S3H Working Paper Series

Number 06: 2017

Efficiency Wages and Employee Work Effort:

A Case Study of Pakistan’s Telecom Sector

Maham Muneer

Verda Salman

December 2017

School of Social Sciences and Humanities (S3H) National University of Sciences and Technology (NUST)

Sector H-12, Islamabad, Pakistan

S3H Working Paper Series

Faculty Editorial Committee

Dr. Zafar Mahmood (Head)

Dr. Najma Sadiq

Dr. Sehar Un Nisa Hassan

Dr. Samina Naveed

Ms. Nazia Malik

S3H Working Paper Series

Number 06: 2017

Efficiency Wages and Employee Work Effort:

A Case Study of Pakistan’s Telecom Sector

Maham Muneer

Graduate, School of Social Sciences and Humanities, NUST mahammuneer27@gmail.com

Verda Salman Assistant Professor, School of Social Sciences and Humanities, NUST

verda.salman@s3h.nust.edu.pk

December 2017

School of Social Sciences and Humanities (S3H) National University of Sciences and Technology (NUST)

Sector H-12, Islamabad, Pakistan

iii

Contents

Abstract ...................................................................................................................................................... v

1. Introduction……………………………………………………………………………..1

2. Review of Literature ....................................................................................................................... 3

3. Theoretical Framework and Methodology .................................................................................. 6

3.1 Shapiro and Stiglitz’s (1984) Efficiency Wage Model .............................................................. 6

3.2 The Basic Model ............................................................................................................................ 7

3.3 Empirical Model ............................................................................................................................ 9

3.4 The Fair Wage Effort Hypothesis and Unemployment ........................................................ 10

3.5 Motivation for the Hypothesis .................................................................................................. 10

3.6 Empirical Model .......................................................................................................................... 12

4. Data………………………………………………………………………………….....12

4.1 Questionnaire Design ................................................................................................................. 13

4.2 Survey Reliability ......................................................................................................................... 13

4.3 Construction of Variables and Descriptive Statistics…………………………………...14

4.3.1. Construction of Variables ....................................................................................................... 14

4.4. Cross Tabulation .......................................................................................................................... 17

5. Results and Discussion................................................................................................................. 20

6. Conclusion and Policy Implications…………………………………………………….24

Appendix A: Construction of Variables .............................................................................................. 25

Appendix B: Summary Statistics ........................................................................................................... 26

Appendix C: Descriptive Statistics ....................................................................................................... 27

References…………………………………………………………………………….............27

iv

List of Tables

Table 1: Cronbach’s Alpha Reliability .................................................................................................. 13

Table 2: Job Separation Rate and Effort Cross Tabulation .............................................................. 17

Table 3: Job Search Time and Effort Cross Tabulation .................................................................... 17

Table 4: Effort and Efficiency Wages Cross Tabulation .................................................................. 18

Table 5: Effort and Perceptions about Fair Pay Cross Tabulation.................................................. 18

Table 6: Job Satisfaction and Effort Cross Tabulation ................................................................... 199

Table 7: Management Relations and Effort Cross Tabulation ......................................................... 19

Table 8: Monitoring and Effort Cross Tabulation ............................................................................. 19

Table 9: Peer Pressure and Effort Cross Tabulation ......................................................................... 20

Table 10: Regression Analysis for the Shirking Model .................................................................... 211

Table 11: Regression Analysis of The Fair Wage Effort Hypothesis ............................................ 223

v

Abstract

The study analyzes the efficiency wage theory and the fair wage hypothesis using the self-

reported survey data of Pakistan’s Telecom sector. The focus of the study is to explore how

efficiency wages, job separation rate, time to find an alternate job and the monitoring intensities

affect the level of efforts. Moreover, it examines the relationship between self-reported effort

levels and fair wage perceptions. The paper finds strong support for the shirking model,

suggesting that the provision of efficiency wages along with the monitoring technology

encourages employees to exert higher effort. The results are in line with the fair wage effort

theory. The study suggests that wage comparisons within and outside the firms are important

determinants of worker effort. Therefore, market compatible wages coupled with good

managerial relations with firms and job satisfaction among workers stimulate increased employee

efforts.

Keywords: Efficiency wages, job separation rate, monitoring, shirking model, fair wage

perceptions.

1

1. Introduction

Work effort affects individual performance as well as organizational efficiency and

effectiveness. To optimize organizational efficiency, firms need to induce their employees to

work at or near their peak productivity levels. The wage policies, supervision, monitoring

technology and other measures undertaken by firms, to enhance worker’s morale are the direct

ways to increase effort per worker. The models regarding work effort, formulated in the 1980’s, discussed the unavailability

of adequate information to the employers about the effort level of their employees. The notion

of imperfect information states that the likelihood of shirking on the part of employees, creates

a wedge between the marginal product of the employees and their wages.

Monitoring enables the firms to detect the shirkers, if the firms are not monitoring its

employees at all, the rational worker will shirk. The models predict that the effort or the

probability of detecting a shirker, positively depends on the type of monitoring technology (i.e.,

whether the technology or the design of the job makes it easy for the firm to determine the

performance levels of the employees) and the level of monitoring applied, closely supervised or

not (Shapiro & Stiglitz, 1984; Kruger & Summers, 1988).

Employees will shirk only when to do so is not costly. There would be some type of cost

of losing a job, otherwise dismissing an employee will not be a significant threat. The cost of

losing a job, increases with the projected unemployment period and upsurges with the wages.

Production techniques are of such types that it is quite difficult to ascertain an individual’s

contribution to productivity. Under these circumstances, how can high effort be elicited from

workers? These types of models instantaneously highlight the question of work incentives.

Efficiency wages are the most widely discussed under these work incentive models. These models

hypothesize that employers find it optimal to set wages above the market clearing level. The

impediments to observe and measure an employee’s productivity hence effort, lead firms to pay

efficiency wages. Efficiency wage models are applicable when worker’s actions are hard to see

by the employer.

Pay fairness consideration attracts the employee’s effort choices, as stressed by Akerlof

and Yellen (1990). This concept coined the fair wage effort theory. Employees develop a concept

of wage fairness, by comparing their pay with other employees with same or similar kind of work.

2

Employees reciprocate when they are paid fairly, by exerting more effort. If they consider their

pay as unfair, adjust their effort downward to correspond to the wages obtained.

Workers usually take their peers as a reference group for a comparison of their wages

(Falk & Knell, 2004). These comparisons are vital for the work morale, whereas quality boosting

performance is motivated by the internal pay schemes (including salary and all type of monetary

and non-monetary benefits). The main issue is to ensure internal pay equity to keep morale high

and hence elicit increased effort (Bewley, 1999; Gachter & Thoni, 2010).

Different approaches are available to measure the work effort. Economists usually used

the physical or monetary measures of production, when the output is tangible. But it is difficult

to measure the production of a worker directly, at the individual level. Some of the researchers,

have used the self-reported effort levels to measure the work effort or the supervisor’s evaluation

to the subordinates. The latter technique is usually used in organizational psychology studies.

