Economic Implications of Public-Sector Comparable Worth: The Case of San Jose,

California

SHULAMIT KAHN*

This study evaluates the wage and employment effects of comparable worth in San Jose, California, one of the first cities in the United States to implement comparable worth wage adjustments. The empirical evidence indicates that, contrary to the predictions of many economists, females posted large employment gains in jobs targeted by comparable worth adjustments. Male employment in these jobs was stagnant. The increas- ing percentage female in the targeted jobs is attributable to the combined effects of comparable worth and pro-female hiring policies. No overall decrease in employment appears to have occurred in the city of San Jose, nor is there any indication of substitution from targeted to nontargeted jobs.

COMPARABLE WORTH ADVOCATES ARGUE that wages in women’s jobs are below those in men’s jobs because of sex discrimination. As a remedy, they urge that wages be set on the basis of “comparable worth,” where worth is evaluated using criteria (e.g., required education and/or number of workers supervised) that specifically exclude sex or proxies for sex. Since, in the United States, comparable worth advocates thus far have been successful only in the public sector, the economic analysis presented in this paper is restricted to that sector.

*The author is at the Boston University School of Management and the National Bureau of Eco- nomic Research. This study could not have been accomplished without the cooperation of Russell Strausbaugh, Research and Transaction Administrator of the Personnel Department of the city of San Jose. The author would also like to thank Mark Killingsworth, Peter Doeringer, Henry Farber, Larry Katz, Tom Kochan, Kevin Lang, Jonathan Leonard, James Medoff, Bruce Meyer, Paul Osterman, Elyce Rotella, Rob Valetta, and the anonymous referees for their helpful comments.

INDUSTRIAL RELATIONS, Vol. 31, No. 2 (Spring 1992). 0 1992 Regents of the University of California

270

Public-Sector Comparable Worth i 271

Most neoclassical analyses of comparable worth predict that, despite the policy’s goal of raising women’s wages and thereby improving their eco- nomic position, comparable worth will in fact cause a large employment loss among female workers (see Killingsworth, 1985; Ehrenberg and Smith, 1987). As this paper will show, however, it is theoretically possible to implement a comparable worth policy and create no employment losses. Moreover, there may be changes in the sex segregation of jobs that are not predictable a priori. A valid assessment of the impact of comparable worth on women therefore must be based on empirical research, and preferably on case studies, since no other wage-setting policy is identical to compara- ble worth.

The focus here is on the case of San Jose, California, arguably the first U.S. case of comparable worth. Although by 1981, 14 state governments had legislation requiring pay equity for women and men performing “com- parable work” or work of “comparable character,” these laws generally were implemented as equal pay laws, and no state had attempted to com- pare dissimilar jobs (Bureau of National Affairs, 1981).

The analysis indicates that females in San Jose posted large employment gains in jobs targeted by comparable worth adjustments (referred to as “targeted jobs” throughout this paper), while male employment in these jobs stagnated. The increasing percentage female in jobs targeted by com- parable worth appears to stem from a combination of comparable worth and pro-female hiring policies. Furthermore, the analysis shows no overall decrease in employment in the city of San Jose, nor any substitution from targeted to nontargeted jobs.

The Theoretical Impact of Comparable Worth

Comparable worth exogenously increases the wages of female-dominated jobs and consequently can be modeled as a wage support, analogous to a minimum wage with a large uncovered sector. The well-known result is that wage supports reduce demand in targeted jobs. Wages are no longer free to clear the labor market; instead, employment is determined by demand alone. Employment decreases and there is an excess supply of labor, creating a queue for employment in female-dominated jobs.

The predictions about employment changes induced by wage supports are not limited to the neoclassical profit-maximizing model. They are valid as long as the demand for labor is downward sloping and employment is set along the demand curve. These conditions apply to most of the objective functions of the public sector (e.g., social welfare maximization, vote maxi-

272 / SHULAMIT KAHN

mization), as well as to most discrimination models.' They are also true for a wide class of models of unionized environments in which contracts spec- ify wages and other terms of employment while management chooses employment along the labor demand curve.

Although public-sector demand elasticities may be near zero if the gov- ernment bodies can absorb or pass on increased costs, estimated values of local government labor demand elasticities are, in fact, of the same magni- tude as estimates for private-sector labor demand elasticities (Ehrenberg and Schwarz, 1986). Moreover, labor demand is likely to be more elastic when governments are fiscally limited by laws such as California's Proposi- tion 13.

Many previous analyses have assumed that the decrease in employment in female-dominated jobs as a result of comparable worth would be female or predominantly female. Ehrenberg and Smith (1987), for instance, esti- mate the sex-specific public-sector employment responses to comparable worth based on demand elasticities calculated from 1980 census data. On the assumption that occupations maintain their historical sex ratios, the authors predict relatively small (5.5%-6%) female disemployment effects of a 20 percent comparable worth increase in female wages.

A decrease in employment in female-dominated jobs is not, however, synonymous with a decrease in female employment since the percentage female in these female-dominated targeted jobs may not remain constant, for two reasons. First, since there is now excess supply to targeted jobs, the government body's hiring and layoff decisions are no longer dictated by market considerations and thus can be based on alternative criteria. Sec- ond, supply elasticities of (identical-quality) male and female workers to these targeted jobs may not be identical.2 The female/male mix in these jobs depends crucially on assumptions about these two factors. For exam- ple, if the sex composition of supply remains in its historical proportions, and if workers are hired and laid off randomly, comparable worth de- creases employment of each sex by equal percentages, implying a larger effect on females in these historically female jobs. On the other hand, the discretion in hiring allowed by the queue may lead governments to hire a higher proportion of one sex than previously. If female hiring policies are implemented, female employment may rise. In a general equilibrium simu- lation of comparable worth, Beider et al. (1986) find that their female/

'These include models based on taste (Becker, 1957; Arrow, 1972; Bergmann, 1971), on language (Lang, 1986). and on statistical discrimination (Lundberg and Startz, 1983).

