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Michigan State

University

This is to certify that the
thesis entitled
ALTERNATIVES FOR THE TRAINING OF
SKILLED INDUSTRIAL LABOR IN
SAO PAULO, BRAZIL

presented by
MICHAEL FRANCIS LUKOMSKI
has been accepted towards fulfillment

of the requirements for

Ph . D degree in ECONOMICS

/ Major professor

 

Date July 3|, I974

 

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ABSTRACT

ALTERNATIVES FOR THE TRAINING OF
SKILLEQ INDUSTRIAL LABOR IN
SAO PAULO, BRAZIL

BY

Michael Francis Lukomski

Over the past several decades the State of Sao Paulo
in Brazil has been industrializing at a rapid pace. Large
quantities of skilled industrial labor have been developed
and several sources of industrial training can be identi-
fied. The most visible is the quasi-private Servico
Nacional de Aprendizagem Industrial (SENAI) which was
established in 1942. Industrial training is also provided
by private training schools as well as in the formal school
system. Given the existing data, a comprehensive historical
view of the total system of industrial skill development has
not been possible.

This study was undertaken with three basic objec-
tives: to establish the origin of the present skilled
industrial workers; to identify the types and sources of
the training they received; to evaluate the effects of
differences in both origin and training on their present
work situations and the time taken to reach the skilled

occupational level.

U

Michael Francis Lukomski

7\

O. The first step in the realization of the study was

the construction of a general model which related factors
important in the development of skilled industrial workers.
The model contained six blocks of variables: (1) present
work situation; (2) present work situation control; (3)
initial conditions; (4) formal education; (5) work experi-
ence; and (6) training. The model was used for two pur-
poses. First, it was used to organize information in a
useful manner and served as a general framework for the
description of the entire system of industrial skill
development. Second, it provided the basis for the develop-
ment of several linear regression models which were used to
estimate the effects of various factors on (1) hourly wage
rates, (2) number and difficulty of operations performed

on the job, and (3) time taken to reach the skilled occu-
pational level.

Data was obtained using the "reverse tracer"
technique. A sample of S40 skilled lathe setter-operators
from the "ABC" area of Greater Sao Paulo was selected and
interviewed. Detailed information was collected on their
present work situations, origins, and past learning experi-
ences (formal education, work experience, and training).

Those who have become skilled industrial workers
were not drawn randomly from the total population. Most
had parents who were well educated and had fathers who
worked in industrial occupations. Almost all had at least

a complete primary education and many went beyond the

Michael Francis Lukomski

primary level. Over two-thirds had at least one "rapid"
industrial training course. Of those who did not, half
had been enrolled in SENAI apprenticeship programs, and
about 13 percent had received industrial training in the
formal school system. Only 11 percent of the total sample
reached the skilled occupational level without some form
of special industrial training.

Most of the rapid industrial training courses were
sponsored by private industrial schools and not SENAI. The
respective percentages were 54 percent and 31 percent. Of
those in the sample who had at least one course, 66 percent
had at least one private school course, while only 33 per-
cent had at least one SENAI course. Private school indus-
trial training has existed at least since 1942, the year
SENAI was established.

In terms of what is done on the job, the pay
received, and time taken to reach the skilled occupational
level, private school training was not shown to be inferior
to SENAI training. Variation in wage rates was explained
by differences in work experience and by the conditions
under which work takes place (size of firm, sector, machine
type, etc.). Differences in initial conditions (origin),
formal education, and training were not significant in
explaining the variation. Both levels of formal education
and past work experiences were found to influence the time
taken to reach the skilled occupational level. Initial

conditions and training were, again, not significant.

ALTERNATIVES FOR THE TRAINING OF
SKILLED INDUSTRIAL LABOR IN

SAO PAULO, BRAZIL

BY

Michael Francis Lukomski

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Economics

1974

ACKNOWLEDG EMENTS

I would like to express my great appreciation to
Dr. John M. Hunter, chairman of the committee, for his
guidance, concern, encouragement and friendship. Without
Dr. Hunter this study would not have been possible. Also,
I would like to thank Dr. Robert H. Rasche and Dr. David
K. Heenan who, as members of the committee, made many
valuable contributions in the latter stages of the
research.

Several organizations have made financial contri-
butions toward the completion of this research. Included
are the Non-Formal Education Study Group and the Latin
American Studies Center at Michigan State University, the
United States Agency for International Deve10pment and the
Departmento Regional de Sao Paulo of the Servico Nacional
de Aprendizagem Industrial (SENAI).

Special acknowledgment is also extended to Dr. Paulo
Enersto Tolle and Dr. Joao Baptista Salles da Silva of
SENAI, who were extremely helpful during all stages of the

research conducted in Brazil.

ii

TABLE OF CONTENTS

LIST OF TABLES . . . . . . . . . . .

LIST OF FIGURES . . . . . . . . . . .

Chapter

I.

II.

III.

IV.

INTRODUCTION . . . . . . . . .

1.1 The Purpose of the Study .
1. 2 Background Notes . . .
1. 3 The Importance of the Study
Footnotes--Chapter I . . . .

THE MODEL . . . . . . . . . .

THE

THE

2.1 Introduction . . . . .
2. 2 The Conceptual Frame .

2.3 Elements in the General Model
2. 4 Summary of the Model . . .
Footnotes--Chapter II . . . . .

COLLECTION OF THE DATA . . . .

3.1 Restrictions On the Study .
3.2 The Sample . . . . . .
3.3 The Questionnaire . . .
3.4 The Field Work . . . .
Footnotes-~Chapter III . .

FINDINGS O C O I O O O O O

4.1 Introduction . . . . .
4.2 Simple Descriptive Findings
4.3 Learning Paths . . . .
Footnotes--Chapter IV . . . .

iii

Page

25
27

29

29
29
34
48
50

51

51
56
60
62
65

66

66
66
108
116

Chapter

V.

VI.

APPENDIX I

BIBLIOGRAPHY

SUMMARY

mamma‘
o o o o o
UTIbUJNH

AND CONCLUSIONS

Introduction

(OP DO)

Objectives of the Study
The Realization of the Study
The Descriptive Findings
The Regression Analysis
General Summary, Implications and
Suggestions for Further Research

Wage Per Hour .
Number of Operations Performed

The Questionnaire .

0

iv

THE ANALYSIS OF THE FINDINGS

Difficulty of Operations Performed
(TOP OP)
Years Taken to Reach Skilled
Occupational Level (YRS AR
Footnotes--Chapter V

LO-SLO)

Page
117

117
127

158
171

185
201

202
202
202
203
206
215
220

230

Table

l.

10.
ll.

12.

13.
14.
15.
16.
17}

LIST OF TABLES

Domestic Product at Constant Prices--Tota1
and Per Capita, 1947-1972 . . . . .

Real Product Index by Sectors, 1947-1972 . .

Industrial Workers in the State of Sao Paulo,
1972 O O O O O O O O O C O 0

Industrial Workers in the State of Sao Paulo,
1946-1972 . . . . . . . . . . .

Hypothetical Experience of 10,000 Students
who Began Primary School . . . . . .

Students Enrolled in Brazilian Secondary
Educational Programs, 1971 . . . . .

Training Certificates Awarded by SENAI of
Sao Paulo, 1943-1972 . . . . . . .

Gross Monetary Earnings (In Cruzeiros) . .

Estimated Gross Monthly Earnings, Excluding
Payments for Overtime Work . . . . .

Operations Performed on the Job . . . . .
Number of 41 Different Operations Performed .

Difficulty of Operations Performed (Scale
of l to 41) . . . . . . . . . .

Factory Size . . . . . . . . . . .
Factory Workers by Sectors . . . . . .
Type of Lathe Used . . . . . . . . .
Entry Level and Years Working in Factory . .

Age Distribution . . . I. . . . . . .

V

Page

10

12

14

16

22
68

70
72
75

76
77
78
79
81

82

Table Page

18. Place of Birth . . . . . . . . . . . 83
19. Place of Birth and Place of Primary Education . 83
20. Educational Levels of Parents . . . . . . 85
21. Occupational Area of Father . . . . . . . 86
22. Occupational Status of Father . . . . . . 87
23. Formal Education, Grade and Program . . . . 88
24. Formal Education Levels . . . . . . . . 90
25. Years Required to Complete Highest Grade of

Formal Education . . . . . . . . . 91

26. Special Program Equivalents for Middle and
High School (Madureza) . . . . . . . 92

27. Occupational Area of First Job . . . . . . 93
28. Occupational Area of First Job and Work
Experience Before Entering Area of

Lathe Operation . . . . . . . . . . 95

29. Type.and Years.of Work Experience Before
Entering Area of Lathe Operation . . . . 96

30. Entry Level Into Area of Lathe Operation . . . 97
31. Years Working as Skilled Lathe Setter-Operator . 98
32. SENAI Apprenticeship . . . . . . . . . 99
33. SENAI Apprenticeship by Year . . . . . . . 100
34. Number of Special Courses Taken . . . . . . 100
35. Types of Courses Taken . . . . . . . . . 102
36. Courses, by Sponsor and Type . . . . . . . 103
37. Courses by Year and Sponsor . . . . . . . 105
38. Courses, by Type, Sponsor and Total Hours

Planned . . . . . . . . . . . . 107

39. Sponsors of Courses Taken . . . . . . . . 108

vi

Table Page
40. The Four Major Learning Paths . . . . . . 110

41. Educational Levels of Parents by Learning
Path (percentage distribution) . . . . . 111

42. Occupational Area of Father by Learning

Path (percentage distribution) . . . . . 112
43. Occupational Level of Father by Learning

Path (percentage distribution) . . . . . 113
44. Educational Level by Learning Path

(percentage distribution) . . . . . . 113
45. Number of Courses Taken by Learning Path

(percentage distribution) . . . . . . 114
46. Percentage Having at Least One Course From

a Given Sponsor by Learning Path . . . . 115
47. General Form of the Basic Regression Model . . 122
48. Regression Analysis of Wage Per Hour for the

Total Sample and Two Subsamples--
Complete Model . . . . . . . . . . 129

49. Analysis of Variance of Wage Per Hour for the

Total Sample and Two Subsamples--

Complete Model . . . . . . . . . . 136
50. Test for Significance of the Regression

Equation for Wage Per Hour for the
Total Sample and Two Subsamples--
complete MOdel O O O O O O O O O O 137

51. Test of Hypothesis that Both Subsamples Come
From the Same Population-~Comp1ete Model . 138

52. Analysis of Variance of Wage Per Hour for the
Professionally Young Subsample--
Complete and Reduced Models . . . . . . 140

53. Tests of Hypotheses in Individual Blocks of
Variables for Wage Per Hour for the
Professionally Young Subsample . . . . . 142

54. Test of Hypothesis on Set of Three Blocks of

Variables for Wage Per Hour for
Professionally Young Subsample . . . . . 144

vii

Table ' Page

55. Regression Analysis of Wage Per Hour for the
Professional Young Subsample-~Reduced
Medel O O O O O I O O O C O O O 145

56. Analysis of Variance of Wage Per Hour for the
Professionally Old Sample-—Comp1ete and
Reduced Models . . . . . . . . . . 147

57. Tests of Hypotheses on Individual Blocks of
Variables for Wage Per Hour for the
Professionally Old Subsample . . . . . 150

58. Test of Hypothesis on Set of Four Blocks of
Variables for Wage Per Hour for
Professionally Old Subsample . . . . . 151

59. Regression Analysis of Wage Per Hour for the
Professionally Old Subsample--Reduced
sample 0 O O O O O I O O O O O O 153

60. Regression Analysis of Number of Operations
Performed--Complete Model . . . . . . 160

61. Analysis of Variance of Number of Operations
Performed--Complete Model . . . . . . 164

62. Test for Significance of the Regression
Equation for Number of Operations
Performed--Comp1ete Model . . . . . . 164

63. Analysis of Variance of Number of Operations
Performed--Comp1ete and Reduced Models ‘. . 166
64. Tests of Hypotheses on Individual Blocks of
Variables for Number of Operations
Performed . . . . . . . . . . . . 167
65. Test of Hypothesis on Set of Four Blocks of

Variables for Number of Operations
Performed . . . . . . . . . . . . 169

66. Regression Analysis of Number of Operations
Performed--Reduced Model . . . . . . . 170
67. Regression Analysis of Difficulty of Operations

Performed--Comp1ete Model . . . . . . 173

68. Analysis of Variance of Difficulty of
Operations Performed--Complete Model . . . 177

viii

Table Page

69. Test for Significance of the Regression
Equation for Difficulty of Operations
Performed--Comp1ete Model . . . . . . 177

70. Analysis of Variance of Difficulty of
Operations Performed--Comp1ete and
Reduced Models . . . . . . . . . . 179

71. Tests of Hypotheses on Individual Blocks of
Variables for Difficulty of Operations
Performed . . . . . . . . . . . . 180

72. Test of Hypothesis on Set of Three Blocks of
Dunny Variables for Difficulty of
Operations Performed . . . . . . . . 182

73. Regression Analysis of Difficulty of Operations
Performed-~Reduced Model . . . . . . . 183

74. Regression Analysis of Years Taken to Reach
the Skilled Occupational Level-~Comp1ete
MOdel O O O O O O O O O O O O O 187

75. Analysis of Variance of Years Taken to Reach
Skilled Occupational Level--Comp1ete
Medel O O O O O O O O O O O O O 191

76. Test for Significance of the Regression
Equation of Years Taken to Reach Skilled
Occupational Leve1--Complete Model . . . 191

77. Analysis of Variance of Years Taken to Reach
Skilled Occupational Level--Complete and
Reduced Models . . . . . . . . . . 193

78. Tests of Hypotheses on Individual Blocks of
Variables for Years Taken to Reach
Skilled Occupational Level . . . . . . 194

79. Regression Analysis of Years Taken to Reach
Skilled Occupational Leve1--Reduced Model . 195

ix

LI ST OF FIGURES

Figure Page
1. The Conceptual Frame . . . . . . . . . 30
2. The General Model . . . . . . . . . . 33
3. Work History . . . . . . . . . . . . 42
4. Occupational Relevance . . . . . . . . . 44

CHAPTER I

INTRODUCTION
1.1 The Purppse of the Study

Over the past several decades Brazil, particularly
the State of Sao Paulo, has been industrializing at a
rapid pace. Growth rates during the past eight years have
been especially high, ranking the Brazilian economy as one
of the fastest growing in the world. Brazil's ability to
sustain high rates of industrial growth over a long period
has been closely related to the large quantity of skilled
industrial labor which has been developed. Industrial
growth and the development of skilled industrial manpower
have apparently taken place at compatible rates.

It has long been appreciated in Brazil that skilled
industrial labor is important in the process of industrial-
ization, and that the development of skilled industrial
labor requires training. Several major sources of
industrial training can be identified. The most visible
is the quasi-private Servico Nacional de Aprendizagem
Industrial (SENAI), an institution with a series of indus-
trial apprenticeship and training programs, which was

eStablished in 1942. It was the first of its kind in

South America, and has served as a model for other South
American countries' national manpower development systems
such as SENA (Servico Nacional de Aprendizaje) in
Colombia and INCE (Instituto Nacional de Cooperacién
Educativa) in Venezuela.

Industrial training also is provided in the formal
educational system. Basic industrial training is available
at the middle school level, and there are technical indus-
trial programs in some high schools. Private industrial
schools offer training in a wide range of industrial
occupational areas. Some firms offer special training
courses for their employees, and several large firms main—
tain their own training centers in lieu of paying taxes
for the support of SENAI. Such training centers are,
however, supervised by SENAI. Various religious and trade
organizations also sponsor industrial training courses.

All these different sources have provided the
training for Brazil's skilled industrial workers. However,
data on what types of individuals received training, the
percentages trained through each of the different sources,
and the effects of different types of training have not
been available. It is known that large quantities of
skilled industrial labor have been developed recently and
rapidly; however, given the existing data, a comprehensive
historical view of the total system of industrial skill

development in Brazil has not been possible. Without such

an over-all frame of reference, it has not been possible
to evaluate the contribution of any individual program.

Presently, parts of the Brazilian training system
are being copied in other countries. Furthermore, laws
have been passed recently in Brazil which will have pro-
found effects on the industrial training system. In 1971,
a national law (Lei 5.692) was passed recognizing the
existing system of non-formal education as a legal supple—
ment (Ensino Supletivo) to the regular, formal school
system. Through a system of special courses and examina-
tions, organizations such as SENAI will be able to offer
diplomas which are legal equivalents to the regular
diplomas of the formal school system. This legislation
and decisions by other countries to copy parts of Brazil's
model are being made with incomplete information. Many
scarce resources are involved, so that any losses due to
"wrong" decisions are great. More complete data on the
development of skilled industrial labor in Brazil are
needed.

The general purpose of this study is to provide
such data. First, a general model or conceptual frame
will be developed to organize logically information on
industrial skill development in Brazil. It will also serve
as a basis for the develOpment of several other models
designed to be used in the evaluation of different forms
of industrial training. Second, based on the organization

suggested by the conceptual frame, the separate parts of

the skill development system will be described in the
context of the complete system of industrial skill develop-
ment. Finally, the effects of different types of indus-

trial training will be evaluated.
1.2 Background Notes

1.2.1 Introduction

The purpose of this section is to provide general
historical and institutional information on Brazilian indus-
trial growth and the development of skilled industrial
labor. Many types of data commonly available in advanced
countries simply do not exist in Brazil. In recent years,
Brazil's data collection systems have improved greatly,
but there is still a lack of reliable, long-term, time
series data in almost all areas. Also, cross-section data
collected in different years is not always comparable due
to variations in coverage and definitions. Even today, in
some areas there are no comprehensive data available.

This is particularly true concerning the operations and
output of private industrial training schools.
1.2.2 Growth, Industry, Skilled Labor,

andisao Paulo

Real Gross Domestic Product in Brazil has increased
over 500 percent since 1947. The average annual rate of
increase has been over 6.8 percent (see table 1). Since

1965, the average annual rate of increase has been over

 

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1947 has grown at an average annual rate of over 3.7 per-
cent, and since 1965, over 5.1 percent. Presently Brazil
is one of the fastest growing and most rapidly industrial-
izing countries in the world.

The leading growth sector in Brazil has been the
industrial sector. Since 1947, real industrial production
has increased by over 800 percent (see table 2). The
average annual rate of growth in industrial production
since 1947 has been over 8.8 percent, and since 1965, over
11.2 percent.

The State of Sao Paulo is by far the leading indus—
trial center in Brazil. In 1949, 49 percent of all
Brazilian industrial manufacturing originated there. Ten
years later in 1959, $50 Paulo's share increased to over
55 percent.1 The Department of Statistics (Departamento
de Estatistica) for the State of Sao Paulo estimates that
in 1967, Sao Paulo accounted for over 57 percent of all
Brazilian manufacturing production.2 It is generally
assumed, though data are not available, that this trend
is continuing. Sac Paulo's share of a rapidly increasing
industrial product is also increasing.

The 1970 census (Censo Demogréfico-Brasil) reported
the total population of Brazil in 1970 as more than 93
million.3 Approximately 66 million (71 percent) were ten
years old or older. Of these 66 million, only 30 million

(46 percent) were economically active, and of those

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a.oaa m.moa m.vaa «.maa o.maa m.~oa mmma

unnooum mooa>umm .oaqseeou mouoeeou mnumooca ousuaooanm< new»
amuoa was .mmcmua

 

.meCau:00|l.m mamda

10

economically active, 5.3 million (17.6 percent) were work-
ing in the industrial sector.4 The National Department of
Labor (Departamento Nacional de Mao-de-Obra) estimates
that in 1970 over 51 percent of all Brazilian industrial

5 The

employees were working in the State of Sao Paulo.
data clearly indicate that S50 Paulo is the dominant and
most dynamic industrial center in Brazil. SENAI estimates

that in 1972 there were more than 1.5 million individuals

employed in Sao Paulo's industrial sector (see table 3).

TABLE 3.--Industria1 Workers in the State of Sao Paulo,

 

 

1972.
Number Percentage
Unskilled 175,541 11.56
Semi-skilled 794,530 52.33
Skilled 266,913 17.58
Foreman, etc. 23,466 1.55
Technicians 18,820 1.24
Engineers 7,502 .49
Other 237,633 15.65

Total 1,518,405 100.00

 

Source: Servico Nacional de Aprendizagem Industrial
(SENAI), Departamento Regional de Sao Paulo,
"Levantamento Industrial 1972." Unpublished
Internal Document.

11

Over 17.5 percent were classified as skilled industrial
workers. According to the SENAI definition:
A skilled industrial worker is a worker capable
of performing all the operations required in a
skilled industrial occupation. His work is varied
and not subject to automation. A relatively long
training period (3,000-4,000 hours) is required to
develop the necessary skills. Two types of train-
ing are possible (1) for a 'new' worker appren-
ticeship training, or (2) for a semi-skilled
worker rapid training courses. Knowledge of the
technical aspects of the occupation is required.6

Time series data on the Specific number of skilled
industrial workers in the State of Sao Paulo are not avail-
able. A good proxy, however, is available. SENAI has
kept records on the number of "qualified" (qualificados)
industrial workers in the state since 1946. The qualified
category contains, in addition to skilled industrial
workers, foremen, technicians, and engineers. In 1972,
20.9 percent of Sao Paulo's industrial workers were classi-
fied as qualified. Within the qualified category, 84.3
percent were skilled industrial workers. The develOpment
of the industrial labor force in Sao Paulo since 1946 is
illustrated in table 4.

Since 1946, the number of industrial employees in
the State of Sao Paulo has increased by over 274 percent.
The number of qualified industrial workers has increased
by at least 280 percent, and perhaps by as much as 350
percent.7 The rapid development of the industrial sector

in Sao Paulo has been "matched" by an impressive increase

in the quantity of skilled industrial labor. There is

12

TABLE 4.--Industrial Workers in the State of Sao Paulo,

 

 

1946-1972.
All Qualified A11 Qualified
Industrial Industrial Industrial Industrial
Workers Workers Workers Workers
Year (000) (000) Year (000) (000)
1946 552.5 109.8 1959 945.6 194.5
1947 597.2 118.0 1960 969.1 194.3
1948 610.1 121.1 1961 1,016.4 203.6
1949 619.2 131.4 1962 1,068.4 213.1
1950 672.9 196.7 1963 1,172.6 231.7
1951 735.9 212.6 1964 1,193.6 232.9
1952 769.2 218.7 1965 1,187.7 227.9
1953 802.6 229.1 1966 1,209.6 232.9
1954 840.8 239.2 1967 1,211.5 236.3
1955 856.5 209.0 1968 1,256.4 245.8
1955* 856.5 168.3 1969 1,331.8 266.9
1956 888.9 178.0 1970 1,391.6 280.9
1957 904.6 182.2 1971 1,449.4 293.9
1958 922.3 186.1 1972 1,518.4 310.7

 

*In 1955 the definition of qualified was changed and a new
series was initiated. Sufficient information was not avail-
able to make the pre-1955 and post-1955 series comparable.

Source: Servico Nacional de Aprendizagem Industrial (SENAI),
Departamento Regional de Sao Paulo, Relatério 1946--
Relatério 1972 (Sao Paulo: SENAI/SP, 194641972).

 

 

13

little doubt that the failure to develop skilled labor

at these high rates would have resulted in significantly
lower rates of industrial expansion. In the following
sections, some of the major sources of industrial training

are identified and briefly discussed.

1.2.3 The Formal School System

 

There are four basic levels in the Brazilian formal
school system:8

1. Primary School (primario)--4 years

 

2. Middle School (ginasio)—-3 or 4 years

 

3. High School (colégio)-—3 or 4 years

 

4. University (superior)--3 or 6 years

 

In 1970, approximately one-third of all Brazilian children
between the ages of 7 (the legal age for starting primary
school) and 14 were not enrolled in any formal school
program.9 For those who do begin the first year of primary
school, the prospects for completing the required four-year
program are not very good (see table 5). Less than 27
percent of those who begin primary school graduate. Of
those who do graduate from primary school, less than 36
percent begin a middle school program. This pattern is
repeated at all levels. Only 4 percent of those who start
in the formal school system graduate from middle school.
Less than .5 percent graduate from a university.

To help overcome this problem the government has

instituted special equivalency examinations at both the

14

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ammumocoz .aanmum Ca coaumosom nonmam mo muommm< oasoooom .Hmuqsm .z anon "condom

 

 

 

HOHHmmom
. o
0am on
. 0a a
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II: In: ooo.oa aoosom mumEaHm mcaccammm
nonmaofiooom doom ucoEaaoucm mmum
mmoa moaomomum
mmwuamoumm Eoum mmoa
ommucoouom

 

.aoonow hHmEaHm ammom 0:3 mucmooum ooo.oa mo moowaummxm amoaumnuomhmll.m mamdfi

15

middle and high school levels, giving adults who have
dropped out of the formal school system an opportunity to
further their educations. Both television and radio are
used to aid individuals with their self-study programs.

The United States Agency for International Development
(AID) reports that very little data on the number of indi-
viduals preparing for the exams or on the number who have
passed the exams are available.10 AID does report, however,
that in the State of Sao Paulo over 85,000 individuals
presented themselves for the exams in 1970. The certifi-
cate granted upon passing the exam is recognized as a legal
equivalent to the regular, formal school diploma.

In 1970, there were over 17.3 million students
enrolled in the Brazilian formal school system: 74.0
percent in primary school, 17.8 percent in middle school,
5.8 percent in high school, and 2.4 percent in universi-
ties.11

Almost 4 percent of all Brazilian secondary students
(both the middle and high school levels) were enrolled in
industrial vocational programs in 1971 (see table 6).

Over 43 percent of these industrial students were in the
State of Sao Paulo.

Publicly financed industrial training in the State
of Sao Paulo has a long history.12 Prior to the 1900's the
government generally felt that industrial training was only

appropriate for "I. . .] the poor [. . .] orphans, the

miserable [. . .] the abandoned, the blind, the deaf and

l6

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"Chanson

 

 

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oo.ooa mma.mmm.v oo.ooa amv.maa.a oo.ooa mom.m¢v.m amuoa
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mo. mmm.a mo. mmm mo. mma.a and "muocuo
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no a m0 w m0 m

 

humocooom amuoa

 

aooaom swam

 

aoonom maooaz

 

.amma .mEmHmoum amooapoosom mumocoomm cmaaaumum ca ooaaoucm

mucmesumuu.o mamas

17

dumb."l3

Though the View changed slowly over time, it was
not until the pressures of a growing industrial sector were
felt that the government actively entered the area of indus-
trial training to meet the needs of the industrial sector.
These pressures developed rapidly. In 1907 there were 314
industrial establishments in Sao Paulo, by 1912 there were
3,321. ($50 Paulo's share of the Brazilian total jumped
from 10.5 percent to 35 percent.)

The federal government was the first to respond to
the need by establishing an industrial training school in
1910. The state established two schools in 1911 and two
more in 1913. Until 1919 academic classes were not taught
in the state schools. When they were introduced, "the
reaction against the introduction of general cultural
courses into the teaching of industrial skills was very
strong."14 The pressures of industrialization slowly
overcame the traditional views and by 1931 there were nine
state-sponsored industrial training schools. In 1936 the
number was 28, and by 1940 the number had grown to 42.

[. . .] industrial education in Sao Paulo progressed!
It passed the phase of indifference and almost
hostility on the part of the people. In 1940,

all realized its advantages and applauded enthu-
siastically whatever new attempt to increase its
realization [. . .] in 1911, 2 schools with 435
students I. . .] in 1940, 42 schools with 11,503
students.15

In 1942, there were two important laws passed which
changed the character of Brazilian industrial education.

The first was the Organic Law of Industrial Education (Lei

Organica do Ensino Industrial) which legally recognized

18

industrial education as part of the formal school system.
The second was the law which created the SENAI industrial
training system. As noted previously, industrial training
within the formal school system still exists today. The
peak of its importance, however, was in the early 1940's.
Though there are no reliable time series data available,
it is clear that the relative importance of the formal
school system in the preparation of skilled industrial

labor has declined.

1.2.4 The SENAI System

 

The SENAI industrial apprenticeship and training
system was established by an act of the federal government
(Decreto-Lei No. 4048) in January, 1942. It was created
in response to the demands of industry for more and better

trained qualified industrial workers. The objectives of

SENAI are : l6

1. To realize in schools installed and maintained
by SENAI or in cooperation with industry, a
system of industrial apprenticeship for youth
between the ages of 14 and 18.

2. To assist firms in the elaboration and execu-
tion of general proqrams of training for
workers at various skill levels, and to
assist in the realization of apprenticeship
programs within firms.

3. To give workers over 18 years of age the
Opportunity to complete their professional
development in short courses given either
in SENAI schools or in the firms where they
are employed.

4. To give study scholarships for the purpose
of upgrading to both SENAI personnel and to
individuals employed in industry.

5.

19

To cooperate in the development of technical
research of interest to industry.

SENAI's initial efforts were concentrated in the

development of apprenticeship courses. Over time the

range of SENAI training has increased to the present

variety of courses:

1.

