LIBRARY OF THE UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN 5I0.84 no.(c>\'-^0 AUG. 51.976 ihe person charging this material is re- sponsible for its return to the library from which it was withdrawn on or before the Latest Date stamped below. Theft, mutilation, and underlining of books are reasons for disciplinary action and may result in dismissal from the University. UNIVERSITY OF ILLINOIS LIBRARY AT URBANACHAMPAIGN JAM. > S APR 8 I 1 1 ■^- ■■ ^l OCT EHoro PHOTO REPRODLCTION OCT 22SEC'n PHOTO REPROUCTION NOV 1 6 ^^ ggjffi f^EPR00UCTtO^ OCT 1 fffo J U » f ? KUIXK^itwi 2 7 KC'B BBjaaSHOOOGTION CCT 2 3 RECD L161 — O-1096 Digitized by the Internet Archive in 2012 with funding from University of Illinois Urbana-Champaign http://archive.org/details/structureoflabor65dauf ENGINEERING LlBRARy UNIVERSITY OF ILLINOIS Center for Advanced CcM»utation UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN URBANA, ILLINOIS 61801 CAC Document No. 65 THE STRUCTURE OF LABOR MARKET SEGMENTATION OF OCCUPATIONS: SOME EVIDENCE FROM FACTOR ANALYSIS By Robert C. DauffenBach February 19, 1973 CAC Doc;jinent No. 65 THE STRUCTURE OF LABOR MARKET SEGMENTATION OF OCCUPATIONS: SOME EVIDENCE FROM FACTOR ANALYSIS BY Robert C. DauffenBach Center for Advanced Computation University of Illinois at Urbana-Champaign Urbana, Illinois 618OI February 19, 1973 ■tfiWKRJNG U8RARV ABSTRACT In this document the empirical results of a factor analysis of labor market attributes of occupations are reported. The purpose of the analysis is to determine the principal structures of the occupation-labor market at- tribute domain enabling a more objective construction of "occupation systems" for use in labor market and manpower research. Nine principal structures of labor market segmentation are delineated and interpreted. Suggestions for application of the results are made. Sample base for this investigation was the detailed occupational categories of the I96O Census of Population. TABLE OF CONTENTS Page I . INTRODUCTION 1 II . OCCUPATION SYSTEMS 2 III . THE RESEARCH QUESTION h IV. THE DATA MATRIX 5 V. THE COMPONENT ANALYSIS MODEL 8 VI . EMPIRICAL RESULTS 12 VII . SUMMARY AND CONCLUSIONS 27 BIBLIOGRAPHY 31 APPENDIX A: PRIMARY FACTOR STRUCTURE MATRIX 32 APPENDIX B : FACTOR SCORE MATRIX 33 LIST OF FIGURES I. ILLUSTRATION OF THE OBJECTIVE OF ROTATION l6 LIST OF TABLES I . UNROTATED FACTOR MATRIX 13 II . PRIMARY FACTOR PATTERN MATRIX IT III . INTERCORRELATIONS OF PRIMARY FACTORS l8 I. INTRODUCTION Since the nineteenth century pronouncement by Cairnes [3] of the "non-competing groups" concept , social scientists have become increasingly interested in the basic anatomy of labor market seg- mentation. In recent years attention has focused on the specialized employment problems of blacks, women, young and older workers. Studies of "bifurcation" of the labor market into "peripheral worker" versus "full-time : full-year" employment, the work experience of minority groups, youth and female emplojnnent patterns and black/white income and unemployment differentials are but a few examples of re- newed interest in labor market segmentation. In contrast to the large majority of past labor market studies which concentrated on various demographic groups, it is the purpose of this investigation to explore the structure of labor market seg- mentation with respect to occupations. The empirical methodology of "factor analysis" is the basic research tool of this study. The over- all goal is to increase the relevance of occupational statistics to labor market research through knowledge of the labor market structure of occupations and, thus, to facilitate the development and construc- tion of "occupation systems." This report is initiated by a brief discussion of occupation systems and their place in labor market and manpower research. A more complete statement of the "research question" of the investigation is then presented. Thirdly, the variables employed in this investigation and the occupational sample base will be stated. Fourthly, the general nature of factor analytic purpose and method and, particularly, one subset of these methods, the "principal component model," will be discussed. The above is followed by the empirical re- sults and interpretation of the basis dimensions of labor market seg- mentation. In the "summary and conclusions" section some implications and applications of