To avoid the difficulties involved to capture the notion of effort, the study relies on self-

reported effort levels to test the efficiency wage theories through a detailed survey. Survey with

self-reported responses leads to ascertain the employees believes, otherwise it is a very

problematic job. The worker’s evaluation is done by himself/ herself, no other objective standard

is used in the study. The present study is a case study of Pakistan’s Telecom sector and uses a

field survey to collect responses regarding the work effort. There are five mobile companies;

namely, Mobilink, Telenor, Ufone, Warid and Zong in Pakistan’s Telecom sector.

The present study also explores the relationship between effort and perceptions of fair

pay, job satisfaction and the relationships between management and employees. The study uses

the fairness perceptions about the wage that is offered, is this wage deemed as fair by the

employees?

The results support the shirking and fair wage hypotheses and suggest that workers exert

high effort; whenever efficiency wages are available, higher the monitoring intensity and higher

the projected time to find an alternate job. Furthermore, the analysis shows that Zong employees

are exerting more effort than other companies.

The outline of the remaining study is as follow: Section 2 reviews the available literature

on the topic. Section 3 presents discussion on theoretical framework used and section 4 gives an

3

insight on the field survey and the data collected. Section 5 gives detailed discussion on the results.

In the last section 6 the study has been concluded.

2. Review of Literature

Below mentioned studies show the empirical evidence against the shirking and fair wage

theories.

Kaufman (1984) surveyed 26 UK firms and collected information on the pay reductions

and effort level of employees. When less than the market wages were offered, employees could

not retain their jobs. The result was not surprising that the pay cut could keep the morale down,

which upsets the workers. The employees could show their anger by putting less effort and

quitting the jobs. Accordingly, the concept of fairness in wages is of special importance.

Raff and Summers (1987) examined the wage policies of Henry Ford and examined the

relevance of efficiency wage theory. The results suggested that the experience of the company to

raise the wages above the market strongly supports the efficiency wage theory. The wage

premiums were negatively linked with the quit rates, positively linked with job tenure and self -

reported effort levels. The introduction of this policy was a type of raising the morale of the

workers to elicit more effort and hence to raise productivity and profits.

Different versions of efficiency wage theories were examined by Blinder and Choi (1990)

by making interviews of 13 firms and targeted managers and workers. A separate questionnaire

was designed for each theory. They found that managers thought that perceptions of fair pay

played a motivational role and the fair pay policy of companies is vital to elicit effort. The survey

results didn’t support the adverse selection theory but were consistent with the shirking version.

Incentives, monitoring and fear to be unemployed were proved to be the most important

determinants of hard work.

Efficiency wage model was estimated by using disciplinary rates and wage premia by

Cappeli and Chauvin (1991). The study provided evidence for the support of efficiency wage

hypothesis. Higher wage premia were linked with higher effort as measured by disciplinary

dismissals. The labor market prospects pressurize worker to exert high effort, when it is difficult

to attain another job.

4

Predictions of the efficiency wage theories were tested by the Levine (1991) using the

data set of employees and employers of US and Japan. The paper used different subjective and

objective measures of performance and some measures of industry, job and employee

characteristics. Quit rates, self-reported effort, absence days, search for another job and job

commitment were used as performance measures. Internal and external pay showed the

comparison of wage within and outside the company and captured the notion of fair pay. Ordered

logit and OLS techniques were used to estimate the models. The results suggested that high wage

workers are more satisfied, reluctant to quit when pay is fair, are good performers, more

committed to their jobs, have lower intentions to find another job and are willing to work hard.

Efficiency wage model was estimated by Wadhwani and Wall (1991) using the production

function of UK’s manufacturing firms. The study examined the predictions of the model for the

productivity of different firms, for which effort was included in the production function. In these

firms, wages are settled between firms and employees through bargaining. This thing didn’t make

the effort wage relations irrelevant. The results suggested that the productivity at firm level is

raised when relative wages and unemployment level boosts. Greater the relative wages, higher is

the production.

Belman et al. (1992) used the micro level data and found some support for efficiency wage

behavior. Some results were consistent with the efficiency wage theory. Ordered probit technique

had been followed on cohesive and noncohesive workgroup samples. The study used the dobbing

strategy (questioned the coworkers about the effort of their colleagues) to capture the effort. The

study found comparatively convincing evidence for the effects of unemployment spells and

monitoring intensity on the level of efforts as suggested by the work discipline model. The paper

concluded that the work discipline behavior is more prevalent than the gift exchange model in

their dataset. The group cohesion increased the effort levels by boosting the morale and

promoting the teamwork.

Agell (1994) used the information of the Swedish Population from time dairies. The study

tested both the efficiency and the fair wage theories. Effort was captured by using the actual time

spent at work. The results suggested that wages and the effort levels had positive but no statistical

meaningful relationship. The study proceeded by saying that higher unemployment rate

compelled the workers to put higher effort, the fear to be unemployed in near future took the

5

predicted sign and induced the employees to put higher effort. The shirking and fair wage

versions of the efficiency wage theory suggested the positive relationship between the effort and

the wage in the Swedish labor market.

Fairris and Alston (1994) contributed to literature by combining the compensating wage

differential theories and the efficiency wage theory. The study simultaneously estimated the wage

and work intensity (a proxy for work effort) equations to test these two models using 2SLS. The

paper found that work effort is the only work condition, which should be compensated. The

dataset was taken from a survey of 1977 US blue collar workers. The results supported the

existence of efficiency wage theory, but not the positive compensating payments. Higher the

employment rents were, higher would be the work effort of the employees, and interestingly

married were receiving higher wages.

Judge and Chandler (1996) studied the individual level determinants of worker shirking.

The study used a sample of employees from US health care occupation. The study investigated

the subjective well-being, job satisfaction and many others are the determinants of shirking. OLS

results showed that the workers who were unsatisfied with their jobs and displeased workers

(with minimal subjective well-being) were significantly more prone to shirk. White employees

were significantly more, and old age employees were less.

Shirking differentials across different regions of Italy banks were estimated by Ichino and

Maggi (1999). Poisson and logit models were used for analysis. Specifically, absenteeism and

misconduct incidents were mostly recurrent in the bank branches of North region of Italy. The

study proposed that individual backgrounds were supposed to be the most dominant factor

towards shirking differentials. North is a privileged region, low unemployment rate and more

branches in North were supposed to be the major reason to be a shirker in North.

The shirking and fair wage model using self-reported effort levels was estimated by Clark and

Tomilson (2001). The study used Britain survey of male and female employees. Ordered probit

and 2SLS techniques were used for estimating effort and wage equations. The study concluded

that effort is positively related to wages and union member, whereas monitoring intensity and

unemployment in the local market didn’t increase the effort level. The results highlighted the fact

that women are exerting more effort than men, but getting low wages.

6

The relationship between wages, monitoring and effort was explored by Strobl and Walsh

(2007). The study tested the efficiency wage model by using a large dataset from the survey of

Ghanaian firms. Effort was suggested as a key determinant for the settlement of wages. Due to

the endogeneity of effort and monitoring, different instruments were used for both. 2SLS

estimation technique was used and study concluded that with low supervision rate firms pay high

to its workers and elicit higher effort from workers. All the results were in line with the theory.

