Numerous Factors of the Education Process Were Analyzed Statistically In

DOCUMENT RESUME ED 025 365 24 RC 003 112 By- Goldblatt. Phyllis K. Education in Relation to Social and Economic Change in Mexico. Chicago Univ.. Ill. Spons Agency- Office of Education (DHEW). Washington. D.0 Bureau of Research Report No- CRP- S-008 Bureau No- BR-5-8266 Pub Date Jun 68 Contract- OEC-4- 10-100 Grant- OEG- 3- 6500-00- 3637 Note-460p iw F /, 73 i4. C,13.10 60.-Descriptors-C,ultural Factors. Diffusion Economic Deve:opment. Economic Factors. Educational CNange. t 1 *International Education Rural Environment. Rural Urban Differences. Social Change. Social Development. Social Factors Identifiers- *Mexico Numerous factors of the education process were analyzed statisticallyin relation to variables relating to social and economic change factors during the period 1930-1960 in Mexico. Major findings included the following: (1) from 1940 to1960 general economic and social conditions became more favorable, but post-primary schooling of adults did not improve (2) little indication was shown fordifferential growth indices between the several Mexican states; (3) the stability of relationships depended to a large extent on lack of an identification with the Federal state. and(4) the areas lagging behind national rdices showed a larger change component over the twenty-year span. All educational variables were related to the amount of change on the social and economic ndices by using multiple correlation statistical analysis UM) R6-T616,6 PP Cgt9s-408. Cs&13g. FINAL REPCRT Project No. S-008 Grant No. 3-65oo-oo-3637 1 0E-4-10-1.00 Research

EDUCATION IN REATION 113 SOCIAL AND ECONOMIC

CHAIM IN 10=00

E D07.5365

June 1968

U. S. DEPARTMIT OF EDUCATI3N, AND WELFARE Office of Education %Mail of Research

1 410414-1.i&s.* Castoecar FINAL REPORT

Project No.5.008 Grant No. 3-6500-00-3637 MN OE-14-10-100Research

EDUCATION IN RELATION TO SOCIAL AND ECONOKIC

CHANGE IN 141=00

Phyllis K. Goldblatt

University of Chl.,ago Chicago, Illinois

June1968

U.S. DEPARTMENT OF HEALTH. EDUCATION A WElfARE

OFFICE OF EDUCATION

TINS DOCIMEM NAS DIII REPIODUCED EMILY AS RECEIVED FROM TNE PINSON OR ONAINZATION OINANIATINA IT.POINTS OF VIEW 01 OPINIONS

STATED DO NOT NECESSAIRY 1E11E1E10 OFFICIAL OFFICE OF EDUCATION

POSITION 01 POUCY.

The research reported hereinwasperformed pursuant to a contract with the Office of Education, U. S. Department of Health, Education, and Welfare. Contractors undertaking such projects under Government sponsorship are encouraged to express freely their professional judgment in the conduct of the project. Points of view or opinions stated do not, therefore, necessarily represent official Officeof Education, position or policy.

U. S, DM'ARTI4 OF lEALTIT, EDUCATION, AND WETYARE

Office of Educatim Bureau of Research I am indebted to the Department of Health, Education and Welfare, Office of Education, for the funds which supported this project.

The members af my committee, Professors C. ArnolTi Anderson, Chairman,

Mary. Jean Bowman, and Phillip Foster have been =unfailing source of sound advice and encouragement.In particular, I am indebted to Professor Mary Jean

Bowman who gave so generously of her time, for her counsel and her interest.

I also appreciate the talents Roger LeCompte and Gerald Pyle expended on the maps and diagrams.

My husband and children stand out as major contributors to the completion of this work.Their support, understanding, and good humor were un- diminished to the end,

ii TABLE OF CONTENTS

Page

ACKNOWAEDGMENTS . .. ..*.. ii LIST OF TABLES iV LIST OF ILLUSTRATIONS P . Viii Chapter I MISODUCTION 1 IL THE SOCIO-CULTURAL GEOGRAPHY OFIMI00 ..

III. EDUCATIONAL ATTAIN/MS AND THESOCIO-ECONOMIC STRUCTURE: THE SITUATION IN 1960

IV. STABILITY AND CHANGE OM TIM: 1940AND 1960 OOMPARED 0 0 146

V. LEVELS OF ADULT SCHOOLING ASINFLUENCES ON THE SCHOOLING OF YOUTH 204,

VI. DETEMINANTS OF THE DIFFUSION OFPRIMARY SCIEOLING 263

VII. SUMMARY AND CONCLUSIONS 310

APPENDICES A. GLOSSARY .. 328

B. CORREIATION MATRICES . .. 353

C. PARAMETERS OF RB3RESSION EQUATIONS 4147 BIBIIOGRAPHY 149' LIST OF TABLES

Table Page

1. Inventory of Variables Used 29

2. Variables Constructed as Ratios or Differences of Table 1 Variables 38

3. Factors Describing Modernization, Agriculture, and Cultural Characteristics ofMAXiC0 53

4. Correlations of Variables Describing Population Distribution . 73

S. Correlations between Urbanization and Occupation Variables, 1940 and 1960 77

6. Correlations between Urbanization and Agriculture

Variables lb 0 0 81

7. Correlations between Urbanization and Industrialization Variables 86

8. Correlations between Proportions of Males Walking Barefoot and in Agriculture and Urbanization Variables 99

9. Correlations between Urbanization Variables and Variables Relating to Transportation, Communication, and Facilities, 1960 106

10. Correlations between the Proportions of Males Walking Barefoot andinApiculture and Transportation Variables 107

U. Correlations between Communication Variables and Characteristics of Agriculture 108

12. Distributions of Literacy Rates by Age, Sex, and Residence, 1960 0 00 115

13. Distribution of Levels of Schooling of the Adult Population by Sex, 1960 119

14. Distribution of the Proportions of Males and Females Who Were Economically Active, 1960 122

iv LIST OF TABLES--Continued

Table Page

15, Correlations of Marriage and Fertility Rates of Females, 1960, with Variables Relating to Education, Occupations, and Urbanization 124

16, Distributions of the Economically Active Population Within Sex and Occupation Categories, 1960 127

Correlations between Occupations and Variables Relating to Literacy and Schooling, 1960 129

Correlations of Indices of Income and Development with Variables Relating to Literacy and Schooling, 1960 134

Correlatione of Indices of Agricultural Development

Education and Other 1960 Variables Showing the Highest Zero-order Correlations with Literacy ID+, No School.;_ng, and 7+ Years of

Schooling . 144

24, Median 1940 and 1960 Values and Intertemporal Correlations; Selected Variables 149

25, Selected 1940 Zero-order Correlations 153

26. Selected 1960 Zero-order Correlations 155

27, Correlations of Variables Measuring Change with Selected 1940 Variables and with 1960 Sex Differences in Literacy 161

28. Intercorrelations of Change Variables among Themselves 165

29, Selected Factors witn One or More High Loadings on Change Variables 168

30. Indices of Intensity of Life-time In-migration; States with Net In-migration, 1960 183 LIST OF TABLES.,-Continned

Table Page

31. Extent oi Out-migration andMajor Destinations of Out- migrants; States with Net Out-migration, 1960 . 184

32, Distribution of In-migrantsfrom States with Various Proportions of Population Engagedin Agriculture 196

33. Correlations of Male In-migrationRates with Other Variables 199

34. Comparisons of Literacy Rates by Age:Mexico and Iran 213

35. Distributions of Literacy Rates: Mexico and Iran Compared 216

36. Correlations among Literacy Rates in States of Mexico and Districts of Iran. 217

37. Correlations between Literacy of Youth and Levels of Adult Schooling, 1960 230

38. Factors with High loadings on Literacy and Schooling of Youth 231

39. Distributions of Intergeneration Gains in Literacyby Age, Sex, and Residence 243

40. Correlations between Adult Levels of Schooling andAge Differences in Literacy, 1960 244

41. Correlations of Urbanization, with TimeDifferences in Literacy of Youth, amiwith School EnrollmentRates, 1930to 1960 246

42. Distributions of Enrollment Rates, 1930to 1960 247

43. Correlations between Enrollment of Youth and Adult Levels of Schooling ...... 249

44. Correlations between Enrollment of Youth and Adult

Literacy in Mexico and Iran * 250

45. Distributions of Rural and Urban Continuation Rates, 1960 and1942 252

46. Correlations between 1940 and 1960 Education-of-youth Variables 253

47. Intercerrelations between Schooling of Children and Middle Levels of Schooling of Adults 1930 to 1950 254

vi LIST OF TABLES--Continued

Table Page

48. Intercorrelations between Schooling of Children and Middle Levels of Schooling of Adults, 1960 256

49. Associations between Age-grade Patterns and Selected Indicators of Lead and Lag in Modernisation 259

5o. Illustrative Correlations of Enrollment Rates and of Child Employment with Selected. Variables, 1960 273

51. Illustrative Correlations of Enrollment Rates and Child Literacy with Selected Variables, 1930 to 1940 282

52. Multiple Regression Analysis, Set 1 Dependent Variabler

Enrollment Rate, 1937 T . . ****** 288

53. Multiple Regression Analysis, Sot 2' Dependent Variables Enrollment Rate, 1960 T 294

54. Multiple Regression Analysis, Set 3 Dependent Variable:

Urban Enrollment, 1960 . . . 295

55. Multiple Regression Analysis,Set 4 Dependent Varieble: Rural Enrollment, 1960 . . .. 304

56. Federal District Enrollment i'rediction Tests 306

57. Correlatioiv Matrix: Population, Transportation, Utilities and Communication Variables against Themselves, Each Other, and Selected Other Variables . 353

58. Correlation Matrix: Occupational and Economic Characteristics against Population, Transportation,

Utility and Communication, and Selected Other Variables . . 363

59. Correlation Matrix: CUltural Attributes by-Demographic, Transportation, Utility and Communication Variables; by Occupational and Economic Characteristics; by Literacy Rates, Youth Employment, and Enrollment; and Population

Wilking Barefoot Variables against Themselves . 370

vii LIST CI TABLES--Continued Page Table 60. Correlation Matrix:Occupational and Economic Characteristics against Thous lves, BachOther,against AdultEducationalAttainmentsandagainst Six Othor Se looted Variables 378 61. Correlation Matrixt EducationVariablos against Each Other:Litoracy of Populationand Diffrencs in Litoracy by Ago, Sox, and Residence;Adult Levels ofSchooling; Iwo llmont Rat's by Tear,Residence, Income and Occupation; Progress in School--ContinuationRates, Age-GradeLevels, Pass Rates, and School Facilities 390

Variables against ihesselvos, 62. Correlation Matrix: Education Literacy Rates, Adult Levels ofSchooling, Secondary Schooling, Primary School Enrollments,Primary School Continuation Raton and SeloctedVariables Showing School Retention 404 63. Correlation Matrixof Education Variables,Literacy, Adult Levels ofSchooling, Prinary School Enrollments, and Progress in School againstNon-Education Variables: Population Distribution andChange,Transportation, Utility and CommunicationFacilities, Occupational mudkonaltic, and Cultural Characterbtics 421 64. Paranoters of RogressionEquations Wet

viii LIST OF ILLUSTRATIONS

Figure Page

1, Economic Indicators for Mexico and for Argentina . 46

2, Map of Elevations, Mexico . 61t

3. States and Major Cities of Meolco . 66

4. Matrix D, Factor 1 . 69

S. Population Distribution and City Size, 1960 . 72

6. Industrial Concentrationsand Locations ofSelected Basic Industries I, 1957 . 89

7. Industrial Concentrationsand Locations ofSelected Basic Industries II, 1957 91

8. Moimatrial Concentrationsand Locationa ofSelected Vital Industries I, 1957 . . 93

9. Industrial Concentrationsand Locations ofSelacted Vital Industries II, 1957 95

10. The Railway Network and Industrial Concentrations 302

U. The Highway System 105

12. Scattargram of Proportions of Adult Males 30+ /ears vith 7+ Tears of Schooling byProportions of Economically Active Bales in Clerical Occupations, 1960 . . 132

13. Scattergram of Percentages of Males Barefoot, 1960 and Percentages of Females Age 40 aad Over Literate, 1960 1112

14. Matrix D, Factor I . . 3.78

15. Matrix D, Factor 2 . . 181

16, Origins of In-migrants u Central and Southern States (Born in Other States), 1960 187

17. Origins of In-migrants to Northern States (Born in Other States), 1960 0 . . 189

ix LIST OF ILWSTRATIONS--Continued

Figure Page

18. Destinations of Out-wigrants Bornin Southern States, 191 1960 .,

19. Dertinations of Out-migrants Born in North and Worth Pacific States 193

20. Destinations of Out-migrants Born in Central States, 1960 195 208 21. Interpersonal Communication Network

22. Scattergraa of Percentages of Literate1D-14 Year Olds, 220 MUGtarFolla1e82 1960

23. Scattergraa of Percentages of Rural Literate10-34 leer 222 Olds, Males by Females, 1960 .

216 Scattergraa of Percentages of Literate Males110+, 1940 bir 1960 - 224

25, Scattergraa of Percentages of Literate Malesiso+ Tears by 10-14 Tears, 1960 . . 226

26. Scattergraa of Percentages of Literate Fmalas 40+ Years by 10-314 Years, 1960 . . . . . 228

27. Matrix C, Factor 1...... 2140

28, Matrix C, Factor3 me

29. EnrolbmentRAtesof 6-14YeaOlds, MalesOm 268 Femaleej 196J

30. Urban Enrollment Rates of 6-14 Year Olds,1960 270

27: 31. Rural Enrollment Rates of6-114Year Olds, 1960

32, Iaformation and Communication Channels 276 278 33. Decision Model

0 303 314. MatrixAsFactor 11 ... CHAPTER I

INTRODUCTION

A problem Oared by most of the developing countriesis that of welding disparate, locally-organized sub-societies into a unifiednatIon.One concomitant of development is the drawing into a nationalnetwork of economic and political participation of many formerly autonomous, self-eufficient groups.

This study is concerned with the socio-economic ecology of human-cesourcede- velopment, its distribution and association with other aspects ofdevelopment of modernization as manifested in adult populations.It deals also with the diffusion of modernizing influences through the spread of literacyand schooling among the members of the rising generation. Conceptually the study is grounded primarily in communication theory, primarily as delineatedby Torsten 'Diger-- straw:11s constructs of "information fields" and °resistance"and in Durkheimits dual concept of social and physical density. Thns, although the data are for geographic units, the relevant "space* is communication apaceand the geographic data are interpreted accordingly.

Mexico is used as an example.Previous studies have described the large proportion of the Mexican impulation living alife marginal to national development, the vast disparities among regions in degreeof modernization, and the advance of the key cities over the rest of the natIon. Casanova identifies the

"marginal° population by literacy and by habits of foodand dress. Certain foods

and ways of dress are customarily used by indigenouspeople; others, as the wearing of shoes, require the adoption of anew styleof life. Intercorrelations

1 2 among these variables of food and dress show that clusters of them(e.g., eating maize, walking barefoot, illiteracy) are highly associated with the proportion of the population living in rural areas. In spite of the fact that Mexico has undergone a genuine social revolution and has experienced industrializationand urbanization, marginal populationspersist.1

Tates, in his study of regional development, contraststhe accelerated growth of favored areas and the continuing gap between prosperous,and poorareas.2

In Education and National Devellpment, Myers emphasizesthe heavy concentration 3 of development of resources in a few industrial areas only. The emphasis in this stu4y is in identifying the educational components of adevelopment nexus so that development potential canbe spotted outside of the clearly advanced areae

These studies implicitly suggest barriers in communication, i.e., in the flow of influence from the modernized to the traditional segments ofthe population. They suggest also the need to stlidy how peripheral groups becomepart of national life, in part by the diffusion of innovations. Innovations maybe of diverse kinds, little and big. Taey include not only the use of new objects

(as wheat bread or bicycles) but also new institutions ar agencieseach as schools--or even acquisition of literacywithoutschooling among heretofare illiterate populations. The ways in which innovations aretransmitted through

the population and the responses of therecipients to new idea., set the conditions for social change.

1Pablo Gonzalez Casanova, "Sociedad plural :y. desarrollo:el case de Mexico," America Latina V, No. 4(OctubromDezeMbro de 1962), 3141.

2Paul Lamartine Yates, El Desarrollo Regionalde Mexico (Mexico, DJ.: Banco de Mexico, S.A. Departamentod6 Investiaciones Industrialise, 1962).

3Charles Nash Myers, Education and, National Eevelopment in Mexico (Princmot&L, New Jersey: Princeton University,rndustrialW.ationsfeotian, 1965). 3

Several of the studies in Meador) have focumian communication links studying the nature of the contacts which reduce the isolation of rural people.

Kunkel, in a study of several communities, found economic dependence (above the level of differentiation of activities within communities) to be present in almost all the cases where new ways had penetrated. He concluded that economic links are a precondition for broader socialchanges.1The Youngs and Moore also explored the conditions under which change occurs.They asked why people leave farming for industrial work. The Youngs found that movement toward industrial work was a !Unction of associations with people who were already involved in the 2 urban occupational structure. The foregoing works used the community as the unit of observation in studying under what conditions a visible commitment to new ideas comes about, why people change their occupation, or why they accept new social customs.

As one aspect of a larger study, Glick identified the most developed areas in Mexico and Classified the remaining states in their locational relation- shi? to these centers.Here development was seen as a function of location, of

MOM/ 1 J.H. Kunkel, "Economic Autonomy and Social Change in Mexican Villages," Economic Development and Cultural Change, X, No. 1 (October, 1961), 51-63.

2 Frank and Ruth Young, "Individual Commitment to Industrialisation in PairalMOxicon (unpublished paper). Of the two villages in Moore's investigation, it was the village that was more physically isolated, had poorer land, and was more traditionally oriented on various indexes, that sent out the greater proportion of factory workers.Moore argued that it was poverty and the lack of alternatives that pushed people off the land in one village and the relattve prosperity and benefits of land reform that kept villagers in the other village. He concimied that while factory workers were most readily recruited from non- agricultural occupations, it was disadvantaged persons in agriculture who would make the move, while those in more developed farm areas might not. While paver- ty might be a factor in recruitment of unskilled labor, at higher occupational levels other sets of complex factors as rewards in goods and services, prestige and esteem were operative. Within this framework there is room:for the interpretation that the communication network bettrem factory workers and the poorer village reinforced the mobility pattern and helped overcome the inertia of the villagers.Wilbert E. Moore, Industrialization and Labor (Ithaca:Cornell University Press, 1951). 4 1 accessibility to a source of change. It was a link through economic activity to a base outside the community thatwas part of the process of change.

What is needed isa panoramic view of the societyl of the structural correlates of social and economic integration; of themovement of ideas and people as mediators between the developed and the traditionalareas.The ecological perspective lends itself to this kind of analysis. Wemay look upon society as an interaction among the physical environment, forms of organization 2 for sustenarce, technology, and population canoentration andincrease. The reduction of isolation presupposesa source of change; it requires channels of communication; and qualities of the recipiente influence their acceptanceor rejection of change. The innovation itself maarbe one that fUnctions as a mediator betwyen the traditional and the modernways of life.The diffUsion of an innovation among a population is enhanced or constrained by the environment as wyll as by circumstances of time and space. The problem than is one of dis- covering the ways in which an innovation is diffusedamong a population, and secondly hm this diffusion fosters other social changes. Rvidently schooling is of key interest in both respects.

Educational systems have inimmaycases been aligned with forces preserving the traditional culture and helped to perpetuate the prerogatives of established elites.However, the diffusion of schooling inevitably supports and nornally fosters modernization. Schooling enables its adopters to acquire new skills, to qualify for a wider range of occupations, or to apply

iMiltonGlick, "The Impact of Economic Development an the Returns to Labor in Agriculture in Mexico" (unpublished Ph.D. dissertation, Department of Economics, University of Chicago, 1963),p. 34. 2 Otis Dudley Duncan and Leo F. Schnore, "Cultural, Behavioral and Eco- logical Perspectives in the Stucly of Social Organization," American Journal of Sociology, Vol. LXV (1959-1960). 5 technological improvements in their activities. Where traditional cultures and languages differ fram the dominaat language of the better educated and more economically auvanced sectors of a population, the,diffusion of education in that language fosters national unity and the integration of heretofore isolated groups into the central stream of an-going national life. Me spatial patterning of educational atininments among the adult members of a population is thua a clue both to spatial patterning of socio-economic development and to the likely ef- fectiveness of the networks for communieation of information and attitudes con- ducive to the diffusion of modernization. The firat four chapters of this sta4y delineates those patterns and their association with other factors in and manifes- tations of socio-ecanomic development.

Primary schooling in developing ccuntries may also be viewed as a. new trait ar innovation, the spread of which is to be explained.The problem then is to trace the patterns in whidh education is diffused through the society and to interpret them.This is the principal concern of Chapters V and VI, where atm tention is centered upon school enrollments, continuation rates, and age-grade characteristics of children. Whereas in the first part of this study, the relationships are such as to make distinctione between cause and effect exceedingly problematic, there can be no adbiguity about taking child literacy and enrollment rates as dependent variables, to be explained by other characteristics of the society (including educational attainments of adults).

The ecological point of viers that is used throughout the study provides a framework by means of which it is possible to synthesise the analysisof interrelationships among a wide diversity of statistical data.But an explanatory developmee, of that framework, and its use for analysis of ccumnnication and diffusion of education and economic change, is needed. 6

Relations of Occupations and Uchnologz to omen

The criteria for choosing variables were derived, from Durkheiala discussion of social morphology. In his Division of Labor in sogalr, DUrkheim defiles two types of social organization. One is a segmented mode of organization characteristic of small and isolated or autonomous aggregates in which little control has been achieved over the local environment. A second type of organization (embracing modern industrial society) rests on an intricate interdependence of specialized parts; this differentiation is dae to the division of labor and exchange of goods and services.The self-sufficient segments are broken down within communities and between societies and isolation among units 1 is reduced.

In an ecological analysis, the description of the economic organization of the society is one of the key indexes to the shift from a type oforganization in which all the members engage in subsistence activities to one based ma an interchange of goods and services.One meaning of development is this transformation cf the self-sufficient segments of the society into differentiatei,integrated, and interacting units.Part of social change is an adjustment to technological change. Adjustment involves relating population to job opportunities, and where these are not locally available, migration becomes alma= of achieving an adjustment.

'EmileDurkheim, The Division of Labor in Society, trans. George Simpson (New York: The Free Press, 10)1: Discusilon alio in, Imo J. Schnore, The Urban Scene (New York:The Free Press, 1933), pp. 3-41. Social and Physical Density

Azdnimm population size and density are necessary to heterogeneous and complex social units. However, population concentration alone does not in- sure weakening of isolation among parts.Durkheim noted that "segmentation" or lack af marked differentiation in a society may persist in spite of high PhYsi- cal density if the contacts among social pwrts are minimal. He introduced the concept of "moral" or "dynamic" density:as labor becomes more specialized, there are more individuals sufficiently in contact to be able to act and react upon one another. Progress in the division of labor is in direct ratio to moral density. Social (or moral) density comes about bydiminishing gaps separating social segments or by multiplying Intra-social relations. He then suggests that dynamic density diminishes the distance between individuals by the concentration of population, especially in cities (physical density) and bydervelopment of transportation and convmmication.Reducing the gaps separating social segments increases the dynamic density of the society. Favorable topographic conditions and technological innovations facilitate movement, reduce barriers of distance, and broaden interdependence.

The Swedish geographer HAgerstrand developed a method for tracing the spatial diffusion of an innovation that offers a basis for prediction of the spatial flow. He found the probability of a new adoption of a particular innovation to be highest in the vicinity of an earlier adoption and to decrease with distance.He described the time pattern of change as one of "deferred accler- ation," by which he meant that after every new adoption the probthilityin- creased that another would occur until a state of saturation is reached. When an e:Loption occurs at some distance from the initial center, a density of 8 adoption develops in the new area which becomes a secondary center. He sug-

gested that a hierarchy Ar centers existed, each one dominating subcenters and

surrounding areas of its awn. Normally adoptions jump from one of these miters

to another long before the local diffusion from any center has reached maxtmum pace of adoption.

Higerstrand provided in his model for the obvious gaps in the spread of an innovation by emphasizing private communication networks, described in the

concepts of "information fields" and of "resistance."The contact or exchange

of information on a person-to-person basis is determined by the communication connections of groups of people (as measured, e.g., by telephone-call distribution and migration). Some people have links extending over national boundaries,

some on a regional level, but the majority maintain connections on a local level 1 on3y. In other =ids, various sub-powlations participate in distinctive, though partially overlapping, information fields. The nature and intensity of these fields and the extent of their overlap are important aspects of social

organization that facilitate or dampen socio-economic change.

Higerstrand found a stability to exist in rural population movements because of the flow of information through private channels, the migration movements reinforcing the spatial patterning of those channels.The telephone- call distribution and the migration field of a rural area both followed a pattern of decreasing contact with increasing distance. However, the adoption of aAy particular innovation is not determined by interpersonal contacts or "tailings" alone. The various factors that may foster or impede adoption are what HAger- strand calls "resistance."Resistances may reflect objective conditions (e.g.,

1Torsten Higerstrand, "Quantitative Techniques for Analysis of the Spread of Information and Technology," Education and Economic Devel t ed. C. Arnold Anderson and Mary Jean Bowman (drago: O. 3), Pp. 244-80. a new fertiliser may produce results only in oertain soils or may require more water than is available insome areas), but resistances may also reflect attitudes that more readily accept one change and resist another. Whatever the determinants of resistances, their thresholds willvary among individuals. Time lags in adaptions are the expression of resistances; some innovations diffuse with great rapidity, others only slowly as complementary factors become avails,- ble or traditional attitudes are modified.

Higerstrand suggests (based on his findings for Sweden) in considering means for planned social change that the communication channels haveto bede- fined first. However, he points out that it is the resistauce sideof.the model where susceptibility to change might be most readily altered.Educational factors operate both in the "information field" side, suggesting more extensive (and distant) communication linksamong populations with more education, and on the

"resistance" side, since education is bothaprerequisite for utilizing more complicated innovations and a precipitant of attitudinal transformations.

Urbanization, Communication4 and Development

HAgerstrandls formulation of the diffusion process does not rely upon an urban-rural dichotomy. While he speaks of parent localities, and a hierarchy of centers with gradients of influence, the role of cities or the process of urbanization is not an explicit part of his analysis. In Durkheim, on the other hand, the concentration of the population in cities is part of the condition under which contacts among diverse social units can be intensified.In

Durkheim'sformalation,social interaction leads to differentiation through competition; the greater the social density, the greater the competition. One means ofresolvingcompetition is through occupational diversity, and through territorial differentiation. Tne definition of urban,even when most simply defined as population concentrations over 2,500 people, does not representa genuine contrast to a rural locality. The folkways that rural migrants bring to the citymay remain essentially intact. Ties among formsr villagers living in the citymay be stronger to each other and to kin back home than to diversegroups living in the city, thus minimizing interaction. The metropolitan area may absorb outlying villages without integrating the residents into the urbaneconomy or society. A rural center of 1,500 population may function as a market town for a madh larger farm population and have ties to the regional and nationaleconomy.

By the same token, a center of 10,000 population might be largely supported by subsistence agriculture with only a slight proportion of its population in specialized occupations, and havinga minimum of outside contacts.

In general, however, industrialization is most likely to take place in or near centers of population, close to the potential labor force and to the potential markets, and industrialization will in turnreinforce these concentrations. In Mexico, industrial concentrationsare found in a few urban centers, there is an uneven distribution of modernization force.

An important concept in diffusion studiesis the gradients of Influence from the city to its hinterlands. Ideally as industrialization proceeds, city and village become increasingly interdependent: the city relies on agricultural products and sends finished goods to the village. The intensity of this interaction between urban centers and environs decreases with distance. This "ideal" pattern is not always found; rural villages are sometimes "surrounded bya wall," apparently impervious to change.The communication systau may be fragmented as it extends across space. This differentiation between the rural and urban communication system is mentioned in Pye: in varying degrees one system is based upon modern technology, is urban centered, and reaches themore 11

Westernized segments of the populatian. There is a separate (samples:network that conforms in varying degrees to the traditional system, which follows patterns of social and communal life.The urban-based communication process may penetrate into the villages only in an erraticform?

The erteneion of transportation and communication system reduces the dependence of the city on its environs and enables urban centers to seek supplies and markets at a distance. The mass media may bring new ideas to the villages directly from a few communication centers, circumventing intervening cities.

Just as there is a separation between rural and urban communication systems, there is a difference between mass-media and inter-person channels of communi-

cation.

In HIgerstrandls theory the by-passing of contiguoue villages for centers

fhrther on is accounted for byethannele of interpersonal commication. He finds

that these dhannels have their roots in history--and remain essentially the same 2 from pmeration to generation4 Sone small communities may have contacts mainl7 with urban centers while others may be part of an biter-village system of c

cation.

In spite of the shortcomings of the rural-urban dimension in a study-of diffusion and economic development, there is justification for using these categories.Most obviously; the rural-urban distinction facilitates the use of available data. In the analysis of correlations, the clustering of traits around specific urban or rural variables allows far variations within the urban or rural framework. Thus some rural qualities may be associated with modernization while other rural qualities describe backward areas. In Chapter II,

Iicien Pye, Communication and Politioal Development (Princeton: Princeton University-Press, 1963), p. 26.

211Agerstrand,op. cit.p pp.262-64. 12 there is a discussion of the ways in which urbanization variables relate to other aspects of modernization, including the rural sector. At the same time association between rural and urban aspects of the 34110 variable point out the extent of interaction.

The Data

The units of observation in the present study are the states of Mexico.

The variables are mainly characteristics of the populations of these states:as a whole or classified as urban or rural, by age, or by sex. Other variables include indexes of infra-structure development (road and railroadnetworks), of facilities or amenities in the community (as libraries,movies), or facilities in homes (as rwming water, beds).

It would have been desirable to have information byumits smallerthan states in order to trace more closely the relationships between population centers and their spheres of influence in the distribution of schooling and of non-educational characteristics. A, preliminary scrutiny of data for municipios

(equivalent to counties) revealed that many of the more interesting variables

(especially pertaining to education) were not available for these small units.

The labor of digging oat the data muld be prodigious, and, in view of the lack of some critical variables, hardly worth while. Without a prior overview, it was hard to make a sound choice of areas in which to studymunicipios. They had to be contiguous, since it would have been desirable to trace the interactionof population centers and their surrounding areas through the etu4y of selected characteristics of the population, the economy, and the educational system.

The data used pertain to entire states, but wherever possible rural and urban portions are distinguished, thus partially offsetting thedisadvantage of working with large geographic unite. Also, one of the most relevant questions 13

is the extent to which rural and urban areas close to each other partake of

each others' traits.For similar reasons data are divided by sex wherever possi-

ble. An Important question is how far differences among states in female par-

ticipation in education or in the economy parallel interstate differences for

males, and where sex differences tend to be large or small.

The main sources of data are the national population...census reports

from 1930, 1940, 1950, and 1960 with emphasis in the analysis an 1940 and 1960.

Industrial and agricultural censuses mere used far supplementary data,as were

other government publications:the Anuario Fetadistico de los Fetados Unidos

Mexicanos, the Est.:...... ,adosdUniMexicanosoEstadistico, amd specialre-

ports. Four hundred variables were selected for each of the thirty-one states.

These are listed ir Tables 1 and 2; mhere theyare also given identifying numbers.

While some of the categoriesmay appear illogical, this was unavoidab3e because

of census changes at each enumeration. (The problems of comparabilitywill be discussed later.) Variables listed in Table 2are all differences of ratios,

derived from other variables.

The first set of variables listed in Table 1 refers to educational at-

tainments in the indicated years for particular residence, age, andsex categories of each state.These data are the basis for the analysis of socio-economic trait clusters and processes of change. The educational data for adults are also among the predictor variables in Chapters II and IV. The second set of variables are concerned with school enrollments, continuation rates, andre- lated aspects of the education of children and youth. FUrther comment an these variables will be deferred.

The demographic variables, which make up the third set in Table 1, call for special comment here.Population density (persons per square mile) is the first of these. In some parts of Mexico high density reflects traditional 14 settled peasant agrarianism, rather than modern industrialization emanating from maior centers, but the agriculturany most advanced states of the North have sparse rural populations. Furthermore, mountainous terrain in central Meolco can reduce average 4ensities even though settlement patterns maybe concentrated, whether in large ar small towns.The interpretation of density data across states at any given time requires that characteristics of agriculture be taken into account; such data aid also in the interpretation of differential changes in density over time.For these and other reasons, the density variable had in itself only limited value.

Migration figures can trace sweeping patterns of population redistribution over successive decades. Urban growth has resulted from and stimulated migration, end rates of movements point oat areas where opportunities have been perceived to be good (and have been so in fact), though at any given time migration responses maybe out of step with growth of job opportunities. Migration patterns also distinguish older central citieswithslackening drawing power from centers with escalating growth and in-migration rates. (The migration rates are proportions of the resident population who were born elsewhere.)Clues to industrial expansion and agricultural developmentcan be derived fmmobservatimns of states attracting high proportions of migrants. Also, migration data provide clues as to where we may-expect urban pockets of unintegratednewcomers to be large.

Two uebanization variables were available: the proportion of the population living in towns of 2,500 or more (reported for all years) and proportions living in cities of 50,000 or more (reported for 1960 only). The two measures have somewhat different implications. There may be less urbanization on the first measure in State A than in State E1, yet more people living in large cities in State B. The factors in the rise and growth of large cities differ 15 from those contributing to "urbanization" under the census definition of proportions living in towns of 2,500 and ove14. Larger cities are neoessary for development; they serve as prime nodes in the communication nem"; they typically are the first tr pick up and transmit innovative ideas and practices, to each other and to their respective hinterlands.And the larger cities support a big enough pool of knowledgeable and alert men to provide cross-stimulation on the forefront of change. Cities are the seedbeds in which adaptation to ngmrways, new technologies, new consumption patterns, and nor social institutions are achieved.

However the degree of urbanization is measured, urbanization and industrialization are by no means synonymous: towns and cities have preceded industrial development; industrial establishments maybe located outside of major cities. Taking the census definition at 2,500 (which ia used uaually), this is particular/7 obvious.In some areas, a village of 2,500 people may be a community of farmers who till outlying fields, whereas in other areas it may te a service center for a scattered farm population; but in neither caae is industrialization involved. A town as large as 10,000 may still have many farmers in its population and very little processing except the traditilonalhame crafts.

There is no clear connection between proportions of males in agriculture in a state, which is probably the best measure of rurality, and proportions of the population residing in places of under 2,500.

Although the data used do not permit a direct, detailed tracing out of inter-city, city-town, or inter-town communication networks within the various states, a word is in order at this point concerning hierarchies of places in information fields, amd their possible relation to size of place. It cannot be assumed that the parent locality for the initiation of an innovation or the first centers in which it will appear or to which it will move is always the 16 largest city, or that transmission of information and innovation follows an urban-size hierarchy. Indeed, as HAgerstrand has demonstrated, we mast look, among other things, at what the nature of an innovation is and to what populations it would be most relevant. On the other hand, there can be little doubt that people living in the larger country townsare typically exposed more rapidly and to a wider range of information and ideas than are those dwelling in smaller places. Myers has pointed out that people engaged innon- agricultural pursuits living in villages of less than 10,000 peopleare denied benefits that accrue to farmers who dwell in larger towns. Furthermore, the high labor-force participation rates in small villagescan indicate concomitant- ly high opportunity costs af schooling, especially when those costsare viewed in relation to total family income./ Larger towns,even when they are still only at a 10,000 level, permita greater diversity of job experience. Lipset, in a study of why men leave farms for factory work, found that the size of the community of orientation was important in predicting non-manual job placement, 2 holding education constant.

The concept of "urban primacy" and its measurement maybe importanton a national scale, but it is a highly ambiguous concept when applied to a state within a nation.The variables relating to the role of the capital city within the urban setting of each stet) were included primarily with the idea that they might pick up distinctive characteristics of urban fanctionsas among the states.

At one extreme, if the capital city is small, is the largest city in thestate, and accounts for a large fraction of tile 1-rban population,we may expect that it will be essentially an administrative and farm-service and marketing center.At

1Myers,op.cit., pl. 17. 2 This studyvas diecussed in Young and Young, op. cit., p. 1. 17

the other extreme, a large capital city that nevertheless aocounts for a not-so- large proportion of the total urban popuLition of the state mull presumably indicate a high level of economic development that was not dependent upon government employment. However, for reasons some of which have been indicated

in preceding comments concerning "urban" places in Mexico, these variables were not as informative as had been anticipated; they definitely tell much less about

Mexico than they tad about Iran in Fattahipourfs study of thatcountry.1

One of the important questions About urbanization and development is the natmre of the links between the rural environment and urban progress. Indirectly

the data supply some insights by the comparisons of patterns of rural and urban

change and the conditions under which rural advance is associated with urban progress.

Glick, in a study of economic development and agriculture in Mexico, devised a measure of urban orientation. States included in this categorywere those which were wholly or largelywithin 200 kilometers of an urban center of

100,000 or more people in 3.950. Fauna labor in 1930 showed greater productivity in the urban-oriented group, but the difference was not large, and contrary to expectations farm labor productivity in nonmurban states surpassed that in the urban-oriented group in 1950. (Glick explained this by saying that the rate of increase between 1930 and 1950 was greatest for non.gurban states.) The level of productivity of farm labor (when correlated with the individual items in an index of development) was related to the level of non-farm earnings per capita, to value added bymanafacturing per capita, and to total manufacturing output per capita--but no significant relationship with population or labor force criteria was revealed.He concluded that "the lack of politive relationships between farm labor productivity levels and cEanges on the one hand, and population and

1AhmedFattahipour Fard, "Educational Diffusion and the Modernization of an Ancient Civilization Iran" (unpublished P4D. dissertation, Department of Education, University of Chicago, 1963). 18

labor force characteristics, on the other, suggests that urbanizatimmay be a

necessary but not a sufficient condition for economicdevelopment.?

Yates, in his study of regional development in/facial:it found that states having high agricultural productivity were those with high levels of "industri-

alization" measured by industrial worth per capita, not by occupational charac-

teristics of the population or location in urban centers.Tates recorded only

the states most industrialized and those least industrialized; including the

full range and their associations on several measures of development night yield

different results.He did not measure urbanization except to point out the

prodigious development of the Federal District in relation to the rest of the

countryP and to mention the concentration of industi7 in a few municipios in the 2 northern states.

Modernization necessarily influences indigenous marriage and family

patterns (Table 1, Group 4).In societies that are urbanized and western in

orientation, marriage takes place later and families are likely to be smaller.

Women are freer to participate in associations outside of the home.The relevance of "females age 20-24 who are single" and the rough fertility measures used in the study is two-fold. One is to see in what ways age at marriage and

size of families are associated with the participation of women outside of the

home. This would be manifested in wider contacts than the village, such as

through occupations or in the adoption of an "urban style of life."The other

questions concern the effect of fertility and woments literacy and schooling

upon the literaay and schooling of youth. With early marriage and largefami-

lies, the efforts of woman will be consumed in daily chores of subsistence and AlrelB

1Glick,op. cit.p p. 34.

2 Yates, pp.. cit., pp. 19 ff. 19 contacts outside of the village or receptiveness to new ideas will be minimal.

If the women are illiterate or unable to speak Spanieh, they maybe a dragging

"resistance" hindering the diffusion of schooling among youth. When woman are literate or schooled, they are more likely to encourage schooling for their children, even when opportunities for child employment abound.

The interpretation of rural-urban differences in fertility is equivocal, however, for many reasons. The proportions of women in the child-;bearing age may be higher than in urban areas, or there may be a higher ratio of males to females. The mortality rate of infants may be higher in rural areas. There may be a heavy migration of rural families to urban centers. In a Catholic country large families do not differentiate the modern from the indigenous population since families in higher income brackets may have many children and employ ample domestic help to care for them.

Fattahipour found Iranian women to have little overall influence in the socio-economic changes that he was investigating, but he stressed that develop- 1 ment was drawing than into the modern sector. The traditional role of the

Mexican women in rural areas is a secondary one, confined to the household, passing on the indigenous culture from one generation to the next.The analysis of family size and age of marriage is designed to define the characteristics of females who are becoming part of the larger society.

In many parts of Mexico, ties to ancient customs and traditions are strong. Traditionalism is found not only in the remote and inaccessible corners of the country but also adjacent to some of the most modern centersas in central Mexico. There are several interpretations of a high adherence to native

1Fattahipour-Fard,pp. cit. 20 ways. A large proportion of the population walking terefoot, sleeping oa the

floor, or eating tortillas in place of breadmaty be interpreted as "poverty."

On the other hand, regardless of income, toa farmer living in a warm climate

there may seem to be no need for shoes andno special status given to those who have them. Certain traits that appear with greater intensity in cities--defined as an "urban life style"--are different from those predominating in rural areas. The former are similar to practices in modern industrial societies elsewhere. For most purposes, in this study the wearing of shoes, eating wheat bread, and sleeping on abed will be interpretedas indicating an urban orientation.

Geographic patterns in the occupational make-up of the population

(variables in Set 5 of Table 1)are important both in themselves and in their relationships to many other traits. For example, how closely are occupational distributions and adult educational attainments correlated? Howare the economic activities of parents related to the education of children? Mbat is the association between urbanization and the proportions of the population in various occupations, and what shifts in the distribution among occupations are taking place over time and where? It is safe to assume that certain occupations require at least the skills of literacy, andsome occupations require or give scope for application of high levels of technical knowledge.The visibility of jobs re- quiring education may attract migrants having qualifications and maybe an incentive for local youth who see around them the evidence that education can be a road to success.

The variables in Sections 6 and 7 of Table I describe the characteristics of agriculture and of manufacturing as industries rather than as occupations.

There will be a preliminary discussion on these points in the next chapter, gm it willbenoted only that the variables included identify points of change in the 21 agricultural system: mechanization, position in the landholding system, agricultural income and productivity. The positions distinguished in the agricultural occupation system are laborers (people who do nklimuil work for a salary ar a daily wage) and proprietors (those who exploit their awn economic enter- prise but employ no help).The ealgofarmers are members of agrarian communities that possess and use land at least in part cooperatively. There are both collective and imlividual ejidos, but the overwhelming majority the crop lands are worked individually while pastures and woodlands areshared./

The presence and expansion of manufacturing is a basic feature of modernization, yet manufacturing establishments may have negligible impact on their surroundings. The variables used were selected to identify those qualities of manufacturing that are part of development* A high percentage of females among employees in manufacturing may indicate a cottage industry where women bring work to their homes. The variable referring to payrolls per month as a ratio to the number employed includes Gray industrial establishments where the annual worth of production was higher than $10,000. The variables referring to income in manufacturing under or above a stated amount consists of those people reporting income from such work in the month of Mcv, 1960; the denomi- nator is the number of people in manufacturing who stated their incame.

The data an roads and railroads (Table 1, Graup 8) show the ratios of route length to square kilometers of land and to population. Paved roads indicate accessibility of travel.However, in general, the data on roads and railroads appeared in highly irregular patterns and caution was used in interpreting them. The variables labeled "useable roads" in Table I are

1 An ejiditario is a farmer who acquired land through the land reforms; he can pass it on to only one heir; the land lapses to the state if not farmed for two years. In the 1960 census, ejiditarios were called "proprietors." 22 equivalent to those with the letter B in the rest of the study, referring to the 1940 category of "traversible" roads and used to compare with pavod roads.

Glickls Index of Development is a composite of the following variables:

(1) percentage of economically active in manufacturing, 1950; (2) percentage of economically active in non-agriculture, 1950; (3) percentage of population urban

(in towns of 2,500.^;, 1950;(4)percentage of population in cities of 10,000+ in

1950; (5) value added bymanufacturingper capita of the total population, 1950;

(6) value of manufacturing productionper capita of the total population,1950;

(7) average earnings of all personnel in manufacturing, 1950. These were combined by ranking the thirty-two states and territories for each item and summing the numerical value of the rank position for each state (those with smaller sums were classed asdeveloped).1Electricity consumption per capita was included among the variables for several reasons; it permits standardized comparisons among states and over time and is a measure of modernization in homes as well as for public works and industry.

In Table 2 are listed variables selected for purposes of measuring changes over time, and differentials by sex and by degree of rural-urban contrast. Variables describing change were in the farm of differences or a ratio.Differences between generations in literacy can be interpreted in several ways: differences were specified between the literacy of youth (15-19 years old) and middle-aged (40-49 years old), or between the middle-aged and aged (over 60 years). When differences were large, literacy in one generation was diffusing rapidly.Where differences mere small, there was either a legacy of high literacy-rates from one generation to the other or a backward area with little progress. These differences included distinctions of males and females and of rural and urban areas. Large differences between males and females or a

;Glick,op. cit., p. 32. 23 high ratio of males to females, who had acquired a "progressive" trait, was indicative of a backward area.Where females matched males in the achievement of literacy (or some other trait) or showed signs of catching up, the area was

"making progress." One set of literacy differences was based on percentages of youth literate in 1940 minus the percentage in 1930 and another on 1960 manna

1940 rates. In general, areas which made the greatest gains in recent decades were those which had the farthest to go or had been the most backwardinitially.

Group 3 includes a variable designed to distinguish recency of urbanisation: the proportions of the population living in urban centers in 1960 minus

1950 divided by the propartions living in urban centers 1960 minus 1940. In

Group4sex contraste and changes over time in theproportions of the population with the indicated culture traitssignal the pace and incidence of trends toward cultural modernization.

Variables measuring the differences between years in the distribution of the economically active in the labor force and changes in theagricultural structure and in agricultural productivity were listel in Groups5and 6.

Group 7 includes variables measuring changes in salaries in manufacturing, and

Group 8 lists variables showing changes in communication, transport, and utility facilities.

Evaluation of data

All census enumeration is subject to errors of collection and tabulation.

As a country continues to utilize data collected, it revises and improves procedures. Myers has this to say about the Mexican censuses:

The reliability of Mcdcan statifitics depends very much on the date and manner of their collection. The largest single body of data is found in the national census taken in 1895, again in 1900, and every ten yearsthereafter. Unfortunately, most of the data compiled before1940 are extremely unreliable. The early compilations are under-enumerated, and the censusof 1921, taken so soon after a period of revolutionaryupheaval, is barely usable. The census of 1930 is somewhat better but still cannot be used withany real confi- dence. After 1940, the dataare more reliable, but still subject to problems of under-enumeration and to the perpetuation, for the sake of consistency, or earlier inaccuracies. Other statistical compilations follow the same pattern but ewe generally less reliable at each interval because of decentralized collection procedures. Over the years individual ministries have compiled data for their own pwposes, and levels of accuracy and comparability have suffered accordingly. In many instances, the inadequately explained sampling procedures wtich have been used cast considerable doubt on the validity of the data cinstructed from them. Recently there has been some improvement, and a few ministries and states are now able to supply relatively accurate information. Unfortunately, some of the educational statistics are still, among the least reliable because of the inadequacy of student accounting.'"

However, errors will decrease rather than increase the computed correlations.

ThrousThout the study, checks on the reliability of the statistics were

made on each of the four hundred variables in the following ways:

1. Watching for qualifying comments and explanations accompanying

the source.

2. Totaling columns of percentages for each state listed and com-

paring the total with national figures.

3. Comparing figures taken from two sources; as enrollment of

males plus fenales 6 to 14 from one table or publication and

total enrollment of 6P to 14-year oils taken from another.

4. During preliminary mapping and graphing of variables, oc-

casional errors were spotted where a state was wildly out of

line.

5. Eoting variables (as some of those on roads) which were so

erratic in correlation matrices as to defy interpretation.

iliFers, loc. cit., 25 $tiies The procedure far selecting variables vas first to seek comparable data within each category for two a- more census years. Some of the data were put into similar forms for comparisons; for example, in tabulating adult literacy rates the 1930 census used 30 plus years of age, the 1940 census used age 40 plus, and the 1960 census used 5-iyear categories through 85 plus years.The percentages were computed far the 30 plus age group for 1930 and 1960 and for the 40 plus age group in 1940.Far 1960 the 5-year age categories were added to form proportions comparable to the preceding censuses.The figmres for each variable were copied in raw form for each state, put into a percentage or ratio form, and checked for comparability and internal evidence of accuracy:

Apart from the teckground and descriptive material, the methodologyof

the stu4y embraoes straightforward statistical analysis.The distribution and diffusion of education among sub-populations (residence, age, sex,occupation)

in each of the states of Mexico is delineated for each of the four census years.

The basic problem is to trace the interrelationshipe between educational and

other data at a given time and to relate tine trends in these two broad categories of data. The statistical techniques were simple correlations, component

analysis (varimax rotation), and multiple regression analysis, together with

statistical mapping and scattergrams.

After the data were edited and percentages derived, all states am eadh item were ranked and the array divided by quartiles to provide information

about variability among states and a basis for deciding whether to use raw or

log scores in the correlation and components analysis. The following equation was computed for each distribution, from both raw scoresand logarithmic values: 26

ahere = quartile 1, trir-tri = median, and

Q3 quartile 3.

The log ar the rawscore was then used depending on which distribution was more

nearly symmetrical (and to what extent closerto a normal curve).The log of

100 minus the raw percentagescore was used mbere this came closer to symmetry.

Specifications concerning log transforms of variablesfor regression and com-

ponents analysis are given in the glossary in the Appendixtables. A starred

item (1) in a table indicates the log transformof 100 minus the raw percentage

score and should be interpreted by a reversal of sign. Tim asterisks (**) or

two reversals are interpreted by the signas it appears. In tables accompanying

the text, sioms have bean changed instead of usingstars on the reported corre-

lations. Alternatively in some cases, as with migration and with incomes under

500 pesos monthly, the descriptiaa of the variable isrevised.

The Federal Districtwas omitted from the correlations and the subse-

quent procedures because its extreme valueswere likely to distort the associ- 1 ations among variables.

To get a clearer picture of possible relationships, certain variables

characterizing education and selected socio-economic aspectswere selected far

scattergramming and variables were chosen for prelirrinarymapping.

From this initial investigation, five correlationmatrices of approximately 100 variables each were computed.2The output was then studied for

1Howevar,the Federal District is included in the discussions ofper- centage distributions and rankings throughout the study. Predictions of the position of the Federal District were made from the regression analysis anda separate section is devoted to this in Chapter VI.

2Eightvariables were repeated in all matrices:proportions of economically active males in agriculture, proportions of males walking barefoot, 27

preliminary substantive insights and for selection of variables to be used in

a factor analysis.One criterion in setting up the factor matrices was to

choose those variables that showed relativeV little correlationadth other

scores from the same matrix but high correlations with scores frau other

matrices. In other words, where several variables in the correlation matrix

formed a tightly knit group, only one variable of that cluster was used in the

factor matrix.An item showinglowintercorrelations with all other variables was eliminated since it would contribute little to the definition of factors.

Four factor matriceswere run and will be designated hereafter by the letters

Al B, C, and D. The purpose of the components analysis was to reduce the origi-

nal number of descriptive variables to a anallar nuAber of amtually independent

factors each of which pulled out components common to a large number of associ-

ated traits. Factor scares that were interpreted to have special relevance for

developaent were mapped to point out the geographic clustering of traits.

Some multiple regressions were run with educational enrollments as the

dependent variables, using selected education awl non-education variables as

possible predictors of enrollment variations.These are discussed in Chapters V

and VI. Variables that had high loadings on the same factor were not included

in the same regression. Ihelaudmun number of vAriables in one regression

useable roads to area, and enrollment of 6- to 14-gyear olds, eadh for 1940 amd 1960. The matrices were organized in the following waylMatrix 1, literacy and characteristics of culture; Matrix:2, occupations, characteristics of the economy; and adult levels of education; Matrix 3, demographic qualities, communication and transportation items, continuation rates of children in school, and variables shoving changes over time; Matrix4,education of youth, as enrollments, age-grade progress, pass rates, and school facilities; Matrix5, the salient qualities of the adult population: literacy; schooling, occupaUons, income, demography. These were chosen after examination of the first four matrices, and are drawn from all of them. 28 equation was five.Educational enrollments were the dependent variables and the independent variables selected to "explo4.1" enrollments ware drawn from various categories of data...agricultural structure, occupations, demographic characteristics, communication, and transportation. 29

TABLE 1

INVENTCRT OF VARIABIAS MED

Total State Rural Urban

411

1930 1940 1950 1960191401960 1940 1960

Education Literacy:age Per cent 11+F 6+ 2142 3143 1/414 10+ 139 340 vas Per cent male 10-114 189 173 35-19 188 172 20-24 187 171 25-29 186 170 30-39 185 169 40-49 184 168 50-59 183 . 167 60+ 182 166 30+ 40+ Per cent fatale 10-114 156 158 160 197 181 35-19 196 180 20-24 195 179 25-29 194 178 30-39 193 177 /40-49 192 176 50-59 191 175 60+ 196 174 30+ 252 140+ 1148 Per cent of population age 6 yrs. and over, literate but no schooling Males 153 Females 2514 30

TABLE 1--Cantinued

Total State Rural Urban

I1930 19401950 1960191015)601940 1960

Law school attainment:

Per cent of males by age and years of school 6+ No school ... 254 ... 256 .. .0. .0. ... 25+ No school ...... 226 .....0...... 30+ No school ... .4'. 228 ... .0 ... ..

25+ 1-6 yrs. school 000 232 000 000 000 30+ 1-6 yrs. school 000 00 234 00 000 000

Per cantof females by age andyears of school 6+ No sdhool 257 00 25+ No school 227 GO 00 30+ No sdhool .2.29 000 00

25+ 1-6 yrs. school 233 111 000 000 30* 1-6 yrs. school 235 000 000 00

High school attainment:

Per cent of males by age and years of school 6+ 6+ yrs. school 261 .0. 25+ 7+ yrs. sdhool 0000 000 000 00 0 000 30+7+ yrs. school 000 000 238

25+ ID+ yrs. school 000 000 240 .0. .0. .0. 30+ 10+ yrs. school 242

25+ 13+ yrs. school 000 000 000 000 000 000 30+ 13+ yrs. school 00 246 000 000 0

15+ baccalaureate 000 248 15+ university 250 31

TAMAR 1Ccntinned

V' Total State Rural Urban

41IW 1930 1940 1950 19601940 19601940 1960

Per cent of females by age and years of sChool 6+ 6+ yrs. school ... 262 ......

25+7+ yrs. sdhool ... .. 237 ...... 30+7+ yrs. school ... 239 ......

25+ 10+ yrs. school .. ... 241 ...... 30+ 10+ yrs. sdhool ...... 243 ...... 40. ...

25+ 13+ yrs. school ...... 245 ...... 30+ 13+ yrs sChool ...... 247 ......

15+ baccalaureate ... 249 ...... 15+ university ... 251 ......

,Educationof youth,

Enrollments:

Ftr cent of 4 and 5 year olds in preschool ... 263 ... 264 ......

Per cent M+F 6-14 yrs. 265 00 60 266 . 274 ... 273

Per cent males 6-10 yrs. old 267 ...... 6-14 yrs. old .. . .. 270 ...... 7-12 yrs. old ...... 276 ...... 15-17 yrs. old ...... 334 0.0 ...... 000 000

Per cent females 6-10 yrs. old 268 ...... 6-14 yrs. oId 000 060 000 271 00 0410 000 000 7-12 7rs. oId ...... 277 ... 060 000 .0 600 15-17 yrs. oId ... . 335 ...... 32

TABLE la -Cmatinsed

Total State Rural Urban

1930 19401950 19601940 1960 1940 1960

Pass rates:

Per cent who pass exam of those present Grade 2 000 0 000 369 377 368 376 Grade 4 000 000 0 ... 371 379 370 378 Grade 6 000 000 ... 373 381 372 380

Per cent who pass secondary school ezmn Males 000 000 000 340 0.0 00 Females 000 341 000 060

Age grade progress in school

Per cant males Aga8 above Grade 1 ...... di.. 314 5 .. 344 Age 10 in Grade 1 ...... 348 .0. 347 Age ID ibove Grade 3 ...... 351 ... 350 Age 12 above Grade 3 ...... 353 .. 352 Age 13 modal grade .. .. 355 354

4 Per cent females Age8 above Grade 1 ...... 357 .. 356 Age 10 in Grade 1 ...... 360 . 359 Age 10 above Orade 3 ...... 363 . 362 Age 12 above Grade 3 ...... 365 ... 364 Age 13 modal grade ... 06O 000 000 367 ... 366

School facilities:

Per cent of schools with 1 to 3 grades only 395 390 394 389 393

Per cent of economically active population who are teachers 387 388 00 .00 33

TABLE 1--Continned

-11MINIIImmENMINOMII

Total State Urban

19301940 195019601914019601940 1960

Continuation rates primary school by percentages:

Beginning of year enrollments Grade /ear 2/1 1943/1942 ...... 291 ... 286 3/2 1943/1942 ...... 292 .. 287 4/3 1943/1942 ...... 293 . 288 5/4 19143/19142 ...... 294 ... 289 6/5 1943/1942 ...... 295 ... 290

2/1 1960/3959 000 0 000 000 000 302 000 297 3/2 1960/1959 ...... 303 000 298 4/3 1960/1959 ...... 304 000 299 5/4 1960/1959 ...... 305 300 6/5 1960/1959 ...... 306 yal

End of year enrollments (day sdhool) Grade Tear 2/1 1943/1942 315 310 3/2 1943/1942 316 311 4/3 1943/1942 *00 317 312 5/4 1943/1942 000 0 318 313 6/5 1943/1942 00 000 .00 000 319 314

End of year enrollments Grade Year 2/1 1960/1959 000 00 00 000 325 320 3/2 1960/1959 ...... 326 . 321 4/3 1960/1959 ...... 327 .. 322 5/4 1960/1959 ...... 328 ... 323 6/5 1960/1959 ...... 329 ... 324

Grade 5/1 Urban 1942 000 000 000 000 330 Rural 1942 000 000 000 000 331 000 000

Urben 1960 000 000 000 000 332 Rural 1960 000 000 000 000 333 000 0 34

TABLE 1-4ont1nued

Total State Rural Urban

.....

1930 194019501960 1940196019401960 1

Continuation rates secondary sdhool by percentages:

Third yearftirst year Males 000 338 0 000 0. 000 Females 000 339 0. 000 000 000

Demographic

Density to area: 1 2

Migration: Per cent of males barn in the state in whidh they are living 00 15 16 17

tbanisation: Per cent of population in towns of 2,500* 3 14 5 6 Per cent of population in cities of 50,000+ 22 Per coat of urban population in capital city 8 ii. 9 Size of capital city 10 ". 11 Capital city is largest city ar not 7

Marriage and fertility: Per cent of 20-24 year old females single ...... 45 ... Per cent of total females under5yrs. old .. 46 ... 47 ...... Number of children born living to females 40-49 yrs. old ...... 49 .. 48 Culture

Per cent oftotalpopu- lation non-Catholic 0. 113 114 Per cent of dwellings that have running water 43 35

TINE 1Continmed

Total State Rural Urban

19301940195019601910196019401960

Per cent of population who sleep on floor and do not eat wheat bread US 000 00 000 000 000 Per cent of population who sleep on bed 116 0..0 000 00 000 000 Per cent of population who do not eat idlest bread 117 118 119 000 000 00 Per cent of population vlio walk barefoot Males + females eoe 000 124 123 Males 126 127 ... Finales ... 128 129 130 ...

Labor force status and occupairons

Economically active: Per cent of males 5o 51 52 53 000 000 00 Per cent of females 54 55 56 57 000 00 000

Per cent of females 10+ mho are econ. active 000 58 Per cent of females 12+ who are econ. active 59

Per cent of 8-11 yr. cads mho are econ. active Males 04.0 62 000 000 Females 000 000 63 000 0.0

Per cent of economical17 active males in Manufacturing 95 96 ... 97 ...... Ulite collar ... 64 ... 65 ...... Clerical ...... 71 ...... Professional ...... 74 ...... Agrizulture 78 79 ... 80 ...... Mining 110 111 ... 112 ...... Public administration 76 77 ...... 36

TABLE 1--Cont4nued

Total State Rural Urban

1930 1940 2950 19601940 19602940 1960

Per cent of economically active females in Widte collar ... 67 ... 68 ...... Clerical ...... 72 ...... Professional ...... 75 ......

Per cent of economically active MtF in Professional .0 000 73 *O. 0 000 000 Clerical 0. 70 . 000 Characteristics of agriculture

Per cent of males in agriculture mho are laborers ... 83 ... 84 ...... Per cent of males in agricultweidm, are proprietors ... 85 ... 86 ...... Per cent of males in agriculture who are Oiditarios ... 82 ...... P-7iirrliiringota1 terra that are mechanised 000 000 89 Per cent of farm w:thout even animal traction 90 Equipment/land value 88 Glick: returns to the %man Agent 92 93 Per cent in agriculture with incomes over 500 pesos monthly 0 000 91 000 600 000 000 pharaoteristics of menu-, facturing

Per oent of females in manufacturing labor foi-ce 99 100 000 101 O410 00000 Payroll per month/number employed in factory 105 106 107 108 41410 .00.0 37

TABLE 1--Continued .

Total State Rural Urban

1930194019501960194019601940 1960

Value added by manufacturing:Glick 104 Per cent of popnlation in manufacturing with Jammu 1,500+ pesos monthly 102 Per cent of population in manufacturing xl.th incomes . over 500 pesos monthly 103

Communication. transportotion anci oilier deveSsment iices

Per cent of dwellings with

radial: ;. 1414 Per cent of popubition using libraries with 500+ volumes 112 Number of seats sold at . cinema/population 39 . Ito ,,, Per cent of population who out bicycles 29 30 Per cent of population who own automobiles 32 33 Railroadstrlation 18 .. 19 Railroads 20 21 Roada/pcpilation 22 24 o Roads 25 27 Usable roads/population 23 Usable roads 26 o Paved roads oads 28 Electricity consumption per capita 36 37 Index of development: Glick S 61 38

TABLE 2

VARIABIZS CONS1RUCTED AS RATIOS CR DIPFERNICES OF TABLE 1 VARIABLES

Total State Rural Urban Variables Shoving Change 4 over Time and Ratios I19301940 )9501960194019601940 1960

Education Literacy percentages Males by age (15-19)440.49) 202 198 (40449)-(60+) 203 199 Females by age (35-19)44049) 204 200 (40-119)-(600 205 201 Males minus femalen by. age 60+ 226 206 50-59 227 207 40-49 218 208 30-39 219 209 25-29 220 210 20-24 221 211 15-19 222 212 10-314 223 213

Males/immales 15-19 225 215 4049 224 214

Males 1940-1930 161-161 1960-1940 163------163 Females 1940.1930 162---162 1960-1940

6+ yrs. 1960-1950/ 1960-1940 165 39

TABLE 2 --Continued

Total State I Rural Urban Variables Showing Change over Time and Ratios 19301940 1950 196011940196019401960

School attainment

Adults age 25+ 1950 Adults age 30+ 1960 No schooling Females/males 252 260 Niles 1950-1960 258- 258 abELLMELIEjeta

Enrollments

Percentage enrolled in school for first time at age 6 by monthly income in pesos 200 278 201-600 279 601-1,000 280 1,000 281 (601-1,000)-(200) 282

Percentage enrolled in school for first time at age 6 by father's occupation Agriculture 283 Professional 284 (Father professional) minus (father in agriculture) 285

Fnmales/Mmles 6-10 year ads 269 6-14 year olds 272

Urban minus rural 6-14 year olds 275...275 140

TABLE 2--Continued

Total State Rural Urban Variables Showing Change over Time and. Ratios 1930 1940 1950 19601940 19601940 1960

Aga gradeprogress in school:

Age 8 above Grade 1 urban minus rural Mhles Females Aga 10 in Grade 1 rural minus urban Males Females

School facilities

Sohodllowith 1 to 3 grades only, 1942 minus 1960 Urban 396---396 Rural 397- ---397

Beginning of year enrollments

Urban minus rural 4/3 1943/1942 296------296 4/32960/1959 1960 minas 1942 4/3 Urban 308 --308 4/3 Rural 309 ....,309

Demographic,

Urbanizatica:

Percentage of population in towns 2,500+ 1960495019604940 1960/1930 1340MMID13 hi

TABLE 2Continued

-NEIL Total State Rural Urban Variables Showing Change over Time and Ratios 1930 1940 1950 1960 VW 19601940 1960

Culture

Percentage of total population Do not eat wheat bread 1940-1950 120---120 1950-1960 121---121 1940-1960 122------a22 Percentage who walk barefoot Maims 1940-1950 133-133 1950-1960 1314-134 1940-1960 Females 1940-1950 136 ---136 190-196o 137 ---137 2940-1960 Maes/females barefoot 131 332

Labor force status and occupations

1960-1940 Percentage of soon. active males in Manufacturing 980041.0m141198 Agriculture *its collar Percentage of soon. active females 60....60 Percentage of econ. active females in white collar TABLE 2--Continaed

Total State Rural Urban Vhriables Showing Change over Time and Ratios 1930 1940 195019601940 19601940 1960

Characteristics of agriculture

1960-1940 Percentage of males in agriculture who are proprietors 1950-1930 01idk returns to the Human Agent 94 --94

Characteristics of manufacturing

Payrolls/number employed in factary 1955/1940 109 109

Communication, transportation, and other development indices

1960-1940 Seats sold at cinema/Pop. 41------Bicycles/pop. Aatos/pop. 1960/939 Percentage of autos to population 1960-1940 Electricity consumption per capita 38- - ---38 CHAPTER II

THE SOCIO-ECONOMIC GEOGRAPHY OF MEXICO

Mexico has been called a society in transition, assuming the direction of development to be toward an industrial and urban nation. How rar has Mexico come in relation to other nations of the world toward this goal? To what extent are the components of development present in Mexico? How do thwy interact?

Nhat are the patterns of concentration and dispersion of modernization.An overview of such questions is the primary purpose of this chapter. At first there will be a brief discussion of summary national indices of Mexican development in world perspectives going an fram this to a presentation of internal geographic variations on three summary modernization indicators (factors fran each of the three matrices used in the components analysis). Later sections present spatial distributions and relate urbanizatioL indicators on urban structure to population distribution, and densities, occupational structures, characteristics of agriculture, characteristics of manufacturing, cultural traits, and transportation and communication facilities. Literacy and schooling are discussed anly as they are important in the modernization factor; they will take a central place in chapters to follows.

Indices of Mexican Development in 'AAA Perspective

The Atlac of Economic Development provides an ecological framework for an assessment of economic growth among nations of the world as of the late1950's.

The world means, given in the Atlasare based on populationaveighted values for

43 44 the countries for which data on the particular item wereavailable. Compu- tations were made for Argentinals relative position aswell as Mexico's in order to provide comparisons between two of the moreadvanced Latin American 1 countries.Figure 1 lists the variables used in the Atlas.

Mexico's (=now has been expanding steadiky inthe last tvo decades.

A. wide range of consumer and producer items arebeing manufactured. At the same time the agricultural sector hasbean producing raw materials for both domestic industry and export.The Atlas gave &atom gross nationalproduct per capita as $187 in Ainerican currency,which was slightl,y below the world mean of $200, and ()compares with$2,343 for the United Stems, $374 for

Argentina, and $66 for Bolivia. A later figure places the Mexican gross national product per capita, corrected to the pricesof 1960 at $30111 the per capita product in the Federal District at$796, and in the state of Nuevo Leon at $566.

This increase took place in spite of oneof the highest annual rates of population growth in the world, 2.9 per cent. (Argentina had a growth rate of

1.9 per cent, the United States1.8 per cent.)Such rapid growth engenders the risk of reducing individual incomesand the level of living.

The proportion of the population aged5 to 14 (26.3 per cent in 1957) was among the world's highest; amongnations the range was from 13.3 percolt to 36.1 per cent (for Argentina19.5 per cent). While youth in9lies potential productivity., a currently high proportionof dependents places a heavior burden on the euonomicallyactive, especially in costs for the uxpansionof the formal educational system.

'NortonGinsburg, Atlas of Economic Development(Chicago: University of Chicago Press, 1961). Fig.14-4cononic indicators for Nolo° and Argentina (fre Norton Ginsburg, Agas of !Canonic Develment (Chicago: The University etChioago Press, 19611), The relative positionsof FiezLoo and Argenttha werein the formof ratiostothe world meansgiven in theWAAL, The world meanisa weighted average for reporbing countries;the weights are oomut7popmlations. 0 Nalco,X Argentina. 46

Relative ValuesMexico and Argentina Map

.1 .2 .3 .4 .5 .6 .7 .8 .9

2 Billions of U. S. dollars--gross national product 0 X 3 U. S. dollars per capita--gross national product II. The Population 4 V'ersons/sq, kilometerdensity of population X 5 Annual rate--population growth 6 Infant deaths/1,000 live births--infant mortality X 7 Proportion in age groups 5-14-- youthfulness of population X 8 Physicians and dentists per 100,000 population 0 9 Calories/capita/dayfood supply III. The Or anization of Population 10 1er cent of active popula1onin agricultural occupations X 11 Per cent of population in cities of 20,000 and moreurbanpop. 12 A measure of primacy--urbanpop. 13 Percentage of adults literate 14 Daily newspaper circulation per 1,000 population 0 15 Proportion of children 5-14 in primary school 16 Percentage of total pop. in secondary and higher education 0 IV. The Resource Endowment 17 geciares/capita--cultivated land 18 Hectares/capita--agriculturalpop. 19 Per cent of land area cultivated 20 Wheat yields--100 kilogralhectare 21 Rice yields--100 kilograms ectare 22 Energy potentials--trillions of kilowatt hours X 0 23 Energy potentials--millions of kilowatt hours/capita X 0 V. Accessibilit 24 RaiJay ensiy-- ilometers/100 square kilometers 25 Railway Octsitykilometers/ 100,000 population 26 Railway density--kilometers/ person to pop. distance 27 Million freight ton--kilometers/ railway kilometers X 0 i la

Relative Velneeaesieo and Argentine

2 3 I

1 4 5 I 0 i 48

11111116, Relative Va lues--Mexico and Argentina Map

.1 .2 .3 .4 .5 .6 .7 .6 .9

28 Million freight ton--kilometers/ 100,000 population 0 29 Road density--kilometers/100 square kilometers X 0 30 tzoad density--kilometers/I00,000 population 0 31 Motor vehicles/1,000 population 0 32 Vehicles/100 kilometers of roads VI. Technoloulk Industrialisation 33 Gross energy consumption--miafins of megawatt hours X 0 34 (iron energy consumption.megawatt hours per capita 35 Commerical energy consumption-- megawatt hours per capita 0 X 36 Commercial energy consumption as per cant of gross coneumption 37 Commercial energy consumption-- annual rate of change per capital 1937-54 38 riectricitygeneration--kilowatt hours per capita 0 X 39 Hydroelectricity generation-- kilowatt hours per capita I 0 40 Water power developmmnt-- proportion of potentials devel. 1 0 41 Consumption of steel--metric tons per 1,000 population 0 42 Petroleum refining capacity=- barrels/day/1,000 population 43 Commercial fertiliser oonsumption-- kilograms/hectare of cultivated land 0 VII. Externallelations 44 International mail Tio7-7-pieces dispatched/1,000 population 45 Imports and exports in millions of U. S. dollars 46 Imports and exports in U. S. dollars per capita 47 Raw materials as per cent of exports 49

SaMIINIIIIIr Relattive ValuesMezLeo and Argentina Mapairoiria&=111111MINC, 1,0 1,5 2,0 2,5 3.0 3.5 4.0 4.5 5.9 5.5

31 32 o r

37 o

ho hi

I o I 0 1 50

The world pattern of primary-school enrollment resembled the geographic

distribution of literacy andgross national prodoct percapita.1Abwat 47 per

ccat of Mexico's 5- to 14-year oldswere enrolled in primary school, compared

with 68 per cent in Argentina; New Zealand rankedfirst with 90 per cent. In

1957, 60 to 65per cant of Mexico's adult population were literate compared with

85 to 90per cent in Argentina.Mexico ranked above the world mean in proportion

of youth in primary school and in thepercentage of adults literate.However,

there was a serious gap between the populationreceiving basic and advanced edu-

cation. The stock of manpower with advancwl educationhas implications for the

ability of a country to use technology.Mexico's position was well below the

world mean in the proportion of the populationenrolled in post-primary schools:

.59 per cent (compared with 2.86per cent for Argentina and, 6.02 per cent for

the United States).

On infant mortality and food supply (measures of the "well being" of

the population), Meaicocame close to the world mean, and slightly below it in

availability of physicians and dentists.

Merico ranked above the worldmean in urbanization (2b per cent living

in cities of 20,000) but far below Argentina (48per cent).On city primacy

(the percentage of the populationof the four largest cities who live in the

largest city), the ratio for MexicoWas 74.3 per cent compared with 79.4 per

cent for Argentina. High proportions of the population employed in agriculture

can be taken as the reverse of an industrial urban society (the data didnot differentiate commercial frmn subsistence farming). In 1957, 58 per cent of

Mexico's population was engaged in agriculture;the range among nations was

5 to 93 per cant. Ten years earlier, in 1947, only 25par cent of the ovu- lation of Argentina had been employed in agriculture. Much attention has teen

h2. r. 14' Si

given to land redistribution in Mexf,co, and for cultivated landper oapita of

the total and of the agricultura population, Mexico'sposition was far above

the world mean. Howevw, widespread distribution and land ownership is not a

reliable indication of agricultural productivity. (Land per capita of the

total population was .62 hectares for Mexico,the world mean was .49; land per

capita of the agricultural populationwas 3.4 hectares for Mexico compared to

the world mean of 2.57.) Wheat andrice yields were above the world mean; but

these are not staple crops in Mexico.In Mexico, a diet of maize, beans, and

squash is part of the pattern of going withoutshoes and adhering to an indige-

nous way of life. A, diet of wheat-bread and rice, whichare non-indigenous

foods, marks the changing parts of the society;increased urbanization has been

associated with an increased demand for theselatter foods.

On, railroad density in ratio toarea and to population, Mexico ranked

above the world mean. On intensity of use of the railway per kilometer of

track, Mexico and Argentinawere both low.Only six countries were above the world mean of 2.57 million freight tan-kilometersper 100,000 population; at

70 per cent of the worldmean, Argentina was the highest ranking Latin American

country on this measure, while Mexico scored only 30per cent of the world mean.

Road density showed a pattern similar to railroads.

The energy consumed in a countrymay- measure the ability of that society to harness resources for prodoltive ends. All of Latin America except Brazil were below the world mean in gross energy consumption, and in megawatthours per capita all except Cuba and Venezuelawere below the mean.However, Mexico and

Argentina alike approached the worldmean in the proportion of total energy consumed from icizsil fuels and hydroelectricpower. Mexico was above the world maan on annual rate of change in commercial energy consumption per capita between 52

1937 and 1954.According to the world pattern, however, changes in this measure mmre not necessarily associated mith rising incomes and levels of living.Among

Latin American nations, Mexico ranked secamd behind Brazil inper capita output

of crude steel.

One index of the application of technology to agriculture is theuse of cammercial fertilizer. Mexico utilized 7.0 kilograms per hectare of alltionetai land and Argentina .4 kilograms; the worldmean was 22 kilograms. However, differences in agricultural systems and in soils must be considered in evalu- ating fertiliser use.

One value of the preceding comparisone is to test the consistencyamong measures of relative development.While each was selected as indicating industrialization, time and again particular measures failed to differentiate at all clearly among countries; developmot is complex and the meaningm ofimmly variables (notably among the "urbanization")vary with the conte.-t.We isy expect that analogous ambiguities and complexities will ariseeven in comparisons among the states of Mexico, for that camary is characterized by great geographic diversity.

Modernization Factors

Turning from an ovnr-view of Mao:Loots position in world perspective, ue can explore the patterns within Mexico.Table 3 delineates clusters of traits associated with differential modernizationamong the states of Mexico as they were revealed by companents analysis of three partially overlapping 1 sets of data: Factor Matrices A, B, and D. The first factor in each case was best described as a summary "modernization" index, but emphasis differs depending upon the variables initially included in the program.

1FactorMatrix C was composed of educational variables only. FACTORS DESCRIBING MDDERNIZATDON, AGRICULTURE, AND CULTURAL TABLE 3 CHARACTERISTICS OF 200EI00 VariableNumber Factor Matrix A Modernization A Aviculture NIMINIIMM Culture and Sax DifferenoesA ,Population distribution and Factor Number 1 1 1 2 3 6 2 2 142 UrbanDensity 1940 1960 clamma -.487 -.490 0000 .306 -.250 ...... 238 .... 12IO 8 Pop.CapitalCapitaliimban 50,000+ size 1960 1940 1940 .065.421.721 .543.052.332.783 .14606...... 678 -.051-.065 .037 -.287-468 .021.110 .147.....10 -.073 .328...... -.113-.320 .....049 -.352 .... :4.4% 151413 In-migrantUrbanUrban 1960-1950/1960-1940 1960/1930 1940 .673o.187 .396 .682.216 .685.196.516 -.029 .271.... -.162-.102-.445 .264.091.309 .087.180 -.191 .... -.109 .022 Transportation 18 RR/Pop. 190 .099 .151 -.017 .125 .... -.080 .... -.357 -.349 28272623 RoadsRoads/areaRoadlop.pRoads paved areaB 1960 196019 1940 40 .311...0000 .,26466 0152 .371.572 ....0000*000 .03100000004,0000 -.123284-.152-.311 ....0000...... 0000.... -.070 ...... 313029 Bicycles/00p.BigraealOop.Bioyoles/Pop. 19601960-19401940 00.06.28i 000*.314600 0156.391 ... -.349 .... .48o...... -.216.«230 .... -.252....223 .... .68 -,006-.I.:: 3531432 AutosAutolop.Antos/00P, pop. 1960/1939 1960.1940 1940 O...*O...737 .705 o546.237.636 .232 -.514 me -.140 .393 .4,126 .118.054 ..345 ..378 .383.079 Utilities and communication 383736 Elect/CapitaEleWcapitaElect/Capita 1960 1960-19401940 .263 .473.234 .166 .403 .4487..658 .551 .404 ....245 -.281 4114039 MoviesMbytes/Pop.Movies//pop. pep. 19601960.1940 1940 .576.847 .647.791 .822 -.089 .038 -.216 ..029 -.046 .267.253 ..107-.048 .102 .4474...JAB 4..249-.214-.240 Ws4342 RunningRadioLibrary 1960 water use/19140 1960 .746.200 .355.839.237 .777.150 .1.106 17 -.267-.197-.289 .228:285 -.113 :06; .:45;-.295 MJL.rr.e...b...oLabor force participation 4745 FSingle under F 20-24 1960 rtili 5 yrs./F 1960 rates .114.019 .098.227 .185.157 -.132-.113 -.101.0545 61605958 Dove.EactEcAct&Act F F F 12+ 10+1960.1940 19601940 1950 .287.422 -.116 .814.151.326 .251.014.297 .619.517 .4,278-.1416-.432.4,188 .587.251.553 .16644098.098 -.148 .128 -.242 .1174,448 baploymtent 6362 &ployEluploy 8-U 8.3.1. Fof 141960 youth 1960 index -.625-.519 -.686-.1785 -.580-.721 -.021-.094 .218.220 -.132-.181 .064.160 .275.502 .478.210 TABLE 3 --Continued Variable Modernization Agriculture Culture and Sex Differences Number FactorFactor Number Matrix A A4 2B 3D 6D 2A 2 White collar and professional workers6664 Collar/EcAct 1414 19401960-1940 .623.946 .632.903 .538.883 .258.036 -.304-.154 .245.153 -009 .082 -.219-.212 -.271-.227 706967 ClerkAcActCollar/EcAct T 2960F 1960-19401940 .935....mi. -.396 .901.... -.501 .866.838 .052.... -.176-.089 .... .147243.034 -.088 .147.018 -.247 .... -.270-.092-.375 7475 Prof/EcActProf/tact 14F 1960 .....785 .684.756 ...... 152 -.304 .003 ...b...... I.it... -.265 .. 4...*E... AgriculturePublic administration 77 P.A./EcAct 14 2940 .840 .732 .... .119 -.213 ...... -.008 818079 Ag/EcAotAgAcActAdEcAot 14 14M 1960.194019601940 -.326-.855-.823 -.253-.856-.866 -.296-.787-.759 -.172-.064-.161 .310.301.274 -.201-.308-.209 .025.020.056 -.170 .357.188 -.285 .199.389 8381482 AgAgEjidos/Ag Labor/Ag Pop 14 19401960 labor/Ag 14 1940 -.263-e024 .266 -.287 .237.228 -.295 .193.082 -.239 .823.723 -.313-.838 .179 -.228 .911.564 -.245-.860-.058 -.268-.198 .224 -.244-.194 .258 8586 AgAg Proakg Prop/Ag M 19140M 1960-19401960 -.259 .202 -.240-.208 .163 -.272-.175 .261 -.829-.155 -.034 .763.856 -.921-.036 -.363 .021.655 .270.134 .2410064.235 918987 ReturnsAgFarm income medhanized Glick over 1950-1930 $500 1950 1960 .625.733 .539.717.681 .607.733.633 0000.189.300 -.356-.315 -.770 0065.300.359 -.056 .167.264 -.422-.599 -.221-.341-.546 Manufac 91498 Mfg/EcAct 14 1960-1940 0.00 0187 0.00 0035 0000 3.07105103 MfgPay/Emp income Fact over 1930 $500 1960 Fact 1950 .373.790 .349.750.596 00000000 .3330000.289 -.663-.600-.443 -.268-.261 00006026 Culture 111109 Mining/EcActPay/Emp Fact /4 1955/1940 1940 0000.011 .207 .024 -.154 '.0304 -.001 0000 -.124 0000 -.140 0151 041400000i 133127125 traits Barefoot M14 M 1940-19501960 1940 -.387 0000 '0610 -.172-.381 0000 -.130 004141 .000.1670000 -.283-.185 -438 .045 .....829 .835.867 1281351.314 BarefootBarefoot M FM1950-1960 19140-19601940 -.507-.222 -.380 is.1496-.190-0186 .....225*** ...224-.179-.020 -.109 .029.040 .768.....846 .791.928.912 138137136 Barefoot 2F 1940-19601950-19601940-1950 .... -..177 .... -.356-.342-.261 .130.... -.116-.208 .043 -.033-.019-.050 ....40-0000.4 .876.880.775 132131123 BarefootBarefoot 14/FM/Furban 19601940 1960 .567.... -.313 .589.454 -.215 .065 -.195 .066.009 .0670000 .1260000 0000.115i. 0000.6690000 Modernisation TABLE 3-Conti Lued Agriculture Culture and Sax Differences VariableNumber IMINIV.N1.71111IIIMNIMININNINEINIWINO,FactorFaotor NuMber Matrix 1A A 28 3 6 A 2 2 114113 43 WaterNon-CatholicNon-Catholio 1960 T 19401960 .294.452 .355.437.314 ern .133 .237 -4,197 .252.125 ern ern ..384 .275 119117116115 NonwheatSleep on T bedfloor19601940 1940 1940 ..166 .388 -.707..718.504 .476 e rn .041 ..266.372 .035 -.037 .v.. .190 .078 vIN 122121120 Non0heatNonwheat 1940-19501940-19601950-1960 ..216 .206-.518 .096 .068 .077.040 -.011-.014-.005 -.121 .105.359 .126 .224.222.155 Adult levels of education228258 Adult 30+030+0 SohSoh 14M 1950-19601960 -.832 .000 0000 0000 0000081 0.000000 -.143 -.111 .315 .319 242239238236 AdultAdultAdult. 30+7+Soh 304.10+Soh 30+7+8oh25+7+Sdh F 24 24141960 1960 19501960 .907.929.939 -.494 458000.851 .124ADO.062 ern .231.210 ern.006.126 .223...225.217 .407is.253 ern Enrollment 267265266 Enrol 6-106-14 MT 193019601937 .675.....703 .777.775.727 .769.803.797 -.128 .....085 -.094 .033.021 -.023 .017.095 -.065-.333 .008 -.011-.197 .. -.105 .025.261 Enrollment and income274273 275in pesos EnrolEnro/Enrol 6-14 6-14 urbanrural EA 19601960 monthlyi 1959 -.090 .339.203 .447.239.41. ma84 .509.212 .004 .117.109 -.039-.145 .... .017.128.133 -.352-.126 .124 -.107-.174 .174 -.066-.216 .301 278282280 EnrolEbrol 6/$601-$1,0006/Inc $200 -.075 -.088 .252 0000 -.273 . .146.079 0000 *0000000 -.063 O0000 000 Enrollment and occupation283 of father, 1959 EnrolEnrol 6/igriculture 6A601-1,000)-(200) -.157 .363 -.197 .448 0000 -.221 184 .130.106 0000 0000 -.182 .288 O 000 Propels in school 284 EnrolContinuation 6/professional rates-primary school .219 .244 ... * 304299293296 B%ginning 4/3 urbanruralurban-rural ofryear 19601942 enrollment1942 .063.378 -.360 .14s.....419 .041.... .112. 148 -.126-.315 ....329 -.069 .156...b. -.143w.049 .031 .429 309308307 4/3B 4/3ruralurban urbanmrural 1960-1942 1960 -.311-.1014 m,208 ... -.093-.2114 -.032-.031 .025 -010. .0000000 -.093-.00 0000 .060.U40000 .132.111,90000 333332331330 B B5/1 5/1.5/3.5/1 urban urhanrural 1942 19601942 0000 .476.298.634.492 00000000 0000O 000 -.156.099-J08-.414 00000000 00000000 O0000 000 0000O 000 000 338 aonContinuation Sec 3/111 1960rates-secondary school 1330000 0000 .0000 028 0000 0000 -.010 0000 Modernisation TOM 3-Continued Agriculture Culture end SexDifferences VariableNumber Factor Number Matrix 1A 1 1 A 2 3 6 2A 2 Age grads pro/tress in348347 349school. 1963 Age 10 OrOr 1 1 14 M urban rural 0000 -.801-.706 -.784-315 0000 .092455 -.063 0000 .1760000 0000 .0350000 Education 361360359 AgeAge 10 10 OrOr Orar 1 1 1 FF Ifurban ruralrural...urban meal-urban -.689 .619 -.860-.774 -.691-.85) 6 :050:000 -.028 -.028 .076.087 .117.2084141 .218.299 4°.1146 .198.1114 4/vt trWr....07 153141140 LiteracyLiteracy 10+ 10+ T T 1940 1960 6+0 Soh 14 19140 -.495 00000000 .852.90C -.196 0000 .045.085 -.018 .055 .132 -.390-.419 159158157156155 LiteracyLiteracy 10-3)410-11410-1410-114 14 F F14 191401940 1930 1930 .803 .946.949.856.924 -.1614 -.091-.286-.013 .0314.020 .105 -.195 -.358-.223 .0.. 162161160 LiteracyLiteracy 10-114 10-14 14 F 19140-19301940-1930 10-114 IIF 19601 50 .634 0000.939.934 .668.6629.920 .001 -447-.042-.090 0000 .076 .126000*.052 -.352 0000 165164163 Lit.Literacy 1041410-14 FM 1960-19401960-1940 6+ 1960-50/1960-140 0000 -.347-.745-.736 .208 0000 -.029-.030 .040.123 -.1419-.312-.353 .149 -.033 -.270-.335 .013.259.004 Literacy ly age, 1,60 170168 40-45CUrban25-29 males ..687 736 -.027 .216 -.0894.'0404 0.00 Age differences in literacy,192 1960 Urban40-14fRural males females .566 1059 -.6b9 200199198 Urban(15-0)440-49) femalesI4o-149 -(6o+) s49) -.536-.398 0000 11000.... -.531-.170-.296 .....213.1$4 ...0.... .444 .033.139 -.173-.169-.234 .637....all -.040 .652.129 203202201 Rural(40-49)-(60+) males40449 6o+) -49) .....380 .... .402.534.279 ..068 ...... 0107.091-.059 ...050 .322.056 0....040.... -.264-.408 .163 Sex differences in literacy,205204 1960 RuralC13-10)-140449)(40-49)-(60+) females ...US ...... me ...Oil .591 ....094 ...... 468 .022 -.335 .161 .....596 452 .687 221218208 20-2416-F40-49 MR-1,14114 -.313-.431 -.3420484 -..098-..046 .052 00.0 -.497..029 0000 -.066 0000.096 .833.879.795 .839.773 Pass rates 368 Pass 2/Pres U 1942 .335 .381 . 443 School facilities 377376369 Pass 2/Pres2Ptes UR 19601942 .059. 386 .217.224.447 .316O 343 -.3520203...2610252 110.0000000.0 -.654-.598 0000.0000000 397396390389 SohSohSdhSoh Incomp Incomp Inoomp U R 1942.1960 U1942.19601942 1942 -.125 .046 .0000000 ..058-.210-469 .0°8 .147 0000000.047 0000000 043.0410255-.028 -.242g..0070052...264 .238.341 .4057 .220.2o1 61

Factor Writ A (which was in fact done last) pulls together selected variables fram eadh of the prior matrices and thus has the largest overlap with each. The variables inawded and their loadings an the first factor that showed up in this matrix are shown in the first column of Table 3.Traits with the highest positive loadings link white-collar occupations and adult school attainments.Thus the higheet positive loadings (all over .900) were for males in white-collw occupations in 1940, the population of both MOB in clerical occupations in 1960, adult males with 7+ years of schooling in 1950 and in 1960 and with 10+ years of schooling in 1960.The high loading for proportion of males in public administration in 1940 (.840, Aso reflects a relationship be-

Usen eztent of education and occupational structure.Negative loadings were correspondingly higilest for proportions of males engaged in agriculture (-.813 in 1940 and -.855 in 1960). Loadings for proportions of incomes over 500 pesos per month in manufacturing and in agriculture in 1960 were positive, at .790 and

.733 respectively. Literacy rates and changes in them had only moderate loadings compared with the high loadings on the variables indicating somewhat higher educational attainments. Negative loadings an recent changes in literacy indicate that recent progress in this respect has bean primarily a catching-up process in the more backward areas. At the forefront of change, an the other hand, are states that scored relatively high an auto ownership, attendance at movies, and use of radios in 1940, all variables with positive loadings above .700.Two other variables, urbanization in 1940 (.721) and in-migration in 194o (.673), were also part of the lead cluster.

