ARE PART-TIME WOMEN PAID LESS? A MODEL WITH FIRM-SPECIFIC EFFECTS

ARE PART-TIME WOMEN PAID LESS? A MODEL WITH FIRM-SPECIFIC EFFECTS

MARK MONTGOMERY and JAMES COSGROVE*

This paper uses data from a survey of child-care establishments to compare part- and full-time wages within two narmw occupations, one high skill and one luw skill. Unlike prmious studies, it controls for firm-specific effects. We find that when fim- specific effects are accounted fol; only the low-skill workers in our sample receive lower wages for working part-time. On the other hand, when compensation is defined to include prorated f i n g e ben$ts, establishments appear to compensate both high- and low-skill part-timers at a lower hourly rate.

1. INTRODUCTION

More than 22 percent of American working women can be classified as part- timers (Tilly 119911). There is some contro- versy over whether a woman in part-time employment receives a lower wage than she would i f she worked full-time. On the one hand, studies by Owen [1978], Jones and Long [1981], and Simpson [1986] have found that part-time women are paid less than equally qualified full-time women. But a recent study by Blank [1990] has challenged these findings. Using a model which incorporates choice of employment status into wage determination, Blank observed that part-time women got paid more than their full-time counterparts.

All of the previous studies on this issue used data on individual workers. None was able to compare part-timers with full- timers in the same firm, the same industry, or even in the same detailed occupation. Moreover, the works described above were only able to consider wages and not total compensation. Yet because part-tim-

* Associate Professor of Economics, Grinnell Col- lege, and Senior Economist, Human Resources Divi- sion, US. General Accounting Office. The authors are grateful to Irene Powell, Rebecca Blank, Julie Hotchkiss and two anonymous referees for helpful comments.

ers are frequently offered fewer benefits than full-timers (Daski [1974], Worklife Re- port [1989]), the distinction between wages and compensation is important in this context. This paper uses a unique survey of child-care establishments, conducted by the U.S. General Accounting Office (GAO), to compare the hourly wages and hourly compensation of part-time and full-time women. The survey is unique in that it provides information on all of the workers (in two skill classes) at each establishment. Having data on all workers permits us to control for establishment- specific effects in estimating wages. The survey also provides detailed information about the availability of fringe benefits, allowing us to consider both hourly wages and hourly compensation. Without controlling for firm-specific effects we find significant differences between part- and full-time wages for both skill levels. For the high-skill workers, the difference vanishes when we use an error-components model to hold constant the firm-specific portion of the error term. For the low-skill group, using error components makes the part-time wage discount slightly smaller, but does not eliminate it. Even with firm- specific effects, however, differences in hourly compensation (wages plus prorated fringe benefits) persist for both skill

Economic Inquiry (ISSN 0095-2583) Vol. XXXIII, J a n v 1995,119-133

119

OWestem Economic Association International

120 ECONOMIC INQUIRY

levels. For low-skill workers the difference in compensation is very large-about 20 percent.

.

II. PREVIOUS STUDIES OF PART-TIME WAGES FOR WOMEN

Given the size and significance of the part-time work force, the literature on the effect of part-time status on women‘s wages is comparatively small. An early study by Owen [1978] found a part- time/full-time wage differential of 17 percent. Jones and Long [1981], using the National Longitudinal Survey (NLS), estimated the wage effect of part-time status to be about 7.5 percent for married women. Simpson (1986), using Canadian data and, unlike earlier studies, controlling for the self-selection of his samples into part-time or full-time status, found a wage differential of 18 percent for single women and 3 percent for married women.

So far, the most econometrically sophis- ticated analysis was conducted by Blank [1990]. Using data from the Current Pop- ulation Survey (CPS), Blank developed a maximum-likelihood model of wage determination which incorporated the probability a woman would enter the labor force and choose, respectively, part-time work or full-time work. Running separate models for each of six occupation categories, Blank found that working part-time increased wages in each category, and that the effect was significant in all but two occupation groups. This result was inter- preted as suggesting that the apparent wage penalty for working part-time was attributable either to unobserved differences between part- and full-time women-factors such as work habits, enthusiasm, reliability-or unobserved differences in the specific jobs they do. It is possible, Blank argues, that the kind of women who tend to choose part-time work would, on average, earn lower wages even in full-time positions.

Looking at wages alone is inadequate in comparing part-time and full-time com-

pensafion. Fringe benefits comprise a substantial share of compensation and there is significant evidence that part-timers get fewer benefits than full-timers. In an early study, Daski [1974] found that part-timers were unlikely to receive medical insurance, life insurance, or retirement benefits. More recently, Blank [1990] showed that for a married woman, choosing part-time work over full-time work reduces the probability of having pension coverage from .55 to .21, and the probability of having health insurance from .59 to .18. In light of these findings, comparisons of wages between part- and full-timers would seem to tell an incomplete story regarding their relative compensation.

