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Occupational segregation by sex Schreck, David Donald 1978

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OCCUPATIONAL SEGREGATION BY SEX DAVID DONALD SCHRECK B.A., Grinnell College, 1969 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Department of Economics) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA June, 1978 David Donald Schreck, 1978 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. i I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the Head of my Department or by his representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of Economics The University of British Columbia 2075 Wesbrook P l a c e Vancouver, Canada V6T 1W5 D a t e April 4, 1978 i i Abstract This thesis is an attempt to describe and explain occupational segregation by sex as evidenced by 196l Canadian census data. Previous writers discussed the ethical question of whether men and women should be occupationally segregated or whether they should receive equal pay for equal work. This literature is reviewed. Irrespective of ethical issues, i f men and women are equally pro-ductive but unequally paid, why should a profit maximizing firm hire any but the cheapest labour? This problem is known as Cassel's paradox. Previous attempts to resolve Cassel's paradox included the use of simple supply and demand models, barriers to competition, theories of monopsony, human capital theory and adjustments for quality differences. These approaches are criticized and alternative concepts of discrimination are reviewed. For the purpose of the thesis, statistical discrimination is defined as a situation in which employers draw inferences about productivity from unalterable attributes of individuals even though the attributes are not correlated with productivity. A model of occupational segregation by sex is developed that permits analysis of statistical discrimination. Employers are assumed to hire labour under uncertainty as to its qualifications. Hiring is assumed to involve a cost. Each occupation is characterized by the traits required to perform in the occupation. The probability that a person is qualified for an occupation is assumed to depend on the traits required for the occupation and the person's sex. From i i i these assumptions the derived demand for the male-female employment ratio by occupation is determined as a function of employer invest-ment, male and female wages, and the required traits. Statistical discrimination is said to be indicated i f a trait is significantly related to the male-female employment ratio and yet there is no significant difference in its distribution by sex. A correlation coefficient of 0.78 is found in a relation between the logarithm of the male-female employment ratio and thirteen indepen-dent variables including a proxy for employer investment, the wage ratio, the male-female education ratio and ten traits. The education ratio, included in the regression analysis to adjust for quality differences, has the greatest impact of any variable. Its negative coefficient is opposite in sign to what was expected. It is possible that the negative education coefficient indicates discrimination. Data was not available for the actual distribution by sex for five of the ten traits. Three of the remaining traits, numerical aptitude, spatial aptitude and form aptitude, indicate the presence of statistical discrimination. The need for further research on how stereotypes affect occu-pational segregation is suggested by this study. iv Table of Contents page Chapter 1: Introduction 1 Notes *f Chapter 2: Review of the Literature 5 Normative Issues 5 Supply and Demand 9 Cassel's Paradox 1^  Barriers to Competition 15 Monopsony 16 Human Capital 19 Quality Adjustments 21 Qualified Discrimination 25 Discrimination Coefficients 28 Statistical Discrimination: Signalling 33 Statistical Discrimination: Employer Investment 39 Notes kk Chapter 3$ A Model of Occupational Segregation by Sex 50 Derived Demand 51 Supply 57 Market Clearing 58 Simplifying Assumptions 60 Notes 64 Chapter 4: Construction and Sources of Data 66 Census Data 66 Worker Traits 72 V Table of Contents (continued) page Chapter 4: Construction and Sources of Data Sample Size 84 Notes 88 Chapter 5s Results 91 Data Description 91 Regression 93 Education 96 Employer Investment 99 Aptitudes 100 Temperaments 102 Interests 103 Working Conditions 104 Elasticity 105 Discrimination 108 Stereotypes 112 Conclusions 112 Notes 114 Selected Bibliography 116 Appendix A: Job Content of Census Occupational Classes 120 Appendix B: Data Base 158 Appendix C: Occupational Code by Occupational Class Title 189 vi List of Tables Table page I Midpoints used to calculate Wk^ 69 II Midpoints used to calculate 70 III Means used to calculate E.. 72 IV Worker Traits 7^  V Quantitative Indicators of' GED 76 VI Quantitative Indicators of SVP 77 VII Definitions of Aptitude Factors 77 VIII Quantitative Indicators of Aptitudes 79 IX- Definitions of Temperament Factors 79 X Definitions of Interest Factors 81 XI Quantitative Indicators of Strength 82 XII Actual and Expected Number of Occupational Groups by 92 Occupational Division and by Interval of Male-Female Employment Ratio XIII Estimates of Parameters 95 XIV Means, Standard Deviations and Impact of 96 Independent Variables XV Estimates of Parameters for Measuring Elasticity 107 XVI Median of Difference in Means for Paired Samples of 109 Boys and Girls of General Aptitude Test Battery Scores v i i Acknowledgements This thesis would not have been completed without the financial assistance I received from the University of British Columbia and from the Canada Council. I appreciate receiving a University of British Columbia Graduate Fellowship for the years l°69-70 and 1970-71. I appreciate receiving a Canada Council Doctoral Fellowship for the years 1971-72 and 1972-73. -1-Chapter 1: Introduction The purpose of this study is to describe and explain occu-pational segregation by sex as evidenced by 196l Canadian census data. Many highly emotional issues arise in discussions of women's role in the labour market. Chapter 2 begins by reviewing some of the normative issues that surround the question of whether men and women should be occupationally segregated. Normative issues may be irrelevant to profit maximizing firms. The usefulness for our pro-blem of simple supply and demand models is examined and rejected. A central question in any examination of occupational segregation by sex, Cassel's paradox, is then reviewed."1" If the men and women who enter an occupation are equally qualified for employment but un-equally paid, why would any profit maximizing firm hire any but the cheapest labour? Chapter 2 examines a variety of approaches to Cassel's paradox. The possibility of restrictions upon perfect competition as the result of either union activity or cultural norms is discussed. Other ap-proaches include models of monopsony and of differing investment in human capital as well as statistical adjustments that reduce apparent wage differences. None of the approaches is free from criticism. The alternative of discrimination is then examined. If the profit motive is subordinate to other motivation, Cassel's paradox could be resolved by the presence of discrimination. Consideration of discrimination poses three problems. First, the use of the term discrimination carries with it a negative connotation. Opinions may vary on whether discriminatory behaviour is in any sense "bad" even though the behaviour satisfied the definition given for discrimination. We must exercise extreme care so as not to allow normative issues to cloud our judgment on whether the criteria of any definition are satisfied by the data we observe. Second, opinions vary on what constitutes discrimination. Three alternative concepts are reviewed in chapter 2. Third, any weakening of the profit motive constitutes an extremely strong assumption. If any single firm is motivated only by profit i t can be expected to eliminate its less profit conscious competitors. It is true that economists have con-sidered other motivations for firms, but this has usually involved 2 relaxing the assumption of perfect competition. An assumption is not necessarily wrong merely because it is strong. We have no trouble accepting that the profit motive is subordinate to statutes such as the criminal code. It is possible that some cultural basis of dis-crimination is more pervasive than the force of law. The model that is developed in chapter 3 is based on a simple model of derived demand suggested by Kenneth Arrow's review of models 3 of discrimination. This approach employs a concept of statistical discrimination. In this context statistical discrimination refers to a situation in which employers draw inferences about productivity from unalterable attributes of individuals, e.g. race or sex, even though the attributes are not correlated with productivity in the population. In chapter 3 Arrow's simple model is extended to consider many different occupations. Each occupation is identified by the quanti-- 3 -tative indicators of factors considered necessary for effective job performance in the occupation, e.g. strength. The model devel-oped in chapter 3 does not restrict the value of the elasticity of substitution between men and women. This is a direct result of the use of Joan Robinson's concept of efficiency labour as a means of aggregating labour inputs. Efficiency labour is the aggregate or output of a process that has male and female labour as its inputs. Chapter 4 describes the data base used in this study and the pro-blems presented by the data base. A major difficulty in any study of occupational segregation by sex is to find data on employment and wage rates broken down by sex and occupation. An additional difficulty for this study is to find data on the distribution by sex of quantitative indicators of factors considered necessary for effective job performance. The model developed in chapter 3 provides the rationale for re-gressing the male-female employment ratio on the female-male wage ratio and a variety of quantitative indicators considered necessary for ef-fective job performance. Chapter 5 presents the numerical results of this regression analysis. A correlation coefficient of 0.78 is found in the relation between the logarithm of the male-female employment ratio and nine quantitative indicators. We say that the evidence indi-cates the presence of statistical discrimination i f a quantitative in-dicator is statistically significant in the regression and yet the available evidence shows no difference in the indicator's distri-bution by sex. No evidence is found on the distribution by sex of five of the indicators. The evidence on three of the remaining four indicators is consistent with the presence of statistical discrim-ination. -4-Chapter 1: Notes 1. Gustav Gassel, The Theory of Social Economy (New York: Harcourt, Brace and Co., 1924), Joseph MacCabe (ed.), p. 315• 2. Joan Robinson, The Economics of Imperfect Competition (London. MacMillian & Co., Ltd., 1965). 3. Kenneth Arrow, "Some Models of Racial Discrimination in the Labor Market," RAND Corporation research memorandum RM-6253-RC, multilith, Santa Monica, Feb. 1971. 4. Robinson, 0p_. Cit.. p. 332. - 5 -Ghapter 2: Review of the Literature The forces that determine whether the labour force is occu-pationally segregated by sex may have little to do with the tra-ditional realm of economic anlysis. The forces that determine interaction between the sexes may dwarf the "invisible hand" of the market. The attitudes that a woman's place is in the home and that a man must be his family's breadwinner may be more powerful than the profit motive. In any event we begin our analysis of oc-cupational segregation by examining the debate over whether men and women should be occupationally segregated. Normative Issues The ethical debate pitted proponents of individual rights against pragmatists whose concern focused on the male's responsi-bility for family support. Positive economic analysis was rare in this debate. The principle of equating wage rates to the value of each sex's marginal product was recognized. However, emotions flared over differing assessments of these marginal products. John Stuart Mill and Harriet Taylor Mill were among the first writers to argue for the rights of women. In his classic essay, "The Subjection of Women" (I869), J.S. Mill argued for a "principle of perfect equality" with, regard to access to jobs?" Mill maintained that women are excluded from many occupations simply "because the generality of the male sex cannot yet tolerate the idea of living 2 with an equal". Later writers were much more pragmatic than Mill in their ap-proach to women's rights. Sidney and Beatrice Webb feared that women "blacklegs" would undercut male wages.J The Webbs argued for a policy of equal pay rates between the sexes so as to eliminate female com-petition. They indicated that, since women were inferior workers (sic), equal wages would lead to occupational segregation as the employers found the male workers more profitable. The widespread employment of women during World War I in tra-ditionally male occupations provided new evidence on women as workers, and reopened the debate on women's rights. Eleanor F. Rathbone ar-gued that "if re-erected they (barriers to female employment) will have to be based frankly upon the desire of the male to protect him-self from competition, and no longer upon the alleged incapacity of the female to compete". Rathbone then addressed her arguments to the question of whether "fair competition" between the sexes is pos-sible without "undercutting male standards of pay".^ She argued that, If the wages of men and women are really based upon funda-mentally different conditions, and i f these conditions can-not be changed, then i t would seem that fair competition between them is impossible, and that women are the eternal blacklegs, doomed despite themselves to injure the prospects of men whenever they are brought into competition with them... If that were really so, then it would seem as i f men were justified in treating women, as in practice they have treated them — as a kind of industrial lepers, segre-gated in trades which men have agreed to abandon to them, permitted to occupy themselves in making clothing or in doing domestic service for each other, and in performing those subsidiary processes in the big staple trades, which are so monotonous or unskilled that men do not care to claim them.7 In the above argument Rathbone maintained that men would not enter the lower paid occupations since they must seek a wage suf-ficient to support a family. Without the burden of supporting a family women would supposedly be able to accept the lower wages. A difference in the minimum wage acceptable to men and women can be interpreted as a difference in the supply curves of male and female labour. Rathbone concluded her argument by recommending a government financed family support scheme. She maintained that with such a scheme the conditions facing men and women would be sufficiently g similar so as to permit men and women to compete with each other. In other words, Rathbone assumed that the male and female labour supply schedules would then become identical. Like the Webbs Rathbone suggested that female workers may not be as efficient as male workers. Competition in her view would equalize efficiency wages but not absolute wages. She wrote ... any permanent recognized disadvantage that adheres to women workers as such should be allowed for as a pro rata rate reduction in their standard rates. The attempt to establish strict arithmetical equality between them goes further than is necessary to protect men against unfair competition and really weights the scales against the women.9 In replying to Rathbone's article, Millicent G. Fawcett ignored the central thesis of the responsibility for family support and con-centrated on Rathbone*s suggestion that women are less efficient than men. Fawcett stated "In reading Miss Rathbone's article I cannot help feeling that she too much disregards the tremendously depressing effect on women's wages of the prewar trade union rules, combined with social use and wont which have kept women out of nearly all the skilled industries".1^ Fawcett went on to argue that "War experience ...has stiffened the conviction of many feminists that a large pro--8-portion of supposed feminine disadvantages exist more in imagination than in reality". 1 1 Fawcett concluded that "The one chance of women "being received into industry by the men already employed as comrades and fellow-workers, not as enemies and blacklegs, is in their stand-ing for the principle, equal pay for equal work, or as it is some-12 times expressed, equal pay for equal results". Fawcett'a reply to Rathbone serves to illustrate the emotional nature of the debate on equal pay. There is no substantial dis-agreement between Fawcett and Rathbone. Both maintained that men and women should be paid equal efficiency wages, i.e. wages equal to the value of each sex's marginal product. However, the exchange was provoked by their differing subjective assessments of the rela-tive efficiency of male and female workers. During World War II women once again moved into traditionally male dominated occupations. Once again worried that female blacklegs would undercut male wages, the male dominated unions joined the fem-inists in the post war period in a call for legislation to enforce 13 equal pay for equal work. The Trades and Labor Congress of Canada recommended "that the federal ana provincial governments enact legis-lation to enforce a policy of equal pay for equal work and thus e l i -minate this competition... In 1951i Ontario was the first province in Canada to pass equal pay legislation. The federal government and eight provinces had passed equal pay acts by 1 9 6 l . 1 ^ By 1963, twenty two states had passed equal pay laws in the United States.1^ Pragmatic definitions of equal pay for equal work were necessary for the new laws. Employers found that the spirit of the laws could easily he escaped by creating slight variations in the duties of male and female employees. In Canada, from the time the Federal Employees Equal Pay Act was passed in 1956 until December, 1971» only eighteen individuals registered complaints under the federal act. In none of 17 the eighteen cases was the decision favorable to the employee. The principle of equal treatment between the sexes had finally been ac-cepted more than 100 years after the appeals of J.S. Mill, but this legalistic solution's success in establishing an ethical standard can be questioned. Supply and Demand Even i f men have the additional responsibility of family support why should impersonal market forces respect this responsibility? With the publication of The Economics of Welfare in 1920, Pigou questioned the role of family support. According to Pigou, In order to understand the matter rightly, analysis is necessary. The common idea is that women are normally paid less than men, because men's wages have, in general, to support a family, while women's wages have only to support the women themselves. This is very superficial.*" Pigou argued that the employment and wage rates of men and women are determined by supply and demand. He suggested that two demand equations, one for each sex, could be expressed as functions of the wage rates of each sex. By setting supply equal to demand for each sex, wage rates and employment are determined in Pigou's model. He described the equilibrium situation as follows. Men alone are employed in all occupations where the ratio of their efficiency to women's efficiency exceeds the ratio of their day wages to women's day wages; women alone in all - i n -occupations in the opposite case; and men and women indif-ferently in the marginal occupations in which their respective efficiencies bear to one another the same ratio as their respective day wages. In these marginal occupations, that is to say, the efficiency wages of the two sexes are equal. This equality of efficiency wages means, with certain allowances, equality of piece wages. The principal allowances are, first, a small extra for men because, since, at need, they can be put on night-work and can be sworn at more comfortably, it is rather more convenient to employ them; secondly, a small extra to the more skilful workers, whether men or women, because they occupy machinery for a shorter time than less skilful workers in ac-complishing a given job. In equilibrium the piece wages paid to the members of the two sexes in the marginal occupations are, with these limitations, equal.19 Pigou noted that the number of "marginal occupations" in which both men and women are employed is small. Pigou*s supply and demand model can be criticized for having no predictive power. Valerie Oppenheimer used a model like this in her analysis of the increase in female labour force participation rates 20 in the United States. Oppenheimer argued that the demand for 21 workers tends to be sex-specific. Taking the occupational com-position of each industry as constant, she argued that the changing composition of demand for final goods among industries, especially the increased demand for services relative to goods, resulted in an increased demand for specifically female labour. She then argued that since the traditional unmarried female labour force could not satisfy the demand, married women responded by entering the labour force. Noah M, Meltz also used a model in which he assumed the exist-ence of national labour markets with separate supply and demand schedules for each occupation for his work on occupational shifts 22 in the Canadian labour force. He attempted to determine whether -11-changes in an occupation's proportion of the labour force were pri-marily due to shifts in demand schedules or supply schedules by examining how the earnings in each occupation changed relative to the average of al l occupations given the change in the occupation's share of the labour force. Meltz did not make the rather strong assertion of Oppenheimer that most jobs are sex-typed. Meltz was clearly aware, however, that some occupations have a large pro-portion of women. He paid particular attention to female labour force behaviour when he discussed changes in clerical and service occupations, the only census occupational groups containing more 23 women than men. J When attempting to explain why a large pro-portion of the increase in the female labour force chose clerical occupations between 1931 and 1961, he argued that males and females are good substitutes in clerical work. Since women's wages were less than men's wages, he concluded that the demand for clerical workers was directed towards women. Meltz went on to argue that with the shift in demand from male to female clerical workers the male-female earnings differential in clerical occupations would be expected to narrow. When he found that the earnings differential did not narrow, the prediction's failure was attributed to the large increase in the supply of female clerical workers which acted 24 to depress female wages. The reason why women were attracted to clerical work, according to Meltz, was that female wage rate dif-ferentials between occupations were such as to shift new entrants 25 to the labour force from service occupations to clerical occupations. -12-The supply and demand model was used as a filing system by both Oppenheimer and Meltz. Oppenheimer did not ask whether the increase in female labour force participation rates could be explained by the occupational supply and demand model. Rather she asked which force, supply or demand, played the greatest role in generating the in-creased female labour force participation. Similarly, Meltz ac-cepted the supply and demand model and asked which force, supply or demand, was responsible for the change in each occupation's share of the labour force. "Supply and demand" is not a very satisfactory answer to the question of what determines occupational segregation by sex and male-female wage differentials. At the very least this answer should be accompanied by a comparison of how supply and demand dif-fer by sex. Unfortunately, there has been no successful empirical attempt to specify a complete model of labour markets along the lines outlined here. Given current data limitations, such a model is not likely to be estimated in the near future. Furthermore, the con-ceptual problems inherent in the supply and demand model are even more severe than the data limitations. The short run situations that are observed are disequilibrium situations in which unemploy-ment exists and in which individuals search for jobs in several labour markets simultaneously. The usefulness of the traditional concept of labour supply for each market is questionable in this situation. Pigou commented on male-female wage differentials when equili-brium in an occupational labour market is not attained. He dis--13-cussed the case where the efficiency wage paid to women is less than the efficiency wage paid to men but equal to the wage women could earn elsewhere. Pigou maintained that women's wages should not be increased in such cases since the disequilibrium provides an incen-26 tive for employers to break down traditions and hire women. Furthermore, Pigou argueds ... since, ex hypothesi. they are more efficient, relatively to men, in these occupations than they are in marginal oc-cupations common to both sexes, their entry would necessarily be beneficial to the national dividend. Hence, generally speaking, interference designed to enforce the payment to them of a 'fair* wage, as compared with the wages paid to men in circumstances when this means an unfairly high wage as compared with women's wages elsewhere, would injure the national dividend. 27 Pigou's argument seems to assume that an equilibrium will always be reached in which men and women are equally paid for equal work. Pigou argued that interference to enforce equal pay for equal work would remove the incentives to reach an equilibrium in which such interference would not be necessary. Pigou*s faith that markets will reach equilibrium may not be warranted. There is merit in the old platitude that the only thing that is constant is change. For growth theorists we might add that even change does not change at a constant rate. These commonplace observations are relevant to our problem of examining occupational segregation by sex as evidenced by 1961 census data. It is reason-able to assume that at any point in time we observe a state of disequilibrium. While rejecting the usefulness of simple supply and demand models as described by Pigou we have not yet explained why we should - 1 4 -consider anything other than some form of impersonal market forces. The inconsistency of assuming the existence of both a powerful profit motive and male-female wage differentials while observing anything other than complete occupational segregation by sex is known as 28 Cassel's paradox. Cassel's Paradox With the publication of The Theory of Social Economy. Gustav Cassel appeared to argue that impersonal market forces would dominate any ethical debate. Cassel wrote, But when it is said that it is unjust to pay female labour less than male, it is not clear why the employer who employs both kinds of labour together does not increasingly sub-stitute the cheaper female labour for the dearer male labour. There you have the heart of the question. If the employer -- and we must assume that he acts from a purely business point of view — does not do this, we must conclude that he puts a higher value on the male labour for some reason or other, in spite of the supposed equal work. Practice does not recognize the equality of work that is affirmed by theorists. In the actual demand there is a very definite distinction between male and female labour, and that is the decisive factor.29 Cassel did not refer to this statement as a paradox. In order to consider the statement to be a paradox it it necessary to accept four conditions as existing simultaneously. The four conditions are profit maximization, perfect competition, less than complete segre-gation, and unequal efficiency wages for the sexes. Cassel maintained "The rule is that men and women do different kinds of work in the same 30 trade, and so get different wages. The existence of complete seg-regation (doing "different kinds of work") would mean that there is no paradox. Cassel did go on to discuss the case of less than com-plete segregation when he wrote, - 1 5 -Even when employment seems externally to he much the same, the demand turns for various reasons, to a certain extent, to male labour. There are certainly many solid reasons for this dif-ferentiations though, objectively considered, they need not be reasonable.31 While Cassel mentioned the possibility of demand turning to male labour for "unreasonable reasons" he went on to offer a warning. He warned that "Until we have examined and tested" all explanations "we must not jump at the theory that female labour is generally 32 'underpaid' relatively to male labour. In the remainder of this chapter we will examine alternative explanations for occupational segregation and wage differentials. Barriers to Competition In his 1922 Economic Journal article F.Y. Edgeworth questioned the condition of perfect competition. Edgeworth wrote "There is much force in Professor Cassel's argument; and his conclusion would be perfectly true i f the implied premise, the existence of perfect competition, were true. Like Fawcett, Edgeworth argued that "The pressure of male trade unions appears to be largely responsible for that crowding of women into a comparatively few occupations, which is universally recognized as a main factor in the depression 35 of their wages. Edgeworth's argument can be termed the overcrowding hypothesis. If labour supply were reduced by institutional restrictions for male dominated occupations while supply were increased for female occu-pations greater wage differences by sex would result. It is not so clear, however, why Edgeworth attributes these restrictions to unions. It is possible that other forces determine occupational segregation -16-by sex and that the resulting male occupations are more easily organized by unions. The presence of unions might add to male-female wage differences given segregation, but i t does not follow that unions cause occupational segregation. Cultural norms could provide barriers to competition with the same consequences as those attributed by Edgeworth to unions. In his 1954 study of the sociology of work, Theodore Caplow included a chapter on the occupational segregation of women in the United 37 States. Caplow argued that two specifically sociological factors are necessary to explain female occupational segregation. Women are barred from four out of every five occupational functions not because of incapacity or technical unsuitability, but because the attitudes that govern interpersonal relation-ships in our culture sanction only a few working relation-ships between men and women, and prohibit all the others on grounds that have nothing to do with technology ... There are two themes: (l) that it is disgraceful for a man to be directly subordinated to a woman, except in family or sexual relationships} (2) that intimate groups, except those based on family or sexual ties, should be composed of either sex but never of both. 38 Caplow concluded by arguing that the same forces which produce occupational segregation also act to prevent women from organizing 39 so as to improve their status. While we cannot dismiss the impor-tance of cultural norms we must consider their conflict with the profit motive and the resulting paradox posed by Cassel. The book jacket to the paperback edition of Caplow's work refers to it as a sourcebook of hypotheses. Unfortunately, they are untested hypo-theses. Monopsony P. Sargent Florence challenged Edgeworth's argument that the - 1 7 -main reason why women's efficiency wages are lower than men's is that /fe-male trade unions crowd women into "comparatively few occupations". Using 1921 census data for England and Wales, Florence attempted to estimate the number of women available for employment. He argued that the female labour supply schedule has a low flat portion and then rises steeply. Florence concluded as follows: The fundamental factor is thus the women's supply-price curve, and this probably rises steeply after a certain amount of 'self-low-priced' labour has been absorbed. Taken in conjunction with elements of unilateral mono-poly, indivisibility and immobility in the women's labour market, it is this peculiar supply-price curve that explains, in my view, Cassel's paradox of women's nonsubstitution for men in spite of their apparent better value for money.