Apprenticeship--These courses are for youth
ages 14 to 18 who are either presently
employed in industry or who are potential
employees. By law, each industrial firm

in Brazil must employ and enroll in SENAI
schools the number of apprentices equiva-
lent to 5 to 15 percent of its total skilled
workers. (In practice this does not occur--
the number enrolled in SENAI schools depends
on SENAI's capacity.) A complete four-year
primary education and entrance tests (general
education and aptitude) are required to enter
the program. The courses take between 1,600
and 3,000 hours and have a duration of between
18 and 36 months. Training is offered in
over 40 occupational areas.

 

Industrial Preparation—-Intended for those
over 16 years old who do not have the quali-
fications for a skilled job. The courses
are divided in three blocks of approximately
180 hours each. Most courses are offered

at night.

 

Upgrading--Intended for individuals who are
already working at the skilled level but who
want to improve and update their technical
skills and theoretical knowledge. The dura-
tion of the courses varies.

 

Specialization-~Intended for individuals who
are already working at the skilled level but
who wish to specialize in a specific area
within their field. The duration of the
courses varies.

 

Technical--Intended for individuals with a
complete middle school education who want to
work at levels between the skilled workers
and the engineer. Course generally takes
over 1,200 hours.

 

20

6. Technical Assistant--Intended for individuals
who wish to assist technicians. The course
duration is approximately 300 hours.

 

7. gthggf-Many other types of training courses
are also provided in response to industries'
needs. Content and duration depend on the
need.

SENAI is a national organization, subdivided into

19 administrative regions. There is a National Council
controlled by the National Confederation of Industry
(Confederacao Nacional da Indfistria) and a regional
council, for each administrative region, which is con-
trolled by the corresponding State Federation of Industries
(Federacao das Indfistrias). Representatives of the Ministry
of Education (Ministério da Educacao) and the Ministry of
Labor (Ministério do Trabalho) are part of the National
Council. Every council has a department under its direc-
tion charged with implementing the policies adopted.

Each region has a great deal of autonomy in establishing
and directing its own programs.

The SENAI system is financed by a compulsory 1
percent tax on the payrolls of all Brazilian industrial
establishments. The tax is collected through the National
Social Security System (Instituto Nacional de Previdéncia
Social) and is channeled to the National Department and

dispensed as follows:

85 percent is returned to the state where it
was collected

5 percent goes to the National Department

4 percent is used to assist less developed regions

21
4 percent goes to the north and northeast regions
of the country

2 percent goes to the National Confederation of
Industry

In addition to the regular 1 percent tax, firms having more
than 500 employees are taxed an additional .2 percent
which is collected by each regional department of SENAI

and transferred directly to the National Department. Over
US$34 million was collected by SENAI in 1971.

Because the State of Sao Paulo has the highest
concentration of industry, the Regional Department of
SENAI in Sao Paulo (Departamento Regional de Sao Paulo)
is the largest in the country. In 1972, SENAI owned and
operated 135 industrial training centers throughout Brazil.18
Of these, 47 (34.8 percent) were in the State of Sao Paulo.
Also in 1972, SENAI supervised 100 training centers which
were owned and operated by large industrial firms. Of
these centers, 20 (20 percent) were in the State of Sao
Paulo. Sao Paulo's share of individuals enrolled in the
various SENAI programs is even greater. The total for all
Brazil in 1972 was 237,126; Sao Paulo had enrolled 114,484
(48.3 percent).

SENAI of Sao Paulo began operation in 1943,
graduating 554 adults from its rapid training and upgrading
courses (see table 7). In 1972, 57,372 adults were
graduated. The first class of apprenticeship trainees was
graduated in 1943 and contained 176 individuals. The 1972

apprenticeship class contained 6,089 individuals. In its

22

 

 

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maoonom moan
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23

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wom.mo¢ vhm.mm Nmo.amm mme.mmm mam.mma mom.mm mmm.mm vmm.mm amuoe
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maoonom moan
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.GOSGHHGOUII.h mamdfi

24

early years, SENAI owned no schools and "borrowed" the
facilities of the public school system. Over the past 30

years the growth of the SENAI system has been impressive.
1.2.5 Other Sources of Industrial Training

Private industrial training schools have existed
for many decades in Sao Paulo. No study has been pub-
lished on the origin and history of these schools, and data
on their outputs are not collected by any central agency.
All private schools which offer training (industrial or
other) are required to register with the State Department
of Technical Education (Departamento do Ensino Professional)
in Sao Paulo. However, schools are not classified in the
department's records by the type of training they provide,
nor are data collected on the number of students enrolled
or graduated. All that is known is that a large system
of tuition-charging, profit-oriented private industrial
training exists and that it has made some contribution to
the development of skilled industrial manpower.

Other sources of industrial training are (1) the
industrial firms themselves, (2) religious organizations,
(3) trade organizations, and (4) correspondence courses.
There are no data available on any of these, and it is
quite possible that other "unknown" sources of training

also exist.

25

1.3 The Importance of the Study

As Myint has noted in The Economics of Developing
Countries:

It is increasingly recognized that many under-

developed countries are held back by [. . .1

a shortage of skills and knowledge [. . .]

attention has shifted from capital to education

[. . .] to investment in human capital.
The importance of education, and particularly industrial
education, has long been appreciated in the State of Sao
Paulo. The high rates of industrial expansion which have
been noted would not have been possible without large
investments in the development of skilled industrial labor.
For the past 30 years, at least 1 percent of all industrial
wages and salaries paid in Sao Paulo have been channeled
to SENAI for the purpose of developing skilled industrial
workers. The magnitude of other similar investments is
not known.

By all measures, 850 Paulo's industrial expansion
is impressive. The State may be viewed as an example of
highly successful rapid industrial development. Much can
be learned from the study of how this was accomplished,
in particular by the study of how the necessary skilled
industrial manpower was develOped. New educational laws
presently being implemented in Sao Paulo, were developed
'without complete knowledge of either the past or the
present system of industrial skill development. Other

parts of Brazil, as well as other countries, are presently

at the stage of industrial development that Sao Paulo was

26

in 30 or 40 years ago. Many of the problems that will

be encountered as these areas industrialize will probably
be the same problems that were solved in Sao Paulo many
years ago.

The major problem is that it is not known how
skilled manpower problems were solved in Sao Paulo. The
SENAI program is the only part of the industrial training
system that is well known. The contribution of SENAI,
or any other source of industrial training, cannot be
evaluated without knowledge of the total skill development
system. The objective of this study is to provide this
type of information.

The methodology employed is that of a "reverse
tracer study." A sample of individuals presently employed
as skilled industrial workers is selected, and information
is gathered which will allow the reconstruction of their
work and learning histories. The type and organization
of this information is discussed in Chapter II, which

deals with the general model used in the study.

FOOTNOTES --CHAP TER I

lBrazil, Governo do Estado de Sao Paulo, Secretaria
de Economia e Planejamento, Departamento de Estatistica,
Secao de Estatisticas da Producao Industrial do Estado de
Sao Paulo. These figures were calculated from Industrial
Census data and released by the above mentioned department.

2Ibid. This figure was calculated from unpublished
data and reIeased by the above mentioned department.

3Brazil, Ministério do Planejamento e Coordena ao
Geral, Fundacao IBGE, Instituto Brasileiro;de Estatfst1ca,
Departamento de Censos, Censo demografico Brasil, VII recen-

seamento eral, 1970, V._I (Rio de Janeiro: Fundacio IBGE,
1970), p. 2, TaEle I.

41616., p. 81, Table 21.

5Brazil, Ministério do Trabalho e Previdéncia Social,
Departamento Nacional de Mao de Obra, Composigéo e distri-
buicao de mac de obra, Sao Paulo (Rio de Janeiro: Departa-
mento Nacionalee Mao de Obra, 1970), p. 13.

6Italo Bologna, A mao de obra industrial (Sao Paulo:
Centro de Estudos Roberto Mange, 1967), p. 2. (Mimeographed)

7This discrepancy is caused by the change in the
definition "qualified" noted in Table 4. The lower estimate
is calculated directly from the published date. The upper
figure is obtained by applying the 1955 adjustment ratio to
the published 1946 data. Both figures should be regarded a
rough estimate.

 

 

8Adapted from: Robert J. Havighurst, and Aparecida
J. Gouveia, Brazilian Secondary Education and Socio-Economic
Development (New York: Praeger Publishers, 1969), p. 20.
There is some variation in the structure of the formal school
system in different areas of Brazil. In some rural areas,
only three years of primary school are offered, while in some
urban areas, pre-primary as well as one or two years of sup-
plemental primary school is offered.

9

 

USAID/BRAZIL, Human Resources Office, Brazil

27

28

Education Sector Analysis (Rio de Janeiro: Human Resources
Off1ce, November, 1972), p. 25. (Mimeographed)

10

 

Ibid., p. 42.

11Brazil, Ministério do Planejamento e Coordenacao
Geral, Fundacao IBGE, Instituto Brasileiro de Estat1stica,
Anuério Jestatistico do Brasil 1972 (Rio de Janeiro: Funda-
cao IBGE,1972).

12The following discussion relies heavily on: Celso
Suckow da Fonseca, Histéria do ensino industrial no Brasil,
vols. I & 11 (Rio de Janeiro: Escola TéCnica Nacional, 1961).

13

 

Ibid., p. 313.

14Ibid., p. 326.

lsIbid., p. 356.

16Italo Bologna, SENAI: origens, evolugao or rgani-
za 50 (Sao Paulo: Centro de Estudos Roberto Mange, 1972),
p. l . (Mimeographed)

17Joao Batista Salles da Silva, and Paulo Ernesto
Tolle, SENAI: An Instrument of Brazilian Industries for
Manpower Training Through Formal and Non-Formal Education
(556 Paulo: SENAI/SP, 197?). pp. 11-14)

8Servico Nacional de Aprendizagem Industrial
(SENAI), Departamento Nacional, Relatério 1972, ed. pro-
viséria (Rio de Janeiro: SENAI/DN, 1972), p. 24.

19H1a Myint, The Economics of Developing Countries
(New York: Praeger Publishers, 1964), p. 173.

CHAPTER II

THE MODEL

2.1 Introduction

 

This study will examine three basic questions
concerning the development of skilled industrial labor in
Brazil. First, what is the origin of present skilled
industrial laborers? The study will attempt to determine
if in fact they were drawn randomly from the Brazilian
population, or if their geographic, socio-economic, and
other characteristics of origin make them significantly
different from the remainder of the population. Second,
how did these individuals learn and develop their mental
and manual skills? An examination will be made of the
institutional arrangements, content, duration, and sequence
of their learning experiences. Third, and perhaps most
important, do variations in an individual's origin and
method of learning influence the nature, quality, and

market value of his work?

2.2 The Conceptual Frame

There are two basic problems in answering the

above questions. First, the questions are all interrelated,

29

30

and second, there is almost no initial information except
that skilled industrial workers do exist. A first step
toward the solution of these problems is the development
of a simple, conceptual frame to identify factors which
may be important in the development of skilled industrial
labor. The frame will also facilitate the organization
of relevant information to describe meaningfully what
has happened in the Brazilian case. Further, it will
allow for the analysis of different types of learning to
determine if some have had more favorable results than
others.

The conceptual frame has three elements: birth,
learning that has taken place over time, and the present

situation (see figure 1).

 

Time —->

 

[A] [B]

.e 1,

 

Birth Present
Situation

\ __________________________________________ /

Learning‘——-+-

 

Figure l.--The Conceptual Frame.

31

At this level of abstraction it is suggested that
the conditions of birth (time, place, socio-economic status,
educational level of parents, etc.) may have had some
influence not only on the present situation of the indi-
vidual (occupation, socio-economic status, etc.) but
also on the types of learning experiences (formal education,
training, work, etc.) to which the individual has been
exposed. It is further suggested that the combination of
both factors, the conditions at birth and the learning
experiences, may have resulted in differences in the present
situation of the individual.

This study is specifically concerned with the
development of skilled industrial labor. Beginning with
the existence of these skilled workers, the simple concep—
tual frame can be expanded. First, skilled industrial
workers can be identified by the general type of work they
perform. Standard occupational titles and definitions can
be used to distinguish different occupations. The standard
definitions, however, only set the limits which define an
occupation but, within a given occupation, there can be
variation in the specific type of work performed. That
is, different individuals working in a given occupation
may perform related but not identical operations. They
might produce at different rates and the quality of work
may vary. Accordingly, there may be variation in the wage
they receive. The model, thus, allows for the hetero-

geneity of labor within specific occupations.

32

Second, the model assumes there are three broad
types of learning experiences relevant to the development
of skilled industrial labor. The first is formal educa-
tion, the learning which takes place in the graded, age-
specific, formal school system. The ability to read, the
knowledge of basic science, and the development of elemen-
tary mathematical tools can be important for specific
skilled occupations. These types of things generally are
learned in the formal school system.

Work experience is the second broad type of learn-
ing. It is the learning which takes place as a normal
by-product of working in a specific occupation, at some
specified level, during a certain period of time. An
unskilled "helper" for a skilled worker probably learns
something about the skilled worker's trade. Over time as
he gains experience his responsibilities might increase
until eventually he reaches the skilled level.

The third type of learning is training experience.
Training is job-oriented learning which takes place under
various forms of sponsorship, for various periods of time,
but is directed toward a specific occupation. Training,
unlike work experience, is planned and has a defined
structure, whereas learning through work experience is
only a by-product of the normal production process. Regard-
less of the lack of structure and planning, work experience
is recognized as a valid form of skill development. The

regular upgrading and promotion of individuals in firms,

33

which is partially based on years of experience, is an
example.
The discussion to this point can best be summarized

and placed in context with the aid of figure 2.

 

 

Time ———>

 

 

 

 

 

 

 

 

 

 

 

 

Present
Birth Situation
[A] [B]
._L .
---------1;—--------Learning ------------ '-: --------
1’ \\
I
1 \ Present
Initial —-|L-—> a—r» Work
Conditions : Formal Work (Training Situat1on
I

 

 

 

 

 

 

I

I
Education Experience Experience : 1

J

 

 

 

 

Work Situation
Control

 

 

Figure 2.--The General Model.

As in figure 1, there are the three basic elements
of birth, learning over time, and the present situation.
The conditions at birth are represented by the block

labeled Initial Conditions. Learning has been divided

 

into the three suggested classifications which are repre-
sented by the blocks Formal Education, Work Experience,
and Training Experience. The position of these three
learning blocks is not intended to suggest ordering in

time or that the types of learning take place during

34

separate periods. The present situation is restricted, in
this case, to the present work situation, which in turn is

divided into two separate components. The Present Work

 

Situation block represents the operations performed on the

 

job and the remuneration received. The second block,

labeled Present Work Situation Control, represents those

 

factors which may influence what is done on the job and
the remuneration, but which are not directly related to
initial conditions or learning. Such factors are the size
and final product of the factory in which the individual
works, the type of machine used, and the length of time
the individual has been working in the factory.

The specific elements of each block of the general
model are discussed in more detail in the following

sections.

2.3 Elements in the General Model

The conceptual frame or general model developed
is a useful tool for identifying and relating the princi-
pal areas of interest in this study. It provides a simple
means for viewing the important aspects of a complex situa-
tion. Most important, it is a basis for the development
of an operational model to answer the questions posed in
this study.

The model will serve three basic purposes. First,
it will be a basis to describe what has actually happened

in the Brazilian case. Second, it will allow the various

35

parts of the study to be placed in perspective with each
other, and with the whole. Third, it will serve as a
frame to analyze the effects of what has happened.

The model may be broken into six major parts,
corresponding directly to the six major informational
blocks in the general conceptual frame. The six major
parts of the model are:

1. Present Work Situation (PWS)

2. Present Work Situation Control (PWSC)

3. Initial Conditions (IC)

4. Formal Education (FED)

5. Work Experience (WEX)

6. Training Experience (TEX)

In the following sections the specific elements of each of

the six parts will be discussed.
2.3.1 Present Work Situation (PWS)

The initial information is that skilled industrial
workers exist. They can be identified by occupation and
located in the various factories in which they are employed.
Within limits determined by the occupation, differences
among individual workers are expected. Specifically, some
individuals may do more varied types of work, some may do
more difficult work, and these differences may be reflected
in labor market prices.

'Two types of Operational information are required--

what is actually done on the job, and the corresponding pay

36

rates. In the first case, previous research by SENAI can
be used. Some Of the most interesting and useful products
Of this research are the analytical tables (quadros
analiticos) Of tasks and operations that are generally
associated with specific skilled industrial occupations.l
In the analytical table a task is defined as the turning-
Out Of some specific intermediate product (e.g., a gear)
in the firms'productive process. The completion of a task
requires one or more distinct, physical operations. As
the complexity Of the task increases, the number and
complexity Of the required operations also increase. The
Specific operations associated with an occupation are
finite in number and arranged in order Of increasing
difficulty. Using the operations table as a basis, two
types Of information can be developed. First, the number
of specific operations performed on the job can be estab-
lished, and second, an index reflecting the degree of
difficulty can be constructed. Information is also needed
on how the market values the work performed. It is assumed
that, other things equal, differences in the quality Of
work are reflected in differences in pay rates. Gross
monthly earnings adjusted for hours of work will be used
as the measure Of market valuation.

In summary, the Present Work Situation (PWS) block
contains three specific elements:

1. The number Of operations performed

2. An index of the degree Of difficulty Of

Operations performed

37

3. A measure Of the market evaluation Of work

performed

2.3.2 Present Work Situation Control (PWSC)

 

It is possible that factors not related to initial
conditions or learning may influence the present work
situation. The final product produced by the firm can
influence the types Of Operations performed on the job.
SENAI has developed and uses an industrial classification
system which is based on the final output.2 All industrial
establishments are classified with a four-digit code, and
using this code it is possible to control for variations
which are due to differences in the final product.

Somewhat related are differences in factory size.
Individuals working in small job shops which concentrate
on specialized, small-scale contract work might be expected
to perform different Operations than individuals working
in large establishments employing mass production techniques.
Even within the larger factories, variations might be
expected between individuals working in production and
others working in maintenance. Differences in the specific
type of machine used might also be expected to lead to
differences in Operations performed.

Given that these factors might be expected to
influence the number and types Of Operations performed,
four specific elements are placed in the Present Work

Situation Control (PWSC) block:

38

1. Factory industrial group

2. Factory size

3. Sector Of work in the factory

4. Type of machine used

In addition to the number and types Of Operations
performed, wage rates may be influenced by the occupa-
tional level at which an individual was first employed in
the factory and by the number Of years he has been employed.
For example, an individual who entered a factory at the
apprentice level and worked his way up to the skilled level
without ever entering the outside labor market might,
because Of his lack of labor market knowledge, Offer his
services to the factory at a relatively low rate. Other
things equal, however, a positive correlation between the
wage rate and the number of years of service might be
expected. Accordingly, two more elements are added to
the block:

5. Factory entry level

6. Number Of years working in the factory

2.3.3 Initial Conditions (IC)

 

It is reasonable to assume that the situation in
which an individual was born (when, where, and to whom)
would influence not only what is perceived as an acceptable
occupational goal, but also the practical possibility Of
achieving a specific goal. More basically, possible

occupations are limited to the occupations actually known.

39

It is conceivable that the son Of an illiterate farm
worker from northern Brazil would have no idea that skilled
industrial occupations even exist. His Opportunities are
limited to what he or his parents know. Within the set
defined by his knowledge he may perceive the occupation Of
farm foreman as an acceptable goal. However, if the occu-
pation of foreman requires some formal education, and if
the costs associated are judged to be too high, the occu-
pation is not part Of the set of practical possibilities.
The individual is limited by both his knowledge and his
resources.

From a different point of view, consider the
hypothetical position Of the son of a doctor living in the
Greater Sao Paulo area. This individual would almost cer-
tainly have the occupation of farm foreman in the set Of
occupations defined by his knowledge. It would also be in
the set limited by his real resources. However, it probably
would not be within the set Of acceptable occupational
goals. The individual is limited by three factors, what
is known, what is possible, and what is acceptable.

Even when an occupation falls within the sets of
what is known, what is possible, and what is acceptable,
additional constraints may influence the particular path
taken toward the realization Of the occupational goal.

For example, there are alternative ways to develop the
skills associated with a qualified industrial occupation.

An individual with few resources may learn through work

40

experience only, while an individual with greater resources
might pay for a special course.

It is therefore possible that the initial conditions
Of an individual may influence the occupation chosen and
the path taken toward that occupation. Consequently, the
following elements are entered in the Initial Conditions
(IC) block:

1. Age

2. Place of birth

3. Place of primary education

4. Educational level Of father

5. Educational level of mother

6. Occupational area Of father

7. Occupational status Of father

2.3.4 Formal Education (FED)

Generally, an individual learns to read and write,
acquires the knowledge Of basic science, and learns the
basic use Of mathematical tools in the formal school
system. As the individual moves through the higher levels
Of the system the Objectives and the specific content Of
learning changes. The lower levels may be viewed as
foundation building for the higher levels. Entry into
skilled industrial occupations may require some critical
minimum Of formal education. On the other hand, there may
be a point beyond which more formal education is not

required for the satisfactory performance Of the tasks Of

41

a skilled industrial worker. There also may be a strong
relationship between the level of formal education and
what is viewed as an acceptable occupation. In Brazil
probably few high school graduates work in blue collar
positions. On the other hand, more formal education might
facilitate specific skill development. In sum, formal
education may be important in the development of skilled
industrial labor, so the following elements are entered in
the Formal Education (FED) block:

1. Age started school

2. Highest grade completed

3. Program of highest grade completed

4. Age when highest grade completed

2.3.5 Work Experience (WEX)

Through informal demonstration, imitation, and
experimentation an individual may learn by simply working
in any occupational area. The importance or relevance of
what is learned depends on the final occupational Objective.
Other things equal, two years Of work experience as a farm
laborer are less relevant to the occupation of tool and
die maker than are two years Of work experience as a
machinist's helper.

The individuals in this study are presently working
as skilled industrial laborers; however, they probably have
not always worked in these positions. Each individual has

a complete work history Of specific sequences and types Of

42

work experience. Variations in individual sequences and
types of work experiences and the associated variations

in learning might be expected to have influenced the present
work situation. The specific manner in which work experi—
ence enters the general frame can best be explained with

the aid of figure 3, as shown below.

 

 

 

Time *>
First job First job
First in area Of as skilled
job present in present Present
ever occupation occupation job
[A] [B] [C] [D]
0—: 44¢: ,, o

 

I work ' work ' work I
_ . fl W . w *- .
experience experience experience

Figure 3.—-Work History.

Time is plotted on the horizontal axis, with four
points of interest. Point [A] represents the first job
Of any type that the individual held, and is the beginning
of his work history. Point [B] represents the first job
the individual had in the area Of his present occupation.
For example, if the individual is presently a skilled
machinist, his first job in the area might have been as a
machinist's helper. The point when the individual was
first hired or classified as a fully skilled worker in his

present occupation is point [C]. Point [D] represents the

43

present work situation. In some cases two or more points
may coincide; however, in the general case the four points
are distinctly separated by time.

The next step is to define explicitly the meaning
Of each point, and to establish the relevance of what took
place between each of these points in time. Assume that
the set Of all possible occupations is defined by the
largest circle in figure 4. Further,assume that the
innermost circle represents all different skill levels in
a specific, given, industrial occupation. The point in
the center Of the diagram represents the skilled level in
that given occupation. In this study the area defined by
the innermost circle is referred to as "the area of the
given occupation." The point in the center is referred to
as "the skilled level in the given occupation." The
second ring represents all occupations related to the given
occupation. All other industrial occupations are repre-
sented by the third ring. The largest ring is divided into
two parts. One represents all agricultural occupations and
the other represents all other occupations not previously
defined. What has been developed is a scheme for classi-
fying occupations with respect to their relevance to the
given industrial occupation. Other things equal, the work
experience associated with an occupation is less relevant

the farther it is from the center.

44

 

All possible occupations

 

 

0’ All industrial occupations

Q" a, """‘\

O T x .
.5'9i5’ ,-1\' \ All related occupat1ons
5 3:1 ’ ”~\ I A e f ' t'
‘9833 ' I ) ' . L4___.———— r a O g1ven occupa 1ons
< 0:! \\ x- ' I "

:Z:::3 \\ ’/
“ritta I . .

”LE: ~_-v’ Skllled level in

'77‘ (”HER given occupation

.. OCCUPATIONS

Figure 4.--Occupational Relevance.

Using this scheme, a scale of work experience
relevance may be constructed as follows (smaller numbers
reflect a higher degree of relevance):

1. In the area of the given occupation

2. Related to the given occupation

3. Some other industrial occupation

4. Agricultural occupation

5. Other occupation

With this scale it is possible to classify the
individual's first job, his work history starting point,

with respect to the present occupation. Further, given
the time sequence Of other jobs held between points [A]
and [B] as defined in figure 3, it is possible to know how
close the individual came to the given occupational area,
POint [B], before actually entering it. Other things

eclual, one would expect that the closer the approach and

45

the longer the duration, the more relevant and beneficial
the work experience to what happens after point [B]. The
skill level of the first job in the area Of the given
occupation, point [B], may be classified as either learn-
ing (apprentice or helper), semi-skilled, or skilled.
Work experience after point [B], the initial entry into
the area Of the given occupation, may again be classified
as to its relevance to the given occupation. If the
individual leaves the area Of the given occupation, the
degree, as well as the duration Of the departure can be
measured. The measurement technique applies equally to
both time spans after point [B], (i.e., the time span
between [B] and [C], the first job as skilled in the given
occupation; and the time span between [C] and [D], the
present work situation).

The index of occupational relevance and knowledge
Of the sequence and duration Of an individual's previous
occupations allows his entire work history be given specific
meaning and dimension. With this information variations
in work history can be discovered and tested for their
effect on the present work situation.

The specific elements in the Work Experience (WEX)
block are:

1. Age at the time of (a) first job ever,

(b) first job in the area Of the given
occupation, and (c) first job as skilled

in the given occupation.

46

2. Position on relevance scale for first
job ever

3. Level Of entry into the area of the given
occupation

4. Relevance and duration of work experience
between (a) first job ever and first job
in the area of the given occupation, (b)
first job in the area of the given occupa-
tion and first job as skilled, and (c)
first job as skilled and the present work

situation
2 . 3 . 6 Training ExperienceJTEX)

Training experience has been previously defined as
job-oriented learning which takes place under various forms
of sponsorship, for varying periods Of time, but is oriented
toward a specific occupation. It differs from work
experience (learning on the job) because it generally has
some type Of structure. In the Brazilian context, training
experience is represented by the many and varied types of
courses available from a wide range Of sources. The SENAI
apprenticeship programs are an excellent example of the
types of long-term training experiences available. SENAI,
as well as various other public and private entities, also
Offers a wide range of shorter courses. A wide range of
courses is available from a great variety Of sources, but

no central clearing house for information on these courses

47

exists. SENAI keeps its own records, and although private
schools generally are registered with the state government,
no records are kept on the number Of individuals who have
taken private school courses. Courses given by the fac-
tories are recorded only by the factories themselves.

Even in the case Of the courses given through the public
school system, no time series data are available. In
short, a training system exists, but there is no way to
estimate its total magnitude or to place its components in
perspective.

Training courses may be extremely important to the
development of skilled industrial labor, and the following
elements are entered in the Training Experience (TEX)
block:

1. Number Of courses taken

2. For each course:

a. Sponsor

b. Occupational area

c. Content (theory/practice)3

d. Total hours planned

e. Duration in months

f. Percentage of courses completed
9. Year course started

h. Geographic location

48

2.4 Summary Of the Model

 

The model to be used in this study has six major

blocks Of variables, which correspond directly to the six

major informational areas suggested by the general con-

ceptual frame.

The variable blocks and their specific

variables are listed below:

I.

II.

III.

IV.

Present Work Situation (PWS)

l.
2.
3.

Gross hourly earnings

Number of Operations performed

Index Of the complexity Of Operations
performed

Present Work Situation Control (PWSC)

O‘UlwaH

Size Of factory

Industrial group Of factory
Sector Of factory

Type of machine used
Factory entry level

Years working in factory

Initial Conditions (IC)

O‘U‘IBUJNI-J
O O O O O

Age

Place of birth

Place Of primary education
Educational level Of parents
Occupational area Of father
Occupational status of father

Formal Education (FED)

l.

2.
3.

Age

a. started school

b. finished school

Highest grade completed

Program Of highest grade completed

Work Experience (WEX)

1.

Age

a. First job

b. First job in present occupational area

c. First job as skilled in present
occupational area

Occupational area of first job

49

. Entry level in area Of present occupation

4. Work experience

a. After first job, but before entry into
present occupational area

b. After entry into present occupational
area, but before reaching the skilled
level

c. After reaching the skilled level

VI. Training Experience (TEX)

1. Number of courses taken
2. For each course taken
a. Occupational area
b. Sponsor
c. Total hours completed
d. Duration Of course in months
e. Age course began

The specific variables used in each block, and their

associated codes, will vary with the specific type of
analysis being conducted. The analysis will be done at
two levels. First, the model will be used to organize and
describe relevant information. Second, the model will
serve as a basis for the development of several regression
models. The most comprehensive regression model will have
the general forms (PWS) = f (PWSC, IC, FED, WEX, TEX).
Regression analysis will be used to determine (1)

which blocks of variables and (2) which specific variables
are significant in explaining variations in the present

work situation.