Limitations of Previous Studies So far, all of the empirical analysis of

relative wages and compensation for part- and full-timers has been conducted using random samples of workers. In none of these studies is there much information about the occupation or the industry in which the workers are employed. The occupational breakdown for these studies is usually a set of six or seven categories like those used by the Current Population Sur- vey and the National Longitudinal Study: Managerial, Professional/ Technical, Sales, Clerical, Service, Craft, Operative and Labor. These are fairly crude occupational distinctions-when a study identifies a woman as a “professional/ technical” worker in the ”services” industry, she could be a dental hygienist or certified public accountant. The implication is that what we observe as the effect of part-time work in these studies almost certainly includes an element of occupational and industrial ch0ice.l Adjustments for sample

1. An interesting discussion of the effect of part- time status on occupation is offered by Leeds [1990], but his analysis is confined to men.

MONTGOMERY & COSGROVE: PART-TIME PAY 121

selection could not eliminate this problem because with these data a choice-of-hours model can no more distinguish between detailed occupations and industries than can a wage-determination model.2 Previ- ous studies could not really say whether a part-timer gets paid less for doing the same job.

This paper is the first to make a direct comparison of part-time and full-time workers in the same detailed occupation in the same detailed industry. Moreover, because we have data on all of the workers at each establishment in our sample, this study can control for establishment-specific effects in comparing wages and hourly compensation. In our sample, any observed difference in pay associated with part-time status will imply that a part- timer receives different compensation than would an equally skilled full-timer in the same job, at the same establishment. The next section describes the empirical model of wages and hourly compensation.

111. THE MODEL

Our empirical model predicts the wages (and hourly compensation) of indi- viduals in a detailed occupation (teachers or teacher aides, respectively) working for one of a sample of child-care establishments. We assume that the wage of the ith individual at the jth establishment is deter- mined by

where Xi is a vector of individual attributes including the job she works, Zj is a vector of firm characteristics, PT is a

2. That is, while these studies may provide an un- biased estimate of the wage implications of “choosing part-time work,” that choice will also involve an unobserved element of occupational/industd choice. They tell us little about the pay implications of switch- ing to part-time status within a given occupation.

dummy for working part-time, and qj is an error term. The coefficient of interest is 6, the discount associated with working part-time instead of full-time. Having controlled for characteristics of the establishment and the individual, 6 should indicate whether establishments pay their part- timers lower wages than otherwise identical full-timers who do the same job. (The implications of omitting some worker and establishment characteristics are dis- cussed below.)

The GAO survey of child-care establishments contains data on multiple workers at each establishment. It is possible to exploit this attribute of the data set to improve the estimates obtained from equation (1). An alternative way of estimating (1) is the error-components model which assumes that

where E(pj) = 0, E(p i2, = o;, and E(pjpk) = 0 for j # k. The error term has one component specific to the firm, pj, and another component specific to the worker, This approach is sometimes referred to as a random-effects model because it assumes that the firm-specific errors, p, are random variables with mean and variance as given above (see Judge et al. [1985, 521-261). In the random-effects model, the error terms will be correlated among workers at the same firm (through the influence of pj) but not among workers at different firms. This model is estimated as a GLS version of (1) in which the covariance matrix, which is block diagonal, can be expressed as

where I, is a JxJ identity matrix, J being the number of firms, and

ECONOMIC lNQUIRY 122

Qj =

.;+a:, 0;. $, ... a;, a;, ...

..., a;, a;, .’,+a:

The variance a: equals E ( E ~ E ~ ) . (See Judge et al. [1985, 5221)

The random-effects model is intuitively appealing. With random effects, pj is the compensation for a set of unobserved characteristics common to all workers at the establishment, and cii is the compensation for attributes unique to the individual. This dissection of the error seems logical if we imagine that each establishment has some minimum requirements, in terms of unobserved worker attributes, that every employee must satisfy. The firm pays each worker pj in compensation for meeting these standards? Though the minimum standards differ among establishments, they are all drawn from the same population and are assumed to be independent and identically distributed.

One potential problem with the random-effects model is its reliance on the assumption that Xij and pj are uncorrelated. This may not be true-an establishment’s standards for unobservable characteristics like enthusiasm and reliability may be correlated with those for observable ones like education and experience. Using a test suggested by Haus- man and Taylor [1981] we were able to reject the hypothesis that Xii and pj are uncorrelated at the .01 level.

