Trainees’ Marketability Of Selected Youth Pre-Employment Training Program: a multinomial Logistic Regression Analysis




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НазваниеTrainees’ Marketability Of Selected Youth Pre-Employment Training Program: a multinomial Logistic Regression Analysis
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  1. OBJECTIVE OF THE STUDY



The specific objective of the study was to determine the relationship between student characteristics, family background, service quality of the training institutes, service quality of industrial practices, labor market condition and the marketability of the trainees.


  1. METHODOLOGY



Population and Sample. A survey were used to achieve the central objective of this study. The population for the study was trainees of wiremen offered by ITIs and MVTIs during the 2000 academic year. Population and sample distribution is shown in Table 1. From the population size for each type of training institutions and location in West Malaysia, representative sample (180 trainees) was drawn using the proportion sampling technique. The sample was stratified according to location (state) of each type of training institutions.



Table 1: Population and Sample Size by Types of Training Institutes

Location Of Training Institutions

Industrial Training Institutes (ITI)

MARA Vocational Training Institutes (MVTI)

Population Size (N)

Sample Size (n)

Population Size (N)

Sample Size (n)

Johor

24

16

29

13

Perak

14

10

31

14

Pahang

27

18

29

13

Melaka

24

16

44

20

Kedah & Perlis

23

16

34

16

Terangganu

20

14

30

14

Total

132

90

197

90

Instruments. Service quality of the training institutes and service quality of industrial practices and acquisition of employability skills were independent variables and labor market outcomes were the dependent variables in this study. Service quality of training institutes was measured using a 26 items instruments mainly adapted from Parasuraman, Ziethaml & Berry (1988, 1991, 1994). The SERVQUAL instrument developed by Parasuraman, Zeithaml and Berry (1988) were to measure the trainees perception of the actual service received at the end of training program. However previous studies of service quality in education by ANTA (2000), Conklin (1996), Cuthbert (1996), Ka-shing Woo (1998), Kwan & Ng (1999), Schmidt (1998), Smith & Wilson (2002), Strickland et al (2001) and Velde & Cooper (2000) were also to be complimented in this study. While service quality of industrial practices was measured using a 14 items instrument adapted from AbdelKarim (1997), Dougherty (1989), Frazao & Oliveira (1999), Gray & Warrender (1992), Metcalf (1985), Parasuraman, Ziethaml & Berry (1988, 1991, 1994) and Thomas (1995). The respondents were required to assess the quality of the training service and industrial practices they had received by using five point scale (1 = strong disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree).

The employability skills questionnaire was developed and adapted through the reviews of literature. Employability skills was measured using a 60 items instrument adapted from ANTA (2000), Azmi (1988), Brodelhewt (1999), Cotton, (1993), Lamb & Mckenzie (2001), Leon & Borchers (2002), UK’s National Action Plan (DfEE, 2001), Employability Skills 2000+ (The Conference Board of Canada, 1998), Judith (1999), Knight & Aucon (1999), Lankard (1990), McNabb (1996), NYATEP (1996), Poole & Zahn (1993), Ryan & Pritz (1994), SCANS (1991, 1994), Van Loo & Semeijin (2000), Wisconsin (2000) and Zirkle (2002). This instrument includes items to identified essential employability skills such as academic skills, thinking skills, personal qualities, job search skills and entrepreneur skills. Participants were asked to rate how each statement related to their behavioral changes after training by using five-point scale (1 = decrease greatly, 2 = decrease, 3 = unchanged, 4 = increase, and 5 = increase greatly). While, the labor market outcomes measured chosen for the study (dependent variables) were selected from McCaslin’s (1990) framework for evaluating vocational education and combined with ANTA, (2000), Dumbrell (2000), Gabbard (1981), Gasskov (2000), Grubb & Ryan (1999), Kim & Harris (1976), Teh Wei Hu (1980), Willett & Luan (2000) and Ziderman (1997). These variables include three dimension of student’s success in industry after a year graduation (i.e., employment status, job relevancy and earning).

