How segmented is the Bogota labor market?

Urban and Regional Report No. 80-1 URR'- 800) HOW SEGMENTED IS THE BOGOTA LABOR MARKET? Gary S. Fields Cornell University January 1980 This report was prepared under the auspices of the City Study Research Project (RPO 671-47) as City Study Project Paper No. 9. The views reported here are those of the author, and they should not be interpreted as reflecting the views of the World Bank or its affiliated organizations. This report is being circulated to stimulate discussion and comment. UIrban and Regional Economics Division Development Economics Department Development Policy Staff The World Bank Washington, D.C. 20433 PREFACE I am pleased to acknowledge helpful discussions and comments on the first draft from Gregory Ingram, Kyu Sik Lee, and Rakesh Mohan of the World Bank and Jorge Ducci, Walter Galenson, and Olivia Mitchell of Cornell University. The views expressed herein are my own and not necessarily those of the World Bank or of Cornell University. This paper is part of a program of research currently being conducted by the World Bank an Bogota and Cali, Colombia. The goal of the program, entitled The City Study, is to increase our understanding of the workings of five major urban sectors - housing, transport, employment location, labor markets and the public sector - in order that the impact of policies and projects can be assessed more accurately. This paper is part of the labor market and income distribution portion of the study which is coordinated by Rakesh Mohan. Another project paper in this series is: Rakesh Mohan. "Population Income and Employment in a Developing Metropolis: A Spatial Analysis of Bogota". City Study Project Paper No. 6. TABLE OF CONTENTS Page No. I. INTRODUCTION 1 II. THEORIES AND DEFINITIONS OF LABOR MARKET SEGMENTATION 5 A. Criteria for Defining and Establishing Labor Market Segm entation ...................................... 5 B. Five Suggested Definitions of Segmentation and Associated Tests .................................. 9 C. The Framework for Modeling a Segmented Labor Market 19 D. Econometric Issues ................................ 24 III. STATISTICAL AND ECONOMETRIC TESTS 31 A. Basic Tabulations and Cross Tabulations ........... 33 B. The Single-Equation Non-Interactive Approach ...... 61 C. Inequality Within and Between Groups .............. 68 D. Segmentation Schemes .............................. 74 E. Segmentation by Exogenous Income-Determining Factors (Type-1) .................................. 75 F. Segmentation by Endogenous Income-Determining Factors (Type-2) .................................. 79 G. Segmentation by Dependent Variable (Type-3) .......87 H. Group Determination and Inter-Group Mobility ...... 92 IV. CONCLUSIONS 95 A. Conceptual Conclusions ............................ 95 B. Empirical Conclusions ............................. 96 C. Needs for Future Research ......................... 98 BIBLIOGRAPHY 100 I. INTRODUCTION The central question confronting development economists as we enter the 1980's is: "Who benefits how much from economic development and why?" In a book now in press (Fields, forthcoming), I try to inform concerned readers both of the lessons of the past and of the questions which remain to be answered. In addition, specifically for the'case of Colombia, I have worked for several years to understand in depth what determines incomes and income inequality. Previous works were sum- marized in a paper recently completed for the World Bank (Fields, 1978a). The present paper is yet one more contribution to this line of research. My point of departure is the question: What causes inequality in the distribution of labor market rewards? One answer that is increasingly being offered by analysts at the World Bank and elsewhere is: labor market segmenta.ion.1/ The purpose of this paper is to evaluate the analytical value of the proposition that labor market segmentation causes income inequality in Botota. Notions of labor market segmentation have a long intellectual history. Mill may have been the first to call attention to labor market imperfections with his analysis of non-competing groups. To Mill, these labor market differences were rooted in capital market differences; without collateral one could not get a loan, and without a loan one could not invest in human capital. Thus, the non-competing groups were seen as resulting from institutional barriers to the ac- cumulation of human capital by the poor. 1/ e.g., Selowsky (1979, p.19) writes: "Two basic trends have prevented improvements in the distribution of income over time. One is demographic growth unparalleled in most development experiences; the second has been the emergence of strong tendencies in the economy toward dualism and segmentation in most factor markets..." (emphasis added). -2- Today, we mean something different by labor market segmentation. One definition, though by no means a universally agreed-upon one, is that labor market segmentation exists when workers face different earnings functions depending on their location in the labor market. In a competitive labor market in full equilibrium, workers with identical education and experience would expect equal earnings for equal hours worked. In a segmented market, workers in the less-favored group earn less than similarly-qualified workers in some other group. Why do different earnings functions occur? The standard explanation of segmented markets in less developed countries (LDCs) focuses on the determinants of wage structure. For example the govern- ment may impose different minimum wage policies on firms in the modern and traditional sectors; modern sector firms are more likely to be unionized; and modern industries may pay higher wages to reduce worker turnover. Add to these such factors as discrimination, nepotism and favoritism, public/private sector differentials, foreign-owned/domestically owned differentials, and individual differences in ability, and we see that the possible reasons for different earniiLgs functions are many. There are other problems beyond just the differentials in earnings functions. Why don't workers in the lower earning groups enter the high earning labor markets? Why don't employers who pay high wages hire more workers until the value of the marginal product of labor is equal between groups? The issues then are what determines the size of the various groups, what determines different workers' access to employment and income opportunities, and why barriers to mobility among some groups persist over time. The answers to these questions turn on the nature of the groupings themselves. -3- Some groupings are based on fixed characteristics. Wor""ers in poor countries cannot choose their sex in order to avoid sex discrimination, nor can they chcoose to be descendents of conquistadores rather than indios, or have parents who are professionals rather than peasants. In these cases, the determinants of group membership are not at issue; the reasons for earnings differentials are. Other groupings are not predetermined. For example, the number of jobs in various occupations and industries, as well as the access of various groups of workers to those jobs, vary with macroeconomic conditions, hiring practices, and the like. All these aspects of group membership are very much of interest to the following discussion, as are differences in earnings functions among these groups. Part II of this paper formulates the question--how segmented is the Bogota labor market?--more precisely. After establishing criteria for a meaningful definition of segmentation, I evaluate various definitions that have been suggested in the literature, set up an economic model of how personal and employment characteristics inter- relate to determine income in a segmented labor market, and formulate an econometric procedure for estimating these relationships. In writing Part II, I searched for useful approaches in the existing empirical literature on labor market segmentation in developed countries; I reviewed the literature surveys by Gordon (1972), Flanagan (1973), Wachter (1974), Cain (1976), and Jackson, Solomon, et al. (1976), as well as many of the basic sources cited therein. I looked also at the less developed country literature, the two most comprehensive references to which are the works of Kannappan (1977) -4- and Harris and Sabot (1978).1/ Unfortunately, I was unable to draw much specific guidance from the available literature. I find the proposition that labor market segmentation causes inequality in the U.S. or LDC labor markets to be ill-defined in many existing studies, to have been "proven" with inappropriate evidence, and to be virtually indistinguishable empirically from alternative hypotheses which main- tain that inequality arises from still-unmeasured human capital differences among workers, non-uniform utility functions, or compensat- ing differentials. This is not to say that the labor market, in Bogota or elsewhere, is a single unified place with equal opportunity for all and equal outcomes for those who work in it, but rather that appeals to the existing segmentation literature do not get us very far in understanding the inequality and associated wate structures that exist. Part III then presents the results of an empirical investigation of labor market segmentation in Bogota. I first present basic tab- ulations and cross-tabulations. Then turning to multiple regression analysis, I review existing studies and present new evidence using single-equation regression models. Next I proceed to different types schemes for segmenting the labor market and running separate earnings functions for workers in the different segments. Three segmentation schema are distinguished and treated empirically in what follows: segmentation by exogenous independent variables, segmentation by endogenous independent variables and segmentation by the dependent variable. Part IV summarizes the paper's conclusions and discusses topics for further research. 1/ See also Fields (1978b). -5- II. THEORIES AND DEFINITIONS OF LABOR MARKET SEGMENTATION A. Criteria for Defining Labor Market Segmentation The purpose of defining and measuring segmentation is to see to what extent the segmentation concept helps explain the distribution of economic rewards. To be fully satisfactory, any definition of labor market segmentation should at aminimum meet the following criteria: 1. The definition should not be equivalent to the phenomena to be explained. If we are seeking to explain poverty and inequality, segmentation cannot be defined as the existence of poverty and inequality. Tautological "explanations" are not very informative. 2. A satisfactory definition of labor market segmentation must distinguish actions by segmenters which lead to labor market inequality from "justifiable" differences among workers. If persons with the same education and experience are paid more in one industry than another, is this prima facie evidence of discriminatory behavior by employers or other actors in the labor markets. Or does it reflect unmeasured productivity differentials among individuals, attitudinal differences among groups toward work, or the luck that some people have in getting higher-paying jobs when not enough good jobs are available to go around? These latter influences do not constitute labor market segmentation in most people's minds. Hence: 3. The definition of segmentation should in principle permit identification of the segmenter. At minimum, any attempt to invoke segmentation as an explanation for unequal labor market outcomes should distinguish between segmentation which occurs in the labor market from -6- that which occurs prior to the labor market. While lack of educational opportunities for children may contribute to inequality in their earnings as adults, this cannot rightfully be attributed to labor market segmentation. A complete segmentation theory should thus establish who is doing the segmenting. The scheme suggested in Becker's (1957) classic treatment of discrimination-by employers, by employees, and by customers--remains equally relevant a quarter century later. In the development context, a further issue is that the lack of development itself may preclude mobility and cause so- called segmentation. 4. The definition of segmentation should in principle permit identification of how the segmenter effects segmentation. Employers, for example, may discriminate by only hiring persons frou a given group. Alternatively, their discrimination may take the form of wage differentials in the "same" job. Either practice might be termed "labor market segmentation." The defiiltion of segmentation should make clear what actions do and do not constittite segmentation. If the aforementioned criteria are adhered to, segmentation analysis can potentially be of great help in explaining inequality and poverty. But these are stringent requirements seldom approached. Consequently, the potential of segmentation analysis far exceeds its realization to date. Segmentation concepts have demonstrated beyond any doubt that labor market conditions are not uniform for different groups in the population. If non-uniformity is all we mean by such statements as: "there is labor market segmentation by sex," then "proof" of segmentation is neither surprising nor analytically helpful. However, -7- the claim of segmentation by sex implies other stronger meanings beyond mere differences. Consider the statement: "Employers systematically discriminate against women by hiring identically qualified men pre- ferentially." This is both more precise than the assertion that "there is labor market segmentation by sex" and, if it were true, would be interpreted by many as evidence of segmentation. Likewise, if it were shown that "employers systematically discriminate against women by paying them less than they do to comparably qualified men," this would also be seen as evidence of segmentation. In other words, there are many labor market actions like preferential hiring and wage discrimination, any one of which is evidence of segmentation by most definitions. Schematically, this might be indicated as: Action A or Action B K Labor market segmentation exists. or Action C or The literature on segmentation commonly suffers from two errors of logic. For example, wage differences between men and women are consistent with labor market segmentation but segmentation may not be inferred from such evidence. This fallacy may be illustrated schematically as: -8- Evidence consistent with action A or Labor market Evidence consistent with action B segmentation exists or Evidence consistent with action C or A,more subtle fallacy derives from the vagueness of the claim that "labor markets are segmented." If there are 10 actions that constitute segmentation by a particular definition and if only one of those actions is shown to exist, there is still segmentation; it is not valid, however, to infer that all 10 possible actions in fact occur. This flawed reasoning can be illustrated as: Action A and Action B Labor market Ai B segmentation exists and Action C and Alas, the segmentation literature is replete with these very mistakes. -9- B. Five Suggested Definitions of Segmentation and Associated Tests To define what segmentation is, it may be helpful to discuss what segmentation is not. In the standard textbook model of a non- segmented (i.e., homogeneous) labor market, (Reynolds, 1978, pp. 84-85): 1. The attractiveness of a job is measured by the wage. 2. All job vacancies are filled through the market. 3. The labor force is homogeneous. 4. There are as many jobs available as there are workers available. 5. Workers and employers are perfectly informed. 6. Vacancies are filled instantaneously. Thus, supply and demand for labor determine the volume of employment and the wage rate paid. The model assumes th:t the labor market processes and outcomes are the same for everyone, i.e., that all workers receive the same labor market returns. The simplest definition of labor market segmentation takes wage equality as the point of departure. Hence, we find in the literature: Definition (i): Hetexogeneity of Outcome. Heterogeneity of outcome is the essential characteristic of many definitions of labor market segmentation. Indeed, heterogeneity of outcome is sometimes the sole defining characteristic in empirical research. According to Freedman (1976), segmentation is easy to document: professionals earn more than manual laborers; better educated workers receive higher incomes than less educated workers; -10- unionized industries pay a wage premium over non-unionized ones; urban incomes are higher than rural incomes; and men are paid more than women. By the heterogeneity of outcome definition, these observations are prima facie evidence of labor market segmentation. These definitions and this type of evidence are unsatisfactory. One problem with the heterogeneity of outcome definition is that no attempt is made to standardize for possible compositional differences between groups. In the case of educational differences, allowance should be made for the period of time when the better educated individuals were in schoolandwere not meceiving income. As for male-female differences, it is desirable to standardize for length, quality, and continuity of labor market experience. Failure to consider heterogeneity of individuals is an important conceptual deficiency in some writings on segmentation. More importantly, however, if the concept of segmentation were only to imply that different groups are rewarded differently in the labor market, there would be little controversy over its existence, since equality of outcome obviously does not obtain in modern economies. However, with such a definition, nothing can be explained: the statement "segmentation explains inequality" is a tautology, since by definition (i), segmentation is inequality. The definition of labor market segmentation as "heterogeneity of outcome" must therefore be rejected. In an attempt to improve upon this definition, some writers have proposed: -11- Definition (ii). Heterogeneity of Outcome Among "Comparable" Workers as a Function of Group in the Labor Market (E.g., Occupation or Industry). Souza and Tokman (1977), for instance, claim (p. 8): "For segmentation in the labor market to exist, persons with equal abilities ought to receive different incomes depending on the stratum of the productive units in which they work." (Translation mine, emphasis added.) Virtually the same conception is used by Altimir and Piera (1977). Likewise, Bourguignon (1979, p. 56) regards segmentation as an "imperfection of the labor market or, in other words, the hypo- thesis that wages in the modern sector are above incomes in the traditional sector" for otherwise identical individuals. (Translation mine.). And, Mazumdar and Ahmed (1977) write (p.1): "A rather stringent definition of labor market segmentation is that a difference in earnings can be attributed to 'institutional' factors after we have allowed for variations in measurable human quality factors like education and experience."' 1/ These authors present empirical tests in their respective studies covering several Latin American cities but excluding Bogota (Souza and Tokman), several Latin American countries including Colombia (Altimir and Pinera), several Colombian cities including Bogota (Bourguignon), and several Malaysian cities (Mazumdar and Ahmed). In each study, the empirical test follows the same form: multiple 1/ Similar definitions of segmentation have been used in the housing market literature. For instance, Schnare and Struyk (1976) regard a housing market as being segmented when the price of an attribute varies with either structural or neighborhood characteristics. -12- regressions involving "human capital" and "segmentation" variables. After standardizing for measurable human capital factors like education and experience, these authors find that the occupation or industry of employment is associated with wages or incomes. Hence, they conclude that the respective labor markets are segmented to a greater or lesser degree. 