CHAPTER VI EFFECT OF DEMOGRAPHIC VARIABLES ON …shodhganga.inflibnet.ac.in/bitstream/10603/17431/12/12_chapter 6.pdf · MEASURES–ANALSYSIS III 6.1 INTRODUCTION The prevalence of

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CHAPTER VI

EFFECT OF DEMOGRAPHIC VARIABLES ON

FACTORS OF LABOUR WELFARE

MEASURES–ANALSYSIS III

6.1 INTRODUCTION

The prevalence of labour welfare measures vary from person to person

and from time to time within the same person. This is mainly due to the

presence of various factors relating to both the job and the individual within the

organization. The present study has been aimed at the identification of both the

organizational variables and personal factors influencing the labour welfare

measures among the employees of SCSM Ltd.

This chapter concentrates more on the effect of demographic variables

like gender, level of education, age, length of service, salary, nature of

employment etc., on labour welfare measures. In order to collect primary data

from the workers employed in SCSM Ltd., a total number of 312 respondents

from all categories and sections of employment have been selected on the basis

of stratified random sampling method. The respondents are classified on the

basis of their demographic variables as previously mentioned.

The classification of clusters of the predominant sub-factors of labour

welfare measures shows the following ten factors :

1. Job requirement

2. Relations with staff

3. Company and management

4. Reasonable pay

5. Working conditions

6. Innovative learning

7. Empowerment

8. Grievances handling procedure

178

9. Opportunity for growth

10. Living conditions and finance

This section deals with the association between the demographic

variables like gender, level of education, age, length of service, salary and

nature of employment and the clusters of predominant sub-factors of labour

welfare measures.

6.2 LEVEL OF EDUCATION

Level of education and intelligence is another important demographic

variable influencing labour welfare measures and too much of education

than the requirement of job may probably lead to job dissatisfaction. It

seems that workers like their jobs when their education skills are

commensurate with job demands. However, the findings of many studies are

inconsistent and inconclusive and may not hold true as in literature. Table

6.1 shows the classification of the respondents on the basis of their

educational qualification.

Table 6.1

LEVEL OF EDUCATION OF RESPONDENTS

LEVEL OF

EDUCATION FREQUENCY

VALID

%

CUMULATIVE

%

Matriculate / Diploma 060 019.2 019.2

Graduate 088 028.2 047.4

Post-graduate 134 042.9 090.4

Professional Degree 030 009.6 100.0

Total 312 100.0

Source : Primary data

It is evident from the table 6.1 that 42.9 per cent respondents are

post-graduates and 28.2 per cent respondents are graduates. It is also known

that 19.2 per cent respondents are matriculate and diploma holders while 9.6

per cent respondents are professional degree holders.

The overall position of the respondents on the basis of their education

have been given in chart 6.1.

179

CHART 6.1

LEVEL OF EDUCATION OF RESPONDENTS

Graduate

Professional

Matriculate / Diploma

Post-Graduate

180

6.2.1 Association Between Level of Education and Clusters of

Predominant Factors

The data relating to the frequency of the respondents grouped on the

basis of their level of education in each cluster is shown in table 6.2.

Table 6.2

ASSOCIATION BETWEEN LEVEL OF EDUCATION AND

CLUSTERS OF PREDOMINANT FACTORS

LEVEL OF

EDUCATION

CLUSTERS TOTAL

1 2 3

Matriculate / Diploma 18 025 017 060

Graduate 20 031 037 088

Post-graduate 17 048 069 134

Professional Degree 01 010 019 030

Total 56 114 142 312

Table 6.2 shows that employees possessing level of education upto

matriculation / diploma level are distributed in the moderate cluster while all

other respondents are distributed in the third cluster which is considered as a

strong cluster in cluster analysis.

In addition to the above, the non-parametric chi-square test is applied

with a view to check the significant association between the above noted

groups.

Pearson chi-square value and its significance is presented in

table 6.3.

Table 6.3

LEVEL OF EDUCATION AND

SIGNIFICANCE OF CHI-SQUARE VALUE

PARTICULARS VALUE DF ASYMP. SIG.

