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INTRODUCTION

Having successfully grown over the last four years, the call center industry has been considered

a sunshine industry. This means that what the Philippines is merely seeing a prelude of what

more is to come over the next several years. However, in the light of our current labor market, itseems that this sunshine would not last that long. This paper argues that although the call center

is being relied upon by various economic pundits in the government and in the private sectors,

the considerable job turnover and inadequate supply of call center specific labor are the

concealed flaws of the industry. The silently bleeding industry in the form of turnover poses a

major factor for potential outsourcers of whether to outsource in the Philippines or in other

countries instead. The first part of this paper is an exposition on the nature of the call center

industry: the nature of work, its players, and its production goals and objectives at the worker

and firm level. This section illuminates our understanding of what type of labor should the

market supply in the light of the increasing demand for call center jobs. The second section

discusses the outsourcing trends in the Philippines. The fourth discusses the review of literature

on job turnover, which pertains to job turnover studies conducted in the US and to studies

conducted in the Asia Pacific and in the Philippines. The fifth section discusses the theoretical

framework with the specification of the equation that will be used.

Statement of the Problem

In the light of the prevalent turnover in the call center industry, the main objective is to identify

the sets of job satisfiers, wage differences, human resources practices, supervisory and

managerial mechanisms that are likely to predict firm- level quit rates then empirically evaluate

the predictive reliability of these variables using data from a survey of agents from two call

center zones.

Significance of the Study

Different articles have already been written about the issues and problems in the call center

industry and identified the factors that lead to job turnover. However, very little to none has been

said about how large would the impact of changes in company policies and company incentives

will be on the job turnover. Being able to identify the probable impact of selected variables will

be able to help firms and their HR department find the appropriate combinations of action plans

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II. THE NATURE OF THE CALL CENTER INDUSTRY

What is a Call Center?

A call center is more prominently called contact center or profit center. It is a central point of

an organization from which customer contacts are managed through voice, e-mail or chat.

Contact centers use special software that allows contact information to be routed to appropriate

people, contacts to be tracked and data to be gathered (Baziotopulos, 2006). A number of such

call center services include inbound sales, directory assistance, technical support, or billing

inquiries.

The Need for Contact Centers and the Importance of Customer Care

The advancements in information technology and in telecommunications has enabled

consumers and even firms to transact in order to take advantage of products or services of a

company simply wither by phone or by internet. The role of contact centers is to expand the

marketing of a product or retain the valued users of a companys products or services. In order

for the company to gain more revenues and at the same time retain its current share of the

market, effective customer care is needed to provide that quality customer service experience.

NATURE OF CALL CENTER WORK

Since the clients are based on a different time zone such as US or UK, call center work in the

Philippines often begins on a graveyard shift. Thus call center employees will inevitably be

exposed to the graveyard working conditions. Call center agents work for at least eight (8) hours

and even render several hours of overtime work in response to the clients needs. Few would

doubt that those work in the call centers have extremely high-pressure jobs. Agents are on the

phone for hours and deal with unseen customers, some of them can be angry, discriminating or

impatient. The upper and frontline managers stay for longer hours in order to complete summary

reports of the agents performance and are submitted and presented to clients.

Call center agents are taking its toll due to the adverse working conditions (graveyard, non-stop

talking on phone for several hours) and its effects on health and leisure (such as stress, loss of the

normal day life routine, staying up to wok at night). Because call center employees are paid

night differentials, additional meal and transportation allowances in order to compensate for

graveyard work, and for traveling to work at night. More often than not, the customer service

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work is indeed repetitive that employers even add additional incentives or performance bonuses

to those who consistently perform and render long periods of employment with the company.

Ideal Type of Workers

Based from the exposition above on the nature of the industry and business rationale behind it,

call center agents must possess one essential or main skill: proficient oral and written

communication in the English language. Furthermore, the call center agent must preferably

possess a good understanding and even experience in business practices, can assimilate to the US

or UK culture and be familiar with the nuances of American or British idioms and their specific

accents.

PHILIPPINE CALL CENTER INDUSTRY: TRENDS AND COMPETTIVENESSAt the start of the millenium, the call center industry was an emerging powerhouse in the

Philippines. Estimates from the Board of Investments (BOI) show that the industry has been

aggressively expanding since 2001 at an astounding rate of 100 percent annually, from less than

1,000 seats in 2000 to more than 69,000 at the end of 2004. In terms of world market share of

contact center services, the Philippines amassed nearly 20% of all the worlds customer contact

services. By 2008, it was forecasted to get a lions share of the worlds customer service market

at 50%. Furthermore, as the industry earned USD 1.12B last 2005, industry forecasts a massive

inflow of more revenues of up to USD 12B by 2010.

Enjoying its rich supply of Americanized labor force, the country has attracted its young

professionals by offering wages far better than the local industry jobs. In fact it offers a salary

range above par compared to the local industries.

Americanized and Amicable Labor Force

In the new wave of outsourcing, Philippines is one of the leading players in the global contact

center market. The American education system combined with the exposure of the country to

various American music and culture enabled the Filipinos to assimilate itself quicker in the

Western way of dealing. In, some able to speak fluently in English and almost mimic the

American accent. This gives Filipinos are huge advantage over its Indian rivals in customer

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service, of which the Indian customer service is tainted to have heavily British- Indian accent

that creates an intimidating factor to Americans.

Another positive impression that Filipinos gained is the delivery of customer service that is

value- added. The Filipinos hospitality and general friendliness is what the people are known for:

taking time to understand the American customers concern, empathizing with the former, and

sometimes going an extra mile to offer a helping hand.

International Competition

A look into Table 4 below shows the Philippines competitiveness relative to other countries of

attractiveness to business outsourcing. Philippines tied with India in terms of overall costs and

placed second best in terms of quality of infrastructure. Furthermore, US companies ranked the

country next to India and Malaysia in terms of attractiveness of location on a scale of 1-5 (with

1= highest, 5= lowest). The country however suffered major setbacks in terms of access to

market, risk profile and business environment where China, Malaysia and Mexico led in these

categories.

Table 4. Outsourcing attractiveness index for offshoring countries, 2004

(1= most attractive, 5= least attractive)

Country Total

cost

Vendor

landscape

Access to

market

Risk

profile

Business

Environment

Quality of

Infrastructure

Philippines 1.5* 4.5 3.5 3.9 3.7 2.8

India 1.5 2.2 3.5 2.7 3.6 3.3

Malaysia 1.7 4.7 3.3 2.2 3.4 2.5

China 1.8 3.7 1.8 3.4 3.6 2.5

Brazil 2.2 3.5 4.2 2.8 3.0 2.0

Mexico 2.2 4.7 2.8 3.5 2.6 2.0

Czech Rep 2.6 4.7 3.5 2.2 3.0 3.0

Hungary 2.6 4.7 3.3 2.3 2.8 3.8

Poland 2.7 4.0 3.3 2.7 3.1 3.0

Russia 3.0 4.5 2.8 3.5 3.3 3.3

Source: Location Cost Index database, McKinsey Global Institute

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In another report by McKinsey (see Table 5), India leads in terms of being a global supplier of

offshore services at USD 12.2 billion in 2003. The Philippines trails behind at sixth, producing a

value of USD 1.7 billion worth of services for its outsourcing clients.

Table 5. Rankings, Services Offered by Offshoring Countries, 2003

COUNTRY VALUE OF SERVICES

OFFERED (billions USD)

India 12.2

Iceland 8.6

Canada 3.8

Israel 3.6

China 3.4Asia (without India & China) 2.3

Philippines 1.7

Eastern Europe 0.6

Mexico 0.5

Australia 0.4

Russia 0.3

Thailand 0.1

South Africa 0.1

Source: McKinsey Global Institute

CHALLEGES OF THE SUNSHINE INDUSTRY

Supply Side Problem

In the first section of this paper, we have witnessed the drastic growth of the investments in the

call center industry as well as the increasing employment. However, the good news may not last

that long. It is true that the Philippines has the stock of labor to meet the requirements of call

center work. In fact more foreign companies tend to outsource every year. The problem is not

about the demand. What is alarming is the countrys supply of call-center specific labor. Simply

put: we dont have enough manpower to meet the ever increasing demand for the growing call

center industry.

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Former DTI Undersecretary Gregory Domingo was able to gather information among several

call centers and noted that only 3-5 out of 100 applicants are hired for the job (Viray, 2003). This

means that if the country produces a yearly average of 380,000 college graduates, then only

19,000 would be hired for the call center job. Since the projected demand for 2004 was

approximately 40,000 agents, this means that the industrys human resources needs to hire other

past graduates, pirate from the currently employed market, and even sign up college

undergraduates and high school graduates.

Figure 2.1 depicts the Philippine call center labor market where demand for the job increases

but with a constant supply of call center labor. Thus, the growing demand (from L 0 to L1) simply

drives the wages up (from W0 to W1).

III. REVIEW OF LITERATURE

7

Wages, Pesos

Labor (# ofworkers)

W0

W1

L0 L1

Fig 2.1

DL

DL

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This section shows the various studies conducted in relation to the factors that affect employee

turnover: from the various effects of wage changes, job performance, employee bonuses, HR

(human resources) practices, to the firms decision- making in job promotions of its employees.