To sum up, several studies estimated the efficiency wage models using the earning

functions, production function and the self-reported performance measures. These suggest the

positive relationship between effort and wages, and give special attention to the unemployment

rate and monitoring intensity. The literature studied supported both models. The studies which

took those sectors for analysis, where the tangible output could be attained. Their measure of

effort was in terms of productivity. But in those sectors where tangible output couldn’t be

obtained, other measures of effort have been used like the self-reported effort levels.

3. Theoretical Framework and Methodology

In this section, the study discusses the theoretical framework that is used to estimate the

model.

3.1. Efficiency Wage Model

Shapiro and Stiglitz’s (1984) shirking model explains the existence of involuntary

unemployment and payment of higher than market wages to workers by the firms. The model

shows that the information structure doesn’t allow the employer to perfectly monitor the

employees on the job effort costlessly. This induces the worker to reduce his work efforts and

indulge in shirking behavior. In case of full employment in the economy, no credible punishments

can be devised for the worker who has incentives to shirk.

With greater prospects of rehiring at the market clearing wage within no time, the shirker

cannot be penalized for his wrongdoing. To deal with this problem, the firms offer more than

the market clearing wage to its employees inducing them to put high effort. An employee, if

detected while shirking is fired as a penalty. When all the firms raise wages, the demand for labor

in the market decreases, and the involuntary unemployment sets in. Efficiency wages prevent

workers from shirking since it raises the worker’s cost of losing the job. The presence of sizeable

7

involuntary unemployment in the economy compels the employees to work instead of taking the

danger of being detected as shirker.

3.2. The Basic Model

The basic model considers the risk neutral workers: Quit rate and monitoring intensities

are assumed to be exogenous. Firm considers the role of unemployment as an incentive, which

discipline workers.

Workers

There are fixed number of homogeneous workers ‘N’. Everyone dislikes exerting effort

but, enjoys the consumption of goods. The utility function of an individual is U (w, e). ‘w’ and ‘e’

are wages and on the job effort respectively. The utility function of an employed worker can be

written as

U = wage – effort or U = w – e

The employees provide only two effort levels: e = 0 (shirking) and e = ē (productive) ē

> 0. Unemployed worker receives unemployment benefits ‘ω’. The employee can be only in two

states in each time: employed or unemployed. The probability of separation from a job is ‘b’ per

unit time due to exogeneous shock. This shock enters the worker into the unemployment pool.

Employees want to maximize the expected discount rate of future ‘r’.

The Effort Decision of a Worker

Employees can only choose the level of effort they want to exert on their jobs. If an

employee does not shirk, he will get a wage ‘w’ until some exogenous shock comes and terminate

his job. ‘q’ (per unit time) is the probability of detecting shirking behavior of a worker. If detected,

worker is fired, and enters the unemployment pool. The expected time of searching for a job per

unit time in the unemployment pool, defines the job acquisition rate. Worker receives

unemployment benefits ‘ω’ when unemployed. The employee chooses that effort level which

maximize his discounted utility.

This makes the comparisons of the utility of a shirker and non-shirker. The lifetime utility

of an employed shirker and non-shirker is ‘ VES ’ and ‘ VE

N ’ respectively, and the utility of an

unemployed individual is VU. The basic asset equation of a shirker is

r VES = w + (b + q) (VU - VE

S) , … (3.1)

8

And for non-shirker is:

r VEN = w - e + b (VU - VE

N) … (3.2)

The Equs. (3.1) and (3.2) can be solved for VEN and VE

S:

VES = (w + (b + q) VU) / r + b + q … (3.3)

VEN = ((w - e) + bVU )/ r + b … (3.4)

The employee will not shirk iff VEN ≥ VE

S which called “No Shirking Condition” (NSC).

Equs. (3.3) and (3.4) can be rewritten as

w ≥ r VU + ((r + b + q) e) /q ≡ ŵ … (3.5)

where, ŵ is the critical wage the firm pays or no shirking wage.

NSC can be rewritten as

q (VES - VU) ≥ e

The above equation shows the implications of the NSC: until penalty is accompanied

with the unemployment, all workers are going to shirk. If everyone is rehired immediately, VU =

VES, the NSC cannot be satisfied.

Equilibrium emerges when every firm, takes the wages and the level of employment of

other firms as given. And finds it most advantageous to offer the prevailing wage rate instead of

a distinctive wage. The important market variable which governs the single firm actions is ‘VU’.

Now we see the expected utility of an unemployed employee ‘VU’ in equilibrium.

The asset equation of VU is

rVU = ω + a (VE – VU) … (3.6)

where, ‘a’ is the job acquisition rate and the utility of an employed employee is VE.

The aggregate NSC is

w ≥ ω + e + e (a+ b + r) / q … ( 3.7)

The expected utility of unemployed will be higher, if the unemployment benefit is larger. The

penalty associated with unemployment will be lower. To induce the workers not to shirk, higher

wages must be offered. ‘a’ is the job acquisition rate per unit of time i.e. the probability of finding

a job, the expected time spell of unemployment is ‘1/a’. The larger the unemployment duration,

the greater will be the penalty associated with unemployment. Hence the lower wage is required

to induce the workers not to shirk.

9

3.3. Empirical Model

The above discussion, highlights following important variables that affect an employee’s

effort level. These include wages, the probability of separation from a job ‘b’, expected discount

rate of future ‘r', ‘q’ the probability of detecting a worker if he shirks, the job acquisition rate ‘a’,

unemployment benefits ‘ω’ when unemployed. The present study also assumes that workers are

exerting some positive level of effort (e > 0) taking it (e = 1) as full effort or shirking (e =0).

The Eqn. (3.5) can be rewritten as

r VU + ((r + b + q) e) /q ≤ w … (3.5A)

By isolating ‘e’ it becomes

e ≤ (w - r VU) q / (r + b + q)

Now we can transform the above equation for Pakistan’s Telecom market. In Pakistan,

there are no unemployment benefits and usually people are unwilling or unaware about the

expected discount rate of future. So, keeping r =0

e ≤ w q / (b + q)

It can be implied that

e = f (w, q, b)

as we see in market equilibrium the job acquisition rate is also associated with the asset equations

of employed and unemployed worker.

The aggregate NSC equation (3.7) is the market equilibrium NSC.

w ≥ ω + e + e (a+ b + r) / q

keeping ω = 0 and r = 0, it becomes

w ≥ e (a+ b) / q

The model can be represented as

e = f (w, q, b, a)

In Pakistan’s Telecom Market, a market survey is annually conducted by each mobile

company to assess the market wage. This survey helps to determine the wages. The companies

align their wages against every job position. So, we can say that the market clearing wage is given.

But it is not the efficiency wage. The notion of efficiency wage is captured by incorporating

incentives. These incentives are in the form of bonuses, salary increments and promotions. The

job acquisition rate i.e. longer duration of unemployment spells is taken as job search time. Job

10

separation rate is used to capture the possibility of exogeneous shocks, which separates a worker

from his job.