1, 2/ Another kind of empirical test consistent with Definition (ii) appears in the literature. This involves three steps: first stratifying the labor force by a variable thought to segment the labor market, then running separate earnings functions for the two groups, and finally comparing the regression coefficients using an appropriate analysis of variance test. 3/ The literature offers innumerable instances of segiented earnings functions based on such alternative segmentation variables as race, sex, region, occupation, and industry. - 1/ Bourguignon sees less segmentation in his evidence than do Souza and Tokman and Mazumdar and Ahmed in theirs. In reading these studies one should be careful to note that the'criteria for establishing the existence of segmentation differ from one study to the next. 2/ In their analysis of housing market segmentation, Schnare and Struyk (1976) look at a sample of housing units in the Boston metropolitan area and at various sub-samples defined according to the number of rooms in the house, whether the house is located in an inner or outer suburb, and income. They find that there are statistically significant differences in the effects of various attributes on rent depending on the housing market in question. However, they also note that there is little gain in precision (as measured by the standard error of estimate) when the housing market is stratified by the above-mentioned variables. From this, they conclude that the Boston housing market is not particularly segmented, at least across the range cf variables with which they deal. 3/ If the earnings function is a single equation, the appropriate test of equality of regression coefficients is the Chow test described in standard econometrics text, e.g., Johnston (1972). If the earnings model is a multi-equation recursive structure and fits the path-analytical modal of sociologists, the test for the system of equations is given by Specht and Warren (1976). / See Fields and Ducci (forthcoming) for a review of this literature for less developed countries as a whole. The Colombian studies are cited below in Part III. -13- These approaches might be criticized at several different levels. At this point, I will mention just two of them. One argument is an empirical problem. Some critics would contend that the <included variables (years of schooling and age) fail to capture other important human capital characteristics such as quality of schooling, continuity of experience, extent of on-the-job training, and such personal characteristics as intelligence and motivation. Without statistical controls for these other influences, the possibility remains that workers in the better occupations or industries possess superior human capital which is reflected in their earnings. The missing variables argument clearly contains considerable truth but it can be pushed to the point of nonsense. Those human capital theorists who disbelieve segmentation arguments sometimes go so far as to attribute all of the unexplained earnings differentials to these omitted characteristics. That will not do. It is about as appealing as "explaining" differences in consumer behaviour by a specified but unmeasured list of "taste" differences in utility functions. The second objection is fundamental. Take occupation and industry as examples of segmentation variables. If occupation or industry is significantly related to income after controlling for personal characteristics, or if different earnings functions are found in different occupations or industries, segmentation is said to exist. A severe interpretation problem arises: Does the test of segmentation "prove" segmentation? If it is established that "segmentation" exists by Definition (ii), what does it imply about the functioning of labor markets? Who are the segmenters? How do they segment the market? Is not the same regression -14- result consistent with both benign and malevolent interpretations?- The observation that seemingly comparable workers earn more in some employment sectors than in others is consistent with discrimination, screening, and other exclusionary practices; it is also consistent with intersectoral differences in unmeasured working conditions, unmeasured differences among workers in productivity-related characteristics, and heterogeneity in workers' preferences. We have a classic identification problem. The "test" of the phenomenon under study is not a sufficient test--it is a necessary test of a pare cular kind of segmentation. Definition (ii) is framed in terms of a symptom which may or may not reflect an underlying pathology: discriminatory barriers to 1/ Here again, the parallel between the labor market and housing market segmentation literatures may offer insights. Just as Schnare and Struyck sought to claim from evidence of different hedonic prices of housing attributes in different markets that the housing market is segmented, many labor market analysts seek to claim that the labor market is segmented insofar as people in different labor force groups receive different gains in income for each additional year of education depending on their occupation or industry. But in Schnare and Struyk's analysis, and in others to which they refer, no attempt was made to explain why it is that people live in housing markets with higher hedonic prices. If, in fact, land is cheaper in Waltham, or if an extra bedroom costs less in Wellesley, why is this? Are there barriers to mobility? Or is the observed configuration an equilibrium one in the sense that people trade off number of rooms for number of acres? Whether the observed pattern can meaningfully be said to reflect segmentation or not depends on why these differences in prices of land and prices of rooms arise. The same holds for labor market segmentation. The critical questions are why there are different wage structures in some occupations or industries as compared with others and why people work in the particular occupations or industries that they do. The mere finding of differences is not sufficient to establish discrimination against some and in favor of others. -15- entry into the higher-paying occupations or industries. Besides studying differences in rewards among various groups in the labor market, we thus need to examine differences in access to earnings opportunities. This suggests: Definition (iii): Heterogeneity of labor market functioning in various submarkets. Edwards, Reich, and Gordon (1975) write: The labor market consists of those institutions which mediate, affect, or determine the purchase and sale of labor power; the labor process consists of the organization and conditioning of the activity of production itself, i.e., the consumption of labor power by the capitalists. Segmentation occurs when the labor market or labor process is divided into separate sub- markets or subprocesses or segments, distinguished by different characteristics, behavioral rules, and working conditions. (Emphasis in the original) (p. xi) This definition has been used in effect by many writers including dualists such as Doeringer and Piore (1971), Bluestone (1970) and Harrison (1972) and radicals such as Wachtel and Betsey (1972) and Bowles and Gintis (1975). This definition of labor market segmentation has the virtue of focusing on the functioning of labor markets; its limitation is that by itself it does not explain why the submarkets or subprocesses are heterogeneous. Economists suggest many reasons why submarkets might differ: heterogeneity among workers, non-competing groups in the labor force, different non-monetary satisfactions received in different jobs, monopsony elements in the labor market, monopoly elements in the product market, limited and costly information,, limited and costly mobility, and institutional regidities and regulations. Any of these real world deviations from the simple textbook model of labor markets would result in non-uniform labor market processes and unequal outcomes. -16- While such occurrences suggest the existence of labor market segmentation, we must ask why segments differ. Indeed, segmentation theorists would have us believe that labor markets function in particularly restrictive ways, i.e., that some individuals are prevented from entering a preferred occupation, moving to a higher paying location, acquiring further education and training, or in some other way improving their economic position. This suggests another, more specific definition: Definition (iv). Limited access to good jobs. A "good job"might be characterized by security, highi wages, safe and pleasant working conditions, andjor opportunities for training and advancement. When good jobs are limited in number, "the crux of any theory of labor market segmentation is the mechanism or institutional barriers which truncate competition by precluding mobility between the various labor market segments" (Flanagan 1973, p. 253). A particularly well-known segmentation theory is the dual labor market approach advanced by Doeringer and Piore (1971). As described by Wachter (1974), the dual labor market model advances four hypotheses: First, it is useful to dichotomize the economy into a primary and secondary sector, Second, the wage and employment mechanisms in the secondary sector are distinct from those in the primary sector. Third, economic mobility between these two sectors is sharply limited, and hence workers in the secondary sector are essentially trapped there. Finally, the secondary sector is marked by pervasive underemployment because workers who could be trained for skilled jobs at no more than the usual cost are confined to unskilled jobs. (p. 639). The critical question that still remains, however, is what limits mobility from the secondary to the primary sector. Since good jobs are not available for all, they must be rationed. This suggests another possible definition: -17- Definition (v): Non-random access to the available jobs. This definition is used in effect whenever one looks at the proportions of workers from particular groups (e.g., racial, sex, regional) who work in different kinds of jobs. Definition (v) differs from Definition (iv) in that it is concerned not just with different outcomes but with systematically different opportunities; it also takes as. given that good jobs are limited in number. Definition (v) concentrates our attention on the rules by which the limited jobs are rationed. If the rationing is found to be at least partly systematic we may then examine why some groups of workers and not others have access to certain jobs. Even now, I worry about using Definition (v) and calling the result "labor market segmentation." In an LDC, good jobs are scarce and must be allocated among would-be employees. What if differences in access among groups of workers are purely productivity based? Partly productivity-based? Not productivity-based at all. Should all non-random rationing of good jobs be considered segmentation? We have come to the same identification problem as before: the same phenomenon (non-random job access may result from varying causes, some discriminatory, some not).Regardless of whether we term the outcome segmentation or not, we have reached another researchable question: what labor market practices determine which groups get the available jobs? Taken togeter Definitions (ii) and (v) are the most helpful concepts of labor market segmentation yet devised because they -18- direct our attention toward the actual wage- and employment- determination mechanisms in labor markets. They take the first step toward explaining why intergroup labor market differentials exist by showing that intergroup labor market differentials exist in particular dimensions. This focus on real world labor market functioning, as distinct from knee-jerk applications of stylized textbook models, explains much of the appeal of theories of segmented labor markets. Segmentation theorists address fundamental questions about the operation of the labor market and of the economic system more generally. Why do some persons have better opportunities than others? Why is discrimination in the economic system perpetuated? Why is poverty transmitted across generations? Why do labor movements in many countries accept the legitimacy of the prevailing economic order? These and other root questions about the operation of labor markets have not received much attention among orthodox economists. As Gordon (1972) writes (p. 14): "Orthodox analysis... tended to take market structure for granted and probe the determinants of behaviour within those given structures. Some economists sought to develop economic models which dealt directly with these basic concerns about the relationship between labor market structure and income." This suggests that the heart of the distinction between orthodox theories of labor markets and segmentation theories may well lie in the nature of the questions that they address rather than in the way of conceptualizing the behavior of individuals and firms. -19- C. The Framework for Modelling a Segmented Labor Market The preceding definitions of labor market segmentation direct our attention to the determinants of income and sector of employment as functions of other individual and environmental characteristics. To estimate the relationship among these variables in Bogota, we require a model of how the labor market might be segmented. Eight alternative models are presented in Table 1. They employ the following notation: Y = Income of the Individual PERSCHAR = A vector of personal characteristics (e.g., education, age, migrant status, sex) JOBCHAR = A vector of job characteristics (e.g., occupation, industry) x = Other exogenous variables = Error term. -20- TABLE 1. Eight Models of a Segrented Labor Market Model Number iMdel Fom of and Name Descripticn Model Mbdel 1. Single Equation Incme as a linear Y = a +B PERSCHAR Structural Estimation., cabination of + y JOBCR + E. Linear Specification, personal and job Full Sarple. characteristics. :tdel 2. Single Equation Incame as a Y. =-. + S. PERSC-R Structural Estimation, linear conbination + 1 1 Linear Specification, of a subset of + J' Exogenous personal and job Separate equations Subsam-ples. characteristics, for various sub- other exogencus samples i. personal characteristics stratified for (e.g., sex). Mdel 3. Single Equation Incore as a Y = a + 6 PERSCHAR Structural Estimation, non-linear ca-,bination * JOBCHAR + e. Interactive Specification. of personal and job characteristics. Model 4. Single Equation Incae as a Y = a + a PERSCRAR Reduced Form Estiraticn. functicn of personal + E. characteristics only. odel 5. Multi-Equaticn Job as a function JOB = a1 1 PEISCHAR Recursive Structure, of personal charac- + E Independent Errors. teristics; incae as 1 a functicn of job Y = a2 2 PEPSCAR and personal + B + I characteristics; 2 2 errors in the COV(E E 2) = 0. two equations independent. Mbdel 6. Multi-Ecq,uaticn Like Model 5 JOB = 1 + PEPSCIAR Recursive Structure, except errors + Dependent Errors. in the two equations are Y =a2 2 PERCHAR dependent. + 2 JOBCLAR + E2 DV(lfE 2) 0. Continued on next page -21- TABLE 1. continued Eight Nbdels of a Seqmented Labor Market model Number Model Form of and Na:e Description Model Model 7. Multi-Equation One set of Y = a + . PERSCHAR Structure Stratified equations determining + 1 1 by JCB. incam within jch f groupings.(e.g., JOB = n + ex + c. occupations); a second set of equaticms determining job grouping. Model 8. Multi-Equation One set of equations Y = a. + 5. PERSCHAR Structure Stratified by determining income + E for NCOE. within an income I grouping (e.g., poor INC GROUP i; versus non-poor); a INCOME GROUP = second set of n + 6 PERSCHRR equations determining + . ince grouping. -22- The choice among these alternative models must be determined by two kinds of considerations: the characteristics of the labor market under investigation, and econometric theory. To model the Bogota labor market, I conceptualize the interrelationships among income, occupation and industry, place of residence, and personal characeristics in the following ways: (i) For workers of either sex, income depends directly on education, age, migrant status, industry, occupation, and residential sector. (ii) Given a choice between two industries or occupations with different average rates of pay, individuals tend to choose the higher-paying one. (iii) The likelihood of being offered a job in a high-paying industry or occupation is a function of the individual's personal characteristics and sector of residence. (iv) Within an occupation or industry, incomes vary with education, age, and migrant status. (v) The sector of residence is affected by income (i.e., a higher income tends to lead to residence in a high-income sector) and by education, age, and migrant status. (vi) The individual's education, age, and migrant status are exogenous. (vii) The average income in an industry or occupation is exogenous. These seven propositions should be regarded as informed hypotheses; some are dubious and are included for purposes of -23- completeness. In particular, one concern of the Bogota City Study is to test for possible spatial effacts on economic status. Thus, Propositions (i) and (iii) allow for a direct role for residential location in determining income, industry, and occupation. In addition, although it is hypothesized that migrant status has both a direct role and an indirect role via occupation and industry, recent re- search findings by Jaramillo (1979) suggest that these effects may be insignificant. The blocks in figure 1, depicting Bogota's labor market, indicate factors which are treated identically in the econometric estimation, where: Y = Individual's income EDUC = Individual's education AGE = Individual's age MIG = Individual's migrant status Y IND Average incomes in each of M industries for individuals like i Y OCCUP Average incomes in each of N occupations for individuals like i IND = An M-dimensional vector of industries, one of which employs the individual OCCUP = An N-dimensional vector of occupations, one of which employs the individual SECTOR = A P-dimensional vector of residential locations, in one of which the individual lives. Arrows depict causal structure. Lower case Roman numerals show how each proposition listed above enters the model. -24- Figure 1 CAUSAL ORDERING OF MODEL OF BOGOTA LABOR MARKET Exogenous Exogenous by (vi) JEDUC AGE MIG IND OCCUP by (vii) IND1~ OCCUJP SETR (i) (iv) ( ) The causal ordering illustrated in Figure 1 makes clear that there are four simultaneous equations and four endogenous variables: (1) Y = f (EDUC, AGE, MIG, IND, OCCUP, SECTOR) (2) IND = g (EDUC, AGE, MIG, SECTOR, YIND) (3) OCCUP = h (EDUC, AGE, MIG, SECTOR, YOCCUP) (4) SECTOR - i (EDUC, AGE, MIG, Y) Let us now address the specific functional forms and stochastic specifications for econometric formulation. D. Econometric Issues Despite a plethora of empirical studies of wage determination -25- and despite many attempts t'o justify the particular econometric models used, no procedure is fully satisfactory for estimating the inter- relationships among employer, worker, and residential characteristics as given by the structure in Figure 1. Hence, one cannot simply borrow an established prodedure developed in some other context and apply it to Bogota. Rather, in the attempt to better understand the workings of the Bogota labor market so as to identify possible sources of labor market segmentation, a theoretically appropriate econometric framework must be developed within the context of available data sets. Hence, the task of this section is to articulate an econometric procedure designed specifically with the economic framework of Section C in mind. Needed modifications of such a framework will also be considered in light of computational intractability. We must begin by rigorously formulating an estimable model derived from equations (1) - (4) of Section C. Linearizing the respective equations, adding a quadratic in age to allow for non- linearities, and recognizing error terms, we have: (')Y = a + b11 EDUC + b12 AGE + b13 AGESQ + b14 MIG + b15 IND + bl6 OCCUP + bl7 SECTOR + e ; (2') IND = a2+ b21 EDUC + b22 AGE + b23 AGESQ + b24 MIG + b25 SECTOR + b 2 ijk e ; 262 IND (3') 00CUP = a3 + b31 EDUC + b32 AGE + b33 AGESQ + b34 MIG + b35 SECTOR + b jk + e* 36 OCCUP 3 (4') SECTOR = a4 + b41 EDUC + b42 AGE + b43 AGESQ + b43 MIG + b45 Y + e . -26- Sex does not appear since it is a stratifying variable. Lines over ij k the IND, OCCUP, and SECTOR variables denote vectors; Y denotes the average income in the industry or occupation in question for persons with education i, age j, and migrant status k. The model given by equations (1')-(4') is fully simultaneous, in that each endogenous variable appears both as a dependent variable and as an independent variable. As with all simultaneous equations systems, identification must be verified before considering estimation. Checking excluded variables restrictions, once it is recognized that ijk the Y variables are vectors, it may be seen that identification requirements are met (i.e., the number of excluded exogenous variables far exceeds the number of included variables in every equation). Having established identification, we may proceed to estimiation. An appropriate estimation procedure must consider the structural form of the system. The choice of an estimation technique depends partly on the stochastic specification of the model. Because of the presence of endogenous variables on the right hand side of each equation, the expectad covarianct's among the various error terms are non-zero. Hence, Ordinary Least Squares (OLS) cannot be used on the model as presently formulated since biased estimates would result. Simultaneous equations methods must therefore be employed. We must also note that the model contains both continuous and discrete variables (i.e., variables with a limited number of possible values, which in our model are are industry, occupation, and secDr of residence). Although procedures for estimating such systems have been developed in the last few years by Schmidt and Strauss (1975), Olsen (1978), and Heckman (1978), computer -27- programs to execute them are not generally available. A compromise must therefore be made. The primary concern in my part of the Bogota City Study is to consider segmenting variables. Given this concern, the sector of residence is the least important of the endogenous variables in the system given by equations (1')-(4'). Sector of residence is also the most troublesome both conceptually and econometrically. The economic relationships between intra-urban location and economic outcome are treated at much greater length in the paper written for the Bogota City Study by Mohan (1979). For all these reasons, relationships involving the intra-city location variable are the leading candidates for modification. Sector of residence enters the model given by equations (l')-((4') in two ways: as a determinant of economic position (opportunities may depend upon place of residence), and as an outcome of economic position (higher income workers can afford to live in better places). From my own experience in Bogota I would suggest that the latter relation- ship is much the more important one. 1/ If we regard sector of residence as a relatively unimportant determinant of income, industry, and occupation, a facilitating assumption is that those effects are absent entirely. That assumption produces a recursive model structure shown below: education, age, and migrant status determine industry and occupation; industry and occupation along with the aforementioned variables determine income; income and the aforementioned variables determine sector of residence. 