FOREIGN LITERATURE

Supervision and Job Turnover

Krackenhardt et al (1981) studied on the effect of supervisory interactions on job turnover. The

study covered tellers in fifty branches of a commercial bank and gathered data on the turnover

tendencies and job performance based on the supervisors ability to implement specific goals the

latter learned from management workshops and a control group (those who did not undergo the

said workshop). The results show that turnover rates during the implementation of the set goals

(during the managers workshop) were significantly lower for the experimental group than the

controlled group. Group meetings (both formal and informal) and counseling sessions have

significantly improved turnover rates. Higher supervisory efficiency combined with group

meetings and counseling have better results for lowering job turnover than low efficiency

supervisors. This is because highly efficient supervisors are able to structure the work

effectively, thereby allowing these managers to have more time allotted to group meetings and

counseling sessions.

Performance & Wage Growth Factors

Leonard (1987) discussed the efficiency wages theory and its implications on job tenure. In his

survey of employment conditions in the technological (IT) sector of one state, the simple and

multiple regression results show the significant effect of wage differentials on job turnover.

Furthermore, additional controls such as total employment and occupational composition reduce

the wage effect of turnover by as much as 14 percent. The study is also limited in such a way that

the data on the profitability of firms remains scarce (if not confidential) as to finding out how

much wages can increase so that the firms can maintain decent levels of profit.

Leonards study was supported Bishops (1990) discussion on job performance and salary

increases (1990). It was shown that productivity was negatively related on the workers job

turnover performance. This relationship holds so long as the measures of an individuals

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productivity are reliable. Furthermore, the consistent and long-run practice of rewarding

exceptional performance yields positive results: the best performers stay and the less productive

ones leave voluntarily. Data was obtained from the National Center for Research in Vocational

Education (NCRVE) with data showing the job matching between the employee and the work

itself and how it affects turnover. Most of the respondents were line managers from small and

large companies from both unionized and non-unionized firms. Among the potential biases was

the fact that the respondents may be reporting past performance of individuals who are already

separated from the company (fired or resigned), who were already promoted or who were still

working with the firm. Another bias would be the fact that there are measurement errors in

productivity indicators.

Table 2.1 Relative Productivity and Separation Rates (In %)

Non-Unionized Establishments

UnionizedEstablishments

200+Employees

20-199Employees

1-19Employees

Total

Quit:Relativeproductivity

106 102 96 93 95

Quit Rate 11.8 10.0 14.6 19.6 16.9

Layoff:RelativeProductivity

104 109 106 93 99

Layoff Rate 14.5 4.7 3.1 5.95.9Discharge

RelativeProductivity

79 73 76 73 75

DischargeRate

2.7 3.0 5.7 6.4 5.7

N 267 173 1,028 1,727 3,195Source: Bishop, 1990

Based from the data above (Table 2.1), larger firms tend to have lower quit rates and at the

same time have relatively higher employee productivity. Dismissed workers were also observed

to have higher quit rates.

Another estimate was used to identify the determinants of turnover behavior. Table 2.2 and 2.3

below shows how quit rates are affected by gender, additional training, tenure, and firm size.

From Table 2.3, we can see that quit rates decreases by as much as 50 percent as the worker

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matches with the job and improves his productivity (3-12 weeks). In terms of involuntary

separations, only the years of schooling is significant although it has a wrong sign (greater

schooling leads to higher quit rates). Women have significantly lower quit rates. In terms of the

effects of training on job turnover, more specific training leads to lower rates. However, it can be

seen in Table 2.3 that 100 additional hours of training lowers the discharged rates but increases

voluntary quit rates. The coefficient on training versus quit rates however was not significant. In

addition, as the productivity hits the 3rd to the 13th week, quit rates significantly gone down and

chances of promotion improves (Table 2.3). Larger firms improve the workers chances of

promotion as the size of the firm improves the employees productivity. Furthermore, higher

training intensities improve ones chance for promotion.

Table 2.2 Turnover & Productivity (Interactive Specification)

Log Tenure Involuntary

Separation

Quit Promotions

Training

Intensity

.045

(1.15)

-.084***

(3.22)

.047

(1.46)

.075**

(2.38)

Productivity

2nd week

-.652***

(2.64)

-.197

(1.21)

.240

(1.19)

-.415**

(2.10)

Productivity,

weeks 3-13

2.198***

(8.84)

-.756***

(4.61)

-.486**

(2.39)

1.201***

(6.07)

Productivity

X union

-.188

(.27)

.878*

(1.89)

.234

(.41)

-.609

(1.20)

Productivity

X logestablishment

-.301**

(2.52)

-.172**

(2.19)

.211**

(2.17)

.273**

(2.63)

Training

intensity X

log

establishment

.072***

(3.48)

R2 .574 .179 .106 .222

NOTEt-statistics are in parenthesis; *significant at .10 level; **significant at .05 level; ***significant at .01 level

Source: Bishop, 1990

Table 2.3 Determinants of Turnover and Promotion

Variable LogTenure

InvoluntarySeparation

Quit Promotion

Quality of Match

Productivity (3-12wks)

2.444***(9.55)

-.779***(4.51)

-.491**(2.34)

1.17**(5.77)

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Productivity -.876***(3.51)

-.167(.99)

.337(1.65)

-.518***(2.61)

Training (1- 12weeks)

.026(.64)

-.070**(2.51)

.052(1.54)

.043(1.31)

Credentials ofNew Hire

Years of school -.019

(1.2)

.018*

(1.65)

-.006

(.47)

-.012

(.95)Coop Student .247**

(2.56)-.037(.60)

-.128(1.62)

.178**(2.32)

RelevantExperience

-.021(.29)

.032(.63)

-.071(1.17)

.049(.83)

Female .112(1.3)

-.013(0.02)

-.131*(1.88)

.082(1.2)

ConditioningVariables

Log potentialtenure

.593***(2.72)

.163(1.11)

.445***(2.50)

.226(1.31)

Log potentialsquared

.040(.96)

.021(.73)

-.068*(2.00)

-.019(.58)

Hours per week .0080(.64)

.00007(0.02)

-.0034(.85)

.016***(4.19)

Intercept .049(1.49)

-.015(.66)

-.012(.46)

.030(1.13)

R2 .585 .153 .094 .208

N 477 447 477 477

NOTEt-statistics are in parenthesis; *significant at .10 level; **significant at .05 level; ***significant at .01 level

Source: Bishop, 1990

Employee Bonus and Job Turnover

When Bishop discussed wage growth and turnover, Blakemore, Low and Orminston (1987)

discussed the effect of employee bonuses on job turnover. The presence of temporal income risk

among risk- averse workers is the motivation behind offering the pay scheme that provides

flexibility in times of uncertainty. The positive correlation between the bonus pay and outside

offers adds a flexible offer-matching ability to the compensation package (Blakemore et al,

1987).

Table 2.4 The Bonus Effect on Voluntary Turnover

(1) (2) (3) (4) (5) (6)Constant -.70

(2.4)

-.38

(.4)

-5.66

(2.3)

4.44

(1.7)

-1.16

(8.3)

1.54

(.5)EARN(X 10-3) -.05 -.03

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(2.0) (1.2)Ln(EARN) ... -.67

(2.5)

-.48

(1.6)

.

BONUS(X 10-3) -.38

(1.9)

-.03

(1.1)Ln(BONUS) -.35

(1.7)

-.38

(1.9)

-1.5

(1.8)

-.40

(1.9)ED -.01

(.1)

-1.5

(1.8)

-.01

(.2)

-.01

(.1)EXP -.01

(.1)

-.01

(.9)

-.02

(1.3)Likelihood

Ratio Test11.88 20.51 14.92 20.95 13.46 18.25

Source: Blakemore et al, 1987

The table above (Table 2.4) shows that bonuses are significant factors in affecting voluntary

separation decision of workers. Bonus pay lowers quit rates by as much as 38 percent as

compared to earnings changes on quit rates which only has a mere 3%. Indeed the flexible

structure of its pay scheme offers its workers risk reduction and counters any outside offers.

Promotion Decisions of the Upper Management

In another research performed by Lam and Schaubroeck (2000), employees whose applications

for promotion were rejected exhibited performance drops, increased absenteeism and high

tendencies of quitting in the short run. However, they asserted that in the long run, these

aspirants performance will eventually go back to their normal levels. The main condition for the

resumption of normal performance is that the management must enable its promotion applicants

to understand that promotions are determined workers own actions.