Hausman and Taylor [1981] propose an instrumental variable estimator to cope with correlation between Xii and pt To briefly outline this technique, we follow the description given by Breusch, Mizon,

3. When p j is negative, of course, the firm is offering lower-than-average compensation in exchange for tol- erating weaker-than-average workers characteristics.

and Schmidt [1989]. First we restate the model more simply, suppressing sub- scripts and letting X subsume the part- time dummy. Then the model becomes

(3) ln(w)= xp + q+ p + E

where X = (X,, X2). X, is correlated with p but X, and Z are not. Now let Px be a matrix which converts X into firm-specific means for each variable, so that PxX is a data matrix in which each observation on X for any teacher (aide) is replaced by the mean for all teachers (aides) at the firm. Let Qx transform X such that in QxX each observation on a teacher is replaced by its deviation from the mean for all teachers at the firm. Now C o v ( p + ~ ) will equal a$, where a-1= Qx + @Px, and @ = 02 /(a: + Ia;), I being the number of employees in each firm.4 Using we can transform equation (3) so that it has a scalar covariance matrix:

Breusch, Mizon and Schmidt [1989] show that the Hausman-Taylor estimator can be implemented as a two-stage-least- squares version of (4), for which the instruments are QxX, QxX,, PxXi and Z. Thus the set of instruments in this model includes (a) the deviations of all the X’s (teacher characteristics) from their firm-

4. In our model there is one minor complication with respect to I, the number of employees in each firm. The Hausman-Taylor estimator is designed for a standard panel data set with each individual in the panel contributing observations for the same number of time periods. This would apply to our model if all centers had the same number of teachers and aides but they do not. In the LIMDEP softwm used to estimate this model, Greene [1989] replaces the e used in the calculation of R with a consistent estimator which is a function of all of the firm sizes. For details see Greene [1989, 174-753.


specific means, (b) the firm-specific means of the teacher characteristics uncorrelated with p, and (c) all of the firm/location variables (which we have assumed to be uncorrelated with p). In the current model we allowed education, experience and part-time status to be correlated with p. It turns out that for the wage models the coefficient on the part-time dummy in the instrumental variables case was very close to that of the regular random-effects case. For the compensation models, the coeffi- cients were similar in the instrumental variables and random-effects models, but in the former case the coefficient was insigdicant.

As a final check on the specification of equation (1) we estimated a fixed-effects model in which all firm-specific information was collapsed into a single parameter. This eliminates the problems described in the previous three paragraphs, though it also eliminates all firm/location variables, Z, from the model. The fixed-effects results (which we do not report) were nearly identical to those of the random-effects models.

Unobserved Diferences between Part- and Full-Timers

We pause now to consider what our specification can and cannot tell us about the difference between part- and full-time wages. The huge literature on self-selection into employment status suggests that the dummy for part-time status, PTi, is likely to be correlated with unobservable individual characteristics which influence both the wage and the likelihood of choosing part-time work (see Blank [1990], Simpson [1986], and Hotchkiss [1991]). The implication of this correlation is that if we observe, say, a negative value of b in our models, we will know that our establishments pay part-timers less than identically skilled full-timers for doing the exact same job; but we won’t know why. It could be because part-time work is

inherently less productive, because part- timers are inferior workers along some unobservable dimension, or for both reasons at once. (It cannot be due to unobserved characteristics of the occupation, industry, job, or f i , as it might in studies using worker data, because we have controlled for those effects.)

The standard way of addressing the problem described above is to replace the part-time status dummy with the pre- dicted probability of working part-time as estimated in a probit model of choosing part-time work (see, for example, Heck- man and Robb [1985]). A more compre- hensive approach is taken by Blank [1990], who incorporates into a maximum-likelihood model of wage determination the woman’s choice among part-time work, full-time work, or staying out of the labor force. Both approaches require (at least) a set of variables which influence the choice of part-time work-variables such as mar- ital status, family income, number of young children, etc. Unfortunately, our data set contains none of these. In princi- ple, therefore, faced with a negative coefficient on part-time status, we will be unable to disentangle lower productivity of part-time work from lower productivity of part-time workers. We return to this question in the interpretation of the empirical results.

IV. THE DATA

The data for this study are drawn from a survey of 205 establishments providing early childhood education. The survey was conducted in 1989 by the U.S. General Accounting Office (GAO) for centers ac- credited by the National Association for the Education of Young Children. It includes establishments in thirty-nine different states. The childcare centers in the GAO sample employed a collective 1876 teachers, 131 of them part-time, and 1922 teacher aides, 504 of them part-time. After elimination of observations with incom-


TABLE I Percent of Centers Having a Given Ratio of Part-Tiie Staff Hours

to Total Staff Hours of the Relevant Type

Percent of Staff Hours Worked by Part-Timers 0 % 1-20% 21-50% 5140% 81-100%

~

TEACHERS 76 % 19 % 5% 0% .5 %

AIDESa 30% 34% 21 % 9% 3%

aIncludes only the 180 centers which himi some aides

plete data, the estimation sample included 1782 teachers, 116 of them part-time, and 1786 aides, 465 of them part-time. The number of teaching staff at these centers (i.e. teachers plus aides, not including directors) ranged from 2 to 87, with a me- dian of 16.