^ 1 Florence's argument was later expressed more clearly at a theoretical level by Joan Robinson. Robinson demonstrated that a monopsonistic firm that faces separate labour supply curves for men and women will minimize cost by hiring each sex to the point where the marginal cost of labour for each sex is equal to the value of the marginal product for each sex. Consequently, even i f men and women are equally productive, male wages will exceed female wages in this model i f the elasticity of supply for males exceeds the 42 43 elasticity of supply for females. Recently Edmund S. Phelps J 44 and Dale T. Mortensen have introduced a concept termed "dynamic monopsony power". An individual firm is said to have dynamic monop-sony power when it must increase its offered wage rate in order to attract labour faster than other firms. Dynamic monopsony power does not depend on the properties of labour supply schedules as does monopsony power in the sense used by Robinson. In the long run the firms modeled by Phelps and Mortensen face perfectly elastic -18-labour supply schedules. In the short run a firm must pay a higher price i f it is in a hurry. While this is a useful concept for dynamic models it is not helpful for examining male-female wage differentials. The traditional concept of monopsony power used by Florence and Robinson is not a persuasive explanation of male-female wage differentials for three reasons. First, it is difficult to believe that the model would apply to anything more than small isolated labour markets like company towns. It seems doubtful that monop-sonistic conditions exist in the large urban areas where most people are employed. Florence took the existence of monopsonistic con-ditions for granted. Second, the theory calls for a clear concept of a labour supply schedule for each occupation. The usefulness of traditional concepts of labour supply schedules for each occupation may be questioned. Certainly, in the short run, what is actually observed is a disequilibrium situation with unemployment and with individuals searching for jobs in several occupational labour markets simultaneously. Florence did not attempt to quantify occupational labour supply schedules. He merely attempted to estimate the pro-portion of women in the total population that might be available as workers. Even i f such schedules exist there is no reason for assuming that the elasticity for males will exceed the elasticity for females. Although the male breadwinner role may imply a greater elasticity the same role may encourage organization to limit the elasticity and increase wages. Third, in order for the theory to be empirically useful an operational definition of the concept of -19-monopsony is needed. The search for such a definition may be sub-jected to all the criticisms that have been applied to operational definitions of monopoly. J In particular i t is necessary to define the extent of the market. A particular job title may be unique to an individual firm. This does not mean that the firm is a monop-sonist with respect to that job title. The appropriate labour supply schedule might pertain to an entire occupational class that includes the job title unique to one firm. Human Capital Cassel's paradox might be explained by relaxing the assumption of equal productivity for the two sexes. Jacob Mincer has offered the following speculation on whether human capital investment de-cisions by the employee affect occupational segregation. Some of the differences between earnings distributions of males and females are explainable by the effects of labour supply behavior on human capital investment decisions. Individuals who expect to spend only a part of their adult lives in the labour force have weaker incentives to invest in forms of human capital which primarly enhance market productivities than persons whose expected labor force attachment is permanent. Women are likely to invest less than men in vocational aspects of education and particularly in on-the-job training. Interpretation of Mincer's speculation is dependent on what constitutes "weaker incentives to invest". The basis of his state-ment is that i f a given investment in human capital increases hourly earnings by the same amount for men and women, i t will increase life-time earnings of an average woman by a smaller absolute amount than the life-time earnings of an average man. Mincer might mean that this lower rate of return on a fixed investment is a weaker incentive for women to invest. Or he could mean that i f the utility of a given absolute increase in life-time earnings is the same for both sexes, there will be a lower incentive to invest for women. However, our problem of labour supply behaviour is not simply the . determination of how much education or training is purchased by men and women. It is, how do men and women choose between hundreds of different occupations. Therefore, Mincer's reference to incentives might refer to comparisons between occupations. In comparing occu-pations that require a high investment with :occupations that require a lower investment, a man might have more incentive to invest than a woman. This does not indicate how a woman would behave relative to other women. When determining their labour supply, women would compare the expected present value of the income streams of different occu-pations open to women. Mincer implies that women might be unwilling to forgo present earnings in order to invest in on-the-job training since they will not be in the labour force long enough to collect the greater but postponed earnings. By contrast, for the same investment, the present value of male earnings would be greater than female earnings since men expect to be in the labour force longer than women. However, the present value of male earnings is not relevant to a woman's decision to enter an occupation. What is relevant is the present value of the earningistreams from other occupations that the woman would consider. For these alternative occupations, i t might again be true that discounted female earnings are less than discounted male earnings. Let M_ (F_) be the present -21-value for males (females) of the income stream from an occupation that requires on-the-job training. Let M (F) be the present value for males (females) of the income stream from an occupation that does not require on-the-job training. Then, it is possible that M^/M « F^/F. In this case, i t is possible that women are equally as likely as men to invest in on-the-job training. Mincer's con-clusion that women are less likely to invest appears to depend on "incentive to invest" referring to a comparison of the rate of return or the utility of an income stream between the sexes rather than referring to a comparison of alternative occupations for each sex. Quality Adjustments If sex differences in education and training are not sufficient to resolve Cassel's paradox perhaps other sex differences might. Several studies have attempted to measure male-female wage differ-entials while adjusting for factors that might affect productivity. This approach takes occupational segregation as a given; however, Cassel's paradox would be resolved i f i t could be shown that wage differences by sex are due to differences in productivity. In 1964, Henry Sanborn made an attempt to quantify male-female wage differentials while compensating for differences between the sexes that might affect productivity. Sanborn constructed indices of the ratio of female to male wage and salary income. A Paasche index of the form (EQ^ YpASQ^ Y^ ) and a Laspeyres index of the form (2QmYf)/(SQmYiii) were constructed. Summation is over census occu-pations. Qp (Qj^ ) is the number of women (men) in an occupation. -22-Yf (Y ) is the median annual wage and salary income for women (men) in the occupation. Sanborn found, using 1950 U.S. census data, that the unadjusted ratio of median female to median male wage and salary income was O.58. Adjusting for occupational distribution through the construction of the indices, Sanborn found a ratio of 0.64 for the Paasche index and a ratio of 0.66 for the Laspeyres index. By taking the ratio of his indices to the aggregate ratio (O.58) he argued that 10.3 to 13.8 per cent of the aggregate male-female wage differential could be explained by observing that men occupy the higher paid occupations. Sanborn then adjusted his indices for differences between the sexes in hours worked, education, age, urbanness, and race. The adjustment consisted of multiplying the female wage in each occupation by an adjustment ratio for the occupation. For example, the adjustment ratio for hours worked consisted of the ratio of hours worked by men to hours worked by 48 women. After adjusting for the factors listed above, Sanborn found a female-male wage ratio for the Paasche index of 0.75 and for the Laspeyres index of O.76. The census data were based on 262 occupations. Using Bureau of Labor Statistics' data that pro-vided more occupational classifications than those of the census for operatives and clerical workers, Sanborn further recalculated 49 the ratios as 0.81 and 0.82. Sanborn went on to argue that sex differences in terms of turnover, absenteeism and work experience further reduce the wage ratio so that "discrimination against women, 50 i f it exists at a l l , is under 10 percent". Not only did Sanborn discount the importance of discrimination, -23-but he also argued that discrimination must be on the part of con-sumers and fellow workers. Finding that his ratios, when calculated by broad occupation groups, were not all equal, he argued that em-ployers could not be discriminating because they would discriminate equally across all occupations.This assertion will be challenged in the theory that is developed below. Sanborn accepted the occupational distribution of men and women as given. Throughout the literature on occupational segregation and male-female wage differentials, writers have recognized that the phenomena are determined by many of the same factors. Given the inter-relatedhess of occupational segregation and male-female wage differ-entials, Sanborn's procedure of adjustment for hours of work, edu-cation, age and other factors is questionable. An attempt should have been made to adjust the occupational distributions of men and women for these factors. Sylvia Ostry calculated Sanborn's indices using 196l Census of 52 Canada data. Ostry used mean rather than median incomes for the indices. The unadjusted ratios of female to male wage and salary income were 5^ .2 per cent for all wage earners and 59.3 per cent for full year wage earners. When both annual hours worked and occu-pational distribution were considered, the Paasche index was 67.2 per cent, and the Laspeyres index was 65.6 per cent. After also adjusting for age and education in a manner similar to Sanborn, the indices were 85.0 and 77.5* Unlike Sanborn, Ostry did not attempt any conjectures regarding her calculations. An alternative method of measuring male-female wage differentials -24-while adjusting for factors that might affect productivity was applied by Gideon Rosenbluth and R.A. Holmes in their study of 5 3 academic salaries in Canada. They postulated that academic sal-aries could be expressed as a linear function of a number of com-ponents including region, university size, control of the university (church, state), rank, field, highest degree obtained, and age. Separate regression equations were calculated for each sex using data for virtually all academic personnel in Canada for the aca-demic years 1962/63 and 1965/66. Using the regression equations, male-female wage differentials were calculated while the independent variables in the regression had the same values for both sexes. They found that, "When all other factors except rank are standardized the average female earned $2,100 less than the average male. For corresponding ranks, the female differential was on the average $l,200".-5*f The Rosenbluth-Holmes study was recently replicated by the Women's Action Group at the University of British Columbia. The Group used data pertaining to U.B.C. academic personnel for the academic year 1971/72. They found that when all factors other than rank are standardized the average female earned $3»071 less than the average male. For corresponding ranks, the female dif-ferential was on the average $1,7^ 0.^  Hartley Lewis has provided an attempt to isolate the influence of a factor, that was not considered in the studies mentioned above, on male-female wage differentials. Lewis estimated that for U.S. manufacturing in 1959, differences in labour turnover costs explain - 2 5 -no more than ten per cent of the male-female wage differential. Lewis arrived at this estimate through a two-stage procedure. Using individual data for each of twenty two industries, he regressed wage rates on a host of demographic variables plus a dummy variable for sex. Lewis considered the regression coefficients on the dummy variable for sex to be a measure of the male-female wage differential in each industry. He then further used the twenty two sex coeffi-cients by regressing them on the male-female labour turnover dif-ferential in each industry. The regression coefficient of this stage was interpreted as a measure of the proportion of the aggregate male-female wage differential due to the differential in turnover.^ The studies reviewed above indicate that even when differences between men and women are taken into account women earn less than men. Of course, it is possible that the researchers simply did not consider enough of the differences between men and women. An alternative con-clusion is that women are paid less than men even when productivity differences are considered. If this conclusion is correct, we are left with Cassel's paradox. Why is it that employers do not simply hire the cheaper but equally efficient female labour and thereby increase profits (reduce costs)? Qualified Discrimination Kathleen Archibald conducted an empirical analysis of sex dis-crimination in hiring practices. Archibald recognized the problem of defining discrimination when she stated "Discrimination may be defined in a number of ways, but it always refers to impairing the opportunity of individuals solely on the basis of their membership -26-57 in a particular group". We will refer to discrimination, in the sense used by Archibald, as qualified discrimination. When dis-cussing Archibald's use of the term "discrimination" we^ must add the qualification "with respect to x", where x is the attribute that defines the group. The normative issues mentioned above may pose a problem when we discuss discrimination with respect to sex. They might be more (or less) of a problem i f we were to discuss discrimination with respect to height in the selection of basketball players. As part of her study of women in the Canadian public service, Archibald conducted a selection experiment. In the experiment, 120 volunteer raters were asked to rank and rate five job candidates. The volunteer raters were participants in Public Service Commission training courses. Each rater was given the same five career resumes. Two of the resumes, say A and B, were constructed so that they would be likely to rank first and second respectively. Male names were assigned to four of the resumes. A female name was always assigned to one of the best two resumes but not always the same one. In analyzing the results from the 117 raters who completed the experi-ment, Archibald reported "... it was found that when Male A competed against Female B, he was at the top of the eligibility l i s t 86 per cent of the time, but when Female A competed against Male B, she was 58 at the top only 58 per cent of the time". These results are statistically significant at the level of 0.005 when a Chi-squared 59 statistic is computed. Kathleen Archibald drew the following conclusion from her experiment. -27-In this experiment, a man had a considerably greater chance of coming out on top of the eligibility l i s t than did a woman of precisely equal ability and experience. Discrim-ination against women, whether conscious or unconscious, did affect the candidate evaluations made by this group of subjects."0 Two similar experiments have been conducted in the United States in an attempt to analyze sex discrimination in academic hiring prac-tices. L.S. Fidell asked the heads of psychology departments in the United States to respond to the desirability of each of ten candidates. Four of the ten hypothetical candidates were given female names. In analyzing the 155 forms that were returned (68 per cent of those sent), Fidell reported, "Clear sex differences were obtained in response to the question, at what level should this candidate be offered a position?"^1 The possible levels ranged from full professor to lecturer. In comparing the distributions of men and women among the ranks at which heads said they would be hired, Fidell reported that the probability that the two distributions were the same was less than 0.01 on the basis of the Komolgorov-Smimov test of the 62 similarity of two distributions. Fidell reported that "... the modal level of offer for women was assistant professor, while for men i t was associate professor". Arie Y. Lewin and Linda Duchan conducted a similar experiment in their analysis of the hiring decision in departments of physical science in the United States. While their results were not statistically significant, they reported, "Comparison of responses to the varying quality resumes yielded results which showed a definite tendency for the chairman to prefer an average male over an average female, but to recognize a superior 64 woman". -28-The studies cited above clearly suggest that occupational segre-gation by sex say be determined at least in part by discrimination with respect to sex. In order to apply Archibald's definition of discrimination in the above studies i t was necessary to observe the qualifications of hypothetical individuals. Our problem is to describe and explain occupational segregation as evidenced by 1961 census data. A definition of discrimination and a methodology that depend on assessing individuals are not directly applicable to our problem. Alternative concepts of discrimination can be applied to census aggregates. Discrimination Coefficients The concept of a discrimination coefficient is the basis of Gary Becker's theory of discrimination. In the passage quoted below, Becker defined the coneept as it applies to the types of discrimination that he considered. By using the concept of a discrimination coefficient, it is possible to give a definition of a 'taste for discrim-ination' that is parallel for different factors of pro-duction, employers, and consumers. The money costs of a transaction do not always completely measure net costs, and a DC (discrimination coefficient) acts as a bridge between money and net costs. Suppose an employer were faced with the money wage rate tr of a particular factor} he is assumed to act as i f m^l-fd^) were the net wage rate, with di as his DC against this factor. An employee, offered the money wage rate 1T-5 for working with this factor, acts as i f irj(l-dj) were the net wage rate, with dj as his DC against this factor. A consumer, faced with a unit money price of p for the commodity 'produced' by this factor, acts as i f the net price were p(l-fdfc), with d^ as his DC against this factor. In all three instances a DC gives the percentage by which either money costs or money returns are changed in going from money to net magnitudes: the employer uses i t to estimate his net wage costs, the employee his net wage rate, and the consumer the net price of a commodity.®5 -29-In a trivial sense, Becker's concept of a discrimination coef-ficient can immediately "explain" Cassel's paradox. We need only to assume that discrimination coefficients exist such that employers do not consider the net cost of female labour to be less than male labour. Since the discrimination coefficients are not observable, this expla-nation is not testable unless additional assumptions are introduced regarding either the behaviour of firms or the determination of the discrimination coefficients. Becker introduced assumptions regarding the behaviour of firms in his comparison of competitive and monopolistic industries. He argued that "... competitive industries discriminate less on the 66 average than monopolistic ones ..." Firms with small discrimination coefficients will have lower costs than other firms since they will hire the less expensive but equally efficient labour. These firms will be able to expand and drive out the higher cost, more discrim-inatory firms. Consequently, in the long run, the discrimination coefficient in competitive industries will be equal to the dis-crimination coefficient that is the smallest among competitive firms. In monopolistic industries, there are no pressures that lead to a reduction in the size of the discrimination coefficient. Consequently, he concluded that the discrimination coefficients of monopolistic firms will, on the average, be larger than those of competitive firms. This argument applies to the aggregate. There could be difference in economic rents, say on the advantage of location, that would permit some competitive firms to discriminate more than others even in the long run. In order to test this argument, Becker presented evidence -30-that the number of nonwhltes relative to the number of whites employed by competitive industries was larger than the number employed by monopolistic industries (the relative numbers per-67 tained to Southern U.S. manufacturing industries in 19M0). This evidence was consistent with Becker's argument. Becker introduced assumptions regarding the determination of the discrimination coefficients in his comparison of white and non-white income ratios. He analyzed the median incomes of white and Negro males in 19^9 for all Southern Standard Metropolitan Areas as classified by the U.S. Census Bureau. He found a signi-ficant correlation between the proportional difference of these incomes and the percentage of non-whites in each area. He observed that "One interpretation of this result is that tastes for dis-crimination and thus market discrimination are positively associated with the percentage of non-whites in each SMA (Standard Metro-politan Area). Before accepting this interpretation, it is necessary to separate the income differentials caused by discrimination from 6 8 those caused by differences in economic capacity." Becker then found a significant correlation between the ratio of the median years of school of whites to non-whites and the percentage of non-whites living in each area. He concluded "The proportion of non-whites in a SMA does not seem to have an important effect on tastes for discrimination that operate through the market." He went on to argue that "... since most education is publicly administered, this suggests that in the South political discrimination against non-6 9 whites is positively associated with their relative number." -31-The preceding argument illustrates a weakness in Becker's 70 approach that is similar to the weakness in Sanborn's argument. In both Becker's and Sanborn's arguments it was shown that the addition of previously unconsidered variables to the analysis of wage ratios tends to reduce any residual that might be defined as discrimination. It is more appropriate to say the addition of variables tends to reduce any residual that could be identified as a discrimination coefficient since Becker used this concept rather than a definition of discrimination. This weakness is also illustrated in the recent work of Ronald L. Oaxaca. He argued that discrimination against females can be 71 measured by the following equation. D - ((«A)-(«A) 0)/(«A)' Oaxaca's discrimination coefficient, D, is the proportionate difference between the actual male-female wage ratio, ^^/tf^, and the male-female wage ratio that would prevail in the absence of discrim-ination, ( W ^ / w p 0 . Measurement of discrimination for Oaxaca then required determining the male-female wage ratio that would prevail in the absence of discrimination. Oaxaca obtained this ratio by first regressing logarithms of wage rates separately by race-sex groups on a wide range of variables including experience, education, industry, occupation, marital status, region, and size of urban area. His data were observations on approximately 60,000 individuals from the U.S. Survey of Economic Opportunity that was conducted for the Office of Economic Opportunity by the Bureau of the Census in February, 1967. H e maintained that given the regressions, (Wm/W f.)° -32-72 could be measured in two ways as given by equations 1 and 2. (1) log (W m A f ) ° - bf(M'-P») (2) log (W m A f ) ° -b^M'-F') In the above equations, b^ and b^ are vectors of regression coef-ficients obtained from the regressions using female and male data respectively. Vectors of the mean values of the independent variables in the regressions for men and women are given by M' and F'. Equa-tions 1 and 2 are then supposed to represent what the male-female wage ratio would be i f men and women faced the same wage structures, i.e. i f the independent variables had the coefficients in both the male and female wage equations. Oaxaca maintained that the difference between equations 1 and 2 is an index number problem. Using equation 73 1, he estimated D as 0.32. The estimate was 0.21 using equation 2. Even i f ( W ^ A p 0 as measured by Oaxaca were an estimate of the wage ratio that would prevail in the absence of discrimination, his interpretation of D could be criticized for being little more than a residual. More importantly this illustrates the problem of not clearly defining discrimination. As shown above, Becker defined the concept of discrimination coefficient. However, he did not define what he considered to be "political discrimination" or even "discrimination" when his context did not relate the term to his coefficients. Becker argued as follows. In the sociopsychological literature on this subject one individual is said to discriminate against (or in favor of) another i f his behavior toward the latter is not motivated by an 'objective' consideration of fact. It is difficult to use this definition in distinguishing a violation of objective facts from an expression of - 3 3 -tastes or values. For example, discrimination and prejudice are not usually said to occur when someone prefers looking at a glamorous Hollywood actress rather than at some other woman; yet they are said to occur when he prefers living next to whites rather than next to Negros. At best calling just one of these actions 'discriminationV. requires making subtle and rather secondary distinctions.^* Becker avoided these distinctions by introducing the concept of a discrimination coefficient. However, he failed to present an analysis of how discrimination coefficients are determined. They simply arise due to tastes. Kathleen Archibald maintained that discrimination: always refers to impairing the opportunity of individuals solely on the basis of 75 their membership in a particular group. ^  Becker's example regarding Negros would be discrimination by Archibald's definition (assuming taste affects behaviour). However, Becker's example of a Hollywood actress would not be a case of discrimination according to Archibald. In this example the preference and behaviour of the observer results from observing a particular individual rather than characterizing that individual as one of a group of actresses. Contrary to Becker's assertion i t might be easier to use a concept of discrimination that isolates a disregard for "objective facts" than i t is to use a concept that labels unexplained residuals. Statistical Discrimination: Signalling Edmund S. Phelps, Kenneth Arrow and Michael Spence have developed 76 models that involve a concept called "statistical discrimination". Phelps provided a brief general reference to this concept in his work on Modern Unemployment Theory and he later provided a mathematical 77 extension of Arrow's model. Although both Spence's and Arrow's -34-raodels employ a concept of statistical discrimination, there are important and fundamental differences between the models, Spence wrote that "The term 'statistical discrimination* refers to a situation in which employers draw inferences about productivity from indices, because those indices are correlated with productive capacity in the population."' Spence defined an "index" as an observable unalterable attribute of individuals, e.g. race or sex. He defined a signal as an observable alterable attribute of indivi-79 duals, e.g. education. Spence emphasized that neither signals nor 80 indices necessarily affect productivity. Spence*s work focused on the individual's decision to invest in a signal. His analysis of statistical discrimination was secondary. For reasons developed below we will not use Spence's definition of "statistical discrim-ination". When elaborating on his definition Spence wrote: One suspects, although it would be difficult to prove, that when one mentions informational bases for discrimination, most people think in terms of statistical discrimination. How else could a productively irrelevant index be useful if it is not correlated with anything productively relevant? ... the statistical discriminatory mechanism is not a mis-conception, but it is a drastically incomplete view of how indices function in a market information system. And i t may not be very important ... If productivity is correlated with some index, for what-ever reason, the result will be average wage differentials over groups defined by the index. But this has nothing or almost nothing to do with the informational structure of the market. It would be true in a world of perfect infor-mation, and i t is true in the world where education by i t -self is a signal. Spence emphasized that an index may not affect productivity} but may nevertheless be correlated with productivity. Due to this -35-correlation he is able to conclude that average wage differentials between persons with different levels of an index would exist even without his version of "statistical discrimination". Recall the emotional exchanges during the normative debate between writers 8 2 whose assessments of female productivity varied. Spence seems to have taken for granted that "statistical discrimination" is based on differences in productivity between the sexes (levels of an index). Spence provided the following assumptions and definitions in the development of his model. Let n = an unobservable unalterable personal character-istic which affects productivity, y » an observable, alterable characteristic which may or may not affect productivity (a signal), z = an observable unalterable attribute of indi-viduals (an index), s •» individual productivity, R(s|y,z) - the employer's conditional distribution over s given y and z, c(y,z,n) » the costs of signalling y for a person of type z,n, WR(y) «• the employer's offered wage schedule to levels of y, given the conditional distribution R(s|y,z), A(n) «• the highest alternative net income outside this market for a person of type n. Spence assumed that the employer adjusts his wages offered to each level of y so that (Spence: 1)°^ WR(y) - J's dR(s |y,z) The individual is assumed to select y to (Spencej 2) max ( WR(y) - c(y,z,n) ) y There are several ambiguities in the above assumptions and definitions. Spence did not define "individual productivity". We may assume from his context that in his one-good world there is an underlying production function and that s is the marginal productivity of this production function with respect to n. By "conditional distribution" we may assume he means a cumulative frequency distribution. Spence failed to motivate his assumption (l) regarding employer behaviour. His equation 1 means that the employers sets wages for each level of y equal to the average marginal productivity for persons employed with level y and supposedly varying levels of n. From his assumptions, however, he established a one-to-one relation between y and n for each level of z. We will further question this assumption after examining more of the model. Spence continued his Q C assumptions and definitions as follows. D Let 0 R(n) - max ( WR(y) - c(y,z,n) ) (Spences 3) y Define the set f R - £n|0R(n) • A(n)J (Spence: 4) The set € R determines the set of people who stay in this market. Define: YR(n) - £y|WR(y) - c(y,z,n) - WR(y) - c(y,z,n)} (Spence: 5) Define: \ m £y.yfcYR(n) for some n$ (Spence: 6) -37-Productivity is determined by n, y and z according to s = S(n,y,z) (Spence: 7) Spence*s equation 7 can be substituted into his equation 1 so as to further examine his assumption regarding the behaviour of the employer. We can see that Spence assumed that the employer acts as if he solves this hypothetical integration problem over the unobservable variable n. In neoclassical models of the firm we usually assume that the firm is a wage taker and that the derived demand for labour is determined from the relation that equates the wage rate with the 86 value of the marginal product. Spence did not explain why his employer would adjust the "offered wage schedule" rather than simply terminate unproductive employees. In his model there is no cost or consideration of cost to the employer i f staff are terminated. Spence*s assumption regarding individual behaviour is equally implausible. Solution of the problem posed in equation 2 requires that the individual behave as i f he were equating the marginal cost of acquiring the signal, y, with the marginal income from having acquired i t . It is difficult to believe that the individual would know the properties of the schedule WR(y) let alone the properties of dWR(y)/dy. Of course, the "as i f " assumption could provide an explanation but that leaves a weak foundation for the model. 87 Spence defined equilibrium in his model as follows: ' For all yrAR let B*(s|y,z) be the empirical conditional distribution of s given y and z which turns up in the sample hired. In an equilibrium, for all y£/lR WR(y) = /S(n,y,z)dR(S(n,y,z)|y,z) (Spence: 8) -38-In traditional neoclassical models equilibrium is defined in terms of market clearing mechanisms, i.e. the condition where supply equals demand in all markets. Spence defined equilibrium in his model as the state where the conditional cumulative frequency distri-bution of s, given y and z as imagined by the employer, is identical to the distribution observed by the employer. Spence failed to comment on why we should assume that such an "empirical distribution" would be observable. Even i f R^(s|y,z) were observable Spence failed to discuss why the employer would take the sample that re-sulted from his hiring decisions as more representative of reality than his imagined distribution. He did go on to discuss the dynamic consequences of various adjustment reactions when R(s |y,z) ^ R*(s|y,z) When the questions surrounding the basic behaviour equations in Spence's model have not been addressed, a cynic might regard the dynamic considerations as mere mathematical extensions along the 89 lines of the traditional literature. The stability of equilibrium can be of interest only after the foundations of the model are accepted. It can be argued that a model should not be criticized on the basis of its assumptions but rather on the basis of its predictive power. Spence deduced several propositions from his model; however, no empirical work was performed by Spence to assess his model against his deductions. His first proposition was a demonstration of the existence of equilibrium. This proof depended on the assertion that dn/dy ^  0 and dn/dz £ 0.^Q The assumption that a relation between productivity and an index, z, or a signal, y, -39-existed was clear from Spence*s definition of statistical dis-crimination. This assumption, however, is inconsistent with 91 Spence's definitions of both signals and indices. His concept of productivity depends on an "unobservable unalterable personal characteristic", n, which may or may not be correlated to signals or indices, i.e. dn/dy = 0 and it is possible that dn/dz - 0. Statistical Discrimination} Employer Investment Kenneth Arrow developed a model that also employs a concept 92 that may be termed "statistical discrimination". In addition to 93 discussing discrimination in the form modeled by Becker and in oh. a form similar to Spence*s model, Arrow suggested a third ap-proach. While Arrow did not define the term "statistical discrim-ination" the definition we will use is consistent with the context 95 of his discussion. v In the balance of this study statistical dis-crimination refers to a situation in which employers draw inferences about productivity from unalterable attributes of individuals, e.g. race or sex, even though the attributes are not correlated with productivity in the population. This definition emphasizes an 96 opposite relation than that chosen by Spence, between productivity and an index. Of course, an employer who statistically discriminates would believe that the index was correlated with productivity. Arrow wrote "... what I have referred to as the discriminatory tastes of the employer might in fact be better described as a problem in perception. That is, employers discriminate against blacks 97 because they believe them to be inferior workers." Statistical discrimination would not last long i f employers •40-learned of their misperceptions and adjusted their subjective assessments of the probability that a person is productive so as to reflect the realities of the actual population, i.e. i f equili-brium as defined by Spence were attained. Like Spence, Arrow dis-cussed adjustment mechanisms and suggested that an equilibrium may 98 not be stable. Stability rather than instability might be the actual reason for the persistence of statistical discrimination. If traditional (and therefore stable) cultural values, e.g. a woman's place is in the home, affect the adjustment of employers' assessments of the actual population, the process of adjustment might takes hundreds of years. Arrow commented on the persistence of discrimination as follows: One possible explanation is to be found in theories of psychological equilibrium, such as Festinger's theory of cognitive dissonance. If an individual acts in a discrim-inatory fashion, he would, according to this theory, tend to have beliefs that justify his actions. Indeed, precisely the fact that discriminatory behavior is in conflict with an important segment of our ethical beliefs will, according to this theory, intensify the willingness to entertain cog-nitive beliefs that will supply a socially acceptable justi-fication for this conduct.99 In his essay, "The Subjection of Women", John Stuart Mill anticipated Arrow's observation by one hundred years when he wrote: So long as an opinion is strongly rooted in the feelings, i t gains rather than loses in stability by having a pre-ponderating weight of argument against i t . And there are so many causes tending to make the feelings connected with this subject the most intense and most deeply-rooted of all those which gather round and protect old institutions and customs, that we need not wonder to find them as yet less undermined and loosened than any of the rest by the progress of the great modern spiritual and social transition; nor suppose that the barbarisms to which men cling longest must be less barbarisms than those which they earlier shake off.l°0 -41-Arrow developed a simple model in which he considered two types of labour, unskilled labour (type l) and skilled labour (type 2). Human capital is invested by both the employer and the employee in type 2 labour. Employers do not know whether a parti-cular job applicant has made the investment necessary to become type 2 labour. "An employer cannot know whether or not a worker is qualified, but he holds subjective beliefs about the respective probabilities, to be denoted by p w and pR, that white and black workers, respectively, are qualified". 1^ 1 If the employer hires an individual who is not qualified to receive the employer's investment, the employer will eventually lose the investment when the employee proves incapable of performing type 2 work. The reasons why an individual might not be capable of performing type 2 work are varied. For example, i f the employee has personal problems, e.g. illness or family problems, the employee might be forced to terminate employment. On the other hand, the employee might simply not possess the aptitudes necessary to perform in type 2 work. In either case, the employee must terminate employment. The employer may choose to reduce the risk of losing employees by purchasing knowledge, i.e. by testing job applicants. For example, the U.S. Department of Labor reported that the use of the General Aptitude Test Battery in choosing power-sewing-machine operators reduced their turnover rate from 4.5 per cent per month to 2,0 per 102 cent per month. The cost of testing can be considered to be part of the investment by the employer. The employer faces a tradeoff -42-between increasing investment in each employee and increasing the expected turnover rate. Knowing the technical properties of this tradeoff, the employer can decide on an optimal testing program. This process is not of importance to Arrow's model since the testing costs can be considered as part of the employer investment. Since all uncertainty is not removed by testing, employers may test and then base their conditional expectations of whether an employee is qualified on a characteristic such as race. Assuming that employers are risk neutral, Arrow maintained that the following relation holds in equilibrium: (A) R - ( f 2 - W2w)pw - ( f 2 - W2n)pn Where: R is the return per worker that employers expect to earn on their human capital investments; f ? is the partial derivative of the firm's pro-duction function with respect to type 2 labour; W2 is the white real wage rate for type 2 labour; W2* is the black real wage rate for type 2 labour. Equation A may be rewritten as, ^ W2w^ W2n + ^ f 2 Where: q» p /p . n w Arrow extended this model in a manner similar to Spence so as to consider adjustment mechanisms for p^ and p^. He considered changes in supply that might affect p^ and p^. These extensions require relaxing the assumption that the employer's perceptions 103 are imperfect. J If we maintain the strong assumption that the employer's perceptions are imperfect, we are left with a simple model that can be solved for the derived demand for each type of labour. -43-Edmund S. Phelps has proposed a mathematical extension of Arrow's model. Phelps maintained that a job applicant's "promise or degree of qualification" might be estimated by an employer as a linear function of the applicant's test score, y^, and an error term. He suggested that another error term that is dependent upon the applicant's race could be added to the model. The manner in which the race determined error term enters the model then affects the comparison of predictions of the degree of qualification for 104 applicants of different race. Phelps' proposal is similar to the traditional errors in the variables problem that is discussed in most econometric textbooks. Determining the effects of errors in the variables requires either more assumptions or more data than is required in the simple case of a single additive error term.1^ These requirements render Phelps* proposal impractical as a means for gaining insight for our problem. The basis of Arrow's simple model is used in the next chapter to develop a model of occupational segregation by sex. The model permits analysis of statistical discrimination. -tt4-I T Chapter 2: Notes 1. John Stuart Mill, "The Subjection of Women" (1869) in Alice S. Rossi (ed.), Essays on Sex Equality (Chicago: The University of Chicago Press), p. 125. 