Table 2.5 Predictors of Annual Quit Rates

Variable Tobit 1 Tobit 2 Tobit 3 Tobit 4

Employee Voice

Union -0.152***

(0.027)

-0.114****

(0.026)

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Problem-Solving

Groups

-0.092***

(0.023)

-0.084***

(0.024)

Self-Directed Teams -0.049*

(0.026)

0.058**

(0.028)

Cost-Cutting HR

Practices

Downsizing in LastFive Years 3.267****(0.687) 8.088****(2.309)

Part- Time Workers

%

0.212***

(0.074)

0.251***

(0.079)

Temporary Workers

%

0.281****

(0.058)

0.236****

(0.059)

Electronic

Monitoring

0.047**

(0.022)

0.050**

(0.023)

Variable Pay 0.066*

(0.036)

0.107***

(0.036)

Commitment-

Enhancing HR

Practices

Mobility % -0.073***(0.026)

-0.040*(0.023)

Training -0.006

(0.006)

-0.005

(0.005)

Pay to Local Cost of

Living

-.152***

(0.034)

-0.102***

(0.033)

Control Variables

Female % -0.026(0.036)

-0.030(0.036)

-0.071*(0.040)

-0.067*(0.037)

Ln of Establishment

Employment

0.0130**

(0.007)

0.016

(0.007)

0.010

(0.007)

0.020***

(0.007)

Branch 0.025

(0.023)

0.033

(0.023)

0.051

(0.025)

0.022

(0.024)

Former BellCompany

0.014(0.024)

-0.034*(0.020)

-0.027(0.022)

0.017(0.023)

Human Resource

Department

0.046**

(0.019)

0.017

(0.020)

0.037*

(0.021)

0.025

(0.020)

College Graduates -0.011

(0.023)

0.009

(0.024)

0.059**

(0.028)

0.035

(0.027)

Constant 0.105***(0.039)

-0.024(0.040)

0.199****(0.046)

0.124(0.048)

Observations 576 545 521 492

Likelihood Ratio Chi 113.150 124.560 84.340 173.120

Pseudo R- Sq 0.674 0.767 0.509 1.303

Likelihood Ratio -27.3/ 4149 -18.91/ 1291 -40.62/ 8212 20/1309

Source: Batt et al (2002)NOTE*significant at .10 level; **significant at .05 level;***significant at .01 level; ****significant at .001 level

Employee Voice and HR Practices

Batt et al (2002) looked into the effect of the firms commitment to set up policies and

practices that improves organizational performance from various telecommunication firms:

mobile phone, cable television and internet services- related providers. Annual quit rate was

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measured in percent, the collective voice union presence was captured by a dummy variable

(1=union and 0 for otherwise), team participation was measured as a percentage of employees in

the firm participating either in problem- solving groups or in self- directed teams. In general, the

independent variables measure the percentage of employees in that firm that has the independent

variable attribute.

Table 2.5 shows the significant effect of organizational initiative towards reducing quit rates.

The presence of unions, self- direct teams and problem- solving groups significantly contribute

to reductions in job turnover. Furthermore, the presence of internal mobility of workers,

additional training and increased responsiveness of employee pay to changes in the cost of living

also significantly reduces quit rates (see Tobit equations 3 and 4). And in contrast, visible forms

of cost- cutting practices sends a negative signal, causes job speculation and employee distrust to

their firms, thereby increasing quit rates.

LOCAL STUDIES

The study of Umali (2005) on the Philippine call center industry, discussed issues and

problems associated with job turnover. It was asserted that stress at work, health and safety

issues while working in the night shift, boredom and limited advancement opportunities are the

main issues that performance needs to be addressed in order to improve he turnover performance

of the industry. The survey by UNI- Apro and UNI- PLC found that nearly one third and nearly

one-half of the call center employees showed heavy and highly moderate pressures at work.

Tough performance appraisals and stringent selection of employees eligible for regular

employment were identified as pressures at work. About a third of the respondents experienced

health- related problems in working in the night shift such as insomnia, hypertension, migraine,

sleeping disorder and respiratory tract infections. Furthermore, since there is little room for

higher positions, career advancement opportunities are simply limited.

A study in a specific call center company (Viray 2004), the turnover rate was at an astounding

rate of 49.78%. The top reasons are as follows: 31% resigned due to lack of opportunities within

the firm or lack of career growth, 16% are said to have left due to the unfavorable work

schedules, 10% are said to have left due to the lack of challenge (considers the call center job as

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a no brainer) or lack of job compatibility (cannot apply what has been learned in college), and

other reasons include unfavorable company policies, lack of recognition in the job, and poor

benefits.

Another study (Sibal, 2006) also showed that the call center industry is bereft of the supply of

competent managers. According to the Call Center Association of the Philippines, only 2-3% of

the applicants qualify for the managerial or supervisory positions in the industry. Moreover, it

has also discussed the fact that SMEs in the country does not automatically produce corporate

managerial talents.

Based from the previous section, only a few percentage of the young labor force are able to

qualify in the call center job. And so the firm pays higher wages to retain its employees and

prevent them from being pirated by rival firms.

International Studies of Job Turnover

An international research firm callcenters.net also conducted a study on the turnover behavior

in the call center industry that focused in the Asia Pacific Region (Wallace 2003 and 2006). In

the initial study in 2003, only 6% of the call centers surveyed in the Philippines said that they did

not experience any agent turnover. More than a third of the firms surveyed declared a small

turnover of less than 5%., although the mean turnover in 2003 was 13%. Outsourcing call centers

(those who were tapped by as vendors by the client firms) were observed to have higher turnover

rates than in-house call centers (those firms that already have call centers on their own that

simply set up their centers in the Philippines). Furthermore, 49% of the total agent turnover in

2003 left the call industry while 42% transferred to other rival call centers. In addition, call

center agents stay up to an average of 19 months then leave. In terms of health, call center agents

on the average have 8 days of sick leaves every year.

In 2006, the same research firm reassessed the job turnover performance of the call centers,

particularly in the Asia Pacific Region. In a survey of 354 organizations that represent 828 call

centers, the respondents were asked to fill out information on their market size, cost per output

and other statistical information such as job turnover.

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Table 2.6 JOB TURNOVER RATES 1999 AND 2003

JOB TURNOVER RATES (%)

None < 5 5- 10 11-15 16- 20 21- 25 26- 30 > 30

1999 33% 8% 25% 3% 8% 6% 6% 11%

2003 6% 35% 18% 8% 14% 8% 6% 4%Outsourcing 9% 18% 18% 18% 9% 9% 9% 9%

In-house 5% 39% 18% 5% 16% 5% 8% 3%

Source: Wallace (2003)

Table 2.7 JOB TURNOVER IN THE ASIA- PACFIC REGION (except India)

Country CHINA KOREA INDIA PHILIPPINES SINGAPORE

Turnover Rate (%) 16 17 31 20 19

Moved to competing

centres (%)

49 36 67 62 17

Tenure of Staff in

months

20 24 11 19 27

Days taken as Sick

Leave Yearly

6 3 15 8 7

Source: Wallace (2006)

Table 2.7 above shows that India and the Philippines take the lead in terms of job turnover

rates at 31% and 20% respectively. Consequently, these two countries ranked last in terms of

length of stay. Singaporean and Korean call center agents stay 6-9 months longer than the former

counterparts. India and the Philippines once again leads in the number of days of sick leaves per

year with 15 and 8 days respectively. Job turnover and recruitment are the highest concerns

among call centers in the Asia Pacific Region.

IV. THEORETICAL FRAMEWORK

The Job Mobility Equation

A workers decision to quit work in his current job is can be summarized by the equation,

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Nt Ot > Ct(1+r)t (1+r)t

where Nt = wages in the new job

Ot = wages in the old jobr = interest ratet = time

Ct = cost of moving

Source: Borjas, 2000; Kaufman & Hotchkiss, 2003; Ehrenberg and Smith, 2003

This means that the condition for moving is that the difference in the present value of the

wages between the current and the new job is significantly greater than the cost of moving. In

this section, the initial discussion will be the effect of wages on turnover (efficiency wagestheory), then move into discussing the non-wage factors that may either impede or provoke job

movement. Among them is employee satisfaction, the firms human resources practices, the

employees confidence in the supervisory and managerial competence of the firm, and lastly the

workers attributes (control variables).

Efficiency Wages Theory

Obviously, paying higher salaries would definitely improve not only the productivity of the

firms current staff, but can also sustain the level of productivity in the long run through reduced

employee turnover (Raff and Summers, 1987). The key assumption behind this theory is that

employee work effort, or efficiency is a positive function of the wage rate. Thus, the higher the

wage the firm pays, the harder its employees work (Kaufman and Hotchkiss, 2003). A higher

wage elicits greater effort because employees value their jobs more and have higher morale, and

at the same time raises the cost of being fired for shirking on the job (Shapiro and Stiglitz, 1984).

.

The wage factor is supported by the efficiency wage theory that higher wages reduces

turnover but that the increases in the wage premium should reach the point where the marginal

reductions in turnover would still allow the firm to reach decent profits.

NON WAGE FACTORS

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Job Satisfaction

Job Match (Sense of fulfillment). Even if the current pay of the worker is far better than the

outside offers, one factor that can negate the responsiveness of job turnover to wage differences

would be the job match. In this context, job match means not only the workers skills in meeting

the job requirements but also the fact that the job itself allows the person to make use of

everything that was learned from the degree earned or such that the job creates a sense of

personal fulfillment. Simply put, the match quality determines job duration (Jovanovic, 1979).

This is a very important factor when analyzing the call center industry, especially with the fact

that it employs generally anyone that has good English fundamentals and basic computer

literacy.

Availability and the Use of Non-Wage Benefits. Job satisfaction not only depends on the number

of non-wage benefits (such as sick leaves and vacation leaves) but mainly depends on the ability

of the employees to actually use them. In fact, Gary Becker (1965) mentions the need for

productive consumption in the form of vacation leaves, sick leaves or the consumption of health

and welfare- improving subsidies offered by the firm are needed to indirectly improve the

efficiency of the worker. Figure 1 below depicts the turnover decision based on wage/ benefit

mix.

The graph shows the firms isoprofit curve JK together with two indifference curves I A and IB.

The optimal combination for the worker is the tangency point between the indifference curve and

offer curve (points Q and Z). In general, most companies cannot provide options for the

employee to choose a combination from the isoprofit curve. Given the wage- benefit

combination WQFQ of worker A in his current firm, the only way worker A can get to his desired

wage- benefit mix at indifference curve IB is to quit the current firm and transfer to the firm that

offers the wage-combination WZFZ at indifference curve IB.