This establishment survey is unique in that respondents were asked to provide information on each individual staff member in these two categories. For each teacher or aide at the center we know years of education, years of total teaching experience, wage or salary and weekly hours worked. We supplemented the survey data with information about the local labor market culled from published sources, as detailed below. We offer one caveat about these data: while this paper claims to be about the wages of part and full-time women, we do not, in fact, know the sex of the teachers and aides in our sample. We expect, however, that all but a tiny fraction are female. A study by Whitebook et al. 119891 found, for example, that women accounted for 97 percent of teaching staff in child-care centers.

Among the 205 centers in our sample, 48 hired part-time teachers and 126 hired part-time aides (only 180 centers hired any aides at all). Table I shows the distribution of the proportionate contribution of part- timers to total hours worked at the centers in our sample. Part-time teachers contrib- uted more than one-fifth of total hours in

only about 5 percent of the centers. In only 11 percent of the centers did part-time aides contribute more than 50 percent of hours. These results rule out the hypothesis that many centers are specializing in part-timers. In nearly all cases, part-timers are serving as a complement to full-time staff of the relevant type. On average part-time teachers worked 16.6 hours per week compared to 34 hours for full-timers.” The range of average part-time hours at these centers was fairly compressed-in forty of the forty-eight centers which hired them (83 percent), part-time teachers averaged between ten and twenty hours per week.6 In contrast, at 90 percent of the centers full-time teachers worked thirty or more hours per week.

Among centers which hired aides, the mean for average weekly hours was 14.7 for part-timers and 34 hours for full-timers. The distribution of weekly part-time hours for aides was similar to that for teachers: at 90 percent of the centers they averaged twenty or fewer hours per week. In about 10 percent of centers part-timers averaged fewer than ten hours per week.

5. These figures include all the teachers in the sample except those for whom weekly hours were unavail- able.

6. There were three centers at which workers deemed part-timer averaged forty hours per week- but presumably worked fewer weeks per year than full-timers.

MONTGOMERY & COSGROVE: P A R T - M PAY 125

Measuring Wages and Hourly Compensation Respondents to the GAO survey were

asked to report either hourly wages for each teacher and aide, or monthly or weekly salary, whichever was easier. When salary was reported we divided salary by hours worked to get hourly wages. Using these measures, full-time teachers earned an average $6.42 per hour while part-time teachers earned $5.20. Full-time aides averaged $4.58 per hour and part-time aides averaged $4.11. How- ever, this method of calculating hourly wages produced some values which were implausibly (and illegally) low-in some cases as low as a few cents per hour-presumably because the directors were inac- curately reporting salary, or, more likely, weekly hours for some staff members. We eliminated from the estimation sample the most egregious cases: hourly wages of less than $2.00 for teachers and less than $1.00 for aides. This process eliminated 3.6 percent of the teachers and 3 percent of the aides. Even though we are still including implausibly low values, using more severe restrictions (say $3.00 for teachers and $2.00 for aides) had very little effect on the results. In the estimation sample, full-time teachers averaged $6.54 per hour and part- time teachers averaged $5.71; full-time aides averaged $4.67 and part-time aides $4.34.

In addition to information on wages, the GAO survey also asked detailed ques- tions about the availability of fringe benefits for teachers and aides. Center directors were given a menu of seventeen separate types of fringe benefits, including pension, health insurance, dental insurance, life insurance, paid vacation, paid breaks, sick leave, worker’s compensation, tuition assistance, reduced child-care fees, and other^.^ For each type of staff (teachers or aides) the respondent was

7. For more details about these benefits and other aspects of the survey, see U.S. General Accounting Of- fice [1990].

asked to report whether a given specific benefit was available to “all” staff of that type or only “some” staff of that type. As a measure of the fringes available to part- timers, we assumed that whenever only “some” staff were eligible, part-timers were excluded. This is imprecise-in some cases the ineligible staff will be those with low tenure, not part-timers per se. In spite of its limitations, however, our measure of the difference in compensation remains superior to anything yet appearing in the literature on part-time pay.

