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Female labour force participation and the choice of occupation
European
Economic
Review
37 (1993)
1393-1411.
North-Holland
Female labour force participation the choice of occupation
and
The supply of teachers P.J. Dolton* University of Newcastle upon Tyne, Newcastle upon Tyne, UK
G.H. Makepeace University of Hull, Hull, UK Received November
1991, final version received
February
1992
This paper examines the relationship between occupational choice and participation decisions by women. It is motivated by recent policy debate in the U.K. concerning the supply of teachers where about sixty per cent of teachers are female and the recruitment of women is vital to the maintenance of the labour force in teaching. Models of earnings determination, occupational choice and labour force participation are estimated for a large sample of female graduates. The choice of occupation and labour market status is modelled as a joint decision between: (i) teaching and non-teaching; and (ii) working and not-working. Estimates from alternative models show that participation decisions are correlated with occupational choice, with individuals who choose teaching being more likely to work. The choice of occupation is also affected by the earnings differential between teaching and non teaching suggesting that teacher shortages could be alleviated by raising teacher’s earnings.
1. Introduction In the United Kingdom, women constitute about sixty percent of the workforce in teaching so the continued recruitment of women graduates is crucial for the supply of teachers. However, since young women are simultaneously making decisions about their family commitments and labour force participation as well as careers, modelling teacher’s labour supply for women is quite complicated. The primary aim of this paper is to analyse Correspondence to: G.H. Makepeace, Department of Economics, University of Hull, Hull, HU6 7RX, U.K. *We would like to thank the following for their comments on earlier drafts of this paper: Brian Main, Gerard Pfan, Jaques Siegers, the referees, participants at conferences of the Labour Economics Study Group and European Association of Labour Economists, and participants at seminars at Maastricht and Utrecht. As always the authors remain responsible for all errors or omissions in the paper. This research was undertaken with the support of Economic and Social Research Council grant number ROOO 23 2045. 00142921/93/$06.00
0
1993-Elsevier
Science Publishers
B.V. All rights reserved
1394
P.J. Dolton and G.H. Makepeace,
Female
labour force
participation
empirically some of the complex interactions determining the career choice of female graduates. This paper focuses on two matters. The first is whether the occupational decision is independent of the decision to participate in the labour force; that is whether an individual’s propensity to become a teacher is influenced by their plans to be in work or out of work given their family and other commitments. The second is whether the choice of teaching as an occupation is affected by the earnings differential between teaching and other graduate occupations. The occupational choice of graduates has been studied by Dolton et al. (1989) for a sample of 1970 graduates and Dolton (1990) for a sample of 1980 graduates. Both these studies examine the effect of occupational choice on earnings and the effect of earnings on the choice of occupation. The study by Dolton concentrates on the common factors affecting the occupational choice of men and women and is not concerned with the special features of the female’s occupational choice. Of particular relevance here is the influence of family role specialisation. If women expect to spend more time on nonmarket work, they are more likely to pursue careers which are complementary to their family role and to be non-participants when family commitments are high. Dolton et al. (1989) recognise these arguments by estimating separate models for men and women and including a female participation effect. However, they assume independence between the occupational decision and the participation decision and do not formally test this constraint. Several reasons may be advanced for a high degree of complementarity between teaching and family support. These include: the relatively low number of hours which have to be spent at school and their convenient location in the day, the timing and quantity of holidays, and the ease with which one can return to teaching after a career interruption. These attributes of teaching as a career are valued differently by different individuals, creating an unobserved factor affecting occupational choice. Similarly there will be some unobserved factors such as motivation for work or the degree of commitment to a family which influence the participation decision. Women who are keen to keep working will, ceteris paribus, be more likely to choose teaching and to work, implying a positive correlation between the decisions. Similarly some women may be especially committed to family support which they realise by choosing teaching. Whatever the reasons, this discussion confirms that the independence of the two decisions should be statistically tested. Results for a bivariate probit model show that the decision to become a teacher and the decision to be a labour force participant are not taken independently of one another if teachers and nonteachers have the same participation model. However there are no compelling a priori reasons for supposing that teachers and non-teachers make their participation decisions in the same way. Indeed the arguments made above suggest that labour force participation for a given level of family commitments is somewhat easier for women teachers. These considerations led us to
P.J. Dolton and G.H. Makepeace, Female labour force participation
investigate a model which allows teachers participation decisions.
2. Estimation
and non-teachers
1395
to make different
methods
We wish to model a female graduate’s choice of labour market status and occupation. In our econometric mode1 an individual chooses whether to be a labour force participant and whether to work as a teacher or a non-teacher. We represent this as two, dependent binary choices in which each decision depends on a list of regressors and is affected through the error structure by the other decision. To make our models tractable,’ our estimation strategies focus on bivariate probit models with and without sample selection. We begin with the ‘standard’ bivariate probit model2 which has the form T*=Wd+rc(lnY,-lnY,)+u,
(1)
P*=Za+v,
In Y, = X,/I, + e,, In Y, = X,& + e,,
(4)
where T= 1 iff T*>O and T=O otherwise, P=l iff P*>O and P=O otherwise, and u, v, e,, e, are jointly normal error terms. The first two equations are ‘selector’ equations describing the choice of whether or not to be a teacher [eq. (l)] and the choice of whether or not to participate in the labour market. The dependent variables, T* and P*, are unobservable, latent variables, measuring respectively the propensity to choose teaching as a career and the propensity to participate in the labour force. The outcome of these choices is indexed by T and P where T= 1 if the occupation chosen is teaching and P= 1 if the individual is a labour force participant. In this type of model, the parameters of the participation probit are the same for both teachers and non-teachers so teachers and nonteachers make similar participation decisions. For policy purposes one important determinant of occupational choice is the earnings differential between teaching and non-teaching. This enters eq. (1) as the logarithm of the earnings ratio in the two occupations. Eqs. (3) and (4) are the earnings equations for teachers and non-teachers, respectively. ‘This issue is discussed in Dolton and Makepeace (1990, section 2) where more general model specifications are considered. ‘By ‘standard’ we mean two probit equations in which the stochastic errors are correlated.
1396
P.J. Dolton and G.H. Makepeace, Female labour force participation
Conditional on Y, and Y,, the occupation and participation equations define a bivariate probit model and can be estimated by maximum likelihood methods given values for Y, and Y,,. Two econometric issues raised by this specification are the endogeneity of the logarithm of the earnings ratio and the possible sample selection effects in the earnings equations arising if the labour force participants working in either teaching or in non-teaching are not random subsets of the population of all individuals. These problems are resolved by the following three-stage estimation procedure. First we substitute the earnings equations into the occupational choice equation and estimate the resulting reduced form bivariate probit by maximum likelihood. If the errors in the two reduced form selector equations are dependent, the sample selection effects in the earnings equation for teachers can be written as the sum of the following terms for 1, and 1, [see Ham (1982, p. 340)].
(5) where
2=wa^-pza (l-jj2)“”
&z”-pws^ (1
_py’
The terms, f( .) and F( .), are respectively the univariate standard normal density and distribution functions and G( .) is the bivariate standard normal distribution function. The hat refers to the estimated value of the parameter. At the second stage we essentially proceed with the familiar two step estimator due to Heckman (1979), adding the two selection terms, At and AP, to the list of regressors in each earnings equation and applying ordinary least squares to the augmented regression equation to give consistent estimates for the coefficients. The standard errors may be consistently estimated using the results in Ham (1982). Similar terms and procedures can be defined for the non-teacher’s earnings equation. We then predict the earnings in each occupation from the two earnings equations and compute the earnings differential. We call these estimates of the earnings differential the ‘reduced form bivariate probit estimates’ and give them the variable name, YDIFFBP. At the final stage the structural form bivariate probit model is estimated by maximum likelihood with the predicted earnings differential included in the list of regressors. One problem with the bivariate probit formulation is that each type of individual has the same participation equation but the earlier arguments concerning family role specialisation imply that the participation decisions of teachers are different from those of non-teachers. If we interpret our data as a bivariate probit model with truncated by the occupational decision,
P.J. Dolton and G.H. Makepeace, Female labour force participation
1397
selectivity is obtained which has different participation equations for teachers and non-teachers and which can be estimated by standard maximum likelihood techniques. Assume that the participation decision is observed if teaching is chosen and is not observed if non-teaching is chosen. The model becomes
T* = W6, + q( Y, - Y.) -I- u,,
(7)
P: = Za, + v,
(8)
(teachers)
where T= 1 if teaching is chosen (T* ~0) and where P= 1 if T= 1 and the teacher participates (Pf > 0), Since u and v, are normal, eqs. (7) and (8) define a bivariate probit with selectivity. Applying the same argument to non-teachers, we get N* = W6, + K,( Y, - Yn) + u,, P,*=Zcc,+v,
(non-teachers)
(9) (10)
where N = 1 if non-teaching is chosen (N* <0) and where P = 1 if N = 1 and the non-teacher participates (P,* > 0). To implement the model with distinct participation equations, we estimate separate bivariate probit models with selectivity for teachers and nonteachers using, with appropriate changes, the procedure described above for the standard bivariate probit model. We call the resulting estimates of the earnings differential the ‘reduced form selectivity model’ estimates and give them the variable name, YDZFFSEL. We note that the cost of tractability is that the estimates of 6 and K are not constrained to be equal for the two models.