FOOTNOTES-~CHAPTER II

1Servico Nacional de Aprendizagem Industrial
(SENAI), Departamento Nacional, Manual do docente de tor-
nearia (Rio de Janeiro: SENAI/DN, 1972).

 

2Servico Nacional de Aprendizagem Industrial
(SENAI), Departamento Regional de Sao Paulo, INPS-codifi-
cacao de atividades (Sao Paulo: SENAI/SP, 1972). (Mimeo-
graphed)

31n Brazil industrial training courses generally
have two parts: one, theory (aula) which is the classroom
study of design, shop theory, etc. and two, practice (Ofi-
cina) which is supervised shoproom work in which machines
are used.

 

50

CHAPTER III

THE COLLECTION OF THE DATA

3.1 Restrictions On the Study

Because Of time and other resource constraints it
was necessary to restrict the study in three ways. First,
it was decided that one important industrial occupation
would be studied, metal lathe setter-Operators (torneiros
mecanicos). Second, the study was limited to the three
highly industrialized counties (municfpios) of Santo Andre,
Sao Bernardo do Campo, and 850 Caetano do Sul. These
three counties are more commonly known as the "ABC” area
Of Greater Sao Paulo. Finally, the study was restricted
to the important industrial group of machinery production
(mecénica). Each Of these restrictions will now be dis-

cussed in more detail.
3.1.1 Metal Lathe Setterjgperator

The machining Of metal in the modern industrial
production process is immensely important. Most modern
industrial products have machined metal components, or

:machined metal tools are used in some stage Of their

51

52

production. The cutting, shaping, drilling, grinding,
and milling of metals is essential to the manufacture and
repair of industrial equipment and machines.

"The lathe is probably the Oldest of the developed
machine tools."1 The distinguishing feature Of the modern
power metal lathe is that "it normally functions by rotating
the workpiece against the cutting edge of the tool held
stationary in a holder."2 The piece Of metal to be worked
is placed between the two centers of the lathe and is
rotated at a chosen speed. TO cut and remove portions, the
cutting tool is moved into position against the metal. The
rotation Of the metal against the tool shaves off parts
of the metal. Typical products are tapered pins, bolts,
screws, pulleys, shafts, disks, etc.

TO set up and Operate the lathe requires basic
knowledge of mechanical design, shop mathematics (geometry
and trigonometry), knowledge of the technical properties
of various metals, as well as knowledge of the techniques
for performing various Operations.

The occupation of lathe setter-operator is coded

8-33.20 in the 1968 edition of the International Standard

 

Classification Of Occupations. The occupational descrip-
tion is:

Sets up and Operates a power-driven metal-
working lathe:

examines drawings and specifications of part to
be made; fastens metal and tools in position on
lathe using chucks, jigs and other fixtures as
required; adjusts guides and stops; sets rota-
tion speed Of metal and starts machine; manipu-
1ates hand wheels, or sets and starts automatic

53

controls to guide cutting tool into or along
metal; controls flow Of lubricant on edges of
tools; checks progress Of cutting with measur-
ing instruments and makes necessary adjustments
to machine setting.

May specialise in a particular type of
lathe and be designated accordingly.

According to the 1970 Demographic Census of the
State Of Sao Paulo, 19 Of every 1,000 workers in the indus-
trial sector are employed in the area of lathe Operation.
SENAI estimates that 30 percent Of the qualified workers
in the machinery subgroup are lathe setter-Operators.
Since 1956, SENAI Of Sao Paulo has trained 9,225 lathe
setter-Operators through its apprenticeship program.5 This
is 18.5 percent of all industrial apprentices trained by
SENAI since 1956. Since 1958 the SENAI school located in
Santo André has trained nearly 37 percent Of its appren-
tices in the occupation Of lathe setter—Operator. In
1972, SENAI Of Sao Paulo had 19,780 individuals younger
than 18 enrolled in its apprenticeship program, of which
3,437 (17.3 percent) were enrolled in the lathe setter-

Operator program.

3.1.2 The "ABC" Area

 

The three counties Of Santo André, Sao Bernardo do
Campo, and $50 Caetano do Sul were selected primarily
because they are extremely important in Brazilian indus-
trial production. The ABC area is slightly more than 600
square kilometers yet, in 1967, it accounted for approxi—

mately 10.8 percent of the total value of Brazilian

54

manufacturing production.6 According to 1970 estimates,
6.2 percent of all Brazilian industrial establishments
and 5.6 percent of all Brazilian industrial employees
were located in the ABC area.7 SENAI of Sao Paulo esti-
mates that in 1972 approximately 12.7 percent Of the
qualified industrial workers in the state were employed
in these three counties.8 Interestingly, only 30 years
ago the three counties consisted only Of small towns and
farms.

In addition to its high concentration Of industry
and skilled workers, the ABC area was selected because Of
its accessibility. It is located only 25 minutes by train
or one hour by car from the main SENAI Offices in downtown
Sao Paulo. Further, relatively recent industrial census
information on the ABC area was available. The industrial
census Of Santo André was conducted in 1971. Sac Bernardo
do Campo and SEO Caetano do Sul censuses were conducted
in 1968. The censuses will be discussed in more detail
in the section that deals with the selection Of the workers

to be interviewed.

3.1.3 The Machinery Industrial Sub-Group

 

Metal lathe setter-Operators can be found in all
types of industrial establishments. Wherever there are
machines to be repaired and maintained a lathe setter-
Operator may be employed. However, the highest concentra-

tion Of lathe setter-Operators is in industries which

55

specialize in the production and repair of machines and
machine parts. It is in this type Of establishment that
the lathe setter-operator works in both production and
maintenance capacities.

Since its beginning SENAI has been conducting
industrial censuses. A11 industrial establishments are
classified according to the type Of final product they
produce. There are 15 major industrial groups and each
is divided into subgroups. Most skilled lathe setter-
operators are found in SENAI Industrial Group 14, Metallurgy—
Machinery-Electrical Material, which is divided into 22
subgroups.9 Seven subgroups (16-22) are related to the
production of electrical materials and are not of present
interest. The remaining 15 subgroups are all related
either to the production or working Of metal, or the pro-
duction or repair of metal products or machines. The sub-
groups of interest are:

14-01 Factories which smelt, refine and laminate
iron and steel.

14-02 Factories which cast and mold metals.

14-03 Factories which produce metal products in
general.

14-04 Factories which cut and shape metals.

14-05 Factories which machine metal.

14-06 Factories which plate metal.

14-07 Factories which build and maintain machines.

14-08 Factories which produce cutlery, guns, and
fine metal work.

14-09 Factories which produce weights, balances,
and precision instruments.

56
14-10 Factories which produce products using their
rolled metal.
14-11 Factories which stamp metal.
14-12 Factories which produce metal furniture.
14-13 Factories which built and repair vehicles.

14-14 Factories which repair vehicles and build
vehicle parts and accessories.

14-15 Factories involved in naval construction.

3.2 The Sample

 

The Objective in selecting the group to be inter—
viewed was tO Obtain the best possible cross-section
representation Of the total population Of lathe setter-
operators in the ABC area. Two basic types of information
were used in the selection. First, the existing SENAI
industrial census data were used to identify firms in
which lathe setter-Operators are employed. This was com-
plicated by the fact the census of Santo André was con-
ducted in 1971 and those Of Sao Bernardo do Campo and $50
Caetano do Sul were conducted in 1968. It was initially
assumed, and later confirmed, that the industrial employ-
ment situation has changed considerably since the censuses.
The census data were, however, the best available source
Of information upon which to base estimates of the current
employment situation.

The second major source of information was the
knowledge and experience Of the field agents of SENAI's

Division Of Census and Control (Cadastro e Controle).

57

The division has been conducting industrial censuses since
1943 in the State of Sao Paulo. Its field agents provide
SENAI with factory level contact in the industrial sector.
Within a given census area, it is the field agents who
locate the factories, assign industrial group and subgroup
codes, and classify each individual in the factory by
sector of work, occupation, and occupational level. There
is probably no other group of individuals in Brazil which
knows and understands the industrial employment and occupa-
tional structure as well. Several of the agents have been
working with SENAI in this capacity since it was founded
in 1942. When needed, it was the field agents who pro-
vided invaluable assistance.

As previously noted, skilled lathe setter-operators
are found in all of the first 15 subgroups of SENAI Indus?
trial Group 14, Metallurgy-Machinery-Electrical Material.
However, they are primarily found in subgroups 03, OS, and
07 which are directly concerned with the production or
maintenance of metal machines or machine parts. Both pro-
duction and maintenance sector work is common. Subgroups
Ol, 11, and 13 also have relatively high concentrations of
lathe setter-operators. The remaining subgroups use lathe
setter-operators generally in a maintenance capacity. The
large automobile producing firms which are part of subgroup
13 do use large numbers of lathe setter-operators in both
production and maintenance capacities. These firms were.

however,excluded from the study as being atypical because

58

most have their own Special training centers, supervised
by SENAI, for the development of their own skilled
workers.

The specific firms from which the representative
group of lathe setter-operators was drawn were chosen
using the following criteria applied to the available
census data.

1. In Santo André--all firms in subgroups 03, OS,
and 07 having more than one lathe setter-
operator.

2. In 850 Bernardo do Campo and 850 Caetano do
Sul--all firms in subgroups 03, 05, and 07
having more than five lathe setter-operators.

3. In Santo André, Sao Bernardo do Campo, and $50
Caetano do Sul--all firms in subgroups 01, 11,
and 13 having more than five lathe setter—
operators, with at least three working in the
production sector.

These criteria were adopted for two reasons: first
it was not possible because of time and other resource con-
straints to conduct interviews in all of the over 3,500
firms in the ABC area; second, the 1968 census data does
not accurately reflect the present employment situation in
the $50 Bernardo and $50 Caetano areas. It was thus
necessary to rely on the knowledge and experience of the
field agents in selecting the firms from which the repre—

sentative group was drawn.

59

In subgroup 03, the size of the firms range from
one or two employees to over 2,000. There is no direct
relationship between the size of the firm and the number
of lathe setter-operators employed. Factories with less
than 1,000 employees which have lathe setter—operators
generally have from one to five. Those factories with
over 1,000 employees tend to have from 15 to 25. In sub-
groups 05 and 07 only one firm had more than 350 employees,
and the majority have less than 50 employees. It is not
unusual to find in firms with less than 50 employees more
than five lathe setter-Operators. In the total ABC area
there are 288 firms in the 03, 05, and 07 subgroups. A
total of 158 (54.9 percent) are located in Santo André,
and of these, 57 had no lathe setter-operators, and 35 had
only one. The remaining 41.7 percent had two or more. In
850 Bernardo and $50 Caetano 47 firms had no lathe setter-
operators and 24 had only one. The remaining 45.4 per-
cent had two or more.

The firms in subgroups 01, 11, and 13 (with the
exception of auto repair shops) generally employ over 350
workers. Lathe setter-Operators are usually found in the
maintenance sector of the factory. There are factories,
however, which do use them in the production line. It
was decided that factories having both maintenance and
production lathe setter-operators would yield a more

representative type of worker.

60

Using the selection criteria developed, 97 firms
were chosen from the census lists as target firms for the
study--4O in the 03 subgroup, 19 in the 05 subgroup, 23 in
the 07 subgroup, and 15 in the 01, 11, and 13 subgroups.

3.3 The Questionnaire

 

The questionnaire was developed with three specific
objectives in mind. First, it was to provide the type of
information required to meet the stated objectives of the
study. Second, as interviews were to be conducted during
regular working hours, the questionnaire had to be
designed to facilitate the rapid collection of the desired
information. Third, in order to reduce the coding time,
self-coding responses were utilized whenever possible.

Work on the questionnaire was initiated three
months prior to the starting date (April, 1973) of the
field work. The supervisor of the SENAI census takers was
involved in every stage of development. His aid was
invaluable. The questionnaire was tested and revised on
three separate occasions. A total of 27 individuals were
interviewed in the testing stage-~15 were skilled lathe
setter-operators who were interviewed in the firms where
they were employed, six were SENAI apprentices studying
to be lathe setter-operators and seven were SENAI office
personnel.

The tests indicated two things. First, the ques—

tionnaire was too long (average interview time was over

61

25 minutes); and second, the section dealing with the

individual's work history was not easily understood by

the person interviewed. Corrections in this section were

made and in the latter tests the average interview time

dropped to approximately 15 minutes, which was considered

acceptable.

In its final form (see Appendix 1) the questionnaire

was 10 pages and was divided into the following general

sections:

1.

Control Information--name of interviewer,
date and hour of interview, time required,
name and address of firm.

 

Ori in--age, place of birth, age arrived in
Sao Paulo, place of primary education,
educational level of parents, occupation of
father, and size of family.

Formal Education—~for each of the three
levels (primary, middle, high) of formal
school (a) age started, (b) age finished,
(c) program enrolled in, and (d) highest
grade completed; also, special rapid
middle and high school programs.

 

Training Courses Taken--For each course

(a) occupational area, (b) name and address
of school, (c) sponsor of course, (d)
duration, (e) total hours per week and
division between classroom and shop work,
(f) percentage of course completed, and

(g) the age or year the course was com-
pleted.

 

Work History-~There were no specific ques-
tions in this section, rather the inter-
viewee was guided by the interviewer in
the completion of a "work history table."
Four important points in the work history
were first established:

 

(1) First job ever (occupation, age, year)
(2) First job in area of lathe operation
(level, age, year)

62

(3) First job as skilled lathe setter-operator
(age, year)
(4) Present work situation (known)

After these points were identified and the
time span between each was established in
the mind of the interviewee, he was asked

to indicate the type and duration of all
other jobs he held between the already
identified major points. The procedure pro-
vided automatic feedback to the interviewer
and insured that the parts of the work his-
tory were consistent with the whole.

6. Present Work Situation--(a) length of time
in firm and entry level, (b) regular and
extra hours worked per week, (c) gross
monthly salary, (d) type of lathe used,
and (e) sector of work.

7. gperations Perfonmed on the Job—-A list of
41 different operations was presented and
the interviewee was instructed to indicate
which operations he used in his present job.
If the operation was not understood the
"don't know" column was marked.

 

Missing data (blank answers) were not permitted.
Where necessary, a "don't know" alternative response was

provided.

3.4 The Field Work

 

In the final week of March, 1973, three days were
devoted to the training of the seven interviewers. Five
were regular SENAI census fieldworkers and two were SENAI
office workers who usually proofed the census field
reports. All were familiar with occupational classifica—
tions and had little trouble grasping the purpose of the
study and understanding how the questionnaire was designed.

The primary interviewing was conducted during

April, 1973. Each interviewer was assigned to specific

63

firms and was given a letter of introduction, written by
the director of SENAI of Sao Paulo, which was to be
presented to the personnel director at each factory. The
letter explained the purpose of the study and that SENAI
was collaborating in its execution. It requested that
all lathe setter—operators presently employed in the firm
be interviewed. To conduct the interviews it was necessary
for the worker to be taken away from his normal duties.
The backing of SENAI and the persuasive powers of the
interviewers were extremely important in gaining entrance
to the factory. In some cases it was necessary for the
interviewer to return to a factory several days in a row
before permission to conduct the interviews was granted.

Of the 97 firms selected, nine refused to colla-
borate in the study. It is estimated that 47 lost inter~
views resulted. Five firms no longer existed, two had
moved outside the ABC area, and seven no longer employed
lathe setter-operators. A total of 74 firms allowed inter-
views to be conducted. Based on the SENAI census data,
it was expected that 738 lathe setter-operators would be
interviewed. The actual number was 546. Given that the
census data for $50 Bernardo do Campo and $50 Caetano
do Sul were over four years old, this divergence between
what was expected and what was realized was not surprising.

The editing process indicated that three question-
naires were not usable and the interviewees could not be

recontacted to make corrections. The three questionnaires

64

were discarded. Only three individuals interviewed did
not have any formal education. This caused severe coding
problems and these three questionnaires also were with—
drawn. A total of 540 questionnaires were usable and were

coded for computer analysis.

FOOTNOTES--CHAPTER III

1Robert S. Woodbury, History of the Lathe to 1850,
in Robert S. Woodbury, Studies in the History of Machine
Tools (Cambridge and London: The M.I.T. Press, 1972), p. 13.

 

 

2Harold V. Johnson, General Industrial Machine Shop
(Peoria, 111., Chas. A. Bennett Co., 1963). P. 168.

 

3International Standard Classification of Occupations
(Geneva: International Labour Office, 1969), p. 199.

4Brazil, Ministério do Planejamento e Coordena ao
Geral, Fundacao IBGE, Instituto Brasileiro de Estatistica,
Departamento de Censos, Censo demografico Sao Paulo VIII
recenseamento geral, 1970, V. I (Rio de Janeiro: Fundacao

IBGE, 1976).

5In 1970 there were approximately 38,000 individuals
working in the industrial sector in the area of lathe Oper-
ation. The number of skilled lathe setter operators was
surely much less.

 

6Brazil, Governo do Estado de Sao Paulo, Secretaria
de Economia e Planejamento, Departamento de Estatistica,
Secao de Estatisticas da Produ 50 Industrial do Estado de
Sao Paulo. This figure was ca culated from unpublished
data and released by the above mentioned institution.

7Brazil, Ministério do Trabalho e Previdéncia Social,
Departamento Nacional de Mao de Obra, Composigao e distri-
buigao de mao de obra (Rio de Janeiro: Departamento Nacional
dé Mao de Obra, 1970).

 

 

8Servico Nacional de Aprendizagem Industrial (SENAI),
Departamento Regional de Sao Paulo, "Levantamento industrial
1972." (Unpublished internal document)

9Servico Nacional de Aprendizagem Industrial (SENAI),
Departamento Regional de Sao Paulo, INPS-codificacao de ati-
vidades (Sao Paulo: SENAI/SP, 1972). (MimeographédTi'

 

65

CHAPTER IV

THE FINDINGS

4.1 Introduction

 

In the first part of this chapter the simple
descriptive findings are summarized largely in tabular
form. The objective is to document and describe within
sample variations and, where possible, to compare the
sample with the total industrial labor force of the State
of Sao Paulo. The format of presentation is based on the
outline of the model previously developed. In the second
part, special attention is given to the learning experi-
ences of those in the sample. Basic "learning paths" are
constructed by combining variables from the Formal Educa-
tion (FED), Work Experience (WEX) and Training Experience

(TEX) blocks.

4.2 Simple Descriptive Findings

 

4.2.1 Present Work Situation (PWS)

Earnings.--As the occupation under study is
generally well defined, and, given that the labor market

area from which the sample was drawn is small geographically,

66

67

the large variation in earnings was unexpected (see table
8).

Gross monthly earning range from a low of Cr$280
to a high of Cr$2,882. A majority of workers (66.67 per-
cent) earn between Cr$900 and Cr$l,800. The sample mean
and standard deviation are Cr$l,443.86 and Cr$462.82,
respectively. Some of the variation may be eliminated by
adjusting monthly earnings for hours of work. Even hourly
earnings range, however, from Cr$1.40 to Cr$ll.16 with a
mean of Cr$6.30 and a standard deviation of Cr$1.69.

The high degree of variation in earnings may have
two basically different, but not mutually exclusive,
explanations. First, it is possible that individuals in
the sample perform more or less the same quantity and
quality of work, and it is the labor market which is very
imperfect. On the other hand, the individuals in the
sample may produce very different quantities and quality
of work, and the market values them accordingly.

There is some partial and preliminary evidence
which tends to support the latter explanation. First, as
noted before, the labor market area is relatively small,
and second, there consistently was a large number of
"help-wanted" advertisements for skilled metal lathe
Operators placed in the local newspapers during the time
the field research was being conducted. This would lead
one to believe that knowledge of the prevailing market

situation should have been very high. Further support is

68

 

 

 

 

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oo.ooa ovm Hmuoa oo.ooH oem Hmuoa
mm. m OHOE HO OH mm. m OHOE HO oo>.N
om.H h mm.m 0» m NN.N NH mmm.m Op oov.m
~m.m we mm.m 0» m om.m «m mmm.m 0» ooa.~
mm.ma ooa mm.h on n hm.HH mm mmo.~ 0» oom.H
HH.H~ vaa mm.m on m HH.H~ vaa mm>.H on oom.H
qo.NN mHH mm.m cu m Nm.mN vma mmv.H ou oom.a
mm.va up mm.v on v vo.>H mm mma.a on com
mm.m we mm.m on m Hm.m mm mam 0» ooo
on.m om mm.~ 0» N mm.~ ea mam on com
om.H n N can» mmmq ma. a com cmnu mmmq

mmmucooumm nonficz mouwmnsuu mmmucmoumm umnasz moufioucuo
mausom hanucoz

 

.Aamouwmusuo ch mmcflcumm ammumcoz mmouolu.m mummy

69

added by two findings of this study to be discussed in
detail later. First, turnover rates tend to be very high,
and second, there is a high degree of variation in the
number of operations performed on the job. At this stage,
however, the intent is only to establish that differences
in market valuation do exist. This phenomenon will be
explained later.

Relative to all industrial employees in the State
of Sao Paulo, the earnings of those in the sample are
relatively high (see table 9).

Over 81 percent of those in the sample earn over
three minimum salaries.1 The comparable figure for all
industrial employees in the state is only 25.4 percent. At
the top end of the scale 4.1 percent of all industrial
employees earn more than eight minimum salaries; no indi—
vidual in the sample has earnings in this class. In
general, those in the sample are in the upper percentiles

of the income distribution.

Operations performed on the job.--Over the past 30

 

years SENAI had been develOping and perfecting its techni-
ques for teaching various skilled industrial trades. In
1972, SENAI published a complete teaching manual for the

development of skilled lathe setter—operators (Manual do

 

docente de tornearia).2 The manual presents step by step

 

the theoretical and practical skills that should be mastered
if an individual is to perform at the skilled level as a

metal lathe setter-operator. One of the most interesting

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HmcoHomz oucwemuummmo .ouHmcmn mp onV we mp om HanHu
.muno mp om: mp choHomz oucofimuummmo mHMHoom mHocmpH>mum
m .onHmnmuB ow oHkuchHz HHNmHm Eoum mH mmmaonEm HMHHumoch HHm new mama «condom

  

.mHMHmm EdEHcHE

on» «o mmHmHuHsE mm pmmmmumxm mum HHnmum CH mmHHMHmm umoz .Amcmechv GOHHMHHcH

mo much on» so comma come who %HMHmm ESEHcHE 0:» CH mucmEumsnpm .chm on cmo mmhonEm
mEHu HHsm m pan» wanmm aHnucoe ESEHCHE may mH pH .BMH an pmme mH >HMHmm ESEHCHE mcaa

 

 

 

 

 

 

o.o0H m.HHn.H Hmuoa oo.00H oem Hmuoa
m.~ m.vv whoa no 0H 00. 0 0H
m.H n.mm mm.m on m 00. o m
0. m.~ m.mv mm.n on m mm.H 0H m
7 m.m m.omH mm.m 0» v no.Hm mum v
m.m m.¢mH mm.m on m mm.>m HmH m
~.o~ n.mvm mm.m 0» m mm.vH an m
m.mv v.mmh mm.H on H on.m on H
H.m m.mmH H cos» mmmq mH. H H swap mmmH
mmmucmoumm Ampcmmsosev muHcD mmmucooumm Hmnfisz «muHcD
monasz AmvhumHmm AmVMHMHmm
EcEHcHz ESechHE
mHmspH>Hch mHmspH>Hch
AchmHlloHsmm 0mm m0 mumpmv
mOOMOHmEm HMHHumsccH HHd mHmEmm

 

.xuoz mEHuum>o How mucmfiamm mchzHoxm .mmchumm mHnucoz mwouw pmumEHummll.m mamfia

71

and useful parts of the manual is the analytical table
(quadro analitico) of tasks and operations generally
performed by metal lathe operators. A task is a desired
end product and one or more Operations is required to
complete a task. As the complexity of the tasks increase
so do the complexity and number of operations required.
Drawing on SENAI's 30 years of experience, a list of 41
operations which skilled metal lathe setter-operators are
generally considered to perform in their daily work was
taken from the analytical table. The operations are
arranged in order of increasing difficulty; two being more
difficult than one, three being more difficult than two,
etc. The individuals in the sample were asked to indicate
if they used each Operation in their present work. Great
care was taken to emphasize interest was only in finding
if the Operation was actually performed, not in if the
individual knew how to perform the Operation. If an indi—
vidual did not understand a particular Operation, the
interviewer was instructed to mark the "don't know" column.
The results are presented in table 10.

As the complexity of the Operations increases,
there is a slight tendency for the percentage of individuals
performing those operations to decline. In the more diffi-
cult range, the percentage of individuals who do not reOOg—
nize or understand specific Operations tends to increase.
The aVerage performance rate for the 41 operations is

74.39 percent.

72

TABLE 10.--Operations Performed on the Job.

 

 

 

 

 

DO DO not do Don't Know

Operation # % # % # %

1. Turn an external

cylindrical surface,

using a universal chuck 487 90.19 50 9.25 3 .56
2. Face 526 97.41 14 2.59

3. Bore center hole 504 93.33 35 6.48 1 .19
4. Turn cylindrical
surface, using chuck

and tailstock 482 89.26 44 8.15 14 2.59
5. Sharpen facing tool 448 82.96 92 17.04

6. Turn an external

conical surface, using

the upper carriage 480 88.89 57 10.56 3 .56

7. Drill, using
tailstock 475 87.96 59 10.93 6 1.11

8. Cut Off working
stock 522 96.67 17 3.15 l .19

9. Cut internal threads,
using tap 426 78.89 114 21.11

10. Turn an internal
cylindrical surface 473 87.59 52 9.63 15 2.78

11. Cut threads, using
dies 347 64.26 191 35.35 2 .37

12. Turn a cylindrical

surface between points,

using dogs 488 90.37 48 8.89 4 .74
13. Knurl 442 81.85 97 17.86 1 .19
14. Center work piece

on chuck with four

independent jaws 499 92.41 37 6.85 4 .74
15. Internal facing 504 93.33 35 6.48 l .19

16. To cut grooves,
with cutting tool 354 65.56 123 22.78 63 11.67

TABLE 10.--Continued.

73

 

 

 

 

 

DO Do not do Don't Know
Operation # % # % # %
17. Finish hole, using
finishing reamer 367 67.96 168 31.11 5 .93
18. Turn convex or con-
cave surfaces, using
bimanual movement 348 64.44 120 22.22 72 13.33
19. Open external tri-
angular thread, using
perpendicular
penetration 351 65.00 145 26.85 44 8.15
20. Turn conical sur-
face, using dislocation
Of tailstock 368 68.15 166 30.74 6 1.11
21. Open external tri-
angular thread, using
oblique penetration 299 55.37 163 30.19 78 14.44
22. Open external
square thread 401 74.26 134 24.81 5 .93
23. Turn pieces, using
mandrel 436 80.74 93 17.22 11 2.04
24. Roll wire 304 56.30 226 41.85 10 1.85
25. Eccentric turning 445 82.41 91 16.85 4 74
26. Turn, using
follower rest 432 80.00 104 19.26 4 .74
27. Drill using drill
chuck fastened to
headstock 358 66.30 151 27.96 31 5.74
28. Open a right internal
triangular thread 426 78.89 103 19.07 11 2.04
29. Mill external cones
and cylinders 308 57.04 224 41.48 8 1.48
30. Turn taper, using
taper attachment 178 32.96 297 55.00 65 12.04
31. Open an internal
square thread 393 72.78 143 26.48 4 .74

74

TABLE 10.--Continued.

 

 

 

 

 

DO Do not do Don't Know
Operation # % # % # %
32. Open internal and
external trapezoidal
threads 381 70.56 154 28.52 5 .93
33. Open internal and
external multiple
threads 298 55.19 216 40.00 26 4.81

34. Turn, using mandrel 328 60.74 197 36.48 15 2.78

35. Sharpen carbide

tools 279 51.67 192 35.56 69 12.78
36. Turn, using face

plate 427 79.07 97 17.96 16 2.96
37. Turn a spherical

surface 387 71.67 141 26.11 12 2.22
38. Turn, using steady

rest 469 86.85 68 12.59 3 .56
39. Turn, using dummy

centers 368 68.15 137 25.37 35 6.48
40. Turn pieces held

with clamps 366 67.78 161 29.81 13 2.41
41. Mill grooves 295 54.63 243 45.00 2 .37

 

The number of different Operations performed by the
individuals in the sample ranges from 1 to 41, the mean is
30.50, and the associated standard deviation is 9.58. The
majority of individuals (65.19 percent) performed 30 or
more different operations (see table 11). Over 13.8
percent performed 40 or 41 Operations. Only 8.7 percent

performed less than 15 operations.

75

TABLE ll.-—Number of 41 Different Operations Performed.

