Malaysia: Enterprise Training, Technology, and Productivity (World Bank Studies)

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9

A WORLD BANK COUNTRY STUDY

Malaysia Enterprise Training, Technology, and Productivity

The World Bank United Nations Development Programme Government of Malaysia

Washington, D. C.

Copyright© 1997 The International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street, N.W. Washington, D.C. 20433, U.S.A. All rights reserved Manufactured in the United States of America First printing September 1997 World Bank Country Studies are among the many reports originally prepared for internal use as part of the continuing analysis by the Bank of the economic and related conditions of its developing member countries and of its dialogues with the governments. Some of the reports are published in this series with the least possible delay for the use of governments and the academic, business and financial, and d e v e lopment communities. The typescript of this paper therefore has not been prepared in accordance with the procedures appropriate to formal printed texts, and the World Bank accepts no responsibility for errors. Some sources cited in this paper may be informal documents that are not readily available. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank Group any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address shown in the copyright notice above. The World Bank encourages dissemination of its work and will normally give permission promptly and, when the reproduction is for noncommercial purposes, without asking a fee. Permission to copy portions for classroom use is granted through the Copyright Clearance Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, Massachusetts 01923, U.S.A. Cover photos: Photos used by permission of the Malaysian Government. ISBN: 0-8213-4059-X ISSN: 0253-2123

TABLE OF CONTENTS FOREWORD FROM ECONOMIC PLANNING UNIT, GoVERNMENT OF MALAYSIA

vii

ABsTRACT

Viii

AcKNo�MENTs

oc

AcRoNYMs/ABBREVIATIONs

x

CHAPIER ONE: INTRODUCTION

1 1

The MITP Survey Analytic Approach

4

Overview of Report

6

CHAPTER Two: OvERVIEW OF TRAINING

10

Incidence of Training

10

Sources of Enterprise Training

12

Workers Getting Training by Source

14

Factors Shaping Training Decisions of Firms

17

Findings and Policy Implications

21

CHAPIER THREE: PRODUCTIVITY AND WAGE OUTCOMES

24

Estimating the Productivity Impact of Training

24

Productivity Effects of Training for Different Firms

25

Productivity Outcomes by Skill Group and Training Source

30

Firm-Level Wage Outcomes of Training

35

Compensation Policy and Labor Turnover

38

Findings and Policy Implications

43

CHAPTER FoUR: TRAINING PoLICIEs

46

Constraints on Training: An Employer Perspective

46

The Double Deduction Incentive for Training Scheme

48

Human Resource Development Fund

52

Findings and Policy Implications

61

CHAPIER FivE: TECHNOLOGY, QUALITY AND SKILLS

63

Technological Characteristics of Firms

63

IS0-9000 and Quality Assurance

70

IS0-9000 and Export Orientation

73

New Technology and Changing Skill Needs

77

Findings and Policy Implications

81

iii

CHAPTER SIX: FIRM EFFICIENCY AND ITS DISTRIBUTION

86

Measuring Technical Efficiency

87

Distribution of Efficiency by Firm Size

90

A Profile of Efficient Firms by Size

92

Ownership, Efficiency Difference and FDI Spillovers Findings and Policy Implications