2. Ibid.. p. 181. 3. Sidney Webb and Beatrice Webb, Industrial Democracy (London: Longmans, Green, and Co., 19©2), p. 506". 4 . Ibid., p. 507. 5. Eleanor F. Rathbone, "The Remuneration of Women's Services", The Economic Journal, March, 1917» pp. 55-5^. 6. Ibid., pp. 57-58. 7. Ibid., p. 63. 8. Ibid., p. 68. 9. Ibid.. p. 59. 10. Millicent G. Fawcett, "Equal Pay for Equal Work", The Economic  Journal. March, 1918, p. 2. 11. Ibid.. p. 4 . 12. 0p_. Cit. 13. Department of Labour, Equal Pay for Equal Work (Ottawa: Queen's Printer, i960), p. 8. 1 4 . Proceeding 6lst Annual Convention. The Trades and Labor Congress of Canada, resolution number 240 by the Toronto Jewellery Workers Local 33, p. 416. 15. Department of Labour, Equal Pay for Equal Work, p. 8; and Report of the Royal Commission on The Status of Women in Canada (Ottawa: Information Canada, 1970), pp. 68-70. 16. U.S. Department of Labor, Economic Indicators Relating to Equal  Pay (Washington: U.S. Government Printing Office, 1963), p. 1. 17. House of Commons Debates (Ottawa: Queen's Printer), May 5 i 1971» p. 10809. Factors that render the equal pay laws inoperable are dis-cussed in The Status of Women in Canada, pp. 75-7&> 18. A.C. Pigou, The Economics of Welfare (London: MacMillan and Co., Ltd., 1920), p. 521. -45-19. Ibid., pp. 522-523. 20. Valerie Kincade Oppenheimer, The Female Labour Force in the  United States (Berkely: The University of California Press, 1970), pp. 156-157. 21« Ibid., pp. 64-120, p. 157; also see, Valerie Kincade Oppenheimer, "The Sex Labeling of Jobs", Industrial Relations. May, 1968, pp. 219-234. 22. Noah M. Meltz, Changes in the Occupational Composition of the  Canadian Labour Force, 1931-1961. Economics and Research Branch, Department of Labour, Occasional Paper No. 2 (Ottawa: Queen's Printer, 1965), p. 35. 23. Ibid., p. 61. 24. Ibid.. pp. 63-66. 25. Ibid.. p. 66. 26. Pigou, 0£. Cit.. p. 525. 27. Ibid., pp. 525-526. 28. P. Sargant Florence, "A Statistical Contribution to the Theory of Women's Wages", The Economic Journal. March, 1931t P« 37. 29. Gustav Cassel, The Theory of Social Economy (New York: Harcourt, Brace and Co., 1924), Joseph McCabe (ed.), p. 315. 30. Op. Cit. 31. 0p_. Cit. 32. Ibid., p. 316. 33. F.Y. Edgeworth, "Equal Pay to Men and Women for Equal Work", The Economic Journal, December, 1922, p. 431. 34. Fawcett, 0p_. Cit. 35. Edgeworth, 0£. Cit., p. 439. 36. Francine D. Blau and Carol L. Jusenius, "Economists' Approaches to Sex Segregation in the Labor Market: An Appraisal" in Martha Blaxall and Barbara Reagan (eds.), Women and the Workplace (Chicago: The University of Chicago Press, 1976), p. I83. 37. Theodore Caplow, The Sociology of Work (Toronto: McGraw-Hill Book Co., 1964), Chapter 10, pp. 220-247. -46-38. Ibid., pp. 237-238. 39. Ibid., p. 246. 40. Edgeworth, 0p_. Git.. p. 439; also see, P. Sargant Florence, 0|>. Cit. 41. Florence, Op_. Cit., p. 37. 42. Joan Eobinson, The Economics of Imperfect Competition (London: MacMillian & Co., Ltd., 1965), Chapter 26, pp. 292-304. 43. Edmund S. Phelps, "Money Wage Dynamics and Labor Market Equilibrium" in Phelps et al., Microeconomic Foundations of  Employment and Inflation Theory (New York: W.W. Norton & Company, Inc., 1970), P. 131. 44. Dale T. Mortensen, "A Theory of Wage and Employment Dynamics" in Phelps ejt al., Microeconomic Foundations of Employment and  Inflation Theory, p. 182. 45. A.P. Lerner, "The Concept of Monopoly and the Measurement of Monopoly Power", Review of Economic Studies. 1934, pp. 157-175. 46. Jacob Mincer, "The Distribution of Labor Incomes: A Survey with Special Reference to the Human Capital Approach", Journal of Economic Literature. March, 1970t p. 23. 47. Henry Sanborn, "Pay Differences Between Men and Women", Industrial and Labor Relations Review. July, 1964, pp. 534-535. Sanborn's article is based on his unpublished University of Chicago, Ph.D. dissertation by the same title. 48. Ibid., pp. 5^ -550. 49. Ibid., p. 535. 50. Ibid., p. 546. 51. Ibid., pp. 546-549. 52. Sylvia Ostry, The Female Worker in Canada (Ottawa: Queen's Printer, 1968), pp. 39-%5~. 53. Gideon Rosenbluth and R.A. Holmes, "The Structure of Academic Salaries in Canada", The Canadian Association of University Teachers  Bulletin. April, 1967. 54. Ibid., p. 23. 55* Women's Action Group, A Report on the Status of Women at the  University of British Columbia (Vancouver: Talconbooks, 1973). -47-56. Hartley V, Lewis, "The Importance of Turnover Costs in the Male-Female Wage Differential", unpublished Ph.D. dissertation, The University of Rochester, 1970. 57. Kathleen Archibald, Sex and the Public Service (Ottawa: Queen's Printer, 1970), p. 1057" 58. Ibid., p. 204. 59. Ibid., pp. 204-206. 60. Ibid., p. 207. 61. L.S. Fidell, "Empirical Verification of Sex Discrimination in Hiring Practices in Psychology", American Psychologist. 1970, pp. 1094-1098. 62. 0p_. Cit. 63. 0£. Cit. 64. Arie Y. Lewin and Linda Duchan, "Women in Academia", Science, September, 1971, PP. 892-895. 65. Gary S. Becker, The Economics of Discrimination (Chicago: The University of Chicago Press, Second Edition, 1971), PP. 1^ -15. 66. Ibid., p. 47. 67. Ibid., p. 48. 68. Ibid., pp. 123-125. 69. Ibid., p. 126. 70. Sanborn, 0j>. Cit.; also see p. 21 above. 71. Ronald L. Oaxaca, "Male-Female Wage Differentials in Urban Labor Markets", Working Paper No. 23, Industrial Relations Section, Princeton University (mimeographed, 1971), p. 2. This is based on Oaxaca*s 1971 Princeton Ph.D. dissertation by the same title. 72. 0p_. Cit. 73. Ibid., p. 32. 74. Becker, 0p_. Cit.. p. 13. 75. Archibald, 0p_. Cit., p. 104. 76. Edmund S. Phelps, Inflation Policy and Unemployment Theory (New York: W.W, Norton and Company, Inc., 1972); Michael Spence, -48-Market Signaling (Cambridge: Harvard University Press, 197*0; and Kenneth Arrow, "Some Models of Racial Discrimination in the Labor Market", RAND Corporation research memorandum RM-6253-RC, multilith, Santa Monica, Feb. 1971. 77. Edmund S. Phelps, "The Statistical Theory or Racism and Sexism", American Economic Review. September, 1972, pp. 659-661, 78. Spence, Op.. Cit.. p. 104. 79. Ibid.. p. 10. 80. Ibid.. p. 10, p. 119. 81. Ibid., p. 104. 82. See page 8 above. 83. Spence, Op_. Cit.. p. 119. 84. Ibid.; Spence: n refers to the n**1 equation as numbered by Spence. 85. Spence, 0p_. Cit.. p. 120. 86. Milton Friedman, Price Theory A Provisional Text (Chicago: Aldine Publishing Company, 1962), pp. 172-174. 87. Spence, 0p_. Cit., p. 120. 88. Spence, 0p_. Cit.. p. 191. 89. Paul Anthony Samuelson, Foundations of Economic Analysis (New York: Atheneum, 1965), Part II, pp. 257-356. 90. Spence, 0p_. Cit.. p. 121, p. 127. 91. 1 Ibid.. p. 10, p. 119. 92. Arrow, 0p_. Cit., p. 48. 93. Ibid., pp. 29-36. 94. Ibid., p. 50. c 95. Ibid., pp. 21-22, p. 49. 96. See page 33 above. 97. Arrow. Op. Cit.. pp. 20-21. -49-98. Ibid., pp. 48-54. 99. Ibid., pp. 22-23. 100. Mill, Op.. Git., p. 126. 101. Arrow, Op_. Cit., p. 48. 102. U.S. Department of Labor, Manual for the General Aptitude Test  Battery. Section III: Development (Washington: U.S. Government Printing Office, 1967), P. 173. 103. Arrow, Qp_. Cit.. p. 23. 104. Phelps, "The Statistical Theory of Racism and Sexism", pp. 659-66I. 105. J. Johnston, Econometric Methods (Toronto: McGraw-Hill Book Company, 1963), pp. 148-175. -50-Chapter 3 s A Model of Occupational Segregation by Sex There would be no occupational segregation by sex i f the dif-ference in the number of males relative to the number of females between occupations were due only to chance variations. The 1961 Canadian census reported 31752,307 males and 1,528,875 females who worked for wages or salary.1 This aggregate male-female employment ratio of 2A5 can be compared to a ratio of 1001.93 for mine labourers or to a ratio of 0.06 for typists. The purpose of this chapter is to develop a theoretical framework that can be used to explain the variance in the male-female employment ratio by occupation. Economists generally model any market through the use of demand relations, supply relations, and market clearing mechanisms. We will develop a model of derived demand for labour by sex and occupation so as to permit consideration of statistical discrimination. As defined in chapter 2, statistical discrimination refers to a situation in which employers draw inferences about productivity from unalterable attributes of individuals, e.g. sex, even though the attributes are not correlated with productivity in the population. In this study we assume that a disequilibrium model exists but we will specify only its demand equations. We will discuss supply considerations and market clearing mechanisms but we assume that employment is always achieved at a point on the firm's derived demand schedule for labour. In other words, we assume pure but not necessarily perfect competi-2 tion. Perfect competition Involves further assumptions regarding perfect knowledge and lack of impediments to long run adjustments. Arrow has shown that i t is useful to relax these assumptions of the -51-perfectly competitive model in the analysis of discrimination. Tradition, including traditional attitudes regarding sex stero-types, can he a harrier both to perfect knowledge and to rapid market adjustments. Derived Demand In micro economic models we typically assume that a firm's output is a function of its inputs of capital and labour. When several occupations are considered, the labour in each occupation can be considered to be a different input in the firm's production function. However, a firm cannot actually hire homogeneous labour for any occupation. In this study we are particularly interested in any difference in labour services between the sexes. Both Spence and Arrow implicitly assumed that male and female labour differed only with respect to the employer's assessment of the productivity of each type of labour. They both aggregated male and female labour through summation after multiplying each factor by its respective probability of being productive. As discussed throughout chapter 2, most writers recognized that even when their particular explanation for segregation or wage differ-ences was eliminated other differences between male and female labour might remain. Joan Robinson's concept of aggregating labour in terms of efficiency units is useful for solving the problem posed by heterogeneous labour inputs. We will assume that the output of a firm is a function of the services of labour measured in effi-5 ciency units. The use of Robinson's concept of efficiency units of labour is -52-an important assumption. It is commonplace in economics to discuss production functions that use labour as an input. The model developed below can be interpreted as one way of explaining the process of aggre-gating different types of labour into one homogeneous type of effi-ciency unit labour. There is little in economic theory that offers guidance in specifying the form of production functions. We will assume that the process, or production function, for producing or aggregating to efficiency unit labour is the same for all occupations. . This approach permits the examination of the elasticity of substitu-tion between male and female labour services. In chapter 2 we reviewed the emotional exchanges over differing assessments of the relative productivity of men and women.^  Con-sideration of an elasticity of substitution may provoke such exchanges. Nevertheless, since we can separate male and female labour in any occupation and since male and female wages are rarely the same, we can consider the elasticity of substitution between male and female labour as we would consider the elasticity of substitution between any two factors of production. Like Arrow, we assume that every occupation requires investment in human capital by both the employee and the employer. A job applicant will be defined as qualified for an occupation when the applicant has made the appropriate employee investment for the occu-pation. The employee's investment includes not only formal training but also the acquisition of work habits; e.g. punctuality, initiative, and proper manners, and the acquisition of worker traits, e.g. apti-tudes, interests, temperaments and tolerances to working conditions.' -53-We will assume that the efficiency labour services in each occu-pation are a function of the amount of qualified male and female labour that the firm hires for the occupation. If hiring unsuitable employees were costless to employers, then employers would not care whom they hired. They would simply hire and fire until sufficiently qualified labour was obtained. However, all hiring involves a cost to the employer. An employer's investment in human capital always involves at least the cost of the paper work of putting a person on the payroll. The employer's investment in each employee may also include costs due to recruitment, training, testing and personal equipment. If perfect knowledge about each potential employee's quali-fications were available without cost, the employer's investment in human capital would never be wasted. The problem in hiring is that it takes place in the presence of uncertainty regarding qualifications. It is both costly to make mistakes and costly to acquire information that would prevent mistakes. Cost minimizing (risk neutral) firms will seek to minimize the expected value of the loss of their investments that results from 9 hiring unqualified workers. We assume that the employer's uncer-tainty is expressed in the form of judgments about the probabilities that male and female workers are qualified. The probability in the employer's mind, but not the amount of investment, depends on the sex of the job applicant. If these subjective probability distributions were used by employers in their hiring decisions we could say that there is -54-"qualified discrimination".10 Owing to the aggregate nature of the census data we seek to explain, we are not able to detect qualified discrimination. We could not determine from census data whether employers actually hire both sexes without discrimination and then dismiss the unqualified workers. An employer's subjective probability that a person is qualified does not depend on sex alone. The U.S. National Manpower Council observed, "Employers hire persons of the sex supposedly possessing the characteristics considered necessary for effective job perform-ance."11 That is, the subjective evaluation will vary by occupation. We will assume that the subjective probability that a person is qualified for an occupation is a function of the person's sex and of the characteristics considered necessary for effective performance in the occupation. We will say that the evidence indicates the presence of statistical discrimination i f we find that a character-istic is significantly related to the male-female employment ratio and yet there is no significant difference in the mean values by sex of the quantitative indicators of the characteristic. It would be possible to assume that employers consider higher moments of their subjective probability distributions as well as joint distributions of two or more characteristics by sex. However, these complications will not be examined here, since we lack the necessary data. Even when we restrict our examination to mean values of single characteristics by sex, relatively little suitable data can be found. The preceding discussion can be expressed as follows. -55-M L e i " * l \ t l \ w J ( 2 ) L q j i * ? / X i ) L j i Where: L . is the amount of the efficiency services of labour e in the i^h occupation that the firm expects to have available; f. is a homogeneous production function of degree one that has continuous partial derivatives of the second order; L . . i s the amount of qualified labour whose sex is ^ j (j-m.f) that the firm expects to obtain in the i"th occupation; X. is a vector of quantitative indicators of the char-1 acteristics considered necessary for effective job performance in the i * " occupation; P.(X.) is the subjective probability that a person whose sex is j (j«m,f) is qualified for the i * * 1 occupation; L.. is the amount of labour whose sex is j that is hired for the i t h occupation. 0> C4 - W ^ + r K iL f f i i + W f iL q f i + r K i L f i Where: is the total cost of labour for the i * * 1 occupation; W.. is the wage rate paid to persons whose sex is j in 3 the i * " occupation; r is the rate of return the firm expects to earn on its human capital investments; K. is the amount of human capital per person (independent of sex) in the i * n occupation that is invested by the firm. As expressed by the following Lagrangean, the firm maximizes the amount of labour available to i t for a given cost. The first order conditions for a maximum are given by equations 5 and 6. We will assume the second order conditions are satisfied. -56-(5) (6) 0 - \(Wf.Pf(Xi) + rKA) + P f(X i)6f i/6L q [ f i ° * ^ m i W + r Ki) + W V V Equations 5 and 6 can be solved to eliminate X. (7) Wf.Pf(Xi) + r K l , P f(X i)6f./6L q f i W V + r K i VV6V6Lqmi Since f is homogeneous of degree one the ratio of the marginal products can be written as a function, F, of the ratio of the factors of production. Equation 8 implies that a relation, G, could be determined between the male-female employment ratio and the wage rates, expected investment return, and the factors considered necessary for effective job performance. Equation 9 establishes a relation for a firm. We wish to explain aggregate-census data; therefore, we will introduce the common Marshallian assumption that the preceding argument applies to the typical firm and, as such, to the entire market. The Marshallian argument is particularly strong here since we are assuming that firms may discriminate. Ignoring aggregation pro-blems by introducing the Marshallian assumptions means that all firms must behave as i f they share the same prejudices. If some firms dis-played prejudice any single nondiscriminating firm might dominate the market by reducing costs. Two reasons can be given for rejecting the argument that one <9> LqmAfi - Gi(Wfi»Wmi'rKi'Xi) -57-nondiscriminating.firm would eliminate all discriminating firms. First, in neoclassical theory we assume that any single firm is small relative to the size of the market but we assume nothing else as to its size. A homegeneous of degree one production function permits a firm to be any size, i.e. its output is not limited by increasing costs. If a firm enjoys an economic profit we usually assume it retains this profit until other firms copy the profitable firm and bid down the price. In our case, a nondiscriminating firm might simply continue to enjoy unusually large profits relative to discriminating firms. Other firms might not copy the first i f they do not attribute its success to its behaviour in the labour market. Second, the traditional prejudices attached to sex are sufficiently 12 strong that adjustment mechanisms are likely to be very slow, supply Equation 9 established a relation that determines the male-female employment ratio. We have assumed that employment is always attained on the firm's derived demand schedule for labour$ therefore, equa-tion 9 provides the basis for our explanation of male-female employ-ment ratios. The purpose of this section is to motivate the assumption that male-female employment ratios can be considered as i f they were simply determined by derived demand. In a comment during a conference held in May 1975 on occupational segregation, Kenneth Arrow described the adaption of labour supply to discrimination. Arrow wrote as follows: Rational adaptation by women to employer attitudes justifies employer attitudes to some extent. I am thinking particularly of high female mobility in and out of the labor force, an - 5 8 -essential part of the mechanism Ferber and Lowry describe. It is correctly referred to as a rational adaptation to female job opportunities. However, it is also true that employers are adapting rationally to this differential female reaction. Female workers perceive a lack of reward for certain kinds of accomplishmentsj therefore, there is a lack of necessary accomplishments, and therefore employers do not expect these accomplishments to exist in women. It is essential to this argument that workers are being treated as groups and not as individuals. If employers would differen-tiate among individuals, all these phenomena would disappear.13 For our problem we do not need to determine which force, supply or demand, determines the male-female employment ratio. In order to analyze the ratio i t is sufficient to be correct in the statement that the ratio behaves as i f it were demand determined. Arrow's comment indicates that separating supply and demand considerations could be like determining whether the chicken or the egg came first. This adaptation of supply to demand (or demand to supply) is the essence of tradition. For the purpose of completing our theoretical framework we can speculate on supply considerations. Both Spence and Arrow provided such speculations in the process of discussing the dynamics of 14 market clearing mechanisms. We can follow their example. Market Clearing The simplest market clearing mechanism exists when we equate supply and demand in each market without consideration of time. Once we deviate from this simple mechanism a wide variety of alter-natives are available. The change in price per unit of time can be made a function of the difference between supply and demand. The rate of change of the wage level, for example, could be expressed as a function of the unemployment rate. The change in quantity -59-supplied (or demanded) could be made a function of the difference between the price asked and the price offered. When expectations are introduced into a model additional market clearing mechanisms become possible. Employers' expectations can be modeled as changing over time as a function of differences in either quantities supplied and demanded or of differences between demand price and supply price. There is little to guide the modeler in the selection of a dynamic market clearing mechanism. Some assistance can be obtained by intro-ducing assumptions regarding the stability of the clearing mechanism. At best this limits the modeler's imagination to alternative monotonic 15 or oscillating stable systems. ^  Arrow dropped his assumption of imperfect employer perceptions when he introduced his dynamic considerations. He assumed that the probability that each group was qualified, and therefore the labour supply, was a function of the difference between skilled and unskilled wage rates paid to each type, black or white, of labour. He then assumed that the probabilities changed over time as a function of the difference between the supply and the demand for labour.1^ Spence defined equilibrium as the condition when employer expectations correspond to actual circumstances. He assumed that each individual's investment in a signal, and therefore the labour supply, was determined by equating the marginal cost of acquiring the signal to the marginal income from increasing the level of the signal. He introduced dynamic considerations by assuming that the employer's derived demand for labour in period t was a function of the productivity experienced in period t-1. Like Arrow, Spence then -60-speculated on alternative forms for the dynamic relationship and the 17 resulting stability of the model, ' We can complete our model by adding assumptions regarding labour supply and market clearing mechanisms. We can start with a market labour supply schedule rather than individual labour market behaviour. We assume that the amount of labour whose sex is j that is offered to th s the i occupation, L .., is a function of all of the alternative wage rates for a person whose sex is j and of the characteristics considered necessary for the i * h occupation. (10) L ^ - L ^ W ^ Wjn,Xi); j-m,f In order to recognize unemployment, we assume that supply is always greater than demand. < u> L j i - L j i Like Arrow or Spence, we could make the model dynamic by adding a time subscript to al l of our variables and by assuming that the rate of change in relative wage rates by occupation and sex is a function of the excess supply in each occupation. While speculation on supply and market clearing mechanisms completes the theoretical framework, i t is not necessary for the purpose of this study. We have assumed that the male-female employ-ment ratio behaves as i f it were demand determined. Simplifying Assumptions Equation 9 established a relation between the ratio of male to female qualified labour and wage rates, expected investment return, and the factors considered necessary for effective job performance. Using census data, we are able to observe the number of men and women -61-who axe actually employed in each occupation. We are not able to observe the number of men and Homen who are hired for any occupation, L^. We assume that the number of men and women who are employed in an occupation is equal to the number of men and women that firms expect to obtain as qualified labour. Similarly, neither r nor are observable. We must choose a quantitative indicator of rK^ that can be observed with our limited data. We assume that rK^ is a function of the amount of specific vocational training time required by each occupation, SVI\. Specific vocational training time can be contrasted with general educational development. General educational development is "education of a general academic nature ordinarily obtained in elementary school, high school, or college which does not have a recognized, fairly 18 specific occupational objective." Specific vocational training includes (a) vocational education, (b) apprentice training, (c) in-plant training given by an employer in classrooms, (d) on-the-job training, and/or (e) essential experience in other jobs.1^ In-plant training and on-the-job training are costs to employers. We assume that in-plant training and on-the-job training are necessary comple-ments to other farms of vocational training. Staff development departments in most large companies demonstrate this complementary form of training offered by employers. There are other assumptions that could be utilized to measure rK^. Employers' investment could be expressed as the sum of a fixed investment (K^) and the product of the wage rate and the training period (T^, = + W-T^ . The use of this alternative assumption -62-does not provide a useful measure for rK^. The alternative assumption merely introduces additional questions. In particular i t can be argued that on-the-job training uses more of the employer's resources than just the new employee's wage. Capital can be tied up by the trainees as well as staff trainers or supervisors. These factors would modify the alternative assumption to « + Q/T^  where is a price representing the price per unit of time of all of the resources used in staff training. It is far simpler to merely assume that rK^ is a function of the amount of specific vocational training time required by each occupation. This means that we are assuming that the cost to the employer for training a person is the same in two occupations i f the specific vocational training time is the same. Furthermore, the fixed cost, K^ , is assumed to be the same for all occupations. The use of SVF\ as a proxy variable for rK^ could present problems of interpretation. Proxy variables must be chosen on the basis of common sense and intuition. If better information were available to assess the reliability of the proxy variable i t might not be necessary to use the proxy. In this particular case it is possible that SVP^  actually serves as a proxy variable for employee investment or for both employer and employee investment. Since we lack both better information and an alternative proxy variable we will have to remain aware of the possible alternative interpretations of our proxy. In order to obtain numberical results we must transform equation 9 into a specific form. Whatever actual functional forms are appro-priate to equations 8 and 9» we assume that the relation in equation 9 can be approximated by equation 12. -63-(12) log ( L q n i A q f l ) - cy.iog(wfi/wmi) + e-svp i + + u± Where: a and 6 are scalar parameters} A is a column vector of parameters; u^ is a normally distributed disturbance term with zero mean; X. is a row vector of quantitative indicators of the characteristics considered necessary for effective job performance in the i * " occupation. By using the logarithm of the employment ratio and by assuming that the wage rates enter equation 12 as the logarithm of their ratio, a can be interpreted as the elasticity of substitution of male and female qualified labour with respect to the observable female-male wage rates. If the actual value of a is zero we would say that male and female labour were completely different factors of production. In other words this would indicate complete occupational segregation by sex; consequently, we will devote particular attention to examining our estimate of the value for a. -64-Chapter 3* Notes 1. Dominion Bureau of Statistics, 1961 Census of Canada, Labour Force, Bulletin 3.3-7 (Ottawa: Queen's Printer, 1963), Table 21, p. 21-1. 2. Joan Robinson, The Economies of Imperfect Competition (London: MacMillian & Co., Ltd., 1965), Appendix, p. 332. 3. Kenneth Arrow, "The Theory of Discrimination", paper presented at conference on "discrimination in labour markets", October 1971. Industrial Relations Section, Woodrow Wilson School and Conference Office, Princeton University, pp. 2-3. 4 . Michael Spence, Market Signaling (Cambridge: Harvard University Press, 1974); and Kenneth Arrow, "Some Models of Racial Discrimination in the Labor Market", RAND Corporation research memorandum RM-6253"RC» multilith, Santa Monica, Feb. 1971. 5 . Robinson, 0p_. Cit. 6. See p. 8 above. 7. Arrow, 0£. Cit.. p. 21, p. 48. 8. Frederick J. Gaudit, Labor Turnover: Calculation and Cost (New York: American Management Association, Inc., I960), pp. 37-38. 9. By risk neutral, I mean to assume that firms consider only the first moments of the subjective probability functions. 10. See p. 25 above. 11. National Manpower Council, Womanpower (New York: Columbia University Press, 1957), p. 233. 12. See p. 40 above. 13. Kenneth Arrow, "Economic Dimensions of Occupational Segregation: Comment I" in Martha Blaxall and Barbara Regan (eds.), Women and the  Workplace (Chicago: The University of Chicago Press, 1976), p. 2357 14. Spence, 0p_. Cit.. p. 191; Arrow, "Some Models", p. 50. 15. Paul Anthony Samuelson, Foundations of Economic , Analysis (New York: Atheneum, 1965), Part II, pp. 257-356. 16. Arrow, "Some Models", p. 50. 17. Spence, 0p_. Cit. -65-18. U.S. Department of Labor, Bureau of Employment Security, Estimates of Worker Trait Requirements for 4,000 Jobs (Washington: U.S. Government Printing Office, 1956), p. 110. 19. Ibid. -66-Ghapter 4: Construction and Sources of Data Census Data A major difficulty in any study of occupational segregation by sex is to find data on employment and wage rates broken down by sex and occupation. For the purposes of this study, data must be found for a large number of occupations so that regression equations can be estimated using each occupation as an observation. The only avail-able Canadian data that meet these criteria are found in the census.1 Since occupational classification schemes change between census years, this study is restricted to the census year 1961. Occupations are divided into groups, classes, and titles in the Canadian census. The 1961 census contained 273 occupational classes divided into 13 occupational groups. There were 16,000 oc-cupational titles in the 1961 census, but data by occupational titles 2 are not published. The Occupational Classification Manual for the 1961 census lists the occupational titles that are aggregated into each census occupational class. In this study, we must treat census occupational classes as i f they were homogeneous occupations. This means that we will assume that men and women do not differ in their distributions among the occupational titles within an occupational class, or it means that there are assumed to be no differences, that are important for this study, between occupational titles within an occupational class. We cannot predict the bias that would result, i f this is an incorrect assumption, without access to data by job title. Of course, the extreme case of complete occupational segre-gation by sex is possible i f jobs are narrowly defined. We assert -67-that even i f this is the case such narrow definitions are useful as nothing more than labels. They should not restrict mobility within a job class. Inaccurate answers to the census questionnaire are an obvious 3 source of error in the census data. Problems would exist, however, even i f the questions were answered accurately. The number of hours usually worked, the number of weeks worked in the census year, and the gross wage and salary income were recorded in terms of intervals. Unfortunately, the possible answers for usual hours worked per week and gross wage and salary income included open ended intervals. The problems posed by this method of recording are discussed below. The precision of the occupational classification of each individual must vary with the type of response given to the question, "What kind of work did you do in this industry?" Furthermore, each individual was classified into only one occupation. Errors are contained in the data resulting from the grouping of individuals with more than one job into only the occupation given in answer to the census question. We hope that individuals with multiple jobs are few in number and importance, or else that the errors will cancel each other. No data are available for fringe benefits. Traditionally, studies of labour markets avoid this problem by assuming that fringe benefits are proportional to wage rates.^ We must also make that simplifying assumption. Employment is measured in person-years, person-weeks, or person-hours, but wage rates can be measured in dollars per year, dollars per week, or dollars per hour. As shown by Sylvia Ostry, a year is -68-not the appropriate unit of time for this study. She found that the ratio of female to male aggregate annual earnings was 5^«2 per cent for all wage earners and 59»3 per cent for only full time wage earners.^ In this study we estimate person hours and hourly wage rates by adjusting the annual employment and earnings data for the differences in weeks worked per year and hours worked per week by men and women. A shorter working period for women could also be the result of discrimination. The problem of participation rates is be-yond the scope of this study. The census provides tabulations of the number of wage-earners who worked for wages or salary and of average annual earnings by sex 7 and census occupational class. These data were converted into units based on time measured in hours by equations 1 and 2. « \a - i 5 i l V j i Where j L is the amoiant of qualified labour whose sex is j (j»ra,f) and who are employed in the 1 t h occupation measured in person-hours; W.. is the wage rate paid to persons whose sex is j in the i * n occupation measured in dollars per hour; L^. is the number of person-years employed in the i * * 1  J occupation whose sex is j; W^". is the annual earnings paid in the i*h occupation J 1 to persons whose sex is j; Wk., is the average number of weeks per year that are worked by persons in the i^*1 occupation whose sex is j; H.. is the average number of hours per week that are J worked by persons in the i * n occupation whose sex is j . -69-The average number of weeks per year, ^ k^, and the average number of hours per week, H\.