the findings of this study for future labor market research will be given. II. OCCUPATION SYSTEMS Initial research in the area of occupation systems, generally attributed to Alba M. Edwards [4] , was structured on a theme of "socio- economic groups." The intent of such a classif icatory scheme was to "bring together into one occupationally homogeneous group all workers belonging to the same social-economic class, with but minor regard to the occupations they pursue...." [5, p. 175]. This theoretical base, now considerably scathed by repeated academic attacks [2] [8] [12], has dominated the summary classifications of Census occupational tabula- tions to date. From these summary classifications, sweeping general- izations on compensation, unemployment, education and other differen- tials between aggregate classifications have been made and, admittedly, some light is cast on basic labor market segmentation through their use. Yet, the inadequacy of the theoretical base by which the detailed categories are aggregated substantially limits their usefulness in specialized research investigations. As evidenced by review of occupational classification litera- ture, there clearly exists a need for sophisticated occupational systems for purposes of labor market and manpower research. Objec- tive criteria are necessary for construction of such systems. Thus, Scoville has developed a reclassification of detailed occupations along "job family" and "content level" lines [11]. Broom and Smith have suggested the structure of a system based on the inherent po- tentiality for mobility of individuals in particular areas of en- deavor [1]. Cain, Hansen and Weisbrod view "cross-elasticities of supply" and "elasticities of substitution" between occupations as the proper conceptual bases for demarcation of such a system [2], These examples of varying (although interrelated) objective criteria are formed from dominant themes of the occupational structure deemed appropriate for classificatory purposes. There are, of course, a multitude of other objective criteria which could be employed in the construction of occupation systems. Age, sex, race, education, earnings and other labor market attributes of occupations could each serve as objective criteria for construction of such a system. Or, one may seek a "structural patterning" of such variables as a basis for aggregating the detailed categories. As is aptly stated by Scoville: The underlying pattern of jobs in the economy influ- ences and is interrelated with other variables relevant to manpower and labor market research. These include educa- tional and training requirements, worker mobility and move- ment, the structure and distribution of compensation, and even the age and sex characteristics of employment. I|^ iMd oAz to kave, h-nlovavit and anatytx.caLiy li6^{^uI data, tkzy i>kouJid be (ion^tJuicit2,d WAXk a va,2W touJa^d tkli> gdneAoZ i>yi>- t2m ol ^^Zatloyii>klp6 bztwdzn job6 and otkdfi tahoK. maAkoX and manpoi^QA. \ja/u.abl [12, p. 317]. (Italics Added) An increased understanding of this "underlying pattern" or dimensionality of occupations with respect to relevant labor market variables could serve as an additional tool for development of an occupational system of greater relevance to current specialized research endeavors. It is the purpose of this paper to further explore the basic lines of segmen- tation of detailed occupations with respect to relevant labor market attributes with hopes of serving the general goal of improving the use- fulness of the detailed occupational categories for purposes of manpower and labor market research. .( III. THE RESEARCH QUESTION The basic research question of this investigation is stated as follows: What are the major, linearly independent, basis dimensions of labor market segmentation that span the occupation — labor market attribute space? Somewhat less technically, the objectives are delin- eation and identification of the principal structures or common themes of labor market segmentation that concisely define the systematic vari- ation in a data matrix of occupations by labor market variables. Each of these basis dimensions is merely a linear combination of labor market attributes and as such, each can be viewed as a separate "structural meshing" of those attributes. To further illustrate this concept of basis dimensions, consider a hypothetical example of