1/ Mohan also regards this as important: "It may be hypothesized that people in the poorer sectors have lower expectations of improvement (in income) over time: indeed they probably move to the richer sectors (of the city) if they do gain in income." -28- The recursive structure is illustrated as follows: Denoting the exogenous variables by X's, we have: IND - f (x); OCC = f2 Y = f3(IND, OCC, X); SECTOR = f4(Y,X). Simplifying further and denoting the endogenous variables by E, the model becomes, in matrix notation, r'E + 'X + S=0. Formally, a recursive model has a triangular r matrix. This model is quite tractable. Hence the econometric work below makes use of the recursive structure: -ij k (21") IND = a + b EDUC + b AGE + b AGESQ + b MIG + b Y I + e 2 21 22 23 24 26 IND 2; (3") OCCUP = a + b EDUC + b AGE + b AGESQ + b MIG + b 1j + e ; 3 31 32 33 34 36 OCC 3 (1") Y = a + b11 EDUC + b12 AGE + b13 AGESQ + b14 MIG + b5 ND+b16 OCCUP + el (4") SECTOR = a + b41 EDUC + b42 AGE + b43 AGESQ + b MIG + b45 Y + e . (The reordering reflects the recursive pattern.) To develop estimation procedures, we must look again at the stochastic specification. Suppose the e's have the property of contemporaneous independence, i.e., the error in one equation provides no information in predicting the value of the dependent variable in -29- some other equation. Such a recursive structure is particularly convenient: a recursive model with this property carn be estimated efficiently and without bias by OLS equation-by-equation.-/ Assuming that the error terms are independent of one atiother, however, does not seem appropriate. Consider the case of an individual who, because of exceptional luck or family background, has a higher-than-expected occupation/industrial position and higher-than-expected income as compared with others less fortunate or well-connected; in the econometric estimation, the errors in various equations for that person would be positively correlated. Likewise, an exceptionally poorly-motivated worker might only be offered poorer jobs(in terms of income, occupation, and industry) than that individual's education, experience, sex, and migrant status might otherwise have warranted; such deviations are also positively correlated. How is the model to be estimated taking account of cor- relation in the error terms? For the occupation and industry equations, discrete data techniques such as logit are in order.2/ 1/ A standard source for this result is Goldberger (1964, p. 355). Ai unfortunate confusion arises since some econometrics books define a recursive structure as one with just a succession of variables ("triangular") while others also require "contemporaneous independence." Only in models with both properties is efficient, unbiased estimation obtained by OLS. 2/ Logit is a procedure for estimating the effect of a set of exogenous variables on a dependent variable with a limited number of outcomes. A good introduction to the technique is presented by McFadden (1976). The pioneering application of logit to occupatlonal outcomes is in a paper by Boskin (1974) using U.S. data. See also Schmidt and Strauss (1975), and Brown, Moon, and Zoloth (forthcoming). -30- Efficient estimates of the effect of the determinants of income may be obtained if the actual occupation and industry are replaced by values uncorrelated with the error, e.g., OCCUP and IND from equations (2") and (3").1 In short, the procedure is to use logit to estimate the mechanism assigning individuals to industries or occupations, and then to use the derived values to explain income by personal and employment characteristics. The final step in estimating the full model would be to use the predicted value of income from equation (1") as an instrument for actual income in equation (4") to predict sector of residence. Another procedure which is econometrically justified is reduced form estimation, which requires that the endogenous variables be dropped from direct consideration. The reduced form of the income equation (1") is: (1"') Y = al + b11 EDUC + bl2 AGE + bl3 AGESQ + bl4 MIG + el from which occupation and industry are excluded. This corresponds to Model 4 in Table 1. Reduced form estimation is incomplete in the sense that it does not distinguish the effects of occupation and industry on income; it is corrent, however, in that the procedure does not bias the estimates of the effects of education and obher exogenous variables on income. 1/ See Goldberger (1964, pp. 355-6) -31- III. STATISTICAL AND ECONOMETRIC TESTS The statistical and econometric work for Bogota is based on a sample of more than 66,000 persons, derived from the 1973 Census of Population. 1/ Persons over the age of 12 who reported that they had worked in the week preceding the Census and those who did not work but who had a job in that week were defined as workers. This group includes more than just wage and salary employees. The variables used in the study are defined as follows: LOGY = Logarithm (natural) of worker's monthly income in pesos. EDUC = Coded into five categories: None; primary (some or all); secondary (some or all); higher (some or all); some education, level not ascertained. AGE = In years. SEX = Male or female. MIG = "Migrant," defined as an individual born o;.side Bogota. INDUSTRY = Coded into six categories: manufacturing; agriculture and mining; construction; commerce; services; other. OCCUPATION = Coded into seven categories: professional, technical and managerial; clerical; sales; production; construction and transport; services; other. SECTOR OF = Divided into 8 sectors: see Figure 2. RESIDENCE 1/ The sample of workers and the definitions of the several variables are as in Mohan (1979). The regression results reported below exclude from the sample zero-income workers, i.e., those individuals who reported themselves as having a job but who did not have income. -32- FIGцRE 2 i � _�,,,_,,, . . ' • � . �, . • ВОССТАк 5ёссот �5раст Еааед сд 1Я71 Coaцnas � ' ' ' ' ' " " ' • . . • � , . 4L а5 � 9� ' • • `'J' sч• . . ' • . . 5у • 54 � . 53 • �г� � 5L �з • 55 "� , 73 • �- �3 51 . . � � 7г вz . • . • . � cs �z Ti � � ЧЗ �� д�� ыi • ' г �� 3� �ЧS ` . . ,� ц� ч� в . 29 xj z; Z = 2 . 2Z � 11 . г� � 2S ' �3 . � -33- A. Basic Tabulations and Cross-Tabulations I/ This section presents tabular evidence on income differentials among workers with different personal characteristics in different kinds of jobs and on the numbers of workers with different charac - teristics found in each job category. I would hardly claim to be the first to report such differentials. The earlier sources include studies by Prieto (1971), Isaza and Ortega (1971), Berry and Urrutia (1976), Mus rove (1978), and Mohan '(1979) among others. I first present a simple table giving average incomes of workers in Bogota by various characteristics. That is followed by twelve cross-tabulations which examine interactions among these characteristics, along with a short discussion of each. Each crous-tabulation includes a cell count, the average income among workers in that cell, and row and column percentages. As a guide to what follows, the order in which the variables are included in the various cross-tabulations is: TABLE NLMBER CORRESPONDING TO CROSSTABULATION Characteristic Characteristic Sex Age Migrant Education Status Occupation 3 6 9 12 Industry 4 7 10 13 Sector of Residence 5 8 11 14 l/ All tabulations are based on weighted data, the weights adjusting Tor varying sampling ratios in various neighborhoods (comunas) of the city. -34- Some of the more interesting questions concerning the various patterns and the empirical answers to those questions are highlighted for easy reference. Further results from multivariate analysis are presented in later sections. To anticipate the results, the main conclusion from this section is: If "labor market segmentation" is defined as "inequality of outcomes" (Definition (i), then the Bogota labor market is segmented. However, since this is not a satisfactory definition of segmentation, the proposition that the Bogota labor market is segmented awaits more sophisticated formulations and tests. 1. Question: How do incomes of workers in Bogota vary by sex, age, education, migrant status, occupation, industry, and sector of residence in the city? (Table 2) The evidence shows: 1. Men earn more than women; 2. Income rises with age in the cross section until the age category 45-54, at which point incomes are two-thirds higher than average; 3. Income increases with education, so that workers with higher education earn more than eleven times as much as the uneducated; 4. Migrants to Bogota on average earn about 15% less than workers who were born there; 5. Occupation is associated with income, e.g., administrators and managers have incomes five times as high as the average, while maids earn only one-fourth of the average; -35- 6. Industry is associated with income, e. g., workers in finance, public instruction, and mining industries earn about twice the average income, while workers in personal and domestic service earn one-fourth the average; 7. Average income is four times greater in the highest income sector than in the lowest income sector. -36- TABLE 2 EAN INCMES OF WOERS IN BOGOTA BY VARICUS CHARACTERISTICS, 1973 (1973 pesos per month) Sex Males 2159 Females 1027 Both sexes (1775) 12-14 270 15-24 929 25-34 1865 35-44 2436 45-54 2897 55-64 2837 65 & over 2604 All ages (1775) Educaticn Ncne 604 Primary 984 Seczdary 2158 Higher 7083 All education groups (1775) .Migrant Status Migrant 1699 Native 2007 Both grous (1775) Occupation Professional & technical 4990 Adin & manager 8827 Clerk & typist 1962' Sales Manag., proprietor 3020 other sales 1642 Service work, not maid 1109 Maid 373 Agriculture 2715 Prod. supervisors 1205 Prod. workers 1182 Constructicn workers 966 Transport workers 1389 Other 701 All occupation (1790) Industry Agriculture 3869 Mining 4056 Food prod., bev., tobacco 1545 Textiles & footwear 1318 Lamber & wood 1308 Paper, printing, publishing 1900 Mineral prod. 1492 Chem & petrcchem -continued- 2497 -37- TABLE 2 Continued Industry Continued Metal ind 1827 Other ind 1857 Utilities 2487 Construction 1277 Wholesale trade 3421 Retail trade 2115 Other camerce 3169 Trans & ccanunication 2578 Financial est 4638 Public adm., soc serv 3247 Public instruction 3684 Personal & damestic service 577 All industries (1999) Sector of the City Sector 1 1499 Sector 2 1066 Sector 3 1327 Sector 4 1536 Sector 5 1659 Sector 6 1530 Sector 7 2638 Sector 8 3940 All sectors (1775) Note: Overall averages differ accross characteristics because of differential non-reporting. -38- The simple tabulations may be open to misinterpretation. For example, in itself, the finding that natives of Bogota earn more than migrants might be considered evidence that migrant workers are dis- advantaged in the Bogota labor force. Migrants are disproportionately young, however, and young workers have lower-paying occupations more often than prime age workers do, on average. It is therefore possible that migrants and natives earn the same within occupations but that the occupational mix differs for the two groups. If the occupational mix does differ, it may be because of age differences between the migrant and native populations or for some other reason. The question here is whether comparable workers receive different incomes in Bogota depending on whether they are migrants or natives, a question that cannot be answered by simple tabulations. Multivariate questions like this require finer breakdowns, which now follow. 2. Question: Do men earn more than women in Bogota because: a) men are disproportionately in higher-paying occupations? b) men earn more within any given occupation? or c) both? [Table 31 Answer; Both, with more weight to the latter. As Table 3 demonstrates, men in Bogota earn more than twice as much as women on average. Part of this difference is due to the fact that men are more likely to be administrators and managers, production workers, construction workers, and transport workers, while women are much more likely to be service workers, maids, and clerks and typists. Since administrators and managers and clerks and typists receive above average incomes, the occupational mix by sex does not clearly -39- TABLE 3. CROSS-TABUIATICN: OCCUPATION BY SEX SMAtE FEMALE TOTAL : .6226.1 17785.1: 540li.2 COUNT PROFESS 67.07 32.13 :00.00 PROW &ITECH: 8.33 8.01 8.22 PCOL : 6033.5 : 2730.5 4989.1 MEAN, INCOME : 9268.1: 1055.9: 10324.0 COUN AOMIN 6 : 89.77 10.23 : t00.00 PROW MANAGER : 2.13 0.48 : 1.57 PCOL : 9412.2 : 3592.1 : 8827.2 MEAN, INCOME : 44G39.0: 3933.2: 83472.2 CCUNT CLERK & : 53.48 : 46.52 : 100.00 PROw TIPISTS : 10.26 : 17.49 : 12.70 PCOL 2137.9 : 1760.6 i 1962.4 MEAN. INCOME SALES MA: 23983.7: 8482.1: 424G5.8 COUNT NAG,PROP: 80.03 : 100.00 PROw RIETOR : 7.8 : 3.82 : 6,4G PCOL : 3339.1 : 1740.9 3019.9 MEAN, INCOME : 37445.1: 175t6.2: 50Gt.l COUNT OTHER 68.13 : 31.7 : 10O.Co PROW SALES : 8.61 : 7.89 8.3G PCOL : 2062.5 : 742.9 : 1642.2 MEAN. INCOME ******************.....***.*****..* SERV : 30722.2: 31311.1: 62033.3 COUNT VORK-AOT: A9.53 : 50,47 : 100.00 PROW MAID : 7.06 : 1410 : 9.44 PCOL 14t7.1 : 806.9 :109.1 MEAN, INCOME : 2009.8: G6076.4: 610OGG.1 COUNT MAIDS 3.03 : 9Q.97 : 100.00 PROW 0.48 : 30.16 : 10.51 PCOL : 564.0 : 3G6.9 : 372.8 MEAN, INCOME : 8313.9, 440.2: 762.1 COUNT AGRICULT: 54.88 : S.12 : 100.00 PROW URE : 1.91 :, 0.20 : 1.32 PCOL : 2698.4 : 2031.8 : 2715.4 MEAN, INCOME q : 22200.7: 7730.0: 20020.7 COUNT PROD SUP: 74.17 : 25.83 : 100.00 PROW ERVISORS: 5.to : 3.48 : 4.55 PCOL : 1328.0 : 853.1 : 1205.3 MEAN. INCOME --*-*--------*-------**---**** :119080.G: 31397.5:149473.3 COUNT PROD : 79.00 : 21.00 : t00.C0 PROW WORKERS 27.14 : 14.14 : 22.74 PCOL 1281.1 : 808.9 : 181.9 MEAN. INCOME CONSTRUC: 46929.2: 258.1: 47187.4 COUNT T WORKER: 90.45 : 0.55 : oo.00 Pqcw 5 : o.78 : 0.12 : 7.19 PCOL 968.6 : 486.a : SGF.0 MEAt, INCOME TRANSPOR: 26258.1: 118.l: 36376.2 COUNT T WORKER: 99.68 : 0.22 : t00.00 PROW S : 8.33 :.532E01: 5.53 PCOL 1388.5 : 1407.9 : l3na0.6 MEAN. INCOME : 8983.6: 156.2: 9139.8 COUNT OTHER : 98.21 : 1.71 : 00.00 PROW 2.06 :.703E*01: 1.39 PCOL 702.7 : 597.4 : 700.9 MEAN. INCOME :435140.4:222068.2:657209.6 COUNT TOTAL : 66,21 : 33.79 : 100.00 PROW 100.03 : 100.00 100.00 PCOL 2169.7 : 1046.3': 1790.1 MEAN, INCOME *-**-***-*-*-*-*---**-***--*-*--**- 59213.7: 31570.8: 90784.5 COUNT NO INFO : G5.22 : 34.78 : 100.00 PROW 0 : 8 0 : 1 . M OPCOL :2082.0 :887.0 :1666.4 MEAN, INCO14E ****.****...*****.*********.*.*.*.* -40- favor men. Hence, differences between men and women in occupational distribution do not account for the bulk of the difference in average income. 1/ It appears rather that income disparities by sex within these occupational groups m%ist therefore account for the overall differential. For most occupational groups (except for clerks and typists, transport workers, and agricultural workers who comprise 20% of the labor force) men's earnings are at least 50% higher than women's. 3. Question: Do men earn more than women in Bogota because: a) men are disproportionately in higher-paying industries? b) men earn more within any given industry? or c) both? [Table 4] Answer: Both, with substantial weight to each. Men in Bogota do, in fact, work disproportionately more in the higher income industries. The five highest-paying industries shown in table 4, are finance (mean income = 5,634), mining (4,056), agriculture (3,869), public instruction (3,684), and wholesale trade (3,421), compared with an average income of 1,999. The proportions of men in these five industries are 70%, 91%, 90%, 42%, and 73%, respectively, as compared with 64% of miaen in the Bogota labor force overall. Public instruction is the in'ly high-paying industry with a 1/ Unlike the United States, where sex segregation is widely claimed as the explanation for male-female income differences. See Kahne (1975) and Lloyd (1975) for extensive bibliographie8 -41- TABLE 4. . о�ss-��ит.�Тгсчv : гrтиsТг�г ЧцЕ . �E�+IE т04а� � , •�----'----бп7�с� � то� r� »es�1 с0ииг ВУ SEX ' аеагSи�та в0 е7 : 1о, п: Ят,с0 гя�ч UAE 7.]Т : O.aR : t.T9 РМПI а�оть.б : 7а76.е : 7!со,о ы�пи, iнсонЕ ................................... 19ав.2: гч7.5: :и2.г СОи!1г MIиtN+ ; 90.95 : 9,05 : 1QO.G'Р pGU': + о,57 : o,rz : о,,Т Pc7t, : дрб0.2 : �O1�.S : a0�G,0� нЕаИ, $иСоlАЕ . .a......... ........................ � ГООо : 159а7.д: ба77,Т: 1]78t.g COUNf • vae�,вcv: тг,ав : 2Т,в2 : го0,ов Раое тааас . s.вг ; �,�в : s,rT гса� : 1т52.0 : ч1т.б : 10.®.s rЕан. lнсаяЕ •-•--°......•-•--.°....-^--.�... тtxit�ES: 2�агб.s: 2ar6e.3: авзз7.о соият 5 aB,Et : 51, г9 : 1CET,i70 рцац f0�1r[аа: д,I7 : tS,7] : 10.59 DCOL � : Ят7в.4 : 975,9 : 171т,7 , нЕли, 1иСайЕ •••••••••-121tt.<:• •Т70�,3:•17.1а0,9 С^.uNf Wьв[R е: 7а.ц : S.ев : 100.1Ю paUY .. raoo : в.7в : о..в : г.вв vcu� : f]25.2 : д.о.б : иQв,г иЕаи, iис0нЕ ................................... ИР(R, : Тl6S.3: Z975,3: 1оЯао,7 COUMT гаtиrlие: Т7,в1 : гТ.ге : гаечоо Pr+or /V5N SH : 7,7в : 1,бА ; t.92 PCOL � : 77+9.в : 10�].1 : 18в9,в MEaN, lгканЕ """""'"' °"" •"""°"".... Sгв9.9: га7J.ве 62г7.7 СОи:1т . krNEaa` : в7.73 : 17.:5 : 100.СО Rд0У � Раао 1,Т7 : 0.6f. : 1.3а Pf.Ot, s 1в7+.Т : вsг,в : t�s1,r wЕаи, iнсонЕ .......° .......................»... t1�uS 9з57.2: sRae.7: 1в��г4.9 c�vиt . СнЕV • i2.5o : 7Т,га : гW,GO � Ра0У вЕтаа 7.Sг : 7.в� : 3,]0 pcoL � : 070г.7 : Т70+,7 : га96,> нЕаю, iиСаиЕ •.°'..••....... .............•°�^ • : ?�697, t: 76I6.7: Ze71E.'7 С7tlРГГ METaL e7./s : 12.бЯ : Яоа.с� paav lюustare в.ав : 2,2t : 5.гв Рсаи • • : 190l.� : 1Т68.3 : 4в7Т.1 ЧЕди, дИСОМ[ .... •". • .... ...... ...... "" `-"" • ; Ч179.1: 39вt,7: 99Т17.0 COUNT ОтнЕО • Т1,57 : Зд.�7 : fOa.OQ PFO� tAOU;taв: +.as : 7.+5 : в.Iб Рса<. : =1as,4 : 9112,в : tвS6,8 нЕви, iАгС0ИЕ .....°°..,. .......,..........°. •... s7ог.т. а+в.а: 7TZV.e соиит � ' uтttt вв,т7 : 1t.�p : го0.оо Рао� rtES 1. и: о.гв : о.еа рсои s 2!62.7 : 1вве,7 : 7�вв,♦ УЕаи, тгкоаЕ ` •••.••~ 's19в�.о:••1Явв�в:�37 юs.в саиит COиStaUC: 97.77 : 7.23 : 100,00 PROm , Т1аУ' ? 17.ав : о.Т♦ : 1t,T3 РСдL : 1:5в.7 : 16]t,5 : t1T6,9 нfаИ, 1ИСаюЕ ""•""""""""•""."• ""' в*�ЕЕ в91е.в: :аSБ.в: дов4.+ сг.иит . 1а1,Е tг,ss : п..ti : гао.са Paav тааоЕ 2,7в : 1.57 : 1,99 РСО� : ]Sвl,в : 91В3.а : 7в77.2 нЕди, 1ЧСдМЕ . ...""°"•' •' .............""""` : 7756�.]: 10Wa.S: 7аглв,l CoUNт atтatt : бв.зи : 71,1о : го0,си Paov тааоЕ в,г0 : 5.вв : т.ss PeoL - : 7379.1 : tOBT.B,s Л гS_7 ЧЕАИ, 2ИСОнЕ ..