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Erects of Excess Supply on the Wage Rates of Young Women ALICE NAKAMURA and MASAO NAKAMURA In this paper we begin with the premise that wages are determined over time by the interaction of supply and demand forces within labor markets. It is assumed that excess labor supply conditions wit! tend to Tower wage rates. Thus, we would expect wages to be lower in a relative sense in labor markets in which the supply of labor is more abundant than the demand. Berg- mann (1974, 1986) argues that excess supply pressures of this sort have been particularly severe in female labor markets ant] that this crowding is one important reason why wom- en s wages are as low as they are. In this paper we address the related question of whether the severity of crowding pressures on wage rates diners among various groups of working women. Reasons for expecting this to be the case are advanced, and sup- porting empirical evidence is presented an discussed. Pay equity programs can be thought of as one type of measure for making female wage rates in jobs covered by these pro- grams less vulnerable to crow(ling effects originating in female labor markets. We en(1 the paper with a (liscussion of pay equity programs viewed from this perspective. 70 INVESTIGATING CROWDING EFFECTS In the language of economists, what is being set by the interaction of supply and demand forces in labor markets are rates of remuneration for the years of schooling and work experience of inclividuals with (liffer- ent occupational specializations an(l other productivity-related characteristics (Pola- chek, 1976, 1981~. The expectation is that the rates of return on human capital re- sources will be lower, anti hence wage rates will be lower after controlling for years of schooling and work experience, in occu- pational labor markets in which the supply of labor is more abundant relative to the (demand. This will be the case because mar- ket forces permit, or even compel, em- ployers to pay relatively less for labor in slack occupational employment markets. It will also be the case because would-be em- ployees in these stack markets must often settle for jobs that do not fully utilize their accumulated human capital. Everyone understancls these relative sup- ply concepts of wage determination on an intuitive level. When recruiting for aca-
EFFECTS OF EXCESS SUPPLY ON WAGE RATES demic staff, for instance, business schools usually filch larger numbers of well-qualifiec! applicants for the business economics and quantitative methods positions than for the accounting positions, ant] it is understood by all concerned that that is why the ac- countants receive higher salary offers. Moreover, some of those who have trained for academic jobs in business economics or quantitative methods end up having to take other sorts of jobs in business or government that do not fully utilize their research train- ing and teaching skills. Excess supply pres- sures on the wages of aspiring artists (mu- sicians, dancers, painters, and so forth) are even more severe. When artistic work can be found, the wages typically provide a paltry return on the investment in artistic training and experience. When artistic work cannot be founcI, aspiring artists end up supporting themselves waiting on tables or cloing other jobs that make little or no use of, and hence provicle little or no return on, their accumulates] artistic human cap- ital. Nevertheless, crowding effects on wage rates cannot be easily observed or measured in a direct sense. For most occupations there are no direct measures of labor supply or demand; only those who were or are actually working in an occupation can be identified. Thus, researchers investigating possible ex- cess supply effects on wage rates have typically proceeded by attempting to demonstrate that conditions that might be expected to cause labor market crowding accompany observed wage rates that are lower than would oth- erwise be expected. Several researchers, for example, have presente(l evidence that men born in large cohorts generally have lower earnings profiles than men born in relatively small cohorts (see, for instance, Easterlin, 1980; Freeman, 1979; Welch, 1979~. Differences in Female Labor Markets Bergmann's assertions about the relative severity of crowding effects in female labor 71 markets hinge on the following empirical observations. First, female labor force par- ticipation rates have risen (Iramatically since World War II. Largely as a consequence of this, the growth rate for the female labor force has been much higher than for the male labor force. Second, female employ- ment is concentrates] in a narrower array of occupations than is male employment (Blau, 1977; Blau an(l Hendricks, 1979; Trei- man ant] Hartmann, 1981~. Moreover, there is little overlap between female and male labor markets. Even within narrow occu- pational classifications that appear from the numbers of female anti male workers to be sex integrated, women and men often have clifferent job titles or work in separate es- tablishments. Based on their study of 400 California business establishments employ- ing nearly 47,000 men ancT over 14,000 women, Bielby and Baron (1984:50-51) con- clude that in most establishments, few job classifications are staged by both men and women. Indeed, com- plete segregation was the norm in establishments studied . . . and segregation levels were virtually constant in these organizations during the late 1960s and 1970s. This occupational segregation by sex pre- sumably limits the potential for a more general diffusion of crowding effects origi- nating from excess supply in female labor markets. Corresponding to these conditions that presumably have resulte(l in crow(ling in female labor markets is the empirical evidence that women have been and con- tinue to be paid less for their market work than men are (see, for instance, Nakamura and Nakamura, 1985; Oi, 1982; and O'Neill, 19854. In the empirical portion of this study, we attempt to examine crowding effects in fe- male labor markets by relating conditions deeme(l likely to result in excess labor sup- ply to observe(l female wage rates. A study of this sort cannot yield precise estimates of the clownward impact on wages resulting