(TWO-SIDED)

Pearson Chi-square 19.458 6 0.003

Likelihood Ratio 20.943 6 0.002

Linear-by-linear association 18.860 1 0.000

No. of valid cases 312

181

Hypothesis: There is no association between the level of education and

clusters of predominant factors of labour welfare measures

It is evident from the table 6.3 that the chi-square value is statistically

significant and hence, it is concluded that there is a strong association

between the level of education and clusters of predominant factors of labour

welfare measures. This implies that employees possessing educational

qualification up to matriculation / diploma level are found to be less satisfied

with the various predominant factors of labour welfare measures than that of

other employees possessing level of education beyond a certain level

consisting of graduates, post-graduates and professional degree holders.

6.3 AGE

Age is one of the inherent demographic variable constituting the labour

welfare measures. It is believed that the status enjoyed by the people is

determined by the age and many studies have predicted a close link between

age and labour welfare measures. During the initial years of employment,

labour welfare measures are high among the young workers who are in their

middle and late twenties or early thirties. In the later years of employment,

labour welfare measures is reported to climb steadily among the employees.

The respondents of SCSM Ltd., are classified on the basis of their age

and the age groups are presented in table 6.4

Table 6.4

AGE OF RESPONDENTS

AGE (YEARS) FREQUENCY VALID % CUMULATIVE

%

Below 30 045 014.4 014.4

30 - 35 061 019.6 034.0

36 - 40 071 022.8 056.7

41 - 45 078 025.0 081.7

Above 45 057 018.3 100.0

Total 312 100.0


182

Table 6.4 shows that most of the respondents are in the age group of

41-45 (25 per cent) and 36-40 (22.8 per cent) respectively. Only 19.6

percent respondents are in the age group of (30-35) while 18.3 per cent

respondents are in the age group of above 45.

The level of age group of the respondents have also been shown in

Chart 6.2.

CHART 6.2

AGE OF RESPONDENTS

AGE ( In Years )

14.40% 19.60% 22.80%

25.00% 18.30%

36-40 41-45

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6.3.1 Association Between Age Group and Clusters of Predominant

Factors

The data pertaining to frequency of the age level of the respondents in

each cluster is presented in table 6.5

Table 6.5

ASSOCIATION BETWEEN AGE AND


AGE

(YEARS)

CLUSTERS TOTAL

1 2 3

Below 30 08 014 023 045

30-35 16 026 019 061

36-40 16 027 028 071

41-45 13 028 037 078

Above 45 03 019 035 057

Total 56 114 142 312

It is evident from the table 6.5 that the maximum number of

employees in the age group of 30-35 years are distributed in the second

cluster which is the moderate cluster. On the other hand, the maximum

number of employees in the other age groups are distributed in the strong

cluster.

The non-parametric Chi-square test has also been applied with a view

to check the significant association between the above noted groups. Pearson

chi-square value and its significance is predicted in table 6.6.

Table 6.6

AGE AND SIGNIFICANCE OF CHI-SQUARE VALUE


(TWO-SIDED)




No. of valid cases 312

184

Hypothesis: There is no association between the age and clusters of

predominant factors of labour welfare measures

From the table 6.6, it is observed that the chi-square value is

statistically significant and hence, it is concluded that there is a strong

association between the different age group of employees and clusters of the

predominant factors of labour welfare measures. This implies that age is one

of the inherent personal character influencing labour welfare measures and it

is found that in the age group of 30-35 years, labour welfare measures is

neither too low nor to high. The study also reveals that labour welfare

measures goes high for all other group of employees due to the pleasant

working environment prevailing in SCSM Ltd.

6.4 LENGTH OF SERVICE

Many studies on labour welfare measures have reported that the level

of labour welfare measures grow with the length of service. Generally

men and women employees with longer years of service identify more

and more with the organisation and they involve with efficiency to a

greater degree than the newly appointed workers completed with lesser years

of service.