-•-•-•.�tг,�л в: •77,��в:•г�а1�.� спvит _ Вт1г[а 67.0т : 76,97 : 100,СО PaUV � СО�^1EkC[: в,]У : в,35 : а.аа PCOI : ыts.в : 11sв,а : 716е.в нЕаи, [иСаиЕ ' ........................"'."-"" � TRaиi 6: 15Т1г,т: 707'1,]: 17Тв7.1 COUNf Са�игиtС: де.Dв : tt,aв : г00.00 vд0� аТlОи l.а0 : t.26 : ].94 гСОL � v 26]1,0 : 7105.7 : 75ТТ.в нЕаи, INCCЯE """"•' ............. .."" • ""' : 1гвs9,в: sэ7е.в: +ете5.в с_.,:+т . ►1+ию. : 70.09 : iO.чS : го0.о0 гасv с Чl Е17: +.Тб : 7,бУ : �.]7 pCOL : вс]а,а : 77оУ,2 : а57Т,7 нЕаи. tHCRнE ................................... ►иаые : 15�е9,1: б�в1,э: г�Отг,1 ссиит , аоЧ.sое : тг.7э : гт.t+ 7 1оо.оа a�Ov SfaY 5.67 : 7,59 : 5,01 рсо1, : 7с9о.1 : 7195.] : ]2вГ,0 �Е�И, tNCOME .......................•'-" "-° " ►VNU С 1: 11797.Т: г�7а6.1: 7Gбв].в СОUиГ иsvоипl: ат.та : sт,п : ко,са vнnv 6N 7.9г : 9,а� ; 5.в4 рСОС ; 55г].Т : 7709.f : 7Gea.2 ЧСM1И, (?дС0ЯЕ , ................. "..._........ " " rccsN� а: п:77.ч; ье:п7.е: Тоsгб.т ссиит Oo�fSтie: 17.7} : е7.п5 : гО0.Сt1 P4UV lfav . в.:0 : �>.07 : 13.56 PcuL ' : 11Тв.1 : ]ПТ.S : 57G.H �Сли, [ИСо>•! "" ""........._........_....•... :г9107J.7:161571,2:в:25аа.3 CCVNr тОтвt . 4a.•,t : Jъ,г0 : ,4•�.а7 Раич � : гсО.СV : t00.t)и : �CJ.CV v401 - : S51t.p : 10G7.3 : t999.e �С:и, INCaиE •••••••-3г0777П.9С•9гП4Т•в•7q5�^9•б с^U9f Na fwC : ce,s2 : 71.п :,oo.w Рси+ . • . ' . • P�..71 , 14аб,5 ; 9i�,9 ; и70.0 >rt+N, IHьCw[ ............." " "'...."'•'•..... -� . . .,__..._ .... „ � _ .,..,.,.. ,...._..Т.,_ >__ ' • -42- below-average share of men. On the other hand, the lowest paying industry--personal and domestic service -- has just 17% male workers. In addition, men often earn twice as much as women within an industry. The two exceptions to this generalization are construction, where womenis average incomes are higher than men's, and mining, where incomes virtually are identical. In these cases, it is likely that the few women in the construction and mining industries are dis- proportionately in 'non-manual occupations, e.g., secretarial work, which are higher-paying. 4. Question: a) Do male workers' incomes vary by sector of residence? b) Do female workers'incomes vary by_sector of residence, and if so, how? c) Does the male-female income ratio vary by sector of residence,___and if so, how? (Table 5] Answers: Males' incomes, females' incomes, and the male-femal-e income ratio all are highest in the high income sectors . Not surprinsingly, the data in Table 5 indicate that both men and women who live in the high income sectors of Bogota earn more. Among males, the income ratio between Sector 8 and Sector 2 is more than five to one. Although women's incomes also vary by sector, intersectoral differences are smaller -- the average in Sector 8 is a 1*1ttle more than twice that in Sector 2. Male-female income ratios rise monotonically with sector income, as indicated below: .Sector Number Average Income Male-Female Income Ratio 2 1066 1.67 3 1327 1.71 1 1498 1.80 6 1530 1.88 4 1536 1.94 5 1659 1.97 7 2638 2.81 8 3940 4.45 -43- TABLE 5. CROSS-TABU.ATICN: SECIOR OF RESIDENCE BY SEX. : MALE : FEMALE TOTAL : t4'171. 8090. 22261. COUNT SECTOR 1: 63.66 36.34 : 100.00 PROW : 2.87 : 3.19 2.98 PCOL ; 1757.2 994.6 1499.1 MEAN. INCOME --**-***-****-*-****-----*-----*--* : 91005. : 36251. :127256. COUNT SECTOR 2: 71.5: 28.49 100.00 PROW : 18.41 14.29 : 17.01 PCOL : 1202.7 : 72t.4 1065.6 MEAN, INdOME .*...******...**.*.******.*..*****. :124757. : 52074. :176831. COUNT SECTOR 3: 70.55 29.45 100.00 PROW : 25.24 20.53 23.64 PCOL 1511.0 : 885.4 1326.8 MEAN, INCOMC : 48509. 22673. 7182. COUNT SECTOR 4: 68.15 : 3t.85 : 100.00 PROW : 9.31.: 8.94 9.52 PCOL : 1815.9 : 935.6 : 1535.5 MEAN. INCOME : 37251. 17929. : 54910. COUNT - SECTOR 5: 67.89 : 32.11 : 100.00 PROW 7.54 : 6.95 : 7.34 PCOL : 1971.2 : 997.8 1658.7 MEAN. INCOME : 85278. : 41371. :126648. COUNT SECTOR 6: 67.33 32.67 : 100.00 PROW 17.25 : 16.31 : 16.93 PCOL *1806.5S: 960.$ IS 130.0 MEAN. INCOMF : 60026. : 44006. :104032. COUNT SECTOR 7: 57.70 : 42.30 : 100.00 PROW * 12.14 : t7.35 13.91 PCOL : 3627.2 : 1289.2 : 2638.2 MEAN. INCOME --*-*----*-*----*-----*--*--------* : 23327. : 31545. 64872. COUNT SECTOR 8: 51.37 : 48.63 : 100.00 PROW 6.74 : 12.44 : 8.67 PCOL 6324.6 : 1420.3 : 3939.9 MEAN, INCOME ...e.e.om...e.....**.......*....... :494354. :253639. :747993. COUNT TOTAL : 66.09 : 33.91 : 100.00 PROW : 100.00 : 100.00 : 100.00 PCOL : 2159.2 : 1026.5 : 1775.1 MEAN, INCOME -44- This rising differential has at least two explanations: women in high-income families are more selective about the kind of work they are willing to perform, and low income females often work as maids in high-income neighborhoods. This is reflected in the dis- proportionately large percentages of females in theligh income sectors. 5. and 6. Questions: a) How do the occupational and and industrial distributions differ by age? b) Does income increase more with age in some occupations and industries than in others? [Tables 6 and 7] Answers: a) Young workers are wore at the Entremes. b) Yes, larger gains in the better occupations, less pronounced patterns by industry. The most noticeable difference in occupational distributions by age, shown in Table 6, is that younger workers are found dis- proportionately at the extremes of the distribution. On the one hand, we see that 34% of the workers in Bogota are between 15 and 24 years old, and 51% of the maids are that age group. On the other hand, while 31% of the workers are between the ages of 25 and 34, that age group comprises 40% of professional and technical workers, 34% of administrators and managers, 35% of production supervisors, and 41% of transport workers. Similar patterns occur by industry. Concerning the question of income gains with age within occupations or industries, differences are apparent. In the cross section, the peak income for professional and technical workers is four times higher than starting incomes, and other high.level -45- TABLE 6. CRCSS-TABULATICN: OCCUPATICN BY AGE :(12.14) :(15.24) :(25,34) :(5.44),:(45.54) :(5.64) :(65,99) TOTAL 65.6: 11274.5: 2530.3: f1f45.0: 6554.5: 2521.0: gt9.5: 54011.2 COUNT PROFESS 0.12 : 20.07 : 39.85 : 20.63 12.14 : 4.67 1.70 : 100.00 PROW 6 TECH 0.60 : 5.0 10.51 9.34 10.06 9.72 : 10.96 8.22 PCOL : 1288.67: 2093.74: 4388.S2: 6761.93: 7876.98: 6577.59: 8300.54: 4989.12 MEAN, INCOME 9.7:. 994,6: 3497.2: 2882.1: f910.5: 847.9: 182.0: 10324.0 COUNT ADM1N & :.I40E-01: 9.63 : 33.87 : 27.92 18.51 8.21 : 1.76 : 100.00 PROW WANAGER :.895E-01: 0.45 : t.71 2.41 : 2.93 3.27 2.17 t.S7 PCOL 0.00: 2674.36: 6852.12:10355.21:12015.84:12035.27: 7833.87: 8627.22 MEAN. INCOME ....................................................--..--------*-----------****- : 685.3: 38177.4: 20030.1: 10035.2: 5210.1: 1470.3: 457.8: 53472.2 COUNT CLERK & : 0.2 : 45.74 : 32.15 : 12.74 : 6.24 : 1.75 : 0.15 : o0.00 PROW TYPISTS : 6.32 : 17.15 : 13.10 : 8.91 : 7.99 : 5.67 : 5.46 : 12.70 PCOL : 6i6.88: 1330.43: 2702.90: 2652.49: 3579.56: 3316.81: 3553.41: 1962.35 MEAN. INCOME ....................................................-********--*****--***--**** SALES PA: 230.7: 7725.7: 12409.8: 10435.0: 6922.2: 3507.5: 1154.9: 424,5.8 COUNT NAC.PRCP: 0.54 : 10.19 : 29.41 : 24.57 : 16.30 : 8.26 : 2.72 : 100.00 PROW RILTOR : 2.13 : 3.47 : 6.10 : 8.74 : 10.62 : 13.52 : 13.76 : 6.4G PCOL : 300.06: 1412.15: 2405.90: 3623.48: 4712. 1: 3933.69: 258G.79: 3019.91 MEAN. INCOME : 844.5: 21779.0: 16594.0: 8.97.9: 4293.9: 2159.3: 792.9: 5496t.3 COUNT OTHER : 1.54 : 39.63 : 30.19 : 15.46 : 7.81 : 3.93 : 1.44 : 100,00 PROW SALES 7.79 : 9.78 : 8.10 : 7.12 : 6.59 : 8.32 : 9.45 : 8.36 PCOL 265.S2: 999.86: 2tt3.53: 2327.87: 2485.36: 1908.08: 997.28: 1642.22 MEAN. INCOME ............. ............ .................... ...... ...... ..................-----. SERV : 503.1: 1G512.7: 20512.1: 14048.7: 7331.9: 2357.3i 767.5: 62033.3 COUNT VORK.?=3T: 0.81 : 26.62 : 33.07 : 22.65 : 11.S2 : 3.80 : 1.24 : 100.00 PROW MAIO : 4.64 : 7.42 : 10.01 : 1t.77 : 11.25 : 9.09 : 9.:5 : 9 44 PCOL 222.77: 780.68: 1012.63: 1250.07: 130t.20: 2811.79: 1687.72: 1109.11 MEAN. INCOME ...... -........................................ ---- ---- ****------------- : 4995.7: 35500.5: 12554.0: 9027.a: 4G31.0: 16-10.2: 717.0: 690..1 COUNT MAIDS : 7.23 : 51.40 : 10.13 : 13.07 : G.?t : 2.37 : 1.04 : 100.00 PROW : 46.10 : 15.94 : 6.13 : 7.56 : 7.11 : 6.32 : 8.55 : 10.51 PCOL : 195.57: 358.29: 422.99: 419.87: 439.80: 414.33: 330.55: 372.84 MEAN. INCOME ---- ..........................................................---- --- -*** *** ** : 137.3: 2231.3: 1732.9: i5tO.9: 1327.8: 1163.2: Gri.7: R72.1, COUNT AGRICULT: 1.57 : 25.47 : 19.70 : 17.24 : tO.15 : 13.28 : 7.52 : 100.00 . PtOW URE : t.27*,: 1.00 : 0.85 : 1.27 : 2.04 : 4.48 : 7.85 : 1.33 PCOL 77.57: 1619.61: 3157.67: 2411.44: 2734.98: 2824.80: 6278.72: 27tS.44 MEAN, INCCME : 225.5: 10951.5: 1051i.3: 48:30. 5: 2676.2: 569.5:. 166.3: 29930.7 COUNT PRGO SUP: 0.75 : 36.59 : 35.12 : 16.14 : 8.94 : 1.90' : 0.56 : 100.00 PROW ERVISoRS: 2.09 : 4.92 : 5.13 : 4.05 : 4.11 : 2.20 : 1.98 : 4.55 PCOL : 34;.36: 812.65: 1191.9t: 1859.53: 1782.26: 1193.53: 839.41: 1205.34 MEAN. INCOME 1986.7: 55369.3: 49838.5: 25180.6: ItS67.8: 4258.4: 1277.1:149478.3 COUNT PqCa : 1.33 : 37.04 : 33.34 : 16.85 : 7.74 : 2.85 : 0.85 : 100.00 PwoW VORXERS : 18.33 : 24.87 : 24.33 : 21.10 : 17.75 : 16.42 : 15.22 : 22.74 PCOL 330.94: 840.05: 12G5.65: 1572.85: 1712:94: 1352.33: 976.73: 1181.94 MEAN. INCOME CONSTRUC: 793.2: 14525.5: 11740.0: 9095.2: 6574.1: 3530.1: 929.3: 47187.4 COUNT T WORKER: 1.68 : 30.78 : 24.88 : 19.27 : 13.93 : 7.48 : 1.97 : 100.00 PROW S : 7.32 : 6.52 : 5.73 : 7,G2 : 10.09 : 13.61 : 11.08 : 7.18* PCOL 1 318.30: 66t.79: 1186.28: 1122.41: 1100.5t: 1030.30: 932.54: 965.99 MEAN, INCOME TRANSPO: 14.6: 4435.3: 14773.4: 10553.9: 5068.5: 1327.0: 203.5: 30376.2 COUNT I WORKER:.40te-al: 12.19 : 40.61 : 29.01 : 13.93 : 3.G5 : 0.56 : 100.00 PROW S : 0.13 , 1.99 : 7.21 : 8.84 : 7.78 : 5.12 : 2.42 : 5.53 PCOL : 1200.00: 1003,13: 121!0.47: 15*10.79: 1717.80: 1401.76: 879.00: 189.56 MEAN, INCOME '344.1: 3171.0: 2266.9: 149.2: 1104.6: 590.6: 164.4: 9139.8 COUNT OTHER : 3.76 : 34.r0 : 24.Cj : 16.39 : 12.00 : 6.4G : 1.80 : 100.00 PnUW 3.18 : 1.42 : 1.11 : 1.26 : 1.69 : 2.28 : 1.96 : 1.39 PCOL 310.0: 504.24: 744.56: 820.85: 901.24: 891.05: 432.80: 700.94 MEAN, INCOME 10835.9:222648,6:204877.1:119340.9: 65173.2: 25942.3: 8390.7:5572CS.6 COUNT TOTAL : 1.65 : 33.88 : 31.17 : 18.16 : 9.92 : 3.95 : 1.28 : 100.00 PROW 100.00 : 100.00 : 100.00 1.00 : 100.00 : 100.00 : !00.00 : too.00 PCOL 277.97: 934.45: 1875.30: 2450.57: 2965.28! 2806.t9: 2707.41: 1790.15 MEAN. INCOME : 1420.3: 31404.A: 27215.7: 17107.9: 8483.1: 3988.2: 11G4.4: 90784.5 COUNT NO INFO : t.56 : 34.59 : 2o.98 : 18.84 : 9.34 : 4.30 : 1.28 : 100.00 PROW PCOL 209.92: 893.19: 1788.23: 2333.36: 2387.82: 3038.02: 1856.89: 1666.44 MEAN, INCOME *****************************************...***.*********************...**** T�I� • 7 . � CFгflSS TABULATION : INDUSTRY ВУ AGE -46- :fr7.la) :С15.:а1 :(:5.7л1 •('?s.ав1 :(ал.SлУ :f55.c.) :t55.99) гОгвС ..............:.................................................._.............. rS9.7: rgr3. r, t1в7.I: t?7:.t: 1150.е: в�5.�: 577.в: ттл;.1 Спгчr � lenteul,t� I.ee : :�.п ; :г.�sг • 1t.1: : 1..п. : гг.п : К.44 : rco�M Ре0ц " инЕ . 1.8+ : 1.:1 : г.'! : 1.е7 : I..a : а.То : V. w: 1.11 Pc�t . 2I7.,7G :I^.ТО.)С 'агУ.. М :79�.4 rI •5a7�.L`г !JGaГ.$в :7в75.1о :70GP,.o9 1'L'AN. 3NСОЧС ................................................................................. г:.3: saз t: втт.r. .iт.3: :�п.о: ra .7: Iв.а: z1,г.г rvчr • уtнtкС : г.sг : Iз.av : I:.7г ::о.э+ : 1г.sг : в.дг : г.+г, : 1tю.rn rcnv о,�т : о.7а : о.�, • а.тс : а r„� : о.^.с : о.ал : o.,r гси� .r r+.ae :17s3.ат аачт.аг гsбъг.•т :7z:•ч..n :.s9г.т9 : ьлг,Iл :аа>!у.ла иЕлн. 1кСОхС .........."-^ "-'^ ........................�......'. "..._.._..._..__... " .'... гсоо 7те.3: 7Рм.н ттсл.т: .,ло.е: 1cra.s: ти�.t; г79.+: Iзэвг.s спvнт raco.ecv: ч.ьl : 3з.гл : 7г.9т : 1�:,ге : л.а� : з.07 : o,so : 1sм.со Р4ац • гвОвс а.]G : S.oO : 9.59 : 5.51 : а.63 : 7.79 : 2.а5 : 5. ц �COI ; �17.GO : те5.17 :t5в5.1] ::Otl4.t9 :7Сл9.в0 :3оf1.5б :17о9.79 :1i..1.ce кЕ+И. INCOM.E ".."� ................" " " " " " " " " "'.._.......-........--'....._..._._._.. 1(![тlLCS: 77a.J: 1тв:7.5: /7G75.5: еЯ5]3.1: а:93.]: г7а5.1: аот.б: а�7и1.0 Соинт в а.тт : гь.аг : 3г..а : re.3+ : e.es ; г.тв : о.ва : гс�.сл гиоы гСОТt�С1Д; 1.77 : 11.р: ' 11.56 : 10.в7 : 9.59 : Т.31 : 7.г1 : 10.59 PCtlI : 3бт.1о : ea�.s9 :+371.IS :гnов.s9 :гэоо.l. :1.за.вs :rnтs.r2 :ro п .1о мСлИ. IнсомС ................................................................................ 1ьО.I: а97а.б: s7s5.a: Zо1г.+: 13t6.9: бсв.б.: 19].в: t7aeQ.� СоиИТ , АиЧ7Fа Ь: 1.47 : 35.:7 : 7r.IP : 1+.л: • 9.с7 : а.99 5 1.ад ; 1г,г�.Сд Рииы woco • 7.оо : 7.гг : з.г1 : 2..т : I.r2 : 7.5в : �.аг : I.9� Рсо>. : sie.as : еъа.ав : иs2.ат :+т.I.sz :1es6.1r :гь9г.ы : uae.IO :+7с9.+а м.ы1. tнсонЕ ...............................•-•---.....-^,---._...._............._.......... •. ►агСв. : 1 ю.е: аагт.з: 7ы,.=: гбо7.а: тес.ь: 3ге.7: 7s.s: 1а9+о.1 ссинт ►Q1нftЧ�: 1.Ot : 79.55 : 7].7t : 14.49 : T.I6 : о.еб : о.49 : 1со.о0 P4U� • M�ottSM : 1,:5 : 3.тв : 3.Бв ; 1.99 : t.1e : 2.о5 : 1.77 : г.а2 РСОс : ?5в.37 :го.'о.5а ::оег.ь5 :I5I5.o2 :з:05.9В :9о79.]7 :2lЗо.57 :7e99.s1 ЧЕSИ, IИСОЧЕ ..'°^°° ..............."-'-------"-"• "^.."'....-..."".......д......... 1sг.г: а+ь�.в: 19о+.г: +1+а.а: sss.9: гоз.з: ss.t: егха.l соим't игжяы � s.+5 : z+.еч : 7о.ьа : 1в.э9 : s.ез : 3.оо : г.s7 : гоо.ао Раоч vдоо : 1.тв : г.�1 : г.ао : 1.•г : t.za : г.гr : 1.sт : г.�е гсог. : ав9.к : тсе.тs :129е.ьг :атоз.ь9 :t99г.зв :1бвв.т9 :вs9э.•: : ияl.аг ЧЕвИ, гисахС --------°-......._..-°...., ...............----�--.........--�---......_._...--- psгus . 77.д: s7.e.e: S5лд.3: :г•пп.о: rna�.r. гог.i: 1оз,�: lаагг,.е сгпс,► • С1цы : 0.7: : 35.19 : 7т.г5 : 1e.ol : т.7а : 1.7в : O.GD : 1оо.с0 PROV ►CiRO Q.7e : ].7t : ♦.OD : ].30 : �.+5 : 7.47 : 1.82 : i.7Q PCOL : 7<ат.3о :1о39.а5 :2165.01 :�5в7.1о :в77Т.6I :в?71.9в :6o9S.97 :2а96.65 xEIN. 1NСОЧЕ '°�°"'-"-"'�."-'-°.�.� ............. ....�......""-"'-"°"...........--. . • гsе.<: 1+сба.ц голаl.l: s9es.9: 1ss2.7: азт.я: +�г,.s: a71e.a сСикт _ 1ЕтвL o.9t : з9.о1 : 7а.1в : t,1.оз : s.+e : з.19 : о..в : 1оо.с0 Ряоц tt�7U5TRr; 7.96 : 1.Со : в.С9 : t.9o : 7.1т : t.e< : I.aO ; Б..'5 PCOL : 213.9О : 990.5ь :t96e.9e :зltо.аэ :74ов.9в :7в77.ео ;ts55.69 :18=т,о9 xEAN, IнСомЕ ' ..«� .....................�............,........""--".._..._...._..........."" fe7.6; ll9в.в: 66o9.S; 2т78.'S: tIRe.O: азт.�: tS9.i: t971].в СсиNГ огига о.9в :.�.ат : �7.s2 :,c.es : в.аа : i.�s ; о.е, : гсо.со РоСц кюиsтяrе 2.ге : s.IS : а.е1 : з.�1 : г.ее : х.зв : г.бf : а.�и Рсо<. ; вб0.10 :1о+а.97 :гeit.tl :7о]7.1в :вfet.a2 :5f59.53 :621а.бв :1eSO.T9 МСви. 1NCOxE .«�..«..... _..�...........� .. ...... . .. ..............."- •"'..."•"".... - •" ao.t: sэв.а: изs.а: so7.s; s97.2: re1.s: s1.s: зтzl.в соикт , ;1д1tS 1.CJ : ]2.а1 : гD.Sб : 21.59 : +0.56 : •.з5 : L о9 : 1Сд.гМ Ряо'�г тгС: о..в : o.s7 : t.as : о 99 : о.ев : а.ее : а.а1 • о..г Рсс� ; 0:e.fS :teoa.l5 :2�69.5С :.ТС5.73 :а2в8.17 ;з0в9.67 :tтв0.22 :2а06.вt ЧЕАН. INCCЯE ««���...,...._....._...� .............._.�..�.............�._....._�.�.-�э..-.. 417.9: 1659].3: ta]]т.2: 9953,]: Бвга.0: 337в.9: 9+5.t; 3Zt65.B СоиИ 6DH*a7CVC; 1.65 ; 31.I1 :'•5.97 ; 19.12 : 1i.e2 : 6.66 : 1.Т3 : 100.С7 РЧ�ы Т10н : t1.7♦ : lо.�0 : 1о.4в : 12.23 : 19.79 У 19.I1 : 1в.бS : 11.75 PCOL � : »t.J3 ; 7о5.97 : N6a,ap :t662.41 :t7tд,96 : Uto.p3 ;faol.o9 :t476.9Y Чf�г2, 1ИСохЕ '-""'-""'. _.�..__..... °-""'-"""'-'-""'-"°'-' -""""""'•"'..." ыWtf . БD.S: ]1с2.2: 3оЗ9.:: +5о7.а: tlo•.t: 27а.5: 9т.у: 6985.а CCtlгtT 5tt,[ : о.бт :�5.5t : 7<.:9 : гв.13 : Э.97 : 2.55 : 0.В6 : 1СО.Со PVO'� iRвof о-17 : :.С7 : I.1� : L6S : 1.Зо : t.29 : 1.36 : t.94 PCQL о •2ь.еs :1ees.ss :79ае.ба :<взе.лб :+ь71.s7 :а795.+9 :автs.91 :за21.г6 мелИ, гиеоЧС --------------------------------------------------------------°----....--------- 74т.�: 11оТ7.6: 915Н.9: fiG99.T: ао5о.а: 112i.6: 99т.З: 3419в.а CQUNГ �Етв R: 1.16 :]7.�в : 27.о1 : 19.59 : it.9a : 5.2о : 7.75 : 10о.С0 P4oV УАвоЕ s.5e : 7.о1 : б.87 : д.:.] : 9.С5 : 11.°.2 : 1О."2 : 1.5о PCOt ; o55.S1 : 9t+.5o :197в,ав ::757.о] :+]15.1( :�tot,бfi :755в.31 :2115.29 г!ЕдИ. INCIIME ""-""""" "'... _.. . _.�.. "' ^"""^ "" ^"" °'........ �... """""."' S7т.6: 6CG1.a: 5G6a.5; 7877.1: ^.5Т?.6: 1oES.5: е3т.1: fo01<.I ССиИГ orMCR 1,79 : 3о.а< :^.9.аа : r9.I1 : 12.9t : 9.з,5 : 2.19 : 1Сд.С0 Риоы Са�нСдСЕ: 7..о : ].$t ; а.1т : a,TZ : 5,т4 : 5.19 : 1.69 : в.аG PCCL : г9о.3т :+о�о.�+ :тт+l.оs :.+es.as :вssч.о9 :е�зо.е9 :,ьаа.lь :згса.79 мслИ, гИСохЕ ._..---°-------`--•---^_..... ........................^--�--•---...---��_._�__• ТявИЪ �: 2п.1: 31в9.9: BJ6t.1: а'921.9: :12а.9: тв9.�: t29.e: t7;a2.1 COUNT CouvtavtC; 0.]] : 1�.t1 : 73.8Т : У7.7а ; 11.9>! : а.а5 : D.77 : 1оо.0о PRCV iTION о.27 : 2.гв : а.ь4 : 8.о5 : 1.1б : а.:9 : 2.:9 : з.92 РСОС : 75т.а0 :17о1.7а :2з+в.тт :г999.sо :7абт.+; aas7.zт : тlв.бв :IS7т.ва мЕлм, INCOxE ,_......._�..° °-°--------------------------------------^-_°_...._......_-�-- s�.s: e9sz.z: ы.s.t: з777.а: I7s1.s: коае.г: гоз.о: ]эlве.в соичт tsx,к- � о.г1 : a.ae : гl.sт : 1е.в� : п.ат : s.7a : r.oz : 1ио.оо Расц С1вС fST; O.G1 ; ].1а : •.15 : а.59 : 5.71 : 5.G4 : �.55 : а,7Т PCCL е 1С6.53 : и+О.та ;af6a.19 :д795.55 :вв27.ат :8563.15 :79г7.ао :eG77.77 ЧЕ4И, 2ИСОЧЕ "°...�°""- ---...... _�...-......."""' °"""""""-"-'--"' �.."'""" Puer,te : s1.e: a+�i.o: т+7s.+: cow.r: зsse.s: +юс.ь: го7.s: :гбг+.1 ссинг аон.sас : о.17 : +в.г7 : 77.ео : 7ь.тs : ,е.в1 : s.;s : о.ьз : 1ао.со Р4сы SE4V ; о.бо : 1.бt : 9.<в ; 7.а5 : е.о5 : 6.SJ : ?.вг : 5.ot РСО� : �51.бт :1171.:t :77о2.55 :7аЛ .05 :в196.1о :5У7?.57 :]го7.71 :7:aG.97 мl'лN. INCOЧE .�.�....е ..... .........'-""-""......."'..-.-.."""'-'..-..."'""-'.....� rwu с У: за.2: 5эв<.т: эвтв.а: sв7т,7: �о79.о: п 7о.7: :ьо.о: �бб�а.е соикт Ni1FUCrI7 o.I] ::2.aG : 7?.Св ::2.CG : 11.5+ : a.GI : 1.10 : 1^О.п0 PCUV си о.�9 : 7.т9 : 1.г9 : т.:г : s.e9 : б.ьв : 9.аь : s.s9 PCOL :1о]7.79 :гб9:.^в :7776,с5 :вв1в.:О :S1a�.79 :1750.10 :9571.а7 :763а.:а ЧЕАИ. 7ИСОМС , . �.........."' . """" ^"--' ."" ...................."""""""' .....' . ". "' патчЕ а: иоь.7: r о.о.т: г7lлт.з: е+б,.v: .sas.1�: гlоь.s: бгг. и 1rs,б.т ссигп СоцС5т1С: 5.02 : 57 57 : 18.Э5 : 12. М: 5.5: • I.id : О.В7 : 1ССт.С0 PROJ 7Едv ss. п :i.,. л.г� . гn <а : га.:п . а.:т го.'6 . г�,.вя ггаг, t 199.98 г at9.]R : 767.г9 ; 92в.+5 ; 987.и7 : 75h.5] : бвз.+в : 51б,е1 иЕАИ, SMGOME ' -"'--....• ..............................." '-'....-'-........" " " " ..-." ""' . fe17.7: NnJIO.1:r3s71Q.0: J17т0.9: ЧгаR.в: 104ti.G: 3Gцг.2:Р5::�рл.9 Cn'�НГ тота� г. n: s..лl :.л.а, : 1г.�в : i.ит : а,от : г. а: гro со гагзv � гм.а7 : 1ги.ио : 1ои.с: : гог].сt1 1о:.ии : юn.ro : гго.со : ico.eo rcou а 2f5.67 : 9tв.+а .IгсS.гI ::e�G,:y ;?�d6.IS :757в.9Т :»с0.]] :1907.ио ИЕаИ, 1NCONE �_ ..........� ...............""'""' -"^"' -"' -'..."""""""'. -.......... � t]ie7.a: 4бН1<.а: 4GJ[7.l: i5oT7.9: :L71t.a: 115гв,в: �873.71I95797.8 CQUNT но 1ЧГо : 1.7( : 7I.$o :�7.6: : 1c.G5 : 9.Ot : 3.9V : 1.7I : rCU,o�'1 Расд ' . . . . . . . . PCCL : IT6.ST : 957.д9 :17I1.o0 ;1в U.oc :1?IIП.лб гtT70.IT : М01.бU �11]O.V1 �fhN, INCOxE ..«........ --........... .-""'"""" ...................."-""...-.,.--...... ._._ „ �...._.. , .: ._,,,. �,.,�.. -47- occupations show similarly steep age-income profiles. In contrast, the peak income for maids is only one-fourth higher than the average starting incomes. The cross-tabulation by industry, presented in Table 7, demonstrates that incomes increase more with age in commerce, finance, and public instruction than in agriculture, manufacturing, construction, or personal and domestic service. Although the industries with larger experience effects tend to be higher-paying, the correlation is not very great. 7. Questions: a) How do the distributions of workers among sectors of residence j1 fftE_LX_aZe? b) Does income rise more with age in some residential areas than in others? (Table 8) Answers: a) High income sectors have older workers on average. b) Income rises more with age in higher income sectors in the cross section. The data in Table 8 reveal that disproportionately more older workers- reside in high income sectors. For example, 21.0% of the workers living in the highest income sector (Sector 8) are more than 45 years of age, as compared with 14.8% of all workers in that age category. The most likely explanation for this pattern is that, as their incomes increase with age, workers tend to move into better neighborhoods; lower life expectancy among residents of poor neighborhoods is also a possible explanation. Regarding the question of age-income profiles, they clearly do differ across sectors. -48- TABLE 8. CROSS-TABULATICN: SECTOR OF FESIDENCE BY AGE :(12.14) :(15.24) :(25.34) :(35,44) :(45,54) :(55,60) :(65.99) : TOTAL ********-**-------------....................................................... : 294.4: 711.7: 6013.2: 3969.5: 2867.3: 1463.3: 521.4: 22261.3 COUNT SECTOR 1: 1.32 : 32.04 : 27.01 : 17.83 12.88 6.57 : 2.34 100.00 PROW : 2.40 : 2.1 2.59 : 2.91 : 3.89 4.89 5.46 : 2.98 PCOL. : 2E9.1 : 852.8 : 17G5.3 1866.9 : 1970.5 : 1945.0 : 1tS.7 : 1499.1 MEAN, INCOME . .