72 from various degrees of excess labor supply. Nor is it easy to see how circumstantial evidence of this nature can illuminate the question of hou: much more severe the crowding effects are in female compared with male labor markets. For one thing, some of the conditions that can readily be identified as likely causes of labor market crowding probably affect female labor mar- kets differently from the way they affect male labor markets. For instance, prime- aged men who are laid offdue to a downturn in some sector of the economy are likely to remain in the labor force as unemployed workers until they locate new jobs. On the other hand, a substantial proportion of prime- aged women who lose their jobs are likely to simply drop out of the labor force. Also young men are more likely than their female counterparts to migrate for job-relatecI rea- sons. In this study we address the more limited question of whether there is any evidence that some segments of the female work force are more vulnerable than others to crowding-related wage erosion. To our knowledge, there have been no previous empirical investigations of this question. Differences by Occupation Our reasons for expecting wages to be more vulnerable to crowding effects in some occupations, ant] in some positions within occupations, than in others include the fol- lowing. First, barriers to entry, such as training requirements, appear to stem the flow of job seekers into some occupational labor markets, making it less likely that excess supply pressures will (levelop. The potential magnitude of these effects can be seen from the numbers presented in Table 3-1. The numbers in the first two columns indicate a (lramatic surge in me(lical school applications following the end of World War II and the return to civilian life of large numbers of young men whose educations had been interrupted by the war and who were now eligible for GI education benefits. PAY EQ UI TY: EMPIRI CAL INQ UIRIES Because of constraints on medical school capacity, acceptance rates dipped for both men and women as the numbers of appli- cants rose (see columns 3 and 4~. Thus, as can be seen from the last column of Table 3-1, the numbers actually admitted to U. S. medical schools (and hence the numbers graduating from those schools) rose only modestly over the 16-year period from 1939 through 1956. In general, higher educa- tional requirements probably act as a barrier to entry into an occupation even in the absence of binding limitations on the ca- pacities of the relevant training programs. This is a consequence of the time and mon- etary costs required to obtain this training. Second, more educated and also relatively more scarce types of workers may be more successful in securing concessions from em- ployers that protect their wages against ex- cess supply pressures when they (lo ~le- velop. These concessions may include long- term contractual agreements, job ladders requiring employers to promote from within to fill most positions, wage scales virtually guaranteeing rising levels of pay with in- creases in seniority, and the institutionali- zation of powerful collective bargaining units that can push the workers' point of view in disagreements with employers. Thus, in general, we expect to find that the returns to education and work experience (that is, the expected wage increases associated with each a(l(litional year of education an(l each additional year of work experience) are low- er and crowding effects on wage rates are more severe for women working in occu- pations with lower educational require- meets. Within occupations, we would also expect wage rates to be more vulnerable to crowd- ing effects for some jobs than for others. Moreover, even after controlling for general measures of human capital, such as years of schooling, women with certain charac- teristics might be more likely than other women to end up in those jobs within an occupation for which wage rates are more
EFFECTS OF EXCESS SUPPLY ON WAGE RATES TABLE 3-1 Applicants and Admissions to U. S. Medical Schools, 1939-1956 73 Percentage of Number of Applicants Applicants Admitted Total Number Year Women Men Women Men Admitted 1939-1940 632 11,168 50 51 6,012 1940-1941 585 11,269 53 52 6,170 1941-1942 636 11,304 57 51 6,128 1942-1943 810 13,233 49 46 6,484 1949-1950 1,390 23,044 29 29 7,086 1950-1951 1,231 21,049 33 31 6,931 1951-1952 1,109 18,811 38 38 7,570 1952-1953 1,021 15,742 47 43 7,249 1953-1954 972 13,706 53 49 7,231 1955-1956 1,002 13,935 54 50 7,508 SOURCE: Cole (1986:Table 1~. adversely affected by excess labor supply pressures. This might be the case, for in- stance, for women belonging to racial, eth- nic, or religious groups that are discrimi- nated against in the labor market; for women having relatively little work experience due to child-related withdrawals from the work force; and for recent entrants or reentrants to the work force. METHODS younger cohorts of working women are larg- er in number, which leads to larger sample sizes for empirical analysis. Second, the careers of these younger women have been less affected by any discriminatory training or labor market practices that were, in fact, reduced as a result of the equal opportunity rulings that came into effect in the 1960s and early 1970s. General Labor Market Variables We estimate log wage equations using Our choice of explanatory variables was microdata from the 1980 census (5 percent motivated not only by the objectives of this A public-use sample) for individual working study, but also by the limitations of our data women 20 to 24 years of age. Separate results are presented for eight broad oc- cupational groups: personal service, other clerical, secretarial, sales, managerial, health professional/technical, and teaching (de- fined in Appendix A). Results are also pre- sented for women (working in all occupa- tions) in various demographic groups. We do not show equations for women sorted by both occupation and demographic char- acteristics because the sample sizes are too small for many occupational-clemographic categories. We focus on the wage rates of women ages 20 to 24 for two reasons. First, the source. Unemployment rates are sometimes used as indices of general excess labor supply conditions in macroeconometric models. We include state-specific unemployment rate variables for women and men ages 20 to 29 in our log wage equations. We have included those ages 25 to 29 along with those ages 20 to 24 in the computation of these state- specific unemployment rates because that leads to more reliable estimates of the un- employment rates (lue to the larger sample sizes and also because employers may regarc] those ages 25 to 29 as potential substitutes for workers who are 20 to 24 years old.