99 105

CHAPTER SEVEN: CoNCLUSIONS AND REcoMMENDATioNs

108

Summary of Main Findings

108

Policy Recommendations

112

ANNExEs 2.1

Probit Estimates of the Likelihood of Formal Training

23

5.1

Introduction of New Technology and Training

83

5.2

Introduction of New Technology and Firm-Level Productivity

85

6.1

Stochastic Frontier Production Functions

107

NOTES

121

REFERENcES

125

TABLES 1.1

Key Variables in the MITP Survey

4

1.2

The MITP Sample and Response rates

5

2.1

Incidence of Training in Manufacturing and by Firm Size

11

2.2

Incidence of Training by Industry

11

2.3

Internal and External Sources of Training

12

2.4

Sources of Training by Firm Size

13

2.5

Workers Trained: Overall and by Firm Size

14

2.6

Number of Workers Trained by Industrial Sector

15

2.7

Workers Getting Formal In-House Training by Skill Group

16

2.8

Workers Trained from External Sources by Occupation

17

2.9

Marginal Effects of the Likelihood of Formal Training

18

3.1

Production Function Estimates by Firm Size

25

3.2

Production Function Estimates by Technology Level

28

3.3

Production Function Estimates by Export Orientation

30

and Ownership

3.4

Production Function Estimates with Predicted Training by Worker Groups

32

3.5

Production Function Estimates: In-house vs. External Training

33

3.6

Production Function Estimates: Training from External Sources

34

3.7

Productivity Effects oflncreased Training Intensity

35

iv

3.8

Wage Model Estimates with Training Indicator and Predicted Values 37

3.9

Wage Effects of Training by Technology, Exports

3.10

Occupation-Specific Wage Effects on Training

38

3.11

Summary Statistics on Quits and Compensation Policies

41

3.12

Compensation and Overall Quit Rates by Training Status

42

3.13

Compensation and Quit Rates by Occupation and

4.1

Reasons for Little or No Training: Overall and by Firm Size

48

4.2

Participation in DDIT by Industrial Sector

50

and Ownership

37

Training Status

43

4.3

Reason Given by Firms for Not Using DDIT

51

4.4

Reason for Not Using DDIT by Firm Size

52

4.5

Use of HRDF by MITP Firms , 1994

53

4.6

Eligible Firms Not Registered with HRDF by Size and Industry

54

4.7

Probit Estimates of Non-Compliance with HRDF

55

4.8

Registerd Firms Not Claiming from HRDF by Training Status

56

4.9

Probit Estimates of Not Claiming from HRDF

57 58

4.10

Training Centers and Training Plans in MITP by Firm Size

4.11

Joint Training Programs in MITP by Firm Size

58

4.12

Pro bit Estimates of Increased Training Under HRDF

60

4.13

Changes in Training Levels Over Past Three Years: Frims Registered with HRDF and Unregisterd Firms

60

5.1

Technology Characteristics by Firm Size and Ownership

64

5.2

Technology Characteristics by Industry

66

5.3

Quality Control and Precision in Production

67

5.4

IS0-9000 Status and Quality Control Systems

71

5.5

IS0-9000 by Firm Size and Ownership

72

5.6

IS0-9000 and Export Orientation

73

5.7

IS0-9000 and Export Propensity by Principal Markets

75

5.8

Introduction of New Technology since 1992

76

5.9

Effects of New Technology on Skill Needs and Employment

76

5.10

New Technology and Changes in Training since 1992

77

5.11

Impact of New Technology on Training

78

5.12

Impact of New Technology on Productivity by Firm Size

80

6.1

Stochastic Frontier Production Function Estimates

89

6.2

Distribution of Efficiency by Firm Size and Economy

90

6.3

Stachastic Frontier Production Function Estimates

6.4

Stochastic Frontier Production Function Estimates with

by Ownership

101

FDI Spillovers

104

v

FIGURES 3.1

Quit rates and Wage Policies: Training and Non-Training Firms

39

5.1

Quality Control Systems by Firm Size and Ownership

68

5.2

IS0-9000 and Exports

74

6.1

Distribution of Efficiency by Economy

91

6.2

Malaysia- Distribution of Efficiency by Firm Size

92

6.3

Technology Attributes of Efficient and Inefficient Firms

93

6.4

Training Attributes of Efficient and Inefficient Firms

94

6.5

Quality Control in Efficient and Inefficient Firms

95

6.6

Quits and Compensation in Efficient and Inefficient Firms

96

6.7

Technology and Training in Past Three Years

97

1.1

Cross-National Enterprise Training Study

BoXES 3.1

Enterprise Training and Productivity in Developing Countries

3.2

Technology Raises the Productivity of Training in Taiwan, China

2 25 27

5.1

Use of External Sources of Technical Support by Firms

69

5.2

Diffusion and Impact ofiS0-9000 in Brazil

70

6.1

Mexico's Proactive Approach to SMI Support

100

6.2

Promoting SMI Networks in Chile

103

vi

FoREWORD FRoM THE EcoNOMIC PLANNING UNIT, GOVERNMENT OF MALAYSIA

The quality of a nation's workforce is the key ingredient to economic growth. Increasing labor productivity and upgrading the skills and flexibility of workers through training and retraining are essential strategies for developing a quality labor force to support sustained growth and economic development of the country. To achieve the status of a fully developed industrialized country by the year 2020, Malaysia has made human resource development one of its major development strategies. The govern­ ment has, and will continue to, play a strong role in strengthening the educational and workforce skills of the population. But the government cannot do it on its own. Most technological innovations now enter Malaysia through industries; furthermore, learning is a lifelong pro­ cess, and relevant skills are best acquired in the workplace. This means that employers­ who have the expertise and technical know-how to train-will have to assume greater re­ sponsibility for training and upgrading the existing skill levels of their employees to meet the skill requirements of new technology. For its part, the government has introduced the Human Resource Development Fund, to encourage and promote enterprise training in industry, as well as complementary research and development (R&D) incentives and policies to assist small and medium industries (SMis). This report, which is based on a large survey of enterprise training, technology and produc­ tivity in the manufacturing sector, is written for policy makers and company executives who have to make critical decisions and design training policies. It provides the first broad­ based look at the existing level and incidence of private sector-led training in Malaysia, and it relates training efforts to corporate strategies on R&D, technology licensing, and quality control, as well as the effects of training on productivity and wages in companies. The analyses reported here can be used to support formulation of more effective public policies and corporate strategies for strengthening industrial training to meet the challenges of sus­ tained economic growth and globalization. It is hoped that this report will encourage the private sector to play a greater role in developing the country's skill abilities to support Malaysia's strategic vision of attaining our Vision 2020. Tan Sri Dato' Seri Ali Abul Hassan b. Sulaiman Director General Economic Planning Unit Prime Minister's Department Government of Malaysia