*............................................................................ : 2007.3: 45052.2: 40270.7: 22412.9: l1587,3: 449s.6: 1427.t:127256.0 COUNT SECTOR 2: 1.58 : 35.40 : 1.65 17.61 9.11 3.54 : 1.t2 100.00 PROW : 16.38 : 17.73 : 17.35 : 15.43 : 15.73 : 15.03 : 14.93 : 17.01 PCOL : 237.1 : 780.1 : 12t6.9 : 1318.3 : 1299.4 : 1117.8 : 942.1 : 106556 MEAN, INCOME - -*** -* ---* --------*--***------------- *-- *- ***---- *---- **--- **.......... .... : 2559.4: 60144.3: 58344.1: 31758.2: 16559.6: 5745.2: 1719.8:17G30.8 COUNT SECTOR 2: 1.45 : 34.G1 : 22.99 : 17.96 : 9.36 : 3.25 : 0.97 : 100.00 PROW : 20.88 : 23.67 : 25.14 : 23.27 : 22.48 : 19.20 : 18.00 : 23.64 PCOL : 319.6 : 891.1 1535.6 : 1702.0 : 1629.3 : 1403.1 : 881.8 : 1325.8 MEAN. INCOME -******************************************************************************* : 944.6: 24469.3: 2339t.4: 13439.7: 5261.6: 2217.0: 468.S: 71182.2 COUNT SECTOR 4: 1.33 ; 34.38 : 32.85 : 19.88 : 8.80 : 3.11 : 0.66 : 100.00 PROW 7.71 : 9.63 : 10.07 : 9.85 8.50 : 7.41 : 4.90 : 9.52 PCUL 272.1 : 939.2 : 1711.4 : 2041.1 : 2132.4 : 1958.6 : 1962.7 : 1535.5 MEAN. INCOME 893.4: 17749.4: 17933.6: 10451.2: 4691.5:' 2250.1: 941.0: 54910.3 COUNT SECTOR 5: 1.63 : 32.32 : 32.66 : 19.03 : 8.54 : 4.10 : 1.71 : 100.00 PROW 7.29 : 6.99 : 7.73 : 7.6G : 6.37 : 7.52 : 9.85 : 7.34 PCOL 251.5 : 985.8 : t779.9 : 2398.3 : 2354.9 : 188t.4 : ¶215.9 : 1658.7 . MEAN. INCOME *- - - - - - - - - -1 - - -- - - - - - - - - - - - - -----** * *--------------------.. .. . . . .. . . .. . .. . . . . . . . . : 2234.6: 43242.3: 39001.4: 24335.8: 12411.0: 4158.7: 1254.5:126648.3 COUNT SECTOR 6: 1.76 : Z4.14 : 30.80 : 19.22 : 9.80 : 3.28 : 1.00 : 100.00 PRCY 18.23 : 17.02 : 16.80 : 17.84 : 16.95 : 13.89 : 13.23 16.93 PCOL 269.2 : 902.4 : 1620.t : 2276.6 : 2208.4 : 1594.9 : 1196.2 : tS30.0 MEAN. INCOME - **- -------- ................................................................ 1953.5: 35006.6: 30266.0: 18265.7: 11136.9: 5469.6: 1934.1:104032.4 COUNT SECTOR 7: 1.88 : 33.65 29.09 : 17.56 : 10.71 : 5.25 : 1.G : 100.00 PROW 15.94 : 13.78 : 13.04 12.39 : 15.t2 : 18.27 : 20.24 : 13.91 PCOL 227.3 : 1117.8 : 2759.8 : 3772.7 : 4765.0 : 4217.1 : 325.1 : 2538.2 MEAN. INCOME 1269.0: 21257.6: 16882.5: 11815.7: 8140.5: 4127.9: 1278.5: 64871.8 CCUNT SECTOR 8: 2.11 : 32.77 : 26.02 : f8.21 : 12.55 : 6.36 : 1.97 : 100.00 PROW 11.17 : 8.37 : 7.27 : 8.66 : 11.05 : 13.79 : 13.38 : 8.67 PCOL 295.3 : 1065.6 : 3854.0 : 5463.1 : 7466.2 : 7436.6 : 8946.5 : 3939,9 MEAN, INCOME **** **--- ----- 4**** .....3 0..... ... ... .... .. 9... . . ..5 . ..:. CC PJ :12256.2:254053.S:232092.8:13448.8: 73656.3: 29930.5: 9555.1:747993.1 COt;NT TOTAL : 1.64 : 33.96 : 3t.03 : 18.24 : 9.8s : 4.00 : 1.28 : 100.00 PR3W 100.00 : 100.00 : 100.00 : 100.00 : 100.00 : 100.00 : i00.00 : 100.00 PCOL 269.6 : 929.4 1865.1 : 2435.9 : 2896.5 : 2836.8 : 2603.5 : 1775.1 MEAN, INCOME -49- Comparison of average incomes among 45-54 years olds with the average incomes among 15-24 year olds yields the following results: Average Income of 45-54 year-old residents Average Income Average Income of of 15-24 year- 15-24 year-old Sector Number Average Income old residents residents 2 1066 780 1.66 3 1327 891 1.83 1 1499 853 2.31 6 1530 902 2.45 4 1536 939 2.27 5 1659 986 2.39 7 2638 1118 4.26 8 3940 1066 7.00 The observed pattern (i.e., income increases more with age in the high income sectors) is consistent with the hypothesis that workers residing in poor neighborhoods have fewer opportunities for training and occupational upgrading. If this were correct, it would be worrisome and would suggest various policy interventions: among the possibilities are subsidies for public transport, establishment of local offices of a public employment service, creation of industrial parks in low income areas, and construction of worker housing near employment opportunities in higher income areas. It is also consistent with a more positive scenario: that many residents -50- of poor neighborhoods do experience income growth over time and they can therefore afford to move to better neighborhoods, as the age distribution of workers by sector suggests. We thus confront an ambiguity of causality: Sector of the city is both a determinant of success in the labor market and a reflection of success in the labor market. The consequent ambiguity of interpretation cannot be resolved with cross sectional data from censuses or surveys. Only imaginative use of longitudinal data -- on workers who ex ante were in different sectors of the city -- can possibly distinguish among these alternative views. 8 and 9. Questions: Do iatives of Bogota earn more than in-migrants because a) natives are disproportionately in better occupations and industries than migrants? b) natives earn more than migrants within any given occupation or industry? or c) both? (Tables 9 and 10) Answers: a) yes. b) as often as not, no. The distribution of occupations and industries is somewhat better for natives than for migrants. The data in Table 9 5how that migrants comprise 76 % of Bogota's total population, yet 91% of the maids, 86% of persons in other service occupations, and fewer than 70% of professional and technical workers, administrators and managers, and clerks and typists are migrants. The differences in -51- TABLE 9. CROSS-TABULAPICN: CCCUPATION BY MIGPANT STATUS :MICRANT :MIG21NT TOTA : 377. : 16836. : 54011. COUNT PROFESS 68.3 : 1t.17 100.00 PROW & TECH 7.49 : t0.45 : 9.22 PCOL . 5042.5 4871.2 : 4989.1 MEAN. INCOME 7147. : 3177. 10324. CCUNT AOMIN & 69.22 : 30.78 100.00 PROW MANAGER : 1,44 1.98 : 1.57 PCOL 9199.1 7990.: 8327.2 MEAN. INCOME : 55070. 28402. : 83472. COUNT CLERK & : 65.97 : 34.03 : 100.00 PROW TYPISTS : 11.09 : 7.6 : 12.70 PCOL : t949.2 : 1987.9 1962,4 MEAN. INCOME **************. e................... SALES MA: 33102. 8564. 424G6. COUNT NAG.PROP: 79.83 : 20.17 : 100.00 PROW RIETOR 6.83 : 5.32 6.46 PCOL 2869.8 : 3614.1 3019.9 MEAN, INCOME 41020. 12942. 54961. COUNT OTHER 74.53 : 25.37 100.00 PROW SALES 8.u6 8.68 : 8.36 PCOL 1637.t : 1657.4 : t642.2 MEAN, INCOME ********************..*..*****.*... SERV : 53354. : 8680. : 62033. COUNT WORK.NOT: 86.01 : 13.99 100.00 PROW MAID : 10.74 : 5.40 : 9.44 PCOL 1086.3 : 1249.2.: 1109.1 MEAN. INCOME : 62864. : 6202. 60066. COUNT RAIDS : 91.02 : 8.08 : 100.00 PROW 12.66 3.86 : 10.st PCOL 276.3 238.2 : 372.8 MEAN. INCOME *********************************** : 7478. 284. : 8762. COUNT ACRICULT: 85.34 : 14.66 : 100.00 PROW URE : 1.51 : 0.80 : 1.32 PCOL 2276.1 : 5273. : 2715.4 MEAN, INCOME .***********..**.**.***...*.******* : 22748. : 7183. : 29931. COUNT PROD SUP: 76.00 : 24.00 : 100.00 PROW ERVISORS: 4.58 : 4.47 : 4.55 PCOL : 1193.1 1244.1 : 1205.3 MEAN, INCOME --*********--*--**--****---*****- :105872. : 43607. :149479. COUNT PROD : 70.83 29.17 : 100.00 PROW WORKERS : 21.22 : 27.15 : 22.74 PCOL 1167.3 : 1216.3 : 1181.9 MEAN, INCOME **********-**-**-*-*-----****-**-- CONSTRUC: 14155. : 12833. : 47187. COUNT T WORKER: 12.80 : 27.20 : 100.00 PROW S : 6.92 : 7.99 : 7.1a PCOL 940.8 : 1033.4 : 906.0 MEAN. INCOME ************-*--*-**-***---*******-- TRANSPOR: 28618. : 7758. : 36376. COUNT T WORKER: 78.67 : 21.33 100.00 PROW S : 5.76 : 4.83 : 5.53 PCOL 1293.6 i 1370.1 : 138.6 MCAN. INCOME 6968. : 2172. : 9140. COUNT - OTHER : 76.24 23.76 : 100.00 PROW 1. 40 : 1.35 : 1.39 PCOL 747.0 : 553.1 : 700.9 MEAN. INCOME *************************........... :496SG8. :16040. :657209. COUNT TOTAL 75.56 : 24.44 : 100.00 PROW 100.00 : ICO.00 : too.00 PCOL 1715.4 : 202?.3: 1700.1 MEAN. INCOME . : 65735. : 25050. : 9075. COUNT NO INFO : 72.41 : 27.59 100.00 PROW : : : * PCOL 1572.2 :19i.7 : t6G.4 MEAN, INCOME **** ** .** .**** *** *** .*** *** T�BLE 1�. CRUSS--TABUI�?'3'IC�`1: Г�DUS?'??У ЕУ ��1?GP.�;T 5ТАТ[1S 1 �-52- ' ивч ;иггидиГ �rcr,uмar : го7аг. ' • ».-•..--° ........................ • 5a87,S: t757,6: '1У5. t сС'JиГ �г.агсиь7i Еа. гr : Уе 7а . гм.м Р:сы U�E г,70 : t.г7 t 7t Dг,pL : 7]а7,2 ; С7зв.t : 7!5Э.0 нг.лгi. 3нССнЕ ................................... гг,.�.0: .в�. г: :�л.. г сгигп ' , ы7н1иС � 7р.7о : 7�.70 :+С] м v:+:ы д.ае . о.вs o,.r Р:ль : 7Г„9.1 : 57:9.7 : <?i5,0 иЕ1и, f�CCME • ........................._......... t00o . гВр7в.б: в5СG.2: :':Эt.S СрциГ ►а�?,веv: еа.77 19.�р . га,. м P:or � пр+с ..9а а.с� s,гт vсОь ; 1577.А : 1L77.0 : г:ае,9 иЕ�Ч, fNCOME . .. .................................. ТЕдТ1ьЕ5: 9т775.1: tгO5i.9: <Е)�7.0 сси7гГ ♦ 77.tG : Х1.е< : гсJ,г.И Р:ры Г001vЕАА; to.95 : 9.90 : YJ.C7 DCUI ' " ; 1787.3 ; 1а77.1 : t7f7.7 МЕди. lhtoNE ....«.«.•946Е,Е:"а0г2.1; г7190•9 с.^цЧГ lUиoER в: 70.�♦ : 29.75 ; гСд.С9 РдСы • . УдОФ 7.7д : ].Э9 : .96 PCCL . : 17вг.в : 1270..А : fOC8.1 MUw, 2ИССИЕ � ....-°-"•" "' .................... /lРС4. : 6759.7: t1в2.<: гСЭ:0.7 СС7цТ tя[иг[ис: ьг.77 : зе.7э : гсv.со л.,. ' ilпОьtsн : г.9а ; �.7s : :..г Рсоь г tв+в.7 : 19аг.в : геа9.s ыии, ггкаИЕ » ..................•--°•------... д7в7.2: lд80.6: Е:77.7 СоииТ и!н(ав� � в9.7е : 7о.7г : iса.м P40I 1i00 1.77 : 1.64 : г.]В PCGi. : tв52.3 : t38t.� : tв9г.а УЕди, INCOИF, tнaus, �--ta0гaa;- 7л9о 7а гз9г: s а.ит снси � т�.s2 : гб.Оз : га;.м оо:ч . lETRO 7.7� : 7.+9 : �.7� PCOL ' t770.) : 29ва.t : 2�7д.7 ИЕаи. tHCOME ...."""' °.........� ......... ..... : г9ве0.4: ее7а.а: 7в7:е.7 сви!гт я(твь ее.ео : 8+.7о : гсл.t•7 Рд0ы IЮUSTRY: 5.71 : 7,92 : 8.;5 а:74 • : 779д.в : tа9д.0 : �рt7.г и(ею. IvC.^нЕ .�...« ..........................._. � : 1в0l1.Т: 5632.о: 19Tt7 1 Сь"�ч( � О(н(4 71.]7 : 7В.Б7 ; t�].Ср Расы 2NCt757R►; в-t2 : 5.06 : :.79 РС7ь : каа.7 : т110.э : 1е=_g.a ыЕаи. гИССИЕ -----•---------•------------------- $576.6: 111<.9: 7721.5 CCL'!!Т urnt e4.za : 7о.а6 : 10о.х гRCv тctcs О.ге : г.в7 : о.вг vсеь ' : 4705.а : 7@9].7 : 2ьЭб.+ иЕSи, 1иССыЕ • ��� тs+е. е:�+ыт=г�о; sa•ss�д с�: п СОиlТяиС: 7t.87 : ?9.76 : га�.С0 Раэv . � TICN 11.20 : 1�.а2 : г1.7! PCCI ' . о nег.4 :+sгs.o : 1776.9 и(дN, tг�ьсоИЕ ..° ............................... - � � vног.Е c77T.s: 2е07.а: а9ц.в соип . �• твьЕ 7о.9а : м.Ог : tо7.Од Раом • Таа0Ё 7.et : 7.7i : :,99 дсоь : рг7,.р : затэ.В : 7.z,.3 иЕан[. гнсОтЕ ......................_............ : sвгае.5: есюв.s: 7eisч.+ саигп дЕУ1l1 : t8.34 : 27.д: t tС�.Сд РSэ'! тя.е( 7.ре : 7.+р : •.ss РсОь , : 7г7а.в : Iv.3.7 : 21ti.a иЕги, tисрИЕ °---°.°.°---------------------- : 15799.3: <52А.9: 19J1t.a Ср!l;�Г ` �-- OTHER 7J.28 : :7.72 : fС4.:д Р9оы СОипЕRСЕ: ♦.51 : +.04 : <,�0 CCCI : оlге.n : 7а7о.7 : �tta.a rЕАн. JНсрнЕ , ...... •. ° .. ^ .. -........ ^....... -. � ТRвюS i: г19Т0.6: вТ71.5: 1"47.1 ГpUNT СО�гмJнЕС: 73.г1 ; 26,В9 : 1M.N PvC% �TtON 7.80 : в.37 ; 7,9Z РСсь 9 2S4Я,� : I5гi.3 : 2S7T:3 и(вN, tNCOMf �. • : пs0а.7: �гег.9: гэтвs.В саинТ � - rswaи- = а7.т0 : эв,з0 : г00.Со p�cv , CTaL ЕSТ: 7.ТО : б.а7 : в.7f PCCL ' . : авоа.5 ; sou.e : ав]7.7 ыЕаи, 1иt0ыЕ .•..°..° .......................^ М.г�lа'С : 169вТ.9: 5Т27.2: 2:575.1 CpL'71Т аон.sОс : 7<.зв : 7s.�a : т�.с0 Р;Сы SERV . а.9Т : 5.t7 : S.Ot vсаь � 7таа.0 : 77ss.a : 7z.7.o ыs.н, гжСИЕ ----------------°--------........_. PVBl2C I: 1CG67,9: 7974.9: tC5<].! COV!!Т Ms7auut: 70.os : 79.з3 : гсю,м л.сv ою s.ar ; 7.гs : s.a9 vcp� : 7T09.t : 7511,7 : 76lг 2 иiаи. tиСрИЕ ............................•-.•-.. /(�SNL 6: б Н97.7: В977.а: ТС'г6,7 CCWR с0�ЕSтгr, вi.7s ; u.cs : кv.ео Рыр+ ucv •, го.о5 : 7.о9 : г5 se Р,r,ь • ,. . 57а,9 ; 4]5.7 ; 5'•5 t иktи, 1NCpYf ..........,. .............._........ :7в0978.а:ггlбгГ. 0:15::?< 9 СрrчТ . ТОТв� 75.Sa ;;а.ББ : гс� го avGы е 1а].оо : го.).оо : и��.са vcct : геаs.г г г7а9.в : г447,е иим, tИсаиЕ ° ...............................° :77г7:,.я: 7.077.e�as��e.s с�•ит на 2иго : 7а,а7 : аS.Се : Y4v w Рааы , - - . - оеэ, г гаге.а е г<ео.я : гио.э иЕа�,, [неОИt ..........^-•^-•••-• ............. -53- occupational mix of migrants compared with natives are significant but not substantial. Differences of similar magnitude appear in the industry breakdowns. Migrant/native income differences within occupations or industries are small. Natives earn more than migrants, in seven occupational groups, mi.,rants earn more in three occupational groups, --nd average incomes are within 30 pesos (about U.S. $1) of each other in four occupations. This suggests that migrants acquire inqome equality with natives in the same occupation within a fairly short time; whetber t'h&y acquire occupational equality as well cannot be determined without an age breakdown. 10. Questions: a) How do the distributions of workers among secto s of residence differ by m b) Is the native/migrant income ratio greater in some residential areas than in others? (Table 11) Answers; a) Very little. b) Yes, reatest in the highest income sector. Migrants and natives do not differ much in their residential pat,--erns. Of persons with identifiable sector of residence, 75.2% were migrants. The proportion of migrants in the eight residential sectors ranges from 72.8% to 79.0% with no apparent relationship to income level. The small size of these differences and the lack of a systematic relationship with income suggest that migrants become integrated into the Bogota labor market over time; whether recent migrants are equally well-integrated within a short time is not clear from the available data. Turning to income difl rentials -54- TABLE 11. CROSS-TABULATION: SECTOR CF RESIDENCE BY MIGRANT STATUS N* : :NON :MICRANT :MIGRANT : TOTAL 1667. 5794. :22269, COUNT SECTOR 1: 73.97 26.03 : 100.00 PROW : 2.93 3.12 2.98 PCOL : 1479. : 1557. 1499. MEAN, INCOME 95577. 31679. :127256. COUNT SECTOR 2: 75.11 24.89 100.00 PROW : 17.00 : 17.05 : 17.01 PCOL : 1047. 1120. : 10G6. MEAN. INCOME :33904. : 42927, :176031. COUNT SECTOR 3: 75.72 : 24.28 :00.00 PROW 23.81 : 23.12 23,64 PCOL S1315. : 1365. : 1327. MEAN, INCOME : 56173. : 15009. 71182. COUNT SECTOR 4: 78.92 21.08 : 100.00 PROW : 9.99 8.08 : .52 PCOL : 1471. 1777. : 1536. MEAN. INCOME : 43349. : 11561. 54910. COUNT SECTOR 5: 78.95 : 21.05 : 100.00 PROW * 7.71 : 6.23 : 7.34 PCUL 1633. : 1755. 1659. MEAN. INCOME 92227. : 34421. :126648. COUNT SECTOR 6: 72.82 : 27.t8 : 100.00 PROW : 16.40 : 18.54 : tG.93 PCOL : 9489. 1639. : 50. MEAN, INCOME : 77072. 2699f. :f04032. COUNT SECTOR 7: 74.08 25.92 : 100.00 PROW 13.71 : 14.S2 : 13.91 PCOL 2500. : 3034. : 2638. MEAN. INCOME 47534. : t7l38. 64872. COUNT SECTOR 8: 73.27 : 26.7: 100.00 PROW : 8.45 9.34 : 8.67 PCOL 3602. : 4866. 3940. MEAN, INCOME :562303. :185390. :747993. COUNT TOTAL : 75.17 : 24.83 : 100.00 PROW 00.00 100.00 : 100.00 PCOL 1699. : 2007. : 1775. MEAN. INCOME -55- by sector of residence, the ratio of natives' incomes to migrants' incomes rises monotonically with income. One possible explanation is that Bogota natives have the advantages from birth of better public health conditions, higher-quality and more plentiful schooling opportunities. 11. Question: Do better-educated workers in Bogota earn more because: a) they are disproportionately in higher-paying occupations? b) they earn more within any given occupation? or c) both? (Table 12) Answer: -Both, with substantial components due to each. As compared with those with no schooling, workers with primary educati,on earn nearly twice as much, those with secondary education three times as much, and those with higher education twelve times as much. The data in Table 12 reveal that the differences in occupational composition across educational groups are considerable. For example, persons with higher education are more than 100 times as likely as persons with no education to be in professional and technical occupations. On the other hand, more than 40% of workers with no education were in service occupations as compared with only 1% of persons with higher education. We also find that within occupztions, better-educated persons earn quite a bit more, e.g., the ratio of incomes of workers with higher education to the incomes of persons with no education is nearly five to one in professional and technical occupations, eleven to one in sales jobs, four to one in production, and eight to one in construction. 니― -57- 12. Question: Concerning the relationshi2 between education and industry in Bogota: a) Within an industry, do better-educated workers earn more? b) Within an educational group, does the average income dep2nd on indu r? (Table 13) Answers: a) Yes,.a great deal more. b) Yes, but relative to the inter-education group differences, the inter-industry differences are much smaller. For those individuals for whom industry information is available, the ratio of incomes of the highly-educated to the incomes of those with no education is twelve to one. The income ratios in various industries are of the same order of magnitude: twenty-five to one in agriculture, twelve to one in textiles; thirteen to one in construction, eleven to one in retail trade, eight to one in transport and communications, and so on. By comparison, inter-industry differences are a great deal smaller, though by no means trivial. Particularly noteworthy is the pattern of incomes for workers with primar-j school education, who comprise 54% of the Bogota labor force: the dispersion of industry averages around the overall average is rematkably small, with two outliers standing out (utilities on the high end, personal and domestic service on the low end). п5�� TABLE Т3. • а v еsесоИОьао о : /в� г�з�17�7����д�T1 /�ц�уΡ 7�д� �7 г7д��� :--ЧОнЕ :РR1МвдУ :У : HICIIER 10ГИС : oTHFR : L.L\�S�1ь'�9V.1aC'11IV1V : I1V1JVSyд,L ......". � ...................'.........._._........ ..._...«. • • 8]г.5: Эдлl1.7: 1010.7`. 7лG.7: 1ri33.0 RO6.1: cCL'ИТ • �сазwСте 10,9о : so.�a : 26,Э2 : п.лд : 1с0,с0 . vr.0�a иаг �.о1 ; з.59 ; з..1в : :.,� : 1.7i 1.sв : PcrC ВУ EDLTГATICx7 . SзВ.] : i715.1 : 5775.в :171ЭП.0 : ЭПG7.7 : ]O61,1 ; нЕди, [иСОМЕ ...."__"' .......................................... .._.._.... ' 1А2.�: 8ПУ.1: 475.5: аа7.1: ?01:В.1 57.9: CaI:NT M1NtNC : 11.G1 : Ф1.Ф7 � 13.7J : 71.1А : 100.г70 - PFCY 0..9п : 0.77 : О.Эг ; 1�t7 : О.•n 0.79 : ЛСОL : 2�ОЭ.� : 11ФО.6 : 97П7.9 :1070А.1 : дОго.0 : 3iG7,8 : ЬiС1гг, SNCCHE ..'ССО• : г,77.а: 15' ....................."........ _... ,. .. � ••••- 7А7.7: 51SG.A: 97G.9: 13072.I Э�9.1' CUUNi PRC:I.6EY: В.В5 ; 66.67 t 24.�0 : J,OT ; 1(Ю,ОО • PpuV �' 1'а9>tC . 5.71 : G.3� : 3.7G : 2,д0 : 5.1G 5.2В ; PCUI . т87.0 : 10г5.е : 2a0J.5 : G909.a :+:60.5 ; 11В6,� ; М[ЛИ, 2ИСОМЕ '_"'..........°° .................'....." __......__ ....__.... TExtSlES: 1266.1: 29770,1: f57W ,7: В97.0: Ф79t7.1 в6Э.9: Са1МГ . 6 2.G8 : 61. U: Э].Э2 : 1.tl7 : 100.00 • РРа� f0o1VEвR: a.GS : t1.31 : 11,GJ з 2.70 : 10,75 8.91 ; PCCL . т7В.2 : 991.9 : гsОд.2 : 9г9э.о : 1a13.s : пл�.г : нели, tЧсомЕ .......................^.__.....^ ^_°..•......._... ...._•_... .9В.о: 9о36.В: Э6ех.Э; 1z0.e: гаЭ3в.7 1вх.1: сииит Wr?ER 6: 3.12 : G7.75 :?7.r,t : 0.93 : f00.00 • PftUV � - vaa0 i.79 : 3.7i : 2.68 : 0.Э2 : Т.79 д.09 : PCCL е ь2вЭ,г : 119е.� : 1509.В : 3в7г,г : n1г,7 97в.6 : нсли, tисанt ..................°•--•----°�-----............._._. :.---.а.-- P4PER, : 1t6.0: 162В.9: 56Вв.6: 559.9: 10аа9.7 51.7: COVNr PR1�TfNa: 1.07 : a2.5t : 5t.19 : 5.11 : 100.00 - PROV PVat15н : Ол 2: 1.91 : ..О7 : 1. и: 2.н o.7s е РсоС : 1вЭ3.s : 1191.9 : г1 п.о : 577s.. : reoa.б i ев7,7 ; иЕАи. гисаие ..................°---°----......----•--....._._... .-----._.. еВЭ.=: Э9+а.В: t?�B,e: Эба.ь: В1о9.в 11а.1: соиит игиЕавС д 9.12 : бв.s7 : 2о.1е : s.9a : 1ао.о0 - Риои Psoo : 2.ав : 1.ь3 : 0.9о : 0.9Э : 1.37 1.ье ч РсаС . 9В8.8 : 9Э8.4 : 15i7.7 : 7hв6.5 : 148о.5 . 2072.в : нЕлИ, iNCOME ' """" "'....а.."'..._'_"" " ""'•_.'.'.___'._'.. ._........ ки�и5 31s.6: ь•оа.а: 5В5В.2: 1в2е.В: 1a7o7.s sот:в: саиит СНЕМ 2.15 : ai.81 : J9.62 : 72.J2 : 100.G0 - PaOV РЕ1а0 1.1в : 2.77 : в.27 : в.бВ : 7.70 9.06 : РСОС . 780.1 : у2t,д : 222Э.6 : В692.5 : 2вВ1.в ! 7503.7 : MEAN, кNtCME """"'�' •_'°'_'____""'..."'....' __'.""..... _.. .""°_" 770.9: ts777.3: 10995.7: 1624.1: 1В119.2 t99,2: COUNi � иfrlL 7. N: ц.ЭТ : 79.10 : 5.Т8 : 1а0.р0 - РАОУ 1MOUS7RY: 7.Т9 : 6.о9 : B.Ot : Ф.17 : 6.3t У.93 : PCOL ебэ.о : 12г+.0 : хо0п.В : еЭ7�.6 : 1В1ь.z � 33Ве.о : мели, гиаоне ........:.........................................°. _..._.._.. 59в.1; 1o7n.7: пss.ls e6z.s: 19ez9.� 2ев.s: еаимт oTHER 3.96 : 55.J1 : 27.19 : 4.а4 : 100.00 - PROW, IM1u5TRY: 2.{5 : 1,44 : l.Z6 : 2.21 : в.36 в.18 : PCaI ' 729.• : t1О7,б : 2352.6 : 7722.Э : 1632.9 : 2l1Э.В : нЕАN, INCJMt ..."""•_____.'_".'.'__."__""...'_"__'_....'_' _"'.'.... 197.1: -1ВО?.В: tв79.5: а77.д: 3673.9 вТ.Т: COUNT • итпг 3.ВЭ : в3.76 : ае.7э : 1z.дв : 1а0.)о - PAav ' тксs О.