Validity & Reliability. Based on comprehensive review of the literature, preliminary draft of survey was completed. Committee members of supervision (two professor and two associate professor) reviewed the draft survey instruments. Additionally, a pilot test with a group of 20 trainees was conducted to determine problem with the instruments. The instrument was also retested on a small group (n=16) of trainees. The survey instruments was evaluated for reliability using the consistency measure developed by Cronbach & Meehl (quoted in Rezin & McCaslin, 2002). According to George & Mallery, (2001) Cronbach alpha () 0.7 is considered acceptable, while 0.8 is good and 0.9 is excellent. Table 2 below shows, most instruments are good and acceptable.



Table 2: Reliability of Instrument


Components of Instruments

Pilot Test

(n = 20)

Retesting

(n = 16)

Actual

(n = 176)

  • Service quality of training institutes

.8018

.8161

.9298

  • Service quality of industrial practices

.8509

.9374

.8491

  • Employability Skills

.9144

.9011

.9262

Data Collection and Analysis. The data was gathered at two stages. The first stage involved data gathering using self-administered questionnaires at the end of training program (end of May and early June, 2002) before leaving the training institutes. The students and their family background, private costs, service quality of training institutes and industrial practices and acquisition of employability skills data were collected. In the second stage, data was collected through phone interviews and mailed questionnaires a year after graduation (end of June 2003). Response rate at the first data gathering stage was 98 percent (176 respondents), while data collection in the second stage recorded at 89 percent (160 respondents) response rate. The multinomial logistic regression was employed to explain four types of Trainee’s marketability (unemployed, further study, job mismatch and job match) by personal endowments (gender, age & socio-economic status), attributes of training institutes (service quality of training institutes & service quality of practical training), learning outcomes (technical skills & employability skills) and labor market circumstances.





  1. TRAINEE’S MARKETABILITY MODEL


Trainee’s marketablity model designed based on human capital theory (Becker, 1994), job matching theory (Arrow & Spence in Tachibanaki, 1994a,b), vocational training system developed by Al–Khayyat & Elgamal (1997), ANTA (2000), Barnard, Veldhuis & Rooij (2001), Dumbrell (2000), Glewwe (2000), Gray & Warrender (1992), Kim & Harris (1976), Kivinen & Silvennoineen (2002), Lynton & Pareek (2000), McCaslin (1990), educational production function (Hanushek, 1986, 1997, 2000) and employment function from previous research (such Afrassa 2000; Arriagada, 1990; Dyrentruth, 2000; Franz, Inkmann, Pohlmeier & Zimmermann, 1997) as shown in figure 2. The determinant of Trainee’s marketability consists of student characteristics, service quality of the training institutes, service quality of industrial practices, learning outcomes and labor market condition.

Based on Catteral (1984) equation 1.1, we using a multinomial logistic regression to determine the empirical relationship between student characteristics (gender, age and socio-economic status), service quality of the training institutes, service quality of industrial practices, labor market condition and the marketability of the trainees. The model considers four discrete trainee’s marketability; 1) unemployed, 2) further study, 3) employed with unmatched job and 4) employed with matched job. With four discrete Trainee’s marketability, Demaris (1992), Hair, Anderson, Tatham, & Black (1998) and Kleinbaum, Kupper, Muller & Nizam (1998) proposed a multinomial logistic regression as appropriate method.

Students Characteristics

a. Gender

b. Socio-economic status




Service Quality

a. Training institutes

b. Industrial practices


Trainee’s Marketability

a. Unemployed

b. Further study

c. Unmatched job

d. Matched job












Learning outcomes

a. Technical skills

b. Employability skills


Labor Market

a. Rural versus urban

b. Low versus high growth region





Figure 3: Trainee’s Marketability Model


In the model, let Yj* denote the Trainee’s marketability j (= 1, 4). Trainee’s marketability equations;

Yi = a0 + amSm + bmJm + e (1.1)

Yi = Trainee’s marketability

Sm = Students characteristic

Jm = other related factors

e = error term

Yj* = a0 + amjSmj + bmjTImj + cmjLOmj + dmjMmj + ej (1.2)

j* (= 1, 4) 1,

Sm = students characteristic

TIm = service quality

LOm = learning outcomes

Mm = labor market condition


Logit pr (Y = 4) = 0 + 1(S1 x S2) + 2S3 + 3 TI1 + 4TI2 +

5LO1 + 6LO2 + 7LO3 + 8LO4 + 9 M1 + 10M2 (1.3)

S1 = student’s age

S2 = gender

S3 = student’s socioeconomic status

TI1 = service quality of training institutes

TI2 = service quality of industrial practices

LO1 = technical skills

LO2 = academic skills

LO3 = ICT skills

LO4 = English proficiency

M1 = Low versus high economic growth region

M2 = rural versus urban

  1. RESULTS AND DISCUSSION

This section consists of two subsections. At the first section, we describe the background of respondents and the second section; we compare the private costs, service quality of training institutions, service quality of industrial practices, acquisition of employability skills among trainees of single phase versus third phase wireman. Of the 180 trainees, 176 returned questionnaires, which mean for a total usable response rate of 97.8%.