The data pertaining to the length of the service of the respondents is

depicted in table 6.7

Table 6.7

LENGTH OF SERVICE OF RESPONDENTS

LENGTH OF

SERVICE (YEARS) FREQUENCY

VALID

%

CUMULATIVE

%

Below 10 090 028.8 028.8

10 - 20 160 051.3 080.1

Above 20 062 019.9 100.0

Total 312 100.0


185

It is known from the table 6.7 that 51.3 per cent respondents have

service of 10-20 years, 28.8 per cent respondents have service of below 10

years and 19.9 per cent respondents have more than 20 years of service with

SCSM Ltd.,

The length of service of the respondents of SCSM Ltd., have been

shown in chart 6.3.

CHART 6.3

LENGTH OF SERVICE OF RESPONDENTS

Below

10 Years

10 to 20 Years

Above 20 Years

186

The data relating to the frequency of the length of service of the

respondents in various clusters are depicted in table 6.8.

Table 6.8

ASSOCIATION BETWEEN LENGTH OF EXPERIENCE AND


SERVICE

(YEARS)

CLUSTERS TOTAL

1 2 3

Below 10 20 042 028 090

10 - 20 34 049 077 160

Above 20 02 023 037 062

Total 56 114 142 312

Table 6.8 shows that the majority of the employees with less than

10 years of service are distributed in the moderate cluster while the

employees of the other service groups are distributed in the strong cluster in

maximum number.

The non-parametric chi-square test has been applied with a view to

check the significant association between the above mentioned clusters.

Pearson chi-square value and its significance is predicted in

table 6.9.

Table 6.9

LENGTH OF SERVICE AND

SIGNIFICANCE OF CHI-SQUARE VALUE


(TWO-SIDED)




No. of valid cases 0.312 (a)

187

Hypothesis: There is no association between the length of service and


It is evident from the table 6.9 that the Chi-square value is perfectly


between the length of service of the respondents and clusters of the

predominant factors of labour welfare measures. This reveals that employees

with less than 10 years of service feel moderate level of labour welfare

measures and all other employees with service of 10 years and more have

high degree of labour welfare measures.

6.5 SALARY

Salary includes the total emoluments including basic pay, dearness

allowance and other allowances. It is noted that the amount of money earned

is important in the determination of the workers labour welfare measures.

Many previous studies have shown that the degree of labour welfare

measures with the job goes up with increase in income.

The data pertaining to the distribution of respondents in different

salary categories is presented in table 6.10

Table 6.10

SALARY OF RESPONDENTS

SALARY

(PER YEAR) RS. FREQUENCY

VALID

%

CUMULATIVE

%

Less than 2,00,000 049 015.7 15.7

200,000 – 300,000 122 039.1 54.8

300,001 – 400,000 088 028.2 83.0

Above 400,000 053 017.0 100.0

Total 312 100.0


From the table 6.10, it is found that a sizable number 39.1 per cent of

respondents are getting Rs. 200,000 to 300,000 per year. Only 28.2 per cent

respondents are drawing Rs. 300,001 to Rs.400,000 while 17 per cent

188

respondents receive Rs. 400,000 and above. It is also known that 15.7 per

cent respondents are getting less than Rs. 200,000 per year.

The salary income of the respondents of SCSM Ltd., has been given

in chart 6.4.

CHART 6.4

SALARY OF RESPONDENTS

Less than Rs. 2 Lakhs

Rs. 2 to 3 Lakhs

Rs. 3 to 4 Lakhs

Above Rs. 4 Lakhs

15.7 % 39.1 % 28.2 % 17.0 %

Rs.3,00,001 – Rs.4,00,000

Above Rs.4,00,000

Rs.2,00,000 – Rs.3,00,000

Less than Rs.2,00,000

189

6.5.1 Association Between the Salary and Clusters of Predominant

Factors

The data pertaining to the frequency of the salary of the respondents

in different clusters are presented in table 6.11.

Table 6.11

ASSOCIATION BETWEEN SALARY AND


SALARY (PER YEAR) RS.

CLUSTER TOTAL

1 2 3

Less than 2,00,000 10 022 017 049

200,000 – 300,000 33 042 047 122

300,001 – 400,000 08 031 049 088

Above 400,000 05 019 029 053

Total 56 114 142 312

It is observed from the table 6.11 that the maximum number of

employees with salary of less than Rs. 200,000 per year are distributed in

the moderate cluster and the other group of employees who are having a

total salary of Rs. 200,000 and more per year are distributed in the strong

cluster in large.