3. The data The data set employed in this investigation is the Survey of 1980 Graduates and Diplomates. This survey is described further in Dolton et al. (1990). A brief description of the variables used in this analysis is given below and a more detailed definition is given Appendix A. The participation decision is affected by family commitments and since these are directly related to marriage and child rearing, we include variables for marital status, number of children and the age of the oldest child. Social background may also influence attitudes to participation so we include variables for parents’ social class. Finally, the opportunity cost of nonparticipation is an important consideration so we include some human
1398
P.J. Dolton and G.H. Makepeace, Female labourforce
participation
capital variables (degree result and age) and earnings in the first job. In the absence of data on the spouse, marital status may also be regarded as a crude indicator of other household income. We assume that the main determinant of occupation choice is the earnings differential between teaching and non teaching although the choice is also conditioned by the individual characteristics such as social and educational background. We therefore include age, social class of parents and whether an independent school was attended to measure the effect of different cultural factors and degree result to proxy ability. Given a sample of graduates drawn from across different institutions and faculties and given the marked differences in the propensity to become teachers across these categories, we have included dummy variables for institutional background and for the faculty awarding the graduate’s degree. The earnings equations are based on a human capital specification and their form is discussed in Dolton et al. (1990). The dependent variable is the logarithm of earnings in the current job and we briefly list the regressors under three headings: Human
capital variables. Degree class, type of degree by faculty, number of months spent in work, square of the number of months spent in work, higher degree, professional qualification. Socio-demographic variables. Married or not, social class of parents. Other control variables. Logarithm of starting salary, number of months unemployed, dummy for location of current job, number of jobs, university degree or non-university degree, graduate job at start of career.
The variables are grouped under various headings to indicate their broad characteristics although different interpretations can be given to some variables. For example, number of months unemployed could be given a human capital interpretation if the value of a person’s capital declines when she is out of work. Alternatively the individual could be searching the job market for the optimal job. Similarly lower earnings for a non-university degree could reflect prejudice or different levels of human capital. The results for the various earnings equations are shown in Appendix B.
4. Results For brevity we only discuss the structural form estimates of the bivariate probit equations for each of our models. These are reported in tables l-3. Throughout the tables a single asterisk means that the coefficient is significant at the 10% level on a two tailed test (the t-statistic is greater than 1.64) and two asterisks that the coeficient is significant at the 5% level on a two tailed test (the t-statistic is greater than 1.96). Our main interests are the
P.J. Dolton and G.H. Makepeace,
Table Structural
form
Female
labour force
participation
1
participation and occupational choice bivariate probit model.”
equations
Married Number
of children
Age of eldest child Age Social class of parents Type of school attended Degree class University
0.705 [0.989] -0.271* [O.lOS] - 1.769** [0.079] 0.014** [0.002] 0.003* [0.002] -0.015 co.0431 _
0.0014** [0.0007] -0.024 CO.0321 -0.274** [O. 1241 -0.039* [0.021] -0.116* CO.0691 _
degree 0.020 [O.lOO] _
-0.827** [0.095] - 1.093** [0.097] -0.682** [0.092]
Arts and languages Difference in the logarithms of earningsb (YDIFFBP)
1.233** 10.2731
Correlation coefficient for errors in the probit equations
0.305** [0.059]
Log likelihood “The dependent variables are, respectively, one if teaching is chosen. bThe difference in earnings is obtained probit model without selectivity.
the
0.052 [0.317]
- 0.026 10.03 l]
Logarithm of starting salary Engineering and technology and science Social science
for
Occupational choice probit
Participation nrobit Constant
1399
- L590.9 one if the woman from
the reduced
participates form
and
bivariate
estimates of the correlation coefficient and the effect of the earnings differential on occupational choice; however, we shall begin by reviewing briefly some features of the remaining estimates. In the results for the bivariate probit model (without selectivity) shown in table 1, the probability of a female graduate working falls if she is married and as the number of children increases but rises as the age of the eldest
P..i. Lhltnn and G.H. Mukepence, Fern&
1400
lahnurfirce
participation
Table 2 Structural
Form participation and occupational bivariate probit model with selectivity:
choice eqquations Teacher’s mu&i.”
Married Number
of children
Age of eldest child
AiS Social ctass of parents
3.434 C2.7381 -0.280 [0.303] ~ 1.405** co. 1871 0.011* [0.006]
0.078 CO.3181
0.002 [0.004] --0.250** [O.@Xc,]
0.0016** [0.OcO7] --0.041 [0.032]
Type of school attended Degree
class
University
degree
Logarithm of starting salary Engineering and technology and science Social science
-0.297+* [O. 1271 -0.051** [0.022]
-0.108* [0.063] _
-0.143** [0.070] _
-0.076 EO.2711
_
-o.s01** [0.095] - 1072** [0.09X]
_
-0.635** [0.092] 0.727** [0.270 3
Arts and languages Difference in the logarithms of earning? (YRIFFSEL) Correlation coeflicient for errors in the probit equations
0.290 CO.2571 - 1,2X2
Log likelihood “The dependent variabies are, respectively, one if teaching is chosen. ‘The difference m earnings is estimated probit model with selectivity.
the
Occupational choice probit
Participation probit Constant
for
one if the woman from
the reduced
participates form
and
bivariate
child increases. This is consistent with the view that the main determinant of the participation decision is family commitments~ Older women may also be more likely to work. The probability of a female graduate choosing to be a teacher increases as her age increases but falls as her class of degree improves and if she has a university degree. An interesting aspect of these results is the possibility that more able students prefer not to teach. Social background
P.J. Dolton and G.H. Makepence,
Female
lnbour force
participation
1401
Table 3 Structural
form participation and occupational choice equations bivariate probit model with selectivity: Non-teacher’s model.”
Married Number
of children
Age of eldest child Age Social class of parents Type of school attended Degree class University
degree
Logarithm of starting salary Engineering and technology and science Social science
-0.056 Cl.1721 -0.254** [0.113] - 1.670;; CO.1161 0.016** [0.003] 0.0047* [0.0024] - 0.050 [0.048] _ - 0.024 [0.034] _ 0.034 [O.llO]
Arts and languages
_
Difference in the logarithms of earningsb (YDZFFSEL)
_
Correlation coefficient for errors in the probit equations Log likelihood
the
Occupational choice probit
Participation probit Constant
for
-0.156 [0.314] _ _ _ -0.0012* [0.0007] 0.029 CO.0321 0.264** CO.1251 0.042** [0.021] 0.104 CO.0691 _ -0.832** [0.093] - 1.110** CO.0961 0.663** CO.0891 - 1.323** CO.2591 -0.847** CO.1481 - 1448.2
“The dependent variables are, respectively, one if the woman participates and one if non-teaching is chosen. “The difference in earnings is estimated from the reduced form bivariate probit model with selectivity. Note the occupational choice variable takes the value one for non-teaching.
affects the choice of occupation and a teacher if she has attended an independent These conclusions continue to apply selectivity for non-teachers shown in table the estimates change. More interestingly
woman is less likely to become a school. in the results for the model with 3 although the numerical values of the results for the teacher’s model
P.J. Dolton and G.H. Makepeace,
1402
Female
labour force
participation
shown in table 2 are somewhat different, supporting the technical specification of heterogeneity in the occupations. Marital status no longer has a significant influence on participation and the age of the eldest child has a less pronounced influence. Although the number of children variable has the same significant effect as elsewhere, the results for marital status and child’s age suggests a different role for family commitments in each occupation. We note also that age is no longer significant and that the social class variable and, at the 10% level, degree performance are now negatively significant. These results show clearly that the participation equation is indeed different for teachers and non-teachers. YDIFFBP is the appropriate measure of the earnings differential for the bivariate probit model and YDZFFSEL for the bivariate probit model with selectivity. This explains their appearance in particular tables. Whatever set of results are selected, as the earnings of a teacher increase relative to those of a non-teacher, individuals in the sample are more likely to choose teaching.3 The signs of the correlation coefficient in tables 1-3 indicate that, if the unobserved factors in the occupational choice equation make it more likely that a person chooses teaching, the unobserved factors in the participation equation make it more likely that the individual chooses to work. Although the correlation coefficient is only significant for the simple bivariate probit model and for the non-teacher’s selectivity model, the evidence does suggest a difference between teaching and non-teaching. The correlation between the unobservables in the choice equations is consistent with the view that people who choose teaching are more likely to work and, if the effects of family role specialisation are not fully measured by the variables included in the model,
3The selectivity models are chosen for their tractability in estimation and do not impose the condition that the coefficients of the earnings differential have the same effect in each model. Integrating over all values of P: and P,f in each model and generalising the notation in eqs. (7)+10), the probability of choosing teaching in the teacher’s and non-teacher’s models are, respectively, Pr(T=l)=l-@(-IV&rc,(Y,-YJ), Hence, the impact dPr(T= d(Y,-
1) YJ
dPr(N=O) d(Y,-
and
Pr(N=O)=@(-WWS,-q(Y,-YJ).
effects are
Y.)
