1. science
2. mathematics
3. statistics

CHAPTER 4
SOCIO-ECONOMIC PROFILE OF THE SAMPLE WORKERS
4.1 Introduction
The main purpose of this chapter is twofold: first, to provide a capsule description of the
socio-economic characteristics of workers surveyed; second, to identify the labour market
features with related to their social and economic characteristics. Thus, this chapter is
presented as a backdrop to the major findings presented in chapter 5, 6, and 7. Although the
three core themes of this study –job search methods, labour mobility, and duration of search are presented in the subsequent chapters, the questions that this chapter attempts to answer
are more of general. The broad questions are as follows. What are the various factors that
determine workers’ decision to opt for unemployed job search or to engage in employed job
search? What are the patterns of workers’ monthly wage earnings? Does the difference in
educational attainment influence the workers’ monthly wage earnings? To what extent, do
monthly wage earnings vary across industry of work and occupations? Does wage increase as
workers change their jobs? How is wage distributed across various social groups? What
factors determine workers’ level of job satisfaction? Within the broad socio-economic
framework, we attempt to answer these questions using simple statistical measures-mean and
standard deviation in particular- and cross-tabulation.
For the purpose of analysis, the socio-economic characteristics presented in this
chapter are broadly subsumed under three classifications. First, general characteristics cover
variables such as age, gender, marital status, district of origin, religion, caste, and level of
education. Second, employment related aspects encompass the nature of economic activities,
type of occupation, job duration, nature of job creation, distribution of monthly wage
earnings, type of social security benefits, level of job satisfaction 31, details of on-the-jobsearch, and training. Moreover, an important point to be noted here is that the study has taken
details of the number of jobs held by respondents previously to explore the degree of labour
mobility and the duration of search. Third, we attempt to provide the major determinants of
level of job satisfaction by way analysing labour market characteristics.
31
According to Locke (1976), job satisfaction is a ‘positive emotional state resulting from the appraisal of one’s
job or job experience’. More specifically, job satisfaction primarily emanates from a set of factors, such as
compensation, quality of work, job tenure, social milieus, organizational behavior, and work environment are
closely connected with each other.
55
The main contour of this chapter is as follows. Section 4.2 presents the sample
respondents’ general characteristics, which include sex, age, marital status, place of origin,
and level of education. Section 4.3 deals with the salient features of present job, and a brief
account of the number of job held by workers. Section 4.4 presents the patterns of the
distribution of monthly wage earnings, and section 4.5 explains the social groups and its
characteristics of the sample workers. Section 4.6 explains the inequality among the different
sections of the workers. Section 4.7 provides a detailed analysis on the level of job
satisfaction and its major determinants. Section 4.8 concludes this chapter.
4.2 General characteristics
One of the salient features of employment in the manufacturing industry, irrespective of
regions, is that the lion’s share of workforce is composed of male workers, generally ranging
from 60 per cent to 80 per cent. In developing countries such as India, according to National
Sample Survey, 62 nd round32, approximately 38 per cent of those employed in unorganised
manufacturing units is female workers. In line with this evidence, the present study further
suggests that the manufacturing units at Peenya Industrial area –in particular, manufacture of
machine-tools and automobile units- are predominantly employ male workers. As indicated
in table 4.1, a substantial proportion of sample workers are male, constituting around 94 per
cent of the total sample size. It is worth noting that documents and directories compiled by
agencies such as Peenya Industrial Association provide little evidence on the composition of
workforce employed. For instance, a report published by the Peenya Industrial Association in
2007 (table 3.1) pointed out that about 40 per cent of the total workforce of Peenya Industrial
area was women. Based on in-depth interviews with compilers of this directory, we find that
a huge projection of workforce, particularly women participation rate, in the report is mainly
due to the lack of scientific investigation. More specifically, the enumeration of
manufacturing units and workers were not based on any empirical attempts, and more
importantly, the size of employment was drawn from an intuitive approach. Equally
important is that the majority of manufacturing firms -mainly large-scale industries, are not
under the purview of Peenya Industrial Association. As a result, firms that are not under the
purview of this association were omitted while compiling most of these reports.
32
Unorganized manufacturing sector in India-Employment, Assets, and Borrowings (July 2005-June 2006)
56
Table 4.1 clearly indicates that age of the workers surveyed ranges between 20 and
68. For the purpose of analysis, age of respondent, given in years, is classified into two broad
intervals: 15-34 and 35-68. While the former represents the youth33 and the latter stands for
the adults34. Considering the present age of workers, a significant proportion of workers fall
in the age group of 15-34 years. In other words, more than three-fifth of sample workers are
youth. Empirical evidences suggest that young workers are more inclined to work in the
manufacturing sector, as it provides not only the status of regular wage/salaried employment,
but also competitive wages. Note that from firm’s point of view, as viewed by proprietors,
employing young workers enhance the overall firm’s productivity. Similarly, with regard to
marital status, married workers are higher than single that consists of never married,
divorced, and separated. It is evident that while nearly three-fifth of the workers is married,
never married represents two-fifth of the sample size. It is striking that widowed and divorced
together represents only a small percentage of the sample.
Bangalore seems to have been a popular target of migrants for a long time. As a hub
of manufacturing and electronic goods, with increasing pace of urbanisation, Karnataka
industrial sector attracts a number of workers -be it skilled or unskilled –from different parts
of India. A closer look at table 4.1 shows that slightly over four-fifth of workers are from
Karnataka, and the remaining from neighbouring states, Kerala, Tamil Nadu, and Andhra
Pradesh. The workers from states, such as Bihar, Uttar Pradesh, Maharashtra, and Punjab
contribute negligible share in the sample size. A discernible regional bias in employing
workers exists in a few manufacturing units, which often occludes workers from northern
states, as a majority of these units are owned and managed by South Indians. Based on indepth interviews with proprietor of 53 firms at Peenya, the present study finds that the lion’s
share of firms tend to hire workers either from their own regions or from their own social
groups. Consequently, a district wise analysis of workers’ place of origin indicates that the
majority of workers are from neighbouring districts like Bangalore rural, Tumkur, Hassan,
Mandya, and Chitradurga.
Table 4.1 also shows the distribution of workers by social groups. As a backdrop, this
study attempts to provide a brief overview of the recent demographic profile of Karnataka to
understand the sample characteristics from a larger context. According to the Census 2011,
33
According to the Census 2001, youth in India is defined as those who are in the age group of 15-34.
This definition is quite different from the conventional notion of viewing adults. See, for instance, the Indian
Factories Act 1948,
34
57
Karnataka accounts for over 61 million population, consisting of approximately 5 per cent of
the total population in India. Considering the distribution of population by religion, Hindu,
which accounts for circa 83 per cent, is considered as the most dominant religion in
Karnataka; the corresponding figure for Muslims is circa 11 per cent, and for Christian, it is
about 4 per cent. It is important to note that Budhists and Jains constitute a negligible share in
the state’s population. The findings from this study are quite consistent with the Census 2011.
More aptly, except the Hindu, the remaining religions constitute merely less than 10 per cent
of the sample size. The Hindu religion consists of about 92 per cent of the sample size.