Matrix B in the components analysis included the occupation variables that were also in Matrix Al but it differed from Matrix A in several respects, the most important of which were the fuller representation of culture variables and variables relating to youth in school (which replace the emphasis on adult 62

schooling). The Matrix B modernization factoraccordingly picks up traits that

were common 4.0 both Matrices A and B and hadhigh loadings on Factor 1 of

Matrix A, but adds positiveloadings of .700 or more referring to schoolen-

rollments: negative loadings of.700 or more on proportion ofover-age school

childrem, wage employment ofchildren, and the persistence of traditional traits

(walking barefoot andnot eating wheat bread). In the absence of adalt schooling,

the litsracy variables receivehigher loadings than in Factor 1 of Matrix A.

Factor Matrix D included allthe change and difference variables, but

the tirst factor is stilldominated by-variables relating to basicoccupation

structures and levels of education.The only change variables with loadings

over .600 refer to literaey of childrenage 10 to 14; loadinge vere positive

for the period 1930to 1940, negative for 1940 to 1960--avery interesting indi-

cator of the phasing of developmentprocesses that will be described in later

chapters. The only-difference variablesadded in Matrix D that loaded .600or

higher on the first factor referredto urban-rural differences in the proportions

of over-age children in Grade 1 (withhigh negative loadings).

One of the outstanding features of developmentis its uneven distribution and wide local variation inspace. The northern states borderingon the

United States and a few metropolitanareas in the center of Mexico (around the historicalV important cities of the FederalDistrict, Guadalajara, and Puebla) have been favored.Abundant natural resources, favorabletopography and climate, proximity to previously developedareas or sites of older population settlements, sach provides a perspective fromwhich to explain regional diversity.The physical profile that defines dissimilarregions in Mexico is paralleled bydiverse subcultures with differing traditions andpositions of lead or lag in the modernization process. Figure 2 presents this physical profile, Figure3 Fig, 2.--;.Map of elevations, Mexico. Elevations 0 to 500 Meters ...... 500 to 1500 Meters ':. . . . . ). Over1500 3000to 3000 Meters Meters ' ..t.'.. :: ,. a.... . ' v- .. . r - . - . ::::.."' . Hi:.*3 OE. .:: :. *:: '14.* . . \. .. ., 'ilikie .... , *1 . !BRITISH .. r G u AT EMALA... i ) 'HONDURAS!L. ... ! ... Fig. 3,--States and major cities of Mexico. *Jana to 1.1exieli 7- c- *1- U N E D AN uo r z I % C-1 S .tyLEALQ.Q 0 Hermosillo1, . 1 .1 I.. -. . ./. s ...... -. Cities greater50-100,000 than 100,000 pop. pop, ... O Cd. .,. N c... -v Chihuahua. e ? t l, l I. Puebla StateCities capitalsless than 50,000 pop. Obregan ; ) I ( %. COAHUILA ce ' -A. . Nueva Mexico Federal Capital c+ I. i A I. ... .7 t 1. ft WLaredo 0 O AGUASCALIENTES V r -T, . I lf, N t..- 1 .... N... v..- ...'. . '''OTorrsion Ni 1 NUEi x VO zcleft...0k MonterrijeNs' ..*._.. Matamoros--= HIDALGOQUERETAROGUANAJUATO o 'A Cul -s. I%i SaltUra . .e '.. aeon:. , DURANGOk I. Duranaq , - - i LEON ` -.0 e°Y. 03 0 l 0 TLAXCALA MEXICO V. A (eG SAN V. *. tCd. VIC oria 0 COLIMA MORELOS ) Ii k. ,e .1 iP0E3LeI 22/ 0 LUIS. 7 V Sn Luis Potosi ) f Tampico TABASCO t NAYARIT *1 f -r Aauas alientee-% ="?'"` I POTOSI *% ve PACIFIC Guadalgjgai 0 Leon_. C® !"inal9/2 eGuanahatp 0 PadhucciN '4;5(1 Qmpech Merida .1 1-90.(//NTANA ROO / OCEAN A L°Urn" I ..s , 5 c-C;r:o-re:01;.%C)S.ta - C A.:1%) ! Mexlcoe, e.T-Itig4191.,51"1-kg Veracruz VAQ 0- V*" C., .... qh_r_w_nol I .;\-+:._116 ° c "itrsh.HPRELPf 1:.- .f _41. v.) %, dirM2V. BRItISHHONDURAS-5' 100 Oaxaca c NIA °Tuxtla Gutierrez GUATEMALA L. ==-=- EE11!! MILES 200 00 1-'0A XA CA 67 identifies the states and major cities of Mexico, and Figure 4 shows the geographic patterning of scares on Factor 1 of Matrix D.

The dominant phymical features of the Mexican landscape are the too mountain ranges, the Sierra Madre Oriental on the east coast and the Sierra

Madre Occidental flanking the west coast. The topography of the central plateau varies fram large flat plUns in the north to hilly areas in the center.The level land in the plateau supports the largest rural population, because of its fertile soil, ease of tillage, and rainfall.However, MR indexes of development the central area has an equivocal position, containing both the largest cities and some highlyrural states. The north-central area is fairly level but has a semi-arid climate; rainfall averaged less than twenty inches and except where there is irrigation or mining, people are supported by grazing livestock. The landb developed in the north are planted with cotton for export, maize, and wheat. The north has been the beneficiary of the greatest efforts in irrigation, and these sites have bean developed with the more modern agricultural techniques. The northern part of the country has sparsely inhabited rural districts, the larger proportion of the population residing in urban areas.

In the tropical lowlands of thesoatheaststhe rainfall patterns make farming difficult except for "plantation crops" of sugar, bananas, rubber, and cocoa; hence the rural population is as scanty as in the dry north.

The Rio Grande which borders Texas is the major riverin Mexico.Al- though many rivers flow from mountains to both coasts, most have little economic value; for part of the year they may be completely or almost dry.Papaloapan in

Veracruz perhaps contributes the most economically. There are few lakes of comm. mercial value; the two most important are Lake Chapala in Jalisco and Lake

Patzcuaro in Michoacan. Lack of natural water resources makes irrigation projects essential for maximum productivity of land. Fig. 4.--Matrix 14 Factor 1.

Variable Factor Loadings Number (t.800 and Above)

39 Movies/Pop 1940 .822 64 Collar/EcAct M 1940 .883 67 Collar/EcAct F 1940 .838 70 Clerk/EcAct T 1960 .866 140 Literacy 10+ T 1940 .900 114 Literacy. 10+ T 1960 .852 157 Literacy 10-14 M 1940 .950 158 Literacy 10-14 F 1940 .920 238 Adult 30+ 7+ Sch M 1960 .851 266 Enrol 6-14 T 1960 .803 348 Age 10 Or 1 MR 1963 -.815 360 Age 10 Or 1 FR 1963 -.850

A short-cut estimate of the rank of the Federal Districtfor these variables is 3.2 (fram a high of 1 to 32). WIN1 . INIM ! . . . . . :411% UNITED ,.... : . . 7. .1 . 6 S a 1 . .* I ' .... Ts . , N . ' .. ' a/ 1 . 0. k .. 1 % 4% Factor Scores . I .. * , I.18to 1.55 l . 1 I TT?, SI I. . 0.12 to 1.17 11' 1.1" .. ' .1 'N .1 . , .1 t 't. - % . % . I I bbbbbbbbbb " e b h s ! t .',... %...... i . -0.43 to 0.11 1 \ r ... ' 7 ....., e.° " 1.05 to 0.44 t' 1.66 to 1.06 1:. : , 6 . . . . 6 . : V . ...7... ,..1-...... 1 I BRITISH .. :: GUATEMALA IiHONDURASL. Population Distribution andUrban Structure Population density in itself,as was pointed out before, does not differentiate the modernisedfrom the isolatedareas. A densely settiod area nay be industrialised and urbanisedor it may be agricultural, its local centers having a minimum of outsidecontacts. Densityper square kilometer does not indicate how skillfullythe people use land.However, the way population density is associated with indicesof agricultural developaentor with urbanisation and commicationcan tell us about the conditions under which densityis a factor in development.In Table 3 (the factors in modernization)population density hada moderately high negative loading. There is wide variationin density fromme state to another; =biding the Federal District, in 19140the range was tram 55.6 to .4and in 1960 from 88.6 to 1.1 peopleper square kilometer. The geographicpattern is clearly displayed on Figure 5.States having the highest densityin 1960 (Tlaxcala, Mexico, Morelos, Puebla,(ivanajuato, Hidalgo, Aguascalientes,Veracruz, and Michoacan)are predominantly in the center. Hidalgoand Michoacan have high proportions of males in aviculture.The states having the mostsparse powlations were predominantlyto the liorth (Baja Califonda Sur, Sonora,Qiinuahua, Coahuila, Durango, Baja California Norte)and close to the extreme eastern tip of the country (Campeche andQuintana Roo).

Density shows low correlations withother measures of populationconcen- tration (Table 14); the relationshipto urbanization was negative except faraise of capital city.The states with the largest capitalshad the highest densities in 1940 (.2914), but that relationship didnot hold for 1960.Density also showed a negative relationship to proportionsof in-migrants (-.1499 in 1940) andto Fig. S. --Population distribution and city size, 1960. *U N 6. .0 ire t. SI 11 II ! Each Dot Reptssents I %...... I i/! k 10,000 PerstIns SIZE3 .0 i! 7. P. I* ti CITY . % I . .0 ../ 1'N I. j*.... . 0?.. ? %- - 3,000,0001,000,000 I *. es 500,000100,000 0 4.. % ' 1/4wv:...iAileVw.,.. : .-. . "0 I / `I I' k1,00 .6% (area of circles proportionate \. t.. . 5 ' :. ! .1 ; ,5 o ei % to city size) et,. '.. ' 1. .,,r., ,' : ' '4.0; :440 45 5,, , ( ?. -;'''firr...";. lili_I: P' . r _fp V...... ,.. % ' .1' (...);.. . : ...r 1.. ',,, . iv,. : : :\ .,. -:reil ::, 1/4:: . 5 . , . .- - ;el ; .r. 46:..... j;'....1.t,4"...,:.:i..b.. .' .. a Orls 1 al* s irc.i.: Ic..j to;;7,;;;I:ii.:c;..:1.3/... . 7 90 ' * / 0# I :164: i..t. .4- s. :1". +41""?`1349%.. ilite..4.1.4.. I. 'AO .... 0. - * - 105 :oft. ' S. . 1. ' t : :. as .0 . . Z., t st 44.1. . o .: 'sm...... :211;..p, .. 0. s. %ea, %pap .k.A L J. . " IBRITISH HONDURAS I ; . . ".: 4A Al p " GUATEMALA : 73

TABLE

=MATIONS OF VARIABLE DEMISING POPULATION DISTRIBUTION

Density Urban

194011960 2930 1940 195o

Variable Amber 2 3 14 5 6

1 Density 1940 1.000

2 Density 1.960 .974 1.000

3 Urban 1930 -.322 -.260 1.000

Urban 1940 -.25o -.192 .981 1.000

5Urban 1950 -.197 -.122 943 .955 14000

6Urban 1960 -.2B8 -.093 .907 .923 .977 L000

7CaPitalidnalar 1960 -.086 -471 -.039 -.075 -.1367 -.053 8 CaPitalbrban 1940 -.315 -.289 .292 .287 ail .153

9Capitalbrban1960 -.375 -.373 .328 .307 .199 .154

10 Capital size 1940 .294 .258 .256 .287 .245 .268

11 Capital sise 1960 .077 .201 .344 .340 .351 *hod

12 Pop. 50,000+ 1960 -.156 -.052 .615 .599 .600 .631

15 Ins.migrantsiiesidents 1940-.499 -.365 .577 .555 .575 .560 16Infisigrantsiresidents1960 -.556 -.374 .457 .517 TABLE 4Continued

Capital Pop. In-aigrants/ Tha w Capital/Urban Capital Size 50,000+ assidants .- 1960 1940 1960 1940 1960 1960 1940 1960 , , 11

7 8 9 10 11 22 1,5 16

L000

547 1.000

.571 .943 L000

.239 .185 .238 L000

.351 .194 .281 .914 1.000

-.029 .236 .297 .482 .638 1.000

-.031 -363 .292 -.141 .054 .469 1.000

-.382 .297 -.240 .017 .440 .855 1.000 75 growth in urbanization.However, by 1960, as we shall see later, density variables began to piok up some of the modernieng elements.

Defiaing urban populations as people living in localities of 2,500+s the 1960 census showed just over half (50.5 per cent) of the total population to be urban. In 1960, the states having the highest proportiona of their population in communities of 2,500 or more were:Federal Didtrict; Baja California

Norte in the northwest; Nuevo Leon, Coahuila and Tamaulipas in the northeast;

Jalisco in west-center; and Yucatan and Campeche on the eastern peninsula.The least urban states were in the center and middle south (Hidalgo, Oaxaca, Chiapas,

Guerrero, Tabasco, and Zacatecas).Of the most urban states, Campeche, Coahuilal

Baja California Norte, and Tamaulipaa were the least densely populated.The states with the highest proportions urban in 1960 had been most urban in 1930.

There was very little correlation between the proportions of the population urban and the Share of the capital cityin the urban population.As we should expect, the more urban states were drawing migrants from other areas, although the highest urbanization-migration correlation which was for 1960, vas only .517.

Cities varyin size, they differ in the proportion of the total urban population of the state they contain, they differ in the functions they perform, and in relationships to surrounding areas. A city maybe the primary one, dominating by its size and influence, or it may be only one of several substantial population centers of the state. In 1960, there were thirty-eight MacLean cities with populations of 50,000 or over. Three of these (Mexico City, Guadalajara, and Honterrey) accounted for three-fifths of the total population living in urban canters of over 50,000.Guadalajara contain. 30 per cent mf the population of

Jalisco, and Monterrey include 56.5 per cent of that of Nuevo Leon. &eluding the Federal District, in 1960 the only city other than Monterrey to contain more 76 than 50 per cent of the state population was Aguascalientes. The correlation between proportions of a state's population living in cities of 50,000 or more and the percentage of the urban populations living in the capital city in1960 was on1y .297 (Table 4). As we should expect, size of capital city dhowed a moderate association with proportion living in cities of 50,000 or more(.638) amd with urbanization in 1960 (.408).

Urbanization and Occupation

Specialization and literacy are two aspects of the modernization of occupational structures, and each is associated with urbanization. Thus, as

Table 5 shows, there are moderate or even high correlations between proportions of the economically active population in white-oollar, clerical, and professional employment and proportions living in urban places (of 2,500 or more). The correlation for white-collar males in 1960 is the highest (.860); it is stronger for both males and females in 1960 than in 1940. Higher proportions in these occupations are also associated, again as we should expect, with net in-migration.

States that stand out in proportions in white-collar occupations together with proportions living in cities of 50,000 or over are Baja CaliforniaNartesthe

Federal District, Nuevo Leon, Aguascalientes, and Tamanlipas.

As already noted, the over-all population density in a state runs counter to the urbanization indexee.High density, especially in 1940, was positively associated with proportions employed in agriculture and described primarily rural settlement patterns, though by 1960 relations between density and urban developments were becoming more important.The negative relations between 1940 density and proportions of both males and femalesinwhite-collar

'WalterThompson de MAxico, S.140 The Mexican Market (Mexico:Walter Thompson de Mexico, S.A.., 1961), p. 18. 77

TABLE 5

CORRELATIONS BETWEEN URBANIZATION AND OCCUPATION VARIABLES, 1940 AND 1960

Utban Capita1/ Cspital Pop. In- Density 2,500+ Urban Sise 50,000+ sigrante

1940

Eck:to F 10+ -.016 .463 .341 .295 .282 .347 &Act F 1960-1940 -.305 -.206 -.096 .022 .004 Collar/Eact M .763 .503 .049 .541 .750 Collar/E6Act F -.726 .586 .211 -.037 .545 .672 Collar/Ed/Lot M 1960-1940 .683 .113 .547 .756 .372 Collar/EcAct F 1960-1940 -.002 -.220 .480 .054 -.328 Public administration/ECAot M .522 .485 -.247 .252 .668 Afg/EdAct .821 .297 .401 .506 .264 legF/M+F Mfg -.242 .062 .316 -.236 -.296

1960

EcAct F 12+ .058 .210 .162 .148 .124 .429 Collar/EcAct -.269 .860 .395 .506 .732 .611 Collar/EC:Act F -.191 .820 .170 .521 .722 .426 Collar/EcAot 1960-1940 .083 Collar/EcAct F 1960-1940 .786 Clerk/EcAot 14 -.494 .753 .397 .659 .673 Clork/EcAct F -.490 .739 .346 .446 .695 .578 Prof/EcAct M -.238 .666 .357 .404 .546 .568 ProEcActgractF -.263 .587 .272 .089 .242 .123 .223 .713 .207 .515 .591 .240 Mfg F/14+F Mfg .176 -.337 .013 .264 -.255 -.307 78

employment at that time are unambiguous: -.624 for males and -.726 for females

(first column of Table 5), and this negativepattern, though damped, is repeated

in the correlation for 1960 between density and proportions in clerical jag.

None of the other correlations with densityare noteworthy excepting the very

interesting high relationship between 1960 density and 1940 to 1960 change in

the proportions of ecanamically active females who were in white-collarem- ployment (.786).

Urbanization and Avicultural Development

According to Cline, fivecrops took up five-sixths of the cultivated land and accounted for nearly three-lourths of the total value of farmpro- duction in 1957; they were maize, beans, wheat, coffee, and cotton.1 Histori- cally maize has been the major foodcrop and it remains so today.While it is the traditional crop of subsistence farming,a surplus is produced in the areas between Mexico City and Guadalajara and in Veracruz and Michoacan.Maize grown on irrigated land is quite a different crop from Maze grown on subsistence farms.With the growth of large metropolitan centers, farming practices changed in the central states; the "extensive" planting of maize was supplanted by intensive grain culture, much of it for shipment to the cities. Within a radius of 150 miles of Mexico City, a dairy belt emergedl and sugar, rice, fruits, vegetables, and flowers were planted, while submarginal lands continue to be

2 used for maize.

2HowardF. Cline, Mexico Revolution to Evolution 2940-1960 (London: Oxford University Press, 1021, p. 265.

2 Edmundo Flores, "The Significance of Land Use Changes in the &anaemic Development of Mexico," Land Economics, MT (1959), 115m24. 7V

NexttO ISSISS,wheat is the major oerlal; it is groin in the northon

irrigatel land and in the west and °enteras vim. RiceiSgrow commercially

primarily in Sonora, withsome harvested in Veracruz, Tabasco, and Michoacan.

Industrial aad export cropsare cocoa awl heneven, as well as cotton and coffee

Coffee plantationsare limitel to Chiapas and Veracruz. Henewen is grown

chiefly ma plantations in Yucatan.Tobacco is prodnoed inNa7arit and Jalisco.

Ths planting of crops for industrialuse, for export, and for urban cm-

sumer markets has been associated with the mechanization of apiculture.

Modernization of the agricultural sector is reflected in relative]; lowper-

centages of the Tork force in agriculture, the application ofsachiv,ry to

farming, and rise of productivity beyond the subsistencelevel.The proportion

of the population in agriculture whoearn over 500 pesos monthly provides a

simple if crude index of productivity.The value of farm equipment as a ratio

to land value supplements information aboutfarm sise and composition of the

agricultural labor force, providing another indexof modernization and com-

mercialization. Glides index of "returns to the human agent"in agriculture 1 was also used as a measure of productivity.

In 1940, the proportion of males in agriculture in the states of Mexico

(excluding the Federal District) ranged from-39to 53 par cent of the economi-

cally active; the state with the lowest percentage still hadover half of its

active males in farming.By 1960, however, the percentage of farmers ranged

from 00 down to 35 per cent.There are moderately high correlations between

1Hearrived at his estimate of returns to the humsn agent by the following procedure. First, he estimated the value of land and capitalem- ployed in agriculture.He then assumed returns of 5 per cent and alternatively of 10 per cent on those values.The resultant estimates of returns to land end capital were subtracted from net famm income toget returns to the human agent as a residual.His estimates using the 10 per cant assumptionsare the ones used in the study.Glick, op. cit., pp. 143-50. 80 modernization of agriculture and urbanization (Table 6). For example, the association of mechanized farms In 1950 with proportions living in population centers of 2,500+ in 1960 is .664 =I with the population living in cities of

50,000+ it is .561.Modern farms attracted laborers from out of state; the correlatiln between farnmechanization and in-migration was .692 and that between farm mechanization and proportions of the agriculturalky employed who were laborers (not shown in the table) VAS .567.Low incomes in agriculture were associated with high population density(.619) in 1960 but with low urbanization, city primacy, and urban growth.Again, where returns to the human agent in agriculture increased between 1930 and 1950, there was a negative association with 1960 population density (-.529). High returns in agriculture were associated also with high migration into growing urban centers

(MO. Contrasts between the traditional peasant farmers of the more densely settled rural areas, and the modernized agriculture in the dry and irrigated areas where scale is typically larger, workers are most literate, and innovative practices have been most important, come through very clearly in these relationships.

Higher incomes were earned in farming in those states with the lowest proportions of men in agriculture. The percentages of the farmers earning under

500 pesos monthly varied from 48 to 96 per cent. However, the state ranking third highest (meaning fewer people earned law incomes) still had 71 per cent of its agricultural population earning less than 500 pesos monthly. The states with the highest proportions of their farming population earning incomes above

500 pesos monthly were Baja California Norte, the Federal District, Sonora, and

Coahuila. Over 93 per cent of farmers had incomes below 500 pesos a month in

Hidalgo, Tlaxcala, Yucatan, Oaxaca, Guerrero, Puebla, and Guanajuato. CCRRELATIONS BED= URBANIZATION AND AGRICULTURE VARIOUS TABLE 6 1940 Density 1960 19140 Urban2,500+ 1960 1940 caPital/Urban 1960 1940 CapitalSize 4960 50,000+ Pop. 1940In-aigrants 1960 AgikoActAg/E6Aot M X 1940 2960 .269.436 ..184.388 -.847-.903 -.873-.803 -.361-.402 -.378-.464 -.299-.234 -.416-.328 -.655-.625 -.595-.635 -.537-.568 Ag/Eelotzjidos/Ag X 1960-19140 Pop 1940 .208 -.316 .158 -.184 .100 .089.... -.083-.031-.100 -.154-.180 -.138-.090..320 -.198 .... -.148-.264-.355 -.005-.158 -473.198 .... Ag Libor/Aglabor/Ag X 1960 1940 209.072 -.395 .247.191 -.037-.207 .284 -.134-.038 .392 -.078 087 -.037 .152 -.018 .060 -.161 .216.127 .092.379 -.058 .304 .416472438 Ag Prop/Ag 14X 19601940 14 1960-1940 -.052-.411 .... -.172 -.265 -.372 ... .091 .41...043 -.034 .006 -.195 .... -.341-.359 -.373-.260 -.416 .... ramEquip/LandAg 1950 Prop/Ag 1950 -.484 .198 -.376 .260072 -.184 .598.272 .664.342 -.298 .083029 -.205 .097 -.069 .267 .247.264 .561.525 .350.813 .692.172 ReturnsImmo Gliokover ;500 amobanised 1950-1930 -.729 -.529-.619 .234.488 .551.... .127.243 Alm.324 -.114-.339 .....190 .503.256 .675.770 .736.... 82 Using Glick 1 s measure of returns to the limn agent In agriculture (series B, 1950), the abates achieving the highest productivity were predominantly northern and coastal whether to the west or on the Golf.The interior °antral states and those across the south ranked very low on thisindex.Click related farm-labor pfroduotivity to urbanization but did not find aclear alma- ation with labor-force characteristics.(An urban oenter mey or way not be industrial in character, and location near a large city did notpredict influences operating to induce or difihse innovations andincreased efficiency in agriculture.)In assessing the relationship between centers ofdevelopment and farm labor productivity there seemed to be differingrelationships in the north than in the center.The centers of development were Baja CaliforniaNorte, Coahuila, i 4 In the northern statos there I Nuevo Leon, Tamaulipas, and the Federal District. was an expected graduation from the highestproductivity in the lead areas to

! favorable located adjacent states and on to the "nearperiphery." However, in

variation and a i the development around the Federal District there was greater different pattern of diffusion.The increase in farm labor productivity was i slowest in the "favorably located" oradjacent areas, greater an theperiphery)*

This finding points to the blockages of diffusion in the central area.

One outstanding feature of the Mexican agricultural systemhas been the I redistribution of the 3and. In 1910, 1 per centof thepopulation owned about

97 per cent of the land. Theland redistribution proven after the Revolution

was carried out in the spiritof socialjustice and only later did economic

, development become an important consideration. Three main types of status in

agriculture were distinguished in1940; e iditarios (those mdio received land

grants under the land reforms and were subjectto associated constraints on

llbid__vI pp 314 and 1414. 83 inheritance and subdivision); agricultural proprietors; and agricultural laborers.The 1940 °ensue enumerated tligtarims separately; at that time they made up 42 par cent of the agricultural population. The 1960 census included them withproprietors.1The agricaltural "proprietors' are defined for this study to include only fermiers working their own lands without hired help, but for 1960 they include e iditarios whereas for 1940 they do not. The majority are on properties of five hectares or less. Most of these mall holdings operate at a subsistence level; general productivity and technology are lour.Soae of these proprietors eftgage in other activities part-time.

Agricultural laborers maybe looal day workers without regular employment who work on small and medium-size properties, they ember migrant workers following the harvest from one part of the country to another, or they maybe employed regularly on caantations. Hired men made up 50 per cent of tbe agricultural labor force in both 1940 and 1960. The proportion of the agricultural population of an area who are laborers is in ;art an indication of the development of commercial farming, awl hence it is to some degree associated with measures of rural modernization.

Mhile the correlations between proportions of proprietors or laborers and urbanization were low, there were some interesting changes in relationihips between 1940 and 1960 (Table 6). In 1940, the proportion of the agricultual labor force who were hired was negatively associated (-.207) with proportion of population in urban places, whereas in 1960 that associationwaspostives as was the association with proportions in cities of 50,000+(.379). The proportion of males in agriculture who were proprietors in 1960 showed the opposite

1Stavenhagenestimates they made up 25 per cent of the agricultural population in 1960. Rodolfo Stavenhagen, mAspectos eociales de la eetructura agraria en Mexico," America Latina (Janeiro to Marco,1966), p. 6. tendencies; a negative association with urbanisation and withurban growth. The corresponding associationof hired farm labor with in-migration reflacts the geographia linkage of urban developmentwith growth of mechanized farming and the spread of irrigation.Nevertheless, there vas a strong negative association between population density and incomesin agriculture in both 19140 and 1960 (-.729 and -.619 in Table 6).Also factors of Matrices A, B, and D that describe agricultural structare give densitya positive loading if the loadings are positive on hired farm labor, and vice versa.As column 14 to 7 of Table 3 show, the clusters involving agricultural structure include moderately high loadings on electricity, female labor force participation, and (in lesser degree) incomes in agriculture and in manufacturing.Factor 14 of Matrix A picks all of these up with signs that indicate relative modernization and economic advancein association with larger proportions of hired labor in the fana populatimu Factor 2 of Matrix B does thesame thing but in reverse, picking out law-level agricultural development along with low incomes inamnufacturing and low electricity consumption.

Urbanization and Nanufacturin1 Since 19140 the industrial sector of the Mexicanmammy has expanded greatly; in 19145 it accounted for 18per cent of the Gross National. Product and in 1960 almost 26 percent.1Much of the increase cane from the expansion of iron, steel, sugar, cement, paper, chemicals, glass, and cordageindustries.2 In 19140 the range of economically active males in manufacturingwas from 33.6 per cent (in the Federal District), followed by Neuvo Leon with 168 per cent, to a losr of 14.0 per cent In Guerrero.In 1960 the percentages were 35.3 in the

1Thompsen,pp. cit., p. 159. 2Cline, pp. cit., p. 280. 85

Federal District, followed by 26.9 in Nuevo Lean to 3.9 in Guerrero.Other states having relatively high proportions of males in manufacturing in 1960 were San Luis Potosi (22.3 per cent), Aguascalientes (15.8), Tamsnlipas (14.2), and Guanajuato (.4.1). Low ranking states were Zacatecas (3.9 per cent),

Chiapas (4.1), Oaxaco (4.8), Baja Califtrnia Sur (6.2), Quintana Roo (6.5),

Tabasco (6.6), Nayarit (6.9), and Durango (7.2).

There was a moderately close relationship between proportion of males in manufacturing and proportion of the population classed as urban (Table 7).

In 1940, the relationship was stronger than in 1960 (.821 and .713 respectively). The correlation between proportions of economicallyactive males employed in manufacturing and proportion of population in cities of 50,003+ was .5914

In 1960, there was a correlation of .515 between large capital cities and proportion of males in manufacturing (in 1940 this relationship was .401).In generall where incomes in manufacturing were high, there was a relatively high proportion of male in-migrants (.738). These areas were less densely populated but also more urbanized.A3 of 1940, factories paying relativelywellwerelike- ly to be in larger cities and to attract migrants from out of state, but these relationships were much lower in 1960.

Where the labor force in manufacturing is composed largely of females, it is typically less modern in character.Females are more likely to be en- ployed in either household or light factory industries, in which productivity and income are low. In 1960, the correlation between 1,,he proportion of wage earners in manufacturing who received under 500 pesos a month and the proportion who were female was .494. The states with higher proportions of females in manufacturing were somewhat less likely to have in-migration of males (-.307).

Mexican manufacturing ie heterogeneous, displaying a wide range in technological development and organization. The most primitive industries 99

CID 10 Po :IP %.0 10 Pi N 10 111) VI -.4 10 N

1 1

%.0 V00 CO 1 13 IN) Lin- jr 8gn 1 :

& E42:1 55- ARiii " I I -. $

C Ch 1-I sel ....,3 N 011.0 N IV 4) jIl i ellt g - IMMIL OD Ch P5-' CO s 1 g ro) Ii,... -4 -.4 .p." AP IV N .k 2 0 I-4 1

-.WI. 43 -4 5-11 CO & La 0 t V% %4W tu ly N Q IND 53 SO 1

U1.-4 N 11-6 43 IV N 1.8 0 No IV -4 11 .L-0 Nt-1 N43 0 N 1-a - k § to 1 1 1

VI. CO -4 -4 -4 N .4 kia to iv uw- -.4 8 IV ta vi, ii-a . 4: t 1 1 11 1 vgi P"o o3 lip le) N V.I S. - .6 . . . kos V% 0% CO 4) 0 I%) -4 N IsVt rs) 1-± ki g - - 1:2gL.-. i I 1 1 11 :

op 4.1 0 4-i- 0 0 V) - : L?' rert% : n' - - - - 1 1 1 1 8?

making objects for home use and a surplus for the local market (however many

people they emplay) do not sigpal development. In transitional society, arti-

cles maybe elaborated by band thatare produced by machine in advanced

countries. This kind of processing can reach a large scale, nevertheless,as

in the shoe industry in Leon, glass blowing in Puebla and Tlaxcala, and fruit

canning inBajio.1 According to Myers:

The manufacturing sectoris comprised of a small number of highly capitalized large establishments that employarelatively smallpercentage of the industrial labor force.The remainder include small or middle- sized establishments and the shops of artisans in whichtechniques are often stUbbornly traditional and personnel administration is based on family relationships. A recentsurvey showed that large plants, representing 34 per cant of the total number of establishments and employing 26.5 per cent of the industrial labor force, accounted formore than 40 per cent of the value of output andmore than 50 per cent of the capital equipment in this sector. Small and middle-sized firma representing 55 per cent of factories employed 65.3 per cent of the labor and 46.3 per cent of the capital equipment and accounted for 55.5per cent of production. The remaining oapital and labor were employed in tbe shops of artisans, 41.9 per cent of total units, which accounted for only 3.5per cant of the total value of output.2

Four maps (Figures 6 to 9) descrite industrial location patterns in 3 Mexico. These are based on information fran a Mexicansource whidh classified

industries into three largegroups: "basic," "vital," and "secondary."Twenty

industries were included, eliminating those that appeared tobe of a workshop

type or represented the indigenouseconomy such as traditional bakeries or mills for grinding maize. Basic industries were defined as those manufacturing machinery and equipment or vehicles thatwere of "essentiaX importance far the industrial or agricultural development of the country"; tbeywere the main

1 jarge L. Tamayo, Geo grafia General de Mellat, Vol. IV: GeosEafia Economica (Mexico: InetitüTMexicano e Invesligaciones Economicas,1962), ir4V37"" 2 Myers, op. cit., PP.19-20.

3Themaps were adapted from:Fernando Zamora (ed.), Diagnoatico Economico Resional (Mexico: Secretaria de Economia e Institilo Mexicano de Investigaciones gconomica),pp. 140-50. Fig. 6.--Industrial concentrations and locationsof selected basic industries I, 1957. IS' AL -6. exicali "ft U NSIND SIM TE 'lb ;1 u a r e z % i 0 Cities Greater than I. ! S 1% .1 Iron100,000 and Steel(1950) %-ds Hermosill t- r) hihuahu \ .. ., *?./ : /- .. 111%. C ^n v .. m PaperGlass and Cellulose rt5)4 ! t. .. r RubberChemicais Processing , , , t_ ,, r . 1 . ( I %.1 : 1 * Construction * I 1t. i Tor re6rvcia' .#* 4 ... o t la . lip nserrey - % ,. koiCtilipcan 1 t-...5....:4...,...... - . .loi_: * c* _. i . 1 - - - .. i i N t 1 aa, - N. 1 119Durango 1 . a, r , , ! 1 1 ; 0 i d k fa . / . C.\ / / .. / , ./...... Mexicol-1 I .4- ---i_A. 6 -A. N xebla 0 i / 1 i/ . )- - ., _....1:11ASn. Luis Pcirtsi . - . .1-,1 .../ ; 1 Merida i- airi .... Leon° 0, A t. .., 611 0 . - - rr i.../ ,,, , . . */ "'m. - ./ op Guaaalajara 0ga 'I./ * 41.4 -/ I ...';,'..i.t. : # - 6% . .. ! '.--,...... -i..I.:.>,0.46. memo., 7 1: 1 .: .: %iv -,.., 1._ .') :4). i . :" .A. 'I -.(.;; 4 ( . . u e ROA. eracruz7 AL 1- AL .A. ..?"--J. 1 . 1...'.1. * J !BRITISH O v N f 0 .A. . A GUATEMALA.."'". iHONDURAS Fig. 7.--Indu5trial concentrations and locations ofselected basic industries II, 1957. 6-Mexicali UNIT 10- Z4N uar ez o Cities Greater than HermosillO k s2 7. s CottonSawmills Gins100,000 (1950) i 1 -0 hihuahuo: ..! . / % p. . - ... 1 ... .7. i - %.,, Torre -.../ i t .-. kr%_,. 6C..iliacan I i ,-4 - . - ..1 onterrey ...... -, I1P N. QDufango. s i. -.-. / i i * c- f. f 0 S t . N . _. iN )-c i4 ..r ... ( ...0-,_ co.:. __../ %...... , j . C. ... .,... .. * C Guadalajara .,..t. 0 i c../ / .)....%. Leona* . i s IgSn. Luis PcitIsi\ ''-'7..r.f.,./ f :-. 1/ SMerida ./ 6 `...... ? - -1 IP 0 -1; -4.. ., -- AXICC):!'ty.-...:,:e.,...:.iiis::: ":":.:,:-Y:4 1 ... s sp. ' ueb 04 eracrUZ i I .s s . p.,., 0 ..- ..,(IP . -- /.... t 'n 0 i v. .." `.. O. ... _... i : -.. _.....: --- I: BRITISH / ( r- ' ''" - ;HONDURAS C i GUATEMALA.,-/ ii Fig. 8.--Industrial ooncentrations and locations of selected vital industries I, 3.957. UNi TED! mluarez '.. S 7. o Cities100,000 Greater (1950) than ermosills; hihuahua* 18. * TextilesSugarCanning Refineries 1... ' r. ' Ak1 %."%... Tarred Or: onterrey i ...... dalipcan .... 'I. 1 .. ( '-' ia / / / 0%..- - ! ..N. LeDufarigo I r , \,, %... . t.... / Mexico' f ! 0- $ . IA ' ,... , /6'. 4, , / / uebla ...... it. . /.. ,, ../ ..... , / i \ .1 c .t / @Sn. Luis P ' . osi ...... , 0 ,c'l i /`- ....)r '1( (.. 1 '1 t. `"r.i.s./...... 0 / , Pe° Merida Guadalajara c..-Fc-'1.....-1 6616 11 r . r / 1. I. ' 0 'Ile . eracruz I. I ...... -* ye DP. .. ', . .4. o ..., L. ' %. " .....*..".. ..". ? ..; . we .. ! BRITISH! (:,.o . L o .i..1 '...- . i .....J `,....: i:HONDURAS i GUATEMALA,,' Cr`I Fig. 9.--In1ustria1 concentrations and locations of selected vital industries II, 1957. ! lb o VegetableFlourCities100,000 MillsGreater Oils (1950) than hihuahua° \.. 4.1 ...... / .,...... 1. 4.44*-41: - ' -. ill PharmaceuticalsFootwear I ...N. 4 t L., i i - . N .. ... 40 .....4 4, ft %, ... i Tarred -....4 Monterrey...... , ....., %. - c u%lipcan I I. .,...... 1.' -1 1 is. k .- \ '.. eN, (3Daango I ., .1-../ ,l, .s., , i-.i( o / Mexico;0`, ", .... \ .. -' /.., t,\ i ill, e N .... r. i @Sn. Luis Pchosi . . ,...... ir III / / 410,' .- .,.; .I &Puebla o .11F4;41,-;Le6n\ i / .( ,. , ) No o ---./ 40 `. ; , .4,o1 / Merida .., .1 " '-r./',/ I .,,, , / 0 Guadal jars oz. 4# C''... '1 ...... % 411....);i*..,:':!....' " ,::'.:'::::,;;;;;:5 .. r 1 o MexIcQ4r k eracruz - 1 -....'-. o - I o J ..,,....._...... % _...; . __.... ! -;-:-. ISRITIISH / / GUATEMALAe."...... :HONDURAS1 i 96 capital goods industries.The "vital" industries were those that 'transform raw materials or the products of basic industry intooonsumer goods. "Secondare industries are not included in the accompanyingmaps; they were light industries that produced such prodactsas beer, cigarettes, beverages, alcoholic drinks, andmatches.1

The most noticeable features of themaps are the concentrations in the center and in Nuevo Leon and Coahuila; this is especiallymarked for the basic capital goods, which aremare tightly clustered around major cities than are the

"vital" industries. On the other handl the maps show that industries in the early stages of processing tend to locate close toraw materials.Comparing the location of the cotton gins (Figure 7) with the location oftextile mills

(Figure 8) shows the earlierprocess to be more dispersed and closer to the growing regions (in Sonora, Durango, Coahuila, andNUMLean).Textile manufacturing is found in the same areasas the initial cotton processing, but also in the Puebla area, Michoacan, and Jalisco. The distribution of sawmills, which are almost ubiquitous, can be compared with paper and cellulose manufacturing to emphasize the wider distribution of the former. Sugar refining (Figure 8) is confined to the sugar-growing area across the center of Mexico and southeast, while food preservation is found in association with relatively denseagri- cultural settlements and consumer markets, closely matchingthe population dot map. Flour mills are, of course, located in wheat-growingareas.

Throughout most of the north and northwest, three-fourthsor more of

1957 manufacturing production is outside of themajor urban clusters outlined on the maps. Though industrial production was growing rapidly in theseareas,

'Thesecondary industries were eliminated in the adaptations formaps, in this study, although they appear in the Dia ostico Ebonomico Regional and are discussed in Tamayos 221.2111, pp, 488 97

more than doubling the national rate of expansion, it was oriented primarAy

to utilization of agricultural products of the region.At the other extreme,

the Monterrey, Saltillo, and Montolova industrial grouping represents a ooncen-

tration of 90 per oent of the total industrial output for its region. In the

central region, whidh generated about 40 percent of the 1957 total national

production, about 80 per cent was concentrated in the urban olneters; however,

the pace of industrial development has been someWhat lower then in the north,

and this central area is marked by the coexistence of modern industries and the

artisan type.

In the Pacific South there is hardly any industrial concentration-smay

a few dispersed raw material processing enterprises.Because of the low levels

of manufacturing production there, any meaningfUl increase in production gives

very high rates or dhange. In the aulf region over half the industry its dispersed. In Thalami% Campeche, and Quintana Roo, 70 per cent of the regional

output is widely scattered.However, areas of high local population density

coincide with these industrial groupings.

The industries that are widely dispersed and are based on processing

of raw materials are likely to be less highly capitalized than toose clustelsd

around the major cities.Those that produce for wider markets do, however, promote contact of the regional sdbcultures with the national society, even when the products and processes are traditional.. The more advanced industries,

concentrated around a few major cities, require a large pool of skilled labor, adequate electrical widgets services, and goal transportation and communiaation facilities. Cultural Characteristics and Urbanization

The cultural characterietics included in the study can be viewed as measures of social isolation. The habits of food and dress indicate the extant of "marginality" of the indigenous population, and changes in these habits often are considered evidence of the adoption of an "urban life style." In 1940, the proportion of a state's population not eating wheat bread ranged from 5 to 80 per cent.In 1960, the range in percentages not eating wheat bread was from

5 to 67 per cent. The proportion of males walking barefoot varied in 1940 from 1 1 to 72 par cent and in 1960 from 2 to 48 per cent. "Indigenous habits" are intercorrelated:walking without shoes accompanies sleeping on the floor and not eating wheat bread. States with the highest proportions walking barefoot and not eating wheat bread were in the central part of the country and in

Guerrero on the Pacific; those most "modernized" in these respects were the northern tier of states together with the Federal District. This again points to the variations among the central states, where sophisticated city life coexists in close spatial proximity to, yet separate from, indigenous culture.

Although the populations that adhere to indigenous mays are not exclusively rtrral, there are moderately high negative correlations between such traditionalism and degree of urbanization. The 1960 correlations with proportion of the population walking without shoes were -.653 for proportions living in towns of 2,500+ and -.688 for proportions in cities of 50,000+

(Table 8).

1Notethat the lowest percentage in 1940 was below the lowest in 1960. In both cases the state was Baja California Norte; the slight increase there between 1940 and 1960 probabky reflects the characteristics of recent in- migrants. 99

TABU 8

=MATIONS BETWEEN PROP0RTIONS OF MALES WAIED1G BAREFOOT AND IN AGRICULTURE, AND URBANIZATION VARIABLES

Agriculture/EcAct. Barefoot/Malas Males

1940 1960 19140 1960

Density

1940 .318 .394 .436 .269 1960 245 .328 .388 .184

Urban 2,500+

1940 -.634 -.662 -.903 -.847 1960 .9628 ...653 ..873

Pop. 50,000+ 1960 -.667 .688 .625 ..655

Capitallurban

1940 ..162 ..149 ..1402 ..361 1960 a..208 ..4614 ..378

Capital size

1940 -.253 -.196 -.234 -.299 1960 8414 ..342 ..326 ..1416

In-migrants

1940 -.624 -.587 -.635 -.595 1950 -.599 -.563 -.608 -.607 1960 -.504 -.537 -.568

Two factors described culture and sex differences (Thble 3). Matrix Al

Factor 21 had high positive loadings on males walking barefoot in 1940 and on changes in this trait between 1940 and 1960; there were positive loadings for large differences between males and females in literaay in both rural and urban 100

areas (1960) and negative loadings far sleeping on a bed (1940). There were

negative loadings also =pass rates of youth in primary school in both rural,

and urban areas and on traits associated with modernised apiculture.

A similar factor (Matrix D, Factor 2, mapped later as Figure 15) had

high positive loadings on "population walking barefoot," on literacy differ-

ences between young and middle.aged females in both rural and urban areas, and

on sex differences in literacy. Taese factors again describe essent10.4 backward rural areas.The reduction in the proportions of males walking barefoot between 1940 and 1960 and the notable progress in literacy of young

girls compared with the middle-aged women points to a lagging rural setting

that is now undergoing change.

Transwtation and Communication Systems

The extent of the transportation and communication systems isan indication of accessibility withinacountry. The national network of roads and railroads tells something about the complexity of the economy and the size of the market.The railroads in Mexico were built during the initial stages of industrial development in the early 19001s. The main lines run north and south with one transverse line; Mexico City is a nucleus in the railway system and people or goods travelling across regions must usually be routed through that city.While the railroad branches run north and south, it is difficult to build branches to smaller places.Figure 10 shows how the railway network connects the areas of industrial concentration.

The principal highways also run mainly north and south, often parallel to the railroads.The modern highway and road networks have been built mainly since World War II.The goal to ereate the main trunk roads as

Federal highways, and then to complete more extensive secondary and feeder Fig. 10. --The railway network and industrialconcentrations. Mexicali -... U N I MEND IMIN. *.. -...... 1 t JuarezN rei I. 1 HermosillO --, ! 8 . 4 .. 7. %) n l.... % hihuahua* ... c Industrial Concentrations / % ( i!I t Oo OtherCities100,000 Cities Greater (1950) than / g

) eri a ../ ./ 0 I k eracruz ----"-joRITISH 1 :HONDURASI ! I / GUATEMALA,,./r--- ..i iI!Le,"': 103

roads to open up isolated areas.Figure 11 depicts the highway system. In

sparsely populated areas there ie a closer relationship between railroad and

road kilometers per capita than in densely populatedareas. However, measures

of railroad or road mileage in relation to populationor area tell little about

the uses people make of the available transportation.

In Table 9 there is a negative correlation of -.654 between railroad mileage per square kilometer of area and relative recency of urban growth whereas road mileage per capita is positively associated with recency of expansion of urban plaoes. This i3 a clear reflection of the fact that Mexico has experienced two major spurts in communications and economic life. The

spread of automobiles is an important element in the present phase, reflected

in the high correlation of autmobiles with urbanization; but equally important

is the increasing use of buses, which have contributol to the habit of travel in the rural areas and bring rural visitors (and migrants) to the cities.

Intercorrelations between transportation facilities, males in agriculture, and males walking barefoot (Table 10) emphasize the remoteness of much of the rural population in 1940, but by 1960 the relationship between proportions of males barefoot and road mileage per capita had loosened considerably, suggesting that as roads began to penetrate the more remote areas, the available measures no longer distinguished nbackward" areas.Table 10 is interesting also for the behamior of the bicycle variable, ',Isiah is negatively associated with proportions walking barefoot and proportions in agriculture, though the negative correlations are weaker than those between autmobiles and indigenous culture traits or rurality.

Attendance at movies and radios per dwelling are measures of the main mass-media network. The correlation of these traits with 1960 urbanization was as strong (Table 9) as for automobiles. As Table 11 showy, these traits, along Fig. 1.1.--The highway system. Mexicali . Juarez%.. 'sr SecondaryPrimary F +...ds Roads rmosillo t t t .. 0 100,000Cities (! 950; Greater than Culiacan Torre Merida Guadalajara eracruz / GUATEMALA ...... ! 106

TABLE 9

=RELATIONS imam URBANIZATION VARIABLES AND VARIABLES RELATING TO TRANSPORTATION, COMMUNICATION, AND FACILITIES, 1960

o...... , _ Urban Pop. Density Urban 2,500+ 50,000. 111-11dgrante

1960-1950/ 1960 1 960 1960-1940

Transportation

Railroads/Pop -.407 .418 .138 Railroads/Area .799 .155 466 -.654

Roals/Pop -.826 142 -.062 ,647 Roads/Area -Sia. -.316 -.1114 -.051

Bicycles/Pop -.091 .285 .075 -.037 Autos/Pop -.311 .784 .699 .582

Communication and facilities

Navies/Pop -.207 .785 .538 491 .342 Radio -.262 .847 .686 218 .527

Electricity/capita -.220 .434 .1470 .528 .1436 &liming Water .053 .638 .560 .1,87

amenk 107

TABLE 10

03HRELATIONS BETWEIN THE PROPORTIONS OF NAM WAIKING BAREFOOT AND IN AGRICULTURE AND 1RA1SPCRTATI0 N VARIABLE

Railroads/Pop

1940 -.290 -.3o6 -.034 -434 1960 -.297 -.353 -.390 -.373

Railroads/Area

1940 -.124 -455 .010 -.097 1960 .042 497 .3.146 -.041

Roads/Pop 19140b (a11-veather) -.697 -.701 -.491 1960 -.182 -.232 -.478 -.304

Roads/Area

194013 (all-weathar) -.065 -.028 -.058 -.3146 1960 .226 .312 4714 .009

Roads paved 1960 -.14146 -.398 -.326 -.1422

Bicycles/Pop

1940 -.293 -.4114 -.1496 1960 -.089 -.190 -.351

Autos/Pop

1940 -.703 -.733 -.775 -.797 1960 -.597 -.626 -.789 -.887 TABLE 11

CORRELATIONS BETWEEN COMMUNICATION VARIABLES AND CHARACTERISTICS OF AGRICULTURE

0, , Movies/Pop. Radio LiubrMovies/Pop.

1940i1960 1960 1940 3960-1940

Ag/loAct 14 1940 -.755 -.639 -.84,3 -.434 -407 Ag/EcAct N 1960 -.693 -.661-.880 -.437 -.099 Ag/EcAct 14 1960-1940 -.152-.276-.386 -.189 -.243

Ejidos/Ag Pcp 1940 -.052 .070 -.078 .139 .03.8 Ag Labor/Ag N 1940 -.162-.063-.050 -.202 .052 Ag Labor/Ag N 1960 .107 .424 .533 .364 .438

Ag ProP4Ag 11 1940 .137 .014 .130 -.101 -.0214 AR Pro PiAg 14 1960 -.412 -.521 -.365 -.1sla Ag Prop/Ag N 1960-1940 -.344-.491 -.270 -.378 Equipfiand 1950 .013 .289 .512 Farm mechanised, 1950 .657 .640 .765 .362 .158 eg Inc. over 1500, 1960 .668 .598 .755 .251 .122 Returns Glick, 1950-1930 .651 .324 .446 -.232 -.140 11111. 109 with 1940 library use, display& moderatelytdiftnegative relationthip with proportions engaged in agriculture.Where there is evidence of commercial

agricultrre (higher farm incomes, mechanized farms, andthe use of muCh farm equipment), there ismore use of radios. The proportion of dwelltags having

radios in 1960 ranged from 9 to 48per cent; again the states in the north had the highest proportions.

The process of linking isolatedgroups into national centers of Change

is accomplished through the diffusion and acceptance ofnew ideas and practices.

A first step in the stu4Tof that diffusion has been the identification ofcom- ponents of modernization througha description of ecological inter-relationships.

This entailed both thepreliminalridentification of trait clusters throughcom- ponents analysis and mapping of the spatial patterningof variables. Lead areas and geographic nodes of modernizationcan be seen clearly enough, and their relationship to transportation networks (Whichare in turn associated, uith topography) is evident. But this is by no means the whole story.Although there was stability in these geographic patterns, theyPalso changed.There are correlations between the adoption of Western and the discardingof indigenous culture traits, but those relationshipsare by no means rigid. Neither are the geographic patterns simple.Why, for example, are the contrasts (cultural andeco- nomic) so sharp between adjacentareas in the center of the country, where development seems to be almost entireVan urban phenomenon, whereas the urbanrural contrasts are less in the north? How farhas development and its geographic pattern been associated with, builtupon, or stimulated the development of human resources? How farare educational differentials associated with contrasts in other cultural economic traitsobserved over relativelT short distances in the high country? Manyof these questions will still have togo unanswered at the end of the present study, butsome of them at least should be 110

HIgerstrandls formulation of an interpersonal network of communi cation, with his constructs of "information fields" and "resistances"may help us here.

Education as a quality of the adult populationincreases the range of personal communication networks and participation in thoile information fields that carry the largest load of knowledge, attitudes,and ideas associated with modernization and change. Education also reduces resistances to those ideas, therdby encouraging adoption of new practices and orientation to life and jobs in modern sectors of theeconomy. The educational attainments of the adult populations of 1960 will, therefore, be studied in Chapter III, which examines the relationships batmen adult qualifications, occupational traits, indicators of the economic and technological levels in agriculture and !ndustry, and the acceptance of "urban cultura traits."Chapter IT will than explore the dynamics of change: the pace at whidh various parts of the country have moved along diverse dimensions, the extent and nature of shifts in the degree to which various traits are geographically associated, and the place of migration and of educational advance particularly in those changes.Focusing on people, this means an interpretation in the framework of an interpersonal system of communication and the relaying of new ideas and practices.Chapters V and VI will than take up the discussion of primary schooling as an "innovation," the diffusion of whidh is to be analyzed. CHAPTER III

EDUCATIONAL ATTAINMENTS AND THE SOCIO-ECONOMIC

STRUCTME: THE SITUATION IN 1960

Mexican adults of 1960 who grew up in the 19201s and 19301s were part

of an era in which national leaders emphasized education as a means toa better

life for the population. Previously, despite verbal adherence to aims of com- pulsory and free education, schooling had been preparatory for elite roles.

Although a law had been passed in 1911 inaugurating rural education, it was not until a decade later that serious rural education efforts were made.Cultural

assimilation of Indians was fostered through a program of cultural missions into

even remote sections of the coufatrn efforts were made to inculcate literacy and to spread improved techniques of farming and homemaking.

From 1934 to 1940 there were mme intensive literacy programs designed

to close the gap between generations. Schools were to be used as community

centers. Programs involving the Indians displayed some ambivalence between assimilating them into Mexican life and preserving idealized indigenous heritages. In recent years a uniform curriculum has prevailed, at least formally, in both rural and urban schools, though allowing ror local adaptations.

Recently secondary education has been extended to larger proportions of the population, ostensibly to support roles in a modernizing industrial society.

Preparatory schools established in the late nineteenth century had been based on

the ideas of Comte and French positivism. These schools widened the gap between

intelliq,entsia and working class or peasantry since the curriculum was designed 112 to prepare far university or professional careers. In 1925, a three-year secondary cycle was introduced, followed by two years that were preparatory either for university or for vocational school. By 1960 pupils finishing a primary school had a choice: general secondary school, prevocational courses, universitT extension work, or secondary night school.

Given these efforts to extend education throughout the population and to adapt schools to new ideals and to a changing economy, to what extent had adults in 1960 become possessed of a practicable amount of schooling?In this chapter the distributiors of literacy and of varying levels of completed schooling are compared among age and sex subpopulations and between urban and rural sectors.

Labor-force participation rates, income levels, indexes of agricultural development, and measures of transportation and communication facilities are stalarlly portrayed. Each of these sets of variables in turn is related to literacy and to level of attained schooling for various subpopulations--again using states as units in the computation of correlations.

Literacy and School Attainments

Whether we look at literacy rates and school attainments of a population as factors in development or as reflections of it, certain characteristics of the distributions of these traits maybe expectel in association with various stages in the modernization of a nation and in the diffusion of development from one location or area to another.First of all, of course, is simply the question as to how widespread literacy has become, and where it has reached the critical mass that characterized initial modernization in the earlier history of Europe and Japan, or accompanies substantial progress in developing countries around the world today?But equally interesting and important is the degree to which urban progress has reached out into rural areas; how far have the hinterlands 113 been integrated with the lead centers as participants in a commonliterate culture?Similarly, marked sex differentials in attainmentof literacy (and then of full primary sohooling or of sohooling beyondthat level) mark steps in the development process in most societies.The sex contrasts initiallywill be sharper where a traditional society puts severe constraints onwomenls activities (as in Moslem countries) than where the traditional eboistywasless differentiated among sex lines. Despite many differences among nations in the mixtures of educational traits and other development phenomena, there ()albs no question as to the importance of these dimensions of educational diffusion, and ohange. Later chapters will concentrate upon ohAnge ano diffusion, but here the fbous is on the situation in !4Xi00 as of 1960 so fareseduoation and literacy rates among the adult population are noncerned.

How far had literacy diffUsed among adult men and womb= ofvarious ages in the Mexico of 1960? Among the thirty-two states, themedian rate for older

(age 40+) males was 63 per cent; for females the corresponding rate was53per cent. The variations among states in these rates wereextremely wide, however;

the lowest figure for adult males was onv 37 per cent,whereas the highest rates were close to 90 per oent. The ranges among females were from a fifth at

the bottam (for older women) to fourmfifths in the most advanced areas.lbese flames may be pat in context by noting that historical researdh showed rates in western countries of at least 40 per cent among males before thebeginnings of

induatrialiaation, and international comparisons aoross nations in the middle

1950Is suggest that 40 par oent male literacy maybe a minimum critical PASS 1 for emergence into the first stages of economic development andmodernization.

1 C. Arnold Anderson. "Literaay and Sohooling on the Development Threshold: Some Historical Cases," in Education and Economic Deve.opmen, edited by C. Arnold Anderson and Mary Jeirlomman (Chicago: Ala ne Pub1i1ting Company, 1966), p. 347. 114

Male literacy rates in Mexico typically exceeded those of females in

the older age groups by approximately 10per cent, but the differences Vera de-

clining, and among children, both urban and rural,they had almost disappeared.

State-by-state comparisons showed largersex differences in the more backward

than in the more advanced states.Among the lowest third of states on male

literacy (all under 60), the medianmale-female difference was almost exactly

20 percentage points, but in the thirdof states with the highest male rates

(all over 70per cent), the median excess of male literacywas less than ten

points.These contrasts are substantial andare in the direction we should ex-

pect, but they are only part of the picture. Even more important are urban-

rural contrasts within eachsex and sex variabilities and differences within

rural versus urban categories. These will now be discussed.

Looking first at males only, and the olderamong them, we find that as

of 1960 the difference between the median urban andrural literacy rates was 19

per cent; for the top ranking state that difference wasr 14y. 11 per cent, for

the lowest state it was 22per cent (Table 12). For the younger adult males

urban-rural differences were smaller. Within each generation,the differences

between median rural and urban rateswere larger for women than for men. For

both sexes, rural sectors of states lagged behindthe urban to a greater degrle

in the less literate states.Generally, and especiallyamong women, rural areas made more advance between generationsthan did urban.

Despite (or perhaps because of) the obviouscultural contrasts between

Moslem countries of the Middle East and LatinAmerica, it is interesting tocompare these patterns with wbat Fattshipour found for Iran in 1956,where over-all adult literacy was, ofcourse, vary much lower than in Mexico.He found that urban females had higher literacy rates than rural malesup to the age of 35 to 44 years, but at older ages rural males exceeded urban femalesin 135

TABLE 12

DISTRIBUTIONS OF LITERACY RATES BY AGE, sa, AND RESIDENCE, 1960

- Lowest 25th 75th Highe0 Dis- F,T1,41. an m Value Percentile Percentile Value°persica°

Age 10+ M+F 87 41 56 70 81 87 .36

Age 3o+ males 90 40 56 67 76 86 .28 Females 76 24 40 59 73 81 .56

Age 40+ Males 88 37 52 63 73 83 .33 Females 73 20 34 53 67 77 .62

Urban

Males 40-49 92 56 71 80 89 93 .23 25-29 94 41 74 83 92 95 .22

Females 40.49 79 36 55 72 81. 87 .36 25.29 85 36 65 79 87 92 .28

Rural

Males 40-49 76 34 49 61 72 82 .38 25-29 82 38 55 70 82 90 .39

Females 40-49 49 16 26 46 64 79 .83 25-29 65 26 39 62 76 88 .60

4F.D. is Federal District.

bkhenthe Federal District is the highest value, the value of the next ranking state is listed,

c(Percentile 75minus Percentile 25)/Redian. 1 literacy. In Mexico, the urban-rural took precedenceover the sex differential

among the older populations as usll; in fact, in 1960,the median literacy rate

for urban femalesage 40 and over vas 72 per cent as against a 61 per cent rata

for rural males of thesame age. Although educationally-selective migration

from rural to urbanareas continuously depresses the rural rates, such migration

would have to be exceedingly selectiveif it were not to depress urb&n ratesas

well; in other words, migrationlowers both urban and rural rates,even though

it has no effect on nationalrates. This is a truism whenever migrants froma

backward area are better qualified thanthose they leave behind tut neverrtheless

are not up to the general ppulation in theirdestination areas.Such migrations

have been important in Mexico inrecent years, as they have been insome parts

of the United States; they contributeto development, but they may also create

problems at both origin and destination.On the other hand, exceptional high

literacy among the older urban femalesconstitutes presumptive evidence that

these are urban areas that led inmodernization but have been growing recently

at a less rapid rate thansome of the newer urban centers.

A priori it would be expected thatproportions of the population without

schooling would very-nearly match proportionsilliterate. These proportions are,

of course, highly correlated, but thecorrelations are by no means perfect. Sig-

nificant numbers can acquire literacywithout schooling (whetheron taeir own or

by participation ina literacy campaign) in a country with so phonetica language

as Spanish. Nor does this preclude the fact thatsame who have attended school

for a limited period,or participated in literacy campaigns,may nevertheless have remained illiterateor lapsed into illiteracy.

1Fattahipour,op. cit., p. 84. 117

In 1940, the cousus reported that the proportions of maleliterates

(over age 6) who had acquiredtheir ability to read and write outside of formal

schooling ranged among states from 29 to 74per cent, with a median of 56 per

cent; for females the corresponding figureswere 32, 74, and 57 per cent.The

important part playad by out-of-school acquisitionof literacy in pre-war

decades is impressive. As development goes forward, the proportions Whoare literate but unschooled diminish. After 1940 the Mexican census did not publiah data similar to those for 1940.

Literacy campaigns have bean launched with =ehinitial enthusiasm and have been described ina number of sources.Vasconceles sponsored the first campaign in 1921, which lasted onlyuntil 1922. In 193e, Cardenas initiated a literacy program that was part ofa three-year plan; local literacy committees were in charge, althcugh it was a federal project,and the states were asked to collaborate.Kneller describes this effortas achieving limited results due to lack of funds and weak support.After this attempt, the Federal government Iett the remnants of theprogram to the Literacy Office of the Cultural Missions De- partment and for stated educationalinstitutions to promote.In 1944, a new literacy campaign was opened. Every literate Mexican was either to teach at least one illiterate person how to read and writeor to pay far such instruction.

"Literacy centers" with paid teacherswereopened.1The proportion of illiterates in the population dropped trom 58per cent to 38 per cent between 1910 and

1960, but the absobite numberof illiterates increased by 1,123,000or 12 per 2 cent.

;GeorgeF. Kneller, The Education of theMexican Nation (New York: Colum)ia University Press, 1351,, 77. 1). 2 Eduardo Ramon Ruiz, Mexico, The Challengeof Poverty and Illiteracy (San )Iarino: The Huntington Library, 1963). 118

Spanish is spoken by the majority of the populatioa, but in the following

states at least 5 per cent of the 1960 population spoke Indian languages only:

Oaxaca 20, Chiapas 16, Quintana Roo 15, Tucatan 13, Hidalgo 12, Guerrero 10,

Puebla 80 Campeche 6, and Veracruz S.In 1960, 1,105,000 people or 3.8 per cent

of the total Mexican population spoke Indian languages exclusively; 6.6 per cant

spoke Indian and Spanish; and 89.1 per cent spoke Spanish exclusively. Tae pro-

portions speaking Spanish only and that speaking Spanish plusan indigenous

language were evenlydivided between males and females. However, of the 3.8

per cent minority speaking indigenous languages only, 1.7 per cent were males

and 2.1 per cent were females.

One aspect of the literacy program was the effort to link literacy to

community-improvement projects. By 1959, it had became apparent that illiteracy

was part of chronic economic and social backwardness and thatmass literacy cam-

paigns did not reach these problems.It was apparent also that the later literacy

programs were of special benefit to urban dwellers; most of the literatepersons

obliged to teach others lived in urban and semi-urban centers.According to 1 Ruiz, nothing was done in the villages.

Litsracy is 1 wild:sal index of educational attainments--especially when

it is recognized that reported literacy maybe illusory.Previous partial

literacy may in fact be lost or never have been really "functional° anda few years of school may t311 us no more.However, at school-completion levels of

7 years or more, the nature of the educaLion imdexchanges. At 10 years or over, we are looking at people who will be qualified for higher roles in theeconomy.

Distributions of schooling for the populationover 30 years old are given in Table 13.As of 1960, the proportions of males who hadgone beyond

1Ibid. 119

TA= 13

DISTRIBUTION OF LEVELS OFSC10OLIN3 OF THE ADULT POPULATION Br SEX, 1960

...... 1 !ears Lowest 75 th F.D. 25th Median Flettest Die- of Schooling ValuePercentlle Percentile Villmeapersionu

Males --"Igyears old

No school 15.4 22.9 28.8 140.0 54.3 68.9 .625

1-6 years 61.1 29.6 43.1 57.0 64.2 67.0 .369 7+ years 23.5 1.4 2.3 3.8 6.8 10.5 14158 104 years 14.8 .8 1.2 149 3.2 5.6 1.010 23+ years 8.8 .4 .6 1.0 146 3.2 1.041 Females I67756arsold

No school 27.7 27.1 3146 48.5 64.7 79.4 .673

1-6 years 56.6 19.9 34.2 51.5 62.4 88.4 .538 7+ years 15.7 .7 144 2.3 5.1 8.2 1.628 10+ years 7.2 .3 .5 .9 149 3.1 14528 13+ years 2.5 .1 .1 .3 .5 .9 141410

%henthe Federal District is thelowest value or the highest value, the value of the next ranking stateis listed.

NPercentile75minus Percentile 25Villedian 120

the completion of the 6-iyear elementary schools ranged among the states from

1.4 to 10.5 per cent, excluding the Federal Distrikt (23.5 par cent)Distri-

butions of those with 10 years or more are similarly skewed and the medians

drop to 1.9 per cent for males and 0.9 per cent for females. The lead of the

Federal District and Nuevo Leon and the gap that separates them from the rest

of the country is obvious. But it is significant and encouraging, nonetheless,

that there are several lead centers or developnent nodes, not merely one.

Granted that Mexico is in a phase of development in which the lead

centers stand out in dramatic contrast to the rest of the country when indicatarr

that pick up the relativeiy higher and rarer leveas of schooling are the focus

of attention, questions still remain as to how functional either literacy or primary schooling mav 'act in the aggregate, and how sharp a break in schooling beyond 6 years may entail with respect to the roles men (and women) play in the

society. Though Mexican data relating an individual's literacy and schooling

to his occupation or income are scarce, much can be learned about these questions by examining literacy rates and schooling among populations in various locatiaas

as these rates are associated with labor force participation and age at marriage,

and with occupational structure and income distributions. The breakthrough among

Indians and mestizos is indicated by diffusion of literacy among populations with

cultural characteristics indicative of indigenous groups. Each variable ar its

inverse represents an aspect of a western-oriented definition of development.

Labor Force Participation Deferred Marria 1'ertiiitfateetand ucation

One of the most interesting indicators of development maybe the variabilities in proportions of the total male and female populations who are employed or are participants in the labor force. In the Mexican case, this is approximated by census data for the "economically active," The defiaition of 121

"economically active" in the Mexican census is relatively easy to interpret in its application tomen.1Proportion of total males economically active is primarily a demographic index reflecting the age composition of the population; the lower the rate, the larger the proportion who are children or too old to work (or, whatever their age, physically unable to work).

Proportions of the tousl male population who were economically active

(first row of Table 1)4) varied remarkably little among the states; this uni- formity undoubtedly reflects the predominance of Catholics among the urban populations and hence minimal effects of modernization differentials on differentials in birth rates between the Latin and indigenous populations. For these same reasons, the ratios are comparatively low:the median among the states was 26.7 per cent. The range from the twenty-fifth to the seventy-fifth percentile was only 2.1 percentage points and even the extreme cases range only from a low per cent of 24.5 to a high of 30.4 per cent.To get some perspective an this, it maybe noted that the median ratio of employed to total population of both sexes in

1960 in the state of Kentudky, a ratio that includes the extreme law rates of the hard-core problem counties of atst Kentucky, was 34 per cent. Most of the

Zast Kentucky counties (and no others in the state) had ratios below 25, as low or below the lowest proportion "economically active" in Mexico; but the lowest

Kentucky county matched the weighted male-plus-female ratio of Naconomically active" for the highest state of Mexico.

Female literacy and the activity of fanales outside the home are associated aspects of a cultural transformation that accompanies modernization in

I Economically active population excludes those with no or antisocial occupations and domestic workers without pay and unpaid family workers. It includes the population from 8 to 11 and 12 years employed and unemployed who said they had a remunerated occupation on the day of the census whether or not they were exercising it on that day. 122

Mexico as elsewhere.As the females become literate, children are encouraged to

attend school and to continue in school longer.At the same tine, marriage and

family customs may change. Womenmany postpone marriage, limit their families,

or migrate in search of opportunities for work.

TABLE 114

DISTRIBUTIONS OF THE PROPORTIONS OF MAWS AND FEMALSS WHO WERE ECONOMICALLY ACTIVE, 1960

Lowest 25th 75th Highest F.D. Value Percentile Median Percentile Valuea

All males 25.1 24.5 25.7 26.7 27.8 30.14

All females 10.9 3.3 14.14 5.6 6.4

Females age 12 and over 30.9 10.8 114.0 15.9 17.6 19.4

0/Mr 111111111

aldhenthe Federa, District is the highest value, the %JAW of the next ranking state is listed.

Whereas it can be assumed that over 90 oar cent of the able-bodied adult man will be economicall,y active, the situation withrespect to females is verydiffer- ent. Although reflecting the same demographic attributes ofthe general population as in the measures for males, in addition (andeclipsing those factors) female activity rates dependupon the ways and extent to which women have become participants in economic life outside the hane. (The Mexicancensus included as

"economically active"women the same definition as for males. Domestic workers who earn a salary are included.) The figures in row 2 of Table 14 can becom- pared directly with the male rates: the sex cantrasts run roughly at 20 per cent at all points of the distribution.These differences are mare easily interpreted looking at the ratea for females aged 12 and over. As of 1960, 123

and the median was only 16 per cent and the differencebetween the twenty-fifth

soventy-fifth percentiles among the states was only 3.6 percentage points. The maximum value of 31 per cent, thoughstill modest, stands out instriking contrast to the predominant pattern and isdouble the median rate. It is evident that the lives of women in a fewdevelopmental centers are quiteunlike the lives of women over most of thenation. The pattern is not a simple one, however, as maybe seen by looking atparticular states.The states with the highest proportions of economicallyactive females aged 12+ include some(not all) of the more advanced states of the North butalso several states in the

center.Of the states with the highestproportions of economically active

females, the Federal District, Nuevo Teon,Morelos, Colima, Sonora, and Baja

California Norte are among the elevenstates with net in-migration rates. The

states with the lowest ratios includeZacatecas, San Luis Potosi, and Durango

in the north; Michoacan andGuanajuato in the center; and the Yucatan,Tabasco,

Campeche, and Chiapas in the southeastand south.

Relationships of proportions of single women20 to 24 years old, and of

fertility to labor force participation,occupations, and education of females

are summed up in Table15. Generall,y the correlations run higherfor proportions

not married than for the fertilityindex. Much more interesting is thefact that

of females who the variables that show thestrongest relationship to proportions who are are unmarried are for traits thatdistinguish P. small group of women

advanced relative to the female population as awhole.Proportions economically

active, female urban literacy rates,and density have the lowestcorrelations

with postponement of marriage;proportions with 10 or more years ofschooling

and employment in white-collar andprofessional jobs have the highest corre-

lations. Once again we are witnessing the emergenceof a small leading minority

at the forefront of societaltransformation, but also, and this too isimportant, 124

TARTE 15

CORRELATIONS OF MARRIAGE AND FERTILITY RATES OF FEMALES, 1960, WITH VARIABLES RELATING 10 EDUCATION, OCCUPATIONS, AND URBANIZATION

Single F F under 5 Years/ 20-24 Years All Females

Female literacy 25-29 years of age

Urban ,222 .190 Rural .398 .217

Adult female schooling 30+ years of age

No schooling -.399 -.135

7+ years of school 484 -.095 10+ years of sdhool .503 -.184

Labor force participatim

Economically active females 12+ years of age .228

Occupations of economically active females

White collar .568 -.147 Professional .554 -.097

Urbanization

Density .199 -.372 Urban 22500+ .416 -.242 Capital size .404 -.418 Pop. 50,000+ .352 -.145 125

the fact that literacy even among females has indeed been spreading widely

through the society.

Education and the Occupation Mix

Whether in the aggregate rising incomes have more effect on the

spread of literacy and successively higher levels of educational attainment,

or whether the direotion of effect is oppositepis a much disputed matter.

Probably the debate will never be resolved, since these are unquestionably

mutually ,:,upportive processes that are manifested in a continuous sequence of

overlapping interactions.At the other extreme, it is comparatively easy to

isolate effects of schooling differentials on occupational and income differ-

entials among the individuals who make up the national labor force at any given

time, or, conversely, to identify the factors that differentiate families with-

in a given area or community in the extent to which they encourage and invest

in schwiling of the rising generation. But this second, "micro" approach re-

quires data that are or have been rarely available, and it by-passes important

questions concerning scale or agglomeration effects that operate in indirect

and more widely diffused ways that carry a whole populace and economy along,

not merely differentiating roles and earnings among the members of the society.

(Later, however, one micro-economic study of effects of schooling on income

differentials in selected Mexican cities will be discussed.) Geographic com-

parison and the use of geographic units of observation partakes of some of the

advantages and the limitations of each of these approaches. In particular, high

serial correlations and high human migration combine to complicateanalysis and

interpretation of geographic differences in incomes and occupations aseffects

versus causes of observed educational attainments of theadult populations; high

education today could reflect high incomes yesterday which are in turn correlated 126 with high inoomen today.On the other hand, analysis of geographicdifferentials enables us to take some of the communication and the complementarityeffects of various factors into account, whereas these are normally lost tosight in analy-

ses of individualeducation-occupation-earnings re1ationships. Bearing in mind

the more serious qualifications, let us considernevertheless what the cross-

section geographic associations between educationalcharacteristics of the

Mexican population and occupational and other economictraits look like in 1960 and what they may suggest with respect todevelopment patterns.

Table 16 provides some key indicators of theactivities of the male and

female populations by occupational and by industrialcategories, as a background

against which we may look at occupation-educationrelationships. States in which

agriculture accounted for less than half of the male laborforce are, of course,

the exception: only 3 of the 31 states; agriculture accountedfor two-thirds of

the male labor force in half of the states, for three-fourths or morein six.

Second in importance are "white collar" workers, but thiscategory is defined

very broadly (to permit comparisonswith other years). It includes professional,

clerical, sales and related workers, which means thatalong with the more modern-

ized occupations there may be many traditionaltraders included under this

heading. Even so, only one in eight Mexican menfell into this classification,

though the range was from a low of under6 per cent (in Oaxaca) to maximum

figures of 26 per cent in Baja CaliforniaNorte, and 42 per cent in the Federal

District.Proportions among the economically activefemales are much higher, but

it must be remembered that the base issmall relative to the total adult female

population. The median proportion of men in manufacturingemployment was a

tenth, with an interstate range from 4 to35 per cent. But "manufacturing"

is of many kinds, from traditional homecrafts to technologically advanced

enterprises, and moderately high(thougb not maximal) rates of employment in 127

TABU 16

DISTRIBUTIONS OF TRE ECONOMICALLY ACTIVE POPULATION WIIHIN SEX AND OCCUPATION CATEGORIES,1960

Lowest 25th 75th Highest Dia- F.D.u ValueaPercentile Percentile Valuespersionb

Males

Agriculture 3.1 35.5 54.7 67.7 74.9 86.1 294

Professional 7.8 1.1 1.4 149 2.8 4.1 .778

Manufacturing 35.3 3.9 6.6 10.4 15.8 26.9 .885

Minthg .8 .4 .5 .8 2.1 5.4 1.912

White collar 42.0 5.6 9.5 12.8 18.1 26.4 .672

Clerical 16.0 143 2.1 3.5 6.0 9.4 1.126

Males + females

Professional 8.0 1.5 2.3 2.8 3.9 5.1 .562

Clsrical 16,6 1.6 2.5 4.2 6.9 10.4 1.067

Females

Professional 8.6 3.3 5.8 8.4 9.5 13.8 .1438

White collar 43.7 16.1 24.1 30.4 36.3 40.6 .401

Clerical 18.1 3.5 4.6 7.6 11.6 15.3 .918

aWhen the Federal District is the lowest value ar the highest value, the value of the next rari%ing state is listed. b (Percentile 75te"IrviPercentile 25)/metian. 128 manufacturing can signal an area in which traditional crafts have specialim- portance.However, even in the latter case a more than average degree of integration into modernising sectors of economic life is like4, since large numbers employed even in traditional crafts mane production for a, wide market, com- mercialization of local life, and communication with the urban development nodes.

A good index of the characteristics of manufactures is the proportion of employees who are women; at the median wtmen accounted for roughly an eighth of the total, but the range in their share was from 8 to 35 per cent. The correlation of proportion of manufacturing employees who were female with literacy rates (omitted from the following table) are consistently negative with coefficients approxi- mating

Table 17 lays out the correlation coefficienos between various educational indexes and proportions of the labor farce in various kinds of employment. The correlations with manufacturing employment are much lower than any of the others. The highest correlations in every case (except females in professional employment) are with proportions completing 7 or 10 years of schooling, rather than with the literacy ar no-schooling proportions. Neverthe- less, the literacy correlations are also huh. Here is an identification problem in that all thd schooling indexes are closely inter-correlatedl and their effects on and responses to economic and occupational structure entail mutual interdependencies. The fact that the higher schooling attainment indicators seem to explain more of the variance emmin proportions employed in agriculture does not mean that they would do so without some minimum critical literacy mass. On the other hand, it does support the inference, from cross- national comparisons, that once a critical threshold is passed, fUrther literacy alone may have very little relationship to economic development until it becomes nearly universal; at that point it is in fact always associated with diffnsian 129

=LE 17

CCELRELATIONS BENZIN OCCUPATIONS AND VARIABLES REIATING TO LIEMAci ANL SCHOOLING, 1960

Pro- White Agri- Kann- fessionsCl erialc Collarcultirefacturing

Literacy Peroentages ofEconomically Active Ma las Males + females 10+ years .692 .823 .7814 -.792 Naas 40+ years .717 .836 .810 -.837 .486 Percentages ofEconcmioally Active Females Females 404 years .759 .858 .831 Adult levels of schooling Percentages of Economically Active Masa Males 30+ years old No schooling -.752 -.851 -.7914 .810 7+ years of school .8148 .960 .941 -.909 .575 W+ years of school .8614 .937 .937 -.900 .561 Percentages of Economically Mtive Females Females 30+ years old No schooling -.756 -.877 -.835 7+ years of school .628 .890 .8814 10+ years of school .639 .847 .863 130 of secondary education among substantial minorities, at least, of thepopulation.

As a whole, Mexico is still at an in-between stage. It is of same interest to note that not only are the correlations of proportions of males inclerical employment with literacy high; they match very closely the correlatims with proportions having seven or more years of schooling. Incidentally, on the average, the latter figure rises above the clerical percentages only in the upper fifth of the states an either measmre(Figare 12).

Education and Income

Although occupational data may provide crude indicators of associations between the distribution of schooling in a population and income levels, they have serious limitations when used for that purpose, The usual aggregative type of measurement is some sort of estimate of per capita income; but such measures are not available by state. There are a number of relevant indices, however.

Some are the more interesting in that they pick out distributional features of economic attainments (percentages at, ar above or below stated levels), and these may be more interesting far analysis of development processesthan mean values.

There has also been a small sample stwiy of individual income differentials associated with schooling in three Mexican cities that calla for brief comment before going on witl- the geographic analysis.

Martin Carnoy studied the costs and incremental income streams associated with successive levels of schooling in across-section sample of 3,901 malo wage earners in the Federal District and in the cities of Monterreyand Puebla in 1962. Using regression analysis, he found that income increased with an increase in age and schooling and with wage and salary work in commerce, wanu- facturing, electricity, and transport rather than in construction or services; and with working in Mexico City rather than in Monterrey or Puebla. Going to Fig. 12.--Scattergraa of proportions of adult males 30+ years with 7+ years of schooling by proportions of economical1y active males in clerical occupations, 1960. i

132

14 0 i-us

0> 8 z0 4 0 n0 6

mar 4co 4 co ur 4-J 2 2 So I I 2 4 6 8 10 12 14 16 PERCENTAGE OF ECONOMICALLY ACTIVE MALES IN CLERICAL OCCUPATIONS, 1960 133 school while working had a negative effect on earnings. The highest internal rates of return to 4he investment in schooling were for completion of the last

2 years of primary school, completion of the first 2 years of secondary school, and the securing of a college degree.There was a high rate of return to the fifth and sixth years of primary school despite the inclusion of income forgone 1 in the cost estimate of primary. school.