= Q(
- W& - k,( yl - Y”)),
= - W$( - W6” - K,( y, - Y”)),
where @ and 4 are the standard normal distribution and density functions. The derivatives therefore depend nonlinearly on the values of all the variables and coefficients. Nonetheless the impact effects are likely to be different given that the absolute value of the coellicient on the earnings differential in table 3 is approximately twice that in table 2. Define as a reference person someone with the characteristics: Age 27 years, class ‘intermediate’, school nonindependent, degree 2.2, university degree, faculty arts. For this person, the derivatives when earnings in teaching are 10% less than elsewhere is 0.22 for the teaching model and 0.40 for the non-teaching model and the corresponding elasticities are 0.41 and 0.85.
P.J. Dolton and G.H. Makepence, Female labour force participation
1403
Table 4 Predicted
joint
probabilities
for occupational choice and participation commitments. Earnings
Teacher Non-teacher Marginal
probability
ratio 0.70
Earnings
assuming
‘heavy’ family
ratio 0.90
Out of work
In work
Marginal prob.
Out of work
In work
Marginal prob.
0.064 0.556
0.084 0.296
0.148 0.852
0.106 0.514
0.124 0.256
0.230 0.170
0.620
0.380
0.620
0.380
Table 5 Predicted
joint
probabilities
for occupational choice commitments. Earnings
Teacher Non-teacher Marginal
probability
and
participation Earnings
ratio 0.70
assuming
‘light’ family
ratio 0.90
Out of work
In work
Marginal prob.
Out of work
In work
Marginal prob.
0.002 0.047
0.146 0.805
0.148 0.852
0.003 0.046
0.227 0.124
0.230 0.770
0.049
0.95 1
0.049
0.95 1
a career in teaching is more complementary to family commitments than a career outside teaching. To give some further intuition to these results, we examine the predicted probabilities of certain states for the results of the standard bivariate probit model shown in table 1. Tables 4 and 5 show the predicted probabilities of different states for women with ‘heavy’ and ‘light’ family commitments, respectively. Both types of woman are assumed to be 27 years old with an ‘intermediate’ social class and state school background, and to possess a 2.2 honours degree from a university. On starting work each woman received the mean starting salary for all graduates. The woman with ‘heavy’ commitments is married with one, six months old, child while the other woman with ‘light’ commitments is single with no children. The probabilities are computed for values of the ratio of earnings in teaching to earnings elsewhere of 0.70 and 0.90. We notice immediately the large influence that family commitments have on the decision whether or not to work. Our main interest lies in the interaction between labour force participation decisions and occupational choice, particularly for those with family commitments. From the prediction in table 4, 38% of women with heavy commitments continue to work. However the incidence of work is much higher amongst teachers than nonteachers. We can calculate the conditional probabilities of work given
1404
P.J. Dolton and G.H. Makepeace, Female labour force participation Table 6 Bivariate
probit
models,
OLS estimates
of earnings
equations.
Bivariate probit
Selectivity
All
Teachers
Non-teachers
Difference in the logarithms of earnings (YDIFFOLS) Correlation coefticient for errors in the probit equations
1.114** [0.260]
0.914** CO.2531
-
0.285** [0.059]
0.364 CO.2421
-0.799** [O. 1561
Log likelihood
- 1591.5
- L221.3
- 1445.5
model
1.346** [0.250]
occupation. In table 4 (at the earnings ratio 0.90) 54% (0.124/0.230) of teachers are predicted to work compared with only 33% of the non-teachers while, at the lower earnings ratio in table 4, the figures are 57% and 35%. Teaching does appear to combine better with work than non-teaching. Interestingly the conditional probability of working is also higher for single teachers than for single non-teachers although the difference in the two is very much smaller.4 The tables also illustrate the effects of changing the earnings differential. A rise in the relative earnings from 70% to 90% increases the proportion who are teachers by 8.2% (0.230-0.148). For single women almost all of these work but for married women just under a half actually work as teachers. Relatively large changes in the earnings differential have relatively small effects on occupational choice, although one might expect the elasticity to be rather small for such a significant decision. It is easy to imagine circumstances in which these models cannot be estimated (for example, if there is a large number of regressors in the model and the reduced form bivariate probits become intractable). Further, some commentators may question the robustness of the results to changes in the measure of the earnings differential. For these reasons we decided to investigate the sensitivity of our conclusions to changes in the definition of the earnings differential. We repeated the estimation of the structural form bivariate probit models with earnings differentials predicted from ordinary least squares estimates of the earnings equations (YDZFFOLS).’ These results are quoted in table 6. The column labelled bivariate probit 4About 98% of single teachers work for both earnings ratios compared with about 94% of single non-teachers (once again for both earnings ratios). 5The results for other definitions of earnings (including the use of YDIFFBP in the selectivity model and YDIFFSEL in standard model) are reported in Dolton and Makepeace (1990). The results for the earnings differential and the correlation coefftcient in each case are similar for those reported in the text.
P.J. Dolton and G.H. Makepeace, Female labour force participation
1405
refers to the estimates of the ‘standard bivariate probit model’ while the columns labelled selectivity model refer to the estimates of the ‘bivariate variable for the probit model with selectivity’. Note that the dependent results of the model with selectivity for non-teachers takes the value 1 for non-teaching so the signs of the coefficients in these results are consistent with the other results. Table 6 confirms that our earlier findings concerning the effects of earnings on the choice of occupation and the correlation between the participation and occupation decisions.
5. Conclusion The results in this paper suggest that the choice of teaching as a career is intimately related to decisions about labour force participation. In the model without selectivity, unobserved factors which make a woman more likely to select a career outside teaching make her less likely to work. Given the same level of family commitments (as measured by marital status, number and age of children), females in occupations outside teaching are less likely to be working than teachers. The results for the models with selectivity support this interpretation but also suggest that the participation decisions of females differ between teachers and non-teachers. The choice of teaching is also found to be significantly affected by the earnings paid to teachers. An increase in the earnings a female graduate receives as a teacher relative to the earnings she receives as a non-teacher increases the probability that she will become a teacher. These results establish the basic economic proposition that occupational choice responds to earnings differentials and such results are not common in the literature. It is interesting to note the differences between the results for the two bivariate probit formulations. There is evidence that the assumption of homogeneity in the choices made by different types of individual obscures the differences in the underlying relationships. A good example is the correlation coefficient between the participation and occupational choice decisions. In one sense this is a key parameter because it motivates the adoption of a bivariate model rather than the simpler alternative of two independent probits. There is evidence that the correlation coefficient is rather different for teachers and non-teachers. Indeed the significant value of 0.305 for the bivariate probit model in table 1 contrasts with the insignificant value of 0.290 for the teacher’s model and the significant value of 0.847 for the non-teacher’s model. These and other results presented above indicate the need to treat teachers and non-teachers as heterogenous groups with respect to their participation decisions. This result may have a wider significance for bivariate probit studies in which the sets of observations distinguished ex ante by the probit equations are treated as homogenous. Simulations of the choices made by women with ‘light’ and ‘heavy’ family
P.J. Dolton and G.H. Makepeace, Female labour force participation
1406
commitments illuminate three features of the results: (i) family commitments significantly affect the propensity to be in work; (ii) teachers are more likely to be in work than non-teachers with the same family commitments (this effect is particularly pronounced for women with ‘heavy’ commitments); and (iii) relative earnings have some quantitative impact on career choice. These results reinforce the case for analysing empirically the links between occupational choice and participation decisions rather than assuming, as is commonly the case, that one can be examined independently of the other.
Appendix
A. Variable
definitions
Brief definitions of the variables are presented much fuller discussion see Dolton et al. (1990)
in this appendix.
For
a
Log earnings. The logarithm of salary in sterling at the end of 1986. This variable is the dependent variable in the earnings equations. Married. A dummy variable taking the value 1 if the respondent has ever been married and 0 otherwise. Number of children. The number of children (aged under 16) up to a maximum of 3. Where an individual has reported more than 3 children, the variable is given a value of 4. Age of the eldest child. The age of the eldest child measured in months. Age. The age of the respondent measured in months. Social class of parents. An ordinal variable for the social class of parents, as determined by the nature of their socio-economic group. A value between 1 and 6 is assigned to each social class as follows: 6 for professional occupation, 5 for an intermediate occupation, 4 for a skilled (non-manual) occupation, 3 for a skilled (manual) occupation, 2 for a partly skilled occupation and 1 for an unskilled occupation. Type of school attended. A dummy variable taking the value 1 if the respondent attended an independent school and 0 otherwise. Degree class. An ordinal variable for the first degree class. The values are allocated according to the following scale: 8 for a first, 7 for an upper second, 6 for an undivided second, 5 for a lower second, 4 for a third, 3 for an ordinary, 2 to a pass or fourth, and 1 for others. University degree. A dummy variable taking the value 1 if the respondent was awarded his or her degree from a university and 0 otherwise. Dummies for postgraduate Professional
qualification.
qualifications
A dummy variable taking a value 1 if the respondent has successfully completed a qualification awarded by a professional body, and 0 if not. Higher degree. A dummy variable taking a value 1 if the respondent has successfully completed a masters degree or a Ph.D., and 0 if not.