As shown by Timmaiah (1983), a major point is that there are diverse social groups
within the Hindu religion. Based on the social, economic, and political milieus, the author
classified the Hindus in Karnataka into four core groups. First, dominant minority castes refer
to socially and economically powerful groups, but numerically insignificant. Generally,
Brahmins are included in this group. Second, dominant majority castes cover those groups
who are not only socially and economically powerful, but also numerically significant in the
state’s population. For instance, Vokkaliga, Lingayat, Reddy are included in this social
groups. Third, non-dominant minority castes include Golla, Kuruba, Naik, Goldsmith, Darji,
Besta, Ganiga, Thigala, and Naidu, who are neither powerful nor significant in social,
economic and political milieus. Fourth, depressed groups refer to scheduled castes and
scheduled tribes whose social and economic position is not quite impressive, albeit
numerically significant.
Taking cues from the above analysis, the present study arranged the sundry social
groups in our sample in a slightly different way, as our sample workers represent all over
India, albeit a gigantic proportion from Karnataka. Depending on the representation in the
sample, all social groups are subsumed under six categories: Foward castes, Vokkaliga,
Lingayat, Scheduled Castes, Scheduled Tribes, and other/unspecified. Like dominant
minority castes, forward castes in this study refer to Brahmin, Shetty, Nair, and Syrian
Christian, who are socially and economically powerful, but numerically either significant or
insignificant. Vokkaliga, Lingayat, although they are notified as other backward caste, are the
two dominant majority castes in Karnataka, and are considered separately. Not only are they
socially and economically powerful, but numerically significant in the state’s population. The
social groups under SCs and STs are identified in accordance with the notification issued by
respective government departments. While others represent most of the other backward castes
58
that include Golla, Kuruba, Goldsmith, Besta, Ganiga, Thigala, and Naidu, workers who
don’t wish to unveil their castes are subsumed under unspecified, which accounts for eight
respondents.
As stated earlier, the SCs, STs, and OBCs, are identified by looking at the notification
issued by state departments. Indeed, most of the state governments have issued independent
resolutions, in which SCs, STs, and OBCs, are notified based on certain criteria, albeit
varying from state to state. In Karnataka, for instance, according to an order issued by the
Governor of Karnataka on 27th July 197735, 101 castes have been notified as Scheduled
Castes, and 39 castes have been notified as Scheduled Tribes. It is important to note that a
few castes in the SCs and STs are notified as per the area of origin. According to the Census
2001, the major scheduled castes in Karnataka comprise Adi Karnataka, which constitutes
25.7 per cent of the total scheduled caste population in the state, Madiga (15.2 per cent),
Banjara (11.6 per cent), Bhovi (11.2 per cent), Holaya (7.5 per cent), Adi Dravida (7.2 per
cent), and Bhambi (6.6 per cent). In addition to this, Adiya in Kodagu district and Bant in
Belgaum, Bijapur, Dharward, and Uttar Kannada districts are notified as Scheduled caste.
Similarly, the present study follows the same method for STs as well. STs in Karnataka
comprise 6.6 per cent of the total population in the state, and 4.1 per cent of India’s ST
population (Census, 2001). Of the total ST population in the state, the notable STs are Jenu
Kurubas, Koraga, Naikda, Naik, Nayak. In addition, five STs, namely Kuruba and Maratha in
Kodagu district, Marati in Dakshina Kannada, Kaniyan in Kollegal Taluk, and Kammara in
Dakshina Kannada and Kollegal taluk, have been notified with area restriction.
Slightly deviating from the conventional wisdom that posits that wage is the function
of investment in years of schooling, this study lays out a broader context to illustrate the
unequal distribution within the different levels of education. By way of analysing technical,
general education, and investment in years of schooling, the educational attainment of sample
workers is classified into six categories: up to secondary, ITI courses, higher secondary,
diploma, graduate (general), and graduate (technical). While ITI, diploma, and graduate
technical are more technical-intensive courses, the remaining courses are more of general in
nature. Up to secondary refers to workers with 1 to 10 years of schooling. The Industrial
training Institute, directed under the control of director general of employment and training,
offers training programmes in technical fields such as electronic, mechanic, welding machine
35
The order is known as ‘The Scheduled Castes and Scheduled Tribes Order (Amendment) Act 1976.
59
operation, and computer application. Further, it also offers training in major sectors like
information technology, electronics and electrical sector, automobile, fabrication, and
hospitality sector. The fields of training and duration vary across the Indian states. A person
who has completed ten years of schooling is eligible to apply for this course. In our sample,
over one-sixth of workers have completed training programmes in various fields, particularly
in the field of technical trades. It is quite natural that workers with ITI courses are more likely
to be absorbed in the manufacturing sector than those who have completed just ten years of
schooling. Higher secondary, in addition to 10 years of successful completion of schooling in
secondary education, requires two years of additional schooling.
Table 4.1
General characteristics of the sample workers (n*= 367)
Characteristics
Sex
Age group (in
years)
Marital status
Place of origin
(State)
Place of origin
(District)
Religion
Caste
Educational level
Categories
Male
Female
Youth (15-34)
Above 35
Never married
Married
Divorced, widowed
Andhra Pradesh
Bihar
Haryana
Karnataka
Kerala
Orissa
Punjab
Tamil Nadu
Uttar Pradesh
Uttaranchal
Within Bangalore
Outside Bangalore, but within Karnataka
Outside Karnataka
Hindu
Muslim
Christian
Others
Forward castes
Vokkaliga
Lingayat
SCs
STs
Others/unspecified
Up to secondary
ITI
Higher secondary
Diploma
Graduate (General)
Graduate (Technical)
Per cent
93.7
6.3
63.2
36.8
40.3
59.1
0.5
4.1
0.5
0.8
81.2
5.4
1.4
0.5
5.4
0.3
0.3
24.3
56.9
18.8
91.8
3.0
4.6
0.5
10.4
29.7
18.5
4.6
9.8
27.0
37.3
17.2
12.3
12.3
15.0
6.0
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
*n represents the total number of sample workers
60
Unlike ITI, diploma courses are quite similar to general under-graduate degrees, but it offers
more specific training, and this caters professionals to the growing industrial demands.
Generally, diploma is offered in various fields such as engineering, design, and
pharmaceuticals. It requires between two and three years of additional schooling, in addition
to 12 years of basic schooling. In our study, diploma holders constitute just 12 per cent of the
total workers surveyed. Compared with higher secondary and ITI holders, diploma holders
are more likely to be absorbed in the occupations such as plant and machine operators and
assemblers. For the analytical purpose, graduates are grouped into two categories: graduate
general and graduate technical. While the former includes general degrees -BA, BSc, and
BCom-, the latter includes professional engineering courses such as electrical, automobile,
and industrial engineering. Interestingly, in the sample size, the proportion of graduate
general is three times higher than the graduate technical. Considering the overall level of
education, over one-third of the workers have competed 10 or less than 10 years of schooling.
4.3 Present job characteristics
Following the general characteristics of workers in table 4.1, table 4.2 presents the workers’
present job characteristics, including type of occupations, major industry of work, duration of
work, social security benefits, membership in trade union, job satisfaction, and so on.