Fig 1. Wage-Benefit Turnover Decision Model

Wage per hour (W)

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WQ

WZ

Employee Benefits per hour (F)

Source: Kaufman and Hotchkiss, 2003

Employer Location. Economic theory predicts that the when the costs of quitting a job are

relatively low, job mobility is more likely. If a change of employer due to locational factors does

not necessarily require a change of residence, then turnover is more likely (Parsons, 1979;

Roback, 1982).

HR Practices and Organizational Commitment

The firms human resources (HR) plays a vital role in understanding how organizationalcommitment can affect turnover outcomes. That role is the ability to reinforce the employees

commitment to the organization, whereby the worker identifies with his particular company and

also maintains ones relationship in the organization in order to facilitate its goals (Blau and

Boal, 1987).

Employee Voice. Organizations that set up focused group discussions to enable workers to

express their dissatisfaction at work and be given the opportunity to be involved in problem-

solving of work concerns indeed improve the performance of workers and generates higher

organizational commitment (Batt et al 2002).

Training and Employee Development. Improving the firms stock of labor and developing

potential candidates for leadership positions through training serves both augment labor

19

Q

Z

IA

IB

J

KFQ FZ


20/62

productivity and prolong the duration of employment.(Kreuger and Rouse, 1984). Furthermore,

by facilitating training workshops for career development, the worker increases ones

dependence on the firm for his overall development.

Supervisory Competence

Supervisory competence plays an important role in turnover outcomes because (a) supervisors

have a degree of control of the work by improving structures that can improve work conditions,

(b) supervisors provide feedback to employees thereby has the ability to dictate work behavior

outcomes and (c) supervisors have control over the rewards system and job security of its

employees (Krackhardt et al, 1981). Providing a positive coaching and counseling experience,

having a good knowledge and understanding the business impacts of team and individual

performance, and the ability to provide an individual career development plan for ones agents

enables the supervisor to significantly reduce quit rates. Furthermore, when supervisors are

perceived to initiate structure, set goals, assist with problem- solving, provide social and material

support and give feedback on job performance, their subordinates experience lower ambiguity,

hence greater satisfaction with their job (O Driscoll and Beehr, 1994).

Managerial Competence

The macro-level of firm decision-making made by the upper management also creates a

significant impact on employee productivity and job turnover. When the targeted performance

goals set by the upper management are unrealistic or inconsiderate (meaning that the goals set

needs to met at the cost higher employee burnouts and higher job stress combined a more strict

performance monitoring, regardless of its effect on employee morale), higher quit rates are very

likely to occur. In addition, if the promotion selection is clearly based the measurable and

reliable performance metrics, then turnover rates will be moderated or even lessen (Bishop,

1990). Furthermore, if the management clearly explains the reason for the promotion of selected

employees, then the post-rejection effects such as low morale, frustration, and job quitting would

also be moderated (Lam and Schaubroeck, 2000).

Worker Attributes

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The worker attributes are intrinsic properties of the employee that can significantly alter the

effects of extrinsic variables (such as wage changes or changes in HR practices) on the turnover

decisions of workers.

The first attribute is age. The young worker will have higher probabilities of quitting due to the

constant effort to search for the ideal fit that suits his personality, current skills and even

lifestyle, and anything that meets the current satisfaction. As the worker ages, temporary

satisfiers are eventually replaced by the desire to look for the work that provides long term

financial rewards and the sense of stability in staying in that particular work. Hence, age appears

to be a deterring factor in the workers decision to quit (Borjas, 2000).

Gender also affects turnover outcomes. Many studies have shown the significant differences in

job tenures and turnover behavior between men and women. Working women usually have

higher quit rates and therefore have shorter job tenures than men (Ehrenberg and Smith, 2003).

Interrupted careers among women due to child bearing and rearing affect the inflow of human

capital, resulting into lower wages, lower job tenures and higher quit rates.

Education is another factor that affects turnover outcomes. Various studies show that

education can either improve or shorten the job tenure. More educated workers are prone

towards being always on a lookout for jobs that maximizes the individuals potential. In fact,

they are very confident about being mobile workers because they possess higher levels of skills

that is almost attractive in every labor market (Royalty, 1998). However, higher levels of

education can also lower turnover incidences, provided that the job highly fits the individual and

the job creates a sense of fulfillment. Education therefore goes hand in hand with job matching in

order to effect lower probabilities of turnover (Barron et al, 1993).

The size of the firm can also dictate turnover outcomes. Large firms offer more possibilities

for job transfers and promotions. In addition, large firms generally pay higher wages (Oi, 1991).

This is because larger firms have greater needs for dependable and steady workers because

employees who shirk their duties can pose greater costs on highly interdependent production

process (Ehrenberg and Smith, 2003).

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Finally, job tenure also affect the workers decision to quit. Highly tenured workers are

assumed to already have matched themselves with the work and in fact mastered their work.

Furthermore, improved job tenure oftentimes entail higher wages in the long run (Borjas, 2000).

Therefore, job familiarization out of longer job tenures may impose larger costs of adjustment

from the current to the new one, a cost that is not strong enough to affect the other wage and

non-wage benefits offered outside.

The relationship of the several non-wage factors can be summarized by the diagram below.

Fig 2. Summarized effects on the decision to quit

CONSEQUENCES OF TURNOVER

Worker Level

The age-earnings profile displays the differences in earnings between workers who have stayed

in their work for a long time as compared to workers who keep on moving. Figure 3.1 obviously

22

Job Satisfaction

Day Shift

Shorter travel time

Use of leaves/ enjoying company benefits

HR Practices

Offers Training

Career Development


Positive coaching experienceUnderstands the business

Helps in career development

Management Competence

Explains promotion decisions clearly

Proper selection of promoted employees

Reduced

Probability of

Quitting


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shows the large foregone earnings of a worker who always moves. Although a worker may earn

higher in the new than the old one, it may just be enough to offset the costs incurred while being

unemployed and at the same time while looking for a new job. Furthermore, the workers who

stay at their current jobs longer will have a steeper age-earnings profile within any given job.

Figure 3.1

Source: Borjas, 2000

V. RESEARCH METHOLODGY

Sampling Design

The data will be gathered from the Eastwood Cyberzone area and the Ortigas Cyberzone area.

Both sites are among the major call center zones in Metro Manila. Due to the major time and

cost constraints in conducting this research, the technique to obtain the samples is through the

-cluster sampling and simple random sampling combination. The Ortigas Pasig Cyberzone area

23

Stayers

Movers

T1 T2 T3

Age

Wage


24/62

and Eastwood Cyberzone will be divided into several clusters. Geographically, the call center

companies are converged into these specific clusters and forms an imaginary triangle

1. Ortigas area beside the MRT Ortigas Station and Unionbank vicinity,

2. Ortigas area within the SM Megamall Discovery Suites vicinity,

3. Ortigas area within the Shangri-la vicinity and Crossings vicinity

EDSA_______________________________________________

The Eastwood Cyberpark will also be divided into several clusters

4. Eastwood Cyberpark :Inside the of Eastwood City beside the Marikina River and FitnessFirst5. Eastwood Cyberpark: Located at the heart of Eastwood City, particularly at the

Cybermall building housing several call centers

6. Eastwood Cyberpark: Near the C-5 road particularly the call centers housed at theCitibank and Epixtar Building

24

MRT-Ortigas Station &

Union Bank Convergys

ICT

Hellcorp

SM Megamall- Discovery

Suites

AmbergrisSolutions

Enfra USA

Client Logic

Shangri-la & Crossings

Teleperforamcne

Sykes Asia

Sterling Global

Citibank Bldg

Citibank NA

E-Telecare

Epixtar Bldg

Epixtar

Shaw

Blvd.

To C-5 Katipunan Ave

Marikina

River

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The first stage is randomly choosing four (4) of the six (6) clusters, using the Table of Random

Numbers (Appendix E of Lind, Marchal and Wathen, 2003). We look at the first digits of the

random numbers to choose from one to six, with the starting point at any sector of the table.

After the selection of the clusters, sixty samples (60) will be drawn from each cluster. On the

second stage, thirty (30) of the sixty (60) sub-samples will be randomly drawn from each cluster.

The gathering of sample data is further limited since the data collection can hardly penetrate

the specific offices of call centers due to security concerns. In this regard, data will be collected

from the areas where the targeted employees take their breaks. In the Eastwood area, the

respondents are randomly picked from the Starbucks Eastwood City, beside the office elevator in

the Cybermall building, beside 7-11, the Foodstreet area, the Fitness First area, and every

smoking area of the call center agents. In the Ortigas area, the samples will be randomly chosen

from the Robinsons Equitable Tower, Seattles Best Robinsons Galeria, and McDonalds

beside the UnionBank Tower. To make the selection of respondents in the clusters random, the

respondents who immediately notice the presence of the surveyor are the ones selected.

Prior to administering the survey, the objectives of the survey will be explained. The

respondents are informed that the survey being completed is related to a graduate thesis. The

samples will be asked to fill out a survey that can be completed in 5-10 minutes. The surveys are

conducted in such a way that the respondents are informed that the survey being completed is

related to a graduate thesis. The respondents must be assured that the survey completed does not

provide an immediate action plan to change their companys business practices.

25

Eastwood Cybermall

SITEL

E-Telecare

SiemensIBM

Marikina River &

Fitness First

Link2 Support

Trend Micro

Emerson


26/62

The initial questions asked in the surveys are questions that profile the respondents basic

information such as age, number of months in the current job, and the number of dependents.