74 The unemployment rate for women and for men in each state was computed as the number who were unemployed in the cen- sus reference week divided by the number who participated in the civilian labor force in that week. Unfortunately, the interpre- tation of the coefficients of these variables is not straightforward. High male unem- ployment rates, for instance, may be in- dicative of more male competition for jobs usually held by women, which results in downward supply pressure on female wages. But high male unemployment rates may also cause some married women to be more serious about finding and hoisting on to jobs that pay relatively well because of the higher degree of uncertainty associated with their husbands earnings. High male unemploy- ment rates could even be indicative of a substitution of cheaper female labor for higher priced male labor. 1 he state- specific enects on la nor markets of general changes in the demand for goods and services, as well as differences among states in other relevant factors, such as the cost of living should be reflected in the ma. . . . a. fir . ~ . ~ . average earnings of prime-aged men (who still make up the largest share of the work force). In our log wage equations, we include a state-specific variable for the log of the average earnings of men ages 25 to 45, working in all occupations. The coefficient of this variable is expected to be positive. That is, it is expected that the effects of general labor market conditions on the wage rates of young women will be in the same direction as the effects on the earnings of prime-aged men. After controlling for more general labor market conditions, we wish to determine whether there are any additional effects on female wage rates due to crowcling in female labor markets. In an attempt to identify acIditional effects, we include in our log wage equations the logarithm of the number of women ages 20 to 24 in each state divided by the corresponding number of women 25 PAY EQUITY: EMPIRICAL INQUIRIES to 29 years of age. We refer to this state- specific variable as the log population ratio. When the female population is growing, the number of women ages 20 to 24 will exceed the number who are ages 25 to 29. In this case, the ratio of these two numbers will be greater than unity and the log of the ratio will be positive. Our expectation is that positive values of the log population ratio will be associated with increases in labor supply in those occupations in which women seek employment. In some occupations, of course, increases in the log population ratio may also be specifically associated with increases in the (lemancl for female labor. Increases in the number of women ages 20 to 24, for in- stance, may lead to increases in the demand for primary schoolteachers as well as day- care and other service workers to care for the children of these young women. In general, however, there are no obvious rea- sons why the number of jobs for young women will expand as quickly as the number of potential jobholders with increases in the population of young women. Excess supply conditions in female labor markets will re- sult when the number of women seeking jobs rises more rapidly than floes the num- ber of jobs. If there are crowding elects on the wage rates of young women after more general, state-specific labor market conditions have been controlled for by the unemployment and male earnings variables, we expect the coefficient of the log popu- lation ratio in our log wage equations to be negative. For similar reasons, increases over time in female employment rates might be ex- pecte~d to lead to excess supply conditions in female labor markets. Thus, we include the logarithm of the state employment rate for women ages 20 to 24 divided by the state employment rate for women ages 25 to 29 as an explanatory variable. We refer to this variable as the log employment ratio. Our presumption is that the more positive
EFFECTS OF EXCESS SUPPLY ON WAGE RATES the value of this variable is, the more likely that there is crowding pressure in female labor markets. Values of the state-specific variables (before taking the logarithms) are shown in Appendix B. Key Explanatory Variables The explanatory variables of key interest are the state-specific log population ratio and log employment ratio variables, the log of the state average for the earnings of men ages 25 to 45, and individual-specific vari- ables for the number of years of schooling and potential labor market experience (age minus years of schooling minus six). Our expectation is that groups of women with more negative values for the coefficients of the log population ratio and log employment ratio variables will also have smaller coef- ficients for the years of schooling and po- tential work experience variables and for the state-specific average male earnings variable. Unfortunately, the relationship between the potential work experience variable in this study and work experience of the sort that might be reflected in higher wages is tenuous. For any given woman, suppose we denote the value of the potential ex- perience variable by PEXP and the actual number of previous years in which the woman worked by EXP. Then, EXP = PEXP L where, L denotes the number of years in which the woman was not employed or in school (plus any discrepancy between com- pleted years of schooling and the number of years required to reach that level of educational attainment). Our data source contains no information concerning the val- ues of EXP or L. That is why we use the potential experience variable. To the extent that the average values of L differ across the cli~erent occupational and demographic groups of working women, 75 there will be systematic differences in the estimated constant terms for the log wage equations for the different groups of women. Because the estimate<] constant terms play no role in our subsequent analysis, this is not a serious problem. Within the groups of women, however, there may also be correlations between the inclividual values of the omitted variable L an(l the values of the potential experience variable. Corre- lations of this sort could contribute to es- timates of the effects of an additional year of work experience on a woman s wage rate that are systematically too high or too low. Moreover, these biases could differ system- atically among the various groups of women. As a consequence, results obtained in this study concerning the return to years of work experience await confirmation from further research basec] on a data source containing information about actual past work expe- rience. In adclition to the variables discussed, we inclucle several other explanatory variables in our log wage equations. To capture in- dustry-specific demand effects on each working woman s wages, we include an in- dustry-specific deman(l index clefined as the log of the earnings of female workers ages 20 to 24 in the woman s industrial group clividecl by the average earnings of all wom- en ages 20 to 24. (Values for this variable are shown in Appendix C.) We include an individual-specific variable for the number of chiIclren ever born (except in the equation tor women with no chilciren), as well as dummy variables for race (black or nonblack, except in the equations for black women and for nonblack women), for whether a woman has ever been marrie(l, and for whether a woman was working 5 years ago (except in the equations for women who were working 5 years earlier and for those who were not). Finally, in the log wage equations for the separate demographic groups, we also inclu(le a set of dummy variables for the eight occupational cate-