P.J. Dolton and G.H. Makepeace, Female labour force participation
Logarithm
of starting salary.
The logarithm
of the (indexed)
starting
1407
salary
in the first job after graduation. A dummy variable taking the value 1 if the Graduate job at start of career. respondent reports that a degree was the minimum qualification necessary to obtain her first job after graduation or if a degree was helpful in obtaining the first job, and the value 0 otherwise. Work experience. The number of months spent in work after graduation. Work experience squared. The squared value of the number of months spent in work after graduation. Number ofjobs. The number of jobs since graduating. The number of months spent unemployed. The number of months unemployed since graduation whilst still seeking work. London. A dummy variable taking the value 1 if the respondents first job after graduation was located in London or Southern England, and 0 if located elsewhere. Faculty dummies. The reference group for these dummies is Education and other subjects. Engineering and technology and science. A dummy variable taking the value 1 if the graduate’s first degree in engineering or technology or a science subject and 0 otherwise. Social science. A dummy variable taking the value 1 if the graduate’s first degree was in administrative, business or social studies (including law) and 0 otherwise. Examples are Accounting, Economics, Sociology, Psychology and Politics. Arts and languages. A dummy variable taking the value 1 if the graduate’s first degree was in an arts or language subject such as English, History or French and 0 otherwise. Lambda. A sample selection correction term. In Yt--ln Y”.,, where Y, and Y,, are earnings in Difference in logs of earnings. teaching and non-teaching, respectively, and where the hat refers to the predicted values. The predicted values are obtained from the relevant earnings equations. Briefly: YDIFFBP YDIFFSEL YDIFFOLS
uses estimates from the reduced form bivariate probit; uses estimates from the reduced form bivariate probit selectivity; uses ordinary least squares estimates.
with
P.J. Dolton and G.H. Makepeace,
1408
Female
lahour force
participation
Appendix B. Earnings equations Table B.l Earnings
equations
for the bivariate probit model variable is log earnings).” _
Married Social
class of parents
Degree
class
Higher
degree
Professional University
qualification degree
Logarithm of starting salary Graduate job at start of career Work experience Work Number
experience
squared
of jobs
Number of months unemployed London Engineering technology Social
and and science
science
Arts and languages Lambda
(participation)
Lambda
(occupation)
constant R2
Sample
spent
Teachers 0.034* [0.020] -0.004 [0.009] 0.012** [0.006] 0.033 [0.040] _
Non-teachers -
0.007 co.02 l] 0.121** [0.036] _ - 0.00096 [O.ooS] 0.00002 [0.00005] -0.024** [0.009] -0.004 [0.003]
co.@w - 0.0092** [0.002]
0.059** co.0221
0.131** [0.024]
- 0.042 [0.040]
-0.095* [0.056] - 0.064 [0.059] -0.129** [0.049]
- 0.005 10.0461 - 0.024 [0.031] - 0.098** [0.042]
0.057 CO.0461 -0.209** [0.104]
0.058 [O.OSO] 6.884** [0.307]
5.518** CO.2611 0.293
489
“The sample selection terms are calculated the reduced form bivariate probit models.
- 0.037* [0.021] 0.018* co.01 l] 0.027** [0.007] 0.026 [0.040] 0.090** [0.036] 0.043* [0.024] 0.236** [0.034] 0.106** co.03 l] 0.013** [O.ooS] - 0.00008 [0.00005] - 0.008
0.218 size
(the dependent
1,263 from
the estimates
of
P.J. Dolton and G.H. Makepeace,
Female
labour force
participation
1409
Table B.2 Earnings
equations for the bivariate (the dependent variable
probit model with is log earnings).” Teachers
Married Social class of parents
0.0338 [0.019] 0.002
w@=Y Degree
class
Higher
degree
Professional University
qualification degree
Logarithm of starting salary Graduate job at start of career Work
experience
Work
experience
Number
squared
Number of months unemployed
spent
London Engineering and technology and science Social science Arts and languages Lambda
(participation)
Lambda
(occupation)
R2
Sample
size
Non-teachers - 0.044, [0.023] 0.018 co.01 11 0.028** [O.OOS] 0.032 [0.042] 0.097* [0.052]
0.007 [0.020]
0.045 [0.0303
0.118** [0.034] _
0.234** [0.038]
-0.0015 [0.005]
of jobs
Constant
0.015** [0.006] 0.03 1 [0.038] _
selectivity
0.00002 [0.OoOo5] -0.025**
0.103** [0.044] 0.012** [O.OOS] - o.OoOO7 [0.00006]
co.@w
-0.007 [0.009]
-0.0045* [0.0027]
-0.009** [0.003]
0.060** [0.020] -0.044 [0.037] -0.006 [0.043] -0.026 CO.0291 -0.015** co.0391 0.052 [0.046] 6.903** [0.285]
0.140** CO.0291 - 0.080 [O.OSS] - 0.046 [0.091] -0.115* [0.068] 0.130** [0.060] -0.182 10.1721 5.498** CO.2531
0.216 489
0.294 1,263
“The sample selection terms are calculated from the estimates the reduced form bivariate probit model with selectivity.
of
P.J. Dotton and GA. Makepeace, Female labour force participation
1410
Table B.3 Participation and occupational choice equations for the hivariate probit with OLS earnings predictions (The dependent variables are respectively the woman participates and one if teaching is chosen). Participation urobit
Constant
0.642 [0.990] -0.&w* [0.990]
Married Number
of children
Age of eldest child
Alre Social class of parents
(Jniversity
degree
Logarithm of salary Engineering and technology and science Social science
Occupational choice probrt -0.012 [0.317]
- 1.750** [0.079] 0.014** ]0.002] 0.003 [0.002] -0.015 [0.043]
Type of school attended Degree class
model one if
- 0.027 [0.031] _ 0.029
_ 0.002** [0.0007] -0.03I LO.032J -0.290** ]os24] - 0.042** [0.021] -0.119 [O&9] _
ro.ioi]
Arts and Languages Difference in the logarithms of earnings (YDIFFULS) Correlation cocflicicnt for errors in the probit equations Log likelihood Sample size ___~ ._~_. __.__~_..
-0.832** /0.095] - 1.102** [O.oSS] --0.474** [0.091] 1.114** [0.260] 0.285** [0.059] - lS91.517 2,056 _.__~_..
_ ..___
Appendix C. The British education system The higher education system in Britain differs between England, Wales, and Scotland. For conciseness we shall describe the system in England and Wales at the time of the Survey. Small modifications are needed to accommodate the Scottish system. Degrees are awarded after three or four years of study at a Higher Education Institution which may be a university, polytechnic, College of
P.J. Dolton and G.H. Makepeace,
Female
labour force
participation
1411
Higher Education or teacher training college. Students typically enter higher education when they are eighteen or nineteen with two or more GCE A level qualifications. For most of these young entrants A levels will have been obtained at the end of their full time education at school. A levels may also be obtained by full time or part time study at other institutions. A levels are graded with grade A as the highest grade down to grade E which is the lowest grade of pass. Students may have attended schools which are fully funded by the government or ‘independent’ schools where fees are paid. At the time of the Survey there were three main routes into teaching, Firstly one could study for a diploma in teaching awarded after three years of study by an institution specialising in teacher education. Secondly one could study for a 3 or 4 year B.Ed. (a batchelors degree in education) which would entitle one to enter teaching. Thirdly, one could take a one year postgraduate diploma course following a degree in another subject. Our definition of teacher includes all types of school teachers, at primary or secondary level and teaching children aged from 5 to 18, who have a degree. Many graduates go into jobs where further training is required and obtain postgraduate qualifications recording their successful completion of that training. These people are recorded in our sample as possessing a postgraduate (non-academic) qualification. References Chesher, A., 1985, Score tests for zero covariances in recursive linear models for grouped or censored data, Journal of Econometrics 28, 291-305. Dolton, P.J., 1990, The economics of UK teacher supply: The graduate’s decision, The Economic Journal 100 (supplement) 91-104. Dolton, P.J. and G.H. Makepeace, 1990, Modelling female labour force participation and choice of occupation: An empirical study of female entrants to teaching, Working paper (University of Hull Labour Economics Unit, Hull). Dolton, P.J., G.H. Makepeace and G.D. Inchley, 1990, The early careers of 1980 graduates: Earnings, earnings differentials and postgraduate study, Research paper no. 78 (Department of Employment, London). Dolton, P.J., G.H. Makepeace and W. van der Klaauw, 1989, Occupational choice and earnings determination: The role of sample selection and non-pecuniary factors, Oxford Economic Papers 41, 573-594. Ham, J.C., 1982, Estimation of a labour supply model with censoring due to unemployment and underemployment, Review of Economic Studies 49, 335-354. Heckman, J., 1979, Sample selection bias as a specification error, Econometrica 47, 1533161. Maddala, G.S., 1983, Limited dependent and qualitative variables in econometrics (Cambridge University Press, Cambridge). Van de Ven, W. and B. van Praag, 1981, The demand for deductibles in private health insurance, Journal of Econometrics 17, 229-252.