We can write the above model as

Effort = f (Monitoring ‘q’, Job Separation Rate ‘b’, Job Search Time ‘a’, Efficiency Wages ‘w’)

The final form of the model to be estimated is

Efforti = β0 + β1 Monitoringi + β2Efficiency Wages + β3 Job Separation Ratei + β4 Job Search Timei

+ δ (Firm Dummies) + μi … (A)

where, μi is the random disturbance term for i = 1, 2, 3,……, 203

3.4. The Fair Wage Effort Hypothesis and Unemployment

Akerlof and Yellen (1990) explains the results of the fair wage effort hypothesis regarding

worker behavior. Employees have an idea of fair pay; when the actual pay is below than the fair

pay, a corresponding normal level of effort is provided by employees. ‘e’ is the effort, ‘w’ and ‘w*’

are the actual and fair wage respectively, then the hypothesis says

e = min (w/ w*, 1) … (3.8)

‘1’ is the normal level of effort and effort is in units.

The hypothesis also elucidates the presence of unemployment in the economy. If the fair

pay ‘w*’ goes above the actual or the market clearing pay ‘w’, unemployment sets in. If people

don’t acquire what, they be worthy of, then they attempt to obtain even. The authors present

various kinds of evidence to prove this hypothesis.

3.5. Motivation for the Hypothesis

Equity Theory

Adams (1963) explains the social exchange from the perspective of two persons.

According to him, the ratio would be equal between the perceived value of both inputs and

outputs. From the labor side, the perceived value of the labor time is input and perceived value

of the wage is outcome. From the firm’s perspective, perceived value of the wage is the input

and, perceived value of the hour of labor is the outcome. In economic terminology, the number

of units of effective labor (e) times the perceived value of a unit of effective labor (w*) will equal

the perceived value of wage (w).

11

e = w/w* … (3.9)

where, ‘w*’ is the fair pay, not the market clearing pays.

Relative Deprivation Theory

The determination of fair pay is very important to find out its economic consequences.

According to this theory, people develop their concept of fairness by comparing their wages with

significant others. The theory guides about the reference group to whom comparison is being

done. The three possibilities are: people compare themselves with the same occupation within

the same firm.

Social Exchange Theory

Blau (1969) and Homans (1961) both proposed the same theory of social exchange. There

will be equal returns on both ends of exchange. If people don’t get even what they deserve, they

get angry. Employees whose pay are below the fair pay w* will be annoyed. The result will be

reduction in effective labor input and reduced level of effort will be supplied if the worker is not

satisfied. This can be written as

e = w/w* for w < w*

Empirical Observations of Work Restrictions in the Workplace

Mathewson (1969) recorded some observations regarding the workplace restrictions,

being a participant. According to him, employees have an idea that they are worth more important

and deserve higher pay than what the organization pays them. It also shows that the pay necessary

to motivate them to be mentally and physically present at work is higher. Furthermore, they

consider the pay of dissimilar workers, in their calculation of fair pay. If they don’t get what they

deserve, then simply adjust their production accordingly. The willingness and the ability to control

effort, in retort to their grievances, underlies the hypothesis.

Jealousy and Retribution

The feelings of envy and vengeance, the relationship among the performance and equity,

aren’t pertinent to the discipline of psychology and sociology: these are the part of everybody’s

experience. Inequitable rewards show the management failure.

12

Personnel Management Texts

Management related textbooks show the importance of equitable treatment of employees.

Dessler (1984) stated that equity is the most important determinant of wage. Pay should be

compared with the other firms, otherwise it will be very difficult to invite and retain the

competent workers. Wages should be equitable within the organization so that every worker takes

his pay as equitable keeping others in mind. According to Kochan and Barocci (1985, p. 249), the

only factor which collapse the labor relations, keep down morale, dissatisfaction towards jobs,

higher turnover, lower production and increased absenteeism; is the inequitable wage rates to the

employees within the same workgroup and in the same organization.

3.6. Empirical Model

This theory suggests that effort depends on the perceptions of fair pay. It is related to the

common sense and motivated by the theory and observation of sociology and psychology.

Employees reduce their effort when they are rewarded less than what they deserve. The above

discussion suggests the important variables to test the fair pay effort hypothesis are: effort,

perceptions about fair pay, job satisfaction and management relations.

We can write it as

Effort = f (Perceptions about Fair Pay, Job Satisfaction, Management Relations)

The final form of the model to be estimated is:

Efforti = α 0 + α 1 Perceptions of Fair Payi + α 2 Job Satisfactioni + α 3 Management Relationsi + γ

(Firm Dummies) + μi … (B)

where, μi is the random disturbance term.

4. Data

The present study focuses on the exploratory research to find out the evidence against

the efficiency wage theory. The sector under consideration is Telecommunication sector of

Pakistan. In this regard, an extensive questionnaire is developed to study the behaviors, which

reflect the efficiency wage considerations.

The data are collected through the online survey. The present study includes the Head

and Regional offices of five mobile companies namely, Mobilink, Telenor, Ufone, Warid and

Zong. A sample of 203 employees was collected through online survey.

13

4.1. Questionnaire Design

The questionnaire was designed to capture the various aspects of efficiency wage theory

as experienced by the employees of different mobile companies. The questionnaire focused on

the self-reported effort levels of the employees, compensation policies, monitoring and

supervision, perceptions of pay fairness and job satisfaction.

Pilot survey was conducted online from 56 employees of all the companies. The survey

consisted of 16 sections with 94 questions including 11 of demographics. In pilot study, at the

end of each section the suggestions about the improvement of section and easiness to understand

the survey were asked. These comments were then incorporated in the principle survey. There

are 7 sections with 64 questions including 11 of demographics in principle survey. The first

section labeled as effort capturing the effort levels of the respondents. The second section was

about the perceptions of fair pay and followed by the questions about job search time,

monitoring, incentives and so on.

Questions about the self-reported effort asked the respondent to tell from scale 1 – 5

about their effort; 1 showing only a little effort and 5 higher effort. Questions about job search

time, monitoring, job satisfaction were measured on the scale 1 – 5 and 1 being lower and 5 being

higher. In the same way, all other questions were constructed.1

4.2 Survey Reliability

Reliability is the internal consistency of the measurement. It shows how closely the items

in a group or section are related to each other. For survey reliability, Cronbach alpha is calculated.

It is calculated by using the number of items and the mean inter item correlation among items.

Table 1. Cronbach’s Alpha Reliability

Variable Cronbach’s alpha Variable Cronbach’s alpha

Effort 0.628 Efficiency Wages 0.621

Perceptions of Pay Fairness 0.770 Workgroups 0.677

Subordinate Evaluation 0.713 Management 0.785

Job Satisfaction 0.744 Job Switching 0.711

1 See this link for questionnaire design: http://discover.ukdataservice.ac.uk/catalogue? Sn%3D5368& source= gmail&ust=1488954135840000&usg=AFQjCNEardvKLDam8bm2z0mPdcNcuTj4ew.

14

Its values lie between 0 to 1. The value greater than 0.6 is usually considered acceptable

in social sciences. The Cronbach alpha for different variables is shown in Table 1:

4.3. Construction of Variables and Descriptive Statistics

The descriptive statistics of all the variables is done to obtain the clear picture of the

responses. There are 64 questions and the sample size is 203. The table in Appendix A shows

different variables and their frequency with percentages. For a clear picture of the data, the

proportion of categories for each variable are shown in the table. Here is the brief discussion of

how variables are constructed.