Looking at the different occupations, which is categorised in accordance with the National
Classification of Occupation 2004 36, it is amply clear that highly technical-intensive
occupations such as plant and machine operators and assemblers constitute a sizable
proportion of sample, followed by craft and related trade workers. Considering these two
occupations together, it constitutes slightly over two-third of total sample size. What is
essentially important is that, on the one hand, professional jobs such as engineers and
accountants comprise about one-fifth, and, on the other hand, managerial jobs such as
mangers, supervisors constitute one-sixth. Analysing firms’ major industry of work, it is
evident that a number of firms at Peenya industrial area are engaged in the manufacture of
machine-tool, contributing slightly less than one-sixth of the sample size. The other activities
such as treatment and coating of metals, manufacture of specialised parts and accessories of
motorcycles, manufacture of electricity distribution and control apparatus, manufacture of
special purpose machinery such as cushion, and manufacture of basic precious and nonferrous metals contribute one-fifth of the sample size.
36
See chapter 6 for a detailed description of NCO
61
Considering the workers’ tenure in the present firm, which is categorized into six
distinct intervals, it is clear that over half of the workers have completed tenure of more than
sixty months in the present firm. Quite importantly, while workers with less than one year
experience in the present job account for around one-sixth of the sample size, the lion’s share
of workers, that is to say, slightly more than two-third, have completed more than one year in
the incumbent firms. The nature of job creation indicates that the majority of the present jobs
were vacant, constituting around three-fourth of the total jobs. It is evident that approximately
one-third of the workers are employed in newly added jobs of the firms. Perhaps what makes
the Peenya distinct from other industrial units is that workers possess a greater degree of
flexibility in terms of entry and exit in the labour market, as is reflected in perfectly
competitive labour markets.
The study highlights two noteworthy features: first, workers are not employed under
the purview of job contract; second, those employed in the manufacturing industries are not
part of any trade unions. In other words, the intervention of trade union is completely absent
in this industrial area. Moreover, taking cues from in-depth interviews with firm managers, a
majority of firms provide social security benefits to only those who have completed a
minimum of 1-2 years of work duration in the incumbent firms. It is important to note that
close to three-fourth of the sample workers avail social security benefits such as provident
fund, bonus, and employment and social insurance.
To assess workers’ degree of job satisfaction in the present job, the study classifies
job satisfaction into two: satisfied and dissatisfied. One of the striking features of this study is
that circa two-third of the workers are not found to be satisfied with the present job. What is
essentially important is that dissatisfied workers are more likely to look for another jobs
while employed. Attempt to pinpoint whether dissatisfied workers in the present job are
looking for another job indicates that about 98 per cent of them are engaged in on-the-job
search. This leads to answer an important question: what factors constitute workers’ job
satisfaction?
Taking cues from an interview with 367 workers, the study finds that the degree of
job satisfaction is associated with factors such as monthly wage earnings, social security
benefits, nature of work, career progression, and more importantly, work environment. A
detailed analysis of the link between wage and job satisfaction is explained in subsequent
62
sections of this chapter. A closer look at the workers’ place of origin indicates that over threefourth of them are migrants, and urban natives constitute nearly one-fifth of the sample size.
Table 4.2
Present job characteristics of the sample workers (N*= 367)
Present job characteristics
Occupation group (1-digit
code as per NCO-2004)
Major Industry of work (4digit code as per NIC200437)
Duration of work
(in months)
Nature of job creation
Job contract
Trade union members
Social security benefits
(PF, ESI, Bonus)
Level of job satisfaction
On-the-job search
Place of origin38
Categories
Managerial
Professional
Craft and related trade workers
Plant and machine operators and assemblers
Manufacture of machine-tools (2922)**
Treatment and coating of metals; general mechanical
engineering on a fee and contact basis (2892)**
Manufacture of motorcycles (specialized parts and
accessories) (3591)**
Manufacture of electricity distribution and control
apparatus (3120)**
Manufacture of other special purpose machinery
(2929)**
Manufacture of basic precious and non-ferrous metals
(2720)**
Less than 6
6-12
12-24
24-60
60-120
120 and above
Replacement
Vacancy
Newly added
Yes
No
Yes
No
Yes
No
Satisfied
Dissatisfied
Yes
No
Migrants
Urban Natives
Per cent (%)
13.1
19.6
30.2
37.1
13.9
5.4
4.9
4.4
4.4
4.4
6.5
10.1
12.8
30.0
20.4
20.2
8.7
61.9
29.4
1.4
98.6
0
100
57.8
42.2
34.3
65.7
63.2
36.8
80.1
19.9
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
*N represents the total number of sample workers
**Figures in parenthesis represent 4-digit code as per NIC-2004
37
NCO 2004 one digit level classification is as follows: legislators, senior officials and mangers (1),
Professionals (2), Associate professionals (3), clerks (4), service workers and shop and market sales workers (5),
skilled agricultural and fishery workers (6), craft and related workers (7), Plant and machine operator and
assemblers (8), and elementary occupations (9).
38
Workers from Bangalore rural are not included in urban natives
63
4.4 The nature of the distribution of monthly wage earnings
To depict the patterns emerging from the monthly wage earnings of workers surveyed, the
present study applies oneway scatterplot and box plot. Figure 4.1 is a one-way scatter plot,
which depicts each observation in the distribution on a vertical scale. A closer look at this
figure shows that workers’ monthly wage earnings spread out across the scale of
measurement, which varies between 0 and 40,000. What is striking is that whilst most of the
observations are huddled in the lower end of the one-way scatter plot, a few observations are
far off the lower end of the scale. Put it in a simple way, it indicates that a few workers earn
higher monthly wage earnings than a majority of workers. If we consider the data range, a
significant proportion of workers earn less than Rs 10,000 per month, a few workers earn
more than 30,000 rupees per month. In fact, the mean is greater than the median, indicating
that the distribution of workers’ monthly wage earnings is positively skewed. This can also
be verified by using measures such as skewness and kurtosis. The positive skewness indicates
that there is greater number of smaller values and right tail is longer than left tail. Similarly,
the large positive kurtosis shows that the monthly wage earnings are more peaked and tails
are heavier than the normal distribution. Succinctly, it implies that most of the observations
are concentrated on the left end of the scale. The observations sitting far off of the cluster of
the distribution can have a profound impact on the mean value.
Mean = 9194.4
Median = 7000
Standard deviation= 5743.08
Skewness = 2.09
Kurtosis = 5.90
N = 367
Figure 4.1
Monthly wage earnings of the sample workers (in INR)
It is worth noting that mean is not resistant to outliers as opposed to median. With the help of
box plot (figure 4.2), it is possible to detect the presence or the absence of outliers in the
distribution using measures shown in table 4.3. It splits the data into three quartiles -first
quartile (Q1), second quartile (Q2 also called median), and third quartile (Q3). The difference
between Q3 and Q1 is called inter-quartile range. The values are given in the third column.
The observations that fall inner or outer fence are called outliers. The box plot given below
pinpoints some distinguishing features of the workers’ monthly wage earnings.