The procedure described above provided an index of benefit availability for part- and full-time staff. To convert this to a measure of hourly compensation we weighted each benefit by the proportion of wages which it represents. This weight- ing was accomplished with data from the Chamber of Commerce [1987] on the ratio of benefit payments to wages, in nonmanufacturing industries, for various types of benefits. The weights used for the most important benefits are listed in Table 11. Formally, letting PT and FT designate part- or full-time, we calculated benefits as a proportion of the wage, B, for a worker of type t at the j* establishment as

17

17

where,

t refers to teachers or aides,

sjk = 1 if the jth establishment offers the kth benefit to some workers of type t, and 0 otherwise;

ujk = 1 if the j* establishment offers the k* benefit to all workers of type t, and 0 otherwise; and

126 ECONOMlC INQUIRY

TABLE I1 Weights Applied to Selected Benefits

(Benefit payment as a percentage of wage bill for nonmanufacturing estabishments)

~~

Benefit ~ ~~

Weight Benefit Weight

Paid Vacation 2.0 Sick Leave 5.8 Paid Breaks 4.5

Pension 5.6 Health Insurnace 5.5

Maternity Leave .3

Life Insurance .5 Dental Insurance .7 Workers Comp 1.3 State UI .9 Tuition Assistant .2 Vision Insurance .5

Some: Chamber of Commerce of the United States, Employee Benefits 1986.

pk = the ratio of payments of benefit type k to wages for nonmanufacturing establishments offering that benefit (Chamber of Commerce of the United States [1987]).

Hourly compensation, C, for the i* worker of type t at the j* firm is then calculated as

where w is the wage, and again, FT and PT designate full-time and part-time. Table 111 reports wages and compensation for teachers and aides in the estimation sample. On average, compensation was 21 percent larger than wages for both full- time teachers and full-time aides. Com- pensation was 14 percent above wages for part-time teachers and 12 percent above wages for part-time aides.

Independent Variables Table I11 lists the independent variables

used in the wage and compensation models. For each member of the teaching staff the survey provided information on edu-

cation and years of total teaching experience. It also reported the age of the children for which the worker primarily cared. (The omitted age category in the estimation is “toddler.”) The part-time dummy indicates whether the employer reported this worker to be part-time. Note that this determination of part-time status is much different than in previous studies, all of which used worker data. In those studies it was the investigator who decided that the worker was part-time by noting that she worked fewer than thirty-five hours per week. In some industries, however, a thirty-four-hour work week could be full-time. In our study a part-timer is someone who works fewer hours than what her employer considers a full work week.

In the random-effects models we are able to include information about the establishment and its location. The bottom half of Table 111 shows the establishment- specific variables. FTE CHILDREN is a measure of establishment size, CLASS SIZE and STAFF/CHILD are assumed to be correlated with the quality of the center’s output-centers with higher quality standards can be expected to pay higher wages to get better teachers. The variables IN- COME/CAPITA and STATE AVERAGE TEACHER SALARY are expected to corre- late with real local wages for teachers.


TABLE111 Descriptive Statistics for Variables in the Estimation Sample

Characteristics of Teachers and Aides

TEACHERS AIDES: Description of Variable Name in Table IV P U P U

Hourly Wage or Salary

Hourly Wages plus Prorated Fringe

Years of Education

Years of (Total) Teaching Experience

Works Part-Time

Primarily Responsible for Preschoolers

Primarily Responsible

Primarily Responsible

For Infants

for School-Age

WAGES 6.59

COMPENSATION 7.97

EDUCATION 14.33

EXPERIENCE 6.47

PART-TIME 0.07

PRESCHOOLERS 0.21

INFANTS 0.09

SCHOOL- AGE 0.56

2.00

2.55

3.93

6.93

0.25

0.41

0.28

0.50

4.72

5.63

12.08

6.72

0.24

0.20

0.09

0.54

1.29

1.63

6.37

16.16

0.43

0.40

0.29

0.50

Characteristics of Centers

Description of Variable Name in Table IV P U

Size of Group Containing

Full-Time-Equivalen t

4-Year Olds CLASS SIZE

Children ETE CHILDREN

16.5 5.8

80.3 54.3

Ratio of Teaching Staff

County Income

State Average Teacher STATE AVERAGE

Located in Midwest MIDWEST 0.36 0.48

Located in South SOUTH 0.32 0.47

Located in West WEST 0.18 0.38

to Children STAFF/ CHILD 8.9 2.1

per Capita INCOME/ CAPITA 11802 2456

Salary TEACHER SALARY 28276 3774

ECONOMIC INQUIRY 128

V. RESULTS

Tables IVa and IVb report the results of the wage and compensation models for teachers and aides respectively. Both dependent variables are measured in logs. The first column in each part of the table gives the OLS results for wages.8 Column 2 reports the random-effects model for wages, and column 3 presents the instrumental variable results for wages. Col- umns 4 and 5 present random-effects and instrumental variables models, respectively, for hourly compensation.

First we consider the high-skill group, teachers. In the OLS model, the coefficient on the part-time dummy suggests that a part-time teacher receives about 8 percent less in hourly wages than her equally educated and experienced full-time counterpart teaching the same type of class (i.e. age group). The difference is highly statistically significant. However, when we control for firm-specific effects, the wage differential drops to about 1 percent and loses its sigruficance in both the random- effects and instrumental variables models. This result implies that while part-time teachers are paid less than full-timers on average, they are not paid less than full- time coworkers at the same establishment. The Compensation model in column 4, on the other hand, shows that part-timers receive about 5 percent less in hourly compensation-wage plus fringe-than full-timers at the same establishment. The 5 percent difference persists in the instrumental variables regression (column 5) though there it loses significance.