two sets of labor market attributes such that there are strong interrelationships among variables within sets but no relationships among variables between sets. Each set of vari- ables is, then, delineating a principal structure of the attribute space, Through a linear combination of the variables in the first set, form a new variable, S- . Similarly, S^ is constructed by taking a linear combination of variables in the second set. By assumption, S, and S- are not related and, thus, they are basis dimensions of the attribute space. Each contains a concise embodiment of one form of systematic variation in the original attribute space. These basis dimensions are said to "span" the attribute space in that any attribute vector can be reproduced by a linear combination of the basis dimensions. In practice, variables do not neatly divide into distinct sets as in the above hypothetical example; therefore, all variables will make a contribution to each of the basis dimensions. Of course, the contri- butions of the variables to a given dimension will vary. In fact, identification of the content of £iny given dimension is facilitated by assessment of the relative contribution of the variables to the dimension. IV. THE DATA MATRIX Although the data on occupations are not as rich as students of labor markets would have them, there does exist a large body of tabu- lations pertaining to earnings, education, weeks worked in the previous year, hours, age, sex, race and marital status. Other relevant variables can be computed from the tabulations such as "unemployment rates." The profile variables selected for use in this study are as follows: per- centage values for male, white male, white female, married, married male; unemplojrment rates: total, male, female, white, non-white; not-in-labor force ratios: male, female, total; and, percentage frequency distributions of earnings, years of education, weeks worked, hours and age on which there are, respectively, ten, seven, five, eight and eleven classifi- catory breakdowns. Use of percentage frequency distributions on the last five groups of variables was decided so as to more accurately account for the variance, skew and sometimes bimodality of the dis- tributions. This information is lost if a simple central tendency measure is employed. All variables were transformed to normality by a "grouping" procedure such that all means and variances were zero and one, respectively. The base on all variables was "experienced civilian labor force" except for "hours" and "age" for which and "employed" base was utilized. Perhaps the only variable in need of an accounting of its con- struction is the "not-in-labor force ratio" (NLFR) . For any given occupation at any point in time there exists a stock of "former workers" who had that occupation as their "last job" and who are non-participants in the labor force. "NLFR" is calculated by simply dividing this total by the "experienced civilian labor force" for the occupation. It would, of course, be hypothesized that "NLFR" would be a direct function of the "marginality" of an occupation, i.e., occupations of a part-time :part-year nature. In a sense, "NLFR" measures the interaction between "not-in-labor force" status and oc- cupational supply. It is necessary to qualify this last statement since measurement of "NLFR" is one directional: from occupation to "not-in-labor force" status, not the reverse. Also, there is no data on the intervening period of unemployment of such workers. In computa- tion of this ratio, "former workers" for 1959 and 1960 were summed to form the numerator. A number of qualifications on the data and variables are in order. Census reliability tests show substantial departures from acceptable levels of response variations in categorizing individuals into their proper occupational class. Also, general homogeneity of the detailed categories with respect to the job titles encompassed within each respective detailed occupation has been severely criti- cized in the literature. Furthermore, the cumbersome size of the "not elsewhere classified" categories raises questions as to the general worthiness of the data. Particular to this research endeav- or, use of the detailed occupational categories and a further indus- trial disaggregation of some of the larger "N.E.C." classifications, which together resulted in 348 cases, creates sampling variability