 

 

 

 

Individuals Individuals
Percen- 47 Percen-
Operations Number tage Operations Number tage
Less than 4 6 1.11 25 to 29 61 11.30
5 to 9 21 3.89 30 to 34 113 20.93
10 to 14 20 3.70 35 to 39 164 30.37
15 to 19 34 6.30 40 or 41 75 13.89
20 to 24 46 8.52
Total 540 100.01

 

4.2.2 Difficulty of Operations Performed

 

As the Operations in SENAI's analytical table were
arranged in order of increasing difficulty, it was possible
to construct an index which reflected differences in the
degree of difficulty of the Operation performed on the job.
The Operations were numbered consecutively from 1 to 41,
one corresponding to the least difficult Operation and 41
to the most difficult. The number of the most difficult
operation performed determined the rank on the difficulty
scale. Results are presented in table 12. Regardless of
differences in the number of operations performed, over 93
percent perform at least one of the four most difficult
operations. The lowest rank is 14, and mean rank on the

scale is 39.4

76

TABLE 12.--Difficulty of Operations Performed (Scale of 1

 

 

 

 

to 41).
Individuals Individuals
Rank Number Percentage Rank Number Percentage
l -—- --— Z --- ---
--- --- 29 2 .37
14 8 1.48 : --- ---
--- --- 35 2 .37
16 2 .37 36 9 1.67
--- ——- 37 8 1.48
18 l .19 38 40 7.40
19 1 .19 39 54 10.00
—-- —-— 40 114 21.00
23 l .19 41 295 54.62
27 3 .56 TOTAL 540 100.00

 

4.2.3 Present Work Situation Control (PWSC)

Factogy Size.--The factories in which the indi-
viduals in the sample are employed range in size from one
employee to over 2,000 employees. The smaller factories
are generally small-scale job shops in which the primary
business is either repair work for other small-scale enter-
prises, Or specialized contract work for larger factories.
The smallest are sometimes little more than worksh0ps
at the back of a house, employing only one or two lathes.

The larger factories are as modern as any in the world.

77

They produce not only for the domestic market, but also for
export. The sample contains workers from the full range of

factories (see table 13).

TABLE 13.--Factory Size.

 

Individuals in Sample

 

 

Number of Employees Number Percentage
Less than 25 55 10.19
25 to 49 36 6.67
50 to 99 33 6.11
100 to 359 81 15.00
350 to 999 67 12.41
More than 1,000 268 49.63
Total 540 100.00

 

The sample's heavy concentration of workers from factories
which employ more than 1,000 is consistent with the total
employment picture in the ABC area. According to SENAI
reports, factories with over 1,000 employees account for

over 49 percent of the total number of industrial employees.3

Sector of work.--Over 69 percent of those in the

 

sample are employed in the production sector of their
firms (see table 14).

For convenience, the factories can be divided into
three categories--small (less than 50 employees), medium

(50 to 349 employees), and large (350 employees or more).

78

TABLE l4.--Factory Workers by Sectors.

 

 

 

Individuals
Sector Number Percentage
Production 375 69.44
Maintenance 165 30.56
Total 540 100.00

The percentage of individuals working in the maintenance
sector is 20.88 percent in the small factories; 17.54
percent, medium; and 27.61 percent, large. The distinc-
tion between maintenance and production in the larger
factories is clear. In the smaller factories there may be
some shifting between sectors. When this was the case, the
individual was asked the sector in which he most generally

worked, and he was classified accordingly.

Type of lathe used.--Basica11y three different
types of power metal lathes are used. The "modern" lathe
is used most frequently. It is distinguished by a special,
external gearbox which allows almost instantaneous changes
in the speed of rotation and movement of the carriage.

The second type of lathe is classified as old. It can
perform the same basic operations as the modern lathe, how-
ever the external gearbox is missing. Any changes in the
speed of rotation and movement of the carriage are accom-
plished by Opening the machine and manually withdrawing one

gear and replacing it with another. The final class of

79

lathes has various highly specialized machines. Several
vertical and hydroelectric lathes were encountered. These
lathes are used for very specialized jobs and in some
cases require two operators. The percentage of modern
lathes in use was found to be high, 87.96 percent (see

table 15). Old and special lathes accounted for 7.41

TABLE 15.--Type of Lathe Used.

 

 

 

Individuals
Lathe Number Percentage
Modern 475 87.96
Old 40 7.41
Special 15 2.88
Don't Know 10 1.85
Total 540 100.00

 

percent and 2.88 percent of the total sample, respectively.
There were 10 cases in which the individual did not know
what type of lathe he was using.

It is interesting to note that the modern lathes
are not concentrated in the large factories. Using the
small, medium, large size class scheme previously defined,
the respective percentages of modern lathes in each class
are 87 percent, 96 percent, and 85 percent. The special

lathes, however, are concentrated in the large factories.

80

Entry_leve1 andpyears in factory.--Over 78 percent
of the workers in the sample entered their present employ-
ment already classified as skilled lathe setter-operators
(see table 16). Of the remaining 118 individuals, 93
entered as lathe Operators but at a less than a skilled
level, and 25 entered in occupations outside the area of
lathe operation. It is rare than an individual is hired
in his first job as a skilled worker, and most of the
individuals in the sample are not presently employed in
the factories where they received their first work experi—
ence in lathe Operation. Over 55 percent of those in the
sample have been employed in their present firm less than
four full years. Two things are indicated: (1) the labor
market for lathe setter-Operators is very active, and (2)
firms tend to place a higher value on lathe setter-Operators

who have work experience in other firms.
4.2.4 Initial Conditions (IC)

Age.--The individuals in the sample are young.

The mean age is 30.7 years. Over 87 percent (472 indi-
viduals) are less than 40 years old (see table 17).

The most noticeable differences between the age
distribution of the sample and the age distribution Of all
industrial employees in the State of Sao Paulo, occur in
the less-than-ZO-years-Old class and the more-than-SO-

years-old class. In both cases the sample percentage is'

81

TABLE l6.--Entry Level and Years Working in Factory.

 

Lathe Setter-Operator

 

 

Other
Less than Occupation
Years Skilled Skilled Area Total
# % # % # % # %
Less than 1 105 24.88 0 1 4.00 106 19.63
1 to 3 169 40.05 24 25.81 2 8.00 195 36.11
4 to 6 93 22.04 31 33.33 6 24.00 130 24.07
7 to 9 23 5.45 8 8.60 3 12.00 34 6.30
10 to 12 21 4.97 14 15.05 6 24.00 41 7.59
13 to 15 7 1.66 3 3.23 3 12.00 13 2.41
16 or more 4 .95 13 13.98 4 16.00 21 3.89
Total 422 100.00 93 100.00 25 100.00 540 100.00

 

significantly lower. The low number of workers under 20
could be explained by the time it takes to reach the
skilled level. On the other hand, the older, qualified

workers with many years of work experience might be found
in supervisory positions. A definitive answer to the
second question is beyond the scope of this study, but the

first will be dealt with later.

Place of birth and place of primary education.--Over

 

78 percent of those in the sample were born in the State of
Sao Paulo (see table 18). The comparable figure for all
employees (industrial and commercial) is just under 69

percent. It was expected the sample would contain a higher

82

TABLE 17.--Age Distribution.

 

(b) All Industrial
Employees (State of

 

 

 

(a) Sample 850 Paulo-~1970)

Age Number

Group Number Percentage (Thousands) Percentage
Less than 20 30 5.56 258.5 15.1
20 to 24 100 20.37 366.3 21.4
25 to 29 120 22.22 292.7 17.1
30 to 34 122 22.59 246.5 14.4
35 to 39 90 16.67 181.4 10.6
40 to 44 26 4.81 147.2 8.6
45 to 49 28 5.19 95.8 5.6
50 or more 14 2.59 123.2 7.2
Total 540 100.00 1,711.6 100.0

 

Source: Data for All Industrial Employees is from:
Brazil, Ministério do Trabalho e Previdéncia
Social, Departamento Nacional de Mao de Obra,
Composigao e distribuicao de mao de obra--
Sao Paulo (Rio de Janeiro: Departamento
Nacional de Mao de Obra, 1972), p. 56.

percentage of individuals who were born, educated, and
trained outside of Sao Paulo who immigrated to Sao Paulo
due to the lack of job opportunities in their home states.
This does not prove to be the case (see table 19).
Although only 78 percent of those in the sample were born
in the State of Sao Paulo, over 87 percent received their
primary education there. In fact.of those born outside
the state, almost 44 percent were educated in Sao Paulo.

On the other hand, only four individuals (.74 percent Of

TABLE 18.--Place of Birth.

83

 

 

All Industrial and
Commercial Employees
(State of Sao Paulo--

 

 

Sample 1970)
Percen- Number
Number tage (Thousands) Percentage

State of Sao Paulo 424 78.52 1,845.4 68.87
Southeast (exclud—

ing State Of Sao

Paulo 31 5.74 283.9 10.60
Extreme South 18 3.33 51.0 1.90
Central West 0 11.6 .43
Northeast 45 8.33 378.9 14.14
North 1 .19 11.0 .41
Other Country 21 2.89 97.7 3.65
Total 540 100.00 2,679.5 100.00

 

TABLE l9.--Place of Birth

and Place of Primary Education.

 

Primary Greater

School

Interior of
Sao Paulo

 

Sao Paulo (rest of Other Other

Birth (city) state) State Country Total
Greater Sao

Paulo (city) 197 197
Interior of

Sao Paulo

(rest of state) 96 127 4 227
Other State 36 4 55 95
Other Country 8 3 l 21
Total 337 134 60 540

 

84

the sample) were born in Sao Paulo and educated elsewhere.
It is also interesting that only 197 individuals (36.48%)
were born in the greater Sao Paulo area, however, 337

individuals (62.41%) received their education there.

Educational level of_parents.--Over 61 percent of

 

those in the sample have at least one parent with a com-
plete four-year primary education (see table 20).

For Brazil this is an extremely high figure.
According to data published in the 1970 Brazilian Demo-
graphic Census, less than 27 percent Of the total popula-
tion of 30 years of age and older has a complete primary
education.4 On the other hand, approximately 14 percent Of
the sample had parents who had no formal education. In
general, the educational levels of the parents must be

considered relatively high.

Occupational area of father.--As stated in Chapter

 

III, the occupational classifications used in this study are
based on the 1968 edition of the International Standard
Classification of Occupations published by the International
Labour Office. Reviewing briefly, the specific occupation
under study is lathe setter-Operator (8-33.20); related
occupations are considered to be blacksmiths, toolmakers

and machine-tool operators (8-3) and machinery fitters,
machine assemblers and precision instrument makers (except
electrical) (8-4); construction refers to bricklayers,

carpenters, and other construction workers (9-5); other

85

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86

industrial occupations are considered to be all remaining
occupations in production and related workers, transport
equipment operators and labourers (major group 7/8/9);
other refers to all occupations not classified within major
group 7/8/9.

Over 51 percent of the sample had fathers who had

industrial occupations (see table 21). Less than 18 percent

TABLE 21.-—Occupational Area of Father.

 

 

 

Individuals
Number Percentage
Lathe Operation 15 2.78
Related to Lathe Operation 20 3.70
Other Industrial (not construction) 172 31.85
Construction 71 13.15
Agriculture 93 17.22
Other 141 26.11
Unknown 28 5.19
Total 540 100.00

 

had fathers who worked in agricultural occupations.
According to 1970 census results, about 17 percent of the
economically active population is employed in the industrial
sector. Some 20 or 30 years ago this percentage was cer-
tainly less. The data suggest that having a father who
worked in an industrial occupation gives an individual a

higher probability of becoming an industrial worker.

87

Occupational status of father.--The lowest level of
occupational status is associated with unskilled manual
workers. Over 63 percent of the sample had fathers who had
occupations which ranked above the non-skilled level (see
table 22). Further, over 39 percent had fathers at or

above the skilled worker level.

TABLE 22.--Occupational Status of Father.

 

 

Status Scale Number Percentage
1. White collar and higher 51 9.44
2. Supervision of manual workers 36 6.67
3. Skilled worker 124 22.96
4. Semi-skilled worker 130 24.07
5. Unskilled worker 171 31.67
Unknown 28 5.19
Total 540 100.00

 

4.2.5 Formal Education

 

In the total sample of 540 individuals, only 32
individuals (5.93 percent) do not have at least a complete
four-year primary education (see table 23). A majority
(59.63 percent) have completed, but not studied beyond the
primary level. Of those who do have some secondary educa-
tion, 66.67 percent were enrolled in standard academic
programs, and 6.45 percent were enrolled in job-oriented

business or commercial programs. The remaining 26.88

88

 

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89

percent (50 individuals) were enrolled in industrial
middle and high school programs. In the total sample 9.26
percent received industrial training within the formal
school system.

When compared to all employees (industrial and
commercial) in the State of Sao Paulo, the sample differs
in two respects (see table 24). First, the sample has a
lower percentage of individuals with less than a complete
primary education. In the general labor force there are
many individuals who work at the unskilled level. It is
expected that a large percentage of such individuals would
not have a complete primary education. Second, the general
labor force has a higher percentage of individuals with
more than a complete middle school education. If the
individuals in the sample had such high levels of formal
education, they probably would be working in white collar
occupations and not as skilled blue collar workers.

Very few individuals (37.04 percent) at any level
completed their formal education in the "normal" time of
one calendar year for each grade (see table 25). It was
not possible to separate those individuals who were
not passed at various levels from those who simply drOpped
out of school and then later returned. It is known, however,
that 23.6 percent of the sample finished their formal
education after they had already taken regular, full-time
employment. If the number of extra years to complete a

grade are taken as a measure of effort, then those in the

90

TABLE 24.--Formal Education Levels.

 

Sample

 

Percen-
Number tage

--.

All Employees
(State of Sao Paulo-
1970)

 

Number* Percen—
(Thousands) tage

 

 

NO formal education --- --- 25.8 1.4
Primary School

Incomplete 32 5.92 295.3 16.0

Complete 322 59.63 954.1 51.7
Middle School

Incomplete 123 22.97 208.5 11.3

Complete 45 8.33 118.1 6.4
High School

Incomplete 14 2.59 66.4 3.6

Complete 4 .74 108.9 5.9
University 0 0.00 68.3 3.7
Total 540 100.00 1,845.4 100.00
*Estimated.

Source: Data for all employees is from:

Brazil, Ministério

do Trabalho e Previdéncia Social, Departamento
Nacional de Mao de Obra, Composigao e distribuicao
de mao de Obra--Sao Paulo (Rio de Janeiro: 7
Departamento Nacional de Mao de Obra, 1972), p. 92.

 

91

 

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92

sample have expended a great deal of effort in Obtaining
their formal educations.

As noted in Chapter I, a special program (madureza)
Offers adults the opportunity to complete a four-year aca-
demic middle school program or three—year high school pro-
gram in one year. Based on the effort expended to Obtain
a regular formal school degree, it was expected that many
in the sample would have completed or be in the process of
completing such special programs. Again, this did not prove
to be the case (see table 26). Over 92.5 percent of those
in the sample have had no contact with these special pro-
grams. Of the 14 who have completed the middle school
program, four are presently enrolled in the high school pro-
gram. A total of 25 are presently enrolled in the middle
school program, and one individual is enrolled in the high
school program who did not complete the middle school

program. NO one has completed the high school program.

TABLE 26.--Specia1 Program Equivalents for Middle and High
School (Madureza).

, I M.--
--——-_._—.‘——._— .m

 

High

School
Middle Completed Completing Never
School Program Program Enrolled Total
Completed Program 0 4 10 14
Completing Program 0 0 25 25
Never Enrolled 0 1 500 501

Total 0 5 535 540

 

93

4.2.6 Work Experience

 

The mean age at which those in the sample first
started working in regular, full-time jobs for a fixed
salary is 14.29 years. The associated standard deviation
is 2.12 years. Over 21 percent started their work lives
before the age of 14; and by the age of 18, 95.75 percent
had started working.

The majority (over 83 percent) started working in
industrial occupations (see table 27). The percentages
starting in agricultural or other occupational areas are
8.15 percent and 8.33 percent, respectively. A large
percentage (39.63 percent) started directly in the area

of lathe operation.

TABLE 27.--Occupational Area of First Job.

 

 

 

Individuals
Area of First Job Number Percentage
Lathe Operation 214 39.63
Related to Lathe Operation 85 15.74
Other Industrial Occupation 152 28.15
Agriculture 44 8.15
Other 45 8.33

Total 540 100.00

 

94

Of the 326 individuals who started outside the area
of lathe operation, 41.1 percent either started in an area
related to lathe operation or had work experience related
to lathe operation (table 28). Another 47.11 percent
either started in other industrial occupations or had such
experience before entering the occupational area under
study. In total, only 37 individuals came directly from
agriculture or other occupations to the area of lathe
operation.

The time span between the first job and the first
job in lathe operation varies greatly in the sample (see
table 29). Over 10 percent of those in the sample had
more than nine years of work experience before entering
the area of lathe Operation. The combinations of type of
work experience and years of work experience are extremely
varied and there are no readily visible patterns.

The mean age at which the first job in the area of
lathe Operation was begun was 17.28 years. The associated
standard deviation was 3.98 years. The percentage younger
than 14 was 3.52 percent. The majority (60.74 percent)
entered between the ages of 14 and 17. By age 21, 86.67
percent of the total sample had entered the area of lathe
Operation.

There are three possible skill levels of entry into
the area of lathe operation. The first is as an apprentice
or helper. The second is as a semi-skilled worker who can

Operate a lathe, but must have a skilled worker set up the

95

 

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96

TABLE 29.--Type and Years of Work Experience Before
Entering Area of Lathe Operation.

 

Years of Work Experience

 

 

Type of Work 9 or
Experience 0 1-2 3-4 5-6 7-8 more Total
L0 214 214
RLO 47 25 6 1 6 85
OTH IND + RLO 3 4 6 6 5 24
OTH + RLO 0 8 l 2 14 25
OTH IND 67 26 18 8 9 128
OTH + OTH IND 3 5 3 2 14 27
OTH l3 5 3 5 ll 37
Total 214 133 73 37 24 59 540

 

Note: LO--started in lathe operation; RLO--started in area
related to lathe Operation; OTH IND + RLO--started
in other industrial occupation but had experience
related to lathe Operation; OTH + RLO--started on
other occupation but had experience related to lathe
operation; OTH IND--started in other industrial
occupation and went directly to lathe operation;

OTH + OTH IND-~started in other occupation, had
other industrial experience, then entered lathe
Operation; OTH--started in other occupation and went
directly to lathe operation.

job for him. He usually does repetitive work using auto-
matic lathes. The third level is as a fully skilled
worker, capable Of reading designs, setting up the lathe
and executing the job. Only 36 individuals (6.67 percent)
entered as skilled lathe Operators (see table 30). The
majority (74.26 percent) entered at the learning level.

The mean number of years from entry into the area of lathe

97

TABLE 30.--Entry Level Into Area of Lathe Operation.

 

 

 

Individuals
Level Number Percentage
Learning 401 74.26
Semi-skilled 103 19.07
Skilled 36 6.67
Total 540 100.00

 

operation until the qualified level was reached is 3.93.
The standard deviation, however, is 2.87 years. The range
is from 0 to 21 years. Leaving the area of lathe operation
is not a significant factor in explaining these great
variations, since only 3.15 percent Of those in the sample
ever left the area.

The mean age at which the skilled level was reached
is 21.20 years; the standard deviation is 4.22 years; and
the range is from age 15 to 45. Only 2.41 percent of the
total sample left the area of lathe operation after the
skilled level was attained.

The number of years those in the sample have been
working as skilled lathe operators ranges from less than
1 to 41 years. The mean number of years is 9.53 and the
standard deviation is 7.61 years. Less than 20 percent
have been at the skilled level for over 15 years (see
table 31). The entire distribution is skewed toward the

lower levels.

98

TABLE 31.-~Years Working as Skilled Lathe Setter-Operator.

 

 

 

Individuals
Years Number Percentage
Less than 1 24 4.44
l to 3 107 19.82
4 to 6 98 18.15
7 to 9 86 15.92
10 to 12 62 11.48
13 to 15 59 10.93
15 to 18 39 7.22
19 to 21 21 3.89
More than 21 44 8.51
Total 540 100.00

 

4.2.7 Training Experience

 

SENAI appgenticeship program.--Prior to the study

 

it was expected that over 60 percent of the skilled metal
lathe operators in the ABC area would have gone through a
three-year SENAI apprenticeship program. In the sample
drawn for this study, the percentage was just under 27.6
percent (see table 32). There is no readily available
standard by which to judge if this figure is high or low.
Certainly it shows that the SENAI apprenticeship program
has made a significant contribution to the development of

this occupation. SENAI officials thought it had done more.

99

TABLE 32.--SENAI Apprenticeship.

 

 

 

Individuals

SENAI
Apprenticeship Number Percentage
None 391 72.41
Lathe Operation

Complete 125 23.15

Incomplete 13 2.41
Other

Complete 9 1.66

Incomplete 2 .37
Total 540 100.00

 

Of the 149 who took SENAI's apprenticeship courses
137 took them in the Greater Sao Paulo area, four were
taken in the interior of the state, and only eight were
taken outside the State of Sao Paulo.

The first SENAI apprenticeship course was started
in 1942, and one individual in the sample was part of that

first group of students (see table 33).

Courses.--Training experience through Special
courses is numerically an important component in the develop-
ment of the skilled workers in the sample. Over 68 percent
Of the sample have taken at least one specialized course
(see table 34). Of the 171 without special courses, 86
have had special training through the SENAI apprenticeship

program and 23 have been enrolled in industrial middle

100

TABLE 33.--SENAI Apprenticeship by Year.

 

 

 

 

 

 

 

Year Number Year Number Year Number
1942 0 1952 10 1962 6
1943 l 1953 5 1963 7
1944 0 1954 7 1964 5
1945 l 1955 10 1965 3
1946 2 1956 4 1966 4
1947 0 1957 5 1967 10
1948 4 1958 6 1968 7
1949 2 1959 5 1969 13
1950 6 1960 9 1970 4
1951 8 1961 4 1971 1
Total 149
TABLE 34.-~Number of Special Courses Taken.

Individuals Courses
Number of
Courses Taken Number Percentage Number Percentage
None 171 31.67
One 219 40.56 219 38.69
Two 103 19.07 206 36.40
Three 47 8.70 141 24.91
Total 540 100.00 566 100.00

 

101

school programs. As the net result, only 62 individuals,
11.48 percent of the sample, have become skilled lathe
setter-operators purely on the basis of work experience.
All the remaining 88.52 percent have had their work
experience supplemented by some form of special industrial
training.

The types of courses taken are divided into four
categories. First, there are courses specifically
designed for teaching the Operation of lathes. There are
generally two parts to the course-~theory and practice.
The theoretical or classroom portion deals with such things
as mathematics, design reading, precision measurements.
The practical or shop work part is concerned directly with
the development of lathe use skills. Though the division
of time between classroom and shop varies, all courses in
lathe operation have both components. Over 35 percent of
those in the sample had at least one course in lathe
Operation (see table 35).

The second category is courses in mechanical design.
The emphasis is placed on reading and interpretation. To
some degree these courses are like the theoretical parts
of the lathe operation courses. They differ because they
are not restricted directly to the area of lathe operation
and cover the area of design in more depth. Over 34 per-
cent Of the sample had at least one course specifically
devoted to the study of mechanical design.

The third category consists Of courses related to

lathe operation. Any course on the cutting, grinding, or

102

TABLE 35.-~Types of Courses Taken.

 

 

 

Individuals

Types of Courses Taken Number Percentage
Lathe Operation (only) 99 26.83
Lathe Operation + Design 39 10.57
Lathe Operation + Design + (Related

to Lathe Operation or Other) 20 5.42
Lathe Operation + (Related to

Lathe Operation or Other) 35 9.49
Design (only) 99 26.83
Design + (Related to Lathe Operation

or Other) 30 8.13
Related to Lathe Operation + Other 3 .81
Related to Lathe Operation (only) 23 6.23
Other (only) 21 5.69
Total 369 100.00

shaping of precision metal tools or parts is included.
Examples are courses in the use of power saws, power
drills, shapers, milling machines, and grinding machines.
The structure of the course is similar to that of the
courses in lathe operation.

The final category is labeled "other" and includes
industrial courses not directly related to lathe operations,
as well as small numbers of courses not specifically
industrial.

The percentage of courses originating from SENAI

was found to be 31.6 percent (see table 36). As with the

103

 

 

 

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104

SENAI apprenticeship program, this figure was much lower
than expected. Most unexpected of all was the high per-
centage of courses sponsored by private industrial schools,
54.8 percent. There simply was no indication or expecta-
tion that private schools would be providing 1.7 times the
number of courses as SENAI. Public schools, the industrial
middle schools, supplied 7.6 percent of the training courses.
Within the "other" category, 13 courses were sponsored by
factories and the remaining 21 were sponsored by various
religious groups and a few unidentified organizations.

Private school courses are not a recent phenomenon
(see table 37). The first private course taken by an
individual in the sample was in 1942, the same year SENAI
was founded. The largest number of private courses are
encountered after 1960. The private courses have existed
for a long time, and they are important in terms of the
total training experience system. The individual student
generally pays for the private course, while SENAI courses
are given free of charge to the student.

The variation of duration among courses is great
(see table 38). The total number of hours planned per
course ranges from 48 to over 5,000, and the time spans
over which the courses are taught range from one month to
over five years. For example, over 48.9 percent of private
school courses in design are planned for over 1,000 hours.
Only 9.3 percent of the SENAI design courses, on the other

hand, are planned for over 1,000 hours. The determination

TABLE 37.--Courses by Year and Sponsor.

105

 

 

 

Sponsor
Total
Year SENAI Private Public Other Number
1941 l 1
1942 l 2 l 4
1943 2 1 3
1944 2 3 5
1945 1 l 2
1946 l l 2
1947 1 l 2
1948 1 2 l 4
1949 l 2 2 5
1950 l 2 3
1951 3 4 7
1952 4 2 1 7
1953 2 4 1 7
1954 4 4 l 2 11
1955 3 5 2 10
1956 7 7 l 15
1957 4 8 2 14
1958 6 6 6 18
1959 4 8 2 14
1960 3 15 3 21
1961 l 14 2 2 19
1962 11 22 1 34
1963 7 25 2 5 39

106

TABLE 37.--Continued.

 

 

 

Sponsor

Total
Year SENAI Private Public Other Number
1964 6 24 1 5 36
1965 10 16 2 28
1966 9 23 2 3 37
1967 12 21 3 4 40
1968 ll 24 3 38
1969 13 24 1 38
1970 19 17 l 2 39
1971 ll 10 l 22
1972 16 4 20
1973 6 12 l 2 21
Total 566

 

as to whether or not these time differences are significant
in explaining the quality of work performed is left to a
future section.

Of those who have taken more than once course,
42.67 percent have taken their courses from different
sponsors (see table 39). The remaining 57.33 percent have
taken each of their courses from the same sponsor. Parti-
cularly interesting is the SENAI (only) case; the number
of individuals having more than one course (36) is almost
as great as the number having one course (38). In other

‘words, almost 50 percent of those who have taken a SENAI

107

 

 

 

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108

TABLE 39.--Sponsors of Courses Taken.

 

 

 

Individuals
Sponsors of Courses Taken Number Percentage
SENAI (only) 74 20.05
SENAI + Private 37 10.03
SENAI + Private + (Public or Other) 6 1.63
SENAI + (Public or other) 5 1.36
Private (only) 190 51.49
Private + (Public or other) 16 4.34
Public (only) 25 6.78
Other (only) 16 4.34
Total 369 100.00

 

course have taken more than one. Of the total number of
individuals who had at least one course (369), 33.06 percent
had at least one SENAI course, and 67.48 percent had at

least one private school course.

4.3 Learning Paths

 

At a highly aggregated level, four basic learning
paths have been taken to reach the skilled occupational
level. A complete four—year primary education seems to
be a necessary base for the development of specific indus—
trial skills. This level of formal education is common
to all four paths. The distinguishing feature of each
path.is the major type of industrial learning experience

after primary school is completed. The four paths are:

109

l. SENAI--those who have been enrolled in
a SENAI apprenticeship program.

2. Industrial School--those who have not
been enrOlled in a SENAI apprenticeship
program but have attended a formal
industrial middle school.

3. Courses--those who have neither been
enrolled in a SENAI apprenticeship program
nor attended a formal industrial middle
school, but who have taken partetime
industrial training courses.

4. Work--those who have no specific industrial
training (neither SENAI, industrial middle
school, nor training courses) and developed
their industrial skills through work
experience only.

In the sample of 540 individuals, 149 (27.6 percent)

have taken the SENAI apprenticeship training path, and 41
(7.6 percent) have taken the industrial school path. The
majority, 288 (53.3 percent) have taken the courses path.
Only 62 individuals (11.5 percent) have reached the skilled
occupational level without some form of special industrial
training.

Those who have taken the SENAI path tend to be
younger than those who have taken other paths. The mean
age for the SENAI class is 28.48 years (see table 39). On
the other hand, those who attended industrial middle schools
tend to be older than the others; the mean age is 35.20.
Within each path, however, there is a great deal of varia-

tion and, thus, the differences between cell means should

be interpreted with care.

110

TABLE 40.--The Four Major Learning Paths.