Background of Respondents. A majority of the respondents were male (89%). Only 11.4% of trainees are female. Almost 62% of the trainee’s age between 19–20 years old. Only 6% of the trainees are above than 23 years old.

Table 3: Trainees Profile (n =176)




Frequency

Percentage

Gender








Male

156

88.6

Female

20

11.4

Total

176

100.0

Age








19 - 20 years

109

61.9

21 - 22 years

57

32.4

More than 23 years

10

5.7

Total

176

100.0


At upper secondary education level, students can choose between four streams of education; namely the art stream, the religious stream, the pure science stream and the vocational and technical stream. The vocational training programs admit not only the science and technical students. Table 4 shows that 63% of trainees are non-science and technical students.


Jadual 4: Trainees Academic Background (n= 176)




Academic Streams

Frequency

PercentageArt

100

56.8

Religious

11

6.3

Science

23

13.1

Technical & Vocational

42

23.9

Total

176

100.0



Sosio-economic status of trainees is presented in Table 5. Only 19.4% of the trainees are from poor household. Overall, two third of the trainees have a monthly household income less than MYR1,380.00. Therefore, vocational training programs still being dominated by trainees with lower socio-economic status.


Jadual 5: Monthly Household Income (n = 176)





Monthly household income

Frequency

Percentage Less than MYR460 (poor)

34

19.4

MYR461 – MYR920

63

35.8

MYR921 – MYR1380

26

14.9

MYR1381 – MYR1840

16

9.1

MYR1841 – MYR2300

11

6.3

MYR2301 – MYR2760

9

5.1

MYR2761 – MYR3220

4

2.3

More than MYR3221

12

6.9

Total


175

100.0

Note: Income poverty line is MYR460 per month




Trainee’s Marketability. Overall trainees from this program are marketable. On the unemployment issue the evidence shows (Table 9) that only 8.1% trainees are unemployed. Those who are graduated from these training institutes also able to continue their studies at diploma level. The findings show that 26.9% of trainees continue their study. Most of them (55.8%) continuing their study at the same year after completing their programs, while the other take more than six months after graduation as shown in Table 11. On the matched employment issue, as can be seen in Table 9, matched job of trainees is 48.1%, while unmatched job is 16.9%.


Table 9: Trainee’s Marketability (n = 160)







Frequency

PercentageUnemployment


13

8.1

Further study

43

26.9

Unmatched job

27

16.9

Matched job

77

48.1

Total

160

100.0



Table 11: Further Study (n =43)







Frequency

PercentageSame year after graduation (July & Oct 2002)


24

55.8

More than six month after graduation (Jan, March & Jun, 2003)

19

44.2

Total

43

100.0


On the issue of earnings matched and unmatched job the empirical evidence shows that statistically there is significant difference in starting and current basic salary, as shown in Table 12.The trainees who’s gained a matched job received higher starting and current basic salary than their unmatched job counterparts.


Table 12: Monthly Earnings of Matched & Unmatched Job a Year After Graduation (n=104)

Monthly Earnings


Job Matching

Mean Score

Standard Deviation

t value

Significant value

Effect size (eta2)

Starting basic salary


Unmatched job

MYR623.00

166.00


3.887


.0001**


0.1290

Matched job

MYR822.00

246.95

Current basic salary

Unmatched job

MYR652.00

159.64

4.128

.0001**

0.1431

Matched job

MYR861.00

246.40

Note: ** Significant at p<.05.

Determinants of Trainee’s Marketability

The result of the multinomial logistics regression model is reported in Table 13 below. There are four groups of independent variables; student related factors, training institutes factors, learning outcomes and market conditions. Our reference of dependent variables is matched job.