2Males may respond in larger proportions to the comparable worth wage increases because the gap between female-dominated and male-dominated jobs has been narrowed or eliminated; alternatively, women may respond in larger proportions because they are attracted from nonmarket activities.

Public-Sector Comparable Worth I 273

male employment predictions change radically depending on whether peo- ple are hired from the queue in historical proportions or in proportions based on the sex ratio of the applicant pool.

In nontargeted jobs, comparable worth is likely to decrease demand: Unless male- and female-dominated jobs are strongly substitutable (in the Hicksian sense), the output effect will dominate.3 There may also be a change in the supply to these jobs; for example, workers queuing for female-dominated jobs may invest less in training or applying for male- dominated jobs (Gold, 1983; Fischel and Lazear, 1986), or people laid off from targeted jobs may apply for nontargeted jobs (Gold, 1983). Similarly, the sex composition of this supply may change, although not in a predict- able direction.

Alternative models. There are several alternatives to this standard model that do not predict a decrease in employment in targeted jobs. If governments have monopsony power over workers so that they face an upward sloping supply curve ,4 comparable worth wage increases may in- crease employment in targeted jobs (Aldrich and Buchele, 1986; Remick and Steinberg, 1984; Ehrenberg and Smith, 1987). There is, however, no evidence of monopsony power among the white-collar, noneducational workers who are the main recipients of comparable worth adjustments (Ehrenberg and Goldstein, 1975).

A more applicable alternative is that, in a unionized environment, em- ployment is not set along a labor demand curve. Empirical evidence shows that in the public sector, unionization increases both wages and employ- ment (Zax, 1989), indicating a movement not along the demand curve.

If labor demand doesn’t determine employment, how is it determined? The leading theoretical model predicting employment off the labor de- mand curve is efficient contracts, where management and unions set em- ployment levels such that marginal product value equals the marginal value of workers’ time. Bargaining over wages, benefits, and work rules divides rents between management and workers. In this situation, comparable worth wage increases would not affect employment, Empirical evidence on efficient contracts (even in the private sector) is limited, and no consistent picture emerges from it (see Abowd, 1989; Brown and Ashenfelter, 1986; MaCurdy and Pencavel, 1986).

To summarize, supply and demand with negative demand elasticities

30f course. if comparable worth causes a decrease in wages in nontargeted jobs because of govern-

4This might occur, for example, because they offer different kinds of employment than that available ment budgetary constraints, there will also be a negative substitution effect.

locally or because incumbent workers have varying mobility costs.

274 / SHULAMIT KAHN

predicts that comparable worth will decrease employment in targeted and nontargeted jobs. However, the sex composition of jobs, especially of targeted jobs, may change strikingly as a result of comparable worth. In an alternative model with efficient city-union bargaining, there would be no employment decline. Given these contradictory theoretical predictions, we must rely on the empirical evidence supplied by case studies of compara- ble worth.

Comparable Worth in the City of San Jose San Jose, with a population of more than 650,000, is located in Califor-

nia’s fast-growing Silicon Valley. Over 4,000 workers were employed by the city in July 1981, when AFSCME Local 101 struck to force the city to implement the comparable worth adjustments suggested by a 1980 Hay Associates wage evaluation of nonmanagement jobs. After less than two weeks, an agreement was reached to implement comparable worth. Wages were adjusted for the 58 percent of female-dominated job titles that devi- ated most from the Haypoint-salary trend. Wages were not adjusted for jobs that were not female-dominated or not represented by AFSCME, even if they fell below trend. Comparable worth wage adjustments came in July 1981, August 1982, July 1983, January 1984, and July 1984, although not all jobs were adjusted each time. These targeted job adjustments affected 809 workers, constituting 20 percent of the City’s employment and 16 percent of its job titles. Of those workers who received adjustments, 87 percent were women (two-thirds of the City’s female employment).

Flammang (1986) describes the combination of factors that led to the successful implementation of comparable worth in San Jose, including economic health, adequate Personnel Department resources, and leader- ship skills. San Jose’s Personnel Department was able to supply data on employment, wages, and comparable worth adjustments for each of the city’s 482 job titles.

AFSCME was responsible, not only for the strike, but for convincing the city to commission the Hay Associates’ study of nonmanagement person- nel. The union’s support for a policy that favored a subset of women, probably at the expense of male employment, is not surprising, given its membership. AFSCME did not represent all municipal workers in San Jose; 37 percent were in the 174 job titles covered by AFSCME bargain- ing; 57 percent were represented by other unions; and management com- prised the remaining 6 percent. The AFSCME-covered sector was heavily female (68%), and 54 percent of its members (and 45% of all AFSCME job titles) were targeted by the comparable worth adjustments.

Public-Sector Comparable Worth I 275

Comparable worth and relative wages. Assessing the impact of compara- ble worth on employment requires first establishing its impact on relative wages. The comparable worth adjustments did increase wages in targeted jobs much more rapidly than in nontargeted positions. Between July 1980 and July 1986, wages in the targeted jobs increased 73.9 percent, while all other wages increased 50.4 percent. As Table 1 illustrates, very little of this large disparity was due to an overall wage increase in union jobs generally or in AFSCME jobs particularly. The comparable worth increases changed the ranking of jobs within the government pay scale, but not dramatically. In 1980, only two targeted job titles were among the top-paid 50 percent of nonmanagement job titles, while in 1986, there were six. Similarly, 20 targeted job titles were among the 80 percent top-paid in 1980, while in 1986, there were 29.