 

 

 

 

Individuals Age

Percent- Standard
Path Number age Mean Deviation
(l) SENAI 149 27.6 28.48 7.10
(2) Industrial School 41 7.6 35.20 9.79
(3) Courses 288 53.3 30.40 7.50
(4) Work 62 11.5 34.77 11.34
Total 540 100.0 30.74 8.39

 

The educational levels of the parents of the
individuals who have followed different paths does not vary
greatly (see table 41). There is a noticeably high percent-
age (58.5 percent) of industrial school path individuals
whose mothers have a complete primary education. The
mothers of individuals in the course and work paths are
more likely to have had no formal education (34.5 and 43.3
percent, respectively). In general, however, the educational
levels do not vary greatly from one path to another.

With respect to the occupational area of the father,
the SENAI learning path has a relatively high percentage
(57.1 percent) who worked in industrial occupations and a
correspondingly low percentage (3.6) who worked in agricul-

tural occupations.

111

 

 

 

 

 

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112

TABLE 42.--Occupational Area of Father by Learning Path
(percentage distribution).

 

 

 

Path

Occupational Industrial
Area SENAI School Courses Work Total
Industrial (not

construction) 57.1 _ 35.0 34.8 30.7 40.4
Construction 12.2 17.5 13.3 17.7 13.9
Agriculture 3.6 27.5 22.6 25.8 18.2
Other 27.1 20.0 29.3 25.8 27.5
Total 100.0 100.00 100.00 100.0 100.00

 

The SENAI path also has a relatively low percentage
(19.3 percent) of fathers who were unskilled workers (see
table 43). The data tend to indicate that having a father
who works in the industrial sector at the semi—skilled
level or higher gives the individual a somewhat higher
probability of entering a SENAI apprenticeship program.

As was noted, the four major learning paths identi-
fied are distinguished from one another by the major type
of industrial learning experience. When other types of
learning are added to the basic paths, they become more
difficult to distinguish. For example, 44.3 percent of
those in the SENAI path have more than a complete four-year
primary education (see table 44). This percentage is much
higher than for the courses and work paths (23.6 and 17.8
percent, respectively). Thus, the SENAI path is not a

"pure" path, i.e., four years of primary education followed

113

TABLE 43.--Occupational Level of Father by Learning Path
(percentage distribution).

 

 

 

Path

Occupational Industrial
Level SENAI School Courses Work Total
Unskilled 19.3 37.5 39.6 35.5 33.4
Semi-skilled 32.1 10.0 23.7 27.4 25.4
Skilled 30.7 30.0 20.4 22.6 24.2
Supervision of

manual workers 7.9 10.0 6.7 4.8 7.0
White collar 10.0 12.5 9.5 9.7 10.0
Total 100.0 100.00 100.0 100.00 100.00

 

TABLE 44.--Educational Level by Learning Path (percentage
distribution).

 

 

 

Path

Educational Industrial
Level SENAI School Courses Work Total
Primary

Incomplete —-- --- 7.3 14.5 5.9
Primary Complete 55.7 --- 69.1 67.7 59.6
More than

Complete 44.3 100.0 23.5 17.8 34.5

Primary
Total 100.00 100.0 100.0 100.0 100.00

 

114

by SENAI apprenticeship followed by work experience. The
paths are even more complicated due to training courses

(see table 45).

TABLE 45.--Number of Courses Taken by Learning Path
(percentage distribution).

 

 

 

Path

Number of Industrial

Courses SENAI School Courses Work Total
None 57.7 56.1 --- --- 31.7
One 27.5 29.3 57.6 --- 40.5
Two 11.4 14.6 27.8 --- 19.1
Three 3.4 ~-- 14.6 --- 8.7
Total 100.00 100.00 100.00 --- 100.0

 

Over 42 percent of those who have SENAI appren-
ticeships have also taken short training courses--27.5
percent have taken only one course, 11.4 percent have
taken two courses and 3.4 have taken three courses.
Approximately 44 percent in the industrial school path
also have taken at least one training course.

It is interesting that of those in the SENAI path
who have had at least one course, over 71 percent took at
least one course from a private industrial school (see
table 45). Over 30 percent also took at least one course
from SENAI. SENAI apprenticeship was followed by SENAI
training courses. Those in the industrial school path

have also taken courses from SENAI as well as private schools.

115

Table 46 shows that "pure" learning paths do not
exist. The number of possible combinations of formal
education, training, and work experience is extremely high.
Simple cross tabulations are not suited to the type of
analysis that is required to evaluate the effects of dif-
ferent types of learning. There are too many variables
to handle and linear regression techniques are required.

In Chapter V several linear regression models are used to
evaluate the effects of different types of learning

experiences.

TABLE 46.--Percentage Having at Least One Course From a
Given Sponsor by Learning Path.

 

 

 

Path
Sponsor of Industrial
Course SENAI School Courses WOrk Total
SENAI 30.2 38.9 33.3 --- 33.1
Private 71.4 72.2 66.3 --- 67.5
Public 6.3 5.6 11.5 --- 10.3
Other 9.5 --- 8.3 --- 8.1

 

Note: The table is interpreted as follows: For the first
path (SENAI)--Of those who have had at least one
training course, 30.2 percent have had at least one
course sponsored by SENAI, 71 percent have at least
one course from a private school, etc.

 

 

FOOTNOTES--CHAPTER IV

1During the time of the fieldwork the minimum
salary in Sao Paulo was Cr$312 (approximately US$50).

2Servico Nacional de Aprendizagem Industrial
(SENAI), Departamento Nacional, Manual do docente de
tornearia (Rio de Janeiro: SENAI7DN,1972).

 

3Servizo Nacional de Aprendizagem Industrial
(SENAI), Depar amento Regional de Sao Paulo, "Levan-
tamento industrial 1972." (Unpublished internal document)

4Brazil, Ministério do Planejamento e Coordena ao
Geral, Fundacao IBGE, Instituto Brasileiro de Estatistica,
Departamento de Censos, Censo demggrafico Brasil, VIII re-
censeamento geral, 1970, V. I (Rio de Janeiro: Fundacao
IBGE, 1970)

116

CHAPTER V

THE ANALYSIS OF THE FINDINGS

5.1 Introduction

 

It was shown in Chapter IV that there is variation
in hourly wage rates and in the number and difficulty of
specific operations performed on the job by metal lathe
setter-operators. In terms of the model developed in
Chapter II, there is variation in the Present Work Situa-
tion (PWS) block of variables. Variation in the time
taken to reach the skilled occupational level also was
shown in Chapter IV. The primary objective of this chap-
ter is to "explain” these variations. Linear regression
analysis is used and the regression model is derived
directly from the model presented in Chapter II.

K

BPWSCj xPWSCji + £31 BICK xICKi +

PWSi = a

.5.
UMQ

=1

L M
i=1 BFEDl xFEDli + g=lBWEXm xWEXmi +

5M2

=1 BTEXn xTEXni + 91’ 1=1:2:---.Q- (5.1)

117

where:

118

Y represents a dependent variable selected from
the Present Work Situation (PWS) block, the X's
represent explanatory variables, and e represents
the stochastic disturbance. The subscript 1 refers
to the ith observation (individual) and the PWSC,
IC, FED, WEX, and TEX subscripts refer to explana-
tory variables selected from the respective
variable blocks of Present Work Situation Control,
Initial Conditions, Formal Education, WOrk Experi—
ence, and Training Experience. The subscript
associated with each summation sign refers to the
number of explanatory variables in each block.

The total number of explanatory variables is P,
where P=J+K+L+M, and P<Q, where Q is the total
number of observations.

The standard assumptions of the classical normal

linear regression model are made with respect to all

observations.1

The primary interest is in how four important

variables are influenced by the explanatory variable in

the regression model. The four variables are:

1. wage per Hour (WA H)

2. Number of Operations Performed (OP DO)

3. Difficulty of Operations Performed (TOP OP)

4. Time Taken to Reach the Skilled Occupational
Level (YRS AR LO-SLO)

A specific regression model for each of the four

dependent variables is constructed using elements contained

in the six variable blocks of the general model. The

general form of each model may be represented as:

WA H (PWS, PWSC, IC, FED, WEX, TEX)
OP DO (PWSC, IC, FED, WEX, TEX)
TOP OP (PWSC, IC, FED, WEX, TEX)

YRS AR LO-SLO (IC, FED, WEX, TEX)

119

where: WA H, OP DO, TOP OP, and YRS AR LO-SLO refer to
the four specific dependent variables, and PWS,

PWSC, IC, FED, WEX, and TEX refer to the variable

blocks used in the construction of each specific

regression model.

The ultimate objective in each case is to identify
and isolate the coefficients of those independent variables
which are statistically significant. The level of statis-
tical significance selected for all tests of hypotheses in
this study is the .05 level. The words "significant" and
”significantly" are to be interpreted as "statistically
significant at the .05 level.”

The first hypothesis to be tested is that none of

the independent variables in any of the variable blocks

has an influence on the dependent variable.

H : 8 =8 =8 =...=B = 0 (5.2)

where: P is the total number of explanatory variables in
the model.

Rejection of this hypothesis would indicate that
at least one of the independent variables in the complete
model has a statistically significant influence on the
dependent variable. The actual number of variables and the
specific variable blocks of which they are a part is not
indicated.

Second, a series of tests of hypotheses is performed
to identify the specific variable blocks which contain
variables which significantly influence the dependent

variable. The hypotheses tested are:

120

H : all 81 = 0

n : all 82 = o (5-3>

ll
0

H : all 8D

where: the subscript refers to the variable block, and D
is the total number of variable blocks in the model.

Rejection of the hypothesis for a specific variable block
would indicate the block contains at least one variable
which has a statistically significant influence on the
dependent variable. Failure to reject the hypothesis would
imply there is no variable in the block which has a signi-
ficant influence. To check this conclusion further, the
hypothesis can be tested that a group of variable blocks
taken together has no significant influence. Specifically,

the hypothesis tested is:

Ho: all 81 = all 82 = ... = all Be = 0 (5.4)

where: the subscript refers to a variable block for which
the hypothesis concerning the block in isolation
was not rejected, and C is equal to the total of
such blocks.

The failure to reject the hypothesis would indicate that

none of the blocks in question contain a variable which

has a statistically significant influence on the depend-

dent variable.

121

This procedure leads to the identification of a
reduced regression model in which all remaining blocks of
variables contain at least one variable which has a
statistically significant influence on the dependent variable.
Finally, a more detailed analysis is made of each variable
in each remaining block.

The basic model from which all specific linear
regression models are formed is presented in table 47.

Both quantitative and qualitative variables are present.
Qualitative variable classes are represented in each
regression model by a set of binary variables where 1
indicates the presence of the designated characteristic and
0 indicates its absence. For example, in the Present Work
Situation Control (PWSC) block, the variable "entry level
into the factory" (ENT LVL F) is divided into three mutually
exclusive categories: (a) skilled lathe setter-Operator
(SKL LSO), (b) area of lathe operation (AR L0), and (c)
other area (OTH). A given Observation may fall into one

and only one category. The category into which the observa-
tion falls is coded 1. The remaining categories are coded 0.

When a set of such binary variables is used in a
linear regression model, one class must be dropped to avoid
singularity in the moments matrix of the regression. The B
coefficient associated with the remaining binary variables
should be interpreted as the estimated difference between
the mean cell value of the omitted category and the mean

cell value of the retained category, other things held

122

 

 

 

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125

 

 

 

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126

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127

equal. 8 coefficients associated with quantitative variables
should be interpreted as estimates of the response of the
dependent variable to a one unit change in the independent

variable.

5.2 Wage Per Hour

 

In Section 4.2.1 it was shown that hourly wage rates
for the 540 skilled lathe setter-Operators interviewed
ranged from less than Cr$2 to over Cr$10. The mean was
Cr$6.30, with an associated standard deviation of
Cr$1.69. Noting that the occupation of lathe setter-
operator is generally well defined and that the labor
market area in which the study was conducted is relatively
small, the basic Objective is to discover why such large
variation exists. Specifically, can the variation in wage
per hour be "explained" in terms of what is done on the job
(PWS), the conditions under which work takes place (PWSC),
the origin of the individual (IC), the learning experiences
to which the individual has been exposed (FED, WEX, and
TEX), or some combination of these factors?

It was suspected that individuals who have been
working as skilled lathe setter-Operators for a long period
might be different, in some respects, from those who have
recently reached the skilled occupational level. Thus,
the total sample was split into two subsamples. The first
contains individuals (229) who have been working at the

skilled occupational level for six or less years.

128

Individuals (311) who have been working at the skilled
level for seven or more years are placed in the second sub-
sample. Henceforth, these two subsamples are referred to'
as the Professionally Young and Professionally Old sub-
samples.

A "complete" regression model containing the vari-
able blocks PWS, PWSC, IC, FED, WEX, and TEX was estimated
for the total sample and the two subsamples. Estimated 8
coefficients, t values, and Rz's for the three regressions
are presented in table 48.

The first hypothesis tested with re5pect to the

total sample and the two subsamples is:

Bpws = BPwsc = B = B = B = BTEX = 0 (5'5)

where: 81, i = PWS, PWSC, IC, FED, WEX, TEX refer to the
vectors of 8 coefficients for the variable blocks
Present Work Situation, Present Work Situation
Control, Initial Conditions, Formal Education,
Work Experience, and Training Experience,
respectively.
In other words, the hypothesis is that none of the variables
in any of the variable blocks significantly influence the
dependent variable, wage per hour (WA H). Summary statis-
tics for the test on the total sample and on the two sub-
samples are presented in analysis of variance form (see
table 49).
In each of the three cases the ratio of the vari-
ance explained by the regression to the unexplained vari-

ance (the F statistic) is sufficiently large to reject the

hypothesis at the .05 level (see table 50).

129

TABLE 48.--Regression Analysis of Wage Per Hour for the
Total Sample and Two Subsamples—-Comp1ete Model.

 

 

 

Sample
Profes- Profes-
Variable sionally sionally
Block Variable Total Young Old
Constant 4.426 4.520 3.990
(4.326) (2.814) (2.739)
PWS OP DO .033 .032 .026
(4.003) (2.247) (2.489)
TOP OP - .019 - .037 .012
(1.143) (1.431) ( .469)
PWSC YRS F .054 .047 .032
(3.613) ( .998) (1.833)
ENT LVL F
AR LO - 1.051 - .734 - .093
(5.495) (2.458) ( .254)
OTH - .172 .633 - .127
( .541) (1.055) ( .257)
SECTOR F
M - .308 - .259 - .241
(2.319) (1.185) (1.378)
LATHE F
OTH - .021 - .470 .514
( .100) (1.467) (1.809)
IND GP & SZ F
03-S - .817 - .663 - .907
(2.400) (1.399) (1.597)
03-M - .446 - 1.038 .353
(1.740) (2.623) ( .849)
03-L 1.474 .896 1.473
(7.291) (2.719) (5.308)
05,7-M — .255 - .l7l - .465
(1.025) ( .472) (1.266)

TABLE 48.--Continued.

130

 

 

 

Sample
Profes« Profes-
Variable sionally sionally
Block Variable Total Young Old
OT—M - .145 .105 - .491
( .473) ( .252) (1.040)
OT-L 1.027 .918 .789
(5.080) (2.921) (2,730)
IC AGE - .031 .038 - .018
( .873) ( .652) ( .372)
PL BIRTH
GSP .363 - .771 .718
(1.055) (1.137) (1.708)
SPI .618 - .504 .693
(1.809) ( .698) (1.711)
OTH ST .216 — 1.203 .514
( .638) (1.660) (1.279)
PL PRI ED
GSP - .286 - .575 - .167
(1.126) (1.299) ( .498)
SPI - .473 - 1.028 - .164
(1.701) (1.925) ( .472)
ED FATHER
PR INC .266 .210 .226
(1.340) ( .679) ( .852)
PR CPLT .305 .143 .220
(1.547) ( .490) ( .829)
MID+ .379 .518 .289
(1.114) ( .879) ( .650)
DN - .373 - .841 .049
( .785) (1.110) ( .071)

TABLE 48.--Continued.

131

 

 

 

Sample
Profes- Profes-
Variable sionally sionally
Block Variable Total Young Old
ED MOTHER
PR INC - .212 - .302 .005
(1.187) (1.053) ( .020)
PR CPLT .000 - .161 .146
( .002) ( .615) ( .675)
MID+ - .217 - 1.466 .171
( .488) (1.975) ( .295)
DN .115 1.272 .276
( .157) ( .929) ( .264)
0C LVL FTHR
wc+ — .482 - .215 - .039
(1.920) ( .525) ( .121)
SUPR MN - .024 .321 — .092
( .088) ( .674) ( .272)
SKL D - .068 .348 - .048
( .348) (1.040 ( .199)
S-SKLD - .094 .466 — .167
( .556) (1.714) ( .779)
OC AR FTHR
IND - .108 - .252 - .285
( .489) ( .702) (1.039)
OTH - .094 - .155 - .359
( .427) ( .429) (1.273)
DN - .376 .237 - .471
(1.141) ( .451) (1.109)
FED LVL-PR SCHL
PRI INC - .309 - .523 - .285
(1.216) (1.018) ( .986)
( .299) ( .425) ( .210)

TABLE 48.--Continued.

132

 

.-.——

 

 

Sample
Profes- Profes-
Variable sionally sionally
Block Variable Total Young Old
M AC CPLT+ - .191 .231 .066
( .623) ( .570) ( .128)
M IND INC - .284 - .032 - .018
( .706) ( .043) ( .035)
M IND CPLT+ .160 .440 .074
( .598) ( .992) ( .216)
EFRT SCHL
+1 .114 - .141 .196
( .769) ( .557) (1.034)
+2 .353 .483 .297
(1.850) (1.516) (1.253)
+3 .171 .107 .198
( .794) ( .284) ( .729)
( .858) ( .926) (1.837)
+6+ .571 .567 .162
(2.038) (1.421) ( .362)
SPECIAL SCHL
M INC - .051 - .046 .064
( .187) ( .129) ( .149)
M CPLT+ - .275 .062 - .347
( .797) ( .115) ( .749)
WEX BFOR AR LO
ST L0 .495 .413 .476
(1.799) ( .980) (1.280)
STR L0 .460 .406 .401
(1.611) ( .907) (1.054)
OI RLO .726 .999 .585
(2.000) (1.658) (1.213)
OTH RLO .568 .515 .753
(1.574) ( .977) (1.533)

TABLE 48.-~Continued.

133

 

 

 

Sample
Profes- Profes-
Variable sionally sionally
Block Variable Total Young Old
OI .427 .523 .359
(1.628) (1.284) (1.006)
OTH OI 1.067 1.191 .705
(3.192) (2.350) (1.504)
YRS WK-AR L0 .014 - .003 - .020
( .408) ( .054) ( .445)
YRS AR LO‘SLO .062 .034 .026
(1.677) ( .594) ( .507)
YRS SLO .087 .244 .033
(2.405) (3.095) ( .669)
TEX CRS HRS CPLT .000 .000 .000
( .329) ( .551) ( .874)
CRS TYPE
LO - .286 .262 - .578
( .859) ( .482) (1.319)
LO + D - .091 .286 - .573
( .204) ( .433) ( .902)
L0 + D + .019 .854 .163
( .033) (1.050) ( .208)
L0 + — .153 .287 - .559
( .346) ( .426) ( .858)
D - .028 - .118 .278
( .083) ( .218) ( .640)
D+ - .254 .119 .593
( .575) ( .189) ( .952)
RLO + - .347 .524 - 1.269
( .875) ( .853) (2.322)

TABLE 48.--Continued.

134

 

 

 

Sample
Profes- Profes-
Variable sionally sionally
Block Variable Total Young Old
NUMBER CR5
1 .350 .584 .161
( .870) ( .565) ( .350)
2 .557 .826 .647
(1.125) ( .721) (1.035)
3 .418 .618 .444
( .714) ( .510) ( .573)
CRS SPONSOR
SN - .130 - 1.194 .486
( .339) (1.137) (1.127)
SN + PR .123 - .703 .194
( .274) ( .647) ( .362)
SN + PR + - .151 - 1.049 .107
( .263) ( .822) ( .158)
PR .074 - .832 .382
( .205) ( .841) ( .967)
PR + .020 - .811 .518
( .041) ( .720) ( .819)
PUB .005 - 1.116 .673
( .012) (1.006) (1.407)
SENAI APR
LO CPLT - .147 .053 - .246
( .860) ( .174) (1.180)
LO INC - .659 - .582 - .308
(1.693) ( .917) ( .618)
RLO CPLT .447 .860 .412
( .956) ( .848) ( .792)
RLO INC 2.000 .744 3.697
(2.114) ( .565) (2.842)

135

 

 

 

 

TABLE 48.--Continued.
Sample
Profes- Profes—

Variable sionally sionally
Block Variable Total Young Old

R2 .5496 .6530 .4782

SEE 1.233 1.152 1.150
Note: Omitted binary variables are: PWSC--(ENT LVL F) SKL

*

LSO, (SECTOR F) P, (LATHE F) MOD, (IND GP 8 52 F)
05, 7 s; Ig—-(PL BIRTH) OTH C, (PL PRI ED) OTH ST OR
C, (ED FATHER) NONE, (ED MOTHER) NONE, (OC LVL F)

N SKLD, (OC AR FTHR) AG; FED--(LVL PR S) PRI INC,
(EFRT SCHL) NORMAL; Eggs—TEFOR AR LO) OTH; Egg:-
(CRS TYPE) NCR 0, (NUMBER CRS) NCR, (CRS SPONSOR)
NCR O, (SENAI APR) NONE. For variable descriptions
see table 47 on page 122.

136

TABLE 49.--Analysis of Variance of Wage Per Hour for the
Total Sample and Two Subsamples--Complete Model.

 

Sum of Degrees Mean
Source of Squares of Freedom Square F
Variation (SS) (df) (MS) Value

 

(a) Total Sample

 

Explained 858.792 76 11.300 7.435
Error 703.696 463 1.520
Total 1,562.487 539

 

(b) Professionally Young Subsample

 

Explained 379.782 76 4.997 3.764
Error 201.821 152 1.328
Total 581.603 228

 

(c) Professionally Old Subsample

 

Explained 283.580 76 3.731 2.822
Error 309.399 234 1.322

Total 592.979 310

 

137

TABLE 50.--Test for Significance of the Regression Equation
for Wage Per Hour for the Total Sample
and Two Subsamples--Complete Model.

 

 

 

 

 

 

 

 

Ho‘ BPWS = BPWSC = 8IC = BEED = BWEx = BTEX = 0
Critical Value Hypothesis
F Degrees of F at .05 (FR) Fail to Reject
Value of Freedom Level (R) Reject
(a) Total Sample

7.435 76/463 1.32 R
(b) Professionally Young Subsample

3.764 76/152 1.37 R
(c) Professionally Old Subsample

2.822 76/234 1.35 R

 

Thus, it is concluded that at least one explanatory
variable in the complete model has a statistically signifi-
cant influence on wage per hour (WA H).

The second hypothesis tested is that the two sub-

samples were drawn from the same population:2

BPWSI = BPwsz, 8Pwscl = BPwsc2, 8IQ = B1C2,

B = B B = B B = B (5.6)

FEDl FEDZ, WEXl WEXZ, TEXl TEX2

where: the subscripts 1 and 2 refer to the Professionally
Young and Professionally Old Subsamples, respec-
tively; and 81 = PWS, PWSC, IC, FED, WEX, TEX which
refers to the vector of B coefficients for the
variable blocks Present Work Situation, Present
Work Situation Control, Initial Conditions, Formal
Education, Work Experience, and Training Experience,

respectively.

 

138

The hypothesis was rejected at the .05 level (see
table 51). It was concluded that the two subsamples
(Professionally Young and Professionally Old) come from
different populations and should be treated separately in

further analysis.

TABLE 51.--Test of Hypothesis that Both Subsamples Come From
the Same Population--Comp1ete Model.

 

H°“)>ws1 = BPws2' BPwsc1 = BPWSCZ' 8Ic2'= BICI' BFEDl =

 

 

BFED2' BWEXI = BWExz' 8mm = BTEx2
Critical Value Hypothesis
F Degrees of F at .05 (FR) Fail to Reject
Value of Freedom Level (R) Reject
1.917 76/387 1.33 R

 

5.2.1 The Professionally Young Subsample

It was established earlier in this section that at
least one explanatory variable in at least one of the
variable blocks of the complete model has a statistically
significant influence on the dependent variable, wage per
hour (WA H). The objective here is to establish if more
than one block contains a significant variable and, if so,
to identify the block(s). The complete model (containing
the variable blocks PWS, PWSC, IC, FED, WEX, and TEX) is
estimated for the Professionally Young Subsample. Next,
six reduced models (a different variable block dropped for

each estimation) are estimated. Finally, a reduced model

139

containing the variable blocks PWS, PWSC, and WEX (variable
blocks IC, FED, and TEX drOpped) is estimated. The results
for these regressions are presented in the analysis of
variance format (see table 52).

The hypothesis that each variable block taken
individually contains no statistically significant variable

is tested. Formally, a set of hypothesis tests is involved:

HO. BPWS - 0
Ho' BPWSC = 0
HO: BIC - 0
HO: BFED = 0 (5.7)
HO: BWEX = 0
Ho: BTEX = 0

where: Bi, i=PWS, PWSC, IC, FED, WEX, and TEX refer to the
vector of B coefficients associated with the vari-
able blocks Present Work Situation, Present Work
Situation Control, Initial Conditions, Formal
Education, Work Experience, and Training Experience,
respectively.

It was not possible to reject the hypotheses concerning

Initial Conditions (IC), Formal Education (FED), and Train-

ing Experience (TEX) (see table 53). The hypotheses con-

cerning Present Work Situation Control (PWSC), and Work

Experience (WEX) were rejected at the .05 level. Further

analysis indicated that the PWS block should be retained

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143

and the model for the Professionally Young subsample should
contain the three variable blocks, PWS, PWSC, and WEX. As

a further check the following hypothesis is tested:

HO: BIC = B = B = 0 (5-3)

where: 81' i=IC, FED, TEX refer to the vector of B coeffi-
Clents for the variable blocks Initial Conditions,
Formal Education, and Training EXperience, respec-
tively.

This hypothesis could not be rejected at the .05 level

(see table 54).
It was thus concluded that the regression model for

the Professionally Young subsample should contain only

the variable blocks Present Work Situation (PWS), Present

Work Situation Control (PWSC), and Work Experience (WEX).
The reduced model containing PWS, PWSC, and WEX

is estimated next. Results in analysis of variance format

are given in table 52, and estimated 8 coefficients, t

values, and R2 are presented in table 55.

5.2.2 The Professiona11y_Old Subsample

 

As with the Professionally Young subsample, it was
established that at least one explanatory variable in at
least one variable block of the complete model has a statis-
tically significant influence on the dependent variable,
wage per hour (WA H). In order to establish if more than
one of the variable blocks contain a significant variable,
the complete model (containing the variable blocks PWS,

PWSC, IC, FED, WEX, and TEX) is estimated for the

144

 

 

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145

TABLE 55.--Regression Analysis of Wage Per Hour for the
Professionally Young Subsample—~Reduced Model.

 

 

Variable Estimated 8 t value -
Block Variab1e* Coefficient (Absolute values)
Constant 3.782 4.529
PWS OP DO .032 2.760
TOP OP ~ .031 1.484
PWSC YRS F .075 1.918
ENT LVL F
AR LO - .681 2.723
OTH .197 .407
SECTOR F
M - .241 1.301
LATHE F
OTH - .705 2.573

IND GP & SZ F

03-S - .780 2.100
03-M - .759 2.333
03-L 1.077 4.144
05,7-M - .139 .447
OT-M .099 .282
OT-L 1.053 4.319
WEX BFOR AR LO
ST L0 .314 .913
ST RLO .405 1.118
OI RLO .769 1.571
OTH RLO .231 .514
OI .507 1.574

OTH OI 1.017 2.336

TABLE 55.--Continued.

146

 

 

Variable Estimated 8 t value
Block Variable* Coefficient (Absolute Values)
YRS WK-AR L0 .019 .746
YRS AR LO-SLO .031 1.071
YRS SLO .301 6.118
R2 .5520
SEE 1.125

 

Note: Omitted binary variables are: PWSC——(ENT LVL F) SKL

LSO, (SECTOR F) P (LATHE F) MOD, (IND GP.& 82 F)
05, 7-S; WEX--(BFOR AR LO) OTH. For varlable
descriptions see table 47 on page 122.

Professionally Old subsample. Next, six reduced models (a

different variable block dropped for each estimation)

are estimated. Finally, a reduced model containing only

the variable blocks PWS and PWSC (variable blocks IC,
FED, WEX and TEX dropped) is estimated. Results are pre-
sented in analysis of variance format (see table 56).