^, are not readily obtained from the census. The response to the question of how many weeks were worked in the census year was recorded in intervals. The published census data contained intervals that were aggregated even further, i.e. 1-4 weeks and 5-13 weeks were reported as 1-13 weeks, and 40-48 weeks and 49-52 weeks were reported as 40-52 weeks. Unpublished data were Q obtained from Statistics Canada. These data reported the d i s t r i -butions of wage earners among the intervals as given in the question-naire by sex and occupational class. For each sex and occupational class, the average number of weeks worked per year, Wk\^r was cal-culated from this data using equation 3. (3) ^ - ( ? i j k r a i ; J k ) ^ 1 J k Where: n. is the number of individuals in the i * n J occupation whose sex is j that were recorded in the k t h interval of weeks; W I t M i j k * S ***e m* dP°* n* °f t n e interval of weeks. The values of ^ kM^^ a r e given in Table I. We assume the interval 1-4 includes 0-4.5 &ncL that the interval 49-52 includes 48.5-52. 0 Table I Midpoints used to calculate k Interval WkM. ., - ijk 1 1-4 2.25 2 5-13 9.00 3 14-26 20.00 4 2? - 39 33.00 5 40 - 48 44.00 6 49 - 52 50.25 The response to the question of how many hours were usually worked each week was also recorded in intervals. The calculation of is further complicated "by the presence of an open-ended interval, 50+. Furthermore, the hourly data were not published by census occupational class. In place of occupational classes, these data were published by broader occupational groupings that were more specific than census occupational groups but more general than census occupational classes. The problem of an open-ended interval was solved by calculating a mean for the interval, 50+, of 5^ .83 hours per week.10 Since we have no other data, the problem of occupational grouping was solved by using the data in each of the broad groups for all of the census occupational classes within each broad group. For each sex and occupational class, the average number of hours worked per week, H^, was therefore calculated using equation 4. Where: n! is the number of individuals in the i * h occu-pation whose sex is j that were recorded in the k t h interval of hours; HM, is the midpoint (except for 50+) of the k interval of hours. The values of HM^  are given in Table II. The calculation of HMg (54.83) is explained above. Table II iBOrl + n «%s»1 m . i l n + a I. j i Midpoints used to calculate H, k Interval HMR 1 1-19 10.0 2 20 - 29 24.5 3 30-3^ 32.0 4 35 - 39 37.0 5 40 40.0 6 41 - 44 42.5 7 45 - 49 47.0 8 50+ 5^ .83 -71-Data on age and education were also obtained from the census.11 These data referred to the labour force rather than just wage earners. The labour force includes the unemployed, the self-employed, and un-12 paid family workers as well as wage earners. However, we could assume that the average age and education of these components of the labour force are the same as the average age and education of wage earners. We cannot determine whether this assumption is reasonable. Alternatively, we assume that the number of "non-wage earners" is sufficiently small so that the data for the labour force will be approximately the same as data for wage earners. There were no ambiguities in the recording of age. Data on the average age of individuals by sex and occupational class were obtained directly from the published census tables. Data on education were recorded by intervals. Furthermore, the data refer to highest grade or year of schooling attained (started) rather than to the highest grade completed. The published census data reported the educational interval "secondary 4 -5 " . Unpublished data were obtained from Statistics Canada that reported secondary 4 13 and secondary 5 separately. J We need to obtain from these data quantitative indicators of education by sex and occupational class. In her work on female-male wage ratios, Sylvia Ostry used the per cent of the work farce that completed high school as an indicator of educational level. She adjusted the wage ratio for the sex dif-ference in education by simply multiplying the female wage rate in each occupation by the ratio of the per cent of males that had com-14 pleted high school to the per cent of females. In this study, we -72-calculated the average number of years of school attended by indivi-duals of each sex and occupational class. The average number of years of school attended was calculated using equation 5. (5) - ( K j f t J / J K i * Where: B i s the average number of years of school J attended by persons whose sex is j and who are in the i occupational class; n ! * i s the number of individuals in the i * * occu-J pation whose sex is j that were recorded in the k^ h educational interval; EM^  is the assumed mean of the k*n educational interval. The values of EM^  are given in Table III. Table III "Means" used to calculate E„ k Interval E Kk 1 0-4 elementary 2.5 2 5+ elementary 6.5 3 1-2 secondary 9.5 4 3 secondary 11.0 5 4 secondary 12.0 6 5 secondary 13.0 7 university 14.0 8* university degree 16.0 * EMft was given a value of 18.0 for two occupational classes: 140 (physicians and surgeons) and 153 (lawyers and notaries). These exceptions were based on the number (greater than 16) of years of general educational development (defined below) required for these occupations. Worker Traits The major data contribution of this study is the construction ' of a series of quantitative indicators of worker traits for census occupational classes. The United States Employment Service has estimated and published data on 48 worker traits for each of 4,000 jobs drawn from the second edition of the Dictionary of Occupational -73-Titles. 1^ Since the occupational classification system for the 1961 Census of Canada was based in part on the Dictionary of Occu- pational titles. many of the 4,000 jobs correspond to occupational titles that make up the 273 census occupational classes.1^ Using the Classification Manual Census of Canada, 1961, the 4,000 jobs were sorted into the appropriate census occupational classes, and average worker traits for each census occupational class were cal-culated. The 4,000 jobs were matched with the census occupational titles that make up the census occupational classes on the basis of job name and industry in which the job is usually found. Not all of the 4,000 jobs correspond to the census occupational titles used to define the census occupational classes. The average worker traits for the census occupational classes are based on 2,526 jobs which are included in both the sample of 4,000 and in the Classification  Manual. No jobs could be found in the sample of 4,000 for 32 of the census occupational classes. Appendix A to this study lists the census occupational classes with the jobs that were sorted into each class. The quantitative indicators of worker traits include measure-ments of the training time, aptitudes, interests, temperaments, and physical capacities that a worker must possess in order to perform at an average level of competence in each occupation, as well as of working conditions that a worker must tolerate in each occupation. The worker traits were quantified by raters trained by the U.S. Employment Service. The raters used the job definitions in the Dictionary of Occupational Titles for their primary source of -74-17 information in making their ratings. Raters were given instruction 18 manuals on how to rate each trait. The manuals defined each trait and provided benchmark jobs for the different levels (values of the quantitative indicators) of each trait. In order to minimize cor-relations between the traits that might result from the rating pro-cedure, no single rater rated more than one or two worker traits for any job. Symbols that are used to identify the quantitative indicators of traits in the remainder of this study and short descriptions of the traits are given in Table IV. Table IV Worker Traits Symbol 20 Short Description Training time GED general educational development SVP specific vocational training Aptitudes G intelligence V verbal N numerical S spatial P form Q clerical K motor coordination F finger dexterity M manual dexterity E eye-hand-foot coordination G color discrimination Temperaments Tl variety and change T2 repetitive, short cyele T3 under specific instructions T4 direction, control, planning T5 dealing with people T6 isolation T7 influencing people T8 performing under stress T9 sensory or judgmental criteria TO measurable or verifiable criteria TX feelings, ideas, facts TY set limits, tolerances or standards - 7 5 -Table IV / continued Symbol Short Description Interests 11 things and objeets 12 business contact 13 routine concrete ik social welfare 15 prestige 16 people, ideas 17 scientific, technical 18 abstract, creative 19 nonsocial 10 tangible, productive satisfaction Physical capacities MW maximum weight lifted P2 climbing-balancing P3 stooping-kneeling P4 reaching-handling P5 talking-hearing P6 seeing Working conditions Wl inside W2 cold W3 heat W4 wet-humid W5 noise-vibration W6 hazards W7 fumes, odors, etc. The 48 worker traits are divided into six catagoriest training time, aptitudes, temperaments, interests, physical capacities, and working conditions. "The Training Time component is defined as the amounts of educational development and vocational preparation necessary for a worker to have acquired the knowledge and abilities essential for 19 average performance in a specific job". General educational develop-ment, GED, is education of a general academic nature that includes reasoning development, mathematical development and language develop-ment. Raters scored the amount of GED required for each job on a scale from 1 to 7. Each level of the scale corresponds to a description of a degree of development in reasoning, mathematics, and language. The rater's manual included benchmark jobs to illustrate the seven 21 levels of GED, The seven levels of GED can be converted to approxi-mate school grade equivalents. However, the levels of GED may be achieved without formal study. For the purposes of this study, the seven levels of GED were converted into the corresponding number of years of formal education. The quantitative indicator of GED for each census occupational class was then obtained as the simple average of the number of years of formal schooling required by the jobs from the sample of 4,000 that were sorted into each census class. Since no data were available on the employment in each job within the census occupational classes, construction of an employ-ment-weighted average was not possible. The seven levels of GED and the corresponding number of years of formal education are given in Table V.22 Table V Quantitative indicators of GED Level Years of School 1 0 2 4 3 7 4 10 5 12 6 16 7 18 Specific vocational preparation, SVP, includes training given in vocational education, apprentice training, in-plant training, on-the-job training, and essential experience in other jobs. This is the type of training "required to learn the techniques, acquire information, and develop the facility needed for average performance -77-in a specific job-worker situation". J Raters scored the amount of SVP required for each job on a scale from 1 to 9. Each level of the scale corresponds to an interval of time required for training. For the purposes of this study, the intervals for levels 2-8 were replaced with their midpoints measured in the number of years of oh, training as shown in Table VI. The quantitative indicator of SVP for each census occupational class was obtained as the simple average of the number of years of training required by the jobs from the sample of 4,000 that were sorted into each census class. Table VI Quantitative indicators of SVP Level Interval^ Average Years 1 short demonstration 0.000 2 up to 30 days 0.G41 3 over 30 days to 3 months O.I65 4 over 3 months to 6 months 0.375 5 over 6 months to 1 year 0.750 6 over 1 year to 2 years 1.500 7 over 2 years to 4 years 3.000 8 over 4 years to 10 years 7.000 9 over 10 years 14.000 The aptitude component of the worker traits is defined as "the specific capacities or abilities required of an individual 26 in order to facilitate the learning of some task or job duty". Table VII, taken from the manual for rating aptitudes, lists the definitions of the eleven aptitudes included in this component. Table VII 2 7 "Definitions of Aptitude Factors" "V—VERBAL: Ability to understand meanings of words and ideas associated with them, and to use them effectively. To compre-hend language, to understand relationships between words and to understand meanings of whole sentences and paragraphs. To present information or ideas clearly." - 7 8 -Table VII / continued "N—NUMERICAL: Ability to perform arithmetic operations quickly and accurately." "S—SPATIALs Ability to comprehend forms in space and understand relationships of plane and solid objects. May be used in such tasks as blueprint reading and in solving geometry problems. Frequently described as the ability to 'visualize1 objects of two or three dimensions, or to think visually of geometric forms." 'T—FORM PERCEPTION: Ability to perceive pertinent detail in objects or in pictorial or graphic material. To make visual comparisons and discriminations and see slight differences in shapes and shadings of figures and widths and lengths of lines." "^ --CLERICAL PERCEPTION: Ability to perceive pertinent detail in verbal or tabular material. To observe differences in copy, to proofread words and numbers, and to avoid perceptual errors in arithmetic computation." "K--MOT0R COORDINATION: Ability to coordinate eyes and hands or fingers rapidly and accurately in making precise movements with speed. Ability to make a movement response accurately and quickly." "F--FINGER DEXTERITY: Ability to move the fingers, and manipulate small objects with the fingers, rapidly or accurately." "M—MANUAL DEXTERITY: Ability to move the hands easily and skill -fully. To work with the hands in placing and turning motions." "E—EYE-HAND-FOOT COORDINATION: Ability to perceive or recognize similarities or differences in colors, or in shades or other values of the same color. To identify a particular color, or to recognize harmonious or contrasting color combinations, or to match colors accurately." "G—INTELLIGENCE: General learning ability. The ability to 'catch on' or understand instructions and underlying principles. Ability to reason and make judgments. Closely related to doing well in school." Raters scored the amount of each of the eleven aptitudes required by each job on a scale from 1 to 5. The scale corresponds to how high an individual must rank in a cumulative frequency dis-tribution of the working population with respect to each aptitude -79-in order to perform satisfactorily in each job. Table VIII shows the correspondence between the scale and the intervals of a cumu-28 lative frequency distribution . For the purposes of this study, the intervals were replaced by their midpoints. The quantitative indicator of each aptitude for each census occupational class was then obtained as the simple average of the ranks (midpoints) required by the jobs from the sample of 4,000 that were sorted into each 29 census class. Table VIII Quantitative indicators of aptitudes Level Interval Midpoint 1 upper 10% 0.95000 2 upper 1/3 less 1 0.78334 3 middle 1/3 0.50000 4 lower 1/3 less 5 0.21667 5 lowest 10% 0.05000 The temperament component of the worker traits "consists of twelve different types of occupational situations to which workers 30 must adjust". Table IX, taken from the manual for rating tempera-ments, lists the definitions of the twelve temperaments included in this component. Table IX 3 1 "Definitions of Temperament Factors" "Tl--VARCHj Situations involving a variety of duties often char-acterized by frequent change." "T2—HEPSGj Situations involving repetitive or short cycle operations carried out according to set procedures or sequences." "T3--USI: Situations involving doing thing only under specific instruction, allowing little or no room for independent action or judgment in working out job problems." -80-Table IX / continued "T4—DCP: Situations involving the direction, control, and planning of an entire activity or the activities of others." "T5--DEPL: Situations involving the necessity of dealing with people in actual job duties beyond giving and receiving instructions." "T6—ISOL: Situations involving working alone and apart in physical isolation from others, although activity may be integrated with that of others." "T?—INFLU: Situations involving influencing people in their opinions, attitudes, or judgments about ideas or things." "T8—PUS: Situations involving performing adequately under stress when confronted with the critical or unexpected or taking risks." "T9—SJC: Situations involving the evaluation (arriving at general-izations, judgments, or decisions) of information against sensory or judgmental criteria," "TO—MVC: Situations involving the evaluation (arriving at general-izations, judgments, or decisions) of information against measur-able or verifiable criteria." "TX—FIF: Situations involving the interpretation of feelings, ideas, or facts in terms of personal viewpoint." "TY--STS: Situations involving the precise attainment of set limits, tolerances, or standards." Raters scored only the presence or absence of each temperament for each job. "Only the two most characteristic temperament factors 32 are indicated" as present for each job. The quantitative indicator of each temperament for the census occupational classes is a dummy variable with values of either 0 or 1. For each census class, the dummy variable corresponding to each temperament was given a value of 1 i f 50 per cent or more of the jobs sorted into the census class had the temperament present. Since each job had only two tempera-ments indicated as present, each census class can have a maximum of only four temperaments whose quantitative indicators have a value of 1. -81 "The Interests Component is defined as a preference for certain types of work activities or experiences, with accompanying rejection of contrary types of activities or experiences. Five pairs of interest factors are provided so that a positive preference for one factor of a pair also implies rejection of the other factor of that pair. Only the two most characteristic interest factors are indicated."^ Table X, taken from the manual for rating interests, lists the definitions of the five pairs of interest factors included in this component. Table X3^ "Definitions of Interest Factors" "II—Situations involving a pre-ference for activities dealing with Things and Objects." "12—Situations involving a pre-ference for activities in-volving Business Contact with People." "13—-Situations involving a pre-ference for activities of a Routine, Concrete, Organized nature." "1*4—Situations involving a pre-ference for Working for People for their presumed good as in the Social Welfare sense, or for dealing with People and Language in Social Situations." vs. "l6—Situations involving a preference for acti-vities concerned with People and the Com-munication of Ideas." vs. "17—Situations involving a preference for acti-vities of a Scientific and Technical nature." vs. "18—Situations involving a preference for acti-vities of an Abstract and Creative nature." vs. "19—Situations involving a preference for acti-vities that are Non-social in nature, and are carried on in re-lation to Process, Machines, and Techni-ques." "15—Situations involving a pre- vs. "10—Situations involving ference for activities resulting a preference for acti-in Prestige or the Esteem of Others." vities resulting in Tangible, Productive Satisfaction." -82-The quantitative indicator of each interest for the census occu-pational classes is a dummy variable with values of either 1 or 0. For each census class, the dummy variable corresponding to each interest was given a value of 1 i f 50 per cent or more of the jobs sorted into the census class had the interest present. Since each job had only two interests indicated as present, each census class can have a maximum of only four interests whose quantitative indi-cators have a value of 1. The physical capacities component of the worker traits "reflects 3 5 the specific physical aspects of occupations that must be performed". Each job was rated on a five point scale for the strength required by the job. The manual for rating physical capacities and working con-ditions defined the scale in terms of an amount of weight lifted and carried. For the purposes of this study, a maximum amount of weight lifted was assigned to each of the five points of the scale. The quantitative indicator of strength, MW, for each census occupational class was then obtained as the simple average of the maximum weight lifted in each of the jobs that were sorted into the census class. The strength scale, its definitions, and the assigned maximum weights are given in Table XI. Table XI Quantitative indicators of Strength Scale Definition-^ Maximum Weight S - Sedentary "Lifting 10 pounds maximum." 1 L - Light "Lifting 20 pounds maximum with 2 frequent lifting and/or carrying of objects weighing up to 10 pounds." -83-Table XI / continued Scale Definition Maximum Weight M - Medium "Lifting 50 pounds maximum with frequent lifting and carrying of objects weighing up to 25 pounds." 5 H - Heavy "Lifting 100 pounds maximum with frequent lifting and carrying of objects weighing up to 50 pounds." 10 V - Very Heavy "Lifting objects in excess of 100 pounds with frequent lifting and carrying of objects weighing up to 80 pounds." 16 * Maximum weight is measured in tens of pounds, i.e. S is 10 pounds. stooping-kneeling, reaching-handling, talking-hearing, and seeing) were rated only for their presence or absence in each job. Each of these conditions was judged by the raters to be present only when a job met certain hazard or precision criteria with respect to the 37 capacity. The quantitative indicators for each of these physical capacities for the census occupational classes is a dummy variable with values of either 1 or 0. For each census class, the dummy variable corresponding to each capacity was given a value of 1 i f 50 per cent or more of the jobs sorted into the census class had the capacity scored as present. For each census occupational class, all five of these capacities may have indicators with values of 1 (or all may be 0). The working conditions component of the worker traits "reflects the physical surroundings in which specific occupational activities The remaining five physical capacities (climbing-balancing, -84-are carried out". Each job was rated "Inside or Outside or Both, depending on whether three-quarters of the working time were spent 39 indoors or outdoors". The quantitative indicator of inside-outside, Wl, for the census classes is a dummy variable with values of either 1 or 0. For each census class, the dummy variable was given a value of 1 if 50 per cent or more of the jobs sorted into the census class were scored "Inside". The remaining six working conditions (cold, heat, wet-humid, noise-vibration, hazards, and fumes, odors, etc.) were rated only for their presence or absence. As in the case of physical capacities, these conditions were only judged to be present when certain specific 40 criteria were met. The quantitative indicator for each of these working conditions for the census classes is a dummy variable. As in previous instances, the dummy variable corresponding to each working condition was given a value of 1 if 50 per cent or more of the jobs sorted into the census class were scored with the condition present. For each census occupational class, all six of these con-ditions may have indicators with values of 1 (or all may be 0). Sample Size The regression equations that are presented in the following chapter were estimated by considering each occupation as an obser-vation. Not all of the 273 census occupational classes could be used for this purpose. Forty five of the 273 census occupational classes contain no women. While zero is an acceptable value for employment, zero is not an acceptable value for the female wage rate in these occupations. We have no way of knowing how much -85-employers would be willing to pay to hire one woman in these occu-pations, or how little a woman would be willing to accept in order to take a job in these occupations. Consequently, these occupations are excluded from our analysis. Similarly, there are five census occupational classes in which less than five women are employed. Employment and wage data are not published for these occupations, therefore they too are excluded from our analysis. As mentioned earlier, data for worker traits could not be found for 32 of the census occupational classes, so these occupations are also excluded from our analysis due to the incomplete data. For two occupations, delivery managers and oilers and greasers, employment, wage, and worker trait data are available, but age and education data are not published. Since these occupations include only 15 women, they are excluded from our analysis. The occupational class "farmers and stockraisers" is excluded from our analysis since it is defined so as not to include any wage-earners. Finally, the occupational class "judges and magistrates" is excluded from our sample. This exclusion was based on the questionable estimate of 14 years for the amount of specific vocational training required for the occupation. This exclusion could also be justified by the difference in selection procedures for U.S. judges as compared to the procedure for selecting Canadian judges. The sample used for the regressions in the following chapter consists of the remaining 194 occupations. Since the data used in these regressions are not easily obtained, Appendix B to this study lists the 194 occupations with their corresponding data. -86-It is possible that the method of selecting our sample could affect the properties of our estimates in the next chapter.We know that 45 of the 273 occupational classes are "bunched" at the value of zero for the male-female employment ratio. Tobin has discussed an estimation procedure that yields consistent estimates i f the underlying model applies both to a continuous range for the variables and to an upper or lower limit for the dependent variable. For example, when studing individual consumer purchases of automobiles we find that most consumers in a given year will not purchase an automobile. Tobit analysis is based on the assumption that the same variables determine both the decision whether or not to buy as 4l well as the decision of how much to buy. The maximum likelyhood estimator for the parameters in such a relation is not the same as ordinary least squares. It is possible that the independent vari-ables that determine the male-female employment ratio for our sample of 194 occupations also determine that no women will be hired in the 45 occupations we have excluded. If this is the case, ordinary least squares will not yield consistent estimates. We will use ordinary least squares in the next chapter. If the assumptions of Tobit analysis apply to our model our estimates will not be consistent, i.e. the probability limit of our estimators will not be equal to the true value of the parameters. The properties of our estimators will always depend on the validity of the under-lying assumptions. The concentration of 45 occupations with no women is cause for concern. However we would have to assume that our model applies to these occupations in the same manner as it does -87-tp our sample of 194 occupations and we would require the corresponding wage data if we were to consider using Tobit analysis. Even i f Topit analysis would yield consistent estimates there is no assurance that 42 it would perform well with the size of our sample. An asymptotic property of an estimator is useful when we can increase our sample size so as to reduce errors. We do not have that luxury. -88-Chapter 4: Notes 1. Dominion Bureau of Statistics, Guide to Federal Government Labour  Statistics, 1969 (Ottawa: Queen's Printer, 1970). 2. Dominion Bureau of Statistics, Occupational,Classification Manual  Census of Canada, 1961 (Ottawa: Queen's Printer, 196l), p. 7. 3. Dominion Bureau of Statistics, l96l Census of Canada, Introductory Report to Volume III, Bulletin 3-3-15 (Ottawa: Queen's Printer, 1965), PP. XXIV-XXVII. 4. Ibid., p. XXVI. 5. Melvin W. Reder, "Wage Differentials: Theory and Measurement" in Aspects of Labor Economics (New York: National Bureau of Economic Research, 1962), pp. 257-299. 6. Sylvia Ostry, The Female Worker in Canada (Ottawa: Queen's Printer, 1968), p. 41. 7. Dominion Bureau of Statistics, 1961 Census of Canada, Labour Force, Bulletin 3.3-7 (Ottawa: Queen's Printer, 1963), Table 21, pp. 21-1 -21-15. 8. I am grateful to Ms. C. Silver of the Economic Characteristics Section, Census Division, Statistics Canada for providing these data. 9. Dominion Bureau of Statistics, 1961 Census of Canada, Labour Force, Bulletin 3.3-7, Table 24, pp. 24-1 -24-2. 10. I am grateful for correspondence with Met A.J. Kempster, Chief, Economic Characteristics Section, Census Division, Statistics Canada and for her comments on the interval of hours, 50+. Ms. Kempster warned that the census data on hours might be affected by seasonality. However, I was unable to find other data on hours that could be used for all of the census occupational classes. The census questionnaire asked how many hours are usually worked each week. Consequently, seasonality might not be a problem. Wei. Kempster suggested using 56 as the mid-point of the 50+ interval if other sources did not indicate a better value. The value actually used was chosen by considering the distribution of all wage-earners among the intervals of hours. The highest average number of hours worked per week during the 1961 census year was 41.0 for June 1961 for all manufactures and 41.3 for June I96I for durable goods (DBS, Review of Man-Hours and Hourly Earnings. 1945-1963, p. 27). Using the mid-points for the first five intervals as shown in Table II and the distribution of all wage earners, a value of 5k.83 was calculated as the midpoint for the open ended interval that yielded an average work week of 41.3 hours. - 8 9 -11. Dominion Bureau of Statistics, 1961 Census of Canada. Labour Force, Bulletin 3.1-9 (Ottawa? Queen's Printer, 1963), Table 17, pp. 17-1 - 17-30. 12. Dominion Bureau of Statistics, 1961 Census of Canada. Introductory Report to Volume III, Bulletin 3.3-15 (Ottawa* Queen's Printer, 1965), pp. XII-XIII. 13. I am grateful to Ms. C. Silver of the Economic Characteristics Section, Census Division, Statistics Canada for providing these data. 14. Ostry, 0p_. Cit.. p. 43, Table 17, note f. 15. U.S. Department of Labor, Bureau of Employment Security. Estimates  of Worker Trait Requirements for 4.000 Jobs (Washington: U.S. Government Printing Office, 1956), hereafter cited as Traits. 16. This method was discussed through correspondence with the Census Division. I am grateful to Mr. Frank Levin for his comments on the methodology followed here. 17. Traits, p. iv. 18. Traits, p. v, pp. IIO-I58. 19. Traits. p. v. 20. Traits, template. 21. Traits, pp. 110-120. 22. Traits, p. Ills also see R.S. Eckaus, "Economic Criteria for Education and Training", The Review of Economics and Statistics. May, 1964, pp. 181-190. 23. Traits, p. 110. 24. Traits, p. 110; also see Eckaus, 0p_. Cit. 25. Traits. p. 110. 26. Traits. p. 121. 27. Traits, pp. 121-122. 28. Traits, template. 29. James G. Scoville used these data in his study of The Job Content of the U.S. Economy. 1940-1970 (New York: McGraw Hill Book Company, 1969Tand The Job Content of the Canadian Economy, 1941, 1951, and  1961 (Ottawa: Queen's Printer, 1967). Scoville apparently aggregated the aptitudes required by the jobs in each census class without allowing for the different sizes of the intervals represented by the scale of 1 to 5« - 9 0 -30, Traits. P* vi. 31. Traits, P- 131. 32, Traits. P? vi. 33. Traits. P. vi. 34. Traits, P. 136. 35. Traits, P. vi. 36, Traits, PP . 145-146. 37, Traits, P. vi. 38. Traits. P. vi. 39. Traits. P. vi, 40. Traits. P. vi. 41. James Tobin, "Estimation of Relationships for Limited Dependent Variables", Econometrica. 1958, pp. 24-36. 