TABLE 1 AVERAGE WEEKLY WAGES IN SAN JOSE CITY GOVERNMENT^

Percentage July 1980 July 1986 Change

Comparable Worth Targeted Jobs $268.3 $466.6 13.9 Nontargeted Jobs

Total 422.8 636.0 50.4 AFSCME 340.7 526.6 54.6 Police/Fire 462.0 684.4 48.1 Other Union 353.7 548.1 55.1 Management 631.4 920.6 45.8

SOURCE: City of San Jose Personnel Department. aWages are weighted by 1981 employment.

The large wage increase in targeted female jobs was not the result of an areawide increase in wages in these occupations. According to the Bureau of Labor Statistics’ annual Area Wage Survey for the years 1980-81 to 1986, wages in a sample of clerical jobs in the San Jose government rose 7.8 times as fast as wages in the San Jose area. In contrast, in skilled maintenance jobs, wages in the San Jose government rose only 1.7 times as fast as area wages; and in computer-related professional jobs, wages rose only .8 times as fast.5

SThe BLS’ annual Area Wage Survey covers only a limited number of occupations. Data presented here are restricted to the 17 occupations that could be classified as identical to job titles in the San Jose government in both 1980 and 1986. In addition, since the city government figures and the BLS’ surveys of the different areas cover slightly different periods, all wage figures have been converted into the instantaneous rate of change of the real wages.

276 / SHULAMITKAHN

Data from other California city governments (see Table 2) indicate that San Jose’s wage increases did not simply mirror a change in relative wages in all local governments. In the neighboring city of Santa Clara, for exam- ple, wages (for the period May 1981-May 1986) in jobs equivalent to San Jose’s targeted jobs increased at the rate of 6.7 percent per year, while wages in other jobs increased at the rate of 7.7 percent per year.6 In contrast, San Jose’s annualized growth rates were 9.7 percent in targeted jobs and 7.0 percent in nontargeted jobs. Wages in targeted jobs (i.e., jobs targeted by comparable worth in SunJose) in the other cities included in Table z7 increased at about the same rate or slower than other wages for all cities except Long Beach, which during this period implemented compara- ble worth adjustments for library jobs (a subset of targeted jobs in San Jose).

TABLE 2 ANNUALIZED AVERAGE PERCENTAGE CHANGE IN CITY GOVERNMENT WAGES

IN SELECTED CALIFORNIA CITIES~

Santa Clara Fresno Riverside Long Beach 5/1981-5/1986 711981-7/1986 711981-7/1986 7/1980-7/1986

Comparable Worth

Nontargeted Jobs Targeted Jobs 6.73 4.89 6.76 9.17

Total 7.69 5.58 6.86 7.30 AFSCME 5.69 4.45 6.77 7.59 Police/Fire 8.91 6.64 6.91 7.54 Other Union 7.45 4.76 6.77 6.70 Management 6.12 4.87 7.15 7.28

SOURCE: The personnel departments of the individual cities. aWages are weighted by 1981 employment. Wage data are based on a sample of approximately 60 job titles for each

city.

On the other hand, in San Jose, wages in the targeted jobs started the 1980s somewhat lower than wages in governments in the comparison cities. On average, wages for targeted jobs were 13 percent lower in the San Jose City government in July 1980 than in equivalent jobs in the Santa Clara government in May 1981 (the closest comparison date), controlling for inflation between these dates. Wages in nontargeted jobs were essentially equal, on average. Evidence from the city governments not within the same

~~

61n Santa Clara, police and firefighters’ wages increased very rapidly during this period; excluding these jobs, other nontargeted jobs averaged a 39.6 percent wage increase.

’1 requested 1980-81 and 1986 wage and employment data from 12 California cities. Only the cities listed in Table 2 were able to provide these data in usable form: none provided a breakdown by sex.

Public-Sector Comparable Worth I 277

SMSA-Fresno, Riverside, and Long Beach-also indicate that the (pre- comparable worth) ratio of targeted jobs’ wages in San Jose to equivalent jobs’ wages in other cities was somewhat lower than the corresponding ratio for other jobs, although the differences are smaller than those between San Jose and Santa Clara.8 The precomparable worth Hay Associates’ study of nonmanagement jobs in San Jose produced similar conclusions: Female- dominated classes were found to pay 5-15 percent below the overall trend in “similar jurisdictions,” while male-dominated classes paid 5-21 percent above the trend in these other jurisdictions.

Wage changes between 1981 and 1985 were not, however, merely mar- ket corrections that restored female-dominated jobs’ wages to the wages in comparable city governments, a movement that might have been expected even in the absence of comparable worth. The wages in comparable worth jobs in San Jose did not just catch up; by 1985, they were higher than those for equivalent jobs in Santa Clara and in other city governments.

Moreover, although the 1980 comparison between San Jose government wages and other local government wages suggests that San Jose’s wages for targeted jobs were below “market,” a comparison with the private sector suggests the opposite. Based on the available sample of jobs from the BLS’ 1980 Area Wage Survey, 1980 clerical wages in the San Jose government were higher than private-sector wages in the surrounding area, and the government/private-sector labor-market ratio was higher in clerical jobs than it was in computer professional, skilled maintenance, or janitorial jobs.