A set of hypotheses that each variable block viewed

individually contains no statistically significant explana-

tory variable is tested:

Ho: BPws = 0

Ho: BPwsc =

HO BIC — 0

H . B - 0 (5.9)

147

 

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149

where: Bi, i=PWS, PWSC, IC, FED, WEX, and TEX refer to the
vectors of B coefficients for the variable blocks
Present Work Situation, Present Work Situation
Control, Initial Conditions, Formal Education, Work
Experience, and Training Experience, respectively.
The hypotheses concerning Initial Conditions (IC), Formal
Education (FED), Work Experience (WEX), and Training
Experience (TEX) were not rejected at the .05 level (see
table 57).
The hypotheses concerning Present Work Situation
(PWS), and Present Work Situation Control (PWSC) were
rejected at the .05 level. Thus in the case of the Profes-
sionally Old subsample, the conclusion seems to be that the
"proper" model contains only the variable blocks Present
Work Situation (PWS) and Present Work Situation Control

(PWSC). To gain further support for this conclusion the

following hypothesis is tested:

H (5.10)

.0
U)
I
m
ll
ID
I)
ID
II
C)

0 IC _

where: Bi, i=IC, FED, WEX, and TEX refer to the vectors
of B coefficients for the variable blocks Initial
Conditions, Formal Education, Work Experience, and
Training Experience, respectively.

The hypotheses could not be rejected at the .05 level (see

table 58), and it was therefore concluded that the regres-

sion model for the Professionally Old subsample properly

contains only the variable blocks Present Work Situation

(PWS) and Present Work Situation Control (PWSC).

150

 

 

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151

 

 

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152

The reduced model containing PWS and PWSC was
estimated and the results are presented in analysis of
variance format in table 56. Estimated 8 coefficients,

t values, and R2 are presented in table 59.

5.2.3 Summary--Wage Per Hour

 

Present Work Situation (PWS).--The Present Work

 

Situation block has two variables: the number of different
operations performed on the job (OP DO) and the degree of
difficulty of the operations performed (TOP OP). The
general hypothesis that all 8 coefficients assoicated with
the PWS block were simultaneously equal to zero, was
rejected at the .05 level for both the Professionally Young
and the Professionally Old subsamples. For both subsamples
the B coefficient associated with the variable OP D0 is
positive and significant at the .05 level (Young 8

OP DO=
.032, Old BOP D0 = .021). Other things equal, performing
more operations on the job is positively associated with
higher hourly wage rates. The B coefficient associated with
the variable TOP OP is not statistically significant for

either subsample.

Present Work Situation Control (PWSC).--There are

 

two groups of variables in the Present Work Situation Con-
trol block. The first concerns the worker's history with
respect to the present factor: how long he has been work-
ing in the factory (YRS F) and at what level he entered

the factory (ENT LVL F). The second concerns the actual

153

TABLE 59.--Regression Analysis of Wage Per Hour for the
Professionally Old Subsample—-Reduced Model.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
Constant 5.306 6.796
PWS OP DO .021 2.166
TOP OP .007 .337
PWSC YRS F .035 2.586
ENT LVL F
AR LO - .225 .716
OTH .005 .012
SECTOR F
M - .222 1.489
LATHE F
OTH .461 1.898

IND GP & SZ F

03-s - 1.185 2.500
03-M .250 .743
03-L 1.387 6.096
05,7-M — .620 1.980
OT-M - .629 1.563
OT-L .579 2.418

R2 .3631

SEE 1.128

 

Note: Omitted binary variables are: PWSC--(ENT LVL F)
SKL LSO, (SECTOR F) P, (LATHE F) MOD, (IND GP &
82 F) 05,7 8. For variable descriptions see
table 47 on page 122.

154

conditions under which the individual presently works:
the sector of the factory (SECTOR F), the type of lathe
used (LATHE F), and the size and industrial group of the
factory (IND GP & 82 F). The general hypothesis that all
8 coefficients associated with the PWSC block were equal

to zero was rejected at the .05 level for both subsamples.

(a) The Professionally Young Subsample.--The number
of years working in the factory (YRS F) is not statisti-
cally significant. However, the second history variable,
level of entry (ENT LVL F), is significant. Specifically,
in comparison to those who entered the factory as skilled
lathe setter-operators (SKL LSO), those who entered in the
area of lathe operation, but at less than the skilled level

(AR LO) earn less per hour (8 -.681). The difference

AR LO=
is significant at the .05 level. The mean wage per hour of
those who entered in an area outside lathe operation (OTH)
cannot be shown to be statistically different from those in
the SKL LSO category.

Though the mean wage rate for those working in the
maintenance sector (M) is lower (BM = -.241) than the mean
wage rate for those in the production sector (M), the dif-
ference is not statistically significant. However, for
those using old and special lathes (OTH), the mean hourly
wage rate is lower (BOTH = -.705) than for those using

modern lathes (MOD-DN), and the difference is statistically

significant.

155

The size and the industrial group of the factory in
which the individual works is important in explaining
variation in wate rates. Taking medium size factories (less
than 50 employees) in the 05 and 07 subgroups (05, 7-S)
as the base category, mean wage rates in large factories

(over 350 employees) are higher (8 = 1.077 and BOT- =

03-L L

1.053) and the difference in the means is statistically
significant. Mean wage rates for small and medium factories
in the 03 subgroups are significant and lower (803-5 = -.780

and BOB-M = -.759). Mean wage rates for the 05, 7-M and
OT-M categories are not statistically different from the

05, 7-S category.

(b) The Professionally Old Subsample.--The number
of years working in the factory (YRS F) is Significant and
positively associated with higher wage rates (BYRS F = .035).
Differences in entry level (ENT LVL F) are, however, not
statistically significant.

Differences in the sector of work (SECTOR F) and in
the type of lathe used (LATHE F) are also not statistically
significant. As with the Professionally Young subsample,
differences in factory size and industrial group are
important. Again using the small factories (less than 50
employees) in the 05 and 07 subgroups (05, 7-S) as the base,
mean wage rates in large factories are higher (BOB-L = 1.387

and BOT-L = .579) and the difference in the means is statis-

tically significant. The mean wage rate for the 05, 7-M

156

category is statistically significant and lower (805 7-M =

I
-.620). The means for the 03—M and 05,7-M categories are
not statistically different from the means of the base

category (05,7-S).

Initial Conditions (IC).--The hypothesis that all 8

 

coefficients associated with the IC block of variables are
simultaneously equal to zero was not rejected for either
subsample at the .05 level. Therefore, it is concluded
that differences in age (AGE), place of birth (PL BIRTH),
place of primary education (PL PRI ED) educational level
of father (ED FATHER), educational level of mother (ED
MOTHER), occupational level of father (OC LVL FTHR), and
occupational area of father (OC AR FTHR) have no statisti-

cally significant influence on hourly wage rates.

Formal Education (FED).--The hypothesis that all 8

 

coefficients associated with the FED block of variables

are simultaneously equal to zero was not rejected at the
.05 level. Thus, it is concluded that differences in

level and type of formal education (LVL-PR SCHL), the num-
ber of years taken to reach a given grade (EFRT SCHL), and
the fact that an individual has or has not been exposed to
the special rapid school program (SPECIAL SCHL), have no
statistically significant influence on wage per hour. Mean
wage rates for those having either more or less than a
complete primary education are not statistically different

from those who have a complete primary education but did

157

not go beyond this level. Regardless of the type of post
primary education (academic or industrial), and regardless
of whether the post primary program was completed, mean wage

rates are not statistically different.

Work ExperienceJWEX).--(a) The Professionally Young

 

Subsample.--Those who started in agricultural or other non—
industrial occupations and then had some industrial experi-
ence not related to lathe operation (OTH OI) have a higher

mean wage rate (8 = 1.017) than those who entered

OTH OI
directly fromaanoneindustrial occupational area (OTH). The
means of the other categories (ST LO, ST RLO, OT RLO, OTH
RLO) are not statistically different from the mean of the
OTH category. The B coefficients associated with the time
spent working before entering the area of lathe operation
(YRS WK - AR L0) and the time span between the first job

in the area of lathe operation and the attainment of the
skilled lathe setter-Operator level (YRS AR LO-SLO) are
also not statistically significant. However, the number

of years that the individual has been working as a skilled
lathe setter-operator (YRS SLO) does have a statistically
significant positive influence on the dependent variable,

wage per hour (8 .301).

YRS SLO =

(b) The Professionally Old Subsample.—-The hypo-
thesis that all of the B coefficients associated with the
WEX block of explanatory variables are simultaneously equal

to zero was not rejected at the .05 level of the

158

Professionally Old subsample. Thus it is concluded that
for those who have been working as skilled lathe setter-
operators for seven or more years, type and duration of
previous work experiences have no statistically significant

influence on wage per hour.

TrainingyExperience (TEX).--The hypothesis that all

 

B coefficients associated with the TEX block of variables
are simultaneously equal to zero was not rejected at the
.05 level for either the Professionally Young or the Pro-
fessionally Old subsamples. There is no evidence to
suggest that those having no courses (NONE) receive either
higher or lower salaries than those with one, two, or
three courses. Nor can it be established that types of
courses taken (CRS TYP), or different sponsors for the
courses (CRS SPONSOR), or the total hours of course work
completed (CRS HRS CPLT) have a statistically significant
influence on hourly wage rates. Further, the fact that an
individual has or has not taken part in the SENAI appren-
ticeship program (SENAI APR) cannot be shown to have a

statistically significant influence on hourly wage rates.

5.3 Number of Operations Performed (OP DO)

 

Section 4.2.1 presented the list of 41 different
operations which skilled lathe setter-operators generally
are considered to perform. For the total sample of 540
individuals, the mean number of operations performed on the

job was 30.5 and the associated standard deviation was 9.58.

159

The number of operations performed (OP DO) was shown in
Section 5.2 to have a positive and statistically signifi-
cant influence on hourly wage rates. The objective in this
section is to "explain" the variation in the number of
Operations performed. The linear regression model employed
is the identical model used in the previous sections,
except the Present Work Situation (PWS) block of explana-
tory variables is dropped. The general form of the model

is thus:
OP DO (PWSC, IC, FED, WEX, TEX)

where: OP DO is the dependent variable, and PWSC, IC,
FED, WEX, and TEX refer, respectively, to the
variable blocks Present Work Situation Control,
Initial Conditions, Formal Education, Work
Experience, and Training Experience which are
used in the construction of the complete model.
Estimated 8 coefficients, t values, and R2 for the
complete model are presented in table 60.
The first hypothesis tested is that no variable in
any of the explanatory variable blocks has a statistically

significant influence on the dependent variable, the number

of operations performed (OP DO):

HO: BPWSC = B = B = 8 = B = 0 (5.11)

where: 81, i=PWSC, IC, FED, WEX, and TEX refer to the
vector of B coefficients for the variable blocks
Present Work Situation Control, Inditial Condi—
tions, Formal Education, Work Experience, and
Training Experience, respectively.

Summary statistics for the test of this hypothesis

are given in table 61.

160

TABLE 60.--Regression Analysis of Number of Operations
Performed—-Complete Model.

 

 

Variable Estimated 8 t value
Block Variable* Coefficient (Absolute values)
Constant 42.355 8.047
PWSC YRS F .183 1.857
ENT LVL F
AR LO - 1.221 .972
OTH - .541 .258
SECTOR F
M 3.799 4.444
LATHE F
OTH - 8.352 6.397

IC

IND GP & 82 F

03-S
03-M

03-L

05,7-M

OT-M

OT-L

AGE

PL BIRTH

GSP

SPI

OTH ST

PL PRI ED

GSP

SPI

ED FATHER
PR INC

1.601

.648
2.024
4.718
4.406
4.454

.315

2.996
2.978

3.934

.201
.616

1.559

.716

.370
1.536
2.905
2.195
3.409
1.345

1.327
1.330
1.778

.121

.337

1.197

TABLE 60.--Continued.

161

 

 

Variable Estimated 8 t value
Block Variable* Coefficient (Absolute values)
PR CLPT - 1.798 1.392
MID+ - 1.051 .470
DN - 3.935 1.263
ED MOTHER
PR INC .196 .167
PR CPLT 1.039 .952
MID+ 2,149 .734
DN - 9.269 1.948
OC LVL FTHR
wc+ — .657 .399
SUPR MN - 1.062 .600
SKLD - .674 .525
S-SKLD - .947 .851
OC AR FTHR
IND - 3.211 2.233
OTH - 2.828 1.964
DN - 2.215 1.024
FED LVL-PR SCHL
PRI INC - .603 .361
M AC INC - .489 .430
M AC CPLT+ 2.440 1.219
M IND INC .275 .104
M IND CPLT+ - 2.189 1.252

TABLE 60.--Continued.

162

 

 

Variable Estimated 8 t value
Block Variable* Coefficient (Absolute values)
EFRT SCHL
+1 - .992 1.018
+2 - .367 .299
+3 — 1.616 1.147
+4 or 5 .085 .049
+6+ - 2.975 1.636
SPECIAL SCHL
M INC — .838 .471
M CPLT+ - 2.528 1.116
WE)( BFOR AR LO
ST LO 1.671 .927
ST RLO 1.412 .753
OI RLO 2.880 1.209
OTH RLO - 3.924 1.659
OI 1.044 .606
OTH OI - .439 .199
YRS WK-AR LO .136 .614
YRS AR LO-SLO .170 .698
YRS SLO .322 1.362
TEX CRS HRS CPLT .001 1.644
CRS TYPE
LO 2.620 1.200
LO + D 5.680 1.950
LO + D + 9.036 2.481
LO + 3.622 1.247

TABI£:60.--Continued.

163

 

 

Variable Estimated 8 t value
Block Variable* Coefficient (Absolute values)

D 3.102 1.426
0* 3.984 1.376
RLO+ 3.614 1.391

NUMBER CRS
1 - 2.437 .923
2 — 3.445 1.058
3 - 5.985 1.556

CRS SPONSOR
SN - 1.687 .668
SN + PR - .916 .310
SN + PR + — .334 .089
PR - 1.609 .681
PR + - 1.324 .402
PUB - 2.396 .838

SENAI APR
LO CPLT 1.109 .993
LO INC 1.820 .713
RLO CPLT - 2.730 .891
PLO INC .746 .120

R2 .3817

SEE 8.108

 

Note: Omitted binary variables are: PWSC--(ENT LVL F) SKL

LSO, (SECTOR F) P, (LATHE F) MOD, (IND GP & 82 F)

*05,7 S; IEf-(PL BIRTH) OTH C, (PL PRI ED) OTH ST or
C, (ED FATHER) NONE, (ED MOTHER) NONE, (OC LVL F)

N SKLD, (OC AR FTHR) AG; Egge-(LVL PR S) PRI INC,
(EFRT SCHL) NORMAL; fl§§--(BFOR AR LO) OTH; TEX-~(CRS
TYPE) NCR O, (NUMBER CRS) NCR, (CRS SPONSORI’NCR O,
(SENAI APR) NONE. For variable descriptions see
table 47 on page 122.

164

TABI&:6l.--Analysis of Variance of Number of Operations
Performed--Complete Model.

 

 

Sum of Degrees Mean
Source of Squares of Freedom Square F
Variation (SS) (df) (MS) Statistic
Explain 18,870.630 74 255.009 3.879
Error 30,566.369 465 65.734
Total 49,436.999 539

 

The hypothesis was rejected at the .05 level (see
table 62). It was thus concluded that at least one
explanatory variable in one of the variable blocks signi-
ficantly influenced the number of operations performed.

TABLE 62.--Test for Significance of the Regression Equation
for Number of Operations Performed--Complete

 

 

 

Model.
H0: BPwsc = 8IC = BFED = BWEX = BTEX = 0
Degrees Critical Value Hypothesis
F of Freedom of F at .05 ((FR) Fail to Reject
Value (df) Level (R) Reject
3.879 74/465 1.32 R

 

To establish if more than one explanatory block
has at least one statistically significant variable, a set
of reduced models is estimated. The first set contains
five reduced models. For each estimation, a different
explanatory block of variables is dropped from the model.
The sixth reduced model estimated contains only the

Present Work Situation Control (PWSC) block of explanatory

165

variables (the IC, FED, WEX, and TEX variable blocks were
dropped). Results for the six reduced models and the
complete model are presented in analysis of variance format
in table 63.

A set of five hypotheses that a given variable

block contained no statistically significant variable is

tested:
Ho: BPWSC - 0
Ho. BIC ’ 0
Ho‘ BFED = 0 (5.12)
Ho‘ BWEX = 0
Ho‘ BTEX = 0

where: Bi, i=PWSC, IC, FED, WEX, and TEX refer to the
vectors of B coefficients for the variable blocks
Present Work Situation Control, Initial Conditions,
Formal Education, Work Experience, and Training
Experience, respectively.
At the .05 level of significance it was not possible to
reject the hypotheses concerning the variable block
Initial Conditions (IC), Formal Education (FED), Work
Experience (WEX), and Training Experience (TEX). The
hypothesis concerning the block Present Work Situation
Control (PWSC) was rejected (see table 64).
These results suggest that the regression model
should contain only the Present Work Situation Control

(PWSC) block of explanatory variables. As a further

check the following hypothesis was tested:

166

 

 

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168
H : B = B = 8 = B = 0 (5.13)

where: 8', i=IC, FED, WEX, and TEX refer to the vectors
ol 8 coefficients for the variable blocks Initial
Conditions, Formal Education, Work Experience,
and Training Experience, respectively.
The hypothesis was not rejected at the .05 level
(see table 65). It is thus concluded that the regression
model for number of operations performed (OP D0) is the
reduced model containing only the variable block Present
Work Situation Control (PWSC). Estimated 8 coefficients,

t values, and R2 for this reduced model are presented in

table 66).

5.3.1 Summary

There is no evidence to suggest that differences in
Initial Conditions (IC), Formal Education (FED), Work
Experience (WEX), and Training Experience (TEX) significantly
influence the number of operations performed on the job
(OP DO). There are, however, variables within the Present
Work Situation Control block (PWSC) which are statistically
significant.

The length of time working in the factory (YRS F)
is significant and positively associated with the number

of operations performed (8 .183). Differences in

YRSP=
mean cell values for factory entry level (ENT LVL F) are
not statistically significant. Both the sector of work

(SECTOR F) and the type of lathe used (LATHE F) signifi-

cantly influence the number of operations performed. The

169

 

 

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170

TABLE 66.-~Regression Analysis of Number of Operations
Performed--Reduced Model.

 

Variable Estimated 8 t-value

 

Block Variable Coefficient (Absolute values)
Constant 30.527 29.680
PWSC YRS F .183 2.425
ENT LVL F
AR LO - 1.187 1.181
OTH - 2.456 1.379
SECTOR F
M 4.038 5.132
LATHE F
OTH - 8.873 7.295

INC GP 8 82 F

03-s .929 .457
03-M .841 .519
O3-L 2.132 1.822
05,7-M - 4.491 2.960
OT-M 3.896 2.092
OT-L — 4.948 4.217

R2 .3053

SEE 8,065

 

Note: Omitted binary variables are: PWSC--(ENT LVL F) SKL
LSO, (SECTOR F) P, (LATHE F) MOD (IND GP & 82 F)
05, 7 S. For variable descriptions see table 47,

page 122.

171

mean number of operations performed in the maintenance
sector (M) is higher than the mean for the production

sector (P) (BM = 4.038). On the average, individuals using
old or specialized lathes (OTH) do fewer operations (BOTH =
-8.873) than those using the more common, modern type lathes.
Differences in the size and industrial group of the factory
(IND GP & SZ F) in some cases has a statistically signi-
ficant influence on the number of Operations performed.
Regardless of size, the average number of operations
performed in the 03 industrial group cannot be shown to be
statistically different from the mean by the base category
(05, 7-S). However, differences between the mean of the
base category and the means of the 05,7—M, OT-M, and OT-L
categories are significant. Both the 05,7-M and OT-L
categories have lower means (805,7-M = -4.491 and BOT-L =
-4.948). On the other hand, the mean cell value of the

= 3.896).

OT-M group 15 higher (BOT-M

5.4 Difficulty of Operations
PerformedOYTOP OP)

 

 

In Section 4.2.1 an index was develOped for the
degree of difficulty of the operations performed on the job.
The index (TOP OP) was used as an explanatory variable in
the Present Work Situation (PWS) block of the regression
model employed in Section 5.2. It was shown that while
the index (TOP OP):E;positively associated with higher

hourly wage rates, the relationship is not statistically

172

significant. As constructed, the index has a range from

1 to 41. With respect to the total sample (540 individuals)
the mean rank on the scale was 39.4 and the associated
standard deviation was 4.3. The objective in this section
is to ”explain" the variation in the index (TOP OP). The
regression model employed is identical to the model used

in Section 5.3. The general form of the model is:
TOP OP (PWSC, IC, FED, WEX, TEX)

where: TOP OP is the dependent variable, and PWSC, IC,
FED, WEX, and TEX refer respectively to the
variable blocks, Present Work Situation Control,
Initial Conditions, Formal Education, Work
Experience, and Training Experience, which are
used in the construction of the complete model.

Estimated 8 coefficients, t values, and R2 for
the complete model are presented in table 67.

The first hypothesis tested is that none of the
explanatory variables in any of the variable blocks have a
statistically significant influence on the dependent

variable (TOP OP). The hypothesis tested is:

8 = B = B = B = 0 (5.14)

H : 8 IC

0 PWSC =

where: Bi, i=PWSC, IC, FED, WEX, and TEX refer to the
vector of B coefficients associated with the
variable blocks Present Work Situation Control,
Initial Conditions, Formal Education, Work
Experience, and Training Experience, respectively.

Summary statistics for the test are given in table 68.

173

TABLE 67.--Regression Analysis of Difficulty of Operations
Performed—-Complete Model.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
Constant 44.417 17.145
PWSC YRS F .046 .943
ENT LVL F
AR LO - .391 .632
OTH .162 .157
SECTOR F
M 1.078 2.561
LATHE F
OTH - 1.590 2.474
IND GP &
03—S .061 .055
03-M - 1.392 1.613
03-L - .850 1.311
05,7—M - 1.462 1.829
OT-M .530 .536
OT-L - 2.370 3.686
IC AGE - .058 .502
PL BIRTH
GSP - 1.572 1.415
SPI - 1.793 1.627
OTH ST - 2.095 1.924
PL PRI ED
GSP .105 .127
SPI .499 .550
ED FATHER
PR INC .396 .619

TABLE 67.--Continued.

174

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
PR CLPT .011 .018
MID+ .547 .497
DN — 2.519 1.643
ED MOTHER
PR INC - .431 .744
PR CPLT - .392 .730
MID+ — .027 .019
DN - 10.011 4.274
OC LVL FTHR
wc+ .397 .489
SUPR MN — .323 .371
SKLD .750 1.188
S-SKLD .352 .642
OC AR FTHR
IND - 1.591 2.248
OTH - .827 1.167
DN - .070 .066
FED LVL PR SCHL
PRI INC .698 .849
M AC INC .583 1.044
M AC CPLT+ 2.572 2.610
M IND INC 2.071 1.595
M INC CPLT+ .416 .483

175

TABLE 67.--Continued.

 

‘v

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
EFRT SCHL
+1 - .253 .527
+2 .165 .273
+3 — 1.037 1.496
+4 or 5 - .630 .748
+6+ - 3.056 3.414
SPECIAL SCHL
M INC .453 .518
M CPLT+ .242 .217
WEX BFOR AR LO
ST LO - .798 .900
ST RLO .292 .317
O1 RLO 1.667 1.422
OTH RLO - 1.406 1.207
OI - .118 .139
OTH OI .249 .230
YRS WK-AR LO - .116 1.058
YRS AR LO-SLO - .024 .202
YRS SLO .068 .589
TEX CRS HRS CPLT - .000 .327
CR TYPE
LO 1.399 1.302
LO + D 2.243 1.564
LO + D + 2.047 1.142
LO+ 2.434 1.702

176

TABLE 67.--Continued.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)

D 2.085 1.947

D+ — .229 .161

RLO+ 1.631 1.276

NUMBER CRS

1 - .505 .388

2 - .951 .593

3 - 1.631 .862

CRS SPONSOR

SN - .981 .789
SN + PRIV .220 .151
SN + PRIV + .698 .376
PR - 1.225 1.053
PR+ .697 .429
PUB - .350 .249

SENAI APR
LO CPLT 1.041 1.892
LO INC 1.820 1.448
RLO CPLT .772 .512
RLO INC - 1.001 .327

R2 .2635

SEE 3.991

 

Note: Omitted binary variables are: PWSC--(ENT LVL F)
SKL LSO, (SECTOR F) P, (LATHE E) MOD, (IND GP &
sz F) 05,7-S; IC--(PL BIRTH) OTH C, (PL PRI ED) OTH
ST or C, (ED FATHER) NONE, (ED MOTHER) NONE, (0C
LVL F) N SKLD, (OC AR FTHR) AG; FED--(LVL PR 5)
PRI INC, (EFRT SCHL) NORMAL; WEXF:(BFOR AR LO) OTH;
TE§--(CRS TYPE) NCR 0, (NUMBER CRS) NCR, (CRS
SPONSOR) NCR O, (SENAI APR) NONE. For variable
descriptions see table 47 on page 122.

177

TABLE 68.--Ana1ysis of Variance of Difficulty of Operations
Performed--Complete Model.

 

 

Sum of Degrees Mean
Source of Squares of Freedom Square
Variations (SS) (df) (MS) F Statistic
Explained 2,649.624 74 35.806 2.248
Error 7,405.709 465 15.926
Total 10,055.333 539

 

The hypothesis that none of the variables at any of the
variable blocks has a significant influence on the depen-
dent variable (TOP OP) is rejected at the .05 level (see

table 68).

TABLE 69.--Test for Significance of the Regression Equation
for Difficulty of Operations Performed--
Complete Model.

 

 

 

Ho‘ BPWSC = 8IC = 8FED = BWEX = BTEX = 0
Degrees Critical Value Hypothesis
F of Freedom of F at the (FR) Fail to Reject
Value (df) .05 Level (R) Reject
2.248 74/465 1.32 R

 

It was thus conluded that at least one variable
block in the complete model contains a statistically signi-
ficant variable. Next a set of reduced models is estimated
to determine if more than one variable block contains a
significant variable. The first set consists of five
reduced models in which a different variable block is

dropped from the model for each of the estimations. The

178

sixth reduced model contained only the variable blocks
Present Work Situation Control (PWSC) and Initial Conditions
(IC). Summary results for the estimations of the complete
model and the six reduced models are presented in table 70.
The specific set of hypotheses tested with respect

to the individual variable blocks is:

“0‘ BPWSC = 0
Ho: BIC - 0
Ho: BPED - 0
(5.15)
“0‘ BWEX - 0
HO: BTEX - 0

where: Bi, i=PWSC, IC, FED, WEX, and TEX refer to the
vectors of B coefficients for the variable blocks
Present Work Situation Control, Initial Conditions,
Formal Education, Work Experience, and Training
Experience, respectively.
The hypotheses concerning the variable blocks
Formal Education (FED), Work Experience (WEX) and Training
Experience (TEX) are not rejected at the .05 level. How-
ever, the hypotheses concerning the variable blocks Present
Work Situation Control (PWSC), and Initial Conditions (IC)
are rejected (see table 71). The results of the tests
suggest that the regression model contains only the vari-
able blocks Present Work Situation Control (PWSC) and
Initial Conditions (IC). As a further check the following

hypothesis is tested:

179

 

 

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180

 

xmB

 

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H : B = B = B = O (5.16)

where: 81, i=FED, WEX, and TEX refer to the vectors of
B coefficients for the variable blocks Formal
Education, Work Experience, and Training Experience,
respectively.
The hypothesis was rejected at the .05 level (see table 72).
Further analysis indicated, however, that the regression
model for the difficulty of Operations performed (TOP OP)
is the reduced model containing the variable blocks Present
Work Situation Control (PWSC) and Initial Conditions (IC).

Estimated 8 coefficients, t values, and R2 for this reduced

model are presented in table 73.

5.4.1 Summary

There is no evidence to suggest that differences in
Formal Education (FED), Work Experience (WEX), and Training
Experience (TEX) have a significant influence on the diffi-
culty of operations performed (TOP OP). Within the
Initial Conditions (IC) block there is one variable which
is significant at the .05 level. The set of binary vari-
ables used to present different educational levels for the
mother (ED MOTHER) contains the binary variable coded DN
which represents the category for which the individual did
not know the educational level of his mother. The mean
cell value for this category is statistically different from
the mean cell value of the base category NONE (BDN = -8.744).
This is the only variable within the IC block which is

statistically significant.

182

 

 

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183

TABLE 73.--Regression Analysis of Difficulty of Operations
Performed-~Reduced Model.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
Constant 42.024 24.785
PWSC YRS F .064 1.344
ENT LVL F
AR LO — .954 1.638
OTH - .818 .861
SECTOR
M 1.082 2.679
LATHE
OTH - 2.063 3.291
IND GP & SZ
03-S .344 .322
03-M - 1.203 1.437
03-L - .466 .763
05,7-M - 1.571 2.006
OT-M .350 .365
OT-L - 2.244 3.692
IC AGE - .023 .789
PL BIRTH
GSP - .852 .793
SPI — 1.039 .980
OTH ST — 1.861 1.741
PL PRI ED
GSP - .220 .286
SPI .009 .011

184

TABLE 73.--Continued.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
ED FATHER
PR INC .763 1.212
PR CLPT .382 .616
MID+ .589 .539
DN - 1.444 .973
ED MOTHER
PR INC - .322 .560
PR CPLT - .145 .274
MID+ .461 .327
DN - 8.744 3.756
OC LVL FTHR
wc+ .398 .510
SUPR MN - .152 .178
SKLD .663 1.075
S-SKLD .430 .804
0C AR FTHR
IND - .736 1.112
OTH - .049 .074
DN .710 .698
R2 .1644
SEE 4.071

 

Note: Omitted binary variables are: PWSC--(ENT LVL F)
SKL LSO, (SECTOR F) P, (LATHE F) MOD, (IND GP &
82 F) 05,7-S; £§--(PL BIRTH) OTH C, (PL PRI ED)
OTH ST or C, (ED FATHER) NONE, (ED MOTHER) NONE,
(OC LVL F) N SKLD, (OC AR FTHR) AG. For variable
descriptions see table 47 on.page 122.