42. Carl F. Christ, Econometric Models and Methods (New York: John Wiley & Sons, Inc., 1966), p. 264. -91-Chapter 5: Results Data Description The male-female employment ratio ranges from a low value of 0.023 for "nurses-in-training" to a high value of 2673.5 for "elec-tricians, wiremen and electrical repairmen". It is interesting to examine our data to determine whether extreme values of the employ-ment ratio are clustered in particular occupational divisions. Table XII presents the distribution of the 1$& occupational groups by occupational division and by intervals of the range of values for the employment ratio. Since our regression analysis uses the logarithm of the employment ratio, the intervals used in Table XII were chosen as equal thirds of the range of the ratio in logarithmic form. A Chi-squared statistic with a value of 37-65 can be computed for Table XII. However, since there are several cells in Table XII in which the expected number of occupational groups is quite small (less than 5), we are unable to interpret the Chi-squared statistic. It is interesting to note that there is no obvious clustering of occupational groups in the table. The two extreme cases are 6 oc-cupational groups dominated by women in the clerical division and 6 occupational groups dominated by men in the "other" division. The two extremes differ from expected frequencies by approximately threefold. Such extremes are unusual in the remainder of the table. -92-Table XII . Actual and Expected Number of Occupational Groups by Occupational Division and by Interval of Male-Female Employment Ratioc Interval of Ratio Division 0.023 -1.122 1.122 -5^ .781 54.781 -2673.490 0.023 -2673.490 Managerial 0 (0.9) 5 (2.9) 0 (1.2) 5 Professional and Technical 7 (7.1) 24 (21.7) 7 (9.2) 38 Clerical 6 (1.7) 3 (5.1) 0 (2.2) 9 Sales 2 (1.3) 4 (*.o) (1.7) 7 Service and Recreation 6 (3.5) 10 (10.9) 3 (4.6) 19 Transport and Communications 1 (2.0) 4 (6.3) 6 (2.7) 11 Craftsmen, Production & Related 14 (17.8) 58 (5^ .9) 24 (23.3) 96 Otherd 0 (1.7) 3 (5.D 6 (2.2) 9 All Divisions 36 111 7^ 194 a) The expected number in parentheses is calculated on the null hypothesis that occupational division and interval of the employment ratio are independent. b) The table is limited to the 194 groups discussed on page 84 above. c) This is the adjusted employment ratio; see page 68 above. d) "Other" includes farmers and farm workers; fishermen, trappers and hunters; miners, quarrymen and related workers; and labourers, n.e.s. -93-Regression The theory developed in Chapter 3 leads to the following equation that relates the male-female employment ratio to the female-male wage ratio, specific vocational training time and a vector of quantitative indicators of factors considered necessary for effective job per-formance.1 (i) log ( L q m i A q f i ) - a.iog(wfiAm.) + e.svpi + x i A + U i Table IV lists variables that may be included as elements in the X vector. A constant, denoted by VC, may be included as an element in the X vector. The inclusion of a constant is justified as a result of the assumption of linear approximation that yielded equation 1. Linear approximation by a Taylor expansion, for example, produces 2 a constant term dependent upon the point of approximation. The wage and employment data were adjusted by the methods described in Chapter 4 for differences between men and women in time worked. It would be desirable to adjust for other observable variables that affect the quantity or quality of work as opposed to the worker trait vari-ables that indicate requirements of each occupation. Age, sometimes used as a proxy variable for experience (and sometime for job in-eligibility) , and education are two observable variables which can be included in equation 1 so as to adjust for their effect on the quantity or quality of male and female labour. The ratio of the average age of males to the average age of females employed in the i occupation will be denoted by MATPA^ . The value of the correlation coefficient between average edu-cation of males and general educational development, GED, is 0.82. -94-The correlation coefficient between average education of females and GED is 0.73. It is not necessary to include each of these variables. GED is included to measure the minimum general educational development required in each occupation. The ratio of the average number of years of school attained by males to the average attained by females, METFE^ , is included to adjust for possible differences by sex in the quality of labour. For convenience, MATFA^  and METSE^  are included as elements of the vector. There are fifty one possible elements in the X vector. In order to reduce the number of elements in X to manageable proportions the parameters in equation 1 were estimated by means of a stepwise re-gression program. The program considered all of the possible elements in X and included only those elements in the regression that had coef-ficients which were significantly different from zero at or below the 0.05 level. Table XIII lists estimates for the parameters in equation 1. In equation 2 the wage ratio was deleted from the regression. In equation 3 the wage ratio was forced to be included in the regression since its coefficient is significantly different from zero at the 0.11 level and the stepwise program would otherwise have deleted i t . Notice that with the exception of the coefficient for TY all coefficients in equation 3 are consistent in size (within 10%) and sign with those in equation 2. When the wage ratio was included in equation 3 the stepwise program added P and deleted II. With the use of a stepwise program this sort of minor change is not surprising for variables that are close to the cutoff point. -95-Table XIII Estimates of Parameters equation 2 equation 3 . Coeffi- Standard Coeffi- Standard Variable cient Error cient Error VC 8.2662 0.7650 8.8724 0.7556 SVP 0.2715 0.0614 0.2585 0.0607 N 2.8191 0.5911 3.0106 0.6033 S 4.2661 0.6632 4.9852 0.7118 P -2.8437 -1.6209 0.8130 K 0.7892 -2.5548 0.8522 E 2.8106 0.9410 2.1950 0.9859 TX -2.0997 0.4126 -2.0988 0.4117 TY -0.6501 0.2116 -0.4868 0.2141 11 0.4270 0.1960 12 -O.665L 0.2721 -0.7209 0.2719 14 -3.0618 0.3579 -3.2848 0.3645 MW 0.1400 0.0620 0.1581 0.0621 METFE -8.9857 0.6218 -8.8841 0.6222 In (Wf/l * ) nr 0.7700 0.4847 Coefficient of Determination 0.7839 0.7866 Standard Error 1.1426 1.1385 a) See Table IV, pp. 74-75 for definitions. We can compare the relative impact of the independent variables on the logarithm of the male-female employment ratio by reference to Table XIV. "Impact" is measured by the product of the square of a variable's regression coefficient and the ratio of the variance of the independent variable to the variance of the dependent variable. -96-Table XIV Means, Standard Deviations and "Impact"8, of Independent Variables Variable ln(L /L ) SVP <lm <lf N S P K E TX TY II 12 14 MW METFE m(w fA f f i) Standard Impact in Impact in Mean Deviation equation 2 equation 3 2.27984 2.38023 2.06423 1.86431 0.40104 0.22108 0.40239 0.19232 0.48310 0.17928 0.37485 0.14527 0.10641 0.09776 0.04639 0.21088 O.56701 0.49677 0.31959 0.46752 0.15979 0.36736 0.07217 0.25943 2.98866 1.64180 0.93357 0.16958 -0.37068 0.17371 0.04522 O.OI543 0.06856 O.078I9 0.11882 0.16225 0.01491 0.03012 0.02431 0.01332 0.00813 0.03460 O.03457 0.01841 0.01032 0.00703 0.01054 0.01238 0.11136 0.12817 0.00933 0.01189 0.40990 0.40069 0.00316 a) Impact is calculated as the product of the squared regression coefficient from Table XIII and the ratio of the variable's variance to the variance of ln ( k ^A^ ) . Education The impact of the education ratio is from 2.5 to 127 times greater than the impact of any other variable. The education ratio, METFE, was included in the regressions so as to adjust for possible differences in the quality of male and female labour. If this is £he actual effect of including METFE and education is considered to increase productivity, we would expect the coefficient for METFE shown in Table XIII to be positive. The negative coefficient for METFE indicates the rather strange result that male employment decreases relative to female employment as male education increases relative to female education. We could simply change our assumption and maintain that there is an inverse relation between education and productivity. -97-A less drastic but similar explanation of this strange result involves "over qualification". Employers might be reluctant to hire staff who are over qualified. A negative correlation between the male-female education ratio and the level of general educational development would be consistent with this explanation. However, the value of the correlation coefficient between METFE and GED is positive, 0.41. Multicollinearity can produce incorrectly signed regression coefficients. The Coefficient for METFE is, however, negative even in the simple regression shown as equation 4. The numbers in paren-thesis are standard errors of the regression coefficients. (4) In (L A , ) - 9.9749 - 8.2426 METFE qm qf (0.7779) (0.8199) R - 0.3449 Extreme observations can produce unexpected results in regression analysis. In order to determine whether the negative METFE coef-ficient is related to extreme observations we can examine simple regressions of In O^^jJ^qf) o n METFE by the intervals of the male-female employment ratio used in Table XII. Equation A is based on the interval 0.023 - 1.122. Equation B is based on the interval 1.122 - .54.781. When an equation was estimated for the interval 54.781 - 2673.490 the results were insignificant. However, when the 7 professional and technical occupational groups were excluded from this interval the results became significant. The average education ratio for the 7 occupations is 1.25 compared to an average of 0.73 for the remaining 40 occupations. Equation C is based on 40 occu-pations for the interval 54.781 - 2673.490. -98-(A) ln (L A J - 5.2552 - 5.95064 METFE 9 *f (0.1676) (1.79341) R « 0.2446 (B) In (L A ,) - 6.0858 - 4.1975 METFE ? *m *f (0.08906) (0.73697) R - 0.2294 (C) ln (L /L .) - 8.12895 - 3-9689 METFE ? *n *f (0.15866) (1.80579) R - 0.1128 The coefficient for METFE remained negative in all cases. It is possible that the negative coefficients in equations A, B, and C result from a relation between the wage ratio and the education ratio. As male education increases relative to female education we might expect the female-male wage ratio to decline. The simple correlation coefficient between METFE and ln (W^^f^) for our sample of 194 occupations is only -0.13. The samples used for equations A', B', and C correspond to those used for equations A, B, and C. (A') ln (L A J - 4.91094 - 5.30001 METFE + 1.00632 ln (¥./» ) ? *m *f (1.96145) (1.87067) (0.86838) ^ m R - 0.2741 (B«) ln (L JL J - 6.13891 - 4.21201 METFE + 0.10102 ln (W./W ) oX qf (0.75523) (0.74417) (0.53123) f m R - 0.2296 (C) ln (L /L _) » 8.73911 - 3.79142 METFE + 2.20211 ln (W./W ) qtf qf (1.27174) (l.70649) (0.92780) f m R - 0.2300 The coefficient for METFE remained negative even when ln (W^/V^) was included in the regressions. The simple correlation between METFE and ln (WfAm) remained low for each of the subgroups. The simple correlations between METFE and ln ( Hf/ W m) are -0.30, -0.16, and -0.04 for samples A', B', and C respectively. -99-It is possible that METFE serves as a proxy variable for strength, MW. The value of the correlation coefficient between METFE and MW is -0.45. As male education increases relative to female education the quantitative indicator for strength decreases. Female employment increases relative to male employment as strength required decreases. Finally, consider occupations that are dominated by one of the sexes. It is possible that employers would only hire members of the non-dominant sex i f they had education in excess of that possessed by the dominant sex. It is sometimes said that a person in a minority group must be exceptional in order to be accepted. A few exceptional women might be accepted into male dominated occupations. A few men training for higher positions, e.g. teachers training to become principals, might be accepted into female dominated occupations. This interpretation is consistent with Gaplow's suggestion that few 3 groups consist of both sexes. It is possible with this inter-pretation that the negative coefficient indicates discrimination. If more education increases productivity, the acceptance of exceptional individuals of the excluded sex could counteract the effect of dis-4 crimination coefficients as described by Becker. Employer Investment Specific vocational preparation, SVP, was defined in Chapter 4 as the type of training "required to learn the techniques, acquire information, and develop the facility needed for average performance in a specific job-worker situation."^ This can be contrasted with GED, general educational development, which was not significant in -100-equations 2 or 3- General educational development was defined in Chapter 4 as education of a general academic nature. Interpreting SVP as a measure of employer investment (as was assumed in Chapter 3) the positive coefficients indicate that men are employed in preference to women as employer investment increases. Across the 19-4 occu-pations the average value of SVP is 2.06 years of training. The coefficient of SVP is 0.27. Since the employment ratio is expressed in logarithmic form, this can be interpreted as meaning that an additional year of vocational training requirement results in a 27 per cent increase in the employment ratio. SVP in our regressions is particularily important since it pro-vides a major reason why employers act under uncertainty. However, as mentioned in Chapter 3» our proxy might actually be a measure of employee investment or a measure of both employee and employer investment. If the alternative interpretation of the proxy were correct, the positive coefficient could be interpreted as being consistent with Mincer's suggestion regarding sex differences in human capital investments.^ Aptitudes The aptitudes N, S, P, K, and E are included in the regressions in Table XIII. The aptitude component of the worker traits is defined as "the specific capacities or abilities required of an individual 7 in order to facilitate the learning of some task or job duty." The aptitudes are measured by the cumulative frequency distribution dis-cussed in Chapter 4. The numerical aptitude, H, is defined as the "ability to perform -101-arithmetic operations quickly and accurately." Men might be believed to be better at numerical tasks than women. Whether there is evi-dence to support this belief will be examined, <below. The coefficients in Table XIII on numerical ability, N, are positive. This indicates that, other things equal, as the amount of numerical ability required in order to perform in an occupation increases, the number of men employed in the occupation increases relative to the number of women employed in the occupation. The spatial aptitude, S, is defined as the "ability to compre-hend forms in space and understand relationships of plane and solid 9 objects." This is "frequently described as the ability to visualize objects in two or three dimensions, or to think visually of geometric forms."10 The coefficients in Table XIII on spatial ability, S, are positive and more than six times greater than their standard errors; furthermore, the impact of spatial aptitude as shown in Table XIV is second only to the education ratio. The sign of the coefficient is consistent with the attitude that men have superior spatial ability than women. The magnitude of the impact of spatial ability is consistent with either an actual and important sex dif-ference in spatial ability or a general acceptance of a sex stereotype. Form perception, P, is defined as the "ability to perceive detail in objects or in pictorial or graphic material" and the ability to "make visual comparisons and discriminations and see slight dif-ferences in shapes and shadings of figures and widths and lengths of lines." 1 1 This is more of an ability related to sorting objects as compared with the spatial aptitude which refers to abstract -non-visualization. Unlike S the coefficient on form perception,p, is negative. This corresponds with a stereotype that men are better at spatial perception and women are better at fine detail such as sorting. These stereotypes are examined below. Motor coordination, K, is defined as the "ability to coordinate eyes and hands or fingers rapidly and accurately in making precise 12 movements with speed." This ability is similar to form perception in that i t is a useful trait for such things as typing or sorting. Across the 194 occupations the correlation coefficient between P and K is 0.59 as compared to a value of 0.07 between N and K. Like P the coefficient on motor coordination, K, is negative which indicates that there are more women (lower L q f f iA q£ ratio) in occupations calling for precision. Eye-hand-foot coordination, E, is defined as the "ability to move the hand and foot coordinately with each other in accordance 1 3 with visual s t i m u l i . T h i s is the sort of ability that is re-quired of a truck driver. The coefficient of E is positive. Temperaments The two temperaments TX and TY of the twelve discussed in Chapter 4 are included in both regressions in Table XIII. The temperament component of the worker traits "consists of twelve different types of occupational situations to which workers must J.4 adjust. The temperaments are measured with a zero-one dummy variable. A value of one for TX indicates "situations involving the interpretation of feelings, ideas or facts in terms of personal -103-viewpoint.1,1 Only 4.6 per cent of the 194 occupations call for this trait. The negative coefficient for this trait corresponds to a stereotype that women are better at expressing "personal view-points" or interpreting "feelings". A value of one for TY indicates "situations involving the pre-cise attainment of set limits, tolerances, or standards."1^ Of the 194 occupations 56.7 per cent call for this trait. The coefficient on this trait is negative. This is consistent with the stereotype that women are better at precision tasks. Compatible results were obtained for the aptitudes P and K. Interests Three interests, II, 12 and 14, are included in Table XIII. "The interests component of the worker traits is defined as a pre-ference for certain types of work activities or experiences, with accompanying rejection of contrary types of activities or experi-17 ences," The interests are measured by a zero-one dummy variable. A value of one for II indicates "situations involving a pre-18 ference for activities dealing with things and objects." This indicates a rejection of "situations involving a preference for 19 activities concerned with people and the communication of ideas." Of the 194 occupations 34 per cent indicate the presence of this interest requirement. The positive coefficient for II in Table XIII corresponds to a stereotype that men are less people oriented than women. II had the lowest impact of the variables included in equation 2. The stepwise program delected II from equation 3» A value of one for 12 indicates "situations involving a pre-20 ference for activities involving business contact with people." -104-This indicates a rejection of "situations involving a preference 21 for activities of a scientific and technical nature." Gf the 194 occupations 16 per cent indicate the presence of this interest re-quirement. The coefficient for 12 is negative which (like l l ) corresponds to a stereotype that women are more people oriented than men. A value of one for 14 indicates "situations involving a pre-ference for working for people for their presumed good as in the social welfare sense, or for dealing with people and language in 22 social situations." This indicates a rejection of "situations involving a preference for activities that are nonsocial in nature, and are carried on in relation to process, machines and techni-23 ques." J Only 7.2 per cent of the 194 occupations indicate the presence of this interest requirement. The negative coefficient corresponds to a stereotype that women are more socially oriented. The value of the coefficient is almost nine times greater than its standard error and the impact of this trait is similar in magnitude to spatial ability. Working Conditions The worker traits also included indicators for working conditions and physical capacities. Physical capacities reflect "the specific 24 physical aspects of occupations that must be performed." Working conditions reflect "the physical surrounding in which specific occu-pational activities are carried out." ^  Of the 13 indicators of working conditions and physical capacities that were discussed in Chapter 4, only MW is included as a significant variable in Table XIII. MW, the quantitative indicator of strength, indicates the maximum weight that may be lifted in an occupation. MW is measured in tens of pounds and has an average value over the occupations of 2.99 or 29.9 pounds. As might be expected the coefficient for MW is positive. However, its impact makes it the smallest of the values in Table XIV. This is a useful result to recall when one hears allegations that strength determines the exclusion of women from many occupations. Elasticity Since the employment ratio and the wage ratio were expressed in logarithmic form, the coefficient for the logarithm of the wage ratio in equation 3» 0.77, can be interpreted as the elasticity of substitution between male and female labour with respect to their wage ratio. However the equation 3 estimate of the elasticity of substitution, a, is not significantly different from zero at the 0.05 level. If it were accepted that the elasticity of substitution were not significantly different from zero, then the answer to Cassel's paradox would be trivial. If this were the case it would mean that male and female labour are completely distinct factors of production. This would be consistent with complete occupational segregation by sex at a job title level within each occupational group. If we weaken the strength of our test to a significance level of 0.11, we can accept the equation 3 estimate as being different from zero. Without weakening the strength of our test we can examine alternative estimates of 0. Examination of alternative estimates and the problems involved in estimating a will allow us to reject the conclusion that there is complete occupational segre--106-gation by sex. Ernst R. Berndt has discussed the problems of estimating 26 elasticities of substitution. Let 0*^  be the estimate of the elasticity of substitution obtained by regressing the logarithm of a factor input ratio on the logarithm of the factor price ratio and 2 a set of other exogenous variables. Let R^  be the squared correlation coefficient for this regression. Let 0£ be the estimate of the elasticity of substitution obtained by regressing the logarithm of a factor price ratio on the logarithm of the factor input ratio and the set of exogenous variables, and let R^  be the squared correlation coefficient for this regression. Berndt has shown that for a simple *C 2 2^ 27 regression (only) that = 0^  since O^/o^ " R i = R2 ™ 1* For purposes of comparing estimates of the elasticity of substi-tution we can regress the female-male wage ratio on the male-female employment ratio. We have not developed a complete labour market model that would rationalize this inverse specification. However, by assuming that some inverse relation exists we are able to more closely examine our estimate of the elasticity of substitution. Equations 5 and 6 provide alternative estimates of the elasticity of substitution. The numbers in parentheses are standard errors of the regression coefficients. (5) m ( w / g = -0.3866 +.7 0.006886 In (L /L J (0.0173) (0.005243) q K ^ R - 0.0092 2.7660 + 1.3H7 In ( W J W J (0.4028) (0.9843) 1 m -107-The estimate of a obtained from equation 5 is 143.1 (1/0.006886). While this estimate is not significantly dif-ferent from zero at the 0.05 level, it serves to illustrate how sensitive our estimate is to variations in the specification of the underlying model. Equations A', B', and C* provide further alternatives. The equation C estimate (p. 98) of o* for male dominated occupations is 2.2, and this estimate is signifi-cantly different from zero at the 0.05 level. Two further alternative estimates can he obtained by utilizing our complete data base. In equation 7 we assume the elasticity of substitution has an infinite value for all occupations. In equation 8, as in all other equations other than 7, we assume that the elasticity of substitution has a finite but constant value for all occupations. (7) In (Wf/Wm) - X»A' + u» (8) In (W^) - X»A" + (l/o)ln(L q J B A q f ) + u" As in the case of equations 2 and 3 "the elements of X' and X" were chosen by estimating the parameters in equations 7 and 8 with 28 the use of a stepwise regression program. The results of this estimation are shown in Table XV. Table XV Estimation of Parameters for Measuring Elasticity equation 7 equation 8 Goeffi- Standard Coeffi- Standard Variable cient Error cient Error VG 0.1255 0.1484 -0.2809 0.0313 N -0.1582 0.0576 T7 -0.2584 0.0699 -0.2419 0.0693 MATFA -0.2109 0.1064 METFE -0.2338 0.0754 In (L Jh ,) 0.0130 0.0052 Coefficient of Determination 0.1275 0.1243 Standard Error 0.1640 0.1643 -108-The estimate of the elasticity of substitution obtained from equation 3 is 0.7700. This estimate is only significant at the 0.1097 level. By contrast, the estimate of one over the elasticity of substitution obtained from equation 8 is 0.0130. This estimate is significant at the 0.0138 level. Hence, from equation 8 we have an estimate of the elasticity of substitution with the value 72.4710 (l/0.0130). The estimate from equation 8 is 94 times greater than the estimate from equation 3. The merits of measurement, let alone estimation, without theory, are certainly open to considerable debate. Nevertheless, the pre-ceding discussion serves to cast doubt on our equation 3 estimate of the elasticity of substitution. Discrimination As shown in Table XIII the traits N, S, P, and K significantly affect the male-female employment ratio. If there is no difference in the distribution of a worker trait between the sexes and yet the trait affects the male-female employment ratio we may say that the evidence indicates statistical discrimination. Numerical aptitude (N), spatial aptitude (S), form aptitude (P), and motor co-ordination (K) are defined in terms of tests in the General Aptitude Test Battery. The manual for the test battery includes a discussion of the effect of sex on the test scores. The discussion is based on 29 pairs of samples of boys and girls. A total of 3,598 boys and 3,852 girls are included in the 29 pairs of samples. Twenty of the pairs were those used in the development of tentative 9 and 10 grade GATB norms — 7 pairs of 9 grade -109-samples, 5 pairs of 10**1 grade samples and 8 pairs of 12^ grade samples. The additional 9 pairs of 12th grade samples were taken 29 from separate state tests. The manual for the test battery re-ported that "For each pair of samples the difference was found between 30 the mean scores of boys and girls on each aptitude."^ The manual did not report standard deviations or other statistics that could be used to test the significance of the differences. Table XVI lists the median of the differences between the mean scores of boys less the mean scores of girls. Table XVI Median of Difference in Means for Paired Samples of Boys and Girls of General Aptitude Test Battery Scores ~, Sign of Regression Symbol Description Median of Difference-3 Coefficient8- N numerical 0b + S spatial +7 + P form -6 K motor co-ordination -8 a) from Table XIII b) The manual reports this difference as being 3 points or less and inconsistent in sign between the paired samples. 32 Further indications of the distribution of aptitudes by sex were reported in the manual for a sample of 2,439 persons from four states. The sample consisted of 1239 males and 1200 females. The ages of the sample subjects ranged from 17 years to 74 years. For each of the GATB aptitudes an analysis of variance was performed to test for the significance of age, sex, and the interaction of age and sex on test scores. The effect of age on test scores was significant at the 0.01 level for all aptitudes except verbal (V) and numerical (N). There was a significant effect of age on numerical aptitude at the 0.05 level. The effect of sex on aptitude -110-scores was significant at the O.Ol level for clerical perception (Q), motor co-ordination (K) , and finger dexterity (F). Mean female scores tended to be higher than mean male scores for these aptitudes. No significant effect of sex was found for the remaining aptitudes. A significant interaction (0.05 level) between age and sex was found only for numerical aptitude (N). j Statistical discrimination is not indicated by the significance in the regression equations of spatial aptitude (s), form aptitude (P), or motor co-ordination (K) on the basis of the 2 9 pairs of samples. These results, however, were not tested for significance. On the basis of the age-sex study only motor co-ordination (K) has a dis-tribution between the sexes that is consistent with our regression results. The age-sex study suggests that statistical discrimination is indicated by the significance in the regression equations of numerical aptitude (N), spatial aptitude (S), and form aptitude (p). Any conclusions regarding numerical aptitude must be tempered by the significant age-sex interaction on numerical aptitude. Due to lack of data our regression analysis did not consider such interactions. Although statistical discrimination is not indicated by the significance of motor co-ordination (k), it is possible that qualified discrimination could be rationalized on the basis of sex differences in K. Employers might simply refuse to hire men for jobs requiring a large amount of motor co-ordination. However it might also be the case that employers hire both sexes without discrimination and then dismiss the unqualified workers. If the worker traits were indepen-dently distributed and i f we knew the variance in the quantitative - I l l -indicator of each trait by sex, we could test between the preceding alternatives. A standard deviation for worker qualification could then be calculated for each sex as the square root of the sum of the variances for each trait by sex weighted by the square of the corresponding regression coefficient. On the basis of the data available to us we cannot explore this alternative. The five remaining traits shown in Table XIII, E, TX, TY, 12, and 14, do not appear to have been analyzed in any study regarding sex differences. While literature exists related to batteries of tests on interests, this literature does not consider sex differences 34 in the same interest. Nor does any study appear to define interests in precisely the same manner as is done in Worker Traits. ^ Eye-hand-foot co-ordination and the temperaments appear to be uniquely defined variables for the study of Worker Traits. Extensive literature concerning sex differences in child develop-37 ment does exist. I have not been able to find any studies that have extended into the traits we considered when examining occu-pational requirements. Lacking any basis for the comparison of the actual distribution of these traits between the sexes we are unable to draw conclusions regarding statistical discrimination that might be indicated by these results. In view of this lack of data, however, i t is also unlikely that any employer could say that he "knows" that men perform better than women in an occupation because of their differences with respect to one of these traits. Generalizations regarding sex dif-ferences must be based on personal experience or stereotypes. Alter--112-natively, employers might view the lack of data as indicating that there is no evidence to prove that men and women are equal in ability. Stereotypes While we cannot use five of the traits to determine whether statistical discrimination exists, we can use the results to suggest a sociological or psychological study. It would be worthwhile for a researcher in one of these disciplines to extend this study to measure sex stereotypes as they affect occupational segregation by sex. Our results suggest the presence of stereotypes that women are better at precision tasks, more people oriented, and more interested in social welfare. It would be interesting for a sociologist to determine the prevalence of such stereotypes and for a psychologist to determine whether the stereotypes correspond to reality. Conclusions 1) With the exception of clerical occupations and unskilled manual labour, extreme concentrations of the sexes do not appear to characterize any single occupational division. Occupational segre-gation is not as apparent at the aggregate census division level as it is at the census occupational class level. 2) The good fi t of the regression results supports the U.S. National Manpower Council's observation that 'Employers hire persons of the sex supposedly possessing the characteristics considered necessary for effective job performance. 3) If it exists, it is difficult to detect any form of dis-crimination through the use of aggregate data. Data are lacking on - 113 -sex differences for worker traits. The significance of numerical aptitude in determining relative male-female employment indicates statistical discrimination but this indication is weakened by the effect of an interaction between age and sex in numerical aptitude. The significance of spatial aptitude and form aptitude in determining relative male-female employment indicates statistical discrimination. -114-Chapter 5 s Notes 1. See p. 63. 2. R.E. Johnson and F.L. Kiokemester, Calculus with Analytic Geometry (Boston: Allyn and Bacon, Inc., 1964), p. 399. 3. See p. 16. 4. See p. 28. 5. U.S. Department of Labor, Bureau of Employment Security, Estimates  of Worker Trait Requirements for 4.000 Jobs (Washington: U.S. Government Printing Office, 1956), p. 110, here after cited as Traits. 6. See p. 19. 7. Traits. P. 121. 8. Traits. pp. 121-122. 9. Ibid. 10. Ibid. 11. Ibid. 12. Ibid. 13. Ibid. 14. Traits, P. vi. 15. Traits, P* 131. 16. Ibid. 17. Traits. P. vi. 18. Traits. P. 136. 19. Ibid. 20. Ibid. 21. Ibid. 22. Ibid. 23. Ibid. 24. Traits. P. vi. -115-25. Ibid. 26. Ernst R. Berndt, "A Reconciliation of Cross-Section and Time-Series Estimates of the Elasticity of Substitution", University of British Columbia, Department of Economics, Discussion Paper No. 95, mimeographed, 1973. 27. Ibid.. p. 8. 28. "UBC TRIP", University of British Columbia, Computing Centre, mimeographed, 1971. 29. United States Department of Labor, Manual for the General  Aptitude Test Battery, Section III (Washington: U.S. Government Printing Office, 1967). 30. Ibid., p. 219. 31. Ibid. 32. Ibid. 33. Ibid., pp. 224-226. 34. E.K. Stong, Jr., Vocational Interests of Men and Women (London: Oxford University Press, 1943). 35. Traits. p. 136. 36. Ibid., p. 131. 37. Eleanor E. MacCoby, The Psychology of Sex Differences (Standford: Standford University Press, 197^). 38. National Manpower Council, Womanpower (New York: Columbia University Press, 1957), p. 233; see p. 5^ above. -116-Selected Bibliography Archibald, Kathleen, Sex and the Public Service. Ottawa: Queen's Printer, 1970. Arrow, Kenneth, "Some Models of Racial Discrimination in the Labor Market". RAND Corporation research memorandum RM-6253-RC, multilith, Santa Monica, 1971. Baetjer, Anna M., Women in Industry. Philadelphia: W.B. Saunders Company, 1946. Becker, Gary, The Economics of Discrimination. Chicago: University of Chicago Press, 1957. Berndt, Ernst R., "A Reconciliation of Cross-Section and Time-Series Estimates of the Elasticity of Substitution", University of British Columbia, Department of Economics, Discussion Paper No. 95t 1973. Blaxall, Martha and Barbara Reagan, Women and the Workplace. Chicago: University of Chicago Press, 1976. Brown, E.H. Phelps, and J. Wiseman, A Course in Applied Economics. London: Sir Isaac Pitman and Sons Ltd., 1964". Brown, E.H. Phelps, "Equal Pay for Equal Work", The Economic Journal. 59:235 (September, 1949). Caplow, Thedore, The Sociology of Work. Toronto: McGraw-Hill Book Company, 1964. Christ, Carl P., Econometric Models and Methods. New York: John Wiley & Sons, Inc., 1966. Department of Labour, Equal Pay for Equal Work. Ottawa: Queen's Printer, 1959. Department of Labour, Implications of Traditional Divisions Between  Men's Work and Women's Work in our Society. Ottawa: Women's Bureau, Department of Labour, 1964. Department of Labour, Occupational Histories. Ottawa: Queen's Printer, 1959. Department of Labour, Wage Rates., Salaries and Hours of Labour. Ottawa: Queen's Printer, 1970. Dominion Bureau of Statistics, 1961 Census of Canada. Ottawa: Queen's Printer, 1963. Dominion Bureau of Statistics, Occupational Classification Manual  Census of Canada. 1961. Ottawa: Queen's Printer, 1961. -117-Edgeworth, F.Y., "Equal Pay to Men and Women for Equal Work", The Economic Journal 32:128 (December, 1922). Fawcett, Millicent G., "Equal Pay for Equal Work", The Economic  Journal 28 (March, 1918). Friedman, Milton, Price Theory A Provisional Text. Chicago: Aldine Publishing Company, 196*2. Ginzberg, E l i , "Paycheck and Apron ~ Revolution in Womanpower", Industrial Relations 7 (May, 1968). Gross, Edward, "Plus Ca Change ...? The Sexual Structure of Occupations Over Time", Social Problems 26 (Fall I968). House of Commons Debates 115s126 (May, 1971). Ottawa: Queen's Printer. House of Commons Debates 115:240 (December, 1971). Ottawa: Queen's Printer. International Labour Office, Job Evaluation. Geneva: La Tribune De Geneve, i960. International Labour Office, Women Workers in a Changing World. Geneva: La Tribune De Geneve, 19W* Lewis, Hartley V., 'The Importance of Turnover Costs in the Male-Female Wage Differential", unpublished Ph.D. dissertation. New York: The University of Rochester, 1970. MacCoby, Eleanor E., The Psychology of Sex Differences. Standford: Standford University Press, 1974. Meltz, Noah M. Changes in the Occupational Composition of the Canadian Labour Force. 1931-1961. Ottawa": Queen's Printer, 1965. Mill, John Stuart, Principles of Political Economy. New York: Augustus M. Kelly, 1961 reprint of Toronto: Longman's, Green and Co., 1929. National Bureau of Economic Research, Aspects of Labor Economics. Princeton: Princeton University Press, 1962. National Manpower Council, Womanpower. New York: Columbia University Press, 1957. Oaxaca, Ronald L., "Male-Female Wage Differentials in Urban Labor Markets", Princeton University, Industrial Relations Section, Working Paper No. 23, 1971. -118-Oppenheimer, Valerie Kincade, The Female Labor Force in the United  States. Berkeley: University of California, Institute of International Studies, 1970. Oppenheimer, Valerie Kincade, "The Sex Labeling of Jobs", Industrial  Relations 7 (May, 1968). Ostry, Sylvia, The Female Worker in Canada. Ottawa: Queen's Printer, 1968. Phelps, Edmund S., "The Statistical Theory of Racism and Sexism", American Economic Review 62:4 (September, 1972). Phelps, Edmund S., et al., Microeconomic Foundations of Employment  and Inflation Theory. New York: W.W. Norton & Company, Inc., 1970. President's Commission on the Status of Women, Private Employment. Washington: U.S. Government Printing Office, 1963. Rathbone, Eleanor F., "The Remuneration of Women's Services", The Economic Journal 27 (March, 1917). Reder, Melvin W., "The Theory of Occupational Wage Differentials", American Economic Review 45 (December, 1955)• Robinson, Joan, The Economics of Imperfect Competition. London: MacMillan & Co. Ltd., I965). Rosenbluth, Gideon, "The Structure of Academic Salaries in Canada", Canadian Association of University Teachers Bulletin 15:4 (April, 1967). Rossi, Alice S. (ed.), Essays on Sex Equality. Chicago: University of Chicago Press, 1970. Royal Commission on the Status of Women in Canada, Report of the Royal  Commission on the Status of Women in Canada. Ottawa: Information Canada, 1970. Samuelson, Paul Anthony, Foundations of Economic Analysis. New York: Atheneum, 1967. Sanborn, Henry, "Pay Differences Between Men and Women", Industrial  and Labor Relations Review 17:4 (July, 1964). Scoville, James G.. The Job Content of the U.S. Economy, 1940-1970. New York: McGraw-Hill Book Company, 196*9. Scoville, James G., The Job Content of the Canadian Economy, 1941. 1951. and 1961. Ottawa: Queen's Printer, 1967. -119-Spence, Michael, Market Signaling. Cambridge: Harvard University Press, 1974. Stieber, Jack, The Steel Industry Wage Structure. Cambridge: Harvard University Press, 1959. Stong, Edward Kellogg, Vocational Interests of Men and Women. London: H. Milford, Oxford University Press, 1943. Thurow, Lester, Poverty and Discrimination. Washington: The Brookings Institution, 1969. Tobin, James, "Estimation of Relationships for Limited Dependent Variables", Econometrica 26 (1958). Turner, Marjorie, Women & Work. Los Angeles: Institute of Industrial Relations, University of California, 1964. U.S. Department of Labor, Bureau of Employment Security, Estimates of  Worker Trait Requirements for 4.000 Jobs. Washington: U.S. Government Printing Office, 1956^  U.S. Department of Labor, Economic Indicators Relating to Equal Pay. Washington: U.S. Government Printing Office, 1963. U.S. Department of Labor, Manual for the General Aptitude Test Battery. Washington: U.S. Government Printing Office, I967. Webb, Sidney, "The Alleged Difference Between the Wages of Men and Women", The Economic Journal 1:4 (December, I891). Webb, Sidney and Beatrice, Industrial Democracy. London: Longmans, Green and Co., 1902. -120-Appendix A: Job Content of Census Occupational Classes The results of this study could not be replicated without reference to the construction of the data series for worker traits. The quantitative indicators of the traits for each census occupational class depend on the jobs that were sorted into the class. This computer produced appendix lists the occupations by number as they are referred to in the Classification Manual Census  of Canada. 