64
Table 4.3
The outlier detection measures
Computations
Measures
Median (Q2)
Lower quartile (Q1)
Upper quartile (Q3)
Inter-quartile range (IQR)
Lower inner fence
Upper inner fence
Lower outer fence
Upper outer fence
25th percentile
75th percentile
Q3 – Q1
Q1 - 1.5 * IQR
Q2 + 1.5* IQR
Q1 – 3 * IQR
Q2 – 3 * IQR
Value
7,000
5,300
11,000
5,700
-3,250
19,550
-11,800
28,100
While the lower quartile is marked at 5300, the upper quartile is marked at 11,000. Therefore,
the length of the box, called hinge-spread, ranges between 5,300 and 11,000. The median is
marked at 7000. In figure 4.2, the observations range between 2000 -the smallest non-outlierand 40,000 -the largest outlier. To differentiate between mild outliers and extreme outliers,
we apply upper inner fence and upper outer fence; more aptly, observations beyond inner
fence, be it upper or lower, are called mild outliers, and observations beyond outer fence are
called extreme outliers. The outliers are marked separately, albeit the presence of identical
observations in the distribution may not easily be identified. It is evident that there are 18
observations outside the upper bound (or upper whisker). Interestingly, there are no values
outside the lower bound. The figure shows that there are 13 mild outliers and 5 extreme
outliers. Looking at the characteristic features of these outliers, it is worth noting that 7
outliers are employed in the top occupations –principally managerial jobs.
Figure 4.2
Box plot: monthly wage earnings of the sample workers (in INR)
65
4.4.1 Monthly wage earnings and educational level
A plethora of studies shows that wage earnings and years of schooling are closely associated.
To be more precise, as investment in years of schooling increases, wages tend to goes up.
Further, the study shows that the type of education that a worker receives has vital role to
play in the determination of monthly wage earnings. To assess the impact of types of
education, as shown in table 4.4, the level of education is broadly subsumed under two
categories: general and technical education. While general education includes Bachelor of
Arts, Bachelor of Science, and Bachelor of Commerce, and so on, the latter includes ITI,
diplomas, and professional engineering courses. A priori assumption is that workers with
technical education are more likely to get higher returns than workers with general education.
In fact, table 4.4 suggests that earning capacities are different for workers with general and
technical education. An interesting characteristic is that a greater percentage of workers with
general education are employed in lower-wage categories; whereas the percentage of workers
with technical education is mostly engaged in higher-wage categories.
Table 4.4
Association between type of education and monthly wage earnings
Monthly wage earnings in INR
Less than 4001800112001- 16001 & Total
4000
8000
12000
16000 above
General
76.5%
75.0%
52.0%
46.9% 47.6%
64.9%
Technical
23.5%
25.0%
48.0%
53.1% 52.4%
35.1%
Sample size (n)
34
184
75
32
42
367
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p = 0.000)
Type of
education
Although the study shows that there is close association between type of education and
monthly wage earnings, there is a great deal of inequality within the technical education. The
study reports that workers with technical education, on average, are more likely to earn
relatively higher monthly wage. For instance, while mean monthly wage earnings for workers
with technical education earn, on average, Rs 11,041, workers holding general education, on
the other hand, earn, on average, Rs 8,193. It is worth noting that the standard deviation for
workers with technical education is higher than that of general education holders. This
implies that there are greater variations in monthly wage earning for workers with technical
education than those of workers with generation education. To understand this phenomenon
in great detail, the level of education is further disaggregated into six broad levels. The box
plot would help us to identify the mild and extreme outliers in the distribution of wage
earnings, more importantly, the difference within the broader types of education holders. As
66
is evident in figure 4.3, while blue-coloured boxes represent general education, red-coloured
education represent technical education. The variations in the monthly wage earnings of
workers with technical education are greater than that of the workers with general education.
Figure 4.3
Box plot: monthly wage earnings for different educational groups
Table 4.5 provides summary statistics for different level of education by workers’ monthly
wage earnings. It is evident that as the level of education increases, the monthly wage
earnings, on average, tend to increase, except for higher secondary. Furthermore, the study
also reports a high standard deviation in line with the increasing average monthly wage
earnings, implying that the observations are spread out over the scale of measurement. In
fact, the difference between the minimum and the maximum monthly wage earnings indicates
that degree of variation from mean values.
Table 4.5
Basic statistics of the wage earnings for workers with different educational level
Level of education
Up to Secondary
ITI
Higher secondary
Diploma
Graduate (General)
Graduate
(Technical)
Total
Monthly wage earnings in Rs
SD
Minimum
Maximum
3625.65
2000
25000
2962.59
3500
16000
2085.15
3000
12000
6935.44
3800
35000
6637.52
4000
35000
8400.68
8000
40000
Mean
6896.4
7845.2
6787.8
12608.9
12517.3
17000
Median
6000
7500
6000
11000
12000
16500
9194.41
7000
5743.081 2000
40000
n represents the number of workers
67
% of n
Skewness
2.174
.922
.610
1.501
.941
1.539
37.3%
17.2%
12.3%
12.3%
15.0%
6.0%
2.094
367
4.4.2 Age of respondent and monthly wage earnings
It has long been established that age and monthly wage earnings are closely associated. In
other words, as age increases, wage tends to increase. As is indicated in table 4.6, while circa
three-fourth of the youth earn less than Rs 8,000 per month, the corresponding figure for the
workers in the age group of above 35 years is just 40 per cent. Interestingly, perhaps because
of the positive relationship between age and duration of work, workers who are in the age
group of 35-68 are more likely to earn higher wage earnings than those in the age group of
15-34 years.
Table 4.6
Association between monthly wage earnings and age
Monthly wage earnings
Age group (years)
in Rs
Total
15-34
35-68
Less than or 4000
10.3%
7.4%
9.3%
4001-8000
60.3%
32.6%
50.1%
8001-12000
14.7%
30.4%
20.4%
12001-16000
7.8%
10.4%
8.7%
16001 & above
6.9%
19.3%
11.4%
Sample size (n)
232
135
367
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p = 0.000)
4.4.3 Occupational group by level of education
As stated in the above analysis, each job requires a unique set of skills and knowledge.
Similarly, some occupations require higher years of schooling, and some other occupations
demand certain specific type of education. Interestingly, the workers surveyed are employed
in a wide variety of occupations, ranging from skilled to unskilled occupations. Table 4.7
shows that there is the strong association of occupation with level of education. More aptly,
more than two-third of workers with 10 or less than 10 years of schooling are engaged in
semi-skilled and unskilled occupations such as craft and related trade workers, and plant and
machine operators and assemblers. It is reasonable to argue that workers with technical
knowledge are more likely to be absorbed in the highly technical-intensive occupations.
Quite clearly, close to half of the workers with ITI degrees are absorbed in semi-skilled and
skilled occupations –principally machine operators, assemblers, and plant in-charge.
Compared with workers graduated in general education, graduate (technical) and diploma
holders are more likely to be absorbed in managerial and professional jobs.