The Impact of Comparable Worth on San Jose City Employment

Output effects. Because comparable worth increased city expenses, stan- dard economic theory would predict a negative output effect on overall employment in San Jose. The facts show otherwise. Despite the adoption of comparable worth, between 1980 and 1984, the rate of growth of govern- ment employment in San Jose (15.5%) was considerably higher than the average growth in city government emplclyment (4.4%) in the other 12 largest California cities. (See Table 3.) Of course, Silicon Valley was grow- ing rapidly, but the San Jose government grew even faster. The rate of

T h e difference between the ratio of 1981 targeted wages in San Jose to targeted wages in Fresno and the ratio of nontargeted wages in San Jose to nontargeted wages in Fresno is 8 percent. For Riverside, this difference is 4 percent, and for Long Beach it is 7 percent. Note, however, that job descriptions were not provided by the comparison cities. So, the occupations that I judged to be equivalent across the cities may actually have involved different tasks and responsibilities.

278 / SHULAMITKAHN

TABLE 3 PERCENTAGE GROWTH IN LOCAL GOVERNMENT EMPLOYMENT, SELECTED CALIFORNIA CITIES~

In Employment In Employment In Populationb 1980-84 10/1978-10/1Y80 10/1980-10/1985

San Jose 12 Largest California CitiesC

Anaheim Fresno Huntington Beach Long Beach Los Angeles Oakland Riverside Sacramento San Diego San Francisco Santa Ana Stockton

Average, 12 cities Standard error

Silicon Valley Citiesc Mountain View Palo Alto Redwood City Santa Clara Sunnyvale

Average, 5 cities Standard error

~~

-3.1 15.5 7.8

-6.7 -2.3 -8.6 -3.3 -7.3

-22.5 7.5 0.2 1.5

-3.5 -6.5 -7.3 -3.7

7.6

-5.9 1.4 6.2

-8.2 -14.8 -4.3

7.4

7.8

7.1 8.7 3.6 2.9 l . Y

-5.9 12.8 14.6 13.4 9.9 4.4 7.5

-3.8

13.9 29.0 -3.2 10.6 2.9

10.7 10.9

5.3 22.5 5.2 4.9 4.4 3.7 6.7

13.4 9.7 5.0

10.6 14.6 8.6 5.3

5.3 1.5 1.3 2.5 3.7 2.9 1.5

SOURCE: U.S. Bureau of the Census, Government Employment, various years. %mployrnent refers to full-time equivalent (FTE) positions. hData limitations restrict population change figures to the period 1980-84. Sari Jose is excluded.

growth was higher in San Jose than in any of the Silicon Valley cities covered by the census figures, except Palo Alto. (This growth was in stark contrast to a 3.1% decline in San Jose government employment between 1978 and 1980 due to Proposition 13.)

There are many ways to evaluate this real employment growth relative to expected growth. During this period, salaries were increasing slightly as a proportion of total San Jose government expenditures. San Jose govern- ment employment and private-sector employment were growing at roughly the same rate, which was about twice the rate of the city’s population growth. To evaluate the population/government-employment ratio relative to other cities, city government employment was regressed on population for a cross section of 82 California cities. (The different specifications that

Public-Sector Comparable Worth I 279

were tried all gave similar results.) The estimated equations were then used to predict San Jose government employment. Two results were salient. First, both in 1980 and in 1985, San Jose’s government employment was about 50 percent lower than that predicted by the equation; and, second, the predicted percentage change in government employment was 8.1 per- cent, considerably lower than the 15.5 percent actual change.

Finally, a comparison of employment growth 1980-85 to employment growth in the two years preceding this period indicates no change in San Jose’s position relative to other cities (see Table 3). San Jose’s pattern is remarkably similar to San Francisco’s. Palo Alto, the only Silicon Valley city with growth surpassing San Jose’s between 1980 and 1985, showed similar relative strength in the 1978-80 p e r i ~ d . ~ Time-series regressions of San Jose government employment in the years 1975-86 provide a final test of the proposition that comparable worth slowed overall San Jose government employment growth. Two versions of the dependent variable, full-time equivalent (FTE) government employment per capita and FTE government employment, were run. Independent variables included a comparable worth dummy variable and different combinations of the variables: total San Jose metropolitan area employment, a dummy variable for Proposition 13, and total San Jose population (for the latter dependent variable). The coeffi- cient on the comparable worth dummy was never significant and it changed in sign.10

In sum, there is no strong evidence that comparable worth had any contracting effect on the scale of city government employment. Whether it caused substitution between targeted and nontargeted jobs is discussed below.

Total employment in targeted jobs. Table 4 presents employment levels in different occupational groups in the San Jose government both for 1981, prior to the adoption of comparable worth, and for 1986.

9While most California cities, including San Jose, were cutting back employment in response to Proposition 13, Palo Alto’s employment grew. If the study period is extended to cover 1975-80, more changes in the relative growth rates of government employment in different California cities are apparent, but San Jose still does not stand out. For example, its overall 4.6 percent growth in this period is dwarfed by Sunnyvale’s increase of 24 percent and Stockton’s 20 percent.

‘OFor earlier years, only FIE data are available. One typical equation result is:

San Jose government FTE = 6.042 + ,00171 Employ. (. OO298)

- ,218 Comp. Worth Dummy

- .766 Prop.13 Dummy

San Jose population

(.408)

(.491)

280 / SHULAMITKAHN

TABLE 4 EMPLOYMENT IN S A N JOSE CITY GOVERNMENT

Total Employment

Percentage 1981 1986 Change Change

Comparable Worth Targeted Jobs 809 983 174 21.5

Total 3,263 3,732 469 14.4 AFSCME 685 746 61 8.9 Police/Fire 1,435 1,638 203 14.1 Other Union 873 1,046 173 19.8

Nontargeted Jobs

Management 270 302 32 11.8

Previous studies of the elasticity of public-sector labor demand to wages generally suffer from an identification problem: The observed relation between employment and wages might represent points along the public- sector labor demand curve, but it might just as well represent points along the labor supply curve, as would occur if the government raised wages to expand employment. Alternatively, the wage/employment observations may be equilibrium points in a map of both shifting demand curves and shifting supply curves. It is usually impossible to isolate demand elasticities using the kinds of data generally available for the public sector because no instruments exist: There are usually no variables that can be clearly classi- fied as affecting only labor supply.