196l. Beneith each occupational class is listed the page number from Estimates of Worker Traits for 4.000 Jobs and the line number from the page that identifies a job sorted into the class. Also given are the volume I and volume II codes that identify the job from the Dictionary of Occupational Titles. -121-'AGE COL. VOL. I VOL. II PAGE GOL. VOL. I VOL. II OCCUPATION 1 2 6 81040 0X35 OCCUPATION 4 60 5 97610 0X810 OCCUPATION 5 94 27 t 97610 6X810 OCCUPATION 6 59 37 97120 0X810 OCCUPATION 8 14 7 74110 0X715 14 8 74120 0X715 76 31 91600 0X715 OCCUPATION 10 4 32 94920 0X71 1 9 3 791 10 4X6295 14 9 91620 0X715 14 10 91630 0X715 17 5 71010 0X825 22 30 72550 1X57 23 7 240210 1X57 23 30 97050 0X711 25 14 94930 0X702 26 33 95030 0X625 26 34 94220 0X625 26 35 94220 0X625 26 36 95010 0X625 26 37 95040 0X625 28 16 118740 1X50 48 31 26100 0X702 59 30 71210 0X825 60 1 98830 0X849 60 4 72210 0X810 60 8 22300 0X810 61 6 95050 0X625 64 20 98570 0X810 75 16 99840 0X625 75 22 97810 0X810 76 8 26100 0X702 78 36 68210 3X994 78 37 99320 0X614 79 1 94330 0X712 80 38 98710 0X849 91 18 98720 0X849 94 19 98810 0X849 94 21 99110 0X842 94 22 98850 0X849 94 25 98860 0X849 94 28 31100 0X600 108 6 553330 4X6181 109 13 98730 0X849 OCCUPATION 101 20 23 16010 0X742 44 28 19010 0X744 49 15 16010 0X742 50 29 16010 0X742 83 21 16010 0X742 OCCUPATION 102 2 18 19010 0X744 2 30 19030 0X742 3 35 19810 0X742 5 18 19010 0X744 22 26 19010 0X744 48 38 19010 6X744 50 29 16010 0X742 58 30 19010 0X744 72 14 19010 0X744 78 31 19010 0X744 107 11 19050 0X744 -122-PAGE COL. VOL. I VOL. I I PAGE COL. VOL. I VOL. I I OCCUPATION 104 61 24 18010 0X741 75 25 18010 0X741 82 26 18010 0X741 99 6 v 18010 0X741 99 7 68730 0X741 104 14 19010 0X741 OCCUPATION 105 14 17 17020 0X748 34 72 14 19010 0X744 77 77 16 17010 0X748 101 28 17010 15 17010 20 553920 0X748 0X748 4X6181 OCCUPATION 107 62 22 20010 0X741 70 82 26 18010 0X74 1 20110 0X741 18 15110 OCCUPATION 108 0X741 19 15010 0X741 OCCUPATION 109 2 11 19100 0X741 61 14 14100 0X704 61 15 14200 0X74 82 37 19910 0X741 101 5 39940 0X849 OCCUPATION 111 4 22 7210 0X704 19 10 7020 0X703 19 11 7020 0X703 19 12 7210 0X703 19 13 7030 0X703 19 14 7210 0X704 19 16 7020 0X703 19 17 7020 0X703 19 18 7830 0X703 19 19 7840 0X703 19 20 7020 0X703 19 21 7030 ' 0X704 70 12 7860 0X703 OCCUPATION 114 70 30 35730 0X704 OCCUPATION 119 45 31 35650 0X730 51 12 46880 0X12 61 18 35680 0X730 OCCUPATION 121 5 29 35330 0X703 8 35 35220 0X703 11 15 35230 0X730 37 23 35300 0X703 68 34 35310 0X703 70 8 25100 0X703 70 13 35340 0X703 72 16 35260 0X703 109 22 35280 0X730 105 10 105 13 34100 34100 OCCUPATION 124 0X702 105 11 0X702 105 14 34100 0X703 34100 0X702 -123-PAGE GOL. VOL. I 2 10 348910 27 24 35140 27 34 11200 75 29 11500 96 27 31010 52 1 32300 75 12 31100 96 25 31010 96 28 31010 96 30 30020 103 22 32200 46 27 32980 3 23 26200 28 17 26100 47 22 26100 60 40 26100 65 20 26100 66 23 26100 76 5 26100 95 1 26100 66 5 13100 65 7 33100 65 9 238300 65 13 33900 65 17 33650 65 19 33270 36 11 52800 65 21 32040 66 4 39920 VOL. I I PAGE COL. VOL. I VOL. I I OCCUPATION 129 0X703 2 12 35010 0X70 3 0X730 OCCUPATION 131 0X600 74 34 11100 0X810 0X600 96 23 '11500 0X600 0X600 103 22 32200 0X600 OCCUPATION 135 0X604 52 3 11500 0X60 4 0X810 96 22 31010 0X603 0X600 96 26 30110 0X600 0X600 96 29 30020 0X600 0X600 96 31 31010 0X601 0X600 OCCUPATION 139 0X600 52 2 32330 0X749 OCCUPATION 140 0X702 15 16 ,26100 0X702 0X702 45 29 26100 0X70 2 0X702 52 23 26300 0X702 0X702 64 25 26100 0X702 0X702 65 22 26100 0X702 0X702 70 29 26100 0X702 0X702 77 19 26100 0X702 9X702 104 12 26100 0X702 OCCUPATION 141 0X702 66 22 13100 0X702 OCCUPATION 142 0X702 65 8 33070 0X702 2X33 65 11 33870 0X702 0X702 65 16 33570 0X702 0X702 65 18 33560 0X702 OCCUPATION 143 0X702 -OCCUPATION 144 0X702 50 33 52800 0X702 0X702 70 28 52800 0X702 OCCUPATION 145 0X702 -124-•AGE COL. VOL. I VOL. I I PAGE COL. VOL. I VOL. I I OCCUPATION 146 19 34 39900 0X702 66 25 39960 0X702 OCCUPATION 147 70 11 25100 0X703 OCCUPATION 148 28 12 50060 4X6348 28 13 50070 0X702 28 14 50060 4X6348 28 15 50060 4X6348 36 9 50050 0X702 61 1 50010 0X703 67 7 50030 0X702 69 7 50010 0X703 70 10 50020 0X703 83 23 66410 0X703 109 2 50040 0X702 109 3 50040 0X702 OCCUPATION 151 53 29 22500 0X712 OCCUPATION 153 30 14 22400 0X7 12 55 33 22700 0X712 55 34 22100 0X712 • OCCUPATION 171 2 5 4 4260 0X12 4 11 44260 0X12 , 8 15 44010 4X6508 22 15 44240 4X6506 22 27 44210 0X12 49 18 44410 0X12 87 31 125930 1X44 i OCCUPATION 172 16 22 4410 ' 0X12 37 35 44030 0X12 67 24 4010 0X12 84 17 4310 0X13 OCCUPATION 174 23 26 6230 0X31 24 36 6920 0X35 24 38 6940 0X35 34 3 6440 0X35 34 4 6430 0X35 34 5 6300 0X35 34 6 6450 0X35 34 7 116010 1X20 34 8 6280 0X749 34 9 6000 0X35 34 10 6540 0X35 34 11 6460 0X35 34 12 6470 0X35 34 13 6480 ' 0X35 34 14 6250 0X35 34 15 6510 0X35 34 16 6520 0X35 34 17 6530 0X35 42 24 68320 0X35 57 14 6040 0X31 72 39 6050 , 0X31 73 11 6060 0X31 76 9 6970 0X35 79 14 6710 0X35 79 30 6930 0X35 84 17 6330 1X20 84 15 6320 0X81 OCCUPATION 176 4 8 24420 0X21 ' 20 22 37 24410 0X21 23 2 24230 0X21 6 24020 0X25 -125-•AGE COL. VOL. I VOL. I I PAGE COL. VOL. I VOL. i : 66 7 24250 0X21 66 8 24430 0X21 66 14 24120 0X26 105 17 24120 0X26 OCCUPATION 182 31 9 4 8040 0X772 31 10 69450 0X772 31 11 4 8050, 0X772 31 12 48060 0X772 31 13 4 8080 0X772 31 14 48010 0X772 31 15 48110 0X778 31 16 48120 0X772 31 17 48160 0X772 31 18 48130 0X773 31 19 48150 0X773 31 20 48160 0X772 31 21 48180 0X774 31 22 48210 0X772 31 23 4 8180 0X774 31 24 48220 0X772 31 25 48230 0X772 31 26 48010 0X772 31 27 48160 0X774 31 28 48250 0X772 31 29 48180 0X774 31 30 48260 0X773 100 37 48300 0X774 OCCUPATION 183 22 34 64500 0X773 45 30 64400 0X773 52 11 64300 0X773 95 2 64100 0X773 95 4 64100 0X773 95 5 64100 0X773 95 6 64100 0X773 OCCUPATION 186 33 28 36110 0X610 OCCUPATION 188 1 9 1200 0X711 1 10 1100 0X711 1 11 1200 0X711 1 12 1300 0X711 1 13 1400 0X71 1 5 2 1600 0X711 18 29 103020 1X29 OCCUPATION 191 29 24 39930 0X703 • OCCUPATION 192 16 24 27500 0X612 16 28 27210 0X612 29 34 27110 0X612 47 11 27,400 0X612 68 38 27200 0X625 -OCCUPATION 195 30 10 s 43300 0X15 42 2 43600 0X15 52 19 43400 0X15 107 36 43300 0X15 OCCUPATION 196 14 33 56410 0X749 70 19 56010 0X749 70 20 56110 0X15 70 21 56910 0X749 70 22 56310 0X749 70 23 56210 0X15 70 24 56450 1X59 -126-•AGE COL. VOL. Ii VOL. I I PAGE COL. VOL. I VOL. i : OCCUPATION 198 5 30 50010 0X703 6 13 66310 0X625 19 9 50220 0X704 21 16 50320 0X704 21 17 50420 0X704 23 33 61100 0X748 23 34 66030 0X748 23 35 66030 0X748 27 25 50450 0X703 27 26 50920 0X703 68 17 50380 0X704 76 16 441910 4X638 85 2 66930 0X703 89 31 66150 0X749 89 32 66020 0X49 90 1 66040 0X810 94 8 61400 3X749 98 9 50340 0X704 109 1 50400 0X749 OCCUPATION 199 6 25 46870 0X12 21 6 46910 0X741 21 s 7 46010 0X15 22 29 46920 0X12 29 2 48420 0X774 37 1 68710 0X614 40 23 68060 3X885 40 24 68070 3X885 48 17 46030 0X15 49 19 36910 0X610 53 17 39850 0X610 59 32 39820 0X810 60 2 39830 0X810 60 29 35760 0X704 61 3 46950 0X13 67 9 68060 3X885 75 28 68500 0X741 76 6 36250 0X610 96 21 66950 4X6309 100 1 48410 0X774 101 19 68320 0X31 OCCUPATION 201 1 14 101310 1X11 1 15 101030 1X20 1 16 101310 1X20 1 18 101310 1X29 1 19 101330 1X29 1 20 117010 1X40 5 1 101320 1X11 6 8 101330 1X29 10 37 101020 1X20 13 12 118840 1X50 16 29 101310 1X29 16 30 101520 1X20 16 31 101530 1X57 16 32 101530 1X57 16 33 101520 1X20 16 34 101530 1X57 23 24 101430 1X29 29 36 101520 1X20 39 17 125130 1X29 45 28 101020 1X20 47 10 103050 1X28 52 24 101410 1X28 52 25 101410 1X28 52 26 101410 1X28 52 27 101410 1X28 53 27 101360 1X29 73 37 101430 1X29 97 15 106020 1X20 97 16 106020 1X20 97 17 106020 1X20 97 18 106020 1X20 102 32 118940 1X57 102 33 101530 1X57 105 20 101310 1X29 105 21 101310 1X11 105 22 125910 1X22 OCCUPATION 203 1 32 125120 1X29 2 1 125410 1X44 8 32 125020 1X22 11 1 102010 1X20 11 2 102020 1X20 11 3 102030 1X29 23 5 125130 1X11 33 17 125220 1X44 1 •127' PAGE COL. VOL. I VOL. I I PAGE COL. VOL. I VOL. I I 33 18 125230 1X44 33 19 125240 1X44 33 20 125250 1X44 33 21 125230 1X44 37 26 125470 1X44 46 35 125430 1X44 54 3 125620 1X29 62 8 125220 1X44 74 1 102040 1X29 78 19 125920 1X44 89 30 125630 1X44 95, 33 125640 1X44 105 7 126660 1X29 OCCUPATION 212 39 27 138010 1'X 2 8 45 22 138010 1X28 54 12 103060 1X28 54 13 138010 1X28 92 34 118650 1X28 92 35 1 18660 1X28 92 37 13 8010 1X28 92 38 138040 1X28 93 20 78 8750 1X28 105 34 101420 1X28 105 35 138010 1X28 OCCUPATION 214 28 10 134010 1X20 38 4 134230 1X11 44 7 112460 1X28 51 7 134230 1X11 78 16 134150 1X28 79 21 134040 1X28 86 34 134150 1X28 87 1 134130 1X28 87 2 134140 1X28 87 4 138010 1X28 OCCUPATION 223 68 40 14 4270 1X50 91 12 144220 0X810 OCCUPATION 232 25 37 137180 1X23 56 20 133010 1X23 56 21 137120 1X23 73 19 137120 , 1X23 84 35 133010 1X23 92 9 137120 1X23 92 10 137140 1X23 96 36 137120 1X23 OCCUPATION 234 20 40 137340 1X22 28 34 137360 1X22 34 2 137360 1X22 64 18 137330 1X22 97 14 137330 1X22 101 9 137360 1X22 103 27 137320 1X22 104 28 137380 1X22 OCCUPATION 241 65 11 33870 0X702 OCCUPATION 249 1 6 101310 1X1 1 1 31 108010 1X11 1 34 101310 1X20 1 35 104010 1X29 2 3 112310 1X50 2 4 101310 1X29 2 8 108410 1X29 3 12 149850 0X730 3 32 118410 1X50 6 22 118810 1X29 6 23 101430 1X29 8 20 6 8770 0X614 8 30 103020 1X29 8 31 118820 1X29 11 9 10 8450 1X29 17 25 136030 1X59 18 30, 101310 1X29 18 31 125910 1X26 -128-/ PAGE COL. VOL. I VOL. I I PAGE COL. VOL. I VOL. I I 18 38 145030 1X28 20 20 1 17020 1X49 20 28 101020 1X20 20 39 105010 1X20 21 33 118830 1X29 22 11 1 15020 1X50 22 35 136060 1X40 23 28 1,12040 1X50 24 35 123020 1X49 25 5 1 18960 1X28 25 17 116010 1X20 25 18 116010 1X20 25 21 103020 1X29 25 22 101350 1X11 25 34 103020 1X29 25 35 106250 1X20 25 36 137340 1X22 26 9 1 12020 1X20 28 22 101530 1X50 30 1 102030 1X20 30 8 126030 1X20 3 0 9 118610 0X849 30 24 117010 1X40 31 7 123140 1X49 31 8 135100 1X40 36 34 118310 1X50 37 24 101360 1X29 37 27 112030 1X50 37 28 101480 1X29 37 30 68640 0X711 38 1 106560 1X20 , - 3.8 2 134020 1X28 38 5 125130 1X11 39 2 117010 1X40 39 3 117030 1X40 39 4 1 17020 1X49 39 5 117020 1X49 39 •6 117010 1X40 39 26 149750 1X29 41 32 118660 1X28 42 18 103040 1X28 42 20 103030 1X28 43 13 118970 1X11 51 5 118320 1X20 51 6 117020 1X29 52 18 101370 1X20 52 29 101020 1X20 52 30 103070 1X29 53 37 107200 1X57 55 32 ,68480 0X712 58 16 629510 1X28 59 17 118010 1X40 59 18 118010 1X40 60 20 112470 6X4309 61 22 149940 1X11 65 25 50010 0X702 65 26 123020 1X49 68 35 118020 1X40 69 25 103020 1X29 69 26 126020 1X1 1 69 27 103020 1X29 70 6 118320 1X20 73 21 125130 1X11 73 22 117010 1X40 73 23 108130 1X11 75 23 1 18660 1X28 75 24 118660 1X20 76 3 149240 1X28 78 5 125130 1X11 78 17 118430 1X543 78 35 118010 1X40 79 2 117010* 1X40 79 10 101430 1X29 79 16 107500 1X29 85 1 137340 1X22 85 8 118680 1X20 91 20 136010 1X11 91 21 136020 1X11 99 2 126030 1X20 99 3 126030 1X20 100 38 118930 1X28 101 4 136610 1X59 101 6 118970 1X11 106 28 145010 1X28 106 29 145010 1X28 107 38 138010 1X28 109 19 145010 1X28 OCCUPATION 307 89 24 155400 ,1X55 97 13 155200 1X50 OCCUPATION 312 41 6 161400 1X57 69 31 161100 1X57 105 1 161200 1X57 -129-PAGE COL. VOL, VOL. II PAGE COL. VOL. I VOL. II 70 185330 OCCUPATION 311 0X703 82 34 175440 1X55 OCCUPATION 325 46 13 175710 1X55 56 30 23200 0X600 56 31 120010 0X600 60 35 175810 1X57 82 31 170100 1X57 82 34 175440 1X55 82 35 175710 1X55 20 25 157400 OCCUPATION 331 1X50 103 31 157300 1X11 OCCUPATION 338 20 24 148730 1X50 38 35 148240 0X603 38 37 148150 0X715 84 4 148220 0X741 94 18 144250 1X28 OCCUPATION 339 -28 11 156010 1X55 42 19 156010 1X55 46 22 155600 1X55 OCCUPATION 401 40 16 263200 0X625 40 18 736250 6X229 OCCUPATION 403 28 24 265010 0X625 28 25 265020 0X625 28 26 266020 0X625 40 22 94940 3X994 73 16 266060 0X625 73 17 266050 0X625 73 18 266030 0X625 73 20 266240 0X625 OCCUPATION 405 45 11 261010 6X2290 46 21 240030 6X229 56 33 261110 0X625 106 11 261030 6X22'9 OCCUPATION 412 2 31 225370 2X56 , 50 10 225380 1X50 50 21 225210 0X825 50 22 225230 0X825 57 2 224510 1X28 60 30 243130 2X56 92 16 228010 2X12 92 17 98630 0X715 OCCUPATION 413 19 4( 226310 2X12 24 5 226320 2X12 24 7 226510 2X12 24 14 226610 2X12 24 17 226550 2X12 OCCUPATION 414 7 8 227130 2X52 -130-AGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 415 15 19 227910 2X52 25 30 227130 2X52 50 11 227140 0X825 89 13 227610 2X52 92 1 227210 2X52 95 28 805210 6X664 105 24 227110 2X52 105 25 227110 2X52 105 26 227110 2X52 105 27 227120 2X52 OCCUPATION 416 40 20 238100 0X702 65 12 238200 2X56 66 12 242100 2X56 • OCCUPATION 417 8 2 123140 1X49 8 3 2221 10 2X59 73 33 292300 2X59 73 34 292100 2X59 73 35 291100 2X59 OCCUPATION 419 6 6 602910 4X6675 13 36 209050 0X825 21 34 229410 2X12 22 33 209030 2X56 24 8 229710 2X12 / 24 11 229010 2X12 24 12 229020 2X12 30 3 229610 6X662 42 22 209060 2X59 55 7 224210 2X59 59 14 282150 2X59 59 15 206110 2X59 65 10 207050 2X33 68 24 229120 2X12 68 37 208010 2X59 83 20 229130 2X12 104 18 209070 2X59 OCCUPATION 431 1 30 2110 0X42 20 1 45210 0X44 27 30 45110 0X44 31 33 2250 0X42 59 10 62050 0X49 76 30 713013 6X4309 OCCUPATION 433 52 4 57410 0X606 52 5 57410 0X606 53 20 243040 2X59 59 29 57510 0X606 OCCUPATION 451 6 26 247120 2X56 7 31 232220 2X56 7 32 232150 2X56 7 33 247110 2X56 36 10 232230 2X56 OCCUPATION 452 33 5 557110 4X6651 , 33 23 557410 4X6651 41 28 957210 6X4457 43 19 557610 6X4651 46 37 557310 4X6651 103 9 957000 6X4651 OCCUPATION 453 36 22 295300 6X2493 36 23 295200 2X59 -131-AGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 454 18 24 282100 6X662 107 35 282300 6X662 OCCUPATION 455 36 25 65100 2X56 36 26 69430 2X56 OCCUPATION 456 47 13 236300 0X810 47 14 236310 1X57 47 15 396100 3X960 47 16 236100 0X810 OCCUPATION 457 6 28 240350 1X55 30 30 245100 2X59 57 30 248300 2X59 63 24 240920 1X57 90 26 556210 6X4409 98 27 240610 1X57 104 13 248100 2X59 OCCUPATION 459 4 33 242300 2X56 1 1 8 234100 2X59 18 36 243510 1X57 19 26 243960 2X33 21 19 243320 2X59 43 18 2439 10 2X59 50 19 243230 2X56 62 34 243420 1X59 79 19 243120 2X59 100 27 222710 1X50 OCCUPATION 510 13 30 749201 0X849 15 22 579020 4X6209 70 3 79210 0X849 77 33 576020 4X6386 78 2 505010 4X6385 101 7 98750 0X849 101 23 79220 0X849 106 22 576010' 0X849 OCCUPATION 520 3 2 41100 0X749 OCCUPATION 531 35 32 541040 4X2492 50 12 541030 4X2492 51 14 541060 4X2492 57 36 541010 4X2492 64 6 543100 4X2492 64 7 543200 ' 4X2492 80 37 541010 4X2492 109 6 541010 4X2492 OCCUPATION 532 40 8 542200 4X2492 40 10 542100 4X2492 40 15 542100 4X2492 OCCUPATION 534 23 17 92110 0X849 23 18 92210 0X849 OCCUPATION 535 12 8 905010 4X2109 12 10 538010 6X2492 -132-PAGE COL. VOL. I VOL. II 106 12 262100 28 18 573760 60 27 88030 76 32 88410 103 6 88020 37 8 88250 37 11 88210 103 7 88230 1 1 548040 10 5 548030 84 23 748010 OCCUPATION 6X2290 OCCUPATION 3X870 3X870 0X810 3X870 OCCUPATION 4X2102 4X2102 4X2102 OCCUPATION 3X870 3X870 3X870 PAGE COL. 537 VOL. I VOL. II 541 32 73 86 103 543 37 73 545 6 77 5 31 33 8 10 32 27 2 88120 88020 88310 88030 88240 88220 54 9102 548020 3X870 3X870 3X870 3X870 4 X 2 1 0 2 4 X 2 1 0 2 3X870 3X870 13 31 536010 18 28 736010 6 7 735100 82 10 735100 OCCUPATION 4X2492 OCCUPATION 6X2492 OCCUPATION 6X2492 6X2492 551 552 554 28 735300 6X610 23 10 736210 64 2 735200 96 32 591401 77 3 70 77 18 97 14 6 9 2 1 0 7 27 23 61600 61700 61300 19 1 4 2 0 1 0 9 1 4 2 3 1 0 OCCUPATION 556 4X2492 33 6X2492 102 OCCUPATION 563 3X996 96 ' OCCUPATION 581 0X42 OCCUPATION 582 0X749 38 0X749 77 0X748 77 OCCUPATION 584 1X11 85 1X50 97 16 14 36 22 24 10 10 736220 736260 33 737100 61500 61330 61320 142030 142320 4X2492 6X2492 3X111 0X748 0X748 0X748 1X57 1X50 -133-IGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 585 30 7 141160 1X28 85 10 142030 1X57 97 4 141120 4X6183 OCCOPATION 588 25 15 123140 1X49 28 8 123150 6X610 59 16 123140 1X49 61 8 932010 6X4211 67 20 248150 2X59 67 21 123140 1X49 83 6 123130 0X810 OCCUPATION 601 17 9 307100 3X112 17 10 307100 3X113 19 22 308100 3X117 38 24 304100 3X112 43 15 305010 3X124 46 31 301100 3X121 46 39 338200 3X128 OCCUPATION 603 6 31 344010 3X111 59 34 337100 0X831 .OCCUPATION. 605 7 40 307700 3X119 25 39 317200 3X113 46 40 339100 3X128 49 11 307100 3X115 65 14 338200 3X128 OCCUPATION 607 44 19 340010 3X128 OCCUPATION 609 9 29 348010 3X117 19 23 348030 3X117 22 23 333220 3X121 30 15 749525 6X2608 48 16 341100 3X117 50 3 349950 3X111 52 36 332150 3X100 53 31 756720 3X119 53 32 756730 3X119 66 6 340130 3X124 85 3 347200 6X2671 86 7 349310 3X114 OCCUPATION 611 11 6 591401 3X996 80 14 591401 3X996 87 23 591401 ; 3X996 i OCCUPATION 613 -26 16 68140 0X730 40 1 68170 3X994 40 17 68180 3X994 88 36 68220 3X994 OCCUPATION 615 9 13 430010 3X996 11 5 831010 6X669 11 23 430310 3X996 13 7 630030 3X996 13 8 591401 3X996 13 11 591401 3X996 18 26 830100 6X669 19 38 830100 6X669 19 39 591401 3X996 38 22 630140 3X996 46 28 830100 6X4409 49 30 591401 0X839 -134-PAGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II 53 6 573330 4X2493 57 22 591401 0X839 58 4 631110 6X4429 65 4 630150 3X996 71 12 630080 3X996 77 27 630340 3X996 81 1 830100 6X669 95 14 830100 6X669 95 34 830100 6X669 108 18 630130 3X996 108 22 591401 3X996 OCCUPATION 631 20 27 387520 3X881 40 26 387420 3X881 40 27 387520 3X881 40 28 387530 3X881 40 29 387010 3X881 40 30 387430 3X881 40 31 387040 3X881 40 32 387230 3X881 40 33 387060 3X881 40 34 387550 3X881 40 35 387560 3X881 40 36 387080 3X881 40 37 387170 3X881 40 38 387580 3X881 40 39 387440 3X881 41 1 3.87130 3X881 41 2 387260 3X881 45 32 619753 6X4359 48 15 341200 3X885 52 31 388220 3X881 83 37 387570 3X881 84 19 387710 3X860 84 22 387730 3X860 • OCCUPATION 633 74 29 397400 3X960 101 24 397300 3X960 OCCUPATION 651 6 21 593110 0X842 62 9 593010 0X842' 66 27 593210 0X842 72 7 5931 10 4X6295 72 9 593210 0X842 84 37 5931 10 0X842 100 22 593310 0X842 OCCUPATION 653 '94 16 521210 4X2494 98 38 522010 4X6220 98 39 590210 4X6220 . OCCUPATION 654 2 7 788480 6X2492 7 24 722510 4X6295 11 19 722620 6X4295 18 4 521210 4X2494 20 8 575240 4X2494 24 1 722530 6X2493 32 16 575020 4X2494 32 18 521010 • 4X6295 32 22 775010 6X2494 32 23 575020 4X2494 53 2 775710 6X4294 57 24 721410 6X2493 57 25 721420 6X2493 58 21 593210 4X2494 62 12 521020 4X6295 62 13 521010 4X6295 62 14 521010 4X6295 62 16 521010 4X6295 62 19 721020 4X6295 62 20 521010 4X6295 62 21 521010 4X6295 64 1 593000 0X849 77 10 521210 4X2494 78 3 575020 4X2494 81 3 521030 4X6295 81 5 575020 4X2494 81 28 593210 4X6295 85 29 522910 4X6220 85 30 521010 4X6295 85 31 521020 4X6295 103 12 521010 4X6295 i ' -135-LGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 655 2 9 690511 6X2695 3 15 490011 4X6695 20 34 490211 6X2695 21 29 722360 6X2695 26 17 722380 6X2695 42 3 490511 4X6695 53 15 522310 6X2695 53 16 490211 4X6695 74 23 7 690312 6X2695 74 24 490520 4X6695 88 32 690211 4X6695 95 30 688036 4X2489 OCCUPATION 656 1 24 520420 4X6694 , 14 16 575270 4X2494 17 19 520020 4X2494 20 35 520820 4X2494 20 36 575280 4X2494 26 15 720410 4X6694 28 7 455030 4X6097 28 20 520825 4X2494 64 16 720830 6X2601 81 33 575050 4X2494 81 34 775050 4X2494 87 13 574030 4X6295 101 29 455310 4X6694 107 15 575230 4X2494 107 16 775220 4X2494 107 17 520010 4X2493 OCCUPATION 657 • 6 14 789311 6X2695 ^  12 18 572010 4X2102 12 19 583641 4X2109 14 24 922000 6X4295 15 17 922300 6X2492 15 21 922500 6X2493 16 13 922100 6X2492 20 9 593210 0X849 20 12 922200 6X4295 28 28 775410 4X2494 28 29 922100 6X2494 30 29 922000 6X2492 33 14 593210 0X846 33 15 573520 4X2493 41 27 593210 0X846 , 57 20 593210 0X849 62 18 721010 4X6295 64 15 593210 0X849 81 7 593210 4X6295 84 6 852310 6X669 95 23 922100 6X664 96 2 573520 4X2493 - OCCUPATION 659 18 37 79130 4X6295 20 8 575240 4X2494 21 22 776140 4X6381 21 23 79110 4X6295 32 22 775010 6X2494 32 23 575020 4X2494 32 25 775020 6X2494 42 6 522920 4X6220 42 7 922300 4X6220 53 2 775710 6X4294 55 ' 12 722970 4X6183 62 20 521010 4X6295 69 5 722810 6X4209 77 1 521040 6X4294 87 16 574020 4X6295- 99 15 593210 0X846 OCCUPATION 701 • 25 12 407100 4X6671 41 16 608212 6X2671 61 30 40 7100 4X6671 61 33 607100 4X6671 62 4 407200 4X6671 81 20 607400 6X2671 OCCUPATION 702 6 ,4 401100 4X6675 6 5 401400 4X6675 7 19 401600 4X6671 8 7 401200 4X6375 8 8 401200 4X6375 24 6 247020 2X12 -136-•AGE COL. , VOL. I ' VOL. II PAGE COL. VOL. I VOL. II 30 19 602123 6X4479 31 2 401700 4X6671 66 34 401800 4X6675 86 9 602125 6X4479 OCCUPATION 703 7 36 409205 4X6376 10 33 409204 4X6376 10 34 409204 4X6376 13 26 8091 10 6X4676 13 33 40 9205 4X6376 13 34 558100 4X6376 13 35 409206 4X6376 17 11 609204 6X4676 47 32 60 9297 6X4376 48 28 8091 10 6X4676 60 ' 36 558100 4X6376 OCCUPATION 704 50 14 809110 6X4379 60 38 809110 6X4379 OCCUPATION 705 • 24 4 226910 2X12 OCCUPATION 706 24 , 16 604360 4X6671 24 18 604380 4X6671 69 35 804100 6X2479 71 2 604660 6X2671 79 23 604450 6X2671 OCCUPATION 707 13 37 406330 4X6671 14 1 776115 6X2383 19 1 406430 4X6671 19 2 4064 10 4X6671 19 37 406560 4X6671 20 6 610511 4X6671 24 22 591011 0X846 26 5 606410 6X2671 32 12 606530 6X2604 69 4 406560 4X6671 104 32 406570 4X6671 104 33 406570 4X6671 OCCUPATION 708 7 37 609351 6X2381 9 16 602012 4X6671 14 25 402311 4X6375 14 38 605242 6X4679 23 36 40 8421 4X6671 39 11 768032 6X4409 39 12 804100 6X2479 39 13 805010 6X2479 41 5 95120 0X625 42 4 810230 6X4679 42 5 804100 6X4479 51 3 602331 6X4479 55 17 409401 s 4X6671 64 26 4084 11 4X6671 79 11 409401 4X6671 88 20 6024 30 6X2479 88 22 602420 6X4479 96 5 6094 15 6X2671 OCCUPATION 709 12 22 403210 4X6671 53 34 403280 4X6671 59 28 403240 4X6671 60 24 403270 - 4X6671 92 22 403260 4X6671 109 20 403030 4X6671 OCCUPATION 711 2 13 657141 4X6357 7 25 592001 0X841 51 20 857010 6X4349 99 17 789421 4X6357 99 18 457112 4X6357 99 19 457111 4X6357 99 22 457212 4X6357 10 1 27 6571 15 6X4356 -137-'AGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II 101 28 789426 ' 6X4356 OCCDPATION 719 17 23 657017 4X6641 27 12 657961 6X4356 99 20 780422 6X2381 99 21 457213 4X6381 OCCUPATION 721 5 27 461011 6X4356 5 33 859010 6X4359 V 12 1 461041 6X4459 26 11 461012 4X6355 27 6 661011 6X4356 27 11 459032 6X4356 49 9 461043 6X4459 66 30 461046 6X4459 89 22 461045 6X4459 89 23 461046 6X4459 OCCUPATION 722 7 34 4676 81 4X6355 14 3 661512 6X6352 21 30 461171 6X4351 25 31 661711 6X4459 46 23 461218 6X4352 46 24 461221 6X4352 46 25 461222 6X4352 49 5 661482 6X4459 49 7 661713 6X4459 49 8 661714 6X4459 51 23 661716 6X4459 55 20 461662 6X4358 57 6 461224 6X4352 60 21 661131 6X4359 76 12 461683 6X4358 91 11 461685 6X4459 92 30 661211 6X4352 92 31 461227 6X4352 100 26 461233 6X4352 104 24 461234 6X4352 107 18 461235 6X4352 OCCUPATION 724 87 11 660100 6X4359 87 12 460100 4X6355 OCCUPATION 729 22 10 462010 4X6335 48 9' 662060 4X6355 48 11 662070 4X6355 1 48 .12 462120 4X6355 51 25 461861 4X6381 56 18 662160 4X6355 58 15 462110 4X6355 92 28 462050 6X4351 OCCUPATION 731 15 18 619031 6X2451 70 35 619012 6X2451 90 6 619024 4X2 OCCUPATION 732 43 11 619055 6X2451 53 4 4150 10 4X2452 90 12 470010 4X2451 90 15 619041 4X2451 90 16 419042 4X2451 90 17 619041 4X2451 98 23 619071 .4X2451 103 24 619071 6X2451 OCCUPATION 733 10 7 661912 6X2451 79 29 619117 6X2451 90 20 619165 6X4359 J -138-'AGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 734 13 1 415020 4X2452 106 23 415020 4X2452 OCCUPATION 735 6 11 957210 6X4358 6 12 619111 6X2451 7 27 419201 4X2451 21 18 619453 6X2459 31 36 619824 6X2451 32 2 419821 4X2452 48 8 415030 4X2452 58 9 415030 4X2452 58 10 416010 4X2105 90 21 619227 4X2451 105 36 619222 4X2451 OCCUPATION 736 21 14 414064 4X2453 39 34 614221 6X2381 50 7 614331 6X4351 54 17 414111 4X6351 54 18 583321 4X2105 54 20 414061 4X2453 54 21 619751 6X4451 54 22 414063 4X2453 57 7 414072 4X2453 57 8 414071 4X2453 93 1 614235 6X2381 93 2 414065 6X4451 OCCUPATION 737 1 27 451030 4X6641 22 14 718320 6X2601 30 35 619605 6X2601 53 10 718050 6X4651 89 12 619252 6X2601 OCCUPATION 738 21 15 819010 6X4651 75 4 627216 6X4457 98 3 819010 6X2459 OCCUPATION 739 9 35 614173 6X4458 21 4 118660 1X20 21 10 619553 6X2381 42 15 619462 6X2459 47 12 619164 6X2451 61 4 6193 32 4X6351 72 36 619082 4X2451 108 29 419991 4X6381 OCCUPATION 741 95 35 426101 4X6352 96 1 426201 4X6352 OCCUPATION 742 25 19 625010 4X6352 32 9' 425030 4X6352 84 29 425020 4X6352 OCCUPATION 743 43 20 421210 4X6356 43 22 4 21110 4X6356 44 3 421310 4X6356 44 4 421010 4X6356 OCCUPATION 744 15 10 424221 6X4351 61 34 423100 4X6352 -139-PAGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II 14 2 627041 27 .9 427043 46 12 662040 60 19 627011 4 5 627071 14 4 627074 16 12 625420 32 9 425030 36 32 827650 39 32 427261 55 15 427071 79 33 70 3000 84 29 425020 85 16 627081 85 18 624234 85 21 627508 102 2 624121 5 16 435610 17 2 635820 48 33 435630 OCCUPATION 745 6X4356 27 4X6352 27 6X4352 60 6X4359 104 OCCUPATION 746 6X435 7 6X4351 14 6X4352 28 4X6352 36 6X4352 38 6X4351 44 4X6352 58 6X4 351 84 4X6352 85 6X4 351 85 6X4351 85 6X4352 92 6X4351 10 3 OCCUPATION 747 4X6353 5 4X6353 18 4X6353 104 3 7994 10 4X6352 10 427041 4X6352 18 627024 6X4359 8 435120 4X6353 13 627071 6X4351 5 627073 6X4 351 3 624234 6X4352 31 ,625910 6X4351 16 627563 6X4352 21 614410 6X4451 11 614420 6X4451 24 627513 6X4352 15 6271 1 1 I 6X4351 17 627082 6X4351 19 627083 6X4351 28 462050 6X4351 4 627576 6X4352 17 799824 4X6353 7 6351 10 4X6353 6 635710 4X6353 OCCUPATION 749 2 29 503010 4X6354 4 9. 713252 6X4309 5 21 627811 4X6354 13 5 624251 6X4359 15 9 427811 4X6354 20 32 627122 6X4359 21 11 427121 6X2381 25 20 427981 4X6352 38 18 503030 4X6354 38 19 780600 4X6354, 48 18 624126 6X4359 53 11 624074 6X4458 55 36 819010 6X664 63 2 624456 6X4459 74 35 713256 6X4463 76 22 627025 6X4359 80 20 627352 6X4359 82 7 624061 6X4356 82 8 624062 6X4459 82 28 462060 4X6355 82 27 427812 4X6352 101 36 703562 6X4208 101 37 627054 6X4356 103 15 827740 6X4359 103 28 513411 4X6352 OCCUPATION 751 10 3 525610 4X6220 15 23 525110 4X6220 15 24 793550 4X6220 15 25 525830 4X6220 15 26 525010 4X6320 15 27 525150 4X6209 15 28 525830 4X6220 15 29 525260 4X6220 15 30 525640 4X6220 15 31 525150 4X6220 15 32 682610 4X6320 15 33 5251 10 4X6220 15 36 525810 4X6220 15 37 525540 4X6220 16 1 525830 4X6220 16 2 525320 4X6320 •140 PAGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II 16 4 525230 4X6220 16 5 525640 4X6220 16 6 525670 4X6220 16 7 525340 4X6320 16 8 525380 4X6220 16 9 525550 4X6220 42 32 525242 4X6220 48 7 525160 4X6220 81 27 525220 4X6220 89 37 525640 4X6220 91 7 525050 4X6320 OCCUPATION 752 -2 2 636130 6X4313 14 13 432100 4X6320 14 14 ' 799815 4X6320 14 15 432100 4X6320 16 3 525030 4X6320 30 28 636120 6X4320 43 35 636050 6X4320 56 28 636960 6X4213 60 23 432200 4X6328 OCCUPATION 754 6 17 431110 4X2029 6 19 678611 6X2416 10 26 631130 6X4429, 24 39 631220 6X4429 44 17 631420 6X4429 54 23 633217 6X4429 84 9 633221 6X4429 9 1 26 631180 6X4429 98 36 633227 6X4429 102 7 633223 6X4429 OCCUPATION 756 • 8 4 633111 6X4425 11 12 633411 6X4429 11 13 633412 6X4429 20 4 633361 6X4429 30 38 633318 6X429 30 39 433461 4X2029 31 4 633314 / 6X4429 32 38 633115 6X4425 42 9 633362 6X4429 43 12 633363 6X4429 44 11 633315 6X4429 46 17 633316 6X4429 53 26 633462 6X4429 55 23 633373 4X2021 60 26 633463 6X4429 62 2 433914 4X6320 62 3 799820 4X6320 72 13 433461 4X2029 85 35 633364 6X4429 85 36 633365 6X4429 90 10 433362 4X2021 92 36 633368 6X2429 93 3 433365 4X6328 97 33 633318 6X4429 103 16 638440 6X2421 108 26 433363 4X20 21 OCCUPATION 758 29 26 '429010 4X6381 58 5 429020 4X6381 58 6 629010 1X28 58 18 429510 4X6381 OCCUPATION 759 3 5 433916- 4X6320 3 6 633925 6X4320 11 28 639114 6X4320 18 5 631840 3X996 24 25 438010 4X6327 31 6 633913 6X4429 39 30 516710 4X6346 54 9 433911 4X6625 55 22 461881 4X6320 66 21 639277 4X6 348 68 21 634120 6X4429 85 12 638620 4X6327 85 33 636820 4X6328 101 31 439423 4X6621 105 2 639473 ~ 4X6625 105 4 439471 4X6327 105 5 639473 4X6625 105 6 636520 6X4329 108 19 638460 6X4329 -141-AGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 761 1 5 455010 4X6697 3 18 651630 4X6697 23 27 451640 4X6601 23 32 655040 4X6697 40 13 655070 4X6697 45 3 652371 4X6693 90 38 455020 4X6697 92 21 455030 4X6697 92 23 455010 4X6693 92 25 455030 4X6697 92 26 652428 4X6697 100 24 655030 4X6697 OCCUPATION 762 -10 32 653421 6X2601 22 3 456010 4X6618 32 10 652426 6X2604 37 36 652312 6X2604 37 37 651758 6X2604 43 25 487412 4X6615 53 35 6505,11 6X2601 56 7 650111 6X2618 57 15 650113 6X2618 81 35 652732 4X6693 OCCUPATION 763 27 37 641060 6X4661 76 15 841010 6X4661 88 5 641250 6X4601 OCCUPATION 765 43 6 441420 4X2109 OCCUPATION 766 14 : 642020 6X4469 68 27 642020 6X4469 - OCCUPATION 768 61 29 667030 6X2601 61 32 452311 4X6671 68 12 650001 6X2601 72 25 657014 4X6641 90 33 657033 6X4651 98 18 650004 6X2601 OCCUPATION 769 1 25 641260 6X2601^ 1 26 655530 4X6694 1 28 495031 4X6285 1 29 655550 4X6694 9 14 651720 4X6601 9 15 852710 6X4601 9 17 652437 6X2601 9 18 655310' 6X2694 17 34 : 591901 4X6694 18 8 452721 4X6693 19 8 452770 4X6611 20 31 655320 4X6694 22 18 653723 6X2601 23 1 455380 4X6694 25 16 451020 4X6641 38 33 452211 4X6601 39 16 655370 4X6694 45 10 754910 4X6693 46 32 652445 6X2602 58 20 652741 6X2601 62 24 853880 6X2601 62 28 452472 4X6601 62 29 652487 6X2601 64 32 652415 4X6601 65 1 452441 4X6601 65 2 452451 4X6601 78 9 455024 4X6694 78 23 651140 4X6641 78 24 650112 6X2618 78 28 451040 6X4451 89 11 453151 4X6601 90 18 451030 4X664 1 95 15 655230 4X6694 ., 99 13 450312 4X6641 106 14 754621 6X2601 -142-AGE COL. VOL. I VOL. I I PAGE COL. VOL. I VOL. I I OCCUPATION 771 6 24 444210 4X6568 22 39 444010 4X6568 22 40 798010 4X6568 51 8 444220 4X6568 57 11 444110 4X6567 57 12 798050 4X6567 59 24 444240 4X6568 59 26 444230 4X6568 63 25 444120 4X6567 75 13 798010 4X6568 OCCUPATION 772 36 29 619030 4X6361 36 30 619040 4X2463 65 28 448050 4X2463 82 2 448060 4X2463 106 25 448030 4X2463 106 26 448030 4X2463 106 27 798430 4X2463 OCCUPATION 773 57 16 448070 4X2463 57 17 649510 4X6508 70 25 446200 4X6588 93 12 446100 4X6508 93 38 447300 4X6588 101 11 446400 4X6588 101 12 446300 4X6588 101 13 446500 4X6508 OCCUPATION 775 70 15 447100 4X6588 70 16 798120 4X6588 70 17 447100 4X6588 70 18 649510 4X6508 72 26 445010 4X6569 OCCUPATION 776 10 35 449010 4X6361 OCCUPATION 778 11 4 649041 4X6361 13 2 44230 4X6506 OCCUPATION 779 36 15 798110 4X6569 OCCUPATION 778 70 2 649011 6X4469 75 1 649012 6X4469 92 29 649016 6X4369 OCCUPATION 779 7 23 445015 4X6569 9 21 649210 4X2029 9 22 986370 .6X4346 12 2 649960 4X6567 17 4 649310 4X6569 26 38 649910 1X28 27 18 649620 6X4415 27 19 649410 4X2463 27 20 448010 4X2463 36 12 445010 4X6569 36 13 445010 4X6569 36 14 445010 4X6569 36 16 445010 4X6569 67 4 448011 4X6508 72 31 649420 4X2463 82 13 649220 6X2414 92 11 445210 4X6569 92 12 798130 4X6569 106 20 445010 4X6569 -143-AGE C O L . VOL. I VOL. I I PAGE COL. VOL. I VOL. I I OCCUPATION 781 8 22 524130 4X6232 8 23 592302 4X6618 9 12 491311 4X6618 18 16 691011 6X2618 18 17 691011 6X2618 18 23 691181 6X2618 26 23 691052 6X2618 26 25 694611 6X2616 26 26 491351 4X6618 34 30 4914 11 4X6618 40 21 491445 4X6618 43 23 691232 6X2618 43 27 488081 4X6615 43 31 500950 4X6618 43 32 491438 4X6618 43 33 491571 4X6618 43 34 687410 6X2615 45 7 6914 8,1 4X6618 48 36 688732 6X2615 52 33 491573 4X6618 73 8 688082 6X2618 79 22 491391 4X6697 79 27 491441 4X6618 84 33 691 183 4X6618 96 18 691726 6X2616 98 35 491572 4X6618 OCCUPATION 782 9 33 693777 6X2611 9 34 893180 6X2615 16 27 487210 4X6615 27 16 687210 4X6615 29 6 487210 4X6615 32 . 1 687130 6X2615 46 11 476220 4X6385 48 5 487220 4X6615 48 39 487010 4X6615 49 1 487020 4X6615 65 3 487010 4X6615 80 29 687010 6X2615 97 20 487310 4X6615 100 8 487230 4X6615 OCCUPATION 783 22 7 573020 4X2493 81 10 488028 4X2487 81 12 688024 4X2487 81 15 688011 6X4487 81 17 488018 6X4487 82 5 688033 6X4 487 86 24 592301 4X2487 89 33 688012 4X2.487 99 10 488023 4X2487 OCCUPATION 784 3 8 486125 4X2489 3 26 488742 4X2487 3 27 486010 4X6315 8 16 486010 4X6315 9 5 486010 4X6315 9 6 486010 4X6315 9 7 486010 4X6315 10 28 486125 4X2489 32 28 584110 4X2015 32 30 686110 4X2489 32 31 486110 4X2489 32 32 486110 4X2489 32 33 486170 4X2489 32 34 486170 4X2489 41 22 488743 4X2489 41 23 694224 6X4489 42 29 486125 4X2489 43 28 486020 4X6315 44 16 486125 4X2489 47 28 486120 4X2489 48 23 486125 4X2489 50 5 486210 4X6315 50 13 486125 4X2489 73 9 6861 10 4X2489 95 10 486110 4X2489 95 12 4861 10 4X2489 95 13 486125 4X2489 100 2 584010 4X6315 100 3 784010 6X4489 100 4 575280 4X2494 104 10 486125 4X2489 105 23 486015 4X6320 - 1 4 4 -iGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 786 28 37 482920 4X6616 41 26 682620 4X6318 55 10 491651 6X2616 56 11 481060 6X2616 58 24 : 681010 6X4443 58 25 481025 4X6343 58 26 481050 4X6343 63 5 481020 4X6343 63 6 481030 4X6343 63 9 491721 4X6343 63 11 481040 4X6343 63 12 481030 4X6343 63 13 481030 4X6343 OCCUPATION 787 24 40 482010 4X6343 25 1 799801 4X6343 25 2 482010 4X6343 25 4 682020 6X4443 25 6 682120 6X2605 OCCUPATION 788 38 10 488991 4X2488 102 29 488311 4X2488 08 7 488511 4X2488 OCCUPATION 789 13 19 783326 6X4419 19 27 878100 6X4415 19 28 678925 6X4319 19 29 478920 4X6315 25 7 682961 4X6343 4 3 3 882100 6X669 56 5 690712 6X2695 56 8 495031 4X6285 56 9 695032 6X4318 62 41 682960 4X6343 74 6 892610 6X4208 76 35 691585 4X6618 83 9 690991 1X28 83 10 682720 6X4319 83 13 482310 4X6641 83 17 682310 6X2601 83 38 488292 4X2487 89 20 494182 4X6618 OCCUPATION 791 17 3 472411 4X6616 21 2 471510 4X6310 28 31 471220 0X730 46 20 471010 4X6318 53 7 471250 4X6338 53 8 475130 4X6310 53 9 471010 4X6318 63 4 481015 4X6343 73 25 872300 ,6X4315 87 39 471310 4X6318 89 16 472318 4X6318 93 11 471210 4X6338 106 9 475010 4X2010 106 10 471510 4X6310 106 13 583971 4X6310 OCCUPATION 793 29 10 473040 4X8508 37 20 473010 4X6508 37 21 473020 4X6508 37 22 473510 4X6508 37 31 799350 4X6508 37 32 693332 6X4319 37 33 473310 4X6508 37 34 473320 4X6508 59 27 913270 6X4356 81 14 473910 4X6508 OCCUPATION 801 28 35 476127 4X6309 28 36 476110 4X2014 29 3 476010 4X6310 29 5 4 76010 4X6310 29 8 476020 4X6508 29 9 476010 4X2010 -145-PAGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II 29 11 794110 4X2010 v 29 18 476120 4X2489 29 19 476120 4X2489 29 21 476120 4X2489 29 23 476010 4X2010 29 25 476010 4X2010 33 31 476010 4X6320 53 33 476910 4X2012 99 35 476040 4X2010 100 15 476210 4X2010 100 16 476220 4X2010 100 21 476210 4X6315 OCCUPATION 802 29 15 475120 4X6410 , 52 7 475130 4X6310 53 19 475160 4X2010 56 1 475140 4X2010 58 34 475010 4X2010 58 35 794100 4X2010 58, 38 475010 4X2010 58 39 475120 4X6310 59 1 583901 4X6310 59 2 475010 4X2010 59 7 475010 4X2010 59 8 583642 4X2100 59 9 475150 4X2100 63 17 475010 4X2010 OCCUPATION 803 13 29 677510 6X4315 30 4 678512 6X4415 38 6 678513 4X2015 39 7 639993 6X4325 39 8 478291 4X2015 45 15 678413 4X2015 45 24 678132 4X2015 45 25 678413 , 4X2015 49 28 478411 4X2015 49 29 678412 6X2415 52 21 478512 4X2015 54 14 784110 6X4415 54 15 584130 4X20,15 61 12 677530 6X4315 83 30 784230 6X4315 85 27 678511 1X2015 94 35 478513 4X2015 94 36 678522 6X2415 98 19 678516 4X2015 100 5 584120 4X6315 100 6 784110 6X4415 / 100 7 5841 10 4X2015 100 12 678413 4X2015 104 20 678632 4X2015 104 22 678518 6X2415 OCCUPATION 805 • 62 6 578100 4X2100 OCCUPATION 806 2 19 580130 4X2103 2 21 580130 4X2103 3 13 694431 4X6310 12 14 6941 14 4X6310 16 25 494351 4X6310 30 ,26 702374 6X4213 30 32 702353 6X4313 37 12 503572 4X6210 39 18 503572 4X6210 39 24 633925 6X4320 41 8 703562 6X4310 41 30 678632 4X6310 45 27 703562 6X4208 52 13 503554 4X6310 64 4 702312 4X6210 68 39 475120 4X6310 79 31 70 3542 6X4213 79 32 703542 6X4213 87 10 702311 6X4310 88 14 503552 4X6213 89 . 34 633925 6X4320 ' 89 35 478031 4X2012 93 21 , 493411 4X6211 108 1 633925 6X4320 108 2 603552 4X6213 108 4 703552 6X4213 -146-AGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 808 6 32 678025 4X2014 6 34 678216 6X2414 7 1 478214 4X2014 7 5 678291 4X2411 7 6 478011 4X2001 7 30 678411 4X2015 '8 19 188742 4X287 10 30 678141 4X2014 11 11 678041 6X2414 1 1 14 478042 4X2014 12 37 475120 4X6310 12 38 678051 4X2013 17 26 678152 4X2014 17 27 678511 4X2015 28 30 584020 4X6318 37 14 478011 4X2011 42 35 688627 6X4419 45 18 478132 4X2012 45 23 178134 4X2013 , 50 1 478042 4X2014 50 2 478031 4X2012 52 22 678121 4X2013 60 10 688733 4X2487 61 35 478031 4X2012 61 36 678031 6X2412 61 37 478031 4X2012 64 17 678081 4X2014 64 30 688626 6X4489 71 7 688351 6X4489 71 20 695055 6X4488 71 21 695056 6X4488 71 22 695062 6X4488 71 25 695057 6X4419 72 4 688346 6X4488 72 12 478071 4X2013 74 20 688664 4X2489 74 25 478011 4X2011 75 30 478111 4X2012 75 31 678111 6X2412 77 9 678082 4X2014 81 37 694204 6X4419 82 6 478011 4X2011 84 10 478011 4X2011 84' 11 678144 6X2411 85 34 478061 4X2013 86 1 478062 4X2013 88 1 678083 6X2414 88 30 478061 4X2013 88 31 478031 4X2012 90 7 478012 4X2011 93 22 688613 6X4419 93 23 688612 6X4489 95 31 688623 6X4489 96 19 678142 4X2014 103 20 478021 4X2011 103 21 678021 6X2411 105 8 478044 4X2014 105 9 478031 4X2012 107 22 678214 4X2014 107 26 678166 4X2011 OCCUPATION 810 44 31 754511 6X4601 71 17 530030 4X6217 71 26 530010 4X6217 71 27 530010 4X6217 71 :« 28 796200 4X6217 71 29 530010 4X6217 71 30 530010 4X6217 71 32 530010 4X6217 71 33 530210 4X6217 71 34 530010 4X6217 72 2 553940 4X6217 73 4 530210 4X6217 73 5 530260 4X6217 73 6 503562 4X6217 91 30 530410 4X6217 91 31 796300 4X6217 91 32 530410 4X6217 91 33 530410 4X6217 91 34 530410 4X6217 109 14 530410 . 4X6217 OCCUPATION 811 3 24 480050 4X6313 7 28 480060 4X6313 24 31 480080 4X6217 24 32 797052 4X6217 25 38 480050 4X6313 33 22 480050 4X6313 68 20 480910 4X6313 86 10 703562 6X4213 86 13 488622 4X6213 86 14 480022 4X6313 -147-PAGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II 86 15 694222 4X6313 86 16 480010 4X6313 86 17 488622 4X6213 86 18 480050 4X6313 86 19 480060 4X6313 86 20 797040 4X6313 86 21 480010 4X6313 86 23 480010 4X6313 89 15 695001 6X4319 89 . 1 7 695010 6X4319 89 18 485310 4X6285 89 19 702015 4X6285 97 22 480020 4X6313 99 11 480010 4X6313 99 12 480010 4X6213 OCCUPATION 812 13 9 684610 6X4284 29 27 695084 6X4284 29 28 688622 6X4419 48 35 685230 6X2615 80 16 695071 6X4419 80 17 695080 6X4284 80 18 695081 6X4284 80 19 684440 6X4284 80 24 684630 6X4489 80 25 695082 6X4284 80 26 484060 6X4284 80 28 684650 6X2615 80 30 695087 6X4419 80 31 484010 4X6211 80 32 695074 6X4419 80. 