76 gories developed from the census codes (see Appendix A for the definitions of these occupations). EMPIRICAL RESULTS The main estimation results of this stucly are displayed in Tables 3-2 through 3-4. We present descriptive statistics and regression results for log wage equations for working women ages 20 to 24 classified by occupation and personal characteristics. Occupation The mean years of education and of po- tential labor market experience for women ages 20 to 24 in each of the eight occupa- tional categories are shown in the first two rows in Table 3-2, with the occupations ordered from lowest to highest in terms of mean years of schooling. From the mean wage figures shown in the third row, women in the top four occupations in terms of eclucational qualifications (managerial; health, excluding doctors and dentists; professional/ technical, including doctors, dentists, and university teachers; and teaching, excluding university teachers) are better paid, on av- erage, than women working in the personal service, other clerical, secretarial, and sales occupations. From the next two rows of the top panel, women working in the four occupations with the highest educational requirements are less likely to have wage rates below $2.50 (approximately the mean wage rate for a secretary) than women working in the four other occupations, and they are less likely to have wage rates below $4. 50 than women working in the personal service, other cler- ical, and sales occupations. On human cap- ital grounds, one would expect the wage rates to be higher for the occupations with higher educational qualifications even if workers in all occupations received the same returns, on average, to additional e(luca- tion. As expected, however, the top two PAY EQUITY: EMPIRICAL INQUIRIES rows of coefficient estimates in the middle pane! of Table 3-2 show that returns to both a(lditional years of schooling and adclitional years of labor market exposure are also generally higher for the four occupations in which the average educational levels of the work forces are higher. Ignoring the teaching occupation, the coefficient estimates for the log population ratio and the log employment ratio are not significantly different from zero for the oc- cupations with higher educational require- ments, but one or the other of these two coefficient estimates is statistically signifi- cant and negative for each of the four occu- pations with lower educational require- ments. These findings are in line with a priori expectations that crowding effects on wage rates will tend to be more severe in occupations in which the educational level of the workers is lower. The results for the teaching occupation seem to confirm that our method for identifying crowding effects will not work for those few occupations for which changes in the size of the specifie demographic group, or in the labor force participation rate of that group, are directly linked to changes in the numbers of jobs available in those occupations. The coefficient estimates for the female and male unemployment rate variables are shown in the fifth ant] sixth rows of the micIdie panel of Table 3-2. With none of the coefficients of the male unemployment rate variable being significantly different from zero, there is no evidence of adverse effects on female wage rates in any of the eight clesignated occupations as a result of men in slack mate labor markets competing for jobs usually or sometimes filled by wom- en. The coefficient estimates for the female unemployment rate variable are only sig- nificantly negative for the high-education professional/technical an(l teaching occupa- tions. Looking at the last row of coefficient estimates in the mi(ldle panel, female wage rates do move together with male earnings,
EFFECTS OF EXCESS SUPPLY ON WAGE RATES over states, for all of the designated occu- 77 over, different sorts of jobs within the same patrons except personal service, sales, and broad occupational group tend to be filled health. The health occupation has relatively by different sorts of workers. high average educational requirements, and To examine the possibility that, regardless it is not one of the occupations for which of occupation, certain types of women are we have found significant crowding effects more likely to end up in those jobs in which on wage rates. The other clerical and see- accumulated human capital is less rewarded retarial occupations are ones with relatively and in which wages are more subject to low educational requirements, end they are crowding effects, we estimated log wage also occupations for which we have found equations separately for women with less evidence of crowding effects on wage rates. than 12 years of schooling and for those In the bottom pane! of Table 3-2 we show with at least 12 years, for women with the predicted wage impacts of changes in children and for those with no chiTclren ever thelog population ratio,thelog employment born, for black women and for nonblack ratio, and the male average earnings variable women, and for women who were working associated with "moving" a woman who was 5 years earlier and for those who were not earring the average wage for her occupation (Table 3-3~. From the first row of Table from South Dakota to Oregon. According 3-3, women with children, black women, to our measures of excess supply conditions anc] (obviously) women with less than 12 in female labor markets, there is less crowd- years of schooling have lower mean edu- ing in Oregon than in South Dakota. Also, cational levels than the other groups of average earnings for men ages 25 to 45 are women. From the wage statistics presented almost $5,000 higher in Oregon. We chose in the top panel, it is clear that the women these two states to demonstrate the mag- in these three groups, as well as those nitude of the estimated wage gains associ- women who were not working 5 years ear- ated with a move from a poorer into a more tier, are more poorly paid in general than favorable labor market for female workers. the women in the other groups. Due to the crude nature of our proxies for T ~ · . .1 a. excess supply conditions, our rudimentary understanding of how wages are cleter- mined, and a variety of possible statistical problems with our estimation results, how- ever, these predictions may be very im- precise. Personal Characteristics One problem with looking for crowding and other related wage effects on an oc- cupation-by-occupation basis is that each one of our broad occupational groups ac- tually contains a range of jobs with differing educational requirements and other barriers to entry; differing pay scales, including dif- fering institutionalized practices governing wage growth with increased seniority; and differing balances of bargaining power be- tween employers and employees. More- Looking now at the first two rows of coefficient estimates in the misfile pane} of Table 3-3, women with less than 12 years of schooling, women with children, black women, and women who were not working 5 years earlier also earn lower rates of return on additional years of schooling or for acI- (litional years of potential labor market ex- perience, or both. These are the groups of women, too, for whom the estimated coef- ficients of the log population ratio are sig- nificantly negative. Moreover, the estimat- e(l coefficients for the female unemployment rate variable are consistently negative for these four groups, and they are statistically significant for both women with less than 12 years of education and women with chil- lren. Again, there is no evidence of adverse effects on female wage rates from men in states with higher unemployment rates com-
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84 peting with women for jobs. (The coefficient estimates for the male unemployment rate are positive, not negative.) Also, as ex- pected, the coefficients for the male earn- ings variable are insignificantly different from zero for those groups of women found to be most subject to adverse crowding effects. In the bottom pane! of Table 3-3 we show predicted impacts for selected variables. The results presented in Table 3-3 raise a number of questions. For instance, would the wages of women with less than 12 years of schooling still be relatively unresponsive to human capital differences and the level of male earnings, but vulnerable to crowding effects, if we limited this low-education sam- ple to nonblack.women? From the results presented in the second column of Table 3-4, the answer to this question is probably yes. Based on the results in Table 3-3 we might also wonder if the wage rates of women with children would be found to be more responsive to human capital variables and the state level of male earnings, and less responsive to crowding effects, if we limite(l the sample of women with children to nonblacks. Comparing the coefficient es- timates in the fifth column of Table 3-4 with those in the second column of Table 3-3, the answer to this question is probably yes with respect to the human capital effects and probably no with respect to the other effects. In Table 3-3, the wages of black women are adversely affected by crowding and un- related over states to the average level of mate earnings. From column 3 of Table 3- 4, these conclusions still hold even if we limit the sample of black women to those with at least 12 years of schooling. Finally, we might wonder whether we wouIc] still find evidence of crowding effects on the wage rates of young women who were not working 5 years earlier if we limitecl this sample to those who are nonblack and chfl~- less. From the coefficient estimates in col- umn 7 of Table 3-4, the answer to this question is yes. In general, the conclusions PAY EQUITY: EMPIRICAL INQUIRIES reached on the basis of Table 3-3 seem to be borne out by the findings for more de- tafled groupings of women presente(1 in Table 3-4. ALTERNATIVE CAUSAL EXPLANATIONS In the empirical literature on female work behavior, log wage equations similar to those estimated in this study are viewe(l as re- flecting the wage offers macle to women with specified productive attributes in com- petitive labor markets in which the wage distributions are determined by marketwide supply and demand conditions not subject to the control of in(liviclual employers or labor force participants. Thus, these equa- tions usually include both individual- specific explanatory variables, such as years of schooling, and certain marketwicle vari- ables, such as the state or county unem- ployment rate (see, for instance, Heckman, 1981; Nakamura and Nakamura, 1981~. The estimate(1 coefficients of the marketwi(le variables are presumed to represent the responsiveness of the female wage (listri- bution, and hence the responsiveness of the wage offers received by indivi(lual women, to marketwide supply and demand condi- tions reflected in the values of the mar- ketwide variables. The log wage equations estimate(l in this study contain a number of marketwide variables. It has been argued that negative coefficient estimates for two of these variables, the state-specific log pop- ulation and log employment ratios, are in- dicative of negative crowding effects on the wage rates of working women. Could it be, however, that the log em- ployment ratio variable is serving as a proxy for other sorts of impacts on the wage dis- tributions of women ages 20 to 24? It is often argued that the probability that a woman Will work is positively related to the wage offers she receives. If marketwide supply-side effects of this sort were strong enough, there would be a tendency for the
EFFECTS OF EXCESS SUPPLY ON WAGE RATES values of the log employment ratio to be higher in states where wages are relatively high for women ages 20 to 24 versus women ages 25 to 29. As a result, the coefficient estimates for this variable might turn out to be insignificantly different from zero or significantly positive, even though there are also negative crowding effects on female wage rates. Because of their positive effects on wage rates, however, supply-side fee(l- backs of this sort could not contribute to an erroneous conclusion that there are crowding effects. On the other hancI, it has also been argued in the literature that employers make min- imal specific training investments in women and, therefore, are generally willing to sub- stitute younger for older female workers (or vice versa) in order to minimize labor costs. If marketwide demand-side effects of this sort were sufficiently strong, there would be a tendency for the values of the log employment ratio to be lower in states where wages are relatively high for women 20 to 24 versus 25 to 29 years old. As a result, the coefficient estimates for the log employment rate variable might be found to be significantly negative even in the absence of crowding effects on the wage rates of female workers. However, when women lose or cannot finct jobs due to the substitution of lower priced