Five income or economic-level indicators are related to educational characteristics of the populations of the Mexican States in Table 18. The firs.;-, two are proportionsof wageearners in manufacturing and in agriculture who received incomes over 500 pesos per month in 1960. No matter What edu- cationvl measure is used and regardless of sex (excepting the highest school attainment category for females) the correlations ran arolind .75.Taking employment of 8- to 11-year old children as a negative index of income or development, thera are similar,relationships with adult schooling: slightly higherpre- diction on vihe literacy and no-schooling indices, less predictive power on indices for schooling beyomd 6 years. In contrast to most of the other economic and occupational indicators, employment of young boys identifies a minority at the lower instead of the upper part of the status structure. The correlations with pay per employee in larger manufacturing firms in 1955 are very low, reflecting the idiosyncratic character of those data in geographic units of the size and diversity of the Mexican states. Correlations of the schooling variables with the Glick development index are strikingly high.

'MartinCarnoy, "The Return to Education in Mexico: A Case Study* (unpublished paper, The Brookings Institution, March, 1966). 1314

TABLE 18

OORRELATIONS OF rNDICES OF Imam AND DEVELOPMENT WITH VARIABLM REIATING TO LITERACY AND SCHOOLING, 1960

Pay to Income inIncome in Number Employment Tw.dex of Mfg. overAg. over :-,..loyed of 8-11 L:velop. 500 Pesos500 Pesos in Year Old ment, 1950 Monthly Monthly Factory, Males 1955

Literacy

Males + females 10+ years .730 .768 .340 -.775 .754

Males 40+ years .724 .745 .341 -.778 .815

Females 40* years .705 .794 .373 -.685 .775

Adult levels of schooling

Males 30+ years old

No schooling -.765 -.757 -.346 .788 -.802 7+ years of school .805 .774 .141 -.654 .922 10* years of school .778 .756 .117 -.631 .909

Females 30+ years old

No schooling -.734 -.785 -.369 .724 -.775 7+ years of school .744 .702 .258 -.683 .931 10+ years of school .678 .641 .232 -.662 .912 335

Literacy and the Agricultural Sector

In a rural setting we are looking for signs of transition from subsistence to commercial farming.Agricultural laborers represent the can- mercialization, while the persistence of small farms run by their owners without hired help are part of the subsistence pattern. In a study of literacy in nineteenth century Russia, Kahan describes the effects of achievement of literacy by rural males in the follawing way:

The rise in the level of literaoy among both the higher income groups of the present population and the agricultural laborers becomes the precondition for introduction of machinery and more modern farming methods. The decline of subsistence farming, a type of farming which had not offered visible incentives for education, made it easier to overcome the long-lasting inertia and maintenance of the status quo and to inject wa additional impetus to mobility and change in the economy and society.-L

In Mexico also, there should be evidence of at least a moderate relationship between the literacy of the rural population and the development of the rural sector.

In general there is a slightly higher association of literacy of 40- to 49-year old men than of literacy of men in their late twenties with characteristics of the agricultural labor forces and with equipment per acre of land

(Table 19), However, this difference disappears in correlations of literacy with agricultural incomes and with farm mechanization, which also show generally higher relationships with literacy. Given the association of agricultural modernization with larger proportions of the agricultural labor force in wage jobs, it should not be arprisingto note that higher agricultural wages go along with large proportions of hired workers and small proportions of proprietors.

1Arcadius Kahan, "Determinants of the Incidence of Literacy in Rural Nineteenth-Century Russia," Education and Economic Development, ed. by C. Arnold Anderson and Mary Jean Bowman (Chicago:Aldine Publishing Co., 1965), p. 302. 1,3 6

TABLE 19

CORRELATIONS OF INDICES OF AGRICULTURAL DEV.0210311T WITH RURAL LITERACY RATES

___._ ____

Income in 1 Farm Ag. Laborers/Proprietors/Age over Equ!lonnnt/ mechani. M in Ag. M in Ag. 500 Pesos 14411" zation 1950 Monthly 1950

Males

40-49 years .251 .579 .389 .517 25-29 years .146 ..127 589 .290 .530

Females

40-49 years .360 -.355 .742 .474 .743 25-29 years .319 -.316 .706 .446 675

Income in Ag. over 500 pesos monthly .1471 -.479 .314 .790 137

It is interesting that in general literacy of rural females showedsub- stantially higher correlations with characteristics of agriculture than did male rates. Given that the mechanization variables referred to1950, and the female literacy figures to a decade later, however, the question remainsvbether female literacy had any real part in the earlier agricultural transformations. There are other grounds also for hypothesizing that theinitiating influence came from the technological and productian side, expanded female literacy being aren't.

Nevertheless, there is every reason for believing that the spread of literacy among rural women may prove in the end to be at least asprofound a force working for changt as the initial revolution in agricultural practims.

Mass Media ?ranortationCmltural VAitst and ucation

Four sets of data (demographic, communication, transportation, and education) all relate directly to communicatial, whether through movements of people, through mass media, ar through education as a process and facilitator in the transmission of intonation. Higerstrandls use of migration fields and of telephone conversations to index the spatial intensities of person-to-person"tellings" and thereby to delineate "information fields" was based on a convictionthat interpersonal communication influences the acceptance of new ideas. State-unit data cannot adequately delineate such influence so indirect indicatorsof differential degrees of contact with "modern" ways must be used. These include mass media and transportation facilities, even though such indicators maybe only loosely related to networks of person-to-person tellings.

Communication and transportation facilities are dispersed very unevenly through the country, as Table 20 shows. Even the proportions of the population owning radios, automobiles, and bicycles are small. Areas in which large proportions of adults were unschooled generally were ill served by mass media TABLE 20

DISTRIBUTIONS OF TRANSPORTATION, COMMUNICATION, AND CULTURAL TRAITS, 1960

n Lowest 25th nth Highest F''Value PercentileMedianPementile Valuea

Transportation

Autos/1,000 pop. 39.5 1.5 6.4 18.9 75.2 Bicycles/1,000 pop. 14.2 3.0 kri9 13.5 21.7 52.0 Railroads/km 204.1 0 8.0 14.r. 28.8 68.0 Roads/Km 34.5 77 16.6 27,4 48.9 120.4 Roads paved/100 Km 100.0 15.2 44.5 6%44 75.5 91.1

Communication and facilities

Radio 48.5 9.3 15.8 24.3 35.5 46.4 Movies/Pop. 16.1 0.9 1.9 4.6 8.6 13.61, Electricity/capita 55.2 1,6 7,0 18.0 53.9 844- Running water 54.8 3.1 114 18,1 32.5 47.5

Culture

Nonwheat bread/M+F 5.4 4.9 13.8 29.7 48.1 63.7 Barefoot/males 2.7 2.3 3.5 4.3 20.9 48.1

4Whenthe Federal District A.s the highest value, the value ofthe next ranking state is listed. b Exclusive of BajaCali:ornia. In the source, data far northern and southern Baja California were :ombined and appearto be erroneous. 139

(Table 21). Radio and auto ownership displayed a strong relationship to local

levels of schooling.

TABLE 21

CORRMATIONS OF MASS MEDIA AND TRANSPORTATION WITH LITERACY AND SCHOOLDO, 1960

Movies Radio Autos Bicycles

Persons ar 40+

Literacy Males .617 .862 .803 Females .711 .860 .787

Persons a2e 30+

No schooling Males -.564 -.853 -.812 Females -.682 -.871 -.789

7+ years of school Males .698 .861 -.912 .168 Females .722 .882 .904

10+ years of school Males .718 .852 .899 .147 Females .738 .860 .872

Communication over distance is partly related to the ease of travel.

In ruraL areas there is no measure of the exchange of information and ideas at the market place and during the long walks to and from town.However, the increase and improvement of roads and the growth of bus service does suggest a flow of people aver longer distances; how far this means effective face-to-face communication over greater distances in geographic and social space is not immediately clear, however.Rural folk may venture into the city for supplies yet rarely come in contact with new ways. They maygo only to the outdkirts and have dealings with pimple who live very much as they themselves. Change in cultural habits pertaining to food, dress, and Musing is

particularly important. Traditionalky tortilla or corn-based food has been

the staple crop of the Mexican countryside, while eating wheat bread (partial-

ly dependent on whether it is a local crop) indicates acceptance of "western

customs."The census records also variations in footwear, from going barefoot

to wearing huaraches (an open sandal) or shoes. Though partly a matter of

climate and terrain, wearing shoes is also a sign of cultural change. The proportion of the population not eating wheat bread varies from 5 to 64 per

cent with a median of 30. The range for males walking barefbot is from 2 to 48 per cent, with a median of 1 (Tame 23).In Table 22 it oan be seen that the proportion of males who are literate shows a negative relationship to the percentage who do not eat wheat bread and who do walk barefoot. Areas where high proportIons of males walk barefoot are also those where older females are illiterate and where communication and transportation facilities are limited

:Figura 13). Once females achieve over 40 per cant literacy, the proportion of males barefoot is negligible.

TABLE 22

OaRRELATIONS OF CUIaURAL CHARACTERISTICS WITH LITERACY, MOM, RADIO, AND macros, 1960

lion'biLeat 1 Walk Barefoot/ Bread/rop. Males M+F

Literacy 10+ years M+F -.568 -.707 40+ years males -.588 -,677 40+ years females -.560 -.792 Movies -.605 -.595 Radio -.623 -.719 Bicycles -.087 -.190 Fig.13,--Scattergraa of percentages of malesbarefoot, 1960 and percentages of females age 140 and over literate,1960, 60 . 4 . 2030 e 0 4 10 20 30 I 40 4 50 , 60 70 80 I 90 100 1 PERCENTAGE OF FEMALES AGE 40 AND OVER LITERATE, 1960 110

Summary

Table 23 summarizes the salient relationshipsamong education, occupation, and communication traitein 1960.

The occupational distribution of the laborforce is one indication of the extent of industrialization. In Mexico, in 1960, a median of 68per cent of the economically active maleswere engaged in agriculture, 10 per cent in manufacturing, 13 per cent in white-collaroccupations, 3 per cent in clerical, and 1.9 per cent in professionaloccupations.

In general, areas that were heavily agricultural showednegative associations with literacy and schooling. In those rural areas where high prtyportions of the porulation were literate, therewas evidence of farm mechanization and of higher income in agriculture. Within the agriculturalstructure, agricultural laborers by 1960 had come to be associated with the modernizingelements.

The proportions of the population in white collar,professional, and clerical occupations were highly correlated with middle andhigh levels of formal schooling, as were proportions owning radiosand automobiles. I

3,44

TAKE 23

CORRELATION COVFICIENTSFOR EDUCATION AND OM 1960VARIABLES SHOWING ME HIGHESTZEO-ORDM CORRELATIONS W1111Limn! 10+, NO SCHOOLING, AND 7+TEARS OF scHansma

No Schooling 30+ Tears of Age Literacy 7+ Tears of Schooling 1D+ Tears 14+F MalesFemales Males Females

Literacy by age State

10+ yrs. 14+F -.953 -.983 .806 .827 140+ yrs. males .974 -.977 -.9143 40+ yrs. females .836 .838 .962 -.891-.990 .813 .847 Urban

10-14 yrs. males -.816 40-49 yrs. females .862 -.807 25-29 -.913 . yrs. iereales -.817 .826 .8o2 1044 yrs. females -.825 Rural

40449 yrs. males .886 -.901-.815 III 25-29yrs. males .891 -.880 -.822 10-14 yrs. males .899 -.918 -.835 1110 140-149yrs. females .943 -.869-.952 25-29 yrs. females .909 -.855-.900 10-14 yrs. femaes .891 -.903 -.846 Urban maes minusfemalas

40449 pa. .8147 IP 25-29yrs. .818 MIS O.-Continued

No Sdhooling 30+ /ears of Age Literacy 7+ Years of Schooling 10+ Years M+F MalesFemales Males Females

Adult levels of schooling 30+ years of age

No schooling Wes -.953 .928 -.853 -.840 Females -.983 .928 0000 -.826 -.859

7+yrs.of school Males .806 -.853 -.828 0000 .953 Females .827 -.840 -.859 .953

10+ yrs. of school Males 00 °10819 '4°0803 0991 .935 Females -.806 -.822 .934 .985 Occupation variables

Ag. MiEcAct M *00* .810 .775 -.909 -.909 Collar/ECAct M 000 0941 .909 CollarAcAct F -.835 .812 .884 Clerk/EdAct M .823 -.851 -.832 .960 .892 Clerk/EdAct F .856 -.853 -.877 .911 .890 Prof/EcAct .848 .802

Other economic varidbles

Mfginc. over $500 0000 0000 .805 Develop. index 1950 0000 -1;2 0000 0922 .931 Miscellaneous

Radio .866 -.853 -.871 .861 .882 Autos/Pop. -.812 000 0922 0904 Urban 0000 *000 0000 .802 .808

a0nlycorrelatices of t.800 and over were included. CHAPTEEt IV

STABILITY AND CHANGE OVER TIME: 1940 AND

1960 COMPARED

One of the most visible characteristicsof the changesIn Mexicohas been the burgeoning of the areas thatwere in aprivileged position even in

1930, while in general thepoor lagged, behind. The backward states have often been described as maintaining tight clusters oftraits representing an unbroken structure of poverty. Did lagging states move ahead even though remaining ina law rank position? Did theymove ahead enoUgh to reduce either relative or absolute gaps or are the gaps betweenpoor states widening? How far have the enormaus efforts to bring literacy and schooling to largersegments of the population con- tributed to diffusion of the development process?Same kinds of variables pick out lead areas in modernization, others distinguish primarilyareas that are following belatedly or not at all. Part of the process of change and adjustment is internal migration, which further complicates spatial patterns because migrants not only respond to opportunities but also carry attitudes and behavior traits with them and enlarge the geographicscope of informal communication networks. This chapter compares the populations of Mexican states in 1940 with those of 1960 to explore the patterns of change,- the last sectionsupplements that analysis with a mapping of the migrations that have takenplace.

The evaluation of changes is fran several perspectives withassociated modes of measurement:

146 147

1. The comparison of medians and quartiles of selected variables in

1940 and 1960 gives a simplesummary of changes in general levels and also of

dispersions over time. In soma cases, skewness, or the tendency of a distri-

bution to have a few distinctively high values (positively skewed) or a few

distinctively low values (negatively skewed) shifts for the same variable over

time. This is an important aspect of the phasing of diffusion of traits through

space.

2. It is asked how stable over time are the relative position4 of the

various states, and whether these stabilities are more evident in some respects

than in others. EXamination of these relationships leads in turn to 3, 4, and

3. The question was raised as to whether intercorrelations among sets of traits were tighter or looser in 1960 than they were earlier? In general, a loosening up of geographic correlations night be expected if change brings initially distinctive sub-populations into closer contact and into participation in a common national system.However, certain traits may display tighter locational clusterine with progress in more advanced stages of development. Since the function of education is of central concern, the question, for example, of whether the relationships between occupational structures and adult educational attainments were tighter or looser in 1960 than in 1940 is high4 relevant.

(Discussion of evidence ooncerning the closeness of relationships between school enrollment rates and other traits is deferred to Chapters V and VI.)

L. How have the rates of change in social and economic variables teen affected by the starting position?The correlations between level of variables in 1940 and changes in the same variables between 1940 and 1960 were examined as one way of describing how far the disparities between states may have 148 narrowed or widened aver time.It would be possible for those disparities to

'Aden (or narrow) without -hifts of rank and without changes in degree of correlations among variables.

5. To what extent were changes in one attribute associated with changes in another? For example, haw did changes in the proportion of the population who were white-collar workers relate to changes in the proportions of males walking barefoot between 1940 and 1960?

Overview of Intertemporal Stability and ange

A. summary inspection of changes from 1940 to 1960, both with respect to average values and for geographic stabilities in relative rankings anselected variables, is provided by Table 244 In a number of instances, correlation coefficients are valid even though direct compariems between 1940 and 1960 means or medians would not be valid.This is the situation, for example, when the definition of a variable alters slightly but correlations between the two measures are extremely high. On the other hand, the change in definition of agricultural proprietors is more drastic.This can be adjusted by adding ejiditarios to proprietors in estimating 1940 means, but not in the intertemporal correlations.

In a developing country, we would expect to see, over a20-year span, a decline in the proportion of males inagriculture, an increase in the proportions of the population in manufacturing and in white-collaroccupations.

There would also be upgrading of skills--evddenced in theliteracy and schooling associations with occupations.In the decades between.1940 and 1960, one important feature of change was in fact the proportion of males inwhite-collar and manafacturing occupations, accompanied by their decline inagriculture.

In 1940, the median proportion of economically active males inagriculture was 149

TABLE 24

MEDIAN 1940 AND 1960 VALUI23 AND INTERTEMPORAL CORRELATIONS; SELECTED VARIABLES

Value in Median State Correlation Coefficient

1940

Population distribution

Density 11.1 17.4 .974 Capital/Urban 21.8 30.7 .943 Urban 2,500+ 29.2 4143 .923 In-migrant/resident (1940 and 1950) 7.3 7.3 .968 allture

Barefoot males 9.0 4.3 .917 Nonwheat bread (M+E) 60.0 29,7 .898

Economy

Econamica11y active males 28.0 26.7 .320 Economically active females 10+ 1940 and 12+ 1960 4.1 15,9 .651

Mfg/EcAct males 7.9 10.4 .785 Mfg F/M+F Mfg 8.4 12.9 .731 Pay/No, employed in factarr 1940 aad 1955 La 4.5 .555

Collar/EcAct males 4.2 12.7 .884 Collar/EcAct females 19.1 30.4 .771

Ag M/EcAct males 75.0 67.7 .902 Ag Labor/Ag males 48.8 50.6 .450 Ag Proprietors/Ag males 12.1 45.9 .063 Ejiditarios/Ag males 19140 4147 .... me TABU 24--Continued

Value in Median State Correlation Coefficient

19140

Transportation, communication, and facilities

Roads/Popb .9 1.5 .462 Roads/Area 38.0 27.4 674

Movies/Pop 2.9 4.6 .504 Bicycles/1,000 Pop 2.5 13.5 .774 Autos/1,000 Pop 2.1 6.4 .906 Electricitylcapita 4.6 18.0 .782

Literaay years of age

40+ males 48.2 63.0 .949 40+ females 37.8 52.8 .973 10414 sales 51.4 75.4 .935 10-14 fauales 53.6 75.9 .946

"Education of adults (1950 and 1960)

30+ yrs. of age No schooling males 37.3 40.0 .982 No schooling females 46.5 48.5 .865

7+ yrs. school males 4.2 3.8 .970 7+ yrs. school females 2.4 2.3 .976

10+ yrs. school males 2.0 1.9 .973 10+ yrs. school females .9 .9 .931 151

75 per cent; over the eneuing decades this proportion dropped by almost 10 per cent. Nevertheless, two-thirds of the 1960 popuIatimilmws in farming. The percentage of males economically active remained the same in1960 as in 1940; the median proportion of males in manufacturing roseslightly, from 8 per cent in 1940 to 10 per cent in 1960. The median proportion of urbanresi4ents rose considerably from 1940 (29 per cent) to 1960(41 per cent).However, the median proportion of in-migrants remained stable.The percentageof maleswalking barefoot and eating non-wheat bread was dramaticallylower in 1960 than 1940.

Literacy of adults was on the average higher in1960 than 1940, but in the median state the percentage of adults with middle and higherlevels of schooling declined.

One way of assessing the geographi* stability of relativeposition in development processes is to examine correlations between 1940 and1960 values for the same variables (Table 24). This was done far all items that were comp. parable in both years (and for some that might seem comparable but actually are not).The consistent amiembrimmilyhigh correlations for itemsrelating to population distribution, literacy or schooling, and culturalindicators stand out.

Equally high are correlations between 1940 and1960 incidenceof automobilesand in proportions of men engaged in agriculture.Almost as high (.884) is the correlation over time in proportions of men inwhite-collar jobs.

At the other extreme, the near-zero correlationfor the proportion of males in agriculture who are proprietorsreflects the fact alreaci,y noted that the 1940 and 1960 definitions are notcomparable; this variable was included in the table to emphasize that fact. However, there was not a similar change in definition of the proportion of agricultural laborers. The modest coefficient on this variable(.450) reflects the major changes that were occurring inthe geographic structure of Mexican agriculture. The low correlation between the 152

two censuses in the proportions ofeconomically-active males reflects primarily

the extent and demographic selectivityof migrations.

Stabilities and Change in Correlation Matrices

The degree of geographic associationamong economic and social variables

can shift substantially over time toa considerable extent Tine independently

of the inter-temporal correlationsjust examined. Ihe exploration of stability

or change in such relationshipsuncovers an important aspect of the diffusion

of development, particularly ofthe sequential stages in it.Tables 25 and 26

sum up the 1940 and 1960 relationshipsamong key sets of reasonably comparable

variables. Certain similarities and contrasts stand out.

Correlations among the proportion of barefoot males, literacyof older

adults, and postoprimary schooling ofyounger adults all are comparatively high in both years, and they changed little during the 20years. Zero-ordar correlations between urbanization and densitywere low in 1940 and remained low.

On the other hand, therewere same marked shifts in closeness of particular relationships.

The proportion of economically active femalesdisplayed consistently higher correlations with other traits in 1940than in 1960. The only exception was the marked increase in the association (positive)with proportions of hired laborers in farming.

The relationship of proportion of manufacturingworkers who were female with law productivity and low income hasbeen mentioned earlier. Over the two decades, proportions of females employed inmanufacturing became more closely associated with the less modern aspects of industrialization.For example, in

1940, the correlation between the femaleshare in manufacturing and the proportion of males in white-collar jobswas aaly 28 but in 1960 it was -.4201 253

TAME 25 5EIEC1E1) 1940 MO-ORDIa ammunois

Schooling Age 25+ A8. Collar Mfg. 7+Tears

, . . . MF /4 F /4 14 II 14 , , . , Variable Number 145 146 236 237 79 83 64 96

Literacy 145 Lit. 40+ M 1146 Lit. 40+ F .917 0000 Schooling; adults age 25+ (1950)

236 7+yrs. school /4.876 .880 0000 237 7+ yrs. school F.818 .881 .938 Labor force participation and economy 79 Ag/EcAct -.839 -.860-.854-.878 83 lig Labor/Ag -.162-.097 -.163-.21t7 64 .890 .876 -.888 Collar/Fact II .915 .8113 -airs 96 Mfg/gact M .522 .494 .559 .574-.752-489 .493 106 Pay/Bsp in Fact .208 .322 .244 .382-.322 .250 ;218 :072 58 Fact F .420 .505 .496 .477-.519 .174 .500 .562 67 Collar ct F .826 .836 .832 .812 -.735-.236 .80o .275 100 Mfg F F Mfg -.435 -.199-.240-.213 .224 .146-ass GNiltural traits

117 Nonsheat M+F -.669 -.691 -.719 -.631 .602 .271 -.709 -.396 125 Barefoot it -.626 -.796-.588 -.717 .666-.124-.540 -.431

Tr ;. tation communicationand facilities

32 Autos/pap .737 .786 .819 .877 -.775-.098 .724 .505 39 Bevies/pop .783 .764 .826 .710-.755 -.262 .862 .448 36 Elect/Capita .305 .395 .385 .537 -.434 -.038 .248 .434

Population distribution

1 Density -.505-.604 -.565 -Ala J436 .209-.624 .068 14 Urban 2,500+ .776 .777 .774 .799-.903-.207 .763 .821 1514

TABLE 25-Continued

4 - 1 -- , 'It --re I

Pay/&ActCollarMfg F/Non"Baron.AutosMoviesElect/DensityUrban bop 10+ M+F wheat Capita 29500+

. , I I i 111116 IF I&

F F M+F M

Al .S

106 58 67 100 U7 125 32 39 36 1 4

131 .342 .198 .071.273 -.332 ....

-.017 -.549 -.579 -.065 -.498 -.371 -.641 .077 .361

.434.435.762-.311-.517 -.703 .209.368.694-.055-.666 -.513 .580.... .288 .1488 .319-.186-.2914 -.05 .682 .01414

-.1140 -.016 -.726-.0149 .388.318 -.351 -.662 .121 .... .1214 .463.586-.242-.665 -.634 .679.715 .412-.250 .... 155

TABLE 26

SELECTED 1960 ZPRO-CRDER CORRELATIONS

Literacy Schooling Ag. Age 40+ Age30+ Ago LaborCollar mfg. 7+ Tears

, V A MFNF 14 14 X 11

Variable Number 147 la 238 239 80 84 65 97

Literacy and schooling

3147 Lit. 40+ N 148 Lit. 40+ F .923

,Adulta age 30+, 238 7+ yrs. school N.836 .813 .... 239 7+ yrs. school F .838 .847 .953 Labor force participation and economy 80 AgiEcAct N -.837-.760-.909-.909 .... 814 Ag laber/Ag N .315 .342 .397 .464 .479 65 Collar/EcAct N 810 .777 .941 .909 .953 97 Nfg/EcAct M .486 .358 .575 .652 .785 .350 ..696 .... 108 Pay/Emp in Fact .344 .373 .141 .258 044 .214 .154 .012 59 &Act F 12+ .127 .109 .391 .326 .343 .5214 .288 .237 68 Collar et F .762 .831 .812 .884 .807 .510 .851 .592 101 leg F,44F in Mfg-.5614-.425 -.347-.371 -.477-.176 -.420-.343 Cultural traits

119 Nonwheat -.588-.560 -.750-.586 -.595 -.154 -.695 -.198 127 Barefoot 14 -.677-.792-.631 -.702-.606-.359-.665 -.364 Transportationis communication, and facilities

33 Autos/pop .803 .787 .912 .904 .887 .579.894 .601 40 Movielipop .617 .711 .698 .722 .661 .424 .743 .436 37 Elect/capita .585 .577 .609 .672 .609 .674 .543 .374 Population distribution

2 Density -.463-.538 -.431 -.336-.184 .191-.269 .223 6 Urban 2,500+ .777 .742 .802 .808 .873 .392 .860 .713 1

156

TABLE 26--Continned

Pay/EcAotCollarMfg F/Non- Elect/n..4 4...Urban Rap 10+ M+F wheat "°) capita"""°".72,500+

.. - . .

, i F F M+F M 1 I i ... . , .... v. . 1 108 59 68 lca 119 in 33 140 37 2 6 I r

-.078.... .391..166.... -.295.151 -.390....

.035 -.3142 -.526 .136 .... 4482 -.053 -.736 .3141 .329.... i

.262.382.8148 -AO -.637 -.626 .217.079.8314-.2145-.605 -.595 .706.... .14149 .350.581-.278-.343 -.1451 .769.498 .."

-.123.058 -.191 .176 .478.328 -.311 -.207-.220 .... .152.210.820-.337-.606 -.653 .784.785 .434-.093 .... 151 the correlation with proportion of males engaged in manufacturing was -.184in

1940 and -.343 in 1960; and correlations with proportions of people living in urban areas rose from -.242 to -.337.While the proportion of males in manufacturing rose from 1940 to 1960, the correlation of that ratio with urbanization became sameWhat looser: .821 and .713.On the other hand, the propartions of males in white-collar and in manufacturing jobs were more closely correlated in the later year (.459 in 1940 and .696 in 1960). The former ratio also became mare closely correlated with residence in urban areas.

In 1940, the proportions engaged in non-farm occupations were mare closely associatedwithliteracy than with proportions of people who had mare than a primary education; by 1960, however, that relationship was reversed. In fact, auto ownership, use of electricity, proportions working in white-collar occupations, and the population possessing middle levels of schoolingshowed stronger relationships among themselves and to urbanization in1960. These shifts in degree of correlation are not random.

Change alters the shapes of the distribution of social or cultural traits, and there is as a result a change in the meaning of the variableeven if the definition remains unchanged and reporting is accurate.Literacy is one example. The diffusion of literacy follows an historical pattern as a lead variable--until it reaches a saturation point in the more advanced localities.

In earlier stages, literacy may show high correlations with occupations and with other indexes of dóvelopment.As larger proportions of the population become literate, the intercorrelations betweenliteracy and development may decline, while adult proportions possessing post-primary schooling became large enough to give reliable observations and to take their place as asignificant lead variable in the later years. Arens with high levels of literacy in earlier years lead in the diffusion ofhigher-level occupations one generatian later. 158

Literacy, white-collar workers, and rising levels of adult schooling were the

key variables in this process.

A variable that is complex may change in meaning aver time also; an

important example is proportion of white-oollar workers. This category may be

composed mainly of sellers in 1940 but have many more clerical workers in 1960,

and selling itself may change in character. Without change in content of the

category, the quality of performance may rise appreciably, in part as a reflection

of supplies of human resources.Where there are more high-gmaityworkers, there

can be more selectivity by employers. This might be manifested in the following way.Literacy led the development sequence and by 1960 was becoming near4r universal except in the most laggard states. Meanwhile, schooling beyond 6 years was spreading in the progressive centers.Ay 1960, the white-collar categories

included more genuinely clerical workers, and correlations between proportions working as clerks and proportions with mare than primarr schooling had risen;

it was now possible for employers to select them out instead of trying to get along with unqualified help.

Changes in Agriculture and Its Correlates

The relationships among agricultural variables and between them and other variables are distinctive and call for special comment. On a national level the proportion of males in agriculture diminished from 70 per cent in

1940 to 59 per cent in 1960, but in spite of that decrease the dispersion among the states in the proportions in agriculture remained aboutthe same.

There were some marked shifts of position.Mexico and Morelos were two states with high 1940 proportions in agriculture that shifted rank between 1940 and

1960 from 28 and 27 respectively to 15 and 14.Jalisco, a state with moderately low relative proportions in agriculture in 1940, dropped further from rank 13 159 in 1940 to 8 in 1960. (The lower numbers of rank indicate smaller proportions of economically active males in agriculture.)

In general the changes of the proportion of males in agriculture re- flict urbanization. The items that are more negatively related to agricultural areas in 1960 than in 1940 are the rise in the use of electricity, the presence cf a greater population with middle and high levels of schooling, the proportions of males in white-collar occupations, and the ownership of autos. These stronger negative associations in 1960 with males in agriculture are accompanied by a decline in the positive associations between population density and proportions in agriculture from .436 in 1940 to .184 in 1960. Both urbanization processes and important changes in the structure of agriculture are involved in these changed associations.

Where relationships with proportions in agriculture show a stronger negative association in 1940 than in 1960, there is the implication that either the characteristic measured had spread more fully into rural areas by 1960, or ruralurban migrants had brought rural traits into the city without yet showing much effect of their urban residence. These are almost opposite kinds of processes.

The characteristics showing the highest 1940 relative to 1960 negative associations with males in agriculture were the literacy of females 40+, the mean pay in factories, the proportion of economically active females, and the attendance at movies (Tables 25 and 26).

In 1940, the presence of agricultural laborers was only slightly associated with other characteristics:positive with ratio of equipment to land, with adults lacking schooling, and with age-grade retardation of rural boys in school.

The spatial selectivity of Mexico's agricultural transformations is reflected in the fact that the relationships between proportions of farm laborers and features of the milieu were mach stronger in 1960 but dramatically reversed 160

from 1940. In 1940, there had beenlow butnegative correlations between pro-

portions of laborers among man employed in agriculture and indicators of economic or educational progress. In 1960, there is instead,a law but definitely

positive association with middle and high levels of schooling, with economical-

ly active females, with degree of farm mechanization, and with proportions of

farm wage workers receiving more than 500 pesos a month. There was also a posi-

tive association with movie attendance and with ownership of radios and autos.

These changes are linked with adjacent urban developments and modernization;

thus correlations with both in-migration and proportions urban were positive

at roughtly .400 L.4 1960 whereas they had been a negative -.200 in 1940. ExenChaninRelationtothe .IEFA11120.2a

The processes of dhange and the diffusion of dhange maybe better under-

stood, as has been mentioned,ifwe think of waves in development sequences.

Lead areas pull away from the rest on characteristics that represent new de-

partures for the particular stage. Gradually some of the other areas imitate

or move toward the new norms while yet others lag behind, catching up belatedly if at all. Meanwiale new changes are sending new waves through the society.

These processes will be reflected in predictable relationships between starting positions and ensuing changes. They are well illustrated by the data of Table 27.

Out in front are the unambiguous lead indicators:notably proportions

of males holding white-collar jobs, followed by automobile density and electrici-

ty use. These are traits that gained the largest amount absolutely where they were already highest in 1940, and in states that werealready relatively urban-

ized at that time and displayed the greatest pace of urbanization from 1930 to

1960. Larger increases in proportions of males in white-collar jobs and in automobiles were associated also with greater proportions of females 161.

TABLE 27

(=RELATIONS OF VARIABIA MASDRING CHANGE V1111 SELETED 1940 VARIABLES AND WITH 1960 SEX DIFFANICES Di ingmtcr

WageVariables

EcAct Ag. Non- wheat

Ii 1940-460 1960-40 1960,401960-401960440

1940 Variables 60 81 66 69 122

Labor fwce partidept.ion

58 EcAot lo+ -.089 -.273 .507 .310 -.219 79 Ag/EcAct M .221, .105 -.643 .192 .351 64 CollsOcAct 14 -.181 -.163 .552 -.403 -.219 67 Collsr/EcAct F -.111 -.176 525 -.598 -.218 Cultural traits

U7 Nonwheat 14+F 4,119 298 4607 .095 .557 125 Barefoot /4 .309 .081 -.549 431 .383 fransyortation raunication awl facilities

32 Autos/Pop .050 -.331 734 -.133 im.357 39 Movies/pop -.106 -.152 .470 -.427 -.245 36 Elect/capita .163 -.250 .510 .227 -.362 4 Urban 22500+ -.305 -.184 .683 -.002 -.558 Literacy 1:7 age

140 Lit. 10+ M+F -.162 -.197 .584 -.422 -.329 157 Lit. 10-114 14 -.232 -.172 .566 -.398 -.305 158 Lit. 10-14 r -.079 -.271 .5914 -.4149 -.325

Sex differences inliteracy...700

208 Lit. 40-49MU-F .14.3 -.070 -.1420 .231 .318 218 Lit. 40-49 14R-F a317 -.100 -.339 .191 .173 162

TABIE 27--Continued

_111111111111/7111=kaINIII0D

Change Variables

Barefoot Antos/Pop Movies igg Urban Median

. , .

1960-50/ 1960/30 1960-40 1940-60 1960440 1960440 1960.40 1960440

135 35 41 38 14 13

-.395 .457 148 .178 .186 .204 3.3 .584 -.506 -.007 -.544 -.357 -.196 -60.7 -.381 .493 -.120 .380 .309 .464 8.5 -.501 .409 .124 .478 255 .510 10.3

.240 -.455 - 202 -.257 -.082 -.393 2149 .937 -.143 -.258 -.467 -.059 -.302 3.6

-.628 -.331 .309 .618 .138 .495 *4 -.317 .333 -.286 .207 036 .495 1.8 -.535 .321 .553 .477 .309 .090 10.8

-.535 .437 .086 .424 .173 .191

-.550 .451 034 .457 .263 .484 -.566 .482 088 294 .3114 .364 -.398 .457 .013 .429 .193 .505

.798 -.352 -.224 -.351 -.380 -.151 .816 -.268 -.237 -.299 -.319 -.183 163 economically active in 1940, and with smaller urban-rural differences in literacy among older women at that time.

The negative correlations between changes in female white-collar proportions and the proportions white collar for both males and females in 1940

(matched also by negative correlations with 1940 literacy and movie attendance) reverse the signs observed in relatIonships for male white-collar changes. This seems puzzling at first sight; females in 'bite-collar employment mdght be expected to be even more a lead variable than males.However, it is important to remember three things.First, the nature of white-collar jobs differ by sex. Even if the definitions of "white collar" for 1940 and 1960 were identical, the change mix within this rubric may be very different for the two sexes. Second, there are same unavoidable discrepancies between 1940 and 1960 definitions that probably add more 1960 rural females than males to the count as compared with 1940.

Third, and more important, data relating to proportions in white-collar work take the number economically active as the balm.The proportions of males economically active may vary little in either tine or space, but among females variance on this measure is high, and the national proportions of females economically active rose dramatically, to a degree that cannot possibly be accounted for by incomparabilities in the statistical measures. There is a multiplicity of factors at play here that cannot be sorted out without a special analysis of the components of female white-collar employment and of statistical analysis of determinants of the observed ratios and changes in them. This will be discussed again in an examination of change variables in the components analysis.So far as the zero-order correlations of Table 27 are concerned, the one unambiguous value is the correlation between increase in female white-collar proportions and the 1940 proportions of females economically active; though the coefficient is only .310, it carries the positive sign that would be expected if female) 164

white-collar ratios constitutelead indicators.The zero-order correlation be-

tween male and female changesin white-collar proportions is alsopositive, but

it is even lower, at .20 (Table 28). TUrning back to Table 26, it maybeseen

also that proportions ofwomen in white-collar jobs in 1960 show correlations

with indicators of advance thatare substantial and that carry the expected

signs. These are firmer indicators than thehighly complex change measure for

whito-collar females,a measure that picks up too many unidentified elements and

carries a large random componenteven when it is run against the 1940 proportions

of females economically active.

TUrning once more to Table 27, attentioncan be given to the lag patterns.

Now modernization is measuredby reduction in the incidence ofa traditional or

lag trait. The operation of this kind of changeappears clearly in the varia-

bles relating to indigenous culture. In fact, the highest correlationcoefficient

is between proportions of malesbarefoot in 1940 and the decline in thatper-

centage between 1940 and 1960:r .937. The analogous correlation forpro-

portions not eating wheat bread is .557.Both of these culture-change variables

Vmded to be somewhat higher instates with law 1940 proportions of malesin

agriculture and low 1940 literacyrates.Particularly striking is the high

positive correlation between change in proportionof males barefoot and even

1960 sex differences in literacyra of older people (40-49) within both

urban and rural sectors. Less dramatic, but part of thesame syndrome, the

strongest correlation with increase from 1940to 1960 in proportions of females

economically active is also withsex differences in 1940 adult literacy. The

other changes reported in Table 27were relative/7 independent of any of the

starting traits or of other measured changes.

Table 28 summarizes correlationsamong the change variables themselves.

The signs in ail casesare those that would be inferred from the relationships 165

TABU28

DITERCCBRELATIONS OF CONGE VARIABLES AMONG MMUS

No Literacy (40-149)-(60+) &Act Collar Schooling

UrbanRural UrbanRural 1950-1960 296o-194o

Variable Number 199 202 201 235 258 6o 66 69

Inte e....aattrationanliteracy

199(40-49)-(60+) M Urban

202(4040)-(60+) M Rural ..3(148 201 (401-149)-(60+) F Urban .327 .609 205(40449)-(60#) I arxal .212 .753 .467

258 Adults )3+ M,no edhooling 1950-1960 .117 -.252 -.320 -.357

Changes in occupations and economy, 19601.1a0

60 EcActF -.285 -.403 -.455 -.287 .208 66 Collar/Eact M -.137 .323 .217 .354 -.283 69 Collar/Eact F -.028 -.269 -.329 -.283 .32o -433 .258 82 Ag/EbAct M .111.135 .109.131 -.031 -040.653 -.220 98 mfg/tact m -.034.130 .146.094 -.083 .297 .492.174 94 Returns Glick (1950-1930)-.147 .271.338 .429 -.430 .046 .153 -4496 Chans in cultural traits 1 40-1

122 Nonuheat Mtle .140 -.342.067 -.236 .350 .348 -.448 -.224 135Barefoot M .001 -.327 -.368 -.535 .384 .306 -.520 -.146 138Barefoot F -.075 -.527 -.502 -.663 .399 .434 -.512 .022

41 Movieypop -.039 -.099 -.086 -.146 -.144 -.037 .426 .356 31 Bikes pop .112 .140 .206.391 -.104 -.360 -.034 -.023 35Autos/pop -.110 -.024 .042 .254 -.031 .135 .530 .028 38Elect/capita .016 .273 .216.401 -.138 .191 .576 .038

Changes in urbanization

14 Urban 1960-50/1960440 -.047 .028.180.289 -.253 -.107 -.202 -.320 13Urban 1960/1930 -.206 .051 .222 .064 -.476 .228 .377 -.329 166

TABLE 28-0ontinued

Ag. MfgRot. Glick Barefoot MoviesBikesAutosgleotUrbanisation

M MO' 1950-1930 1960-19140 1960-50/196030 1960410 196049140

81 98 94 322 135 138 /41 31 35 38 14 13

.... -.551...... 089 025

.109 .080 .1146 -.012 0169 is.213 .331 ..086 ...088 *4343 .3710922Sae*

is.2143 .093 ....1140 a..256 ....354 239.... .2211.075 .025 -.077 -.036 -472 0.562 .... a...389.255 .059 .0614.167 1914 .088 .151 380 1405 .329 121 ....Wilt .375 .396°.207.437....

.1423 ..250 -.078 130 ....10317304.019 .002 315202 .552 .182 .658 is016 -.0714...0714.090..171.067.160.281 167 already examined, but it might have been more difficult to predict the magnitude of the correlations. Changes in proportions of males and females going barefoot are most closely correlated (.922), but no other coefficients are as high as

.700. There are, of course, definite associations among changes in the various literaay variables and among those relating to male occupations. There are clear connections also between changes in proportions of white-collar males and changes in movie attendance, electricityuse, and possession of automobile's.

The Components Analysis and Change Patterns

A more systematic identification of patterns of Change and ofnon- change emerges from an examination of the components analysis, giving particular attention to those factors that came out with high loadings on one or mare indicators of change (or non-change). Details for seven such factors are presented in Table 29.

The first two shown (the third factor of Matrix A and Matrix B) are very similar. Both pick up modernization lead traits in their high positive loadings on proportions of young females unmarried in 1960, on dwellings with running water in 1960, and on size of capital city in 1940.Both have strong negative loadings on proportions non-Catholic in 1940, and on Changes in proportions not eatingwheat breadsBoth have moderately high loadings (.438 and .440) aa changes in male white-collar employment. The only variables that were not common to the two matrices but took a high loading on the third factor of either were: the difference in literacy-rates between adult urban males and those in their teens (a positive loading of .534 in Matrix A); rural school continuationrates in 1942 (.463 in Matrix B); and a high positive loading of

.637 on increases in the proportions of females in white-collar employment. SELECTED FACTORS WITH ONE OR MORE HIGH LOADINGS ON CHANGE VARIABLES TABLE 29 VariableNumber Factor NumberMatrix 3A 3 6A 5 6 2 population 842 UrbanDensity 1940 1960distribution and ollEIRE .351.359 -.119 .354.343 ....:... -.048-.022 213 -.061-.331 .120 -.436 .038 -.352 ...... I 131210 UrbanCapitalPop.Capital 1960/193050,000+ urban size 19401960 -080 ....561.... -.430 .263.660 .173.473 .....272 -.245-.414-330 -.089 .186087068 ...429 .022 Transportation 1415 In-migrantsUrban 1960-1950/1960-1940 1940 -.245 .012 -.306 .063 -011-.337 -.872 .260 -.222 .933 .194.105 -.349-.109 2618 RoalsiareaRRitiop. 1940 3 1940 -.037 ... .182 .....922 -015 -.220 30292827 RoadsRoacsAreaBicyclelop.Bicycles paved pop. 19601960 1960 1940 -.003 .497.... mil,.283013 .485mil,.... .121.609.... -094 ....038 -.528 .063.... -.122 4...... 34323135 Autos/pop.Bicycles/130P. 1960-19401960/19391940 1960-1940 .155 .182 -449 .019.246.306 .120 -.098 .271 -.378 .383.079 Utilities and communication 3736 Elect/CapitaElect/capita 1960 1940 :;1.41 1 .259 -.198 .328.176 .078.149 .6.281 403938 Movies/pop.Moviesbop.Elect/capita 19401960 1960.1940 -.061 .399 -.124 .355 -.025 .352 .017.181 -071-.177 .209460 -.214-.240-.249 .... 4142 RadioLibrarymovies/pop. 1960 use 1960-19401940 .767.201...... 248.771 .162.216'AB -.053-.023 .... -.020 .024...... 031.....165 -.380-.373 rc....jzzja....i.e.rriaenu.i.ates 4445 Single F 20-24 1960 6560 .728 .... -.148 .136 .166 .... Labor forceyarticipation 47 F under 5 yrs./F 1960 -.887 -.872 -.087-.088 .045.103 -.031 .129 -.242 .... 60586159 Devel.EcAct&Act F F index 10+1960-194012+ 194019601950 .144.504.40...... -.066 .412.286.107 ....270.132.070 -.026 ...... -.135-.010-.091 -.083-.159 .158 .....448.117 Employment of youth 62 EMploy 8-11 14 1960 .035 .060 -.206 .011 -.075 .025 -.255 .046 .210.478 White oollar and professional63 Employ 8-11 F 1960 workers .036 .051 -.077 .008 -.139-.067 .074 .118 -.227 69676664 CollarAcActCoColler/EcActCollarAcAct llar/EcAct 14F14 F19401960-15140 19401960-1940 -.029 .438 -.087 .637.440 .328.032.515 .296 -.272-.409 -.194-,091 -.375-.271-.092 TABLE 29--Cantinued Variable Number Factor NumberMatrix 3A 3B 7D 6A 5 6 2 7470 Prof/EcActClerical/EcAct M 1960 T 1960 .157.059 .034.117 .208 .040.092 -.062-.106 -.003 .117 -.270 PUblic administration 7775 P.A./EdActProf/Eact FM 19601940 ..229 -.339 .331 .207.356 .025.019 Agriculture 8079 Ag/EcActAgiEcAct M 19601940 -.173-.222 -.227-.205 -.318-.061 -.215 .025.099 -.100 .079 -.188 .065 .199.389 838281 AgEjidos/AgAgAg/EcAct Labor/Ag Labor/Ag PopM 14 1960-1940 M 1940 19601940 -.223-.177-.177 -.206-.150 .029 -.206-.672 .064 -.305 .168 -.025-.212 .522 -.126 .125.405 -.294-.185 .158 878685 AgAg Prop/Ag Prop/Ag M M1960-1940 19601940 -.136-.036 .144 ..058-.098-.083 .105 -.047-.141 .077 -.021-.194 .....071 -.159-.008-.028 .217 -.011-.179 .105.007 -.244 .064.241.235 949189 ReturnsAgFarm inc. mochanised Gliokover $5001950-1930 19501960 -.131-.052 ....mi. -.532-.165-.043 -.158-.078 .cao460 -.101 .150 .4,204-.170 .105 .158.231.314 -.221-.341-.546 It jansfactsir 103 98 MfgMfg/EcAct inc. over 14 1960-1940$500 1960 -.166 -.210 .033 -.015 .131 .016 105 -.080 .100 109107111 Mining/EcActPayAmpPaetinPPaYitemP FactFact Fact 1419501930 1955/19401940 .261.163 0174.212 -.062 0000"" -.044 "".086 -.070-.195 .230 .483.134 -151 Culture 127125 traits Barefoot M 19601940 -.118 -.133 -.076 -.228 .207 -.570 .867 128135134133 BarefootBarefoot F 14 M1940 1940-19601940-19501950-1960 -.094410.261 00000000 0385 0000 -.018-.146"110064.110194 -.162-.199 0001;0000 .113 -.600 .791.928.912.835 138137136 BarefootBarefoot F 1940-19601950-19601940-1950 -.262 -.010-.024 .01.3 .... .039fp...... 0000 -.533 ....0000 .876.880.775 132131123 BarefootBarefoot M/Furban 19401960 1960 -.114 .4.. .007.249.251 40...... 123 -.255 ....lb... -.051 .319.237 -.163-.789 .206 .....669 115114113 43 RunningNon-CatholicSleep onwater floor T1960 19601940 1940 -.647 .659 -.389-.784 .215.662 ...... 011b...177 -.079-.268-.153 .006 -.024-.402-.083 .102 .... 119117116 NonwheatNonWheatSleep on T bed19601940 1940 -.021-.022 -.031 .295.000 .... 40....013.112 -.091..00s .065 .090.157.237 ....478 122121120 NonwheatNanWheat 1940-19601950-19601940-1950 -.604 000040... -.513 b.. -.076-.010 .081 -.185 .4, .209 .080 .224.222.155 TABLE 29--Continued VariableNumber Factor NumberMatrix 3A 3 7 6A 5 6 2 =RimEducation 140 Literacy 10+ T 1940 0000 0000 -.010 0000 0000 0000 .R0390 156155153141 LiteracyLiteracyLiteraay 10-14 10-1410+6+0 TSch F 14 19601930 M1930 1940 -.106 .0270000 -.013 .0260000 -.005 .00700130000 -.031 .0180000000 -.021 .1620000 °.0419 0000 160159158157 LiteracyLiteracy 10-1410-14 10-14 14F M19601940 1940 .641,.0000 .4020 .010.023.016 .....032050 ...... -015-033 .025.004 -029 .052.144.021 -.358-.213 .... 161 Literacy 10-14 M 1940-1930 .020 .101 .072 -.270 163165164162 LiteracyLit.Literacy 6+ 10-141960-1950/1960-1940 10-14 MF F1960-19401940-1930 1960-1940 -.007 me.... -.036-.122 .084.070 -.115 .... .0000 6)000...... -.135 .004.259 Literacy by age, 1960 Urban males 0000 0000 .013 192170168 Rural0-4925-29 females -.267 .002.042 e -.177-.067 .072 * 199198 differences Urban(40-49)-(60+)CK-191440449) males 14U MU literacy, 1960 .534 -.008-.104 4.076 -.040 .129 200 UrbanC15-19)-(040) females .006 .058 .131 .652 202203201 Rural(15-19)-10449)040449)-(60-0(40-49)-(60+) femalesmales -.098 0000 0000 -.047-083 .065 .1410000 0000 me,0000000 -.408 .163 Sex differences in literacy.205204 (40-49)-(60+)(1549)-(40-49) -.186 4,41,41,4, otliodo 095 .155 it.oe.192 odoodo ern '.01452 .687 221218208 40-4920-2440-49 MU-F NR-FMR-F -.093-.090-.158 00000000 .167.134 .243.091.120 000004,00 .839.773 Adult leitel-of schooling258228 Adult 30+0 Sch14Sch M 1950-19601960 .027 .098 .066 .319 242239236238 Adult 30+7+Sch25+7+Sch30+10+Sdh30+7+Sdh 14 FM 1960196019501960 .2410095.192 0000 .274.193 -036-.014 470 0000 -.407-.253 0000 Mar olbment 265266 Ehrol 6-14 T 19601937 -.155 -.146 -.039 .028 -.049 .101.093 -.345-.08 -.105 .161 275274273267 EnrolEnrolEhrol 6-14 6-106-14 ruralurban MUR 1930 1960 1960 -.035-.042-.079 .042 -.223-.070-.045 -.116-.118 .037.029 -.424-.307-.015-.045 -.098 .616.00 -.629-.204 .212 -.216-.066 .301.025 TABLE 29 --Continued VariableNumber Factor Matrix A B D A Enrollment incomeat in pesos, 1959 Factor Number 6 yrs. and monthly 3 3 6 5 6 2 Enrollment at 6 282280278 Enrol 6/(601-1,000)-(200)6/inc.6/$60141,000 $200 yrs. and -.018 .002 -.052-.016 .028 -.090-.120 .077.120.087 -.113-.074 4004 0000S000 father's284 283occumlanala Enrol 6/Ag6/Pro 1959 1959 .014 -.169 .063 0000 "90235 -.124 .215 .077.014 ....4". Ingress in school ContinuationBeginning rates-primary school enrollments 304293299296 B B4/3 B4/134/3 urban-cural urbanrural 19601942 1942 rural 1960 :i4i.033 0249.046 -.082 .139.... -.127 .052 -.213-.043 .218 -.178-.225 .110 .429.008 309308307 B 4/3 urbanruralurban-rural 1960-1942 19604/3 -.143 .109 -.054 -.022-.091 .... -.065 .230 ....165 ..026 .232.31$9 333332330331 B B5/15// 5/1 ruralrural urban 191421960 1960 5/1 urban 1942 me00.0.... .119 .041.463.2141 .4"....do.0.0.... mt.00040....000. -.024 321.002.020 -.090-.153"'0185 .0214 ....eeeo0000... 338 Stichrirgahoo1 0 ..226 0.00 0.00 -.192 .000 00. Age grade progress, 314834719633149 Age 10 GrOr 1 MUMR .15500.0 mI0202 0086.40.. 00.0 OM000. ....130"80152 0..0 .12 1 00.0.03y? . ....0124.0$ 5 Pass rates 359361360 AgeAge 1010 1010 Or Or Gr 1Or 1 MR-MU 1FU FR1 FR-FU .....4,4'.01400000 ....1160140 -.149-.000...Di-.021 11.00 -.022-.019 ....0..0 ...0036.003 -.042413148 .... .19804000 377376369368 PazsPassPass 2/Pres2/Pres 2/Pres urbanrural urbanrural 196019142 is.065 0018.000.091 .133.1714.268.028 .....00.00**OM .0266.4-4 .....177 4...0064...189 .192.132 4...121 .064.1580261 .....0..0040. School facilities, Schools inc , - 0037 00.0 000178 411011:4 397396390389 Soh3chSth InocupLump Lwow urban ruralrural 1942-19601942.496019142 Inaomp ur. - 1942 te - .... 117 OA.... -.202-.13.9 .044 .....073.033...... 0041000 00.0....0.00 00057-03D6 .201220 176

The last is particularly interesting in view of the zero-order correlations

discussed earlier.

The next factor delineated in Table 29 (Factor 7 of Matrix D) is also

in part a modernization lead factor, but it is centeredon changes in male

occupational structure. The highest loadings are .864 for increased employment

in manufacturing, -,672 for 1960-1940 proportions in farming, and .515for in-

creased white-collar proportions. Scares on this factor have been mapped in

Figure 14. Supporting them are moderately high scoreson proportions living

in large cities in 1940 and possession of bicyclesin 1940 (which carry a heavier loading than automobiles).

Columns 4 and 5 of Table 29 both pickup urbanization or its timing, but they are in same respects opposites.Factor 6 from Matrix Alma a strong negative loading of -.872 on recency of urbanization (variable 14). This goes along with relatively early development and a good transport network; there is a positive loading of .922 on railroad mileage per capita in 1940 and of .609 on paved roads in 1960.Howevor, none of the occupational, cultural, or educational variables Come° 4.4.rough in this cluster with the lone partialexception of a negative 1o4ding of -.424 on urban enrollment rates in 1960.

Evidently where transportation facilities backedup early growth but the pace of urbanization was not sustained, other changes reverted towardaverage patterns.Factor 5 from Matrix B is predominantlya specification of areas of belated growth, with low rates of urbanization for 1930-1960 takenas a whole (loadings of .933on recency of urbanization and -.414 on ratio of 1960 to 1930 urban population). Tnese are areas in which proportions in agricultural employment have been maintained (loading of .522) and proportions of males in white-collar jobs have not grown, nevertheless theseareas do not stand out for frequency of indigenous culture traits. Fig. 314.--Matrir. Ds Factor 7.

Variable Factor loadings Number (I.800 and Above)

98 Mfg/EcAct M 1960-19140 .8614 81 Ag/EcAct M 1960-1910 -.672 66 Collar/EcAct M 1960-1910 .515 A short-cut estimate of the rat* of the FederalDistrict for these variables is 12 (from a high of 1 to 32).

1 N /TED . deA Pt AM t.6 . . . OA 114 4 Factor Scores . 0.63 to 2.61 . . 1, . . . 1 / 0.26 to 0.62 I. 'try :::, . IiiOtha.k.S ;Ad"' a ""rv -0.24 to 0.25 , .P. -2.05-0.67 to to -0.66 -0.23 5 I . 5 6 % We: //A.A.^. 4%) rt: . :IZ::: 4A :Z::: a a )f .2... 6a .% :" ) 0.. 'S 1: !BRITISH GUATEMALA HONDURAS 179

The last two columns of Table 29depict factors that focus dieuinctly

on the presence or absence of indigenous populations with associated behavior

and conditions. The last factor (Factor 2 from Matrix D) is repeated from

Table 3and is by now a familiar pattern. It has high loadings on all the

barefoot items and their changes andon sex differences in literacy of 1940

adults. The geographic pattern (see Figure 15) isas simple and unambiguous

in its regional division of Mexicoas aqy one could draw; the indigenous Indian

populations determine the darkerzones to the south and east and Just around

the Federal District, but they donot extend beyynd it to the northor west.

It is interesting tocompare this factor with Factor 6 of Matrix B (shown to

the left of it in Table 29). The latter factor is mainlyIn inverse of the

former, with negative loadingson indigenous cultural traits and changes in

them. It adds also an interesting twist thatmight not have been anticipated:

negative loadings an road mileage relativeto area and on rural school en-

rollments in 1960. (The road variable points immediatelyto the sparsely

settled states which generally had light shadingson Figure 15). The sign

of the enrollment variables is especially interestingand will be reconsidered in Chapter VI.

Migration

One of the aftermaths of the Revolution of 1910was the shaking loose of people from the land, enabling then tomove in search of better opportunities. In 1940, the rangeamong states was from 38 per cent to 98per cent of the population native to the state inwhich they were residing. While this range in percentagescadnot change, in 1960 the medianwas 89 7:qr cent compared with 93 per cent in 1940; themean far all Mexico was only slightly lower, at six-sevenths of the total populationof 35 million. Fig. 15.-4latrix: Do Factor 2.

Variable Factor Loadings Number (I.800 and Above)

125 Barefoot14 191,,0 .867 BarefootM 1940-1950 .835 Barefoot141950-1960 .912 135 BarefootM 1940-1960 .926 13 7 BarefootF 1950-1960 .880 13 8 BarefootF 1940-1960 .876 218 Literacy40449 MR-Fit .839

A short-cut estimate of the rank of the Federal, District for these variables is 17 (from a high of 1 to 32)...... N ...... "'"pUNIT...... " . ED ' ...... , . . :. : . i . . ' ...... S7-4 Factor Scores . . . , . 'e::::::::::::::.:::::::::::::::::::: . N .::::::::::::::itr::::::::::::::::::-:::::::' ii% , -0.89 to -1.62 I::: A It . i :::.;1:.). ',.. 1 ...... :...... 4. : , .... 1 N:,...... ,. iiiiiiii::iigirililill:di:iigv.r...,...v...... ::....-4::-:::::::::::::::::::::::::,1::::::::::::::.r.c:::.:::::::ii, "' - [7771 -0.58 to -0.88 I; ./ - i .,.... 4:::::::::::::::::::::::::::::::::?h41:::::::::::::::::::::::::::::qi::::::::::::::::::::::::: 0 : ' . :.:, . .4, -0.18 to -0.57 , tn.';;;;;;... .. ,... i,4 *4* 44" Ms . ...:1111:::. . 0.95 to -0.17 -...... 1 ..... %. elt:::, I 1.92 to 0.94 .. 4.' N % P. k 1 I, .1 r.. f ' 4:4 ?i t ...... 1 '. . g /. i ,ve : .). /F:.... 144'4/ .-..: .% 4i l; r 3...... 4 / .1.. i . .. , a.% . ) , ...'I 'I * . t GUATEMALA !BRITI1SH HONDURAS 182

Since 1940 the major net movements of the Mexican population has been

fram villages to cities and from smaller to larger cities. Most of the migrants

have settled in states contiguous to the ones in which they ware born, but there

has been considerable movement to the Federal District and to states in the

north. In rural areas there has also been movement from areas of dry or rela-

tively unproductive land to places where irrigation is available.

The census of 1960 asked respondents the state in which they were barn?

The evidence supports a priori expectations of movement predominantly toward areas of economic development. States with the highest proportions of residents who had cmme from elsewhere included the Federal District, Baja California Norte,

Nuevo Leon, Sonora, Chihuahua, and Tamaulipas in the north.Aguascalientes,

Colima, Quintana Roo, and Nayarit are also among those witha relativelyhigh proportion of in-migrants. Table 30 shows numbers of in-migrants, their percentage of the population of the state in which they were living in 19601 and the net coefficient of migration for each state. (The coefficient was computed by dividing the number born in the state, whether resIdent or not in 1960, into the net in-migration that had occurred to 1960; net in-migration was the population living in the state who were not born there minus those who were born in the state but were living elsewhere.) According to the 1960 census, out of the five million living in a state other than where theywere born, about two million, or 40 per cent, were living in the Federal District.

1Myers( cit p. 69) lists some weaknesses of this enumeration: (a) there wasno indication of when migration took place; the migrant could have left his state of birth at any time after infancy, (b) those who had migrated but returned or who migrated but died prior to enumeration were not included, (c) there was no separation of rural from =ban migrants, (d) there was no indication of movement within individual states. 183

TABLE 30

INDICES OF INTENSITY OF LIFE-TIME IN-MIGRATION; STATES WITH NET IN-MIGRATION, 1960

Total In-migrants States with Net Coefficient of I Number of as % of Present In-migration Attractiona ln-migrants Population

Baja California Norte 62.02 289,010

Colima 26.44 42,859

Chihuahua + .107 16.10 191,14814

Distrito Federal + .569 4G.33 1,913,638

Morelos + .184 26.09 99,915

Nayarit + .022 15.70 60,878

Nuevo Leon 23.58 251,270

Quintana Roo 4007 19,401

Sonora 18.03 139,717

Tamaulipas 66.212703i 28.71 288,315

Veracruz 9.85 267,369

a The coefficient of attraction is (Mi-M0)/B where: Xi = number of in-migrant residents, Mo = number born in the state who live elsewhere, and B = total number (of living Mexican population) born in this state.

Table 31 lists the states with a net out-migration and the proportion

of the resident population of each state living elsewhere.The Federal District drew most of its in-migrants from states in the center, while migration in the north was mainly to contiguous states. Long distance streams over several

states are also apparent in the movements fram the central statesto the north and from the north to center. The states with low rankings on indices of de-

velopment (as Oaxaca and Guerrero) appear here with relatively low gross 184

TABLE 31

EXTENT OF OUT-MIGRATION AND MAJOR DESTINATIONS OF OUT-MIGRANTS; STATES wns NET OUTAIGRATION, 1960

Outeeigrants as Per Cent Out-migrants as of Present Population at States with Net Per Cent of Origina Now Living In Out-migratian Present Population at Origin: Total

DJ. I North&

Pacific North

Baja California Sur 33.3 5.4 18.6 Sinaloa 14.0 1.2 10.1

North

Coahuila 20.9 2.7 13.6 Durango 22.3 2.1 13.4 San Luis Potosi 21.4 3.6 12.3 Zacatecas 31.3 3.7 11.4

Canter

Aggascalientes 7.9 Guanajuato gl. 33:(9) 4.2 Hidalgo 25.0 17.2 .8 Jalisco 180 5.1 5.0 Mexico 17.5 :9 Michoacan TO:39 11.1 2.9 Puebla 13.5 7.0 .4 Queretaro 33.2 28.1 2.2 Tlaxnala 23.7 13.7 .9

Gulf of Mexico

Campeche 18.0 4.6 144 Tabasco 10.8 2.2 .4 ibcatan 10.8 3.9 .6

Pacific South

Chiapas 6.3 3.0 .9 Guerrero 9.7 3.9 .4 Oaxaca 12.3 5.7 .6 _

aStatesof ths North with net in-migration: Baja California Norte, Chihuahua, Nuevo Leon, Sonora, and Tamaulipas. 185

movements either out or ins a phenomenon that ties invery neatly with

HAgerstrand's treatment of migration fieldsas indicators of mean private

information fields and with his emphasison the latter and on gaps between

information fields in the explanation of spatial diffusion of innovative be- 1 haviors. Figures 16 to 20 portray the migration streams discussed above.

Most striking on the in...migration maps (Figures 16 and 17)are the relatively

local concqntrations of recruits to the southern and central cities, including

the Federal District, in contrast to the long.distance origins of recruitsto

the north and to the northwest in particular. This is in part a reflection of

the initial disparate population bases, whieh should, ofcourse, be taken into

account if our purpose were to explain the migration patterns. On the other

hand, the effects of various in-migrantgroups on observations concerning

traits of resident populations depend upon their representation at destination

regardless of the total size of populations at either originsor destinations.

Figures 16 and 17 are indicative of the kinds of cultureareas from which the in-migrant populations derive.

When in-migrants come from a rural setting, they are likely to enter

the occupational and social streams at a different level tk.ln those who come from another urban area. It is of oonsiderable interest, therefore, to dis-

tinguish migrant streams by the likelihoods that they will originate inmore urban or more rural places.Table 32 does this by showing the distributions of in-migrants by the proportions of males employed in agriculture in the migrant's state of birth.Relatively high percentages of in-migrants to Nuevo

Leon and Tamaulipas come from predominantly urbanized states, and the origins

1Torsten Higerstrand,DiffUsion of Innovations as a Spatial Process, trans, from Swedish (1957) by AnanTred (chicago:'university ok Chicago Press, 1967). Fig. 16.--Origins of in-migrants to central and southernstates (born in other states), 1960. Se- MEM o.,b UN /Teo I 't N. *.. \i i1 k I . %.. . S ...... Total in migrants %. ! %...,.. 74 t.. 1% SI as percent of population IM ..! ,4' i 1 i I. ., .. at destination 40 and over 1 i %, ,4 :..- 1 .-4 1 . . . . 4 under20 to 39 20 i' 1 i '%. N, e. %. ... o 0. , ,s.I. I I..1 .0 . % . , --. In migrant streams as I % .1' \ . t... I percent of population .%. 1 L.%. .% ,- ''''' 1.... N 1 'ft...* ..01' ' at destination --+ I. 0 to 4. 9 i I .. i i , f ,o .// I I .4 ( o' k =0 5. 0 and over %. 0 /MI 4 4, 0 e .0t .1VAS .r.; 0 I f . . "''. 0 . 'V " i. ..)., ' ''. .4'. , "A . ..61 j` i . . , . ... ) , f - of " - g, . . . ' . s '... . , j ) i # ' 11 .r... a i:' 0. :i . ;4- , : %. b.V1 ..,\ --"IIIIIPP.i.% .:',Z1,111- r "11-.1,§ 130. . 4Cr I Fig. 17.--Origins of in-migrants to northern states (born in other states), 1960. Total in migrants atas destinationpercent of population NM 40 and over 20under to 39 20 atpercent destinationIn migrant of population streams as --bg I.5. 0 0 to and 4. 9 over i Fig, IL...Destinations of out.migrants born in southern states, 1960, MM. MM. MM. , ,, UNirepono ,Ima 1.10e MM. MIN. S asTaal percentin ste3/.e out ofmigrants population of birth 1 /1. t.t -.% am 20 under to 39 20 ,! .... 4, 1 t I. . N. is. %. %. 1 / . \ Out migrant streams as t. 1 ....- I ! % :I...... "...1 percent of population i...., ! %. ', .i. 1 1 .'''% e*.- ). in state of birth l. 0 to 4. 9 s , ... t ...... if 5. 0 and over t. s, t. %. i / .° __- ../ I .1 .7.i. ; %. I 4. 1 . ... /.,.?,. i -. .7 !.-, . .. iv* ... / .4.i. f e 4., "./ .. V. r' 1 . .r. ! sr ,....° .1... 5 ;i ...... ,., ... . )! ...... 4,.. . 4...... ' .# !"*". 1 ) V. . ...,...r 11 P't .... . I. . i 'd / k e . t ) ;L. ** . f . 1 P.. 4°6 k ON D 1 Fig. 19.--Destinations of out-migrants born in north and north Pacific states. asTotalin percent state out migrants of of birthpopulation C=Z3 under 20 20 to 39 percentinOut state migrant of ofpopulation birth streams as ...mo+ 5.01. 0and to over4. 9 r -0.... - I Fig. 20...Destinations of out-migrants born in centralstates, 1960. NMI ...0. . 0 Mil ... IL .... N.. U ..- MED N ITE-0 ! - -. i i --,.\ .. 1 . ....' ..y...... t.. l.. / - cz asTotal percent out migrants of population r) .. ..i . / k. 0 in state of birth 1111111111111 .)*)*-* i If ? t under20 to 39 20 ., 1...... - ..... A i *. % i e) ... \ A... " `.. - Out migrant streams as * ..., c.... / I.i ... 1. i \ l...... inpercent state of populationbirth ... 'N./ i i 1 i r k1 3 / IN.0--) I. 5. 0 0to and 4. 9over _ I i e . . ( - - - - : 1 B i ....; 1. i..-? .. . \ .--,I ",.., - -a --.. ,.... -I t---; .. i icr H OND ...... CD i i of in-migrants to Colima and Nayaritare from cagparativelyuxbanized settings.

At the other extreme, Morelos and Veracruz attracted individuals from highly

agricultural states, followed closely in this respect by the Federal District.

Daja California Norte is especially notable for the wide diversity in the

origins of its in-migrants.

TABLE 32

DISTRIBUTION OF IN-MIGRANTS FROM STATES WITH VARIOUS PROPORTIONS OF POPUIATION ENGAGED M AGRICULTURE

Percentages of Total In-migrants fram States in Which the Following Percentages of Economically Active States with Net Lifetime Males were Engagel in In-migration, 1960 Agriculture in 1960

75+ 70-714 6o-69 50-59 Under 50

Baja California Norte 22.6 6.0 24.3 42.1 4.9

Colima 25.1 1.4 4.7 64.9 3.8

Chihuahua 22.5 34.8 8.9 14.8 19.0

Federal District 30.9 16.5 38.9 10.9 2.8

Morelos 48.7 110 27.6 4.4 7.9

Nayarit 150 4.7 14.8 6149 3.2

Nuevo Leon 13.1 33.2 8.5 17.4 27.8

Quintana Roo 2.7 3.3 87.6 4.2 2.2

Sonora 16.8 7.5 41.6 23.8 10.4

Tamaulipas 10.6 24.9 20.0 8.5 364

Veracruz 34.1 38.9 9.9 10.7 6.4 197

The remaining maps take the opposite perspective,looking out from the point of origin to various destinations.Here, the long-distance attractive power of the Federal District is somewhat more in evidence. Even though the southeastern states contribute relatively little to thepopulation of the

Federal District relative to migrants of ciTher origins, and the proportions of their populations that have left for Mexico City are small, the orientation of the people of the southeast to the nation's capital is clear enough (Figure 18) and there can be no doubt that this is the node from which informalmessages

(attitudes and information)are carried to the people back home.Long-distance out-migrant streams from the north also tend toconverge on the Federal District, but this is almost by definition, and the role of the Federal Ldstrict in the orieat.tion of mdgrants is by no means dominant even for urban-directed migrants from the north (Figare 19). The Federal District's dominance in tbe attraction of migrants from the central states is undisputable, in fact, formost of these states the number of native sons (and daughters) resident in Mexico Cityrims over 5 per cent of the local resident populations. The two-directional play of these central miratica fieldsare unadstakable despite the competitive attractions of northern areas.

To infer that all of these gross migration streame,even those over long distances, were predominantly to urban destinations would be a serious misinterpretation. It is no accident, for example, tt most of the states attracting large proportions of long-distance migrants were inclAded among those with heavy investments in hydraulic projec:4 between 1947 and1958.1

'Shorter-distancemigrations normal4 account for a much larger proportion of rural-to-rural than of urtan-directed movements, and these shifts are vary inadequately-represented using area boundaries so gross as states. 198

Further evidence of a relationship between rural development and mi-

gration lies in correlations among variables relating to agriculture. Table 33

displays all the zero-order correlationswithmigrationmagyarea.The most

agricultural areal; maintained a stable population (in terms of interstate mi-

gration) from 194C to 1960.Areas where farms were mechanized and where incomes

from agriculture averaged above 500 pesos monthly also attracted migrants to

both farm aad city.

Though areas with high proportions of maw in manufacturing gemerally

attracted out-of-state migrants, the correlationa ware not high. Manufacturing

characterized by high prcportions of females again was part of a low in-migration

(or a net outmmigration) pattern, but where greater proportions of the popu-

lation in manufacturing earned over 500 pesos there was significant inmmigration

(.738). Overall, migratim flows were quite in line with the movements from

lagging to leading areas, from those with less to those with more modern facili-

ties and opportunities, as would be expected. Less predictably, the magnitudes

of most of the zero-order come:ha:time of migration with other variables were

remarkably stable over time.

Preceding studier of migration in Mexico have also attributed movement

as a response to economic opportunity. Randall demonstrated an economic motive

for migration by ber finding that states which had a large number of migrants in

1950 were those in which there were relatively high minimum wages in1940.1

Zenteno found migration to be correlated with the proportion of the population

in non-agricultural occupations, earning higher incomes, and with higher

llouraRandall, "labor Migration and Mexican Economic Development," Social and Economic Studim, I (March, 1962), 73-81.

I 199

TABLE33

CORRELATIONS OF PULE IN-MIGRATION RATES VIM OMER VARIABLES

Male In-migrants as Percentage of Resident Hale Population

1940 1 1950 1 1960

Population distribution

Density-1940 -.499 -.451 -.556 Density 1960 -.365 -.304 -.374

Urban 1940 .555 .543 .457 Urban 1950 .575 .585 Urban 1960 .566 .598 :517 Pop. 50,000+, 1960 .469 .475 .440

Capital/I:kw 1960 .031 -.021 Capitkl/mOban 1940 .363 .316 .382 Capital/urban 1960 .292 .242 .297 Capital size 1910 -441 -.149 -.240 Capital size 1960 .054 .061 427

Urban 1960/1930 .684 urban 1960-1950/1560-1940 -.030

In-migrant 1940 .968 .855

Transportation

RR/Pop. 1940 8379 .374 .... RR/Pop. 1960 .366 .335 RR/Ares 1940 .026 .0514 4.... RR/Area 2960 ..o86 -.063 ....

Roade/Pop.b19W) .691 .673 Roads/Pop., 1960 .398 .360 Roads/aream 1940 .196 .230 Roads/area 1960 -.215 -.152

Antos/Pop. 1940 .6I0 .609 .614 Antos/Pop. 1960 .582 .606 .642 200

WIZ 33-Cont4n1ed

mieIn-migrants as Percentage of Resident Male Population

1.94. F. 1960 Utilities and communication facilities

Mectricity/capita 1940 .150 .177 .152 Electricity/capita 1960 .313 .353 .436 Electricity 1960-1940 .2143 .321 Movies/pop. 19140 .751 .736 .764 Movies/pop. 1960 .430 .424 .342 Movies/pop. 1960-19140 -.154 -.138 Library use 19140 .129 .190 Radio 1960 .600 .623 .527 Culture Barefoot M 1940 -.6214 -.485 Barefoot M 1960 -.587 -.5oh Nonwheat 1940 4477 -.503 Nonwheat 1960 -.472 -.590 Running water 1960 .216 .187 Sleep in bed 19140 -.593 .518

Occupation

Econ. act. F1914u .347 .3To Econ. act. F 1960 4.330 .429

White collar/EcAct X 19140 .750 .749 White collar/Eact X 1960 .628 .611 White collar X 1960-19140 .372

White oollar/EcAct 111 1940 .671 0000 .629 value collar/EoAct F 1960 .575 0000 .1426 White collar F1960-19140 -.328 0000 .. Clerical/EcAct M 1960 .695 .673 Clerical/EcAct F 1960 .671 .578 201

TABLE 33-Continued

Male In-migrants as Percentage of Resident Male Population

1940 1950 I 1960

Professional/EcAct 14 1960 552 .568 Professional/EcAct F 1960 .26o .123 Public admin./EcAct N 1940 .668

Ag/EcAct N 1940 -.635 -.537 Ag/EcAct 14 1960 -.595 -.568 Ag/EcAct 14 1960.1940

Ag Labor/4 Pop 1940 .198 .072 Ag Labor/Ag Pop 1960 .394 .416

Ejidos/kg Pop 1940 -405 .... -.173 Ag Prop/kg Pop 1940 -458 ... .138 Ag Prop/kg Pop 1960 -.373 .... -.416 Ag Prop 1960-1940 -.260 ......

EquidLand 1950 .350 .... .172 Farm mechanized 1950 .813 .... .692 Income over 500 pesos1960 .675 .... .736

Mfg/H 1910 .264 .206 Mfg/EcAct 14 1963 .300 .240 Mfg/EcAct 1960-1940 .162 .....

Mfg F/14+F in Mfg 19140 -.296 ... .,264 Mfg VW in Mfg 1960 -.422 -.307

Mfg income over 500 pesos 1960 .706 .... .738 Pay/No. emp in fact. 1940 .400 . .222 Pay/No. comp in fact,1955 .259 .221

Employment of youth

llaploy 14 1960 -.629 -.626 -.542 &ploy F 1960 -.642 -.605

Literacy

Literacy 10+ T 1940 .769 .718 Literacy 10+ T 1960 4690 .578 I i

202 TABU 33--Continued

1401e In-edgrants as Percentage of Resident mai. Population

19140 1950 1960

Literacy 140+ M 1940 .799 .781 Literacy 140+ F 1940 .757 .694

Literacy 40+ M 1960 .683 .623 . Literacy 40+ F 1960 .666 .565 Literacy 30-39 MU 1960 .600 .512 Literacy 30-C9 FU 1960 .672 .585 Literacy 20-24 1,111-F 1960 -.600 -.528 Literacy 20-24 MR-F 1960 -.401 -.369

Literacy 1044 M 19140-1930 .5148 .520 Literacy 10-14 F 19140-1930 .508 .1460 Literacy 10-14 /4 1960-19140 -.1450 -Jai& Literacy 10-14 F 1960-1910 -.5714 -.531 Enrollment 6-114 T 1937 .710 .710 Enrollment 6-14 T 1960 .489 .481 Adult levels of schoolimg No schooling Adult 25+ 11 1950 -.715 -.719 Adult 25+ F 1950 -.691 -.633 Adult 30+ M 1960 -.6814 -.642 Adult 30+ F 1960 -.691 -.573 7+ years of schooling Adult 25+ M 1950 .7314 .722 Adult 25+ F 1950 .692 .583 Adult 30+ M 1960 .669 .647 Adult 304. F 1960 .650 . 051$11 10+ years le schooling Adult 25+ 14 1950 .700 .701 Adult 25. F 1950 .647 .540 Adult 30+ M 1960 .645 .640 Adult 3C+ F 1960 .606 .510 Bac. and univ. education Adult 15+ Bac 14 1940 .1451 .253 Adult 15+ Bac F 19140 .278 .039 Adult 35+ Univ M 1940 .631 .503 Adult 15+ Univ F 19140 .708 .565 203 literacyrates./Burnight and Whetten concluded that agricultural and industrial expansion were taking place simultaneously with substantial interstate migration of workers.2

That economic pull (and push) go a long way toward explaining migrant origins and destinations will hardly be denied by anyone, and has been observed often enough in Mexico by previous writers. But the partiaalar linkages within these systems have rarely received comparable attention. In winding up this chapter, it is worth stressing once again the Liam; that the migration fields delineated by these movements are indicative of spatial structures of interpersonal communication.While people may hear of opportunities from several sources, repeated contact with family or friends living elsewtere is a powerful factor both in migration itself and in the spread of information and ideas by

"feed-backs" to the home folk. It would take another vary substantial research project to identify more precisely the relations between migration patterns in the development and operation of informal information fields, and the pace and spatial patterning of the diffusion of modernizing culture traits and behavior, or to distinguish incole from attitude and information elements in such diffusion. It will be necessary; nevertheless, to consider migration patterns in the interpretation of diffusion of spatial schooling among children and the regression analyses of school enrollment rates that are focus of Chapters V and VI.

1RaulBenitez Zenteno, Analisis Demografico de Mexico (Mexico: Instituto de Investigaciones Sociales Universidad Nacional, 1961), pp. 41-59.

2Nathan Whetten and Robert Burnight, "Internal Migration in Mexico," Eetadistica, XVI (1958), 65-77, CHAP= V

LEVELS OF ADULT SCHOOLING AS INFLUENCES ON THE

SCHOOLING OF TOMS

Once primary schooling came to be seen as an important direct responsi-

bility of government, the task of implementing educational policies remained.

In glancing back over the period from 1930 to 1960one is looking for con-

sistencies in the waym in which primary schooling became part of the local

scene. The interest here lies in the patterns of diffusion of primary

schooling among youth in 1960 and in identifying the factors thatencourage

or restrain such diffusion. This is to treat primary education as an effect, not as a cause of social change.In other words, the concern is with the

diffusion of education viewed as an "innovation" in local life.

In tracing the dispersion of primary sdhooling several aspects of

Hagerstrandls model are adopted. The most effective way a new idea is introduced is through interpersonal communication or contact with a person who has adopted the new idea.The frequency of contact influences the speed at which

an innovation is accepted; the highest rates of adoption are in areas where

there hare been preceding adoptions.agerstrand speaks of "density of adoption" or the increasing acceptance ofan innovation where there have been earlier ones until a point of saturation has been :eached. He speaks also of "resistances" of a population to an innovation,or the ease with which particular new ideas are accepted for any givan intensity of telling's.

Some new ideas maybe adopted almost immediately, 30M8 only after repeated

2014 20

tailings, some noteven then, and individuals may differ in their degrees of

resistance to any particular change.All empirical observations reflect the

operations of both "information fields"and oresistances"--i.e., conditions

that foster or impede the adoptionof a new idea.Hence this Ohapter, as well

as the next one, is in part concerned with "resistances."However, the concen-

tration here will be primarilyon the "information fields" or fields of

utellings."Indeed, they have been anticipated inmany ways, including in.

particular the networks thatwere implicit in the patterning of migration

streams, just discussed at the end of Chapter IV.

One way in whidh variables describing education of adults fit into the

model is as they indicate potential frequencies of directexposure of youth to

literate ar educated persons who are prior "adopters." That is,areas where

there are high prowrtions of literate adults (or adults with middleor upper

levels of schooling) represent a high density of communicationof youth with adopters of the innovation.

Urban centers, with their diversity of activities andcircles of interpersonal contacts, are normally the nodes of modernisationwithin and frost which influences may readh out to break down rural traditions and draw less urban people into the wider national culture.However, urban centers vary in population, in the intensity of their participation in the national network, and in their dominance of surrounding hint3rlands. There mayalso be rural as well as urten "parent localities" or "innovation centers" which appear again and again as early adopters of new ideas and as centers from which these spread.

As Higerstrand pointed out, the stability of these patterns does not mean that early adoptersof various innovations will be the same individuals in exactly the same places. The stability of leadership is rather in adoption hierarchies of entire subiapopulations. 206 Also, while thereare geographic patterns with an upstream and down- stream direction, anotherdimension to the flow of influence ison the scale of social-status and occupational-roledifferentiations in the clusterings of

individualsexchanginginformation.In this; treatment of communicationpatterns, the interest in in whichadults (maleor female) have the greatest influence

on schooling of boys and girls.DIthe:$se patterns vary asbetween rural and urban areas?Have they changedover time? In Figure 21 the schooling of youth is treatedas an innovation being diffused.Central to the acceptance of echooling is knowledgeof the effects of education, or the attitudes concerning educationsthat might =courage or restrain schoolenrollments and retention.The diffusion of Imowledge about education in the model is dependentupon the comication system--the most effective channels being person toperson tellings.The spatial distribution of educational attainment of adults is theproxy variable for "intensity of telling," in this respect.That is, it la assumed that educated adults have more contact with new ideas, and a locality with a large proportion ofeducated adults would have a acre rapid interchange ofinformation relevant to decisions about schooling. The literacy, enrollment,and retention rates of childrenare treated as the innovation or dependent variable.In Figure 21 the indicator variables available for empirical analysisare in the rounded boxes; the concepts or hypothetical variables&re in the square boxes. Within an urban setting there is a high physical. density anda high

over-all frequency of "tellings," Vat urban societymay be segmantalized.The

critical factor for social change is how often tellings link diverse subgroups of the population.Recent studies have disclosed groups of urban dwellers living in a traditional rural way of life, barely touched by their surroundings.

In spite ofincreased physical density, social distances may become greater Fig. 21.Interpersonal comnuaioation networks. 1

? 208

(Educationof Adults flntra-Urban Tellings i 1 1 + 1 KNOWLEDGE OF EFFECTS 1 * OF EDUCATION AND/OR ATTITUDES TOWARD Urban-Rural EDUCATION Tellings

1 (Urban Size, \ ProportionsUrban)

1 209

among groups within a city.The in-migrants who flock to citiesmay swell the numbers engaged in marginal economicactivities. At th. me Ulm, cities attract those in search of higher educationand higher levels at jobs. Urban areas contain not on1y disiroportionate numbers of the better educatedbut also increasingkv large limbers of ieziginal individmals.To what =tont intraurban commnication will reach thosegroups, how far disparate communication networks are maintained side by side, is basic for determining thepace and patterns a modernisation both within cities and out into the hintesiands.Infra-urban tailings nay be limited and highly differentiated,or they'my bemore diffuse and matiplicative in their effects. At the traditional, rural extreme the rural-to-urban comeonication nay dominate the life of the snall city that is a meeting place for the traditional rural people.The arrow reversing back de- pieta the power of traditional matinee in the life of such ommemities, which may reinforce traditional culture instead of opening channels to modern and more national sorts of thoughts and behavior. Active urban to roral 1;ellings will typicalky lead towardincreased difftsion of knowledge about eduoation, given that the urbanareas are also the initial lead areas--the parent localities from whichnew ideaa swiss or through which they are carried to subordinate or secondary runil difftsion nodes.The dominant role of primary cities such as the Federal District, Monterrey, and Guadalajara has been discussed. With the breekdown of the hacienda system and increased mobility of the population, the mall and middle-sized regional cities have taken on changing roles as intermediaries / in the comonicaticin networks.They are not only distributive centers for goods and services but also localities through which migrants say spend some time as they leave the farms in search of greater opportunitLes in the larger cities or in the thriving agricultural centers in the North and Northwest. 210

The economies of these regional citiesare tied to the agricultural production

of the surrounding country in diverse waya.1

The urbanisation variables that distinguish city size help to dis-

tinguish the primary cityasidiffision node.Indexes based an the 2,500+

base pick up the smaller cities as wen, including those that are most locally

oriented. When communication linksare strong between cities and their hinter-

lands, with high "social densities" rather than segmentalization -2f communi-

cations, there will also be high correlations between urban and rural education

variables applying to the same general localities. Low correlations in this

respect indicate a societal geography in which the "hinterland° concept of the

West has little application.