This will be followed by a set of questions about their rate of satisfaction and confidence levels

with their current work, the human resources, their supervisors and the management. Their rates

of approval are quantitative measured using a Likered Scale of 1 to 5 (with 1= Strongly

Disagree, 5= Strongly Agree). In the econometric model, it is hypothesized that higher Likered

Scores lowers the probability of quitting given a set of various external offers (such as higher

wages). In addition to the rates of approval, the contingency question (yes or no) of whether to

quit the current job or not will be asked in the survey in order to have the response as a

dependent dummy variable (with 1= YES, I wll resign, and 0= NO, I will not resign).

Empirical Specification of the Model

The empirical application of the job turnover model is with a binary choice model in which the

decision to quit depends on the independent variables. The distribution of the outside offers (in

the form of higher wages) is given by the equation,

Y = + 1WAGE + i CONTROLi + i

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Y = + 1WAGE + i CONTROLi + j JOBSATi + k HRSATi+ L SUPSATi

+ m MGTSATI + + i

where i, j, k, L, m = 1, 2, 3 subcategories per index

= constant

WAGE = wage difference (%) increase being offered outside

CONTROL = the i number of control variables in the regression such as

AGE of the worker in years

GENDER of the employee: 1= if male and 0 = otherwise

TENURE = number of months in the current jobEDUCATION = number of years in school

SIZE = firm size based on the number of employees

DEPEND = the workers number of dependents

To measure the effect of the non-wage outside offers on turnover decisions given the current

assessment of their situation with their employer, we have:

Y = + 1WAGE + i CONTROLi + j JOBSATi + k HRSATi+ L SUPSATi

+ m MGTSATi + i

where i, j, k, L, m = 1, 2, 3 subcategories per index

Y would have different representations,

YSAT = jobsatisfaction improvements offered by the outside firm

YHR = human- resource practice improvements offered by the outside firm

YSUP = supervisory enhancements offer by the outside firm

YMGT = upper management enhancements offered by the outside firm

= constant

JOBSAT = Job satisfaction index with i number of questions (agents score is 1 to 5 with 1= poor and 5=

27


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excellent)

HRSAT = Human Resources Practices Index with i number of questions (index with i number of

questions ; agents score the variable with 1 to 5 with 1= poor and 5= excellent)

SUPSAT = Supervisory Confidence Index with i number of questions (index with i number of


MGTSAT = Management Confidence Index with i number of questions (index with i number of


CONTROL = the i number of control variables in the regression such as

AGE of the worker in years

GENDER of the employee: 1= if male and 0 = otherwise

TENURE = number of months in the current job

EDUCATION = number of years in school

SIZE = firm size, based on the number of employees

DEPEND = the workers number of dependents

To measure the effect of the shorter travel (offered by the outside firm) on turnover decisions

given the current assessment of their situation with their employer, we have:

Y = + + 1TRAVEL + i CONTROLi + j JOBSATi + k HRSATi+ L SUPSATi

+ m MGTSATi + i

where TRAVEL = travel time of the worker going to work in minutes

The a priori results are given in the table below

REGRESSORS

EXPECTED SIGN

(Probability of Quitting)

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Outside Wage Increases Offered (10%, 20%, 30%, 40% and 50%) Positive

Control Variables

Age Negative

Gender Negative

Number of Dependents Negative

Job Tenure Negative

Education (in years) Positive

Travel Time (in minutes) Positive

Non-Wage Factors

JOB SATISFACTION INDEX

A. Job match: can use what has been learned in college Negative

B. Working on night shift Negative

C. Work Benefits: can use leaves for rest or leisure Negative

HR Practices

A. Adequate Provision of Training Workshops for Career Development Negative

B. Presence of Employee Voice Negative


A. Delivers coaching and counseling with a positive experience Negative

B. Supervisor has a hard time explaining the business impact of agent performance Positive

C. Supervisor cannot help his agents towards career growth Positive

Managerial Competence

A. Selects employees for promotion on performance Negative

B. Management cannot properly explains how the decision for promotion was made Positive

C. Management cannot set realistic targeted performance for the workers Positive

VI. DATA FOR

DESCRIPTIVE STATISTICS

290

10

20

30

40

50

60

0 500 1000 1500 2000 2500

SIZE

TENURE

0

10

20

30

40

50

60

16 20 24 28 32 36 40 44

AGE

TENURE


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Figure 7.1 Tenure and Size Figure 7.2 Tenure and Age

Figure 7.3 Tenure and Years of Schooling

Table 7.1

Size and Tenure

Size (number of

employees)

Average Tenure (in

months)

100- 350 4.61

400- 750 5.98800- 1100 8.04

1200- 2000 28.41

Table 7.3 Size and Human Resources Competence

Size (number

of employees)

Average

Score

HR1

Average

Score

HR2

Average

Score

HR3

100- 350 3.00 3.00 3.36

30

Table 7.2

Size and Job Satisfaction

Size (number

of employees)

Average

Score

JS1

Average

Score

JS2

Average

Score

JS3

100- 350 2.72 3.20 3.44

400- 750 3.16 3.55 3.42

800- 1100 3.52 3.52 3.28

1200- 2000 3.10 3.54 2.92

0

10

20

30

40

50

60

11 12 13 14 15 16 17 18 19 20

SCHOOL

TENURE


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400- 750 3.16 3.19 2.97

800- 1100 2.80 3.32 3.24

1200- 2000 3.10 3.10 3.62

Table 7.4

Size and Supervisory CompetenceSize

(number of

employees)

Average

Score

SUP1

Average

Score

SUP2

Average

Score

SUP3

100- 350 3.24 2.84 3.16

400- 750 3.26 2.48 2.97

800- 1100 3.32 2.20 2.48

1200- 2000 3.79 2.15 2.46

Table 7.5

Size and Management Competence

Size (number ofemployees)

Average ScoreMGT1

Average ScoreMGT2

Average ScoreMGT3

100- 350 2.80 3.00 3.12

400- 750 3.29 3.29 3.35

800- 1100 2.76 3.08 3.28

1200- 2000 3.49 2.26 2.28

Table 7.6 Age and Tenure

AgeAverage Tenure

(in months)

20- 24 9.76

25-29 16.61

30-35 15.1536 yrs and older 6.80

Table 7.8

Age and Human Resource Competence

Age

Average

Score

HR1

Average

Score

HR2

Average

Score

HR3

20- 24 3.13 3.07 3.41

25-29 2.96 3.18 3.23

30-35 2.92 3.23 3.23

36 yrs &above 3.20 3.40 3.60

Table 7.10

Age and Management Competence

Age

Average

Score

MGT1

Average Score

MGT2

Average Score

MGT3

31

Table 7.7 Age and Job Satisfaction

Age

Average

Score

JS1

Average

Score

JS2

Average

Score

JS3

20- 24 3.33 2.54 2.72

25-29 3.66 2.13 2.6630-35 3.00 2.85 3.31

36 yrs & older 3.20 2.80 2.40

Table 7.9

Age and Supervisory Competence

Age

Average

Score

SUP1

Average

Score

SUP2

Average

Score

SUP3

20- 24 3.33 2.54 2.72

25-29 3.66 2.13 2.66

30-35 3.00 2.85 3.3136 yrs & older 3.20 2.80 2.40


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20- 24 3.26 2.67 2.80

25-29 3.05 2.84 3.00

30-35 3.23 3.31 3.00

36 yrs & older 2.80 3.40 3.40

Table 7.11Mean Results on All Genders

Variable ALL Female Male

AGE 26.10 25.98 26.22

GENDER - 0.00 1.00

SIZE 879.48 810.00 948.97

DEPEND 0.85 0.92 0.78

TENURE 13.41 12.73 14.10

SCHOOL 14.97 14.97 14.97

TRAVEL 53.10 51.28 54.92

JS1 3.13 3.17 3.08

JS2 3.47 3.27 3.67JS3 3.23 3.15 3.32

HR1 3.03 2.98 3.08

HR2 3.15 2.97 3.33

HR3 3.32 3.28 3.35

SUP1 3.44 3.22 3.67

SUP2 2.39 2.45 2.33

SUP3 2.74 2.70 2.78

MGT1 3.14 2.93 3.35

MGT2 2.85 2.97 2.73

MGT3 2.94 2.92 2.97

Table 7.12

Schooling and Tenure

Schooling

Average

Tenure (in

months)

12-14 yrs 11.77

15-17 yrs 14.10

18 yrs & above 19.19

Table 7.14

Schooling and HR Index Scores

Schooling

Mean Score

HR1

Mean

Score

HR2

Mean

Score

HR3

32

Table 7.13Schooling and Job Satisfaction Index Scores

Schooling

Average

Score

JS1

Average

Score

JS2

Average

Score

JS3

12-14 yrs 3.00 3.45 3.35

15-17 yrs 3.30 3.50 3.18

18 yrs &above 2.78 3.33 2.89

Table 7.15

Schooling and Supervisor Competence Index

Schooling

Average

Score

SUP1

Average

Score

SUP2

Average

Score

SUP3

12-14 yrs 3.33 2.49 2.80

15-17 yrs 3.55 2.36 2.73

18 yrs & above 3.44 2.00 2.44


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12-14 yrs 3.00 3.02 3.24

15-17 yrs 3.14 3.30 3.32

18 yrs &above 2.56 3.00 3.78

Table 7.16

Schooling and Management Competence Index

Schooling

Average

Score

MGT1

Average

Score

MGT2

Average

Score

MGT3

12-14 yrs 3.09 2.91 3.18

15-17 yrs 3.04 2.86 2.80

18 yrs & above 4.11 2.44 2.33

INTERPRETATION OF RESULTS

Tenure

Based from Figure 7.1 and Table 7.1, job tenures tend to be higher as the size of the firm

increases. Firm size indicates general stability that assures longer employee- employer

relationships. In addition, Tables 7.2 and 7.3 show that workers in larger firms gave better

feedback to their employer as expressed by better scores in job satisfaction (JS), HR competency

(HR), and supervisory and upper management competency indexes (SUP and MGT).