female labor from another age bracket, those women are being "crowded out" in terms of employ- ment opportunities. Thus, negative coeffi- cient estimates for the log employment ratio variable can still be considered to be in- dicative of crowding effects in female labor markets, although these crowding effects may be employment rather than wage re- lated. CONCLUSIONS The evidence presented in this study suggests that increases in the female work force, brought about by population increases and increases in female employment rates, 85 have resulted in wage erosion that has been more serious for working women in some demographic and occupational groups than in others. In particular, we find there have been adverse crowding effects on the wage rates of women employed in the personal service, other clerical, secretarial, anti sales occupations, and on the wage rates of wom- en with less than 12 years of education, those with children, black women, and women who are relatively recent labor mar- ket entrants or reentrants (that is, those who were not working 5 years earlier). On the other hancI, we find no evidence of crowding effects for working women who have at least 12 years of education, or who have no children, or who are not black, or who were aIrea(ly working 5 years earlier. Nor do we find any evidence of crowding effects for women working in managerial, health, an(l professional/technical occupa- tions. We conclude that either there is less crowding in the segments of the female labor market in which these women are employed or the wage rates for the sorts of jobs these women have are better protected by institutional and other factors from ero- sion due to excess supply pressures. To the extent that these results reflect reality, it is appropriate to ask what the policy im- plications of these findings might be. Bergmann (1986:128) made the following observation: Every woman now on the women's labor market who would be allowed into a job in the men's market would reduce the pay gap between the sexes. Her move would push the wage scale in the two markets toward equality by increasing the supply of labor to the men's market and de- creasing the supply to the women's. Our empirical results inclirectly support this position. They also lend abided credence to Bergmann's assertion that"continue(l toc- cupationall segregation would make the ef- forts to close the pay gap between women and men a continual uphill struggle." If this is the case, the continued importance of
86 vigorous affirmative action and other pro- grams promoting employment opportuni- ties should not be lost sight of in the political rush to institute pay equity programs. The results of this study also suggest a general need to examine practices and in- stitutions that prevent the development of excess supply conditions in some labor mar- kets or prevent wage declines in the face of excess supply conclitions. Women could seek an expansion of such practices and institutions in female labor markets. The push to increase the representation and power of women in labor unions falls under this rubric. Pay equity programs can also be viewed as an institutionalized concession from employers that may serve to insulate the wages of jobholders in some sectors of the female labor market from excess supply pressures. Affirmative action and equal opportunity programs act to break clown barriers reg- ulating female entry into occupations and cushioning the wage rates of those holding jobs in these occupations against excess sup- ply pressures that might otherwise clevelop. Thus, affirmative action and equal oppor- tunity programs contribute to the devel- opment of a labor market in which excess supply pressures on wage rates spread more evenly and more quickly through all sectors of the labor market and, hence, are borne more equally by all groups of workers. On the other hand, we have characterized pay equity programs as yet another measure for protecting the wages of those who have jobs against potential excess supply pressures. We do not see any incongruity in the fact that groups pushing for improve(1 labor mar- ket conditions for women are supporting both of these types of measures. The goal is clearly to reduce the sensitivity of wages to excess supply pressures in the secretarial and certain other female labor markets, ant] at the same time open up to women a wider range of what have traditionally been male occupations. Finally, we believe that the results of this PAY EQUITY: EMPIRICAL INQUIRIES study convey a cautionary message. We have found that some groups of women are more likely than others to have their wage rates adversely affected by excess labor sup- ply conditions. These results suggest that those implementing pay equity programs for the other clerical and secretarial occu- pations, for instance, must be careful to (resign those programs so that the employ- ment opportunities of groups of women, such as blacks, those with children, and those with low educational levels, are not improperly infringed upon. The other cler- ical and secretarial occupations have offered black women and women with low levels of education some of the most attractive jobs, in wage terms, available to them. If wages rise in these occupations due to pay equity adjustments, competition for these jobs will presumably intensify. What needs to be guarded against is the formal or in- formal institutionalization of educational and other requirements for these jobs that are not dictated by productivity considerations an(l that woul(1 exclude from consideration for these jobs some of the sorts of women who currently rely on them for employment. Policy conclusions are the result of judg- ment as well as logic. As such, they inspire disagreement. In this particular case, more- over, there is bound to be disagreement not only about the particular policy conclu- sions stated, but also about whether any policy conclusions at all can be drawn from a study such as ours. Some will argue, for instance, that no policy implications can be (Irawn from our study because we have not established the extent to which excess supply pressures in female labor markets are the result of vol- untary career decisions rather than a re- flection of sex-relatecl employment discrim- ination. A woman might have become a secretary because of a preference for this sort of work, because she thought this job woul(l blend more easily with homemaking responsibilities than other possible jobs, because this was the best job she could find