=of Effects of Adult Lona Attainments

Geographic patterns in the-influence of adult schoolingon youth are measured in the following ways:

1. Educational characteristics af youth in earlier and in later years are compared: 1930 with 1940, with 1960.Sudh comparisons can tell us hat great the cverall changes have been and how stable over time are geographic rankings in these respects. It is only indirect]; that they measure adult educational influences, however.Since luny who were young in 1930 ma; have migrated to other areas, looking at the locality data fa' youth in 1960 and

1930 can talus only whether those qualities of the population in the ;articu- lar areas are the same or have changed.

1Stavaihagoiargues that the capital accumulated in these cities while flowing from an agricultural beee is redirected toward the large canters of development, but he nevertheless perceives the groups of people offering the goods and services in these regional cities as havingan influential rola in the dynamics of change, op. cit., pp. 16-17. 211

2.Comparing adults and children in 1960does offer direct evidence concerning effects of educationalatta'amont of the parental generationupon schooling among youth. If there isnot such idgeation, then these relationships viU resembl those obtainedin procedure 1. 3.The adult population of 1930nay be compared vith the youth of 1960.If the extent and educationalselectivity of migration is not too great this provides evidence °oncominga three-generation sequenc in the transmission of eduoationma attitudes andattainiont.

ILteracy Rates in Mesioo and Iran In analysing the levels ofliteracy, school enrollment, and school retention of youth in Moodco,sours comparisons with the findings ofthe Fattabipour study of Iran mill be made. This is interesting because, not merelydespite, the fact that the settingsare quite different. According to the 1956census in Iran, on4 15 per cant of thepopulation were literates in cob=areas one- third of the populationova. 10 rare old, in rural areas only 6percont.1In contrast, in Mexico, in 1960,three-fifths of the population over age 6were literate; rural-wban differenceswere also Narked but less so than in Iran; 76 per cent for urban and 48per cent for rural populations. Given these widely divergent starting points, thecomparison is between countries at two distinct stages of developmint. Time are other criticalcontrastsnotably between a traditional Islamic cultureon the one hand an an analgaa of indigenous Indian cultures and a colonial 3pa33ish-Cath03ic heritage. Inthese very different settings, are there nevertheless similar patterns ofinfluence of adults on children, in the role of females, in theAnaction of cities in relation to

2rattalLipour, pp. 82 and 81s. 212

their surroundings?Such comparisons raise fundamental questionsconcerning the spectra of develomentprocesses and their Interpretations. In both countries malesare more literate than females, and in Iran the gap is extreme.(It is impossible to ten to whatextent this is due to the role of women in a Moslem country and howfar this disparity is a more universal feature of an earlier stage in development.)In the Mexico of 1960 literacy was about thesame for young boys aid girls and differed in equal degree among the states.Although urban literacy was above that in ruml areas for youth in Mexico as well as in Iran, the rural lag in Iranwas mach more dramatic (Table A). In both Mexico and Iran the literacy ofyounger age cohorte in rural areas lagged behind that of theolder generation In urban areas This was strikingly true for Iranian males bat less marked for females

because in Iran the oDier urbanwomen were still so low in literacy.Among the states of Yoxico, the median literacy rate ofrural males age 1044 was 70 per cent as compared with a rate of about 80per cent for urban miles 10-119 years old. In Iran, urban females receivedmore education than the rural males up to age $5, but at older ages rural maloswere higher than urban females. Mexico, on the other hand,even females 404j9 years old in cities had hig116: literacy rates than rural males in thesame age category and thsy matched the literacy of rural male children of 10-111years of age. Literacy in Iran increased most rapidly betsJen older andyounger generations among females, and it increased more between generations in urban than. in rural areas.In Mexico, where literacy watt mach more widespread among adults, there had been a similar increase between generations,again especially

among females, bat it was the rural females who showed the greatest gains.The extent to which literacy was approaching universality in Mexican citiesand the 213

TABLE 34

COMPARISCIS OF =Ear RATES Br AGE:MEXICO AND BIANa

Percentages Literate

Mexico, 1960 Iran, 1956

F M 1...1.._P Federal District (urban) Teheran (City)

Age 60+ 85 68 50-59 89 73 55-64 ;a 4049 92 i; 45-514

30-39 92 82 25-29 94 85 .. 25-24 52 32

20-24 94 85 35-19 95 89 10-14 95 91 79 72

All Urban

55-64 27 5 50-59 ill 65 40-149 83 71 45-54 .4, .. 32 .6

30-39 85 75 25-29 86 78 25-314 411 18

33-24 88 81 16 21 35-19 88 85 58 32 10-34 K 814 69 148 234

TABLE 34Contimed

Percentages Literate

Mexico, 1960 Ira% 1956

All Rural

55-64 4'. 6 .1 50-59 47 24; 110-49 55 37 .. 45-54 7 .2

30-39 60 114 25-29 62 49 .. 25-34 10 .7

20-24 64 54 32 1 15-19 64 60 16 2 10-14 70 70 20 3 aSource for Ira% Fattahipour,op. cit.,p484 andI). 90. 215

slowing down of its diffusion both there and among Tiral males is manifest;

this is undoubtedly in some degree a reflection of selective rural-wrben mi-

gration.

Some quite general sequences in the diffusion of literacy through a

population are illustrated in the comparisons between Mexico and Iran in

Table 35. The youngest males in Iran in 1956 were still somewhat behind the

older females of Mex_ino in 1940, and both these distributions were still posi-

tively skewed. Though third-omoment measures of skewness were not computed for

Iran, the data indicate positive skewness for the young males and, most emmi

phatically, for the young females.Skewness in dieseibutions far the older

generation would be more evident if urban areas or Tehran, in particular, were

set apart.

The shifting signs and magnitude of the measures of skewness in the raw

figures for Mexican states as moving from both the oldest persons and children

of 1940 to the children of 1960 dramatize the patteAs of change. In 1940

there were only a few states with literacy rates in the upper ranges while the

modal cluster of states was below the 50 per cent mark, giving a positive

dkewness.However, by 1960, there were more cases in the upper ranges,

especially among youth, and the exceptions were "lawstates with rates around

50 per cent (as shown by the negative skewness).The distribution of literacy

among the older generation had also shifted by 1960, with fewer cases in the

lower percentages, especially among males.

Literacy Correlations:Mexico and Iran

Associations in literacy rates for various age, sex, andresidence

groups among the states of Mexico and among the districts of Iran are summarized in Table 36.The most striking impression from the Mexican data is of 21i

TOLE 35

DISTRIBUTIONS OF LITERACI RATES: MIMIAND IRAN COMFAREDa

Percentages Literate

Age 10-14 Age 110* Age55-614

11042914_1.940___.

Docile l 90 91 77 72 Median 75 76 63 53 Docile 9 5o 5o 44 24 Skew -.499 -.526 -.271 -081 ..

Mexico, 1940,

Docile 1 78 79 65 54 Median 51 54 48 38 Docile 9 27 21 28 16 Skew .166 .042 -.001 .146

Iramil Districts, 3956

Decile l 49 22 15 Median 30 9 lo Docile 9 16 2 414. 5

at Source for Iran, Fattahipour cp. cit"p. 90. 217

TABLE 36

CORRELATIONS AMONG LITERACY RATES IN STATES OF MEXICO AND DISTRICTS OF IRANa

Iran Mexico

Rural-trban comparison!).

Age 40-49 . ... .463 . .864 25-34 .23 .52 1044 .37 .54 .565 ..570

Urban Rural Urban Rural

Males: me comparisons

55464/10-14 .30 40-49/10-14 .923 .931 25-34/10-24 0 00.0 Females: age comyarisons

4040/10-14 ... .911 .859 2 5-34/10-14' .87 :41Z ......

Sex comparisons

Age 404,49 ..., .891 .859 25.14 .i6 .77 .... 10-14 .60 .74 .987 ..;;;

Age-Sex crosses

m 55-64/P 10-14 .36 ...... m 40-49/1 10-14 .913 .906 m 25-39A 10-14 .52 °SO ......

F 40,49/14 10 -14 ... .925 .820 F 25-34/M 10-14 :45 .74 ......

a Source for Iran, Fattahipour, op. cit., p. 131. 218

vary high correlations aororx sex awl age within urban and within rural residence categories. All of the coefficients exoept those involving urbanrural comparisons exceed .800, explaining two-thirds or more of the variance in the designated younger age cohorts. The only Iran correlations at this level were between young adults aid children for rural males and for both urban arA rural females.There are some distinctions among the Mexican figures nevertheless, even when rural-urban comparisons are ignored.In particular, there is a definite jump in the sex correlations frma the older age (40449) to the children.The coefficients for the former are .891 urban and .859 rural while those for the Children come close to unity, at.987 awl

.960 respectively. In Iran, the sex correlations were not so high, and those within the rural category (.77 end .74) exceeded those irithin the urban(.56 and 43) for eaoh age.Even more striking are the sharp contrasts in the degree of correlation across ages in the Iranian as againstthe Mexican data for urban males and in all the age-sex cross-combinatiJne.Some of the Mexican comparisons are displayed in mare detail in scattergrams (Figures 22 to26).

The urban-rural correlations for literacy. (within each sex and age category) serve to indicate how closely adjacent urban and rural communities are linked with each other and therebydifferentiated from other areas.

Excepting the correlations for older females, the degrees ofrural-turban association in literacy rates are generally modest;those for Mexican males and younger females nevertheless approximate the Iranianrelationihips for females. These findings suggest significant but neverthelessweak local urban-rural communications, along with activenetworks linking urban oenters of widely scatterel areas.This should 00R0 as no surprise.The surprising figure is rather the high Mexican correlation betweenurbaniind rural literacy Fig, 22,-.Scattergram of percentages of literate 10-14 year olds, males by females, 1960.

Fig. 23.Scattergram of percentages a rural literate 10-114 year olds, males by females, 1960, N ea co PERCENTAGE OF RURAL FEMALES AGE 10-14 .6 cs cn .4 al LITERATE, 1960 w 5 s5 0 0 0 0 0 0 0 o a0-4 0 gss Fig. 24.--Scattergram of percentages of literate males 40+, 1940 by 1.960. 2214

100

ge >ia 70 0 z0 4 v0 60 ia 4CD cn 50 ur 4-.J 2

as_ 0 40 ia 40 z1- la 12 30 ia a.

20 20 30 40 50 60 70 80 90 PERCENTAGE OF MALES AGE 40 AND OVER LITERATE, 1940

I Fig. 25.--Soattergran ofpercentagesof literate males 40+ years by 10-14 years, 1960. 226

100

111/

50 I

30 30 40 50 60 70 80 90 PERCENTAGE OF MALES AGE an AND OVER LITERATE, 1960 Fig, 26.Scattergramof percentages of literate females 140+ by 10-14 years, 1960. years 228

,Aum

100

.9+

15 20 30 40 50 60 70 80 90 PERCENTAGE OF FEMALES AGE 40 AND OVER LITERATE, 1960 229 rates for women aged 4049. Putting this the opposite way, it points to some of the effects of migrations, which can easily confound urban-rural correlations even when the migrants themselves carry 'tellingly* back home end draw their kin to the cities.The migrant and dissociated male dwellers in the cities of Iran may be a more extreme maple of segmentalism in urban life than what we observe in Mexico, but the evidence is clear enough there as welt

Sohoolins of Adults and Mild Literacy Given the generally high correlations between literacy of adults and of children in Mexico, close correlations between adult sbhoolimg and ohild literacy as ireU might..be anticipated. This is by no means a foregone con- olusion, however.It would be quite possible to find high correlations only where minimal attainment levels are involved, with schooling beyond mere literacy or primary stage being a distinct attribute in whioh only limited and distinctive minorities participate. Which of these patterns prevail is an important development question to which a partial answer is given in Table 37. In those areas having large proportions of unschooled parents, there were larger gaps between urban and rural children in literacy.If men were unschooled, young rural males and females were less likely to be literate; whether women were unschooled had slightly less effect.The opposite tendon- cy prevailed for urban youth literacy; that is, the relaticnship was stronger with unschooled females than with unschooled male adults. There are moderately high relationships between literacy of youth and porportions of adults with at least some post-prisary schooling; these 230

associations are higher for the urban than the rural child-literacy rates

regardless of sex.

TABLE 37

CORRELATIONS BETWEEN LITERACT OF TOM AND LEVELS OF ADULT SCHOOLING, 1960

Literacy of 10-14 /ear Olds

Urban

Femaes Females

Levels of adult schooling Age 30+

No sdhooling

Males -.725 -.706 -.928 -.903 Females -.816 -.825 -.835 -.846

7+ years of school

Males .759 .735 .670 .676 Females .769 .757 .671 .699

10+ years of school

Males .739 .716 .630 .638 Females .732 .722 .635 .653

Matrix C, Factor 1 (Table 38) sums up these aLeA related associations; it has loadings of .800 or more on the literacy of youth (both male and female) from 1930 through 1960, and on middle and high levels of adult schooling. When the factor scores are mapped (Figure 27), a clear spatial pattern emerges, emphasizing the adjacency of developed areas in the North, the pocket of progress centered on Yucatan, and contiguity of the backward areas of the

South. On this factor again the developed Federal District is surrounded TABLE 38 Factor Matrix FACTORS WITH HIGH LOADINGS ON LITERACY AND SCIDOLING OF YOUTH A B D D D C B A VariableNumber Factor NUMber 3. 31. 4 5 9 13 3 9 5 L'ulation distribution and chma 2 Density 1960 .... -.176 -.063 ...... 068 -.035 1210 48 Pop.CapitalUrbanCapital/urban 50,000+ 1940 size 1960 1940 1940 .615...... D. -.029-.037 .....130 -.179-.040-.081 .027 .157...D.....177 -.131-.057 ...... -.057 .....004 -.024 ...... fp -.103-.010 .023.160 -.152-.085 .....073 141513 UrbanIn-migrant 1960-1950/1960-19401960/1930 1940 ...... -.079-.051 .... -.030-.074-.376 -.1484 .218.299 -.038 .017.054 - -.056-.186.002 ...."di. -.001-.059 -.122 .155.167 Transportation 18 RR/pop. .... -.033 ...... -.025 -.000 28272623 RoadsRoads/areaRoadlop. pavedareaB 196019601940 19W 1940 ...... -.155 ...0...do.... -.127-.184 ...... -.092-.102 .055.064 .028 313029 Bioyolesipop.Bioycles/Oop.Bicyclesbcp. 194019601960-1940 ...... -.039 ...... 212 .057.....090 -.086 .....015 -.084 4..070 ...... 170..... -.104 .....4... 353432 Autos/pop.Autos/pop. 1960-19401960/1939 1940 ...... 050 -.065 ...... -.123-.154 .217 -.122 .215.136 -.032-.031 .272 ....me.4.... -.115 ...... :T. Utilities and communication 363837 Elect/Capitarlect/capita 1960-19401940 1960 ....0000ADM 0000....0050 -0000".0085 0000 -.203-.064 0.00 -.143 .041*000 .0680000.049 00.2....0000 '0146-.174 ...... 269 4139 40 Movies/pop. 1960-194019601940 ...... -.055 .151... -.228 ..)..027 -.019 ...dB.135 .048.062...dB -.014 0000.143 0000...... -.096 ...dB.248 -.117 .....107 444243 RadioBanningLibrary 1960 mater use 19401960 .... -.140 .....122 -.400-.293-.053 -.049 .....065 -.008 .....201 .043....(149 0000.... -.014-.060 0016 .315.072000 Marriage and fertility 45 Single F 20-24 1960 rates .167 -.120 -.370 .4...... 0000 ....0000 .0500143 -.026 .061 .010.121 g Labor force participation 47 F under 5 yrs./F 1960 .084 -.087 .081 -.214-.136 -.059 .... .442 .295 .097 .054 -.051 53616059 Devel.&ActEcAotEcAct FindexF 1960-194022+10+ 195019601940 .....311.....135 .....119 -.025-.146 .022 ...fp.054.001 0000.295.522 -.053 0000.194 .....3780000 -.826-.522 014514 ...... 369 Employment of youth 6362 Employ*p1oy 8-n F 1960 8-11 M 1960 -.604-.701 .487.590 .156.121 .022.069 .113.088 .411.363. -.309-.321 -.162 .054 -.204-.194 W 38--Continued VariableNumber FactorFactor NuMber Matrix 11 A A White collar and professional workers64 Collar/Wet H 1940 1 -.028 -.021 -,113 5 -.016 9 .034 3 .021 9 .008 5 696766 Conar/ScActCollarAcActCollar/ECAct M FF1960-1940 1960-19401940 -.075 -.097 -.179 .251.121 .424 .208.065 -,089 .193.157 .123.041 -.162 757470 Prof/WetProf/EoAotClerical/Pocket /4 F1960 1960 T 1960 -.023-.177 48001S5mo015-.058 -.116 00000000 0,0.0170000 09000000.127 00000000 -0029-.050 0309 -.056 .017 AgriculturePublic administration 77 P.A4/EoAot 14 1940 0000 -.007 .091 ... ..v. -.012 .070 7980 Ag/SoAotAg/EoAot 14M 1940 N 3.960-19140 1960 .050.095.077 .042.092 -.267-.013 .084 -.171 .090.019 -.221-.076 .039 -.030 015900142 -.120 0007 8148382 AgEjidos/Aq Labor/AgLaboriAg 14PopX 1940 1940 -.177 .085 -.507 .025 -464 .191 -.173 230 -.080 .075.003 -.013 .030 -.128 .097.007 CT8685 Ag ProP/AgProp/kgProp/Ag 1414M 1960-1914019401960 1960 -447.,084 .085 -.328 .224.336.095 -.018-.156 .099.030 -4144-.008-.070 .017 -.040 .114.039 -oat-.150 .078.007 -.083 .193 Manufacturini 948991 ReturnsAgFarm inc. mechanised Glickover $5001950-1930 1950 1960 .689 -.016 .045.41.. -.315-.219-.414 -.133 .093.197 -.057-.048 .054 .040.233.016 .128 -.059-.011-.038 480.090 105103 98 mfgPay/DapMfg/EcAat inc. overFact m 1960-1940 $5001930 1960 .725 -.133 -.136 .119 .086 -.043 -.075 .092 -,149-.164 i.075 1111097.07 Mining/tOActPay/Emp PactFact 14 1955/19401950 1940 0000/000 -.031 0000.242 "0192-.122 -.048 0000.117 0000.060 0000 6.0139-.179 0000.632.266 Culture traits, 125 Barefoot M 1940 -.005 .... .121000 .076 .043 ...... -.121 133127 Barefoot 14M 19601940-1950 ...... 4"..... I...234 .058.... .116.... -.030 .... .4". -.158 .... 41....4".I.. 128135134 BarefootBarefoot F14 1940 1940-19601950-1960 ....41...... -.004-.010 .... .425.... -.061-.079-.063 -.229 .116.009 -.007 .039.121 ...... -.196 ...... 084-.089 138137136 BarefootBarefoot F F1940-1960 1950-19601940-1950 .... "I..41...... 41".458 -448 .134467 -.204 .331.270 -453-.119 .058 .... -.252 mg,...... 131123 Barefoot 14/Furban 1940 1960 "I..Ge00 -.021 0000 452.131 .....299 ....009 I...067 ...... -.101 011 -.128 .... 114113130 Non-CatholicBarefoot M/F T 196019601940 .... -447 .... -.342 .065-.269 .40..I...... 40...... 41...... -.116-.051 .311 -.181 .... Factor Matrix 40111111111111111MOINIMINIMMININEW TABLE 38-Continued A 111011111110111 A VariableNumber Factor Number 1 11 4 5 9 23 3 1111111111111111111, 9 5 316us 143 RunningSleep on water floor 1960 19140 00060060 "'.090 ft.345-.400 41000000410660 0004101110 MID 0600410111 119117 NonwheatSleep on T T bed19140 1960 1940 0.0. -.062 .017 90 -.092-.007-.520 .12141000 -.076 040MID -.162 01110 0060 Education 122121120 NonwheatNonwheat 19140-1950 19140-19601950-1960 00041 .167 -.061 -.165-.242 .003 -.035-.039-.016 .086.022.135 0600 titeracy 153141140 Literacy 6+010+ SohT 19601940 M 1940 -.400 .888.912 -.307 ...... -.002-.003 -.042-.358 .... -.089-.056 -.071 .243.172 -.326 158157156155 LiteracyLiteracy 10-14104410-114 14F M 1930 19401930 .942.833.884 -.384 I.. -.024..047 .....070 .012.... -.0514 ...... -0024 600.0000 .102.236.273.158.298 -418 .060.064.043 .230 1)15r262162 LiteracyLiteracy 10-14 M 19140-1930 10-1410-31410414 FM 1960 19401960 .891.875.913.680 .135...... -.203-.166-.107 -.020-.Ohl .....0.. -.128-.010 .... .536.0...035 -.203-.326 *209 .055.007 -4D9 165163164 Lit.Literacy 6+ 1960-50/1960-40 10-114 10-14 FM 1940-1930 1960-19401960-19140 -.739-.707 -.307 .667 -.103 ... -.171 .160.3114.095 -.091-.334-.052 .017 -.281 .56o.502 .129.047 -.On 168 40-4.4Urban ma1es.1960 .727 .120 -.279 -.120 Age differences 192170 40-49Rural25-29 females 1960, in literacy; 1960 .630.741 -.229 .157 (ow -.210 .266 -.265 .189 198199 Urban femalesmales140-49 )- ( 60+ )9 ) 000 .114 0000 000 .176.351 -.824 .546 .140.054 0000 00000000 .082 202231200 Rural1117.331:(0"-49)(40-49)-(60+)(15-19)-(1i'-49) males -.040 000.037 0000 -.091 479.377 -.530 .238.134 -405 .256.128 000000 0000000 -.090 .067 20205204 T13:13,741r49)Rural(40-49)-(60+)(40-49)-(60+) females -.437 0000 0000000 .6017.256.088 -.361-.591 .057 -.039-.171-.002 00000000 0000000 -.083 0000 Sem differences 218238221 40.44920-2440-49 MU-F14R4MR-F in literacyt 1960 -.391-.532 -.175 .107 000*.129.157 -.040-.087 -.184 0000.057 -.055-.097 0000 ..039-.058 .022 Adult levels of schooling228258 Adult 30+0 Soh 14 1950-601960 -.435-.944 .259 -.209 000 .498 -.248-.192 00 -.278 0000 242239238236 AdultAdult 25+7+Sch 30+7+Sch30+10+Soh30+7+Sch 14 14 F141950 1960 1960 .856.846.901.894 -414 .049.040 -.124 .052.097 .144.059 .414 .051.056 0000 .026.032.083 TABLE 38-Continued VariableNumber FactorFactor Matrix Number C1 11 A 4B 5D D9 23 D 3C B9 A5 Enrollment 266267265 Enrol 6-106-14 24T 196019371930 .704.626.630 -.476-.180 -.194 .231.057 -.068-.057 .111 .220.175.172 -.131-.417 .024 .099.368.153 -.207-.032 .060 50.090 Enrollment and income274273 275in EnrolEnrolEhrol 6-14 6-14 U-R ruralurban 1960 1960 b...... -.625-.138 .275 -.079 .....083 -.860-.873 .368 .081.015.2142 -.197-.023 .103 ...... 0000 -.087-.042 .00 °00039-.030 .065 282280278 lihrollalrolEnrol 6/$60141,0006/(601-1,000)-(200)6/Inc. $200 pesos month1za.2251 -.028-.297 .165 -.029 .139 -.001 .097.033 0000...... 0.000000 00000000 -.098-.045-.054 -.219-.152-.020 .032.149 Ift...... w...... a.nallmen284283 EnrolEnrol 6/agriculture 6/professionaland oc ation of father, .426.170 -.135 .... -.061 .021 ...E...... 015.012 -.018 .229 .176.... Progress in school296293 BTTGontinuatton 1.13 urban-rural rates--primary 1942 school r171swizsr- -.3114 -.197 0000000 4018 .1974000 °0181.094 -63;8 .333 .:... -.498 .238 .735 307304299308 4/3B 14/34/3 urban urban ruralurban-rural 1960-.1942 1960 1960 ...... 0000 -.327 0000 -.147 .067 ...... 0880000 .iudi....0000 ....0000 -.218 0000.034.... "10049 :ii;...... 309 4/3 rural 1960-1942 -.202-.096 ...... I. 284.067 .111.0147 -.028-.065 ..... 0000 ...049 331330 B 5/1 urbanrural 1942 .222.642 -.152-.106 .480.151 -.529-.347 me, 338333332 B 5/1 ruralurban 1960 -.160-.226 -.140-.579 ..114 le grade progress in school,.347 1963 4S251r"171249#60Age ID Or 3 MU n ec -.634-.125 -.214 .397 -.303-.199 .327 361360359349348 AgeAgeAge 10 10 1010Or GrOr Or1Or 1 FR-FU 1PRFUlie MR-MU -.577-.787-.686-.729-.711 .302.179 .152.294.046 -.033-.097-.246 .007 -.004 .125.067.045 .364.181.199.294 -.366-.180-.363-.385 .230.341 327 -.384-.491 Pass rates 368 Pass 2/Pres urban 1942 .360 .122 ...... -.285 .016 -.181 .073 co School facilities 376377369 Pass 2/Pres ruralurban 19601942 .205.190.371 -.146 .100 -.60341816441-.705 ...... -.035 .025.026 -.093 .020.116 -.028 .....017 397389396390 SohSdh IncompInoompIAcomp ruralurban 19421942-60 -.028-.150-.040 .045 .....032..017 ...... -.087-.164.022 .095 -.040 .124.022.008 .179 .096.255.046 -.174-.738a...033.3014 ...... -.899-.559 .... Fig. 27.-44atrix C, Factor 1.

Variable actor Loadings Number ( -.800 and Above)

140 Literacy 10+ T 1940 .912 141 Literacy 1.0+ T 1960 .888 155 Literacy 10-14 14 1930 .884 156 Literacy 10-14 F 1930 .833 157 Literacy 10-14 14 1940 .942 1 158 Litwacy 10-14 F 1940 .913 159 Literacy 10-14 14 1960 .891 160 Literacy 10-14 F 1960 .875 236 Adult 25+ 7+ Sch 14 1950 .901 238 Adult 30+ 7+ Sch 14 1960 .894 239 Adult 30+ 7+ Sch F 1960 .846 242 Adult 30+ 10+ Sch 14 1960 .856

A short-cut estimate of the rankof the Federal District for these variables is 1.5 (frama high of 1 to 32). _ by. less developed states, implying impeded flow of influence out fromthe capital to its nearest neighbors and the diluting of the Federal District values by the in-migrants from surrounding backwardareas.'"

The apread of literacy was an important first step in the long pull toward an integrated economy and a unified society. Mexico had made prodigious efforts to diffuse the Skills of reading rad writing Spanish to the people of the cities and countryside, both witUn and outside the formal school system.Mhere did these changes manifest themselves and under what conditions?

Direct measures of change in literacy are of two main kinds:comparisons among different age groups at a givendate, and comparison of data obtained at different dates.Although both are used, even if the complications introduced bymigrations are ignored, the second approach presents twv special problems: definitions could have been altered in successive censuses, and in earlier years recorders may have been generous in interpretationof literacy. (as they were in 1930). For example, one might suepect the evidence

(referring back to Table 35) that the literacy in 1960 of males above age40

(63 per cent) exceeds that for 10-14 year old boys of 1940 (51 percent).

However, this seeming discrepancy is in the opposite direction from what might be expected if literacy definitions had been tightened over time.

Taking the figures at face value, if older men have higher literacythan younger men of the same cohort, they can haveacquired literacy after age 14.

In substantial measure that did occur in earlier years, as is borne out by

ITheFederal District was not included in the factor analysis. Its mean rank on the heavily weightedvariables of Factor 1 of Matrix C was 1.53 (out of a possible 1 to 32). 242 proportions of the population who acqutred literacy without schooling.There is no way to be at all definite about the size of the error. However, inter-

(Ansel discrepancies are more serious in their effects uponassessments of changes in absolute levels through time than in effects an measuresof relative positions among states and of change or stability inthat respect. nirther use of inter-censal changes in literacy of adults willtherefore be confined to correlation and factor analyses.

For a preliminary description of the extent of change and of variabilities in the 1960-data are relied upon for different age groups, taking age- differences in literacy as indexes of change.But this also raises problems.

Especially where migration has been substantial, successive past cohorts of children in certain states mayhems undergone an educational development sequence larger or smaller than age comparisons amongpresently (1960) resident populations would suggest.

Bearing these qualifications in mind, Table 39 supplies an interesting overview.For each of the sex:and residence categories there are statesin which there was little or no advance in literacy(under 5 per cent) between the oldest and middle or the middle and teen-age categories. There were two exceptions:middle compared to teen-age urban and rural females.Median ivogress was between 14 and 17percentage points for all except rural girls, who were rapidly catching up (23 per cent), and the urtan and ruralboys (6 and 9 per cent). The largest differences (in states with tho most age-changein literacy) ran close to 40 per cant for olderrural males and younger rural females.

Changes in literacy rates were systematically associated with both initial level of literacy and initial proportions of adultswho possessed more than primary schooling. Table 40 gives the correlations between proportions mith post-primary schooling and age differences in1960 literacy rates for 243

TABLE 39

DISTRIBUTIONS OF INTERGENERATION GAINS INLIIIRACT Br AGE, SEX, AND RESIDENCE

Lowest 25th 75th Highest F.D. Median Value Percentile Percentile Value

Literacy

Urban males

15-19 minus 40-49 3 -1 4 6 9 17

40449 minus 60+ 7 0 11 14 17 29

Rural males

15-19 minus 40449 10 3 7 9 13 16

40-49 minus 60+ 25 4 15 17 25 39

Urban females

15-19 minus 14 0449 10 7 10 15 21 31

40-49 minus 60+ 11 o 11 15 18 25 Rural females 1549 minus 40-49 30 9 18 23 27 38

40-49 minus 60+ 23 -4 8 17 21 32 244

TABLE 40

CORRELATIONS BETWEEN ADULT LEVELS OF SCHOOLIN3 AND AGE DIFFERENCES IN LITERACY, 1960

Percentages of the Older Adults Age 30+ with Age Category Literate in 7+ Years of Schooling 1960

MOW

Males Females Males Females

Age differences in literacy

Urban males

(15-19)-(40-49) -.293 -.281 -.661 (40-49)-(60+) -.258 -.197 -.158 -.129

Rural males

(15-19)-(40-49) .313 .201 .199 .213 (40449)-(60+) .373 .384 .433 .447

Urban females

(15-19)-(40-49) -.645 -.706 -.722 (40-49)-(60+) .308 .344 .6e1 .543

Rural females

(15-19)-(40-49) -.296 -.363 -.091 -.464 (40449)-(60+) .548 .573 .622 .699

4Forcorrelations with literacy-change from age (40-49) to (15-19) this refers to literacy rates of those aged 40449.For comparisons with literacy- change from age 60+ to age 40-49 the literacy rates of the "older" age category refer to those aged 50-59. 216 sex-residence sub-populations. As one might by nov expect, all intergeneration differences in literacy of urban males, and those for age 15-19 minus age 4049 for both rural and urban females, are negatively correlated with proportions of adults possessing post-primary schooling.Small intergeneration changes in literacy in these sub-porwlations generally reflected relatively high literaqy starting points in the older age groups. On the other hand, correlations of literaqy change with adult post-primary schoolism w_fre positive for all rural male inter-generation comparisons and for older urban and rural females. The fact that younger as well as older rural males shared in an early-stage literacy- change pattern is once again evident. Me ,equential processes of development in diffusion of literacy are clearlydisplayed.'

The evidence concerning these geographic develoment patterns is bolstered by a reexamination of Factor 1 of Matrix C (Table 38) with its loadings of over .800 an virtuallyanmeasures of se' oling and literacy regardless of sex, age, ar date. That factor also had high positive loadings

(.600+) an 1930 to 1940 change in literaqy of both boys and girls aged 10-14.

It had high negative loadings on these changes far 1940 to 1960, high positive loadings io the directions of urbanization (at 50,000+ population) and comparatively high incames in agriculture and in manufacturing.The reversals between earlier and later periods in relationships between advance in literacyand urbanization are dramatic (see the top half of Table41).

Similar differentiations between lead and lag areas are shownin the coefficients in the lower half of Table 41 as well. (1) There are high

lInelfmection with cohort analyses, it is of some interest to notice that correlations between reported 1937 enrollment rates of6-14 year olds and all measures of educational attainment among adults of1950 were consistently a little higher than thonc between 1960 enrollmentsof 10-14 year olds and the various 1960 measures of adult schooling. 2146

TABLE 41

=RELATIONS OF URBANIZATICK, WITH TME DIFFERENCES IN LITERACY OF YOUTH, AND WITH SCHOOL ENROLLMINT RATIM, 1'30 TO 1960

Urban Urban In-migrant/ Pop. urban 19604950/ % Gains in Literacy 2,500+ 2,500+ Resident 50,000+ 1960-1940 1940 1960/1930 1940 1960 yteracz 10-114 year olds 1940 minus 1930

Males .167 .387 .549 .496 .039 Females .358 .297 .508 .406 .075 1960 minus 1940

Males -.1436 -.514 -.1449 -,597 -.077 Females -.554 -.482 -.574 -.595 -.122 airollment 6-14 year olio urban-rural, 1960 -.230 -.402 -.287 -.118 .487 6-10 year olds mass, 1930 .631 .407 .554 .315 202 6-114 year olds M + F, 1937 .339 .593 .710 .301 .207 6-14 year olds M + F, 1960 .4014 .385 .489 .087 .142 247 1 urban-rural contrasts associated with backwardness.(2) Early high enrollment rates are quite strongly $orrelated with rapid and extensive urban development. (3) /core recent enrollments showa positive (but weaker) association with urbanization.The backgmund in enrollment rates and their distributions are sumworized in Table 42.

TABLE 42

DISSIBUTIONS OF ENROLLMINT RATES 1930 TO 1960

F.D.Lowest 25th 75th ilighost valuePercentileMedianPercentils Values

Enrollment of youth

1960 6-14 years old

Males 75 37 59 67 77 Females 74 32 44 56 64 76 1930 6-10 years old

Males 814 25 38 45 59 73 Females 80 24 314 44 59 72 1937 6-14 years old

Males + females 96 22 52 85 99

19606-114 yearsold

Males + females 85 39 52 69 75 Males +femaleP 75 35 16 58 66 76

aWhen the Federal Districthas thehighest value,the value of the nest ranking state is listed.

bEstimated valuesare usedto adjust for obvious error.Enrollments males plus females in 1960 as theystand exceed rates for males and femalos separately. 248

Generally, in both earlier and recantyears, literacy of adults aged

40+ manifested closer correlations with school enrollments than did indioators

of middle and high levels of adult schooling (Tables 43 and 44). This is true

despite the decline in predictive value of literacy in the older generation.

The progressive weakeniag in correlation of adult literacy with primary en-

rollments of dhildran is displayel in Table 44. In Iran associations between

literacy and school enrollment are substantially higher than in Mexico, even

the Mexico of 1937, when correlations were in tarn above 1960.

School Retention: Patterns

Three indices are available to trace the movements of children through

the primary schools: continuation rates, pass rates on examinations, and pro-

portions of pupils over-age. The presentation begins with the most difficult to

measure, continuation rates, which are subject to large error.In the first

place, they suffer from all the defects of reported enrollment rates; the number

enrolled at the beginning or end of the year may have only a loose relationship

to the number attending regularly. Subsequent compariams are more sensitive

to even minor errors in this respect when the data are used to estimate progress

through school. Also, while normal/7 it takes a year to complete a grade, those

enrolled during a year include repeaters fram last year, those transferring from

other schools, those mho may have dropped out far a whiLla and returned. Nor can

continuation rates alone tell how many who passed the year-and examination were

eligible to continue but did not.

Continuation rates were computed for each grade year up to sixth by

taking entrants to a given grade as a ratio to eutraats to the preceding grade

in the prior year, for rural and urban areas for 1942 and 1960.For example, the number entering fourth grade in 1960 were divided by the number entering 249

1113LE 43

CORRELATIONS BMW= ENROLMENT OF YOUTH AND ADULT LEVELS OF SCHOOLINI

Enrollment of 6-14 Adult Levels of Schooling Year Olds

1960 1960

Wales, 30+ years of age At least 1. year of schooling .638 7+ years of school .570 10+ years of school .565 Females, 303+ years of age At least 1. year of schooling .576 7+ years of school .491 10+ years of school .477

1950 1937

Males 25+ years of age At least 1 year of schooling .715 7+ years of school .659 10+ years of school .619 Females, 25+ years of age At least 1 year of schooling .633 7+ years of school .589 10+ years of school .515 250

TABLE 44

CORRELATIONS SEMEN ENROLLMENT OF YOUTH AND ADULT LITERACY IN MEXICO AND IRANa

1 Enrollment of6414 Year Olda

1960

Meidco

Literacy, 1960

10+ years of age males + females .64 40+ years of age, males .67 40+ years of age, females .56

1937

Literacy, 1940

10+ years of age males + females .75 40+ years of age, males .73 40+ years of age, females .70

Iran

Enrollment 10-14 Year Olds

Literacy, 1956 10+ years of age Males .95 .86 ?males .86 .92 55-64 years of age, males .77 .76 25-34 years of age Males .91 .83 Females .84 .92 a Source of Iran, Fattahipour, op. cit., Table 30, p. 143. 251 third grade in 1959. Computations were mad also for enrollments at the end of the year relative to enrollments at its beginning, butmay of the data using end-of-year figures were inadequate. (Correlations between the alterna. tive sets of continuation rateswere highnevertheless.)1In the discussion emphasis will be primarily an the ratio of fourth/third grade enrollments,

Which is particularly interesting in itsown right and is also a good progy for most of the other rates (whichwere omitted in most of the appendix tables as well). The completion of at least threeyears of school might be considered a requisite for sustained literacy. Tabulations were made for primary schools in rural areas offering at least three years of schooling. Transition from thirl to fourth grade was obviously a point at which dropoutswere large.

("Incomplete schools" was definedas schools with less than four grades.)

The fourth/third continuation rates in rural areas in 1960 varied from

24 to 81 per cent with a median of 47; for urbanamps the range VAS 76 tO 98 per cant with a median of 89 per cent. In 1943, the range for rural sectors of the states had been from 14 to 72 per cent and for urban areas 53 to 94Per cent, with respective medians of 36 and 71 per cent. These, along vith ratios and distributions at other grade levels, are summarised in Table 45.

Factor 9 of Matrix B (Table 38) gave more veight than any other to continuation rates. The highest loading an that factor is -.826, for dhanges between 1940 and 1960 in proportions of females occupationally active; along with this are negative loadings on economically active females in 1960 (-.512) on fourth/third continuation rates in rural areao in 1942 (-.498), and an fifth/first continuation rates in urban sectors for 1942 and 1960.

a The correlations ran at or above .890 with two exceptions: sixth,/ fifth grade rural schools in 1942 (.648) and fifth/fourth grade urban schools 1960 (.874). One of the appendix tables gives a full set of intercorrelations. TABLE 45

DISTRIBUTIONS OF RURAL AND URBAN CONTINUATION RATES, 1960 AND 1942

2/1 3/2 4/3 5/4 6/5 5/1

12.62.Essa

High 61 95 81 78 95 26.7 75th 56 81 57 59 81 8.5 Modian 50 63 47 50 74 5.5 25th 48 51 35 39 66 1.9 Law 40 35 24 16 36 .8

1960 urban

High 88 102 98 95 um. 54.6 75t2t 79 99 93 85 92 47.4 Median 72 95 89 89 90 41.9 25th 64 89 84 93 85 32.9 Law 46 77 76 64 75 17.9

1942 rural

High 49 84 72 37 107 6.59 75th 44 62 49 24 67 2.34 Median 38 49 36 17 42 .86 25th 34 41 28 12 27 .34 Law 23 22 14 0 0 0

1942 urban

High 83 94 94 89 106 49.5 75th 65 86 79 77 94 26.4 Median 56 76 71 70 80 18.5 25th 48 69 65 66 71 140 Low 36 56 53 39 47 9.6 253

The correlation between 1940 and 1960proportions of incomplete schools

(only three grades)in rural areaswas fairly high (.615), but urban systems

shifted their relative positions in this respectvery stbstantially (an inter-

temporal correlation ofonly 470, Table 46). Thehighest of the inter-temporal

correlations relating toschooling of youthwere for total enrollments and for

urban pass rates at Grade2. (Paso ratesare the number passing the year-and

examination compared tothose present at the timeof the examination.)

TABLE 46

CORRELATIONS BETWEEN 1940MD 1960 EDUCATION- OF-1OUTR VARIABLES

Enrollment 6-14 year (M+F) . J19 Continuation 4/3 urban .310 Continuation 4/3 rural .496 Schools incomplete urban .170 Schools incomplete rural .618 Pass 2/Present urban .726 Pass 2/present rural .. JO

Factor 4 of Matrix B (Table38) had its heaviestloadings (all negative) for second grade pass rates (both rural and urbanin both 1942 and 1960). It had moderately high positive loadings fartraditional culture traits,negatift loadings an farm mechanization and hired farm labor andfor facilities suchan water supply, cleully describing a backwardsetting.Pass rates had higher correlations with other traits in 1942 than 1960 (Tables47 and 46). In 1942, rural pass rates showed stranger associationswith enrollment and withurban continuation rates thanin 1960.

Age-grade relationships proved to be the mostinteresting of the measures in degree of association with non-education traitsindicating development (or retardation). The associations withother education variablesalso 254

TABLE 47

INTERCORRELATIONS BENEEN SCHOOLING OF CHILDREN AND MIDDLE LEVELS OF SCHOOLIO3 OF ADULTS 1930 TO 1950

Ehrol Adults 30+ Literacy 10-14 6-14 7+ Ire of SChool 1940 1 37 342g2

Variable Number 265 157 158 236 237

265 Enrol 6-14 T 1937 157 Lit. M 10-14 1940 .775 158 Lit. F 10-14 1940 .787 .979

Adult levels of schooling, 1950

Ag.e125± 236 7.77-iiifis m .659 .890 .894 OtO 237 7+ Years F .589 .820 .860 .938

Continuation rates--primarz school, 1942

293 B 5/3 R .413 .477 .437 .402 .353 331 B 5/1 R .293 .359 .339 .299 .346 330 B 5/1 U .474 .657 .639 .636 .633 390 Sch incomp. R ..237 -.302 -.291 -.249 -.291 389 Sdh incomp. U -.113 -.201 -.203 -.070 -.075

369 Pass 2/pres R 1942 .587 .495 .588 .499 .599 368 Pass 2/Pres II 1942 .347 .389 .468 .497 .600

1930_ 267 BEFF1 6-10 /4 .596 .762 .700 .654 .542 155 Literacy 10-14 M .703 .938 .897 .800 .733 156 Literacy 10-14 F .702 .929 .923 .865 .823 TABLE b,7-4ontimted

c Cont.Ratea 19142 Schools Pau Rates kcal 4/3 5/1 Incomplete 1942 6-10 Lit 10-14

R R U R U R u 14 m

293 331 330 390 389 369368 267 155

.6140 .633 .640 -.783 -.695 -.500 -.523 -.14142 -.1412 .632

.219 .1450 .474 -.328 -.272 .169 .318 .479 -.324 -.275 .836

.1428 .262 .345 -.276 -.032 .1149 4714 .517 .339 .548 -.332 -.128 .3147 .257 .912 .489 .1444 .569 -.403 -.220. .545 .14314 .7144 .8g 2 56

TABLE 48

INTERCORRELATIONS BETWEEN SCHOOLING OF CHILDRENAND MIDDLE LEVELS OF SCHOOLINGS OF ADULTS,1960

Enrol Lit. 13-14 Adult Levels of Schooling 6-114 e 30+

No School 7+Tears X

Variable Number 266 159 160 228 229 238 239

266 Enrol 644 T 159 Lit. M 10-14 1960 .725 160 Lit. F 10-14 1960 .719

Adult levels of schooling

228 Nro school M -.638 -.944 -.937 .... 229 Nb school F -.576 -.931 -.959 .928 .... 238 7+ years M .570 .796 .791 -.853 -.828 .... 239 7+ years 1 .491 .796 .813 -.840 -.859 .953 004* 242 10+ years X .565 .767 .761 -.819 -.803 .991 .935 243 10+ years F .477 .771 .777 -.806 -.822 .934 .985

Continuation

304 Cont. B 5/3 R 1959 - 1960 .250 .302 .237 -.239 -.134 .072 .064 .540 333 Coat B 5/1 R 1959 .363 .623 .603 -.646 -.557 .493 .470 332 Cont B 5/1 U 1959 .463 .522 .506 -.508 -.397 .506 -.571 395 Sch incomp. T -.257 -.634 -.624 .610 .646 -.515

.418 377 Pass 2/pres R .006 .299 .372 -.343 -.437 .268 .369 .443 376 Pass 2/pres U .099 .361 .376 -.338 -.401

Age-grade progress

.698 -.634 -.654 348 Age 10, Gr 1le -.674 -.761 -.798 .796 .734 -.694 -.733 347 Age 10, Gr 1 KU -.586 -.756 -.779 .721 .832 -.699 -.718 360 Age 10, Gr 1 FR -.717 -.903 -.911 .868 -.700 -.694 359 Age 10, Or 1 FU -.689 -.773 -.802 .740 .743 257

TARO 148-Carbinued

ContinuationRates Aga 10, Grade 1 ...... E,S). 511 10+ Tears Ma1e Females R R U SchoolPass 2/ Inc. Pres . M F UR U

243 304 333 332 395 377 376 348 347 360 359

... .932

.070 .071 .... .482 .533 .655 .... .477 .4414 .490 .593 -.519-.561 -,623-.777-.316 .281 .423-.221 .315 .021 -.290 diOlt A814 -.025 .328 .293-.279 .710

-.605-.617-.329-.6148-.593 .1488 -.319-.215 m. -.676...699-.294 -.537-.701 .1435 -.378-.543.7a.... -.669 -.684-.364-.618 -.666 .564 -.285 -.285 .867 .794 -.667-.627-.194-.1424-.635 .370 -.230 -.368 .696 .906 ...827 .... 258 were neatly patterned.A, high proportion of students who are aver-age far

their grade measures retardation. Some of the associations are shown, by way of illustration, in the factor clusters (titriZ Cs Factor 1 in Table 38

and Matrix B, Factor 1, in Tible 3). In both of these factors there mere high

positive loadings on enrollments and literacy of youth with negative loadings

an slow passage through school (age 20 in Grade 1). The first matrix shoved high loadings on middle levels of schooling of adults and the lattar on pro-

portions of economicalkv active 16RJ-collar, clerical, and professional occu-

pations. Table 49 summarizes some of the relation,mps between age grade

progress and lead or lag variables.One measure of the isolation of areas

characterized by retardatim is the high negative correlatiomswith. radios,

and, on the level of interpersonal communications, the lmr literacy of adult

females.Slow progress through school was most pronounced for urban females where incomes from manufacturing were low.

In combination with continuation and pass rates, these variables for

age-grade progress mayreflect late entry to sdhool and high absenteeism, with associated failure in examination, repeating and dropout.But the patterns are not always so Obvious. An area might have low continuation rates, saw

overage pupils, and low pass rates: obviously a backward area.But one may find a high continuation rate alongwith high proportions over age and low pass rates--whici might be interpreted as evidence of a lagging area in which nevertheless there is a rising determination to keep children in school (e.g.,

Oaxaca, Guerrero, Mexico). Or there may be a low continuation rate with mew

overage pupils but a high pass rate, meaning that there are many dropouts even

though relatively large proportions of those who take the examination pass and a large proportion of eadhgrads are repeaters ar late starters (Michoacan,

Guanajuato).A, high continuation rate, "normal" age-grade patterns, and a 259

TABLE 49

ASSOCIATIONS BETWEEN AGE.GRADE PATTERNS AND SELECTED INDICAMIRS OF LEAD AND LkG IN MODERNIZATION

Age Grade Patterns, 2963

Retardation Progress Grade 1, Age 10+ Age10,Grade 3+

Rural Urban Rural

14 IM F M i

Lit. 40-49 FU .677 -.1o4 0000 0000 0000

Lit. 40-49 FR -.651 -.774 0000 4000 0000 0000

Clerical/EcAct -.658 -.741 -.611 -.704 000 00 0000 0000

Ag/EcAct 14 .605 .606 .652 .692 -.510 -.575 -.571 -.1149

Farm mech. 1950 -.698 -.725 -.529 -.663 0000 0000 0000 410000

Ag inc. over $500 -.655 -.684 -.577 -.630 0000 0000 0000 0000

Mfg inc. over $500 -.659 -.748 -.650 -.654 0000 0000 0000 0000

Employ 8-11 .731 .747 .780 .774 0000 0000 0000 0000

Barefoot)! .509 .530 455 .546 -.324 -.36o -.327 -.256

Radio -.704 -.671 -.713 -.709 0000 0000 260 high pass rate describe a system in which studentsprogress ateadily taking ono year per grade. An interesting mixture of traitswas picked up by Factor 3 of Matrix C, mapped in Figure 28.This factor might be described as rural progress in schooling.Here there was a negative loading of -.738an the number of incomplete rural schools in 19142 andan urban-cural differences in continuation rates in 196e, together with positive loadingson rural continuation rates in 1960 and age-gradeprogress in rural schools.The geographic pattern is very unlikeany previous map.It is particularly interesting bes cause some elusive traits of the rural environments of the high scoring states seem to be involved, traits that are not revealed by all-state variablesor by the main agricultural variables. Coming back to the broader 130a04C011oilliC context,a pattern that summed up educational and economic traits associated with lead positions in development very neatly was Factor 1 of Admix C, mapped above in Figure 27. This flotor related high literacy attainments of youth with high levels ofschooling of adults. A comparison of the more generalized over-all spatial pattern of Figure 27 with areas of rural progress in schooling of youth (delineatedin Figure 28) poses fUrther questions concerning the constraintsupon diffusion of schooling and the attributes of the population thatmay distinguish areas where acceptance of schooling is manifest.Can we identify with an'ty more 're- cision the characteristics that raise or lower propensities to attend school or to continue through the primary years?Chapter VI attempts to explore this question by the use of multiple regression analysis takingenrollment rates as the dependent variables. Fig. 28.--MAtrix Cs Factor 3.

Variable Factor Loadings Number (t,800 and Above)

1 296 Cont B 4/3 Urbanarural 1942 -.1451 331 Cont B 5/1 Rural 1942 .480 390 Schools Incomplete rural 1942 -.738

A short-cut estimate of the rank Di tha Federal Districtfor these variables is 8 (from a high of 1 to32). EL ...... U N ":711Fil:"^it::in . . . ' . .0 . Fa c tor 0.69 to Scores 2.15 . . . - : -0.24 to0.25 to 0.240.68 -2.09-0.67 to - 0.250.68 t BRITISH! HON. DURAS GUATEMALA * CHAPT1R VI

DEMMINANTS OF THE DIFFUSION OF PRIMARY SCHOOLING

In the preceding discussion of the relationships of adults levels of education to the schooling of youth, a simplified framewwk was introduced to clarify the diffusion process. This model will be expanded to explain the geographic distribution of primary enrollment (and retention) rates as a function of (a) the spatial structures of comunication networks, and (b) indicators of differences in economic alternatives and attitudes that influence decisions aboutschooling.

The theoretical framework integrates certain diffusion theories of human geographers with economic decision theory. Scholarsworking withcentral-place theory have developed a technique for measuring the influence of one population aggregate upon another as a function of size and distance* It has been assumed that the larger the population of place B, the mr.sre influence it will have on a population in place A, but the greater the distance from B to A the less is Ws effect upon A. To obtain the "population pritential" of A we would add to the population of A the population of all other places, B1, B2,B3, each divided by its distance from A. Fattahipour tested this concept of population potential in his study of the diffusion of education inIranbut found it inadequate; both the central and intermediate cities remained self-contained with little spillover of educational stimulation to thehinterlands.1

1Fattahipour,2p. cit." pp. 289-91, Appendix B.

263 264

The HIgerstrand model, with its "information fields" and"resistances"

to diffusion, is quite different. For Sweden, he found a remarkable stability

in geographic patterns for diffusion ofmagy innovations (including schooling)

over a century and a half. He identified established canters from which inno-

vations spread, and these centers appeared to forma status order; ideas from

the centers were mare Likely to be accepted.What he calla private information fields are person-to-person communication networks, and he demonstratedthat the most effective flaw of information followed such "tellings."As indexes of these interpersonal linkages he used telephoneusage and migration routas. Mass media appeared to promote new ideas adly when supported by person-to-person communication.

But people do not always accept new ideasupon first hearing of them.

Reactions depend upon the economic and culturalsetting and upon how readi37 a particular innovation fits into that setting--hence the conceptof "resistance," as the degree of ease with which particular new ideasare accepted for any given intensity of "tellings."Some new ideas may be adopted almost immediately,some only after repeated tellings,some not even then.

Using Higerstrandls general modelas a guide, this chapter treats enrollment of 6-14 year olds as the innovation beingdiffused within the states of

Spatial variation in enrollment will be treatedfirst as a function of information fields, approximated by patterns of communicationfrom urban to rural areas and across regions of the country. These variations are then examined from the resistance side, in a crude decision model thatanalyzes how the economic and cultural setting affects behavior withagy given information flow.

In many instances, direct measures of information fields and decision factors cannot be obtained and indirect indexes must be used. 265

The spatial distribution of educational attainments of adultsas dis-

cussed in the preceding chapter could be interpretedprimarily as an independent

indicator of "intensity of tailings" with respectto events and ideas beyond the

local scene and on the frontiers of change. This is in contrast to enrollment rates of children, the innovationor dependent behavior variable. It is assumed that educated adults have more contacts withnew ideas and that a locality with a large proportion of educated adults would havemare interchange of information relevant to decisions about schooling.

But the level of adult educationcan be considered from the resistance side, also, making people moreeager (or more opposed) to keeping their children in school for both economic and non-economicreasons. The more educated the adults, th3 greater the presumption that aspirations foreducation of children will be high, whether as foundation fora etreer or simply viewing education as a value in itself.

The Geoa hic Patterns of Primar chool ollment

Though access to schools in Mexico hasbroadened over the past generation or more, wide variation in their use and in their effectivenesspersists; same states remain traditional while othershave most of the earmarks of modernity.

It is in the north generally andin particular parts of the central plateau

(mainly in the capital district)that progress has been most marked. The Federal

District is one of the most modernized urbanareas in the world. By most measures, the three states facing southon the Pacific are the most retarded: Guerrero,

Oaxaoa, and Chiapas. The northern states are better off than moston the central plateau, the latter displaying extremely diverse levels ofdevelopment wAhin a densely populated region. Bat there are marked variations within regions and even within states. 266

Three maps (Figures 29 through 31) show state rankings on enrollments

of 6-14 year old children over-all and in urban and rural areas.These reflect the complex diversities of development among the parts of Mexico. The over-all enrollment rate (Figure 29) provides a good general image of regional development, both oultural and economic.The marked disparity between north and south,

the complex variations in the central area, and the lack of clear associations between urban and rural rankings suggest that the gradients implied in con- ventional population-potential models would not provide an adequate explanation for the observed pattern.

Many factors that could be presumed to underlie these geographic patterns have been examined in the preceding dhapters. A few that are put together in

Table 50 bring out a looseness of rural-urban relationships that is suggested in most of the findings. The literacy of urban males aged 40-49 has a modest posi-

tive relationship with the enrollment of urban 6-14 year olds (.385) and a nega-

tive relationShip to the employment of children aged 8-11 (v.315).With rural males the pattern is similar, but the correlationa are somewhat higher (.490 with

literacy of rural males in their forties and -.;71 for employment of young boys).

There is a stronger negative relationship between literacy of older men and em-

ployment of children in the rural than in the urban areas (-.777 and -.512), but

the urban-rural cross-correlations on enrollment rates are negligible. Maw of

the coefficients in the first column of Table 50 tend to be higher than corre-

sponding coefficients in the second and third columns because the variables in-

volved are in part predicting degrees of urbanization or rurality (and hence the weights of rural and urban enrollment rates in the total) but do not differentLate

efficiently among urban or among rural populations taken separately. However, the

one variable that is beat associated with all others in this table is the rate

of employment of 8-11 year olds. Fig. 29.--Enro1lment rates 3f 6-14 year olds, males plus females, 1960...... ,..., u '"'" N ". 6 6 twi erlIfIlefftest 1 4 I. pft us 6 ft? i.. , . .1.....erPercentage Enrolled )s I 1...... ma Over 74.8 t .. e ,..%, . 71.1 to 74.8 1 . 1 i 62.9 to 71.0 % . . 6 . ' .. . 4 ,t4 - a51.9 to 62.8 % 14 t .. 38.7 to 51.8 7: . 1 .. " . . ...' . ' 6.i ...%: i. '1BRITISH 1 ii1 HONDURASL. GUATEMALA ;!. .... ,..; Fig, 30.--Urban enrollment rates of 6-14 yearolds, 1960. U N =Era /Teo "NM Mb I 3744 7' Ic A'''' ' oe""A t Percentage E nrolled et !iNn!i! ...... 61.5Over to 64.0 64.0 ...... , , ,P. '''''...... , ...... ''' iirj` 'vift . I Ii 475.9 4to to 578 61.4 TT" 42.0 to 47.3

IMP Fig. 31.--Rural enrollment rates of 6.14 year olds,1960. UN IT E D A Percentage Enrolled Over 53.7 Yeo , 45.7 to 53.7 . , ...... 39.0 to 45.6 , . . ... 33.3 to 38.9 .. ' 22.7 to 33.2 . , ...... GUATEMALA.., I:HONDURAS t 273

TABLE%

ILLUSTRATIVE CORRELATIONS OF HIROLUA1TRATE3 AND OF CHILD EMPID/RENT WITH SELECTED VARIABLES, 1960

Fiirollment Rates, Age 6-14 EMployment of Males Age 8-11 ITotal Urban Rural

Enrollment rates; rural .543 -.085 .... -.571 Elployment of males age 8-11 -.709 -.315 -.571 ....

Percentages literate: Urban males, 40-49 .303 .385 -.055 -.512 Rural males, 404i9 .585 .105 490 -.777 All males 10-14 .725 -.790 All females 10-14 .719 -.83.1 Proportion of males employed in Nhite collar jobs, M .568 .125 .269 -.648 Clerical jobs, T .607 .241 .252 -.680 Agriculture -.569 -.129 -.338 .679

Proportion ,If females 12+ economica14 active .213 .228 .175 -.169 Proportion of males barefoot -.156 .004 .112 .480 Incomes (per cent over 500 pesos) Manufacturing .579 .404 .278 -.727 Agriculture 493 .316 .176 -.644 Urbanization, 1960 Proportions 2,500+ ...... -.541 Proportions 50,000+ .087 -.132 .009 -.511 Urban 1960/1930 .385 -.155 .423 -.381

Proportions of in-migrants .... -.016 417 Radio .526 .157 .257 -.684 Movies .475 .100 .215 -.510 274

In this zero-order correlation matrix, traditionalism (or poverty)as

indicated going barefoot, has little connection with either urbanor rural en-

rollments (.004 and .112 respectively).Yet there are negative correlations

between going barefoot and adult literacy, which is correlated with enrollments.

Proportions of males barefoot is related inversely to the literacy of older urban

males (-.656) and of older rural males (-.492).This prepares one for the re-

ordering of some of these relationships that willemerge when soma multiple re-

gressions and partial correlations are explored later in this chapter.Indigenous

components of local cultures are not the dragan diffusion of primary schooling

that might have been expected a priori.

Variations in Enrollments as an Aspect of Rural-Urban

Two diagrams (Figures 32 and 33)were drawn to represent the "information" and the "resistance" sets of factors respectively. (The actual variables available for analysisare in the rounded boxes amd the concepts or hypothetical variables in the square boxes.)The objective is to explain enrollment of children in school, and the network of relationships displayed reflects the causal hypotheses derived from combining the HAgerstrand and human-invvetment models. Although overall enrollment rates by states are given first, it is the distinctive characteristics of the ruralas against the urban influences and be. havior that are especialiy interesting.

Beginning with Figure 32, which isan elaboration of the diagram presented in Chapter V, information can flaw within either urbanor rural areas and between them. (The intra-rural tellingswere not put in the diagram.)The straight arrows indicate positive influencr,on diffusion of knowledge about the effects of education and tailings that presumablyconvey attitudes favorable toward eincation. The arrows that turn back on themselves indicate presumptively 1

: I 1

;

Fig, 32.--Informati3n and communication channels.

i i

1 276

(Net Migration

(Educationof Adults lntraUrban --- Tellings i .... KNOWLEDGE OF EFFECTS OF EDUCATION AND/OR ATTITUDES TOWARD Qk Rural-Urban ,0 EDUCATION Tellings .i0 A (Proportions Percent of White Collar) Barefoot Not Clerical

( Urban Size, ) Proportions Urban Fig, 33. . -Decision model, 278

Proportions Urban, (Proportions CIncomes in City Size, Barefoot, Incomes Manufacturing Proportions Clerical in Agriculture Proportions in Agriculture

Opportunities Ability to Visible for Child Pay Economic Low Income in Employment Returns Manufacturing it (Foregone Small Trade Child \ income) (High white collar, 1 Employment ENROLLMENT/ but low clerical) RATES Proportions in Z.. Agriculture Attitudes Direct Costs and of Schooling Values

Rectangles: Concepts, not measured directly Education of\ Entries in boxes with rounded ends: Adults, Each entry indicates a variable or Proportions set of variables used in the Barefoot i empirical analysis 279

negative effects. Whenan element in the chart has a traditionalist influence,

arrows from it to "Knowledge of effects of education and/Ur attitudestoomml

education" tarn back orreverse themselves.Here are factors that presumably

impede or delay the spread of information and theorientation to education upon which the decisions to enroll and continue in schoolare based. (Hereafter such

arrows mill be referred to as "reversing arrows."

One would expect innovations to diffuse first within and among urban places, wi.th weaker influence upon the surrounding hinterlands (exceptas they maybe specifically related to agricultural practices).Also, areas with high rates of in-migration pre-1,0 bly offer greater opportunities and displaymore economic modernization; higher enrollment rates might be expected there. These assumptions are generally upheld in the zero-order correlations. However,more refined analysis points to distinctive urbantypes. In 1960, themelms =relationship (a correlation of -.016) between urban enrollmentsand net in- migration. Furthermore, controlling for other key variables, urbanenrollments for children aged 6-14years were depressed bylarge in-migration ranes; these findings will be presented later.For the moment it is sufficient to note this fact, which is one of the justifications for thereversing arrow in Figure 32.

Evidently, newly arrived udgrants bring their traditional ruralculture with them, tend to live in self-contained neighborhoods, and only slowlyaccept the elphasis on schooling.

Factor 5 of Matrix D (Table 38) would give highscores in a setting in which rural progress in schoolingvas marked but urban rates lagged despite the fact that whatever urbanization of the population had occurredhad come relatively early.Urban enrollments are lbw in 1960 (a factor loading of -.873) gaps between rural and urban areas in enrollments are minimal (a loading of

-.860), ana the factor loading forurbanization (1960-1950/(1960-1940)was 280 -.484.It is interesting that in overall zero-ordercorrelations for 1960, the data in Table 49 of Chapter V indicate that highproportions at average urban male and female pupils tended to occur moreoften in states that were pre- pondorantly agricultural, with correlations of.605 and .606; these are closer than might be expected to the analogouscorrelations of .652 and .692 respectively for over-age rural boys mid girls.Yet the negative correlations between proportions of males in agriculture and eitherrural or mina enrollments were low (-338 and -.129 respectively).It is evident that late entryand retardation due to absenteeism differentiate amongthe sub-populations of Mexicotoday in ways that are not picked up in the data onenrollment rates. A delayedrelaying of information and attitudes is bringing someof these groups into theechools,

where resistances to repeatedschooling-oriented tailings are belatedly,and-

still only partially, overcome.

The nature of recent influences byprogressive urbanization upon school

progress in rural areas isevidenced in a number af ways. Taus (a) there is a moderate positive association between developekentof cities (proportion urban 1960/1930) and 1960 rural enrollment rates (shown in Table50).Also (b), there are stronger negativeassociations of urban growth withruralthan with urban proportions of pupils over-age and yetslightly higher associations of urbanization with urban than with ruralcontinuation rates. With reference to(b) the correlations of urban growth with1960 proportions in Grale 1 who were 10 years old or more wereonly -.195 and -.360 for urban,but-.368 and -.404 for rural boys and girls.On the other hand,correlations of 1963urban and rural fotubth/third continuationrates (takingboth sexes together) withurban growth were .571 and.436.Examination of thespatial patterns taken by these and

associated relationships providesclearevidence that the extent andeffective-

ness of urban-to-ruraltel differssubstantially from one part of Mexico

! 281

to another, however.Also, some of the diffusion of tellinga and adoptions

take place primariV within an agricultural context.This hail been quite evident

where extension of irrigation and farm mechanization has been accompanied bya

reduction in the differences between rural and urban enrollment rates--a pattern

indicated for states with high scores on Factor 5 of Matrix D, discussed above.

The preceding comments on 1960 relationshipswere focused primarily

uppon either rural or urban performance, rather than theaveragea that combine

urban and rural populations. Unfortunately, the earlier data allow compariaons

for entire state populations only.Some correlations that maybe comparedwith

those in the first column of Table 50are presented in Table 51. As ex-

pected in the light of evidence presented in Chapters IVand V, the earlier

associations of both enrollment and child literacy with proportions of thepopu-

lation urban were much higher than those of 1960. The mass-media indicator, at-

tendance at movies, was also substantially higher in the period 1930 to 1940

than more recently; althouel the associationwas still 3n evidence in 1960, it

was weak. Proportions of households owning radios (not available for the earlier

years) was the better predictor of enrollmentsin 1960.

Determinants of "Res4stance" to Diff Usion of schooling;'the %del

Considerations that influence people in deciding whetherto send their children to schoolpositive and negative companentsof resistance--have been

anticipated in previous pages. However, there has been no attempt to lay out systematically a model of the factors that determine givenammunication how readily innovative behavior will be adoptedor imitated.

A human-investment decision model that can be used to study that agerstrand calls aresistancies" is portrayed in-figure 33. Enrollment rate is the dependent variable: it ia an investment indicator and measures diffusion 282

TABLE 51

ILLUSZIATIVE CORRELATIONS OF 21ROLLME2T RAMS AND CHIDLITERACY WmMEC= VARIABIES, 1930 to 1940

Enrollment Rates, Literacy of Children Age 6-14 Age 10-14

1930 1940 1930 1937

Enrollment rates 1930 .596 .912 .744 .762 .700 1937 .596 .... .703 .702 .775 .787

Percentages literate Males 40+, 1940 .728 .920 .912 .939 .925 Females 40+, 1940 .695 .819 .915 .892 .942

Proportion of males employed in White-collar jobs, 1940 .680 :al .821 .828 .889 .904 Agriculture, 1930 Agriculture, 1940 -:490 -4.790 Proportion of females 10+ economical1y active, 1940 .288 .350 .296 .383 .365 .413 Proportion of males barefoot, 1940 -.349 -.502 -.709-.577-.671

Urbanization Proportions 2,500+, 1930 .". Proportions 2,500+, 1940 .631 -339 .731.736 .7 Urban 1960/1930 .407 .593 .424 .581 .505 .473

Proportions of in-migrants, 1940 .554 .710 .648 .716 .714 .743 Movies, 19140 .636 .623 .756 .774 .831 .833 283 of the innovation, going toschool.' (Again rectangles are concepts and rounded boxes represent independent variables observed.)The two attached rectangles to the left refer to costs of schooling: costs in the earnings that are foregone with attendance at school and direct outlays.The far-right rectangle represents visible economic returns to schooling, such as the improvement in job opportunities and earnings associated with acquiring an education.The rectangle at the bottom represents attitudes and values as these affect behavior, including preferences relating to schooling as a consumer value, along with "tastes for leisure," and strength of preferences relating to more narrowly defined expected economic costs and returns.A fourth rectangle specifies oonstraints on ability to pay:the total resources on which the individual (or family or local community) may draw in deciding whatresources to put into education.A fifth rectangle might have been included to represent information, which was at the center of the first diagram.However, it is more appropriate to think of the information field as in another dimension, through which perceptions of the parameters relevant to the enrollment decisions are carriedl screened out, or magnified.Intensities, directions, and contents of infokmation are in turn altered by feed-back from behavior am: experience.

The independent variables include two that were in the previous information-fieId diagram (Figure 32):education of adults and proportions of adults going barefoot. In Figure 33, these are both shown as proxies far the concept-box of attitudes and values. The barefoot variable doubles also as an indicator of ability to pay. This illustrates the fact that empirical measure- ments of societal dharacteristics using geographic units of observation are

lInsteadthe dependent variable could be some other indicator, such as persistence in school beyond the third year. 284

almost inherently multiple inmeaning.As a careful examination of Figmre 33

shows, there are multiple elementsin the interpretation of most of the other

variables as well.

Despite this use of keyvariablesas stand-ins for several things,

direct cost of schoolingwas not represented by any variable.For elementary

school such costs are small;moreover, available data do not allow one to identiry

or infer inter-area differences in such costsvery dependably.Interpreting the model in terms of individual decision-making,availability of sohools might have

been inserted in the direct-costrectanglo. This would point up the fact that where local facilitiesare missing or are too few for the applicants, there will be sharp and large shifts in directcosts to those individualsor families that seek schooling despite failure in crowdingthrough the narrow entry gates.How- ever, this special case is already coveredconceptually by the mare general concept of direct cost. If a community or aggregative view of decisions is taken, which is the statistical implicationof using data for geographicareas as a whole, availability of schoolplaces is no longer an indicator of costs. Local availability is itself a function oflocal pressures for schooling, anda satis- factory measure of it would bevery closely correlated with enrollments by-definition. A related but different empirical variableis "proportion of elementary schools lacking the full range of six grades."This variable was not used in the multiple regressions, however.For that purpose it would have quite ambiguous meanings, since it maybeas much an indicator of pace of expansion as of limited availability, and it is highly sensitive toshort-run idiosyncratic influences.

Far more important on the cost sideare opportunities for employment of children and the costs in foregone incomethat such opportunities produce.Though children legally are not to be employed, childemployment is widespread enough for 285

the census to tabulate the 8-11year olds separately in data on the economically

active population.

The zero-order relationshipsselected for summarypmesentation inTables 50 and 51 are, of course, reflectors of bothinformation-field and resistance

aspects of the diffusion of schooling, which are intercorrelatedin state-by-

state observatif!hs. However, in directing attentionto the resistance aspects of the model, special interest is focusedupon variables that may serve as indicators of area differentials in visible returns to schooling,ability to pay for it, and the costs of its acquisition.Especially interesting in thisconnection are the income and white-collar indicators and those relating toemployment of

young children. The strength of this lastvariable in all the 1960 sero-order

relationships, with the partialexception of urban enrollmentrates, has already been noted.

The occupation variables (proportionsin white-collar jobs and inagriculture) generally came through stronger for the earlieryears than for 1960 an the all-state enrollment and child-literacy rates, andbhp 1960 all-statepre- dictions were in turn very much better than those for urbanor rural rates taken separately. In fact occupation structures in themselves tadalmost nothing about 1940 urban enrollments, though occupatian variablescame through as well on urban as on rural continuation rates (both around .450) andretardation rates

(both around .650). Income in agricultureas well as in manufacturing displays a modest association with 1960 urban enrollments; but in therural settings the only "economic" indicator thatshows a high zero-order relationshipis on the cost rather than thevisible-benefits side; this is thenegative correlation

(-.571) for child employment,which will be discussed againlater. 286

The Multiple Regressions

The interplay among the various traits measured in this study as explanations of patterns in diffusion of schooling is complex. Detailed intensive study of numerous zero-order correlations together with idtntificatioa of absolute values and their shifts through time can providean important part of the picture, but such detail is not easi4 summarized, andsome simple correlations are misleading.Relationships among the variables has been further elucidated by the use of components analymis, which identifies clusters of traits thathave important elements in common.However, the main concern of the present chapter is with the determination of particular dependent variables--in this instance pri.marily school enrollment rates--by means of multiple regressions.

The dependent variables are four: enrollment rates for satire states in 1937 and in 1960 aid urban and rural rates in 1960. In view of the fact that correlations between enrollment rates for boys and girls are extremely high, little should be lost in failure to differentiate by sex. The independent variables were selected on the basis of the theoretical framework outlined in

Figures 32 and 33, taking into account the components analyses. Where two or more good candidates for inclusion as independent variables had loadings exceeding .800 on the same factor, oayone of these was included in any single regression equation. The regression program eliminated automatically any variables that had F values less than 1.0 in a particular trial equation.The results are summarized in Tables 52 through 55. The median state values and the simple correlation coefficients are shown in the first two columns. The medians present a better clue to the relevant development levels than would be provided by the moan values coming out of the regressions, since the latter are in various 287

transforms of the initial indicators. Independent varitbles that were thrown

out by the F value cut-offare indicated 'by an entry of "d" for eadh of the

equations with which they were tried.1 The FederalDistrict was omitted in all

the regressions. Values for the District were then computed frau each of the

equations and compared with the otserved enrollment rates. The results of these

prediction tests, shown in Table 56, will be discussedat the end of this chapter.

The regressions for enrollment rates in 1937are by far the most success-

ful if the criterion of success Is the proportion of variance in en-

rollment rates that is explained. This is wbat by now might have beenexpected,

not only on the basis of the zero-ordercorrelations with enrollment rates, but

also the generally higher associationsbetween literacy measures aad other socio-

economic indicators a generationago. (It will be remembered that thereverse

was the case for associations involving schooling of adultsbeyDnd primary

levels, but that is another matter.) The maximum R2 in Table 52, explaining

70 per cent of the variance in 1937 all-stateenrollment rates, was obtained

from equation 1.5.That equation included literacy of older females,males

barefoot, males in white-collar jobs, andmales in agriculture. Hbwever,

equation 1.1 was the more efficient statistically;literacy of males aged 40

or more, males in white-collar jobs, and males in agriculture jointly yieldan

R2of .688.

'Variablesdropped by theFtest in trial runs that left onlyone independent vwciabls in the regressionare identified separately.In view of the transformations that were used to convert variablesinto more nearly normal form before entering them in the regressions, theregression coefficients are awkward to interpret andare not included in Tables 52 through 55.The equations and forms of the varidblesare given in Appendix C, however. In all cases N is 31. 288

TABLE 52

MULTULE REGRESSION ANALYSIS, SET 1 DEPENDENT VARIABLE: EURO RATE, 1937 T

Equation (1.1) (1.2) Simple Numbers: R2 MedianCorre- .688** .607** Valueslations 19.87 21.63 Zero- order F

Dependent variable

265 Enrol 6-14 T 1937 51.7

Independent variables

145 Literacy 40+ m 1940 48.2 .728 32.77** .454 *000 .5914** 146 Literacy 40+ F 1940 37.8 .695 27.09** 125 Barefoot M 1940 9.0 -.349 3.91

64 Collar/EcAct 11 19140 4.2 .713 30.07** .507* 0.00 79 Ag/Eact/4 1940a 75.0 -.490 9.17** .553* .447* .381 58 EcAct F/10+ 1940 1.4 .350 14405 0000 0000

39 Movies/Pop. 1940 2.9 .623 18.43** 0000 .241 46 F under 5/F 1940 14.4 .070 04100 0000

Equations tried and reduced to one independent variable by the F test: When variable (79) is in: (125) and (58) dropped4 when variable (39) is in: (79) dropped.

*Significant at the .05 2Jvel of probability:

**Significant at the .01 level of probability. 289

TABLE 52 --Continued

(1.4) (1.5) (1.6) (1.7) (1.8) (1.9) (1.10) (1.11) .580** 705** .6914.4-* .668** .649** 619** .596** .527** 19.36 15.51 14.55 13.09 16.67 14.62 20066 15.62

Partial Correlation Coefficients

.467 .723** .675** 733 ..gO** 735** .243 435 .470 .508 .468*

.397 .326 .515 .279 .413 .364 .196 .293 d.

.231

.345 :1414i

IllnymiC.:arlIMINIMENONOINel11..

aBecauseof its marked negative skew,this variable was used in the general form Log (100-x). All signs have been reversed,however, to facilitate interpretation of the table. The partial correlation coefficientsconsistently carried signs the opposite ofthe simple correlation, this is nota tranwposing error.

dDeletedby the F test. 290

The most interesting features of these regressions, however,are in the

details, the ways in which particular variables perform in diverse combinations

with others and how they diverge from theirown zero order performanne. Equation

1,8 is a good starting point. In that equation the coefficient for female litera-

cy has been raised slightly, from a zero-order value of .695 to a partial corre-

lation coefficient of .733, just above the zero-order value on literacy of older

males. This is not particularly surprising.What is striking is the reversal

of signs on the coefficients for both proportions of males in agriculture and,

especially, proportions of males barefoot. In a zero-order correlation with 1937

enrollment rates, these variables carried the negative values of -.490 and -.349

respectively, whereas the former is now a positive .364 and the latter (barefoot)

a significant positive .508. In fact both of these variables carry positive signs

in all equations in which they appear with literacy of either males or females

age 40 or over. However, the barefoot variable is dropped by the F test when male literacy is substituted for female (compare equation 1.4 with equation 1.8),1 which implies a much greater overlap between the male literacy and proportions barefoot than the female literacy and (again male) proportions barefoot in their associations with enrollment of children in primaryschools.2Inclusion of the agricultural employment and barefoot variables has no effect on the coefficients for female literacy.

One of the most interesting things that these equations tell us is that back in the late 1930's the larger the indigenous populations (proportions

lltis also dropped when put in an equation with agricultural employment only. 2 The zero-order correlations for males as against females do not shaw this, however. They are as follows: Barefoot M 1940 with Lit. 40+ M 1940 and Lit. 40+ F 1940 coefficients are -.626 and -.796 respective/ye The corresponding correlations with Ag./EcAct 14 1940 are -.839 for the male and -.860 for the female literacy variables. 291 barefoot) for any given litem:4rates in the older generationl or among older women in particular, the greater was the propensity to sand Children to school.

These results refleCt a feature of the complexityof socio-economic patterns

that showed up in the components analysis forsome of the factors emerging at the third round or later for Matrices C and D, reflected,as an example, in the

last map of Chapter V (Figure 28).

Equation 1.5 differs from equation 1.8 only in adding proportions of males in white-collar employment, which is the only variable that cuts female literacy down below a .05 significance level.White-collar employment survtred the F test in equation 1.5 at a positive modest level, to signal the effects of visible returns to schooling. While such returns are reflected indirect4 in some of the other variables as well, the reversals of signs on the agriculture and barefoot variables and the relative strength of that for female literacy might seem to underline the great importance of information fields and cultural attitudes in the early diffusion of primary schooling.1 The high zero-order correlation with white-collar proportions (.713) and the characteristics of equation 1.1 warn against overstressing this inference,however. In equation

1.1 male literacy was substituted for female andthe barefoot variables was not included.The highest partial correlation coefficients in thiscase are on the male occupations, that for white-collarproportions being somewhat the more significant; also, the white-collarmeasure retains its zero-order positive sign, whereas the coefficient for proportion of males in agricultureis again reversed, from a negative zero-order toa positive partial correlation coefficient. Given the nature of these occupationvariables, equation 1.1 suggests that for any given literacy rate among adult males the lowest enrollment

1, ahnically and geographically differential effects of early activities of the cultural missions are undoubtedly part of this picture. 292

rates of children will be found wherethere are the largest proportions of

non-agricultural laborers, self-employedartisans, and huckstersthe unlanded

poor in the more densely settled districts withtheir many small towns. This

is also where child employmentis high. Given earlier settlement patterns,

the changes in Mexican agriculturesince 1940 and the overaiall advance in

diffusion of primary schooling,ors should not necessarily expect a repetition

of the 1940 relationships in 1960.

Before turning to the regressions for 1960,a brief comment on the

performance of the variable for movieattendance in the 1937 regressions is in

order. By itself that variablewas significant at the .01 level, and it knocked

out the variallle for males employed inagriculture when the two variables were

paired. It survived the F test in equation 1.7, thoughat a low level, in

partnership with female literacy, proportionsin agriculture, and proportions

barefoot, though proportions offemales economically active dropped out.How-

ever, it did not survive in this combination whenthe competition for inclusion

was with white-collar employment,on the one hand (1.5), or even when the

competitor was theproxy for fertility rates (in equation 1.6),on the other.

Again, there are seriousproblems of multi-collinearity thatare unavoidable

in working with these data. Also, certainlymeasures lack the precision needed

to discriminate definitelybetween densities of face-to-face and formalcommuni-

cations. However, the nature of the selectivityof survival of the 1940 movie attendance variable reflects notonly the fact that it picksup a wide range of related socio-economic differentialsamong areas and that it has a corresponding lack of precision asan indicator of economic level (vis.a.vis the 1940 white. collar variable), but also that it failsto separate out degree of informal face-to-face involvement in local-traditionalversus national information fields.Among other things, in brief, it t& less about what Bagerstrand 293 would call the "private" than about the 'public" information fields, and loses its force as a communication measure accordingly.

The regression equations taking 1960 enrollments for total state populations as the dependent variable (Table 53) were set up to provide as close a match with those for the earlier period as possible. The fact that the zero- order correlations are generally weaker has been noted in several contexts, and the multiple regressions were correspondingly less successful in explaiming vari- 1 ance of enrollments. The same five variables that were significant in one or more of the 1937 multiple regressions survived the F cut-off in one or more of the 1960 regressins, but with some decided changes in theways in whiEh they made their appearance.Proportions of maIes engaged in agriculture in 1960 dropped out of the 1960 regressions wherever either literaey of older maleror proportions of men in white-collar employment was included in an equation.On the other hand, whereas proportions of males barefoot had dropped out of the

1940 regressions when male literacy was included, they retain a place beside male literacy in the 1960 regressions, and again with positive coefficients.

Whether they refer to 1937 or to 1960, analyses of determinants of enrollment rates for total sta4e populations are confounded hy the various weightings of urban and rural people that are involved.Although this very fact increases the proportion of inter-state variance that is statistically explained by the available data, it also clouds the picture of theprocesses at work. Fortunately, for 1960 we can distinguish between urban and rural enrollment rates (Thbles 54 and 55). The urban analyses are considered. first.

1Thiscannot be attributed to the transposed form of the dependent variable, since it prevailed throughout in correlations involving literacy variables as well, regardless of the forms in which theywere entered, TABLE 53 mularam RD3RESSION ANALXSIS, SET 2 DEPENDENT Equation VARIABLE: ENROLLMENT RATE, 1960 T MedianValues lationsSimpleCorre- orderZero-Numbers' F RzF 10.39 (2.1) .536** 15.30 (2.2) 522-** 9.31(2.3) .508** 8.58(2.4) .380** Dependent266 variable Enrol 6-14 T 1960a Partial Correlation Coefficients Independent147 variables Literacy 40+ M 1960 63.059.2 .625 18.63** .7110HI .... 127148 65 Collar/EcActBarefootLiteracy M 40+1960a M F1960 1960 12.752.8 56R.513 13.84**10.3434 .4326.1.42:4; 1 .....464* .....506*.1.4g .....288 80 Ag/EcAct M 1960a 67.7 -.569 13.89** d 404759 FMovies/Pop. EcActunder F5/F 12+ 1960 1960 6.74.64.9 .125.475.213 ..... 1.388,414** .... d .... d -.372 d ...... droppedand (40) separately dropped separately and jointly. and jointly. Equations tried and reduced to one variable by the F test: (Note equation 2.3, however.) When variable (80) is in: Wben variable(148), (147) (65),is in: (59), (4o), and (47) ... (65), (80), ism anthoseto extremefacilitate for variables positive the interpretation (127)skew inand its (80). raw of form,this table,was put all into signs the haveregression therefore as abeen rank reversed variableaBecause with rank 1 for the of its marked negative skew, the dependent variable (266) Variable (80) was in the form log (100-x) and variable (127), which has was used in the form log (100-x); with the exception of highest proportion barefoot. dDeleted*Significant at the .05 level of probability. by the F test, **Significant at the .01 level of probability, 295

TABLE%

MULTIPLE REGRESSION ANALYSIS SET 3 DEPENDENT VARIABLE: URBAN MOLD ,1960

laination Simple NumbersI (3.1) (3.2) (3.3) 473** .408** Median Corre - V .264* F Value lation 12.57 5.02 9.64 Zero- order F

Inderendent variables

273 Barol 6-14 U 1960 58.7 168 Lit. 40-49 HU 1960 80.0 385 5.06* .514 0000 176 Lit. 40-49 FU 1960a 71.5 .445 7.16* .688.31* .630**

127 Barefoot !I 1960a 63 .004 0.02 586** .368 62 Feaploy 8-11 M 1960 24.1 -.315 3.19 ....

65 Collar/EcActM 1960 12.7 .125 ...... 59 EcAct F,12+ 1960 4.9 .228 1.60 ...... 45 Single F 20-24 1960 32.1 .159 1.71 ......

103 lug Inc. over 500, 1960 38.1 .404 5.67* 00000 0000

9 Capital/Urban 1960 28.1 .332 3.58 0000 12 Pop. 50,000+ 1960 14.3 -.132 -.512*

17 In-migrant 1960 10.6 -.016 000 0000 * 44 Radio 1960 24.3 .157 00000 0000

a Because of its narked negative skew, variable (176) was used in the form log (100-4. Vhriable (127) was entered by rank, with the highest proportion barefoot as rank 1. Signs for these variables have therefore been reversed to facilitate interpretation.