The non-parametric Chi-square test is applied with a view to check

the significant association between the above noted clusters.

Table 6.12 shows the Pearson Chi-square value and its significance.

Table 6.12

SALARY AND SIGNIFICANCE OF CHI-SQUARE VALUE


(TWO-SIDED)





190

Hypothesis: There is no association between the salary and clusters of

predominant factors of labour welfare measures

It is noted from the table 6.12 that the Chi-square value is relatively


between the salary income of the respondents and clusters of the

predominant factors of labour welfare measures. The study reveals that the

level of labour welfare measures increases with the higher limits of salary.

However, the labour welfare measures is neither high nor low for the

employees earning salary of less than Rs. 2 lakhs per year.

6.6 NATURE OF EMPLOYMENT

In SCSM Ltd., the classification of the workers has been done on the

basis of conditions of employment and appointment of the respondents.

There are two categories of workers namely supervisor and non-supervisors

in SCSM Ltd., The data regarding the employment classification is given in

table 6.13.

Table 6.13

NATURE OF EMPLOYMENT OF RESPONDENTS

NATURE OF

EMPLOYMENT FREQUENCY VALID % CUMULATIVE %

Supervisory 127 040.7 040.7

Non-supervisory 185 059.3 100.0

Total 312 100.0


It is seen from the table 6.13 that 59.3 percent respondents are from

non-supervisory cadre and 40.7 percent respondents are from supervisory

cadre.

The nature of employment of the respondents is shown in chart 6.5

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CHART 6.5

NATURE OF EMPLOYMENT OF RESPONDENTS

Non-supervisorSupervisor

Supervisor : 40.7 % Non-Supervisor : 59.3 %

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6.6.1. Association Between Nature of Employment and Clusters of

Predominant Factors

The relevant data pertaining to the frequency of the nature of

employment of the respondents in each cluster are shown in table 6.14.

Table 6.14

ASSOCIATION BETWEEN NATURE OF EMPLOYMENT AND


NATURE OF

EMPLOYMENT

CLUSTERS TOTAL

1 2 3

Supervisory 12 047 068 127

Non-supervisory 44 067 074 185

Total 56 114 142 312

It is clear from the table 6.14 that the maximum number of middle

level employees who are in the non-supervisory group are distributed in the

moderate cluster and the maximum number of supervisory employees are

distributed in the strong cluster.

The non-parametric Chi-square test has been applied with a view to

know the significant association between the above mentioned groups.

Table 6.15 shows the Pearson Chi-square value and its significance.

Table 6.15

NATURE OF EMPLOYMENT AND SIGNIFICANCE OF

CHI-SQUARE VALUE


(TWO-SIDED)





193

Hypothesis: There is no association between the nature of employment and


From the table 6.15, it is observed that the Chi-square value is

statistically significant and hence it is concluded that there is a close

association between the nature of employment and clusters of the

predominant factors of labour welfare measures. It is found that the

supervisory level of employees seem to be satisfied with the predominant

factors of labour welfare measures as their needs are not wider than

the non-supervisory employees. However, the labour welfare measures

has been declining to some extent in the case of non-supervisory

employees.

6.7 RELATIONSHIP BETWEEN AWARDS AND MERIT AND

COMMENDATION CERTIFICATES AND PREDOMINANT

FACTORS OF JOB SATISFACTION

Recognition means acknowledgement of work well done by the

employees and the workers feel that the appreciation enable them to perform

their work more efficiently. Recognition has been given to the workers of

SCSM Ltd., in the form of awards, merit and commendation certificates and

this appreciation promotes healthy competition among the employees of

SCSM Ltd.

In this section, the relationship between the number of awards and

merit certificates obtained by the SCSM Ltd., employees and the

predominant factors of labour welfare measures is established by applying

Karl Pearson’s co-efficient of correlation.

The para-metric correlation co-efficient between the above mentioned

variables is presented in table 6.16.