EFFECTS OF EXCESS SUPPLY ON WAGE RATES when forced to look for work by economic necessity with little or no career prepara- tion, or because she was rejected for training programs or jobs in her chosen career area (perhaps because of discrimination). Our data source only provides information on a woman's occupation, not on her reasons for being in that occupation. Some economists would argue that the erosion offemale wage rates due to crowding pressures is an appropriate public policy concern only if the crowding can be shown to be largely due to employment discrim- ination against women. Demonstrating this point would probably require a fully artic- ulated model of occupational choice. Thus, the acceptance of this position would cer- tainly forestall any timely (rebate of policy measures intencled to reduce or counter- balance crowding pressures on female wage rates because occupational choice is one of the most poorly developed topic areas in labor economics. A number of other econ- omists are also pushing forward with the analysis of implications of pay equity pro- grams, despite the paucity of empirical ev- idence on key behavioral responses (see, for instance, Beider et al., 1986~. Others would argue that no policy im- plications can be drawn from our empirical results because we have not clemonstrated that crowding effects on wage rates are more severe in female than in male labor markets. It is true that our results do not shed light on the question of whether differences in the severity of crowding pressures in female versus male labor markets are an important cause of the female-male wage gap. But we do not agree that consideration of policies to deal with crowding effects in female labor markets must await evidence that this crowding is due to discrimination or that the wage effects of crowding are more severe for female than for mate workers. Perhaps our position on this question can be clarified by pausing for a moment to consider some of the issues in the health care area that have come to be viewed as appropriate 87 topics for public policy debate and potential government action. Virtually everyone agrees that providing some minimal level of health care for the nation's elderly is an appropriate public concern. This is despite the fact that many elclerly indivicluals are suffering from ailments that are the result of their own voluntary life-style choices (both present and past). There is also special public concern about factors that appear to affect adversely the health status of those in subgroups of the elderly population that have particularly severe overall health problems. There is special concern, for instance, about the ef- fects of high blood pressure and poor nu- trition on the health status of el(lerly blacks. This is the case even though these same factors may have just as severe adverse effects on the health of those belonging to groups for whom the overall health picture is more satisfactory. Demonstrated need provides both the justification for public expenditures on health care for the elderly and the rationale for making extra funding evade tor programs designed to provide special assistance to certain subgroups of the elderly population. The case for public policies to improve female labor market conditions rests on a similar foundation. There is clear evidence that the earnings situation for female work- ers is poor in an overall sense, even though employment and wage conditions for wom- en have improved in recent years. More- over, the poor earnings situation of large numbers of working women causes suffer- ing. There is widespread and growing pov- erty, for instance, among widowed, cli- vorcecI, and separated women and the depenclent children of those women. To us the question is not whether, but how, public policy should be harnessed to improve the economic status of women. We undertook this study in the hopes of improving the understanding of how wages are determined in female labor markets, for it is this un- derstanding that must form the basis for
88 policy discussions on how the wages of working women can be enhanced. ACKNOWLEDGMENTS We are grateful to Francine Blau, Harriet DuTeep, Ronald Ehrenberg, and particu- larly, Arthur Goldberger for their helpful comments on earlier versions of this paper. REFERENCES Beider, Perry C., B. Douglas Bernheim, Victor R. 1985 Fuchs, and John B. Shoven 1986 Comparable Worth in a General Equilibrium Model of the U.S. Economy. Working Paper Series, No. 2090. Cambridge, Mass.: National Bureau of Economic Research. Bergmann, Barbara 1974 Occupational segregation, wages and profits when employers discriminate by race or sex. O'Neill, Eastern Economic Journal 1 :103- 110. 1985 1986 The Economic Emergence of Women. New York: Basic Books. Bielby, William, and James Baron 1984 A woman's place is with other women: Sex segregation within organizations. Pp. 27-55 in Barbara F. Reskin, ea., Sex Segregation in The Workplace: Trends, Explanations, Remedies. National Research Council, Com- mittee on Women's Employment and Related Social Issues. Washington, D. C.: National Academy Press. Blau, Francine 1977 Equal Pay in the Office. Lexington, Mass.: D. C. Heath. Blau, Francine, and Wallace Hendricks 1979 Occupational segregation by sex: Trends and prospects. Journal of Human Resources 14~2~:197-210. Cole, Stephen 1986 Sex discrimination and admission to medical school, 1929-1984. American Journal of So- ciology 92(3~:549-567. Easterlin, Richard A. 1980 Birth and Fortune: The Impact of Numbers on Personal Welfare. New York: Basic Books. PAY EQUITY: EMPIRICAL INQUIRIES Freeman, Richard B. 1979 The effects of demographic factors on age- earnings profiles. Journal of Human Re- sources 14:289-318. Heckman, James J. 1981 Heterogeneity and state dependence. Pp. 91- 139 in Sherwin Rosen, ea., Studies in Labor Markets. Chicago: University of Chicago Press. Nakamura, Alice, and Masao Nakamura 1981 A comparison of the labor force behavior of married women in the United States and Canada, with special attention to the impact of income taxes. Econometrica 49:451-489. The Second Paycheck: A Socioeconomic Anal- ysis of Earnings. Orlando, Fla.: Academic Press. Oi, Walter 1982 The fixed employment costs of specialized labor. Pp. 63-116 in Jack Triplett, ea., The Measurement of Labor Costs. Chicago: Uni- versity of Chicago Press. June The trend in the male-female wage gap in the United States. Journal of Labor Eco- nomics 3:S91-116. Polachek, Solomon 1976 Occupational segregation: An alternative hy- pothesis. Journal of Contemporary Business 5:1-12. 1981 Occupational self-selection: A human capital approach to sex differences in occupational structure. Review of Economics and Statistics 58:60-69. Treiman, Donald, and Heidi Hartmann, eds. 1981 Women, Work and Wages: Equal Pay for Jobs of Equal Value. National Research Coun- cil, Committee on Occupational Classification and Analysis. Washington, D. C.: National Academy Press. Welch, Finis 1979 Effects of cohort size on earnings: The baby boom babies' financial bust. Journal of Po- litical Economy 87:565-597. White, Halbert 1980 A heteroscedasticity-consistent covariance matrix estimator and a direct test for het- eroscedasticity. Econometrica 48:817-838.