Age

From Figures 7.2 and Table 7.6, job tenures tend to be lower for the younger age brackets (20-

24 yrs) and the oldest age bracket (36 yrs and above). Graphically, the age and tenure

relationship shows an inverted U- shaped curve. In terms of job satisfaction, workers in the 25-

29 yrs age bracket gave the best scores in job match between current profession and the related

degree in college (JS1 in Table 7.7). At the same time the 25- 29 yrs age bracket had the lowesttolerance of working at night (JS3 in Table 7.7). People in the 30- 35 yrs age bracket had the

highest dissatisfaction in terms of not being to take enough time off for leisure (JS3 scores in

Table 7.7). In addition, the workers in the 36 yrs & above age bracket gave the best scores on

their supervisors overall competence (SUP1- SUP3 in Table 7.9) but gave the lowest scores on

the managerial competence of the upper management (MGT1- MGT3 in Table 7.10). This may

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indicate the vast experience of these older workers allow them to identify and compare the types

of management that sustains the high morale and performance of its employees.

Gender

Based from Table 7.11, both male and female workers nearly have the same average ages.

However, male workers are employed at relatively larger firms. This implicitly argues that larger

firms employ dependable and steady workers that male labor can offer more than the female

labor (Zabel, 1993). Better scores of men in JS2 shows that men obviously are more tolerant to

the hazards of working at night. In terms of HR competence, female scores in the employee

voice index (HR2) are lower than men, showing that women are more aggressive in voicing out

their job concerns than men. In the supervisory competence category, both male and female

workers gave good feedback on their supervisors business and leadership knowledge and the

effort to help in employee career growth (SUP2 and SUP3). In terms of management

competence, both genders gave neutral scores in categories (MGT- MGT 3).

Schooling

Figure 7.3 shows the graphical relationship between years of schooling and job tenure,

resembling an inverted U- shaped curve (if plotting the extreme points of the scattered diagram).

Based from Table 7.12, average job tenure is higher for workers with more years of schooling.

The reason is that several of the workers in the 18 yrs schooling & above bracket are taking up

their post-graduate degrees (MBA and MA in Education) while working in the call center. From

Table 7.14, the more schooled workers in the 18 yrs schooling & above category registered

lowest scores in job match (JS1) between current profession and the related college degree.

Compared to the other years of schooling brackets, the former surprisingly gave very good

scores in the supervisory and upper management competence indexes (Table 15 & 7.16).

Overall Mean Scores

From the first column of Table 7.11, the mean age of the sample group was 26 years old and

were employed at firms that have an average employee size of 880 workers. The average tenure

of the samples was 13 months and generally underwent close to 15 years of schooling. The

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average travel time of the workers in the sample was close to an hour (53 minutes) indicating

that workers reside farther from the central call center cyber-districts of Ortigas and Eastwood.

Moreover, the samples gave neutral to good scores in almost all of the indexes. Looking into the

probit regression results (in the next section of this paper) however will show that although

workers gave these decent scores, these workers will still be attracted to outside firms that offers

better wage and non-wage benefits.

VII. PROBIT REGRESSION: DATA & RESULTS

table 8.1 PROBIT REGRESSION RESULTS: The new company offers a WAGES INCREASE

Variable Equation (1) Equation (2)

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C-2.509(0.746)

-2.240(0.998)

WAGE 0.067**(0.005)

0.076**(0.006)

AGE 0.022(0.016)

0.027(0.017)

GENDER 0.192

(0.122)

0.168

(0.147)SIZE -0.0004**

(0.0002)-0.00031**(0.00018)

DEPEND0.024

(0.055)-0.013(0.062)

TENURE0.007

(0.006)0.007

(0.007)

SCHOOL-0.021(0.041)

-0.024(0.046)

JS1-0.115*(0.063)

JS2-0.031(0.075)

JS3-0.043(0.056)

HR1-0.218**(0.076)

HR2-0.044(0.089)

HR30.019

(0.073)

SUP1-0.014(0.070)

SUP2-0.103(0.066)

SUP30.332**(0.068)

MGT1 0.046(0.059)

MGT2-0.164**(0.080)

MGT30.126*(0.070)

S.D. dependent var 0.4903 0.4903

Akaike info criterion 0.9695 0.8972

Schwarz criterion 1.0281 1.0438

Hannan-Quinn criter. 0.9923 0.9543

Avg. log likelihood -0.4714 -0.4153

McFadden R-squared 0.2996 0.3830

Total obs 600 600

NOTE: * indicates significant at 10% level; ** significant at 5% level; Standard errors are in parenthesis

Table 8.2 PROBIT REGRESSION RESULTS: The new company offers a WAGE INCREASE

YWAGE10

(10% salary

increase)

YWAGE20

(20% salary

increase)

YWAGE30

(30% salary

increase)

YWAGE40

(40% salary

increase)

YWAGE50

(50% salary

increase)

Variable Equation (1) Equation (2) Equation (3) Equation (4) Equation (5)

C-3.778

(10.217)-1.872(2.521)

0.478(1.991)

1.293(1.960)

-1.637(2.646)

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AGE0.358

(0.314)0.088*(0.047)

0.037(0.035)

-0.026(0.032)

0.063(0.055)

GENDER -0.006(0.005)

-0.0005(0.0005)

-0.0001(0.0004)

-0.0001(0.0003)

-0.050(0.375)

SIZE-

-0.165(0.388)

0.436(0.305)

0.222(0.282)

-0.0005(0.0004)

DEPEND

1.810

(1.490)

-0.072

(0.193)

-0.131

(0.144)

-0.056

(0.115)

0.236

(0.166)

TENURE-0.272(0.348)

0.020(0.019)

0.001(0.014)

0.004(0.014)

0.012(0.019)

SCHOOL-0.704(0.840)

0.019(0.116)

-0.022(0.088)

-0.034(0.095)

-0.003(0.123)

JS1-2.672(1.795)

-0.482**(0.172)

-0.061(0.127)

-0.044(0.126)

0.255(0.168)

JS21.385

(1.067)0.154

(0.198)-0.124(0.153)

-0.013(0.145)

0.092(0.196)

JS3-0.981(0.759)

-0.024(0.140)

-0.097(0.113)

-0.002(0.110)

0.152(0.159)

HR1-0.789(1.394)

-0.112(0.198)

-0.177(0.148)

-0.400**(0.154)

-0.317(0.210)

HR23.192

(2.991)-0.133(0.232)

-0.317**(0.187)

0.046(0.180)

-0.313(0.256)

HR3-1.343(1.129)

-0.009(0.194)

0.102(0.144)

0.008(0.145)

0.363**(0.217)

SUP1-0.702(1.260)

-0.043(0.171)

-0.122(0.143)

0.078(0.141)

0.178(0.173)

SUP2-1.847(2.137)

-0.012(0.148)

-0.102(0.129)

-0.119(0.135)

-0.284(0.217)

SUP32.926

(2.512)0.466**(0.168)

0.287**(0.130)

0.378**(0.141)

0.536**(0.229)

MGT11.339

(1.072)-0.128(0.152)

0.054(0.121)

0.129(0.116)

-0.161(0.155)

MGT2-1.904(1.982)

-0.323(0.199)

-0.082(0.155)

-0.088(0.159)

-0.282(0.222)

MGT3 1.533(1.781) 0.072(0.170) 0.162(1.40) 0.094(0.138) 0.217(0.186)

S.D. dependent var 0.2189 0.3666 0.4790 0.4920 0.3666

Akaike info criterion 0.4385 0.9452 1.3585 1.4544 0.9870

Schwarz criterion 0.8567 1.3866 1.7999 1.8958 1.4283

Hannan-Quinn criter. 0.6083 1.1244 1.5377 1.6336 1.1662

Avg. log likelihood -0.0693 -0.3143 -0.5209 -0.5689 -0.3352

McFadden R-squared 0.6511 0.2807 0.1954 0.1547 0.2329

Total obs 120 120 120 120 120


Table 8.3 PROBIT REGRESSION RESULTS: The new company offers a BETTER JOBSATISFACTION

YSAT1

(better job match)

YSAT2

(Day Shift)

YSAT3

(non-wage

benefits)

VariableEquation

(1)Equation

(2)Equation

(3)Equation

(4)Equation

(5)Equation

(6)

C 5.763(2.165)

4.856(2.926)

4.311(2.150)

3.102(2.744)

0.900(1.988)

1.197(2.543)

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AGE 0.006(0.045)

0.068(0.062)

0.011(0.046)

-0.007(0.054)

-0.018(0.041)

-0.057(0.052)

GENDER 0.247(0.330)

0.498*(0.455)

-0.298(0.342)

-0.493(0.430)

-0.508(0.333)

-0.571(0.411)

SIZE -0.0007(0.0005)

-0.001(0.001)

-0.0004(0.0005)

-0.0002(0.0006)

0.0002(0.001)

0.001(0.001)

DEPEND -0.086

(0.163)