+д : о.еб : г.об : 1.z1 : О.да 0.7о � Рс01. : 1в00.3 : 15ЬО.В ; 2329.2 : 6Ф43.1 : 74В9.Э : 2460.2 : MElN, 1NCOME ..'."""".......'......""' ................�.._.. ._�._..... 6Э]в.7: 7ео69,в: е030.3: 1ВВв.7: 5пЭд3.8 782,г: СОVиТ � еоиысис� 1s.оэ : i2.67 : 11.иа : Э.61 : 1оо.0о - Ра0ы iION . 25.91 : 15.3в : в.вв : 4.84 : 11.75 11.50 : PCOt. Т19.2 : 907.9 : 1789.♦ : 9о79.0 : 12В2,2 . 912.5 : MEhN, IЧСОМЕ .'._......«.._.__ .............'.._'._'.'.."__'.._.. ."__'_'.' � VHO1.E s7o.a: 29s6.6: +гбо.2: 1зЭ7.о: е7те.2 zs9.г: соичт 5цЕ 2.1о : ЭЭ.ее :.7.ВВ : зS.Эл : 1оо.оо - . Раах TaioE 0.98 : t.22 : ?.0Э : 3.а7 : t.96 7.В1 : PCOL . 665.] : 1106.8 :?707.9 : 759Э.9 : 796а.1 : 7S76.в : ЧЕАN, lNСОмЕ ...." " "'.___"___.'._"_'_.._'__'_'.._.'_'....'.__ ___"'...' 19аг.1: +ь9о1.s: t3693.a: 1ьое.В: ЭЭ7и.В +s3.e: саигп АЕГа(L : 3.75 : а8.90 : го.68 : а.Т7 ; 100.00 • PRCY ТRд0Е . т.02 : 6.В2 : 9.97 : в.11 : Т.57 8.67 : РСа1. . 610.J : 119G�6 : 2699.9 : 6698.Э : 2116.9 : 2021.В : нЕАН, 1ИСОМЕ ......_""_.'__._......_'.__•'__•__"..._'. "...._.' "'___'_`_ . 671.9: 955А.0: 7709.1: 1в71.7: f9591.0 727.<: COUNi OTHER а.�55 : а6.79 ; 3з.25 : 7.Э1 : 100.00 - PaOV сам'•Е+ТСЕ: 2.Z2 : 9.75 : 5.61 : ].67 : Ф,19 д,76 : PCOL � . Вtl9.в : 1в12.5 : 4477.1 : 968/.О : аТ,00.0 : 1177.0 : нЕеи, INCCHE ._....'."°'.'_'_"_..'..'__"".""..'...'."...... '°._....,. TI7AN5 8: a7t.G: В7а0.7: 7042.Э: 1Эз6.В: 1755t.в з9о.7: СОииТ СОю��и1С: 2.69 : в9.дО : ФО.01 : 7.50 : 10D.00 • PROY 1TICH 1.7t : 7.61 : 5.1I : 3.78 : 7.Ча 1.6о : PCOL . Вбо.В : 1+9е.9 :�19i,a : т1аq,� : 2sаз.6 : 1еоВ.В : нЕви, кисанЕ ....._..о_... .............................�--...-•--- - ' ' гао.е: Эо1s.в: 99гs.а: Ваоа.е: +9цВ.+ .за.ц соимт fкм�N- • 0.32 : 13.Вд : 51.а3 : Э1.В9 : 100.00 - РаоЧ С1АС Е51'; 0.7G : t.3! : 7.12 : 16.l6 : в.7< Ф.Ф7 : РСОС Sае.В : 1.4е.в : цВ9.Э : s2a1.e : вs9г.а г В7о1.а : ибаи, кисомЕ ........:........................---.••....._....._... _..---•�-- PV8C1C : 701.0: 6В87.3: 97вЭ,1: 5о10.Т: 21157.1 319.0: CCUNT Аан.SаС : 1.26 :]1,09 : а3.9! : 12.В6 : 1а0.о0 - PROY SЕдУ 1.81 : 2.Н9 : 7.10 : 12.87 : 4.9Т 7.6Э : PCOL . 96В.6 : 1вТ2.6 : 7562.В : 7179.6 : 31Эа.1 : 3791.3 : ИЕИN. INCOHE ...............'_'..«......^ ^_'_'......._-...._._. ." ""'__ PUOCIC I: 711.2: Э51Э.9: 119д7,е: 10754,7: 26177.6 а66.2: coUNT HSTAUCTI: 1,t9 : 13.а6 : ав.17 : Ф1.09 : 1(Ю.ОО - PRO'/ ON 1.1Э : 1.+6 : 8.44 : 17,57 ; 5.В7 8.68 : PCUL : 159В.В : 1160.G : 1067.0 : 5Э72.2 : �88В.О : Эв79.2 : ЯЕАи, 1NСамВ '_'.'•_."._'_" "'ь' ..................'........_..__ ."'__._.. Р[АSнг. а: 969s.o: 5fэ11.о: 77o9.s: 2аВ,В: ь71а7.о 1ц9.1: ссииТ ааЧЕ5т1С: fФ.71 : 7в.1б : 1i,ta : 0.79 ::ОО.СО - PaOV $ЕАУ 75,80 : 7t.22 : 5.61 : 0.69 : 15.52 . 19.5В : PGOC . 273.Э : 499,1 : 1�:0.< : в11G.1 : 57В.0 . 519.1 : МЕлN, 1NCONE " "'"""_' ........................'__.._........... .'_....'._ : 27650.0:2а16Эг.9:197ЭСq.в1 Э70Fв.О:Ч5778.2 679B.t: СОUл1 TOr�L 6.10 : 5а,25 : ЭО.а0 : В.75 : t00.00 - PaOL ' : 1С0.00 : 17I.00 : 1W.OJ : t00.G0 : 100.о0 : 1Q0.00 : PCCI , 429.1 ; 971.0 : 2Ф71.2 : 7510.1 : 1975.д : Z762.T : нЕИ:г, INCOME "'_.'_" "'..._......'_"__.' ....................... ....'_.... : 197е2.Ф:сбох21.1: 9sc06.o: 1+sВЭ.Э:я7о1п .в епгб.в: соимт на tиго : В.е2 : 5s.12 : 22.цs : s.o2 : 1ао.оо - гаои . . . . - PCOL sсе.я ; v71.2 i 17оs.в : e73r.9 ,++ц.0 s з:ол.д s МЕИи. lисамЕ ..__.._.:.._....---.._..--• ..................°•-•--. ..-----°- -59- 13. _q tio ns: a) How do the distributions of workers among sectors of residence differ by educational level? b) Does income rise with education more in some sectors of the city than in others? Answers: a) The best-educated workers are concentrated in the highest-income neighborhoods; b) Yes, hiEhe.st gains in the highest- income neighborhoods. Differences in residential patterns across educational groups are considerable (Table 14). For instance, 7% of workers in the Bogota labor force havc higher education; of residents of the highest income sector (sector 8), however, 25% have hignar education. At the other end of the income distribution, the poorest sector (sector 2) contains 22% of the people with no education compared with 5% of the people with higher education. If we turn our attention to income-education profiles within residential sectors, we find: i) income 'rises with education in the cross section more among residents of some parts of the city than among others; ii) the largest income gains are found in the highest income sectors; l/ 0 and iii) all of the difference, however, comes at the secondary and higher education levels; among workers with lower levels of education, residence in a high income sector is not associated with a higher income. Once again, interpretation problems are paramount: are the lower incomes received by workers with secondary and higher education who reside in low income neighborhooods due to limitations imposed by the location, or is it that the unsuccessful among the better-educated have little choice but to live in poor neighborhoods? And still, the alternative views cannot be distinguished with the available data. l/ Cf. Mohan (1979, p. 41). -60- TABLE 14. CROSS-TABULATION: SECTOR OF ESIDENCE BY EDUCATIC : : :SECONOAR: NONE :PRIMARY :Y : HIGHER :-TOTAL: OTHER --- *******---*-* ------ --***** *** .*.***** ***** - *---- - 1816.6: 11160.1: 7232.6: 1606.3: 21815.5 445.7: COUNT SECTOR 1: 8.33 st.16 : 33.1s 7.36 : 100.00 : : PROW : 3.83 2.78 : 3.11 : 3.00 2.96 : 3.71 PCOL 484.3 :90.S 1915.4 4968.5 : 1502.3 : 1342.6 : MEAN, INCOME ------------------------------******* **--*-------- ----*-*- 10552.6: 798t7.6: 31925.1: 2745.5:125070.3 2185.2: COUNT SECTOR 2: 8.46 63.92 25.53 2.20 : 100.00 - PROW : 22.21 19.85 : 13.71 : 5.12 : fG.99 : 18.17 : PCOL : 621.1 : 884.5 : t449.5 : 3737.3 : 1009, : 866.2 : MEAN. INCOME --- *- - ********- -- -- - ---------** ** ** * ** ** * ------ **- ....***.** : 8090.3: 97364.0: 61978.7: 6210.4:174543.4 2287.4: COUNT SECTOR 3: 5.1S : 55.78 : 35.51 : 3.56 : 100.00 : : PROW i.95 : 24.22 : 26.61 : 11.59 : 23.72 : 19.02 : PCOL 613.4 : 1039.3 : 1653.3 : 3654.2 : 1328.4 : 1200.5 MEAN. INCOME **... *****...-****** .** ..********* --*-***** - -- -- - ******--- : 3243.7: 27633.9: 25988.3: 3399.7: 70265.7 : 916.6: COUNT SECTOR 4: 4.62 : 53.56 : 3G.99 : 4.G4 : 100.00. : - : PROW : 6.84 : 9.36 : i.16 : 6.35 : 9.55 7.62 : PCOL : 661.S : 1158.5 : 1862.3 : 4073.3 : 1536.9 : 1428.7 : MEAN, INCOME **... ******.. ****.. ***. ***** I *.. *. **.*.*.*.. *. *****.. ---- ---- : 3319.8: 28938.7: 17974.5: 3834.9: 53988.1 : 922.2: COUNT SECTOR 5: 6.15 : 53.42 : 23.33 : 7.10 : 100.00 : * : PROW 7.00 : 7.17 : 7.73 : 7.t6 : 7.34 : 7.67 : POOL 554.7 :1042.7 : 1985.2 : 5738.8 : 1660.4 1557.3 : MEAN, INCOME : 8565.7: 70789.7: 38466.3: 631i.7:124G33.4 : 2014.9: COUNT SECTOR 6: 6.87 : 56.80 : 30.36 : n.47 : 100.00 : . : PROW : i8.05 : 17.61 : 16.52 : 12.71 : 16.93 : 16.76 : PCOL : 594.9 : 999.5 : 1896.8 : 6001.6 : t528.9 : 1597.4 : MEAN, INCOME ***- - -- - --------------------- * -** **- ***-- ----*-- ----****- : 6020.3: A5439.1: 34214.7: 15304.2:101977.1 : 2055.3: COUNT SECTOR 7: 5.90 : 45.54 : 33.55 : 15.01 : 100.00 : - : PROW 12.69 : 11.55 : 14.69 : 28.56 : 13.36 .: 17.09 : PCOL 489.1 : 943.9 : 3005.4 : 7789.1 : 2636.0 : 2747.4 : MEAN, INCOME -*-----------------------------------*--------- * --------- :ASS3.6: 30010.8: 151OS.1: 12664.8: 5"674.2 :19.: COUNT SECTOR 8: 7.69 : 47.13 : 23.72 : 21.46 : 100.00 : - : PROQ 10.32 : 7.46 : 6.49 : 25.50 : 8.65 : 9.96 : PCOL 571.7 3 46.1 :5294.2 :10435.3 :3938.1t 4034.4 : MEAN. INCOME : A7432.5:402053.0:232905.4: 53577.4:735968.2 : 12024.9: COUNT 70TAL : 6.44 : 54.63 : 31.65 : 7.28 : 100.00 : * : PROW : 100.00 : ICO.00 : 100.00 : 100.00 : 100.00 : 100.00 : PC. : 604.0 : S83.7 : 2157.4 : 7022.7 : 1774.7 : 1802.9 : MEAN, INCOME -61- B. The Single-Equation Non-Interactive Approach Beyond simple tabulations, the most frequently used test for labor market segmentation is a multiple regression earnings function relating a worker's earnings to socioeconomic and employment characteristics. Among the personal characteristics commonly included are education, age or experience, sex, migrant status, etc. Job characteristics may include size of firm, capital intensity, worker productivity, among others. A test of segmentation which is commonly employed is the following: If, after controlling for personal characteristics, we still find that job characteristics are significant determinants of income, then the labor market is said to be segmented. This test of segmentation corresponds to Definition (ii): workers with "equal" human capital are rewarded differently depending on the segment of the labor market in which they work. Earnings functicns have been run for a large number of countries. The evidence Psacharopoulos (1978) has synthesized covers the earnings functions for 16 less developed countries. 1/ In general, earnings functions estimated on LDC data are found to perform well. Education and age systematically appear as important explanatory variables. The effects of education are quantitatively large as well, each year of education adding from 5% to 17% to one's annual earnings. 1/ The countries covered are Brazil, Colombia, Cyprus, Iran, Kenya, Malaysia, Mexico, Morocco, Nigeria, Peru, Singapore, Taiwan, Thailand, Turkey, Vietnam, and Yugoslavia. -62- Colombia, too, also offers numerous multiple regression earnings functions dating back a decade (see Table 15). Education consistently has an important positive effect on income. Age or experience are also found to be related positively to income. Other variables, such as city of residence, family background, and employer characteristics, although statistically significant determinants of income, are not very important in magnitude. Finally, these studies explain up to fifty percent of the variance in individual incomes. What of wage differences for seemingly "comparable" workers depending upon their sector of employment? An illustrative empirical test is offered by the work of Bourguignon (1979), using the following variables: Y = income, EDUC = years of schooling, EXP = labor market experience, EXPSQ = " "1 squared, WORKTINE = hours per week D = dummy variable for modern sector employment.1/ Following Souza and Tokman (1976) and Webb (1974), Bourguignon (p. 47) distinguishes between traditional sector employment (productive units with five or fewer workers) and modern sector employment (those with six or more workers). All persons with university education and all government employees are considered members of the modern sector regardless of firm size, while all domestic servants are included in the traditional sector. I/ In empirical work, Bourguignon used the logarithm of D. It is unclear how he has taken the logarithm of a 1/0 dummy variable. TADLE 15 Principal Results of Studies Using Microeconomic Survey Data to Con3truct Earnings Functicns in Colombia STATISTICALLY YEAR OF DATA GEOGRAPHICAL SAMPLE DEPENDENI' INDEP-NDrr 2 ALMIOR .iiD_SO[mE ODVERAGE SIZE VARIABLE VARIABlES R Schultz (1968) 1965 Bocgota 1,000 Logarithm Educational level, .17 - .24 Survo:y of individuals of wage adjusted age, other family Employmant and both sexes for a 48 hour incan Unnip1loyFEnt work week (wanon only) (CEDE) Gonzales (1971) 1967-68 Bogota 918 Income Educational level, .38 Survey o individuals age, inccme source Fam] lyu- both sexes (capital, independent gets and work, mixed or Expenditures salaried), sex (CEDE) Musgrove (1974) 1967-68 Bogota 2,949 Logarithm of Interactive variables .49 Survey of Barranquilla, families imputed "relative involving educational Fanily D.Id- Cali, Madellin long term income" level and age of a gets and of family family head, head's Exponditures marital and family (CEDE) status, presence of capital income, number of workers in family, city Urrutia (1974) 1967 Bogota 331 IncCMe Educational level Approx- Survey of Bucarawanga, individuals age, sex imately Occupational Manizales, both sexes .45 and Geograph- Medellin ical Mblt (CEDE) continued on next page TABLE4 15. Continued STITCALLY SIGNIFXCWP YEAR CF DATA CEOGRAPIICAL S7IPIE DEPENDNITf IN)I-IWNpNT2 ALIOR AND SCUI=' COJEJUGL SIZE VAlATLE VARIAjIES R2 Kuglor (1975) 1970 National 607 Logarithm Educational level .50 National individuals of inoame and experience Iouna'-old both sexes level of individual, Survey parents'inccme (DANF.)* Fields (1976) 1967 Bogota 331 logarithm Educational level, .55 Survey of Bucaramanga, individuals, of inccme experience, sex, Occupational Manizales, both sexes city of residence, and feograph- Medellin occupation, parents' ical Mobility education and income (CEDE) Fields and 1973 National 860,000 Logarithm Educational level, Up to .35 SJhultz (1977) C-nsus individuals of income age, department, rural/urban, elployer/ employee Fields (1978a) 1967-c8 Bogota 877 Logarithm Educational level, .41 Survey of Barranquilla, manufacturing of income age, sex, industry, Family Cali, - workers, of employmient Budgets and bledellin both sexes Expnditures (CEDE) Bourquignon (1979) 1974 Barranquilla, 4,700 logarithm Educational level, Up to .38 Iousel-old Bogota, salaried of inocme experience, work Survey Bucaramanga, workers, tine, modern/traditional (DANE) Cali, both sexes sector anizales, Medellin, Pasto -65- The regression evidence he presents (p. 66, reg. l.a) for males in Bogota is: 1/ Log Y = 5.266 + .145 EDUC + .074 EXP - .001 EXPSQ + .196 WORKTIME (.004) (.003) (.000) (.040) 2 + .123 log D, R = .316, n = 3713, (.021) All coefficients are statistically significant at the 1% level. Bourguignon himself interprets the significance of the modern- traditional variable as evidence of a degree of dualism in the Bogota labor market (though he later argues that the degree of dualism is not great). Some new evidence for the workers of Bogota appears in this and the several subsequent sections. The data set used for the econometric work presented here is the Public Use Sample from the 1973 Census, the same source used in the tabulations of Section A, but with certain restrictions. Only the male sample was used, and it was further limited to workers who had an income and who reported that they were employed. The explanatory variables used in the regressions are education, age, industry, and occupation. The migrant variable is omitted due to its insignificance in past studies of urban Colombia (Fields, 1976; Jaramillo, 1979). The sector of residence is omitted in keeping with the model in equations (1")-(4"). 1/ Standard errors are given in parentheses. -66- The first regressions were run on the entire sample and are reported in Table 16. Regression (1) expresses the logarithm of income as a function of education categories and age. The four education dummies correspond respectively to primary education (some or all), and some education, level not ascertained. Thus, the omitted category is no education. Age and age squared are measured in years, the latter to allow the curvilinear effects. We find in this sample of male workers that education and age do indeed contribute significantly to the explanation of income, the coefficients being many times their standard errors. The estimated values are quite reasonable in magnitude. The R2 is .41, a highly respectable figure that compares well with the explanatory levels found in the other studies reviewed above (cf. Table 15). Regression (2) of Table 16 adds a series of industry and occupation categories to the education and age variables and estimates the full set using Ordinary Least Squares. Performance of the industry and occupation is poor: the estimated magnitudes are not very large; many of the estimated effects are statistically insignificant, which in a sample of 44,000 cases indicates really weak performance; and the contribution of these variables to the proportion of variance explained is only 2%, though sti1 statistically significant by standard F tests. From this evidence, we might draw the following inference about labor market segmentation in Bogota based on job type: If labor market segmentation is defined as a statistically significant effect of sector of employment on income for seemingly-comparable workers, the evidence is weak but nonetheless statistically significant that by this definition the Bogota labor market is segmented. Table 16 Regressions on Full Sample Regression (1) Regression (2) Regression Standard Regression Standard Coefficient Error Coefficient Error EDUC1 (Primary, some or all) 0.437 (0.015) 0.405 (0.015) EDUC2 (Secondary, some or all) 1.022 (0.016) 0.908 (0.016) EDUC3 (Higher, some or all) 2.060 (0.020) 1.717 (0.622) EDUC4 (Some education, level not ascertained) 1.301 (0.051) 1.113 (0.050) AGE 0.110 (0.001) 0.108 (0.001) AGESQ -0.001 (0.00002) -0.001 (0.00002) IND1 (Agriculture and mining) -0.155 (0.028) IND2 (Construction) -0.210 (0.015) IND3 (Commerce) 0.065 (0.016) IND4 (Services) 0.020 (0.014) INDS (Other non-manufacturing) -0.108 (0.009) OCC1 (Professional,technical, managerial) 0.505 (0.028) OCC2 (Clerical) 0.119 (0.027) OCC3 (Sales) 0.114 (0.026) OCC4 (Production) 0.020 (0.025) OCC5 (Construction and transport) 0.024 (0.026) OCC6 (Other non-service) 0.002 (0.025) CONSTANT 4.224 4.343 2 R 0.41 0.43 SEE 0.711 0.695 n 41,307 41,307 -68- Actually, the evidence is so weak that I would prefer to say: Only weak support is found for the proposition that the Bogota labor market is segmented by sector of employment. C. Inequality Within and Between Groups In Section B, weak effects for industry and occupation were found in an earnings function which also inclueded education and age. Although these weak results might be symptomatic of model misspecification, I would suggest that the cause for the weak oc< cupation and industry results appears to lie elsewhere. When one looks in Table 17 at the extent of correlation between income, industry, and occupation, a deeper empirical problem becomes evident: Income is not very highly correlated with the broad industry or occupational categories used. Even the highest cor- relation (between TIOGY and OCCl) implies that just 14% of the variance in LOGY is associated with knowledge that the worker is in a professional, technical, or managerial occupation or not. Although it might be argues that the categorization used is too broad or that measurement errors in the Census are severe, it is also possible that incomes of Bogota's workers are not determined primarily by the industry of occupation of employment. What I just said has important implications for the degree of labor market segmentation in Bogota. Any test of segmentation should recognize differences within groups as well-as differences between them. Dual economy theorists hyphotesize that the labor market is divided into a primary and a secondary segment (or modern and traditional). If the dualists are right, earnings in the two -69- TABLE 17 Correlation Coefficients Coefficient of Correlation Industry of Employment Between Log of Income and: (Agriculture and Mining) -.015 (Construction) -.145 (Commerce) .072 (Services) .191 (Other Non-Manufacturing) -.053 Occupation (Professional, technical, managerial) .373 (Clerical) .060 (Sales) .047 (Production) -.122 (Construction and Transport) -.118 (Other Non-service) -.073 -70- segments should be rather distinct. Two alternative possibilities are depicted in Figure 3. The frequency distributions in Panel A are quite consistent with labor market duality. In that case, the sector of employment is rather decisive in predicting an individual's income. Such a finding would suggest that differential access to jobs in the various sectors is an important source of inequality. Further research inot employers' hiring practices might prove particularly fruitful in understanding why the labor market rewards different persons differently. However, if the data were as in Panel B, with much income dispersion within each of the sectors and much overlap between them, it would be much harder to claim a dualistic labor market. Tabulations like those presented in Section A seem to show that workers with various personal characteristics or employed in various kinds of jobs receive quite different returns in the Bogota labor market. But I would caution readers to treat these data carefully before inferring that labor markets are segmented, since no evidence on intra-group income variability is presented in those sorts of tables. Studies conducted in a number of LDC labor markets, including that of Bogota, have shown that despite large differentials in average incomes between one labor force group and another -- where groupings are by education, industry, or other income-determining characteristics -- the great bulk of income inequality is within the groupings. Simply put, no one variable,nor set of explanatory variables combines, is decisive in predicting income with a high degree of precision. -71- Figure 3 DISTRTBUTION OF INCCE WITHN AND BEINEEN LABOR MARKET SECTORS Nu.ber of Persons Earning that Distribution of Distribution of Incorm Amng Those Incce Among Those Emoloyed in Secondary Employed in Primary Sector Sector A. Income Number of Persons Distribution of Incore Distribution of Incame Earning that Amn Those EmEployed Amng Those Emoloyed in nc. in Secondary Sector Primary Sector B. Income -72- In the four major cities of Colombia including Bogota, the following income differences have been observed: Group Mean Income (in pesos per three months) All urban manufacturing workers 6,570 Education breakdown: Primary 3,820 Secondary 8,020 Higher 16,180 Sector breakdown (selected industries): Clothing 4,100 Transportation 5,920 Foodstuffs 6,730 Textiles 8,200 Elec. Machinery 8,240 Chemicals 12,320 Although this may at first seem convincing evidence of labor market segmentation, Figures 4 through 6 demonstrate that there is a great deal of overlap between one education or industry group and another, especially in the industry plots, and no sign of bimodality. Given the more disaggregated presentation of the available data in Figures 4-6, we should be much less willing to conclude that the labor market in urban Colombia is segmented, at least in these dimensions. *'Fields (1978a), derived from 1967/68 family budget data from CEDE. -73- o 50. .r4 0- FIGUPE4 3 DISTRIBUTION OF (LOG) I`CO.'2-, 20 MANUFACTURIG WORERS, URBA:l . 