Comparable worth provides natural instruments. The choice of which jobs receive wage increases does not depend on labor demand factors. Therefore, these jobs can be used as instruments to evaluate the labor demand elasticity in a two-stage process:

+ = b C W (1)

(2) Employment = X B + a + where CW is the comparable worth adjustment variable and the X are other factors affecting labor demand. For evaluating only the labor de- mand response to comparable worth, the following reduced-form estima- tion provides unbiased estimates:

Employment = X B + c CW. (3) This equation directly assesses the impact of comparable worth adjust- ments in targeted jobs. Here, “c” is numerically equivalent to “a b” in the two-s tage estimation.

Public-Sector Comparable Worth I 281

Of course, data measuring the cross-sectional relationship (at a given point in time) between the number of workers employed in a particular job and the job’s wage will be dominated by the pyramid aspect of workplaces, where higher-paid jobs tend to be toward the top of the pyramid and hence also tend to have lower employment. Assessing the employment response to wage changes thus requires controlling for the job itself. This could be accomplished by combining data into a fixed-effect model with job dum- mies or, equivalently, by using the employment change or rate of change as the dependent variable.

Table 5 reports the results of the reduced form estimation (as in equation

TABLE 5

(standard errors in parentheses) REGRESSION RESULTS, EMPLOYMENT CHANGES (1981-86)’

Dependent Variables

Proportion Proportion Absolute Absolute Proportion Proportion Change Change Change Change Change Change Employ. Employ. Employ. Employ. Employ. Employ.

(1) (2) (3) (4) ( 5 ) (6)

Comp. Worth

Comp. Worth

Managerial Job

PolicelFire Job

Other Union Job

Clerical Job

Admin. Job

Employ.-1981

Constant

Adjust ./l,000

(dummy variable)

(dummy variable)

(dummy variable)

(dummy variable)

(dummy variable)

(dummy variable)

RZ N = 370

.86 -

- ,152 (.113)

-.161 - ,102 (.088) ( ,093) .045 ,104

(.203) ( .205) ,201 ,260

(3.93)

(.095) (.099) - -

.045 -.014 (.069) (.075) ,048 ,053

.81 (3.23) -

,424 (.730)

-3.707 (1.96)

306 ( .788) - -

,172 (.008)

(.576) .604

-.912

-

1.607

1.058 (.637)

-3.012 (1.976) 1.437 (325)

(.934)

-

-

.172 (.OOS)

- 1.538 (. 627) .608

-

.143 (.116)

- . lo6 (.093) ,111

(.206) ,261

(. 102) -

.027 (.080) -

- ,021 (.078) ,053

-

,109 (.118) - ,086 (.093) ,121

(.205) ,277

,171 (. 140)

(. 100)

-

-

- .031 (.076) ,056

Wariables are defined as follows: Proportion Change Employ. = proportion change in employment, 1981 to 1986; Absolute Change Employ. = absolute change in employment, 1981 to 1986; Comp. Worth Adjust. = number of comparable worth step adjustments; Comp. Worth = dummy which equals 1 if job had any comparable worth adjustments; Managerial Job = dummy for managerial job; PoliceEire Job = dummy for police or fire job; Other Union Job = dummy for union job other than AFSCME or policelfire; Clerical Job = dummy for clerical jobs; Admm. Job = dummy for administrative jobs, including clerical, fiscal, data processing, personnel, legal, and general administrative; and Employ.-1981 = employment in 1981.

282 I SHULAMITKAHN

3) of the effect of comparable worth adjustments on changes in employ- ment." Two possible comparable worth variables-a dummy variable for jobs adjusted by comparable worth (Comp. Worth Dummy) and the num- ber of steps (each .5% of wages) that comparable worth adjusted each job (Comp. Worth Adjust.)-were tested. The X control variables were lim- ited by the available data. Dummies for major job groupings (managerial, police/fire, or other union; the excluded class is AFSCME unionized jobs) were included in all regressions.

Table 5 indicates that the point estimates of the effect of the comparable worth variables, both the dummy and the number of comparable worth step adjustments, are generally positive, although never significant at the 95 percent level; the Comp. Worth Dummy does, however, achieve signifi- cance at the 90 percent and or 80 percent levels (see columns 4 and 2, respectively). Columns ( 5 ) and (6) control for clerical or administrative jobs and show that the positive point estimate on the comparable worth variables is not accounted for by a tendency for clerical jobs-or, more generally, administrative jobs-to grow during these years. These results suggest that there was no contracting labor demand response to comparable worth.

Examining average changes produces the same implication about the relationship between employment effects and wages. Average percentage changes in employment (1981 to 1986) for comparable worth jobs and for the other occupational groups are given in Table 4. These average changes are exactly equivalent to an employment-weighted regression of the percent- age employment change as a function of a dummy variable for comparable worth and dummies for each occupational group, that is, an employment- weighted version of equation (2) of Table 5.12 Employment in targeted jobs increased 21.5 percent in this period, 50 percent faster than the 14.4 percent in other jobs and more than twice as fast as nontargeted AFSCME posi- tions. (The latter averaged 8.9% growth.)