68
Table 4.7
Occupational group by level of education
Occupation group
Level of education
(1-digit code as
Up to
ITI
Higher
Diploma Graduate Graduate
per NCO-2004)
Secondary
secondary
(General) (Technical)
Managerial
5.8%
9.5%
4.4%
20.0%
25.5%
40.9%
Professional
5.8%
17.5%
17.8%
44.4%
32.7%
31.8%
Craft and related trade
43.1%
25.4%
37.8%
20.0%
16.4%
4.5%
workers
Plant and machine
operators and
45.3%
47.6%
40.0%
15.6%
25.5%
22.7%
assemblers
Sample size (n)
137
63
45
45
55
22
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p = 0.000)
Total
13.1%
19.6%
30.2%
37.1%
367
4.4.4 Monthly wage earnings by occupation
From the above analysis, the study highlights two noteworthy features of the wage
distribution in an urban economy. First, those who are in the age group of above 35 are
employed in the top job positions. Second, the nature of labour market for high-skilled
workers is quite distinct from the labour market for low-skilled workers. As is indicated in
table 4.8, nearly three-fifth of managerial workers reports the highest monthly wage in the
distribution, while the corresponding figure for craft and related trade workers is just 2.7 per
cent. What is essentially striking is that close to half of the total professional workers particularly engineers, product designers, and sales executives- earn less than Rs 8,000 per
month. With regard to monthly wage payment, the study shows that there is a great deal of
mismatch between professional and managerial jobs in the study area. Although managerial
occupations offer relatively higher monthly wage earnings than the other occupations, an indepth interview with professional workers sheds light upon the fact that oversupply of
engineers has reduced the demand for professional workers. Obviously, the state has
witnessed a number of engineering colleges over the last two decades, leading a surge in the
number of engineering graduates. As a result, the majority of professional workers in our
sample are compelled to accept low-profile occupations that offer less impressive
compensation packages. Many engineers in the sample are employed in the occupations they
are not trained for. Taking cues from this analysis, we attempt to find the association between
job satisfaction and type of occupations. Although there are several dimensions to job
satisfaction, the highly-skilled occupations such as professionals are found to be less satisfied
with the present job.
69
Table 4.8
Association between monthly wage earnings and the type of occupation*
Monthly wage
earnings in INR
Less than 4000
4001-8000
8001-12000
12001-16000
16001& above
Sample size (n)
Occupation group (1-digit code as per NCO-2004)
Managerial Professional Craft and
Plant and machine
related trade operators and
workers
assemblers
0.0%
4.2%
17.1%
8.8%
20.8%
48.6%
55.0%
57.4%
16.7%
30.6%
16.2%
19.9%
22.9%
5.6%
9.0%
5.1%
39.6%
11.1%
2.7%
8.8%
48
72
111
136
Total
9.3%
50.1%
20.4%
8.7%
11.4%
367
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p = 0.000)
4.4.5 Age of respondent and occupation
As was indicated above, age is positively related to monthly wage earnings, and managerial
jobs are more likely to offer higher monthly wage. In this context, it would be interesting to
examine the link between age and occupation. Interestingly, about two-third of workers
engaged in professional occupations are youth, while the managerial jobs largely are filled
with established workers who are found to be in the age group of 35-70 years.
Notwithstanding a significant proportion of craft and related trade workers in the age group
of 35-68 years, just one-fifth of workers are offered monthly wage earnings above Rs 12,000
(table 4.9)
Table 4.9
Association between age and type of occupation
Age category
15-34
35-70
Sample size (n)
Present occupation (NCO 2004 one-digit level)
Managerial Professional
Craft and
Plant and machine
related trade
operators and
workers
assemblers
47.9%
73.6%
61.3%
64.7%
52.1%
26.4%
38.7%
35.3%
48
72
111
136
Total
63.2%
36.8%
367
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p = 0.037)
4.4.6 Social security benefits by monthly wage earnings
The majority of firms provide at least one social security benefit to Peenya industrial
workers. Before inducting social security benefits to the workers, firms adopt two strategies.
First, in case of newly employed workers, firms provide social security benefits to its
employees after inducting workers to the either formal or informal training, which generally
varies between 6 and 28 months. Second, in case of workers with previous work experience,
70
firms start providing social security benefits after gaining a minimum of one-year experience.
Interestingly, a number of workers in our sample pointed out that less technical-intensive
workers are unlikely to get social security benefits, as their job turnover is very high.
Presumably, because of these reasons, firms are reluctant to offer any social security benefits
to the craft and related trade workers, at least for initial 2-3 years. As is evident in table 4.10,
there is a close association between monthly wage earnings of respondent and social security
benefits -providence fund, employment insurance, and bonus. For instance, while more than
four fifth of workers, who earn less than Rs 4,000 per month, do not get any form of social
security benefits, over two-third of workers, who earn above Rs 16,000, get social security
benefits. More specifically, workers are less likely to receive social security benefits if their
monthly wage earnings are very low.
Table 4.10
Association between monthly wage earnings and social security benefits
Monthly wage earnings in Rs
Total
Less than
400180011200116001
4000
8000
12000
16000
& above
Yes
17.6%
53.8% 73.3% 71.9%
69.0%
57.8%
No
82.4%
46.2% 26.7% 28.1%
31.0%
42.2%
Sample size (n)
34
184
75
32
42
367
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p = 0.000)
Social security
benefits
4.4.7 Wage function: Major determinants
From the above analysis, it is amply clear that monthly wage earnings are closely associated
with educational attainment, age, and occupational type. To identify the major determinants
of workers’ monthly wage earnings, we fit a regression function, where monthly wage is the
dependent variable and age, type of education (dummy), type of occupation (dummy), caste
and firm size are the independent variables. The significance level of each variable is given at
the end of table 4.11.
Table 4.11
Regression analysis of monthly wage earnings of workers
Independent variables
Coefficient
(Constant)
4010.38
Present age
103.21
Education (=1 if technical; = 0 general)
1481.16
Occupation (=1 if highly-skilled; = 0 otherwise)
1753.09
Caste (=1 if forward caste; = 0 otherwise)
1201.41
Firm size
17.69
R2= .161
n= 349 F-statistic 12.55
71
Robust
standard error
754.76
28.33
461.20
515.61
868.67
9.80
t-statistic
Sig.
5.31
3.64
3.21
3.40
1.38
1.80
.000
.000
.001
.001
.168
.072
It is important to note that when we looked at the wage function in SPSS, the presence of
heteroscedasticity is detected even after excluding the outliers in the distribution. The
correction for heteroscedasticity is done using robust command in Stata by adjusting t and f
statistic. Similarly, we run a wage function for workers engaged in low-skilled occupation
such as craft and related trade work. An interesting factor that binds both these functions is
that caste is not a significant variable in the determination of wage. Contrary to this evidence,
empirical evidence suggests that caste is an important determinant of wage earnings
(Banerjee and Bucci 1995). This leads to explore the characteristics of social groups in our
sample (table 12).
Table 4.12
Regression analysis of monthly wage earnings for low-skilled workers
Independent variable
Coefficient
(Constant)
2276.97
Present age
146.95
Education (=1 if technical; = 0 general)
2171.77
Caste (=1 if forward caste; = 0 otherwise)
1368.73
Firm size
37.67
R2= .222
n= 242 F-statistic 17.28
Robust
standard error
788.70
31.20
518.76
941.06
10.92
t-statistic
Sig.
2.89
4.71
4.19
1.45
3.46
.004
.000
.000
.147
.001
4.5 Social groups of the sample workers
In this section, based on the data collected from Peenya, we explore some general
characteristics of the labour markets using cross-tabulation and basic statistical measures
such as mean and standard deviation. The main focus of the analysis in this section is based
on respondents’ social groups and its link with variables such as place of origin, social
security benefits, monthly wage earnings, number of jobs held and so on. As far as social
groups of respondents are concerned, a majority of them are from Karnataka. In fact, the
sample consists of more than twenty-five social groups, which spread across all over India.