10 COLOMMIA, FULL o SAMPLE, Log of 0 3 4 5 6 7 8 9 10 Income Primary Education 50 ·Secondary Education o 40 \ ligher Edulcation FIGURE 5 \ o30 \ DISTRIBUTION OF (LOG) INCOME 20 BY EDUCATIO:W , 14. GROUP 0 ----'Log of 3 5 6 7 8 9 10 Income e 60 50 ö 40 FIGURE 6 3 DISTRIBUTION OF (LOG) INCOmE BY 2 INDUSTRY GROUP 0 ° 10 - - - , Log of 3 4 5 6 7 8 9 10 Income Key: ___ Foodtuff ___ Chemicals .... Textiles ... Elec. Machinery Clothing --- Transportacion Source: Fields (1978a) -74- We have thus reached a partial answer to the title question: The Bogota labor market is not sharply segmented if, by'labor market segmentation," we mean large income differences between workers in various occupations, industries, or other labor force divisions as compared to variations within those groupings. D. Segmentation Schemes A somewhat different notion of segmentation arises, one that requires a different kind of test. It may be that the earnings functions themselves depend on the segment of the labor force in which an individual works. Accordingly, some researchers have proposed stratifying the labor force into various segments and examining the determinants of income within each. With few exceptions, however, these studies have largely ignored the causal structure of the labor market, i.e., no attention is paid to how the segmenting variables enter the income determination process.- Some, like sex and race, are given attributes of the individual. Others, such as firm size and public/private sector employment, reflect the choices made by individuals in the pursuit of higher economic status and the constraints imposed upon those choices. A third kind of segmenting variable sometimes used is income itself. 1/ See, for example, Psacharopoulos (1978), where segmentation by income, occupation, race, and sex are treated identically. -75- Once these different kinds of segmentation are recognized, they are readily seen to fit in with the causal structure of the labor market. The first kind of segmentation (e.g., sex, race) is by exogenous income-determining factors; the second (e.g., firm sixe, kind of employment) is by endogenous income-determiig factors; and the third is by the dependent variable (income itself). In the next three sections, I shall show that the validity of various segmentation schemes depends critically on how the segments are defined. To summarize the results, I claim: Validity of Intra-Segment Type of Segmentation Earnings Functions Type-1: Regmentation by Exogenous Independent Variable Valid Type-2: Segmentation by Endogenous Independent Variable Questionable Type--3: Segmentation by Dependent Variable Invalid These points are developed at length in Sections E-G. E. Segmentation by Exogenous Income-Determiniiig Factors (Type-1) Some factors are clearly exogenous to the income-determination process. Without question, these include sex, age, race, and family background; somewhat less certainly exogenous, but usually treated as such, are migrant status, education, and religion. What all these factors have in common is that for all practical purposes they are unalterable, i.e., in the pursuit of higher economic status, the individual cannot do anything about these factors. -76- The key conclusion about Type-i segmentation (by exogenous independent variables) is that meaningful results are obtained when the labor force is segmented in this way. l/Mr-reiey Segmentation by exogenous variables produces unbiased estimates of the parameters of the earnings_function for workers in each segment. By this, I mean that an undistorted estimate of the earnings function is obtained for each group. For example, for both men and women in th~e labor force, income (Y) is partly determined by education (X). Figure 7 depicts the pattern found in the raw data for men and women, denoted respectively by M and W, and the fitted regression lines (Table 18). The line in the center is fit to the whole sample; Segment ing the sample into men' s and women's observations, we obtain the upper and lower lines. These two lines respectively give unbiased estimates of the income which a man or woman with the specified education would be expected to receive. It is apparent that the predicted values for the two sexes straddle the line fit to the entire sample. Put differently: The regression fit to the whole sample is not a good predictor of income for anyone: it systemaicll overstates predicted income for women and understates predicted income for men. 1/ This same conclusion holds for the U.S. li terature on differences in male-female and black-white earnings functions. -77- FIGURE 7 STRATIFICATION BY EXOGENOUS INCOME-DETERMINING FACTOR Y (Income) *_Fitted Regression Line, Men Only Fitted Regression Line, Whole Sample SFitted Regression Line, Women Only (Education) -78- TABLE 18. Separate Earnings Functions for Men and Waren in Bogota Dependent Variable: Logarithm of Incare Independent Salaried Salaried Variables Men Wmen Educaticn .135 .094 (.004) (.006) Experience .075 .024 (.003) (.003) Experience Squared -.001 -.000 (1000) (.000) Hours per week .098 .050 (.045) (.046) Mbdern Sector Employent .182 .017 (.025) (.029) Ccnstant 5.625 6.683 R- .384 .180 Number of Observations 2761 '1986 Note: Standard errors in parentheses Source: Bourguignon (1979, p.66) -79- In addition, the regression coefficients (as illustrated by the slopes of the lines in the figure), we see something even stranger: The regression fit to the whole sample systematically overstates the effect of an extra year of education on income for both men and women. The message, very simply, is that when different groups in the labor force receive different incomes, when these incomes are generated by different underlying earnings function, and when the groupings are based on an exogenous characteristic, the sample should be stratified and separate earnings functions run for each segment. In summary: If labor market segmentation is defined as a situation where workers in different groups have different earnings functions, and if it is believed that the labor market is segmented according to exogenous independent variables, unbiased estimates of the parameters of the earnings function may be obtained by stratifying the sample by these alleged segmentation variables. Empirical evidence shows that by this definition, the Bogota labor market is segmented by sex. F. Segmentation by Endogenous Income-Determining Factors (Type-2) Endogenous independent variables are those income-determining factors which result from choices made by and opportunities open to workers in their quest for an improved economic position. These variables include: occupation; industry of employment; characteristics of the occupation, industry, or firm; and place of work. The unifying feature is that incomes vary from one occupation/industry/firm/work place to the next, even for workers with identical personal characteristics. In Bogota, as elsewhere, workers presumably prefer the occupation/industry/ firm/work place combination which pays best. -so- When earning functions differ significantly from one occupational, industrial, or other group to the next, the result is usually taken as evidence of labor market segmentation. This is compatible with definitions of segmentation that emphasize income differences. The empirical evidence is incomplete, however, in that the rules determining access of individuals to various oc- cupations or industries remain unexamined. A review of the literature turns up many studies where earnings t4tructures have been compared for workers in separate occupations, industries, firm size categories, and places of work of residence. Included among the research studies on Colombia are papers by Kugler et al. (1979), Altimir and Pinera (1977), Mohan (1979), and Bourguignon (1979). How valid are within-group regressions when the groups are defined according to endogenous income-determining factors? The answer has three parts: When the labor force is grouped according to endogenous income-determining factors, if there is no mobility between groups, and if the labor force is homogeneous with respect to omitted variables, then within-group regressions are valid. Under these assumptions, intra-group regressions provide meaningful estimates to questions such as: Does education pay off more in the modern sector than in the traditional sector? Given one's education, does income vary with occupation? The stated assumptions imply a -81- completely-segmented labor market, i.e., one in which otherwise identical workers do in fact receive different wages depending solely upon the segment of the labor market in which they are first employed, with no opportunity to move to better segments. When the labor force is grouped according to endogeneus income-determining factors and there is intergroup mobility, however, within-group regressions are invalid. Any degree of mobility between segments means that some of those who start in group i move up to group j. This mobility is ignored in within-group regressions. The result is sample selectivity bias: looking only at the incomes of those individuals who end up in group i underestimates the incomes expected by individuals who started in that occupation or industry or who at some time have worked in it. This bias occurs even if upward mobility takes place without regard to the individual's race, sex, or other characteristics. When the labor force is grouped according to endogenous income-determining factors, the labor force is heterogeneous with respect to omitted variables, and if the effects of these unmeasured variables are ignored, then within-qroup regressions are also invalid. In labor economics, the likely omitted variable is ability. In earnings functions, the coefficients on variables such as education that are correlated with ability are biased upward, since part of Phe estimated return attributed to education is in fact a return to -82- superior ability. If one stratifies by variables correlated with ability and runs separate earnings functions within each stratum, however, the likely effect is to reduce the apparent contribution of education in determining earnings in the lower strata even, 4n extreme cases, producing an apparent ngative relationship between education and earnings. This small or negative relationship is erroneous since it is the (unmeasured) low ability rather than the (measured) high education which results in low income. For example, the low incomes of college graduates working as street vendors more probably reflect the unmeasured physical and mental limitations of those individuals more than it does the lack of skills acquired during 16 years of schooling. Stratifying a sample by endogenous income-determining factors thus yields invalid results except under strong assumptions that do not hold. In each case, the problem is a form of selectivity bias: education tends to raise people's Incomes by moving them out of lower occupational categories into higher ones, and this effect is missed when income functions are estimated within occupations. Estimates of income determination from intra-occupation regressions thus are conditional on remaining in that occupation, and as such are biased downward. An ordinary factory worker who acquires one more year of education is less likely to remain a factory worker, so adverse selection determines the sample. 1/ See, for example, Griliches (1975). -83- Figure 8 illustrates these relationships: FIGURE 8 Segmentation by Endogenous Income-Determining Factor INCOME True effect of EDUC on INCOME Estimated effect of EDUC on INCOME eve *among Office Workers co* Estimated effect of EDUC on INCONE among Factory Workers EDUC Let us now turn to the empirical results. Intra-industry and intra-occupation regressions for the 1973 Census sample of workers in Bogota are presented in Tables 19 and 20. These regressions results are subject to an unknown degree of sample selectivity bias. It is clear by inspection that income structures within industries and occupations are not the same from one to the next. Indeed, by the standard Chow test, these differences are highly statistically significant at all tabulated levels. Comparisons of the regression coefficients suggest that: (1) The effect of education on income is larger in agriculture and mining and commerce than in the other industries. (2) Somewhat offsetting the pattern in (1), commerce which has a small slope is the industry with the highest intercept;i -84- Table 19 Regressions Within Industries IND1 IND2 IND3 IND4 IND5 IND6 Agriculture Construction Commerce Services Other Manufacturing and Non- Mining Manufacturing EDUC1 0.558 0.279 0.666 0.280 0.427 0.350 (0.109) (0.031) (0.069) (0.069) (0.023) (0.033) EDUC2 1.802 0.741 1.455 0.963 0.933 0.836 (0.124) (0.043) (0.070) (0.070) (0.024) (0.034) EDUC3 3.076 2.395 2.270 1.899 1.858 1.955 (0.144) (0.074) (0.085) (0.071) (0.031) (0.046) EDUC4 3.099 0.774 2.149 1.695 1.014 0.964 (0.656) (0.215) (0.229) (0.125) (0.074) (0.103) AGE 0.066 0.082 0.133 0.127 0.106 0.119 (0.012) (0.004) (0.006) (0.005) (0.002) (0.003) AGE2 -0.0005 -0.0009 -0.0013 -0.0013 -0.0012 -0.0013 (0.00015) (0.00005) (0.00007) (0.00006) (0.00003) (Q.00004) CONSTANT 4.368 4.803 3.520 4.034 4.353 4.231 R2 0.560 0.303 0.431 0.573 0.331 0.392 SEE 0.920 0.672 0.821 0.679 0.693 0.655 n 673 4342 3308 4024 18982 9978 -85- Table 20 Regressions Within Occupations OCC1 OCC2 OCC3 OCC4 OCC5 OCC6 OCC7 Professional Clerical Sales Production Construction Other Service Technical, and Non- Managerial Transport Service EDUC1 -0.160 0.251 0.502 0.316 0.382 0.499 0.469 (0.147) (0.083) (0.049) (0.027) (0.026) (0.031) (0.108) EDUC2 0.409 0.751 1.236 0.683 0.724 1.131 1.021 (0.144) (0.082) (0.050) (0.028) (0.032) (0.034) (0.114) EDUC3 1.194 1.322 2.041 1.298 1.425 2.160 2.171 (0.144) (0.085) (0.067) (0.071) (0.156) (0.047) (0.199) EDUC4 0.826 1.120 1.515 0.701 0.467 1.325 1.623 (0.172) (0.141) (0.187) (0.091) (0.161) (0.129) (0.546) AGE 0.125 0.127 0.122 0.112 0.099 0.096 0.113 (0.006) (0.004) (0.004) (0.002) (0.003) (0.003) (0.011) AGE2 -0.0012 -0.0013 -0.0012 -0.0012 -0.0011 -0.0009 -0.0011 (0.00008) (0.00006) (0.00005) (0.00003) (0.00004) (0.00004) (0.00015) CONSTANT 4,811 4.187 3.923 4.444 4.545 4.309 3.982 2 R 0.426 0.483 0.346 0.239 0.192 0.372 0.413 SEE 0.739 0.566 0.828 0.637 0.637 0.730 0.758 n 3266 3780 5944 12472 7117 7885 843 -86- (3) Although age-earnings profiles are steeper in some industries (commerce services and manufacturing) than in others, these differences are not systematically related to the education- earnings relationships. (4) Turning from industries to occupations, we observe that education-earnings profiles are steepest for sales and service workers. (5) As an offsett to the pattern of education-earnings slopes, the occupations with the smallest slopes are those with the largest intercepts (professional, construction, and production workers). (6) The age-earnings profiles do not differ among occupations in any large or systematic way. (7) Intra-industry and intra-occupation earnings functions vary in explanatory power (as measured by R 2); no systematic pattern emerges. To summarize .these results: Significantly different earnings functions are found for workers in different industrial and occupational groupings. This might be interpreted as evidence of labor market segmentation in Bogota, at least according to some of the more common definitions. I should repeat: I have my reservations about these results which cloud the interpretation. Workers in Bogota do not remain in the same industry or occupation throughout their lifetimes; that many change categories is reflected in the age compositions of oc- cupations and industries in Tables 6 and 7. Cross section regressions like those in tables 19 and 20 cannot take adequate account of these -87- changes, so I hesitate to give much weight to these results. Perhaps in the future, once we know the income profiles of individuals who startedout in particular industries or occupations, we will be better able to assess the effect of occupation or industry or employment on workers' life chances. G. Segmentation by Dependent Variable (Type-3) The dependent variable in this study is (the logarithm of) income. Some earlier work has actually or in effect stratified by the dependent variable and run separate earnings functions for each. To do so is methodologically inappropriate. Segmentation by the dependent variable results in severe truncation bias.. Truncation occurs when a sample is limited to include only those cases within a particular range of values. When samples are truncated by the dependent variable, in this case income, the result is a misestimated regression coefficient which is biased toward zero. In particular, this means that when income functions are estimated on a sample of low income workers, they tend in- correctly to find little or no apparent effect of education on income, Es illustrated in the following figure: -88- FIGURE 9 ILLUSTRATION OF TRUNCATION BIAS The essence of the truncation bias is adverse sample selectivity. Intuitively, the reason that bias is introduced when a sample is truncated by income is that one of the effects of education is to raise people's incomes and hence remove them from the sample. It is evident that education has an effect on income even at the lowest levels but this effect is distorted because of truncation. The literature offers many examples of tests for labor market segmentation which suffer from truncation bias. Several authors have looked at the determinants of income for low.income workers and inferred from the small magnitudes of regression coefficients or analysis or variance effects that income is not affected by education (i.e... direct segmentation by the dependent variable). Nothing can 1/ Source: Cain (1976) -89- be learned about labor market segmentation from such invalid "evidence. Equally invalid are similar tests conducted within low income occupations (e.g., among small farmers in poor countries) or within low income neighborhoods (e..g., in urban ghettos or squatter settlements). A direct examination of truncation bias in the case of Bogota is highly revealing. I divided the workers into two groups -- those with incomes above and below 1,000 pesos per month -- and ran separate regressions within each of the two groups. The results, presented in Table 21 and Figure 10, are actually quite extra- ordinary: in the sample as a whole, incomes rise steadily with education; however, within the higher income sample, the income gain is attenuated, because low income workers are systematically excluded; and in the low income sample, the apparent effect of education on income is even smaller and becomes negative for higher education! To reiterate, these estimated relationships are totally biased and ought not to be believed because of adverse sample selectivity and the resultant truncation t_as. To infer from invalid evidence that the human, capital model does not apply to the poor is bad enough just ac ak matter of social science. But the policy implications of that conclusion are far more serious. It would be disastrous if a policy-maker were to back away from educational investments because of evidence like this. This confirms my worst fears: that policies deleterious to the interests -90- TABLE 21 REGRESSIONS WITHIN INCOME GROUP WHOLE SAMPLE INCOME . 