Additional analysis. Killingsworth's analysis (1989) of the employment response to comparable worth in San Jose concludes that comparable worth decreased employment. This conclusion is derived from an estimated negative effect of wage on employment in San Jose government female- dominated jobs.13 The t-statistic on this coefficient was never greater than

Wsing data from the two endpoints, 1981 and 1986, rather than linking year-by-year employment to year-by-year wages is preferable here because employment effects of wage changes are generally not instantaneous but rather are gradually adjusted, often by attrition.

'2An additional difference is that the regressions included only job titles with employment in 1981. 13As previously discussed, because of a standard demand-supply identification problem, this method-

ology is inferior to one based on Comparable worth variables used either as instruments for wage or, as above, in a reduced form.

Public-Sector Comparable Worth I 283

1.18, and it was often much lower. Despite this statistical insignificance, Killingsworth used the point estimate to estimate an effect on employment.

Neither Killingsworth’s parameter on wage nor the parameter on the Comp. Worth Adjust. variable used here are statistically different from zero, but the opposite signs obtained on these parameters bear further investigation. Accordingly, his general fixed-effects methodology for the years 1981 through 1986 was replicated in order to estimate his key parame- ter, the effect of wage on employment in jobs identified by the Hay Associ- ates’ study as female-dominated. Different specifications were then tested. (All specifications were also estimated combining female-dominated, male-dominated, and mixed jobs.) For comparability, the analysis was limited to the subset of job titles used by Killingsworth.

Results generally comparable with Killingsworth’s were obtained using his specifications (i.e., using the log of both employment and wages and including time variables as control variables). Thus, the effect of log(wage) on log(emp1oyment) is positive when no time variables are included, but it turns negative when time variables are included.

However, the coefficient on wage is positive, not negative, when estima- tion is based on the levels of wages and employment (with or without time variables), rather than on the logs. It is insignificant in some specifications, significant in others. Similarly, when levels of employment but log of wages are used, the effect of wage on employment is always positive, significant for female-dominated jobs, and varying in significance when all jobs are combined. Furthermore, even with Killingsworth’s log-log specifi- cations and time variables, wages have a positive rather than a negative effect on employment when weighted least squares is used (with weights equal to 1981 employment). Finally, in terms of goodness-of-fit , Kill- ingsworth’s log-log specification is inferior to a specification using levels rather than l0gs.1~ Moreover, the levels functional form can predict cases of zero employment; the log specification not only cannot predict these observations, it must exclude all cases of zero employment for estimation, introducing truncation bias.

Thus, the finding of a negative effect of wage on employment, either for all employment or for female-dominated employment, is not a general one for San Jose. A negative effect only appears in a single functional form and

14The sum-of-squared residuals in the levels specification is 2,288, while the analogous figure from the log-log specification is 2,460. This number was calculated using the coefficients from the log-log specification, translating their predictions of log(Emp1oy.) into a prediction of the level of employment and calculating the implied residuals.

284 / SHULAMIT KAHN

specification, when observations are unweighted, and even then is gener- ally statistically indistinguishable from zero. Moreover, this functional form is inferior to others in terms of explanatory power. In all other forms estimated, including simple levels, wage has a positive effect on employ- ment, sometimes significant and sometimes not.

As argued above, the best specification would use comparable worth variables as instruments for wages. It would also use real, not nominal wages. This specification (with variables expressed as deviations from the jobs’ means) yields (standard errors in parentheses):

Employment = 3.40 real wage - 0.97 time (3.87) (0.52)

(0.08)

(2.50) (0.23)

(0.03)

+ 0.27 time2 (female-dominated jobs)

Employment = 3.83 reai wage - 0.62 time

+ 0.15 time2 (all jobs)

Instruments for wages include cumulative comparable worth step adjust- ments, its lag, and time variables. Here, wage is positive but insignificant.

The general conclusion both from the exercise replicating Killings- worth’s methodology and the evidence previously presented in this paper is that the comparable worth adjustments in San Jose did not induce any decrease in employment and might even be interpreted to have increased employment.

Factors affecting employment growth in targeted jobs. Although tar- geted jobs did not grow more slowly than nontargeted ones, it is neverthe- less possible that whatever caused the rapid growth in employment in targeted jobs during this period would have led to even greater increases in the absence of comparable worth. One factor behind this rapid growth may have been a general expansion of clerical-library jobs in California local governments during these years. The growth in Fresno and Santa Clara (cities for which disaggregated employment data are available) can be compared with San Jose’s growth, Employment growth in jobs equiva- lent to San Jose’s targeted positions was ,061 percent in Fresno and .056 percent in Santa Clara, while employment growth in other jobs was .046 percent and .068 percent, respectively. Thus, in Fresno, employment in targeted jobs increased 33 percent faster than in other jobs; and in Santa Clara, it increased 18 percent more slowly, compared with 50 percent

Public-Sector Comparable Worth I 285

faster growth in San Jose.15 Although these cities’ patterns of employment growth were very different, neither comparison indicates that San Jose’s growth in targeted jobs would have been larger in the absence of compara- ble worth. In state and local governments in the United States as a whole, office and clerical employment grew less, not more, than other occupa- tions between 1980 and 1985.16

A second factor behind the growth in targeted jobs in San Jose is that government growth might entail a shift toward more layers of management and thus more associated clerical jobs. The census category that includes clerical workers, “financial administration and general control,” groups together the management-related employment that may increase with gov- ernment size. Regressing the log of the 1985 city government employment in “financial administration and general control” (Finance) on the log of the 1985 city government employment (Employ.) for the 85 California cities in the census yields (standard errors in parentheses):

Log(Finance) = -1.18 + -843 Log(Emp1oy.)