Deleted by the F test. 296

TABLE 54--Continued

(3.4) (3.5) (3.6) (3.7) (3.8) (3.9) (3.10) (3.11) .322** .441** .289* .248* .196* .181 .187 .152 6.66 7.09 3.67 4.63 3.42 3.09 2.06 2.52

.390 a

0000 -.201 0000 -.284 -.218

-.311 -.373 -.318 -.195 0000 0000 .237 0000 0000 0000 .234 0000 0090 0000

.651** .> .486* 0

,254 .243

-.506

Tried anddropped by F test:

Whenvariable(168) is in: (62) (44) (9). Whenvariable(176) is in: (62) (44) (9). Whenvariable(103) is in: (59). Whenvariable(59) iS in: (65) (17) (45). Whenvariable(45) is in: (65) (17). Whenvariable(9) is in: (127) (44) (12) (65). Whenvariable(62) is in: (65) (44). 297

That high levels of income for employees in manufacture was one of the most successful predictors of 1960 urban enrollments in a zero-order correlation was shown by the data of Table 50, even though the coefficient was only .404.

This variable comes through much more strongly when it is introduced into a multiple regression with other relevant variables. In fact when it is combined with proportirs of males in white-collar employment and proportions of in- migrants, the partial correlation coefficient on manufacturing income is a strongly positive .651. This indem of abi4ity to pay for education, and probably of visibility of economic returns from education, was much more important among uroan populations than the levels of employment of children.The latter were dropped in most trials by the F test cut-off, surviving at a low level only, alone or in combination with Capital/urban, Barefoot M 1960,or Eact F 1 12+ 1960, all in weak regression equations, none of which gave multiple correlations significant at the 05 level.

The negative signs of the partial coefficients for males in white-collar employment and for in-migration in equation 3.5 both call for special comment.

In the zero-order relationships, the coefficients for these variables were virtually zero (.125 and -.016 respectively).Controlling for inmost, in manufacturing, both become significantly negative partial coefficients (-.311 and

-.SOO. This equation distinguishes types of urban places very clearly, thereby sharpening the independent variables and their meanings. "White-collar employment" is a very heterogeneous designation, and alone it may be a poor indicator of the character of a local economy.However, whencontrollingfor incomes in manufacturing and for the proportions of the state population who are

lInaddition to equations (?.10) and (3.11), &ploy 8-11 M 1960 appeared with a negative coefficient of -.288 in combination with EcAct F 12+ 1960 (coefficient ,187) and with a coefficient of -.361 in combination with Barefoot M 1960 (coefficient -.186). 298 in-migrants, the proportion in white-collar employment becomes ammo explicit indicator. The fact that in this case it carries a negative sign is almost certainly indicative of the depressing effect on enrollment rates of a large proportion of men engaged in either trade or local bureaucratic employment relative to the degree of progress or lag in the modernization of industry and to the proportions of urban newcomers. (It should be remembered that the

Federal District is not in these regressions.) The stronger effect of the in- migration coefficient adds substantial clarification to this picture of kinds of urban places and of how contrasts among them mays/Tact primary-school enrollments. The evidence of a continuing drag of in-migration on the diffusion of primary schooling is unambiguous. Indeed, given the limitations inherent in the gross units of observation available, equation 3.5 provides a very clear picture of the operation of information fields, ability to pay, and visible returns on decisione to attend primary school.These crude measures explained

44 par cent of the inter-state variance in urban enrollment rates.

Along with income in manufacturing, the best zero-order predictors of urban enrollment rates were the literacy rates of men and women in their forties, with coefficients of .385 and 445 respeotively.This is hardly surprising, except that these correlations might have been expected to be higher, asindeed they were a generation ago. The first inclination of most sociologists is to interpret inter-generation correlations of this kind as indicative of the importance of attitudes toward schooling that are distinc:: from or outside'of economic considerations. However, empirical observations of data for geographic units mover provide such easy answers. The proportion of an area's adults who are literate (or have attained any given level of schooling) maybe proxies far almost any of the components in the models of Figures 32 and 33 (with the partial exception of foregone earnings).Adult educational attainments pick up 299

geographically associated aspecte of ability topay, visibility of returns,

attitudes, and participation in wider information fields.Nevertheless, the

contributions of measures of adult literacywere raised by codbining them with

either Barefoot M 1960 or Pop 50,000+ 1960.

In the urban setting it was literacy of older females rather than of

males that came through most clearly. In fact the best equation so far as

explanation of variance is concerned (equation3.1 with an R2 of .473) con-

tained the two independent variables female literacyand males barefoot, with

partial coefficients of .688 and .586respectively.The incidence of literaqy

among women in their forties, still distinguishes urban populations that have

been established as leaders in modernizationfrom the general run of urban

places, thereby pickingup some of the same traits that are associated with

higher versus lower incomes of employees in manufacture and the modernversus

traditional types of white-collar activities. The tendency for the literaqy

variables to eliminate other variables because-of major overlapwith them must

serve as a warning against a research procedure that stops with finding the highest R?, and that gives theoretically better-specifiedvariables no further chance to demonstrate their validityonce they have been eliminated by F tests in an equation that includesa variable with multiple theoretical connotations.

Equation 3.3 also presentsa warning; given the small number of observations and the particular Characteristics ofthe Mexican states in which the urban populations are most concentrated, the highnegative coefficient of -.512 on proportions in cities of 50,000or more maybe in large measure a proxy for the more meaningful in-eigration variable of equation 3.5. It would be fallacious to generalize on the basis of equation 3.3taken alone.

Child employment wae placed centrally in the diagramon resistance

(Figure 33) because of its logicallykey place in schooling decisions. Yet 300 this variable had only a moderate sero-order correlation of -.315 with urban enrollment rates, and aswunoted earlier, it faded out in the urban multiple regressions. These facts cannot be dismissed solely on the ground that the child employment measures refers to entire state populations and fails to discriminate among urban settings.Hy-1960, there can be no doubt that the relative economic importance of foregone earnings of urban children under 12 years of age had declined. Whether foregone earnings play an important part in continuation rates and in schooling beyond the primary years is not here in question. Carnoy's study is decidedly pertinent, however. He made no estimates of proportions of urban children employed, but in a small survey of earnings among working Children he found that employed children earned etbstantially more in the cities he studied than in the rural localities.For urban families foregone earnings are a substantial cost in the lastyears of elementary school.

Nevertheless, even taking these foregone earnings into account, Carnay estimated urban rates of return to investment in the fourth to sixthyears of primary 1 school to run 30 or even 40 per cent. His finding is consistent with these regressions.Despite high opportunity costs, relatively fewer childrenare employed in Mexico's cities than in rural areas, and rates of child employment fail to explain differences in urban enrollment rates precisely because other factors dominate: visibility of returns to schooling, high parental incomes, parental education, and participation in modern culture versusmany unassimi- lated in-migrants living in thepoorer residential areas of cities.

In rural areas returns to schooling are less visible, ability to pay is generally lower, and there is less variation in these traits. There the opportunity-for child employment plays a pervasive part in determining school enrollment; in fact the eleventh factor of Matrix A centerson enrollment of

lcarnoy,op. cit. 301 youth in rural areas (loading of -.615) and employment of top ag4 8 to 11

(loading of .590). The geographic pattern is shown in Figure 34.

While the employment opportunities open to children cannot be meaeured directly from the available data, the uses people make of those opportunities and the correlates of that use can be studied. The data indicate that the exp. tent of opportunities and of their utilization in rural areas are greatest where there are moderate proportions of males in agriculture, low incomes in manufactwring, and many small traders (indicated by high white-collar but low clerical proportione). In the set of multiple regressions for 1960 rural enrollments (Table 55), the child-employment variable comes through strongly every time it is given a chance. All other variables, including literacy of rural males (age 40-49) were eliminated by the F test when included with the exceptions shown: proportions of males barefoot in 1960 (.536 in equation 4.1); agricultural incomes over 500 pesos (.305 in equation 4.2); literacy of rural females (-.270 in equation 4.3); ownerships of radios (-223 in equation 4.4), and proportions of the urban population living in the capital city 1960 (-.200 in equation 4.5).

Furthermore, with the exception of the barefoot measure, none of these was significant, and all had signs that reversed their zero-order coef ients. Al- though the zero-order coefficient for proportions barefoot was only .112, it came through significant at the .01 level when combined with male literacy

(equation 4.6) as well as in combination with child employment. The rural enrollment pattern is revealed as very clearly a function of foregone earnings and cultural attitudes4

Federal District Enrollment Prediction Tests

Throughout this stu4y, the Federal District has been analyzed part from the other states of the country. On many indices of development it stands at the Fig. 34.-4tatrix 144 Factor U.

Variable p.ctor Loadings Number (-.800 and Above)

274 Enrollment 6-14 rural 1960 -.615 62 Employment 8-3.1 males 1960 .590

A short-cut estimate of the rank of the Federal District for these variables is 32 (from a high of 1 to 32).

TABLE 55 MULTIPLE REGRESSION ANALYSIS, SET 4 DEPENDENT Equation VARIABLE: RURA,L ENROLLMENT, 1960 ValuesMedian lationsCorre-Simple Zero-NumbersA F11` 15.17 (4.1) .520** 8.92(4.2) .389** 8.42(4.3) 376** 7.87(4.4) .360** 7.65(4.5) 353** 9.48(4.6) .404** 4.23(447) 232* 184Enrollment 6-14 R 1960 Lit. 40-49 MR 1960 1.00c order F 127192 62 EmployBarefootLit. 40-49 8-11 M 1960aFRM 1960 45.560.524.1 4.3 -.571 .112.239.490 - 14.05**1.759.14** .39 -.717** -.608** -.270-.581** -.561** -.593** .465*.629** .431*.471* 91898480 Farm mechanised 1950 Ag.AgiEcAct incomeLabor/Ag. M over1960 M 5001960 50.667.7 1.69.0 -.338 .237,176.162 3.73 4412 9 RadioPop.Capitallurban 50,000+ 1960 1960 1960 24.314.328.1 .257.060.009 2.05 -.223 -.200 ble (89)( 2) .?1)is in: (44) (9); when Variable (192 is in: aEquationsSignes tried haveand droppedbeen reversed by. F test:to facilitate When variable interpretation (80) is in: since this (80) (84) (89). (91); when variable (89) is variablein: was entered(84); whenin thevariable regressions (184) ia in: (127) (94) (91) (9) (12); when varia- (84), ! as a rank variable with the highest proportions barefoot as rank 1. dDeleted*Significant at the .05 level of probability. by the F test. **Significant at the .01 level of probability. 305 apex of modernisation. It is the focal point from which change and ideas diffUse at a varying pace throughout the country. It has direct lines of commuoicatimi with foreign centers and with the regional capitals: Nanterre'',

Ouadalajara, Puebla, and Oaxaca.In 1960, the Federal District accounted for over 20 per cent of the total urban population. It contains the chief industrial complex of the nation. Out of the 7,000 industrial establishments beginning operations from January, 1960 to June, 1960, 4,000 were in the Federal

District.1

Because of the extreme values for this area on most measures of modernization, it was listed separately in tables Showing distributions of traits, and it was omitted from the simple correlations, the components, and the regression analysee.It was feared that otherwise its extreme values might distort the relationships among variables.However, the question remains as to howwell the Federal District fits the patterns revealed by the other states.The values for the Federallaistrict on independent variables were put into the regression equations to see how closely its enrollment rates wouLi be medicted.The results, for a selection of the best equations, are given in Table 56.These are presented in three forms.The first column, headed (Ip-X0)A, is the value predicted by the equation minus the Observed value divided by the standard error of the regression.The second measure expressed the predictel enrollment rates as ratios to the observed rates for the Federal District.The third measure gives the absolute differences between the predicted and observed values. The second two columns are designated as Zp/20 and Zp-Z0 respectively instead of using the letter X becalm in the case of total enrollments for 1960 the form of the dependent variable in the regression equations was log (1,000 - 10Z),

1Thompson,pp. cit., p. 99. TABLE 56 FEDERAL DISTRICT ENROLIMENT PREDICTION TESTS Description of Equations by Order of F Value on Partial throllmentDependent 1937variable: ir kr".3--00 Zp/Z Zp-Z0 Correlations and by Signs of Regression Coefficients Equation (1.5)(1.1)(168) 1.102 .301.563 1.11.21.3 14.631.0 8.1 + LitAg M/EotM/Ect40+ 1940 1940 + BareLitCollar 40+M 1940 mF 19401940 + AgCollarLit M/Ect 40+ M M1940 1940 + Bare M 1940 IhrollmentDeRendent variable:19616 t 1.359 .870 1.51.3 49.828.7 + Lit 40+ M 1940 Collar M 1940 Equation (2.2)(2.1)(2.3) -1.516-1.265 .332 .916.936.985 - 5.4 7.01.3 + -Lit Bare 40+ M M1960 1960 + -Lit Bare 40+ M F1960 1960 + -Collar Ag M/Ect 1960 M 1960 - .662 .963 - 3.1 + Collar 196o UrbanDependent enrollment, variable: -1.795-1.094 .875.938 -10.6- 5.2 + Lit 40+ FM 1960 M 70b EquationEquation (3.5)(3.3) (3.1) -1.632-1.373-1.025 .7261.7675.8359 17.214.610.3 + +MfgLit Lit Inc40-49 40-49 500+ FU FU 1960 1960 -+ In-mig.Urb 50,000,160 1960 Bare M 1960 Collar M 1960 307

where Z is the enrollment percentage.Also, to make the results comparable the

signs in the first columnwere, of course, reversed for the set 2 regressions,

since they would otherwise refer to proportions not enrolled instead ofpro-

portions enrolled.

Because the regression equations of sets 1, 3, and 4, putno contraints

an the upper or lower limits of predicted values, it was, ofcourse, possible

for these to exceed 100per cent or to fall below zero.This happens for pre-

dictions from set 1.The results are shown in Table 56.Convincing evidence

is provided that around 1940 the FederalDistrict's concentration of white-

collar man was grossly out of linewith other traits. So, in lesser degree,

were the District's reported ratesan adult male literacy. lhese facts are

reflected in the large discrepanciesassociated especially with equation 1.1

and with the predictions from white-collarproportions only.

By 1960, things had changed substantially. Also, fortunately, the data

in general and for the Federal District inparticular are better. In addition

the dependent varidble for Enrollments 1960 Twas in a form that precludes pre-

dictions exceeding 100 per cent. All of the 1960 equations pertaining to state

totals in enrollment rates under-estimate the rate in the Federal Distrit.t,but equation 2.1 comes extremely close.1So, for that matter, does white-collar proportions taken alone. On the other hand, female literacy by itselfor in argy combination that does not include proportions in white-collar employment leads to substantial under-estimation of 1960 enrollmentrates in the Federal

District as a whole.The under-estimates in the regressions for 1960 urban enrollments are much larger, primarily because they failto give the positive weight to proportions white-collar that wouldtend to pull the estimates for

1Thereis no reasonwhy a priori the use of the change in the foraof the dependent variable should have this effect, whichis repeated in set 3 also. 308

the Federal District more nearly into line. By the same token, the distinctive role of the Federal District as a communication node is inadequately reflected

in the urban enrollment equations. It is interesting in this connection that

the best urban prediction far the Federal District was the equation that combined proportions barefoot and urban female literacy, despite the fact that the

latter variable tended to produce underestimates of enrollments in the District as a whole. The fringe settlements aa the edges of the city are relevant in this connection.

Summary Consent

In summary, the regression analyses support both Higerstrandts emphasis on face-to-face "tellings" (as basic to the functioning of an information field) and the complementary ecanomic-decision theory of investment in schooling

(stressing the counterplay of opportunity costs and visible returns).Evidence supporting Hagerstrandls theme includes low correlations between urban and rural enrollment rates and low adult-literacy correlates in the central states.Even stronger is the support provided by-the negative insmigration effect in the urban regressions:in contrast to Sweden, Mexico is a bi-cultural eociety that has recently been experiencing an upheaval of old geocultural patterns as subcultures move into new urban locations. The strength of the barefoot variable in all the

1960 regressions underlines this situation.Both the face-to-face communication argument and the economic-decision model find expression in the differential impacts of a diversity of urban "settings."Those range from the so-called

"urban" that is only a rural crossroads gathering plter3 to modern cities with visible returns to education and the ability to expand enrollments. On the coat side, the economic model isbmst dramatically supported in a rural setting, by the close correlations between diffusion of schools among rural populations and 309 opportunities (and demands) for employment of &Wren. Visible returns usually are too low to offset the inhibiting effects of foregone-income opportunities. CHAPTEM VII

SUMMARY AND CONCLUSIONS

Those societies that are composed of plural culturesare faced with the

task of superimposing a commitment to national goals through the development of

economic, cultural, and social ties within the nation. For many Latin American

countries universal education through a free public school systemwas held to

be an indispansible vehicle for achieving integrationon a national level.

Recently critics havebegun to attack this ideal of the public primary schools

as a dream--impossible to fulfill in view of past efforts and present problems,

and based on the analogy of the United States experience that has little rele- 1 vance for Latin America.

The questions of relationships between sbhooling (whether formalor

informal) and development has been approached inmany different ways. The

choice in this investigati= has been to take a case in point, that of Mexico,

and to present a sweeping panorama of interrelationships in the development

process. Questions at the heart of the study have been concerned with develop-

ment centers in a spatial context, and the sequences of dhange over time. In

Chapters V and VI the focus was narrowed to concentrateon analysis of the

determinants of patterns of diffusion of literacy and of primary schooling

among rising generations.

Prior to this, Fattahipour, in a study of Iran, had usedcensus data for small districts as source material. A much richer variety of data could

01111. 1 See, for example, Ivan Illich, "The Futility of Sdhooling in Latin America," Saturday Review, April 20, 1968,pp. 57 ff.

310 311

be obtained for Mexico for individualstates, enabling one to explore changes

over the past three decades.On the other hand, data limitationswere severe

at the next lower level, of municipios,and at the same time the municipios

are so many as to make full coverage for Mexico totallyunfeasible. The choice

between limited amounts of information forsmall geographic units over a narrow-

ly circumscribed part of Mexico and much fullerinformation using the grosser

observations for states was a relativelyeasy one at this stage. Clearly analy-

sis of observations by states took priority,even though later research at the

municipio level might well be rewarding. This decision was made in full

awareness of the disadvantages of using data for geographicunits as large as

states, even when theconcern is with ecological patterns. The heterogeneity

of sub-areas within states blurs therelationships that can be observed. (Aay use of geographic units of observation, largeor small, will of course entail

serious problems of multi-collinearity whenresults are interpreted as clues to determinants of individual behavior,a problem that must be kept in mind in any attempt to interpret the findings. In that context multiple regression coefficients will bespuriously high, even as partial correlation coefficients tend to be reduced.)Fortunately something of the internal geographic heterogeneity problem inherent inItae of areas as large as states as the units of observation could be mitigated byuse of measures that differentiated rural from urban residents and that distinguishedby sax and age.

The theories of the Swedish cultural geographer,Hagerstrand, provided a rationale for the ecological study and incidental clues toways of analyzing the data, even though the latter would not permitduplication of his refined micro-analysis. His construct of "information fields" in thepatterning of interpersonal communications, on theone hand, of "resistance" to messages and 312

to acceptance of innovations, on the other, formedthe starting point for the model presented in Chapters V and VI. There communication and economicdecision

theory are joined to study thediffusion of Children'seducation.

"Modernization" and its distributionin the Mexican contextwas identified in three factor matrices with only a few overlapping variables(plus a

fourth containing onlyeducation items). In each case a first factorthat

clearly delineated relativeprogressiveness in one or another combinationof

degree of urbanization, economic levels and structure, educationalattainments of the population, and cultural traits. In one matrix loadingswere highest for

males in white-collar workand for intermediate levels ofadult schooling (rather than literacy). Another highlighted the importanceof children's enrollment in

primary school and the disappearance of the elements of traditionalculture. Although one of the matrices included most of the change andthe difference indexes, these were passed by in the first factor, whichpicked up the rela-

tively few variables relatingto occupational structures andschooling.

One of the factors that emergedin the components analysis hadhigh positive loadings on persons walking barefoot, on differencesbetween males and

females in the acquisition ofliteracy, and on degree of differencebetween the

younger and the older females with respect toproportions literate (in both rural and urban areas). These features characterize backward,predominantly rural

statos. Isolated groups become linked intocenters of Change only slowly.

47 1960, Mexico had reacheda point at which well over half of the adult male population was literate. In fact the proportion in the medianstate was nearly two-thirds even among the oldermales--with a rural median /ustunder that level and an urban medianat 80 per cent. However, there wasa very large range among states and particularlyas among their rural parts.For older females, proportions literatewere about ten points less than for males,with even bigger gaps in rural parts of statesand in lagging states generally. 313

Inter-state ranges were largerthan for males. Urban-rural somewhat exceeded

male-female differences.'While literacy had becomemore pervasive typically

among the younger cohorts, the inter-staterange in proportions literatewas

not appreciably lower amongyounger individuals. But in most respects, by 1960,

Mexico clearly had a larger proportionof states well beyond the 40 to 50per

cent level of literacy than is oftenregarded as essential for development. As

of the 1940 census, about half theliterates had acquired their literacy outside

of school, though this itemwas not repeated in later enumerations, therecan

be no doubt that by 1960 the proportionsof literates without schoolingwas

substantially smaller.

Nevertheless, Mexico can hardly be calleda well-schooled society. In

the median state nearly half thepersons over age 30 have had no schooling, less

than 5 per cent hadgone beyond 6 years, and barely 2 per centeven of males

received 10 or moreyears. For each of these indexes, again,inter-state

contrasts are largealthougheven the top-ranking state (apart from the

Federal District)can hardly be said to havemore than a handul of individuals

with more than working literacy.

The role of womenwas explored in several ways.Their participation in

the society outside the homewas clearly associated rith degree of modernization.

Fertility rates displayed the expectednegative associations with indexes of

economic advancement, but this isa Catholic country and with states as units

the coefficients were quite moderate. The proportions of young-adultwomen who

have remained unmarried showed associationoof .6he opposite sign to those for

fertility, and generally the coefficientswere larger. Thus in states with more

unmarried young women therewere more adult women with schooling beyond primary,

distinctly larger proportions ofwomen in white-collar jobs, and somewhat larger participation by women in the labor force,generally. That the coefficientsare 314

larger for more-than-minimum schoolingthan for literacy testifies to the fact

that as yet it is a rather smallset of woman who are sharing in modernization.

Relationships between the occupational structureand levels of literacy

and schooling were expected to shownegative associations with occupations.The

states of Mexico are still preponderantlyagricultural; in only a handfulare

less than half of the malesso employed. In no state, on the other hand, do

workers in manufacturingor "white-collar" man represent more than about a

quarter of the employed. Among economically active women, however, the medlan

state has 30 per cent in white-collarjobs (very broadly defined); this relates,

of course, to small proportions ofwomen working outside the home. On all these

occupational features, the relative dispersionamong the states was rather large,

but absolute ranges were smallon most items. Literacy rates did display quite

high correlations with workers in agriculture,but very modest correlations with

employment in manufacturing. They were much more closely associated with other

kinds of indicators in 1940 than in 1960. The 1960 coefficients were slightly

larger using indexes of adult schooling than ofliteracy.

Uhere large proportions of individualswere earning over 500 pesos

monthly in manufacturing, levels of adultschooling exceeded literacy in the

magnitude of the correlations, For agriculture it was literacy thatgave the

larger coefficient. But in each sector both sets of associationswere strong.

Employmmat of 8 to 11year old boys was impressively correlated (negatively) with adult education.Aswould be expected, literacy of rural males and femalesparticularly the latter--was associated,state by state, not only with incames in farming but also with indicators of theuse of equipment and mechanization generally.

The features of lead areasare related to the transportation networks but none of these linkages is rigid. Individual states are very dissimilar as regards transport and communication facilities. Even in 1960 possessian of 315

bicycles, let alone autos,was very sparse in most states. Attendance at movies

and possession of radios likewisewere still at low levels and distinctl,y uneven

as among'states.Even so modest a facilityas running water in houses was a

rarity, but distinctly lessrare in some states than in others. Possession of

radios and autos ;:fut not bizycles) and attendanceat movies were quite highly

correlated with literacy and schooling of adults, andto about an equal degree

for each sex.

Two indexes of traditionalor indigenous ways of life than run through

this report are the proportions of the population eating only non-wheat bread

(which reaches a maximum of three-fifths) andthe proportions of males who

typically walk barefoot (nearly half the males in the highest ranking state).

But, once more, states differ greatly on each index, and median proportions

are law. Each traditional trait is negatively correlPted to an impressive de-

gree with literacy rates of the states' adults.

Patterns of change over time (from 1940 to 1960 in most instances)were

set forth in Chapter IV, using several differentways of cutting into the voluminous statistical material. The basic questions an this topicare, of wurt:el whether states that had taken a lead drew frrther aheador whether there was a catching-up process at work, whethersome states that mere behind forged ahead, and also whether initial signs of advancement driedup. The findings may be conveniently summarized under five headings:

1. Changes in median and 'quartile values of indicators.Median-state values in most instances shifted in the expected direction from 1940 to 1960.

Both bicycles and autos became more common, fewer men went without shoes, white- collar workers became more numerous, and literacy rose, but post-primary schooling of adults did not improve. 316

2. Stability of state relative poaitionsover the period.On most

items that could be repeated the correlations between 1940 and 1960values were

impressively high. There is little indication of displacement of leadingor

lagging states.

3. Stability of intercorrelations among traits.Aloosening-up of the

correlations (with states as units) Ian be expected if hithertolagging sub-

mulations are being brought into a common society; yetsome traits can become

more tightly connected as advance is made to a more developed stage.Comparison

of the correlation matrices for the two years (1940 and 1960) showed, forex-

ample, that both proportions of males barefoot and indicators ofadult schooling mere closely associated with economic variables in both years.But the pro-

portion of economically active females was less closely associated with other

features in 1960 than in 1940. The positive associations between female proportions of total numbers engaged in manufacturing and other indicators dis-

tinguishing between less and more advanced types of industrialization increased.

Proportions working in white-collar jobs and proportions possessingmiddle levels of schooling became more closely connected in 1960. Seemingly areas with higher higher levels of liten,ay in earlieryears lead one generation later in diffusion of the higher-level occupations.The 19401s saw major changes in agriculture as irrigation, was extended and agricultural technology ehenged. One of the results is that whereas the proportion of laborersamong farmers was negatively associated with positive indicators of social advancement in 1940, by1960 that relationship had become positive.

4. Effect of starting level upon amount of change.This way of putting the figures together is congruent with thinking of development as occurring in successive waves. Thus early-stage lead traits gained most over the period i940-1960 among lagging states that were catching up. Literacy of most 317

sub-groups is an example. So is the very high correlation between proportions

of males going barefoot in 1940 and theextent of the decline in that practice

between 1940 and 1960.On the other hand, more aditanced lead traits gained the

most where in 1940 they were already highest: proportions of males in white-

collar jobs and density of autos--associated alsoin 1940 with smaller urban-

rural differences in literacy among olderwomen.

The components analysis contributes toan understanding of these change

patterns. One illustration is the factor discussed previously, in which high

positive loadings appeared on 1960 inter-generation differences inliteracy

(15 to 19 minus 40to 49 year old females in urban and rural areas), highpro-

portions going barefoot in 1940 andon changes between 1940 and 1960, high posi-

tive loadings on differences between male andfemale literacy in the older

generation. This reflects the way in which the diffusionprocess operates in

its later stageson any-given trait. If the diffusion of literacy is thought of

as characterized by the usual ogive growthcurve, what is involved here is the

attainment by 1940 or earlier ofa position above the steep part of the curve among the lead states, while at the time members of the oldergeneration were young in the laggard states, theywere still inor only approaching the zone of rapid growth rates. At the opposite extreme traits maybe identified thatwere still in an early phase of growth even in lead states. Thus, a factor that appeared in two matrices tdentified a certain cluster, namely: high 1960 proportions of unmarried women and of houses with runningwater, small change in proportions eating wheat bread, small proportions of non-Catholicsas of 1940, and large 1914 to 1960 increases in proportions of malesin white-collar employment (but low proportions in 1940).

5. Correlation between amount of change in differentvariables. In the table displaying this matrix (Table 28), thesigns of the coefficients are 318

as expected, but one might have difficulty in foreseeing their sizes.The

salient coefficient is .922, for changes in proportions of males and famaUs

going barefoot. Changes in occupational patterns are interlinked,as expected.

General shifts in literacyare moderately associated for the urban and rural

sectors of the aame states, and that association isslightly larger among

females.

Development inevitably entails, and is bulwarked by, largeflows of

human beings as migrants from economically lagging toadvancing parts of a

country. Furthermore, migrants carry with them something of the features of

the communities from which they come, and theyserve as communication links bringing glimpses of life at their destinations to the folk back home.Mi-

gration patterns and their correlates are clearly important.

Mexican migrations have been by no means confined to urban destinations.

Localities with large new irrigation projects in the 19401s and thereafter have attracted heavy inflows of farmers (not merely of migrant laborers).Indeed,

states in which farms were mare mechanized and yielded larger incomesto tarn people attracted migrants to both farm and city. In-migrants were relatively more numerous in states equipped with better road networks and possessingmore autos, wheremass media were more used, where white-collar employees were more numberous, and where young boys of schoolage were less often holding down jobs.

States with higher literacy attractedmore migrants. It is the states with presently moderate rather than maximal proportions of adults withpost-primary schooling who had the greater proportions of in-migrantd in their populations; but this reflects the fact that migrants constttute part of the population base on which the percentages are taken. Large numbers of in-migrants will typically pull down the percentage figures on traits that characterizea distinctively leading minority. 319

The information fields in whichmen participate are determined by the society and community in which theygrew up, by where friends and relatives have gone (migration patterns), by their associates at work.They are clearly affected, directly or indirectly, by their educational experiences and the degree to which their parents participated in and introduced them to the "educated community." In an area in which there is a relatively high level of educational attainment of adults, a wider exposure of youth to "modern" parts of theeconomy and of the society can be expected, and, ofcourse, a more direct exposure to education itself.Larger proportions of youth in areas in which there are many educated adults are directly exposed to prior "adopters" of education. The impact of these exposures on behavior of youth may differ, however, with characteristics of the educated populatim and of those still in school.Fbr example, do men or woos* have greater influenceon the schooling of boys or of girls, or is there any difference?Haw far do urban patterns take their messages into adjacent rural communities? Not surprisingly, adult schooling beyond primmu years is definitely associated with literacy of teen-age children especially those living in urban areas.The level of literacy of rural youth is rather better predicted (negatively) by the adult index of "no schooling."Yet, for each sex and residence category there are states that have experiencedno residual inter-generation improvements in literacy after migrants havecome or gone.

Inter-generation differences in the literacy of urban males and for the young-to-middle-aged urban and rural females are negatively correlated with proportions of adults who possess post-primary schooling, again evidencing the ft successive waves" features of development. Areas high on 1960 proportions of adults with post-primary schooling reflect high literacy starting points in the older age groups. (The fact that the association is reversed for rural males is almost certainly attributable to the confounding effects of selective male 320 rural-durban migration.) The sequential phasing of diffusion of literacy is illustrated also in the fact that urbanization indexes were positively correlated with 1930 to 1940 literacy gains of adolescents, but the correlation turned negative for the following decade.

FUrther traces of sequential developments were evident in that urbanization was more closely associated with high enrollments of children in earlier years than in 1963. Nevertheless, rates barely reached 60 per cent for the

6- to 14-year olds in the median state in 1960. Literacy of older adults tends to be a better predictor of childrenls enrollment than is adult possession of post-primary schooling, and literacy was a somewhat better predictor in early than in recent years.

As remarked earlier, school retention can be measured byproportions of over-age pupils, bypromotion rates, or by pass rates on examinations.Continu- ation from third to fourth grades (many rural districts have only three grades) ranged among states in 1960 from 24 to 81 per cent in rural schools and from 76 to 98 per cent in urban districts. (The corresponding 1942 rates had been: 14 to 72 and 53 to 94 per cent.)The lowest urban rate in 1960 was almost equal to the highest rural one. As would be expected, continuation rates were high in states with numerous progressive features. Between 1940 and 1960, state levels of enrollment remained relatively in the same order, as did urban (but not rural) pass rates on exams.Continuation rates displayed only modest stability over time.

Age-grade relationships proved to be associated with other traits in interesting ways. Thus, one measure of the isolation of areas marked by high retardation is the clear negative correlation with presence of radios, with literacy of adult females, and with indexes of farm mechanization (despite positive correlation with high proportions of man engaged in agriculture). 321

The size of these coefficients with retardationwas about the same for rural

as for urban pupils and of about the same size for pupils of eachsex.

Effects of urbanization are evidenced in severalways. For example,

there is a moderate positive association between development of cities and 1960

enrollments. Overage is more a rural than an urban affair.Continuation rates

are higher in urban areas. Obviously, communication of attitudes favorable to

schooling is uneven from state to state, and much of it goes on within the urban

or rural population only. The data on enrollments up to 1940 cannot be separated

by urban and rural residence, but correlations of enrollment for total state populations with urban proportions and with adult literacy are higher than those for recent years.

In Chapter VI, multiple regressions were used to explain the geographic distribution of primary enrollments. The independent variables were selected to provide good indicators of (1) visibility of economic returnsto schooling (2) ability to pay for it, (3) costa of schoolingto the individuals of the community

(represented by foregone employmentopportunities, and (4) degree of exposure to attitudes and "tellings" presumptively favorable toschool attendance. Because of inevitable problems of multicollinearity, itwas not always possible to separate these elements in the empirical findings; almost any variable selected would inevitably pick up some elements of the model other than the main feature that it was supposed, hopefully, to represent.Nevertheless, by comparisons among a series of multiple regression equations, it was possible to draw some reasonably firm conclusions. In all cases the dependent variable was enrollment rates o: children age 6 to 14: total for 1937, total for 1960, urban 1960, and rural

1960.

At first sight it might seem that one of the most surprising resultswas the behavior of the variable "proportions of males barefoot," whichcomes through with highly significant positive coefficients in multiple regressions that 322

include any variable relating to either adult literacy or occupation, which

ever the dependent variable. However, the real surprise is not in this, but in

the negligible zero-order correlations between enrollment rates and proportions

of men barefoot. At the same time, there are strong negative associations be-

tween proportions barefoot and such variables as adult literacy (male or female)

or proportions of males in white-collar employment and strong positive associ-

ations with proportions employed in agriculture. Given this combination of

facts, it was expected that when controlling for education or occupational

characteristics, the proportions barefoot would have the positive sign it dis-

played.The more indigenous populations were doing remarkably well. with respect

to enrollment of their children in 1960; for any given economic or adult edu-

cational level they were apparently doing much better than other people.Pro-

portions barefoot had been introduced to pick up an attitude cluster that was

assumed to be less rather than more favorable to schooling. The results (which

are consistent from 1937 to 1960 and for rural and urban populations alike) are

extremely. persuasive. The attitude proxy performs with marked success in an

inverse direction that is at once surprising and encouraging.

Both the highest multiple regression coefficients, and the highest partial

coefficients on some variables (notably literacy of older females) were in the

1937 equations for total state populations. However, to discuss further either

1 the 1937 or the 1960 "total" sets would be less interesting than to concentrate

on the results of the separate regressions for the urban and the rural 1960 en-

rollment rates. The urban regressions will be considered first.

Already among the better predictors of urban enrollments in a zero-order

relationship, the proportion of high incomes in manufacturing is raised further,

to display a highly significant partial coefficient of .651 when it is combined 1 323

with proportions of in-migrants and of males in white-collar jobs.It is almost

certainly serving primarilyas an index of visibility of economic benefits of

schooling, though partially also asan indicator of ability to pay for it. Pro-

portions of in-migrants, proportions in white-collar employment, and proportions

with high income from wages in manufacturing together explained 44 per cent of

the 1960 urban inter-state variance in enrollment. But once again it is extreme-

ly interesting to analyze the signs of these variables. That for in-migration

is a highly significant negative506. (In zero-order relationships with urban

enrollment rates, in-migration told virtually nothing.)Together with other

evidence, this is an unambiguous indicator of Vie segmentalization of urban

society and the separate sub-cultures and information fields with which large

proportions of migrants to the cityare identified. Almost all the features of

the school decision modelare picked up in this particular equation, with one

exception.The exception is "proportions ofyoung children (age 8 to 11) in

paid employment," whichcame through in the urban equations only when it was

combined with certain other equally weak variables (and did not attaina 5 per

cent significant level). In other equations in which urban enrollment was the

dependent variable, literacy of older women proved to be somewhatmore important

than that of older men. The combination of literate older females with barefoot

males gave the highest multiple regression coefficient obtained on urban en-

2 rollments (an R of .473). It must be stressed, however, that literacy of

older females may reflect such a range of other factors as to blur its meaxiing;

it can be interpreted at one and the same time as an attitude and information-

field variable, an indirect indicator of ability topay, and an indirect indi-

cator of visibility of returns. The slightly higher R2 (and higher F value)

is reached at the sacrifice of greater precision in identification ofprocesses

bearing upon urban enrollments. 324

The regressions for rural enrollments were quite different. Variables

that might have indicated local visibility of returns to schooling (or, more probably, local ability to pay for it) were of little or no explanatory value.

The child-employment factor, on the other hand, emerged as important (with a

high negative coefficient) in each equation in which it was entered. The

highest multiple correlation was obtained by combining percentages of boys age

8 to 11 who were employed with percentages of males going barefoot (an R2 of

.520), the former variable taking a negative and the latter again a positive

coefficient. That the negative correlation between child employment and enrollment is not spurious is evident from the failure of the farmer to come

through in the urban regressions--and despite the fact that the only evidence available suggests that earnings of employed childrenare greater in cities.

In the countcy the children's earnings are amore important marginal contri- bution to the household.Summing up all the rural regressions (of which only a few were picked out for comment), they give clear evidence that the important determinants of enrollment in the countryside are attitudes about schooling and the degree of fiaportunity for atyl importance of foregone earnings of school-age children.Such indexes as were available on ability to pay or local visibility of returns to education were of no importance as explanatory factors.This contrasts with the urban picture, in which the important factors were visibility of returns and the extent to which the urban population was segmentalized, with large numbers of in-migrants participating only in a limited degree in the forward edge of Mexican modernization and in the information fields common to the more advanced sectors of the society.

If only because data for individuals and families that can be used to analyze the role of education in development are almost non-existent, analysis of area data in developing countries will yield large returns. Actually, there 325 are advantages in using area data, as discussed earlier, despite the undeniable ambiguity that is called the "ecological fallacy." As Fattahipour demonstrated for Iran, the vast amount of census material availableon sub-national areas in

soma developing countries will reward exploitation by the development researcher.

Comparing his findings for Iran with the present analysis for Mexico reveals many similarities and many contrasts; these are quite different cultures and countries at quite different stages of development, with very different historical heritages and physiographic conditions. Yet evidence of the patterns in which information flows through a changing society and the factors determining how slowly or rapidly residents of different places respond toor resist the stimuli to school attendance emerges clearly in bothcases. It reveals a systematic sequential process that can be traded from the older generations of

Iran in 1956 to those of Mexico in 1937 andon down to the yodng adults of the

Mexico of 1960. Even those differences in sex patterns thatare expected in comparing a Moslem with a Catholic-Indiannation, though they are evident enough, still fall within the general franework ratherthan overriding it.

Probably every researcher who has probed into anything witha complexity approaching the problems that have been tackled inthis resarch comes out with a list of things that should still be done and of research endeavors he suggests others might well take up. This author is no exceptim. The major suggestions are broadly two.

First, the use of smaller geographic units,as the municipio in Mexico, would permit a finer drawing of spatial communication patterns, both within hierarchies of cities and between town and adjacent country. By reducing the heterogeneity of the observation units, it would permit alsoa sharper identification of relationships among those variableson which small-unit data, can be obtained. This would be well worth doing for selected parts of Mexicodespite 326 the fact that educational data in particular are severely limited at the municipio level.

Second, Chapter VI barely touched the factors influencing education of youth, even if looking only at the primary levels.Other transforms of the variables already used should be tested.More important, however, is experi- mentation with other dependent variables, such as continuation rates and age- grade indicators of progress through the schools.There is reason to suspect that some of the results might be quite different sin certain respects than those undertaken using enrollments as the dependent variable. Continuation rates pick up to a greater degree the more advanced aspects of the development process and provide clues in predicting the conditions under which rising proportions of young people will move on into post-primary education.

Over-all, it is suggested that the joining of "information field" concepts and communication theory with economic decision models in the analysis of development processes should yield increasing insights into those processes. This study only taps a few of the possibilities in suchan approach. I

APPENDIX A

i GLOSSARY :

;

I GIASSARY Source Fore Variabl NuMber Population Distribution and Change Variable Name Description bda p.pp.pp.p. v 14C 1and and 39 39 3421 DensityUrban 1940193019401960 PerPopulationACM2 cant of total population in towns d opp. 59-681-9 p. 26 B. 765 CapitalUrban 19501960 Dummy 1960 Durway variables;largestof 2,500+ 1 cityif capital in the city state; was 0the otherwise b dpp. pp. 1 68and ff. 39 pp. 1 and 39 10 98 CapitalCapital/UrbanCapita1/Urban Size 1940 19401960 Populationpopulation of the capital city/Urbancity d pp.p. 3959 ff. It 22 PopCapital 50,000+ Size 1960 1960 Per cent of oftotal 50,000+ population in cities 1413 UrbanUrban 1960-1950/1960-1940 1960/1930 RatioPer (seatef 1960 portionoflation 1940to 1930 ofinto populationtowns1960per cent change2,500+ of in inpopu- towns pro- 2,;(n+ bdc PP.pp. 14550-51 ff. 1 and 31 LT 151716 BornBorn Instate Instate 1950 19401960 Males born nowinblethat theliving/population occurred state in between which of they1950 the are andstate. 1960 sometimes used as In-migrant/Resident Varia- Transportation pih ipp.PP. 104 ff. p.PP. 1051 1041051 ff. and 24 20211918 RR/AreaRR/PopRR/Pop 196019601940 Railroad kilometers/km?kilonotersbopulation ph p. 764 p. 12461 and K, 242322 Roads/PopRoads/PopBRoads/Popk 1960 1940 KmRoads of allKm/population weather roads/Population h pp. 764 p. 12 and K, 2625 Roads/AreaBRoads/AreaA 1940 1940 KmRoads/km2 of all weather roads/km2 of area of area hk p. 769 p. 467 282927 RoadsBicycles/PcpRoads/Area Paved/Roads 1940 1960 1960 Proportion of road kilometers paved, 1960 k1k p.p. 468 92471 33323130 Autos/OopAutos/PopBicyclesBicyoles/Pop 1960-194019401960 1960 AutosBicycles/Population registered in 1939/population 1940 3534 Autos/PopAutos 1960/1939 1960-1940Utility and Communication Facilities Autos/population;Autos/Population; 1960 1960/1939 minus 1940 kh p. 676 p. 411 383736 Elect/Capita 1960-194019601940 Electricity consumption/per= capita Source Form!1 VariableNumber Variable Name Description kd p.pp. 271 289 and 24 414039 Movies/Pop 1960-194019601940 CinemaCinema sales/Population sales/Population 1961) manus 1940 d1 pp, 629 and 601 p. 35 R 43 RunningLibrary Water Use 1940 1960 PercentageLibrary use/Population;braries of dwellings with 520+ withincludes volumes running only water li- d pp. 631 and 601 R 44 Radio 1960 Percentageforincludesnumber of oonaercial dwellings present.of places radios useswith for owned, raios: rather than the Definition of dwelling living rather than the d pp. 112 ff. 45 Single F 20-24 1960 Marriage and Fertility Rates Per centunmarried of females age 20-24 who are db pp. 170 and ff. 39; and PP.pp. 32 94 ff. and 69 L 4647 F under 5 its/t 1940under 5 Yte/F 1960 Per cent of females under 5 years of age d dpp. PP. 632 632 ff. ff. 49'48 Child/FChild/F 40449 40-49 U R1960 1960 Children evereverold;old; born boraurban rural toto womenwommt40-49 10-49 yearsyears Labor Force Participation da ocpp. 1-9 PP.p.p. 67 84-8588 53505251 EcActEcActM M 1940196019501930 Male populationeconomicthepopulation.an populationoccupation economically activity 12+for for active/maleyearspay the or of familywho age exercise who without have an Economically active includes a p.p. 67 88 RL 54 EcAct F 1930 Female populationpay.population economically included 8-11active/female year olds In 1960, the economically active bd d6pp. p. 11-9363 and 39 pp. 84-85 RLL 5958575655 EcActEcAct F F12+1960 10+19501940 1960 1940 Economicallyhomeaspopulation. female7/fenalesfemales/femalesfor men; excludes 12+10+ domestic work in own "Economically active" defined years of age g pp. 32, 240443 RL 6160 Devel.EcAct indexF 1960-1940 1950 CompositeEconomicallyminus indexthe female following1940 of development; female variables: population rank 1960 (1) Per cent of sums or Centagriculture,economicallylation(2) of Per populationclassed cent 1950.active ofas economicallyin urban,in cities manufacturing, 1950. of active'in10,000 1950. (3) Per cent of popu- (4) Per pernon- Averagepercapitaor(6) capitamore. Value earningsof ofthe of the totalmanufacturing oftotal allpopulation, population,personnel production 1950. in 1950. (5) Value added by:manufacturing manu- per (7) forrankingintothefacturing, each anumerical composite thestate. states1950. value index for of eachof the development andrank summing position by These were synthesised Source VariableNumber Variable Name Description d dp. p. 363 363 6263 EmployEmploy 8-11 8-U. 14 F 19601960 Employment lyactiveof 8 to U year olds/Economical- b pp. 30-67 614 Collar/EcActWhite MCollar 1940 and Professional Workers White collar/Economically active; white proprietorios,port,mining,pluscollar (b) commerce; inworkers ustries, !Elm,andare: (c)c funcionarios and "softer-and (a) directares, ca Z on, trans- sates from d p. 417 65 Collar/EcAct 14 1960 White collar/EconomicallyqMpleados(41) professionistaa. in public active; administration, white and 66 Collar/EcAct 14 1960-1940 White collaroficinistas,tecnicos,collar males/Economically includes (d) (a)vendeiores professionistasactive y (b) personal directive, (c) b dpp. p. 30-67417 696768 Collar/EcAct F 1P40-194019401960 WhiteASAs 64, 65,collar but but females,males, for females/Econemicallyfor females females 1960 1960 minus minus 1940 1940 active d dp. p, 417 417 p. 1417 L , 727170 Clerk/EcActClerk/EcAct 14 FT 19601960 Clerical/Economical.17 active d p. 417 73 Prof/Eact T 1960 d 1p. 417 P. 417 7574 Prof/EactProf/EcAct FM 19601960 Public Administration Profesionistas y tecnicos/Economically active ba pp.pp. 78 30-67 ff. 7776 P.A./EcAct 14DI 19401930 Agriculture Public administration/Economicallymales active dba pp.p. 36719 and 57 p. 78 LT 807978 AdEcActAg/EcAct 14 M 196019401930 Males in agriculture/Economicallymales active i p. 802 3281 Ejidos/AgPopAg/EcAct /4 1960-1940 1940 MalesEjiditarios/Male in agriculture/Economicallymales 1960 population minus 1940 in agriculture; active Per cent nationtheoflation malesproportion under whoin agriculturecultivatespecial of the legislation agriculturallands who areconceded popu- by the b bdpp. p. 19367 and 59 pp. 19 and 59 LL 838584 Ag Labor/AgProp/Ag MM 194019401960 Per cent whoofjornaleros malesreceive in aagricultureand salary obreros, or awho i.e.,daily work manualwage workers d p. 367 8786 AgAg Prop/Ag Prop/Ag M11 1960 1960-19140 Per centproprietorsbut alone,of havemales orno in who1960one agriculture are workingminus proprietors 19140 far who them are of for enterprises, a salary Source FornlU VariableNumber Variable Name Description pp. 53-56 53-56 8988 FarmEquip/Land Mechanized 1950 1950 PerInvestment cent of implementsfarmsin equipment; mechanized in thousandvalue (includes of pesos/landmachinery, farms value pp. 53-56 90 . Farm Nonmechanized 1950 Per cent mechanizedof farms without and farms even having animal mixed traction) a Table 1 LT 91 Ag Inc under $500 1960 Per centwith Agvariableoftraction Inctheincomes overpopulation is under$500somettms 1960 500in agricultureusedpesos converselymonthly. This g pp. 143-50 pp. 1143-50 9392 Returns Glick 19301950 Returms to matedprices,laborthe human valuein agriculture agent; of land productivity and in capitalpesos atof employed current in This measure was based on an esti- as farmvalues.agriculture.tocent landincome and and katernately to capital get returns were of 10 subtractedto per the cent human fromon those net The resultant estimates of returns Glick assumed returns of 5 per 94 Returns Glick 1950-1930 Returns to theagent.1950turns human minus are agent used1930 inin agriculturethis study The estimabes using 10 per cent re- a p, 78 95 Mfg/EoAct M 1930 Manufacturing and Mining Males in manutacturing/Economicall.y active db pp.p. 36721 and 58 969897 Mfg/EcActMfg/EcAct M M1940 1960-19401960 Males in manufacturing/Eamon:ix:allymales 1960 minus 1940 active a p. 78 99 Mfg F/M+F Mfg 1930 Female percentage of total employedsin db spp. 21 and 58 p.Table 367 1 102101100 Mfg F/m+FIncF/M+F $1,500+ Mfg 19601940 1960 Per cent manufacturingofwho those had engagedincomes inover manufacturing 1,500 pesos monthly Table 1 103 Mfg Inc under $500 1960 Per cent ofpesos.Thiswho 1y,those had variableas engagedincomesper cent is in under sometimeswith manufacturing 500incomes pesosused over converse-monthly. 500 gf pp.pp. 5-9 240-43 105104 Mfg.Pay/EMp Glick Fact 1950 1930 AverageValue addedmonthlypopulation in paymanufacturing/Per per factorY employee. capita of f pp. 5-9 pp.pp, 5-9 5-9 108107106 Pay/EmpPay/EMpPay/EMp Fact Fact 19551950 1940 wasmoremining,Theless limited 1930per of petroleum,yearcensussize. to firmain coversbusiness. anddoing quarrying)all 10,000 firma pesos (exceptregard- ar Beginning in 1935 the census In 1950 the 109 Pay/Emp Fact 1955/1940 Ratio of monthly1955farrestriction firms topay monthly perincluded on factor sizepay in perofem the firmsplayeefactory census was in employee removed dba pp.p. 3677820 and 57 Mining/EdActMining/EcActMining/EbAct /IMN 193019401960 Males in mining/Economicallyin 1940 active males b dpp. pp. 14 282-89 and 51 L 114113 Non-CatholicNon-Catholic T T1940 1960 Cultural Traits Per cent ofCatholic the total population non- Source Fo rmu VariableNumber Variable Name Description b pp. 34 and 71 LT 116115 Sleep on BedFloor T 1940T 1940 Per cent ofafthe total floor population and do not who eat sleep wheat inan bread PP.pp. 34 and 71 75-76, 87-88 LT LT 118117 Nonwheat T 19501940 Per cent ofwheat atotal bed bread population who do not eat pp, 280-81 122121120U9 NonwheatNonwheatNonWheatNom/heat T T1940-1960 1950-19601940-19501960 Change in doper not cent eat of wheat total bread population who pp.p. 2801 and 39; PP. 35.and 72 124125123 BarefootBarefoot 14RuralUrban 19140 T 1960 PerPer cent cent ofbarefoot total population who walk who walk barefoot p.274PP. 75-76,87.88 127126 Barefoot M14 1960 1950 States mereproportionsstates ranked on barefoot with a of males high rank had the lowest this variable; the b 0pp. 1 and 39; PP. 75-76,87-88 pp. 35 and 72 L 128129 Barefoot F 19501940 Per cent of females who walk barefoot d p. 274 a 131130132 Barefoot FM/F 1960 1960 MA 1940 Ratio of malebarefoot to female per cent it 133 BarefootBarefoot MM 19140-1950 Change in per cent of males barefoot itit 1373381363353314 BarefootBarefoot M F F 19140-1960 39140-195019140-1960 1950-19601950-1960 Change in per cent of females barefoot a 55 Literacy: 139 at 10+ TLiteracy of Populations 1930 above Per cent ofwho the are population literate:Decimated 10+ Ages literacy is defined as years of age db pp.290if.and pp. 7 and 45 P. PP. 94 ff. R 11411140 Lit 6+ T 1940 10+ TT 191401960 Per centwrite ofthe the Spanishpopulation population who 6+ sayyears they of canage read and bd cpp. 77651-52 andand 4545 pp. 82-83 1145143144142 Lit 40+6+ TM 195019601940 who are literate bd pp. 290 ff. and pp. 7 and PP. 914 ff. ff. 45 RR 148147114 6 Lit 40+ FM 19601940 Per cent 140+ years of age who are literate ad P.pp.290if.snd 55 P. 55 PP. 914 308 1501149 Lit 30+30+ 14MF 19301960 Per cant 30+ years of age who are literate b dpp. 19 and p. 19 and 56 LT R 154153152151 Lit 6+06+030+ SohSohF 196014 F 191401940 Per cent 6+ schoolingwho are literate without Source Form Variable Number LiteracyVariable of Name Youth Description b app. p. 7 and 45 p. 55 LT 157156255 LitLit 10-14 10-14 F HM1930 19401930 Per cent of 10-14 years old who are d bpp. 290 ff. and pp. 7 and 45 pp.pp. 94 94 ff. ff. RR 160159458 LitLit10-llii1960Lit 10-14 10-14 F F1960 1940 literate LTLT 164163162161 LitLit 10-14 10-14 FM FM1940-1930 1960-1940 Change in perliterate cent of 1D-14 years old 165 Lit 6+ 1960-1950/1963-1940 Urban Literacy by Age Per cent ratesof 1940 that to occurred1960 change between in literacy 1950 and 1960 d dpp. 290 ff. and Includespp. 94 ff. all RR 167166168 Lit 50-5940-4960+ MU MU 1960 1960 MU 1960 dd inruralforageliteracy 1960 entriesurban pop. by and RRL 173169172171170 Lit 10-1415-1920-2425-2930-39 MU 19601963 Per cent byof designatedurban malej ages who are literate d LTLT R 176175174 LitLit 50-5960+ 40-47 Fu FU 1960Fu 1960 1960 dd LT RR 181180179178177 LitLit 20-24 30-3925-2910-1415-19 FU Ft7FUFu 1960 1960196o Per cent ofby urban designated females ages who are literate dd LT R 184183182 Lit 60+4o-4950-59 mR MRtiR 1960 1960 Rural Literacy by Age dd LT RR 189188186187185 LitLit 30-325-2935-1920-24 10-14 9 MRKRNR lemR 19601960 1960 Per cnt ofby rural desigtiated toles who ages are literate dd RR 190193192191 LitIttLit 60+50-5940-49 30-39 FR PR ER1960 1960 1960 Per cent of rural females who are literate dd LT RR Age197196195194 Differenoes in Literacy by 3ez and Residence LitLit 20-24 10-1425-2915-19 FR 11R EREl 1960 1960 by designated ages zz RR 199198 (35-19)-(40-49)(140-49)-(60+) NUkw 19601960 PerPer cent cent of ofper urban =ban cent males malesof urbanInban 15-19 40.49 males literate literate minas =Inuit 60+14449 literate literate Var iable Description Source Form Number 200 (l5-19)-(40-49) Fu 1960 Variable Name Per cent ofminus urban per females cent of15-19 urban literate females 40449 201 (40-49)-(60+) FU 1960 Per cent minusliterateofliterate urban per femalescent of 40-49urban literatefema1e6 60+ 203202 (15-19)-(40-49)(140-49)-(600 142 MR 1960 Per cent ofper rural cent males of rural 4044915-19 ma/esmales literate 40449 minus literate 60+ literate 205204 (40449)-(60+)(15 -19) -(40-149) FR FR1960 1960 Per cent minusafof literaterural per femalescent of 40-4915-19rural literatefema.les 40-49 Sex Differences In Literacy by Age and Real:lance minusliterate per cent of rural females 60+ 208207206 Lit.Lit. 60+50-59140-449 Mu.F MU.F 14141960 1960 1960 Per cent of urban males minus per cent of 210209213212211 Lit.Lit, 20-21410-1115-1930-3925.29 MU-FMU.FMINFmU-Fmu4 19601960 designatedurban females ages who are literate far 214 Lit. 40-49 FU/M 1960 Ratio of femaleurban topopulation male perconts age 40449 literate; LT 216215 Lit. 15-1960+ MR-F FU/11 1960 1960 Ratio of femaleurban topopulation male percents age 15-19 literate; 220219221218217 Lit. 25-2940-4920-2430-3950-59 MR -F 1960 Par cent agesruralof rural females males yearsminus literateper cent fbrof designated 222224223 Lit.Lit. 10-1415-19 40-49 MRMR-F FR/M -F 19601960 1960 Ratio of femalerural topopulation male per agecents 40-49 literate; LT 225 Lit. 15-19 FR/M 1960 Adult Levels of Schooling Rati- of femalerv to male per cents literate; L population age 15-19 p.pp. 30856-57 229228227226 AdultAdult 25+30+ 30+ 0 OMF111950 1950 1960196D PerPar cent oent of of schoolingadults schoolingadults age age 30+25+ with no pp.pp. 30856-57 308 LT 231230233232 Adult 25+30+25+30+ 1-61-41-61.4 14F 1950196019501960 PerPer cent cent of of adults schoolingadults age age 30+25+ with mith 1 to1 to4 years6 years Source Variable Variable Name Description P.p. 308 308 F0u LT Number 235234 Adult 30+ 1-6 FM 1960 Per dent ofof adults schooling age 30+ with 1 to 6 years pp.PP. 56-57319-29 56-57 239238237236 AdultAdult 30+25+ 30+25+ 7+ 7+7+ F 14 M19601950 19501960 Per cent of schoolingadults age 30+25+ with 7+ years of pp. 319-2956-57 56-57 242241240 AdultAdult 30+25+ 25+ 10+10+ 10+14 14F 19501960 1950 Per cent ofof adultsadultsschooling ageage 30+25+ withwith 1C,-10+ years of pp.pp. 319-29 56-57 56-57 245244243 Adult 25+30+ 13+10+ FM 19501960 Per cent ofschooling sdhoolingadults age 25+ with 13+ years of years of pp. 17319-29 and 55 L 248247246 AdultAdult 30+ 30+15+ 13+ 13+BAC M FM1960 19601940 PerPer cent cent of of schoolingadvlts adults age 15+ with age 30+ with 13+ years of pp.PP.pp. 17 17 and and 5555 L 253250249 AdultAdult 15+ 15+ UN UNBAC M F1940 F1940 1940 Per cent ofschoolingbaccalaureate adults age 15+ with 13+ years of p. 309 R 253252 7-147-14 No No Sch. Sch. M F1960 1960 Per centosnt offemalesmaIes 7-14 year olds without schooling; b pp. 17 and 55 LT 254 6+ No Sch. M 1940 db pp. 17 amd 55 p. 308 LT R 257255258256 Adult646+ No 0Sch. Sch MF14 19601940 1950-1960 PerPer cent cent of of adults 6+ population with no sdhoolingwith no schooling x R 260259 6+06+0 Sch. Sch. F/MF/14 1950 1960 Ratio of femalelation1950-1960; to 6+ male with males per no centschooling of popu- b pp. 19 and 56 LL 262261 6+6+ YrsYrs SchSchM1940 F 1940 Enrollment Rates Per cent 6+of yearspopulation of schooling 6+ years of age with m kp. 100 pp. 181-83 L 264263 Preschool 19601944 PerPer cent cent of kindergartenof 4 populationand 5 year 6-14cld populationyears old en-in ak1a p. 66 p. 2266181 LT LLL 266265268267 EarolEnrolEnrolEnrol 6-146-10 6-10 T14 19601937F 1930 1930 Per cent rolledof 6-10 inyear primary olds enrolledschool kx kp. p. 181 181 LT R 269271270 EnrollihrolEnrol 6-10 6-314246-14 F/M F 196019301960 RatioPer centof female of 6-14(age to year6-10)male oldsper centenrolled enrolled k 13. 196 R 272273 Enrol 6-14 Urban 1963 6-14 F/M 1960 PerRatio cent of offemale enrolled;5-14(age to age6-14) male cOhort,urban per cent6-34 enrolledyear olds Source Fo rmu VariableNumber Variable Name Description k p. 197 275274 IhrolEhrol 6-14 6-34 Rvral Urtan-Jtural 1960 1960 Urban minus rural per cent enrolled Per cent enrolled;of 5-14 age rural cohort,-6-14 year olds 00 PP. 54-55 It 277276 EnrolEnrol 7-12 7-12 11 F 1950 1950 Per cent of (age7-12 6-14)year olds enrolled q Table 5 L 278 Percentages of Children First Enrolled inEnrol School 6/Inc $200 1959at Age 6 or Younger by Family Income Monthly income under 200 pesos q TableTable 5 5 RItR 282281280279 Enrol 61(6016/$1,00o+6/4601-1,0006/3201-600(200) to 1,000)- 19591959 1959 MonthlyPerMonthly oents income income forfrom framfromfamilies 1,000600 601201to pesos 1,000towith 1,000600 and monthlypesos pesos averpeace minus income per cents for Percentages of Children First atEnrolled Age 6 arin YoungerSchool by Father's Occupation families with incomes under 200 pesos q TableTable 6 6 It 285284283 Enrol 6/Prof-Ag6/Professional6/Agriculture 1959 19591959 PerProfessionsFarming cents forminus children per cents of professionalsfar children of farmers (Number of entrants to Grade 1 in the Gradeyear t(i-1) as a inratio year to (t-1). number of entrants to Primary-School Continuation Rates The ratios in the variable refer to the two grades compared in each case) Beginning of year enrollments:Urban areas: i PP. 363 and PP. 193 392285 and RL11L 288289287286 ContCantCont B B6/52/15/44/3 3/2 Uu U1942 1942 Sth14th3rd2nd grade gradegrade grade 194314th 1943/3rd1943/2nd 1943/1st grale gradegrade grade 1942 1942 1942 RLR 293292291290 CantCont B 3/24/32/1 R 1942 Rural4th6th3rdand grade areas:grade 1943/3rd1943/5th 1943/2nd1943/1st grade grade 1942 1942 L 295296294 ContCant B4/3B 5/146/5 U-B. Itit 19421942 Ratio of per1943 cent to of per entrants6th5th cent grade of in entrants1943/5th1943/4th 4th grade ingrade 3rd 1942grade k pp. 207-12 R 298297 CantCont J33/23 2/1 U 1960 1942 urban 3rd2ndUrbanminus grade areas:rural 19600/2ad1960/1st areas gradegrade 1959 LT R 302301300299 ContCoatCant B 2/14/36/55/4 RuU 1960 Rural6th5th4th gradeareas: 1960/5th1960/4th1960/3rd grade 1959 k pp. 207-12 RRIt 306305304303 ContCant BB 6/54/35/43/2 itR 19601960 4th6th5th3rdand grade gradegrwle 1960/3rd 1960/5th1960/4th1960/2nd1960/1st grade grade 1959 1959 Source Ft) VariableNumber 307 4/3 Urban-Rural 1960 Variable Name Ratio of per cent of entrants in 4th grads Description 308 4/3 Urban 1960-1942 Ratio of per195919601959 cent into urban ofurbanper entrants centmims areas of rural in1960entrants 4th minus grada in 1942 3rd grade 309 4/3 Rural 1960-1942 NuMberFourth enrolledgradeas1960 1960/3rda ratiominusin Grade toruralgrade number1 at 19421959 end enrolled ruralof year in Grade ij pp. 363193 aidand 392 285 R 312311310 ContCont E E2/1 3/24/3 U U1942 1942 (i-1) at4th3rddgradeDay end2nd school:grade ofgrade year 1943/3rd1943/2nd 1943/1st (t-1) grade grade 1942 1942 urban RF 316335314313 ContCantCantCant E E 2/16/5 E5/4 3/2 RU U 1942 R1942 1942 Day3rd6th5th2nd school: grade grade 1943/2nd1943)5th1943/4th 1943/1st grade grade 1942 1042 rural k pp. 207-12 RRL 320319318317 ContContCont E EE 4/3E 5/14 2/16/5 R URR1942 1960194219142 Day5th6th4th2nd +grade grade nightgrade 1943/4th 1943/5th1943/3rd schools:1960/1st grade grade grade 1942 1942 1959 urban RR 3214322321323 CantGontCont EE 5/1414/36/53/2 UU 19601960 4th5th3rd6th grade grade grade 1960/3rd 1960/4th1960/2nd 1960/5th grade grade grade 1959 1959 1959 328327326325 ContCont E E5/43/22/1 4/3 R R1960 1960 5th4th3rdDay2nd gradegrade+ night 1960/2nd1960/1st 1960/4th1960/3rd schools: grade grade 1959 1959 rural LT 330329332331 ContCont B BE5/1 5/15/i6/5 Rural UrbanR 1960 1942 19601942 6thComputed ofby2/1 yearcumulation + 3/2enrollments; + 4,6grades + 5/4 ratio + 6/5 of forpergrade beginningcent 1960/5thof grade 1959 333 Cant B 5/1 Rural 1960 Sec ondary School studentsstudents completing enrolled ingrade grade 5 to1 per cent of k cpp. 246 and260 pp. 54-55 LT L 337336335334 Present/EnrolledEnrolEnrol 15-17 15-17 M F1950 1950 14 1960 RatioPer centof students of secondary15-17 present year school olds at endenrolled of year in 339338 ContPresent/EnrolledCont Sec Sec 3/1 3/114 F 1960 1960F 1960 Ratio of studentssecondaryschoolto those into enrolledthirdschoolstudents year at ofbeginning secondary in firot year of k pp. 246 and 260 L 3143.340 Pass SecSec ExamExam 14F 19601960 Per cent katonomaof340, ofstudents thoseand Nacional 3141 present.passing data de for secondaryMexico Universidad and Institut° Nacional For variables 336, 337, exmn 343342 EnrolEnrol 15-17/1-12 15-17/7-12 F M1950 1950 Ratio of 15-17cohortsecondaryPolitednico year ads sdhool Nacionalenrolled to 7-12 notin yearavailable) old age Source ForMu VariableNuMber Variable Name Description Age Grade Progress in School Proportionby ofsex age and group by residence in or above a grade Unpublished1963 data RLL 3473463453144 AgeAge 108 8 Grl+ GrlGrl+ MUMU-R MR 1963 19631963 AgeAge 8 810above above in gradegrade grade 11 1urbanrural urban minus rural RL 350349348 AgeAge 1010 10 Gril0r3+ Grl 111141U MUMR 1963 1963 AgeAge IO10 10 inabove above grade grade grade 1 rural 3 3urban rural minus urban RRL 394353351352 GrAge 14013 1210 Gr3+GT3+ MU 1963MRMU 1963 ModalAge 12 gradeAboveabove atgrade age 313 urban ruralurban RL 357356355 AgeGr 8M013 Grl+ MR FRFU 1963 1963 AgeAgeModal 8 8above gradeabovirgi:M grade at age 1 rural Fema lea 1 13 urban rural RRL 360358361359 AgeAge 10 GrlGrl FUFR4T 1963 1138 Gri. al.+ FU-R FR 1963 1963 1963 AgeAgeAge 10 810 10inabowe in ingrade grade gradegrade 1 1 rural 11urban ruralurban minus rurs.1urban LT RL 365363364362 AgeAge 12 1210 Gr3+ GT3+Gr3+ FR FUFR 1963 1963 AgeAge 10 1012 above abovt.above grade gradegrade 3 3rural urbanrural It 367366 Or M013 FRPU 1963 Modal grade at age 13 urban Pass Rates Per cent passingend grade of those present at year p. 377 LT RLL 372370369368371 PaasPass 2/Pres4/Pres4/Pres6/Yves2/Pres ItftU 19421942 Grade 426 ruralurban or RR 375374373 PassPass 1-6 1-66/Pres U 1942 R 1942 Et 1942 OrAdeGrades 6 rural1 through 6 ruralurban 220 and 226 LT RL 379378376377 Pass 4/Pres2/Pres RU 1960 Grade 42 urbanrural RRR 382380384383381 PassPass 6/Pres1-6 1-66/Pres TU R 1960 U1960 R196C 1960 GradesGrade 61-6 urbanrural urbanrural + rural rural R 386385 Pass 1-6 R/U 19601942 School Facilities Ratio to urban or per cent passing grades 1-6 rural k1 p. 20530 It 387388 Pri.Pri.:Teachers/ACAct Teachers/EcAct 1940 1960 Per cent whoof economicallyare primary schoolactive tem:heirspopulation Source Variable Variable Name Description Pone LT L NuMber 390389 SohSch Incomplete RU 1942 kn pp. 165-68 p. 192 RL 394395393392391 SobSohSch Incomplete Incomplete R UTR1960 19501960 Per cent ofthrough primary 3 schoolsor less withby urban grades or 1rural area x alstados Unidos Mexioanos, Secretaria de la Economia,R Dirsocion General de Bstadistioa, V Canso de 397396 Soh Incomplete RU 1942-1960 Differences schoolsin per cent1942 ofminus incomplete 1960 Poblaoion: 1930. Resumsn GeneralVII Canso(Mexico, de Poblacion:1934). Congo de Poblacion: 1940. 1950. Resumen GeneralResumsn °Mexico, General 1943). (Mexico, 1953). VIII Canso de Poblaciont..1960. d°Estado. UnidosEstados Mexicenos, Unidos Mexicanos, Secreteria Seoretaria de Eoonomia, de IndustrieDirecoion yeomen:do,General de DireocionEstadistioa, General III Cansode Estedistica, Resumer: General (Mexico, 1962). Agrioola.Como IndustrIal: Ganadero yEdidal: fEstudos Untdos Mexicenos, Seoretarle1956. de Industrie Resumen General (Mexico, 1959). 1950. Rem= General (Mexioo, 1956. y Comeroio, Direocion General de Estadistioe, Estadistioo:(unpublished Ph.D. dissertation, Department of Economics, University of Chicago,hEstadosgMiltan 1963). Glick, The Impact of Economic Development 1941 (Maciolos 1943). Untdos Mexioanos, Seoretaria de la Economia, Direocion General de Estadistioa, Anuario on the Returns to Labor in Agriculture in Mexico" iEstados Unidos Mexicanos, Senretaria de la Economia, Direccion General de Estadistica, Anuario Estadistico: k 1942 (Mexico, 1948). Anuario Estadistico: 1943-1945 (Mexico, 1950). AnuarioCompendio Estadistico: Estadiatico: /Estados Estados Unidos Mexicanos, Secretaria de Industria7Comercio, Unidos Mexicanos, Secretaria de la Economia Nacional, 1960-1961 (Mexico, 1963).1941 (Mexico, 1941). DireccionDireccion General de Fatadistica,Estadistica, CompendioCompendio Estadistico: Estadistico: lag19531947 (Mexico,(Mexico, (Mexico, 1959).1953), 1740. EducacionCompendio EstadiaticosPrimaria" Juniogosto 1960 (Mexico, (Mexico, 1960). PEstadosgEstados UnidosUnidos Mexicanos,Mexicanos, DepartaleVIdeMuestreo,.'ecretaria de Industrie Fundamento y Oomercio, Estadistico Direccion General del "Plan de Estadistics,de Once Anos de Departamento de los Censos, rs EstadosAge-grade Unidos progress; Mexicanos, unpublished Secretaria data, de1963, Industrie y Comercio, Direccion General de 4 esos or Traba o de la Poblacion Economicamente Active Jefes de Familia Estadistica, Departamento(Mexico, 1964). Tecnico Seccion de Estadistica, ut Estados Unidos Mexicanos, Secretaria de Educacion Public*, Direccion General de Segunda Ensenansa, elsewheretransformation: in the Glossary, The The variables we used in one of the followingvariable forms: is in Rthe for form raw of a ratio or difference, logthe (100separate - items of which are described value, L for log, and LT for log IMMIX B

CORREIATION MURICES

1 CORRELATION MATRIX: POPULATION, TRAMJPQRI'ATION, UTILITU.S AND OMR VARIABLES TAPLE 57 COMMUNICATION VARIABLES AOAINS2 TILEOLIVES, EACH OTHSR, AND SELECTED Population Distribution Urban and ChangeCap. Cap. Urban _ Pop w Urban Born Instate 1940 Density 1960 1930 19140 1950 1960 Dummy 1960 1940 1960 1940 Cap. Size 1 1960 50,000+1960 1960/30 196040/1960440 1940 1950 Variable Number 1 2 3 14 5 6 7 8 9 10 11 12 13 14 15 16 Population distribution1 and chana -.188 -.086 -.315 -.375 .294 .077 -.156 -.356 -.353 423 UrbanDensity 19401930 19601940 1.000-.250-.322 .974 -.192-.2601.000 .974 -.3321.000-.260 .981 1.000-.192-.250 .981 -.112-.197 .955943 -.093 .923907 -.075-.039-.071 -.289 .287.252 -.373 .328.307 .256.287.258 .340.344.101 -.052 .599.615 -.152 ..201 191 -.411 .173.177 -.365* .555.577 : -.304* 565*. 576 Capitar/AammyUrban 19501960 1960 -.086-.188-.197 -.071-.112-.093 -.039-.907 .943 -.075 .955.923 -.0671.000 .977 1.000-.053 .977 1.000-.053-.067 .547.153.211 .571.154.199 .239.268245 .351.408 -.029 .631.600 .076.418.342 -.025-.002 .103 .031,.566.575- * .598.585; 543 10 98 CapitalCapital/UrbanC.:vital/Urban Size 1940 19601940 -.375-.315 .294 -.373-.289 .258 .256.328.292 .287.307 .245.199.211 .268.153.154 .239.571.547 low .185.943 1.000 .233.943 1.000 .238.185 .914.281.194 .482.297.236 -.234-.023 .036 -.129 .446.231 -.363; .2924 -.316*-.021: 242* kab.nkab 131211 UrbanPopCapital 50,000+ 1960/1930 Size 1960 1960 -.356-.156 .077 -.152-.052 .101 .201.615.344 .599.191.340 .342.600.351 .418.631.408 -.029 .076.351 .086.236.194 -.023 .297.281 -.234 .482.914 1.000 .073.638 1.000 .358.638 1.000 .358.073 -.281-.057-.144 -.1414-.054. .634:.469" .684:.475.0614149* Transportation151416 BornUrbanBorn Instate 1960-1950/196049140 Instate 1940* 19 b* -.499'-.353 -.411-.304*-.365* .565-.577:.177, 543-555:.171., -.002 .585-.575! -.025 .598-.560: -.021-.031* .103* -.316-.363* .231* .242-.292:.446 -.149--.141:-.129, -.144, .061-.054: -.057 475-.469: -.281, .684-.634: -.041--.030..1.0004 1.000**-.030** -.041**.968 1.00o .968** 21101918 RR/AreaRR/PopRR/Pop 1960 19601940 1940 -.365 .787.748.144 -.407 .799.773.221 -.032 .105.362.164 .034.154.360.159 .130.400.275.206 .155.218.310.428 -.085-.068-.069-.106 -.242-.129-.025-.083 -.383-.228-.073-.265 .326.356.057.181 .196.232.245.106 .066.217.138.315 -.082-.110 . 064363 -.485-.257-.654-.853 -.086- -.063-.026:.366:.379! .054:.335:.374: 232224 Roads/PopRoads/Popt 19601940 1940 -.807-.528-.816 -.826:.7730 .524.449.311 .272.506.411 .372.178.499 .142.472.369 .259.040.178 .431.313.335 .511.359.393 -.391-.052-.320 -.277-.126 .145 -.062 .514.167 .455.099.337 .647.264.46o .398-.691:.5214'!, .36°-.673:.521! 25282726 Roads/AreapBoads/Area!Roads/PavodRoads/Area 1940 1960 1960 .467.722.084.817 .841.526.726.155 -.125-.010 .086.458 -.054 .470.154.066 -.013 .198.597.110 -.016 .607.176.123 -.008 .134.113.063 -.056-.059-.141 .173 -.120-.103-.170 .105 .379.047.076.123 -.101-.012 .484.007 -.144-.062 .514.124 -.23.8-.203 .085 -.010-.045 -:g-.2244 -.146! -.IN: 313029 BicyclesBicycles/PopBicycl3s/Pop 1960-1940 19601940 -.035-.069-.050 -.052-.028-.091 .345.423.573 .344.424.589 .249.341.598 .197.585.285 .232.247.115 -.521 545.390 .432.461.311 -.025 .081.350 -.130-.018 .342 -.006 .075.405 -.171-.165 .095 -.037-.310::M .002 .220*.184':33019* .191*.114441.32929* i110011011.011V.00.1 Mr.*-ft...... ,...... 1.RII.110/ 0 ...... --,,,,,,,,,

354

* * ** ,* ss *erg,,

A Mai 1U *aliti t5 _ _ _ ilA .ii, t e e I ***** v "yeg:14. AA aatFa ua zio It it 'iv: *6 . 8. e 1

*,..... * 5VOR AEI riR AI m.u.. IN MIrt. ii ( tang to VA§ § 51 IA AR am A . 5'; e e

* *

ai al Icx4 FAME HI au P: s * * AEA OiR t R5 11 IA PM7Ngsog * * IC yi 1119n RW, TM 3 58 fE Pqr1 A* IM.*Ch0 0. NgNA im BR 0 R41 4.1 c). IS 1115 ** CO 5 ct 0.182sills 515 1 R5 IA ii Ittg 1 * * ...t 65 oola gnI. 88

UNMV-0 Is.OUNIA19. UNCOMI IsIsr1%010NWAIMI" II° 'fit 1 1 II *241.1 LIN ZAgn 00%- WW RPA' 55 55 . 11 * * v-m Za,42:2" -423 ni2 86 n°41 r4sn r4 chr.g do ; * * .0 111 &O .c.472,11 :E WW 15

0/ Aoar-g *ca NMNNA ME aaa 5 '2VA1 eN o e ee 1111 II , II gNine-cg m.ugm 555 HZ om.rn I 1 II I 1

/ I nth

A Alig, 0 Mb 13

ARAW04 AAA AS1 2 21 Population Distribution and Change Density Urban Dummy Cap. Cap. Urban Cao. Size 50,o0a4. Pop ----, Born instate Variable Number 1940 1960 2 1930 3 1940 4 I 1950 5 1960 6 1960 7 1940 8 1960 9 19O 10 1960 11 1960 12 196030 13 193966g-zy 14 1940* 1$ 1950* 16 49484746 Child/FF underChild/F 540-49 Yrs/F40-49 u 1960R19601940 1960 -.150-.449-.224 .011 -.186-.372-.230 .062 -.189-.596 .001.306 -.196-.592 .359.016 -.265-.623-.143 .314 -.112-.242-.599 .337 -.257-.104-.098 .033 -.030-.041 one.259 -.117 .010.134.202 -.007-.536-.329-.113 -.418-.321-.058-.070 -.145-.275 .322.023 -.175 .148.391.129 -.167 .085.165.126 -.075: .432.138,.410, -.156: .383.360,.109, Zeployment 6362of yuuth Employ 3-118-11 FM 1060 .328.338 .244.266 -.575-.500 -.549-.510 -.527-.507 -.559-.541 -.022 .125 -.202-.345 -.265-.373 .003.046 -.188-.114 -.555-.511 -.362-.381 -.191-.260 -.642-.629: -.605-.626: 266265Child enrollment Enrol 6-146-14 TT 1960-1937 -.599, . 375 -.512, .336 -.373 .371, -.4o4 .339, -.457 3e4, -.479 .421' -.131 .165, -.239* .223 -.208* .235 -.165 .246* .115*.069 -.087* .361 -.385* .593 -.162* .207 -.489 .710 * -.481 .710 * nvi 162161Changpm in youth literacy LiteracyLitexacy 10-1410-14 IIF 1940-30_1940-30 -.5360-.464 -.511, -.411 .379,.473 .358,.457 .475.343, .513.369, -.056 .ca5 .259.154 .291.204 .140.155 .301.327 .406.490 .297.387 .075.039 .508-.548: .520: 165164163 Lit.Literacy 6+ 1960-50/1960-40 10-1410-14 1.1 F 1960-40*1960-40- -,5960-.643 -.50;. 466 -.563 .363 -.155 .607.485, -.141 .554.4360 -.114 .588-.481, -.077 .574.4740 -.185 .083.09 -.342-.318 .257 -.355 .291.348 -.021 .085.080 -.074 .306.219 -.269 .597.595 -.445 .482.514 -.303 .112.077 -.321* .574--.53: 4 -.289* .531--.414:: 293235Continuation rates B 4/3 U 1942 4/3 R 1942 -.325 .228 -.292 .155 .250.028 .254.077 .237.050 .248.080 -.158-.176 -.268 .069 -.151 .130 -.084 .250 .021.184 .180.265 -.338 .202 .337.164 -.Z02: .293: -.o9e: .398: 299296 B 4/3 U-RU 1960* 1942 -.103*-.062 -.118*. 551 .008.471 -.226 .092.220* -.192 .062.194* -.191 .136*.172 -.166 .196.103* .120.099*.064 -.087-.225 .245* -.112-.231 .162' -.252 .438*.312 -.101 .359*.147 -.047-.159 144* -.054*-.45o .328 -.001-.241*-.323 -.432- .202:.173** -.457- .3*A,*.051** 309307304308 3B 4/3B 4/3 RU 1960-10421960-19421960U-R 1960 -.140 .275. 112 -.058 .311.046 -.135-.222-.217 -.173-.164-.254 -.043-.185-.266 -.018-.176-.265 -.126 .273.189 -.111-.ow .106 -.209 .049.059 -.415-.125 .152 -.087-.293 .00 -.243-.308 .07 -.309 .377.123 -.023-.364 .102 -.066"-.282- 079: -.395--.003- .060: 333332331330 B 5/1 RU 104?194219601960* -.3h6-.10 . 3722o5 -.027-.321 .279-.048, -.331- .524.438.352, -.313 .436.376,.517 -.347 .502.424,.531 .375-.520.533.452, -.o21-.138--.243 -.294--.259, .004.070 -.16o,-.306- .042.W41 -.110 .195-.137,.107 .118.007.223,.206 -.34o- .462.381,,.582 -.436- .571.164..165 -.157- .222...139.193 -.571"-.610:- .16841..41,4* -.712,-, .645".295*.505* 356

I am E0 1Wn EIO f.:01: r r r 9 I ,4 :M M E i ,

A EMI99,41CO ItOR M ElUi ra riA g i III. 0e eeet e .eeg. et? ei e

i el urri,... all .. Ut009 filt00* 5

Pt In au a El§g 4.11 ...... I i I

g gt15 gf, ink §va § II .. . e i III II..i.,

R AA5 iiia Pi Ma...... H2 9416 I t II I I I

10 rX°._tht-7011 M.