Economic
Review
37 (1993)
1393-1411.
North-Holland
Female labour force participation the choice of occupation
and
The supply of teachers P.J. Dolton* University of Newcastle upon Tyne, Newcastle upon Tyne, UK
G.H. Makepeace University of Hull, Hull, UK Received November
1991, final version received
February
1992
This paper examines the relationship between occupational choice and participation decisions by women. It is motivated by recent policy debate in the U.K. concerning the supply of teachers where about sixty per cent of teachers are female and the recruitment of women is vital to the maintenance of the labour force in teaching. Models of earnings determination, occupational choice and labour force participation are estimated for a large sample of female graduates. The choice of occupation and labour market status is modelled as a joint decision between: (i) teaching and non-teaching; and (ii) working and not-working. Estimates from alternative models show that participation decisions are correlated with occupational choice, with individuals who choose teaching being more likely to work. The choice of occupation is also affected by the earnings differential between teaching and non teaching suggesting that teacher shortages could be alleviated by raising teacher’s earnings.
1. Introduction In the United Kingdom, women constitute about sixty percent of the workforce in teaching so the continued recruitment of women graduates is crucial for the supply of teachers. However, since young women are simultaneously making decisions about their family commitments and labour force participation as well as careers, modelling teacher’s labour supply for women is quite complicated. The primary aim of this paper is to analyse Correspondence to: G.H. Makepeace, Department of Economics, University of Hull, Hull, HU6 7RX, U.K. *We would like to thank the following for their comments on earlier drafts of this paper: Brian Main, Gerard Pfan, Jaques Siegers, the referees, participants at conferences of the Labour Economics Study Group and European Association of Labour Economists, and participants at seminars at Maastricht and Utrecht. As always the authors remain responsible for all errors or omissions in the paper. This research was undertaken with the support of Economic and Social Research Council grant number ROOO 23 2045. 00142921/93/$06.00
0
1993-Elsevier
Science Publishers
B.V. All rights reserved
1394
P.J. Dolton and G.H. Makepeace,
Female
labour force
participation
empirically some of the complex interactions determining the career choice of female graduates. This paper focuses on two matters. The first is whether the occupational decision is independent of the decision to participate in the labour force; that is whether an individual’s propensity to become a teacher is influenced by their plans to be in work or out of work given their family and other commitments. The second is whether the choice of teaching as an occupation is affected by the earnings differential between teaching and other graduate occupations. The occupational choice of graduates has been studied by Dolton et al. (1989) for a sample of 1970 graduates and Dolton (1990) for a sample of 1980 graduates. Both these studies examine the effect of occupational choice on earnings and the effect of earnings on the choice of occupation. The study by Dolton concentrates on the common factors affecting the occupational choice of men and women and is not concerned with the special features of the female’s occupational choice. Of particular relevance here is the influence of family role specialisation. If women expect to spend more time on nonmarket work, they are more likely to pursue careers which are complementary to their family role and to be non-participants when family commitments are high. Dolton et al. (1989) recognise these arguments by estimating separate models for men and women and including a female participation effect. However, they assume independence between the occupational decision and the participation decision and do not formally test this constraint. Several reasons may be advanced for a high degree of complementarity between teaching and family support. These include: the relatively low number of hours which have to be spent at school and their convenient location in the day, the timing and quantity of holidays, and the ease with which one can return to teaching after a career interruption. These attributes of teaching as a career are valued differently by different individuals, creating an unobserved factor affecting occupational choice. Similarly there will be some unobserved factors such as motivation for work or the degree of commitment to a family which influence the participation decision. Women who are keen to keep working will, ceteris paribus, be more likely to choose teaching and to work, implying a positive correlation between the decisions. Similarly some women may be especially committed to family support which they realise by choosing teaching. Whatever the reasons, this discussion confirms that the independence of the two decisions should be statistically tested. Results for a bivariate probit model show that the decision to become a teacher and the decision to be a labour force participant are not taken independently of one another if teachers and nonteachers have the same participation model. However there are no compelling a priori reasons for supposing that teachers and non-teachers make their participation decisions in the same way. Indeed the arguments made above suggest that labour force participation for a given level of family commitments is somewhat easier for women teachers. These considerations led us to
P.J. Dolton and G.H. Makepeace, Female labour force participation
investigate a model which allows teachers participation decisions.
2. Estimation
and non-teachers
1395
to make different
methods
We wish to model a female graduate’s choice of labour market status and occupation. In our econometric mode1 an individual chooses whether to be a labour force participant and whether to work as a teacher or a non-teacher. We represent this as two, dependent binary choices in which each decision depends on a list of regressors and is affected through the error structure by the other decision. To make our models tractable,’ our estimation strategies focus on bivariate probit models with and without sample selection. We begin with the ‘standard’ bivariate probit model2 which has the form T*=Wd+rc(lnY,-lnY,)+u,
(1)
P*=Za+v,
In Y, = X,/I, + e,, In Y, = X,& + e,,
(4)
where T= 1 iff T*>O and T=O otherwise, P=l iff P*>O and P=O otherwise, and u, v, e,, e, are jointly normal error terms. The first two equations are ‘selector’ equations describing the choice of whether or not to be a teacher [eq. (l)] and the choice of whether or not to participate in the labour market. The dependent variables, T* and P*, are unobservable, latent variables, measuring respectively the propensity to choose teaching as a career and the propensity to participate in the labour force. The outcome of these choices is indexed by T and P where T= 1 if the occupation chosen is teaching and P= 1 if the individual is a labour force participant. In this type of model, the parameters of the participation probit are the same for both teachers and non-teachers so teachers and nonteachers make similar participation decisions. For policy purposes one important determinant of occupational choice is the earnings differential between teaching and non-teaching. This enters eq. (1) as the logarithm of the earnings ratio in the two occupations. Eqs. (3) and (4) are the earnings equations for teachers and non-teachers, respectively. ‘This issue is discussed in Dolton and Makepeace (1990, section 2) where more general model specifications are considered. ‘By ‘standard’ we mean two probit equations in which the stochastic errors are correlated.
1396
P.J. Dolton and G.H. Makepeace, Female labour force participation
Conditional on Y, and Y,, the occupation and participation equations define a bivariate probit model and can be estimated by maximum likelihood methods given values for Y, and Y,,. Two econometric issues raised by this specification are the endogeneity of the logarithm of the earnings ratio and the possible sample selection effects in the earnings equations arising if the labour force participants working in either teaching or in non-teaching are not random subsets of the population of all individuals. These problems are resolved by the following three-stage estimation procedure. First we substitute the earnings equations into the occupational choice equation and estimate the resulting reduced form bivariate probit by maximum likelihood. If the errors in the two reduced form selector equations are dependent, the sample selection effects in the earnings equation for teachers can be written as the sum of the following terms for 1, and 1, [see Ham (1982, p. 340)].
(5) where
2=wa^-pza (l-jj2)“”
&z”-pws^ (1
_py’
The terms, f( .) and F( .), are respectively the univariate standard normal density and distribution functions and G( .) is the bivariate standard normal distribution function. The hat refers to the estimated value of the parameter. At the second stage we essentially proceed with the familiar two step estimator due to Heckman (1979), adding the two selection terms, At and AP, to the list of regressors in each earnings equation and applying ordinary least squares to the augmented regression equation to give consistent estimates for the coefficients. The standard errors may be consistently estimated using the results in Ham (1982). Similar terms and procedures can be defined for the non-teacher’s earnings equation. We then predict the earnings in each occupation from the two earnings equations and compute the earnings differential. We call these estimates of the earnings differential the ‘reduced form bivariate probit estimates’ and give them the variable name, YDIFFBP. At the final stage the structural form bivariate probit model is estimated by maximum likelihood with the predicted earnings differential included in the list of regressors. One problem with the bivariate probit formulation is that each type of individual has the same participation equation but the earlier arguments concerning family role specialisation imply that the participation decisions of teachers are different from those of non-teachers. If we interpret our data as a bivariate probit model with truncated by the occupational decision,
P.J. Dolton and G.H. Makepeace, Female labour force participation
1397
selectivity is obtained which has different participation equations for teachers and non-teachers and which can be estimated by standard maximum likelihood techniques. Assume that the participation decision is observed if teaching is chosen and is not observed if non-teaching is chosen. The model becomes
T* = W6, + q( Y, - Y.) -I- u,,
(7)
P: = Za, + v,
(8)
(teachers)
where T= 1 if teaching is chosen (T* ~0) and where P= 1 if T= 1 and the teacher participates (Pf > 0), Since u and v, are normal, eqs. (7) and (8) define a bivariate probit with selectivity. Applying the same argument to non-teachers, we get N* = W6, + K,( Y, - Yn) + u,, P,*=Zcc,+v,
(non-teachers)
(9) (10)
where N = 1 if non-teaching is chosen (N* <0) and where P = 1 if N = 1 and the non-teacher participates (P,* > 0). To implement the model with distinct participation equations, we estimate separate bivariate probit models with selectivity for teachers and nonteachers using, with appropriate changes, the procedure described above for the standard bivariate probit model. We call the resulting estimates of the earnings differential the ‘reduced form selectivity model’ estimates and give them the variable name, YDZFFSEL. We note that the cost of tractability is that the estimates of 6 and K are not constrained to be equal for the two models.