-0.122

(0.231)

0.084

(0.153)

0.083

(0.190)

-0.0004

(0.161)

-0.011

(0.214)TENURE 0.008

(0.019)0.018

(0.030)-0.002(0.019)

-0.011(0.028)

-0.022(0.021)

-0.041**(0.024)

SCHOOL -0.194*(0.109)

-0.253*(0.144)

-0.056(0.107)

-0.017(0.124)

-0.212**(0.108)

-0.292**(0.133)

JS1 -0.804**(0.155)

-0.920**(0.202)

-0.501**(0.159)

-0.493**(0.185)

0.245(0.160)

0.391**(0.196)

JS2 -0.2712*(0.158)

-0.237(0.223)

-0.793**(0.187)

-0.721**(0.224)

-0.289*(0.168)

-0.645**(0.245)

JS3 -0.0640(0.125)

-0.169(0.156)

-0.020(0.128)

-0.005(0.149)

0.990**(0.149)

1.244**(0.217)

HR1 0.200

(0.258)

-0.150

(0.205)

-0.004

(0.232)HR2 -0.601**

(0.297)-0.198(0.217)

0.400(0.291)

HR3 0.034(0.253)

-0.029(0.237)

0.217(0.201)

SUP1 -0.028(0.208)

0.227(0.204)

-0.148(0.209)

SUP2 0.288(0.188)

0.242(0.176)

-0.004(0.194)

SUP3 0.470**(0.206)

0.289(0.178)

0.317(0.194)

MGT1 0.353(0.230)

0.053(0.173)

-0.196(0.197)

MGT2 -0.2083

(0.250)

-0.045

(0.245)

0.226

(0.241)MGT3 -0.2025

(0.212)-0.117(0.199)

-0.243(0.192)

S.D. dependent var 0.4247 0.4247 0.4193 0.4193 0.5015 0.5015

Akaike info criterion 0.8710 0.8299 0.8313 0.8965 0.8784 0.8914

Schwarz criterion 1.1033 1.2712 1.0636 1.3379 1.1107 1.3327

Hannan-Quinn criter. 0.9654 1.0091 0.9256 1.0758 0.9728 1.0706

Avg. log likelihood -0.3522 -0.256596 -0.3323 -0.2899 -0.3559 -0.2873

McFadden R-squared 0.3517 0.527684 0.3767 0.4562 0.4857 0.5847

Total obs 120 120 120 120 120 120


Table 8.4 PROBIT REGRESSION RESULTS: The new company offers BETTERHUMAN RESOURCES PRACTICES

YHR1

(Presence of Career

Devt Program)

YHR2

(Employee

Involvement &

Participation)

YHR3

(Timely given:

Bonuses & Incentives)

Variable Equation (1) Equation (2) Equation (3) Equation (4) Equation (5) Equation (6)

C 5.478(4.280)

-13.854(11.774)

3.712(2.006)

3.082(3.007)

3.229(2.128)

2.998(3.159)

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AGE

0.009(0.048)

0.017(0.179)

-0.006(0.044)

-0.022(0.064)

-0.034(0.047)

-0.083(0.062)

0.048 0.179 0.044 0.064 0.047 0.062

GENDER 0.952*(0.554)

0.003(0.03)

0.517(0.343)

0.097(0.469)

0.405(0.343)

0.317(0.458)

SIZE -0.0004(0.0008) -

-0.001(0.001)

-0.0004(0.001)

0.0006(0.0005)

0.0009(0.0006)

DEPEND 0.175(0.213)

1.114(0.905)

-0.013(0.150)

-0.215(0.195)

0.062(0.143)

0.013(0.183)

TENURE -0.021(0.031)

-0.059(0.141)

0.032(0.021)

0.035(0.028)

-0.045*(0.024)

-0.055*(0.032)

SCHOOL 0.171(0.231)

1.674(1.201)

-0.020(0.102)

-0.057(0.125)

0.033(0.111)

0.080(0.134)

JS1 -1.138(1.039)

-0.289(0.192)

0.214(0.215)

JS2 -1.461(1.104)

-0.078(0.224)

-0.348(0.276)

JS3 1.229(0.796)

0.120(0.163)

0.396**(0.194)

HR1 -2.491**(0.554)

-9.182*(5.374)

-0.340**(0.146)

-0.536**(0.203)

-0.366*(0.165)

-0.505**(0.212)

HR2 -0.327(0.296)

-2.173(1.750)

-0.839**(0.194)

-1.020**(0.282)

-0.136(0.183)

-0.074(0.230)

HR3 -0.080(0.243)

0.918(0.937)

-0.112(0.174)

-0.102(0.214)

-0.744*(0.187)

-0.845(0.230)

SUP1 1.006(0.961)

0.062(0.218)

0.036(0.226)

SUP2 1.611(1.218)

-0.136(0.202)

-0.142(0.209)

SUP3 1.632*(0.983)

0.422**(0.174)

0.471**(0.196)

MGT1 2.116(1.535)

0.313(0.204)

-0.183(0.199)

MGT2 -0.157

(0.641)

0.284

(0.250)

-0.104

(0.250)MGT3 0.922

(0.930)0.070

(0.210)-0.004(0.219)

S.D. dependent var 0.4920 0.4920 0.4484 0.4484 0.4396 0.4396






Total obs 120.0000 120 120 120 120 120

Table 8.5 PROBIT REGRESSION RESULTS: The new company offers BETTER SUPERVISORS

YSUP1

(Positive Coaching

Experience)

YSUP2

(Business & Leadership

Knowledge)

YSUP3

(Provides

Individual

DevProgram)

Variable Equation (1)Equation

(2) Equation (3) Equation (4)Equation

(5) Equation (6)

C

1.282(3.323)

13.360(6.973)

-1.120(2.772)

-0.314(3.354)

-1.569(2.560

-0.190(3.014)

3.323 6.973 2.772 3.354 2.560 3.014

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GENDER

-0.780*(0.466)

-1.938**(0.895)

-0.714(0.447)

-0.953(0.532)

0.067(0.047)

-0.648*(0.480)

AGE 0.120*(0.063)

0.205**(0.100)

0.072(0.053)

0.078(0.059)

-0.748*(0.415)

0.102(0.059)

SIZE -0.0001(0.0007)

-0.0013(0.0013)

-0.0005(0.0006)

-0.0005*(0.0007)

0.0002(0.0006)

0.0001(0.001)

DEPEND -0.653**

(0.321)

-0.692

(0.469)

-0.280

(0.232)

-0.358

(0.284)

-0.062

(0.205)

-0.180

(0.241)TENURE -0.003

(0.026)0.027

(0.041)0.024

(0.025)0.029

(0.029)-0.010(0.025)

0.003(0.031)

SCHOOL -0.217(0.196)

-0.900**(0.431)

-0.100(0.149)

-0.160(0.189)

-0.054(0.129)

-0.106(0.148)

JS1 -0.320(0.308)

-0.171(0.240)

0.139(0.232)

JS2 -0.370(0.393)

0.008(0.293)

-0.437(0.344)

JS3 -0.754**(0.344)

-0.128(0.217)

-0.095(0.179)

HR1 -0.468(0.477)

0.047(0.285)

0.067(0.245)

HR2 1.622**(0.767)

0.188(0.339)

-0.006(0.341)

HR3 -0.162(0.156)

-0.106(0.276)

-0.032(0.245)

SUP1 -1.051**(0.272)

-2.193**(0.695)

-0.573**(0.212)

-0.635**(0.267)

-0.513*(0.193)

-0.717**(0.287)

SUP2 0.794**(0.269)

1.830**(0.702)

1.534**(0.343)

1.631**(0.402)

-0.105(0.228)

-0.220(0.265)

SUP3 0.150(0.193)

0.250(0.301)

-0.447**(0.249)

-0.623**(0.294)

1.067*(0.212)

1.066**(0.260)

MGT1 -0.093(0.280)

-0.026(0.206)

-0.020(0.209)

MGT2 -0.012(0.337)

0.161(0.296)

0.345(0.260)

MGT3 -0.212(0.287) 0.159(0.233) -0.057(0.202)

S.D. dependent var 0.4703 0.4703 0.4703 0.4703 0.5010 0.5010






Total obs 120 120 120 120 120 120

Table 8.6 PROBIT REGRESSION RESULTS: The new company offers BETTERMANAGEMENT

YMGT1(Performance-based

promotion)

YMGT2(Promotion Decision-

Making)

YMGT3(Realistic Performance

Goals)

VariableEquation

(1)Equation

(2)Equation

(3)Equation

(4)Equation

(5)Equation

(6)

C -0.977(2.002)

-2.951(2.917)

-3.763(2.321)

-3.929(3.430)

-1.802(2.346)

0.117(3.791)

AGE -0.003(0.046)

0.013(0.057)

-0.067(0.054)

-0.081(0.073)

-0.023(0.048)

0.037(0.080)

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GENDER -0.478(0.381)

-0.963*(0.536)

-0.306(0.429)

-0.203(0.694)

-0.749(0.462)

-1.228*(0.706)

SIZE 0.0009(0.0006)

0.0013*(0.0008)

0.0015**(0.0006)

0.0018**(0.0009)

0.0013**(0.0006)

0.0015*(0.0008)

DEPEND -0.201(0.173)

-0.330(0.210)

0.030(0.180)

0.078(0.276)

0.042(0.197)

-0.429(0.340)

TENURE -0.009

(0.022)

0.009

(0.028)

-0.011

(0.025)