vii

ABsTRAcT This report presents the findings of a study of enterprise-based training in Malaysia's manufacturing sector which was jointly sponsored by the World Bank, the United Nations Development Programme, and the Economic Planning Unit, Prime Minister's Department. Using data from a survey of 2,200 companies, the study investigates the incidence and productivity outcomes of employer-sponsored training in in-house com­ pany programs and from external training providers, and the role of government poli­ cies and incentives in encouraging private sector training. The study also looks more broadly at technology in firms, their use of quality control systems, and the skill re­ quirements associated with the use of new technology and organizational change . The report concludes that while some firms, especially the larger, more technologically progressive ones and the multi-national companies do provide training, in general, most Malaysian firms underinvest in employee training. It documents the primacy of the private sector as the most important source of in-service training, and suggests that existing public sector training institutions need to become more demand responsive. It demonstrates that training firms are also making complementary investments in new technology, and that the productivity of local firms lags behind that of foreign-owned firms, in large part because local firms invest relatively less in training and new tech­ nology. The report also offers recommendations on improving collection and dissemi­ nation of training information, making training and technology policies more effective, and developing better coordinated, proactive policies to support small and medium industries.

viii

AcKNOWLEDGMENTS This report was prepared as part of the Malaysian Industrial Training and Productiv­ ity (MITP) Study, a joint project of the World Bank, the United Nations Develop­ ment Programme (UNDP), and the Economic Planning Unit (EPU), Prime Minister's Department. The project, directed by Hong Tan, was conducted by several teams -- a World Bank team, including Hong Tan and Geeta Batra; a local team including Professors Rajah Rasiah, Osman Rani, and Anwar Ali from Universiti Kebangsaan Malaysia; and staff from the Human Resource Section of EPU, especially Puan Faizah Mohd. Tahir (Director), Dato Zainol Abidin Rashid (former Director), Yap

Kim Lian, Asri Hamidon, Mohd. Hanafi Sakri and Muhd. Fikri Nawawi. The MITP survey was fielded by Survey Research Malaysia (SRM) under the able direction of Eugene Wong, Cheah Swee Kit and Christine Kwan. The MITP survey relied on a sampling frame provided by the Department of Statistics (DOS), and used a survey instrument developed by the World Bank and adapted for the MITP Study by the project team and SRM. This report was written by Hong Tan and Geeta Batra of the Private Sector Development Department. This MITP Study would not have been possible without the financial support of UNDP, the World Bank Research Committee (RPO

678-39), and EPU. We thank

Ameerah Haq, Neil Buhne, and Selva Ramachandran of UNDP for their support. We gratefully acknowledge the active support of Dato Annuar Ma'aruf, Deputy Director General ofEPU, and the many insightful comments provided by members of the project's Steering Committee, including representatives from EPU (Human Resources, Industry and Social Sections), the Ministry of Human Resources, Minis­ try ofintemational Trade and Industry, Malaysian Industrial Development Author­ ity, Human Resources Development Council, Ministry of Science, Technology and theEnvironment, and the Federation of Malaysian Manufacturers. We benefited from interactions with numerous individuals and both public and pri­ vate sector groups. In particular, we acknowledge the staff of DOS, especially Dorothy Robert, Mat Noh b. Russin, Lok Chung Lee and Tan Hoe Seng for their invaluable assistance with surveys and data; and Mr. Yau De Piyau and his staff at HRDC for data and insights into the operation of the Human Resource Develop­ ment Fund. We gained many insights from interviews with the Penang Develop­ ment Corporation, the Penang Skills Development Center, Standards and Industrial Research Institute of Malaysia, National Productivity Center, and the National Vo­ cational Training Council. Finally, we acknowledge the many companies that con­ tributed their time generously to participate in the MITP Survey; we trust that you will find the research and policy recommendations in the Report useful in formulat­ ing your skills and technology development strategies.