IIi ta t it

1 10

t g 44! wi5 Wa.14 a.MI

Q Rep piM llii: kalg.§. i .1:. % WE59.1 IPA 4$.4 44111 t e et AgOgi o arsR 4.545.. 000 HO 08000EQUi i 111 ell 'It' 0 04 Mg 10§ 4355 REA Mg! t .4 IIII I II1t

P AM 55E OAS 5At AMR: t or3 . .. He . . .i. 5. I ; unA1., $glII 1MN Ie ng 555 .09ri er r

1 iiin ml11 ilroi

I 1 1411III Tit'IA1 1 1T'.1 ii 1 NMI nfi VIM Wig ARAA.1in* ASil 2 357

m :&314 1 MI . . . Is 15 16 iA II

:1 Ifg fA APmg , gt RR Ell IS Is II I

aP leg m 1MR Ai ea 5 444 I e e I IS l; I 1 I l mm RP W46 R MR ig A 14 1"41. . 5 5 II /I 1 1 I 1 1

Ch :g gUt"4 n A Mli* * I I A 0 IS e I e e I I I /I * * g* tAts g$ Ofi § 1M %sl A .R 11 8 i t I i I I I I I

0. it Effl 8 1,13g ft 6, tt: FA 5 . II I III I I

C9, *et Ma a U a a ft 8 k Wo P I e e I I I I I I

4tida.. , n rI8. HOn gRO ecm I le le II I II I

mbZ. wg rot g. M N2 lel 6 S S S S II III III I I I IS

In : IN : 0, Yg. 0. 5201 WA A16 . .. I I° I

CO m 0( :A VIarn 1 eg a na 4t0 §.40: IN .. .I. . II 1 * t. Cl .4.4 WA Ag NIA g 6 taR15 5 ant 6 6iq VI II OWII II I

P, gin fra ea li an 6 e I I I le I I e I , .._1 :§ IS *4 P. II O555 a III IV 16 I I I"

0 - 01 IA g k*'s .g. */§1 ROM WO MA5555 in . I I I e I I l e I I 0 *4 k174; HIR 6 6 f 6 I I II ISe I

UN 0,1 cli10 g ;AU il n VA D 6 10 6 ci I lele I 6 16 6 I e e i 1 "i gilt 1 i 4* * V Ma; !! il ti $all 11 1 1 xm00 OR 1 H!MI' h g N111 Transportation 1940 RR/Pop 1940 RR/Area 1960 1940k Roads/Pop 19403 /960 1940 A Roads/Area 1940 B 1960 RoadsPayed 1960 1940 Bicycles/Pop 1960 1960-40 1940 Autos/Pop 1960/39/ 1960-40 Variable Umiber 18 1960 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 1 35 Changes162161 in yuuth literacy Literacy10-14Literacy 10-14 14P 1940-30*1940-30 .089*.126 .388 -.239*-.213 -.274*-.194 .334*.367 .225..294 .418,.342 -.508*-.343 -.386,-.351 -.343-.2534g*- ' .178*.274 .224.2391, -.045 .056 -.036*-.095 .oce .599*.536.421 .652-.510,.607 .105w.124,,.029 .275*.442.432 104163165 Lit.Literacy 6+ 1960-50/1960-40 10-1410-14 14F 1963-40-1960340, .112'.207.088, .282.184;405,.257 -.287-.313* .382 -.382'-.376, .480 ,..386 .496.413* -.531 .580-.520; -.313 .475.394; -.495-.515* .328 -.134-.207-.185* -.4657 .292 -.031 .315-.224_ .125.328-.286: .100.115*.055* -.003* .135 -.319 .662* -.327 .697* -.272 .016* .151.361* 288Continuation rates B 4/3 U 1942 -.156 .154 -.203 .156 -.310 .168 -.134 .374 -.126 .479 -.075 .320 -.104 .233 .029.024 -.170 .182 -.007 .046 -.087 .021 -.177-.196 -.197-.160 .445.122 .439.113 -.072-.367 .166.310 299296293 B 4/3B 4/3 U-R Ru 194219601960* -.175 .110356*.188 .044.095.203*.135 .429.115.119* .027.036*.543 -.520-.096* .236 -.103*-.595 .308 -.436-.066* .099 -.292* .175.325 -.096* .304.049 -.256* .107.351 .210.136*.000 -.004 .333*.02 -.077 .147.332* -.042 .290*.199 -.392 .120.129* -.024*-.392 .188 -.204*-.176 .117 -6094*.019.045 309308307304 B 4/3 uU-R 1960-1942 1960 4/3 R 1960-1942 -.080-.279 .341 -.275-.183..102 -.233-.187 .367 -.066 .374.207 -.132-.208 .192 -.147-.379 .030 -.232-.087 .083 -.003-.055 .306 -.283 .307.045 -.016-.009 .284 -.059-.273 .195 -.182-.197 .054 -.106-.121 .158 -.107-.136 .212 -.308-.202-.158 -.236-.094-.166 -.056 .188.469 -.140-.237 .039 331330333332 B 5/1B 5/1 U U1942R 1960--19601942 -.068--.145, .101.027 -.258- .051.083,.406 -.060 .123.064'.037, -.125-.020 .152-.003, -.533- .414.392.253, -.421- .680.332,.435 -.417- .300.166.024 -.003-.039 .186.319* -.167-.023 .351.182* -.236 .116.056.103* -.216 .330*.338.328 -.215-.082* .20.068 -.037-.039-.170-.399* -.162-.123-.4284-.099 -.395 .671.628.506* -.507 .667500*.611 -.084-.184-.312*-.295 -.339 .203*.395.140 - Bevel.Index Electricity/Cupita Utilities and Communisation iiovies/Pop Library- Use Water Radio Variable Wilber 1950 61 1940 1960 37 1960 -40 19140 39 1960 1960-40 19140 42 1960 43 1960 55 Utilities and61 communication Bevel. Index 1950 1.000 .549 36 .656 38 .716 .694 140 141 .507 .564 .859 Elect/Capita 1960-194019601940 .656.549 LOCO .477.782 1.000 .715.782 1.0oo .715.477 .207.268.01414 .529.498.514 .396.385.553 .439.540.738 .539.421 .690.511.644 Movies/Pop 1960-194019601940 .694.716 .553.514 .385.498.268 .396.529.207 -.286Loop .504 1.000 .621.504 1.000-.286 .621 .4142.518.162 .579.259 .323.778.626 RunaingWaterRadioLibrary 1960 Use 191.01960 .859.564.507 .738.511.539 .6114.1421.540 .626.259.162 .778.579.518 .323.442 1400 .451.655 1.000 .647.655 1.00o .647.451 Proportion of8079 wales in agriculture Ag/EcActAd &Act N M 3,9140* 1960* -.902-.885: .496.434: .6o9 .544:.615 .693.755: .661.639: .007,.o99 .437-.434: .509.502: .88o*.843* Proportion127125 of BarefootBarefoot N 141960 1940 males without shoes -.681-.620 -.415-.310 -.451-.467 .®: -.528-.467 -.609-.513 -.595-.628 -.155-.258 -.361-.477 -.05-.543 -.702-.719 169211177 Literacy-30-39Literacy 20-24 FUmu MU-F 19601960 1960 .... -.299 .215.186 -.328 .475 -.216 .428.435 -.396*.684 .610 -.606 .586.478 -.235-.009-.068 -.335-.241 .149.083 .....234.148 -.946 .699.601 Marriage221 and45 fertility-rates SingleLiteracy F 20-24 MR-F1960 1960 -.409 .... -.450 .595 -.487 .360 -.437 .506 -.045-.300 -.685 .443 -.397 .421 .631 -.492 .645 -.707 .542 11111 Utilities and Conuunication Library - Electricity/Capita No vies/Pop 1960-40 19140 Use 1-later 1960 Radio Variable Ihmiber 195061 1940 36 1960 37 1960-140 38 1940 1960 140 41 112 43 1960 55 49484746 Child/FF underChild/F 540-495 yirs/FIrs/F40-49 u R19601940 1960 .153.419 -.339-.539 .188.146 -.,019-.308 .233.022 -.109-.286 .334.148 -.175-.009 .068.142 -.300-.155-.509 .177 -.237-.443-.342 .095 -.538-.153-.769 .032 -.509-.717 -.082-.443 .446.033 Employment 6362of youth EmplgyEmploy 8-118-11M F 1960 .600.604 -.317-.282 -.485-.568 -.485-.567 -.472-.483 -.535-.510 -.194-.166 -.228-.219 -.316-.312 -.653-.684 266265Child enrollment Ebro1Enrol 6-14 T 19371960* -JAL* .427 -.062 .190* -.262* .403 -.251- .254_ -.491- .623_ -.475- .514.. -.064- .061_ -.050- .088. -.133- .222_ -.526* .620 162161Changes in youth literacy Literacy 10-14 F14 19140-30 1940-30 .... .066.216 .258,..360 415 .610 .573 .085 .170 .297 .686 163165164 Lit.Literacy 6+ 1960-50/1960-40 10-1410-14 'A F 1960-140*1960-40* ...... -.106 .257*.187* -.369 .398.373; .079.208*.169.314* -.280 .823*.733.627* -.070 .578-.428,..449* -.002--.016.-.096* .060 .082.267*.126-.009...... 190 -.155 .635*.525-.55_9. 293283Continuation rates 3 4/3 RU 1942 -.450 ... .374.273 .573.195 .390.401 -.157 .342 .177J360 -.021 .166 .221.188 .... .317 304299296 B 4/3B 4/3 n 1960*RU-R 1960 1942 -J079 .352 -.186*-.238 .137 -.172*-.491 .203 -.023*-.085 .097 -.457 .101.2144* -.001-.141 .135* -.118* .040.119 -.040-.104 .266 -.004 .292.....229 -.199 .140.039.412 309308307 8B 4134/33 4/3 RU 1960-1942U-R 1960 .....146 -.234-.100-.024 -.342-.060-.113 -.232-.353-.060 -.216-.053-.223 -.160-.084-.007 .048.003.061 -.103-.234 .051 -.245-.243 .054 -.246-.31414-.147 330333332331 B 5/1B 5/15/1R u 1960*R 19421960 5/1 u 19142 -.735-.508*-.464 .447 -.225* .578.714.400 -.380* .648.649.514 -.300* .564.415.505 -.530* .521.351.103 -.224* .425.491.435 -.489 .287.201*.037 -.224* .449.609.369 -.293-.375* .622.501 -.414* .586.519.653 VC

*a ,*a , , E 0,... ayw ti HIE ; . . U . : : J r: 1::: w a 4vtWV Aw g 13 cou.... 7.4kb ifilx 1

g Mi9g h Egi6 g I J JJJ rt, JJJ1 J WM 3§ wleom 14 ihi .r ktr-w W 1 :::. 111J

t-1 nu ela 40 itAg i ... :: 1 JJ..

c R za t wig.r. J: .: WO1111

v gi v a0i g US t i Yilit :: : es :I : * U tg58 )gv i c ib 0600%0 n : bb i 44 i e 1 1

* COW IS 0,-.47V Q u4Z4- SW WN c 9 -9 9 WN t I-4 SI - ill i.lb

f g 111 i jiff il wx.qm 4:;'41 Ix 44 0.0V.

T I it qn I g i ;""i" 1111

5 5 C t.st;c R ee R gl F6g5 & 362

W. sort...1., 4utIAJL omNf6P El NIX 1; t . r . er . re i4 A RH Via 13liga rr151Sr r m0.1 II e e e

§ § vt5t ri 6"S II o e e

Eg5 *Ngg ir i 14hir

gi 5AW o of es e

at Ei I I

945§ 2.71 2N,Eq- IIIII I II I

111, elg 51e st gra 1A5-- o I o e o

ii!11gtli SSS SSS S5 $$$$ mm mm mm 111101420

ERI 363

S (V .43 ggiv- r- rAl NO441frl 01 Si:AEE 13 II II % t Ci.:C * **** in el**NO P-211 .0`"-,A% VC! l'A 3 12 8A

0. Ing cy o 1.1% In el IN Aria A : : 0. Os 01 -

GAO Ir1 01 I.. 04 :1:- : g 0 IN .13 r4 4.1 0i ert 01 LIN 4 IA -.1 I I

pc! r4 AZ2 %ANA r4 0 .'.11 .43 N 01 8 f- ago "S sk-1 7-1

....0g ....i 0 . , filA gg ...... F. ,... 1 ceisc):,1 SA --t -vo. ri A : II

GOA I.. IA A a SIX 1 riIft I.- 2 V1.1.1 . 010%-.01 0 4.1...7 No4 0I I13% g5§ SRa 17.. I: 2 I. I I il I

.02i :2 )71 LILA Oeg:-.%0 er% pi CM a:Er.*::NA ; ; :

0 In 03 m lig p .-41 f... 0 %0 II0 1 %0 0.. 11% ala gm- gtg 524.&, gm 0ID 0.- Att;

1 1 1

A2 . fit In 0% 1.1% 01 10 P§ NOr4' 03 2 IP- 0- UN. c.N. i:il I

00.1% CD 000 03 - 01 1.1% Moo No I.. AAZ 5Fif

tr,IA ;71NA AvzV r4 II ...The %ON: 0IUN Me4 0 : g ap *I% CM IN 01 0r lLINV% MN cgE itCi 1*%13. Cit II t IS g S0, I Cg it; ...... ,...... 4 t.".:4'441...... r. V, h iAAA 44 41'.k 144 1AM MMA sls'11 i 88 gd8IT: 1 1O 888 8 3 ...i. u. a ,41 Z41.%geg r.e.4 ...... ,- ....-,me/~IMORMINW~pa

Population Distribution and Change

Dsmaity Orbon Capital Sise P°P Urban Burn Instate capital/orb= 50,0030*

1940 1960 1940 1960 19110 1960 1940 1960 1960 1960/193019W-199531d 1940* 1960

'Salable Number 1 2 4 6 8 9 10 U 12 13 14 15 17

Ss AgProp/AgPt 1940 -.411 -.395 -.037 -.038 .087 .152 - sou .127 .092 .150 .255 -.138 36 Iterop/Ag11 1960 -.052 -.172 -.265 -.372 .091 .043 -.034 -.195 -.359 -.399 -.132 -.373, .416 81 Wrop/Ag N 1960-1940 .02 .029 .... .006 -.341 -.148 -.255 -.260

38 Ekinielmnd 1950 .198 .260 .272 .342 -.298 -.205 .167 .264 .525 ...... 350 -.172 59 Fare Mechanised 1950 -.434_-.376_ .598_ .664_ .083_ .097_ -.469, .147, .561 .558. .042 -813,. -.694 91 Ag Inc. under 4500 1260)-.729--.619- .488- .551- .243- .324- -.114 .190 .503- .606r .330" .675-- -.736

94 Beta:rpm Glick lOWD-1930 .... -.529 .234 .... .127 .... -.339 .256 .658 -4078 .770. ....

Mannfactsring and nining

96 mtticAct 11 1940 .068 .106 .921 .775 .297 .335 .401 .404 .506 .. .264* -.206 97 HfcAct M 1960 .163 .223 .661 .713 .215 .207 .499 .515 .591 .3oo* -.240 95 Mfg/dcictM 1960-1910 .00. 0.0 .203 .441 aii .16211 WO Mfg Ft4F Mfg 1940 -049 -.125 -.242 -.238 .062 .099 .316 .276 -.136 ...... -.296* .264 101 Mfg F/H+F itts 1960 .214 .176 -.337 -.337 .001 .013 .222 .164 -.155 ...... -.422* .307

103 Mfg Inc =der 600 1960 .550 .428 -.421 -.527 -.209 -.257 .048 -.226 -.554 -.653 -.211 -.706: -.738 105 PayAmp Fact 1930 -.183 .473 .060 .017 .... .687 .600 .030 .57o-_ .... 106 Pay/Sip Fact 1940 -.1.1s0 -.110 .121s .147 -.022 .001 .266 .331 .500 .... .400- -.222 107 Pay/Rap Fact 1950 -.003 .261 -.306 .183 .552 :Ca .085 .308: .465 loS Fay/Inp Fact 1955 -.1i; -.123 .144 .152 -.272 -.238 .074 .159 .281 .... .259- -.221 109 Pay/dmp Fact 195511940 .... .050 .233 ...... -.064 *Ai .122 .005* .050

111 Mining/EcAct M 1940 -.292 .137 -.311 .178 .266 -.090 .323 .128 01 is n ::§g ig& S II U.11111 S S S 00 515

:9 p :: gs tt ti$ .O 5a 11 gt4 IS I.1.

Ai if i i :i . . 5 5 f RI. I I I 1 I

A /i Inar r RI: 41g :a E:g OS0 OSO OSGo 40O g4 ,

kT :a OOS 00 SS OS 1 ISO

O SOO 000 11110 000 SO 550 000 SOO 00000S SS S 0 0110 0040 0 00 40SOO 00 O SOO000 SO 000110

S :A S O O S O 00 :SO &SOO OO iggSS

II 555 555

VO00S I R riO?II

i I 1 log 1ili iii111 11 1 zun hmk011 Felear NMI X A4Y 1 ill ii 1 Ili11 1 iili1 III111ggli n i 4 amirims t$8 RgA AVOg Roads/Pop Transportation Roads Autos/Pop RR/Pop I 1960 Paved 1940 Bicycles/Pop 1960 1960-40 1940 2960 Variable Number 1940 18 1940A 22 1940B 23 I 24 1960 28 29 30 31 32 33 878685 AgProp/Ag rUM 1960-194019601940 -.318 .249.031 0000 -.362-.409 .053 0.00004. -.230-.151-.077 -.218 .300.159 -.277 .493.355 -.306 .645.276 -.612-.529 .075 0000.04. 899491 FarmReturnsAg Mect.snizedInc ulier Glick ,i500 1950-19301950 1960* -.041* .?96.264 .667*.337.678 0000 .129.197-.427_ .054-.091.249... -.006-.088* .032 -.mow .052_.025 .768.731* Manufacturing98 and mining Mfg/EcAct M 1960-1940 000. 4914 .075 .322.393 103109107105 Pay/Ehplay/EmpMfgPay/Eep Inc Factunder Fact 1955-19401950 15001930 1960 -.072 .032.204 .0...00.0..0006. -.617 .487.614 -.256 .350.402 .1180.00 -.065-.527-.116 .096 -.123 -.767 .314.761.817 00000040000. Mining111 Mining/EcAct 1940 .1111.45 ...0 .315 .053 00.0 03613 4.0 4140 Utility andConnunication Facilities

3lectricity per LibraryWater Radio Capita Movies/Pop Use

vas JEMMIED

1940 19601960-40 191) 1960 10,4).440 1940 1960 1960

Variablo Number 36 37 35 39 Ia 143 44

Labor force particition

58 &Act F 10+ 1940 .488 .355 .178 .365 .432 .1118 .530 .610 .508 59 EcActF 12+ 1960 .397 .350 .201 .226 .079 -.031 .216 .336 .31s0 60 ScAct F 1960-1940 .163 .157 .191 -.106 -.223 -.037 -.057 .003 -.001

White collar and professional workers

64 CollarAcAct a 1940 .2148 .517 .350 .862 .554 -.120 .239 .329 465 65 Coller/ScActti1960 .419 .543 .764 .743 .915 .869 66 collarAcut m 196o-194o .510 .436 .470 461 .14;01 .612 .784

67 Collar/ScActF 1940 .319 .591 .475 .694 .644 .224 .218 .292 .760 68 collar/a:Act F 1960 .575 .581 .525 .534 .633 .590 69 collar/zcAct F 1960 -1940 .227 -.196 .038 -.427 .040 .356 .337 -.072

73 CLark/RcAct T 1960 .318 .524 .500 .804 .677 .037 .271 .438 .539 71 Clerlc/EcAct N 1960 .296 .509 .... .819 .657 .1117 .025 72 Cl3rici3cAct F 1960 .338 .535 .720 .741 .460 .524

74 FroalcAct M 1960 .445 .533 .561 .605 .254 .401 .766 75 Frof/Ecict F 1960 .442 .468 .... .306 .553 .435 .342 .646 Public achinistration

77 p.a./EcAct N 1910 .098 .1429 .... .749 .345 .036 486 .532 Agriculture

81 AG/3cAct :I1960-1940 -.250 -.207 .544 -.152 -.276 .037 -.189 .502 -.386

82 sjidos/Aeop 1940 .043 -.118 004 -.052 .oTo .018 .139 -.361 -.078 83 AaLabor/AgX 1940 -.038 .693 A03 -.162 -.063 .052 -.202 .021 -.050 314 AgLabor/Agli 1960 -.093 .601 .444 .107 .424 .438 .364 .460 .533 _

Electricity per Utility and Communication Movies/Pop LibraryFacilities Water Radio 1940 Capita 1960 1960-40 1940 1960 1960 -40 1940 Use 1960 1960 85 Variable RuMber -.074 36 .216 37 .062 38 .137 39 .014 4o -.024 -.101 42 .177 43 .130 44 838756 AgProp/AgAgPropiAgAgFrop/Ag Mn 1960-194019601940 -.570-.707 .585 -.721-.680 -.382-.413 -.134-.094 .013 -.344-.412 .239 -.373-.442 -.270-.365 -.531-.454 .462 -.491-.521 .512 949189 ReturnsAgFarm apiip/LandInc Mechanized under Glick 35001950 1950-1930 1950 1960' -.176 .294*.414 .139.657-.497.486 .129.511-.424, .651.668*.657 .324.598*.64o -.140 .122* -.232 .251*.362 .085.408-.486, .446.755-.765, Manufacturing96 and mining Mfg/EcAct A 1940 .434 .405 .448 .403 JO .553 .58o.620 101100 9897 MfgMfg/ZcActAfg/2;cAct PA;+F MfgM 1960-1940 1940 1960 -.136 .273.468 -.273 .374 .4o5 -.055 .133.385 -.050-.245 .436 .093 .249 .271.234.424 -.388-.139 .290 103 Mfg FAI+FInc under Mfg 1960$500 1960 -.358-.057 .507 -.665-.278 -.609-.273 -.543 -.140 .315 -.303 .230 -.760 .602 109108106105107 Fay/UmpPoy/EmpPay/EmpPay/EMp Fact 1955195019401930 .333.254.681.23f! .442.731.346:704 00000000 .028.039.146.209.568 .533.217.173.446 .2310000 .539.442 .072.243.378 .102.329.598.378 lining111 Minthril.icActPay/Cnp Fact M 1955/19401940 .504 .597 .183 .044 .371 .332 .335 .420 1

Marriage and Fertili.V Rates and Child Fauployment

Employ Employ Single F F Under 8-11 8-11 20-24 5 /rs

1960 19140 1960 1960 1960

Variable NuMber 145 46 47 62 63

Labor force participation

58 2cAct F 10+ 1940 .439 -.640 -.233 -.252 59 EcAct F 12+ 1960 .228 -.294 -.057 -.169 -.022 60 EcAct F 1960-1940 -.023 -.055 .015 .187 61 Devel. Index 1950 .409 -.419 -.153 -.604 -.600

White collar and professional workers

64 Collar/EcAct /t1 1940 .114 -.207 .204 -.665 -.610 65 CollarAcAct1I 1960 .301 -.353 -.048 -.643 .... 66 Collar/EcAct 11 1960-1940 450 .... -.329 -.490 -.435

67 Collar/EcAct F 1940 .160 -.022 .268 -.754 .... 68 Collar/EcAct F 1960 .568 -.443 -.147 -.677 .... 69 Collar/EcAct F 1960-1940 .463 .... -.605 .330 .226

70 Clerk/EcAct 1960 .209 .... .083 -.680 -.599 71 Clerk/EcActM 1960 .180 -.188 .095 -.666 .... 72 Clerk/EcAct F 1960 .278 -.174 .061 -.715 ....

74 Prof/EcAct H 1960 .293 -.233 -.046 -.662 -.583 75 Prof/EcAct F 1960 .554 -.422 -.07 -.618 -.547

Public administration

77 P.A./EcAct M 1940 -.131 .397 -.535 -.h40

AlEIMPala 79 Ag M/EcAct 1940* .431: -.438: 009, -.663 003* 30 Ag A/EcAct 1960* .385 -.408 -.038 -.679 -.013 31 AgM/acAct H 1960-1940 -.049 -.138 -.210 -.004

82 Ejidos/AgPop 1940 .007 -.031 -.005 -.119 -.067 83 AgLabor/Ag M 1940 .091 -.072 .213 .169 -.283 84 AgLabor/Ag 11 1960 .380 -.408 .078 -.4o4 -.276

35 AgProp/Ag 111940 _059 .170 .050 -.141 .030 86 AgProp/Ag 111960 -.367 .394 -.091 .356 .274 37 AgProp/Ag 11 1960-1940 -.213 -.082 -.190 .204

38 Equip/Land 1950 .645 -.312 -.008 -.406 89 ?arm Mechanized 1950 .322 -.269,, .224 -.584 -.095 91 Ag Inc under ;500 1960* .134* -.139- .327* -.644* -.170*

94 Returns Glick 1950-1930 -.180 .462 -.409 -.461

Manufacturing and mining

96 14/EcAct M 1940 .484 -.623 -.298 -.h03 97 Hfg/.7mict M 1960 .405 -.380 -.229 -.442 93 Mfg/2cAct M 1960-1c40 -.283 -.112

100 Mfg Ft.:+F Mfg 19110 .079 -.166 -.346 .541 101 Mfg F/:14-F :".fg 1960 -.029 -.243 -.423 .581

103 Hfg Inc under 1500 1960 -.139 .028 -.302 .726 .670

105 Pa7/Emp Fact 1930 .053 .156 -.604 -.581 106 Pay/Elp Fact 1940 .344 -.020 .C94 -.275 107 Pay/:mp Fact 1950 .392 -.064 -.401 -.440 108 Pay/alp Fact 1955 .162 .029 .147 -.296 109 Paybamp Fact 1955/1940 .056 -027

Minila

111 lining/3cActM 1940 .506 -.041 -.237 -.299 CORRELATION MATRIX: L'ULATIU.LCT LaACLIII3TIC.3;ATTRI3U ? CO SY DI:SI LI RATES, 'MUM 1.1* IOCZA, I ;72 +: Lr0iTCI0I, TABLE. UTIUD LITY59 ?IOU .141,1111 3,aar.xCo LTC A I'D!: Vrii. I 3.1,a3; By OCCul. Z-i..,73SLFsM AND -ZONU.IIC l Non-Catho lic Water Sleep on Sleep on Non-wheat Bread Barefoot 1 floor bed T 1940 1940 F 1960 1960 1940 /4/F 1960 1940-1960M F 1940 T 1960 1960 1940 1940* 1940* 19600 194o-196o Variable Numbor 123 128 127 123 131 132 135 138 113 114 43 115 I 116 117 118 122 Fopulation distribution 1and chance Density 1940 .318 -.394 .121 .3230000 -.424 0000 -.279 .053.013 ,526 .325: -.388: .473.552 -.103 642 DensityUrban 19601940 -.628-.634 .245 -.666 00641.363 -.662-.653 .328 -.191 "O..563 -.569 0.00.231 -.632 .485.000 -.535 -.609 -.013 .149 .013.638.566 -.340 -.449-.353* .220; -.321' .724--.665: -.606-.506 -.112-.558 10 89 CapitalCapital/UrbanCapitalibrban Size 19601940 Size 1940 1960 -.253-.231-.162-.4114 -.183-.206 -.208-.140-.342-.196 -.402 .011; -.278 .211 -.054 .249 -.387-.075 -.296-.175 -.309 .340 -. 101 .068 0 .527.433.098 -.389 .173 -.312,-.023,-.059, .031: .318:.21e,.151:.210* -.137-.201-.206 .025 -.271 14131211 UrbanPopCapital 50,000+ 1960-1950/1960-19401960/1930 1960 -.059-.667-.302 -.117-.367-.678 -.044-.313-.63- -.237-.055-.213 .138.274.322 .438.030.269 -.103-.074-Ai; -.072-.615-.173 -.135 .502.042 -.281 .509.090 ..560 .106.102 -.017-.311-.043 -.512:-.621-.110 .032.393.413- -.129-.522-.289 -.016 .130 363 Transportation1715 Born3orn Instate Instate 1960 1940* .1485 .587*.504 -.452* .469.324* .423* -.460* -.537* .440.428 * .356* -.187 .245- -.228* -.593:* .518 -.SOY .477!* -.472* .590 -.175* 2218 RR/PopRoads/Po011940 1940 -.445-.290 -.224 -.701-.440-.306 -.008-.536 -.246 .259 -.231 .397 -.223-.532 0000 -.151-.566 0000 .175 .033"00.232 .277.055 -.004-.153 -.544 ....* .373gm.101 000, -028 000 -.219-.298 4.0015 2423282726 Roads/Pop°Roads/AreaRoads/AreaDaoadsaoads/pop Paved1940 1940 1960 n196° 1960 -.124-.132-.697 .042 -.423 -.C55-.232-.398-.097 .427.luo -.3714-.023 0000000. -.426 .054 -.410 .12300000.00 .2360000 -.161 .338.0750000 -.296 .1640.000.0. .526.032 -.107 .367.0000000 -.276 .179,0000*0000 0000 -.266 .284 -.653-.o98 293130 Bicycles/PopBicycles/Pop 1940 19601960-1940 -.293-.031 -.266-.042-.059 -.190 . -.269-.153-.243 -.126 0000 .045 -.036-.042-.237 -.072-.039-.233 .207 .102 -.097 000. -.417 0000 .2710000* .132 -.037 -.077-.181-.327 Barefoot A Non-Oatholic Water Sleep an.floor Sleep on bed Non-Wheat Bread Ii Ii 11/F M I F T T Variable Number 1251940 1940 128 1960 127 1960 123 1940 131 1960 132 1351940-1960 138 1940 113 1960 114 1960 43 1940 115 1940* 116 1940* 117 1960 118 1940-1960 122 343332 Autos/PopAutos/Pop 19601940 1960/1939 -.527-.703 .334 -.755 .264 -.733-.626 -.317 .036 .332 .386 -.623 .501 -.635 .411 -.081 .111 .512.4680060 -.099 06000000 -.614 4.060 .641-.517!, -.637-.468 0060 -.357 .064.347.... Utility and35 communication facilities Autos/Pop 1960-1940 -.143 -.180 .073 -.167 -.194 0000 .3070600 06.0 000..294* -.136 0006 -.362 383736 Aect/0apita.I;lect/CapitaMect/Capita 1960-194019401960 -.467-.467-.415 -.470-.429 -.451-.310 -.163-.226 .002 .400.10: ...1.A;.103 -..432-.515-.444 -.414--r<-.409 -.159-.494 -.231-.183 .421.539 .035 -.543--.429! .666364 -.733-.343 -.245-.121-.211 413940 Movies/PopMoviesPopMovies/Pop 19401960-1940 1960 -.253-.623-.513 -.239-.600-.657 -.609-.595 -.422-.131 .206 .452.212 .461.418 -.317-.354-.593 -.239-.603-.454 -.067 .492 .1:'5.111 .259.655 -.609_ 12.7 .123 -.399:-.307*-.603 .477*.675- -.241-.605 -.623-.256-.421 4342 WaterRadioLibrary 1960 1960 Use 1940 -.543-.702-.477 -.756-.455 -.719-.435-.361 -.235 .031.003 -.031 .114.339 .351.).133 r70 -.603-.559-.553 -.445-.655-.525 -.022-.427-.627 -.037-.345 .138 .647 -.169 .089 -.633-.473* .670-.60), -* -.403-.623 -.233-.458 :Terrine and 44fertility rate _ , 474645 F underFSingle uneer 5 FYrs5 20-24Yrs 1940 1260 1960 -.046-.494 .450 -.416-.095 .... -.045..395.370 -.415-.116 .... -.279 .239 .180 -.672 .097 -.577-.015 -.695 .570 -.477 .134 -.509-.717 .645 -.055 1425.... -.36't-.122 .141, -.302--.434: .131, * .005.126.216 -.232 .398.... 21ployment 6362of youth LImployEmploy 3-11 3-11 F M1960 1960 .662.504 .687.564 .430.633 .436.296 -.133-.339 -.416-.438 .605.403 .594.442 -.140-.177 -.033-.135 -.316-.312 .127.193 .572*.626* -.347--.429: .326.403 .216.223 Cultural Attributes by Occuptional and Zconomic Characteristics Barefoot Non-Catholic 11 a* br I Sleep on floor Sleep on bed Non-wheat Bread 19'40 1940 1960 11 I 1960 1940 1960 1940-1960M F 1940 T 1960 1960 1940 1940* 1940* T 1960 1940-1960 Variable Number 125 123 127 123 131 132 135 I 138 1 43 115 116 117 118 122 Labor force58 participation 2cAct F 10+ 1940 -.371 .417 -.247 -.102 .225 .298 -.395 -.336 -.202 W .067 .61c -.093 .324! .549: -.219 Uhite collar616059 and professional workers Bevel.L'cAct74Act F Index F1960-1940 12+ 19501960 -.075-.620 .309 -.118 .310 -.631-.053 .196 -.242 .212.046 -.074 .367.163 -.479-.037 .438 -.529-.053 .3o6 -.595 .434.034 -.030 .045.003 .195.117.059 .564.0010.336 -.213 .419.191 -.230- .530*.011* -.657--.119: .252_ .494 -.601-.005-.342 -.361 .348.173 666564 Col1arAcActCollarb;cActCoIlar/Ze:xt Hn 1960-19401960 1940 -.549-.602-.54o -.564-.641 -.665-.570-.604 -.022-.443 .125.517 .230.597 -.510-.381 -.512-.533 -.078 .410 .17585 .612.505.329 -.182-.539 -.475--.522-.482: .72%..607-.709: -.488-.695-.741 -.219 0 686769 Collar/EcActCollarA;cActCollarAcAct FF 194019601960-1940 -.549-.641-.773 -.564 -.736-.116-.665 .339 -.434 -.359 -.146 .012 -.621 0000 -.407 .339.633.292 .451.... .694*.160 -.095 .645*579: -.526-.605 .287 -.448 0000so** 727170 Clerk/LcActClerkAcActClerk/ZcAct M7T 1960 -.584-.667-.561 -.633 -6694-.627-.642 -.362 .510 .585 -.449 -.587 .306 .407 .46o.417.438 -.463 0000 -.647-.539, .566: .682.723;.720, -.668-.745-.738 -.239-.224 Public administration747375 Prof;:cActProf/EcLct?rof/EcAct TFM 1960 506)433 ' ".).50 0000 -.525-.433-.574 _4,285-.243 .179.343 .379.603 -.501-.h07 -.434-.553 -.238 .167 -.083 .316 .342.401 -.279-.323 -.336-.508, .464.624* -.676 .353 -.369-.125 Agriculturs7776 P.--../..tActP.A./EcAct Mn 1930194o -.238 0000 -.400 0000 -.366 O000 -.396 0000 .5250000 so*..454 -.093 -.230 .541 0000.!:02 0080.036 -.557 0080 -.315 . . . . .5700000* -.692 0000 -.013 31So79 Azt.:cgAcAct'g/.,cAct Act MH M1960-19401960 1940' -.55,-.6661 .081 d* -.616-.726* .090 * -.705*-.111-.606 * -.227-.210-.426* ,* -.03 .333.36e .223.422-.617* -.012-.449-.584* * -.516-.086-.694 -.056 .053* 00* -.307 .219*.102 -.231 .509.532* -.273-.22* .057 * -.459* .206 -.298 .6467.602** -.595-.531: .308 -.323 .109 Cultural Attributes by Barefoot Occupational and Economic Characteristics non-Catholic Water ISleep on ISleep onfloor bed Mon-wheat Br oad 1940 1940 1960 1960 1940 VF 1960 1940-1960M F 1940 T 1960 1960 1940 1940 * T 1960 1940-1960 82 Ejidos/AgPopVariable number 1940 1= .117 125 .171 128 -.095 127 .009 123 -.258 131 -.249 132 .068135 L.138 .137 -.075 113 -.029 -.361 -.07, 115 .283: 116 -.036: 117 .111 118 -.189 122 348335 AgProplleAgLabor/AgAgLabox/Ag n M194o 1960 M 1940 -.467-.124 .112 -.492-.073-.016 -.355'-.071 .158 -.1) .252 -.205 .551.160 -.087 .127.367 -.451-.136 .216 -.373-.076 .068 -.199-.050 .158 -.189-.260 .365 .177.460.021 -.135 .305.399 -.181-.515 .074* .200:.209.271* -.250-.154 .266 -.138 .230 e73688 Equip/IandAgProp/Ag M1950 1960-19401960 -.604 .304.454 .405.437.... .189.3h4 -.063 .093.... -.475-.194 -.303-.143 .241.1145 .248.369 .107.212 .007.19C -.531-.454 .462 -.149-.303 -.649* .514.510, -.007*-.312-.2070 .158.296 .095050.127 949139 ReturnsAgFarm Inc Mclaanized underGlick ;5001950-1930 1950 1960* -.790-.403-.611* -.436-.836-.727* -.434-.759, .597- -.394-.430*-.491 .433.605*.315 .560.432.654* -.213-.679-.466" -.343-.568*-.741 .639.249-.111 .510.218*.167 .085.4G8*.486 -.371-.329- -.715--.589* .393*.584ww.515!., -.662* -.555-.497 -.212-.109* .146 Manufactutim95 Mfgr.lcActMfg/LcAot MM 19301940 -.131-.376 .... -.432-.336 0000 .553 00 -.198* .396* -.245 00000.00 101100 989796 :tfg21fgIlfg/EcAct:Ifg/LcAct F/M+F7/1+F Mfg1M 19601960-1940 19401960 -.116-.324 .229.07? -.085 .... .341.110...... 073 Ol00 -.169 -.068 0000 1 .271.234.424 -.203* .218*.101* -.044* .065'324* -.119-.198 .136 -.080 .000 106103105 Pay/EipPay/Smp:Xs Inn Factunder 19401930 )500 1960 -.490 .515 .....615 -.426-.509 .547 -.160 .....379 -.503 .466 -.462 .231 -.343 .379 -.391 .452 11011 -.339 .242 -.310 .210 -.303 .243.230 -.141 ....236 -.511"-.600! .714* -.523.' .017.427 -.409 .039.639 -.137-.028 109108107 Pay/ipPay/ZnpPayAmp FactFact 1955/194019501955 -.483-.218-.464 -.159-.492 .... -.482-.426 .... -.03?-.017 .... 0000.197 .205 -234-.!,64 -060-.%1h -.159 0000.028 ...072.373 ...... 220 -.439*-.663* ...... 004.297 -.257 ....035 -.183-.120 .... 111tining MininzAksAct N 1940 -.432 -.377 .0960000 .397 -.5C5 -.542 -.253 -.267 335 .317 -.536* .060* -.036 -.005 Cultural Attributes by Literacy Rates, Youth ENployment and Enrollment Barefoot non-Catholic Water FloorSleep on Sleep Bedon non-wheat Bread 1940 1940 Urban Rural M/F 1960 1940 N 1960 1940 1960 1960 1940 1940* 194o* 1940-60 Variable Numbor 125 128 1960 127 1960 130 1960 123 1960 124 1940 Ui 132 135 138 113 114 14.3 115 116 117 960U9 122 140Literacy Literacy 10+ T 1940 -.714 -.796 -.727 -.769 -.418 -.800 Ilml .474 .693 -.550 -.666 .244 .345 .426 -.146 -.639* .707* -.680 -.329 146145141 LitoracyLiteracyLiterncy 10+40+F 4o+n T 19401960 1940 -.796-.626-.711 -.864-.700-.778 -.762-,.640-.707 -.831-.65P-.758 -.480-.307-.397 -.557-.714-.769 .424.425.412 .723.584.739 -.657-.443-.566 -.752-.528-.686 .130.289.168 .227.379.292 .494.373.422 -.333-.368-.392 -.566*-.642*-.665* .691*.669*.625* -.639-.568-.644 -.319-.305 153148147 LiteracyLiteracy 6.040+M 40+? Sea 1960 1960 N 1940 -.818-.643 .435 -.871-.725 .438 -s792-.677 .531 -.699-.863 .512 -.04-.312 .389 -.735-.87o .548 .071.366.445 -.091 .763.662 -.693-.483 394 -.792-.599 .331 .002.061.199 .225.162.319 -.319 .482.416 -.033-.374-.350 -.669*-.559* .166* -.182* .646*.623* -.560-.588 .147 -.346-.360-.338 .224 155Utorncy of louth Litericy 10-14 M 1930 -.02_ -.532_ -.559 -.579_ -.241 -.623 .784* -.488* .429 -.569* .532 -.347 .541* .614*.446 -.198* .300 -.284* .431 -.476* .368 -.481 .421* .656_ .701" -.663 .678* -.347 .429* 16n159158157156 LitoracyUttrneyLiteracyLiteracy, 10-14 10-14 10-1410-14 FF1F 19601P4019401930* 1960 -.671-.621-.577 .709w -.733-.690-.761-.635 .795' -.667-.612-.697-.620-.723- -.720-.653-.737-.633 .713' -.363-.277-.279-.403 .340* -.735-.688-.764-.680 .331435.496.560 .674.675.635681 -.398-.531-.482-.520 -.632-.573-.648-.540 .187.192.272.320 .390.467.323.371 .4lo.429.427.394 -.389-.437-.517-.361 -.598--.633--.573!-.665* -.720 .6104.744,:.751'.632': -.734-.737-.61$-.589 -.294-.302-.325-.332 Chaves161 In youth literasy Litoracy 10-1410-14 7:F 1940-30 -.419-.589 -.317-.450 ..229-.276 .535 -.177-.353 -.398-.531 .212 .2970000* -.678* .5084'1*.535* ...... -.177:-.098 .037, ;;nrollment164163162 14tor1c7Literacy 10-14 10-14 M 1960-4042 1960-404 -.482*-.276"-.305_-.482 -.404*-.596* .550*.401* -.184* 01I IDOOO.596' 000O -.129--.357* -.503*-.285- .....444 + .190 il .728**41614 ....OOOS -.224' 26265 .;nrol.:Icrol 6-146-14 T 19371960* -.347_ .106' -.455_ .194- -.156*..333 -.387 .257* 0 -.266 .088* -.394 .258* -.392 .506* .46o. -.012*-.235 -.321 .106* -.366' .431_ -.452' .531, -.133* .222 -.399 .546* -.6o4* .241** -.629** .626* -.727 .673* .010.163* Cultural Attributoc by Litaracy Aatec, Youth ::np1oyment and 3nrollment 3aroroot Non-Catholic Water on on F Urban :tura1 11/17 71oor Bed Non-wheat Bread Variable Nunber 1940 125 1940 123 196J 127 1960 130 1960 123 1960 124 1940 131 17;i0 132 1940 135 1960 133 1940 113 1960 114 1960 43 1940 1940* 116 1940' 1960 1940-60 122 Urban literacy 1962 %ales MN. L- 115 117 119 168173170169 rsnales25-2930-397V7110-14 -.503...6b8-.312-.503 -.729-.659-.665-.426 -.632-.326-.641-.656 -.764-.632-.400-.612 -.66-.322-.666-.674 -.632-.712-.405-.629 .372.502.393.418 .659.399.647.644 -.585-.251-.504-.495 -.712-.356-.645-.653 .265.312.332.383 .149.275.205.233 .200.306.1%3.119 -.336-.352-.325-.322 -.579'-.515: .439*.535*393*.361* -.493-.535-.1407 428 -.146-.070-.040 .000 .4-4 131178177176 30-391D-1425-29* -.700-.718 .571*.763* -.778*-.750 .928.675 * -.700-.571-.730, .7591. -.771-.Bo& .654.334* -.693-.700* .517 -.730-.757.1, .633'.791* -.524'-.4133 .332.315, -.644-.7676 .631.7374 -.621,1,-.620 .434'.673* -.128-.7531 .620'.790 * -.2564-.167* .255.237 -.2544-.113* .160.212, -.347*-.321* .230234 -.367-.345 .348*.204* -.613*-.632- .598.646, -.468 .471* .160* 184 urat litlracy Males1960 -.496 -.567 -.492 -.505 -.143 -.531 .403 .616 -.344 .250 .303 .496' -.502 -.160 139136115 Femalesna=77L.;-1425-2930-3.' -.464-.423-.494 -.549-.565-.559 -.465-.4'.16-.466 -.487-.533-.493 -.154-.251-.173 -.516-.539-.506 .448.393.418 .577.664.630 -000-.328-.335 -.410-.456-.459-.h57 .197.217.151.091 .409.293 .308.222.275 -.305-.286-.229-.213 -.52e-.475"-.522: .573.41%.446",.443: -.524-.364-.370-.346 -.247-.210-.245-.301 197123194 7747--10-1442)-29.30-39 * -.707*-.792 .561'.701 -.760*-.841 .6454.761. -.650,-.730-.549,-.6372 -.703,-.796 .558*.693- -.362*-.439 .334, -.6954-.794 .537.679'* -.3927 .357.353 -.6074-.7977 .774_.737 -.600,-.676 .4144.5907 -.702,-.764 .515'.6877 -.112--.034 .0331:.()12 -,2573-.100- .201.086 -.368-.415! .423.452 -.200,-.209 .205'.167.! -.708 .612**.683** -.441** .491.48e, -,421, .469*.36T406 -.230*-.295 .236'.251, !:!1,.198 differences in literacy 1960 Urban(13-19) melee - (0-49) .17° .23/ .335 .272 .430 .214 -.307 -.33 .128 .31h -.475 -.279 .254 .121 .205: .125: .006 -.265 201200199 Urban(40-49)(15-19)(o-49) fenales -- (604.)(60+)(40-49) -.432 .001.718 -.461 .763.007 -.432 .715.043 -.460 .791.037 -.519 .631.042 -.408 .744.057 -.325-.011 .207 -.736 .460.065 -.368 .656.001 -.502-.075 .510 -.009-.059 .411 -.137 .123.014 -.003-.328-.380 -.104 .202.213 -.3°5* .529*.020.-- -.3,6,-.501- .010' -.036 .314.517 .067.219.140 Cultural Attributes by Literacy Rates, Youth 4nployment and 2nrol2iment Barofuot ---N Non-Catholic Water FloorSleep on Sleep Bedon Non-wheat Bread 1940 1940 1960 I I 1960 1960Urban 1960Rural 1940 M/F 1960 it/F 1940 1960 1940 1960 1960 1940 1940* 1940* 1960 1940-60 RuralVariable males Number 125 128 127 130 123 124 131 132 135 138 113 114 43 116 117 119 122 204202203 Rural(15-19)(15-19)(40-49) females - (40-49)- (60+)(40-49) -.437-.033 .587 -.519-.112 .537 -.465 532.017 -.417-.027 .601 -.204 519.047 -.426-.053 .568 -.100 436.345 -.481 .508.195 -.327 .600.101 -.527 .620.038 .277.172.389 .169.645.385 -.295 .223.048 -.246-.429 .025 -.335-.254: 345: -.103: .291509: -.163-.578 .043 -.003-.342 .192 Male-female205 differences in literacy by age 1960 Urban(40-49) males - (60+) ninus urban females -.659 -.705 -.736 -.713 -.433 -.703 .318 .692 -.535 -.663 .057 .101 .308 -.248 -.434 .363 -.306 -.236 213211710208 40-4?10-1420-2425-29 .634.550.693.523 .630.708.817 .733.476.618 .630.742.874 .521.564.621 .605.689.841 -.094-.176-.111 -.468-.634-.679 .590.670.798 .593.730.023 -HEIT-.021 .111 -.158-.126 .096 -.16P-.446-.325 .283.254.243 .587.610 -.448-.461: .412.384 .172.318 213 40-49lural males minus rural fenales .543 .847 .735583 .840 .629 -.155 -.687 .316 .832 .174 .147 -.457 .123 .6r,.6*:F6* -5*-.351: .320 .173.1100070 Child223221220 enploymant 10-1420-2425-27 .726.726.811 .674.729.801 .630.616.682 .7370000.736 5100000.395 .698.727.005 -.125-.087 .110 -.467-.657 .... .742.755.312 .720314 .154.106.231 .162.072.... -.325-.492-.603 013.031 .605.723':.771, -.184"-.254:-.406 .145.....305 .121.083.274 6263 Zmployavloy 1-118-11 NF 1960 .612.504 .687.564 .633.480 436.296 -.339-.138 -.416-.433 .605.403 .5.-4.442 -.140-.177 -.033-.135 -.316-.312 .127.198 .62e -.347-.422: .326.403 .216.223 Males Barofoot Females Barefoot Barofoot N+F 1940 1960 1940-1950 1950-196o I 1940-1960 1940 1960 / 1940-1950 1950-1960 1940-1960 Urban 1960 Rural Barefoot M/F1940 1960 Variable Number 127 I 128 130 136 137 138 123 124 131 132 127125 BarefootBarefoot M 1940M 1960 1.000 .917 125 1.000 .917 .870.747 133 .796.387 134 .820.937 135 .892.973 .934.917 .740.837 .763.855 .8o4.906 .639.585 .941.953 -.045-.o5o -.497-.567 135134133 Barefoot M 1940-19601950-19601940-1950 .937.387.870 .820.796.747 1.000 .951.751 1.000 .919.751 1.000 .919.951 .874.826.812 .546.828.764 .849.877.691 .874.962.705 .922.857.869 .576.499.591 .843.827.760 .125.115.120 -.467-.440-.434 136130128 IlarefootBarefoot F F1960 1940-19501940 .837.917.973 .740.934.892 .877.764.812 .691.828.326 .849.846.874 1.000 .901 1.000 .303.901 1.000 .803.853 .724.806.834 .956.364.908 .576.766.553 .979.774.930 -.210-.031-.277 -.726-.676-.616 124123138137 BarefootBarefoot UrbanF 1950-1960F 1940-1160 1960 .585.855.206 .639.804.763 .499.869.705 .591.962.857 .576.922.874 .558la.908 .766.864.806 .576.956.724 1.000 .543.394 1.000 .604.894 1.000 .604.543 .649.842.799 -.1$7-.048 .014 -.410-.571-.714 132131 BarefootBarefoot M/F RuralM/F 1960 1940 1960 -.567-.o5o .941 -.497-.045 .953 -.440 .760.115 -.434 .120.827 -.467 .843.125 -.676-.277 .930 -.616-.031 .979 -.726-.210 .774 -.571-.048 .799 -.7114-.157 .842 -.410-.014 .649 -.1041.000-.563 1.000-.104 .588 1.000-.563 .588 CoRRELATIoN HATRIX: OCCUPATIONAL AND Ecorionlo CHICACT3IISTICS AGAMST SIX OTHIll =ECM VARIABLES TABLE 60 AGAINST THEMSELVES, EACH OTHUR, AGAINST ADULT EDUCATIONAL ATTAINMMS AND Enrollment 6-14-T Roads/Aroa Barefoot Males Vales Economically Active Females F 10+ IF 12+ IndexDevel. - 1937 1960* 1940B 1960 1940 1960 1930 1940 I 1950 1960 1930 1940 1950 1960 1940 1960 19401960- 1950 265 266 26 27 125 127 51 I 52 53 265 SnrolVariable 6-14 Number T 1937 1.000 -.738444 -.252, .031- -.144--.015, -.349, .106- -.333, .156- -.013 .184* 50 -.167 .17o, -.069-047* -082* .090 -.087 .313*54 -.158 .249* 55 -.074 .262*56 -.123 .284* 57 -.264 .350* 58 -.213 .395* 59 -.011 .125* 60 .484* 6]. 266 2126 Roads/AreaRoads/AreaBEnrol 6-14 1940T 1960* 1960 -.252-.738* -.144*Low .031* 1.000 .674 1000 .674 -065 .226 ...0213 .312 -.103 068 -.144-.203 -083-086 -.156-.348 -002 .057 .074.224 .221.244 .2.i7.271 .181.258 .209.210 .227.101 -.427-.250 .023 12?Labor125 force participation Barefoot M 19601940 -.333:0314: .156-.106* , .322.226 -.028-.065 1.000 .917 1.000 .917 -.288-.300 -.112 .011 .039.187 .138.263 -.378-.500 -.271-.4o7 -.108-.241 -.030-.064 -.247-.371 -053-.075 .196.309 -.681-.620 5150 &ActEcActM M 19401930 .170.184 -.013*-.167 ': -.203-.103 -.144 .068 -.300 .011 .-.288 .112 1.000 .763 1.000 .763 .691.555 .320.176 .256.110 .026.155 -.119 .125 -.030 .148 .099.242 .088.233 -.016 .081alo 54535255 EcActEcAct&Act M F1950FM 193019401960 -.032-.047 .313.248 -.158:-.087-069; -086.090 : -.348 .224.057 -.002-.083-.156 .074 -.500-.4o7 .197.263 -378-.271 .138.039 .155.256.176.555 .026.110.320.691 -.211-.0241.000 .451 -.227-.0501000 .451 1.000-.050-.024 .875 1.000-.227-.211 .875 -.058-.091 .784.769 -.072-069 .619.520 -.239-.246 .975.852 -.120-.042 .582.487 -.168-.192-084 .201.096 -.012-.010 .493.463 565857 EcAct F 195010+1960 1940 .262.330.284 -.264:-.123'-074: .258.271.244 .237.221.181 -.371-.064-.241 -247-.030-.108 .242.148.125 -.030-.119 099 -.146-.069-.091 -.239-.072-.058 .852.520.769 .975.619.784 1.000 .798.860 1.000 .664.860 1.000 .66u.798 .651.972.794 -.089 .645.312 .495.348.394 White collar606159 and professional workers Deve1.EcAct F Index12+1960-1940 1960 .484.125.395 -.427*-.011-.213 -.25o .101.210 .227.023.209 -.075-.620 .309 -.053-.681 .196 -.016 .110.233 -084 .081.088 ..010-.042 .096 -.012-.120 .201 -.192 .463.487 -.168 .493.582 .394.312.794 .348.645.972 -.089 .651 4Q5 1.000 .332.632 -0451.000 .632 1.000 .045.332 6564 Collar/EcActCollar/LcAct M 19601940 .340.591.713 -.3131*-.568*-.634* -.146-.272 .036 .132.073.016 -.602-.54o-.549 -.570-.665..604 -.053 .236.127 -.090 .163.307 -.094-.059-.039 -.252-.177-.057 .425.459.404 .510.546.457 .404.330.189 .275.267.166 .507.573.500 .213.288.252 -.110-.182-.055 .773.895.802 69636667 oolar/EcActCollar/EcActCollar/ScAct n 1960-1940F 1960-194019401960 -.477 .554.748 -.478*-.634* .393* -.141-.518 .634 -.138 .109.352 -.773-.641 .032 -.736-.665 .116 -.159 .017.117 -.345-.138 .111 -.065-.174-.097 -.283-.155-.128 .223.279.503 .355.434.164 .412.353.022 .234.185.001 .310.491.198 .074.166.087 -.033-.166-.111 -.110 .827.734 11. .t alrollment 6-14T Roads/Area 1 Barefoot Hales Males Economically Active Females F 10+ F 12+ IndexBevel. Variable Number 2651937 2661960* 1940B 1960 27 1251940 1271960 1930 50 1940 SI 1950 52 1960 53 1930 54 1940 55 195056 1960 57 1940 58 1960 59 19401960- 60 1950 61 727170 Clerk/EcActOlerk/EcAct MT F1960 1960 .682.711.708 -.577-.608*-.607: -.416-.345-.350 4 -.112-.060-.063 -.667-.584-.561 -.627-.695-.642 .068.128.123 .051.189.162 -.124-.078-.090 -.150-.103-.119 .402.420.432 1 .391.466.47o .234.270.289 .133.235.248 .406.496.499 .174.291.300 -.150-.093-.083 .863.870.879 PUblic administration757473 Prof/EcActProf/ScActProf/EeAct FMT 1960 .402.637.654 -.574-.607*-.730* -.101-.046 .004 .086.106.129 -.506-.545-.483 -.483-.525-.570 -.139 .191.087 -.085 .187.117 -.029-.234-.083 -.304-.256-.295 .481.478.312 .314.494.534 .020.465.374 -.080 .442.346 .308.570.585 -.068 .492.381 -.388-.060 .095 .513.760.801 Agricultiwe7776 P.A./EcAct M 19401930 .694.619 -.635*-.532* -.192 -.281 -.026 .018 -.238-.277 -.362 .366 .329.411 .423.469 .058.064 -.057-.046 .258.137 .338.179 -.045 .095 .193.101 .414.260 .323.266 -.080-.072 .554.405 807978 Ag/EcActAg/EcAct M 1930*M1940* 1960* _.234 .533.490.483: -.569**-.484**-.468 -.009-.174,.274w ** -.198-.280: -.069: -.145 .146-.058, -.555-.666*-.522: -.705-.599:.081 -.606* .111 -.099 .052*.o57-.124: -.113 .1057.089*.294: -.128-.117--.172.*-.015: -.238--.112* .229.056: -.079 .457*.416-.376: -.202 .558.535*.412: -.317 .328-.066: -.407 .329.170*.053: -.273 .582-.519.,.399: -.393 .343-.194,,.110: -.339-.224:-.260: -.377 .902.885:.783* 838281 AgLabor/AgEjidos/AgPopAg/&Act M 1960-1940M 1940 -.136-.015 .274 -.170-.301, .169, .202.285.308 .313.131.289 -.467-.124 .117 -.359-.o7o .095 -.025 .170.212 -.220-.099 .142 -.221-.074 .287 -.532-.333-.103 -.216 .435.074 -.215 .537.148 -.344 .543.260 -.294 .332.522 -.176 .174.562 -.285 .524.365 -.138 .169.196 -.125-.086 .431 66853487 AgProp/AgAgLabor/i1gAgProp/Ag MPI 19601940M 19601960-1940 -.354-.272 .234 .191.138* -.047-.298-.387 -.281-.405-.037 .454.304.112 .189.344.158 -.002-.170-.314 -.204 .295.223 -.273 .328.229 .294.539.203 -.434-.452 .067 -.507-.541 .073 -.573-.545 .160 -.544-.531 .145 -.527-.584 .021 -.552-.534 .154 -.224-.172 .110 -.399-.413 .131 898890 FarmFarmEquip/Land Mechanized Nonmech 1950 1950 under 4500 1950 1960* .717'.003,.652.132 -.493-'-.080,_-.432, -.431*-.049.057: -.232 .2o4 -.058_ .054.253 -.5114"-.790-.604 .510, -.488-.759 .597*.411 -.052 .237*.004.318 -.387 .082*.292.065 -.106-.271-.165* .216 -.087*-.356-.271 .400 .422*.058.428.284 393*.054.396.280 .235*.061.246.328 -.027 .224*.241.301 .424*.430.106.249 -.005 .239.254.310* -.053*-.086-.061 .164 -.175 .683*.692.409 94939291 ReturnsAgInc Glick 1950-193019501930 .737.772.664 -.466*-.529'-.579: -.455-.369 -.235-.262-.296 -.403-.381-.109 -.434-.429-.168 .456.466.385 .357.423.523 .133.184.287 .036.060.061 .263.157.194 .080.094.142 .104.085.021 -.029 .125.082 .153.236.173 .256.219.106 _.014-.156 .046 .373.401.312 Enrollment 6-1.4T Roads/Area BarefootHales Males Economically Active Females F 10+ F 12+ IndexBevel. Variable Number 2651937 2661960* 1940 26 1960 27 1940 5 1271960 1930 50 1940 1950 52 1960 53 1930 54 1940 55 1950 56 1960 57 1940 58 1960 59 19401960- 60 1950 61. Manufacturing95 and mining Mfg/EcAct M 1930 .106 -.187: .138 .008 -.376 -.336 -.076 .054.028 -.084-.055 -.086 .086 .485.506 .612.518 .364.276 .255.063 .562.439 -.026 .190 -.201-.366 .708.594 989796 Mfg/EcActMfg/EcAnt M 1960-194019601940 .227.180.034 -.133*-.275-.211 .110.237.192 .151.226.135 -.116-.324-.431 -.364-.432-.169 -.125-.036-.058-.007 -.020-.095 .004 .019.057.017 -.107-.174 .113 -.104 .233.234 .125.023422 .292.281.101 .259.190.297 .108.046.418 .256.159.237 .146.297.064 -.460 .349.632 102101100 99 Mfg F/M+FF/m+F Mfg 196019401930 -.329-.125-.193 .688 .506.285!.381 -.181-.108 .109 -.354-.143-.267 .020 .229.077.331 .341.110.339 -.070-.022 .210 -.223-.018-.078 -.083-.187-.189 -.264 .297.209 -.335 .243.357 .294.196.346 .313.375.307 .280.252.211 .273.346.141 .343.151.156 -.075 .100.103 -.409-.212 .652 105104103 Mfg GlickInc under31$00+ 195G 3500 1960 1960 -.776 .502.461 -.449"-.428* .579: -.159-.318-.402 .275 -.017-.039 .011 -.476-.395-.583 .515 -.509-.531-.559 .547 -.222 .285.210 -.1100 .207.266 .116.117.210 -.010 .053.228 -.326 .151.284 -.356 .238.191 -.298 .245.150 -.354 .224.285 -.428 .266.233 -.448 .262.339 -.148 .230.141 -.726 .708.786 109108107106 Pay/EmpPay/EmpPgy/Emp Fact FactFact 1950 1955/40194019301955._ -.082 .192.132.386 -.153'1-.321! .107'.011.0914:, , -.282-.249-.060 .014 -.076 .206.027.025 -.218-.483-.464-.498 -.426-.275-.482 -.076-.017-.091 .241 -.253-.312-.268 .145 -.032-.089-.263 .393 -.1$1-.252-.172 .214 -.029 .251.307.103 -.168 .262.181.024 -.030 .151.205.392 -.113 .173.342.198 -.176 .038.266.131 -.078 .133.320.198 .035.139.169.245 .241.305.564.406 Mining112111110 Mining/EcActMining/EcAct M 19601940M 1930 .195.260.210 -.130. .016*004 -.295-.291-.087 -.246-.263 .054 -.223-.449-.551 -.185-.432-.449 -.399-.239-.174 -.480-.443-.434 -.369-.410-.351 -.330-.270-.266 -.093 .169.426 -.033 .169.342 -.098 .143.287 -.121 .107.184 -.071 .098.291 -.095 .147.097 -.104-.085 .020 .230.320.331 Employment 6362of youth Employ 8-11 FM 1960 -.476-.651 457.709: .157.037 -.248-.269 .682.504 -638.480 ...... -.223-.251 -.022-.169 .036.232 -.600-.6o4 Erirelboent Roads/Area Barefoot Economically Active Devel.Index 1937 6-14-T 1960* 1940B 1960 1940 Males 1960 1930 1940 Males 190 196o 1930 1940 Females 1950 1960 F 10+ 1940 F 12+ ,A, 19401960 F 1950 Adult levels of schooling Variable NuMber 265 266 26 27 125 127 50 I51 52 53 54 55 56 57 58 59 I 60 61 255254 6_j_l_li.Lteve: +6 0+0 6SchSchM 4. F Yrs 1940*1940* Sch M 1940 .635.790.723: -.677 -.279-.278--.225 -.131-.137: .066 -.612-.369--.212: -.668-.392--.236: -.006: .224:.206.089 .260.255 -.144-.143: -.011:.015 -.076 .033 .451.356.303: .464.370.329: .314.162157: .277.146.142: .488.407.362: .246.227: -.146-.110: ..520:...601 226262261 6 +Adult 6 + 25Yrs25 4. .SCh 00 FMF 19501940 -.634-.715 .603 -.500-.549;-.708 .604: c -.255 .362.290 -.042 .079.071 -.727 .632.671 -.745 .630.656 -.166-.108 .164 -.013-.105 .145 -.080 .178.115 -.082 .092.060 -.364-.316 .509 -.369-.278 .526 -.248-.184 .341 -.133-.140 .269 -.378-.307 .534 -.186-.201 .294.319 -.101-.038 .142.063 -.810-.793.017.933 .0co 258229228227 Adult 030 Sch + 0014M F1950-1960 1960 -.469-.714-.687 .638;.376*.576.462. .134.304.351 -.001-.210 .032 .520.780.601 .461.769.645 -.197-.091-.042 -.049-.058-.093 .045.224.136 .118.177.056 -.214-.399-.289 -.160-.360-.263 -.140-.153-.153 -.068-.131 .014 -.198-.364-.287 -.039-.128-.197 .208.233.044 -.432-.802-.775 -, 237239238236 AdultAd1tHighAdult levels: 25+70430+7+1125+7+F 30+7+F 195019501960 1960 .610.680.589.659 -.491--.57%-.447;-.555: -.314-.191-.253-.255 -.063 .130.043.120 -.688-.575-.717-.588 -.702-.631-.743-.648 .052.135.137.255 -.007 .148.083.260 -.184-.092-.119 .016 -.151-.054-.134-.015 .494.519.464.444 .524.540.454.444 .394.385.310.315 .326.365.259.292 .543.576.477.492 .326.391.274.336 -.012-.043-.007 .011 .924.921.931.922 243242241240 AdultAdult 25+10+M 30+10+F30+10+M25+10+F 1950 1?6019601950 .566.656.515.619 -.565.-.57e.-.477-.351, -.114-.218-.172-.251 .084.190.066.182 -.670-.572-.632-.570 -.623..671-.624-.649 .125.025.171.281 -.042 .170.095 -.195-.072-.044 .047 -.149-.C51-.058-.004 .519.522.569.504 .588.526.564.476 .438.452.363.357 .345.404.265.326 .579.628.504.576 .328.425.253.356 -.036-.059-.045 .004 .912.909.12s 248251250249 AdultAdalt 15+Un15+Bac 15+Un 14 F M F19401940 1940 .193.530.400.076 -.260:-.352-.336,-.136; -.264-.338 .036.167 -.001 .211.174.001 -.734-.472-.584 .438 -.717-.657-.444 .395 -.102 .141.130.107 -.150 .077.189..2272: -.146-.027-.098-.043 -.128-.166-.043 .045 .470.343.421.381 .541.400.484.501 :Z.360.207 .273..100.071 66 .524.389.434.498 .280.164.001.160 -.110-.109-.301-.333 ..842.855.516.566 White Collar Clerical Professional Public Admin. 19110 Males 1960 1960-40 1940 Females 1960 1960-40 T Males 1960 Females T Mies 1960 Females 1930 MAles 1940 'Alta collar and professional uorkers Variable Number 64 65 67 69 70 71 72 73 74 75 76 77 646665 Collar/EcActCollar/EcAct M 1940M 1960 1.000 .552.884 1.000 .871.884 1.000 .871.552 .800.525.746 .859.851.677 -.099-.403 .258 .927.741.949 .727.949.940 .758.906.850 .734.865.780 .687.826.739 .536.531.431 .485.779.037 .669.901.254 6867 Co11ar/=-41ActCollar/EcActCollar/ECActcollar/EcAct FF 19601940n1960-1940 1960-1940 -.403 .677.800 .851.746 .258.859.525 1.000-.598 .771 1.000 .049.771 -.5981.000 .049 -.296 .820.847 -.308 .796.835 -.279 .890.892 -.134 .825.747 .749.691 -.117 .675.615 -.617 .595.251 -.553 .372.651 72717069 Clerk/EcActClerk/Act TFM 19601960 .850.940.927 -.099 .906.949 .758.727.741 .892.835.847 .796.890.820 -.279-.308-.296 1.000 .968.998 1.000 .954.998 1.000 .954.968 .827.837.316 .817.802.329 .560.497.507 .632.502.612 .609.763.739 Public administration757473 Prof/EcActProf/3cAct FMT 1960 .531.739.780 .536.826.865 .431.687.734 .615.691.747 .825.675.749 -.117-.140-.134 .329.837.507 .497.827.817 .560.802.316 1.000 .780.906 1.000 .550.906 1.000 .550.780 .310.487.478 .358.600.611 Agriculture7776 P.A./EcAct M 19401930 .901.779 .669.485 .254.037 .651.595 .372.251 -.553-.617 .739.612 .763.632 .609.502 .611.473 .600.487 .358.310 1.000 .905 1.000 .905 797880 Ag/EcActAg/ECActAg/EcAct M NM1930! 1960*1940" -.163 .848-.890:.888; -.473 .953-.868;766: -.653 .822.643*453: -.176 .735:.691".695: -.386 .807".763,.590: -.210-.068*-.192-.347: -.360 794:.882".845; -.350 .B49;.884".810: -.327 .728:.822".811, -.469 .889.787*.679: -.510 .790-.6624.547: -.066 .617-.659;.584: .058.557,.678:.488* -.058 .649-.676;.742: 838182 AgLabor/AgEjidos/AgPopAg/EcAct M M1960-1940 1940 -.184-.032 .313 -.130-.012 .440 -.062 .473.035 -.236-.010 .297 -.092 .510.035 .177.257.059 -.155-.109 .349 -.097-.176 .326 -.144-.107 .345 -.1L8 .426.119 -.102-.086 .443 -.326 .195.315 -.075-.037 .180 -.117-.018 .278 86858487 AgProp/AgAgLabor/AgAgProp/Ag M M1940 M1960 1960-19401960 -.326-.302 .175 -.369-.420 .068 -.336-.038-.447 -.371-.288 .265 -.384-.490-.022 -.166-.444 .097 -.373-.336 .230 -.315-.359 .229 -.367-.328 .220 -.409-.351 .028 -.402-.431 .084 -.102-.183-.103 -.239-.187 .1$7 -.340-.281 .221 White Collar Clerical Professional Public Admin. 1940 Males 196o 1960-140 1940 Females 1960-40 IT Males 1960 Females Males 1960 I Females 1930 Males 19140 Variable Number 64 I 65 66 67 68 69 70 71 72 73 74 I 75 76 77 908988 FarmFarmEquip/land Eechanized Mon-mach 19501950 1950 .792*.060.711.121 -.066_ .706.276.708- -.194 .461-.565.415 .779.283.845- .686-.779.561 -.46o*-.009-.240 .262 -.032 .748.817*.228 -.000 .807*.733.196 -.145 .819*.765.317 -.133 .628*.641.256 -.013 .569.234.606* -.284_ .405-.438.241 -.055 .054_.674-.523 -.066 .158_.567.713- 94939291 ReturnsAgIncReturns GlickGlic Glick 19301950-1930 1950 under 4500 1960* .579.634.581 .416.422.462 .153.177.152 .597.643.563 .348.376.294 -.496_.535-.512 .601.557.521 .528.551.598 .554.594.493 .355.414.442 .441.506.527 .140.067.260 .642.681.625 .580.636.631 Manufacturing95 and mining Mfg/EcAct M 1930 .445 .538 .649.542 .275.246 .550.531 .262.284 .449.499 .500.452 .460.448 .610.520 .453.375 .555.506 .128.021 .165.250 99989796 Mfg/EcActMfgMfg/EcActMfg/EcActli F/M+F M 14Mfg1940 19601960-1940 1930 .166.453.493 -.436 .378.654.696 -.357 .492.764 -.432 .176.295 -.408 .358.592 .166.174.282 -.363 .311.542 -.374 .309.543 -.370 .297.493 -.338 .287.595 -.220 .311.506 -.388 .356.059 -.305-.079 .070 -.006-.344 .214 102101100 Mfg F/t.to.FlIfgIncF/H+F 41500+ Mfg 196019401960 -.457-.188-.446 .623 -.168-.421 .664 -.262-.116 .555 -.530-.332 .791 -.391-.193 .720 -.333 .341.279 -.426-.122 .758 -.424-.121 .732 -.464-.155 .810 -.482-.279 .597 -.441-.215 .687 -.475-.324 .271 -.469-.243 .428 -.430-.220 .524 104103 Mfg GlickInc under 1950 4500 1960 -.797 .683 -.769 .657.664 -.547 .541.46o -.810 .66o.659 -.706 .595.523 -.284-.378 .381 -.356 .653.683 -.844 .647.685 -.840 .648.654 -.710 .513..641 -.782 .587.639 -.368 .227.378 -.657 .486.408 -.728 .529.601 109107106105108 Pay/Emp FactFact 1955/19401950194019301955 -.004 .339.125.218.626 -.020 .154.478.297 -.027 .540.338.191 .077.426.509.342 .113.391.632.1414 -.004-.179 .029.021 -.060 .200.472.365 -.068 .175.445.342 -.051 .327.537.430 .139.413.115 .089.134.137.490 -.028 .201.161.203 -.044 .002.023.055 -.012-.008 .227.014 110Mining112111 Mining/ZcActMMining/EcAct :11930M 19401960 .172.169.186 .179.184.090 .016.181.191 .428.369.347 .259.413.471 -.221-.152 .014 .238.214.196 .190.180.210 .339.287.287 .187.292.197 .191.135.141 .376.289.460 -.029-.018 .101 .090.040.045 19C

Uti ;Ramp/ igg.tw 1 1 .1 .1

. *.1* :ght, :%.,...14k 1 1 1 1 1

* * H Wig 41*Pti q1/441M OoF waR 0

am e ERIE !*Ing . O.

g 610 F- S S S thlk vtak tkkt , 1 1 1 1

g ** z Ta , g411 "gg Pr') S 1111 OO * * vs 4 %a V`43W . 03.1.0 wwv.v% ME mr.0 g O 1 1 / 1

31g 4.as s11S3,11' '. 1 1

ma% -4%ammWW Cha- ro-,%4 Cragl4 00g- 0 1 1 1 1 1 1 1 1 1 1 ** Hag k .11.6:4 k II% 1111 1 VI

VO 43RE. Wde k '0,A

* * gICCO1 Wel .4 'WA

* * ....,.., ,...... ,,.. .. , ,...., . ...., , 01 lgn PV ,.., 1...... § t....AL, . 1 1 1 1 1

w tJ

im*-C1* 0 1 1 1 1 1

C) CD 4- 4- 11%i f §§154 an Vt .. 0000 v...... ++++ g ." vliv Ylt! "IF Imml o Init+1 VM 5555 0 0 CP CO 0 0 0 0 0 CO 0 CO 0 0 0 0 pggp i ufl1 MF kW

101.%11. MODH1A Vt W13113N Nu eug sec

,q16 t &OA.": I:: ik

A RI Vig t_ WM, II III

pf*b iki I I I I cw m wvw v 0 Pe 0 III Okk Liwia ;: Vt *** A fE5 5M.. O kiii tii iikk . I

SP11§ Eit 1St ikkg, kii hhii II I I I I 6Vg gi ikii Wee 1111 6S S S *** 4.4. 61 fa kikits OA kiA k,kkk t?iik ":0 **** * * tay*** 4 rks kelk: OA kik OA i*. I I *** zisni wi kw o.y. VA i.,w itM 5.1y1sb uziAm ... I 11 r : I .I I ....I

1 1*** t pi A. liw NI' "." Isxm, iii; ikl ilt ttf 444 PRI I v. III ilif 1ft c"1 HI fiff Pig tri $828 34t1 11 P843 Pea UM oax ( ggl ggg 316

0%0 eo 44E1 anla 5 aA.5 qt1.4$0 0 2A§...... 1 , 1 11111

,2 .. .. . Ale.. .. . 555 ....3 nil... .

11 * o girrotiso Ort IS IA V Po et .0 ei .. St og p-1.6. Vell al ...." !tilt 011..0! ac. er02.4. Oa ses . 5 4.1113...14 . 4. 4. . . . . 1 il ...... IS I I $ $ 0 IP * 0 0 * *11710,..,14 * * * * ri 0PM Via" 4 13 4441 ogo Ir.. 1... Po an I.-i.n tWitg A 4. A 41'frAl9...... Soo soso 1 1 1 1111 1 41* . . Po Os ID g do . liVg$00 SEMI W V f53R4S SSSS o .... 5555 hi 11111 1 1 1 I mINNI * * 110 14.1 %MIN P..Fti4i5 Af ki 1ka"..q 0000 S oo. 0400 1 10 1 Ir * * R IA0% IA MA&T, R1 .5fc'd125at OW. N o0NI Jr. lise o oollo S I] 1 1 1111 1 * * Ars aav51 en 1.-11AVAliriN . F,R1. ... f o l i o 1 1 1 : : ...... * . 01e1ellell Po l oRsIsi vvuog ...let Ills. 141Ill sell. . 1111....