0.012

(0.036)

-0.016

(0.022)

0.025

(0.033)SCHOOL 0.022

(0.116)0.050

(0.129)0.025

(0.134)0.116

(0.193)-0.079(0.142)

-0.024(0.184)

JS1 -0.455**(0.224)

-0.386(0.257)

-1.052**(0.391)

JS2 0.315(0.255)

-0.011(0.289)

-0.078(0.329)

JS3 0.329(0.214)

-0.185(0.245)

0.076(0.257)

HR1 -0.060(0.231)

0.010(0.265)

-0.046(0.323)

HR2 0.173(0.269)

0.083(0.342)

0.246(0.411)

HR3 -0.231(0.256)

-0.135(0.333)

-0.630(0.422)

SUP1 0.069(0.216)

-0.570**(0.280)

-0.238(0.263)

SUP2 0.006(0.185)

0.596**(0.280)

-0.136(0.267)

SUP3 0.284(0.192)

-0.398(0.277)

0.498*(0.287)

MGT1 -0.662**(0.153)

-0.904**(0.225)

-0.168(0.157)

-0.172(0.224)

-0.348**(0.168)

-0.530*(0.271)

MGT2 0.469**(0.181)

0.408*(0.233)

1.152**(0.241)

1.799**(0.503)

0.185(0.220)

-0.202(0.337)

MGT3 0.371**(0.166)

0.462**(0.211)

0.359*(0.190)

0.423(0.313)

1.242**(0.261)

1.818*(0.444)

S.D. dependent var 0.4996 0.4996 0.4951 0.4951 0.5015 0.5015






Total obs 120 120 120 120 120 120


Table 9.1 PROBIT REGRESSION RESULTS: The new company offers TRAVEL TIME REDUCTION

Variable Equation(1) Equation(2)

C -1.9783(0.509)

-2.599(0.901)

TRAVEL 0.0289**(0.005)

0.033(0.005)

AGE 0.0179(0.018)

0.017(0.020)

GENDER -0.0447(0.152)

-0.059(0.183)

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SIZE 0.00001(0.0002)

0.00024(0.00022)

DEPEND -0.0273(0.035)

-0.043(0.048)

TENURE 0.0008(0.008)

0.006(0.009)

SCHOOL -0.0003

(0.008)

0.004

(0.009)JS1 -0.077

(0.079)

JS2 -0.232**(0.098)

JS3 0.013(0.072)

HR1 -0.132(0.093)

HR2 0.117(0.109)

HR3 -0.034(0.095)

SUP1 0.050(0.088)

SUP2 0.216**(0.080)

SUP3 0.032(0.085)

MGT1 0.013(0.075)

MGT2 0.023(0.105)

MGT3 0.127(0.090)

S.D. dependent var 0.4368 0.4368

Akaike info criterion 1.0595 1.0121

Schwarz criterion 1.1459 1.2280

Hannan-Quinn criter. 1.0938 1.0979

Avg. log likelihood -0.5075 -0.4505

McFadden R-squared 0.1070 0.2074

Total obs 360 360


Table 9.2 PROBIT REGRESSION RESULTS: The new company offersTRAVEL TIME REDUCTION

Variable 10% travel reduction 25% travel reduction 50% travel reduction

C -4.646(3.672)

-0.041(2.275)

0.927(1.889)

AGE 0.062(0.047)

-0.011(0.042)

0.026(0.032)

SIZE -0.365(0.467)

0.006(0.334)

0.0004(0.272)

GENDER -0.0001(0.0005)

0.0002(0.0004)

0.0004(0.0003)

DEPEND -0.100(0.211)

0.043(0.144)

0.006(0.113)

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TENURE -.100(0.211)

0.043(0.144)

0.006(0.113)

SCHOOL -0.009(0.177)

-0.032(0.105)

-0.133(0.088)

JS1 -0.121(0.193)

-0.232(0.142)

0.061(0.122)

JS2 -0.292**(0.283)

-0.170(0.176)

-0.288**(0.150)

JS3 0.095(0.179)

-0.111(0.132)

0.048(0.108)

HR1 -0.231(0.264)

-0.025(0.170)

-0.197(0.141)

HR2 0.392(0.304)

0.00003(0.203)

0.126(0.165)

HR3 -0.066(0.268)

0.029(0.170)

-0.074(0.137)

SUP1 0.192(0.235)

0.038(0.154)

0.034(0.133)

SUP2 0.687(0.285)

0.155(0.139)

0.111(0.123)

SUP3 -0.364(0.292)

0.139(0.147)

0.120(0.123)

MGT1 -0.0003(0.211)

-0.014(0.136)

0.033(0.113)

MGT2 0.125(0.275)

-0.046(0.174)

-0.044(0.145)

MGT3 0.333(0.245)

0.158(0.150)

0.063(0.130)

S.D. dependent var 0.3224 0.3886 0.5010

Akaike info criterion 0.7873 1.1266 1.5371

Schwarz criterion 1.2287 1.5679 1.9785

Hannan-Quinn criter. 0.9666 1.3058 1.7164

Avg. log likelihood -0.2353 -0.4050 -0.6102

McFadden R-squared 0.3467 0.1500 0.1168

Total obs 120 120 120


PROBIT REGRESSION INTERPRETATION OF RESULTS

Wage Offers

Based from Table 8.1, equations 1 and 2 shows that higher wage offers (WAGE) by outside

firms significantly increases the workers decision to quit (YWAGE) at 5% level. Going back to

equation 1, the size of the firms (SIZE) significantly reduces the workers decision to quit,

indicating that firm stability can significantly deter quit behavior. Incorporating the other

variables as in equation 2 shows that firm size loses its significance. However, better job matches

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between the work and college degree (JS1), the presence of a career development program by the

companys HR and its supervisors (HR1 and SUP3), and the managements good promotion

decision making (MGT2) and realistic goal setting (MGT3)all these factors help in

significantly deterring the workers decision to quit his current job given attractive wage rates

offered outside. The significant effect of number of years of schooling on quit decision may be

attributed to the fact that the samples show prolonged studying to earn a postgraduate degree

makes the part-time student workers stick to the current job until they have completed their post-

graduate degrees (see Table 7.12).

Table 8.2 looks into the turnover behavior at each rate of wage increase offered by outside

firms. At 10 percent salary increase offer (see equation 1 where the GENDER variable was

dropped to prevent the singular covariance error in the probit regression), there are no

significant factors that can either deter or encourage the worker to quit. At 20 percent salary

increase offer, job match (JS1) and the supervisors career- pathing ability (SUP3) are significant

deterrents of quit decisions while age (AGE) significantly increases the probability of quitting.

At 30 percent wage offer, the supervisors career- pathing ability (SUP3) and the presence of

employee voice (HR2) can significantly decrease the probability of quitting. At 40 percent wage

offer, the supervisors career- pathing ability (SUP3) and the presence of the companys career

development program (HR1) significantly deter quit behavior. At 50 percent wage offer, the

firms timeliness in giving out work incentives and bonuses (HR3) and the supervisors career-

pathing ability (SUP3) significantly discourages the tendency to quit the current job.

Job Satisfiers

Based from Table 8.3, equation 1 shows that an outside firm offering a job that matches with

the workers course in college (YSAT1) encourages the worker to quit the more the worker is

dissatisfied with the current job match (JS1). From equation 2, the probability of quitting

decreases, the more the worker is satisfied with the job match (JS1), the more the worker is able

to see an employee voice in the company (HR2), and the more worker sees his supervisor

helping to build a career path for him (SUP3).

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Given an outside firm that offers a daytime shift (YSAT2), equations 3 and 4 shows that the

worker will not quit his job the more the worker can tolerate the hazard of the graveyard shift

(JS2) and is satisfied with the job match of his current work with his college degree (JS1).

Based on equations 5 and 6, better scores in the tolerance of the night shift (JS2), more years of

schooling (SCHOOL) and job tenure (TENURE) significantly reduces the workers chances of

quitting the current job, but the probability will increase if the worker finds it hard to get leisure

time or time-off from work (JS3).

Better HR practices and systems

Based from Table 8.4, the worker will significantly be discouraged from leaving the company

given an external offer of a career development program in the outside firm (YHR1) if the

worker sees the same career development plan set up in his current employer (HR1) and the quit

decision is more likely of the worker were male (GENDER), all based from equation 1. In the

second equation, better scores in HR1 supported by the supervisors ability to create a career

path for the worker (SUP3) will significantly deter the workers decision to quit.

Suppose there is an outside offer that provides an avenue for employee involvement in the

managements decision making and a voice for grievances (YRH2). From equation 3 and 4, the

chances that the worker will quit decreases if the worker sees the presence of career development

plan from his current employer (HR1), sees same voice and employee involvement mechanism

in his current company (HR2), and the supervisor can offer help in the employees career growth

in the company (SUP3).

Based from equations 5 and 6, suppose that the worker is offered by the outside firm an HR

system in which the performance incentives and bonuses are given on time (YHR3). The worker

will be deterred from quitting if he is already tenured in his current job (TENURE), if he sees a

career development plan set up by his current employer (HR1), sees the same timeliness of

giving the incentives and bonuses in his current employer (HR3), and is able to find it easy to

take time-off from work (JS3).

Competent Supervisors

45


46/62

From Table 8.5, suppose there is an outside firm that has a set of supervisors who can deliver

effective coaching on performance improvement (YSUP1). Given that offer, equation 1 shows

that the worker will significantly be discouraged from leaving if the worke

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