As indicated above, all these social groups are classified into six categories: Forward castes,
Vokkaliga, Lingayat, Scheduled Castes, Scheduled Tribes, and other/unspecified.
4.5.1 Caste of the sample workers by place of origin
Table 4.13 shows the social groups of the sample respondents by place of origin. Over half of
the forward castes in the sample are from outside Bangalore, but within Karnataka state. In
case of Lingayat, it is striking that not even a single worker belongs to outside Karnataka.
Nearly three-fifth of the scheduled castes is from outside Karnataka, mostly from
72
neighbouring states -Tamil Nadu and Kerala. Others/unspecified groups are largely from
outside Karnataka, as the lion’s share of this group is composed of other backward castes.
Table 4.13
Caste of the sample respondents by their place of origin
Place of origin
Forward
caste
13.2%
Vokkaliga
Caste of respondent
Lingayat SCs
STs
Others/
unspecified
25.3%
Within
31.2%
16.2%
29.4%
25.0%
Bangalore
Outside Bangalore
57.9%
65.1%
83.8%
11.8%
72.2%
33.3%
but within Karnataka
Outside Karnataka
28.9%
3.7%
0.0%
58.8%
2.8%
41.4%
Sample size (n)
38
109
68
17
36
99
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Total
24.3%
57.5%
18.3%
367
4.5.2 Caste of respondent and occupation
It has long been established that there is strong association between caste hierarchy and
occupational structure (Driver, 1962; Rocher, 1975). Although caste system in India is
inextricably embedded in a set of dynamic and complex realities, it is reasonable to argue that
castes, to some extent, seem to have originated from the stratification of occupational
structure. A principal feature of Indian labour market, taking cues from Driver (1962), is that
the percentage of workers who deviate from their father’s occupations is quite conspicuous.
A brief analysis on this aspect is provided in the last section of this chapter. Furthermore, a
major phenomenon that was observed in Indian labour markets is that while the majority of
socially and economically deprived groups engage in unskilled and semi-skilled jobs, the
forward and dominant castes are largely employed in highly skilled and high-status
occupations that offer higher earnings.
As indicated in table 4.14, close to one-third of the forward castes are employed in the
managerial and professional occupations; the corresponding figure of Lingayat, one of the
dominant social groups in Karnataka, is two-fourth. What is essentially striking is that,
compared with other social groups, the lowest proportion of scheduled castes employed in the
unskilled and semi-skilled occupations –particularly welders, moulders, helpers, sheet metal
workers and turners. Moreover, circa two-fourth of the scheduled castes are employed in the
plant and machine operators and assemblers, which require relatively high technical
knowledge; the corresponding figure for Scheduled tribes is nearly one-third. What lies
beneath this pattern? Taking cues from table 4.13, it would be interesting to note that close to
three-fourth of these scheduled castes are migrants, mainly from Tamil Nadu and Kerala.
73
Table 4.14
Present occupation of respondent by caste
Occupation group (1digit as per NCO-2004
Forward
caste
13.2%
18.4%
31.6%
Vokkaliga
Caste of respondent
Lingayat
SCs
STs
Others/
unspecified
16.2%
16.2%
32.3%
Managerial
12.8%
8.8%
23.5%
8.3%
Professional
14.7%
38.2%
5.9%
16.7%
Craft and related trade
26.6%
26.5%
23.5%
44.4%
workers
Plant and machine
36.8%
45.9%
26.5%
47.1%
30.6%
35.4%
operators and
assemblers
Sample size (n)
38
109
68
17
36
99
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p =0.019)
Total
13.1%
19.6%
30.2%
37.1%
367
4.5.3 Social security benefits by caste
Social security benefits play a greater role in the workers’ decision to quit the jobs. The
likelihood of engaging in employed job search or unemployed job search is greater if the
workers do not avail social security. The study finds that there is the strong association
between social security benefits and caste of respondent. While over half of the forward and
dominant castes receive social security benefits, scheduled castes and scheduled tribes are
least likely to obtain these benefits (table 4.15).
Table 4.15
Social security benefits by caste
Social security
benefits
Caste of respondent
Forward
Vokkaliga Lingayat SC
ST
Others/
caste
unspecified
Yes
68.4%
62.4%
50.0%
47.1% 36.1% 63.6%
No
31.6%
37.6%
50.0%
52.9% 63.9% 36.4%
Sample size (n)
38
109
68
17
36
99
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p =0.018)
Total
57.8%
42.2%
367
4.5.4 Number of jobs held by caste of respondent
Like perfectly competitive labour markets, the entry and exit are relatively flexible among the
Peenya industrial workers, more obvious among the less technical-intensive workers such as
helpers, office assistants, and welders. The occupational changes vary across social groups in
our sample and there is no association between number of jobs held by the respondents and
their social groups. It implies that social group does not account for any discernible pattern in
occupational changes (4.16).
74
Table 4.16
Number of jobs held and caste of respondent
Number of
jobs held
Caste of respondent
Forward
Vokkaliga
Lingayat
SC
ST
Others/
caste
unspecified
0
15.8%
19.3%
25.0%
5.9%
13.9%
18.2%
1
42.1%
37.6%
39.7%
41.2%
50.0%
33.3%
2
34.2%
24.8%
26.5%
47.1%
22.2%
39.4%
3
7.9%
18.3%
8.8%
5.9%
13.9%
9.1%
Sample size (n)
38
109
68
17
36
99
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p = 0.243)
Total
18.5%
38.7%
30.8%
12.0%
367
4.5.5 Monthly wage earnings by respondents’ caste
One of the salient features of our sample respondents is that there is a great deal of disparity
in the distribution of monthly wage earnings. Figure 4.4 shows that dominant social group
such as Vokkaliga reports the highest number of outliers.
Figure 4.4
Box plot: monthly wage earnings for different social groups
4.6 Gini Coefficient for different groups of workers
To measure the inequality in the distribution of workers’ monthly wage earnings, we, in this
section, attempt to calculate Gini coefficient, as shown in table 4.17. Using Deaton’s (1997:
75
p.139) formula39, we estimate Gini coefficient for different groups of workers. Gini
coefficient varies between 0 and 1. While the former represents equal distribution, the latter
coveys the message of perfect inequality in the distribution. In the given formula, N depicts
the number of observations,  represents mean wage, Pi X i is the wage multiplied by rank of
worker i, provided that worker who receive the highest monthly wage earning will be given a
rank of 1, and the lowest a rank of N.