problems especially with occupations which are sparsely populated. It should also be noted that the "earnings" variables group pertains to income year 1959. Thus, for occupationally mobile individuals, the earnings figure will not conform with their current occupational clas- sifications. Furthermore, because of the "experienced civilian labor force" or "currently employed" base of the variables, individuals who worked in 1959 or early 1960 but who were "not-in-labor force" at Census taking time are not included in the tabulations. A qualification on the "unemployment rate" variable is also necessary. This is because the unemployed individual's job horizon will generally encompass options other than his most recent line of work. Thus, to speak of an unemployment rate for "welders" is a more tenuous concept than that of the "male" unemployment rate since it is more difficult for an in- dividual to escape the classification of "male." V. THE COMPONENT ANALYSIS MODEL The "principal component analysis" model is a type of factor analytic technique. As such, this model has in common with other factor analytic techniques the general goal of producing a more parsimonious description of a data matrix, i.e., disentangling the patterns of interrelationship between variables in the attribute space into primary independent lines or dimensions of variation. These basis dimensions, which are a concise embodiment of the sys- tematic variation in the original data matrix, outline the basic structure of a domain and, thus, produce the desired parsimonious description of the data. Furthermore, objects or cases can be "scaled" along each of the basis dimensions of the attribute space enabling a clustering of cases into groups based on similarity of profile values. It is seen, then, that factor analytic techniques serve as a tool for development of an empirical typology. Unlike other factor analytic techniques in which it is assumed that each variable can be decomposed into "common" and "unique" parts component analysis is concerned with the space defining the total variance of the variables in question. Component analysis produces the basis dimensions of the data making no assumption regarding the existence of common factors. In fact, since the component model analyzes total variation, the common, specific and random error variances are all intertwined in the basis dimensions. When the communality of the variables is high (as measured by the squared multiple correlation coefficient of each variable with all other variables), however, the results of component analysis will be quite similar with those of common factor analysis [10, p. 112]. Component analysis has the additional advantage that an exact solution for the "scaling" of cases can be computed whereas only approximations are possible with the common factor analysis method [10, p. 436]. Mathematically, the "component analysis" model can be stated as follows : h = ^'21^1 -^ °'22^2 -^ ••• + ^^2^ +•••■' W?' • • • • • • • • • • • • • • • Z. = a.,S, +a.„S„ + ... +a,,S, + ... +a S , J Jl 1 J2 2 jk k jp p' Z =a,S-+a,S, +...+a,S, +...+a S, m ml 1 ml 1 mk k nip p where: Z.is the i-th standarized variable data vector: S, is the k-th J ' k factor dimension; a., is the "loading" weight of factor dimension "k" on variable "j"; "m" is the number of variables; and, "p" is the dimen- sionality of the data. Stated in matrix form: Z = SF' , (1) where "Z" is an "n by m" matrix with rows of cases and columns of vari- ables, "S" is an "n by p" matrix of "factor scores" or "scale values" of each case on each factor dimension of the data and "F" is a matrix size "m by p" of factor loadings. From the above it should be apparent that there is a relationship between multiple linear regression and component factor analysis. The factor dimensions of "component analysis" are analogous to the independent 10 variables of "multiple regression analysis" and the loadings of the former are the regression coefficients of the latter [10, p. 204]. Yet, despite this relationship, the questions asked of the data are different. "Regression analysis" measures dependence of one variable on a set of other variables. In factor analytic techniques, the basic research question is one of interdependency , i.e., interest is in the smallest number of independent dimensions that will span the vector space defined by the original set of