Looking at the aides regressions, the OLS model tells a similar story, but the random-effects model does not. The OLS coefficient on the part-time dummy implies that a part-time aide gets 9 percent less in wages than an identically qualified full-timer at the same establishment. In the random-effects model, the wage differ-

8. These models also include a constant term.

ence is reduced but not eliminated-there remains a statistically significant 7 percent wage discount for working part-time. As with the teachers, the difference in hourly compensation is larger than the wage difference-about 20 percent-and highly significant. In the instrumental variables model for compensation, the difference is somewhat smaller and less significant.

Note that for both teachers and aides, the part-time dummy became insignifi- cant in the compensation models when instrumental variables was used. We attribute this result mainly to the weakness of our set of instruments. In (unreported) fixed-effects models for compensation- for which there is no potential bias in the firm-specific effects-the results were nearly identical to the random-effects models.

The above findings can be summarized as follows: when we control for firm-specific effects, a part-timer in our high-skill group gets lower hourly compensation but not lower wages when compared with her full-time counterparts in her own establishment. A part-timer in our low-skill group gets both lower compensation and lower wages.

Interpreting These Findings The results described here offer some

important insight into part-time and full- time wage differentials. Even within a narrowly defined occupation in a specific industry we observe wage differences between part- and full-timers of equal education and experience. This was true for both the high- and low-skill groups. For the high-skill group, however, the wage difference appears to be attributable not to firms paying their part-timers lower wages, but to differential sorting of part- timers among firms. Part-time teachers earn lower wages because they tend to work for (relatively) low-wage firms. This finding casts doubt on the idea that the mere fact of teaching part-time makes a

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056

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ple:

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min

ants

of A

ide C

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nsat

ion

WA

GES

W

AG

ES

WA

GES

C

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ION

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ION

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stru

men

tal

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rum

enta

l V

aria

bles

In

depe

nden

t O

LS

Ran

dom

Eff

ects

V

aria

bles

R

ando

m E

ffec

ts

Var

iabl

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Coe

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Std.

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or

Coe

ff.

Std.

Err

or

Coe

ff.

Std.

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or

Coe

ff.

Std.

Error

Coe

ff.

Std.

Error

EDU

CA

TIO

N

EXPE

RIE

NC

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FUEN

CE~

PAR

T TI

ME

PRES

CH

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SCH

OO

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ILD

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STA

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SS S

IZE

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T

SOU

TH

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T

INC

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TA

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TATE

TE

AC

HER

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L.

0.2

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004

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9

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0.07

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0.00

006

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2

0.00

3

0.00

2

-0.0

2

-0.0

2

0.04

0.01

0.00

24

0.00

26

0.00

004

0.01

8

0.02

6

0.03

2

0.02

2

0.00

012

0.00

41

0.00

15

0.02

5

0.02

9

0.02

8

0.00

37

0.00

29

0.01

0.02

-0.0

005

-0.0

7

0.06

0.04

0.05

0.00

03

0.01

0.00

3

-0.1

0

-0.1

6

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3

0.03

0.00

2

0.00

16

0.01

0.00

18

0.03

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1 -0

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7 0.

04

0.01

3 0.

05

0.00

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0.08

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98

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0.02

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10

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3 0.

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0.59

0.

03

0.21

0.

01

0.77

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0.78

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1.02

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0.03

0.

05

0.26

0.

003

0.00

31

0.00

36

0.00

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0.02

2

0.02

9

0.035

0.02

6

0.00

081

0.02

3

0.00

82

0.14

0.16

0.16

0.02

0

0.01

6

0.01

0.04

-0.0

01

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3

0.04

0.08

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5

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7

3.17

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0

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30

0.00

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04

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3

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4

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4

0.38

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0.58

0.44

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8.11

1.31

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6.88

5.78

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= .3

1 Sa

mpl

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786

R2 - .3

1 R2

- .31

MONTGOMERY & COSGROVE PART-= PAY 131

teacher inherently less productive. If this were the case, firm-specific effects would not eliminate the part-time wage differen- tiaLg But why would part-time teachers tend to work for lower-wage firms? One potential explanation, suggested in section 111, is that part-timers are less frequently hired by high-wage firms because they less frequently meet the standards those firms have for (unobserved) quality characteristics. As pointed out in section 111, earlier studies have shown that there are very likely to be systematic, unobserved differences between part- and full- time workers (Blank [1990], Simpson [1986]). In the previous work on part-time wages, these unobservable influences included characteristics of the (detailed) occupation, of the industry, of the firm, of the job and of the worker. Our model eliminates all but the last of these-there can be no interoccupation or interindustry effects, and we control for both the job (age group taught) and the firm. There- fore, any systematic differences between part- and full-timers in our model are likely to reflect unobserved differences in the workers themselves-such factors as reliability, enthusiasm, amiability, etc. which employers reward with higher wages.