1,000 INCOME < 1;000 EDUC1 0.437 0.1109 0.183 (Primary) (0.015) (0.022) (0.015) EDUC2 1.022 0.509 0.343 (Secondary) (0.016) (0.022) (0.018) EDUC3 2.060 1.370 0.143 (Higher) (0.020) (0.024) (0.081) EDUC4 1.301 0.783 0.141 (Level Unknown) (0.051) (0.047) (0.106) AGE 0.110 0.060 0.048 (0.001) (0.002). (0.002) AGE2 -0.001 -0.003 -0.006 (0.00002) (0.00002) (0.00002) CONSTANT 4.224 5.872 5.338 R2 0.410 0.371 0.061 SEE 0.710 0.555 0.568 N 41.307 24.693 1.6614 -91- FIGURE 10 PREDICTED VALUES FOR WHOLE SAMPLE AND TRUNCATED SAMPLES LOGY ..- High Income Sample Whole Sample Low Income Sample II I EDUC N P S H NONE PRIMARY SECONDARY HIGHER -92- of the poor may be promulgated from a basis of ill-designed research findings. Many times, it is better to take on a small problem and do it right rather than to take on a larger problem and do it wrong. H. Group Determination and Inter-Group Mobility In Sections D-G, we examined the determinants of income for different groups of workers. That kind of analysis did not address the determination of group membership. Let us now indicate how that gap might be filled in future work. . The preceding sections distinguished between segmentation by exogenous income-determining factors, by endogenous income- determining factors, and by income itself. It is not of much interest to social scientists to predict sex, race, or other exogenous characteristics with one exception: predicting educational attain- ments by family background and local opportunity variables is of interest. We have ample evidence fro. Colombia and other countries that educational attainments are closely-linked across generations (i.e., the children of highly-educated, well-off parents are them- selves more likely to achieve higher schooling levels) and by location (i.e., where there are more schools, more children attend).1/ For a limited group of workers in Bogota -- namely, those who live with their parents the 1973 Census data could be used to relate the 1/ The Colombia evidence includes studies by Rama (1969), Parra (1973), Urrutia (1974), Kugler (1975) and Fields (1976). -93- worker's income to his/her own education and the ed-acation, oc- cupation, and income of the parents; but I would not advise such a research undertaking since the sample of workers who live with their parents cannot be thought to be representative of the labor force as a whole. Turning our attention to endogenous income-determining factors, this study has identified industry and occupation as such factors. Tabular evidence is available on the characteristics of workers in different occupations and indtstries (cf. Section A above and the references cited therein); but these efforts have not proceeded to the point of formal modeling of occupational/industrial outcomes, in Colombia or in other LDCs. Finally, on the determination of membership in the poverty group (i.e., segmentation by the dependent variable), the studies by Mohan (1979), Bourguignon (1979), and earlier works cited therein give a clear picture of who the poor of Bogota are. I need not go further into poverty profiles at this time. There remains the question of inter-group mobility or lack thereof. Up to now, no researcher has had access to data on changes in economic status over time. Consequently, we have been unable to gauge the extent to which today's poor were also yesterday's poor or whether different persons have taken their place. In addition, it 1/ That is what Kugler (1975) did using an earlier national statistical office (DANE) sample. -94- has been impossible to look at changes over time in occupation, industry, and other measures of labor market status. Even fine microeconomic cross sectional data sets like the Census sample cannot be used for dynamic purposes. Fortunately, new kinds of data sets are being generated. It would be particularly useful in these new data sets to solicit retrospective cross section information and/or longitudinal panel data to answer questions about inter-group mobility. We could, if we had such data, move ahead toward assessing the degree of mobility in the Bogota labor market, relate the observed inmobility to labor market barriers, and thereby move even further toward determining the degree of segmentation in the Bogota labor market. Studies such as the ones outlined above would help clarify the proximate causes of poverty. The root causes of poverty in Bogota remain to be understood. -95- IV. CONCLUSIONS A. Conceptual Conclusions Four criteria for defining labor market segmentation were articulated: the definition should not be equivalent to the phenomena to be explained; a satisfactory definition must distinguish actions by segmenters which lead to labor market inequality from "justifiable" differences among workers; the definition should in principle permit identification of how the segmenter effects segmentation. The existing definitions never fulfill and seldom approach these criteria. Five definitions commonly used in the segmentation literature were reviewed. These are: (i) Heterogeneity of outcome. (ii) Heterogeneity of outcomes among "comparable" workers as a function of group in the labor market (e.g., occupation or industry). (iii) Heterogeneity of labor market functioning in various submarkets. (iv) Limited access to good jobs. (v) Non-random access to the available jobs. Taken together, Definitions (ii) and (v) are the most helpful concepts of labor market segmentation yet devised. Jointly, they direct our attention toward the actual wage - and employment - determination mechanisms in labor markets. They take the first step toward explaining why intergroup labor market differentials exist by showing that inter- group market differentials exist in particular dimensions. Eight statistical models for empirical estimation of a segmented -96- labor market were presented. The available data set for Bogota affords measurement of income, sector of residence, education, age, migrant status, industry and occupation of employment, and average incomes in the several industries and occupations. The workings of the Bogota labor market suggest a model with four equations in four endogenous variables -- income, industry, occupation, and sector of residence. None of the eight models which have been applied in the literature fully capture the interrelationships among the variables. Hence, one cannot simply borrow an established procedure developed in some other context and apply it as is to Bogota. Upon further consideration, some econometric models appear more justified by the underlying labor market conditions than do others. These econometric issues condition the interpretation of the empirical evidence, discussed beiow. B. Empirical Conclusions In answer to the empirical question of how segmented Bogota's labor market is, the following results were found: 1. There are differences in income among various groups of workers. The sample of workers in Bogota was divided according to sex, age, education, migrant status, industry, occupation, and sector of residence in the city. For each such grouping, differences among groups were evident, both in the simple tabulations (Table 2) and in the cross- tabulations (Tables 3-14). - 97 - 2. None of these characteristics decisively segments the labor force. We find much inequality within each group and much overlap between the groups. The data exhibit neither a clear duality nor any sign of bimodality (Figures 4-6). 3. Among male workers in Bogota, there are significant differences in income given education and age for workers in different industries and occuDations. (Table 16) However, not all differences are economically significant in magnitude or statistically significant in sign. Thus: 4. Overall, only a weak correlation apears between income and occunation or industry of employment. While there are tendencies for workers with the same education and age to earn higher incomes*in one industry or occupation rather than another, these tendencies are not at all pronounced. (Table 17). 5. Segmenting the labor force according to exogenous exlanatory variables results in significantly different earnings functions for different labor force grouvs; these grounings are statistically valid. Earnings functions estimated separately for men and women are significantly different from one another (Table 18). 6. Segmenting the labor force according to endogenous explanatory variables results in significantly different earnings functions for different industrial and occupational groups; these groupings are only approximately valid. The earnings functions are noticeably different for workers in various industries (Table 19) and occupations (Table 20). This kind of segmented earnings function with the segments chosen according to - 98 - endogenous explanatory variables would be exactly valid if labor market segmentation were complete (i.e., no worker who starts working in a low-paying segment is able to enter a higher-paying segment subsequently) and if the distributions of abilities among workers in thn various segments were identical. Since these assumptions presumably do not hold in the Bogota labor market, however, selectivity bias occurs in the within-industry and within-occupation regressions, rendering the estimated earnings functions only approximately valid. 7. Segmenting the labor force according to income results in significantly different earnings functions for the poverty and non- poverty groups; inferences of labor market segmentation drawn from these grouoings are totally invalid; When separate regressions were run for poverty and non-poverty groups, there appears to be a positive relationship between income and education for the non-poverty group only; for the poverty group, a weak relationship between income and education appears in the lower educational categories and a negative relationship is found between income and education at the higher educational levels. (Table 21) These results are completely biased due to the adverse sample selectivity and consequent downward truncation bias which is introduced when a sample is chosen on the basis of the dependent variable. Income and education are in fact positively related throughout the entire range of observations (Table 16) and hence the slopes of the earnings functions estimated for both the poverty and the non-poverty groups are biased downward (Figure 10). C. Needs for Future Research The empirical work presented in this paper used a particular kind of data (microeconomic Census data on individuals) and focused on a particular kind of question (segmented earnings functions). The -99- needs for future research fall into three general areas: doing a better job on the same kind of question with the same kind of data, addressing a broader range of questions, and using different kinds of data. These are neither mutually exclusive nor jointly exhaustive. Perhaps the least expected empirical result reported here is the weak effect of the industry and occupation on income. The weakness of these variables is open to a variety of interpretations, most prominently: a genuinely integrated labor market or mismeasurement. Further work on segmentation by type of employment needs to establish which is the better explanation. There is value in moving beyond data based solely on cross sections of individuals. We need to look at household data on chaiges over time changes in wages, in kind of employment, in labor force participation rates and and their relationships to education, sex, occupation, initial income, and the other variables considered in this paper. We need also to examine differences in firms wage structures, employment decisions, hiring practices, and promotion policies. We need to observe information and misinformation, mobility and immobility, access and barriers to access, and stratification in the labor market. And we need to integrate these different kinds of information and relate them to poverty, inequality, and development. -100- Bibliography Altimir, Oscar and Sebastian Pin-era. "Decomposition Analysis of the In- equality of Earnings in Latin American Countries." Economic Commission V for Latin America and World Bank, mimeo. August, 1977. Becker, Gary S. The Economics of Discrimination. (Chicago: The University of Chicago Press, 1957). Berry, R. Albert and Miguel Urrutia. Income Distribution in Colombia. (New Haven: Yale University Press, 1976). Bluestone, Barry. "The Tripartite Economy: Labor Markets and the Working Poor." Poverty and Human Resources. 1972. Boskin, Michael J. " A Conditional Logit Model of Occupational Choice." Journal of Political Economy. June, 1974. Bourguignon, Francois. "Pobreza y Dualismo en el Sector Urbano de las Economias en Desarrollo: El Caso de Colombia." Desarrollo y Sociedad. Enero, 1979. Bowles, Samuel and Herbert Gintis. "The Problem with Human Capital Theory: A Marxian Critique." American Economic Review. May, 1975. Brown, Randall S., Marilyn Moon, and Barbara S. Zoloth. "Incorporating Occupational Attainment in Studies of Male-Female Earnings Differentials." Journal of Human Resources, forthcoming. Cain, Glen. "The. Challenge of Segmented Labor Market Theories to Orthodox Theory: A Survey." Journal of Economic Literature. December, 1976. Doeringer, Peter and Michael J. Piore. Internal Labor Markets and Manpower Analysis. (Lexington, Mass.: Heath, 1971). Edwards, Richard C., Michael Reich, and David M. Gordon. Labor Market Segmentation. (Lexington, Mass.: Heath, 1975). Fields, Gary S. "Education and Economic Mobility in a Less Developed Country." Economic Growth Center, Yale University. Discussion Paper No. 237. revised version. June, 1976. Fields, Gary S. "Analyzing Colombian Wage Structure." World Bank. Studies in Employment and Rural Development. No. 46. May, 1978 (197 8a). Fields, Gary S. "On Labor Market Segmentation." Paper presented at the Conference on Economic and Demographic Change: Issues for the 1980's. International Union for the Scientific Study of Population. Helsinki. August, 1978 (1978b). -101- Bibliography, continued Fields, Gary S. Poverty, inequality, and Development. (New York: Cambridge University Press, forthcoming). Fields, Gary S., with the assistance of Jorge H. Ducci. "Education and Income Distribution in LDC's: A Review of the Literature." World Bank, forthcoming. Fields, Gary S. and T. Paul Schults. "Sources of Income Variation in Colombia: Personal and Regional Effects." Economic Growth Center, Yale University. Discussion Paper No. 262. June, 1977. revised version forthcoming in Economic Development and Cultural Change. Fields, Gary S. and T. Paul Schultz. "Income-Generating Functions in a Low Income Country: Colombia." Cornell University and Yale University, mimeo. July, 1979. Flanagan, Robert J. "Segmented Market Theories and Racial Distrimination." Industrial Relations. October, 1973. Freedman, Marcia. Labor Markets: Segments and Shelters. (New York: Allanheld, Osmun/Universe, 1976). Goldberger, Arthur S. Econometric Theory. (New York: John Wiley, 1964). Gonzales, Helena. "Determinantes de los Patrones de Ingreso y Consumo para la Ciudad de Bogota." Tesis de Grado, Universidad de Los Andes, Bogota'. Junio, 1971. Gordon, David M. Theories of Poverty and Uiideremployment. SLexington, Mass.: Heath, 1972). Griliches, Zvi. "Estimating the Returns to Schooling: Some Econometric Problems." Presidential Address, 3rd World Congress of the Econometric Society. Toronto. August, 1975. revised January, 1976. Farrison, Bennett. "Education and Underemployment in the Urban Ghetto." American Economic Review. December, 1972. Heckman, James J. "Dummy Endogenous Variables in a Simultaneous Equation System." Econometrica. July, 1978. Isaza, Rafael and Francisco Ortega. "Encuestas Urbanas de Empleo y Desempleo: An£Lisis y Resultados," Centro de Estudios sobre Desarrollo Economico, Universidad de Los Andes, Bogota. Monograffa #29. 1971. Jackson, John E., Arthur P. Solomon, et al. "Urban and Regional Development: A Critical Review of the Literature." Harvard University, mimeo. 1976. -102- Bibliography continued Jaramillo, Helena. An Analysis of Migration in Colombia. Unpublished Ph. D. dissertation. Yale University, 1979. Johnston, J. Econometric Methods. (New York: McGraw Hill, 1972). Kahne, Hilda. "Economic Perspectives on the Roles of Women in the American Economy." Journal of Economic Literature. December, 1975. Kannappan, Subbiah, ed. Studies of Urban Labor Market Behavior in Developing Areas. (Geneva: International Institute for Labour Studies, 1977). Kugler, Bernardo. "Influencia de la Educacion en los Ingresos del Trabajo: El Caso Colombiano." Revista de Planeacion y Desarrollo. Enero, 1975. Kugles, Bernardo, Alvaro Reyes, y Martha Isabel de Gomez. Educacin y Mercado de Triabajo Urbano en Colombia: Una Comparacion entre Sectores Moderno y No Modernos, Monograflas de la Corporacion Centro Regional de Poblacio5n, Vol. 10, Mayo, 1979. Langoni, Carlos. "Income Distribution and Economic Development: The Brazilian Case." Paper presented at the World Econometric Society Congress, Toronto. 1975. Lloyd, Cynthia B., ed. Sex, Discrimination, and the Division of Labor. (New York: Colombia University Press, 1975). Mazumdar, Dipak and Masood Ahmed. "Labor Market Segmentation and the Determination of Earnings: A Case Study." World Bank, mimeo. November, 1977. McFadden, Daniel. "Quantal Choice Analysis: A Survey." Annals of Economic and Social Measurement. Vol. 5, No. 4, 1976. Merrick, Thomas. "Employment and Earnings in the Informal Sector in Brazil: The Case of Belo Horizonte." The Journal of Developing Areas. April, 1976. Mohan, Rakesh. "Population, Income and Employment in a Developing Metropolis: A Spatial Analysis of Bogota, Colombia." World Bank, mimeo. November, 1979. Musgrove, Philip. "Urban Household Income and Consumption Patterns in Latin America: A Comparative Analysis of Colombia, Paraguay, Peru", and Venezuela." (Washington; Brookings Institution, 1974). Musgrove, Philip. Consumer Behavior in Latin America. (Washington: The Brookings Institution, 1978). -103- Bibliography (continued Olsen, Randall, J. "Comment on 'The Effect of Unions on Earnings and Earnings on Unions: A mixed Logit Approach.'" International Economic Review. February, 1978 Osteman, Paul. "An Empirical Study of Labor Market Segmentation." Industrial and Labor Relations Review. July, 1975. Parra Sandoval, Rodrigo. Analisis de Un Mito: La Educacion como Factor de Movilidad Social en Colombia. (Bogota: Departamento de Educaci6n, Universidad de los Andes, 1973). Prieto, Rafael. Estructura del Gasto y Distribucion del Ingreso Familiar en Cuatro Ciudades Colombianas: 1967-68. (Bogotd: Centro de Estudios sobre Desarrollo Economico, Universidad de los Andes, Mayo, 1971). Psacharopoulos, George. "Inequalities in Education and Employment: A Review of Key Issues with Emphasis on LDC's. Paper prepared for participants in the IIEP/Inter-Agency Seminar on Inequalities in Educational Development. International Institute for Educational Planning. Paris. November, 1978. Rama, German D. "Educaci'n Universitaria y Movilidad Social: Reclutamiento de Elites en Colombia." C.I.D. Universidad de Colombia. Noviembre, 1969. Reynolds, Lloyd G. Labor Economics and Labor Relations, Seventh edition. (Englewood Cliffs, N.J.: Prentice-Hall, 1978). Schmidt, Peter and R. P. Strauss. "Estimation of Models with Jointly Dependent Qualitative Variables: A Simultaneous Logit Approach." Econometrica. July, 1975. Schnare, Ann B. and Raymond J. Struyk. "Segmentation in Urban Housing Markets." Journal of Urban Economics. Vol. 3, No. 2, 1976. Schultz, T. Paul. Returns to Education in Bogota, Colombia. (Santa Monica, Calif.: The Rand Corporation, RM-5645-RC/AID. September, 1968). Selowsky, Marcelo. "Balancing Trickly Down and Basic Needs Strategies: Income Distribution Issues in Large Middle-Income Countries with Special Reference to Latin America." World Bank Staff Working Paper No. 335. June, 1979. Souza, Paulo R. and Victor E. Tokman. "The Informal Urban Sector in Latin America." International Labour Review. November-December, 1976. -.04- Bibliography (Continued) Specht, David and Richard Warren. "Comparing Causal Models." in David Heise, ed. Sociological Methodology. (Jossey-Bass, 1976). Urrutia, Miguel. "La Educacion Como Factor de Movilidad Social." CEDE. Universidad de Los Andes, Documento de Trabajo No. 12.Julio, 1974. Wachtel, Howard and Charles Betsey. "Employment at Low Wages." Review of Economics and Statistics. May, 1972. Wachter, Michael. "Primary and Secondary Labor Markets: A Critique of the Dual Approach." Brookings Papers on Economic Activity 3: 1974. Webb, Richard. "Income and Employment in the Urban Modern and Traditional Sectors of Peru." Princeton University, Mimeo. November, 1974