variables. The objective of "component analysis" is to determine the elements of the factor loadings matrix (F) and the factor score matrix (S). Generally, the above is accomplished through "factoring" of the cor- relation matrix (R) . The dependence of the solution on the correlation matrix is seen in the matrix manipulations below. From equation (1) , Z'Z = (SF')'(SF') = FS'SF'. (2) The inner product (S'S) of the right most expression reduces to (nl) , where "n" is the number of cases and "I" is an "m by m" identity matrix, when the factor scores are orthogonal (uncorrelated) and column stan- dardized. Thus, multiplying through by the scaler "1/n", one obtains: (l/n)Z'Z = FF'. (3) The left side of equation (3) is the familiar matrix expression of the product moment correlation matrix; therefore, R = FF' (4) or r.. = E a, .a., . ^ , , >. jk ^ ji ik (4 ) 11 Equation (4) is known as the fundamental theorem of "factor analysis." The solution for "F" involves determination of the characteristic roots and vectors of "R" and is commonly known as the "principal axes" techni- que. Defining "E" as a matrix of eigenvectors and "X" as a diagonal matrix of corresponding eigenvalues , R = EAE' = (EX-'-^^XA-^'^^E') = FF' . (5) Thus, the matrix of factor loadings (F) is equal to the eigenvector matrix of "R" with each column eigenvector of "E" rescaled by the square root of its corresponding eigenvalue. The solution is mathe- matically unique. The columns of "F" are generally extracted in a "principal axes" algorithm such that the first factor explains the largest percent of variance, the second factor explains the largest percent of the remaining variance given that it is orthogonal to the first factor and so on. Each additionally extracted factor is orthogonal to those previously extracted. Generally, the number of possible factors is equal to the number of variables since, in most empirical investiga- tions, the rank of the correlation matrix is equal to the number of variables. But, typically only a small number of these dimensions will be of interest in terms of their importance in accounting for variance in the original variable space. For the "principal component" model, the solution for the matrix of factor scores (S) is straightforward. Employing equation (1) under the assumption that only "p" non-trivial factors were extracted from the data: ZF = SF'F (6) 12 and thus , S = ZFCFT)"-*-. (7) If "F" is unrotated, "F'F" will be a matrix with the eigenvalues along its main diagonal [10, p. 436]. The "s., " element of "S" is the "score" or "scale value" of object "i" on dimension "k". VI. EMPIRICAL RESULTS A. The Unrotated Factor Matrix. The "principal axes" method of factor analysis was applied to the product moment correlation matrix (R) of the variable set. Table I. presents the mathematically unique unrotated factor "loadings" matrix (F) for the first nine factors. The (i,j) loadings of "F" is the pro- duct moment correlation of variable "i" with factor dimension "j"; consequently, approximately 17 percent of the variance of "percent male" is explained by the first factor. In factor analysis there is a general problem of when to stop extracting factors. The "eigenvalue-one" criterion was employed in this investigation, i.e. , only those factors with eigenvalues greater than unity were extracted [10, p. 362]. The first nine factors account of 83 percent of the variance in the trans- formed data matrix. Or, stated alternatively, the remaining possible 45 factors explain only 17 percent of the variance. The column vector 2 "h." has as its elements the sum of squared factor loadings across factors and is known as the "communality" of a variable. This measures A number of criteria have been developed for the "number of factors" problem. For a discussion of these the reader is referred to Chapter 15 of Ruinmel [10]. In recent years the "eigenvalue-one" criterion has been widely employed. 13 r CD N -O 3 PJ rsj • fO f\J «• tM o ♦ tn 9> # <# lo o lm pg^ CO o • • o o M « CD mt h- in 3 pg S) O l»l * ■/> P- ^ ee i t Po E^ p» CO • o o 1 - 1 1 N. 