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Table 6.16

CORRELATION BETWEEN AWARDS, MERIT AND

COMMENDATION

CERTIFICATES AND PREDOMINANT FACTORS OF

JOB SATISFACTION

PREDOMINANT FACTORS OF

LABOUR WELFARE

MEASURES

CORRELATION

CO-EFFICIENT

SIG.

(TWO-

TAILED)

N

Awards 1 312

Job requirement 119* 0.036 312

Job attachment 154** 0.007 312

Job informations -0.076 0.182 312

Relations with staff -0.012 0.833 312

Communication -0.091 0.110 312

Feeling of recognition - 0.030 0.598 312

Company and management -0.046 0.417 312

Reasonable pay -0.098 0.083 312

Competency - 0.020 0.720 312

Transparency in reward system -0.047 0.410 312

Working conditions -0.095 0.095 312

Comfort and safety -0.050 0.376 312

Innovative learning -0.047 0.408 312

Training conditions -0.053 0.348 312

Empowerment -0.031 0.580 312

Grievances handling procedure -0.001 0.989 312

Grievances evaluation - 0.073 0.199 312

Opportunity for growth - 0.044 0.440 312

Promotion and education - 0.004 0.942 312

Living conditions and finance - 0.079 0.165 312

Relations with family members - 0.080 0.157 312

* Correlation is significant at the 0.05 level (2-tailed)

** Correlation is significant at the 0.01 level (2-tailed)

It is evident from the correlation matrix shown in table 6.16 that the

number of awards and certificates received by the employees of SCSM Ltd., is

deeply correlated only with the two predominant factors of labour welfare

measures namely job requirement and job attachment. This implies that the

employees are able to get most of the merits and commendation certificates and

awards for their meaningful contribution towards the performance of their job.

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6.8 RELATIONSHIP BETWEEN THE TRAINING PROGRAMMES

AND SUB-FACTORS OF TRAINING

Training improves the ability of the workers to do the job on which

they are placed. The increasing technical nature of work demand systematic

training and employees with adequate training definitely give better results

in quality and quantity of output than the untrained employees. In this

section, the link between the training programmes attended by the

employees of SCSM Ltd., and the two sub-factors of training namely

innovative learning and training conditions is established by applying Karl

Pearson’s co-efficient of correlation. The data pertaining to the correlation

matrix is shown in table 6.17

Table 6.17

CORRELATION BETWEEN TRAINING PROGRAMMES AND

PREDOMINANT SUB-FACTORS OF TRAINING

SUB-FACTORS PARTICULARS INNOVATIVE

LEARNING

TRAINING

CONDITIONS

Innovative learning

Pearson Correlation 1 0.432 **

Sig. (two-tailed) 0.000

N 312 312

Training conditions

Pearson Correlation 0.432 ** 1

Sig. (two-tailed) 0.000

N 312 312

Training programmes

attended

Pearson Correlation 0.153 ** - 0.078

Sig. (two-tailed) - 0.007 0.169

N 312 312

Training programmes

attended

Pearson Correlation - 0.023 - 0.015

Sig. (two-tailed) 0.681 0.798

N 312 312

Seminar participated

Pearson Correlation 0.010 - 0.042


N

312 312

Seminar participated

Pearson Correlation 0.001 - 0.008


N 312 312

* Correlation is significant at the 0.01 level (2-tailed)

** Correlation is significant at the 0.05 level (2-tailed)

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From the table 6.17, it is found that the two sub-factors of training

namely innovative learning and training conditions are significantly

correlated. This implies that the training conditions in SCSM Ltd., are highly

conducive for the employees to know the innovative technological

advancement in their job. Further, it is also found that the employees of

SCSM Ltd., are highly motivated to attend the training programmes,

workshops, and seminars on their own interest. From this one can

understand the interest shown by the SCSM Ltd., employees in knowing the

latest innovative ideas in their working environment.

Documents

CHAPTER VI EFFECT OF DEMOGRAPHIC VARIABLES ON …shodhganga.inflibnet.ac.in/bitstream/10603/17431/12/12_chapter 6.pdf · MEASURES–ANALSYSIS III 6.1 INTRODUCTION The prevalence of