A plausible interpretation of our results for teachers is that part-timers are differ- entially sorted into low-wage firms because such firms have lower hiring standards for the unobservable attributes on which part-timers score lower.1° Within

9. A possible counterargument to this proposition is that part-timers and full-timers could be highly seg- regated so that low-wage firms tend to specialize in the former and high-wage firms in the latter. In this case we would observe little intraestablishment pay differential because few establishments have both type of worker. But Table I shows that all of the establishments that hired part-time teachers also had full-time teachers.

10. There are, of course, other possible explana- tions. Perhaps, for example, low-wage f i s are less able to attract highquality full-timers and prefer good part-timers to inferior full-timers.

these firms they receive the same pay (though not the same benefits) as equally educated and experienced full-timers. If true, this explanation would be consistent with Blank's conclusion that the fact of working shorter hours does not, in itself, lead to a lower level of wages.

In contrast to our results for teachers, for aides we found that firm-specific effects did not eliminate the part-time/full- time wage differential. A part-time aide does receive a lower hourly wage than her equally skilled full-time counterpart working in the same center. As we stated in section 111, our model does not allow us to say whether this differential results from productivity differences in part- and full-time work of this type, or from productivity differences in the workers themselves. It may be that two half-time aides are inherently less productive than one full-time aide, or it might be that aides who choose part-time work tend to have fewer of the (unobserved) personal quali- ties that employers reward with higher wages.

We saw that for both the high- and low-skill workers, part-timers got lower hourly compensation even after controlling for firm-specific effects. Why would part-timer teachers get lower compensation, but not wages? Because providing benefits to part-timers is more expensive per hour worked, than providing them to full-timers. For benefits which are not strictly prorated by earnings-many types of insurance premiums, for example-average benefit payments per hour fall with average hours worked. Even for prorated benefits-sick leave, paid vacation, etc.- fixed per-person costs like record keeping and eligibility review make it more costly per hour to extend them to part-timers.'l So, the results for wages and for compen-

11. For a discussion of the effect of fringes and other per-person labor costs on the employment of part-timers, see Montgomery [1988a, 1988b], and Montgomery and Cosgrove [1993].


sation still tell a consistent story for the teachers. The marginal hour of part-time work is as productive as a comparable full-time hour and therefore receives equivalent marginal pay-wages. That part-time hour receives lower average pay (compensation), however, because average hourly compensation includes fixed (per-person) components which impose a cost disadvantage on part-time hours.

VI. CONCLUSION

This paper adds to a small but growing literature on the difference between part- time and full-time wages. The analysis is unique in several ways. First, this is the only paper to compare part- and full-time wages within detailed occupations in a single industry. All previous papers used broad occupational categories and con- tained little information about the industry. Second, this is the first paper to control for firm-specific effects in wage determination. Thus our paper is the first to observe whether part-timers get lower wages than equally skilled full-timers at the same firm. Third, this paper is the first to examine hourly compensation (wage plus prorated fringe) rather than just wages. The distinction between wages and compensation proved to be substantial in terms of the relative pay of part- and full-timers. Finally, this paper is the first to compare workers who were designated as part- or full-time by the employer rather than by the person conducting the study.

Our results were somewhat mixed. For the high-skill workers we found that once we control for firm-specific effects, establishments are not paying part-timers lower wages and salaries than equally skilled full-timers. In OLS models they appear to earn 8 percent less, but the difference vanishes in a random-effects model. This result shows that controlling for firm-specific effects is important in analyzing this issue. For the low-skill

group, wage differences were observed even after firm-specific effects were controlled for. We interpret this result to sug- gest that apparent differences in wages for teachers come from differential sorting of part- and full-timers among high- and low-wage firms. Such sorting does not appear to be happening among the aides.

The findings for the teachers are consistent with those of Blank [1990]. Blank argues that most of the part-time/full- time wage gap is attributable either to differences in the jobs part- and full-timers take or to an unobserved productivity gap between the two types of worker. Because we have effectively controlled for job attributes, our findings lend support to the hypothesis of unobserved differences between the workers themselves. The fact that a wage gap remains in the aides models even with firm-specific effects- indeed, those effects change the coefficient of the part-time dummy only slightly- implies that part-time aides are not being sorted (much) into low-wage establishments. They are simply being paid less. This could result either from the inherently lower productivity of part-time work, or it could be differences between the part-time and full-time workers themselves.