185

The Present Work Situation Control (PWSC) block
contains three sets of variables which have a statistically
significant influence on the difficulty of operations
performed (TOP OP); (SECTOR FL (LATHE F), and (IND OP &

82 F). On the average, those working in the maintenance
sector (M) do more difficult operations than those working
in the production sector (BM = 1.082). The mean cell
value for those using old and specialized lathes (OTH) is

lower (8 = -2.063) than for those using the common

OTH
modern lathes. In some cases, differences in the size and
industrial group of the factory also have a significant
influence on the difficulty of operations performed. Using
the 05,7-M class as the base, both the 05,7-M and OT-L
classes have lower mean cell values (805,7-M = 1.157 and
BOT-L = -2.244) which are statistically significant. The
remaining classes do not have cell means which are signi-
ficantly different from the cell mean of the base category.
5.5 Years Taken to Reach Skilled

OccupationaIiLevel
(YRS AR LO-SLO)

 

 

 

The objective in this final section of Chapter V is
to "explain" the variation in the time individuals take to
reach the skilled occupational level (YRS AR LO-SLO) . This
time span is determined by two events in the work history
of the individual. The first is his initial job in the
area of lathe operation. Possible occupational titles for

this first job are apprentice lathe operator, lathe

186

operator's helper, operator of automatic lathe, operator
of pre-set lathe, or skilled lathe setter-Operator. The
other event is his first job as a skilled lathe setter-
operator. In only 16 cases did the two events coincide;
that is, the first job in the area of lathe operation was
as a skilled lathe setter-operator. The mean number of
years taken to reach the skilled occupational level is 3.9
and the standard deviation is 2.9.

The general form of the regression model used is:
YRS AR LO-SLO (IC, FED, WEX, TEX) (5.17)

where: YRS AR LO-SLO is the dependent variable, and IC
FED, WEX, and TEX refer, respectively, to the
variable blocks Initial Conditions, Formal
Education, Work Experience, and Training Experi-
ence which are used in the construction of the
complete model.
Estimated 8 coefficients, t values, and R2 for the
complete model are presented in table 74.
The first hypothesis tested is that none of the
variables in any of the variable blocks has a statistically

significant influence on the dependent variable (YR AR

LO-SLO):

HO: BIC = B = B = B = 0 (5.18)

where: Bi, i=IC, FED, WEX, and TEX refer to the vector
of B coefficients for the variable blocks Initial
Conditions, Formal Education, Work Experience, and
Training Experience, respectively.

Summary statistics for the test are given in table 75.

187

TABLE 74.-—Regression Analysis of Years Taken to Reach the
Skilled Occupational Level--Comp1ete Model.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
Constant 3.356 2.825
IC AGE .089 5.278
PL BIRTH
GSP - .920 1.295
SPI - 1.149 1.635
OTH ST - .526 .751
PL PRI ED
GSP .358 .693
SPI .798 1.391
ED FATHER
PR INC .721 1.757
PR CLPT .298 .741
MID+ 1.026 1.451
DN .501 .511
ED MOTHER
PR INC - .488 1.325
PR CLPT - .163 .476
MID+ - 1.405 1.522
DN - 1.148 .763
OC OVL FTHR
wc+ - 1.092 2.118
SUPR MN - .799 1.430
SKLD - .257 .631
S-SKLD - .390 1.097

TABLE 74.--Continued.

188

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
OL AR FTHR
IND .346 .767
OTH .267 .596
DN - .430 .628
FED LVL-PR SCHL
PRI INC .112 .214
M AC INC - .397 1.118
M AC CPLT+ — 1.316 2.081
M IND INC - 2.654 3.207
M IND CLPT+ — 3.306 6.319
EFRT SCHL
+1 - .023 .076
+2 — .666 1.722
+3 - .441 .993
+4 or 5 .030 .055
+6+ .773 1.371
SPECIAL SCHL
M INC - .009 .016
M CPLT+ - 1.656 2.322
WEX BFOR AR LO
ST LO - .249 .442
ST RLO - 1.801 3.120
OI RLO - 2.389 3.213
OTH RLO - 1.103 1.516

189

TABLE 74.--Continued.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
OI - 1.055 1.967
OTH OI - .236 .342
YRS WK-AR LO - .148 3.746
TEX CRS HRS CPLT - .000 .070
CRS TYPE
LO - .563 .708
L0 + D .385 .336
L0 + D + 2.954 1.664
LO + .667 .589
D .466 .576
D + 1.227 .973
RLO .563 .578
NUMBER CRS
1 .173 .167
2 - .280 .212
3 - 1.447 .873

CRS SPONSOR

SN - .175 .187
SN + PR 1.057 .918
SN + PR + - 1.428 .755
PR - .161 .185
PR + - .163 .130

PUB - 1.213 1.213

190

TABLE 74.--Continued.

 

 

 

Variable Estimated 8 t value
Block Variable Coefficient , (Absolute values)
SENAI APR
LO CLPT .336 1.016
LO INC .440 .548
RLO CLPT - 1.620 1.681
RLO INC - .224 .116
R2 .2702
SEE 2.600
Note: Omitted binary variables are: IC--(PL BIRTH) OTH C,

(PL PRI ED) OTH ST or C, (ED FATHER) NONE, (ED MOTHER)
NONE, (OC LVL F) N SKLD, (OC AR FTHR) AG; FEET-(LVL
PR S) PRI INC, (EFRT SCHL) NORMAL; W__E_X_--(BFOR AR LO)
OTH; IEXf-(CRS TYPE) NCR 0, (NUMBER CRS) NCR,

(CRS SPONSOR) NCR O, (SENAI APR) NONE. For variable
descriptions see table 47 on page 122.

191

TABLE 75.--Analysis of Variance of Years Taken to Reach

Skilled Occupational Level--Complete Model.

 

Sum of Degrees Mean
Source of Squares of Freedom Square
Variation (SS) (df) (MS) F Statistic
Explained 1,196.432 61 19.614 2.901
Error 3,232.033 478 6.762
Total 4,428.465 539

 

The hypothesis was rejected at the .05 level (see
table 76). It was thus concluded that at least one vari-
able in one of the variable blocks significantly influences

the time taken to reach the skilled occupational level.

TABLE 76.--Test for Significance of the Regression Equation
of Years Taken to Reach Skilled Occupational
Level--Complete Model.

 

 

Ho: 8IC = BFED = BWEX = BTEX = 0
Degrees Critical Value Hypothesis
F of Freedom of F at (FR) Fail to Reject
Value (df) .05 Level (R) Reject
2.901 61/478 1.38 R

 

To establish if more than one explanatory block has
at least one statistically significant variable, a set of
four reduced models is estimated. For each estimation a
different explanatory block of variables is dropped from

the model. The fifth reduced model estimated contains only

192

the variable blocks Initial Conditions (IC), Formal Educa-
tion (FED), and Work Experience (WEX). Results of the
estimation of the five reduced models are presented in
analysis of variance format in table 77.

A set of four hypotheses that a given variable

block contained no statistically significant variable are

tested:
Ho: BIC = 0
Ho: BFED - 0
Ho' BWEX = 0
Ho‘ BTEX = 0
where: i=IC, FED, WEX, and TEX refer to the vectors

61,8 coefficients for the variable blocks,
Initial Conditions, Formal Education, Work
Experience, and Training Experience, respectively.
At the .05 level of significance it is not possible to
reject the hypothesis concerning the variable block Train-
ing Experience (TEX). The hypotheses concerning Initial
Conditions (IC), Formal Education (FED) and Work Experience
are rejected (see table 78).

It is thus concluded that the regression model for
the time taken to reach the skilled occupational level
(YR AR LO-SLO) should be the reduced model containing only
the variable blocks Initial Conditions (IC), Formal Educa-
tion (FED), and Work Experience (WEX). Estimated 8 coeffi-

cients, t values, and R2 for this reduced model are

presented in table 79.

193

 

 

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194

 

 

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195

TABLE 79.--Regression Analysis of Years Taken to Reach
Skilled Occupational Level-«Reduced Model.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
Constant 2.937 2.589
IC AGE .088 5.506
PL BIRTH
GSP - .764 1.099
SPI - .929 1.344
OTH ST - .445 .645
PL PRI ED
GSP .401 .792
SPI .711 1.267
ED FATHER
PR INC .802 1.991
PR CLPT .250 .633
MID+ 1.160 1.659
DN .772 .799
ED MOTHER
PR INC - .524 1.432
PR CLPT - .169 .499
MID+ - 1.273 1.409
DN - 1.400 .935
OC LVL FTHR
WC+ — .984 1.931
SUPR MN - .786 1.415
SKLD — .223 .553
S-SKLD - .273 .781

196

TABLE 79.--Continued.

——-'o-

 

Variable Estimated 8 t value
Block Variable Coefficient (Absolute values)
0C AR FTHR
IND .411 .919
OTH .215 .487
DN - .308 .453
FED LVL PR SCHL
PRI INC .275 .530
M.AC INC - .149 .434
M AC CLPT+ - 1.346 2.203
M IND INC - 2.626 3.183
M IND CPLT+ - 3.066 5.977
EFRT SCHOOL
+1 .015 .050
+2 - .705 1.847
+3 — .581 1.320
+4 or 5 - .151 .284
+6+ .672 1.216
SPECIAL SCHL
M INC .284 .512
M CPLT+ - 1.419 2.009
WEX BFOR AR LO
ST L0 .017 .030
ST RLO - 1.864 3.360
OI RLO - 2.188 3.041
OTH RLO - .876 1.245

197

TABLE 79.--Continued.

 

 

Variable Estimated 8 t value
Block Variable Coefficient (Abvolute values)
OI — .948 1.798
OTH OI - .312 .456
YRS WK-AR LO - .141 3.678
R2 .2299
SEE 2.614

 

v—

Note: Omitted binary variables are: lgf-(PL BIRTH) OTH C,
(PL PRI ED) OTH ST or C, (ED FATHER) NONE, (ED MOTHER)
NONE, (OC LVL F) N SKLD, (oc AR FTHR) AG; Egg--
(LVL PR S) PRI INC, (EFRT SCHL) NORMAL; WEX--

(BFOR AR LO) OTH. For variable descriptions see
table 47 on page 122.

198

5.5.1 Summary

There is no evidence to suggest that differences
in Training Experience (TEX) significantly influence the
time taken to reach the skilled occupational level (YRS
AR LO-SLO). Variables within the initial Conditions (IC),
Formal Education (FED) and Work Experience (WEX) variable
blocks are statistically significant.

The variable (AGE), which should be interpreted
as a time trend variable in the present analysis, is
statistically significant and positively related to the
time taken to reach the skilled occupational level
(SAGE = .088). Other things equal, as one moves further
into the past, more time was taken to reach the skilled
occupational level. The second significant variable in
the Initial Conditions (IC) block is the educational level
of the father (ED FATHER). Within this set of dummy
variables, the mean of the category primary incomplete
(PR INC) is statistically different from the mean of the
omitted category, no formal education (NONE), (BPR INC =
.802). The cell means of the other categories in this set
of dummy variables are not statistically different from
the cell mean of the omitted category.

Variables within the Formal Education (FED) block
are statistically significant. In the set of dummy

variables for the level and program of school (LVL PR SCHL),

the cell means for (1) academic middle school complete or

199

higher (M AC CPLT+), (2) industrial middel school imcom-
plete (M IND INC), and (3) industrial middle school com-
plete or higher (M IND CPL+) are significantly different
from the cell mean of the omitted category, complete primary
education (PRI CPLT). The respective 8 values are:

= -1.346, 8 -2.626, and B

8M AC CPLT+ M IND INC = M IND CPLT+
= -3.066. This result for individuals who received indus-
trial training within the formal school system was expected
as they received their training before they entered the
labor market. If the time spent in full-time industrial
study were added to the work years taken to reach the
skilled occupational level, these individuals would not be
greatly different from those who did not have formal indus-
trial school training. The case of those in the complete
middle school or higher category (M AC CPLT+) is different.
Whether or not these individuals entered the labor force
before they completed their formal academic educations,
they reached the skilled industrial level more rapidly.

The same is true for individuals who complete their middle
school academic education through the special rapid
(madureza) program. They reached the skilled level more
quickly than those who did not complete the program

(8 a -1.419).

M CP LT+
In the Work Experience (WEX) block, both the type
of work experience before entering the area of lathe

operation (BFOR AR L0) and the duration of this work

experience (YRS WK - AR LO) have a significant influence

200

on the time taken to reach the skilled occupational level
(YRS AR LO - SLO). Compared to the mean cell value of the
other type of work experience (OTH) omitted category, the
mean cell values for those who started working directly

in an area related to lathe operation (ST RLO) and those
who started in some other industrial occupation and then
had some work experience related to lathe operation (OI RLO)
= -l.864) and (B

are lower, (8 = —2.188).

ST RLO OI RLP
Other things equal, individuals having these types of work
experiences prior to entering the area of lathe operation
become skilled lathe setter-Operators more quickly. The
longer the duration of the work experience before the

area of lathe operation is entered, the more rapidly the
skilled occupational level is reached (8 =

YRS WK "' AR L0
-0141).

FOOTNOTES--CHAPTER V

lJan Kmenta, Elements of Econometrics (New York:
Macmillan Co., 1971), p. 202.

 

2The test used is generally referred to as a "Chow
Test." See: Gregory C. Chow, "Tests of Equality Between
Sets of Coefficients in Two Linear Regressions,"
Econometrica, 28, pp. 591-605.

 

3This result was considered to be odd and further
investigations were made. Since the educational level of the
father (ED FATHER) and the educational level of the mother
(ED MOTHER) are correlated, a second set of regressions
(for the complete and all reduced models) was run in which
the set of binary variables for ED MOTHER was dropped from
the Initial Conditions (IC) block. The results for all
tests of hypotheses concerning blocks of variables was the
same. The significant variable in the IC block was ED
MOTHER - DN. In the total sample of 540 individuals, 10
did not know the educational level of their mother, six did
not know that of the father and three who did not know the
educational level of either. Though the variable ED LVL -
DN may be statistically significant it cannot be regarded-
as "important."

4The variable block Initial Conditions (IC) is
identical to blocks used in previous models. Formal Educa-
tion (FED), Work Experience (WEX), and Training Experience
(TEX) blocks have been adjusted to include only those learn-
ing experiences which took place prior to reaching the
skilled occupational level.

201

CHAPTER VI

SUMMARY AND CONCLUSIONS

6.1 Objectives of the Study

 

This study of the development of skilled industrial
labor in Sao Paulo, Brazil was undertaken with three basic
objectives:

1. To establish the origin of the present
skilled industrial workers.

2. To identify the types and sources of
the training they received.

3. To evaluate the effects of differences
in both origin and training on (1) wages
received, (2) what is done on the job,
and (3) the time taken to reach the
skilled occupational level.

6.2 The Realization of the Study

 

The first step in the realization of the study was
the construction of a model or conceptual frame. Six
areas of information or blocks of variables were involved:

1. Present WOrk Situation

2. Present Work Situation Control

3. Initial Conditions

4. Formal Education

202

203

5. Work experience

6. Training experience
The model was used for two purposes. First, it was used to
organize information in a useful manner and to serve as a
general framework for the description of the entire system
of industrial skill development. Second, it provided the
basis for the development of several linear regression
models. The regression models were used to estimate the
effects of various factors on (1) wage per hour, (2) number
of operations performed, (3) the difficulty of operations
performed, and (4) the time taken to reach the skilled
occupational level.

Basic information for the study was obtained
through detailed interviews with 546 skilled lathe setter-
operators who were employed in the "ABC" area of Greater

Sao Paulo.

6.3 The Descriptive Findings

 

The most important simple descriptive findings of
the study can be summarized. The format of presentation
is based on the major informational areas of the general

model used in the study.

6.3.1 Initial Conditions

 

l. The mean age for those studied is 30.7
years. Over 87 percent are less than
40 years old.

S.

In general

relatively

the skilled industrial workers studied had

high socio-economic starting points. Their

204

Approximately 78 percent were born in the
State of Sao Paulo. Over 87 percent,
however, received their primary education
there.

Over 61 percent have at least one parent
with a complete four—year primary education.

Over 51 percent have fathers who worked
in industrial occupations.

More than 63 percent have fathers who were
at least semi-skilled workers.

parents were well educated in comparison to the general

population.

Most are second generation industrial workers

(only 17 percent have fathers who worked in the agricul-

tural sector). Also, most had fathers who worked at least

at the semi-skilled level.

6.3.2 Formal Education

 

1.

Over 94 percent have at least a complete
four-year primary education.

About 33 percent went beyond the primary
level.

Of those who did go beyond the primary
level, 66.7 percent enrolled in academic
programs, 6.4 percent in business oriented
commercial programs, and 26.9 percent in
industrial programs.

Only 9.3 percent of the total sample
received industrial training at either
the middle or high school levels of the
formal school system.

Less than 8 percent have had contact with
the special rapid middle and high school
equivalency programs (madureza).

205

A complete four-year primary education seems to be a

necessary base for the development of specific industrial

skills.

6.3.3 Work Experience

 

1.

Over 83 percent started their working lives
in industrial occupations. Almost 40 per-
cent started in the area of lathe operation.
Few, 8 percent, started in the agricultural
sector.

Over 74 percent entered the area of lathe
operation at the "learning" level. About
19 percent entered at the semi—skilled
level, and only 7 percent (36 individuals)
entered at the skilled level.

Once the area of lathe operation was entered,
less than 4 percent left for jobs in other
occupational areas.

The mean age at which the skilled level was
reached was 21.2 years.

The time taken to reach the skilled occupa-
tional level (the starting point being the
first job in the area of lathe operation)
ranged from 0 (see 2 above) to 21 years.
The mean was 3.9 years.

In general there tends to be great variation in

the type and especially the duration of work experience

before the area of lathe operation is entered.

6.3.4 Training Experience

 

1.

Only 28 percent had been enrolled in SENAI
apprenticeship programs.

206

2. In the total sample, over 68 percent had
taken at least one industrial training
course. Of those who did not have a
training course (32 percent) half had
been enrolled in the SENAI apprenticeship
program, and 13 percent had been enrolled
in a formal school industrial program.
Thus, in the total sample only 62 indi-
viduals (11.5 percent) reached the skilled
occupational level without some form of
special industrial training.

3. Over 54 percent of all the courses taken
by the individuals in the sample were
sponsored by private industrial schools.
SENAI sponsored only 31 percent of the
courses and public schools sponsored less n
than 8 percent. ' .

 

4. The variation in the duration of the

training courses was great, ranging from
48 hours to over 3,000 hours.

5. Private school courses have been offered

at least since 1942, the year SENAI was
founded.

Two findings are particularly significant. First,
only 11.5 percent reached the skilled occupational level
without some form of special industrial training, and
second, the private schools have trained many more of the
workers than SENAI. Private schools sponsored over 54
percent of the training courses, while SENAI sponsored
only 31 percent. Further, of those who have taken at
least one course, 66 percent have taken at least one private

school course, while only 33 percent have taken at least one

course from SENAI.

6.4 The Regression Analysis

 

Linear regression techniques were used to explain

differences in four variables:

207

l. wage per hour
2. number of operations performed
3. difficulty of operations performed
4. time taken to reach the skilled occupational
level
The most comprehensive regression model contained
six blocks of variables:

1. Present WOrk Situation

 

 

(a) number of operations performed
(b) difficulty of operations performed

2. Present Wbrk Situation Control

 

(a) years working in firm

(b) entry level in firm

(0) industrial group and size of firm
(d) sector of work

(e) type of lathe used

3. Initial Conditions

 

(a) age

(b) place of birth

(c) place of primary education
(d) educational level of parents
(e) occupational area of father
(f) occupational level of father

4. Formal Education

 

(a) level and program of formal school
(b) effort to complete highest grade

(c) special equivalency program (madureza)

208

5. Work Experience

 

(a) type and duration of work experience before
entering the area of lathe operation

(b) years taken to reach skilled level

(c) years working at skilled level

6. Training Experience

 

(a) types of training courses
(b) Sponsors of training courses
(c) hours of training courses completed

(d) SENAI apprenticeship

6.4.1 Wage Per Hour

 

There was great variation in hourly wage rates.
The range was from less than Cr$2 to more than Cr$10. The
mean hourly wage rate was Cr$6.30 and the associated stan-
dard deviation was Cr$1.69.

The linear regression model used to explain the
variation in hourly wage rates contained six blocks of
variables:

1. Present Work Situation

2. Present Work Situation Control

3. Initial Conditions

4. Formal Education

5. Work Experience

6. Training Experience
Differences in initial conditions, formal education, and

training experience were not significant in explaining

209

the variation in wage per hour. Initial conditions may
have an influence on the occupational area that is entered
and the type of learning experiences to which the individual
is exposed; but after the skilled occupational level is
reached, the effects of such differences are lost. There
was no evidence to suggest that having more than a complete
four-year primary education resulted in higher earnings.
Regardless of the type of secondary education (academic

or industrial), there was no statistically significant
difference in earnings. Likewise, hourly wage rates were
not significantly influenced by differences in training
experience. It could not be established that SENAI appren-
ticeship graduates earn either more or less than those who
did not have apprenticeship training. The hourly earnings
of those who had private school training courses were not
statistically different from those who took short-term
SENAI training courses.

Work experience was found to influence hourly wage
rates. The influence, however, is felt only in the early
years of the professional work life. As the number of
years working at the skilled level increases, the effects
of prior work experience fade.

The two major factors which have an influence on
hourly wage rates are: (l) the present work situation,
what is done on the job; and (2) the present work situation
control, the conditions under which work takes place. With

respect to the present work situation, other things equal,

210

the greater the number of operations performed on the job,
the higher the hourly wage rate. Regardless of the stage
of the profeSsional work life, the size and industrial
group in which the individual is employed has an influence
on what is earned. Larger firms in general tend to pay
more than smaller firms. The entry level into the firm
and the type of lathe used have an influence early in the
professional work life. This influence fades with time
and the number of years that the worker has been employed
in the firm begins to have a positive effect on wage rates.
In general, it does not seem to matter where the
individual comes from, if he has more than a complete
primary education, or what type of industrial training he
receives. It is important only to obtain four years of
primary education, take some type of industrial training
course, and to start working as soon as possible. Once
the skilled occupational level is reached, wage rates
will be influenced primarily by the length of time working
and the type of firm in which the individual is employed.

6.4.2 Number and Difficultyiof
Operations Performed

 

 

The number of different operations performed on
the job ranged from 1 to 41. Over 65 percent performed
more than 30 Operations and approximately 9 percent per-
formed less than 15. The mean number was 30.5 and the

associated standard deviation was 9.6. As noted, there

 

211

was a positive relationship between the number of operations
performed and hourly wage rates.

The observed ranking on the index of the difficulty
of the operations performed ranged from 14 to 41. Less
than 7 percent ranked lower than 38 on the scale. The
mean was 39.4 and the standard deviation was 4.3. Differ-
ences in rank did not influence hourly wage rates.

The regression models used to explain differences
in what was done on the job (both number and difficulty
of operation performed) contained five blocks of variables:

1. Present Work Situation Control

2. Initial Conditions

3. Formal Education

4. Work Experience

5. Training Experience
Initial conditions, formal education, work experience and
training experience had no significant influence on what
was done on the job. Only the present work situation con-
trol block contained variables that were statistically
significant. There was a significant positive relationship
between the number of operations performed and the length
of time employed in the firm. Those working in the main-
tenance sector performed both a higher number of operations
and more difficult operations than those working in produc-
tion. Those using old and specialized lathes performed
both fewer and less difficult operations than those using

modern equipment. Workers in small and medium sized firms

212

(less than 350 employees) performed more operations than
those in large firms. The difficulty of the operations
performed is also influenced by the size and industrial
group of the firm. There is, however, no observable general
pattern.

After the skilled occupational level is reached,
differences in origin and learning experiences are not
important. What is done on the job is determined by the
conditions under which work takes place.

6.4.3 Years Taken to Reach the
Skilled Occupational Level

The time taken to reach the skilled level was
defined as the time span between the first job in the area
of lathe operation and the first job as a skilled lathe
setter-operator. The number of years taken to reach the
skilled level ranged from 0 (36 individuals were hired in
their first job in the area of lathe operation as skilled
lathe setter-operators) to 21. The mean number of years
was 3.9 and the associated standard deviation was 2.9.

The regression model used contained four blocks of
variables:

1. Initial Conditions

2. Formal Education

3. WOrk Experience

4. Training Experience

213

Differences in training experience had no significant
influence on the time taken to reach the skilled occupa-
tional level. It was not possible to establish that

SENAI apprenticeship graduates reached the skilled level
any more quickly than those who develOped their skills
through a combination of work experience and short courses.
There was no statistical difference between private school
courses and SENAI courses. Neither the form of the train-
ing (apprenticeship or course) nor the sponsor of the
training (private or SENAI) was important in explaining the
variation in time taken to reach the skilled level.

In the initial conditions block one variable (age)
was statistically significant. Age in this case should be
interpreted as a time trend variable. Those who have
entered the area of lathe operation in the more recent
past have tended to reach the skilled occupational level
more quickly than those who entered in the more distant
past. There are two possible explanations. First, it may
be that as the pace of economic expansion increased stan-
dards for the first job in which an individual is classi—
fied as a skilled worker were lowered. This would have
the effect of reducing the observed time which is taken
to reach the skilled level. There is little doubt, however,
that as the individual works at the skilled level (gains
work experience) his deficiencies are quickly overcome.

The second possible explanation is that individuals enter-

ing the area of lathe operation have simply gotten "better"

214

over time. There is some partial evidence that those
entering the area of lathe operation in the more recent
past have somewhat higher levels of formal education than
their predecessors. The effects of more formal education
are discussed below.

Differences in formal education influenced the time
taken to reach the skilled level. Compared to those who
had only a complete primary education, those who either had
industrial training or who graduated from an academic middle
School, reached the skilled level more quickly. Further,
those who completed the special middle school equivalency
program (madureza) also reached the Skilled level more
quickly. The results for the industrial school were
expected, Since industrial training is generally completed
before the work life begins. The industrial school graduate
starts his work life already trained. This is not the case
with formal academic education. Generally, industrial
training (work experience and courses) does not begin until
after the formal education is completed and the work life
begins. The data indicate that more formal academic educa-
tion facilitates the more rapid development of specific
industrial skills. As noted, a skilled industrial worker
must be able to do much more than perform certain physical
operations. He must be able to read designs and make some
rather difficult mathematical calculations. The classroom
part of most industrial training courses is devoted to

such tOpics. The better base developed in mathematics in

215

the formal school system, the less that must be learned in
courses as on the job. Further, it is possible that the
formal school system teaches an individual how to learn.
That is, the techniques of learning and the discipline
obtained in the formal school system may be transferable
to other types of learning situations. Though definitive
answers are not possible, the study does show that more
formal academic education is associated with reaching the
skilled occupational level more quickly.

Work experience was also found to be important.
Both the type and duration of work experience prior
to entering the area of lathe operation had an influence
on the time taken to reach the qualified level. Generally
the closer the work experience approaches the area of
lathe operation and the longer the duration of the experi—
ence, the more beneficial it was in terms of reducing the
time required to reach the skilled level.

6.5 General Summary, Implications, and
Suggestions for Further Research

 

 

l. The present skilled industrial workers in Sao
Paulo were not drawn from the lower socio-economic levels
of Brazilian society nor were they drawn from the agricul-
tural sector. Other areas in Brazil which are now begin-
ning to industrialize do not have such an urban-lower
middle class pool from which to recruit potential skilled

industrial workers. The problems to be faced in these

216

areas may be the problems faced in Sao Paulo during the
first decades of the 1900's. The transfer of the indus-
trial educational technology presently used in Sao Paulo
may not be appropriate. Research, of a nature similar to
this study, conducted in an area less industrialized than
Sao Paulo would be useful to establish if such transfer
problems exist.

2. More than a complete four-year primary educa-
tion was not shown to result in higher earnings. Such
formal education must be judged on other grounds if it is
to be justified. SENAI's apprenticeship courses are
currently being "upgraded" to include academic subjects
which will make the SENAI program equivalent to the middle
school level of the formal school system. Graduation from
the SENAI program will give the individual the right to
enter the formal high school. If the objective of the
SENAI apprenticeship program is to develop skilled indus-
trial workers, then two problems may develop. First, the
type of individual who is drawn to the SENAI program may
change. As has been noted, the Brazilian formal school
system is extremely selective at all levels. Very few who
begin a particular level complete that level. Of those
who do, very few go on to the next. As graduation from
the SENAI program will give the individual the legal right
to reenter the formal educational stream, the SENAI
apprenticeship program will become an alternative path to

the university. Those who enter the SENAI program may

217

have no intention of ever working in a blue-collar indus—
trial occupation. Second, even if the intentions of the
students are to become skilled blue-collar workers, the
added cost of providing academic subjects probably cannot
be justified in terms of increased earnings. A Clear
statement of SENAI objectives would help to clarify the
issue. Research on the characteristics of entering students
before and after the new program is implemented would give
an indication of the problems that might develop.