33 636850 6X4419 80 35 695075 6X4419 80 36 695086 6X4284 96 13 484060 6X4284 OCCUPATION 813 10 14 483100 4X6211 10 15 484013 4X6211 10 16 797020 4X6211 10 17 483100 4X6211 10 22 483400 4X6211 10 27 6841 10 6X421 1 41 13 483300 4X6211 61 17 184030 4X6211 66 18 484020 4X6211 66 19 797760 4X6211 86 30 484012 4X6211 86 31 797770 4X6211 92 3 684115 6X4211 94 3 484010 4X6211 96 7 438050 4X6220 96 8 484030 4X6211 96 11 438050 4X6220 OCCUPATION ^ 815 44 12 674110 6X2611 72 33 474010 4X6611 72 35 474010 4X6611 OCCUPATION 817 1 21 685215 4X6283 3 34 685280 4X6281 9 10 485060 4X6280 10 31 685110 4X6281 12 16 485310 4X6285 41 18 685215 4X6283 41 19 685240 4X6283 49 31 485060 4X6280 87 18 685120 4X6280 106 31 485020 4X6281 106 32 799030 4X6283 106 33 799035 4X6280 106 34 485020 4X6281 106 35 485060 4X6280 106 36 685010 6X4281 106 37 685020 6X4281 106 38 188341 4X2488 106 39 485040 4X6280 107 1 485040 4X6280 107 2 685130 4X6281 107 6 485020 4X6281 107 7 685100 6X4281 107 8 685060 6X4318 107 9 685080 6X4281 107 10 685070 4X6285 PAGE COL. VOL. I 73 27 884510 5 20 695051 6 9 672205 8 10 472321 21 38 693667 72 11 672298 82 14 694235 90 14 494201 93 25 693183 102 24 695060 102 26 695054 107 23 505030 2 20 580130 2 23 580130 2 27 580130 2 34 580100 3 4 580120 75 34 580130 5 6 581510 5 9 581410 5 11 581010 5 14 581530 10 12 702342 12 4 902010 12 7 581120 13 32 581035 48 27 781420 64 3 581020 101 2 581040 107 21 581210 1 17 583121 14 28 583123 91 22 583126 3 11 579510 87 29 579170 97 29 579020 -148-VOL. II PAGE OCCUPATION 818 6X4315 OCCUPATION 819 6X4213 5 4X6310 8 4X6318 - 13 6X4419 57 6X4315 73 6X24.14 84 4X6313 90 6X2419 102 6X4319 102 6X4419 102 4X6211 108 OCCUPATION 821 4X2103 2 4X2103 2 4X2103 2 4X2103 3 4X2104 37 4X2104 OCCUPATION 822 6X4213 5 4X6183 5 4X2103 5 4X2109 10 4X6208 12 6X4310 12 4X2109 12 4X2103 15 4X2109 63 4X2103 98 4X2103 102 4X2109 107 OCCUPATION 824 4X2106 1 4X2106 65 4X2106 103 OCCUPATION 825 4X2109 23 4X6183 93 4X6211 97 COL. VOL. I VOL. II 24 4789 11 4X6616 9 672114 6X4309 20 491721 6X2616 33 472313 4X6318 12 672015 4X6318 27 693072 4X6313 35 493661 6X4419 23 488342 • 4X2487 25 695053 6X4419 27 695052 6X4419 17 505610 6X4208 22 580130 4X2103 26 580130 4X2103 28 580120 4X2104 1 580120 4X2104 15 580352 4X2103 7 581520 4X6320 10 581010 4X2103 12 581650 4X2103 11 781671 4X2109 3 581110 4X2011 6 581120 4X2109 9 581120 4X2109 13 781610 4X2103 34 579630 4X2109 7 581630 4X2103 16 581030 4X2103 27 771210 4X6315 33 583122 4X2106 27 583.1 11 4X2106 25 583127 4X2106 31 579550 4X6183 30 779070 6X4 208 31 579120 4X6211 -149-PAGE CpL. VOL. I 4 6 583541 22 25 583326 29 17 583931 38 38 583641 44 ! 26 583040 45 6 583032 47 19 583542 51 9 783512 52 10 583972 58 12 583323 61 23 583465 65 31 583024 78 33 583941 85 23 583641 93 6 583022 100 18 475160 100 20 475160 107 34 583961 VOL. II PAGE OCCUPATION 829 4X2107 8 4X2105 23 4X2103 36 4X2100 43 4X2109 44 4X2109 45 4X2107 47 6X4209 51 4X6310 54 4X2105 58 4X6310 62 4X6209 76 4X2109 83 4X2109 87 4X6183 100 4X2012 100 4X2014 106 4X2100 L. , VOL. I VOL. II 27 583881 4X2109 22 583641 4X2109 24 583921 4X6183 24 583023 4X6209 32 583948 4X2109 8 583947 4X2100 23 583972 4X6310 13 583945 4X6310 19 583322 4X2105 32 583311 4X2029 10 783511 6X4208 21 58364 1 4X2102 36 583641 4X2109 30 583872 4X6209 17 475160 4X2015 19 475160 4X2012 17 783471 6X4219 4 1 475010 34 20 583041 35 2 70 3562 35 5 583447 35 7 497915 35 9 795200 35 11 497420 35 13 497010 35 15 497210 35 20 497921 35 22 553295 35 24 497520 35 26 497270 35 28 497120 35 30 497010 35, 33 783451 36 5 783012 104 16 583011 4 2 699011 4 24 700928 34 19 500933 36 3 583432 52 8 500912 74 22 499172 82 4 499163 101 14 698240 108 10 909410 , OCCUPATION 831 4X2010 7 4X6183 34 6X4208 35 4X6185 35 4X6183 35 4X6183 35 4X6183 35 4X6181 35 4X6183 35 4X6181 35 4X6181 35 4X6188 35 4X6183 35 4X6181 35 4X6181 35 4X6181 36 4X6183 64 4X6183 108 OCCUPATION 832 6X4307 4 6X4308 4 4X6308 34 4X6183 44 4X6308 59 4X6308 82 6X4308 95 6X4308 101 6X4309 22 589411 4X6183 27 497420 4X6183 4 497010 4X6181 6 497910 4X6181 8 795100 4X6181 10 581420 4X6183 12 497150 4X6181 14 497010 4X6181 19 497010 4X6181 21 497410 4X6183 23 497510 4X6188 25 497210 4X6183 27 497220 4X6183 29 497910 4X6181 31 497230 4X6183 4 583433 4X6183 24 583871 4X6181 8 497010 4X6181 4 699011 6X4307 30 700921 1 6X4308 21 678634 4X6310 5 854010 6X4310 13 499012 6X4307 3 499162 6X4308 22 700921 6X4308 16 55336 2 4X6183 --150-PAGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 833 74 16 551030 4X6188 74 17 551510 4X6188 74 18 551020 4X6188 74 19 49010 0X744 94 12 551210 4X6188 95 21 551130 4X6188 103 13 551120 4X2102 76 7 583415 77 20 583447 77 25 583411 OCCUPATION 835 4X6185 77 18 783415 4X6185 4X6185 77 21 583447 4X6185 4X6185 77 26 583411 4X6185 63 31 555010 OCCUPATION 836 6X2409 OCCUPATION 838 14 20 553950 4X6181 14 22 553340 4X6181 17 32 553235 4X6183 22 21 553210 4X6181 56 37 553420 4X6181 56 38 553410 4X6181 56 39 753410 4X6181 57 1 553410 4X6181 57 31 553220 4X6181 72 18 753020 4X6181 91 16 753030 4X6181 91 17 553040 4X6181 91 19 553250 .4X6181 97 1 553070 4X6181 97 2 553270 4X6183 97 3 553050 4X6183 97 5 553260 4X6183 97 6 553060 4X6181 98 11 553910 4X6181 98 24 753040 4X6183 99 5 753050 4X6181 99 27 553310 4X6181 OCCUPATION 839 7 10 700070 6X4308 7 11 900910 6X664 21 36 699167 6X4308 2 1 37 699161 6X4308 21 40 898010 6X4308 21 41 699120 6X2409 22 1 699014 6X4307 23 16 698070 - 6X4307 41 25 900910 6X4310 64 10 700020 6X4308 64 12 700016 6X4308 ' 64 13 898010 6X4308 84 20 89 8010 6X669 84 21 500011 6X4419 92 5 500012 6X4308 -OCCUPATION 841 9 2 727120 6X4246 14 27 527010 4X6246 15 20 527910 4X6506 46 8 577010 4X6233 46 9 577010 4X6233 46 10 797720 4X6233 67 25 797540 4X6246 67 26 516910 4X6246 67 27 527110 4X6246 67 28 727110 6X4246 67 29 716940 6X4346 67 30 527010 4X6246 67 31 527110 4X6246 67 32 516740 4X6346 67 33 516710 4X6246 67 34 ' 466511 4X6506 68 5 527010 4X6246 68 6 527010 4X6246 68 7 727010 6X4246 68 8 861010 6X4346 68 9 527310 4X6246 68 10 527020 4X6246 68 11 527110 4X6246 68 28 797580 4X6246 -151-PAGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II 72 28 577010 4X6233 104 29 716152 6X4246 104 30 527140 4X6244 OCCUPATION 843 28 4 716971 4X6346 28 5 716970 4X6346 30 36 516930 4X6246 55 4 716320 6X4346 91 5 716111 6X4346 93 34 716610 4X6346 OCCUPATION 852 13 17 79010 0X742 13 18 79020 0X715 49 16 79010 0X742 94 2 776950 6X2381 OCCUPATION 854 9 27 724630 4X6232 12 23 524110 4X6232 12 24 524010 4X6232 12 25 524120 4X6232 12 26 797/410 4X6232 12 30 524010 4X6232 12 31 724610 4X6232 12 32 524020 4X6232 26 24 524130 4X6232 55 9 724130 4X6244 60 12 524020 4X6232 60 15 524310 4X6231 63 26 724210 4X6231 65 37 524130 4X6232 93 14 524210 4X6231 98 30 732332 6X4297 98 31 524410 4X6233 98 32 797480 4X6233 OCCUPATION 855 17 n < 526100 4X6244 17 20 526100 4X6244 17 22 520200 4X6244 23 8 797450 4X6244 23 12 526100 4X6244 98 4 524510 4X6244 OCCUPATION 856 -55 26 799860 6X4213 55 27 532763 6X4229 55 28 532761 6X4213 55 29 532762 6X4229 66 20 529200 4X6244 72 19 529100 4X6244 72 20 797460 4X6244 94 6 529300 4X6244 OCCUPATION 857 4 13 799850 4X6244 4 14 533100 4X6244 4 17 533100 4X6244 71 23 5331 10 4X6244 78 32 639561 6X4208 OCCUPATION 859 4 18 532010 4X6244 9 11 574010 4X6295 16 10 525150 6X4208 30 16 589011 4X6209 42 1 532752 6X4208 43 1 732072 6X4211 72 1 732320 6X4297 79 4 . 732251 6X4211 81 21 797730 6X4208 81 22 731100 6X4208 81 23 932010 6X4208 81 26 731400 6X4208 83 32 484010 0X6211 83 33 525230 4X6220 93 16 732577 6X4435 103 10 732375 6X4208 -152-GE COL. VOL. I VOL. I I PAGE COL. VOL. I VOL. I I OCCUPATION 861 8 24 508020 4X2035 32 19 708025 4X2035 33 29 708067 4X2035 51 28 508060 4X6330 52 9 508066 4X6330 56 22 708665 4X2035 56 23 909010 4X6381 56 24 708071 4X2035 56 25 909010 4X6508 64 11 708028 4X6330 66 3 508010 4X20 35 74 26 508071 4X2035 74 27 799610 4X2035 7*4 28 508081 4X2035 75 15 508065 4X6385 • OCCUPATION 862 40 3 666611 6X2605 40 9 666611 6X2605 54 7 666251 6X2605 54 8 666265 4X6232 85 11 666261 4X6232 OCCUPATION 864 20 18 669010 6X2436 24 28 669320 6X4239 32 26 669180 6X2439 46 34 669160 6X4435 83 12 469160 6X4339 92 7 469610 4X6508 93 9 468100 4X6337 93 10 797320 4X6377 93 15 469150 4X2033 93 17 669360 6X4339 93 18 669140 6X2439 108 11 669040 6X2436 OCCUPATION 869 11 16 606710 6X2381 12 33 666321 6X2386 12 34 666322 6X2385 22 12 666211 4X6346 28 2 465910 4X6506 39 31 666561 6X4349 45 34 465430 4X6342 45 35 465440 4X6342 46 1 865010 6X4399 46 2 665240 6X4335 46 3 665220 6X4335 47 1 666164 6X4349 51 29 666523 4X6381 53 12 666453 4X6348 56 26 665462 4X6645 56 27 465446 4X6645 62 1 666014 6X2601 63 7 666116 4X634 8 63 19 666411 4X6348 69 2 666361 4X6233 75 3 666455 6X4449 75 5 666162 6X4489 83 11 665230 6X4339 103 14 466453 4X6348 OCCUPATION 871 10 21 572930 4X2102 40 14 770040 6X2608 OCCUPATION 872 2 15 772580 4X2102 11 7 572921 6X2964 23 4 572924 4X2102 29 16 572210 4X2102 32 6 772530 4X2102 38 25 572010 4X2102 39 36 772510 4X2109 44 27 572920 4X6693 44 29 572945 4X2T03 45 1 572920 4X2102 45 2 572920 4X6693 50 17 572050 4X2102 53 3 772570 4X2102 76 18 772510 4X2102 76 19 572550 0X846 76 20 572925 4X2012 78 30 572313 4X2102 91 13 572010 4X2102 - 153 -PAGE COL. VOL. I 99 16 572010 55 8 540110 64 5 556020 91 36 541060 1 23 573010 26 4 773010 35 3 573040 36 6 773030 49 24 573520 49 26 812100 57 21 573330 58 7 573520 60 11 : 573510 63 23 573020 82 22 573030 84 7 573520 88 15 573550 95 18 573330 107 31 773740 107 33 773710 3 3 580500 79 34 520000 80 1 591401 80 5 630350 80 7 573330 87 5 505570 4 20 723530 9 9 723010 23 11 523350 32 4 523010 71 10 523610 74 21 573210 81 2 723940 101 33 523030 17 7 591401 101 3 736510 65 32 771010 VOL. II PAGE 4X2102 106 OCCUPATION 873 4X2492 55 4X6181 66 4X2492 OCCUPATION 874 4X2493 17 6X2493 34 4X2493 36 6X2493 37 4X2493 49 6X2493 51 4X2493 57 4X2493 59 4X2493 63 4X2493 76 4X2493 84 4X2493 88 4X2493 94 4X2493 102 6X2493 107 6X2493 109 OCCUPATION 875 4X6208 14 4X6209 79 3X996 80 3X996 80 4X2493 80 4X6207 87 OCCUPATION 876 6X2492 4 6X2492 23 6X2601 31 4X2493 66 4X2494 , 71 4X2493 80 6X2492 100 4X2494 OCCUPATION 877 3X996 98 6X2492 OCCUPATION 878 4X2102 COL. VOL. I VOL. II 2 572010 4X2102 18 740050 6X2492 28 740050 6X2492 8 573050 4X2493 32 573010 4X2493 1 573020 4X2493 9 573330 4X2402 25 573520 4X2493 15 573010 4X2493 35 573060 , 4X2493 19 573520 4X2493 22 573080 4X2493 10 773520 6X2493 5 773060 6X2493 12 830100 6X669 32 573510 4X2493 11 573070 4X2493 32 773720 6X2493 7 573330 4X2492 19 789051 6X4209 35 430000 4X996 4 430320 3X996 6 830100 3X996 8 520841 4X6207 6 505570 4X6207 21 523510 4X6601 9 523330 6X2601 31 573320 4X2493 1 523910 4X2102 11 523620 4X2494 39 523530 6X4297 29 523020 4X2493 29 788410 6X2492 I I -154-AGE COL. VOL. . I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 881 30 21 947200 6X669 55 2 389060 6X669 58 8 947100 6X669 92 14 747100 0X849 OCCUPATION 883 5 8 902010 6X4208 102 13 949220 4X2492 102 15 988400 6X664 104 3 8034 10 6X664 105 33 988400 6X669 OCCUPATION 890 • 100 40 932010 6X4209 OCCUPATION 900 83 22 95160 0X625 OCCUPATION 911 13 23 612321 6X4379 20 14 968300 6X4359 20 15 612361 6X4379 58 19 612511 6X4379 66 11 612022 6X4679 73 3 612513 6X4379 94 1 612043 6X4479 OCCUPATION 912 10 20 517350 4X6211 56 3 513311 0X779 58 1 517245 0X772 58 2 517210 4X6320 58 3 79 9071 4X6320 62 33 517240 4X6320 62 35 44620 0X13 62 36 517120 4X6320 62 37 517070 4X2010 63 18 476031 4X6318 69 13 517030 0X772 69 14 517248 4X63 48 69 16 517080 4X6320 69 18 799832 4X2010 69 19 517010 4X2010 69 20 517020 4X6320 69 21 517020 4X2452 97 23 517255 4X6313 97 24 517220 4X6320 97 26 517260 4X6217 OCCUPATION 913 6 35 803010 6X4479 11 17 591011 0X846 11 18 591011 0X846 11 26 768811 6X4409 11 29 768212 6X4463 12 13 804100 6X44 12 17 802100 6X4479 14 37 805210 6X4379 15 2 768831 6X2479 15 3 591901 0X846 15 7 900910 6X4409 15 11 968100 6X2479 19 3 606450 6X4379 20 13 768225 6X4463 27 35 789351 4X6346 54 26 768214 6X2409 60 22 968200 1X28 67 14 968300 6X4309 70 1 968200 1X44 88 6 861010 1X28 108 35 968300 6X2409 108 36 842010 6X4469 OCCUPATION 914 11 27 968900 6X4369 84 18 591011 6X2479 -155-AGE COL. VOL. I VOL. II PAGE COL. VOL. I VOL. II OCCUPATION 915 9 32 786330 4X6588 27 31 586510 4X6588 70 14 449940 4X6588 70 26 786310 1X59 OCCUPATION 916 7 18 459911 4X6651 22 16 661833 6X4346 22 17 518520 4X6651 30 37 •659261 6X4359 33 24 518510 4X6651 36 27 659926 -6X4459 49 12 659437 6X2381 56 6 4594 13 4X6651 56 17 459912 4X6381 71 3 659065 4X6651 71 4 659915 4X6651 89 27 459912 6X2381 90 22 459031 6X4459 90 29 659235 6X4346 96 14 459501 4X6651 OCCUPATION 917 2 32 503810 4X6385 2 33 580910 4X2103 10 8 702742 6X2381 10 13 79030 0X742 18 3 576040 '6X2381 20 16 699433 6X4308 34 23 497916 4X6386 34 24 497930 4X6181 34 29 493010 4X6381 35 35 583452 4X6183 37 18 580350 4X2103 39 21 503812 4X6213 39 23 499432 4X6183 44 9 476220 4X6 385 44 10 688214 6X2385 45 21 678690 4X6386 47 5 900910 4X6381 47 6 498020 4X6381 48 6 687910 4X6386 50 24 505511 0X74 2 51 27 478671 4X6385 51 33 6981 10 4X6381 53 14 476220 4X6385 63 32 5816 30 4X2103 63 33 579610 4X2109 81 11 694658 6X2381 86 8 688215 4X6385 100 11 476220 4X2010 107 12 485070 4X6381 OCCUPATION 918 34 18 776110 6X2381 42 17 95110 0X625 46 33 95130 0X730 108 28 776130 4X6381 i OCCUPATION 919 1 8 513351 4X6309 3 14 494341 4X6386 3 28 713321 6X4309 4 10 509430 4X6328 7 9 662201 6X4351 7 12 480010 4X6313 13 6 713758 6X4309 14 35 583901 4X6310 41 3 713013 6X4310 41 9 509442 4X6348 50' 9 583981 4X2T00 51 17 710016 6X4449 60 31 436414 4X6353 60 32 636422 6X4352 66 16 512200 4X6309 66 24 509410 4X6309 66 29 91610 0X715 69 38 713118 6X4309 70 31 583222 4X6309 70 32 512100 4X6383 73 2 510930 4X6348 76 2 556540 2X59 78 21 69 8760 6X2383 88 28 651528- 6X4469 89 2 913510 6X430 89 , 3 513511 4X6328 89 4 783902 4X6328 97 32 713051 6X4309 V PAGE COL. VOL. I TOO 35 513011 4 15 932010 5 13 781010 5 28 809110 6 29 841010 8 28 892010 9 8 893710 9 31 583021 10 18 893450 12 27 866010 12 29 932010 13 22 913750 15 34 90 5010 17 12 80 4100 18 6 787200 20 7 932010 21 24 988400 24 10 205030 30 31 892010 32 36 80 3210 35 17 905510 35 34 954100 38 14 ,827010 39 9 812100 42 10 849010 45 12 849010 48 2 583641 49 20 932010 51 11 849010 51 35 903010 53 21 842010 54 31 867610 54 33 859010 54 35 932010 55 . 3 932010 58 22 983410 59 4 905510 65 24 833050 67 35 932010 68 1 716895 68 3 903010 68 22 802100 68 29 932010 70 33 831010 71 35 932010 72 21 932010 74 7 809010 75 10 654056 77 30 989850 -156-VOL. II PAGE 4X6320 105 OCCUPATION 920 4X6244 4 4X2103 5 6X4359 6 6X4429 7 6X664 8 4X6315 9 4X6183 10 4X6211 10 4X6232 12 4X6232 12 6X4309 . 15 4X6220 15 6X4379 17 6X669 19 6X4297 20 6X669 23 2X11 27 6X2615 32 6X664 35 4X6181 35 4X6181 37 6X4459 38 6X4379 42 4X2463 44 1X28 46 4X2109 48 6X669 51 6X4469 51 4X6210 53 6X610 54 6X2601 54 6X4651 54 6X669 55 6X669 55 6X664 N 59 4X2100 59 6X664 65 4X6246 67 4X6246 68 6X4 346 68 6X4379 68 4X6246 69 6X2381 71 6X4297 72 4X6244 73 6X4676 74 6X2419 75 6X2381 77 iL. VOL. I VOL. II 18 513353 4X6328 16 932010 4X6244 23 987100 3X996 10 988400 6X664 2 8030 10 6X664 34 892610 6X664 30 812100 6X664 6 819010 6X4451 19 693421 4X6211 28 932010 4X6232 35 583631 4X6209 6 831010 6X664 35 932010 4X6220 21 9320 10 4X6244 31 682910 6X4319 33 893070 6X4315 23 932010 4X2109 32 819610 6X4346 27 842010 6X4469 16 900950 4X6181 18 903010 4X6181 3 893580 6X2611 15 8240 10 6X4459 8 810610 6X2493 1 949200 6X4209 16 853410 6X669 3 495031 4X6285 1 810320 6X664 21 849010 6X4309 1 866010 6X4294 30 850010 6X669 32 892010 4X6601 34 855010 6X669 1 988400 6X669 35 841010 6X664 3 878100 4X2010 5 905510 4X2100 33 672955 6X4319 36 916210 4X6246 2 905510 4X6246 4 916210 6X4346 23 802100 4X6675 34 830831 3X996 31 732811 4X6217 10 902010 6X4213 7 732812 4X6217 8 809010 6X4379 14 849010 4X6568 31 v 819010 6X610 -157-PAGE COL. VOL. I VOL. II 80 M 2 903010 4X6208 81 24 932010 6X4208 82 - 9 920100 6X669 83 14 916940 6X4315 83 18 989960 6X669 84 13 89201Q 4X2487 86 32 905510 4X6211 89 6 878000 6X4415 90 28 808010 6X669 91 27 968900 6X4469 92 40 809110 6X663 93 36 859010 6X664 95 3 64200 3X773 96 9 851410 6X4409 98 33 732543 4X6233 99 36 988400 6X664 101 26 802100 6X4379 106 8 988400 6X669 107 4 695045 4X6281 109 9 204030 6X662 PAGE COL. VOL. I VOL. II 80 3 .932010 4X6207 81 25 932010 6X4208 82 16 457910 - 4X2021 83 15 6331 17 6X4325 83 35 804100 6X2671 86 22 694231 4X6313 87 28 732952 4X2493 89 29 631860 6X2381 '91 1 988400 6X669 92 32 988400 1X28 93 35 716580 6X4346 93 37 685215 4X6283 96 3 842010 6X664 98 5 732542 4X6244 98 34 932010 4X6233 100 31 841010 6X2601 105 31 866510 \6X664 107 3 69504-1 4X6283 107 5 69504,3 4X6280 109 10 959010 6X669 - 158 -Appendix B: Data Base Since the data used in this study are not easily obtained this computer produced appendix presents tables containing the data necessary for replication of the study. -159-OCCU- ANN, EARNING PATION MALE FEMALE 1 7659 4496 4 7468 3647 6 5883 3845 8 5652 3755 10 6758 3364 101 7112 4368 102 7055 4866 105 7329 4675 108 7625 5463 109 7411 4961 111 6187 3873 1 14 7439 5303 119 7182 3387 121 5991 3850 124 6362 3317 129 5232 3673 131 7112 5038 135 5529 3399 139 4748 2598 140 6883 4315 141 7303 2819 142 3458 2751 143 1795 514 144 3772 2852 145 6409 1871 146 4746 2359 147 5321 3427 148 3513 2354 153 7359 4362 171 4982 2897 172 54 56 3600 174 5817 3201 176 4097 1906 182 4416 3083 183 3553 2019 186 6993 3753 188 6194 3984 191 4543 2998 192 4024 3020 195 3725 2057 REEKS/YEAR HOURS/WEEK HRLY EARNING MALE FEMALE MALE FEMALE MALE FEMALE 49.4 47.5 44.0 41.1 3.52 2.30 49. 6 47. 3 44.0 41.1 3.42 1 .88 49.4 48.6 44.0 41.1 2.71 1.92 49.0 48.4 44.0 41.1 2,62 1 .89 49.2 46.9 44.0 41.1 3.12 1.75 47. 3 45.8 40.9 38. 8 3.68 2.46 48.1 43.5 40.9 38.8 3.59 2.88 48. 1 43.8 40.9 38. 8 3.73 2.75 48.0 46.9 40.9 38.8 3.88 3.00 48. 5 45. 2 40 .9 38. 8 3.74 2.83 47.4 42.7 40.8 38.4 3.20 2.36 46. 3 48. 3 40.8 38.4 3.94 2.86 47. 1 41.8 40.8 38.4 3.74 2. 1 1 45. 1 43.0 42.7 38. 3 3.11 2.34 45.8 41.0 42.7 38. 3 3.25 2. 1 1 45. 8 40. 5 42.7 38. 3 2.68 2.37 48.8 46.2 41.0 36.9 3.56 2.96 48. 8 46.8 41.0 36. 9 2.76 1 .97 47.4 43.7 41.0 36.9 2.44 1.61 46.7 44. 0 44.6 39. 4 3.3 0 2.49 45.3 43.4 44.6 39.4 3.61 1.65 47.8 42.6 44.6 39. 4 1.62 1 .64 39.4 43.3 44.6 39.4 1.02 0.30 47. 4 42. 2 44.6 39. 4 1.78 1 .72 48.3 38.9 44.6 39.4 2.98 1.22 48.2 44.7 44.6 39.4 2.21 1 .34 46.9 42.7 44.6 39.4 2.54 2.04 45. 6 44.0 44. 6 39. 4 1.73 1 .36 47.3 45.3 42.7 39.5 3.64 2.44 47. 3 44. 1 39.9 31.9 2.64 2.06 46.6 42.9 39.9 31.9 2.93 2.63 47.7 44.9 39.9 31.9 3.06 2.23 43.6 42.4 39.9 31.9 2.36 1.41 47.0 45.4 40.5 37. 6 2.32 1.81 41.1 34. 1 40.5 37.6 2.14 1.57 48.2 41.9 40. 5 37. 6 3.58 2.38 49.4 47.8 40.5 37.6 3.10 2.22 45. 8 4 2.7 40.5 37. 6 2.45 1 .87 44.2 42.2 40.5 37.6 2.25 1.90 46.5 42.4 40.5 37.6 1.98 1.29 - 160 -OCCU- ANN. EARNING PATION MALE FEMALE 196 4171 2342 198 4373 2571 199 5529 3072 201 3461 2170 203 3829 2531 212 3298 1913 214 3229 1890 223 4498 3236 232 3918 2640 234 3175 2187 241 2585 2034 249 3392 2184 307 3623 666 312 1819 717 314 5279 2531 325 2897 1307 331 5282 2944 338 5027 2899 339 3406 1229 403 4328 2930 405 2798 1590 412 3055 1895 413 2616 1418 414 2644 1430 415 1979 995 416 2680 1614 417 2352 1494 419 1527 857 431 3487 2411 433 3920 2744 451 2642- 1678 452 2553 1423 453 2482 1588 454 2500 1039 455 4060 2814 456 1366 964 457 1064 717 459 2494 1456 510 4579 1522 520 8352 4439 WEEKS/YEAR HOURS/WEEK HRLY EARNING MALE FEMALE MALE FEMALE MALE FEMALE 46.7 44.3 40. 5 37. 6 2.21 1.41 44.4 43.5 40.5 37.6 2.43 1.57 47.4 44.4 40.5 37.6 2.88 1 .84 47.2 44. 1 40.4 37.3 1.82 1.32 47. 5 44.8 40.4 37. 3 2.00 1.51 46.1 43.2 40.4 37. 3 1.77 1.19 46. 1 42.9 40.4 37.3 1.73 1.18 48.6 45.6 40.4 3 7.3 2.29 1.90 47. 3 45.6 40.4 37. 3 2.05 1 .55 45.8 43.2 40.4 37.3 1.72 1.36 40.7 43.8 40.4 37. 3 1.57 1 .24 45.7 43.0 40.4 37.3 1.84 1. 36 43. 1 31.8 43. 1 33. 9 1.95 0.62 35.9 28.1 43. 1 33.9 1.18 0.75 48. 2 41.7 43.1 33.9 2.54 1.79 44.5 39.6 43.1 33.9 1.51 0.97 48. 2 46.5 43. 1 33. 9 2.54 1 .87 48.0 46.5 43.1 33.9 2.43 1. 84 40. 2 33.3 43. 1 33. 9 1.97 1 .09 48.5 44.8 42.8 38.4 2.08 1.70 43. 8 38.7 42.8 38.4 1.49 1 .07 44.9 44.1 44.1 39.6 1.54 1.09 42. 1 38.5 44. 1 39.6 1.41 0.93 44.4 40.0 44.1 39.6 1.35 0.90 41.5 34.7 44. 1 39.6 1 .08 0.72 45.7 41.1 44.1 39.6 1.33 0.99 42.4 33.6 44. 1 39.6 1.26 1.12 36.4 37.3 44. 1 39.6 0.95 0.58 39.0 35.6 41.6 30. 2 2.15 2.24 39.4 37.4 41.6 3 0.2 2.39 2.43 44. 4 40.0 41.3 34.0 1.44 1.23 44.6 41.5 41.3 34. 0 1.39 1.01 45. 5 42. 3 41.3 34.0 1.32 1.10 44.8 41.7 41.3 34.0 1,35 0.73 48. 1 45. 2 41.3 34.0 2.04 1 .83 24.8 25.5 41.3 34.0 1.33 1.11 32.0 32. 1 41.3 34. 0 0.81 0.66 41 .9 36.8 41.3 34.0 1.44 1. 16 48. 8 40. 1 42. 6 39. 7 2.20 0.96 47.8 49.0 40.0 27.9 4.37 3. 25 -161-OCCU- ANN. EARNING PAT ION MALE FEMALE 551 3583 1302 552 2449 1472 554 2805 1512 556 3048 2005 581 5036 2677 582 4701 2772 584 4020 2170 585 4093 2873 588 1718 777 603 3135 1546 605 1182 579 607 1892 955 609 1888 1055 613 2199 935 615 1913 1321 631 1531 658 657 3253 1895 701 2953 2190 702 2946 1666 703 3288 2071 704 3472 2289 705 1428 666 706 2541 1140 707 2925 1746 708 3213 1708 709 4121 2237 711 4017 2675 719 3479 2032 721 2786 1597 722 2505 1605 724 2251 1710 729 2590 1626 731 2706 1967 732 2717 1899 7 33 2335 1852 734 2854 1925 735 3098 2027 736 2684 1642 737 2986 1654 738 2826 1642 WEEKS/YEAR HOURS/WEEK HRLY EARNING MALE FEMALE MALE FEMALE MALE FEMALE 47.1 38.3 45.1 32.0 1.69 1.06 43. 4 38.4 45. 1 3 2.0 1.25 1.20 44.3 41.1 45.1 32.0 1.40 1. 15 42.4 40.9 45. 1 32.0 1.59 1.53 46.6 42.4 39.6 3 7.6 2.73 1.68 47. 5 46. 2 39. 6 37.6 2.50 1.60 47.5 44.3 39.6 37.6 2.14 1.30 48.7 46.5 39.6 3 7.6 2.12 1 .64 39.5 34.8 39.6 37.6 1. 10 0.59 47.0 39. 3 47.9 41.4 1.39 0.95 34.2 20.9 47.9 41.4 0.72 0.67 33. 2 28. 3 47.9 41.4 1.19 0.82 33.7 34.1 47,9 41.4 1. 17 0.75 3 3.5 20.8 48. 4 48.5 1.36 0.93 27.7 22.3 48.4 48.5 1.43 1.22 25.8 17.6 50.2 41.0 1.18 0.91 42.0 31.7 42.8 42. 1 1.81 1.42 46. 3 47. 2 44.3 40.2 1.44 1.15 46.2 42.3 44.3 40.2 1.44 0.98 46.7 4 2. 1 44.3 40. 2 1.59 1 .22 46.0 41.6 44.3 40. 2 1.70 1.37 30.7 23.4 44. 3 40.2 1.05 0.71 38.6 29.3 44.3 40.2 1.49 0.97 44. 7 40. 1 44.3 40. 2 1.48 1 .08 46.0 41.4 44.3 40.2 1.58 1.03 47. 2 41.6 44.3 4 0.2 1.97 1 .34 46.5 43.6 42.2 42. 1 2.05 1.46 46.8 42.5 42. 2 42. 1 1.76 1 .14 45.6 41.4 42.9 41.7 1.42 0.93 44. 2 41.8 42.9 41.7 1.32 0.92 43.2 39.5 42.9 4 1.7 1.22 1.04 45. 3 42. 2 42.9 41.7 1 .33 0.92 45.6 43.9 43.1 41.1 1.38 1.09 45.9 42.9 43. 1 41.1 1.37 1 .08 42.9 41.8 43.1 41.1 1.26 1.08 46.4 43. 3 43. 1 41.1 1.43 1 .08 47.8 44.6 43.1 41.1 1.50 1.11 43. 5 41.7 43. 1 41. 1 1.43 0.96 46.6 42.3 4 3.1 41.1 1.49 0.95 45. 9 42.0 43. 1 41.1 1.43 0.95 -162-OCCU- ANN. EARNING PATION MALE FEMALE 739 2786 1836 741 2967 1710 742 2638 1553 743 3331 1944 744 3005 1734 745 3222 16 25 746 2701 1592 747 3187 1951 749 2748 1534 752 3012 1835 754 2602 1633 756 2826 1928 758 3324 2187 759 2800 1875 761 5131 2371 762 4019 2013 763 4236 2034 766 4079 2123 768 3902 2399 769 4161 2133 771 4206 2331 772 4359 2166 773 4873 2333 775 5876 2029 776 4055 1987 778 3722 1923 779 4376 1951 781 4416 2182 782 4331 2812 786 3682 2182 787 3645 2444 788 3901 2552 789 4069 2321 791 3420 1713 793 4230 2253 801 4484 2472 803 3762 1961 806 3598 2439 808 36 33 2277 811 3640 1921 WEEKS/YEAR HOUES/WEEK HRLY EARNING MALE FEMALE MALE FEMALE MALE FEMALE 46. 2 4 2.9 43. 1 41.1 1.40 1 .04 45.8 42.4 41.0 39.6 1.58 1.02 44. 7 4 2.0 41.0 39. 6 1.44 0.93 44.0 40.7 41.0 39.6 1.85 1.21 45.7 4 2. 1 41.0 39. 6 1.60 1 .04 46.2 41.2 41.0 39.6 1.70 1.00 43. 6 41.6 41.0 39.6 1.51 0.97 45.4 41.0 41.0 39.6 1.71 1.20 44. 3 4 1.3 41,0 39. 6 1.51 0.94 44.6 41.7 43.4 41.6 1.56 1.06 38.0 35. 2 43.4 41.6 1.58 1.12 43.2 40.2 43.4 41.6 1.51 1. 15 42. 4 40. 2 43. 4 41.6 1.81 1.31 42.4 3 9.5 43.4 41.6 1.52 1. 14 48.9 4 1.0 40.9 40.3 2.57 1 .44 46.7 42.8 40.9 40.3 2. 10 1.17 47.0 37. 1 40.9 40. 3 2.20 1.36 46.9 43.0 40.9 40. 3 2. 13 1. 23 47.4 47. 3 40.9 40. 3 2.01 1.26 47.5 43.3 40.9 40.3 2.14 1.22 48.0 44.3 39.6 38.0 2.21 1.39 48.2 45.1 39.6 38.0 2.28 1.26 48. 3 43.4 39.6 38. 0 2.55 1.42 48.7 41.8 39.6 38.0 3.05 1. 28 47.7 43. 2 39.6 38. 0 2.15 1.21 46.0 42.8 39.6 38.0 2.04 1.18 48.0 43. 2 39.6 38.0 2.30 1.19 47.2 39.9 41.0 40.3 2.28 1.36 47. 2 45. 0 41.0 40. 3 2.24 1.55 45.5 41.0 41.0 40.3 1.97 1.32 45. 6 41.5 41.0 40.3 1.95 1.46 46.5 42.6 41.0 40.3 2.05 1.49 45. 6 42. 1 41.0 40. 3 2.18 1 .37 47.2 42.4 41.7 38.8 1.74 1.04 47.9 44.9 41.7 38. 8 2.12 1.29 47.3 42.3 41.4 40.4 2.29 1.45 45.0 39. 5 4 1.4 40.4 2.02 1.23 44.3 41.9 41.4 40.4 1.96 1.44 44.9 42.0 41.4 40.4 1.95 1.34 44.1 38.1 41.4 40.4 1.99 1. 25 - 1 6 3 -OCCO- ANN. EARNING PATION MALE FEMALE 812 3251 1907 815 3524 199 1 817 3807 23 54 818 3369 2147 819 3513 2413 821 4439 2866 822 3279 2300 824 3845 2892 829 3812 20 05 831 4140 2963 832 3753 2411 835 3357 2527 836 3779 2175 839 3454 2324 841 2751 1508 843 3315 1850 854 3050 1786 859 2685 1456 861 3458 2171 862 3463 2015 864 2743 1911 869 3390 2147 877 3284 2086 900 4970 2765 911 3442 2795 912 4244 2352 913 2595 1652 914 3416 1937 915 3312 1939 916 3116 1864 917 4286 2663 918 3682 1494 919 3038 1784 920 2156 1449 WEEKS/YEAR HOURS/WEEK HRLY EARNING MALE FEMALE MALE FEMALE MALE FEMALE 41 .3 38.4 41.4 40.4 1.90 1. 23 45.9 42. 1 41.4 40.4 1.86 1.17 44.1 41.9 41.4 40.4 2.09 1.39 44. 8 4 1.3 41.4 40. 4 1.82 1.29 44.3 42.8 41.4 40.4 1.92 1.40 48. 1 48.7 43.8 37. 6 2.11 1 .57 45.4 42.2 43.8 37.6 1.65 1.45 47.8 46. 2 43.8 37. 6 1.84 1 .66 46.2 43.3 43.8 37.6 1.88 1.23 45.7 44. 1 41.3 40. 2 2.19 1 .67 46.1 42.8 41.3 40.2 1.97 1.40 45. 6 46. 6 41.3 40.2 1.78 1 .35 46.5 44.7 41.3 40.2 1.97 1.21 44. 9 43.0 41.3 40.2 1.86 1 .35 37.6 33.4 41.5 38.9 1.76 1. 16 44. 1 41.7 41.5 38. 9 1.81 1.14 36.9 28.5 42.9 40.5 1.93 1.55 36.2 29. 4 42.9 40.5 1.73 1.22 47.8 45.4 42.3 40.6 1.71 1. 18 43.9 41.0 42.3 40. 6 1 .86 1.21 39.7 38.7 42.3 40.6 1.63 1.22 45. 2 41.3 42.3 40.6 1.77 1.28 41 .9 39.4 44.3 43.5 1.77 1.22 49. 1 48.0 41.6 39. 4 2.43 1 .46 45.6 44.6 41.6 39.4 1.82 1.59 47. 1 42. 3 41.6 39.4 2.17 1.41 41 .9 39.5 41.6 39.4 1.49 1.06 46. 2 43.0 41.6 39. 4 1.78 1 .14 45.7 42.9 41.6 39.4 1.74 1.15 46.0 41.8 41.6 39.4 1.63 1.13 47.4 44.9 41.6 39.4 2.17 1.51 45. 3 39.6 41.6 39. 4 1.95 0.96 44.1 40.8 41.6 39.4 1.66 1.11 35. 5 37.4 41.8 37. 8 1.45 1.02 -164-OCCU- WAGE-EARNERS EMPLOYMENT RATIO AGE TION MALE FEMALE UNADJ. ADJUSTED MALE FEMALE BAT IO 1 ' 2175 193 11.269 12.547 39 40 0.97 4 22589 610 37.031 41.572 41 42 0.98 6 11946 2847 4. 195 4.565 41 42 0.98 8 13005 1500 8.671 9.398 41 43 0.95 10 194985 13760 14. 170 15.914 45 47 0.96 101 11086 24 443.440 482.750 37 34 1.09 102 7923 15 528.267 615.744 38 36 1.06 105 8564 39 214.125 247.873 37 31 1. 19 108 29 37 13 209.786 226.327 35 34 1.03 109 4592 13 353.231 399.533 39 33 1. 18 111 5593 433 12.919 15.238 37 32 1.16 114 661 24 26.440 26.929 35 35 1.00 1 19 1357 69 19.681 23.563 36 34 1. 06 121 13 73 267 5.146 6.017 37 34 1.09 124 712 10 64.818 80.725 40 36 1.11 129 2623 61 42.323 53.359 40 33 1.21 131 7256 1213 5.978 7.016 40 42 0.95 135 46878 108406 0.432 0.501 36 36 1.00 139 4821 4048 1. 191 1.436 40 40 1.00 140 71 12 933 7.623 9.158 42 38 1.11 141 449 166 2.711 3.203 46 36 1.28 142 2255 54759 0.041 0.052 39 36 1.08 143 2 36 10671 0.022 0.023 24 20 1.20 144 445 1842 0.242 0.308 44 35 1. 26 145 183 17 10.167 14.289 44 40 1.10 146 43 35 1 .257 1.534 42 47 0. 89 147 3479 785 4.433 5. 512 43 38 1.13 148 4017 8748 0.459 0.539 36 29 1. 24 153 3595 181 19.758 22.302 42 40 1.05 171 34 96 735 4.758 6.383 36 33 1.09 172 870 438 1.989 2.702 41 45 0.91 174 8817 2998 2.940 3.907 39 39 1.00 176 3102 3105 0.999 1.285 38 44 0.86 182 19554 852 22.924 25.562 33 32 1.03 183 7977 56 142.446 184.929 30 36 0.83 186 1895 262 7.233 8.962 37 34 1.09 188 24869 1394 17.841 19.860 40 41 0.98 191 64 1779 0.036 0.042 42 37 1. 14 192 4944 5262 0.940 1.060 39 39 1.00 195 2050 1364 1 .504 1.776 35 37 0. 95 -165-OCCU- WAGE-EARNERS EMPLOYME PATION MALE FEMALE UNADJ. 196 1984 226 8.779 198 35557 3904 9. 108 199 22369 7010 3.191 201 58169 94755 0.614 203 5991 22335 0.268 212 32994 3756 8.782 214 52341 3725 14,048 223 7225 1312 5.507 232 4494 159169 0.028 234 2307 48535 0.048 241 132 3678 0.036 249 1571 13 162981 0.964 307 6991 4109 1.702 312 398 37 10.757 314 67651 815 82.906 325 94475 120542 0.784 331 22632 1444 15.662 338 49 13 550 8.933 339 143 804 0.178 403 29576 369 80. 152 405 33559 1194 28.107 412 3949 9920 0. 398 413 23934 20496 1 .168 414 9043 246 36.615 415 16455 59423 0.277 416 13062 46899 0. 279 417 5067 64 79.187 419 159 80 114876 0. 139 431 1024 787 1 .299 433 2442 943 2.590 451 7321 13647 0.536 452 7793 21401 0. 364 4 53 3842 1404 2.736 454 67669 30533 2. 216 455 1472 21 70.095 456 2545 135 18.721 457 4041 930 4.342 459 3201 2152 1.4 87 510 17735 410 43.256 520 2649 4 530.000 RATIO AGE ADJUSTED MALE FEMALE RATIO 9.968 37 38 0.97 10.014 33 32 1.03 3.669 39 39 1.00 0.712 35 33 1.06 0.308 30 30 1.00 10.150 38 38 1.00 16.350 37 35 1.06 6.357 40 32 1.25 0.032 40 32 1.25 0.055 29 28 1.04 0.036 31 33 0.94 1.110 36 34 1.06 2.932 40 40 1.00 17.472 43 40 1.07 121.835 39 40 0.97 1. 120 34 37 0.92 20.641 39 40 0.97 11.723 40 39 1.03 0.273 35 40 0. 88 96.714 36 39 0.92 35.456 52 42 1. 24 0.451 44 45 0.98 1.422 40 43 0.93 45.262 40 34 1.18 0.369 35 32 1.09 0.345 39 36 1.08 111.282 37 41 0.90 0.151 36 39 0,92 1.961 36 32 1. 13 3.758 32 29 1.10 0.723 43 32 1. 34 0.475 38 37 1.03 3.575 49 33 1.48 2.892 49 45 1.09 90.608 42 52 0.81 22.116 38 28 1.36 5.257 30 27 1. 11 2.056 40 40 1.00 56.486 43 39 1. 10 741.248 35 32 1.09 -166 -OCCU- WAGE-EARNERS EMPLOYMENT RATIO AGE TION MALE FEMALE UNADJ, ADJUSTED MALE FEMALE RATIO 551 17281 4 39 39.367 68.230 41 40 1. 02 552 13553 240 56.241 89.585 41 40 1.02 554 48223 373 129.284 196.397 32 34 0.94 556 143305 279 511.807 747.783 35 33 1.06 581 1529 105 14.562 16.856 29 33 0.88 582 3335 157 21 .242 23.001 33 33 1.00 584 1685 33466 0.050 0.057 40 33 1.21 585 3918 455 8.594 9.480 36 38 0.95 588 6371 639 9.956 11.902 32 21 1.52 603 3048 57 53.474 73.991 43 44 0.98 605 82207 8496 9.676 18.320 30 40 0.75 607 21597 221 97.724 132.644 43 45 0. 96 609 4154 208 19.876 22.726 37 39 0.95 613 7539 15 502.667 807.914 38 3 4 1. 12 615 63162 78 799.531 991.092 34 31 1.10 631 10286 147 69.973 125.590 39 37 1.05 657 14876 19 743.850 1001.929 36 32 1.13 701 2039 10 185.455 200.472 39 35 1. 11 702 9991 1819 5.490 6. 608 37 37 1.00 703 19292 593 32.478 39.701 38 34 1. 12 704 2828 2970 0.952 1. 160 37 32 1.16 705 6071 4529 1 .341 1.938 36 34 1.06 706 1494 20 59 0.726 1.053 37 39 0.95 707 5755 337 17.027 20.916 35 33 1.06 708 5369 3369 1.594 1.951 38 34 1.12 709 2866 2 34 12.252 15.319 39 36 1.08 711 2538 181 13.945 14.908 39 39 1.00 719 3661 1862 1.967 2.171 38 34 1. 12 721 2246 4 39 5. 105 5.784 38 31 1.23 722 5725 7020 0.816 0.887 33 32 1.03 724 1362 32 42.563 47.889 48 45 1.07 729 11 90 2088 0.570 0.630 39 34 1. 15 731 1331 334 3.973 4.328 39 35 1.11 732 1901 1862 1 .020 1.145 34 32 1.06 733 751 20 34 0. 370 0. 398 30 35 0.86 734 3206 1246 2.574 2.892 35 34 1.03 735 14 53 276 5.264 5.917 39 35 1.11 736 1946 3774 0.516 0.564 33 33 1.00 737 1821 116 15.564 17.981 38 32 1.19 738 1689 718 2.350 2.694 36 34 1.06 - 167 -OCCU- WAGE-EARNERS EMPLOYMENT RATIO AGE PATION MALE FEMALE UNADJ, ADJUSTED MALE FEMALE RATIO 739 5003 4041 1 .238 1 .398 36 33 1. 09 741 4288 866 4.947 5. 533 44 43 1.02 742 545 1 0312 0.053 0.058 44 46 0.96 743 2383 1748 1. 363 1. 526 41 42 0.98 744 1 52 717 0.212 0.238 44 44 1.00 745 4887 1614 3.028 3.515 38 34 1.12 746 5211 5 0363 0.103 0.112 40 34 1. 18 747 4484 299 14,997 17.193 37 36 1.03 749 44 51 5928 0.751 0.834 39 35 1. 1 1 752 7036 183 38.448 42.901 39 36 1.08 754 12131 92 130.452 146.922 38 31 1. 23 756 9262 742 12.467 13.977 38 32 1.19 758 6225 221 28.172 30.999 39 30 1. 30 759 10866 1072 10.128 1 1.342 3 8 32 1.19 761 1376 24 55.040 66.623 39 37 1.05 762 1395 21 63.455 70.267 41 31 1.32 763 36 21 61 58.403 75.089 41 39 1.05 766 111 16 796 13.949 15.440 38 31 1.23 768 1071 19 53.600 54.513 42 33 1. 27 769 13383 1989 6.729 7.492 38 34 1.12 771 14626 903 16.180 18.270 36 35 1.03 772 82 37 495 16.640 18.533 36 34 1.06 773 2862 130 21.855 25.347 33 33 1.00 775 11 16 30 36.000 43.709 37 29 1.28 776 1295 2520 0.514 0.592 37 37 1.00 778 537 1354 0.397 0.445 35 39 0.90 779 1901 781 2.434 2.818 35 34 1.03 781 5838 24 243.292 292.802 41 36 1.14 782 1020 15 68.000 72.563 43 39 1. 10 786 6663 65 100.970 113.998 40 35 1.14 787 913 69 13.246 14.808 43 42 1.02 788 865 61 13.952 15.49 3 39 36 1.08 789 8876 92 95.441 105.171 40 36 1. 11 791 27 51 5 23 5.26 2 6.295 41 34 1.21 793 704 133 5.301 6.078 38 34 1. 12 801 10407 43 236.545 271.053 39 32 1.22 803 5532 111 49.847 58.193 42 32 1.31 806 15668 1875 8. 356 9.054 39 34 1.15 808 251 02 2946 8.521 9.335 40 34 1. 18 811 15548 638 24.333 28.863 36 32 1.13 -168 OCCU- WAGE-EARNERS PATION MALE FEMALE 812 1298 96 815 1979 106 817 36352 764 818 2647 125 819 8461 1420 821 6755 24 822 77835 125 824 3720 48 829 70856 507 831 45201 17 832 8251 7079 835 4798 66 836 1307 11 839 1460 1963 841 33548 286 843 71 80 362 854 17783 22 859 12390 13 861 1380 183 862 1151 13 864 1559 19 869 6576 1140 877 26720 45 900 65462 4958 911 1387 2673 912 1894 46 913 19960 28098 914 57 82 4144 915 1647 1246 916 2300 3 89 917 12182 2404 918 3000 1295 919 1 1104 5802 920 290613 20281 EMPLOYMENT RATIO AGE UNADJ. ADJUSTED MALE FEMALE RATIO 13.521 14.902 39 35 1.11 18.495 20.664 38 34 1.12 47.519 51,252 37 31 1.19 21.184 23.548 39 37 1. 05 5.955 6. 316 38 35 1.09 281.458 323.830 37 33 1. 12 622.680 780.359 35 32 1.09 77.521 93.431 32 34 0. 94 139.755 173.704 40 40 1.00 2511.167 2673.482 36 40 0.90 1. 166 1.290 37 32 1.16 71.627 72.008 35 32 1.09 109.000 116.492 46 38 1.21 0.744 0.798 36 32 1. 13 117.301 140.877 40 37 1.08 19.780 22.316 37 34 1. 09 773.217 1060.438 36 29 1.24 885.071 1154,362 36 35 1.03 7.546 8.278 35 36 0.97 88.538 98.770 41 31 1.32 82.053 87.697 39 35 1.11 5.769 6.578 36 31 1. 16 593.778 643.067 36 30 1.20 13.201 14.257 43 41 1.05 0.519 0.561 39 34 1.15 40.298 47.376 41 33 1. 24 0.710 0.796 33 32 1.03 1 .396 1.583 34 33 1.03 1.321 1.486 33 31 1.06 5.897 6.852 40 35 1. 14 5.067 5.648 41 36 1.14 2.315 2.796 41 36 1. 14 1.914 2. 184 36 33 1.09 14.329 15.041 36 33 1.09 - 169 -OCCU- EDUCATION TION MALE FEMALE BAT IO 1 12.4 12.1 1 .02 4 11.6 11.9 0.97 6 11.7 11.7 1 .00 8 11.0 11.5 0.96 10 10.1 10.8 0.94 101 15.4 12.2 1. 26 102 16.1 12.2 1 .32 105 16.7 12.4 1. 35 108 17.4 13.2 1 .32 109 16. 4 12, 2 1.34 111 16.2 14.5 1.12 114 17.5 16.8 1.04 119 16.5 13.2 1 .25 121 17. 1 16.5 1.04 124 15.5 13.6 1.14 129 14. 2 12.6 1.13 131 17.5 16.0 1 .09 135 14.2 13.4 1.06 139 11.0 8.9 1 .24 140 19.7 18.8 1.05 141 17.7 14.1 1 .26 142 12.8 12.7 1.01 143 13.0 12.5 1 .04 144 12.0 13.3 0.90 145 15.1 13.0 1.16 146 16.4 12.8 1.28 147 15.4 14.4 1 .07 148 11.7 12. 2 0.96 153 18.6 16.5 1.13 171 11.2 12.0 0.93 172 11.6 12.4 0.94 174 13.4 13.0 1.03 176 11.9 12.2 0.98 182 11.8 12.0 0.98 183 11.9 12.0 0.99 186 15.9 13.9 1.14 188 13.5 12.1 1.12 191 13. 0 13.4 0.97 192 13.2 13.7 0.96 195 10.6 10.7 0.99 TRAIN! NG APTITUDES GED SVP G V N 16.00 3. 00 0.7833 0. 7833 0.5000 16.00 7. 00 0.7833 0. 9500 0.7833 16.00 7.00 0.7833 0. 7833 0.5000 13.33 3. 83 0.8389 0. 8389 0.7833 14.70 5. 71 0.7851 0. 7077 0.6239 15.60 6. 20 0.9500 0. 9167 0.9500 17.27 5. 91 0.9348 0. 8288 0.9348 16.33 6. 33 0.8750 0. 7917 0.8750 18.00 7. 00 0.9500 0. 9500 0.9500 17. 20 4. 60 0.9500 0. 8500 0.9500 17.85 3. 92 0.9500 0. 9372 0.9372 18.00 3. 00 0.9500 0. 9500 0.9500 16.67 3. 00 0.9500 0. 8389 0.8944 17. 1 1 3. 44 0.9500 0. 9130 0.7204 16.00 5. 00 0.9500 0. 9500 0.7250 13.67 3. 39 0.8C00 0. 5944 0.6500 17.33 6. 17 0.8944 0.8944 0.7639 16.00 2. 95 0.8439 0. 8136 0.5515 16.00 2. 25 0.7833 0. 7833 0.7833 17.75 7. 00 0.9500 0. 9500 0.7125 1 8.00 7.00 0.9500 0. 9500 0.7833 11.87 2. 65 0.7125 0. 7125 0.5000 12.00 0. 0. 0.7833 0. 7833 0.7833 13.50 1. 63 0.6417 0. 6417 0.4292 16.00 3. 00 0.7833 0. 7833 0.7833 17.00 5. 00 0.8667 0. 7250 0.5000 16.00 3. 00 0.9500 0.78 33 0.7833 12.33 1.86 0.6556 0. 6083 0.5708 17.33 8.00 0.9500 0. 9500 0.5944 13.71 2. 79 0.6214 0.5405 0.3381 14.50 6. 00 0.7958 0.7542 0.5708 16. 37 4. 85 0.9025 0. 9315 0.4056 16.50 9. 62 0.8458 0.7833 0.4646 12.87 2. 75 0.7804 0. 5370 0.7659 15.43 2. 57 0.7833 0.6619 0.8548 18.00 3. 00 0.9500 0. 9500 0.9500 15.14 4. 29 0.8857 0. 7667 0.8857 16.00 3. 00 0.7833 0. 7833 0.7833 16.40 5. 10 0.9167 0. 9167 0.5567 13.00 1. 31 0.7125 0. 5708 0.3167 •170-OCCU- EDUCATION TRAINING APTITUDES ' E H A L E RATIO GED SVP G V N 11.3 0.93 12.29 2.83 0.6619 0.5405 0.4190 11.8 0.97 13.16 2.27 0.7237 0.6430 0.5597 12.2 1.01 15.62 4.08 0.8143 0.7738 0.6079 10.5 1.12 10.33 0.32 0.5394 0.5315 0.5944 10.4 1.14 8.43 0.21 0.4865 0.4325 0.3976 10.3 0.89 9.64 0.41 0.5515 0.5000 0.5515 10.0 0.86 9.90 0.31 0.5283 0.4717 0.4433 11.9 0.90 11.00 0.75 0.5000 0.5000 0.5000 11.3 1.14 10.25 0.94 0.6417 0.8042 0.2875 10.5 1.21 10.50 0.42 0.5000 0.5000 0.2521 10.9 1.17 12.00 3.00 0.5000 0.7833 0.5000 10.6 1.03 10.17 0.45 0.5486 0.5178 0.4930 9.6 0.99 10.00 0.10 0.5000 0.5000 0.5000 9.5 0.75 8.00 0.12 0.4056 0.4056 0.4056 11.9 0.90 12.00 1.12 0.6417 0.6417 0.3583 9.3 1.08 11.00 1.26 0.5810 0.5810 0.4595 11.9 0.93 16.00 1.50 0.8667 0.8667 0.8667 11.7 0.97 13.60 2.25 0.6700 0.6700 0.5567 10.0 1.14 9.67 0.12 0.5944 0.6889 0.2556 11.8 0.83 13.25 2.39 0.6417 0.6417 0.4292 10.4 0.71 10.50 0.19 0.4292 0.4292 0.1750 8.6 1.15 11.50 1.61 0.5563 0.5208 0.3375 7.7 0.97 12.00 3.80 0.5000 0.2733 0.3300 10.6 0.77 7.00 0.16 0.5000 0.5000 0.5000 8.3 1.08 7.50 0.10 0.4150 0.3300 0.2850 8.9 1.12 9.67 0.68 0.5000 0.5000 0.2556 11.1 0.77 5.80 0.04 0.5000 0.4433 0.2167 7.4 1.15 7.12 0.27 0.3500 0.3167 0.1520 11.3 0.90 12.17 2.60 0.7167 0.5750 0.1528 12.9 0.89 11.75 3.29 0.6417 0.6417 0.3583 9.1 0.92 11.20 0.71 0.4433 0.5000 0.1833 7.3 1.08 8.83 0.83 0.4528 0.3583 0.2556 8.6 0.85 7.00 0.04 0.2167 0.2167 0.2167 7.2 1.00 7.00 0.04 0.2167 0.2167 0.0500 11.8 0.87 12.00 1.58 0.5G00 0.5000 0.5000 11.3 0.57 10.50 1.55 0.5708 0.7125 0.2458 9.7 0.89 7.86 0.24 0.5000 0.5000 0.1452 9.2 0.98 7.50 0.04 0.3583 0.3017 0.1617 11.2 0.79 12.00 2.75 0.5708 0.5354 0.4292 12.0 0.98 12.00 1.50 0.9500 0.7833 0.9500 PATION MALE 196 10. 