Table 4.17
Gini coefficient for different categories of workers (n*= 367)
Variables
Categories
Age group
(in years)
Youth (15-34)
Above 35
Within Bangalore
Outside Bangalore, but within Karnataka
Outside Karnataka
Managerial
Professional
Craft and related trade workers
Plant and machine operators and assemblers
Forward castes
Vokkaliga
Lingayat
Scheduled Castes
Scheduled Tribes
Others/unspecified
Up to secondary
ITI
Higher secondary
Diploma
Graduate (General)
Graduate (Technical)
All
Place of origin
(District)
Occupation
Caste
Educational
level
Total
Sample
size (n)
232
135
89
211
67
48
72
111
136
38
109
68
17
36
99
137
63
45
45
55
22
367
Gini
coefficient
.31
.35
.32
.34
.35
.32
.28
.31
.32
.35
.35
.28
.47
.40
.29
.31
.25
.24
.31
.34
.33
.34
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
*n represents the total number of sample workers
4.7 Level of Job Satisfaction:
In general, Peenya Industrial area seems to be a case of labour markets operates in a perfectly
competitive markets condition. An interesting factor that binds Peenya and perfectly
competitive labour markets is that there is free entry and exit of agents, be it firms or
workers. A noteworthy feature of Peenya industrial area is that the turnover rate for certain
categories of workers seems to be very high. As shown above, the study reports that there is
high attrition rate among workers employed in less technical-intensive jobs. Such workers
39
G
N 1
2

(in1 Pi X i )
N  1 N ( N  1) 
76
often engage in job-to-job changes, and in effect, the demand for craft and related trade
workers -welders, helpers, tool setters, fitters, moulders, and sheet metal workers- are very
high in this industrial area. Equally important is that a gigantic proportion of workers, mostly
craft and related trade workers, seem to be dissatisfied with their present job. In this context,
we attempt to answer an important question: what factors constitute workers’ job
satisfaction? An interview with workers sheds light on three core factors: wage, career
progression, and work environment.
4.7.1 Monthly wage earnings and job satisfaction
Although it is a bit thick to argue that workers’ level of job satisfaction is merely determined
by their wage, the study attempts to pinpoint various socio-economic aspects that influence
the job satisfaction. Considering the link between wage earnings and job satisfaction, it is
clear that there is a positive relationship. It should be noted that higher the wage, greater the
job satisfaction. Moreover, workers are more inclined to engage in jobs that provide
indemnity and fringe benefits. It is worth noting that, workers, to a great extent, are
dissatisfied because there is a mismatch between the level of education and industry of work,
which in effect, provides a trivial scope for career progression. For instance, a great deal of
professionals such as engineers, designers, and drawers are employed in low-profile activities
that provide little space for career progression. Further, due to the excess supply of
professionals like engineers, jobs seekers are inclined to work for lower monthly wages. As
indicated in table 4.18, the least satisfied workers are found to be in low-wage categories. In
fact, the level of job satisfaction increases as the level of monthly wage earnings increase.
Table 4.18
Association between monthly wage earnings and level of job satisfaction
Job satisfaction
in present job
Satisfied
Dissatisfied
Sample size (n)
Less
than 4000
8.8%
91.2%
34
Monthly wage earnings in Rs
40018001120018000
12000
16000
26.6% 38.7%
56.2%
73.4% 61.3%
43.8%
184
75
32
16001
& above
64.3%
35.7%
42
Total
34.3%
65.7%
367
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
Significance level by chi-square test (p = .000)
4.7.2 The sequence of job-changes and wage earnings
Why workers tend to quit jobs? As was explained in table 4.18, the monthly wage earnings
and level of job satisfaction are strongly associated, and, therefore, dissatisfied workers are
77
more likely to quit jobs. Nor is there any strong evidence to suggest that job satisfaction is a
linear or a positive relation with job changes (Fasang et al, 2007). One possible way of
finding out this relation is to examine whether job change influences other variables. Is there
any evidence to suggest that whether monthly wage earnings change, as workers move from
one job to another? Table 4.19 shows the relationship between the sequence of jobs held by
workers and monthly wage earnings. A discernible pattern emerging from table 4.19 is that
the monthly wage earnings, on average, tend to increase as workers switch over one job to
another. For instance, the study reports that while moving from first job to second, the
workers’ monthly wage earnings, on average, increase by more than 50 per cent. With this
evidence, it is reasonable to argue that when workers engage in job-to-job changes, the
monthly wage earnings, on average, tend to increase, albeit not perfectly reliable. However, it
is important to note that the standard deviation for first job is higher than the mean value.
This is due in large part to the fact that the workers surveyed held their first, second, third,
and fourth job not only at different points in time, but also at different geographical locations.
Hence, a useful exercise is to standardise their wage data by taking into account appropriate
index, and this seems beyond the scope of the present analysis.
Table 4.19
Summary statistics for sequence of jobs held and monthly wage earnings
Sequence of
job held
First job
Second job
Third job
Fourth job
Wage earnings per month in Rs
Average
Standard
Skewness
deviation
3925.08
4042.44
2.77
6388.76
6366.40
5.09
8642.67
6176.08
2.08
10459.09 9975.15
3.62
Sample
size (n)
367
299
157
44
Source: Field Survey, Peenya Industrial Area, Bangalore, Karnataka, 2011-2012
4.7.3 Age and job satisfaction
A number of studies pointed out that there is a significant positive relationship between age
and job satisfaction (Glenn et al., 1977; Warr 1992). However, Clark et al (1996) show that
the relationship between age and job satisfaction appears to be U-shaped, indicating that over
a period of time, workers tend to adjust with their aspirations (Herzberg et al, 1957; Warr,
1992; Erikson, 1979; Levinson, 1978). In fact, table 4.20 shows that a significant proportion
of youth are not satisfied with their present job; similarly, the corresponding figure for those
who are in the age group of 35-70 is close to half. What is the major reason for this change?
The grinding down hypothesis, which pinpoints the relationship between job satisfaction and
age, posits that new entrants, mostly found in the age group of 15-34, experience a gap
78
between actual and desired level of labour market outcomes, which, in effect leads to
disutility (Kohn and Schooler 1973). In other words, workers in the age group of 15-34 enter
the labour markets with a lot of expectation. This expectation will ‘ground down’ as age
increases. This implies that higher job satisfaction in the age group of 35-70 is primarily due
to the reduction in the gap between expected and actual experience.
Another possible explanation for this pattern is that older workers are more satisfied
because they change jobs many times and undergoing many types of training programmes
during the course of their working life. With duration of job, they are able to find better jobs.
The job-change hypothesis states that moving from one job to another enables workers to find
out suitable jobs that fit their skill and aspiration. Older workers, who have changed jobs
previously, are found to be satisfied because they accumulate skill and knowledge over a
period of time. In brief, job-change hypothesis states that older workers are more satisfied
because they are more likely to find out better jobs after changing a number of jobs.
Table 4.20
Job satisfaction, by age of respondent
Job satisfaction
Satisfied
Not satisfied
Sample size (m)
Age group
15-34
Above 35
25.9%
48.9%
74.1%
51.1%
232
135
Total
34.3%
65.7%
367
Source: Field Survey, Peenya Industrial Area, Bangalore 2011-2012
Significance level by chi-square test (p = .000) (CC=.22)
4.7.4 Education and job satisfaction
It is generally found that as level of education increases, the degree of job satisfaction tends
to decline (Fasang et al, 2007). This is because of the fact that workers with higher
educational attainment tend to expect higher prospective returns. Moreover, workers with
higher education attempt to engage in high-profile jobs rather than low-skilled occupations.
Interestingly, high-profile jobs provide better career progression through occupational
mobility than low-profile jobs. Although the association between education and job
satisfaction appear to be fuzzy, table 4.21 shows that workers with high secondary education
are not satisfied with the present job, perhaps because they are employed in low-profile
occupations that provide low degree of occupational mobility. This is quite clear from table
4.22, which shows that workers employed in craft and related workers are not satisfied with
the present job.