4.3.1. Construction of Variables

For Effort index, the variable is constructed by using 4 questions including one reversed

question with a five point Likert scale. The reversed question is transformed into different values

of (1=5 and 5= 1 and so on). Below are the questions:

1. The level of effort you put in your job.

2. How often you didn’t complete your tasks/targets within the allocated time?

3. How often you completed your tasks/targets before the allocated time?

4. How often you were above average in your performance evaluation report in the

recent past?

The responses of all the questions were added and then dived into two cut points. For

simplicity, values less than and equal to average, i.e., ‘14’ were assigned ‘0’ showed shirking and

‘1’ for values greater than mean showed full effort. Responses showed 22.17% employees are

shirkers while 77.83% employees are exerting full effort.

Monitoring variable consist of 4 questions. The questions are:

1. My work is monitored.

2. I have to report about the progress of my tasks/targets (i.e., how much I complete

daily/weekly, etc.).

3. It is easy for my supervisor to closely monitor my tasks/targets.

4. It is easy to falsely report about the completion of my daily/weekly/monthly/quarte-

rly tasks.

15

Questions 1 and 2 are binary and question 3 and 4 scaled from 1 – 5. For simplicity two

categories formed; no monitoring at all as ‘0’ and high level of monitoring as ‘1’. Values less than

and equal to average was assigned ‘0’ and ‘1’ for values greater than mean. 14.29% employees

are not monitored whereas, 85.71% employees are perfectly monitored.

The variable Efficiency wages consists of three questions are:

1. I received bonus due to my better job performance in the last one year or less.

2. I am promoted due to my good performance (for how many times?).

3. I received increment in my salary (for how many times?).

All variables were added and then 0 and 1 values were assigned. The employees who

report they didn’t get efficiency wages were coded as ‘0’ and 1 for the provision of efficiency

wages. 22.17% of the respondents reported they didn’t get and 77.83% received efficiency wages.

Job Search Time Variable consists of 2 questions based on scale 1 – 5 including one reversed

question:

1. It is easy for me to obtain a similar job at another company with comparable pay.

2. My job prospects will decrease, due to the probable merger of two mobile companies.

For simplicity, it was coded 0 and 1. ‘0’ for less time to find an alternative job and ‘1’ for

more time. 37.93% responses showed that less time is required to find a job whereas, 62.07%

reported that it would take more time to find an alternate job.

Perception of Fair Pay variable consists of 3 questions scaled on 1 – 5. The questions are:

1. My pay is fair, compared to Coworkers working in my company, with same kind of

work.

2. My pay is fair, compared to Workers working in other Companies, with same kind of

work.

3. I am compensated fairly relative to my local market.

Then these questions were coded 0 as unfair and 1 as fair. 40.39% and 59.61% employees

reported 0 and 1, respectively.

Management Relations variable consists of 4 questions scaled on 1 – 5, then coded as 0

and 1. The questions are:

1. In general, the relationship between management and the employees is very good.

2. Management has failed to create equity among employees.

16

3. All employees have equal chance of promotion.

4. The job performance evaluation system is objective.

Value 1 showed good and 0 bad relations with management. 22.66% reported badly and

77.34% reported as good relations with management.

Job Satisfaction variable consists of 5 questions scaled on 1 – 5, including 2 reversed

questions.

1. In general, I am satisfied with my job.

2. I am proud of my company's brand.

3. I do not willingly devote my free time to job.

4. I am satisfied with my overall job security.

5. In general, the work environment in my company creates stress.

For simplicity two categories were formed. Value 0 showed unsatisfied and 1 showed

satisfied.

Job Separation Rate variable consists of 1 question scaled on 1 – 5, is:

1. Thinking about the next year, there is a possibility I will lose my current job.

2. My job is at stake, when I see the present market conditions (i.e., the probable merger

of two Mobile companies).

The responses were added and divided into two cut points 0 being the no possibility to

separate from the job and 1 being the possibility to be separated from the job.

Peer Pressure variable consists of 4 questions based on scale 1 – 5. The questions are:

1. The level of effort my Coworkers (working in my department) put in their jobs.

2. My Coworkers didn’t complete their tasks/targets within the required time.

3. My Coworkers were above average in their performance evaluation report in recent

past.

4. My Coworkers meet their targets/ tasks/ objectives completion deadlines.

All responses were added and then dived into two cut point ‘0’ showed no peer pressure

and ‘1’ the presence of peer pressure. 46.80% employees reported no peer pressure and 53.20%

reported they took pressure.

17

4.4. Cross Tabulation

Tables 2 to 9 present the cross tabulation of effort along with independent variables. (The

direction of cross tabbed variables is horizontal) and highlight the association of independent

variables like monitoring, Job Search time and job separation rate etc. to the effort levels.

Table 2. Job Separation Rate and Effort Cross Tabulation

Effort Job Separation Rate

Probability of not losing job (0) Probability of losing job (1)

Shirking (0) 57.78% 42.22%

Full effort (1) 40.51% 59.49 %

Pearson chi2(1) = 4.2335 P value = 0.040

The cross tabulation of job separation rate and effort shows the significant relationship.

Job separation rate is the probability of separating or losing the job due to exogenous shock.

57.78% employees shirk when there is not the possibility of being separated from the job.

Whereas, 42.22% employees shirk with the possibility of being fired. This shows that lower

percentage of people shirk when they feel there is high probability of losing the current job.

59.49% employees exert full effort.

Table 3. Job Search Time and Effort Cross Tabulation

Effort Job Search Time

Less Time (0) More Time (1)

Shirking (0) 57.78% 42.22%

Full effort (1) 32.28% 67.72%

Pearson chi2(1) = 9.6730 P value = 0.002

Job search time shows the easiness to obtain a similar job in another company. Table 3

reveals that 57.78% employees feel it’s easy to obtain job with less amount of time and keeping

this in view they shirk. Whereas, 32.28% put full effort with less time to find a job. Similarly,

42.22% employees shirk when it takes more time to find an alternative job whereas, 67.72% are

more likely to provide high effort. The results show a clear picture of the relationship between

18

these two variables. It can be argued that the people with more time to find an alternative job are

working at their peak levels.

Table 4. Effort and Efficiency Wages Cross Tabulation

Effort Efficiency Wages

No (0) Yes (1)

Shirking (0) 44.44% 55.56%

Full effort (1) 29.75% 70.25%

Pearson chi2(1) = 3.4217 P value = 0.064

Table 4 shows the relationship between efficiency wages and effort. When efficiency

wages are given 70.25% employees exert full effort. Whereas when there are no efficiency wages,

44.44% employees shirk. The percentage of employees is quite low who exert full effort in the

face of no efficiency wages.

Table 5. Effort and Perceptions about Fair Pay Cross Tabulation

Effort Perceptions about Fair Pay

Unfair (0) Fair (1)

Shirking (0) 68.89% 31.11%

Full effort (1) 32.28% 67.72%

Pearson chi2(1) = 19.4973 P value= 0.000

Table 5 shows that 68.89% employees shirk who take their wages as unfair. The

percentage of employees who shirk with fair wages significantly reduces to 31.11%. Only 32.28%

employees exert full effort with unfair wages, whereas the number raise to 67.72% when wages

are deemed as fair.

Table 6 reports that 57.78% employees are shirkers and unsatisfied with their jobs.