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From the relative size of the first eigenvalue it is apparent why the first factor dimension is often regarded as the general factor. More than one-third of the variation is dependent on this dimension. Moreover, the pattern of factor weights of this first dimension are readily interpretable in a labor market sense. Percent white female, the unemployment rates, the NLFR variables, low earn- ings, education, weeks worked, hours and age all possess appreciably positive loadings. Strongly negative weights are found on the male, white male, married and married male variates as with high earnings, education, full-year weeks worked, long hours and middle age. If a name were to be attached to this dimension, a "prime-aged male" label would seem appropriate. Occupations dominated by women, blacks and young workers of a general part-time :part-year nature with low educa- tion requirements and meager compensation would score high on this factor while those occupations inhabited by prime-aged males would score low. The second dimension of the unrotated factor matrix indicates another male dominated dimension while factor three is weighted heavily on older worker age categories. Beyond these three factors, loading patterns are less interpretable. Techniques of matrix 15 rotation to "simple structure" have been developed to facilitate in- terpretation of dimensions. Rotation is performed to discern the cluster patterning of variables as is seen by the example in Figure 1. In this example it is seen that the variables load highly on both di- mensions as evidenced by their respective projections onto the unrotated axes. For the orthogonally rotated axes, the cluster patterning of the variables is more readily discernable. It is apparent from this example that "oblique" rotation even better identifies the cluster patterning of the factor space. In fact, it is the general solution for rotation whereas "orthogonal" rotation is a subset of "oblique" rotation. Unlike "orthogonal" rotation, "oblique" rotation permits intercorrelations of basis dimensions. The question of which technique to employ was em- pirically determined in this investigation. On the basis of a number of significant intercorrelations of obliquely determined dimensions, it was decided not to impose orthogonality through use of such a rotation model. Complexity of the factor space necessitated an "oblique" rota- tion. B. Oblique Rotated Factor Matrix . Table II. presents the results of an oblique "binormamin" rota- tion of the unrotated matrix of Table I. There are two matrices generated by oblique rotation: the "factor pattern" and the "factor structure" matrix. The first, which is presented in Table II , is best for discernment of the clustering of variables on factor dimensions, There are a number of methods for oblique rotation [7, ch. 15] [10, sect. 17.3]. The "binormamin" method is useful when the factor space is relatively simple or highly complex. On the basis of the complexity of the space, the "binormamin" method was selected. 16 FIGURE I. Illustration of the Objective of Rotation Unrotated Factor 1 Orthogonal Factor 1 Factor 1 Unrotated Factor 2 Oblique Factor 2 Ortho.gonai^Factor 2 IT M «M 4 IT or ■r ^ M rg O O C ^ OC OC C or 4 ■4 r^ r> r- ru o ss P- -£ «M tr, « f- ^H # - * r- O D o a sr o <» r' ^ tr o ^ ffi p. •c «' 4 »-* •t p^ ON »■' o - o O o o o o o < O O o o «\» 1^ »VJ »■ ^ t- If OD ri c ^ ( 3 O O ^ a o c o o O ^ .- fVJ CV o o o o o •• D • o 1 o o o 1 O 1 o o 1 = co d d o o do o o o o e d o o o d 1 d< 1 • • • 3 O O 1 1 1 d d 1 o o o o 1 o o d d • ^ O O o o 1 • o • o 1 o o 1 did 1 II H O O o < 3 — tvj O o o O — «\ '- - o o - r fv. •- t-i -- " c o - o o o o 1 o o e o o 1 o « 3 O O 1 1 d C o o d d d 1 1 d d 1 1 o c 1 d d d o 1 1 d c 1 3 O O 1 • o d o 1 1 d d 1 • • o o o c o o 1 d d e o 1 o o o o o W « • * • • k • • • • • o «v en r^ 0^ ir, IT <0 (M OD c C <* <« 0- (N. O ro © O fVi •C oo »v o- If > - r- o> r- e o r- ^ •C .J fv -< OP ■c >c ri .» u> OD r VI (V ^ tr <* r^ p» IP o f^ ^ <0 If fvj * O t\i a rv o e 3 « en ^ (T .-< fVJ >M p-l o- .t <-• tr oc * (v. ,0 ,_1 a ^ CM M -' o o o •-■ r VI «M O o o c o o -. -• o o o o o o — CM — c o O o o O o c e 1 o e 1 • • c O • c t • • i 3 O O c d o o o o 1 1 d d o o c o e o 1 o o 1 1 O « 3 c d • : o c 1 1 • m c o 1 • o o 1 • * d d d d 1 1 o o • • o cr o o • • oc o e o ro • • • • • • IT •-■ u> •-« ir o o- .-. r- •« r <»> ir ^^ ^-i o •* r- 00 o n — o O" *n ^' r- If ^ •- r n ft) or vf r 0 If C 3 f- a> tr r M O .