3. The data The data set employed in this investigation is the Survey of 1980 Graduates and Diplomates. This survey is described further in Dolton et al. (1990). A brief description of the variables used in this analysis is given below and a more detailed definition is given Appendix A. The participation decision is affected by family commitments and since these are directly related to marriage and child rearing, we include variables for marital status, number of children and the age of the oldest child. Social background may also influence attitudes to participation so we include variables for parents’ social class. Finally, the opportunity cost of nonparticipation is an important consideration so we include some human
1398
P.J. Dolton and G.H. Makepeace, Female labourforce
participation
capital variables (degree result and age) and earnings in the first job. In the absence of data on the spouse, marital status may also be regarded as a crude indicator of other household income. We assume that the main determinant of occupation choice is the earnings differential between teaching and non teaching although the choice is also conditioned by the individual characteristics such as social and educational background. We therefore include age, social class of parents and whether an independent school was attended to measure the effect of different cultural factors and degree result to proxy ability. Given a sample of graduates drawn from across different institutions and faculties and given the marked differences in the propensity to become teachers across these categories, we have included dummy variables for institutional background and for the faculty awarding the graduate’s degree. The earnings equations are based on a human capital specification and their form is discussed in Dolton et al. (1990). The dependent variable is the logarithm of earnings in the current job and we briefly list the regressors under three headings: Human
capital variables. Degree class, type of degree by faculty, number of months spent in work, square of the number of months spent in work, higher degree, professional qualification. Socio-demographic variables. Married or not, social class of parents. Other control variables. Logarithm of starting salary, number of months unemployed, dummy for location of current job, number of jobs, university degree or non-university degree, graduate job at start of career.
The variables are grouped under various headings to indicate their broad characteristics although different interpretations can be given to some variables. For example, number of months unemployed could be given a human capital interpretation if the value of a person’s capital declines when she is out of work. Alternatively the individual could be searching the job market for the optimal job. Similarly lower earnings for a non-university degree could reflect prejudice or different levels of human capital. The results for the various earnings equations are shown in Appendix B.
4. Results For brevity we only discuss the structural form estimates of the bivariate probit equations for each of our models. These are reported in tables l-3. Throughout the tables a single asterisk means that the coefficient is significant at the 10% level on a two tailed test (the t-statistic is greater than 1.64) and two asterisks that the coeficient is significant at the 5% level on a two tailed test (the t-statistic is greater than 1.96). Our main interests are the
P.J. Dolton and G.H. Makepeace,
Table Structural
form
Female
labour force
participation
1
participation and occupational choice bivariate probit model.”
equations
Married Number
of children
Age of eldest child Age Social class of parents Type of school attended Degree class University
0.705 [0.989] -0.271* [O.lOS] - 1.769** [0.079] 0.014** [0.002] 0.003* [0.002] -0.015 co.0431 _
0.0014** [0.0007] -0.024 CO.0321 -0.274** [O. 1241 -0.039* [0.021] -0.116* CO.0691 _
degree 0.020 [O.lOO] _
-0.827** [0.095] - 1.093** [0.097] -0.682** [0.092]
Arts and languages Difference in the logarithms of earningsb (YDIFFBP)
1.233** 10.2731
Correlation coefficient for errors in the probit equations
0.305** [0.059]
Log likelihood “The dependent variables are, respectively, one if teaching is chosen. bThe difference in earnings is obtained probit model without selectivity.
the
0.052 [0.317]
- 0.026 10.03 l]
Logarithm of starting salary Engineering and technology and science Social science
for
Occupational choice probit
Participation nrobit Constant
1399
- L590.9 one if the woman from
the reduced
participates form
and
bivariate
estimates of the correlation coefficient and the effect of the earnings differential on occupational choice; however, we shall begin by reviewing briefly some features of the remaining estimates. In the results for the bivariate probit model (without selectivity) shown in table 1, the probability of a female graduate working falls if she is married and as the number of children increases but rises as the age of the eldest
P..i. Lhltnn and G.H. Mukepence, Fern&
1400
lahnurfirce
participation
Table 2 Structural
Form participation and occupational bivariate probit model with selectivity:
choice eqquations Teacher’s mu&i.”
Married Number
of children
Age of eldest child
AiS Social ctass of parents
3.434 C2.7381 -0.280 [0.303] ~ 1.405** co. 1871 0.011* [0.006]
0.078 CO.3181
0.002 [0.004] --0.250** [O.@Xc,]
0.0016** [0.OcO7] --0.041 [0.032]
Type of school attended Degree
class
University
degree
Logarithm of starting salary Engineering and technology and science Social science
-0.297+* [O. 1271 -0.051** [0.022]
-0.108* [0.063] _
-0.143** [0.070] _
-0.076 EO.2711
_
-o.s01** [0.095] - 1072** [0.09X]
_
-0.635** [0.092] 0.727** [0.270 3
Arts and languages Difference in the logarithms of earning? (YRIFFSEL) Correlation coeflicient for errors in the probit equations
0.290 CO.2571 - 1,2X2
Log likelihood “The dependent variabies are, respectively, one if teaching is chosen. ‘The difference m earnings is estimated probit model with selectivity.
the
Occupational choice probit
Participation probit Constant
for
one if the woman from
the reduced
participates form
and
bivariate
child increases. This is consistent with the view that the main determinant of the participation decision is family commitments~ Older women may also be more likely to work. The probability of a female graduate choosing to be a teacher increases as her age increases but falls as her class of degree improves and if she has a university degree. An interesting aspect of these results is the possibility that more able students prefer not to teach. Social background
P.J. Dolton and G.H. Makepence,
Female
lnbour force
participation
1401
Table 3 Structural
form participation and occupational choice equations bivariate probit model with selectivity: Non-teacher’s model.”
Married Number
of children
Age of eldest child Age Social class of parents Type of school attended Degree class University
degree
Logarithm of starting salary Engineering and technology and science Social science
-0.056 Cl.1721 -0.254** [0.113] - 1.670;; CO.1161 0.016** [0.003] 0.0047* [0.0024] - 0.050 [0.048] _ - 0.024 [0.034] _ 0.034 [O.llO]
Arts and languages
_
Difference in the logarithms of earningsb (YDZFFSEL)
_
Correlation coefficient for errors in the probit equations Log likelihood
the
Occupational choice probit
Participation probit Constant
for
-0.156 [0.314] _ _ _ -0.0012* [0.0007] 0.029 CO.0321 0.264** CO.1251 0.042** [0.021] 0.104 CO.0691 _ -0.832** [0.093] - 1.110** CO.0961 0.663** CO.0891 - 1.323** CO.2591 -0.847** CO.1481 - 1448.2
“The dependent variables are, respectively, one if the woman participates and one if non-teaching is chosen. “The difference in earnings is estimated from the reduced form bivariate probit model with selectivity. Note the occupational choice variable takes the value one for non-teaching.
affects the choice of occupation and a teacher if she has attended an independent These conclusions continue to apply selectivity for non-teachers shown in table the estimates change. More interestingly
woman is less likely to become a school. in the results for the model with 3 although the numerical values of the results for the teacher’s model
P.J. Dolton and G.H. Makepeace,
1402
Female
labour force
participation
shown in table 2 are somewhat different, supporting the technical specification of heterogeneity in the occupations. Marital status no longer has a significant influence on participation and the age of the eldest child has a less pronounced influence. Although the number of children variable has the same significant effect as elsewhere, the results for marital status and child’s age suggests a different role for family commitments in each occupation. We note also that age is no longer significant and that the social class variable and, at the 10% level, degree performance are now negatively significant. These results show clearly that the participation equation is indeed different for teachers and non-teachers. YDIFFBP is the appropriate measure of the earnings differential for the bivariate probit model and YDZFFSEL for the bivariate probit model with selectivity. This explains their appearance in particular tables. Whatever set of results are selected, as the earnings of a teacher increase relative to those of a non-teacher, individuals in the sample are more likely to choose teaching.3 The signs of the correlation coefficient in tables 1-3 indicate that, if the unobserved factors in the occupational choice equation make it more likely that a person chooses teaching, the unobserved factors in the participation equation make it more likely that the individual chooses to work. Although the correlation coefficient is only significant for the simple bivariate probit model and for the non-teacher’s selectivity model, the evidence does suggest a difference between teaching and non-teaching. The correlation between the unobservables in the choice equations is consistent with the view that people who choose teaching are more likely to work and, if the effects of family role specialisation are not fully measured by the variables included in the model,
3The selectivity models are chosen for their tractability in estimation and do not impose the condition that the coefficients of the earnings differential have the same effect in each model. Integrating over all values of P: and P,f in each model and generalising the notation in eqs. (7)+10), the probability of choosing teaching in the teacher’s and non-teacher’s models are, respectively, Pr(T=l)=l-@(-IV&rc,(Y,-YJ), Hence, the impact dPr(T= d(Y,-
1) YJ
dPr(N=O) d(Y,-
and
Pr(N=O)=@(-WWS,-q(Y,-YJ).
effects are
Y.)