EFFECTS OF EXCESS SUPPLY ON WAGE RATES APPENDIX A Definitions of Occupational Groups 89 Group Name 1980 Census Codes Personal service Other clerical Secretarial Sales Managerial Health Professional/technical Teaching 403-4()7, 449, 453, 457, 467, 468 275, 276, 319, 323, 325-329, 335-339, 343-349, 353, 354, 364, 365, 368, 374, 377-379, 383-387, 389 313-315 256, 259, 263-269, 274, 283 004-009, 013-019, 024-029, 033-037 095, 097-099, 103-105, 203-208, 445-447 023, 043-049, 053-059, 064-069, 073-079, 083-089, 113-119, 123-129, 133- 139, 143-149, 153, 154, 164-169, 173-179, 183-189, 193-199, 213-218, 223- 229, 233-235 155-159, 163 APPENDIX B State-Specific Variables Calculated Using Public-Use Sample (5 percent) Data from the 1980 Census Ratio for Women 20-24 Versus Women 25-29 Unemployment Rate Mean Annual Employment Women Men Earnings for State Population Rate 2()-29 20-29 Men 25-25 ($s) Alabama 1.13 .94 .15 .08 15,684 Alaska 1.00 .96 .06 .06 26,031 Arizona 1.22 1.00 .11 .05 15,992 Arkansas 1.21 .98 .10 .06 14,482 California 1.05 .97 .07 .09 18,192 Colorado 1.10 1.01 .08 .06 18,481 Connecticut 1.03 .96 .03 .06 19,442 Delaware 1.16 ~ 1.05 .11 .02 17,140 District of Columbia .92 1.02 .10 .10 14,726 Florida 1.17 .97 .07 .06 16,345 Georgia 1.11 .93 .11 .09 15,731 Hawaii 1.05 1.04 .10 .04 17,284 Idaho 1.64 .89 .07 .08 16,589 Illinois .98 .96 .08 .12 19,227 Indiana 1.03 .98 .10 .12 18,033 Iowa 1.19 .95 .05 .09 17,332 Kansas 1.22 1.00 .10 .09 17,511 Kentucky 1.18 .97 .08 .11 16,934 Louisiana 1.11 .90 .09 .07 17,726 Maine 1.17 .99 .09 .16 12,764 Maryland 1.14 .96 .09 .07 18,963 Massachusetts 1.10 .96 .04 .07 17,576 Michigan 1.01 .98 .12 .19 20,020 Minnesota .98 1.03 .04 .10 18,313 Mississippi 1.48 .92 .16 .12 14,101 Missouri .96 .99 .10 .11 17,080 Montana 1.06 1.00 .08 .16 17,355 Nebraska .81 .96 .02 .01 17,596 Nevada .89 1.03 .04 .04 19,081 New Hampshire 1.57 .97 .02 .08 18,610 New Jersey 1.09 .95 .09 .08 18,815 New Mexico 1.14 .99 .10 .09 16,153 New York 1.06 .91 .08 .12 17,428 North Carolina 1.18 .98 .10 .07 14,314 Continued
so APPENDIX B Continued PAY EQUITY: EMPIRICAL INQUIRIES Ratio for Women 20-24 Versus Women 25-29 Unemployment Rate Mean Annual Employment Women Men Earnings for State Population Rate 20-29 20-29 Men 25-25 ($s) North Dakota .87 1.00 .08 .06 18,091 Ohio 1.06 .99 .11 .14 18,004 Oklahoma 1.01 .95 .05 . 06 16,487 Oregon . 74 .97 .10 .11 18,204 Pennsylvania 1.26 .94 .07 .11 17,506 Rhode Island 1. 12 .98 .02 .07 15,328 South Carolina 1.20 .99 .08 .06 14,243 South Dakota 1.29 1.05 .10 .11 13,565 Tennessee 1. 53 .99 .08 .12 16,166 Texas 1. 07 .96 .07 .04 17,960 Utah 1.16 .94 .07 .04 17,314 Vermont 1.05 1.06 .12 .15 14,394 Virginia 1. 13 .95 .07 .06 16,953 Washington 1. 13 .95 .10 .12 18,439 West Virginia 1.09 .99 .04 .11 15,172 Wisconsin 1. 07 .99 .09 .08 17,171 Wyoming .84 1.07 .03 .02 19,137 NOTE: Natural logarithms of these variables were used in the regressions for which results are reported in Tables 3-2, 3-3, and 3-4. APPENDIX C Definitions of Industrial Groups and Values for an Industry-Specific Relative Earnings Variable 1980 Census Industry-Specific Industrial Group Codes Demand Indexa Durable goods 230-391 1.29 Food and kindred products 100-122 1.02 Textile products 132-152 .92 Other nondurable goods 130, 160-222 1.11 Transportation 400-411, 420-432 1.37 Communications 440-442 1.47 Utilities and sanitary services 460-472 1.28 Wholesale trade 500-571 1.19 Retail trade 580-691 .76 Finance, insurance, and real 700-712 1.21 estate Business and repair services 732-760 1.12 Personal services 761-791 .77 Entertainment and 800-802 .70 . . recreation services Health services 812-840 1.12 Legal services 841 1.31 Educational services 842-851, 860-861 .83 Other services 852, 862-892 .86 Other All remaining codes 1.01 aDefined as the average earnings of women 20-24 in the given industry divided by the average earnings of all women 20-24. The natural logarithm of this variable was included in the log wage equations to account for demand effects.