Whereas among unsatisfied 41.14% exerts full effort. Similarly, 42.22% employees are although

shirkers but satisfied with their jobs 58.86% employees are exerting full effort and satisfied with

their jobs.

19

Table 6. Job Satisfaction and Effort Cross Tabulation

Effort Job Satisfaction

No (0) Yes (1)

Shirking (0) 57.78% 42.22%

Full effort (1) 41.14 % 58.86%

Pearson chi2(1) = 3.9205 P value = 0.048

Table 7. Management Relations and Effort Cross Tabulation

Effort Management Relations

Bad (0) Good (1)

Shirking (0) 37.78% 62.22%

Full effort (1) 18.35% 81.65%

Pearson chi2(1) = 7.5397 P value = 0.006

Table 7 shows that 37.78% employees who have bad relations with management are

shirkers and this percentage further decreases to 18.35% who put full effort. Similarly, good terms

with management rises the effort level to 81.65%.

Table 8. Monitoring and Effort Cross Tabulation

Effort Monitoring

No (0) Yes (1)

Shirking (0) 28.89 % 71.11 %

Full effort (1) 10.13% 89.87%

Pearson chi2(1) = 10.0691 P value = 0.002

Monitoring and effort cross tabulation in Table 8 indicates that 28.89% of shirking

behavior is influenced by lack of monitoring. Within no monitoring category the percentage of

full effort is 10.13% whereas within monitoring category, the percentage of full effort is 89.87%.

We see that highly monitored employees are almost 8 times more likely to put full effort than

with not monitored workers. It can be concluded that the monitoring positively affect effort.

20

Table 9. Peer Pressure and Effort Cross Tabulation

Effort Peer Pressure

No (0) Yes (1)

Shirking (0) 66.67% 33.33%

Full effort (1) 41.14 % 58.86%

Pearson chi2(1) = 9.1671 P value = 0.002

A positive relationship between peer pressure and effort is revealed in Table 9. 66.67%

employees are shirkers with no peer pressure. Whereas, 58.86% employees are exerting full effort

with peer pressure. Similarly, the percentage of employees who are exerting full effort reduces to

41.14% when they don’t feel peer pressure. Peer pressure turns out to be an important

determinant of effort.

5. Results and Discussion

This section discusses the results of shirking model and fair wage hypothesis.

Monitoring is positively associated with effort in Table 10, Model A(1). Higher the

monitoring level, more likely it will be, that an employee put effort or work as compared to those

who are not monitored. The significance shows that the monitoring induces the workers to do

their tasks well. One unit increase in monitoring level increases the likelihood of exerting high

effort by 1.95 times. The increase in monitoring level, enhances the likelihood of being caught

as shirker and thus fired and thereby induces him to work hard (Chang & Lai, 1999).

Efficiency wage is the reward when performance is more than expectation. The positive

and significant coefficient of efficiency wage shows that, it is more likely the workers will put

high effort as compared to those who don’t receive efficiency wage (Goldsmith et al., 2000).

Efficiency wage motivates the workers to show good performance. The result is consistent with

the shirking model.

The positive relationship between job separation rate and effort shows that, the possibility

of losing the current job is high, induces the worker to put high effort as compared to those who

don’t think to be separated. With high probability of losing job stir up a worker to exert full on

21

the job effort, so that he may not be separated from the company. Firm considers him a valuable

employee, separating him will not be beneficial for the company.

Table 10. Regression Analysis for the Shirking Model

Variable Model A(1) Model A(2)

Coefficient Z-Score

Odds-Ratio

Coefficient Z-Score Odds-Ratio

Monitoringi 0.672* (0.249)

2.70 1.959 0.561** (0.263)

2.13 1.752

Efficiency Wagei 0.766*** (0.403)

1.90 2.152 0.820*** (0.453)

1.81 2.271

Job Separation Ratei

0.996* (0.386)

2.58 2.707 0.935** (0.387)

2.42 2.548

Job Search Timei 1.250* (0.380)

3.29 3.492 1.216* (0.389)

3.13 3.373

Peer Pressure 0.826** (0.393)

2.10 2.285

Career Level -0.0829 (0.277)

-0.30 0.920

Teamwork 0.267 (0.657)

0.41 1.305

Marital Status 0.180 (0.392)

0.46 1.198

Telenor -0.0465 (0.585)

- 0.08 0.955 -0.033 (0.595)

-0.06 0.968

Ufone -0.0506 (0.499)

- 0.10 0.951 0.042 (0.503)

0.08 1.048

Warid -0.383 (0.573)

-0.67 0.682 -0.401 (0.584)

-0.69 0.670

Zong 0.223 (0.635)

0.35 1.249 0.485 (0.669)

0.72 1.624

Constant -1.910** (0.778)

-2.46 0.148 -2.410** (1.105)

-2.18 0.090

Hosmer & Lemeshow Test

1.98 (0.982)

5.70 (0.681)

Number of observation = 203, LR chi2(8) = 27.94, Prob > chi2 = 0.0005, Pseudo R2 = 0.130

Log likelihood = - 93.419

Number of obs = 203, LR chi2(12) =32.52, Prob > chi2 = 0.0012, Pseudo R2 = 0.151

Log likelihood = -91.132

Effort is dependent variable and Logistic Regression is used for analysis. *, **, *** shows significance at 1%, 5% and 10% levels respectively. Standard errors are in parantheses.

Job Search Time is positively associated with the effort. With a greater amount of time,

to find an alternative job, it is more likely an employee put a higher level of effort as compared

22

to those whom required less time to find a job. The result shows that whenever a worker finds it

harder to acquire a job of same worth he is currently doing, or it takes a long time to find job, his

intention is to put much more effort on the current job. The longer unemployment duration,

higher is the effort (Cappelli & Chauvin, 1991; Wadhwani and Wall, 1991). One unit increase in

Job search time increases the likelihood of exerting full effort by 3.49 times.

In the company Telenor ,Ufone and Warid the employees are exerting less effort than

Mobilink. As the base category is Mobilink here. Zong employees are exerting much effort than

the Mobilink.

The Model A(2) produces results by incorporating some important variables in shirking

model as suggested by empirical researches.This model incorporates the 4 new variables namely,

peer pressure, teamwork, marital status and career level. By incorporating these variables, the

overall and individual significance of important variables is not disturbed. The effort of the

colleagues also effects one’s own effort levels. If one’s peers are working hard, ultimately one will

also put more effort. The positive significance shows that this variable is an important

determinant of one’s effort.

Teamwork shows that to be the part of a team enhances the morale of the workers,

encourages joint venture, and thus raises the effort levels (Chang & Lai, 1999). Marital status

shows the responsibility level of employees, married are exerting high effort than singles.(Fairris

& Alston, 1994). Career level is negatively related to the effort level. To be in a high position

shows the low level of effort. These three variables are turned out to be insignificant, but takes

the predicted sign as suggested by the literature. The model is overall significant at 15%, shows

that, by incorporating these variables. Hosmer and Lemeshow goodness of fit test shows that

model is a good fit, as the null hypothesis of good fit is not rejected here (P value in parantheses).

Perceptions about fair pay is positively associated with effort in Table 11, Model B(1).