5 198 11.4 199 12. 3 201 11.8 203 11.9 212 9.2 214 8. 6 223 10.7 232 12.9 234 12.7 241 12.8 249 10.9 307 9.5 312 7.1 314 10.7 325 10.0 331 11.1 338 11.3 339 11.4 403 9.8 405 7.4 412 9.9 413 7.5 414 8.2 415 9.0 416 10.0 417 8. 6 419 8.5 431 10. 2 433 11.5 451 8.4 452 7.9 453 7. 3 454 7.2 455 10.3 456 6.4 457 8.6 459 9.0 510 8.9 520 11.8 -171-OCCU- EDUCATI PATION MALE FEMALE 551 8.1 10 .5 552 7,8 10.9 554 8.3 11.4 556 7. 5 11.7 581 12 .3 1.2.2 582 11 .0 11.6 584 12 .3 9.7 585 10. 2 11.1 588 8.3 10 .0 603 8. 5 11.0 605 7.6 7.4 607 7. 3 10.6 609 7.6 9.5 613 7.5 11.8 615 6.2 11.2 631 6. 2 9 .7 657 7.2 11.8 701 7.4 10.8 702 7.6 8.1 703 8.0 10.0 704 7.8 7.9 705 6.4 6.9 706 7.8 7.2 707 8. 1 9.4 708 7.8 7.6 709 8. 3 9 .2 711 8. 1 9.0 719 7 . 7 7.6 721 7.3 7.9 722 7.3 7 .3 724 6.5 9.5 729 7.5 7. 1 731 7.0 7.5 732 7. 3 7.3 733 7.5 7.4 734 7. 2 7 .7 735 7.1 7.8 736 8.0 7.5 737 7.3 9.3 738 7.3 7 .5 N TRAINING RATIO GED SVP 0 . 7 7 1 2 . 0 0 0 .75 0 . 7 2 7 .00 0 .04 0 .73 1 0 . 0 0 0 .04 0 .64 9 .25 0 .13 1.01 1 6 . 0 0 0 .38 0 . 9 5 1 2 . 6 7 1.92 1.27 1 0 . 5 0 0.31 0 . 9 2 11 .33 1.37 0 .83 7.71 0 .09 0 . 7 7 14.00 5.00 1.03 1 0 . 2 0 1.65 0 .69 7 .00 1. 50 0 .80 8 .42 0 .62 0 . 6 4 12.00 0 .90 0 .55 7.61 0 .77 0 . 6 4 7 .00 0.11 0.61 7 .36 0 .98 0 . 6 9 10. 33 1. 87 0 .94 1 0 . 0 0 1.88 0 .80 8 .82 1. 37 0 .99 5 .50 0 .04 0 . 9 3 10 .00 0 . 7 5 1.08 7.60 0 .30 0 . 8 6 10 .75 1.41 1.03 8.33 0 .40 0 . 9 0 11. 33 2. 23 0 .90 7 .89 1.38 1.01 9. 25 0. 70 0 .92 8.80 1.54 1.00 7.71 0 .70 0 .68 1 0 . 0 0 1.58 1.06 10 .00 1. 33 0 .93 7 .00 0 .39 1.00 8. 37 1.22 1.01 8 .00 0 .19 0 .94 12.00 3.00 0.91 8 .45 1.25 1.07 9 .50 1.63 0 .78 9 .40 0 .33 0 . 9 7 5.00 0 .08 APTITUDES G V N 0.5000 0 .5000 0 . 5 0 0 0 0 .5000 0 . 2 1 6 7 0 . 2 1 6 7 0 .5000 0 .5000 0 .5000 0 .5000 0 .2875 0 . 2 1 6 7 0 .7833 0 . 7 8 3 3 0 . 5 0 0 0 0.7361 0 .6889 0 .5000 0 .5000 0 .5000 0 . 3 5 8 3 0 .5G00 0 .5000 0 .5000 0 .4595 0 .5000 0 . 2 7 3 8 0 . 6 4 1 7 0 .5000 0 .5000 0 .6133 0 .5000 0 .4433 0 .5CC0 0 .5000 0 .2167 0 . 4 2 9 2 0 . 3 3 4 7 0 .2181 0 .5708 0 .5000 0 .4292 0 .3522 0 . 2 5 8 7 0. 1399 0 .4261 0 .2094 0 .1804 0 . 3 7 1 2 0 .3068 0 .2053 0 .4528 0 .3583 0 .2556 0 . 4 7 1 7 0 .2450 0 . 2 2 8 3 0 . 4 2 2 7 0 .2788 0 .1773 0. 3583 0 . 2 1 6 7 0. 1333 0 . 2 1 6 7 0 . 2 1 6 7 0 .2167 0. 3867 0 . 2 1 6 7 0. 1833 0 .5000 0.3111 0 . 3 3 4 7 0 . 3 8 9 8 0 . 2 7 9 6 0 .2361 0 .5944 0 .5000 0 .5000 0 .4056 0 . 2 4 8 2 0. 1741 0 .5000 0 .2875 0 .3167 0. 4433 0 .2450 0 . 1333 0 .4595 0 .2167 0 .1056 0 .5000 0 . 2 1 6 7 0 . 3 5 8 3 0 .4646 0.2521 0 .1542 0 .3111 0 . 2 1 6 7 0 . 2 1 6 7 0 .4292 0 .2875 0 . 1 4 7 9 0 .3111 0 . 2 1 6 7 0 .0500 0 . 5 0 0 0 0 .3583 0 .3583 0 . 3 1 9 7 0 .2530 0 .2076 0 .5000 0 .4056 0 .2694 0 . 5 0 0 0 0 .2733 0 .3300 0 . 2 1 6 7 0 .2167 0 .0500 -172-OCCU- EDUCATION - TRAINING PATION MALE FEMALE RATIO GED SVP 739 7.5 7.6 0.99 9.00 1.03 741 7.5 8.5 0.88 1 1.00 2.25 742 10.8 7.8 1. 38 10.00 3.00 743 7.9 7.7 1.03 10.50 2.62 744 8.9 8. 1 1. 10 10.00 1. 87 745 8.0 7.9 1 .01 9.62 1.42 746 8. 2 7.0 1. 17 7. 35 0. 57 747 8.0 9.3 0.86 10.67 2.03 749 7.2 7. 1 1.01 7. 68 0.85 752 7.9 10.5 0.75 10.56 1.52 754 7.0 10.4 0.67 8. 70 0.98 756 7.3 8.5 0.86 9.38 0.75 758 8.5 10. 4 0. 82 10.50 4. 29 759 7.4 8.7 0.85 9.26 1.12 761 9.2 11.3 0.81 10. 83 1.06 762 7.5 10.8 0.69 9. 10 0.68 763 7.6 10.4 0.73 8. 00 0. 32 766 7.9 9.4 0.84 7.00 0.10 768 7.7 10.4 0.74 8. 50 0. 50 769 8.5 9.0 0.94 9.83 1.21 771 9,6 11.0 0. 87 11,60 3. 60 772 9.0 10.3 0.87 12.00 4.67 773 9.6 11.3 0.85 11. 50 6.00 775 9.6 11.4 0.84 12.00 5.63 776 9.3 8.3 1.12 12.00 3.00 778 9.3 8.3 1.12 10.00 0.23 779 8.9 8.5 1.05 10.53 3. 37 781 7.4 11.4 0.65 9.88 1.28 782 7.9 11. 1 0.71 10.36 2. 44 786 7.2 10.9 0.66 9.23 2.37 787 7.2 8.4 0.86 11. 20 1. 68 788 8.0 9.0 0.89 10.67 2.25 789 7.5 10.8 0.69 7.94 1.01 791 8.9 9.8 0.91 10.93 3.87 793 9.2 9.6 0.96 10.60 3. 32 801 9.4 11.9 0.79 11.78 3.50 803 7.8 10.6 0.74 9.00 0.73 806 8.1 9.3 0.87 9.92 1.08 808 7.8 8.9 0.88 9.58 0.91 811 8.4 10.0 0.84 11.00 2.39 APTITUDES G V N 0.3938 0.2875 0.2104 0.5000 0.3583 0.5000 0.5000 0.4056 0.3111 0.5000 0.2875 0.2042 0.6417 0.3583 0.1333 0.5000 0.3229 0.3583 0. 3801 0.2667 0. 1526 0.5000 0.2639 0.3583 0.4093 0.2507 0.2180 0.4685 0.2796 0.2611 0.4150 0.2450 0.2167 0.4891 0.3474 0.3192 0.5000 0.3583 0.5000 0.4553 0.2614 0.2649 0.5708 0.4056 0.4056 0.5000 0.3300 0.2567 0.2167 0.2167 0.3500 0.5000 0.2167 0.2167 0.4528 0.2167 0.1889 0.5000 0.3624 0.3495 0.5000 0.5567 0.3867 0.5000 0.5000 0.4190 O.5C00 0.5000 0.5000 0.5000 0.5000 0.4433 0.5000 0.2167 0.2167 0.4056 0.2167 0. 161 1 0.4702 0.3956 0.3009 0.5000 0.3256 0.2654 0.4595 0.3179 0.3060 0.5000 0.4564 0.4090 0.4433 0.4433 0.4433 0.5000 0.3111 0.4056 0. 4370 0.2639 0. 1926 0.5567 0.4811 0.4322 0.4717 0.4150 0.3983 0.5000 0.5000 0.4843 0.4660 0.2960 0.2673 0.4782 0.2821 0.2917 0.4717 0.2875 0.3472 0.5113 0.4207 0.4527 - 173 -OCCU- EDUCATION TRAINING PATION MALE FEMALE RATIO GED SVP 812 7.2 8.8 0.82 9.16 0.74 815 7.9 9.6 0.82 10. 33 2.01 817 8.0 10.4 0.77 9.92 0.59 818 7.4 9. 1 0.81 4.00 0.04 819 7.7 8.5 0.91 9.23 0.95 821 9.6 11.9 0.81 11.82 3.73 822 8.1 11.8 0.69 11.33 2.41 824 10.0 11.9 0.84 1 1.00 3.00 829 8.2 11.5 0.71 1 1.26 2.51 831 9. 3 12.0 0.77 1 1. 83 3. 17 832 8.6 8.4 1 .02 9.71 1.36 835 9.7 11.8 0. 82 12.00 2. 75 836 8.9 11.7 0.76 12.00 3.00 839 8.7 8.4 1.04 6.53 0. 27 841 7.7 11.3 0.68 10.00 2.22 843 7.8 9.4 0.83 8.00 0.68 854 7.2 11.8 0.61 11.06 2.06 859 7.2 11.8 0.61 9.81 1. 60 861 9.6 10.4 0.92 10.33 1.70 862 7.0 10.4 0. 67 9.40 0.71 864 7.1 11.0 0.65 9.92 1.39 869 7.6 8.5 0.89 9.67 1. 17 877 7.7 11 .6 0.66 8.67 0.68 900 8. 8 10. 1 0. 87 16.00 3.00 911 7.8 7.4 1 .05 7.43 0.09 912 9. 1 11.0 0.83 11.70 3.72 913 8.3 7.8 1 .06 6.09 0.81 914 8. 2 7.9 1.04 8.00 0,77 915 10. 1 9.7 1 .04 11.00 1.58 916 7. 2 7.4 0.97 9.73 1.05 917 9.2 9.6 0.96 11.31 3.06 918 9.7 8.6 1. 13 11. 25 1.04 919 8.0 7.9 1 .01 9.57 1.82 920 7. 1 8.5 0.84 5. 24 0. 16 APTITUDES G V N 0.3509 0. 2316 0.1965 0.5000 0. 4056 0.3500 0.5000 0. 3300 0.3053 0.2167 0. 2167 0.0500 0.4614 0. 2682 0.2432 0.5258 0. 2939 0.4742 0.4882 0.2993 0.2597 0.5000 0. 5278 0.4056 0.5000 0.4433 0.4433 0.5236 0. 4843 0.4685 0.4833 0. 3333 0.2578 0.5000 0. 4528 0.4056 0.5000 0. 5000 0.5000 0.3678 0. 2167 0.1056 0.4685 0. 4056 0.3432 0.5000 0. 2167 0. 1333 0.4843 0. 2796 0.3583 0.4646 0. 3229 0.3344 0.5189 0. 4433 0.3867 0.4433 0. 4433 0.2733 0.5000 0. 3583 0.3583 0.44 10 0. 2757 0.1847 0.4056 0. 3111 0.1611 0.7833 0. 7833 0.5000 0.2976 0. 2167 0.0500 0.6133 0. 5142 0.5850 0.3068 0.2682 0. 1288 0.3583 0. 2750 0.2750 0.5000 0. 4292 0.3167 0.5000 0. 3489 0.2211 0.5782 0. 4316 0.4144 0.6417 0. 5000 0.4292 0.4622 0. 3961 0.3417 0.2667 0. 2176 0.1110 - I N -OCCU-PATION S P 1 0 . 5 0 0 0 0 . 7 8 3 3 4 0 . 2 1 6 7 0 . 2 1 6 7 6 0 . 2 1 6 7 0 . 2 1 6 7 8 0 . 4 0 5 6 0 . 7 8 3 3 10 0 . 3 7 7 5 0 . 4 221 101 0 . 8 2 6 7 0 . 5 5 6 7 102 0 . 9 3 4 8 0 . 6 8 0 3 105 0 . 8 4 7 2 0 . 7 8 3 3 108 0 . 9 5 0 0 0 . 7 8 3 3 109 0 . 8 8 3 3 0 . 7 2 6 7 111 0 . 6 1 2 8 0 . 7 6 1 5 114 0 . 9 5 0 0 0 . 7 8 3 3 119 0 . 8 9 4 4 0 . 7 8 3 3 121 0 . 6 1 3 0 0 . 8204 124 0 . 9 5 0 0 0 . 7 8 3 3 129 0 . 3111 0 . 5 9 4 4 131 0 . 3 3 0 6 0 . 1889 135 0 . 5 0 0 0 0 . 5 0 0 0 139 0 . 3 5 8 3 0 . 3 5 8 3 140 0 . 8 3 8 5 0 . 8 2 5 0 141 0 . 9 5 0 0 0 . 7 8 3 3 142 0 . 4 6 4 6 0 . 4 6 4 6 143 0 . 7 8 3 3 0 . 7 8 3 3 144 0 . 3 5 8 3 0 . 4 2 9 2 145 0 . 7 8 3 3 0 . 7 8 3 3 146 0 . 7 2 5 0 0 . 5 0 0 0 147 0 . 2 1 6 7 0 . 7 8 3 3 148 0 . 5 7 0 8 0 . 7 1 2 5 153 0 . 1611 0 . 1 611 171 0 . 5 8 1 0 0 . 7024 172 0 . 8 3 7 5 0 . 8 6 6 7 174 0 . 2 5 6 8 0 . 2 3 7 7 176 0 . 2 3 1 3 0 . 7 8 3 3 182 0 . 8551 0 . 7 8 3 3 183 0 . 7 8 3 3 0 . 7 0 2 4 186 0 . 2 1 6 7 0 . 2 1 6 7 188 0 . 0 5 0 0 0 . 1 9 2 9 191 0 . 2 1 6 7 0 . 5 000 192 0 . 2 0 6 7 0 . 2 4 0 0 195 0 . 5 0 0 0 0 . 5 0 0 0 APTITUDES Q K F 0 . 5 0 0 0 0 . 2 1 6 7 0 . 5 0 0 0 0 . 5 0 0 0 0 . 2 1 6 7 0 . 2 1 6 7 0 . 5 0 0 0 0 . 2 1 6 7 0 . 2 1 6 7 0 . 5 0 0 0 0 . 2 1 6 7 0 . 2 1 6 7 0 . 3 6 2 2 0 . 2 6 8 9 0 . 2 7 7 9 0 . 4 4 3 3 0 . 2 1 6 7 0 . 3 3 0 0 0 . 2682 0 . 1712 0 . 2682 0 .3111 0 . 4 0 5 6 0 . 4 5 2 8 0 , 3 5 8 3 0 . 2 1 6 7 0 . 3 5 8 3 0 . 2 1 6 7 0 . 1 8 3 3 0 . 2 1 6 7 0 . 4564 0 . 4 5 6 4 0 . 4.782 0 . 2 1 6 7 0 . 5 0 0 0 0 . 5 000 0 . 4 0 5 6 0 . 4 0 5 6 0 . 5 0 0 0 0 . 2482 0 . 5 3 1 5 0 . 5944 0 . 2 1 6 7 0 . 7 8 3 3 0 . 8 6 6 7 0 . 2 1 6 7 0 . 4 0 5 6 0 .3111 0 . 3111 0 . 1889 0 . 2167 0 . 5 000 0 . 3 9 7 0 0 . 4 4 8 5 0 . 2 1 6 7 0 . 3 5 8 3 0 . 3 5 8 3 0 . 2 6 9 8 0 . 8 1 7 7 0 . 8 4 1 7 0 . 2 1 6 7 0 . 7 8 3 3 0 . 9 5 0 0 0 . 3 2 2 9 0 . 4 6 4 6 0 . 4 6 4 6 0 . 2 1 6 7 0 . 5 0 0 0 0 . 5 0 0 0 0 . 2 8 7 5 0 . 3 5 8 3 0 . 4 292 0 . 2 1 6 7 0 . 5 0 0 0 0 . 5 0 0 0 0 . 2 1 6 7 0 . 5 0 0 0 0 . 7 2 5 0 0 . 5 0 0 0 0 . 2 1 6 7 0 . 5 0 0 0 0 . 2 6 3 9 0 . 5 0 0 0 0 .6181 0 . 5000 0 . 1611 0 . 1611 0 . 4 1 9 0 0 . 5 0 0 0 0 . 5 8 1 0 0 . 2 1 6 7 0 . 8 2 5 0 0 . 8 2 5 0 0 . 4 3 2 7 0 . 2 2 7 2 0 . 2 272 0 . 6 0 6 3 0 . 4 2 9 2 0 . 3 9 3 8 0 . 4 8 7 7 0 . 7 2 1 7 0 . 7 0 9 4 0 . 4 5 9 5 0 . 3 7 8 6 0 . 4 1 9 0 0 . 5 0 0 0 0 . 2 1 6 7 0 . 2 1 6 7 0 . 7 4 2 9 0 . 1 9 2 9 0 . 2 1 6 7 0 . 2 1 6 7 0 . 2 1 6 7 0 . 2 1 6 7 0 . 2 7 3 3 0 . 2 4 0 0 0 . 2 7 3 3 0 . 1 750 0 . 3 1 6 7 0 . 4 292 H E C 0 . 2 1 6 7 0 . 0 5 0 0 0 . 5 0 0 0 0 . 2 1 6 7 0 . 0 500 0 . 0 5 0 0 0 . 2 1 6 7 0 . 0 500 0 . 0 5 0 0 0 . 2 1 6 7 0 . 0 500 0 . 5 0 0 0 0 . 2 9 3 2 0 . 0833 0 . 2 1 6 7 0 . 3 3 0 0 0 . 0 8 3 3 0 . 1500 0 . 2 2 2 7 0 . 0652 0 . 1 2 1 2 0 . 4 5 2 8 0 . 0 500 0 . 1889 0 . 3 5 8 3 0 . 0500 0 . 5 0 0 0 0 . 1 8 3 3 0 . 0 500 0 . 2 9 6 7 0 . 4 7 8 2 0 . 0500 0 . 5654 0 . 5 0 0 0 0 . 0 5 0 0 0 . 2 1 6 7 0 . 5 C 0 0 0 . 0 500 0 .3111 0 . 5 3 1 5 0 . 0 6 8 5 0 . 5 9 4 4 0 . 6 4 1 7 0 . 0 500 0 . 5 0 0 0 0 . 4 0 5 6 0 . 1 0 5 6 0 . 4 0 5 6 0 . 2 6 3 9 0 . 0500 0 . 0 5 0 0 0 . 4 4 8 5 0 . 1 1 0 6 0 . 2 3 3 3 0 . 3 5 8 3 0 . 0500 0 . 0 5 0 0 0 . 7 8 6 5 0 . 0 8 8 5 0 . 5 3 5 4 0 . 7 8 3 3 0 . 1333 0 . 3 5 8 3 0 . 5 0 0 0 0 . 1 750 0 . 3 5 8 3 0 . 5 0 0 0 0 . 2 1 6 7 0 . 5 0 0 0 0 . 5 7 0 8 0 . 0 9 1 7 0 . 2 4 5 8 0 . 5 0 0 0 0 . 0 500 0 . 5 0 0 0 0 . 7 2 5 0 0 . 0 500 0 . 2 7 5 0 0 . 5 C 0 0 0 . 0 500 0 . 7 8 3 3 0 . 5 2 3 6 0 . 0 6 3 9 0 . 3 6 8 1 0 . 1611 0 . 0500 0 . 0 5 0 0 0 . 5 4 0 5 0 . 0 5 0 0 0 . 6 6 9 0 0 . 7 5 4 2 0 . 0 917 0 . 5 0 0 0 0 . 2 2 7 2 0 . 0500 0 . 0 8 0 9 0 . 3 9 3 8 0 . 1833 0 . 0 9 1 7 0 . 5 2 4 6 0 . 0 500 0 . 2 2 5 4 0 . 4 1 9 0 0 . 1452 0 . 0 7 3 8 0 . 2 1 6 7 0 . 0500 0 . 0 5 0 0 0 . 2 1 6 7 0 . 0 500 0 . 0 5 0 0 0 . 2 1 6 7 0 . 0 500 0 . 2 1 6 7 0 . 2 7 3 3 0 . 1 400 0 . 1 4 0 0 0 . 3 0 4 2 0 . 0 7 9 2 0 . 5 1 6 7 - 175 -occu-PATION S P 196 0.7024 0.7024 198 0.4254 0.5895 199 0.6071 0.5270 201 0.1565 0.3301 203 0.3302 0.4381 212 0.2682 0.3712 214 0.2283 0.3867 223 0.2167 0.2167 232 0.1750 0.7833 234 0.2313 0.4292 241 0.5000 0.5000 249 0.1778 0.3340 307 0.2167 0.2167 312 0.2167 0. 311 1 314 0.3583 0.3583 325 0.2976 0.5000 331 0.2750 0.5000 338 0.2967 0.5000 339 0.2167 0.3111 403 0.2167 0.3229 405 0.2167 0.2167 412 0.2521 0.2875 413 0.2167 0.5000 414 0.2167 0.2167 415 0.2000 0.3583 416 0.3111 0.4056 417 0.2167 0.2167 419 0.2069 0.3000 431 0.3778 0.3306 433 0.2875 0.2167 451 0.2967 0.7267 452 0.2167 0.4528 453 0.2167 0.2167 454 0.2167 0.2167 455 0.5000 0.5000 456 0.2875 0.2167 457 0.2095 0.2095 459 0.1333 0.2283 510 0.4292 0.4292 520 0.7833 0.7833 APTITODES Q K F 0.2167 0.4595 0.4595 0.3360 0.2974 0.3509 0.2976 0.3516 0.4191 0.7046 0.4292 0.4056 0.4730 0.4730 0.4460 0.5879 0.2167 0.2424 0.5567 0.3017 0.2167 0.5000 0.3583 0.3583 0.7833 0.7479 0.5354 0.6417 0.6063 0.6417 0.2167 0.5000 0.5000 0.5702 0.3209 0.3516 0.3583 0.2167 0.2167 0.2167 0.2167 0.2167 0.2167 0.2167 0.2167 0.4191 0.2976 0.2167 0.6417 0.2167 0.2167 0.3300 0.2167 0.2167 0.1611 0.2167 0.3111 0.3229 0.3229 0.3229 0.1750 0.2167 0.2167 0.2813 0.2521 0.2167 0.2167 0.2167 0.2167 0.0500 0.2167 0.2167 0.2850 0.3867 0.2450 0. 161 1 0.3111 0. 4056 0.2400 0.2167 0.2167 0. 1353 0. 2402 0. 2833 0.2556 0.4806 0.4528 0.2167 0.2875 0.2167 0.1833 0.6133 0.6133 0. 1611 0. 2167 0. 2639 0.2167 0.2167 0.2167 0.0500 0.2167 0.2167 0.2167 0.5000 0.5000 0.1750 0.2167 0.2167 0.2095 0.2333 0.2333 0. 1167 0. 2283 0. 1833 0.2875 0.2521 0.2875 0. 5000 0.7833 0. 5000 M E C 0.5000 0.0738 0.5000 0.3360 0.0588 0.3281 0.3571 0.0738 0.2802 0.3662 0.0500 0.1412 0.4190 0.0500 0.0817 0.2939 0.0955 0.1561 0.3300 0.0667 0.1950 0.3583 0.0500 0.1333 0.5000 0.0500 0.0708 0.4646 0.0500 0.0708 0.5000 0.2167 0.2167 0.3183 0.0516 0.0989 0.2167 0.0500 0. 1333 0.4056 0.1611 0.2167 0.2167 0.0500 0.3583 0.3786 0.0500 0.4357 0.2167 0.0500 0. 1333 0.2167 0.0833 0.1733 0.3111 0.0500 0.1056 0.3229 0.1333 0.0917 0.2167 0.1625 0.0917 0.3229 0.1625 0.1750 0.5000 0.0500 0.2167 0.5000 G.0500 0.2167 0.4433 0.1333 0.1333 0.5C00 0.1611 0.1611 0.3300 0. 1733 0.0500 0.3333 0.0598 0.1912 0.3583 0.3972 0.2083 0.3583 0.2750 0.0500 0.5000 0.0500 0.4667 0.4528 0.0500 0.3972 0.2167 0.0500 0.0500 0.3583 0.4167 0.0500 0.5000 0.0500 0.5000 0.2167 0.1333 0.2167 0.2738 0.0738 0. 1857 0.3017 0.0667 0.1617 0.3229 0.1833 0.1125 0.5000 0.5000 0.2167 -176-occu-P A T I O N S P 551 0.5000 0.5000 552 0.5000 0.2167 554 0.4056 0.2167 556 0.4292 0.2167 581 0.2167 0.2167 582 0.5944 0.5944 584 0.2167 0.3583 585 0.3111 0.5000 588 0.2333 0.1929 603 0.5000 0.3583 605 0.3300 0.5000 607 0.2167 0.2167 609 0.2875 0.4292 613 0.3583 0.2875 615 0.3275 0.2145 631 0.2167 0.2413 657 0.3197 0.2735 701 0.3583 0.5944 702 0.3017 0.5283 703 0.3712 0.4485 704 0.2167 0.2167 705 0.2167 0.5000 706 0.2167 0.3300 707 0.3347 0.4292 708 0.2639 0.3898 709 0.3583 0.5472 711 0.2796 0.4685 719 0.2167 0.4292 721 0.3133 0.4433 722 0.3516 0.3921 724 0.2750 0.5000 729 0.3938 0.5354 731 0.3111 0.4056 732 0.3229 0.5000 733 0.3111 0.4056 734 0.5000 0.6417 735 0.3712 0.5258 736 0.5236 0.7125 737 0.2733 0.3867 738 0.2167 0.3111 A P T I T U D E S Q K F 0.5000 0.5000 0.5000 0.2167 0.5000 0.2167 0.2167 0.4056 0.2167 0.1750 0.4292 0.2167 0.2167 0.2167 0.2167 0.3111 0.4056 0.5000 0.3583 0.3583 0.3583 0.5944 0,4056 0.5000 0.3143 0.2167 0.2167 0.2167 0.2167 0.2167 0. 1833 0. 3867 0.5000 0.2167 0.2167 0.2167 0.1194 0.2875 0.3111 0.1750 0.2167 0.2167 0.1058 0.2659 0.2167 0.0572 0.2536 0.2413 0. 1462 0.2682 0. 2424 0.1611 0.2167 0.2639 0.1783 0.3300 0.3867 0.0803 0.3712 0.3455 0.2167 0.2167 0.3583 0.2167 0.2167 0.2167 0.1500 0.2167 0.2167 0.2028 0.3111 0.2403 0.1796 0.2639 0.2482 0.2639 0.2639 0.2639 0.1185 0.4056 0.3741 0.1750 0.3583 0.2875 0. 1000 0.4150 0. 4717 0.0738 0.4460 0.4056 0. 2167 0. 5000 0. 5000 0.1542 0.5000 0.5000 0.0500 0.4056 0.4056 0.1125 0.4646 0.6063 0.0500 0.4056 0.5944 0.2167 0.5000 0.5000 0. 1364 0.4742 0. 4742 0.1986 0.5708 0.5236 0.2167 0.2167 0.2167 0.0500 0.3111 0.3111 M E C 0. 5000 0.5000 0.2167 0. 2167 0.5000 0.2167 0.4056 0.4056 0. 161 1 0. 4292 0.5000 0.1750 0. 2167 0.0500 0.0500 0. 5000 0.0500 0.2083 0. 3583 0.0500 0.1750 0. 3111 0.0500 0.1611 0. 2167 0.1381 0.0976 0. 3583 0.1333 0.2167 0. 5C00 0.1733 0.3867 0. 2167 0.2167 0.2167 0. 4528 0.1389 0.1708 0. 2875 0.2167 0.2167 0. 3768 0.1913 0.0717 0. 3029 0.1326 0.0862 0. 3 06 8 0.1417 0.0727 0. 3111 0.0500 0. 1333 0. 4717 0.0500 0.2233 0. 5000 0.0500 0.2227 0. 5000 0.0500 0.2167 0. 5000 0.0500 0.2167 0.3300 0.0500 0,2067 0. 3111 0.0500 0.2458 0. 3898 0.0500 0.2306 0. 3583 0.0778 0.2639 0.5000 0.1056 0.0870 0. 2875 0.0500 0. 1333 0. 5000 0.1167 0.0833 0. 5135 0.1056 0.1429 0. 6417 0.0500 0.6417 0. 4292 0.0500 0.3229 0. 4056 0.0500 0.1056 0. 4646 0.0500 0.1542 0. 5000 0.1056 0.1611 0. 5000 0.0500 0.3583 0. 4742 0.0500 0.2076 0. 5236 0.0500 0.2875 0. 4433 0.0500 0.3867 0. 4056 0.0500 0. 1056 -177-occu-PATION S P 739 0.2875 0.5000 741 0. 6417 0.5000 742 0. 5000 0.5000 743 0.3583 0.6417 744 0. 5000 0.5000 745 0. 3229 0.4646 746 0. 3256 0.4673 747 0. 4528 0.5472 749 0. 3393 0.4773 752 0. 4056 0.4056 754 0. 3300 0.3583 756 0. 5000 0.4891 758 0. 2875 0.5000 759 0. 2912 0.4404 761 0. 4764 0.4528 762 0. 3583 0.3867 763 0. 2167 0.3111 766 0. 3583 0.3583 768 0. 3111 0.4056 769 0. 3819 0.4352 771 0. 4717 0.6700 772 0. 5000 0.6214 773 0. 5000 0.7479 775 0.5000 0.7833 776 0. 5000 0.5000 778 0. 3111 0.4056 779 0. 3272 0.5447 781 0. 4128 0.3583 782 0. 2774 0.4595 786 0. 4346 0.4346 787 0. 4433 0.4433 788 0.4056 0.5000 789 0. 2796 0.4370 791 0. 6133 0.7645 793 0. 4150 0.6983 801 0. 6574 0.7204 803 0. 3980 0.5907 806 0. 4782 0.4891 808 0. 4622 0.5425 811 0. 6020 0.5340 APTITUDES Q K . P 0.1688 0.3938 0.3938 0.2167 0.5000 0.5000 0.2167 0.5000 0.5000 0.1333 0.5000 0.5000 0.2167 0.5000 0.6417 0.2167 0.5354 0.4292 0.1269 0.5000 0.5000 0.1611 0.4528 0.5000 0.1167 0.4433 0.4207 0.1611 0.4685 0.4370 0.1333 0.3583 0.2450 0.2147 0.4019 0.4019 0.2458 0.2167 0.2167 0.1789 0.3509 0.3509 0.2403 0.3819 0.3819 0.1833 0.2167 0.2450 0.1056 0.2167 0.2167 0.2167 0.3583 0.3583 0.1333 0.2167 0.2639 0.2233 0.2652 0.2814 0.5283 0.4717 0.5567 0.2167 0.3786 0.5000 0.2167 0.5000 0.5708 0.2167 0.6133 0.6133 0.2167 0.5000 0.5000 0. 1611 0. 4056 0. 4056 0.2175 0.4254 0.4553 0.1462 0.3147 0.2494 0.1690 0.2369 0.2369 0.1397 0.2821 0.3038 0.2167 0.2733 0.2167 0.1611 0.3111 0.2167 0.1426 0.3269 0.3583 0.1944 0.6511 0.6700 0.2567 0.6700 0.6700 0.2167 0.5472 0.5315 0.1567 0.4093 0.4093 0. 1718 0. 4455 0. 4891 0.2011 0.4481 0.4244 0.2013 0.5113 0.4367 H E C 0.4292 0.0917 0.3521 O.5G00 0.2167 0.6417 0.5000 0.0500 0.4056 0.5C00 0.0917 0.4292 0.5000 0.0500 0.5000 0.5000 0.0708 0.3375 0.4891 0.0821 0.2827 0.5000 C.0500 0.3111 0.4547 0.0833 0.2293 0.5630 0.1000 0.1926 0.4433 0.0667 0.1000 0.5545 0.0756 0.0756 0.2167 0.0500 0.2458 0.4851 0.0675 0.1088 0.4764 0.0639 0.2694 0.3583 0.0500 0.1783 0.2167 0.0500 0.1056 0.5000 0.2167 0.0500 0.3583 0.0778 0.1611 0.4029 0.0629 0.2229 0.5000 0.0500 0. 1 1 17 0.5000 0.1452 0.3143 0.6063 0.0708 0.3229 0.7267 0.0833 0.2967 0.5000 0.2167 0.5000 0.5000 0.0500 0.1611 0.4553 0.0588 0.1860 0.4237 0.0821 0.2551 0.3786 0.0619 0.3107 O.5C00 0.1141 0.0628 0.4433 0.0500 0.0500 0.4056 0.1611 0.2000 0.4685 0.0843 0.0963 0.5944 0.0500 0.2444 0.5283 0.0500 0. 1783 0.7361 0.0593 0.0750 0.5227 0.0500 0.0567 0.6090 0.1212 0.0692 0.5236 0.0789 0.0528 0.6360 0.1640 0.0767 -178-o c c u -PATION 812 815 817 818 819 821 822 824 829 831 832 835 836 839 841 843 854 859 861 862 864 869 877 900 911 912 913 914 915 916 917 918 919 920 APTITUDES K H 0 . 3 8 6 8 0 .3868 0 . 1 2 0 2 0 .4851 0 .3509 0 .4702 0 .3070 0 .0912 0 . 2 1 6 7 0 .5944 0.1611 0 .4056 0 .4056 0 .4056 0 .0500 0 . 4 0 5 6 0 . 4 4 3 3 0 .5000 0 . 2 0 3 3 0 . 4 6 6 0 0 .4207 0 . 5 0 0 0 0 .1080 0 .2520 0 . 2 1 6 7 0 .5000 0 .0500 0 . 2 1 6 7 0 .2167 0 .5000 0 .0500 0 . 2 1 6 7 0 . 4 3 5 6 0 .5644 0 . 1 2 5 8 0 . 4 2 2 7 0 .4614 0 .5386 0 .0727 0 .0780 0 .7576 0 .7576 0 .2424 0 .5000 0 .5000 0 . 7 5 7 6 0 .2273 0 . 0 6 5 2 0 . 5 1 1 8 0 . 5 2 3 6 0 . 1 4 0 3 0 . 4 6 4 6 0 . 4 6 4 6 0 . 6 0 6 3 0 .1062 0 .1333 0 . 6 8 8 9 0 .6889 0.3111 0 .4528 0 .5472 0 . 5 9 4 4 0 .0500 0 . 1 3 3 3 0 . 5 6 4 8 0 . 6 2 1 4 0 . 2 1 5 2 0 . 4 5 9 5 0 .5324 0 . 6 4 5 7 0.0933 0 .1200 0 .5157 0 .7440 0 .2120 0 .2875 0.4921 0 .7204 0 .3310 0 . 2 3 5 7 0 . 5 0 0 0 0 .6000 0 .1480 0 . 4 8 3 3 0 .5000 0 .6889 0 .2167 0 . 5 0 0 0 0 .5000 0 . 7 8 3 3 0 . 2 1 6 7 0 . 7 8 3 3 0 . 3 4 8 9 0 .4622 0 .0722 0 .4244 0 . 2 4 8 2 0 .4790 0 .1920 0 .3951 0.3111 0 .5472 0 .2083 0 . 4 5 2 8 0 .4370 0 . 5 0 0 0 0 . 1 8 8 9 0 . 4 5 2 8 0 . 3 2 2 9 0 .4469 0 .1542 0 .3406 0 . 4 6 2 2 0 .7078 0 . 2 0 5 6 0 . 5 7 5 6 0 .2733 0 .3867 0 .1833 0 .2733 0 . 4 2 9 2 0 . 4 2 9 2 0 . 1 8 8 9 0 . 5 0 0 0 0 .2924 0 .5000 0.1542 0 .3465 0 . 5 0 0 0 0 . 2 1 6 7 0 .1611 0 . 4 0 5 6 0 . 5 0 0 0 0 .5000 0 .2167 0 . 2 1 6 7 0 . 2 1 6 7 0 . 2 9 7 6 0 .0500 0 . 2 9 7 6 0 . 7 7 1 7 0 .7408 0 .2308 0 .5992 0 . 2 9 1 7 0 .3250 0 . 1 3 3 3 0 . 3 3 2 6 0 .3583 0 .3583 0 .1333 0 . 2 1 6 7 0 . 3 5 8 3 0 . 5 7 0 8 0 .1750 0 . 3 5 8 3 0 .2433 0 .5189 0.1611 0 .2544 0 .4316 0 . 6 5 6 3 0 . 2 2 4 7 0 . 3 0 4 6 0 . 2 1 6 7 0 .6417 0 .1750 0 . 2 1 6 7 0 . 5 0 3 9 0 . 5 0 9 4 0 .1872 0 . 4 2 4 4 0.2411 0 .2788 0.0991 0 .2687 0 .5000 0 . 5 6 6 7 0 .0765 0 .0990 0 .5000 0 . 6 8 8 9 0 .0778 0 . 1 8 8 9 0 .7833 0 .5000 0.0500 0 .5000 0.4811 0 . 4 0 5 6 0.0611 0 . 0 9 4 4 0 .4265 0 . 5 0 0 0 C.3395 0 . 5 5 6 8 0 .5000 0 . 5 0 0 0 0 .0500 0 . 3 7 7 8 0 .4056 0 . 6 4 1 7 0.4093 0 .1676 0 .3406 0 . 5 0 0 0 0 .4333 0 . 0 8 1 3 0 .6133 0 .5000 0.0611 0 .1322 0 .3300 0 . 3 3 0 0 0 .0833 0 . 3 5 3 3 0 .4056 0 . 5 2 3 6 0 .0639 0.1431 0 .3632 0 .4646 0 .0500 0 . 2 0 6 3 0.3111 0 .4056 C .4056 0 .0500 0 .2167 0 . 2 1 6 7 0 .0500 0 . 2 1 6 7 0 .4595 0 . 5 4 0 5 0 .0738 0 . 1 6 1 9 0 .5567 0 . 6 7 0 0 0 .0833 0 . 1 2 5 0 0 .2939 0 . 3 7 1 2 0 .0652 0 .1538 0 .3583 0 . 5 0 0 0 0 .0500 0 .0500 0 .4292 0 . 4 2 9 2 0 .0500 0 .4292 0 .2733 0 . 3 3 0 0 0 .0500 0 . 4 5 8 9 0 .3828 0 .4023 0 .1195 0 .1362 0 .2875 0 . 3 5 8 3 0 .0500 0 . 2 8 7 5 0 . 4 7 1 7 0 .5661 0.0706 0.1561 0.2651 0 . 3 4 3 7 0 .1678 0 . 1 2 3 8 -179-OCCU- TEMPERAMENTS L T I O N 1 2 3 4 5 6 7 8 9 0 X Y 1 0 0 0 0 0 0 1 0 1 0 0 0 4 0 0 0 1 1 0 0 0 0 0 0 0 6 0 0 0 1 1 0 0 0 0 0 0 0 8 0 0 0 0 1 0 0 0 1 0 0 0 10 0 0 0 1 0 0 0 0 0 0 0 0 101 0 0 0 1 0 0 0 0 0 0 0 0 102 0 0 0 1 0 0 0 0 0 1 0 0 105 1 0 0 0 0 0 0 0 0 1 0 0 108 0 0 0 1 0 0 0 0 0 1 0 1 109 0 0 0 0 0 0 0 0 0 1 0 0 111 0 0 0 0 0 0 0 0 0 1 0 1 114 0 0 0 0 0 0 0 0 1 1 0 0 119 1 0 0 0 0 0 0 0 0 1 0 0 121 0 0 0 0 0 0 0 0 0 1 0 0 124 0 0 0 0 0 0 0 0 1 1 0 0 129 0 0 0 0 0 0 0 0 0 1 0 0 131 0 0 0 0 1 0 0 0 1 0 0 0 135 0 0 0 0 1 0 0 0 0 0 0 0 139 0 0 0 1 1 0 0 0 0 0 0 0 140 0 0 0 0 1 0 0 0 0 1 0 0 141 0 0 0 0 0 0 0 1 0 1 0 1 142 0 0 0 0 1 0 0 0 0 0 0 1 143 1 0 0 0 1 0 0 0 0 0 0 0 144 0 0 0 0 1 0 0 0 0 0 0 1 145 0 0 0 0 1 0 0 0 0 1 0 0 146 0 0 0 0 1 0 0 0 0 1 0 1 147 0 0 0 0 0 0 0 0 0 1 0 1 148 0 0 0 0 0 0 0 0 0 1 0 1 153 0 0 0 0 0 0 0 0 1 0 0 0 171 0 0 0 0 0 0 0 0 1 0 0 1 172 0 0 0 0 0 0 0 0 0 0 1 1 174 0 0 0 0 0 0 0 0 1 0 0 0 176 0 0 0 0 0 0 0 0 0 0 1 1 182 0 0 0 0 0 0 0 0 0 1 0 1 183 0 0 0 1 0 0 0 0 0 1 0 0 186 0 0 0 0 0 0 0 0 1 0 0 1 188 0 0 0 1 0 0 0 0 0 1 0 0 191 0 0 0 1 0 0 0 0 0 1 0 0 192 0 0 0 1 1 0 0 0 1 0 0 0 195 0 0 0 0 0 0 0 0 1 0 1 0 INTERESTS 1 2 3 4 —' 6 7 8 9 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 c 0 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 -180-OCC0- TEMPERA KENTS LTIOH 1 2 3 4 5 6 7 8 9 0 X Y 196 0 0 0 0 0 0 0 0 0 0 1 0 198 0 0 0 0 0 0 0 0 0 1 0 1 199 0 0 0 0 0 0 0 0 0 0 0 0 201 0 1 0 0 0 0 0 0 0 0 0 0 203 0 1 0 0 0 0 0 0 0 0 0 1 212 0 1 1 0 0 0 0 0 0 0 0 0 214 0 1 0 0 0 0 0 0 0 0 0 1 223 1 0 1 0 1 0 0 0 0 0 0 0 232 0 0 1 0 0 0 0 0 0 0 0 1 234 0 1 1 0 0 0 0 0 0 0 0 1 241 0 0 0 0 1 0 0 0 0 0 0 1 249 0 1 0 0 0 0 0 0 0 0 0 0 307 0 1 0 0 1 0 1 0 0 0 0 0 312 0 0 0 0 1 0 1 0 0 0 0 0 314 0 0 0 0 1 0 1 0 0 0 0 0 325 0 0 0 0 1 0 1 0 0 0 0 0 331 0 0 0 0 1 0 0 0 1 1 0 0 338 0 0 0 0 1 0 0 0 0 0 0 0 339 0 0 0 0 1 0 1 0 0 0 0 0 403 0 0 0 0 1 0 0 1 0 0 0 0 405 1 1 0 0 1 0 0 1 0 0 0 0 412 0 0 0 1 1 0 0 0 0 0 0 0 413 1 0 0 0 0 0 0 0 0 0 1 0 414 0 0 1 0 1 0 0 0 0 0 0 0 415 0 0 1 0 1 0 0 0 0 0 0 0 416 0 0 0 0 1 0 0 0 0 0 0 0 417 0 0 1 0 1 0 0 0 0 0 0 0 419 0 1 1 0 .0 0 0 0 0 0 0 0 431 0 0 0 0 0 0 0 0 0 0 1 1 433 0 0 0 1 1 0 0 0 0 0 0 0 451 0 0 0 0 1 0 0 0 1 0 0 0 452 0 1 0 0 0 0 0 0 0 0 0 1 453 0 1 1 0 1 0 0 0 0 0 0 0 454 0 1 1 0 0 0 0 1 0 0 0 0 455 0 0 1 0 0 0 0 0 1 0 0 1 456 0 0 0 1 1 0 0 0 0 0 0 0 457 0 0 0 0 1 0 0 0 0 0 0 0 459 0 0 1 0 1 0 0 0 0 0 0 0 510 0 0 0 0 0 0 0 0 0 1 0 1 520 0 0 0 0 0 0 0 1 0 0 0 1 INTERESTS 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 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CAPACITIES WORKING CONDITIONS iTION MW 2 3 4 5 6 1 2 3 4 5 6 7 551 5.0 0 0 1 1 1 1 0 0 0 0 0 0 552 2.0 0 0 1 0 1 1 0 0 0 0 0 0 554 3.0 1 0 1 1 1 0 0 0 0 0 0 0 556 2.8 0 0 1 0 1 1 0 0 0 0 0 0 581 1.0 0 0 0 1 0 1 0 0 0 0 c 0 582 1.5 0 0 1 1 1 1 0 0 0 0 0 0 584 1.5 0 0 1 1 1 1 0 0 0 0 G 0 585 1.3 0 0 0 1 0 1 0 0 0 0 0 0 588 3.0 0 0 1 0 0 1 0 0 0 0 c 0 603 2.0 0 0 0 1 0 0 0 0 0 0 0 0 605 3. 2 0 0 1 0 0 0 0 0 0 c 0 0 607 5.0 0 1 1 0 0 0 0 0 0 0 0 0 609 2. 8 0 0 1 0 0 0 0 0 0 0 c 0 613 4.3 1 0 1 0 1 0 0 0 0 0 0 0 6 15 7. 3 0 0 1 0 0 0 0 0 0 0 0 0 631 4.9 1 1 1 0 0 0 0 0 1 0 0 0 657 4.4 0 0 1 0 0 1 0 0 0 0 c 1 701 2.5 0 0 1 0 1 1 0 0 0 1 0 1 702 5.9 0 0 1 0 1 1 0 0 0 0 0 1 703 5.2 0 0 1 0 0 1 1 0 1 0 0 0 704 7.5 0 0 1 0 0 1 1 0 0 0 c 0 705 5.0 0 0 1 0 0 1 0 1 1 0 0 0 706 6. 4 0 0 1 0 0 1 0 0 0 0 c 0 707 2.5 0 0 1 0 1 1 0 0 1 0 0 0 708 4.7 0 0 1 0 1 1 0 0 0 0 0 0 709 3.8 0 0 0 0 1 1 0 1 0 0 0 0 711 4.6 0 0 1 0 0 1 0 0 0 0 0 1 719 4.3 0 0 1 0 1 1 0 0 0 0 0 0 721 1.9 0 0 1 0 1 1 0 0 0 0 c 0 722 1.9 0 0 1 0 1 1 0 0 0 0 0 0 724 2.0 0 0 1 0 1 1 0 0 0 0 0 0 729 2.6 0 0 1 0 1 1 0 0 0 0 0 0 731 4.0 0 0 1 0 0 1 0 0 1 1 0 1 732 2.0 0 0 1 0 1 1 0 0 1 1 0 1 733 2.0 0 0 1 0 1 1 0 0 0 c 0 0 734 3.5 0 1 1 0 1 1 0 1 1 1 1 1 7 35 2.8 0 0 1 0 1 1 0 0 1 1 0 0 736 1.6 0 0 1 0 1 1 0 0 0 0 0 0 7 37 6. 6 0 0 1 0 0 1 0 0 1 0 0 0 738 2.0 0 0 1 0 0 1 0 0 1 0 0 0 -187-OCCU- PHYSICAL C A P A C I T I E S WORKING CONDITIONS PATION KW 2 3 4 5 6 1 2 3 4 5 6 7 739 2.3 0 0 1 0 1 1 0 0 0 0 0 0 74 1 2. 0 0 0 1 1 1 1 0 0 0 1 0 0 742 2.0 0 0 1 0 1 1 0 0 0 0 0 0 743 2.0 0 0 1 0 1 1 0 0 0 0 0 0 744 2.0 0 0 1 0 1 1 0 0 0 1 0 0 745 2. 0 0 0 1 0 1 1 0 0 0 0 0 0 746 1.6 0 0 1 0 1 1 0 0 0 0 0 0 747 4.8 0 1 1 0 1 1 0 0 0 0 0 0 749 2. 1 0 0 1 0 1 1 0 0 0 0 0 0 752 3.7 0 1 1 0 1 1 0 0 0 0 0 0 754 4.0 0 0 1 0 1 1 0 0 0 1 1 1 756 2.7 0 0 1 0 1 1 0 0 0 0 0 1 758 2.8 0 0 1 0 1 1 0 0 0 0 0 0 759 3.4 0 0 1 0 1 1 0 0 0 0 0 0 761 2.0 0 0 1 0 1 1 0 0 0 0 1 1 762 3. 1 0 0 1 0 1 1 0 1 0 0 0 1 763 3.0 0 0 1 0 0 1 0 0 0 0 0 0 766 2.0 0 0 1 0 0 1 0 0 0 c 0 0 768 3.3 0 0 1 0 0 1 0 0 0 1 0 1 769 3. 3 0 0 1 0 1 1 0 0 0 0 0 1 771 2.8 0 0 1 0 1 1 0 0 0 0 0 0 772 4. 1 0 0 1 0 1 1 0 0 0 1 0 0 773 3.1 0 0 1 0 1 1 0 0 0 0 0 1 775 2. 4 0 0 1 0 1 1 0 0 0 c 0 0 776 2.0 0 0 1 0 1 1 0 0 0 0 0 0 778 2.0 0 0 1 0 0 1 0 0 0 0 0 0 779 3.6 0 0 1 0 1 1 0 0 0 0 0 0 781 6. 1 0 0 1 0 1 1 0 1 0 0 1 1 782 7.3 0 0 1 0 1 1 0 1 0 0 1 0 786 6. 6 0 1 1 0 1 1 0 1 0 1 1 1 787 6.4 0 1 1 0 1 1 0 0 0 0 1 1 788 7.7 0 0 1 0 1 1 0 0 0 1 0 0 789 5.4 0 0 1 0 1 1 0 0 0 0 0 0 791 2. 3 0 0 1 0 1 1 0 0 0 0 0 0 793 2.4 0 0 1 0 1 1 0 0 0 0 0 0 801 4.7 0 0 1 0 1 1 0 0 0 0 0 0 803 3.2 0 0 1 0 1 1 0 0 0 1 0 0 806- 4. 3 0 0 1 0 1 1 0 0 0 0 0 0 808 4.3 0 0 1 0 1 1 0 0 0 1 0 0 8 11 4.7 0 0 1 0 1 1 0 0 0 1 c 0 -188-O C C U - P H Y S I C A L C A P A C I T I E S W O B K I N G C O N D I T I O N S P A T I O N MW 2 3 4 5 6 1 2 3 4 5 6 7 812 4,3 0 0 1 0 1 1 0 0 0 1 0 0 815 3.0 0 0 1 0 1 1 0 0 1 0 1 1 8 17 5.7 0 1 1 0 1 0 0 0 0 0 1 0 818 5.0 0 0 1 0 0 1 0 0 0 0 0 0 819 3.4 0 0 1 0 1 1 0 0 0 0 0 0 821 4.4 0 0 1 0 1 1 0 0 0 0 0 0 822 4.8 0 0 1 0 1 1 0 0 0 0 0 0 824 3.5 0 0 1 1 1 1 0 0 0 0 0 0 829 4.7 0 0 1 0 1 1 0 0 0 0 0 0 831 3.7 1 1 1 0 1 1 0 0 0 0 0 0 832 3. 5 0 0 1 0 1 T 0 0 0 0 0 0 835 2.5 0 1 1 1 1 1 0 0 0 0 0 0 836 2.0 0 0 1 1 1 1 0 1 0 0 0 0 839 1.7 0 0 1 0 0 1 0 0 0 0 0 0 841 4. 3 1 1 1 0 1 1 0 0 0 0 1 1 843 1.7 0 0 1 0 1 1 0 0 0 0 0 1 854 6. 3 1 1 1 0 1 0 0 0 0 0 1 0 859 6.7 1 1 1 0 1 0 0 0 0 0 1 0 861 1.7 0 0 1 0 1 1 0 0 0 0 0 0 862 3.2 0 0 1 0 1 0 0 1 0 0 0 0 864 7.7 0 0 1 0 1 1 0 0 0 1 0 0 869 2.8 0 0 1 0 1 1 0 0 0 0 0 0 877 2.0 0 0 1 0 1 0 0 0 0 c 0 0 900 2.0 0 0 0 1 0 1 0 0 0 0 0 0 9 11 1. 4 0 0 1 0 0 1 0 0 0 0 0 0 912 4.2 0 0 1 0 1 1 0 0 0 0 0 0 9 13 2. 6 0 0 1 0 0 1 0 0 0 c G 0 914 2.0 0 0 1 1 0 1 0 0 0 1 0 0 9 15 2.0 0 0 1 0 1 1 0 0 0 0 0 0 916 4.7 0 0 1 0 1 1 0 0 0 0 0 1 9 17 2. 8 0 0 1 0 1 1 0 0 0 0 0 0 918 3.8 0 0 1 0 1 1 0 0 0 0 0 0 9 19 2.7 0 0 1 0 1 1 0 0 0 c 0 0 920 6.6 0 0 1 0 0 1 0 0 0 0 0 0 -189-Appendix G: Occupational Code "by Occupational Class Title Occ. Code Occupational No. Class Title Division 1 — MANAGERIAL OCCUPATIONS 001 Advertising managers 002 Credit managers 004- Sales managers 005 Delivery managers 006 Office managers 00? Postmasters 008 Purchasing agents and buyers 010 Owners and managers, n.e.s. Division 2 — PROFESSIONAL AND TECHNICAL OCCUPATIONS 101 Civil engineers 102 Mechanical engineers 104 Industrial engineers 105 Electrical engineers 107 Mining engineers 108 Chemical engineers 109 Professional engineers, n.e.s. 111 Chemists 112 Geologists 114 Physicists 119 Physical scientists, n.e.s. 121 Biological scientists 124 Veterinarians 129 Agricultural professionals, n.e.s. 131 Professors and college principals 135 School teachers 139 Teachers and instructors, n.e.s. 140 Physicians and surgeons 141 Dentists 142 Nurses, graduate 143 Nurses-in-training 144 Physical and occupational therapists 145 Optometrists 146 Osteopaths and chiropractors 147 Pharmacists 148 Medical and dental technicians 149 Other health professionals - 1 9 0 -Occ. Code Occupational No. Class Title 15L Judges and magistrates 153 Lawyers and notaries l6l Clergymen and priests, n.o.r. 163 Nuns and brothers, n.o.r. 169 Religious workers, n.o.r. 171 Artists, commercial 172 Artists (except commercial), art teachers 174 Authors, editors and journalists 176 Musicians and music teachers 181 Architects 182 Draughtsmen 183 Surveyors 184 Actuaries and statisticians 186 Economists 187 Computer programmers 188 Accountants and auditors 191 Dietitians 192 Social welfare workers 194 Librarians 195 Interior decorators and window dressers 196 Photographers 198 Science and engineering technicians, n.e.s. 199 Professional occupations, n.e.s. Division 3 — CLERICAL OCCUPATIONS 201 Bookkeepers and cashiers 203 Office appliance operators 212 Stock clerks and storekeepers 214 Shipping and receiving clerks 221 Baggagemen and expressmen, transport 223 Ticket, station and express agents, transport 232 Stenographers 234 Typists and clerk-typists 241 Attendants, doctors' and dentists' offices 249 Clerical occupations, n.e.s. -191-Occ. Code Occupational No. Glass Title Division 4 — SALES OCCUPATIONS 301 Foremen, trade 303 Auctioneers 307 Canvassers and other door-to-door salesmen 312 Hawkers and pedlars 314 Commercial travellers 316 Newsvendors 323 Service station attendants 325 Sales clerks 327 Advertising salesmen and agents 331 Insurance salesmen and agents 334 Real estate salesmen and agents 336 Security salesmen and brokers 338 Brokers, agents and appraisers, n.e.s. 339 Other sales occupations Division 5 ~ SERVICE AND RECREATION OCCUPATIONS 401 Firemen, fire protection 403 Policemen and detectives 405 Guards, watchmen, n.e.s. 407 Commissioned officers, armed forces 408 Other ranks, armed forces 411 Lodging and boarding house keepers 412 Housekeepers (except private household), matrons, stewards 413 Cooks 414 Bartenders 415 Waiters 416 Nursing assistants and aides 417 Porters, baggage and pullman 418 Baby sitters 419 Maids and related service workers, n.e.s. 431 Actors, entertainers and showmen 433 Athletes and sports officials 451 Barbers, hairdressers, manicurists 452 Launderers and dry cleaners 453 Elevator tenders, building 454 Janitors and cleaners, building 455 Funeral directors and embalmers 456 Guides 457 Attendants, recreation and amusement 459 Service workers, n.e.s. -192-Occ. Code Occupational No. Class Title Division 6 — TRANSPORT AND COMMUNICATION OCCUPATIONS 510 Inspectors and foremen 520 Air pilots, navigators and flight engineers 531 Locomotive engineers 532 Locomotive firemen 534 Conductors, railroad 535 Brakemen, railroad 537 Switchmen and signalmen 54l Deck officers, ship 543 Engineering officers, ship 545 Deck ratings (ship), barge crews and boatmen 547 Engine-room ratings, fifemen and oilers (ship) 551 Bus drivers 552 Taxi drivers and chauffeurs 554 Driver-salesmen 556 Truck drivers 561 Operators, electric street railway 563 Teamsters 569 Transport occupations, n.e.s. 570 Inspectors and foremen, communication 581 Radio and television announcers 582 Radio and television equipment operators 584 Telephone operators 585 Telegraph operators 587 Postmen and mail carriers 588 Messengers Division 7 — FARMERS AND FARM WORKERS 601 Farmers and stockraisers 603 Farm managers and foremen 605 Farm labourers 607 Gardeners (except farm) and groundskeepers 609 Other agricultural occupations Division 8 — LOGGERS AND RELATED WORKERS 611 Logging foremen 613 Forest rangers and cruisers 615 Lumbermen, including labourers in logging -193-Occ. Code Occupational No. Class Title Division 9 — FISHERMEN, TRAPPERS AND HUNTERS 631 Fishermen 633 Trappers and hunters Division 10 — MINERS, QUARRYMEN AND RELATED WORKERS 651 Foremen — mine, quarry, petrol, well 652 Prospectors 653 Timbermen 654 Miners,, n.e.s. 655 Millmen 656 Well drillers and related workers 657 Labourers, mine 659 Quarriers and related workers, n.e.s. Division 11 — CRAFTSMEN, PRODUCTION PROCESS AND RELATED WORKERS 701 Millers of flour and grain 702 Betkers 703 Butchers and meat cutters 704 Meat canners, curers, packers 705 Fish canners, curers, packers 706 Fruit and vegetable canners and packers 707 Milk processors 708 Other food processing occupations 709 Beverage processors 711 Tire and tube builders 713 Vulcanlzers 719 Other rubber workers 721 Leather cutters 722 Shoemakers and repairers — factory, n.e.s. 724 Shoemakers and repairers — not in factory 729 Other leather products makers 731 Carders, combers and other fibre preparers 732 Spinners and twisters 733 Winders, reelers 734 Weavers 735 Loom fixers and loom preparers 736 Knitters 737 Bleachers and dyers — textile 738 Finishers and calenderers 739 Other textile occupations -194-Occ. Code Occupational No. Class Title 741 Tailors and tailoresses 742 Dressmakers and seamstresses — not in factory 7^3 Furriers 744 Milliners; hat and cap makers 745 Cutters, markers — textiles; garment and glove leather 746 Sewers and sewing machine operators, n.e.s. 747 Upholsterers 749 Apparel and related products makers, n.e.s. 751 Carpenters 752 Cabinet and furniture makers — wood 754 Sawyers 756 Woodworking machine operators, n.e.s. 758 Inspectors, graders, scalers, log and lumber 759 Woodworking occupations, n.e.s. 761 Batch and continuous s t i l l operators 762 Roasters, cookers and other heat treaters, chemical 763 Cellulose pulp preparers, n.e.s. 765 Paper makers 766 Paper making occupations, n.e.s. 768 Crushers, millers, calenderers, n.e.s. — chemical 769 Chemical and related process workers, n.e.s. 771 Compositors and typesetters 772 Pressmen, printing 773 Lithographic and photo-offset occupations 775 Photoengravers 776 Bookbinders 778 Other occupations in bookbinding 779 Printing workers, n.e.s. 781 Furnacemen and heaters, metal 782 Heat treaters, annealers, temperers 783 Rolling mill operators 784 Blacksmiths, hammermen, forgemen 786 Moulders 787 Coremakers 788 Metal drawers and extruders 789 Metal treating occupations, n.e.s. 791 Jewellers and watchmakers 793 Engravers, except photoengravers -195-Occ. Code Occupational No. Class Title 801 Tool makers, die makers 802 Machinists and machine tool setters 803 Filers, grinders, sharpeners 805 Millwrights 806 Fitters and assemblers, n.e.s. — metal 808 Metalworking machine operators, n.e.s. 810 Plumbers and pipefitters 811 Sheet metal workers 812 Riveters and rivet heaters 813 Boilermakers, platers and structural metal workers 815 Electroplaters, dip platers and related workers 817 Welders and flame cutters 818 Polishers and buffers —metal 819 Metalworking occupations, n.e.s. 821 Mechanics and repairmen, aircraft 822 Mechanics and repairmen, motor vehicle 824 Mechanics and repairmen, office machine 825 Mechanics and repairmen, railroad equipment 829 Mechanics and repairmen, n.e.s. 831 Electricians, wiremen and electrical repairmen 832 Fitters and assemblers ~ electrical and electronics 833 Power station operators 835 Mechanics and repairmen, radio and television receivers 836 Projectionists, motion picture 838 Linemen and servicemen — telephone, telegraph and power 839 Electrical and electronics workers, n.e.s. 841 Painters (construction and maintenance), paperhangers and glaziers 843 Painters, except construction and maintenance 851 General foremen — construction 852 Inspectors — construction 854 Bricklayers, stonemasons, tilesetters 855 Cement and concrete finishers 856 Plasterers and lathers 857 Insulation appliers 859 Construction workers, n.e.s. 861 Lens grinders and polishers; opticians 862 Furnacemen and kilnmen, ceramics and glass 864 Stone cutters and dressers 869 Clay, glass and stone workers, n.e.s. / -196-Occ. Code Occupational No. Class Title 871 Boiler firemen (except ship) 872 Stationary enginemen 873 Motormen (vehicle), except railway 874 Hoistmen, cranemen, derrickmen 875 Riggers and cable splicers, except telephone, telegraph and power 876 Operators of earth-moving and other construction machinery, n.e.s. 877 Materials-handling equipment operators 878 Oilers and greasers — machinery and vehicles (except ships) 881 Longshoremen and stevedores 883 Warehousemen and freight handlers, n.e.s. 890 Sectionmen and trackmen 900 Foremen, n.e.s. 911 Tobacco preparers and products makers 912 Patternmakers (except paper) 913 Bottlers, wrappers, labelers 914 Paper products makers 915 Photographic processing occupations 916 Tanners and tannery operatives 917 Inspectors, examiners, gaugers, n.e.s. — metal 918 Inspectors, graders and samplers, n.e.s. 919 Production process and related workers, n.e.s. Division 12 — LABOURERS, n.e.s. 920 Labourers, excluding those engaged in agricultural, fishing, logging or mining operations Division 13 ~ OCCUPATION NOT STATED 980 Occupation not stated 

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