= Q(
- W& - k,( yl - Y”)),
= - W$( - W6” - K,( y, - Y”)),
where @ and 4 are the standard normal distribution and density functions. The derivatives therefore depend nonlinearly on the values of all the variables and coefficients. Nonetheless the impact effects are likely to be different given that the absolute value of the coellicient on the earnings differential in table 3 is approximately twice that in table 2. Define as a reference person someone with the characteristics: Age 27 years, class ‘intermediate’, school nonindependent, degree 2.2, university degree, faculty arts. For this person, the derivatives when earnings in teaching are 10% less than elsewhere is 0.22 for the teaching model and 0.40 for the non-teaching model and the corresponding elasticities are 0.41 and 0.85.
P.J. Dolton and G.H. Makepence, Female labour force participation
1403
Table 4 Predicted
joint
probabilities
for occupational choice and participation commitments. Earnings
Teacher Non-teacher Marginal
probability
ratio 0.70
Earnings
assuming
‘heavy’ family
ratio 0.90
Out of work
In work
Marginal prob.
Out of work
In work
Marginal prob.
0.064 0.556
0.084 0.296
0.148 0.852
0.106 0.514
0.124 0.256
0.230 0.170
0.620
0.380
0.620
0.380
Table 5 Predicted
joint
probabilities
for occupational choice commitments. Earnings
Teacher Non-teacher Marginal
probability
and
participation Earnings
ratio 0.70
assuming
‘light’ family
ratio 0.90
Out of work
In work
Marginal prob.
Out of work
In work
Marginal prob.
0.002 0.047
0.146 0.805
0.148 0.852
0.003 0.046
0.227 0.124
0.230 0.770
0.049
0.95 1
0.049
0.95 1
a career in teaching is more complementary to family commitments than a career outside teaching. To give some further intuition to these results, we examine the predicted probabilities of certain states for the results of the standard bivariate probit model shown in table 1. Tables 4 and 5 show the predicted probabilities of different states for women with ‘heavy’ and ‘light’ family commitments, respectively. Both types of woman are assumed to be 27 years old with an ‘intermediate’ social class and state school background, and to possess a 2.2 honours degree from a university. On starting work each woman received the mean starting salary for all graduates. The woman with ‘heavy’ commitments is married with one, six months old, child while the other woman with ‘light’ commitments is single with no children. The probabilities are computed for values of the ratio of earnings in teaching to earnings elsewhere of 0.70 and 0.90. We notice immediately the large influence that family commitments have on the decision whether or not to work. Our main interest lies in the interaction between labour force participation decisions and occupational choice, particularly for those with family commitments. From the prediction in table 4, 38% of women with heavy commitments continue to work. However the incidence of work is much higher amongst teachers than nonteachers. We can calculate the conditional probabilities of work given
1404
P.J. Dolton and G.H. Makepeace, Female labour force participation Table 6 Bivariate
probit
models,
OLS estimates
of earnings
equations.
Bivariate probit
Selectivity
All
Teachers
Non-teachers
Difference in the logarithms of earnings (YDIFFOLS) Correlation coefticient for errors in the probit equations
1.114** [0.260]
0.914** CO.2531
-
0.285** [0.059]
0.364 CO.2421
-0.799** [O. 1561
Log likelihood
- 1591.5
- L221.3
- 1445.5
model
1.346** [0.250]
occupation. In table 4 (at the earnings ratio 0.90) 54% (0.124/0.230) of teachers are predicted to work compared with only 33% of the non-teachers while, at the lower earnings ratio in table 4, the figures are 57% and 35%. Teaching does appear to combine better with work than non-teaching. Interestingly the conditional probability of working is also higher for single teachers than for single non-teachers although the difference in the two is very much smaller.4 The tables also illustrate the effects of changing the earnings differential. A rise in the relative earnings from 70% to 90% increases the proportion who are teachers by 8.2% (0.230-0.148). For single women almost all of these work but for married women just under a half actually work as teachers. Relatively large changes in the earnings differential have relatively small effects on occupational choice, although one might expect the elasticity to be rather small for such a significant decision. It is easy to imagine circumstances in which these models cannot be estimated (for example, if there is a large number of regressors in the model and the reduced form bivariate probits become intractable). Further, some commentators may question the robustness of the results to changes in the measure of the earnings differential. For these reasons we decided to investigate the sensitivity of our conclusions to changes in the definition of the earnings differential. We repeated the estimation of the structural form bivariate probit models with earnings differentials predicted from ordinary least squares estimates of the earnings equations (YDZFFOLS).’ These results are quoted in table 6. The column labelled bivariate probit 4About 98% of single teachers work for both earnings ratios compared with about 94% of single non-teachers (once again for both earnings ratios). 5The results for other definitions of earnings (including the use of YDIFFBP in the selectivity model and YDIFFSEL in standard model) are reported in Dolton and Makepeace (1990). The results for the earnings differential and the correlation coefftcient in each case are similar for those reported in the text.
P.J. Dolton and G.H. Makepeace, Female labour force participation
1405
refers to the estimates of the ‘standard bivariate probit model’ while the columns labelled selectivity model refer to the estimates of the ‘bivariate variable for the probit model with selectivity’. Note that the dependent results of the model with selectivity for non-teachers takes the value 1 for non-teaching so the signs of the coefficients in these results are consistent with the other results. Table 6 confirms that our earlier findings concerning the effects of earnings on the choice of occupation and the correlation between the participation and occupation decisions.
5. Conclusion The results in this paper suggest that the choice of teaching as a career is intimately related to decisions about labour force participation. In the model without selectivity, unobserved factors which make a woman more likely to select a career outside teaching make her less likely to work. Given the same level of family commitments (as measured by marital status, number and age of children), females in occupations outside teaching are less likely to be working than teachers. The results for the models with selectivity support this interpretation but also suggest that the participation decisions of females differ between teachers and non-teachers. The choice of teaching is also found to be significantly affected by the earnings paid to teachers. An increase in the earnings a female graduate receives as a teacher relative to the earnings she receives as a non-teacher increases the probability that she will become a teacher. These results establish the basic economic proposition that occupational choice responds to earnings differentials and such results are not common in the literature. It is interesting to note the differences between the results for the two bivariate probit formulations. There is evidence that the assumption of homogeneity in the choices made by different types of individual obscures the differences in the underlying relationships. A good example is the correlation coefficient between the participation and occupational choice decisions. In one sense this is a key parameter because it motivates the adoption of a bivariate model rather than the simpler alternative of two independent probits. There is evidence that the correlation coefficient is rather different for teachers and non-teachers. Indeed the significant value of 0.305 for the bivariate probit model in table 1 contrasts with the insignificant value of 0.290 for the teacher’s model and the significant value of 0.847 for the non-teacher’s model. These and other results presented above indicate the need to treat teachers and non-teachers as heterogenous groups with respect to their participation decisions. This result may have a wider significance for bivariate probit studies in which the sets of observations distinguished ex ante by the probit equations are treated as homogenous. Simulations of the choices made by women with ‘light’ and ‘heavy’ family
P.J. Dolton and G.H. Makepeace, Female labour force participation
1406
commitments illuminate three features of the results: (i) family commitments significantly affect the propensity to be in work; (ii) teachers are more likely to be in work than non-teachers with the same family commitments (this effect is particularly pronounced for women with ‘heavy’ commitments); and (iii) relative earnings have some quantitative impact on career choice. These results reinforce the case for analysing empirically the links between occupational choice and participation decisions rather than assuming, as is commonly the case, that one can be examined independently of the other.
Appendix
A. Variable
definitions
Brief definitions of the variables are presented much fuller discussion see Dolton et al. (1990)
in this appendix.
For
a
Log earnings. The logarithm of salary in sterling at the end of 1986. This variable is the dependent variable in the earnings equations. Married. A dummy variable taking the value 1 if the respondent has ever been married and 0 otherwise. Number of children. The number of children (aged under 16) up to a maximum of 3. Where an individual has reported more than 3 children, the variable is given a value of 4. Age of the eldest child. The age of the eldest child measured in months. Age. The age of the respondent measured in months. Social class of parents. An ordinal variable for the social class of parents, as determined by the nature of their socio-economic group. A value between 1 and 6 is assigned to each social class as follows: 6 for professional occupation, 5 for an intermediate occupation, 4 for a skilled (non-manual) occupation, 3 for a skilled (manual) occupation, 2 for a partly skilled occupation and 1 for an unskilled occupation. Type of school attended. A dummy variable taking the value 1 if the respondent attended an independent school and 0 otherwise. Degree class. An ordinal variable for the first degree class. The values are allocated according to the following scale: 8 for a first, 7 for an upper second, 6 for an undivided second, 5 for a lower second, 4 for a third, 3 for an ordinary, 2 to a pass or fourth, and 1 for others. University degree. A dummy variable taking the value 1 if the respondent was awarded his or her degree from a university and 0 otherwise. Dummies for postgraduate Professional
qualification.
qualifications
A dummy variable taking a value 1 if the respondent has successfully completed a qualification awarded by a professional body, and 0 if not. Higher degree. A dummy variable taking a value 1 if the respondent has successfully completed a masters degree or a Ph.D., and 0 if not.