In contrast to the wage results, we found that for both types of worker hourly compensation was considerably lower for part-timers. This finding is consistent with the view that it usually costs more to pay part-timers a given benefit than it does full-timers. It may also be the case that part-timers, many of whom are married with children or students still dependent on their parents, are more willing to forgo benefits such as health and life insurance because they are already covered through a spouse or parent. We warn the reader, however, that given the way we structured the compensation variable, our results for compensation should be considered less reliable than those for wages.

MONTGOMERY & COSGROVE PART-TIME PAY 133

Limitations of This Study We remind the reader of some caveats

about these results. First, we have looked at only one industry. While this enabled us to avoid many of the limitations of previous studies, it does reduce the generality of our results. One thing that makes the child care industry a good one to study for our purposes is the fact that child-care establishments hire part-timers more often than do other service establishments (for information on the latter see Montgomery [1988a]). But this industry is also atypical in terms of wages. Hartman and Pearce [1989] show that childcare workers are poorly paid even relative to other tradi- tionally female occupations. They are also better educated than other workers. This suggests a group well-dedicated to the kind of work they pursue, a fact which could make their wage elasticities of labor supply lower than those for other workers.

A second disadvantage of focusing on narrow industry-occupation categories is that we face an additional problem of sampling bias besides selection into part- and full-time status: selection into these particular occupations. To the extent that unobserved special characteristics of child-care workers are correlated with our independent variables, other sources of bias might be present.

REFERENCES

Blank, Rebecca. "Are Part-Tie Jobs Bad Jobs?" in A Future of Lousy Jobs, edited by Gary Burtless. Washington, D.C.: Brookings, 1990,123-64.

Breusch, Trevor S., Grayham E. Mizon, and Peter Schmidt. "Efficient Estimation Using Panel Data." Econometrica, May 1989,695-701.

Chamber of Commerce of the United States. Employee Benefits 2986. Washington, D.C.: 1987.

Daski, Robert S. "Area Wage Survey Focuses on Part- Timers." Monthly Labor Review, April 1974,6042.

Greene, William H. LIMDEP Version 5.2. New York: Econometric Software, 1989.

Hartman, Heidi I., and Diana M. Pearce. High Skill and Low Pay: The Economics of Child Care Work. Wash- ington, D.C.: Institute for Women's Policy Re- search, 1989.

Hausman, J. A., and W. E. Taylor. "Panel Data and Unobservable Individual Effects." Econometrica, November 1981,1377-98.

Heckman, James J., and Richard Robb. "Alternative Methods for Evaluating the Impact of Interven- tion," in Longitudinal Analysis of Labor Market Data, edited by James J. Heckman and Burton Singer. New York Cambridge University PESS, 1985.

Hotchkiss, Julie L. "Part-Tie Status and Hourly Earn- ings: Specification Issues." Research Paper No. 14, Policy Research Center, Georgia State Univer- sity, April 1991.

Jones, Ethel B., and James E. Long. "Married Women in Part-Tie Employment." Industrial Labor Rela- tions Review, April 1981,413-25.

Judge, George G., W. E. Griffiths, R. Carter Hill, Hel- mut Lutkepohl, and Tsoung-chao Lee. The The0 y and Practice of Econometrics. New York John Wiley & Sons, 1985.

Leeds, Michael A. "Part-Tie Status and Hourlv Earn- ings of Black and White Men." Economic inquiry, July 1990,544-54.

Montgomery, Mark. "On the Determinants of Em- ployer Demand for Part-Tie Workers." Review of Economics and Statistics, February 1988a, 112- 17.

-. "Hours of Part-Tie and Full-Time Workers at the Same Firm." Industrial Relations, Fall 1988b, 394-406.

Montgomery, Mark, and James Cospve. "Fringe Ben- efits and the Demand for Part-Tiers." Industrial and Labor Relations Reoiew, October 1993,47244.

Owen, J. D. Working Hours. Lexington, Mass.: D.C. Heath, 1978.

Powell, Irene, and James Cospve. "Quality and Cost in Early Childhood Education." Journal ofHuman Resources, Summer 1992, 472-84.

Smpson, Wayne. "Analysis of Part-Tie Pav in Can- - ada." hnadian Jo;rnal of Economics, November 1986,798-807.

TUy, Chris. "Reasons for the Continuing Growth of Part-Tie Employment." Monthly Labor Review,

U.S. General Accounting Office. Early Childhood Edu- cation: What Are the Costs of High Quality Pro- grams? GAO/HRD-90-43R. Washington, D.C., 1990.

Whitebook, Marcy, Carollee Howes, and Deborah Phil- lips. Who Cares? Child Care Teachers and the Quality of Care in America: Executive Summary of the Na- tional Child Care Stafing Study. Child Care Em- ployee Project, 1989.

The Worklife Report. "Benefits for Part-timers." July 1989.

March 1991,lO-18.

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