- on r- a M 00 c ,0 an t^ 0" ^ ^t tfv If ir\ o e 3 vC or r- -c o o o- ^ cr .r •»• •»• t^% hi t vO S-i it •^ ^« - 0- r' or o ^ t « If tr, o f" f^ «N •• -■ ^ rsi rM <1 IT. tr <1 oc pi m o DO -3- r-< — o 0' ri o o o o C f- - C - O o o o c o o o o o t- ro ^ O o o o c 3 c o C O o o o o o o °.': ri vi ■c ^ IT O r^ o^ <^ Oj r^ O 1 o o 1 O O o 1 1 c c5 1 O « 1 3 O O o d o o d o o o d d 1 o d o d 1 t d d d c 3 O O d o 1 o o o o o o t o o do d d 1 1 O 1 o o o 1 o d o ir\ • i,o *^. • • • : : : : • • : : • • • « ITi ^^ flC (■ er .O a r~ o «• IT n o ^ cc •* .» t- yC rn r- If c c 3 f^ r- -> <* tri (f- f- o IT If O vt tr rvj .r CM OO CVi If « Pvl ►J o •-• OD -C r- p vj f> f>d o- en OP (\) ^ <#■ 4> If If fM r* * If »t ^- ac ■C f 'i If n o .» ^ 00 0> ri i a t^ u. —■ (SJ M ri »r. O- If O ^ 00 ^ If »-• s tr — « CM 00 <■ fM r- »- <\j lr^ OP ir t^ OP O u ■N •* IT •r «»> fsi O «n o t^ If •* CVJ 00 OD M f r; eg «M ^ f If ^ ^^ ^ o> o rr< ^ a * .-• f^ o — pu vl- « O p- r- 9-, s C^ t-i rj ^ * >o — «v o .- -1 r - in * IT ••■ O O tx (»^ ri (Ti O ^ « If O — «M (^ O M * If 5 o «n f- * >o rr> O tr '- »- r U 00 « « a m u> 00 IT o — If If v O r u v. rH fTl t>. If f - o o r- -■ r- o O tr, ff m PJ fvj -« vO ^ If ^ CM •O tr. Pvi m o 00 CM a: cc \t •4 •I »VJ o rvj .- ■. H O ^ IT tf^ (»N <\J ■t) 00 OC r^ If fTl o •« ti o o o O f ■1 fvj Pvi rsj O «M fM rt r\j •~ O tr, pj m IT, Pvl ^ tr, •»■ O O o p-i pg w o o o 1 O o o o o o O C 3 o d o 1 O 1 1 d d do d o do o o o d d c 1 3 O O 1 o o d o • 1 O O 1 1 O o O O o 1 1 d d 1 o d O o o o 1 o oo f H^? 0\ c\ r ^ If ill ,. ii^l ii H tv -* If ^s 1 1 1 1 3 ■1 -ICE <\J r ■^ IT \o t- «s VOO lf> i^ H its L 1 ! 1 1 £ E r^ O H 1- C\J S J- -3 tr,^ -3- O irv IT -3- O VD VT J- ^ l> ^ ^ Eg o < 8S P s ci ci ,^i irtvo fLJ, ilQ 1 1 I 1 1 1 t 5 io! O u- O lA 1 1 l/\ IT d. lA t o |S z =• i 1 r r : a 4 3 See 1 1 I 1 1 1 1 1 H 1 1 1 1 1 1 1 1 1 1 3 H E £ i £ E H r- j 1 1 j CI f 1 1 1 1 \OV£ ! 1 p- 1 2 E gS s s 3 "o S- E e E E E i E w ' 1 1 ' 1 1 1 1 1 i> e& M a ^ p 8 E E E £ E S E E £ y ^8 (r> ^ CO O Jt 0\ ± i/\ o 3= £ £ § i 1 3 1 < h «*- i E E E E i" £ £ E £ 1 CO ^ 5 3 8 ^' £ £ ( E '- § 1 1 1 1 1 1 w *} 1 g 1' - - SEE fi S E S s : E E £ E i ' 1 1 1 1 1 I S= = = = = £ E £ £ E o 1 ^ § 1 1 E e ^ I E E E B £ » E E E H B E E B B £ 1- E B E E E £ B e ' 18 0^ O O OO O o CO Pi o H O Pt4 vO O O o o in CO I CO CM o h4 Pi PM Pm O CO o M H o o PS M H in CO CNJ o o o o CO O o in o o o o o CO CM CO in CO in ON 00 o CM CO CN CO <» 00 00 o Oco 0000 O CD rvj ^ •-< h- •M IT * IT ff «r «V r- «4 o t" « ff ir O K cc r> («^ ff « (P o c ff OO •♦ O — tr ^.°. § If n pg P- -4 o -c .-1 O tr p- •C pg «- IT K cv 1* PJ ff ff r^ ff <4 OBi(\> ^ o O ^ O O — o O O »- o .-« »-< IM pg ^H p>« o o (V. O ir\ 00 o •-• pg pg pg — e pg (p »-< o C cv. t- CV CM O o c e — pg O • • o e 1 t • e 1 1 • • o o 1 t • • o o 1 1 o 1 1 o o oo 1 1 o o O O o o c o 1 O O 1 1 OO oo d 1 I c5o 1 1 O O 1 o o 1 1 O o 1 o o o O 1 1 o c • • o o o o o 1 1 • • V coo 1 1 1 «v> IT 00 or -c -.r^ 00 •- Its O »• * «« P.I ^ c e c <>< ff o ir> <« ^ ff -4 "i -t r- r rvj t- «c o o pg i r» IT c «» 0> ff PI IT or Sit PU M •-« f- ir IT ■4 P .- « d >o or 4 — N OD * ff -c pi If. 00 o ^ (OlPl 00r~ e o O Oliv «NJ IV p^ vr o p ff ff o CM C p. .^•^ O M O '4' O dC n-- o PJ M 0- r^ •» o or w h O CD ■# OO tr or 00 pg Ki (p (P p- p- pg P- (« tr * - tr CM w-t ^1 tr C ► • <»• C 00 ^ r~ (T O .-• IT o *~p" f. »- PvJ d * r. (VI ■* M f py -• O f n -♦ * 1" «-" «M 1-1 ^: P (T pg p\ (p CM r-i o o .~ o p- p- « OC t- (CI 4 ^ o oi^ ^ pg p ■4 • o • • c o t • • • • o o o o 1 • • • O O 1 t • • c e o o • 1 • 1 o o t t t • O O 1 1 • • OO 1 • • o o • t o o • • o o t ) o 1 1 • • o o • o • O 1 • • O O • 1 • • o o 1 o d 1 • • o 1 1 • e 1 t o • • o o • • O O 1 1 dd 1 1 • • t » o o o o i • • o o o » (V r- fli.- a!r- (V tc \r -■ a or 4 u^ O tr ir IT <» tr o ■* — PJ iTi ff c •4 pg O CM OO ff r-, tr 00 O on 4 «t p. p CM •- Ifi ff < J ff If ^ If p. If ff -s >* IT f opi^ e? ■4 «^ rr IT <* or ir Pi IT 1^ o PI * K fp f- o •4 <> ft ff or p- r- o ..• or or O cc p 4 ff .-. K p ^ p- ff ff (\i ir Pi er fM o f- oc P- rf ^ <» r^ tr fP o M ir o «> fl (»> If fn (« O * «t * «» tr 4 iry 4- (\i pg fy pg CM cv CCi P '*.**. •» P o n •-PJ Pi pg — ff o 1 o o o e o o • 1 { • t o e * • • • • • oo • # o e • 9 o 1 1 t * o 1 1 9 • c o ) 1 • t o o O C3 1 • • o 1 1 • o 1 • o • • o o • • O O 1 • • O O • • C O o o 1 * • O O 1 1 • • O O oV • • o o 1 o 1 1 • • o 1 1 o 1 cmI^ r on 4 ■C 00 r- «B C -' O f^ f- ff or tr «» ir — o 00 u * — r- tr ff o M PI ff ^ r- 00 — Klp,l ff -■ c .t ff^'(^^ O If. irvio- r- f^ o- I'- <<■ — ■^ ff pi -tl OC ff ff oc •e 0« O PJ p.1 tr. rP ff ^ t^ »o ff .