P.J. Dolton and G.H. Makepeace, Female labour force participation
Logarithm
of starting salary.
The logarithm
of the (indexed)
starting
1407
salary
in the first job after graduation. A dummy variable taking the value 1 if the Graduate job at start of career. respondent reports that a degree was the minimum qualification necessary to obtain her first job after graduation or if a degree was helpful in obtaining the first job, and the value 0 otherwise. Work experience. The number of months spent in work after graduation. Work experience squared. The squared value of the number of months spent in work after graduation. Number ofjobs. The number of jobs since graduating. The number of months spent unemployed. The number of months unemployed since graduation whilst still seeking work. London. A dummy variable taking the value 1 if the respondents first job after graduation was located in London or Southern England, and 0 if located elsewhere. Faculty dummies. The reference group for these dummies is Education and other subjects. Engineering and technology and science. A dummy variable taking the value 1 if the graduate’s first degree in engineering or technology or a science subject and 0 otherwise. Social science. A dummy variable taking the value 1 if the graduate’s first degree was in administrative, business or social studies (including law) and 0 otherwise. Examples are Accounting, Economics, Sociology, Psychology and Politics. Arts and languages. A dummy variable taking the value 1 if the graduate’s first degree was in an arts or language subject such as English, History or French and 0 otherwise. Lambda. A sample selection correction term. In Yt--ln Y”.,, where Y, and Y,, are earnings in Difference in logs of earnings. teaching and non-teaching, respectively, and where the hat refers to the predicted values. The predicted values are obtained from the relevant earnings equations. Briefly: YDIFFBP YDIFFSEL YDIFFOLS
uses estimates from the reduced form bivariate probit; uses estimates from the reduced form bivariate probit selectivity; uses ordinary least squares estimates.
with
P.J. Dolton and G.H. Makepeace,
1408
Female
lahour force
participation
Appendix B. Earnings equations Table B.l Earnings
equations
for the bivariate probit model variable is log earnings).” _
Married Social
class of parents
Degree
class
Higher
degree
Professional University
qualification degree
Logarithm of starting salary Graduate job at start of career Work experience Work Number
experience
squared
of jobs
Number of months unemployed London Engineering technology Social
and and science
science
Arts and languages Lambda
(participation)
Lambda
(occupation)
constant R2
Sample
spent
Teachers 0.034* [0.020] -0.004 [0.009] 0.012** [0.006] 0.033 [0.040] _
Non-teachers -
0.007 co.02 l] 0.121** [0.036] _ - 0.00096 [O.ooS] 0.00002 [0.00005] -0.024** [0.009] -0.004 [0.003]
co.@w - 0.0092** [0.002]
0.059** co.0221
0.131** [0.024]
- 0.042 [0.040]
-0.095* [0.056] - 0.064 [0.059] -0.129** [0.049]
- 0.005 10.0461 - 0.024 [0.031] - 0.098** [0.042]
0.057 CO.0461 -0.209** [0.104]
0.058 [O.OSO] 6.884** [0.307]
5.518** CO.2611 0.293
489
“The sample selection terms are calculated the reduced form bivariate probit models.
- 0.037* [0.021] 0.018* co.01 l] 0.027** [0.007] 0.026 [0.040] 0.090** [0.036] 0.043* [0.024] 0.236** [0.034] 0.106** co.03 l] 0.013** [O.ooS] - 0.00008 [0.00005] - 0.008
0.218 size
(the dependent
1,263 from
the estimates
of
P.J. Dolton and G.H. Makepeace,
Female
labour force
participation
1409
Table B.2 Earnings
equations for the bivariate (the dependent variable
probit model with is log earnings).” Teachers
Married Social class of parents
0.0338 [0.019] 0.002
w@=Y Degree
class
Higher
degree
Professional University
qualification degree
Logarithm of starting salary Graduate job at start of career Work
experience
Work
experience
Number
squared
Number of months unemployed
spent
London Engineering and technology and science Social science Arts and languages Lambda
(participation)
Lambda
(occupation)
R2
Sample
size
Non-teachers - 0.044, [0.023] 0.018 co.01 11 0.028** [O.OOS] 0.032 [0.042] 0.097* [0.052]
0.007 [0.020]
0.045 [0.0303
0.118** [0.034] _
0.234** [0.038]
-0.0015 [0.005]
of jobs
Constant
0.015** [0.006] 0.03 1 [0.038] _
selectivity
0.00002 [0.OoOo5] -0.025**
0.103** [0.044] 0.012** [O.OOS] - o.OoOO7 [0.00006]
co.@w
-0.007 [0.009]
-0.0045* [0.0027]
-0.009** [0.003]
0.060** [0.020] -0.044 [0.037] -0.006 [0.043] -0.026 CO.0291 -0.015** co.0391 0.052 [0.046] 6.903** [0.285]
0.140** CO.0291 - 0.080 [O.OSS] - 0.046 [0.091] -0.115* [0.068] 0.130** [0.060] -0.182 10.1721 5.498** CO.2531
0.216 489
0.294 1,263
“The sample selection terms are calculated from the estimates the reduced form bivariate probit model with selectivity.
of
P.J. Dotton and GA. Makepeace, Female labour force participation
1410
Table B.3 Participation and occupational choice equations for the hivariate probit with OLS earnings predictions (The dependent variables are respectively the woman participates and one if teaching is chosen). Participation urobit
Constant
0.642 [0.990] -0.&w* [0.990]
Married Number
of children
Age of eldest child
Alre Social class of parents
(Jniversity
degree
Logarithm of salary Engineering and technology and science Social science
Occupational choice probrt -0.012 [0.317]
- 1.750** [0.079] 0.014** ]0.002] 0.003 [0.002] -0.015 [0.043]
Type of school attended Degree class
model one if
- 0.027 [0.031] _ 0.029
_ 0.002** [0.0007] -0.03I LO.032J -0.290** ]os24] - 0.042** [0.021] -0.119 [O&9] _
ro.ioi]
Arts and Languages Difference in the logarithms of earnings (YDIFFULS) Correlation cocflicicnt for errors in the probit equations Log likelihood Sample size ___~ ._~_. __.__~_..
-0.832** /0.095] - 1.102** [O.oSS] --0.474** [0.091] 1.114** [0.260] 0.285** [0.059] - lS91.517 2,056 _.__~_..
_ ..___
Appendix C. The British education system The higher education system in Britain differs between England, Wales, and Scotland. For conciseness we shall describe the system in England and Wales at the time of the Survey. Small modifications are needed to accommodate the Scottish system. Degrees are awarded after three or four years of study at a Higher Education Institution which may be a university, polytechnic, College of
P.J. Dolton and G.H. Makepeace,
Female
labour force
participation
1411
Higher Education or teacher training college. Students typically enter higher education when they are eighteen or nineteen with two or more GCE A level qualifications. For most of these young entrants A levels will have been obtained at the end of their full time education at school. A levels may also be obtained by full time or part time study at other institutions. A levels are graded with grade A as the highest grade down to grade E which is the lowest grade of pass. Students may have attended schools which are fully funded by the government or ‘independent’ schools where fees are paid. At the time of the Survey there were three main routes into teaching, Firstly one could study for a diploma in teaching awarded after three years of study by an institution specialising in teacher education. Secondly one could study for a 3 or 4 year B.Ed. (a batchelors degree in education) which would entitle one to enter teaching. Thirdly, one could take a one year postgraduate diploma course following a degree in another subject. Our definition of teacher includes all types of school teachers, at primary or secondary level and teaching children aged from 5 to 18, who have a degree. Many graduates go into jobs where further training is required and obtain postgraduate qualifications recording their successful completion of that training. These people are recorded in our sample as possessing a postgraduate (non-academic) qualification. References Chesher, A., 1985, Score tests for zero covariances in recursive linear models for grouped or censored data, Journal of Econometrics 28, 291-305. Dolton, P.J., 1990, The economics of UK teacher supply: The graduate’s decision, The Economic Journal 100 (supplement) 91-104. Dolton, P.J. and G.H. Makepeace, 1990, Modelling female labour force participation and choice of occupation: An empirical study of female entrants to teaching, Working paper (University of Hull Labour Economics Unit, Hull). Dolton, P.J., G.H. Makepeace and G.D. Inchley, 1990, The early careers of 1980 graduates: Earnings, earnings differentials and postgraduate study, Research paper no. 78 (Department of Employment, London). Dolton, P.J., G.H. Makepeace and W. van der Klaauw, 1989, Occupational choice and earnings determination: The role of sample selection and non-pecuniary factors, Oxford Economic Papers 41, 573-594. Ham, J.C., 1982, Estimation of a labour supply model with censoring due to unemployment and underemployment, Review of Economic Studies 49, 335-354. Heckman, J., 1979, Sample selection bias as a specification error, Econometrica 47, 1533161. Maddala, G.S., 1983, Limited dependent and qualitative variables in econometrics (Cambridge University Press, Cambridge). Van de Ven, W. and B. van Praag, 1981, The demand for deductibles in private health insurance, Journal of Econometrics 17, 229-252.