2 IXHIGRANT

WORKING FIJLGTTWE ‘POORER' 4 NON I~IG~

WORKING FUL&TIMFi 'POORER' 7 IMMIGRANT

0.38 0.14 0.22 0.27

0.27 o.e5 0.27 0.11

0.29 0.11 0.22 0.38

0.25 0.34 0.30 0.11

0.31 0.18 0.28 0.23

0.09 0.09 0.16 0.66

0.15 0.05 0.10 0.70

WOFfKING I+7JLL-TIME 'RICHER' 6 NON IMKIGRANT

0.12 ( 0.17 1 0.24 j 0.47

WORKING FULL-TIME 'RICAER' 6 BlMGMNT

0.21 I I 0.09 0.16 I 0.54 TOTAL 100 0.33 0.20 0.21 0.26 (~=396)

SEPS Vol. U.No. 6-d

310 I. PLEWIS

Table 6. Matrix of estimated cross-product ratios for desire for pre-school provision arising from model 2

POFTLATiONS % DESIIE FUR PRFe~OOL Z'EWISION

(N = 523) (N&o) .

UNDERlyEAR

~~ 6 3.81 0 0.02 1

lJNDJ!BlYSAR

NON l'MIW 12 18.1 0 0.04 1

1 YEAROLD

IMMI(iRANT 6 2.18 0.12 0.13 1

1 YEAR OLD

NON ?XMKIW 10 10.4 0.56 0.28 1

2YEm OLD

IX-MI- 9 0.65 0.28 0.10 1

2 YEAR OLD 14 3.10 1.27 0.20 1 I

NONIMlIGRAN'!! I 3 YFAR OLD

7 0.15 0.42 0.36 1 IMMIGRA?Ir

3 YEAR OLD NUDGE 15 0.71 1.89 0.74 1

4wmoLD

lx?frGRANT 5 0.21 0.22 0.48 1

4YEdR OLD

NON IMMIGRANT 15 1 1 1 1

Table 7. Comparison of ho ‘s per week attended (HWA) and desire (DES) by age of child

France are in some form of pre-school-further details some form of pre-school provision; the tigures for the 5 are given in [2]. age groups were 21,42,73,88 and 91%. These figures are

very similar to those presented here (see Table 7) and 6.DLWJSSION provide reassuring evidence that the models for DES

The national sample of 2501 children under 5[6], could apply to a variety of areas and not just to these showed, that (excluding “don’t knows”) 65% desired areas in Inner London. U~ortunateIy, data on hours

Planning pre-school services: a s~io~emo~aphic analysis 311

wanted were not presented and it is for this that area differences are likely to be greatest. A comparison of the predictions for DES for “richer” non-immigrant mothers who were not working (45, 29, 19 and 8%) with the overall prediction from Table 5 (33, 20, 21 and 26%) indicates the possible extent of area differences. The predictions from Table 5 will be inaccurate if there are other independent variables which vary from area to area and which have a “partial” association with desire. One candidate might be “a history of pre-school provision”- desire might be greater in those areas which have had extensive provision for many years, this history of pro- vision raising the expectations of these mothers compared with mothers living in areas with little or no present or past provision. There is no evidence from these data to support this view in that desire was similarly distributed in all 3 areas despite the fact that one of the areas had benefitted from many pre-school services for several years while the other two had been less well served. It might also be argued that the “history” effect is likely to be quickly eroded by the provision of a service and thus has little relevance from a planning point of view.

difficult to explain but may be partly due to the asso- ciation of income with both marital status and housing, the stresses of the latter two conditions resulting in “poorer” mothers wanting their children to have more pre-schooi provision.

A study such as this inevitably leaves some questions unanswered-statistical questions such as the applicability of log linear models to “sparse” data and substantive questions such as the predictive value of the concept named here as “desire”. Can data collected by the survey method accurately predict the extent to which people will take up services? Section 5.3 suggests that it might be able to but more evidence is needed. Neverthe- less it is hoped that the data presented here will assist in the rational planning of services in the future and that they shed some light on a social group about which we know little.

Acknow&xfgemenfs-I would like to thank Prof. Harvey Gold- stein, Peter Moss, Don Sharpe and other colleagues for helpful comments on earlier drafts of this paper, the mothers who provided the information and the team of interviewers who collected it.

One of the most important findings of this study is the influence on DES of mother’s country of origin. It is particularly striking because the immigrant group consists of mothers from several different cultural back- grounds, the two most important being the West Indies and Spain and Portugal, while the non-immigrant group includes some British born black mothers. Both these points would seem likely, a priori, to attenuate any differences. Other studies (e.g.[14]) have shown that ~rn~ant mothers of pre-school children have higher employment rates than co~esponding non-~rni~ant mothers and this is confirmed by the present study (analysis not presented here). However, even after eli- minating the effects of hours per week worked and “income”, immigrant mothers want more pre-school provision than non-immigrant mothers. It might be argued that this result is an artefect arising from the method of analysis. Thus if the dist~bution of imm~nt mothers working full-time was more heavily weighted at the upper end (i.e. >40 hr/week), this would lead them to want more provision but would not be picked up in the model-building process. However, there was no evidence to support this, nor did the substitution of “desire to work now” for HWW eliminate the CO effect. Possible explanations are that non-English speaking ~mi~ant mothers want more pre-school provision so that their children learn English while, in the West Indies, child care is the responsibility of the extended family rather than just the mother so that, in this country, West Indian mothers see institutional care as a substitute for the extended family,

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The relationship between “income” and desire is 14. G. Lomas, Race and employment. New Sot. 32,4t3 (197s).