The fairness in pay induces the workers to exert high effort as compared to those who take their

pay as unfair (Blinder & Choi, 1990). It is significant in case of Pakistan’s Telecom sector. The

employees know the worth of their jobs, and the compensation schemes all around the local

market. They give much considerations to the fairness of pay. The more the pay is fair, higher

will be the effort.

23

Table 11. Regression Analysis of Fair Wage Effort Hypothesis

Variable Model B(1) Model B(2)

Coefficient Z Odds-Ratio Coefficient Z Odds Ratio

Perceptions about Fair Payi

1.753* (0.402)

4.36 5.77 1.485* (0.423)

0.00 4.415

Managemnet Relationsi 0.780*** (0.411)

1.90 2.182 0.575 (0.455)

1.26 1.777

Job Satisfactioni 0.705*** (0.384)

1.83 2.024 1.587* (0.424)

3.74 4.890

Monitoring 1.374** (0.555)

2.48 3.951

Job Search Time 0.977** (0.408)

2.40 2.656

Telenor -0.244 (0.606)

-0.40 0.783 -0.111 (0.678)

-0.16 0.895

Ufone 0.366 (0.507)

0.72 1.442 0.205 (0.561)

0.37 1.227

Warid -0.497 (0.575)

-0.86 0.609 -0.630 (0.628)

-1.00 0.532

Zong 0.739 (0.640)

1.15 2.094 0.542 (0.674)

0.80 1.719

Constant -0.619 (0.503)

-1.23 0.539 -2.551* (0.814)

-3.13 0.078

Hosmer & Lemeshow Test

7.59 (0.475)

9.65 (0.290)

Number of observation = 203, LR chi2(7) =32.12,

Prob > chi2 = 0.0000, Pseudo R2 = 0.149

Log likelihood = -91.333

Number of observation = 203, LR chi2(9) = 54.16,

Prob > chi2 = 0.0000, Pseudo R2 = 0.252

Log likelihood = -80.313

Effort is dependent variable and Logistic Regression is used for analysis. *, **, *** shows significance at 1%, 5% and 10% levels respectively. Standard errors are in parantheses.

Job Satisfaction is positively related to effort which shows that workers who are satisfied

with their job are going to exert more effort unlike those who are unsatisfied (Falk & Knell,

2004). Management relations show that the good relations among the workers and management

induces the employees to put high effort as compared to those who have bad relations with

management.Telenor and Warid employees are exerting less effort than Mobilink whereas Zong

and Ufone employees are putting more effort. All variables in the above regression are significant

suggests that fair wage hypothesis exist in case of Pakistan’s Telecom sector.

24

The Model B (2) in Table 11 shows results by incorporating some important variables in

fair wage model as suggested by empirical researches. This regression controls some important

variables which captures the workplace characteristics. The most important are the job search

time and monitoring. The inclusion of these results makes the overall result significant. All of

them have predicted signs but the management relations coefficient turns out to be insignificant.

Good management relations are associated with increase in the level of effort exerted.

The insignificance shows that good relations with management, to somehow, compensate the

reduced effort by the employees. If an employee exerts low effort and at the same time, he has

good relations with management, will exploit it.

When an employee is being monitored and at the same time, there are longer duration of

unemployment spells, it compels him to put high effort. The results also show that local market

prospects are dim, employees have to spend more time to find an alternate job, if they lose it

today. The odds ratio interpretation is same as above.

6. Conclusion and Policy Implications

Companies believe that a change in the effort levels could bring a substantial change in

the profits of companies. Companies give strong incentives to uplift the effort. The study

concludes that efficiency wages play a significant role in Pakistan’s Telecom sector and motivates

the employees to raise their effort. Employees repeat those acts which have some positive

consequences. Higher effort gives them the positive outcomes in the form of efficiency wages.

At the same time, firms try to give that pay which is perceived as fair. Firms consider the concept

of pay fairness crucial to the labor market and for attracting best from the large pool of

unemployed workers. The fair pay hypothesis exists in case of Pakistan’s Telecom sector.

Employees give much importance to fair wages. They have some perceptions about the wages, if

the wages would not be fair, then there may be some issues or resentment among employees.

Companies align pay structure with their competitors, to make pay fair. Firms take the notion of

fairness serious while making the compensation policies.

Employers believe that pay influences effort by impinging on the employee’s attitude

towards job and the company. The more technically advanced firms, offer salaries which are

perceived as fair. By doing this, it is possible for them to bring a slight change in the effort level,

25

which could have even a much bigger effect on their profits. It can also be concluded from the

results, work environment like the peer pressure is a very powerful tool to elicit increased effort

and to model the human behavior is a complex phenomenon.

To sum, it can be implied from the study that the firms can elicit effort from employees

by giving them a good package of incentives. More work pressure can harm the objectives of the

employer. There is a need of friendly environment where the abilities can be best flourished.

Appendix A: Construction of Variables

Variable Categories

Effort Frequency Percentage

Shirking (0) ≤ 14 45

22.17

Working/ Full Effort (1) >14 158

77.83

Monitoring Frequency Percentage

No (0) ≤ 7 29

14.29

Yes (1) >7 174

85.71

Efficiency Wages Frequency Percentage

No (0) ≤ 5 45

22.17

Yes (1) >5 158

77.83

Job Search Time Frequency Percentage

Less Time (0) ≤ 5 77

37.93

More Time (1) >5 126

62.07

Job Separation Rate Frequency Percentage

Probability of not separated (0) ≤ 3 90

44.33

Probability of separation (1) >3 113

55.67

Perceptions about Fair Pay Frequency Percentage

Unfair (0) ≤ 8

82

40.39

Fair (1) >8

121

59.61

Job Satisfaction Frequency Percentage

Not satisfied (0) ≤ 16 91

44.83

Satisfied (1) >16 112

55.17

Management Relations Frequency Percentage

unsatisfactory (0) ≤ 10

46

22.66

Good (1) >10

157

77.34

26

Peer Pressure Frequency Percentage

No (0)

95 46.80

Yes (1)

108 53.20

Fear of Penalty Frequency Percentage

No (0)

97 47.78

Yes (1)

106 52.22

Team work Frequency Percentage

Yes (1)

188 92.61

No (0)

15 7.39

Supervisor Frequency Percentage

Yes (1)

146 71.92

No (0)