Po r41,1%,,a. foN §1°;: N 0 N 0 littp. a el looses 0 sees sees 1 11111 les ert....1 re 41411A.1100.1.... Uffi 4. F.. g, kg§ §Al ,A.01 pi IUNN 3 VA 1 1 1111 1111

N *. en 0Or1 0 aAriii§? r* a. 0SO 1 1 1 1111 1111

et ,... 4.6110/ 01 -1 N N N v.10 *1AA"*1 4 1 111 1 1111 IttA* * * :Se** * * **cn ....1.41.N.422r r- r-1 a 1 2t2R co.:o Imo I 3 3 * * wet %neneqii,401 N .00: .00. soo. els, I * 0 0 ** it, * * * 1 4011[0#env mil Taw!fr, ..,, 0 .4 0, N 0 0% traC-sedA gS2g S I.CE!Ili 1111 1

a st3g * *z!14 It SSA ff§friar.r4 tad 0000X: Or 00 +4,4.g el 4. 4. ill 44 p0.04 m los) R 00%04D 0000 0 0000 VAR s MLA: 4i IS ill 1 ;1 Ii ,01CCI% cl"^u."Zia on cm OM OM 11M P..n WiU2 NNN, NNNN %INV Aviculture Economically Active in As. Males Ejidos/ f Am, Labor As. Proprietors 144P/ Land MeddFano Non-Forma underAg.Ino Returns Glick WINNewa=0. 1930* 194041 1960* 1960-40 As.Pop. 1940 1960 1960.40 1950 1950 1950mach $500 1964/1 1930 1950 1950-30 Variable Number 78 79 ao 81 19140 82 19140 83 1960 84 85 86 o7 88 139 90 91 92 93 94 , 243242241240 AdultAdult 3041007 25.010+1430010+M25+10.7 1960 196019501950 .7514-.779:.761:.787: .882-.852:313:.841: .904-.900:.339:.878: -Joe -.347-.359-.369 -.018-.129-.019-.098 -.159-.130-.189-.130 ..406 .448.318.381 .060.165.011.150 -.433..403-.317-.372 -.388-.425-.244-.386 .393.217.331.169 .697.699.682.698 -.137-.054 .020.024 .641.756*.590*.753: .208.444.248.486 .296.481.312.527 .434.270.280.478 251250249248 AdultAdult 15.0Bac 15.0Un1544in154.:40 M7m 1940F 194P 1940196.0 .741-.750,-,.354:.629: .797:.649:.834-475: .646t.827-.789:.546: -.346-.328-.313-.248 -.106-.123 .144.054 -.024-.043-.071-.102 .385.168.185.077 -.199-.171 .093.097 -.356-.150-.036-.137 -.305-.123 .027.087 .458.362.208.164 .777.600.369.389 -.191-.038 .021.188 .660.534*.1034,.194: -.144 .224.210.085 -.078 .456.410.160 -.080 .465.404.145 Manufacturing dconalical1y Active Males $ of Foolos in Mfg Wnrk Force 31500+Income IncomeUnder 0500 Glick Mfg Pay/Humber AMployed in Factory Aiconomicelly Active males .-- Variable Aumber 19301 95 1940 96 1960 97 I 1960-40 98 1930 99 1001940 1011960 1021960 1031960 1041950 1051930 1061940 1071950 1081955 1955/44 109 1101930 1940111 1121960 Nannfacturinuand95 Mfg/EcAct M 1930 mining 1.000 .796 .606 .223 -.406 -.037 -.107 .008 -.112 .239 .289 .072.049 .107.161 -.030-.006 .146.125 .120.140 -.015 .023 -.030-.010 989796 1tfg/EeAct3fg/EcAct4fg/EcAct HM 1960M1960-40 1940 .223.796.606 1.000 .256.785 1.000 .785.764 1.000 .764.256 1.030-.330-.505-.422 -.155-.263-.184 .724 -.230-.343-.225 .594 -.187 .156.196.097 -.326-.206-.240 .306 -.393 .124.339.359 -.542 .347.486.377 -.188 .374.260 -.258 .303.300 -.272 .012.138 -.040 .039 .044.086.044 -.037 .067.139 -.001-.150 .047 100102101 99 Mfg F/H+PF/4+F IffgMfg 19401930 F/11+7 Mfg 1960 -.107-.037-.406 .038 -.225-.184-.422 .097 -.505-.34)-.263 .196 -.155-.230-.330 .156 -.187 .594.724 -.1441.000 .731 -.3751.000 .731 1.030-.375-.144 -.910 .494.231 -.535-.292 .611 -.456-.463 .704 -.115 .513.072 -.267-.261 .762 -.295-.341 .465 -.009-.036-.187-029 .049.329.141 -.102 .501.110 -.374-.111 .311 134103 ilfgNigMfgPay/Elp O1ic.::.IncTao under31500+ Fact 1950 $500 19301960 1960 -.112 .289.239 -.240 .377.359 -.326 .486.339 -.206 .347.124 -.542-.393 .306 -.463-.252 .231 -.456-.535 .494 -.910 .704.611 1.030-.737-.736 1.003-.706 .603 -.7371.000 .603 -.449 .342.400 -.645 .740.469 -.329 .384.423 .163.295.047 -.234 .152.203 -.370 .295.302 -.a' .083.314 109108107106105 Fay/EmpPay/EmpPay/RepFey/Emp Fact Fact 1950 1955/194019551940 -.086 .107.049.125 -.030 .146.161.072 .039.012.303.260 -.040 .300.138.374 -.029-.272-.258-.188 -.341-.261-.187 .072 -.295-.267-.115-.036 -.009 .513.465.762 -.329-.645-.449 .047 .295.342.384.469 .163.420.740.400 -.1201.000 .555.515 1.000 .261.633.515 1.000 .381.633.555 1.000-.120 .381.261 .446.469.405.452 .115.550.592.632 -.067 .305.444.4h4 lining112111110 Mining/fa4ctMining/EcActHining/EcAct N 196019401930 -.010-.015 .140 -.030 .120.023 -.001 .067.086 .044.047.139 -.150-.037 .044 -.111 .141.110 -.374-.102 .049 .501.329.311 -.234-.241-.370 .295.203.314 .152.083.302 .632.405.444 .469.305.592 .452.444.550 -.067 .446.115 1.000 .553.807 1.000 A07.727 1.000 .727.553 Employment6362 of yvuth dIployEmploy 8-11 FM 1c601960 -.408 -.442 .... -.022-.169 .541 .581 .670.727 -.581-.604 -.275 .... -.440-,401 -.296 .... -.127 .056 -.299-.237 000. Manufacturing Mining Economically Active Hales % of Females in Mfg. Work Force 31$00+Income ,_,w'Income""" ;ZOO Glick Mfg Pay/Number bap1oyed in Factory ------Economically Active Males 1930 1940 1960 1960 -40 1930 99 1940 1960 1021960 1031960 1041950 1051930 1061940 1071950 1081955 1955/40 109 1930110 1111940 1960112 Adult schooling Variable Nunber 95 96 97 98 100 101 , 255254 6 +Law o0 levels:SohSdh FM 194011940* .557.355.352: .639.344316: .614.305.286: .086.309.090 -.477-.304-.284 -.151-.151:-.236 -.416-.351-.276: .60,.561.458: -.729-.741-.636: .700.534.456: .684.540.498: -.052: .340.048 .397.253.206 -.035: .035.211 -.110-.144: .107 .207.150.058: .174.153.059: .134.100.039: 226262261 6 +Adult 6 + ItsYrs25 +Sch Soh0 HFM F 1950 19401940 -.438-.429 .584 -.395-.477 .673 -.426-.302 .641 -.080-.204 .326 -.460 .488.578 -.206 .204.418 -.412 .416.479 -.662-.760 .637 -.735 .804.775 -.661-.728 .679 -.665-.733 .666 -.238-.445 .424 -.575-.504 .470 -.424-.379 .279 -.101-.099 .139 -.385-.249 .363 -.460-.275 .320 -.393-.238 .229 VDtl 258229228227 Adult 030 Sch + 0N FH1950-1960 1960 -.470-.468-.174 -.307-.462-.482 -.448-.208-.378 -.134-.222 .558.429.449 .243.396.378 .467 -.415-.666-.634 435.765.734 -.201-.638-.701 -.468-.678-.553 -.015-.317-.247 -.439-.461-.329 -.311-.369-.346 -.114-.076-.131 -.169-.252-.440 -.143-.424-.281 -.026-.297-.343 237239238236 HighAdultAdilt levels: 25+7+1430+7+M30+7+F25+7+F 196019501950 .525.531.469.557 .574.620.582.559 .597.538.652.575 .379.285.344.245 -.430-.358-.445-.418 -.240-.175-.118-.213 -.420-.425-.371-.347 .695.670.698.670 -.740-.744-.805-.806 .691.693.727.722 .636.645.634.682 .399.277.382.244 .442.548.482.523 .258.284.170.141 .123.144483.195 .454.276.408.189 .382.308.130.220 .305.161.252492 243242241240 AdultAdult 30+10+F25+10+F 25+10+1430+10+H 19601950 19601950 .602.537.619.515 .660.600.608.615 .666.561.628.558 .374.251.376.231 -.417-.303-.436-.364 -.152-.168-.177-.084 -.300-.298-.301-.351 .557.612.676.664 -.678-.778-.610-.760 .606.654.705.681 .587.612.574.643 .224.292.185.339 .520.464.420 .232.117.224.120 .157.108.219.129 .441.252.315.149 .341.179.149.070 .274.108.149.016 251250249248 AdultAdultAdult 15+Bac 15+Un15+Bac 15+oh MF F1940 19140 H1940 1940 .537.643.519.628 .627.576.606.536 .604.605.653.641 .275.423.388.345 -.270-.315-.336-.372 -.017-.045 .050.024 -.300-.088-.230-.312 .147.648.503.206 -.575-.176-.260-.692 .348.596.583.184 .501.199.207.602 .315.173.593.386 -.018 .459.277.222 -.100 .022.248.126 -.051-.057-.094-.127 .086.192.032.334 -.094 .360.095.032 .083.071.040.220 CORRELATION MATRIX: IMMATIONTZVEIS VARIABLZ OF SOBOOTIFO; A0AI137 artoT.I.:01T I'd.T.r,g AY Y..AP, 11,;J/C:2!Ci:, LarV1,OF.::Fts PAS3 HATSS, LIMACY OF TABLE 61 POPULATIONLiteracyANDOCCUPATION; =cm ANDFACILITIES PROMESE IN SCHOOL.-CONTIVUATION RATE, AGE DI1raval37.3 LIT%T.Afri BY AlL, snx AND 1USIDANCE; ADULT GRADE Age 10+ Age 40+ SchoolAge 6+0 Literacy of Muth 10-14 Ire. Changes 1buth 10-14 res. in Literacy of 1960-1950/Lit. 6+ Yrs. 1940 ] 1960 19160 1960 1940 1930 1940 1960 ' 1940-1930 1963-1940 1960-1940 11 14 F* 14 F M F 161 M 162 r 163 Mit 164 F* 165 Adult levels of education Variable HuMber 1140 141 145 1.46 iia 148 153 155 156 157 158 159 160 , 226 AdultLow levels. 25 + 0 M 1950 -.957 -.932 -.830-.954 -.888-.903 -.844-.966 -.870-.887 ;lift 229223227 Adult 3025 + 0 FM 19601950 -.964-.952-.373 -.953-.845-.983 -.935-.889 -.963-.885 -.943-.977 -.990-.891 .326.341 -.843-.913 :i1;11*.878* -.901 -:;ii-.948 -.931- -.959-.937 --:!Pg681.48 _670* .315 238237236 AdultHighAdult levels25+7+1125+7+F 30+7+14 19501950 1960 .857.875.869.898 .827.606.820.806 .807.835.818.876 .861.880.365.865 .838.836.852 .857.816.847.813 -.545-.603-.578-.579 .733.734.761.800 -.808*-.827*-.865: .818.872.820.890 .560.858.888.894 .796.771.797 .813.791.793.795 .648.710.631.654 .569.587.678.622 .705*.618*.746*.571* .743* -.282-.245-.299-.378 239241240 Adult 25+10+1425+10+F30+7+F 1960 19501950 .781.858 .719.755 .843.750 .793.857 .756.806 .750.778 e.iii00.0 0000 0000 000. 0004*0 * .761 .0004 690 .410041 647 .690*00.0 .811*000 -.302 243242248 Adult 30+10+1415+Hac30+10+F M1960 1940 .822.855.481 .473.793.775 .471.788.822 .838.462.859 .490.812.809 .817.797.445 -.587-.5148 :Ali:.704 -.770 .783 *4100.818 *040.771.767 .7770.0004 .614 .545 .525* 0000.669* -.247 249251250 AdultAdult 15+Hac 15+Un F FM1940 1940 .788.719.337 .354.744.657 .680.299.727 .793.697.359 .339.731.695 .359.760.663 0000 0000 0000 SO*.0000 0000 265Enrollment267 EnrolEnrol 6-14 6-14 T 1937M 1930 65o .631 -.055 .703_.912 -.702,0-.741C .775.762 .787_.700 .737_ ..743_.737 .627.171 .159.638 -.416** .582*.482* -.419** .431*.656* -.338 .506 266 Enrol 6-14 T 1960* :la. _137. -:625* _.513* on* -.725- .569 -.697* -.675- -.725- -.719- -.413* -.442* .234* Ase 6+0 arm. Literacy Chanses in Literacy of Lit. 6* Age 10+ 1940 Age 4o+ School 1930 Literacy of Youth 10k3)4 Yrs. 1940 1960 1940-1930 Youta 10-14 Its. _ 1963-1940 1960-19401960-1950/ Yrs. - 19; 1960 I. 1960 1940 11 M F* M F li F M F M * F * Varia'alc Pumber 1110 1/13 1116 1187 I 148 153 155 156 157 158 159 160 161 162 163 164 165 275274273 IlnrolJnrol_;nrol 6-146-14 R.aralU=RUrban 196C 19601960 -.090 .213.359 -.099 .214.377 O0...000. 0.0 00..000.00.. 006000.0 . 000. 00. -.074 .152.017 -.219 .503.143 -.343,-.144* .128 -.160 .183.443 -.069 .364.252 -.214 .180.524 -.145 .472.233 .006.196.207 .109.155.325 .142!.1431'.007- .100*.068*.233* -.169-.173 .002 282280279 IEFOIalnrol..-krtoairol 6711-1c and6/(601-1,000)-(200) 6/401 income;200 to A,000in pesos monthly 1959 .... 00...000 00. 000. SO..600.0000 0006000.. 00. 0.0000000600 -.247-.009 .220 -.144 .020fl.353- -.362 .227.038 -.342 .242.011 -.271-.006 .207 -.301-.010 .209 00....00 SOO....a.0.0 ...a SOO..0..SOO. 284283285 ;InrolAnrolAnrol.1nro1. and6/Profes5ion1 6/Prof-Ag occupation 6/Alricu1ture l of father 1959 0.00 000000.. 000.. 0.0 0...00.. .271.483 -.?37-.36e SO. * .21/48.468 .247.454 .257.470 .258.453 O...SOO. .0.0SOO. 660.SOO.SOO. .00. MalnuationProrress inrates--primary school school )esinnins of year anrollment 00.. .00. 0.0. 00.. * 296293283 3 4/33 4/3 Urban-Rural aural 1942 19424/3 Urban 1942 -.473 .1/459.... -.317 .382 00..0000 .000.00.. 00..000. -.358 .361S OO. -.512 .517 -.489'. .570* -.483 .477.0.0 -.438 .437 -.341 .439 -.329 .422 -.134 .179.102 -.557-.11 .382, 6* -.491-. .334, 112* -.374 .668.312 304299 3 14/34/3 RuralUrban 19601960* .214.03? .01/45*.165 000. 000. 0.00 000.0.0. .023. 039 -.423 .372 -.280-.007* .245 -.223 .168.087* -.344 .302 -.272 .237 -:1;3* .172 -.054--.on** .218**448: -.011- -.067* -.228 .234.042* 309303307 4/34/33 Urban4/3 Rural Urban-Rural 1960-1q42 1960-191/42 196u -.222-.125-.233 -.183-.207-.194 00..00.0000. 000.00..0.0.000. 000.8.00 060.0.00...0 .143.361 -.116-.100 3534 -.133-.219 -.136-.251 -.295-.154 -.192-.371 -.371- .061: -.373--448! -.309 .16o 333330332331 13" 5/13 5/1 UrbanRural Rural 19421960191/42 /1 Urban 1960* -.485* .621.347.594 -.441e .579.565.359 000.0.0.00.00.00 .0000000004.6000 .000.0.410000 0000000.6000 -.259-.254-.499 .405* -.4T7* .633.339.548 -.569P-.733- .493!* .613.359.657 -.4954 .639.566.339 -.522- .379,.594.613 .581 :435* .589 .119.069..563 -.363- .556*.379- -.345:- .17D:.569!.325- -.065-.390-.139 .158- Age 10+ 4ge 4O+ Age 6+0 -- Literacy Literacy of 76oth ID-14 Tes. Changas in Literaa7 of Lit. 6* 1.960 School 1930 1960 19140-1.930 7PloLb 10-1h 21-s. 196)4940 isto1960-1950/ 49140 7rs. isho -A 11 .1: F K Y A F :ilk Fe Ii 155 1 i56 157 3.58 159 160 161 162 163 164 1 165 339338Secondary 90=1 Cont. 3/I3/1M F 19601960 .23h.139 .259.131 000 000000O0 0000 0000 -..2e3-.030 .233.11t9 -.131-.057: .228.175 .237.168 .160.126 .233.133 .136.188 .222.287 .282.r1.3:: .181186: -.295-.2.57 347349348Age grade progress is sdhool Agekm Age10 10 Gr IDar 1 lr 11111 la-Mf1 :a 1963 19631963 -.777-.756-.716 -.771-.744-.734 -.789-.666 -.701-.655 -.692-.730 -.650-.702 .358.343.366 -.794-.802-.617 .713-.734:.630! -.770-.779-.695 -.737-.754-.735 -.795-.761-.756 -.792-.798-.779 -.421-.417-.613 -.366-.390-.551 -.453-.546,-.359: -.430-.1164,-.472: .336.308.031 359353 AzeAce 10 12Gr 3r1 3+FU 'a1963 1963 .613 -.743 .619 -.691 -.699 -.694 -.687 -.259 .324.261 -.671 .669 -.64e -.71L3 .644 -.777 .58*, -.773 .696 -.502 .680 -.636 .337 -.630 .194 -.445 .323* -.534: .255* -.159 .136 365361360 AgeAre 1210ID CrGr 341 YR-FLFRYR 19631963 1963 -.577-.860 .643 -.560-.865 .635 -.877 -.792 0000 -.866 al -.737 -.153 .214 -.693-A45 .697 -.633* .602.70, -.542-.625 .669 -.539-.566 .615 -.561-.903 .706 -.911-.595 -.208-.539 .333 -.201-.505 .206 -.523-.444, .366* -.343-.484, .301* -.251 .375.284 376Pass rates ?ass 2/Pres 0 1960 .319 .423.104 0000 -.161-.227 .151.114 -.270-.256: .141.178 .255.259 .361 .372.376.695 .262.338 .243 -.059: 065: -.012 School377 facilitios hiss 2/Pres R 1960 .303.727 .679 -.205 .674 .740 .674.199 .656 .539 .056 -.110 -.027 -.034 388387389 SchFri.Pri. Inconplcte TeachersikAct ThecherstkAct C 1942 1940 1960 -.199 .343*.663 -.208., .297-.669 000. 0000 O 000 0000 -.425* .129.020 -.128 .332.690 -.693:-.943 .220,, -.201, .302-.689 -.203, .291746.633 -.195, .325.719 -.205, .328.7111 -.270, .090.455 -.019-.145, .1490.530 -.135* .411605 -.131: .132--.451.652: -.209-.487 .056, 390397396395 Sch IneomplateIncoapleteIncomplete R7 1942-6019601942* 19h.2-60 -.001-.047-.666 -.047-.623 ggei O0000 0000000S 000 0000 000O 000 0000O 000006 .278.19s.331 -.325-.635 .005 .097*.020*.715- -.062-.073-.567 -.011-.972-.569 -.091-.634 .075 -.064-.624 .065 -.037-.184-.178 -.044-.175-.014 -.276*-.017*-.227 .1427 -me-.129*-.304* -.103-.170 .160.343 LYban Li.teracy by Age 1960 Males Rural Literacy by Age 1960 Fasales 40-49 30-39 Males 25-29 10-34 40-4911 I 30-39 Females 25-29* 10-31, h0-49 30-39 25-29 10-14 40-49 30-39 25-29* 10-3.11* Adult levels of educationVariable NuUber 168 169 170 173 176 177 178 181 184 185 3.86 189 192 193 194 197 228226227 AdultAdultLow 25-4.levels 3025 4. 0 0M 1950F 1950 1960 -.697-.705-.673 o.,_ -149-.534-, 7 -.725-.744-.707 .807*8o0.836* ** .769*748*738*.817 -.706-.743 -.815-.901-.681-.878 -.522-.673-.055 -.835-.918-.697-.882 -.952-.069-.811-.063 0000 .900*.855*147: 69 .846*.933.761111* 236229 dithAdaltAdult Ievels 254.741130 1950 0 F 1960 H -.743 .682 -.552 .711 -.816 . 738 .913 -.792* -.825 .708 .623 0000 .590 .642.682 .743.685 0000 -.684 688* 40 239236237 AdultAdult 254.7*F 301.741M30474F 1950 19601960 ..723 707706 .0000.00 .691.661.771 ..759 769760 -.790-.758: 00000000 -.790*-.802*-.826* .757735.747 599.617:631 00000000 .577.516.574-559 .671.670.615 .626.740.650 000000,0.000 -.620*-.699*-.:-.676 242241240 AdultAdult 254.104f 254.1041430+104)! 1950 19601950 .660.625.623 41,0000000 .689.744.594 .739.643. 702 -.666* 0000 -.788*-.666*-.733* .722.716.629.675 .605.559.533.553 080000000000 .495 .556.635.630 .705.655.617 0000000000.0 -.558*-.542' -.653*-.638*-.587* 24.3248 Adult 35+3ab301104F 1960 1940 .451.649. 293 0000 413.651 . 732453.334 -.238--.365:-.733- 0000 -.285--.384!-.V.2* .354.437 .204.319 .136.544.519.331 .216.363 .320.255 -.233*-.655* -.228*-.315* 251250249 !AultAdultAdult 151.3ac1540n 15+Tin F F)41940 1940 1940 .696.739 .667.626.343 . 764694 -.628*-.734* -.735*-.647* .658,758 .500.439 .464.408 .577.487 .642.501 -.528*-.390* -.482*-.584* 266265267dorollsent EnrolalrolEnrol 6-14 6-14 m 1930T 19371960. -.303 .565*.000 -.356 .596*0255 -.419- .627,. -.467- .635 -.626 ....*.337'' _ -.444- .663_.375 -.492"-.731 -.484* .668 0000000. 000.000. --592* 0060.580 -.737* .7270000 -.460* .5830000 00000000 -.609* -.699*.475** .644** 275273274 -Jura:braEnrol 6-14 6-146-14 Rural UrbanMa 1960 1960 -.055 .385.275 -.022 .282.434 .064.311.197 ..445.065 239 -.290_416* .222.064.415 -.160*-.176--.419! .091.217.443 0000000. 00000000 -.213 .489.129 -.260 .616.178 .011.239.229 0000 -.022*-.281*-.286* -.540*-.177* .207* Urban Literacy by Ago 1960 Rural Literacy by Age 1960 males Fenales 25-29* 10-14 40-49 30-39 Malan 25-29 10-14 40-49 30-39 Females 25-29* 10-34* Variable Number 40-49 169 30-39 169 25-29 170 10-14 173 40-49* 176 30-39 177 178 181 184 185 186 1 189 192 193 194 1 197 278 ?tar. .67m_rizand income in nes= month .... .130 00.0 0000 .017.155 ..00 0000 000. 0000 0000 000. 0000 0000 0000 0000000. 282253280 ZiarolEnrol2nrol and6/(601-1,0W)-(200)6/Agriculture0601 occupation to A,003 of father 1959 .303.307 00.0.00.0000 000000.04.000000 00000000 -.358 000C.000000. 000.000000.0 000.00.00000 00.00000000. 0000 0000000. 0000 00000000 0000000. 0000 284Conb.nuationratei--prinaryProsreas285 in school school Enroltbro1 6/Professiona1 6/Prof-Ag -.153 .031 0.000000 0.000000 0000 -.209 .137.365 0000 0.000000 000000.0 00000006 00000.00 0000 00000000 000.0000 0000 0000 293289 BBesinni. 4/3 RuralUr ng of 1942 ye ar enrollnont 42 -.231 .202 -.220 .200.012 -.162 .175 -.295 .321 -.345* .363* -.007-.270 .247 .295- -.267 .269 000.0000 00.0000. .000.333.368 -.368 (1930000 0000.399 0000 .....332." * .332.".... * 304296299 BE 4/34/1 RuralUeban-RuralUrban4turalUrban 19601960a 19421960 -.278 .141 -.274 .167.425* -.111 .137 -.133 .051 .03L.....*.05e- -.162 .053.360* .0or.(boe*0000_ -.139 .0430000 00.0000.0000 00.00.000000 -.305 .2970000 -.381 .4060000 -.226 .2100000 0.000000 .217-239..c....* .335* 307309308 4/3413 RuralUrhan1960-1942 1960-1942 -.470-.267 -.235-.464 -.302-.099 -.386-.201 -.174-.170 - .500* 331330333332 B 5/1.5/1B 5/1 RuralUrban UrbanRural 1942 196001960 -.016 .476 -.262* .469.204.055 -.511. .117,.481,212 -.2* .272.543 -.488:-.399 -..13r0i. .464.30e -.378--.215*-.545* .495t! -.332* .283.145.487 .000000.0000 -.415- .584.279_.441 -.580* .674.411.582 -.378* .615.406.496 00000000 -.630'-.444* .388:11 -.450*-.713* .573** 339333Secondary school Cont. 3/1 7F 1960 .396.316.133 .384.290 .243.244 .428.341 -.337*-.220* .362.271 ,299* -0 * .290.354 .166.235 .177.139 .228.094 -.249--.148: -.178--.129: Urban ,itcracy by 410 1960 Rural Literacy by Age 1960 40-49 30-39 Hales 25-29 10-14 4o-49* 30-39 Females 25 -29* 10-14 40-49 30-39 Males 25-29 10-14 40449 ------30-39 Females 25-29* 10-14. Variable Number 168 169 170 173 176 177 178 181 184 185 186 189 192 193 194 I 197 349348347Age grade progress in school Age 1010 GrGrOr 111/91 I9JMR-HU 1963 1963 -.556-.463-.460 -.466-.478-.568 -.259-.323-.552 -.530-.546-.714 .565*.559".677: -.560-.554-.659 .486".523:730! -.530-.507-.690 -.624-.751 0000 -.604-.784-.714 -.831-.840-.732 -.693-.651-.701 0000 .680*.666*.719* .778*.803*.823* 353 Age 12 Or 3+ MR 1963 .153 -.642 .176 .249 .285 -.354* .7014: .290 -.1420* .273 0000 .658 .802 .606 0000 -.636* -.826* 365359361360 AgeAge 1210 10OrGr OrGr3+1 FU1FR FR-FUFR 19631963 1963 1963 -.172-.289-.577-.647 -.308-.583 .199 -.115-.431-.609 .279 -.296-.689-.754 .315 -.392* .351.690; -.352-.696-.726 .316 -.448* .660!.279-773* -.290-.675-.746 .306 -.601-.814 00000000 -.598-.617-.819 .663 -.683-.910-.747 .805 -.459-.774-.655 .627 00.00000 -.650' .476*.75r.650! -.808* .628*.874-.753! Pass376 rates Pass 2/Pres RU 1960 .296.267 .351.307 .C72.279 .339.388 -.337: .387337 -.264--.345: .359.378 .337 .269.304 .516.434 -.541-.486"! -.363*-.394* 377387School facilities Pri, Teachers/EcAct 1940 .489 .552 .564 .625 -.635!-.415- .620 -.689* .623 .593.293 .652 .629 -.670* -.628* 390389383 SchPri.Sch Incomplete Teachers/EcActIncomplete U R 194215042N- 1960 -.063 .036*.336 -.065_ .030-.419 -.040- .094_.470 -.128_ .132-.496 -.515--.241**.217 -.134 .113*.520 -.131**-.585* .068* -.113 .504.108* 000000.0 00000.00 -.218 .661.307* -.237 .382*.724 -.342 .406*.577 000000.0 -.536* .342* -.410:7-.667* .2641. 397396395 Sch Incomplete RU7 19112-601942-601960 -.218 .003.019 -.214 .010.004 -.126 .039.042 -.038-.313 .031 -.024- .023!.500 -.015-.362 .067 -.047-.017: -.009.356* -.334 .076 0000 00000400 -.581 .036.019 -.001-.078 .582 -003-.076-.690 .4005*.045*.626- .020.023:.606- Ale Di'forencao in Literacy 1960 Nale-ecmale Differences in Literacy. brAge 1960 Halos Urban Females Hales Rural Females Urban Rural (15-19)- (40-49)- (40-49)- (15-19)- (40-49)- I 25-29 20-24 10-14 20-24 10-14 Variable NuMber (40-49) 193 (60+) 199. (40-49)(15-19)- 200 (60+) 201 (40-49)(15-19)- 202 (60+) 203 (40,49) 204 (60+) 205 40-49 208 210 911 213 40-49 218 25-29 220 221 223 226Adult levels of schooling AdultLow levels 25 + 0 M 1950 0060 1 0000 0000 .711.629 .652.564 .544.443 .652.506 .619.539 .... .488.383 228227229 AdultAdult 30 25+ 0+ F0M 1960F 1950 .317.375 -.317-.349 .7060000 00000000 .823.706 .405.21.8 .218 00000000 .847.618 .818.607 .694.523 .641.447 .747.490 .755.530 .544.765 .643.387 236238237 AdultHigh levels 30+74M25+7+F25+7+H 19601950 -.322-.293-.356 .192.313 -.645-.663 .308 -.645-.751 .296 -.296-.391 -.296-.274 .548 -.586-.533-.632 -.565-.556-.642 -.526-.586-.522 -.499-.408-.511-.450 -.583-.514-.607-.492 -.606-.494-.585-.445 -.520-.466-.623-.599 -.441-.474-.354-.323 239241240 AdultAdult 30+7+F 25+10+1125+10+F 1960 1950 -.281 000040060 .2010000 -.706-.623 0000 .3440000 -.705-.623 0000 -.363-.312 0000 -.363 O0O00000 00600000.573 -.550-.565-.671-.589 -.534-.490-.491-.637 -.596 -.438-.410-.392 -.479-.470-.516 -.478-.495-.435 -.511 .... -.325-.316-.325 243242249 ltdultAdultAdult 30+10+F30+10+M15+2ac -5+Bac Ii 1960191.0F 1940 -.195-.222 0000 0000.176239 0000 0000000*SOO -.656 -.365 0000 -.312 0000 00000000 -.336-.653-.285 ...292-.295-.573 -.569-.522 0000 -.335-.262-.492 -.196-.218-.551 -.335-.184-.593 -.592 0000 -.203-.397 2503nrallment251 Adult.1du1t 15+Un15+1jn FM 19401940 0000 0400000.0000 0000 00000600 0000 0000 0600 -.599-.456 -.452-.611 06000000 -.347-.535 -.564-.380 -.548-.343 -.414-.242-.258 266265267 EnrolalrolIhrol 6-14 6-14 MT T19301960* 1937 -.000"-.In_-.055 -.304,-.275 .254- -.391"-.396 .239 -.055- .299_.032 -.537- .712_.404 -.075* .182.191 -.327* .049.139 -.264;* .413.349 -.460 ....3704 -.576 .43S*.... -.714 .629*.... -.616 .418*.... -.419 .127*.... -.276*-.465 .. -.503_ .339- .340.311* 275274273 .nrolL;nrolEnrol 6-146-14 U-2UrbanRural 1960 1960 -.325-.259 .211 -.093-.021-.220 -.1.00-.436 .142 -.0?1 .066.121 -.293 .105536 -.128-.171 .004 -.205-.499 .571 .C27.053.027 -.191-.048-.335 -.081-.336-.203 -.C77-.330-.437 -.163-.315-.075 -.291-.290 .161 -.114-.028-.214 -.031-.393-.246 -.175-.037-.136 Urban A3s Differonces in Literacy 100 Rural Hale-FemaloUrban Difforancss in Literacy IgrAag 1960 :tural (15-1S)- hales 1 (15-19)- (15-19)- Femelos (4°-49)- Variable %mbar (Z10.0) 1(40-49)- (600 (40-49)(15-19)- 200 (40-49)- (60+) 201 (40-49) 202 (600 203 (40-49) 204 (600 205 40-49 208 25-29 210 20-24 211 10-14 213 40-49 218 25-29 220 20-24 121 10-14 223 280278 2nro1ilnralJnrol 6/.401 andMInc income to 31,000 in 00 Eason monthly 1959 0000 0000 0004100041 0.041 00410000 0004141.00 0004100010 00010 000.0000 0000 0000 000000044 0000 00000000 0000 283282 1;nro12nro1 6/601-1,000)-(200)and occu ro1 b/Artou1tur. tion of father 1 .... 0000 0000 0000 0000 0000 0000 0000000O 00000000 00004000 0000 000000 00000000 0000 0000 0000 continuationProgres285284 in school rates--primarysshool JcrolEnrol 6/Prof-ag 6/Professional .... 00000000 000000 000000041 00041000 004000 000O0000 000.0000 0.000000 0000000 00000000 0000000 00004.000 00000000 00000004400 00000000000 296293288 1523,141A-2141F2ff41--1:1111ntPA 4/3 RuralUrban-Rural '942 1942 n . -.027 .109 .268.116 -.302 .352 .012 -.169-.116 -.136-.057 -.136 .226 -.109 0000 .0257 .263 -.172 .156 -.193 .256.108 -.001 .088 -.171 .135 -.200 .065 -.254-.267 .099 -.016 .0050000 304307299 0 h/3n3 4/3 Urban RuralUrban-Rural 1p60* 1960 1960 -.231-.409* .449 -.402-.169 .398 * -.249* .101 ....396* -.026 .016.398 * -.147 .218.11$ * -.255* .218 0000.182* -.031 .1220000 -.020 .0790040 -.066-.143* .108 -.002 .128.... .020.0480000 -.054 .0330000 .0030-.034 .047 4 .032.0210000 330309308 D 4/3'4/3 Urban Rural 1960-191/42 19604942 .419.268 -.033-.061 .3?5.201 -.427-.143 .141.230 -.309-.310 .375.110 -.205-.467 .185.122 0000 .0052 .085 we .124.185 .289.225 333331332 3D0 5/1 UrbanRural 19601942 -.023-.245 .035.1240 -.436 .296.035,.170 0000.0000000 0000000410000 -.312-.405 .150 -.069-.093- 151 0000 0000 .0370-.253,1.-.253 .150 .0299-.199,-.305 .227 -.364-.213,-.227 .273 -.251-.074,-.146 .049 -.323-.234,-.257 .144- -.424-.466*-.359 .164 -.339-.394*-.373 .201 .300-.158,-.115 .090 Soc,ndary339338 chool Cont. 3/1 F 196J 1960 -A03-.356 -.21i9-.016 -.221 -406-.221 -.20 -.014/1 -.223-.037 -.176-.070 -.137-.141 -.020 .019 -.134-.026 -.122 .039 -.245-.138 .106.001 Age Differoncna in Literacy 1960 Male-Female Differences in Literacy by'Ag. 1960 Halos Urbnn Females Males Rural Females Urban Rural (15-19)%4C-49) (40-49)-(60+) (40-49)(15-19)- (40-49)- (60+) (40-49)(15-19)- (40-49)-(60.) (40449)(15-19)- (40-49)- (600 40-49 25-29 20-2 10-14 40-49 25-29 20.04 10-14 Ve:iable Number 198 199 I 20u 201 202 203 204 205 208 210 211 213 218 220 221 223 347Az. zrads progress In school Ago 10 Or 1 1W 1963 .190 -.339 .... .562 .11 .129 .... .552 .619 .635 .416 520 ,5So .642 .501 34835311s9 1...... AgezoAge 12 101C Or IrOr 3+ 11 MRAI-itU!GR 1963 1963 1963 .105.191.25F -.084 .390.018 .....452.1.45 -.213-.154 .... -.237-.399-.338 -.220-.315 .138 -4043 ....470 -,.4i;-.527 .... -.299 .465.429 .465,434.147 -.389 .416.536 -.159 .288.329 -.213 .320.276 -.362 .343.386 -.347 .341..441 -.277 .290348 .4os.0 oso .506 .581 360361359 AgeA;eAao 10 10Or lror1 OR-FU1 rnru 19631963 .226.233.332 -.447 .031.073 .269 -.158-.252 -.264-.459 .545 -.156-.311-.006 -.120 .034 -.335 .239.513.581 -.385 .213.621.657 -.428 .321.634.709 -.190 .189.445.515 .423.069464 -.391 .183.466 -.380 .505.216 .152,,443492 I-ass365 ratan AgaPass 12 Or2/Froo 3+ FRU 19601963 .035.132 .394 .*000 000 -.291-.280 -.156 .101 0000 0000 -.360-.346 -.262 -.374 -.240 -.406-48 -.574 -.548 -4;3-.342 School377376 facilitiee Pass 2/Fres R 1960 -.271 025 -.)4146 -.446 -.340 -.340 -.h74 -.341 -.324 -.339 -.491 -.618 -.641 -.536 3 19397 .T4.Pri. TemehorsAcAct ;'lachare/jcAct 1960 1940 -.105 -.526 .404 -.226 .070 -.559-.473 -.492-.527 -.586-.628 -.447-.3/7 -.226-.459 -.466-.299 -2-.1434 -.379-.318 3'.11369 .ich;eh Inonmpleto Incomplete U R1942 1942* -.925, .077w -.022, .042'' -.246--.422 .257 -.139'-.013, -.246" .257,.409 -.232* -.232- -.?32_ .239- -.239-.274* -.125* .130 -.159* .04!, -.027 .011* -.28e .331 -.303- .367_ -.274- .332 -.177* .155 3r7326305 Sch:ch Tncomplrto;ch Incomp1ote Incomplote 7 U1960 n1942-60 124?-6o -.052-.C73 1ex) -.044-.132 .214 -.030 .090 .C11.051.503 .193.168 .198.168 -.112 .050 -.136 07n.594 -.234 .010.29 -.192-.085 .429 -.227-.102 .306 -.065 .085.506 -.001 .102.516 -.010 .082.429 -.257-A16 .479

= ro 3ohoJling 21au1t bpvals af aiuo. tion roars 1.:. Years .Ic:.00ling 3ao !Thin Ar.:e 25+ 1950 kle 1960 I Aso 25+1:50 Aso 30+ 1960 1750 25+ AGe 30+ 196U Age 1940 L54 L. F F wwwwwww,.. wwwm ro, Veriable Amber 22A 227 225 I 299 236 238 239 I 21/40 242 243 243 249 250 '51 332333 n 5/1!I 5/1 Vrben Rural 196o* 1960 00.0 -.646 .503' -.557 .327' -.521 .531 -.411/4' .524 237 -.506 .493 -.470* . 540 0000 000011..0 .482.1477' -.444 .533 .0..... 0 3393g:icon:Lary333 school L'ont.ent. 3/13/1 e 1960 1960 0.0. 0...00.0 -.252-.112 -.249-.1n0 .239.104 .246.041 .225.101 .259.060 000. .113.236 .264.053 ...... a 34831/47Age -.rano pro,7ress in school AgeACIe 1010 OrCr 11 Itrt:411 1963 .678.783 .628.596 .796.721 .698.73h -.650-.663 -.624-.679 -.634-.694 ..65h-.733 -.-.630 o 2,-e x, -.521-.584 -.605-.676 -.617-.699 -.151-.301 -.260 -.506-.435 -.459-.621 8 31/4?3.53 AgeAge 10 12 Or 3r 1 3+At-rU 1E'. 1q631963 -.687 .808 -.569 . 733 -.655 .518 -.627 .506 -.602 ..51 _.617 .553 ..00 .483 5'2 -.593 .528 0.0. -.033 0.60 .000 360159 10 103r Or 1 1FU n!: 1963 1963 .657.333 .673.603 .740.868 ..743 501.532 -.701i-.737 -.705-.676 -.44u-.610-.700 -.71?-.694 .442 -.676-.648 -.609-.555 -.669-.41c-.667 -.634-.627 -.369-.347 -.195-.214 -.583-.563 -.641-.664 Pass361365 rates Co 10 12Or Or1 FR-FU34 Fl 1963 -.702 .642 -.593 -.465 .544 -.434 .521 .530 .564 .506 .412 .535 00.0 00.0 377376 ?assPass 2/1ras2/Pros RU 196n1960 ..00 -..31/43-.333 -.401-.1/437 .2:10.276 .331.369 .26.369 .413.4'.3 00.00.00 .231.401/4 423.434 .0.00... 317Selaol finalities Teachm-9/cAct 1940 000.0000 000.00..0.00 -.6-.701 -.631 .570.675 .594.467 .557.684 .435.622 00.0WOO. ..541.694 .457.629 00.0.0.0 39031933' jcn3oh inconpleteInconolete3e, Ircoanleteri. Teachers/cAotU 1942 T 1960 1060 1242* 0.00 00.0 .203*.610 -.2 p3- .215. .249' -.075* .291 -.079-.515 .23n* -.571-.115 333 * -.519 ncIRqnn* -.561-.122 .344* 0.0.0.00 OO..0.0 39737f ion.i0:1 Incolplute IncoApiote U 1942-60a 1942-60 .0.033 -;3 -.065 ..646 035 -.570-.003 -.C2-.593 3 .030 -.043-.C41 -.065-.032 -.043-.03n -.12 00.. O....000 000. $.0.O...00.0 throlbnent 1937 Alo 6-14 1960 1930 6-10 Age 6-14 1960 Income in Pesos 1:ontt1y nnrollmant at 6 tsars 1959 Occupation Variable OuK4ar 11 265 Totn1 266 267:!a1cr. Urban 273 Rural 274 RuralUrban - 275 278200 601 to 1000 280 (601 to 1000 - 282200 283 As Prof 284 --- 121.2,222; in school '112Z11.M.III1_,2L2221:_enrollmont 296293218 3 4/30 5/3 Rural Urban-Rural 1942 1942 /11..1-517--- -.075*-.4nr;-.043 .413 -.35%.-.01.5: .213**.334- -.129*-.501 :11iti -.232 .034*.227 -.241 .211 -.010 .036.... -.033 .147 -.281 0.00.370 -.213-.003 0.0. 308307304299 4/30 4/38n Urban4/3 Rurol Urhan-aura1 1960-1942 1960 19604/3 ;than 1960* -.248 .106.290 -.250*-.075* .193* -.488 .452.091 -.031-.134 .141 -.280-.451* .337.420 -.353-.239-.265* .276 -.122 .054..*. -.030 .057 -.073-.021 3313303n9 234/3 5/1 .blral 2ura1Urban 1960-1942 1942 -.132 .223.1/474 -.1/4011-.24941_ * .064* .262.34.5.059 -.014-.327 .123 .271.132.244 -.030-.102-.387 -.010 -.064 .225 -.177 .36541.0. -.017 3ccowlar!.333332 23 5/1 11ra1Urb.m 19601960* sT:,00l .44 -.363* .463*" -.470 .545;:: -.032" .04 -.537:' -.234 .323* -.093 .001-.040_ -.237-.070* -.260*-.200 .146 -.303 .072.093, -.006-.159, .065" 33'33R .:Int.::ont. 3/1313 I .! 7961 196n .01'7.213 -.073-.219: .205.11A .230.376 -.06P .160 .209.164 .461.430 .256.311/4 -.340-.266 .471.5111 .115.150 A-.1 -.-1.,:, n73,T ,-.T.. I:, sn:lool. ,. 3L7 .+-,1 1.01. :r10 1 % 1 '21 1263 -1.: 1963 -.667-.632 ....,36 , * ..r -.712-.469 -.271-.37,11 -.450-.362 -.030 .088 .009.034 -.270...215 .174.198 -.367 -.037 Y.353349 .173Ar I., l'i) lr 1 '77-M 1963 '72- 3+ '21 1963 -.574 .7., '06 -.561* .561o74* -.711 .6o3 .012.039 -.470 .590 -.302 .329 -.064-.153 -.014-.231 -.239 .279 -,405-.333 .156 -.167-.349 .001 hro/lm,:At 6-14 .:ge 6-10 ..,,e 6-1Ic Income in Peson nonth1y rollmont at C Ycirs 1959 Occupation 1937 Total. 1930 Urban Rural 1960 Rural";rhan- 200 601 to 1000 (601 to 1000)- 200 A3 Prof 359 Age 10 32. Pumber I. FU 1963 -.774 265_1 .639* 266 -.521 267 -.357 273 -.420 274 .025 275 .143 27 I -.272-.303 280 I .123 222 -.397 233 -.163 294 3.:5361360 AgeAle 1011 Orr 3+1 rn7a 19631263 r 1 PR-FT: 163 -.519-.713 .6Wc -.595* .717*.532* -.746-.646 .645 -.140-.172 .122 -.537-.362 .593 -.277 .121.225 -.031-.072-.029 -.035-.213 -.233 .225.135 -.222-.440 .194 -.279-.157 .033 327376l'Arig rates la.:,.c.'ass 2/1r.:s7/1-res 7 V1F60 1960 .091.249 -.099-.Ne * 005.069 21U.053 -.043 .129 -.059 .177 -.020 .030 -.053 -.141-.239 143.060 -.029 .011 317 Pm.:arilitios Teachers/2.eAct 15.4; .735 -.731! .606 .1.72 462 .026 .241 .398 -.209 .260 339319 jch.;crrri. inco-toletc Teacharo/...;cectIncomplete 1.96() 1942, -.113* 237.673 -.0227-.0171.-.879- -.031, 276-.633 -.066_-.036- .536 ..g,332*.576 -.067-.0804 .)23 -.290-.052 140* -.401_ .305 -.54:51 .2557.421 -.324, .157-.162 327396.3)5 3cn3cr Incomlnleto;ch Tnen-71.z.te U 191. 7 194?-1,0 T 1960 1-60 -.365-.032 .104 -.117- .033:.25r -.532 .095.042 -.025 .003.09.; -.179 .113.035 -.011-.049 .107 -.405-.203 .200 -.200-.202 :g- .o58 -.247-.231-.101 -.093-.151 .027 Pro7x3sa in School Continuation Rates Primary Scbool .0000010000.10001,0010 Secondary School 1942 Crades 4/3 1960-1942 Grades 5/1 1942 Grade 3/1 1960 Urban Tivral RuralUrban- Urban Rural 1960 RuralUrban - Urban aural Urban aural 0 1960 Rural 7 412. nrade nro,lress in school Variable Number 239 293 2 299 307 318 30.9 330 331 Urbanl 332 333 338 339 343347 Ace 10 10Gr 3r1r 3. 1MU Ift--.111IR 1963 19A3 1963 0000 -.654-.457 .504.285 .949 * -.324-.254 .363.260.139 .135 .273 -.649-.735 -.503-.478 .593593*.701: -.648 -.617-.537 -.15)-.231 215330.26 359353349 AgeAre 12 1r 10 Cr 3+ 1*21 FU 19631963 O 000 -.556 .600 -.1:24 .475 0000.160 -.194-.375 .682 -.537 0000.097 .139 -.642-.548 .565 -.398-.256 .681 -.685* .635* -.424 .875 -.254-.072-.034 -.068-.200 365361360 A7eAgetr,r3 1210 CrGr 34 r 1963 1r 1 rara-Fu 1263 1263 -.573-.56C .642 .46F.305.450 IR, 0000*.120 01,2* -.272-.364 .653 .223.136.360 .104.1240000 .257.137 -.422-.680 .562 - :383-.392 .678 -.700* .346*.666* -.538-.619 .836 -.044-.062-.137 -.038-.277-.242 37'376Pas rates PassPass 2/Pres 2/Pres U P.'960 1960 .000 .053.017 .140.177 O 000 -.025-.221 .275.112 -.239 0000 -.256 0000 .171.297 359.410 -.021't-.293* .315.328 .044.130 .334.324 3'13337Jenool facilities Pri. Tanchers/ct 1940 503 -.622 ...... 1.55.158 -.131-.057 0000 .501 .136.395 -.484* .322.475 .321.378 .113.300 39039 Lc-SepLeh incolpl.:teIncomplete Inlo7plete L2 1942T19424 1960 Toachers/LcIlt. 1960 0600 -.399-.513, .352.733' -.572--.370 .394 -.623-.228 .601- -.492' .707.080, -.104* .103 -.114* .2360000 -.254-.412.. .500'..357 -.426-.42 .695' -.522-.334* .294* -.263 .659' -.073*-.103 .323 -.110 .107.032* 3973963,5 Sch.e., Incomplete Inco-,n1:7te U a1942-60 150..2-60 O06000000 600 -.514-.405 .125 r" .364.259! -.091 -.056 -.030-.128 .100.180 .542.237 -.463-.402 -.443-.547 .221*.251*.31e -.777 -.251 .022 -.247 .030 -.253 .006 TABLE 62 CORRELATION MATRIX: ;;J:^, V.L.-2,13L23 37:100T. 1'7 PT 1.1ATTP.T.; 71T ;3, Ailll LIT 2.:..CY RAM, hy3.V.APHLT ;CT 11.2.7RL3 0 7.1.1tIAI3L:!:3 OF 3flIr i 3.3-no T., .11100LII!r.1, 3,:C0NDIZZY LC71001..1.1(1, ILY 3:31400L Emorui crrs Literacy 10+ Yrs Literacy 40+ Yrs Literacy Rates LiteracySchool 6+0 A7e, Sex and Year Literacy of Youth 10-14 Yrs Changes in Literacy of Youth 10-14 Yrs H M F* U F M 1 F H F / M* F * 1940 1960 a 1940 71-1 1960 F 1940 1930 1940 1960 I 1940-1930 1960-1940* Literacy rates by age, sex, and year Variable Number 140 141 145 11 6 117 148 153 1 155 1 156 157 158 159 160 161 162 163 I 164 141140139 Literacy 10+ T 196019401930 1.000 .960.990 1.000 .960.934 .911.963.954 .929.971.966 974.059.$46 .937.962.951 -.392-.411-.289 .876.912.899 -.942._-.953:-.864- .917.968.958 .965.940.983 .965.947.9/3 .915.992.953 .677.687 591.616 .50r.663_ .607-.764 142144143 LlteracyLiteracy 6+ 6+T 1940T 19601950 .961.931.999 .955.998.989 .9-.5.042.961 .923.945.970 .978.975.956 .954.956.948 -.398-.300-.323 .587.916.912 -.868"-.903:-.944: .971 .927.948 .945.961.936 .970.964.946 .985.974950 0 148147146145 LiteracyLiteraQyLiter-myLitemcy 40+40+ MF 19401960 nF 19601940 .971.963.951.959 .962.974.929.911 1.000 .917.868.949 1.000 .973.909.917 1.000 .909.923.949 1.000 .923.973.868 -.381-.404-377 .914.920.803.819 -.875-.885:-.915: .934.892.939.867 .924.925.942 .936.893.955.390 .926.954.908.921 151150149 LitemcyLiter,cyLiteracy 30+30+ MFU 19601930 .877.960.961 .903.906.399 .869.898.988 .814.913.955 .913.891.942 .860.825.951 -.413-.408-.335 .352.904.905 -.810:-.921_-.928: .368.945.896 .935.925.856 .837.895.861912 .881.893.898.874 154153152 LiteracyLiteracy 64030+ 6+0 SellF 1960Sch M F1940, 1940 -.392 .436*.894 -.239_ .926.340- -.404_ .440-.805 -.423, -.381,.918.4*)0" .416.88h -.377_ .438- 33 -.335-.914*1.000 -.258, .756.303" -.447-.819' 435* -.315, .801.356- -.340, .392--.863 -.237, -.2$4 -.109 ...... 0 .096 0000.337 * 4,800.396* 156155Literacy of youth LiterlcyLiteracy 10-1410-14 MF 1930,1930' -.042 .°12, -.864- .876, -.912* .920 -.215- -.885k.319, -.875' .914, .803, -.283 .435* -.895*1.000 1.000-.815 -.929* .038 -.923 .897 -.846* .915 -.850 .900 -.552 .443 -.445 .420 -.647:*-.741, -.774 .801*.638:* 157160159153 LitemcyLiteracyliteracyLiter.c:, 10-14 10-1410-14 :1 :Y1940 1960 F 1940 1960 .933.963.°53.°47 992.965.940.017 .921.925.930.c36 .903.942.890.502 .025.°55034.054 .926.393.924.567 -.254-.237-.340-.315 .915.597.938.900 -.95e-.846-.023:-.920 1000 .935.024.979 1.000 .946.°35.979 1.000 .935.034035 1.000 084.046.924 .666.664.755.714 502.504.663.703 .494'.490:.717,.767* .593*.591*.821* Literacy Rates by Age, Sex, and Year Literacy 10+ Yrs Literacy 40+ Yrs LiteracySchool 64.0 Literacy of Youth 10-14 ire Changes in Literacy of Youth 10-14 Yrs 19140 19140 1960 1940 1930 1940 1960 19140-1930 1960-1940*x Variable Number 1140 147 1 248 153 155 156 157 158 159 161 1 162 163 I 1614 161Changes in literacy of youth Literacy 10-14 M 1940-30 .687 .677 *000 0000 0000 -.109 .448 -.552 .714 .755 .664 .666 1.000 .937 .562* .714: Literacy164163162 by age 1960 Literacy 10-1110-11$10-14 4 FF 1960-40*1940-30 fl 1960-40* .764*.663*.616 .607*.508*.591 0000 0.0000000000 0000000 0000 -.396*-.337* .096 -.613; .420 -.445 .78g1:.663 .717*.821*.703 .591*.490*.594 .593*.494*.592 .714*.562*.937 1.n00 .725*574* 1.000** .902**574* 1.000- .902::.725_ 167168 40-4950-59Urban males .712.766 .744.714 .678.609 .707.758 .670.719 .722.753 -.418-.398 ..600 525 -.593:-.646: .655.725 .729.782 .622.675 .662.701 .680 .742.... .511 .655* 173172170169 Urban10-1415-1925-2930-39 females .781.748.577.706 .774.744.537.722 .674.639.495.599 .303.773.570.715 .710.583.663.510 .804.774.531.734 -.396-.379-.327-.362 .543.430.592.510 -.669--.644:-.494-.592; .688.718.600.651 .801.767.636.727 .666.704.520.625 .753.703.525.673 .687.....683.678 .726.....748.728 .498.549-.472: .679.672-631: 176175 40-14950-59! -.858*-.880* -.861*-.862* -.762* -.888*-.9114* -.8014*-.809* -.930*-.918* .396.374: -.673-.693: .805797: 71* -.769 .7 -.848-.872: -.761--.768: -.811--.812: -.653 -.634 - ... 49i i . L.* * i a *. 181180178177 10-1415-1925-29*30-39 -.793* .768.829.780 -.787* .779.785.845 -.678* .659.708.671 -.792* .811.799.845 -.732* .702.695.768 -.799* .813.817.873 -.336-.320-.327* .319 -.627- .58o.633-. 557 -.671-.650*-.709 .696, -.769 .707.682.755' -.827 .804.779.843. -.732 .710.700.753* -.764 .764.749.809* -.749- -.763*.....694.721.. .717.....727 -.576-". -.722--.14;9* .66:6*...6806 .,...* 185184183 30-3940-49Rural males06-59* -.821* .809.812 -.833* .894.886 -.849* .824.833 .725.728 -.884* .898.912 -.771* .774.777 -.176 .274* -.822* .835.844 -.745:-.735 .791** = -.810 .811.810 * -.758* .770.761 -.828* -.811*.889.885 .883.872 ...... 189188186 Rural10-1415-19*25-29 females -.821* .864.810 -.861* .899.891 -.842* .823.883 -.728* .763.729 -.884* .905.893 -.751* .777.782 -.127-.128_ . 174 - -.836* .904.833 -.785--.717- .771: -.841* .891.792 -.783* .758.853 -.883- -.864*.952.882_ .936.873 ..4li5. ;71 .504.....368 .549* :1:g;* 193192191 30-39_40-4950-59 .895.866 .937*.943.909 .798.830.323 .. .853_.904.923 .876.875.895 .910.957.952 -.267-.299 .769747.763 -.7841.-.822*-.847* .808.811.807 .839*.854.847 .876_.878.836 .908.865 0.;;i 197196194 10-14-15-19_25-29- -.833* .879_ -.891--.909 .946_ -.844*-.773- .856 -.774*-.82r .839 -.883--.860* .907_ -.800--.892* .879_ -.137 .191*. 199' -.837* .836 -.7961, .793--.766-- -.864*-.764* .837 -.843*-.792 .846 -.914*-.845- .937 -.917--.870* .951.905 _ :.4418:' - :14ig**:11;i* -:fer;** :* -:04** Literacy Rates by Age, Sex, and Year Literacy 10+ Yrs Literacy 40+ Yrs LiteracySchool 6+0 Literacy of Youth 10-14 Yrs ahanges in Literacy of Youth 10-14 Yrs 21 19140 I M M F* m F M F m F 14 F Variable 'Tumber 1940 1'40 1960 3145 1146 )17 1960 1940 153 155 1930 156 157 1940 158 159 1960 160 1611940-1930 162 163 1960-19140* 164 Age differences in literacy 1960 Urban males 200199198 Urban(40-49)-(60+)(15-19)-(40-49) females -.757-.108-.319 -.756-.331 .012 -.644-.118-.275 -.802-.151-.242 -.723-.351 .002 -.044-.849-.234 .435.114.271 -.613-.166-.255 .190".211 -.654-.121-.321 -.725-.147-.323 -.634-.054-.247 -.687-.037-.276 -.261-.101 .080 -.280-.122 -.315-.216: 184.135 203202201 Rural75:1-91719)(40-49)-(60+)(15-19)-(40-49) males .491.394.397 .541.335.483 .397.396.314 .422.320.385 .536.236.412 .487.253.456 -.134-.180 .240 .461.237.430 -.471-.333-.292 .694: .525.471.340 .496.459.396 .482.471.353 .146.422.400 -.234 .283.240 -.212-.391 .177.159 -.265-.497: .226.255: -.436-.520: .543.429: Male-female205204 differences T15-19)-(40-49)Rural(40-49)-(60+) females in literacy by age 1960 -.315 .740 -.284 .770 -.168 .654 -.446 .692 -.210 .749 -.497 .756 -.315 .419 -.062 .670 -.649- .320. -.183 .703 -.283 .723 -.118 .734 -.167 .733 .265.318 .350.296 .513.305: .351.325: 203207 40-4950-59Urban males minus females -.761-.666 -.776-.670 -.649-.533 -.350-.768 -.685-.570 -.891-.313 .292.163 -.487-.590 .635 -.526-.614 -.731-.633 -.683-.573 -.747-.641 -.450 .... -.353 .... -.270* .... -.509* .... 212211210209 20-2425-2930-3?15-19 -.769-.590-.644-.707 -.655-.660-.748-.790 -.516-.551-.626 -.627-.759-.315 -.668-.541-.618 -.684-.316-.356 .096.059.091.071 -.426-.545-.546-.590 .470,.553*.605.667,693, -.547-.617-.677-.463 -.578-.645-.734-.783 -.578-.626-.671-.723 -.746-.646-.702-.785 -.405-.558 ...... -.544-.377 .... -.221*-.312* ...... -.392*-.523* .... 215214213 40-4910-1415-19 FurA Fu/n 1960, 1960-- -.58o*-.572 .75o -.640*-.591 .794 -.482-.501* -.627-.661 -.527* -.627 .363., -.455 .700 -.677--.629 .39% -.339 .117.020 -.393-.331 .578, -.6841 .636*.461----.451.499 -.479 -.572-.602 .761* -.554-.544 .691* -.624-.604 .758* -.536 -.492 00009000 -.137* 0000.000 -.441* 00.0.000 Literacy Rotes by me, Sex, and Year-12LINFT:=.111Er..1.1=.13111111111Mi.....--=' -----91S111:11=2 Literacy 10+ Yrs Lttoracy 40* Yrs LiteracySchool 6+0 Literacy of Youth 10-14 Yrs Changes in Literacy of Youth 10-14 Yrs H =MEW AMIN. 194m 1960 14)60 1940 1930 1$40 I 1960 1AI^ 1960-1940* Variable Number 1 1146 148 153 155 156 157 1 4;t1 I 161 I 162 163 1614 217 77-37Rural 'also minus females -.527 140 -.519 14 -.37) 1.45 -.665 -.40? 147 .694 .219 -.247 .51141 -.384 , -.$16 -.381 -.457 ...... 0, ....0, 221220e1921, 20-2425-2n30-3940-4" -.637-.633-.646-.603 -.112-.690-.624-.644 -.1191-.433-.45ts .... -.729-.703-.7ob .... -.560-.489-.524 .... -.784-.750-.802 .... .163.126.145.275 -.426-.358-.377 .425 .550.602:.544:.595: : -.510-.479-.513-.496 -.637-.631-.606-.634 -.599-.589-.503-.527 -.685-.662-.574-.596 -.532-.520-.540 .... -.416-.348-.413 -.262-.177--..iii! -.392- -.141;!-.537 224223222 40-1,210-1415-1 FRA: -.534-.551 .808 -.598-.628 .847 -.439-.446 .697 -.612-.652 .865 -.471-.498 .754 -.672-.700 .922 -.269 .135.118 -.368-.363 .616* -.7241, .468-.504; -.385-.409 .679_ -.515-.538 -.511-.527 -.607-.620 .817 _ -.324 ...... 176.... -.023 ....0..... it -.220 .... 26626522; .rirs:4nro'15-0 6-1.4 FRAI*6-14 T 1937T 1*)1 -.640*-.610" .748 -.637'-.639e .698 -.663*-.5174 .723 -.556*-.633* .695 -.625*-.57o* .650 -.513*-.743* .631 -.028*-.055 .119* -.72,.k-.446 .703 -.702* .543--.569** -.697*-.469- .775 -.585--.675* .757.779 -.725--.597- .737_754_ -.68e-.719- .743_ -.413- .627_.... -.442- .....638_ -.416-- .5821,.... -.419-- .....656_ Urban Literacy by Age 1960 Rural Literacy by Age 1960 40-49 30-39 Mals 25-29 1044 Fanalea 2549* 1044 4049 30-39 Mals 25-29 10-14 40.49 30-49 Females 25-29* 10-14* Litoracy by ag 1960 Variabl Number 168 169 170 173 40-,49* 176 1 30-39, 177 .., 178 181 18h 185 1 186 189 192 193 194 197 167168 40-4950T57-----Urban males 1.000 .978 .983.967 .686.702 .923.928 -.1301.4-.889: .943.936 -.538:-.831, .913.915 1463.487 .497.518 .514.537 .497.541 .620.627 .600.611 -.600:-.613! -.502: -.527! 173172170169 Urban15.4.925-2910-1430-39 .923.955.686.983 1.000 .941.973.680 1.000 .698.734.680 1.000 .966.698.941 -.915-.9004-.625,-.69, .955.951.660.954 -.891,-.870-.868,-.831, .987.965.669.943 .496.298.457.456 .531.490.332.499 .530.495.271.510 .565.514.403.497 .700.661.415.630 .6E6.629.410.617 -.355;-.673--.630.-.627_; -.54. -.575--.419;-.498; 178177176175 30-3940-49-50 -59! females -.831-.891--.866: -.867:.943, -.831-.891- .954, -.891-.625--.6W): -.90A:.660, -.870--.915- .955, -.962,;1.00L -.962.987:: -.952: -.882*1.000 -.882aa .880,.872:: -.911--.905: .986a -.549--.612:-.633- .570 -.584--.653--.627: .606. -.666--.640: -.633:.623. -.64o- .623* -.853*-.864* .790. -.817*-.797* .763* -.753:. -.639..823..799:, .670..657:* 183181180 Rural10-11415-1925.29' males -69* -.429* .913.921 -.426* .943.952 -.326* .669.674 -.468* .987.938 -.911--.911: .636:*.880: -.542* .966.967 -.353--.545:1.000:- -.441*1.000-.853- .962 -.931' .476.485 -.905* .518.520 -.552- .521.541 -.627 .560.548 -.833*-.725- .707.720 -.810*-.711 .677.682 -.677--.681; .684:7 -.570-.545; .668- 188186185184 25-29,40-493015-19-30-39 -.450 .514,.497.463 -.438 .510,.457.499 -.365 .271,.332.298 -.486 .530.496.531 -.666;0 -.653-.633; -.558 .623*.606.570 -.5524-.584-.549, .593;.559:* -.467- .521a.516.476 -.960-1.000 .968,.985 -.9551.000 .980,.985 -.9371.000,-.902* -.669*.980.968 -.953 .9404922'.931 -.835- .858..853.859 -.859 .899.893,.874 -.894;-.873, -.586.8437 -.906, .856:* 192191189 40-49Rural10-14 finales .620.629.497 .630.497.640 .415.441.403 .700.705.565 -.864-.380:-.640- .612,' .790.790.623 -.725,-.742:-.627- .708.707.560 .808.859.931 .853.800.940 .858.804.922 1.000 .820.771 1.000 .973.820 .918.853 -.927:-.828 -.805:.858-: -.960 .959;- 197196194193 15-1978-31--25-29'33-3910-114* -.502-.600 .526,.600, -.627-.498 .536,.617, -.419-.355 .374,.410, -.575-.673 .613,.666, -.817; .763, -.738 .670-w.823,- -.753 .700, -.657*.-.711,, .684, -.677 .627*.677, -.873 .926*.874, -.890 .936*.899, -.894 .927..893, - -.960--.828- .935..653. -.859-.963- .939..966 -.969*1.=-.888* .956 -.9281.000:: :* .865** 1.oco** -.944-:::ig' .865*: Ale198 differences in literacy 1960 Urban males ..!51 -.272 -.420 -.392 -.283 -.301 -.338 -.240 -.253 -.271 201200199 r1167077105.49)(4o-49)-(60+)c15-19)-t40-49)(I0-49)-(60.)Urban females -.722-.057 .760 -.123-.586-.696 .749 -.406-.162 .448 -.T35-.228 .587 -.603' .905*.080-.485: -.518-.801 -.072 .694 -.527 .693:.112.332: -.717-.234 .612 -.595 .323.203 -.590 .362.169 -.608 .392.170 -.523 .262.034 -.816 .430.106 -.752 .101.427 -.429-.073 .763:.266: -.267-.122 .566:.254: Urban Literacy by Age 1960 Rural Literacy by Ase 1960 Males Females 4 40..49 Males Females Variable Number 40449 3o-39 25-29 170 10-14 173 40-49* 30-39 177 25-29* 178 10-14 181 184 30-39 185 25-29 186 10-14 189 40.49 192 30-39 1.73 25-29* 194 o. 10-14* 197 202 Rural males 168.208 169.221 .332 .254 176 .292 .296 .199 .290 .498 .213 .300 205204203 TIMM:(O-4Rural(40-49)-(60+)(15-19)-(40-49) females40-49)-(+) 609) -.434 .64o.572 -.435 .576.509 -.232 .377.349 -.441 .603.449 -.208:-.135--.515" .591! -.476 .683.511 -.600-.447-.351: .399: -.423 .557.417 -.091 .712.547 -.047 .521.667272 -.085 .700.513 .670.044.435 -.464 .786.514 -.321 .732.495 -.480-.707--.229: .384! -.696"-.425-.420: .042! Aale-fenale207 dif:erances in literacy by 41_1240 T6=TOUrban males minus females -.413 -.444 -.247 -.578 .731: -.656 .548* -.617 -.442 -.433 -.451 -.435 -.792 -.696 .682: .494: 211210209208 20-2425-2930-3940-49 -.646-.560-.504-.643 -.567-.676-.65'-.594 -.395-.501-.509-.360 -.650-.767-.754-.704 .333*.651..828*.848* -.695-.837-.772-.830 .775.643*.605:,.783* -.717-.803-.789-.739 -.477-.534 -.576-.567-.533 -.511-.575-.558 .477 -.542-.551-.611-.529 -.769-.851-.617-.830 -.748-.796-.769 .... .607:.697:764.740 .58s:.654,,.562; 214213212 40-4910-1415-19 FU/3 1960, -.633-.558 .719* -.702-.613 .751* -.334-.319 .469* -.674-.584 .8180 .655.669* -.754-.721 .879* .561".535; -.785-.667 .853. -.418-.275 501. -.334-.444 .512. -.497-.351 .541. -.391-.471 .513* -.543-.660 .8220 ..541-.615 .745. -.737L .511-.607. -.536* .400-.461. 213217215 ;0-5940-49Rural15-19 malesFU/A 1960'minus females -.568-.545-.609 -.669-.595-.569 -.394-.375-.362 -.676-.631-.631 -.893. .808*.758.683-- * -.749-.750-.666 -.74, .657*.614:.555" -.711-.660-.712" -.236-.351-.372" -.359-.251-.401" -.451--.389-.255 -.364-.231-.427 -.781-.673-.627 -.7010-.593-.579- .695*.592*.574-- .465*.334*.415** 222220219221 20-2425-2930-3915-19 -.334-.528-.452-.514 -.547-.421-.589-.514 -.309-.230-.421-.363 -.530-.674-.609-.603 .765*.706*.737* -.605-.733-.671-.694 .501..67>.57S.61g, -.586-.662-.646-.709 -.461-.429-.355 -.447-.479-.385 -.459-.493-.402 .435 -.424-.489-.466-.378 -.739-.508-.796-.747 -.772-.713-.750 .... .678*.775*.757*.699* .559*.58o'.493* 225224223 15-1940-4210-24 FR/3WI 1960"1960 -.388-.317 .5884 -.423-.347 .60* -.166-.229 .372* -.527-.454 .691, -.835**.572.642.6390 -.60-.555 .795* -.67446* .502.434 -.533*-.523 .726 -.511*-.410 .644 -.526*-.432 .640 -.444-.521* .666 -.505*-.411 .634 -.693-.7744 .925 -.666-.747* .857 -.8331. .706-'.616* -.694" .593**.521.539: 266265alrollment of youth :,nrolairol 6-146-14 TT 19371960* -.303 .565, -.356 .596* -.419 .6274 -.467 .6$ % -.6264L .387- -.444 .663 -.731 .492- -.484* .668 -.585* .530 -.630* .608 -.592* .580 -.736* .727 -.460* .583 -.520* .659 -.(09* .475** -.699* .61414** =""=====1.. A. Differencs in Literacy 1960 Male-Female Differences in Literacy by Age 1960 mM, Male Urban Femal.e Male Rural Femal Urban Rural throl. Rates T 14.0f114;015 (40-49)- (60+) sol(40(15-29) -49) (4o -49) - (60.0 (15-19)- (40-49) (60+) (40-49)(15 -19) - (4c-49)(60*) 40-49 25-29 20-24 10-14 413-59 25-29 20-24 _ 10-14 1937 1960* Variable Number 199 200 (4o-49) 202 204 205 208 210 211 213 218 220 221 223 265 266 Age differencs in literacy 1960 Urban males 201 203 200299190 Urban(15-19)(40-49)(15-19) females - (40-49)(60+)- (40-49) 1.000-.339 .519 1.000-.339 .000 1.000 .519.000 -.450-.644 .327 -.284 .034.050 -.485-.460 .308 .680.017.163 -.480-.702 .212 .834.094.109 .728.101.178 .455.183.196 .766.053.117 -.015 .652.068 -.158 .580.068 -.391-.304-.179 -.000* .239*ti.254* 20320e201 (40-49)Rural(157101-:740(40-49) males - (60+)- (60.) -49) -.644-.485 .050 .308.284.327 -.460-.450 .034 1.000 .609.026 -.0111.000 .026 2000-.011 .609 -.315-.291 .327 .467.753.138 -.278-.376-.135 -.302-.307-.390 -.184-.374-.546 -.274-.246-.393 -.282-.252-.340 -.120-.054 .308 .182.712.299 -.075'-.537*-.055* - Male-female205204 differences in literacy by ago 1262 ftral(40-49)(15-19) females - (60+)(45-49) -.480 .163 .222.017 -.702 .600 -.315 .467 .138.327 -.291 .753 -.3641.000 1.000-.364 -.609 .611 -.549 .458 -.371 .243 -.569 .737 -.527 .508 -.482 .312 .349.049 -.264*-.32746 209208207 40-491./030-39Urban -59 males minus females -.018 .196.109 .015.094.041 .742.834.751 -.422-.376-.237 -.366-.135-.117 -.368-.278-.213 .477.611.626 -.633-.609-.527 1.000 .870.953 .851.842.964 .739 .710.676.602 .909.567.910 .797.861.857 .....870 .76o.014.800 -.624-.460-.371 -.370* .42241.2781 212211210 20-2415-1925-29 .206.129.178 -.082-.379 .101 .499.....728 -..gi;-.390 -.314-.307 .553 -.190-.30, .106 .2;6.456 -.1.4132-.549 .851.759.739 1.000 .687.759 Locoo .759 .808.731.730 .697.616.825 .678.696.798 .736.829 .707.726.7413 -.522-.714,.576 .392.601.435- - 215214213 40-4915-1910-14 Fo/m ruin 1960 1960* -.252, .223-.183 -.o4o-.099* .196 -.841* .498.455 -.650-.546 .536, -.293--.374 .160, -.194--.184 .342, -.604_ .32o-.243 -.453--.371 .622_ -.969 .749*.676 -.359 .682*.730 .782 -.762_1.000 .333- -.892 .693*.659 -.823 .683.666* ....:gill -.761, .644.672 -.616-.513- .504, -.375: .418*.376-- 3 Age Differences in Literacy 1960 Male-Female Differences in Literacy by Age 1960 4===mmum. Mal* Urban Female Hal* Rural Female Urban Rural Enrol. Rates (40-49)(15-19)- (40-49)(60+) (40-49)(15-19)- (40-49 )(60+) (40-4T)(15-19)- (40-49)-(60+) (40-49)(15-19)- (40-49) -(60+) 40-49 25-29 20-24 1044 40-49 25-29 20-24 10-14 1937 1960* Variable Humber 198 199 200 201 202 203 204 205 208 210 211 213 218 220 221 I 223 265 266 219218217 30-5940-49Rural males minus females .115.117.100 .042.053.087 .695.766.716 -.383-.393 -.146-.064 -.274-.233-.255 .737.784 -.569-.420 .909.807 .825.785 .616.... .659.590 1.000 .943 .993.828 .56 .768.642 -.419-.357 -429: .127n E 222221220 20-2425-2930-3915-19 -.015 .061.110 -.091-.111 .068 .708.613.651 -.320-.340-.354 .... -.152-.222-.059 .703 -.188-.282 55n .5o5.508.60 -.519-.517-.536 .... .825.87....86'.817 .754.829.798 .736.696...... 654.642.688.636 .829.883.893.933 1.000 .919.948.905 1.000 .....948 .832.934.814.753 -.389-.503-.465-.471 .279-.339;.276:.144: 224223 40-4910-14 FR/M 1960 -.189 .068 -.150 .109_ -.789 .580 -.308 -.054 .172_ -.120 .396 -.546_ Jo.312 -.482 .722 -.947* .014 -.837* .740 .716 .644 .760 .5314 .332 1.000 -.34u .492,, -.373* .311- 266265225 'enrolSnrol15-19 6-14ra/M6-14 T1960*T 19371960* -.00o--.179_ .087* -.254--.304_-.137' -.391_ .239-.614* -.055*-.3g* .299 -.537--.087' .712 -.075*-.215* .182 -.327 .049*.306' -.264-.548* .349* -.460, .370-.839 -.576 .438-.751 .629- -.616_-161* -..;*.418- -.419,, .127- .276- -.503,, -.340* .311?43 -.735-1.000_-.417- 1.000 -.7381_ .363" Adult ilvels of Schooling 25+ 1950 No SchoolingAdults Age 30+1960 25+ 1950 7+ Years Adults Ageof School 30+ 1960 25+ 1950 10+ Years of Sdhool Adults Age 30+ 1960 Adults Ago BAC 15+ 1940 Adults Age Univ Variable Number 226 227 228 229 236 237 238 m 239 F 240 H F H F 248 M 249 F 250 K 251 F Adult levels of schooling Law levels 262261255254 6 ++ -060 +Sdh30h Yfs F Sch1940* F 1940 6 + fres Soh M 1940 h 1914o* -.850-.870-.S12*-.750* -.683* -.807-.785-.585* -.850-.867-.825*-.765* -.777--.665: -.875-.929 .927.953.761.679: .949.914.540:.646- .930.936.768-.692. * .952.904.661.566: .912.934.719640: .899.882.562486: .913.738.661:.916 .929.879.619536: .635.407364:.637 .523.466.188159: .864.882.561.488: .617-.924.881.514: 227226228 AdultAdult 30+025+0 30+0 MNF 19601950F 1960 lono .982.910.854 1.000 .865.839.854 1.000 .928.839.982 1.000 .910.928.865 -.858-.825-.800-.867 -.863-.836-.828-.SOO -.828-.797-.844 .853 -.859-.840-.797-.825 -.775-.796-.760-.818 -.759-.761-.707-.756 -.803-.819-.774 -.763-.822-.806-.800 -.475-.443-.356-.392 -.297-.269-.377-.312 -.677-.680-.626-.660 -.731-.709-.775-.723 258260P29259 0 Soh MF/HFA 1950-1960 19501960 -.037 .684 .373.552.474 -.042 .556.127 .560.461.183 -.557-.207-.U93 -.489-.364-.184 -.460-.238-.129 -.456-.344-.177 -.563-.203-.100 -.440-.112-.254 -.479-.249-.132 -.484-.317-.151 -.097-.017-.069 -.112-.037-.193 -.379-.176-.091 -.373-.360-.238 235234233232 Adult 30+30+25+ 1-61-6 14FM 1960*1950*1960* .948*::::*.982:.433 .418.791,..823: 820 . .876.448.981*.983: * .473.915,.9734.920: -.809:-.762. -.6184 -.411-.782* -.463-.764*-.784: -.770.-.775;-.446--.791: -.381-.767.-.810;-.789t, -.374-.711;-.701a-.753: -.526--.680a-.697.-.707: -.7534-.763:-.412--.728a -.327-.7304-.7744-.766: -.409.-.405:-.174--.438a -.173-.290.-.301*-.267: -.280--.614;-.606:-.618* -.313--.658;-.703,-.664: Adult Lavas of Schooling No Schooling 7+ Years of School 10+ Years of School BAC I Unix 25+ 1950 Adults Age 30+ 1960 25+1950 Adults Age 30+ 1960 25+ 1950 Adults Age 30+ 1960 Adults Age 15+ 1940 I, Adults Ag6 14 11 H F H _ F H F M F M F VariableNigh levels Number 226 227 228 229 236 237 238 239 240 241 242 243 248 249 250 251 239238237236 AdultAEU 304.74.m 30+7+F25+7+F25+741 19601950 19601950 -.825-.844-.828-.867 -.797-.797-.800 -.84o-.853-.836-.858 -.863-.859-.828-..925 1.000 .910.970.933 1.000 .976.945.938 1.000 .953.945.970 1.000 .953.976.910 .889.962.906.982 .934.915.958.895 .935.991.923.960 .880.985.934.946 .611.591.613.562 .567.483.548.425 .834.845.850 .383.848.877.826 242241240 Adult 30+10+1425+10+F25+10+H 19501960 -.919-.758-.318 -.774-.707-.760 -.819-.761-.798 -.80-.759-.775 .960-R95.982 .923.958.906 .991.915.962 .935.934.889 1.000 .973.894 1.000 .905.894 1.000 .905.973 .932.931.882 .583.680.582 .475.637.453 .815.844.842.915 .835.302 245244243 Adult 25.13.1425+13+F30+10+F 195019601950 -.515-.766-.800 -.515-.736-.763 -.510-.739-.806 -.505-.746-.822 .662.949.880 .775.890.946 .700.935.934 .724.877.985 .670.882.986 .875.887.931 .696.932.958 1.000 .719.982 .464.595.625 .518.478.604 .838.799 .803.851.815 248247246 Adult 30+13+1415+Bac30+13+F m 19601940 -.392-.692-.771 -.356-.759-.719 -.693-.770 -.475-.792-.747 .562.795.924 .613.884.918 .591.851.964 .611.921.932 .582.793.944 .680.835.890 .583.849.978 .625.931.933 1.000 .595.569 .794.613.536 .788.762.828.572 .843.741.843.583 251250249 AdultAdult 15+un15+Bac 15+Un m FF1940 19401940 -.709-.663-.269 -.297-.731-.626 -.723-.680-.312-JO -.775-.677-.377 .826.850.425 .877.845.548 .948.834.483 .883.815.567 .802.842.453 .844.835.637 .815.475 .799.851.604 .741.788.794 1.0400 .686.667 low .917.667 1.000 .917.686 Enrollments Primary School nnrollments Males Urban Rural U/R Monthly income in Pesos Enrollment at 6 Tears 1959 Occupation 1937 Total 1930 1960 4200 31,0004601 o t 1,000)(601 to- ()nn) Ag Prof Enrollment Variable Number 265 266 267 273 2714 275 278 280 282 283 284 ---- 265264263 PreschoolEnrolPreschool 6-14 1960 1944T 1937 1.000.. .496.470 -.603--.377- .318.614...410 .341.437.150 .523.407.432 -.072 .141.027 -.151-.028* .044 .261*.240.041 -.183 .199.022 -.051 .284.102 -.068 .008.180 268267266 Enrol 6-14 FMT 19301960* -.719- .631.614 1.000-- 1.000-.744- .962 -.341- -.750*.111.068 .575.634 -.187* .o96.15o .042.123 -.297 .311.348 -.048-.084 .030* -.353* .482 -.195* .392.361 271269270 EnrolEnrol 6-146-14 6-14 IF/h F 19301960* 1960* -.7,5-.636: .317 -.7374-.287 .872**.876!* -.653:-.629 .312 -.524 .187 -.715 .062 4: -.286--.224: .232 .186*.1149*.051 .268 -.140-.027*-.058* -.223*-.228* .363.524 .258 273272274 Enrol 6-14 RuralUrbanF/M 1960 1960 .523.341.308 -.750...-.341:-.202- .634.068.056 1.000 .276 1.000-.085-.035 .095.236.114 -.198-.049 .123 .218.075.097 -.116-.014 .071 .091.121.247 -.261 .205.165 277276275 Enrol 7-126-14 FHU-R 1950 1960 -.072 .674.637 -.752-.732:-.187- .726.736.096 -485 .403.363.095 .418441.114 1.000 .121.129 -.405 .058.063 -.200 .270.218 -.273-.301 .249 -.247 .528.495 -.097 .088.040 282280278 EnrolNrol 6/Inc6/(601-1,000)-(200)6/3601and income 0200 to 31.000 in pesos -.183monthly 1959 -.025 .261 -.297-, .030'.123! -.048 .31101i2 -.198 .071.075 -.014 .218.123 -.200-.405 .249 -.3631.000 .383 1.000 .347.383 -.3631.000 .347 -.095 .749.523 .007.402.615 285284233 EnrolNrolEnrol 6/Agricultureand6/Prof-Ag 6,"Frofessional occupation of f ather 1959 -.064 .180254 -.195,-.353 .08e , ,034.361.482 -.380-.261 .091 .014.205.247 -.247-.097 .126 .073.402.523 .132.6157h9 -.095 .095.007 -.1351.000 .559 1400.713.559 Continuation Rates Based on Beginning of Year ihrolbeente 41 Urban 1942 Rural U-R Urban 1960 P.ural Urban' Rural1960-1942 4/3 5/4 6/5 4/3 5/4 6/5 Grades 4/3 4/3* 5/4 6/5 4/3 5/4 6/5 4/3 4/3 Continuation rates--TeUstry school Variable Nunber 288 289 290 293 294 295 296 299 300 301 304 305 306 307 308 I 309 290289288 BV.612 5/4B 6/5Ulif:...... 42 rural 11101:481* .497.795. enrol. 1.000 .653.795 1.000 .653.497 -.,105 ,295.424 .215.678.447 .384.167.469 .212.371.252 -.298--.432*-.310: .092.235042 .311.399.269 .008.109.064 .121.313.049 .361.425.153 .128.106.105 -.302-.479-.782 -.207-.357 .022 296295294293 4/3B 6/5urhanrural 19425/4 .252.384.447.424 .212.469.678.295 -.005 .371.167.215 1.Ctt-.741 .1s6. 35 1.000 .561.135.152 1.000 .073.561.166 1.000-.741 .073.152 -.268;-.403*-.389: .231 -.153-.001 .041.197 -.002 .380.547.115 -.416 .094.165.496 -.171 .288.488.219 -.169 .279.162.411 -.380 .384.051.059 -.321-.160-.085-.241 -.080-.429 .297.023 301300299 B1960 5/4B 6/5urban -.310 .269.092 -.432* .399.042 -.298* .311.235 -.389* .115.197 -.001-.403* .547 -.268* .380.041 -.153-.002 .231* 1.000**-.570-.631* 1.000-.631* .333 1.000-.570* .333 -.487* .211.332 -.358* .187.022 -.122-.089* .177 -.041 .063.108* -.296* .167.351 -.082* .047.122 306305304 B1960 6/55/4 rural .153.049.064 .361.313109 .425.121.008 -.169 .219.496 .411488.165 .162.288.094 -.171-.416 .279 -.358* -.122 .022.332 .177.187.211 -.0031.000 .423 1.000 .312.423 1.000-.003 .312 -.302-.908 .062 .183.200 .147.227.551 309308307 4/3 ruralurban-ruralwban 1960-19421960-1942 1960 -.357-.782 .105 -.207-.479 .106 -.302 .012.128 -.429-.241-.380 -.055 .023.059 -.080-.160 .051 -.321 .297 -.082-.296:-.089* .108 -.041 .122.351 .047.167.063 -.908 .551.200 -.302 .227.183 -.046 .147.062 -.591-.0951.000 1.000-.095-.o46 .425 1.000-.591 .425 3114313312 12111urban--endMI 5/4 of year day school4-136/5 .787.478.50 .529.886.759 .904.774.581 -.001 .174.222 .100.429.501 .o45.244.346 .368.355.394 -.319*-.229:-.179 -.009 .115.031 .299.341.357 -.032 .009.006 .069.021.004 .201.354.314 .104.144.129 -.384-.715-.572 -.021-.179-.221 319318317 EDErri:/r-191_42 6/5 rival .314.362472 .303.677.223 -.044 .24o.211 .142.928.169 .382.993034 .648.574.052 -.805 .106.135 -.171 .041.061.155 .307.567.142 .020.189.437 .195.435.197 -.225 .215.404 -.353 .082.053 -.211-.083-.191 -.111-.473 .015 4 1942 Continuation Rates Based an Beginning of Year alroll_renbs 1960 1960-1942 Urban aural FU:17 Grades Urban Rural U-R Urban I Rural Variablo Nunber 233 5A 6/5 293 2245 6/5 14/3296 2994/3* 3005/4 3016/5 3044/3 3055/4 3066/5 308 4/3 309 160 urban--ond of year daerand nipt school 239 290 .403 .465 .207 295 .702 1 .615 .447 .286 .034 307 324322323 1960E3L 6/5413 rural 5/4 .066.229.042.347 .129.438.036.425 .009.250.161.225 .491.117.216 -.033 .199.648 .093.451.054 -.413-.222-.235 .015 -.565-.657*-.914: .319.274.874 .229.965.349 .996.192.350 -.040 .460.234 -.139 .001.203 -.898-.056-.042 .093 .208.151.414.285 .549.025.117.017 329327330328 BE3t 5/i5/46/54/3 urban 1942 .524.174.039 .332.396.293 -.145 .404.112 -.124 .632.212 .291.392.445 .320.218.138 -.339-.166 .236 -.228:-.080-.312*-.422: -.151-.005 .132 -.019 .206.163 .184.080.432 .161.403.991 -.246 .948.280 -.065-.029-.331 -.378-.072 .164 -.451 .174.244 333332331 B3 5/1 ruralurban 19421960196n* -.148- .035.477_ .057.024-.542_ -.200 .125-.044_ -.497- .515.640_ -.049- .306.777_ -.130* .218.547 -.510-.354_ .405- -.390 .469, -.5424* -.627- .127.173_ -.14e. .185.509_ -.491* .655.482 -.248* .738.545 .272*.148.097 -.549-.240 .288* -.179*-.078 .182 -.022*-.133 .159 Day Sdhool Continuation Rates Based on &ad of Tear &fro llsant Day and Night School Beginning of Year Shrollnent Urban 1942 Rwral Urban 1960 Rural Urban I Rural 1542 Urbanal 2960 Rural 4/3 5/4 6/5 4/3 5/4 6/5 Grades 4/3 5/4 6/5 4/3 5/4 6/5 5/1 5/1 Continuation rates Variable NuAber 312 313 314 317 318 319 322 323 I 3214 327 328 329 33( ] 331 332 I 333 1942 urban-end of year 14/3 1.000 .657864 1.000 .777.864 1.000 777.657 .028112.137 .125.526.436 .131.191.304 .098.989.262 -.003-.071 .019 .235.349.396 -.036 .018 . .073.009.021 .368.357.231 -.188 .157.369 -.044 .268.396 -.009: .1449,.187 -.181-.237-.070 EDRD 6/5 .304.137.526 .191.436.112 -.028 .131.125 1.000 .035.068 1.000 .419.068 1.000 .419.035 .138.510.332 -.010 .021.146 .363.667.108 .023.224.444 .172.444.209 -.144 .264.388 .345.333.608 .562.438.808 -.226*-.132*-.468* .243.34).511 WAWA7....14/31960ED 6/5 urban 5/4 -.071 .396.262 .349.019.289 -.003 .235.098 .108.146.332 .667.021.510 -.010 .363.138 1.000 .848.749 1.000 .333.749 1.000 .648.333 .215.336.459 -.066 .209.252 -.172 .250.048 .036.297.048 .576.597.148 -.144*-.536--.553! .385.257.115 EiT7F.SALz 5/46/5S 5/4 .231.021.018 .357.073.018 -.036 .368.009 -.144 .209.444 .388.444.224 .264.172.023 .048.252.459 -.172-.066 .336 .250.209.215 1.000 .094.474 1.000 .399.474 1.000 .399.094 -.189 .160.201 .175.518.511 -.261--.506! .205* .742.219.681 B 5/1nB 5/1 Urban Rural 19421960* 19601942 -.070-.009- 396_.369 -.181 .144-.268..157 -.237--.044_-.158 .187 -.468- .511.562_.608 -.132* .340.808.333 -.226- .243.438_.345 -.553- .385.597_.297 -.536- .115.148_.048 -.144- .257.576_.036 -.506- .681.511_.201 -.261- .742.518_.160 -.205--.189 .219.175_ -.642-1.000 .427.640_ -.496-1.000_ .651.640 -.593-1.030",-.496*-.642* -.593-1.000 .427.661_ OgR n lh gE4tWO gg II IIII 6 11 ON 'Ufa gt 294 8N II 6 I t 1 1

5 Aff lig tf1§ Z1 mlAm I II ** * * 0411 * * *** su,0 R I III I I I

gc.4 RS1 EPNM4- VNMPI SS I se flf.1555 I 1 1 1 I II I I 1 NMWf...47)N gPN ** lOgA RP M.WNmmcm MN clenlg I I I I III

Af .1014.-m RM gaE LINO S I I I I I I I gf SSalM *S na 1.11 1.11 11..1 e II 11

ChOWIA, CAOgO 26:4N WR taa fl§IR 11 . I I S.. I I I I I I 14 r r-zi grg 1PgfilSM V.1 4:1fg

II I I 1 1 I I 1 1 Cr

ZMIA M.1M NO". -.°2% NUNM lAr.1 14-.0 .aRsu7; 6cO' C0%0 . II 1 I 1 II II 1 1 1 Ntf faZe. . '111.1 I III

Chte.r4 IP mop- %431 giA 34'tr% gEA e . 11 1 I I I 1 . N. 1.1\ tR117K 110 iE aria two":". . 11 I I 1 I II PRE 114 R& NO0

I I I I 1 1 1 1

+++ mm mm tbtttig tt big momm ASA SS 1114, $$$ H $q4P

-0.0ch 0,4 Nm %01.M MOel AAA AAA 'Au"mm mm%Jo^ %ALAI^MMM MONmUNO.%4 R31i§S. q0615 at ?A RI r :: I es ft Vi f I es 011ig egaw ea ig nil tg .... 11 1 1 I el i 1 11144.0.13%.2.a * I AM2 t SI (0,1e Ma R2 e 11 1 taIIIleII EESO BARU 6 fA rla 15 Hi 5 rt Sri° .4(

Intik: Ai.max fl ri §s1 ug° t' er He Se *sr, lg tiid Won s

I *a§ MA BAUR BE lEf 11 rr 1 r* rt rtr Cr

1 6 2*, itie la lot: f2: Ufa Wil55 1118 1 1 1 I

A lf Siigt; ; ; fikriot A Al Ili q r II 1 1 C a RA 160 lekiM Ti Or% so 1 8 1 1111 II III 1 1

eqtAgA m ow4a ftw2Ah *2 2g -.49= 1 I 1 1 II III II

A RIM*, t1P,111§ .& S S Sla 3 115 ll 115 II II St El a fil4. . BAER. 1 1 1 III1 II II at ag 1 II 1 1 1 1 1 1 III

lgiii t am=m0 amoo=m CE P2 #411 ttitAi 0,0,_ .0 Nes= '41:142

&t1 i

a& StV. 2ft 'n a t ISM MEO mm mm mmm mm 1

Secondary Schooling

Enrol 15-17 Cant Sec 3/1 Pass Sec Exam

lowier.

1950 1960 1960

M F M F M

Variable Number 334 335 338 337 340 341 .

3314 Enrol 15-17 M 1950 1.000 .852 .152 .116 .198 -.WO,* 335 Enrol 15-17 F 1950 .852 1.000-.019 .057 .179 .045" Continuation rates in secondary school 338 Cent Sec 3/1 14 1960 .152 -.019 1.000 .612 -.197 .24e, 339 Cont Sec 3/1 F 1960 .116 .057 .612 1.000 -.214 .215w Paso secondary school exalt

340 Pass Sec lims 14 1960 .198 .1_ _79 -.197, -.214, 1.000 -.838.* 3111 Pass Sec Exam F 1960* -.08041 .045w .247' -.215" -.838' 1.000w

i i URIM la *41 a mug00000 30 00 RRES0000 a's WO 11 1: 1 111 1 111 * *****u** ** v aaggabla 4gi * woviirtik 4s4 A a At esv, a/ t: .. As

Ii X

15:11.41 es : lipmm, 4 g :f3R.0g :

2 6:221 Oili 1 1 X a *04I§REII. 3E15 .5§5 lye so * *ID 21 Ser. :w *t 3 O OOOOO:8 :Z 0. 1 nOOOOO:11g:5 a ..4:es :10 :".1/4" 3 If *a 2 I:1SA

0 4141 O S 0

:38; :1 :a ON CIpl Pi X 111 ILAt : 1 1 1

Inel 011% 0 In sit . irs a 10 OAII "-v. S 41n1n041 . RP% X OW : 1

ino4 3 'KO

X AIA ' IREEIFIN Ak 1 1 . . :"R 11. g MOWN 0 08AA.4 I emorom,A0 e2 emrmage : a O 11125 1

Hs h wx1,411xx 2i/11$ Mimi 11 si P101.410°°'SON 21 Ng.; IN 24111t2 tRA ARA* AMEN',111M1 Literacy 10+ Literacy Literacy 40+ Changes in Li Wracy of Literacy fre. Yrs. 6+0 Literacy of Youth 10-14 I'M School Youth 10-14 Yrs. 6+ Tr.