Since the coefficient on Log(Emp1oy.) is less than one, the percentage employment in financial administration and general control decreases with size rather than increases. Thus, the growth of the San Jose government alone cannot account for the higher growth rate of targeted jobs; instead, it would tend to slow their growth.

Since we have not explained the somewhat faster growth rates of tar-

(.23) (0.034)

15These numbers can be given a more sophisticated interpretation. One way to control for the employment effect of comparable worth-type jobs in other California city governments would be to run the (Generalized Least Squares) regression:

% Change Employment,, = 7 A, * City Dum,

+ f: B, * (City Dum, * Comp. Worth Jobs,)

where i indexes the job, j indexes the city, City Dum is a vector of dummy variables for the city (San Jose, Frcsno, Santa Clara), Comp. Worth Jobs is a dummy variable for jobs equivalent to those targeted by comparable worth in San Jose, and there is an A and B parameter for each city. If the B parameter for San Jose were different from the B parameter for the other cities, then the employment changes in San Jose comparable worth jobs would be different from employment changes in equivalent jobs in other cities. The B parameter corresponds to the difference in the average percentage employment changes for the different cities; e.g., for Fresno it would be B = ,061 - ,046 = ,015. Similarly, for Santa Clara, it would be B = ,056 - ,068 = - .012, while for San Jose, it would be B = .215 - ,144 = ,071. The B parameter for San Jose is much larger, indicating that targeted employment grew much faster.

I6According to the Equal Opportunity Employment Commission (1982, 1988), office and clerical employment in governments grew by 16.2 percent versus 19.8 percent for other occupations.

286 / SHULAMIT KAHN

geted jobs in San Jose, the possibility remains that the growth might have been more rapid still in the absence of comparable worth. However, no evidence has emerged from any of the analyses and comparisons here to suggest that comparable worth lowered employment in targeted jobs.

Another reason we may not observe a labor-demand motivated employ- ment adjustment to comparable worth is the presence of an efficient union/ management employment contract. Employment levels would then be set efficiently considering only marginal products and workers’ value of time, and wage adjustments would change only the rents to the different parties in the bargaining process. There is no general consensus that efficient contracts are widespread, and in San Jose, AFSCME and city management do not explicitly set employment levels as part of joint bargaining. How- ever, for efficient contracting to exist, there need be only an implicit agree- ment about the rules employers use to determine employment.

Given the positive point estimates of comparable worth on employment in Table 5, it is even possible that comparable worth induced a faster rate of growth of targeted jobs. An increase in employment might arise if the San Jose city government had misjudged market wages prior to compara- ble worth, so that the comparable worth adjustments moved them up along the labor supply curve from a lower-than-market wage. When wages are set by administrative rules or job evaluation procedures (see Kaufman, 1988; Treiman and Hartmann, 1981), there need be no correspondence between internally set wages and external market wages. In 1980, San Jose government wages in targeted jobs were lower than in equivalent jobs in other California governments but higher than in the surrounding private market. Which is the appropriate comparison? In general, females have been shown to receive higher wages in local and state government jobs than in the private sector (see Krueger, 1987). If the wage premiums females receive in local governments are compensating wage differentials, then the appropriate comparison is other city governments’ wages, and San Jose clerical wages were below “market” in 1980. If, on the other hand, females in local government receive above-market wage premiums for political reasons, the appropriate comparison is the local private mar- ket, and San Jose did not pay below-market wages.

The Effects of Comparable Worth on Sex Segregation The most striking results are the effects on sex segregation (see Table 6).

Males were not attracted into targeted jobs by the higher wages; in fact, the opposite seems true. The net increase in male employment in targeted jobs was one individual, less than 1 percent of the total 174-person increase

Public-Sector Comparable Worth I 287

TABLE 6 SEX COMPOSITION OF EMPLOYMENT I N SAN JOSE CITY GOVERNMENT, 1981-86

Percentage 1981 1986 Change Change

Comparable Worth Targeted Female Male

Female Male

Female Male

Female Male

Female Male

Female Male

Nontargeted, Total

Nontargeted, AFSCME Jobs

PoliceiFire Jobs

Other Union Jobs

Management Jobs

702 107

351 2,912

247 438

25 1.410

33 840

46 224

875 108

498 3,234

305 44 1

64 1,574

59 987

70 232

173 1

147 322

58 3

39 164

26 147

24 8

24.6 0.9

41.9 11.0

23.5 0.7

156.0 11.6

78.8 17.5

52.2 3.6

in targeted employment, despite the fact that in 1981, males held about one-eighth of the targeted jobs. Although employment grew faster in tar- geted than in nontargeted jobs, less than 1 percent of male employment growth was in targeted jobs, while about 3 percent of 1981 male employ- ment was in these jobs.

At the same time, the proportion of women in nontargeted jobs also increased. In 1981, only 11 percent of nontargeted employment was female, but 32 percent of the net employment growth in these jobs was female, a 42 percent growth in female employment. (In fact, female employment in unionized police and firefighter jobs increased 156% between 1981 and 1986.) Thus, while targeted jobs became more segregated, nontargeted jobs became less segregated. These facts can be explained by recalling that com- parable worth creates a queue of both women and men to the targeted jobs and therefore gives the employer new discretion over whom it hires.17 In San Jose, this discretion led to a tendency to hire women rather than men from the queue for targeted jobs. There may have been a general pro-female policy in the City of San Jose, and both comparable worth and the hiring priorities were manifestations of this pro-female policy.