Empirical evidence showed that employers less preferred young trainees. Age of respondent was significant and inversely related with unemployment. For both genders, the older youth have better chances of finding a job. This finding consistent with priori expectations based on empirical evidence done by Afrassa (2001), Arriagada (1990), Franz et al (1997), Hammer (1993), Le & Miller (2001), Makinen, Korhonen & Valkonen (1999), Ryan (1999, 2000), Stromback, Dockery & Ying (1998) and Tzannatos & Sayed (2000). Younger trainees are less experience (Hammer, 1993; ILO, 2001; van der Velden, Welters & Wolber, 2001), less job search skills (Krahn, Lowe & Lehman 2002) and less social networks in labor markets (Rosenbaum, Kariya, Settersten & Maier, 1990). therefore more vulnerable to be unemployed. Further, younger trainees are not committed to their early career (ILO 2003; Osterman in Hammer 1993) because they want to experiment and try out new social roles in different works setting.

Socio economic status only affects positively the probability of being continuing study for trainees of these programs. Trainees from high socio economic status, significantly more likelihood of participating in further study than those from low socio economic status backgrounds. However, this factor does not affect the probability of being employed, which challenge the previous finding of Athanasou (2001), Franz et al. (1997), Foster (2002). Lamb & McKenzie (2001), Rojewski (1997) and Ziderman (1997).

The service quality of training institutes also affects the probability of trainee’s marketability. In particular, the service quality of ITIs is exerting a negative effect on the probability of being employed unmatched job. The improvement service quality of training institutes will enhance marketability of their trainees. Bishop (1988) has shown that higher quality of institute’s responsiveness to the students needs could increased the job placement of the trainees. Service quality of industrial practices is important element of getting hands-on experience and actual works environment to the trainees. Service quality of industrial practices especially MVTI’s trainees have negative significantly correlated to unemployment, further study and unmatched job. Similarly with previous studies (Cummings, 2002; van der Velden, Welters & Wolbers, 2001) providing better service quality of industrial practices will enhance the employability skills of the trainees and further likelihood of getting matched job.

Employer select the best potential workers based on the requirement of the job and suit to their organization. Empirical evidence confirmed that the employer evaluates the trainee’s technical capability and related employability skills. This result regarding likelihood of getting matched job is consistent with previous studies (e.g. Callan, 2002; Cummings, 2002). However, achievement of academic skills affects positively the probability of getting unmatched job for both trainees of these programs. The reason for this finding is related to enrolment of uncommitted trainees (World Bank, 1990b) and those who are excellent in academic skills have diversity of jobs choice (Bannell 1993). The study found that trainees with computer literacy have additional advantages in reducing the risk of getting unmatched job in the labor market and compatible with previous study by Frazao & Olivera (1999), Green (1999) and Mallough & Kleiner (2001). Prior study shows that trainees lack in English more difficulties in making transition from training institute to full time work than those proficient in English (Lamb & Mackenzie 2001). However, the result of this study found, the proficiency in English has a positive significantly related to participating in further study and consistent with previous finding such Callan (2002), Isengard (2001, 2003), Judith (1999), Leon & Borchers (2002, 1998), NYATEP (1996) and Stevenson & Bowen (1986).

The marketability of trainees who are in the rural area, especially in low economic growth region has higher probability being unemployed and participating in further study. Meanwhile, trainees from rural area at high economic growth region have relative higher probability getting unmatched job. In sum, trainees from rural area tend to have a more difficult time making the transition to matched job than those from urban. The significance effect of labor market circumstances further justifies preceding finding (such Arriagada 1990; Carnoy 1994; Gusstafsson 2000; Isengard 2001, 2003; Lindsay, McCracken & McQuaid 2003; Mallough & Kleiner 2001; Ryan, 1999, 2000; McVicar & Anyadike-Danes 2000; Pereira, Budria, Figueira & Freitas, 2003; Sheehan & Tomilson 1998). The reasons for these differences are related reluctant of trainees to migrate to urban due to attitude & culture factors (Ahamad & Blaug, 1973) and limited matched job creation in rural (Gusstafsson, 2000; Malaysia, 1991a,b; Lindsya, McCracken & McQuaid, 2003; Mallough & Kleiner, 2001; Ryan, 1999, 2000; Varma 1999; Ziderman 1997).

  1. CONCLUSION AND RECOMMENDATIONS



1   2   3   4   5

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