II H F H Fit MI,MIF MIF lifF M*1I*

1960 19140 19140 1960 19140 1930 1940 1960 191404931 1960-1940 1963495°/

Variable Number Ito 141 114.5 146 147 148 153 155 156 157 158 159 160 161 162 163 164 165

Utility and comuniisation facilitiss

36 Bloot/Capita 1940 .320 .294 * * .305 .395 .3)4 .378 -.401 00041 .207-.418. .267 .306 .252 .282 .216 .066 .1870.2570 -.106 37 Elect/dapita 1960 .556 .543 .548 .588 .585 .577 00041 .451 -.633- .512 .527 .480 .509 .360 .258 .373*.398* -.369 38 Elect/Capita 1960-1940 .457 .537 SOSO .429 .456 .145 .314 .169 .208 .079 39 Movies/FOP 1940 .819 .657 .783 .764 .727 .699-.522 0000 .756 -.774:.831 .833 .724 .711 .610 .627 .733 .623: -.280 40 Movies/70p 1960 .662 .655 .580 .706 .617 .711-.298 41000 .510 -.667 .613 .689 .600 .645 .573 .449 .428*.578* -.070 41 Merles/70p 1960-1940 .034 .088 .000 .031 .051 .085 -.096 -.01/5 -.002 .060 42 Library Use 1940 .319 .288 -.393 .247 -.404*.264 .312 .281 .286 .170 .009 .126* .267* .082 43 Running Water 1960 .426 .422 .373 494 .462 -.319 len* .416 .366 -.476: .3911 .1127 .429 .410 .297 44 Radio 1960 .865 .866 .623 .864 062 .860-.399 .738-.823 .830 .863 .838 .857 .686 .86* Marriage and fertility rates

45 Single 7 20-24 1960 .267 .342 .202 .350 .284 .417 -.205 .... 156-203*169 265 300 31111 207 .077 -117*066* -.307 46 F under 5 Tre/F 1940 .260 -.223-.253-.365 -.241-.344 -.193 47 F under 5 Trs/F 1960 160 184 ,4202 .145 1514 .113 .003 .111 -.1014 .1.00 0! II .138 .1.63 .039 .071-007*-.061* -.340 62 Employ 8-11 M 1960 * -.756-.775-.779-.691-.778 -.685 .296 -.768 .747 -.768-.750 -.790-.811-.452-.393-.455' -.426* .317 63 Employ 8-11 7 1960 -.741-.724 .345 -.686 .The-.676-.705 -.683-.738 -.363-.299 -.1420* -.Ida* .345 Low faros particisatdon

58 &Act F 10+ 1940 .415 .317 .420 .505 .318 .387 -.267 .296 -.383:.365 .413 .386 .346 .389 .325 .199:.432: .006 59 ScAct F 12+ 1960 .223 .093 .301 .250 .127 .109 -.215 .158 -.218*.229 .229 .221 .167 .264 .250 .173*.2850 -.035 60 EcAct F 1960-1940 -.162 -.232 0000 00441 0000 -.390 .088*-.079-.133 -.149-.159 ...092 -.048 .065-.054 61 pevel. Index 1950 -.025 0818 .754 .795 .810 .815 .775 701-784 802 818 7141 .742 ....i 14 Aig tx *E*04 'AlR EF. 5 I5 1 1 I 1 I

**** st* It-orizti E E :fta *v.lemA 'A:am 1 1 1 1 1 1

***** .4..., 41. ,,,.`g2 ig e. :1 r- : :m.: ial Virf 114 iiiig 3 . I II II 1 ir68 : : : : :: : * * et No elk arasl ag§ lo

4 4 5 4 5 5 *Ili f. 41- 1 ,.., q cal ... n"A NIP, ViEf, . I II II * 40 a sbi ...V.° St a :* A'fi: : :g, 61 10riiR . . a 1 I Ift :al: : : :Ma r- 43 lEa a : II N lip S S a : : S : S S S 911 1 1 1 1

,.. pil ,..e. C.- **ali mu.. nmm :g i-40.1. ol...10 K 40.0 0%... 1.-q10- g 8 1,7rt 1.1 ce,::: ...... I ... 1 II II * * Ch .a 0% ab, 5115 I.- 15.1 S .IAIN : 7,./ ry. in re, : :2 1. ag5fRE 12 4 4 5 5 5 5 * * in SS* ustot Ofa to IroOi :*t :: a o : : : E abi 1. ia. 10 51.15 in CO IA .0 MIS0555a.... sz 1 I 4 i s 4 s a 4 4 **A p Ix ril IMO in 4:a ::,... i:: !se, a in C.- CO X C.- I.. PI a in Oi ri Oi 5.4ii ill 4 4 4 a s

* (tI m glg gg: a 4. : 1.0 ,om m I 5 5 I I II * * %I.. k tipg eon .0 ot (AS :ta%RA : 40 E., :84o Mr.- r- o m * * °In S I.- 40a). O. LA. :.4)8 EPet 5 rut: 1 1 581:: * 4eit 1.7q: SIR 'Acre 400 4040 0240 er...4) ..a 555 if:11 P49c: 'A:5. II * * et in CiM %A 04 EVA SS ?Arm g dn0 Oi .0CD CO I.. CO PI in5°in I

*1..01 * CNI Oi Oi CM OS Oi triez Fa :VA Oi 110° OD in CO CO CO Ws C.- pi Oi ri I

**1.-1. -1.4)er% &lig oi 1ga rag p-.0 ZgA Crir: II 55

3 4 IT1 ZWIt Rag P.4102 Literacy 10+ Yrs. Literacy 40. Yrs. Literacy 6443 Literacy of Youth lo -24 ire. Chang** in Literacy of Muth 10-14 Tea. Literacy6+ Tre I14 School Fa m F* 1960 19110 1960 1.914o 1930 1940 1940-193o 1960-3.940 1960-19401960-1950/ Variable ltember ga 145 1146 147 148 153 1514 155 156 157 1 158 159 1 160 261 I US2 163 1 164 165 9491e9ee MewsAiPaueipip/tima NS Glick UMW 2950 1950-1930 1ftshonti06 1950 Oa 1916060 .4117.834.787.41..322 .546.768*.752.362 .7491".711,.310 845*.824.348 745* 794* .'.383- .685.329 .787.368 -.418 .515.644-.595a -.813**-.771' .710a.773" -.55/* .612 .636.816*.759 .568.711*.719 .579735*.759 .672* .606* .576.639 .584.509 .480.613**.441 .566.628* -.322-.146 amsftSiam...V.Aant96 MitAmetm also .41.8 .1418 .1463 .486 .358 0.00 0000.00 0000 0000 0.000000 0000 1,00 979e mteloatMfgmegAmet r/m.9 m 19160-1940 mMrs 1960 1260 -.940 .154.495 .162Jags 716 -.435 .522 -.199 .376Jos -.419 .572 -.209 .490 ..000 00000000 . 0000...0 00000.0000000 00.00000 .000.00 0000 0000.154 0000.1140 0000.178- 0000.104- .015 101 rAto mfg 1 96o " 18 -.529 -.390 -.564 -.425 00.0 0.00 0000 00.0 0000 000. 00.0 00000.0 0.0. 0000, .000 00000 1051,03 MfgPMFilot lee ink" 35(1" .70t .430 -.772 -.772 0..0 -.724 .000 -.705 -.323 .... -.640 .616 -.767* .755* -.777 .705 -.804 .660 -.713 .562 -.738 .568 -.705 106 340,04, Fast 1.950/116D r..et/4440. 1901$ .128.3290.00.276 .05100.263.3140 W.00.327040041.40.206 .3220000.355.0.0 .20600.A41.000 0306...37300000 '0216 .040....00 00.0.0.0O...... 301 -.529- ...e_ .424 .443 .354 .387 -0.72 .433 -.292 .... MI600.0 -.036 00.00000 11 mums/msailtm 0.04 .000 M 000 .00 0 00 00 .149 -.289* .216 .319 .274 .3;9 00.0 0..0 0000:1;5e Urban Literacy by Age 1960 Rural Literacy by. Age 1960 Males I Females Males Females 40-49 30-3911111. 25-29 10-14 0.1111.41.0. 40449* 30-39 25-29* _ 10-14 40-49 3049 25-29 10-14 40,49 30-39 25-29* 10-14* Variable Number 169 170 -1[- 173 I 176 177 178 181 184 185 186 189 192 193 194 197 Ponuation distribution1 and chantta Density 1940 -.678 .... -.500 -.672 .718! 0000 .686* -.636 -.333 0000 -.474 -.439 -.524 00000000 .521: 8642 Capital/UrbanUrbanDensity 1940.1960 1960 1940 -.6149 .428.356.462 0000We0000 ..0448 .433.433.403 ...at/ .458.442.508 -.494*-.572* 685; 0000 ...350*-.503*....511* .646* -.606 .453.500.407 -.331 .591.172.641 0000000* -.427 .227.515.589 -.378 .204.620.624 -.477 .175.638.681 0000000*0000 -.142*-.91211 .488 -.130*...61,04* .376 121110 9 PopCapitalCapital/Urban 50,000+ Sise 1960 19601940 1960 .557.323.208.477 0000.... .482.512.342.182 .556.336.187.530 -.558-.304;-.125;-.471* 0000000 .0544-.302*-.106*-.447* .329.466.534.183 -.091 .048.429.250 0000000 0 ...040-.170 .331.283 -.116 .398.048.240 .513.185.002.304 00000000 -.430,-.096*-.278 .062* -.040*-.192 .138: 1413 UrbanUrban 1960/1930 1960-1950/1960-1940 243 0000 0000.222 00000000 0000 0000 0000 0000000 0000 0000 0000000* 0000,..0 .4080000 0000000 0000 0000 Transportation1715 Born Inatzteinstate M 19601940* -.501 621* 0000 -.478 .1486* -.513 .671* -.633** .53300 * 0000 .539 618** AL -.516 .683* -.458 .522* 0000oefae .-.499 .580* -.567 .652* -.495 .599* .449* -.wry .542* Amn** 2318 Roads/P0pBB/P0P 1940 B 1940 .....076 .572.016 ...044 0000 0000.... .°062741 0000 0064 0000 0000 0000 000000.0 0000000 000.0000 -.033 0000 0000.... 0000.... 0000.... 28272624 Roads/AreaBRoads/PopRoadsRoads/Area Paved/Roads 1940 1960 1960 1960 -.552...235 0000.122 -.501.175 .11605415 -.257...179 .1770000 -.492...111 0000oliee 00004,411141,.523.171* -.520-.189 0196...599 0000.4517.....*.212.m -.453-.089 .0000 -.131 0000.051... -.150...ISO 00000 -.198 00.0350000 -.133 0000.081 -.280-.046 0253000 -.256-.011 0000 -.001: 000*0000.,.256- 0000.148-.064,0000* 313029 Bicycles/PopDicycles/PopDicyclos/Pop 1940 1960-19401960 .1850000 0000 00.001550000 .000000*0000 0000000* 0000 00000000 0.000000000* 0000 0000000 00000000 00000000 .100000000 0000000* 0000000000* 000000 32 Autos/pop 1940 .622 .506 .635 ...701* -.635: .639 .544 0.... .462 .549 .657 0000 ..0594: ..05911: 353433 Autos/PopAutos/PopAutos/Fop 1960-1940 1960/19391960 .593 00000000 0000.685 0.0.6500000 -.712 0000 0000 -.760- 0000000*.643 0000.627 0000...... 000.544 0....647 000.702 0000.... 00 ....631- 0000 ....678- 00000 0 426

** * wg .. 14*A. 1 1 . 1 1 1 1 1 1 1 1 1

* *.4 *t% * v0 * ** Is% 0% 'irt N Yert IA 101 V% V% CA VI ig r

:30%

AA :sta sq, MIA on& 0 0

1 : s

o : ,t; : : rl CO 01 A IA in ;48A Pd 8 'V II

S p N S SSS cr, : gaD o IN en re% gr yl

g,N (-41 : 2A 11.110Al eyV% Ca WO /.r ND * * *fr **1..03 ...V 0 re% IA Z IA r% cj 1 1 1 1 1 1

: : :: : 0ka , %/0

1 I

* * ** * * *ft*Iel 41,A* CO% IAel 0 Is. IA* oi pi t IV% 0 r N ri II II I 1 1 1

: PO : f.°,& R.4 .-1 CO ...V 1 N .0 V3 : In1"1-.1 el IA0 II

0% rit 9 N co 01 Sgt 116k -mv% to ri N 01 E r4 0 0Th inIA II 0. 41) %0 . : g : 01 CO

V 410 0%...1 143 : V% N & CO V% 011) 1%. 0/ r : r1 1.4.4 ri 0 NO 1 1

*1'n-A1:141 3 2S 8.2 1427

:*A $ * * * * /41 r4 litglIA A R.92 : PS e 1.41 %0 %0 RS: APR Pm 4)08 * .SS . 1 1 I 1 II II II S I St *** ** OOP., l' P.. 0% eq *A ,..*, ...... wo $*:s. tng ,. ii . I II I 1

I N I SS A ** yerz %0

rou 1,1 nta W I * * 000 0%4:0 43.43 :MS PZW . . Y.

:*V in C.. A S1: RI"ir-vu, :at la:

A * * 0.0% 50 ti in e- fr% I" In14 S g$ so intr% 4 :At% : :Sf2 :dg iD Puy CY C1i

IA 101 :%1A 1r1 0 0: la: 10 16,12%, 0 0 0 1,*L.cv

tft*eN *v* :*4I kt Nen P..1.. 010 0 0 41#555: 555 1 1 I 1 II II II

- S SS ** .11441A 144-4 **ey I,- rir) :Ca *F31 I 1 55 II II 1 I I 1 * * CYp in_i IV. so p : fq : ..013:1 R4 i-t--..8,0I.e. .:aro,oe.: v, !..A. 0 0 N I I ea; * * . .1k4rig ilE (NI 0 In I II

S SS

4g1 PP: "f 0% In In el %/3 C0 r-V% P.- 1.1N i018 II 1

O A 6 1 ill til inin aa 0% 0. 0 === tisk.% e4 64X014 tit 0. 000 00g 1471S% 0 000 40 000 zwr 4) arrtil AAA da 111 '':'6L911 MMM MMM III In i a 144 it&It 8 888 888 VAiAiai 0 11 604' .TAPO. A 0%014 01M.4 W0t.- tit RgA 2 101.01 WWW COMW 0,10.

428

* * * * el .. . :-111 ":

* * . . . III t e I I

IS OOOOO OOOOOOOOO

eg4: P ; 13 II I ch ev *mai 2 O OOOO:

a g*t :g1. 1g IAIA 1 t11 Oca OOOO .

OOOOO OOOOO OOOOO

ea0% .A I.- P8 11 O OOO

tr M...0'1 1.1 :A :8r4 g O OOO I I ** * : tyCO e4 Al ON +014 4. II I I

OOOOO OOOOO

:* In & II I 1 T 1 aa: -.1. CV II O OOO ;talea gt/21 an: §av av Cli CY In a 1 I

OOOOO OOOOO 051fr 04.4 '41. Pi man.

niit MB I %. I min +4 ilMNnteI s114 gN 11 tE111

magi, us 5F,', 1 Urban Age Differences in Literaey1960 Rural Hale-Female Differences in Literacy by AgeUrban 1960 Rural Hales Females Miles Females - _ Variable Dumber (1/4o-1/49)(15-19)- 198 (40-49)- (600 199 (40-49)(15-19)- 200 (40-49)- (60+) 201 (40-49)(15-19)- 202 (4049)- (60+) 203 (40-49)(15-19) 204 (10-49)- (604) 205 40-49 208 25-29 210 20-24 211 10-14 213 40-49 218 25-29 220 20-24 221 10-14 223 Population distribution and change 6006 60000000 -.287 000 000.00 639200.0 0000.000 .41.0.456 .544.541 .390.425 .283.328 .1.69.490 .277.299 .381/4.... .180.199 21/41 DensityUrban 131/40 19601940 1960 -.184 .1431/4 -.007 -.576 0000.590 .2830000 .1170600 1,000.4115 -.295 0000 0000.635 -.571-.621 -.1*1.6-.424 -.401-.1127 -.375-.337 -.463-.445 -.521-.460 -.450-.451 -..11.1-.477 1110 936 CapitalCapited/OrbanCapital/BrbanUrban Size 19601940 19401960 -.019-.173 -.159-.296 ....0600 ...0000....060 -.169 0000.169 ....0000 -.143-.266 ....0000 ....00060600 -.150-.233-.363-.290 -.291-.123-.200-.090 -.019-.162-.254-.189 -.215-.120-.096-.107 -.251-.110-.280-.114 -.345-.183-.084 .065 -.254-.117-.282-.007 -.02-6022 .070 12 Pop 50,000-4 1960 -.272-.032 -.036 -.508 .410 -.050 .431.. .051.540 -.390 .098 .511.064 -.151-.410 -.374 0000.... -.284-.209 -.309 ....0606 -.1/415-.183 -.470 46460.0000 -.390.....424-.092 -.267 .... 17151413 BornUrban Instate 1960-1950/1960-191/401960/1930 191/40* 1960 -.123-.307* -.01/47-.206 1314* -.084- 471 .....1462.11 .180.....3614*.112 -.138 ....1/470* .5...273*.028 -.059.424 .... * .....467*.189 -.491*-.380 .360 -.500* .296 .215 .....639* -.547* -.459* .394 6319 .347 ...... -.406* -.458*.233 .203 Transportation1823 811/201)Roads/PopB 1940 191/40 -.073 O 000 000.109 0000 .256 0.1.000600.00 02770.600000 006006.6.0000 0000.032 .006000.0000 ..526-.061 ....0000 ....06.0.033 .....060 -.432...IL% .155 ....0060 2821/42726 Roads/AreaRoadsRoads/Ara/0Roads/Pop 1960 1960191/40 1960 .129...... 0000OIDO0.000O 000 ..0235 000001.060 000O0000 000000600 0 0060...... 0000... ..296 .... 6.00...... 223...I%.491 .167 00060006 ..0196 00.60006 006000000006 -.135 6212.064 0000000*.... 313029 Bicyeles/PopBicycles/1'ppBieyelos/Pop 19601960-1940 Paved/Roads 1914 -.185-.2n9-.244 .112.017 -052-.245-.1.00 .206.0990000 . 181.189.161. 069 .1400006.223 -.071 .139.097.041 .391.....418 -.167-.188-.237 0.000006.... 1446151-.236 00.006O6...... OM-.071 .002 O0460006.... .009.055.082 .... 35323334 Autos/Pop 19401960/1939196019604940 -.311. .041.1240OADO -.110-.093-.u6 0600 -.636-.28, .224 00.042.072.306 .17l.3200000.092 -.024-.143 .....385 -.052-.362 .28200.60 -.152 .2540000.513 -.352-.570 .138.581 -.494 .....545 -.37r;-.501 .003.521 ...1145-.435 .... -.268-.537 .1940519 -.565-.51/47 .... -.445-.558-.524 .174 -.393-.387 ...... A WE ii AR 71' 51 1 : .. i9ii'1 $ e $ 1 1i 444 Ilik15: 4k ag 1 1 g IIIIII 1 II k izt.: P. r ,r5. eit. ft gt;.... : 1 5 I i 5 i , 1 4 4 OSE 31IB 1Si. I 1 :5. rI. I : 5 II I . OP :A a liki Ii tlAll rig :e: li.:5 5 .i.: . . I 1 1 1

614 Ilt g :a ERI.. I. Rd. 1 g s' s' 55 1 r i i s' ' i P : a WA i 51 !iRI. A ..i.. 2 le i t 1 1 il

4 1',0 &tt. 1 1 is1 litsit I se 111

4 . LA §.. i.1 .ia A . 4 5 , II qlkiII A.-. , te,In R IN 'Hit5 1 fii 5 la . Hil1. i. I 1 5 I 5 I 5 5 5 s

Mtn: FE. i it.1 i 31 g 6,121 : I . let

1 :0 VII.4 N 01 IR 4 :. 0,45 i 1 I 1 s 5

,..,IVI ,..*A N,,, pi1 ON OW% ii 0 N. .a 01 e. Slfa : . . . i i 1 1 1

p.- .1 4 :& is ors PIE I § ik 4V4 4 Asi O. 04 _ ft I I III I II I I 5. I a I - 55 4 II g .$ 4 F. o SSE 0 trialW 5,.. ..., I 1§. 11 ii 5 5 5 III I i 4 14 NO 25N IIe, II I 1 V i 1

Jire" MAScil .... -.352 -.433 -.433 -.351 -.291 -.234 ...323 .130 -.115 .207 1960 II Lsbor/Ag Aa 44 .... .060 -.206 .013 -.053 -.097 -.162 -.062 -.065 -.104 -.117 .055 .036 -.159 1940 It LRbor/Aa Ag 33 000 0..0 000 000, .061 -.039 .166 -.313 -.110 -.051 .106 .067 1940 Pop 82 0057 6380 -.051 .199 -.165 .141 .242 .118 Ejidos/Ag 0000 0,00 *SOO 0000 Me -.r5o .131 -.?3, .109 -.15e .135 -.166 .111 -.175 rein-194o n Aggniot 81 -.353 -.%r' -.hir., -.46? -.451 -.541 .551 -.171 .322" -.529 -.231 1960* M AzIt4Aot SO -.,u0 .367* .1)4* -.071e 79 gqi.* -.412: -.534, -.61 -.3574, -.033 1040a m Ag/Toaot -.454 ,* * ,: .727: o .425: .516* .120* -.379: Ar.rioultura

-.317 -.429 -.104 .444 -.446 1940 M/InAot P.A. 77 administration Public .... 0000 -.4546 -.629 -.540 -.483 -.624 -.743 40000 1960 P Prof/toAnt 75 0000 0000 0000 -.339 -.436 -.409 -.463 ..0502 .4517 -.142 0000 -495 1960 14 Pre/SoAct 74 0000 Sege 0000 00.0 0000 0000 000 0000 SOO. .282 -.533 0000 40000 SOSO 1960 I Prof/CoAot 73 0000 -.472 -.613 -.586 -.615 -.700 -.656 1960 P Clerk/BoAot 72 .... 00040 000O 0000 -.331 -.469 -.493 -.536 -.569 O572 1F40 N 0000 0000 0000 0000 00040 Clark/ftiot -.516 -.590 .616 -.259 .477 .337 .429 -.653 -.204 -.366 1960 T Clerk/lbAct 70 0000 0000 0000 0000 0000 -.263 .127 1.329 .4,028 1960-1940 P *got 69 0000 ..26, .4339 .391 0 Conn./ -.642 -.770 -.644 -.649 -.700 -.721 1960 P Callsr/MoAnt 66 0000 -.536 .641 .639 -.766 -.726 .625 -.336 .484 .318 .460 -.770 -.390 1940 P Collar/tostot 67 5 .... -.339 -.420 .354 .323 .069 .217 -.333 -.137 -430 1960-1940 x Collar/taiga 66 -.366 -.469 -.461 -.517 -.527 -.526 000O 00040 00.0 0000 3360 m 0011br/s0Abt 65 00040 -.343 -.413 -.489 -.505 -.537 -.612 .636 -.243 .460 .303 .459 -.6514 -.191 -.404 1940 M Dellor/NoAst 64 workers vrofessienal sod oollar Whita

223 221 220 218 213 211 210 208 205 204 803 132 201 200 199 Number Variable

t (40-49) (60+) (40,49) 1044 20-24 2549 4049 1044 2024 2549 4049 40-49 (irdotr" i1541m (6013).. (15-1$ (1(120-1:3).. (15-19)- (40-4)- (15-19)-

Females Holes haales Halos

, Rural Urban Racal Vrben

1960 A. by Literacy in Difforenoon Mele.Feeale 1960 Literal.", in Differenoes Age .40116wr.,Aw.Aw.domiu--, . AN.Ax3A+10,..'. t....84010

Urban Age Differanceo in Literacy 1960 Rural Male-Focal. Differences in Literacy by Age Urban1960 Rural Mmles Females (40449) Males Females (40149)' Variable Number (4049)15-17)-198 (140.449)- (60+) 199 05-19)-(40-49) 200 (60+)201 c15-19)-(4049) 202 (40-49)-(60+) 203 (4049)(1549)- 204 (604)205 40.49 208 25-29 210 20-24 211 10-14 213 40-49 218 2549 220 2024 221 1044 223 876685 Ag Prop/Ag 14M 1960-194019601940 -.100-.119 .108 .211.066.097 -.030 .056.210 ..137-.009 .341 .181 .212.255 .4010-4059 .037 .198.156.125 -.115 .158 132 .322.013 .22a.005 0000 275.210 -.030 .370 .440.056 .4061 .435 .336.304 89949138 FarmAgSquiatAnd MechanisedInc under 1950 35001950 1960s -.362-.256*-.322 -.189- .147 081 .527.31.4304* .432*.398 ..648*-.591 .383*.380 .373 .6.. -.625.330 0000 601r .523w000 .504w .2470000 -.788_ .650w0.0 ..533 .604w .4701.. .626w0000 ..671 000 Manufacturing96 Rotuma Rick 1950-1930 ...... 271 -.364 .338 .005 -.429 e me 5.. Mrea3ActMfg/ScActMfg/2cAct N 1960M 1940 H 1960-19140 -.185 .... .034.... .041.... .130....mi. -4021 ...... 146...... 276 .094 -.190 .365 -.098.4128 -.127-.004 -.032.4222 -.257 -.254 103101100 MfgNfq IncF/M+F under Mfg 1940 FCI+T Mfg 1960 500 1960 .3510000 0000 .5550000 0000 .3240000 0000 .1790000 0000 .498.263.044 .563.306.006 .... -.020 .552.310 .485.061.177 .039 .460.099 .535 .3.377 106108107105 PaY/ftvay/LtspPay/SkpPay/Uftp Fact Fact netFact 19551940 17501730 118 00....0000 -.367 ...... 0000 ...... 000 -.029 .... 00.000000000 -.270 0000 -.324-.269 0.00 -.272 359 ....0000 -.382...416 00 -.322-.396 -.384-.402 -.446 -.249 SIS 109 Pay/zhp Pact 1755/1940 .126 -.075 -.072 +.262 -.196 -.440 0000 '.025111 .036 0.00 0 000 0000 5 000 .000 0000 Adult Levels s.m.s of Schooling 25* 1950 No SchoolingAdults Age 7+ Years Adults Agitof School 30+ 1960 25+ 1950 10+ Years of School Adult Age 30* 1963 Multi Ago Bac 15. 1940 Adults Age Univ 14 30* 3.960 25.1950 M F H F H F M F M F Population distribution and change Variable Number 1226 227 228 229 236 237 230 239 240 241 242 243 248 249 250 251 6b21 Urban:tensityDensity 19601940 19401960 -.720-.738 .478.579 -.655-.673 .475.562 -.713-.731 .491.566 -.732-.755 .532.586 -.463-.565 .807.774 -.375-.447 .801.799 -.431-.513 .6ce.775 -.336-.397 .808.807 -.510-.405 .831.792 -.263-.337 .802.807 -.399-.483 .808.774 -.264-.322 .829 -.005-.021 .584.659 .603.578.236.248 -.318-.38n .749.760 -.290-.365 .752.739 1110 98 CapitalCmpitalCapital/Urban Size 19601940 19601940 -.230-016-.376-.322 -.284-.012-.271-.183 -.235-.045-.297 -.301-.112-.363-.271 .357.2142.451.428 .472.403.339.312 2143.396.434.4114 .507.368.396.320 .397.187.451.471 .517.390.391.358 .450.264.434.474 .425.355.818 .516.489.483.425 .635.126.177 .561.452.464.440 .578.435.358.356 141312 UrbanPop 50,0004. 1960-1950/1960-19401960/1930 1960 -.642 -.583 -.295-.606 -.586 .... .173.665 .726 .240.454.673 .254.328.726 .646 .699 .248000..655 .73.8.536428 .481 .557.599 .745 5555.770 Sr:deportation1715 BernBorn Instate 19601940* -.715* .719 -.691* .633 -.684* .642 -.691* .573 -.722 .734* -.584 .692* -.647 .669* -.544 .650* -.701 .700* -.540 .647* -.640 .645* -.510 .606* -.253 .451* -.039 .278* -.503 .631* -.565 .708* 242318 Roads/PopRoads/Pop3RR/Pop 1940 1940 1960 000 000.00 -.062 60000000 00.1.00010 0000.154 .001.0000 .071 0.00 00000.00 0000000 .000.031 0.00 28P726 Roadsaoads/AreaRoads/Area8 Pavod/Roads 1940 1960 1960 -.0112 .2900 .....362.0.0.079 -.327-.001 .304 .....351.032 -.31h .452.000.063 -.258 .....120 -.253 .417.043 -.191 .13000.0 -.251 .084 -.172 .182 -.218 .412O....066 -.1114 .190 .036.174 .167.211 -.338 :Oti 0 -001-.2614 000 313029 Bicycler/PopSicycles/PoPBicyclos/Pop 19401960-19403960 6600000.0000 0.000000 -.149 0000.000 00000.0 .218 000060000000 .079.168.436 .071.4520000 ..00 00000000000. .11470.00 00000.00 0000.0000000 6.000.411. 00.00000000. 006.0000 35343332 Autal/PopAutos/Pop 1960-19401960/193919601940 -.329-.771 -.765-.735 -.735-.812 -.789-.734 00 .8914.819 .889.877 -.031 .648.912.825 -.225 .619.904.874 .876.797 .818.826 .899.802 .872.840 .368.367 000.361.413 00005664.666.000 .746.772 of Schooling No SchoolingAdults Aze Adult, Lavels 7+ Tsars Adults Ageof School 10+ Years of School Adult Age Adults Age Bac Adults Age Unix 25+ 1950 30+ 1960 25+ 1950 m30+ 1960 r m25+ 1950 F M30+ 1960 F M F15+ 1940 X F Utility and communication facilities Variable Number 226 227 228 229 236 237 238 239 240 241 242 243 248 249 250 251 383736 Slact/BapitaElect/Capita 19601940 1960-19140 -.350-.633 -.669-.423 -.595-.315 0000 -.555-.346 .552.385 .537.600 .524.609.465 .612.672.608 .537.396 .507.524 .605.471 .663.629 .038.086 .045.305 .261.182 .432.366 414039 Moviss/PopMovies/Fopmovios/rop 1960-19401940 1960 -.585-.735 fOff -.644-.639 0.0. -.564-.721 .000 -.682-.706 0000 .00..661.82600.0 .691.710000. .093.698.784 .181.722.682 .705.813 .653.669 .718.769 .738.648 .332.525 .4980Off.282 000..558.778 .623.748 444342 RadioRunningLibrary 1960 Water Uss 19401960 -.839-.416 .... -.804-.462 .... -.853-.399-.261 -.871-.462 .... .814.334452 .852.521 .861.413.535 .882.598.543 .801.490 .773.543 .557.433.852 .860.639 .455.428 .459.552 ..690.459 .791.560 Marriage 45and46 fertility rates FSingle under F 20-24/F5 beg 1940 1960 -.190 .176 -.304 .234 -.239 .150 -.399 .277 -.274 .195 -.348 .421 .332.300 -.410 .484 -.361 .201 -.383 .366 -.393 .311 -.493 .503 -.302 .297 -.475 .478 -.250 .230 -.315 .391 Cultural h7characteristics ProportionsF under 5 Trs/F of population 1960 walking barefoot -.193 -.117 -.162 -.135 .059 -.019 -.037 -.095 -.017 -.224 -.082 -.184 -.169 -.461 -.084 -.032 130127128125 BarefootBarefoot FN 1960F 1940 m 19140 .....656.632 .630.....671...... 645.698.601 .....769.780 -.648-.679-.588 .... -.743-.717 .... -.631-.575 000. -.702-.688 S5S -.623-.570 0000 -.649-.632 000. -.624-.572 -.671-.670 000. -.444-.438 000. -.39;-.472 00. -.657-.584 0000 -.717-.734 0000 124123 Barefoot RuralUrban T 1960 00000000 0000 0.0.000. 000000. O 000 000000 -.310 040. -.354 000. 000.0.0. 000. 00.0000f .0.0000 0000 000. .000 000. Adult Levels of Schooling 25+ 1950 No SchoolingAdults Age 30+1960 25+ 1950 7+ Years Adults Ageof School 30+ 1960 25+ 1950 10+ Years of Sdhool Adults Age 30+ 1960 Adults Ago Bao 25+ 1940 , Adults Age Unix Variable Number 226 227 228 TI 236 237 238 M 239 F ' 240 M 241 F 242 M 243 F 248 M 249 r 250 m 251 r 132131 alrefootBarefoot m/P 1940 1960 0000 O 000 0000 229 0000.474 .482 0000 00.0000 .458 00 00000 0000 0000000 0000000 0000 113138135 Non-CatholicBareootBarefoot F M1940-1960 190-1960 T 1940 0000O 000 000000S. -.227-.524 .429 000000 -.444 0000 00SSSS -.572-.464 00 -.6,9-4637 000 -.463 00000 000000 00000000 0000 0000 114 43 %min..:Non-Catholic ;Ater T 1901960 -.416 -.1162 -.339 -.462 .275 .535.179 .490 .543 .150 .639 555 117116115 Non-wheatSleepSleep on T Bed 1940* 1140* on Floor 1940 .0000* 608 3* -.660* -.661* *Ai* .579"S. * -.640* ..671 * -.521'. .719*452 -.581 .631....*.521 * -.F45* 4i88*7594 -.SA* .672*.598 .7491 .666* -.;211° -AT*.772*.557 Zigi* -NO* 45;.;* .689* .455*.418 .460*.552 .;64*.459 .584.560 122119 NonWheatNonAlbeat TT 1940-19601960 .614 .611 .310.616 .581.... -.289-.723 -.572 -.750-.280 -.360-.556 -.747 -.560 -.763-.297 -.572 .... -.337 -.204 -.513 -.503 Total Primary School Enrollments Hales Urban Ltrollment at 6 Years 1959 1937 1.960* 1930 1.960 Rural 1960, 1960U-R 8200 Monthly income in Pesos 3601$1,000 to (601 to 1,000)- (200) AgOccupation Prof Porantion distribution and channe Variable Number 265 266 267 273 2714 275 278 280 282 283 284 421 DensityUrban 19601940 -.512-.599 .339 -.404w .336!.375* -.264 .631.... -.099-.398 .236.066 -.230-.308 .... .076.011.... -.086 .178 .325 -.302 -.111.... 968 Capital/UrbanCapitelArbanUrban 1960 19401960 .235.223.421 -.239--.479! 08 ...391.... .195...... 184 -.002 ...... 2g .1.11.4g -.168 .068 .573.....399 .251.208.... 121110 PopCapitalCzpital 50,000+ Size 1960 19601940 -.165 .301.069 -.087--.2 * .115:.246* -.1;6 .315... . -.132-.059 ... -.320 .009... -.118 ...... 132 :..116.... -.092 .120.... -.336-.055 .074.301...... -.200 .058.... 17151413 BornBornUrban Instateri instate1960-19401960/1930 1960M 1940* .710*.207.593 -.489**-.162-.385! '''' .554*.202.407 -.155 .027*.602 -.083 .445*.423 -.287*-.402 .487 -.067 .....112 .167*.160.048 -.167*-.160-.023 -.013 .367.189* -.038 .254*.242 Trans-r-tation18 RR/Pop 1940 .025.... -.007 00.0 * .10400.0 0 0000 .00 .000 0 00.0 .00 0000 2324 Roads/PopRoads/PopB ,1960 1940 .610.501 -.541--.281: .470 -.485 .236 .068.139 -.442 -.057-.158 -.113 .080 -.183-.046 -.233 29282726 RoadsRoads/AreaRoads/Area° Paved/Roads 1940 1960 1960 -.252-.015 .106 -.227-.1414:'. .031: -.013 .312.313 -.265-.134-.056 .201.303.295 -.327-.272-.230 -.167-.081 .036 -.172 1401 -.045-.072 -.005 .047 323130 Autos/PopBicycles/PopCicycles/Pop 1940 1960-19)4019601940 -.0?1,-.010 .090 -.163...26C-.208* .354.411.461 -.098-.099-.184 .168.152.160 -.177-.169-.236 .118 4000:1466 :6:1; 334 533 Autos/PopAntos/PopAutoe/Pop 1960-19401960/1939 1960 .1428.306.636.516 -.520-.3474-.513,-.339* .358.283.428 -.024 .299.200 .236.447.063 -:5iL. .058.091 -.146 .160 Primary School alrolbaents tbrollmant at 6 Yews 1959 1937 Total 1960* Males 1930 Urban 1960 Rural 1960 1960U-R 3200 Monthly Income in Pesos 31,0003601 to (601 to 1 000)- (2005 AgOccupation Prof Utility and communication facilities Variable NuMber 265 266 267 273 274 275 278 280 292 233 284 39383736 Movies/PopE1oct/CapitaElect/:apitaelect/Copita 1940 1960 1960-19401940 .254.403.190 -.251-.262*-.062: .184.308.109 .175.441.256 -.008 .054 .190.0B1 -.074-.018 .... -.067-.127 .... -.264-.261-.116 .... -.031 .248.... -.286-.306 .... 414n Library:levies/PopMovies/Pop Use 19601960-19401940 .088.514623.061 -.050*-.064-.475*-.491: -.125 .1i7.357.636 .015.10c.059.... -.042 .269.018.215 -.097-.195 .029.022 -.034-.087 ....124 -.082 .....049.354 -.161-.271 .... .199.456.032.... -.097 .....154.311 Marriage and 4443fertility rates RadioRwrling 1960 Water 1960 .620.222 -.526-.133: .538.291 -.112 .157 .257.068 -.064-.145 -.168-.127 -.059 .144 -.211-.093 .317.179 -.108 .089 474645 F undarunderSinsls 5 F1ra/FYrs/F 20-24 1940 19601960 .269.021.070 -.125-.038: .038* .....117.040 .154.159 -.128 .184 -.004 ....204 -.158 .017 .060.085 -.061 .036 .052.172 -.065 .160 Employment6362 of youth SmployDriploy 8-118-11 Fn 19601960 -.651 .457.709* -.678 -.315 -.571 .143 -.019 .078 -.209 .221.203 -.242-.442 -.162 Labor force58 participation EcAct F 10+ 1960 -.476 .350 -.265: -.555 .288 -.208 .215 -.275 .120 .059.025 -.049 -.057 .178 .034 .226 -.082-.219 White collar6160 and59 professional yorkerrs navel.ScActEcAct FIndax 1960-194012+ 19601950 .484.125.395 -.011,.-.213*-.427 -.032 .516.178 .039.228.129 .135.081.175 -.014 .....047 -.098-.238-.199 .106.062.264 -.211 .280.277 -.194 .298.107 -.242-.131 .078 666564 Collar/EcActCo11ar/2CActCollar/ScAct M M1960N 19401960-1940 .340.713 -.381-.634* .356.680 -.060 .261 .185.319 -.183-.041 -.113-.001 .070.288 -.190-.183 a5;.466 .003.155 Primary School Enrollments Total Males Urban 7 Rural U-R Monthly Income in Pesos throllinent at 6 Tears 1959 Occupation Variable Nunber 2651937 2661.960* 2671.930 1960 27141960 2751960 2783200 31,0003601 280 (601 to 1,000)- (200) Ag Prof 696867 Collar/McActCollar/EcActCollar/ScAct r7 F1960-19401960 1940 -.477 .748 -.393-.634 * -.380 .605 .338273 .284 .....046 ...... --. 0000252 283 284 727170 Clork/EcActClerk/ScActC1erk/3cAct F 1960MT 1960 0000000..711 000 .569 -.330 .241 -.211 .252 -.014-.099 -.093 0000000. -.114 0000000..219 -.209 .289 -.322 .398 -.178 .124 Public administration757473 Prof/Ea/lotFrof/cActvrof/Zciot TM 1960F1960 1960 .402.687 -.604-.574 .574.5149 .360.317 .289.326 .0060000 -.097 .0540000 .195.233 -.085-.161 .425.347 .246.147 ,ftriculture77 P.A./EcAct M 1940 .694 -.635* .63h .278 .395 -.074 -.005 .315 -.085 .425 .183 318079 Asi2cActAs/ScActAgAdAot MNM 1940*1960-19401960* -.234 490:.533 -.5694:-.484* .274 ** -.210 .621-.619: .195.129-.211. -.350 .330.184: -.135 .374.023* -.004-.01r .003! -.098 .262-.*472 -.003_.114* -.013 .428*.500* -.057 .103-.142: 848352 kgAgXjidos/Ag T.aboriALabor/As Pop M 19601940 1940 -.137-.015 .274 -.170-.301: .169* -.342 .138.137 -.009 .201.058 -.064 .162.291 -.130 .037.024 -.276-.283-.067 -.155-.033-.074 -.045 .278.047 -.113-.254-.202 -.314-.091 88378586 *Inip/LandAg Prop/AgPrep/Ar 1550M 19601940 Prop/As ;; 1960-1940 -.354-.272 .234 -.121, .191.138; -.190-.106 .110 -.2h5-.205 .130 -.144-.131 .032 -.079-.060 .075 .204.274.030 -.037 ./69.161 -.238 .176.046 -.055 .125.289 -.054 .308.319 Manufacturing949189 and nining acturnaFarnA; :lechanizedInc Glickunder 1950-1930'0500 1950 1960* .737.717' -.466-.4913-4: .402.445 .067.316- .364.176.237*0000 -.207-.100* .0910000 -.055-.095-.170* .222.067*.119 -.195-.249_ .182.272*.2090000 .348089*0000.1)t0 959697 Mf7/1cActIrg/.:c\ctMfg/ScAct MM 1960-194CM1960 1940 .227 -.133* 0000 -:"i 000. .1920000 -.237 0000 00000000 0000000O 00000060 0000 0000 7 Total Primary School Enrollments Urban Enrollment at 6 Years 1959 1.937 1960* .01.1 Hales 1930 1.960 Rural 1960 1960U-R 3200 MonthIy Income in Pesos $1,0003601 to (601 to 1 000- (2005 AgOccupation Prof 100 Variable NUmber 265 266 267 273 274 275 275 280 282 253 284 103101 llfgMfg F/M+FF/H+FInc under Mfg 19601940t500 1260 -.776 .579*00000004 -.453 0000 -.404 M000000 -.278 -.093 . 211 -.120 -.210 105107106 PayipPaytOTPay/EtpPar/Emp Fact?act Fact 19501940 19551930 .386.502 -.153*-.320* .075.444 .168.074 -.015 .246 .124 -.257 . 013 -.196 0.00.000 .192 .153 -.157-.015 .018 111109Cultural characteristics Hiring/EcActPty/Emp Pact 1955/1940M 1940 -.032 .260 .016*.107* -.117 .139 -.029 .387 -.245-.226 :410;00 -.155 000 -.102 00 -.181-.296 .022 -.254 130127128125 Barefootii'ff,...tdtv.onrT9aotiozg._LzraikinbarqA FH 19601940 oo -.333-.387-.455 .194:.257*.156-.106! -.354-.346-.?92 -.073 0000.004 -.025 .112.039 -.048-.016-.003 -.063 - 04. 8 -.286 .254 -.334- .3180000 -.235 000 131124123 Barefoot HAPRuralUrban 1940 T 1960 -.394-.266 .506 -.391* .258*.088* -.108 .277 -.394 .3300000 .2590000.236 -.407 .073 -.038 .051 -.108 .066 -.398-.357 .1080000.404 -.169 .167 133195132 Barefoot FHIP 1940-1960 1960 -.321-.235 .460 -.012* .106* -.147-.198 .290 -.156-.085 .338 .176.123.052 -.175 i:115104 -.019 .016 -.021-.028 .383.249 -.281-.200 .159 -.149-.143 0.213 114113 Pon-CatholicNon-Catholic 19401960 .531.431 -.482-.366 .400.321 -.107 .010 .428.341 -.232 -.005 .149 .029.211 -.083-0.01 .018 .108 116115 43 BunmingBleepSleep an4:Aaron FloorDed 19601940* 191,0 -.39%-.604- .222 -.133, .2111.546;m, -.222--479_ .291 -.068-.112 -.135*-.294 .068 -.145 .002 -.127-.u6*-.175 -.075*-.261-.059 -.093 .438*.232 -.221*-.357 .179 -.192*-.376-.108 122119117 NonOheatHon-wheatNon-Wheat T T T 194101* 1940-19601960 -.727 .010.626* -.629, .163..673; -.323-.591 .626* -.205-.Me .230.072 * -.222-.334 .395* -.119 .3U * -.007-.150* .179 -.138 .192.014* -.237* .265.156 -.204 .002.1",1* -.015-.093 .026* Continuation Rates Based an Progress in School Beginning of Year Enrollments 1942 Grades 4/3 1960 1960-1942 1942 Grades 5/1 1960 _ Secondary School Grades 3/1. 1960 Variable Number Urban 238 Rural 293 U-R Urban* 299 Rural 3o4 307U-R Urban 308 Rural 309 Urban 330 Rural 331 Urban* 332 Rural 333 338 M 339 F Labor force58 participation ScAct F 10+ 1940 . 182 -.131 .125! .141 -.172 -.112 -.030 .257 .272 -.277* .282 .068 .009 White collar616059 and professional workers Bevel.FtActEcAct F Index1960-194012+ 1960 1950 000000.*O 000 .450..495 437 -.352-.192-.299 -.395*-.176- .... .079.350.422 -.211-.146-.352 -.106-.159 0000 -.01t2-.042 0 .735.296.347 .464.304.323 -.447*-.393*-.479! .508.227.374 -.038 -.170 666564 Gollar/EcActcouar/EcAct mM 19401960-19401960 .000O**00 000 .139.369 -.434 .046 .189....*.214* -.063 .040 -.135 .034 -.299-.125 -.173-.316 .499.557 .416.172 -.211*-.436* .368.427 -.140 .217 00000.0000.0 696768 Col2ar/SCActGollor/ECActCallar/Eact F 1960-19401940F 100 0000O0.** 00. -.413 MO -.515 .6190.00 .155.027* .... .13400*.000* -.239 0000.018 m0307 .2470000 -.181 000*0.00 .0450000 .326*.00.000 -.181 00000 .000 00600000 727170 Clork/ECActClerk/ItActClerk/EcAct TFM 1960 0000 .349 -.339 S.2214" OO* 000*.009 -.081 6000 -.198 00.0 -.328 0000 .660 0000000*.256 -.463* *00* .4230000 .131 0.000000 757374 Prof/2cActProg/EcActProf/EcAct ? M1960T 1960 0.00.00.O 00. .203. 334 -.143-.383 0000*000O 000 moo.136. 000.054 -.200 .07106000000 -,Z6 000* -.232 .3130000.457 .336.263 -.114*-.292 ....*0.00 .1.3840000 51 0000 AgriculturePublic administration77 P.A./Eckct M 1940 .399 -.524 . 143 -.194 .024 -.255 .379 .055 -.497* .375 .231 818079 Ag/EcActmAg/EcAct 1M 1960-19401960*1940* -.014 .371-.434: -.042-.231'-.319.: .067.104*.1984 ** -.121 .002*.049 -.095: -.091 .074 -.276'-.038-.347! -.120-.279--.391: -.157 .614".643: -.238 .396-.349: -.452:7-.412= .187- -.304 .495-.430: .286.042-.206* Progress in School Continuation Rates Based on Grades 4/3 Beginning of Year 311'ollments oracles 5/1 Secondary School Grades 3/1 Urban Rural 1942 U-R Urban* 1960wa U-R Urban 1960-1942 Rural Urban 1942 Rural Urbmm* 1960 Rural M 1960 F 82 Variable litmber 288 293 299 3014 307 308.052 I. -- .256309 -.188 330 -.066 331 332 333.064 338 339 858384 AgEjidos/Ag AgLabor/Ag Labor/Ag Pop M 111940 1960 0000 -.116-.167 .448.320 -.352-.157 .205.214 -.098--.025* .214_ -.149 .273.029.220 -.129-.150-.059 .110 -.023-.091-.121 -.193-.056 .001 -.172 .404.243 -.127 .302.498 -.310-.011* .052: -.129 .143.587 -.077-.193 .049 0000 888786 2quip/tandAgfig Prop/AgProp/kg M1950M 1960-194019401960 0000000 -.510-.300 .372.160 -.344:-.320 .110* -.368-.203 .208.124 .072.008 .133.069 -.398-.227 -.615-.491 -.274: 296*....*.411 -.576-.581 .176.091 0000 Manufacturing949189 amirdning ReturnsAgFarm Inc Mechanized underGlick 35001950-1930 1950 1960* 00100000 .282.2148.413* -.1425-.475'-.278_ -.124- .002:- OA.185*.119 -.1914-.162*-.143 -.102* .127.037 -.106-.228-.168* .308.562.544* -,004 .345.359* -.547*-.054**-.434 .306.541 -.003 .105- .121:. 79896 Mfg/EcActMfg/EcActMfg/teAct M M1960 1960-19401940 00000000 0000 0000 0000*.000 0000 0000 -.211 -.181 0000 0000 000400000 000000011 0000000.10000 0000 101100 Mfg F/M+FF/H+F Mfg 19601940 -.025 .447 -.005 000400000 00000000 .061 -.583 0000 107106105103 FAY/amPPay/EmpMfg Inc Fact Factunder 1950 19301940 ZOO 1960 0000 -.449 .520 -.312-.558 .00do -.149 .134.203 -.038-.174 -.238 0000 -.367 0000.281 .524.572 -.298 .446.618 -.224-.486 ....*.528 * .715tio5611 -.132 0000 0000 111108109 Mining/EcActPay/EmpPay/Eftp FactFact H1955/19401955 1940 000.0000 .4690000486 -.207-.312 0000 023* otood, -.003 do.doo .0140 -.029 000. .1260000 .405 .497 -.062 0.00000 .21900000000 0000 111. 0000 Continuation Rates Based on Progress in School Beginning of Year Enrollments 1942 Grades 4/3 1960 1960-1942 1942 Grades 5/1 1960 SecondarySWhool Grades 3/1 1960 Variable Number Urban 288 Rural 293 U-R Urban* 299 Rural 304 307U-R Urban 308 Rural 309 Urban 330 Raral 331 Urban* 332 Rural 333 338 m 339 F 125Cultural characteristics BarefootProportion N1940 of zop walkil; barofoot -.0o5 -.198 .183 -.33o* -.064 .196 .105 -.338 -.294 -.501* .188 .023 -.167 12313n127128 BarefootBarefoot IIF 1960UrbanF1960 1940 1960 -.041 -.116-.230-.238 .261.194.264 -.487-.275* ....* -.086 .304.022 -.127 .155 .1o7.333.137 .23o.134.385 2.11Oi 07 -Hi; 272 .027....*:139* -:5;S 04,00.045 O0000.038 00o 0000.241 132131124 Barefoot3arefootBarefoot 2f,tr Rural MMiF 1940-19601960 19601940 00000000 .164.208 -.169-.393 .0750000 0000....,.... -.087 .040.10200.0 -.044 0000.045 .2240000.284 -.148 0000.168 -.4.-.279 .4070000.430 -.291 0000.164.176 -.169-.387: 0000 -.373 .210.307 .2810000 00000000 114135113139 Non-CatholicBarefoot r T1940-1960 19601940 0000dd..* -.102-.152 .045.053 -.158-.350 .078 -.350--.356, 0000 -.156 .116.189 -.131-.081 .085.077 .276.269 -.190 .295 -.023-.384 .202 -.047-.389-.220 -.235-.196* .116.058, -.085-.301 .123 -.015 .397 0000000O 116115 43 RunningSloepSleep Wateron Bedrloor 19601940* 1940 ...... 0000 -.274- .076.229 -.004 .255w.259 _ ...... 0000 -.108- .067_.292 -.243 .046-.047_ -.023-.245 ....* .054.184....* -.486--.04% .501 -.438- .224_.622 -.293* .392.053** * -.589 .044*.375 -.103-.194 0....* .000 122119117 Ion-wheatNon-wheatNon-wheat T 1940-1960T1940* 1960 .... -.020-.255 214* -.278* .058.382 -.010 ....*.... -.185-.207 .260* -.292* .213.257 -.0.'r-.025* . -.178 .123*.... -.227-.407 .470* -.410-.197 .372* -.368* .111.339* -.374-.293 .403* -.016* .224 Ago 0rade Programs in School 1963 aradoAge 1C 1 nftles Grade 3+ Age 12 GradeAge 110 0rade 3* A. 12. Pass Rates Orals 2 1960 1942 Echools 1m:owlets 1960 1942-1960 Urban Rural ,111. 11-U Rival Urban Rural Rural Urban Rural Urban Rural * Total Urban Rural Variable Humber 347 I - 349- 349 353 359 360 361 365 376 377 359 390 395 396 397 ME1201:12AM2318 Raads/PopBRR/Pop 1940 1940 -.o09-.548 -.03, -.191 .... -.525 .177 .212.140 .260 0000.20 1 -.028 * 0OOO 000. 28272624 RoadsRoads/ArgoRoads/AreeRoads/Pop Paved/Roads 19601940 1960 1960 -.256-.053 .199 -.582-.116-.203 .1)44 -.256-.14, .257 .079.261 -.274 .033. 335 -.303-.127-.621 ..O 2:A10. .: .085 000 .066.2690040 .136.151.178 .036.146 .026.144.166 000 0*.168.MO -400-.338 000000. .229000..12300OO 00OO.0810:00 313029 picyolso/PopEtcycles/Popacycles/Pop 19401960-19401960 -.059 0 -.067-.098..273 -.151-.209-.369 00000O0 -.148 -.225-.409 -.025-.187 0000 -.224 -.291 0000 .222.217 -.152*-.168 .08*.3.16* 2 -.168 .459.134 35343332 Autos/P7pAutos/Pop 19601940196o/193) -.626-.593 00000000 -.1/07-.041-.678-.623 -.293-.114-.520 00000004 -.612-.554 0000 -.116-.650-.582 -.126-.451 0270 00000000.328 0000.409 -.202 :iig.072 -.002- 0000* ...... 000. .. ii;.095.091.1 -.230 .279.199.... Uttlity and communioation facilities Autos/pop 1260-1940 -.30, -.138 0000 -.152 -.19h4425 -.200 .3830000 .5n1000000 -.140 .470 * -.145 -.349 353736 Eloct/Capita:1.ot/capitaStoat/Capita 19601940 1960-1240 -.5b2-.516-.369 -.555-.529-.557 -.675-.379 00. -.378-.613 -.450-.704-.425 -.440-.405 0000 .089.365 -.053 .526 -.076-.307 ...... *.220.423 00OO -au)-.113 -.252-.015 42414039 NovissAopMovies/PopMovioaftopLit44mr Use 1960194010-194n 1940 -.334-.$46 -,C20-.452-.205 -.203..0346 .197 **Of -496-.1.77 -.195 -.452 .133 -.009-.090 ... .304....457 .237.408 -.151-.085-.082 .4h2*.085.185c, -.231 .043 -.18o-.161 4443 RunningRadio Attar1960 1960 -.704-.446 -.201-.713 -.61.3-.173 .. -.671-.283 -.244-.709 -6.5115 a000 0527.601 .59,.372 -.243-.225 .348.316* -.070 ... -.056 log

§ :g szsitA In0 a.5555 :4 en

.A 0 el 4, II ;; III

et0 REg .111 .1 SO 1 I II ** * ** ***** *Itn.* 0.1g 011.0 11.- P- 00 3 2 : on N 11PIE 0.1° 2 I I ;

0. 0:1 .0 0. lA N-1 CR CP 01 IV A ,. of IV 0 (IP 161V, 000 t g : : I f 555 I I ; ; 2 on W :ize/ tt%.13 FAN 0 in :A :A 44'11 CM1 1 f.a0 g O a-10.9 Idar.1 - .0 01 -wr 01 0 cv o% 5.1 5.1 0, i' .

A 03 : .4 el 11.-IA 01 s 1.-1A 01.0 SI S

/0 11-1 .0 0-1 a..2 . e -1 .... §Ea 1Allip 'LAIN . .. 01IA 1..0 I I 5 5 I I I I p-om 0 "....vIA Iln Ot el 0. §01 s: 2Ag ss r.53,0 1243 ps1 frazi 1 1 ; ; ; i ii' i' i 555 5 5 g v 5 n 8 so .. 1 AA g CP ;Ina t2 C 01 on cr r c-ctc ....i.c- ir- %ct ...3 loll 5 5 5 5 5

11.- 555 01 g 01 t 9 a a r 0

0 41.0 0 .42 cl WV% IP- ch CP A m 01 01 34A: c.- A 01 *0 IA N-1 4:A S:a I 1 III I I

1.. r4 4) 0 IA chr, 01 0 IA W V 01 0 .4 P-W CM 01 NO 25F. ERB555..lta 1 1 1111 ; ; 5 I rlrI p.. 42 11.-1" 0, it- Aga W09-01A0-70.50 IA.*IA Mao .0 IA N mone1,0 0 01*0 *0,0 WA I I ISIS 1111 IIIII I 1

§1§ N-1 N -1 00°13 r1115)1 t"44 Inv% I.. 6-1 Aii 159 .§ 3 E 51. ar3OL:t 0 IA0 01 N W 0.0 A WIAM0 ttS ggg Pglo- Age Grade Propose in School 1963 Me 10 Males Ago 12 Age 10 Females Age 12 Pap Rates Crada 2 1942 Schools Incomplete 1.960 1942-1960 Grade 1 Grade 3+ Grade 1 , Oroda 3+ 1960 . Variable NuAber Urban 347 Rural 348 n-u349 Rural 353 Urban 359 Rurel 360 R-U361 Rural 365 Urban 376 Rural 377 Urban 389 Rural* 390 Total 395 Urban 396 Rural 397 PUblic administration77 P.A./ScAct M 1940 -.441 -.537 -.565 .... -.541 -.634 .... .051 -.040 .044 .137* .. AgriculturaSO79 As/EOActAsjICAct M 1960*1940* -.605--.670: -.652--.660: -.609--.628: 088000...000 .191.620 * .208.692 * .247.504 * 000. .365.382* .372.379 -.103 .323*256* 00000000 .417-.164* -.020-.061* 838281 AgAs/EcAct Ejidos/AgLabor/Ag M 1960-1940MPop 1940 1940 -.051-.033 .074 -.294 .251.208 -.175 .230.224 000,.00.0000 -.041-.031 -.190-.238 .136 -.113-.233 .401 0000.00000.0 -.249-.122 .308 -.112 .3110.155 .441.134 -Alf:-,040 .0.004.000060 474.105.000 .319.216.002 8485 Ag Prop/AsLabor/Ag M M1940 1960 -.391 .124 -.127-.279 .245 -.134-.086 .062 0000000. -.296-.123 .267 -.224 .201 -.105 .086 ..00000000.. .064.512 ....534-.152 .530 -.522-.086 .089.012 -.066 .246*.352- # .400000.0000 -.463.404 .oce -.387-.062 868887 FarmAsAg Prop)AgProp/As Mechanised 14M 1960-19401960 1950 -.456 .378.368 -.349 .272 -.545 .102 ....0.00 .278 .216 299 .124 0000 -.477-410 ..362 *SOO .3480000 ...423 .335*....* .000 0000.225 0000.273.068.051# manufacturing949189 and mining Returns&quip/LandAs IncGlick under 1950-1930 350o 1960* -.497-.655-.698* -.577-.529* .453 -.513* oits50 .481 * -.677-.684r -.630w-.572 -.287-.377w .348.396#.194 .061.2.93.392# -.175-.297-.197# .156.305* -.543 ....* -.043* .075.077 -.057 .023 o796)8 Mfg/EactMfg/FactMfg/EcAct M 1960-19401940M 1960 -.369 ....tb14014 -.326-.423 6361 -.220 ....000 . -364 000. -.252 -408 .250 .318 .000S.S0800 .0.2015 0 0 .276...*0505 0000.100.0600 00050000 091 -.324 005000.0 103101100 MfgMfg IP/M+F Incilmq underMfg Mfg 1940 1960$500 1960 .659.319.163 .650.547.532 .491 . .4A0.748.176 .537.455.654 .... 00OO5 0.0.2840000 -.296 600080 .1830015b 0 -.274* OO.4000 0.6.0.... - rnI1001,01., 01.00110111' ...... 1...... 1r0.Y...... ," me.

Hales Age Grade Progress Females in School 1963 Pass Rates -, Schools Incas() lets Urban GradeAge 10 1 Rural R-U Grade 3+ RuralAge 12 Urban GradeAge 110 Rural R-U Oracle 3+RuralAge 12 Urban Grads 2 1960 Rural Urban 1942 Rural* / Total 196n Urban 1942-1960 Rural Variable Number 347 348 349 353 359 360 361 365 376 377 389 390 395 396 397 107106105 Pay/rnpPay/BmpPay/Bap Fact 195019401930 -.361-.372 .448 -.250 .425.605 -.261 00.00000 -.297-.377 .450 -.293-.261-.521 .... .320.134 .435.....185 -.276 468....* 111109108 Mininc/3oActPay/rA3Pay/an"p Fact Fact 1955/1940M 19401955 -.397-.165 .... -.280-.179-.371 -.233 ...... 060001.00 -.325-.159 .... -.228-.107-.298 -.221 .... .266 .470 -.147 0000.533....* 000 *000.217 0ultural characteristics Prn'ortione ofpon walking barefoot .534 .453 .4346 .523 .167 -.476 .536 . 276 .677 -.028 00. .003 128123130127125 DarefootBarefoot M UrbanFM1946 19601940 T 1960 .337....509 .455.416366 .502.480.236 ...293 ...,...... 442.530....492 .....343.546.571 .076.....309.194 -.059-.318 -.229-.503 .466 .230.073.250 -.061-.324-.354;..381: ...* .596 -.150-.043 .011 -.121 .006.024 132131124 Barefoot M/FRural 19601940 T 1960 -.399 .575... -.283 ....21.327 -.204 .. ...0000 -.526-.394 .... -.494...297 ...... 0000 .448.1h9 .349.0147 -.078 S..**.052* 138135 Barefoot FM 1940-1960 .032.570.483 -.L.19 .294.258 -.266 .320.267 *000011.100000 -.255 .506.401 -.267 .365.439 .....052.056 O0000 000 0 0 -.259..537-.487 -.302-.549 .561 ..194 170. 217 -.194....35911. .* 0000 00 -.010-.079 -.022 .030 114113 43 RunningNon-Catholic Water T 196019601940 -.446-.077 -.201-.302 -.173 .... . -.283-.296 .223_ -.244-.360 .275 ...... cas .601 -.290 .372 -.225 00410 -.a4 0 0410O 000 000000410 115116 Sleep an Flow T 1940 .589w .485w.166, .382w .567w .480* ...52173*...531690* ....* -in456s* 4* 117 Non-wheatSleep on BedT 1940* T 1940* -.471* -.433* -.306* -.446* .503 -.506* .478 -.091 .247* -.010* .106 ..3.1.2* 129119 Nun-wheatNon.wneat T 1940-19601960 .182.422 .099.471 OINNONIRMINNye.01. .251... .009 .212 .013 -.198 -.on 070 .196 -.047 APPENDIX C

PARAMETERS OF REGRESSION EQUATIONS

! PARAMEMS OF RB3RES3I0N SWIMS Squat/An Numbers Dependent variable s R2 Standardised B Vanes for Bach Independent Variable Set 1. Enrollment 19)7 T (265f (1.2)(1.1) .605.607.688 13.7719.8721463 - .8300J791.3197i614 -.1077i-79.0.6824x79. 8784x64 .2629x39. 6430x245 00000O O0000 0000 (1.5)(1,46)(1.14)(1.3) .668.691.580.705 13.0914.551.5.5119.36 - 1.3972=461.0729x1451.43 9403,0458 -.43.05Yrit.. 467978 .2392146. 7334E64 -..3313'79 .28311225O 0000 (18)(1.9)(1.7) .5960619.649 20.6616.6714.62 1.131.0339E461.5452=46 . 5782=5..5229x2i5 3373146 -.251479-.4536x79-.5363r9 O 0000 O .2291[390000000* Set 2. 1131033iSSIIeXpreell (MD)(141) iS 'Mint variableskalkALUIl_ g Wel .527 15.62 1.04 -.024179.5552=5 O 0000 00000 (2.3)(2.1)(2.4)(2.2 ) .380.508.522.535 10.3915.30 86589.31 c'- .95 .97493x80 .6737n27.738 7 -6924:248-.7181m8.297%1274926x127 -.4317no..5226x650000000 00000*0000O 0000 Set 3. Urban enrollment 1960 (273) Dependent(302)(3.1) variable: .408.264. 473 12.57 5.02 -1.056 .6; 76 -.14502n27-.8055n27 or (3.7)(3.6(3.(3.3)(3.4 5) ) ) .2/48..289.322 1441 3.676.669.644.637.09 -1.1816x1n3- .75142=38027110300,u1168753,1n76 -.4551165-.5572x65-.5021=-.5521n2. 653417 -.3843x65.2150x45 (3.8) .196 3.142 . 3799=8 . 21897,59 41 (3.9)(MD ) .187.181 2.063.09 - 43207162 .514714n68 -.24201652558t9 . (3.11)(3.13(3.114)(3.12) ) ..130.152 141131 2.522.102.302.11 - .281411540. 2148819302 X62 -.20331127...2221X62.1793x59. 1803'59 -.2180=7 Set /4. Rural enroll/ant 19W (271) Dependent(4.2(14.1) ) variable: .389. 520 15.17 8.92 - 8321x62 47825i-62 -.3278191-.5016n27 (14.7)(14.6)04.5)(4.3) .376.232.4014.353.360 14.239.1487.657.878.42 - .7431/62.637462.786462 .68)14x292718611814 -.6121=7-.14653=7-.1n3x9-.25122m,-.30871192 BIBLIOGRAPHY

Age-Grade Progress in Sohool.Unpublished data on primary schools in Mexico, 1963.

Anderson, C. Arnold, and Bowman, Mary Jean (eds.).Education and Economic Development. Chicago:Aldine Publishing Company, 194.

Anderson, C. A."Literacy and Schooling al the Development Threshold:Some Historical Cases," in Folucation and Economic Dave Ill-II

C. Arnold Anderson and Mary San&wan. Chicago: , publisllingEklited by Company; 1965. Arms, Margarita Nolasco."Notas sobre la educacion rural en Mexico," Amulets (Instituto Nacional deAntropologia e Historia, Secretaria de oPkVirroon Publica), Vol. UV, No. 43 (1962). Bataillon, Claude."Comunicacion de masa y vida urban& en MexLco," Ciencias Politicas y Sociales (Revista dela Escuela Nacional de Ciencias roliticasy Sociales), IX, No. 32 (Abri./"Tunio, 1963), 3Z-58. Batalla, Angel Bassols.El Estado de Mexico.Mextoo, 1956. Beals, Ralph L."Social Stratification in Latin America," American Journal of ,Sociology, LVIII (19524953), 327-39.

Belshaw, Michael. A Village &ono : Land and People of Huecorio. New 'York: Columbia University rress, vt.

Berry, Brian J. L. "City Size Distributions and Economic Development," Economic Development and Cultural Charm, IX(1960-1961), 573-87.

Bowman, H. J., and Anderson, C. A. "Concerning the Role of Education in Develop sent," in Readings in the Economics of Education.Paris: UNESC091968.

Bravo, Manuel. "The Changing Role of Education at DifferentStages of Economic Development," A paper presented at theInternational Institute of Edu- cational Planning, First Seminar on: "Problems and Strategies of Edu- cational Planning in Latin Amerioa," 31/1/13.Paris:UNESCO House, April to May, 19614.

Burnight, R. "DifferentialRural-Urban Fertilityin Mecico,"AmerioakSociologi- cal Review, XXI (1956), 3-8.

Campos, MoisesOchoa.Guerrero.Mexico: Editorial F. Trillasi S.A., 1961..

Carlson, Fred A.Geography. of Latin America.New York:Prentice-Hal Inc., 1952.

4149 450 1 Carnoy, Martin, "The Return to &lactation in Mexico:A Case Study."Unpublished paper, The Brookings Institution, Maroh,1966.

Carreno, Alberto Mario. "Las clues sociales de Mexico," Revieta Mexicanade Socio logia, XII (1950), 33340.

Casanova, Pablo Gonzales. "Sooiedad plural y deearrolloel case de Mexico," America Latina V, No. is (Ootubro-Desembro de 1962),3141.

Cline, Howard F.Mexico Revolution to Evolution 1940-1960.New York: Oxford University Press, 196/.

The United States and Mexico. Cambridge: Harvard University Press, 195).

Cocking, Walter. "The Regional Introduction of Eduoational Practices inUrban School Systeme of the United States," Institute ofAdministrative Research Study No. 6.New York: Columbia University Teachers College, 1951.

Crawford, William Rex.A Century of Latin-American Thought.Cambridge: Hartrard University Press, 1944.

Davis, Kingsley, and Casio, Ana. "Urbanization in Latin America," Milbank Memorial 'kind Quarter Ur, Vol. XXIV (April-June, 3946).

Davis, Kingsley, and Golden, Hilda Hertz. "Urbanization and the Development of Pre-Industrial Areas," Economic Developnient and 0ultura3. III (19544955), 624.

De Gomezgil, Luisa Rodriguez Sala. "Incremento de las oomunicaoiones en Me7ideo e influencia en algunos aspectos socio-eoonanicos,"Revista Mexicana de Sociologic, Vol. XXV, No. 1 (Enero-Abril, 1963).

DeVoretz, Don. "A Programming Model of Education inMexico." Workshop on Econondos of Human Resources, Paper 6601.University of Wisconsin Social Systems Research Inetitute, September,1966. (Mimeographed.)

Dickens, S. N."Monterrey and Northeast Mexico," Annals of the Associationof American Geographers, Vol. JOCLT(1939),127458.

Duncan, 0. D. "Populatten Distribution and CommunityStruoture9"in Population Studies: Animal Ecology and Demos'. YIII (Cold Spring Harbor .ym:posia on Gruantitative Biologyj. 'tow Toric: Biological laboratory Cold Spring Harbor, 1957. Pp. 35771.

. "The Measurement of Population Distribution,"Population Studies, (1957), 27-45.

Duncan, Guzzort, and Duncan. Statistical Geojrapbr:Problems in Analyzing Areal Data.Glencoe: the Wee Press, 1961. 451

Duncan and Schnore. "Cultural, Behavioral4A,ndEcological Perspective* in the Study of Social Organisation," Amerioan Journal ofSociolcaz,Vol. LXV (1959-1960).

Duncan, Walter Raymond. "Education and Ideology:An Approach to Mexican Po- litical Development with Special Emphasison UrbanPrimary Education." Unpublished Ph.D. dissertation, Fletcher School of Las and Dip lomaoy, 1964.

Durkheim, BMile. TheDivisionof Labor in Society. Translated by George Simpson. The Free Press, 1933.

Estados Unidos Mexicanos.Departamento de Muestreos Aindamento Estadistioo del "Plan de Once Anos de Educacion Frimaria, JunioAgosto." Agostos19617

Estados UnidosMexicanos,Secretaria de la Economia, Direccion General de Estadistica, Anuario Estadistico: 1941.Mexico, 1943.

Anuario Estadistioo: 1942.Mexico,1948.

. Anuario Estadistico: 1943-1,945. Mexico, 1950.

III Canso Agrioola,_ Ganadero y Ejidal: 1950. ResumenGeneral. Mexico, 1956;

. V Censo de Poblacion: 1930.Rasumen General.Mexico, 1934.

VI Canso de Poblacion: 1940.Resumen General.Maxim), 1943.

VII Canso de Poblacion: 1950.Resumen General.Mexico, 1953.

Compendio Estadistioo:1941.Mexico, 1941.

Compendio Estadistioo: 1947. Mexico, 1947.

Mexico, 1953.

gmpendio Estadistico: 1958. Mexico, 1959.

. Nomenclatura Naoionalje Ocupaciontql 1940.Merico, 1941.

Estados Unidos Maxicanos. Secretaria de Educacion Publica, Direccion General de Segunda Ensenanzas Departamento Tecnico Seccion de Estadistica. published data on secondary education.

Estados Unidos Mexicanos. Secretaria Educacion Publica.Educacion Pedagogica Revista de Orientacian (Organo del Consejo Naoional tecnioo de la Magrori), -rj".-7Wril,1961).

Estados Unidos Maxicanos. Secretaria de Indastria y Camercio, Direccion General de Estadisticas Anuario Estadistioo: 1960-1961.Mexioos 1963.

VIII Canso de Poblacion: 196o. Resumen General.Mexico, 2962. 452

Eitados Unidos Mezicanos. Secretaria de Industria y Comercio, Direcoion General de Estadistica, Cons* Resumen General. Mexico, 1959.

Cosnpezxiio Estadiatico: 1960.Mexico, 1960.

BOB or Traba o de la Poblacion Economical:onto Activa Jefes -7de 00,

FattahiPourPardoAhmed. "Biucational Diffusion mad the Modernization of an Ancient Civilization, Iran."Unpublished Ph.D. dissertation, Department of Education, University of Chicago, 1963. Flores, Edmundo."The Significance of Land Use Changes in the F000nomic Development of Mem Lco," ,Isend Economics, WV (1959), 11544.

Friedmann, John, and Alonso, William (eds.).Re ional Developet and Plannin, Cambridge:Massachusetts Institute of T.olmology Press, Gallo, Martinez Victor.Estructura economic& de la efts:scion Mexicana.2 vols. Mexico, 1959.

Gill, Clark Cyrus. "The Role of the Federal Goverment in Public Education in Mexico."Unpublished Ph.D. dissertation, Universityof Minnerota, 1948.

Gillin, John. "Bbhos Components in Modern Latin American Culture," American Anthropologist" Vol. LVII, No. e (June,1955).

Ginsburg, Norton.Atlas of Economic Development. Chicago: University of Chicago Press, 1961.

Glade, William P., Jr., and Anderson, Charles W.The Political Economy of Mexico. Madison:The Universityof Wisconsin Press, 1963.

Glick, Milton. "The Impact of Economic Development on theReturnsto Labor in Agriculturein Mexico." Unpublished Ph.D. dissertation, Department of Economics, University of Chicago, 1963.

Goldblatt, Phyllis. "The Geographyof Youth Employment Rates in Mestco," in Schools in Transition. Edited by Andreas Kazamias and ErwinEpstein. boaton:Allyn and Bacon Inc., 1968. Hagerstrand, Torsten.Diffusion of Innovations as a Spatial Prooess.Translated from Swedish (1957) by Allan Prod.Chicago:University of Chicago Press, 1967.

"Quantitative Techniques fcrr Analysis of the Spread of Information and --"rechnology, Education and Economic Development. Fated by C. Arnold Anderson and Mary-Jean -Bowman.alloagos WinePublishing Company, 1963.Pp. 214-80. liamley, Amos. Human Ecology: A Theory of Conummity Structure. New York: Ronald PressCompany, 19. 453

Hawthorn, Harry B., and Engle, Audrey. "Stratification in a Latin American City," Social Forces XXVII (October, 1948 to May; 1949), 19-29.

Hayner, Norman S. "Differential Social Change in a Mexican Town," Social Forces, XXVI (October, 1947 to May; 1948), 381-90.

New Patterns in Old Mexico: A Study- of Town and Metrmslac New HaverirTaierstyess, 1

Hoselitz, Bert F. "Generative and Parasitic Cities," Economic Development and Cultural Change, III (1954-1955), 278-94.

"Mexican Social Structure and Social Development," Economic Development and Cultural Change,

"The Role of Cities in the Economic Growth of Underdeveloped Countries," Journal of Politica; Economy; LXI (1953), 195408.

Humphrey; Norman. "Social Stratification in a Mexican Town," Southwestern Journal of Anthropology, V (1949), 13844.

Illich, Ivan. "The Futility of Schooling in Latin America," Saturday Review, April 20, 1968.

Infield, Henrik F., and Freier Koka. People in Edidos. New York:Frederick A. Praeger, 1954.

Iturriaga, Jose E. La Estructura social y cultural de Mexico.Mexico: Fondo de Culturea"-Zan71)3oz1.

James, Preston E. Latin America.Revised edition.New York: The Odyssey Press, 1950.

Johnson, John J. Political Chan in Latin America: The Emergence of the Middle Sectors. vsrs y ess, B.

Johnston, Marjorie C. Education in Mexico. (U. S. Department of Health, Edu- cation and Welfare, office of Education.) Washington: U0 So Government Printing Office, 19560

Kahan, Arcadius. "Determinants of the Incidence of Literacy in Rural Nineteenth- Century Russia," in Edacation and Economic Develonei0 Edited by C. Arnold Anderson an. y 3n Bowman.ChlcagoiAldine Publishing Company; 1965.

Katz, Levin, and Hamilton, Ao "Research on the DiffUsion of Innovation," American Sociological Review, XXVIII, No. 2 (April, 1963), 237$20

Kneller, George F. The Education of the Mexican Nation. New York: Columbia University Press, 1951.

Kunkel, J. H. "Economic Autonomy and Social Change in Mexican Villagge" Economic Development and CUtural Change, X, No. 1 (October, 1961), 31-63. lat

Larroyo, Francisco. Historia de la educacion en Mexico. Mexico: Editorial Porrue,S.A.rnalia.da

Lewis, Oscar.The Children of Sanchez. New :ark:Bandon House, 1961.

Pedro Martinez,New York: RandomHouser 1964.

"Urbanization idthout Breakdown," Scientific Monthly, LXXV (July, 1952), 3144.

McClelland, D. C. "Does Biucation Accelerate Economic Growth?" Economic Development azi. Cultural Change, Awil, :966.

Magee, Mary Catherine. "Monterrey, Mexico: Internal Patterns and External Geography."Research Paper No. 599Department of Geography, University of Chicago, 1958.

Mendieta y Nunes, Lucio. La Economia del Indio. Mexico, 1938.

aMexico Today," Annals of the American Amiademy of Political and Social Sciences, Vol, CCCV (May, 1956).

Moore, Clarence A. "Agricultural Development in Mexico," Journal of Farm Eco- nomicl, =VII (February, 1955), 7240.

Moore, Wi Dart E. Industrialisation and Labor.Ithaca:%;ornell University Press, 1951. Moore, W. E., and Feldman, A. S. (eds.).Labor Commitment and Social. Change in Newly DevelopedAreas. New York:Social Science Research Council, 1960.

Moak, Sanford A.Industrial Revolutionin Mexico. Berte ley: University of California frees, 1954. Myers, Charles Nash.Eclucation and National Development in Mexico.Princeton: Industrial Relations Tection, Department of Econced.ce, Princeton Uni- versity, 1965. Nash, Manning."Industry for Indians," ,Americaet, XIII (February, 1961), i:1443.

. "Multiple Society in Economic Development; Mexico and Guatemala," American Anthropologist, LIX (October, 1957), 825-33. ."Reaction of a Civil-Religious Hierarchy to a Factory in Guatemala," Human Organizations XIII (Winter, 1955), 26-28. "Recruitment of Wage Labor and Development of New Skills," The Annals of the American Academy of Political and Social Science, CCCV"ow7 1956), 23-31.

"The Role of Village Schools in the Process ofCultural and Economic Modernisation," Schools in Transition: in ative Education. Edited by Andreas Kasamias and Irvin Epstein. 1117n W1Q-c-as Inc., 1968. 1455

Navarrete,Ifigenia N.de. Ladislasibuciondeeso yel desarrollo economico de Mexico.Mexico: EecueIiNió1onaUde kcoromlh, 06o.

Negrete, Julio Cesar Olive, and Chan, Beatriz Barba de Pins."Estudio de las clases sociales in la ciudad de Mexico," Annales (Instituto Nacional de Antropologia e Historia Secretaria deNk=onPublica Mexico), Vol. XIV, No. 43 (1962).

Northrop, F.S.C.The Meeting of East and West.New York:Macmillan, 1956.

Olsson, Gunnar.Distance and Human Interaction: AReview and Bib Bibliograp .mm .1:6_ .1 o Institute, 1965.

Parker, Franklin (ed.).Latin American vilucationResearch:Annotated Bibliography of 269 united hates Doctoral Diseertations.Austin, Tema:Inatitute ofLatin American Studies, University of Texas, 1961s.

Parks,Richard W."The Role of Agriculturein. Mexican Economic Development," IntermAmerican Eoonomic Affairs, XVIII,No. 1 (Summer, 1964), 1427.

Platt, Robert S.Latin America: Countrysides and UnitedRegionso New 'York: McGraw-Hill Book Company, Irc., 1942.

Putney, Shell, and Putney, Gladys."The Locus of Influence in aRural Mexican Village,"Research Studies of the State Collegeof Washington,XXI (September,1953),230-36.

Pye, Lucien. Communication and Political Development.Princeton: Princeton University Press, 1963.

Randall, Laura. "Labor MigratJanand MexicanEconomic Development,"Social and Economic Studim, I (darch, 1962), 73-81.

Redfield, Robert.Peasain Society and Culture. Chicago: University-of Chicago Press, 1956-

Redfield, Robert, and Singer, Milton."The Cultural Role of Cities," Economic Development and Cultural Change, Vol. III, No. 1 (October, 1954) 7--

Rogers, Everett M.Diffusion ofInnovations.New York:The Free Press, 1962.

Romanell, Fttrick.Making of the Mexican Mind: A Study in Recent Mexican Thought. Lincoln: University of Nebraska Press, 1952.

Ruiz,EduardoRamon.Mexico, The of Poverty sad Illiteracy0 San Marino:TheHuntington Library, 19b3.

, "Mexico's Struggle for Rural Education 1910-1950."Urpublished Ph6D. dissertation, Department of History,University ofCalifornia, 1954.

Sanchez, George I.Mexico: A Re-yolutian by Education. New York:The Viking Press, 1936. 456

Sauer, Car1.0. "The Personality of Mexico," The Geographical Review, X= (Julor, 1941), 353-614.

Schnore, Leo F. The Urban Scene. New York:The Free Press, 1965e

Service, Elwin R. "Indian-European Relations in Colonial Latin America," American Anthropologist, Vol. MI, No. 3 (June,1955).

Stavenhagen, Rodolfo. "Aspectos sociales de la estructura agraria en Mexico," America Iatina Janeiro to Marco, 1966.

Stewart, John Q. "Empirical Mathematical. Rides Concerning the Distribution and Equilibrium of Population," Geograehical Review, =VII (1947), 461-85.

Stewart, John Q., and Warntz, William. "Physics of Population Distribution," Journal of Regional Science, I (1958), 99-123.

Sviatlovsky, E. E., and Ells, W. C., "The Centrographical Method and Regional Anal7sis," Geographical Revimm, XXVII(1937), 240-54.

Tamayo, Jorge L.Geografia General de Maxi%) Vol. Geografis Biologioa Humana. co: Inetitlito Mexicano de Investigaciones Economical, 11§5127-

GeoErafia General deMaxico,Vol. IV: Geografis Economics. Mexico: Instituto Mexicano de rnvestigaciones Economical', 196.

Tisdale, Hops."The Process of Urbanization," Social Forces XI (1941,1942), 311-16.

Tucker, William P. The Mexican Government Todaz.Minneapolis: University of Minnesota Press, 1957,

UMS00. La situacion educative en America Latina:la ansenanza es o&_ prob ,perspectives.Paris:

Readings in the Economics of Education. Paris:UNESCO, 1968.

Universidad de Nuevo Leon Centro de Investigaciones Economicas.Estructura economica del Noreste de Mexico un analisis regional1955.Monterrey9 TOL Urbanization in Latin America:Seminar on Urbanization Problems in Iatin America. Edited by Phi.lip }rawer.Santiago, Chile, 1961.

Van Patten, James Jeffers. "Education in the United Maxigan States."Un- published M.D. dissertation, University of Term, 1962.

Wagley, Charles, and Harris, Marvin. "A Typology of Latin American Subcultures," American Anthropologist, Vol. LVII, No. 3 (Juna, 1955).

Walter Thompson de Mexico, S.A. The Mexican Market,2d ed. MaxLco, Walter Thompson de Mexico, 1963. 167

Whetten, Nathan L. "The Rise of a Middle Class in Mexico," In la Class Media en Mexico y Cuba, Vol, II. Washington, D. C.: Pan American Union, 1950. Pp. 1-29.

Rural Meaco. Chicago:University of Chicago Press, 1948.

Whetten, Nathan L., and Barnight, R. "Internal Migration in Mexico," Rural Sociology, MCI(1956)9141-51. Wilson, Irma:Mexico: A Cent of Fducational Thought. New York:Hispanic Institute of the United States,

Wolf, Eric R. "Aspects of Group Relations in a Complex Society," American Anthropologist, LVIII No.6 (December, 1956), 1065-1077.

Wythe, George.Industry in Latin America.New York: Columbia University Press, 1945.

Yates, Paul Lamartine,El desarrollo reV.oml de Mexico. Investigaciones Industriales, Banco de Mexico, a.A., 1961.

Young, FrankW. °Location andReputationin a Mexican Intervillage Network," &man Organization, La II, No. 1 (Spring, 1964), 36441. Yowl& Frank W., and Young, Ruth C."Social Integration and Change in 24 Mexican Villages," Economic Development and Cultural Change, VIII, No.4(JulY 1960)2366-77.

"Two Determinants of Comunity Reaction to Industrialization in Rural Mexico," Economic Development and Cultural Change, VIII (1959-1960)9 257-63.

"IndividualCommitment to Industrialization in &real Mexioo" Un- published paper,

Zamora, Fernando (ed.).Diagnostico EconomicoRegional.Mexico:Secretaria de Economia e Institut° Mexrcano c1Investigaciones Economia, 1957.

Zenteno, Raul Benitez, Analisis demografico de Mexico.Mexico:Instituto de Investigaciones Sociales UniversiAad Nacional, 1961. - OE 6000 (REV. 9-66) DEPARTMENT OF HEALTH. EDUCATION. AND WELFARE ERIC ACCESSION NO. Ut-I-ItU/- tUUt-ATION (TOP> ERICREPORT RESUME

CLEARINGHOUSE ACCESSION NUMBER RESUME DATE P.A. T.A. IS DOCUMENT COPYRIGHTED? YES 0 No ERIC REPRODUC TION RELEASE' YES NO ' 001 30 68 1 TITLE 100 EDUCATION IN RELATION TO SOCIAL AND ECONOMIC,CHANGE IN MEXICO 101 102 Final Report 103 PERSONAL AUTHOR(S) _ 200 GoldblattPhyllis K.

INSTITUTION (SOURCE) I SOURCE CODE 300. University of Chicago, Chicago, Ill. Comparative Education Cente 310 REPORT/SERIES NO. OTHER SOURCE SOURCE CODE 320 330 OTHER REPORT NO. OTHER SOURCE SOURCE CODE 340 350 OTHER REPORT NO. PUB°L. DATE CONTRACT'GRANT NUMBER 400 HEW-OE-4-10-1_00: 3 6500 3637 . PAGINATION, ETC. 500 ii-457 501 RETRIEVAL TERMS 600 601 . 602 603 604 605 606 IDENTIFIERS 607 ABSTRACT 800 301 The reduction of social distances among diverse groups depends upon 5402 economic, social and cultural ties binding these groups to each other and 803 to national networks. In many Latin American countries, universal education 804 through a free public school system was held to be an indispensible vehicle 805 for achieving integration on a national level. This research takes a case 806 in point, that of Mexico, and asks two main types of questions concerning 807 the relationships between education, economic development and social change.

808 , 809 First, literacy and schooling levels of adults are studied as part of what 810 makes for a viable development process. Interest is in the stages by which an

811 industrializing economy may move from an increasingly literate labor force on, 1 812 at lead pointg, to progressively higher levelS of schooling of larger proportion- 813 of the population. Second, attention is directed to the process of widening 814 distributions of literacy and schooling. The problem is one of identifying 815 components of a development nexus such that development potential can be spotted 816 outside of the clearly advanced areas, and an analysis can be made of the flows 817 of influence between the modernized and traditional sectors. Inthisorientatio 818 cducation is viewed as an innovation; its distribution among the population and 819 its relationship to development are analyzed as a process of diffusion in both 820 spatial and time dimensions. 821 822