57 28.08

Subordinate Evaluation Frequency Percentage

≤ 3

38 26.03

3 < S ≤ 3.9

65 44.52

> 3.9

43 29.45

Experience Frequency Percentage

Less than 1-3 years 46

22.66

4- 10 years 105

51.72

More than 10 years 52

25.62

Company Frequency Percentage

Mobilink 55

27.09

Telenor 30

14.78

Ufone 57

28.08

Warid 30

14.78

Zong 31

15.27

Appendix B: Summary Statistics

Variable Obs. Mean Std. Dev Min Max

Effort 203 .7783251 .4164004 0 1

Monitoring 203 .8571429 .3507922 0 1

Job search Time 203 .6206897 .486415 0 1

Job separation rate 203 .5566502 .4980085 0 1

Perceptions of fair pay 203 .5960591 .491899 0 1

Management relations 203 .773399 .4196672 0 1

Job satisfaction 203 .5517241 .4985469 0 1

Peer Pressure 203 .5320197 .5002072 0 1

27

Appendix C: Descriptive Statistics

Age Marital Status 20 – 29 30 – 39 40 – 49

44.83% 49.75% 5.42%

Married Single

58.13% 41.87%

Gender Job Classification Male Female

89.66% 10.34%

Administrative Non-Administrative / Technical

51.72% 48.28%

Education Career Level 14 years 16 years 18 or more

6.40% 49.26% 44.33%

Officer Manager/ Asst. Manager Executive/ Specialist HoD/ Senior Manager

75.86% 16.26% 5.42% 2.46%

Nature of Employment Employee Type Permanent Contractual

67.00% 33.00%

Office based Employee Outdoor Employee Both

71.92% 4.93% 23.15%

Monthly Pay Company

Under 15,000 – 75,000 76,000 – 150,000 151,000 – 225,000 Above 225,000

55.17% 24.63% 11.82% 8.37%

Mobilink Telenor Ufone Warid Zong

27.09% 14.78% 28.08% 14.78% 15.27%

Department

Customer Service Sales & Distribution Accounting & Finance IT Others

28.08% 20.20% 18.72% 12.32% 20.69%

References

Adams, S. J. (1963). Towards an understanding of inequity. The Journal of Abnormal and Social

Psychology, 67(5), 422-436.

Agell, S. A. (1994). Swedish evidence on the efficiency wage hypothesis. Labour Economics, 1(2),

129- 150.

Akerlof, G. A. (1984). Gift exchange and efficiency-wage theory: four views. American Economic

Review, 74(2), 79-83.

Akerlof, G. A., & Yellen, J. L. (1990). The fair wage-effort hypothesis and unemployment. The

Quarterly Journal of Economics, 105(2), 255-283.

28

Belman, D., Drago, R., & Wooden, M. (1992). Workgroups, efficiency wages and work effort.

Journal of Post Keynesian Economics, 14(4), 497-521.

Bewley, T. F. (1999). Why wages don't fall during a recession. Harvard University Press,Cambridge.

Blau, P. M. (1969). The Dynamics of Bureaucracy: A Study of Interpersonal Relations in Two Government

Agencies.University of Chicago Press, Chicago.

Blinder, A. S., & Choi, D. H. (1990). A shred of evidence on theories of wage stickiness. The

Quarterly Journal of Economics, 105(4), 1003-1015.

Campbell III, M. C., & Kamlani, K. S. (1997). The reasons for wage rigidity: evidence from a

survey of firms. The Quarterly Journal of Economics, 106(3), 759-789.

Campbell, C. M. (2006). A model of the determinants of effort. Economic Modelling, 23(2), 215-

237.

Cappelli, P., & Chauvin, K. (1991). An interplant test of the efficiency wage hypothesis. The

Quarterly Journal of Economics, 112(3), 769-787.

Chang, J. J., & Lai, C. c. (1999). Carrots or sticks? A social custom viewpoint on worker effort.

European Journal of Political Economy, 15(2), 297-310.

Chen, P. (2007). Reciprocity at the workplace: Do fair wages lead to higher effort, productivity,

and profitability? In Proceedings of the Annual Meeting-Labor and Employment Relations

Association, 59, p. 219. Labor and Employment Relations Association.

Clark, A. E., & Senik, C. (2010). Who compares to whom? The anatomy of income

comparisons in Europe. The Economic Journal, 120(544), 573-594.

Clark, K., & Tomlinson, M. (2001). The Determinant of Work Effort: Evidence from the Employment in

Britain Survey. University of Manchester, School of Economic Studies.

Dessler, G. (1984). Personnel Management. Reston Publishing Co, Reston.

Drago , R., & Heywood, J. S. (1992). Is worker behaviour consistent with efficiency wages?

Scottish Journal of Political Economy, 39(2), 141-153.

29

Fairris, D., & Alston, L. J. (1994). Wages and the intensity of labor effort: efficiency wages

versus compensating payments. Southern Economic Journal, 61(1), 149-160.

Falk, A., & Knell, M. (2004). Choosing the joneses: endogenous goals and reference standards.

The Scandinavian Journal of Economics, 106(3), 417-435.

Freeman, R., Kruse, D., & Blasi, J. (2008). Worker responses to shirking under shared

capitalism. Working Paper No. 4227, National Bureau of Economic Research.

Gächter, S., & Thöni, C. (2010). Social comparison and performance: experimental evidence on

the fair wage–effort hypothesis. Journal of Economic Behavior & Organization, 76(3), 531–

543.

Goldsmith, A. H., Veum, J. R., & Darity, W. J. (2000). Working hard for the money? Efficiency

wages and worker effort. Journal of Economic Psychology, 21(4), 351-385.

Homans, G. C. (1961). Social behavior: Its elementary forms. Routledge &Kegan Paul Publishers,

Oxford.

Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Wiley Series in Probability

and Statistics, Wiley Inter-Science Publication, New York.

Ichino, A., & Maggi, G. (1999). Work environment and individual background: explaining

regional shirking differentials in a large Italian firm. The Quarterly Journal of Economics,

115(3), 1057-1090.

Judge, T. A., & Chandler, T. D. (1996). Individual-level determinants of employee shirking.

Relations Industrielles/Industrial Relations, 51(3), 468-487.

Kaufman, R. T. (1984). On wage stickiness in Britain's competitive sector. British Journal of

Industrial Relations, 22(1), 101-112.

Kochan, T. A., & Barocci, T. A. (1985). Human Resource Management and Industrial Relations: Text,

Readings, and Cases. Little Brown & Company, Boston.

Leonard, J. S. (1987). Carrots and sticks: pay, supervision and turnover. Journal of Labor

Economics, 5(4, Part 2), S136-S152.

30

Levine, D. I. (1991). You get what you pay for: tests of efficency wage theories in the United

States and Japan. Institute for Research on Labor and Employment, UC Berkeley.

Long, S. J., & Freese, J. (2001). Regression Models for Categorical Dependent Variables Using Stata.

Stata Press, Texas.

Mathewson, S. B. (1969). Restriction of Output Among Unorganized Workers. Southern Illinios

University Press, Carbondale and Edwardsville.

Pascale, R. T. (1978). Personnel practices and employee attitudes: A study of Japanese and

American-managed firms in the United States. Human Relations, 31(7), 597-615.

Raff, D. M., & Summers, L. H. (1987). Did Henry Ford pay efficiency wages? Journal of Labor

Economics, 5(4), S57-S86.

Rebitzer, J. B. (1995). Is there a trade-off between supervision and wages? An empirical test of

efficiency wage theory. Journal of Economic Behavior and Organization, 28(1), 107-129.

Shapiro, C., & Stiglitz, J. E. (1984). Equilibrium Unemployment as a Worker Discipline Device.

American Economic Association, 74(3), 433-444.

Strobl, E., & Walsh, F. (2007). Estimating the shirking model with variable effort. Labour

Economics, 14(3), 623–637.

Verhoogen, E., Burks, S. V., & Carpenter, J. P. (2002). Fairness and freight-handlers: A test of

fair wage theory in a trucking firm. Working Paper No. 56, Center for Labor Economics,

University of California, Berkeley.

Wadhwani, S. B., & Wall, M. (1991). A Direct test of the efficiency wage model using UK

micro-data. Oxford Economic Papers, 43(4), 529-548.

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