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Table 4.21
Job satisfaction by level education
Job
satisfaction
Satisfied
Not satisfied
Sample size
Up to
Secondary
33.6%
66.4%
137
ITI
25.4%
74.6%
63
Level education
Higher
Diploma
secondary
24.4%
42.2%
75.6%
57.8%
45
45
Graduate
(General)
40.0%
60.0%
55
Graduate
(Technical)
54.5%
45.5%
22
Total
34.3%
65.7%
367
Source: Field Survey, Peenya Industrial Area, Bangalore 2011-2012
Significance level by chi-square test (p = .069)
4.7.5 Type of occupation by job satisfaction
Defining the association between occupation and job satisfaction is a complex task, as each
job is linked with certain symbolic items that are distinct from each other. Moreover, the
nature of activity associated with each occupation is different. For instance, the activity of a
welder is different from an activity of a plant manager, which, in effect, is different from a
manager. In general, those who are employed in managerial and professional occupations are
found to be more satisfied because they enjoy the autonomy and responsibility. It implies that
the discretionary power at their disposal plays a crucial role in determining job satisfaction.
As shown by Mintxberg (1973), this may not be true in case if power and responsibilities are
varying across managerial occupations. In our sample, a small part of the managerial
occupations account for young workers, who search for an alternative job. Obviously, they
are less likely to be satisfied. On the other hand, craft and related trade workers seems to be
less satisfied because of low degree of occupational mobility.
Table 4.22
Job satisfaction by type of occupation
Job
satisfaction
Satisfied
Not satisfied
Sample size (n)
Managerial
52.1%
47.9%
48
Type of occupation
Professional
Craft and
related trade
workers
27.8%
26.1%
72.2%
73.9%
72
111
Plant and machine
operators and
assemblers
38.2%
61.8%
136
Total
34.3%
65.7%
367
Source: Field Survey, Peenya Industrial Area, Bangalore 2011-2012
Significance level by chi-square test (p = .006)
4.7.6 Career progression
Although the study finds evidence for the close association between monthly wage earnings
and satisfaction, a few workers pointed out that they give priority to career progression rather
than wage increase. Career progression implies the various stages of advancement paths in
working life with varying quality of employment and upward occupational mobility. To be
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more precise, it indicates the sequence of job positions, be it industry wise or occupation
wise, held by workers in the labour markets. Taking cues from table 4.22, workers employed
in highly specialized occupations with less scope for mobility appear to be more dissatisfied
than that of occupations with decision power such as supervisors, operation managers, and
plant head. One f the respondents said that employing in occupations that require high degree
of specialisation is an impediment to the career progression. For instance, two designers in
our sample reported that they have engaged in on-the-job search for more than three years,
but, because of limited offers, they tend to continue with the present firm. Presumably, such
occupations may not provide them ample scope for any career advancement and labour
mobility. Interestingly, Fasang et al (2007) show that, unlike workers with less educational
attainment, workers with higher education are more likely to expect career prospects.
4.7.7 Unpropitious work environment
A major point that was emphasised in describing the attrition rate is that monthly wage
earnings, on average, tend to increase as workers move from one firm to another. Departing
from this conventional wisdom, the study attempts to answer an important question. Are
dissatisfied workers willing to accept a lower wage than that received in their previous job?
Workers quit jobs on account of unpropitious work environment, which results from a set
factors such as poor quality of working condition, rigid working hours, sparse social ties
within the firm, and less-flexible management system. Unlike monthly wage earnings, it
cannot be measured in monetary terms as such. The interview with our sample workers
shows that workers find it rather difficult to work in untoward milieus. One of the sample
respondents reported that, because of unpropitious work condition, he had left one job
previously and wage in previous job was higher than the present job.
4.7.8 Life-cycle hypothesis
The life-cycle approach indicates that as we move from one phase to another, our needs,
duties and responsibilities rise. In a socialised set up, people involve in multiple activities
such as taking care family members, children’s education, cost of living in an urban
agglomeration, particularly Bangalore. It is obvious that job satisfaction is inextricably tied
up with such cultural and social characteristics. A missing element in the present study is that
we have not taken into account the evidence of how is the level of job satisfaction influenced
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by the respondents’ own social milieus, particularly the number of children and sources of
non-labour income.
4.7.9 Major determinants of job satisfaction
From the above analysis, we find that there is evidence to suggest that the level of job
satisfaction is associated with monthly wage earnings, age and occupation. Taking cues from
the above description, we address an important question: what are the major determinants of
job satisfaction? Of course, the present study does not account for a large sample size to
support a rigorous model with a number of control and experimental variables, our attempt is
primarily limited to frame a simple function, which consists of job satisfaction as dependent
variable and marital status, monthly wage earnings, and job tenure in the present job as
independent variables. It is important to note that while the level of job satisfaction and
marital status are measured as binary variables, monthly wage earnings in the present job and
job tenure are measured in ratio scale. Estimates are presented in table 4.23. It is striking that
our explanatory variables are statistically significant at 1 per cent and 5 per cent level. For
marital status, the statistically significant coefficient carries negative sign, implying that
single workers are more likely to be dissatisfied than its counterparts. As monthly wage
earnings and job tenure increase, workers appear to be satisfied.
Table 4.23
Binary logistic estimates of job satisfaction* (n=367)
Independent variables
Constant
Marital status (= 1 if single; and 0 = if otherwise)
Monthly wage earnings in present job
Tenure of work in present job
Estimate
-2.226 (.3155) ***
-.540 (0.272) **
0.000 (0.00) ***
0.003 (0.0018) **
Wald test
65.69
3.94
24.64
4.54
Odds ratio
.090
1.717
1.000
1.004
Log likelihood = -207.33, Cox & Snell R Square = 0.145, Nagelkerke R Square =0.200
Figures in parenthesis represent robust standard error.
***Statistically significant at 1 per cent
** Statistically significant at 5 per cent
Source: Estimated from survey data
4.8 Conclusion
The main purpose of this chapter was to lays out a broad context of the respondents’ socioeconomic characteristics. By way of analysing their socio-economic features, we highlighted
a noteworthy feature: unequal distribution in the monthly wage distribution across different
groups of workers. In fact, a great deal of disparity in wage earnings is due in large part to
difference in castes, age, level of education, firm size, and type of occupation.
Notwithstanding the difference in respondents’ personal and employment characteristics, the
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wage function shows that caste is not statistically significant in the determination of wage.
Presumably, this is because of the fact that there are differences within social groups in terms
of their earning capacity, which is manifest in measures of Gini coefficient. The regression
estimates indicate that age and education are the two major determinants of wage. In addition,
with a view to find out the determinants of wage for low-skilled workers, particularly craft
and related trade workers, we run a regression. The findings suggest that age, education, and
firm size are the major determinants. Moreover, the analysis of workers’ job satisfaction
indicates that satisfaction is a function of not only wage, but also age, education, type of
occupation, career progression, and unpropitious work environment. The logistic estimation
of on-the-job search is significant for marital status, age and tenure of work in the present job.
Against the backdrop of socio-economic profile, the subsequent three chapters analyse the
three core themes of this study –job search methods, labour mobility, and the duration of
search. In the next chapter, we address the theme, the job search methods, in great details.
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