17This is true even if total employment in targeted jobs remains unchanged (due, e .g . , to efficient contracting).

288 / SHULAMITKAHN

A related explanation may stem from affirmative action. San Jose’s vol- untary affirmative action plan did not require women to be hired instead of equally qualified (or more qualified) men into jobs already predominantly female, such as the targeted jobs. However, a department’s affirmative action progress was evaluated in terms of its overall percentage female, and this could have provided a weak incentive for male-dominated depart- ments to choose females even for clerical and other female-dominated jobs.

An alternative explanation for the increased feminization of both tar- geted and nontargeted jobs could lie in the national trend toward increasing female labor force participation and hence greater female employment. However, figures for California and the United States (Equal Opportunity Employment Commission, 1982, 1988) indicate that the proportion female in state and local governments remained virtually unchanged between 1980 and 1985: For California, it fell from 41.8 percent to 41.6 percent; for the United States, it remained steady at 41.1 percent. When the figures for the United States are decomposed by occupational group, they show a slight increase in percentage female in male-dominated and mixed-sex occupa- tions, from 25.2 percent to 26.4 percent, and a slight decrease in female dominance in office and clerical occupations, from 98.5 percent to 97.8 percent. Thus, the increased feminization of jobs in San Jose deviates from general trends for state and local governments in California and the United States.

Is there any evidence that comparable worth wage adjustments contrib- uted to the increased hiring of females? Even if comparable worth had not created a queue of workers, an active pro-female hiring policy or a large increase in female labor supply could have led to females comprising virtu- ally all of the increase in employment in targeted jobs and to rapid in- creases in the rate of growth of females in male-dominated job titles.18

If comparable worth had no effect on the sex composition in targeted jobs, we would expect the trends in these jobs to be similar to the trends in female-dominated jobs that were not targeted (because their wages were not sufficiently below the Haypoint-wage trend line). Unfortunately, there were few such jobs (see Table 6 ) , and employment changes were small and negative (net = -19). However, the number of women in these jobs de- creased whereas the number of men increased. It would be unreasonable to give too much weight to this finding. Given the small number of workers

18Females need not comprise all of the growth in male-dominated jobs if these jobs required (or were thought to require) skills that many female applicants did not have.

Public-Sector Comparable Worth I 289

involved, a chi-square test cannot reject that the percentage female in these jobs was different between 1981 and 1986. On the other hand, we can statistically reject the hypothesis that the change in the sex distribution of jobs was the same in comparable worth targeted jobs and nontargeted female-dominated j0bs.1~ This suggests that comparable worth did contrib- ute to the increased proportion of women in targeted jobs, possibly because of the additional latitude it gave management in hiring from among the queue ~

Alternatively, comparable worth may have increased the elasticity of the supply of females to these jobs so that the queue became increasingly composed of women, more so than in other female-dominated jobs (see note 2). This difference in supply elasticities may have been magnified (or even caused) by women attracted to the city of San Jose because of the visibility of the AFSCME strike and the publicized comparable worth adjustments.

Whether these results could be replicated in other governments is uncer- tain. If it is the San Jose government’s pro-female policies that are dominat- ing hiring from the queue, then other governments that adopt comparable worth policies but do not espouse a more general pro-female hiring policy may not experience an increased proportion of females in targeted jobs. If the increased proportion female in targeted jobs is due to affirmative action, then the same effects should be found in other governments with similar affirmative action guidelines, but not in governments which do not measure affirmative action progress by entire departments. Finally, if the heavily female hiring is due to an increased female labor supply attracted by San Jose’s new visibility, the change in sex ratios for targeted jobs may not be replicated in other less publicized cases.

It might be argued that comparable worth would be likely to desegregate targeted jobs, since more men would be attracted by the higher wages associated with these positions. However, an increase in the supply of men affects the sex composition of the queue for targeted jobs, but it does not necessarily similarly alter the sex ratio of the people hired from this queue. Finally, there is no evidence that comparable worth contributed to job segregation in male-dominated jobs, since desegregation of these jobs proceeded quickly.

’9The difference between the change in proportion female in targeted versus other female- dominated jobs was computed. Assuming that the sex of each job has a binomial distribution. this difference is a sum of binomials and is distributed normally. The value of the t-statistic is 1.9, which, given the one-tailed nature of the test, allows us to reject the possibility that the changing proportions are the same in the two kinds of jobs.

290 / SHULAMIT KAHN

Conclusion By lowering employment in targeted jobs, comparable worth has been

alleged to hurt the very group that it was intended to benefit. The evidence from San Jose suggests that the implementation of comparable worth in the public sector may not have a notable disemployment effect, and, in fact, it may even increase employment in the targeted jobs. In San Jose, there was no evidence of either substitution between male- and female- dominated jobs nor of output effects. An efficient uniodmanagement contracts model could explain these results.

The results regarding the sex composition of employment suggest that analyses of comparable worth must pay particular attention not only to how individuals decide to join the queue for targeted jobs created by the nonmarket clearing wages, but also how individuals are selected from the queue. At least in San Jose, it appears that women rather than men were chosen from the queue for targeted jobs, thus decreasing the already low proportion of men in female-dominated jobs.20

If the San Jose experience reflects a general phenomenon, it would imply that comparable worth may have a negative employment effect on men and also that it may promote occupational segregation in female- dominated jobs. Of course, the impact on overall segregation will be small since these occupations are already very heavily female. On the other hand, San Jose’s experience may be atypical. As a pioneer in comparable worth, the City may also have had an unusually high commitment to hiring and retaining female employees, a commitment less likely in cities or states that adopt comparable worth because of legal requirements.

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