* K * : • • oo o e e e • • o o o o 1 • • o r • • O O 1 • c? o 1 1 • • O o O O 1 o 1 1 O o od o o 1 O O O o o 1 o 1 o o 1 t c? o d c> 1 e olo o 1 1 i 1 1 o d'd 1 »r r-« »-4 4 »t •« (^ Oti ■C <£ O tfi CM tr o o p. OD (C t- ! O P-'-< * ff tr Pj OlfM .1 00 4 «» rjlO ^ fr^ rvj OD O r> -< <■ «i.-i r- •-• ■» ^ »• (Cl PJ pi (O If o If a- .-< ff pg or r~ -»OjO o n ^ i ss r» f\ d tr tf> Ifl O — «J^ IT o f» c p^. ff ff X pg ;: f- o c-t <, CM OC I O If pg OC 4 on CM O <0 ■* O lf> * m oo p- or 1^ o o « S 00 f < 00 tr ^- •* IT <^ o ^i ■C tr r- ff ff •f> N o e PI P- CM (CI O cmIcc, '*^r\j|0 rj O IMI<1 *|.* * \r 4 O - c\. <0 t-i O 0' w r- fP PsI oo t- c. a (\j ir i-l fO * ff 4 n o >c © — •C ir 00 pg N If PJ ff -. o o ^ O '-' P- c ai pg i PI •» -1 00.O> (^ •^ ^ » t o ir ir ff CM o ff -H O r^ p- PI rri or ff ff >»• 4 P (C CMC fM O tl • • ir -c 4 * o «<• M or. ir> •« fT ^■ — o -< u\ r^ i-i O Pg 0- ff> t-i »-' ■*> ■* 00 ff' 00 « o or 00 ff- If CTl r- ff f -4 00 pg ir t -■ o O ^ pg 9 1 o o o o 1 t o 1 1 o o 1 1 o o t 1 o o • • o O • • o o o O o o 1 o o o o o t O o 1 1 o o 1 o 1 • d o 1 1 o o o o o 1 1 O O 1 d d d d O O 1 o 1 n ^i? c^cy u r-l ■Jt ir H 3 II H 1 iti c!.ci 1T „ 1 M 1 1 . t- ^ ir. H ♦ E S S E I ^ « a |5 o u^ IA« ^^ 1 1 :52 04 CV -3- ff lA ir VO S< II 11 SI E E CM on so ^- 00 H t!-oi ii 1 1 1 1 1 id H r- W C\ S rr -J IT SsC 6 r- [3 3 1 j 1 1 1 H i' 1 1 1 1 1 W a a ei ' i i B B £ E 1 1 1 1 1 1 1 •^ S * iSc c e B H E B j^ 1 1 1 1 1 1 ! 1 i 1 1 1 1 1 1 1 1 1 1 1 1 . i 1 1 1 1 Z l>>. gg S e ^>= B B B E B 9 u ^ £ (C OE E a s t S E E B g ^R ^? oS ?5i R- E r . . . - . - . a 3g S a g E B ^^ M B S E : ^J m (»■ ■^ 1 <^^ g 32 V 5 A 2 5 •3 OE r- ^ J» Se g S» E s ' 15 E S i fe' B B B B B s 1 1 1 1 i I 1 1 Cs E B S E E B B £' s M B B E E B E E S TO CQ o C E S S 1 J B X.: ^' S E E B B E a E E 5e E E B B B B B B S B B E S S B col 33 fM 00 r- •A C « cc or ©■ « IT IT (M C f^ fM IT .-■ tr\ <^ «M ■« U C' ,r ^ or 1" -i* 9^ <0 IT -< r- ^ a CM ^ in .1 OC 4 c e a c » i .- fvl f- f- CM ^ OC ^ o .- •* C»V (T\ ^ •» ♦ - IT i»i r ^ o o o 1 f O 1 c o 1 ■x (M 1 1 fS O 1 (V O 1 o — t C f~< 1 .- o 1 1 O o 1 o .- 1 1 CM O 1 1 O O 1 - o 1 1 O fM 1 o o 1 »^ o 1 1 fM O 1 • • • • • C> O 1 o o o c • • O ^ 1 1 • c 1 • • CM O 1 1 c o (r r ^ « «» c c r- f' r> ► • f (C o- .C fv- ^ u IT or OC CM IT fV' f- * -' V If <0 vC r — r, cc u f-* or IT u i\ K rv' IT -1 r-< f~ f- 4 U- IT < u IT U; ■4 < r- or If fr * -1 IV O ir M * m Ci cr o « r^ c\ cc IT r ^ h- w- .» c fv f" f ^ -f rr or o *co O O «Vi • 1 1 1 -- o 1 1 o 1 1 • • o o 1 • ♦ o c 1 1 o o • 1 « o 1 1 ^4 ^i4 1 ) 1 1 • • #-• fM • 1 ^ O • 1 • • o c O -« 1 1 o c • • c .-. 1 • > 1 1 t • — O 1 1 • • o 1 1 • • — o 1 t • • o c 1 • • o c 1 O O 1 • c • o C Oj — ir ir or If e ^ o or f\.. fvi IT OC ^ IT »•■ fvi 0- a o fM h- ^ fv f. fM ff. ^ CM O f^ IT er o OC nC f^ fvi cr\ ^ ^ CM fw (T' c r- .f C?v — o •c ^ •c ^ fr. O - c o IT O « rf -• a - «» t^ r. !-• ^ o f\j (M O ^r <■ CN fM OC J — «» r-- •» O ■4 h- o o CM f IT OC O u vl — If r^ IT, CVI ri r, - C CO-' - o o o w — e o 1 -^o oo O -' 1 o o 1 O o 1 1 O O 1 o ^ O o c c> ■" ^ • • — o 1 O O 1 1 fM fM 1 • • CM C • • '- O cv.' o o o • • • • c o o c 1 1 • O ) • • O O 1 1 O 1 i h- IT IVi IT ot ^ ^» O < < e o ^ u rr ... a a O O) rv, ^ K 0- or r^ — < or CM fM C^ -) o tr f>- OC c o ^ in m — If - a IT a. o — c ri* o •-' •-< -- c^ C CM r- »- ^ u in O- u Pvl CM fr, CO ^ t- o h- •* — If a OC' orljvi o 1 r' -' c o 1 c o 1 1 ^ o 1 «V O t o 1 1 o o o 1 1 o 1 1 o 1 1 r- O 1 1 o — 1 — 1 w^ 1 1 — o 1 1 o es 1 1 1 1 pj o 1 1 •" o 1 fM O o c po O 1 1 • t O o 1 1 C ■ 1 r-' old 1 11 1 o -. If r- OP .- c^ •» fv f — e m a ^ •* — fo or r- cr-, O C*N * CM »- (»^ -. c ^ •- vf r o- ■c C -t O fM ^ K r^ •-■ * -c 4 c c r a o .o ^< r- o rr- U" (C r^ o n If rr\ o a ^ ri — i-t c « r^ o o fM rvj < fn CM .» « r- or fvi C^ m < 0- o a C f~ <0 K IT, ,» or IT ^ ^f^ -< a r^ 1^; in m ri 1^ C^ 4 o •o r- in (M t' -. r- fv 1 i 0> lor rv i ! or vjic o:p^ a O lA fM O — >C «\J OC r^ ir ir f- rg O IT O If CM f- a a a <« 00 IT M f .o >c — ■ ■* c » ' r-< ir cv. or CM r o CM >J >i •« IT in;or r" CVI CMIfr, o c — II 1 • • o c 1 • • • • fM &• o> .t fr IT .f i o j 1 1^ OC .1 t~ Cm 0- n t' cr fM O lf> f^ c n. r- ^ OC •i; o •* ff. 1- O- cr r- lf> CM o ^ j O -.CM OC sJ a OC >j in r- '« f»i r- ^r^ CM _ IV' r- •4 O — f- f f" »r O CM t^ o ff. o- in .c 1^ o «M C fM C xr\ O « c»^ rn (^ fM fr. 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