3. Industrial training of some sort is generally
required to reach the skilled occupational level. The two
major sources of such training are the private industrial
schools and SENAI. Numerically, the private schools have
given more courses and reached more individuals than SENAI
has. In terms of earnings, what is done on the job, and
the time taken to reach the skilled occupational level,
private school training was not shown to be inferior in
any way to SENAI training. This would tend to imply that,
for those who do attain skilled industrial positions, the
benefits of private school training are the same as the
benefits of SENAI training. Other things equal, society's
decision to invest in one form of training or another
should be based on the comparison of relative costs and
benefits.

The major question in comparative cost/benefit
analysis is not whether the entire private school system

is an alternative to the entire SENAI system but rather,

218

whether some types of private school training are alter-
native to some types of SENAI training. Alternative,
programs are those designed for the same "types" of
individuals and which have as an objective the development
of the same specific type of skilled industrial worker.
For example, the SENAI apprenticeship programs are designed
for individuals 14 years of age who have recently completed
their primary educations and have little work experience.
If the objective is to train this type of individual, there
is no alternative private school program. The only area
in which real alternatives do exist is in the training of
adult workers. Again the problem is that both the private
schools and SENAI offer a wide range of courses for many
different types of individuals. Even within a given
occupational area there are entry level courses, rapid
development courses, normal courses, up-grading courses,
etc. Each is designed for a different type of individual.
Further, courses for the same type of individual vary
greatly in duration. Some private school courses in the
area of lathe operation are planned for less than 200
hours, others for over 1,000 hours. In sum, the identifiv
cation of real alternatives is not a simple task. Both
the private school system and the SENAI system must be
disaggregated to the point where real alternatives can be
identified and comparative cost/benefit analysis becomes
meaningful.

Even when real alternatives are identified the

costing-out of the programs is not straightforward. First,

219

it must be established who is paying the cost--government,
society, or the individual. Second, costs must be identi-
fied. They may be explicit, implicit, or joint costs.

The relevance of a specific type of cost depends on whose
costs are being considered. Joint costs are probably the
most difficult to handle. Both SENAI and the private
schools have many different types of programs. Somehow,
the general costs of administration, depreciation, and
equipment used for different programs must be allocated

to specific programs.

In sum, the identification and costing-out of
alternatives is extremely difficult. Yet, if resources
are to be allocated rationally and the most efficient
programs identified, such research is called for. This
study has provided some basic information on alternatives.
The next logical step is to cost-out these alternatives.

4. This study dealt with only one important
industrial occupation. The results probably are applicable
to other similar skilled industrial occupations. There
are, however, many industrial occupations which are dis-
similar and to which extension of these results may be

doubtful.

APPENDIX I

THE QUESTIONNAIRE

PESQUISA SOBRE A FORMAQAO PROFISSIONAL

‘Qg TORNEIROS MECANICOS

 

Realizagao: Michigan State University - Estados Unidos da América

SENAI - Departamento Regional de Sao Paulo

DADOS DE CONTROLE

 

Numero do questionario:

Nome do

Nome da

Bairro:

Data da

Hora do

Hora do

Duracao

 

agente:

 

empresa:

 

 

entrevista :

 

inicio da entrevista:

 

término da entrevista:

 

da entrevista:

 

AGENTE: Pergunte ao entrevistado

1. o senhor trabalha atualmente como OFICIAL DE TORNEIRO MECANICO?

NAG ------- PARE COM A ENTREVISTA

2. 0 senhor trabalha em

produgio

anutengao

 

Questionério

1

2

 

(N30 escreva debaixo desta linha)

Tamanho Grupo

P 3 U produgao

M 5

G 7 manutenQZo
,, U

2220

l.

2.

3.

4 -

6.

Quantos anos tem?

Estado onde nasceu?

Cidade

NH

 

2221

Estado

 

GRANDE 3A0 PAULo

S50 Paulo - interior

SUDESTE (MG - ES - RJ - GB - n26 SP)

EXTREMO SUL (PR - sc - RS)

CENTRO-OESTE (co - DF - MT)

NORDESTE (MA - PI - CE - RN — PB -.AL - SE - BA - PE)
NORTE (AM — PA - Ac - RR — AP - R0)

 

7
8[:]0utro Pais

 

Se n30 nasceu no GRANDE SAO PAULO, em que idade veio morar no

GRANDE sAo PAULO?

5. Grau de escolaridade dos pais?

Estado onde fez o curso primario? Estado

Cidade

 

 

 

 

 

PAI 09 MAP ou
(responsavel) (responsavel)
1 analfabeto 1 1
2 semi-analfabeto 2F 2
3 primario incomplete 3
4 primario completo Zl-q 4
5 ginisio incompleto 5
6 ginisio completo :l-fl 6
7 colEgio incompleto ;L_1 7
8 colégio completo
9 superior incompleto 9 9
10 superior completo 10'“-1 10—1
ll NEO Sabe Informer 11 11
.h—. DJ

...:

2

 

 

3
l:
5
6
7

SDutro Pais

 

 

 

 

 

RANDE 3A0 PAULO

Sao Paulo - interior

SUDESTE (MG - ES - RJ - GB - n50 SP)

EXTREMO SUL (PR - sc — RS)

ENTRO-OESTE (co - DF - MT)

NORDESTE (MA - PI — CE - RN - PB - AL - SE - BA - PE)
ORTE (AM - PA - Ac - RR - AP - R0)

 

2222

7 - 8. Ocupagao do psi ou responsivel (na época em que o entrevistado tinha 15 anos)?

Ocupacao

 

O que ele fazia (cargo)?

 

[:1 N30 Sabe Informar

AGENTE: Usando a informagao acima escrita, classifique-a de acordo com as tabelas

N9.7 e N9.8.

(7) Escala Ocupacional (do pai ou responsivel)

 

 

niversitirio e alta administracio

 
  
  
  
  

upervisao de ocupagaes n30 manuais e técnicas
ITE COLLAR (n50 manuais de baixo nivel)
upervisZo de ocupagaes manuais (mestre, contramestre, etc.)
anuais QUALIFICADAS
anuais SEMI-QUALIFICADAS
anuais M210 QUALIFICADAS

50 Sabe Inf ormar

NOU§WNH

(8) Area de Ocupagao (do pai ou responsivel)

 

l torneiro mecEnico

2 area de torneiro mecanico
3 outro tipo de mecanico

4 outta oeupagio industrial
5 agricola

6 outra

7

N30 Sabe Informar

 

9. Nfimero total de pessoas na familia do entrevistado (na época em que o entrevistado

tinha 15 anos)?

 

Nfimero total de pessoas

 

 

 

 

 

 

 

2223

 

10. CURSO PRIMARIO
a. Idade quando comegou o primario?
b. Quantos anos completos fez (com aprovagZo)?
primeiro
segundo
terceiro
tirou diploma
c. Idade quando terminou o filtimo ano completo do primirio?
d MOBRAL
1. Tirou diploma de MOBRAL? aim ----- em que ano?
n30
2. Est; fazendo MOBRAL agora? sim
n30
ll . CURSO GINASIAL
a. Comegou o ginisio? sim
n30 ---""'5> (Passe a f.) --------- ;>
b. Idade quando comegou o ginisio?
c. Curso feito?
_ O ‘ l
A ginasm comum
industrial
— ’ .
industrial basico
comercial
E agricola
d. .Quantas séries completas fez (com aprovagao)?
— nenhuma
primeira
.4
r—J segunda
terceira
—
tirou certificado
e. Idade quando terminou a Eltima série do ginisio?
f. MADUREZA (ginasio)

1. Tirou certificado de MADUREZA

2. Esta fazendo MADUREZA agora? Hsim

sim ----- em que ano?

nao

 

~

1180

2224

12. CURSO COLEGIAL

 

a. Comegou o colégio? sim

n50 -------- ;> (passe a f.) -------- ;>

b. Idade quando comegou o colégio?

c. Curso feito?

- f o
Cientifico
classico

técnico industrial ----- especialidade?

 

comércio
'
agricola

normal

 

 

[IIIIIT

d. Quantos anos completos fez (com aprovagZo)?

nenhum
primeiro
segundo

terceiro (ou tirou certificado do cientifico ou classico)

 

tirou certificado

 

[[111

 

e. Idade quando terminou o Ultimo sno completo do colégio?

f. MADUREZA (colégio)

 

1. Tirou certificado de MADUREZA? ::1 sim ----- em que ano?
n50
2. Esta fazendo MADUREZA agora? sim
L...“3°

 

 

13. CURsos DE TREINAMENTO ou PoRMAgxo PROFISSIONAL — inclusive cursos rapidos

realizados dentro ou fora desta empress.

a. Fez pelo menos um curso de treinamento ou formacao profissional?

[:1 SIM ----- quantos cursos
DRAG ---) (passe a pagina 7, pergunta 119.14) -------- >

b. RELACIONE NAS SECUINTES PAGINAS TODOS OS CURSOS DE TREINAMENTO 0U FORHACAO
PROFISSIONAL QUE FEZ - inclusive cursos rapidos realizados dentro ou fora

desta empress.

2225

CURSO N9.l

torneiro mecinico
outro tipo de mecanico
desenho

outro aficio industrial
outro

escola do SENAI

’escola particular

escola estadual on federal
PABRICA

outra

GRANDE sAo PAULO

S50 Paulo - interior
outro estado

outro pais

SENAI

PIPMD (programs do MEC)
EMPRESA

escola particular
outro

5. Duragao do curso ----- QUANTO COMPLETOU?

1. flfigig que aprendeu

 

2. Home da escola on fabrics onde o curso
foi dado

 

WW

 

3. Local da escola ou fabrics onde o curso
foi dado

Cid. Est.

 

4. Quem ministrou o curso

 

 

fifin’ "[311ij

 

 

6. Horas por semana - Total / aula oficina

7. AND de conclucao B'El DIURNO Dnomo

 

CURSO N9.2

torneiro mecanico

outro tipo de mecinico
desenho

outro oficio industrial
outro

escola d0 SENAI

escola particular

escola estadual on federal
FABRICA

1. Oficio que aprendeu l
2
3
l.
5
1
2
3
l.
5 outra
1
2
3
4
1
2
3
A
5

 

2. Nome da escola on fabrics onde o curso
foi dado

 

3. Local da escola on fabrics onde 0 curso RANDE SKO PAULO

£01 dado S30 Paulo - interior
Cid. Est. outro estado

'
outro pais

 

SENAI

IPMO (programs do MEC)
EMPRESA
escola particular

utro

4. Quem ministrou 0 curso

 

 

5. Duragio do curso ----— QUANTO COMPLETOU?

6. Horas por semana - Total / aula oficina

7. AND de conclucao B‘E] DIURNO EINOTURNO

 

2226

CURSO N9.3

1. Oficio que aprendeu

 

2. Nome da escola on fabrics 0nde 0 curso
foi dado

 

 

3. Local da escola on fabrics 0nde o curso
foi dado

Cid. Est.

4. Quem ministrou o curso

 

 

5. Durasao do curso

6. Horas por semana - Total / aula

8.[::]DIURNO

7. AND de conclucio

CURSO N9. 4

1. Oficio que aprendeu

 

2. Nome da escola on fabrics 0nde 0 curso
foi dado

 

3. Local da escola 0u fibrica 0nde 0 curso
foi dado

Cid. Est.

4. Quem ministrou 0 curso

 

5. Duragio d0 curso

 

6. Horas por semana - Total / aula

£L[::|DIURN0

7. ANOde conclugio

----- QUANTO COMPLETOU?

torneiro mecinico

outro tipo de mecanico
desenho

outro oficio industrial
outro

escola do SENAI

escola particular

escola estadual on federal
PABRICA

outra

GRANDE sAo PAULO

830 Paulo - interior
outro estado

outro pais

SENAI

PIPMO (programs do MEG)
EMPRESA

escola particular
outro

fafjj if?“ aft.” ......”

----- QUANTO COMPLETOU?

 

oficina

[::INOTURNO

torneiro mecinico

outro tipo de mecanico
desenho

outro oficio industrial
outro

escola do SENAI

escola particular

escola estadual «u federal
PABRICA

outra

GRANDE sxo PAULO

Sao Paulo - interior
outro estado

outro pais

SENAI

PIPMD (programs do MEC)
EMPRESA

escola particular
outro

Maw ..ij 33:2: EEENH

 

oficina

[::INOTURNO

2227

14. HISTORIA PROFISSIONAL

AGENTE: Aqui estamos interessados na vida profissional do entrevistado. Queremos
saber todas as profissoes que 0 entrevistado teve desde a profissao do
primeiro emprego ate a sua profissao atual como oficial de torneiro meca-
nico. Todas as profissoes as quais ele exerceu sao importantes, mas tres
sao de maior importancia:

A. a profissao do primeiro emprego
B. a primeira profisszo na area de torneiro mecinico

C. qual a data de seu primeiro emprego como oficial de torneiro mecinico

RELACIONE ABAIXO (em ordem de tempo) TODAS AS PROFISSOES QUE O ENTREVISTADO TEVE

 

Para cada profissEo anotada, classifique de acordo com as tabelas.

   
  
     

U

   

  

sobre
mecanica
sim/nao

    
   

SETOR
Ag.

   

   

      
 

PROFISSOES

 
    

 
     

dade Ano DuraQZO Ind. Outro

   
    
  
 
 
    
 
 
  

Profissao do primeiro emprego

(B) Primeira profissao na area
de torneiro mecanico

apr. de T. M.

ajd. de T. M.
1 Operador de torno
1/2 oficial de T. M.
oficial de T. M.

 

 
   

(C) Primeiro emprego como
1 oficial de torneiro mecanico

 

2
3
l.

 
 
 
 

(D) Emprego atual como
oficial de torneiro mecanico

15.

16.

17.

18.

19.

20.

2228

H5 quanto tempo trabalhs nests empress?

Qual foi a sua primeira profiss§0 nests empress?

 

 

1 oficial de torneiro mecEnico
2 1/2 oficial de torneiro mecEnico

outra profissso na area de torneiro mecanico

 

4 outra profissao na area de mec§nics

 

outra

 

Atualmente, quantas horas por semana trabalhs nests empress?
Horas REGULARES

Horas EXTRAS

 
 

SALARIO ATUAL nests empress

  
  
 

 

  

   
  
  

TOTAL PERIODO

(dinheiro)

    
 

a O

  
     

b Horas extras

P

      

C 08

     
 

tros

 
 

Atualmente, 0 torno em que 0 Sr. geralmente trabalhs E:

1 sem caixa NORTON
2 com caixa NORTON

N30 Sabe Informar

Na re1a9§0 da pagina seguinte, est30 algumas das gperacaes que os torneiros
mecanicos geralmente fazem. Indigue para cada operscio se, em seu trabalho
atusl, 0 senhor FAZ, NAG EAZ, ou NAO SABE INFORMAR.

AGENTE: Explique so entrevistado que n68 queremos saber 86 se ele faz on

n30 faz as operacaes descritas. N53 n30 queremos saber se ele sabe

on n30 sabe fazE-las.

Se ele n30 entender a operacio descrita, ele deveri marcar a coluna

N30 sabe informar.

2229

 

 

OPERACOES

FAZ

NAO
FAZ

NAO SABE
INFORMAR

 

1)

Tornesr superf. cilindrica externa ns placa universal

 

2)

Facesr

A

 

3)

Fazer furo de centro

 

4)

Tornesr superf. cilindrica na placa e ponta

 

S)

Afisr ferramenta de desbastar

 

6)

Tornesr superf. conica externs usando o carro superior

 

7)

Furar, usando cabecote m6ve1

 

8)

Ssngrar e cortsr no torno

 

9)

Roscar com macho no torno

 

10)

Tornesr superf. cilindrica internar (passante)

 

11)

Roscar com tarrsxs no torno

 

12)

Tornesr superf. cilindrics entre pontas

 

13)

Recsrtilhar no torno

 

14)

Centrar na placa de qustro castanhas independentes

 

15)

Tornesr rebaixo interno (facesdo interno)

 

16)

Perfilar com ferrsmenta de forma

 

17)

Calibrar furo com alargsdor n0 torno

 

18)

Tornesr superf. concavas ou convexss (movimento bimanual)

 

19)

Abrir rosca triang. externa por penetracio perpendicular

 

20)

Tornesr superficie c3010a com deslocamento da contra ponta

 

21)

Abrir rosca triang. externa por penetracio obliqua

 

22)

Abrir rosca quadrada externa

 

23)

Tornesr pegss em mandril

 

24)

Enrolar arame em forms helicoidsl no torno

 

25)

Tornesr excentrico

 

26)

Tornesr com lunets m5vel

 

27)

Fursr com brocsApresa n0 eixo principal

 

28)

Abrir rosca triangular direita interns

 

29)

Retificsr superf. canicas e cilindricas externas

 

30)

Tornesr c3nic0 com aparelho conificsdor

 

31)

Abrir rosca quadrsda interns

 

32)

Abrir rosca trapezoidal externa e interns

 

33)

Abrir rosca mfiltipla (externs cu interns)

 

34)

Msndrilar n0 torno

 

35)

Afiar ferramenta de carboneto

 

36)

Tornesr em placa lisa

 

37)

Tornesr superficie esférica

 

38)

Tornesr com luneta fixa

 

39)

Tornesr com centros posticos

 

40)

Tornesr pecas presss em cantoneira

 

41)

Fresar rasgos no torno

 

 

 

 

 

\DwNO‘UlbUND-fi

a- b- u: u: u: u: u: u: a: u: no N) h) «a h) h: h) h) N! h) pa F‘ pa h‘ pa pa _. a. pa -
Hoomuguewwwgomaameunwoomuauewnwo

BIBLIOGRAPHY

BIBLIOGRAPHY

Araujo Filho, Mauricio Leite de. SENAI System. Rio de

 

SENAI/DN, 1972.

Bologna, Italo. Formacao profissional na indfistria: o

 

SENAI. Rio de Janeiro: SENAI/DN (n. d:].

. SENAI: origens, evolucao, organizaqéo. Sao
Paulo: Centro de Estudos Roberto Mangé, 1972.
Mimeographed.

 

Borus, Michael E., and Buntz, Charles G. "Problems and

Brazil.

Brazil.

Brazil.

Brazil.

Brazil.

Issues in the Evaluation of Manpower Programs."
Industrial and Labor Relations Review, XXV (1972),
pp. 234-245.

Governo do Estado de Sao Paulo. Secretaria de
Economia e Planejamento. Departamento de Estatis-
tica. Anuario_estatistico 1971. Sao Paulo:
Secretaria de Economia e'PIanejamento, 1972.

 

 

Governo do Estado de Sao Paulo. Secretaria de
Economia e Planejamento. l? Regiao Administrativa.
Diagnéstico. Sao Paulo: Secretaria de Economia e
Planejamento, 1973.

 

Governo do Estado de Sao Paulo. Secretaria de
Estado dos Neg6cios da Educacao. Documentos
bésicos para a implantapéo do ensino supletivo.
S50 Paulo: Secretariagde Estado dos NegOCios da
Educacéo, 1973.

Leis. Decretos, etc. HabilitagOes profissionais
de ensino do 29 grau. Rio de Janeiro: Expressao
e Cultura, and Brasilia: Instituto Nacional do
Livro, 1972.

 

Ministério do Planejamento e Coordenacao Geral.
Fundacao IBGE. Instituto Brasileiro de Estatistica.
Anuario estatistigppdo Brasil 1972. Rio de Janeiro:
Fundach IBGE, 1972.

 

230

231

Brazil. Ministério do Planejamento e Coordenagao Geral.
Fundacao IBGE. Instituto Brasilerio de Estatistica.
Departamento de Censos. Censo demografico Brasil.

VIII recenseamento eral 1970. Vol. I. Rio de
Janeiro: Fundagao IBGE, 1970.

. Censo demo rafico Sao Paulo. VIII recenseamento
geral, 1970. V01. 1. Rio de Janeiro: 'Findacéo
IBGE, .

Brazil. Ministério do Trabalho e Previdéncia Social.
Departamento Nacional de M50 de Obra. Cadastro
brasileiro de ocu a Oes. la edicéo preIiminar e
pardial. Rio de Janeiro: Ministério do Trabalho
e Previdéncia Social, 1971.

 

 

 

 

. Composipéo e distribuigéo de mao de obra. Rio
de Janeiro: Departamento Nacional de Mao de Obra,
1970.

Castro, Claudio Moura de. "Educacao, ensino técnico e
perfis de idade renda.” In Ensaios econémicos.

Homenagem a Octavio Gouvea de Bulhdes. Rio de
Jane1ro:APEC EdItora S. A., 1972, pp. 115-129.

 

 

. Eficiénc1a e custos das escolas de nivel médig:
um estudo iloto na Guanabara. Rio de Janeiro:

IPEA/INPES, 1971. Mimeographed.

 

: Pereira, Milton de Assis: and Oliveira, Sandra
Furtado de. Ensino técnico: desempenho e custos.
Rio de Janeiro: Instituto aeJ Planejamento Econ-
6mico e Social, and Instituto de Pesquisas, 1972.

Chow, Gregory C. "Tests of Equality Between Sets of
Coefficients in Two Linear Regressions." Econ—
'ometrica,XXVIII, pp. 591-605.

Costa, Roberto Hermeto Correa da, ed. Mao-de-Qbra na

2W
Rio de Janeiro: Fundacao Getulio Vargas, 1972.
Mimeographed.

 

 

A economia brasileria e suas ers ectivasl PRE-APEQAO 73.
Rio de Janeiro: APEC EditSra S. A.,vl 3.

EditOra Atlas 8. A. Divisao Editorial. Plano piloto.
Ensino de 2 grau. Cursos do setor terciério.

Sub—setores: administragao e comércio. Sao Paulo:
Editara Atlas S. A. [1971].

 

 

 

Fonseca, Celso Suckow da. Histéria d9 ensino industrial no
Brasil. Vol. I II. Rio de Janeiro: Escola TEcfiica
Nac1onal, 1961.

Hardin,

232

Einar. ”Benefit-Cost Analysis of Occupational
Training Programs: A Comparison of Recent Studies."
In Sommers, G. G., and Woods, W. D., eds. Cost-
Benefit Analysis of Manppwer Policies. Kingston,
Ont.: Queens University, 1969, pp.*97-118.

. ”On the Choice of Control Groups." In Borus,
Michael E., ed. Evaluating the Impact of Man-

power PrOgrams. Lexington, Mass.: D. C. Heath
an 00' I pp. 41-570

, and Borus, Michael E. Economic Benefits and
Costs of Retraining Courses in Michigan. East
Lansing: School of Labor and Industrial Relations,
Michigan State University, 1969. Mimeographed.

Havighurst, Robert J., and Gouveia, Aparecida J. Brazilian

SecondaryEducation_and Socio—Ecpnomic Devélbpment.
New York: Praeger Publishers, 1969.

Henry Ford Trade School. Shop Theory. Rev. ed. New

Hunter,

York: McGraw-Hill Book Company, 1942.

John M. Economic Aspects of Higher Education in
Brazil. East Lansing: Latin AmeriCan Studies

Center, Michigan State University, 1971.

; Borus, Michael F.; and Mannan, Abdul. Economics
of Non-Formal Education. East Lansing: Institute
for International Studies in Education, Michigan

State University, 1974.

Instituto de Desenvolvimento da Guanabara (IDEG). Demands

e formagéo de méo-de-obra de fabricagéo e manuten-
§§o mecanigas noperiodo 1969-1973. R16 de

Janeiro: Centro de Pesquisas de Mao de Obra [1973].
Mimeographed.

. Matriz ocu acional no Estado do Rio de Janeiro.
Niteroi: SENAI7RJ, 1972.

, and Centro de Pesquisas de Mao de Obra (CEPEMO).
Demands e formapao de méo-de-obra na indfistria
editorial e gréfica da Guanabara. Rio de Janeiro:
Instituto de Desenvolvimento da Guanabara, 1971.

 

Instituto de Pesquisas e Estudos Sociais (IPES/GB). A

educacéo que nos convém. Rio de Janeiro: APEC
Editors S. A., 1969.

International Standard Classification of Occupations.

Geneva: ’International Labour Office, 1968.

233

J

Johnson, Harold V. General Industrial Machine Sho .
Peoria, Ill.: Chas. A. Bennett Co., 1963.

Kmenta, Ian. Elemgnts'of Econometrics. New York: The
Macmillan Company, 1971.

Langoni, Carlos Geraldo. Distribuicéo da renda e desen-
yolvimento econOmico do Brasil. Rio de Janeiro:
Expresséo e Cultura, l .

Laurindo, Arnaldo. 50 anos de ensino profissional. Estado
de 550 Paulo, 1911-1961. 2 vols. SEO Paulo:
EditOra Grafica Irméos Andrioli [1962].

A méo de obra industrial. 85o Paulo: Centro de Estudos
Roberto Mange, 1967. Mineographed.

Myint, Hla. The Economics of the Developipg Countries.
New York: Praeger Publishers, 1965.

Non-Formal Education: A SelecpedjList for A.I.D. Tech-
nicians. WaShington, D. C.: Agency for Interna-
tional Development, 1971. Mimeographed.

Pastore, José; Campino, Antonio Carlos; Bianchi, Ana
Maria F.; and Ceotto, Eny Elza. Profissionais
especializados no mercado de trabalho. $50 Paulo:
Instituto de Pesquisas—SConOmicas, I973.

 

Rolt, L. T. C. A Short History of Machine Tools. Cambridge,
Mass.: The M.I.T. Press, 1966.

Salles da Silva, Joéo Baptista, and Tolle, Paulo Ernesto.
SENAI: An Instrument of Brazilian Industries for
Manpower Traininnghrough Formal and_Non-Formal
Education. SEC Paulo: SENAI7SP, 1974.

Servicio Nacional de Aprendizaje (SENA). Subdireccién
General de Planeacién y Control. Evaluacién de
la formacién profg§ional-aprendizaje. Bogoté:
SENA, Divisién dé Recursos Humanos, 1971.
Mimeographed.

 

Servico Nacional de Aprendizagem Industrial (SENAI).
Departamento Nacional. Analisis ocupacional--
anélise ocupacional. 2nd? ed. Rio de Janeiro:
SENAI/DN, 1973.

 

 

. Coleténea de leis. Rio de Janeiro: SENAI/DN,
1967

 

. Coletanea de leis. Rio de Janeiro: SENAI/DN,
1970.

Servico

Staley,

234

. Manual do_§ocente de ajustagem. Rio de Janeiro:
SENAI/DN, 1972.

. Manual do docente de tornearia. Rio de Janeiro:
SENAI7DN , 1972 .

. Regimento. Rio de Janeiro: SENAI/DN [1962].

 

. Relatério 1972. Ed. provisOria. Rio de
Janeiro: SENAI/DN, 1972.

 

. O sistema SENAI. Rio de Janeiro: SENAI/DN
[1972].

 

. O sistema SENAI--empresas. Rio de Janeiro:
SENAI7DN, 1972. Mimeographed.

. Tprneiro mecanico. Rio de Janeiro: SENAI/DN,
1972.

 

. Treinamento de adultos no SENAI. Rio de
Janeiro: SENAI/DN,1972. Mimeographed.

 

Nacional de Aprendizagem Industrial (SENAI).
Departamento Regional de Sao Paulo. C6digo de
atividades. Plano do enquadramento sihdiCal,
Art. 577 da C. I. T. 850 Paulo: SENAI/SP,SI972.
Mimeographedf’

 

. INPS. CodificaQAO de atividades. Sao Paulo:
SENAI7SP Tn. d.I. Mimeographed.

. "Levantamento industrial 1972." Unpublished
internal document.

. Mao-de-obra industrial e formapéoprofissional.
Levantamento piloto realizado em Jundai(l967).
530 PauIO: ASENAI/SP, 1969.

 

. Obrigatoriedadejescplar e trabalhp_dos menores
de 12's 14 anos. 85o Paulo: SENAI/SP, 1968.

 

. Relat6rio 1972. s56 Paulo: SENAI/SP, 1973.

 

Eugene. Plannipg Occupational Education and
Training for Development. New York: Praeger
PubliSher, 1971.

United Nations Educational, Scientific, and Cultural

Organization. Readings in the Economics of
Education. Paris: UNESCO, 1968.

 

235

U.S.A.I.D./BRAZIL. Human Resources Office. Brazil:
Education Sector Analysis. Rio de Janeiro:

Human Resources Office, November, 1972. Mimeo-
graphed.

Wiedmann, Luiz Felippe da 8., ed. Brasil: realidade

e desenvolvimento. Sao Paulo: Sugestées
Literarias, 1972.

Woodbury, Robert S. Studies in the History of Machine

Tools. Cambridge and London: The M.I.T. Press,
1972.

MICHIGAN STRTE UNIV.

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