ix

AcRoNYMs/ABBREVIATIONS APITD

Action Plan for Industrial Technology Development

ClAST

Center for Industrial and Advanced Skills Training

DDIT

Double Deduction Incentive for Training

DOS

Department of Statistics

EPU

Economic Planning Unit

FDI

Foreign Direct Investment

GMI

German-Malaysia Institute

GTS

Group Training Scheme

HRDC

Human Resource Development Council

HRDF

Human Resource Development Fund

IKM

Institute Kemahiran Mara

IMP

Industrial Master Plan

ITI

Industrial Training Institute

JMI

Japan-Malaysia Institute

JTS

Joint Training Scheme

MASTIC

Malaysian Science and Technology Information Center

MFI

Malaysia-France Institute

MIDA

Malaysia Industrial Development Authority

Mill

Ministry of International Trade and Industry

MITP

Malaysia Industrial Training and Productivity Survey

MLFS

Malaysia Labor Flexibility Survey

�

Multi-national Corporation

NPC

National Productivity Corporation

NVTC

National Vocational Training Council

OJT

On the Job Training

Q:C

Quality Control Circles

QIP

Quality Improvement Practices

soc

Skill Development Center

SIRIM

Standards and Industrial Research Institute of Malaysia

SMI

Small and Medium Scale Industry

SMIDEC

Small and Medium Industrial Development Corporation

SPC

Statistical Process Control

SRM

Survey Research Malaysia

1NA

Training Needs Analyses

UNDP

United Nations Development Programme

VEf

Vocational Education and Training

VIE

Vocational and Technical Education

YIC

Youth Training Center

X

CHAPTER ONE: INTRODUCTION This report seeks to inform policy discussions on

employers' technology- whether they invest in re­

private sector-led training through a survey offinns

search and development (R&D) or purchase their

and rigorous analyses of their responses.

technology through licensing agreements, whether

The Malaysia Industrial Training and Productivity

relate to training strategies. It characterizes the dis­

they have quality control systems- and how these (hereafter MITP) survey, was fielded to 2,200 manu­

tribution of employers' technical efficiency levels

facturing firms in 1994 and 1995. The MITP sur­

relative to the best-practice frontier, and identifies

vey elicited infonnation on firm-sponsored training,

the key training and technological factors associated

and on a wide range of firms' attributes including

with high efficiency levels.

size, industry, local or foreign ownership, equip­ ment, technology, quality control systems, markets and exports, work force characteristics, wages and

The MITP Survey

other compensation and production. The firm-level data needed to study private sector These data allow us to document, for the first time,

training do not currently exist in Malaysia. A primary

the incidence and characteristics of training in Ma­

data collection effort was deemed necessary to

laysian industry, throughout finns of different sizes,

develop the requisite data from a statistically

ownership, and industrial sector. The data also pro­

repre s e n t a tive s a m p l e of m a n u f a c t u ri n g

vide unique insights into where firms get their train­

enterprises. The MITP project team adapted, to

ing- from in-house training programs, from private

Malaysian conditions, a survey instrument

sector providers, and from different government

developed by the World Bank as part of its cross­

training institutions; which groups of workers get

national study of Enterprise Training and

training and how much; and what are the outcomes

Productivity (see Box 1.1).

of training on firm-level productivity and wages.

Survey Questions This report addresses the issue of whether firms in

Table l.llists the main types of questions asked in

Malaysia under-invest in training. It asks employ­

the MITP survey. It elicits a variety of information

ers about why they do little or no training, and in­

about the attributes of the enterprise; its market and

vestigates the factors which shape employers'

technology, including research and development,

training decisions. It evaluates the efficacy of dif­

technology licensing, equipment, and quality con­

ferent training incentives in promoting in-service

trol systems; its work force structure, skills and

training, and suggests ways of overcoming their limi­

compensation system; its training facilities and

tations.

worker training by source and type; and produc­ tion inputs and outputs.

It investigates the links between training and firm­ level productivity, a critical issue not only for firms

The MITP survey asked detailed questions about

but also for policymakers. It addresses this issue by

employer-sponsored training. The multifaceted

estimating the productivity and wage outcomes of

nature of training makes it notoriously difficult

different kinds of training provided to different

to quantify. It can either be provided informally

groups of workers.

on-the-job through instruction from co-workers and supervisors, or formally through structured

Finally, the report studies the role of new tech­

courses of classroom instruction combined with

nology in raising skill requirements. It looks at

on-the-job training.

2

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Training can take place in company training centers

Employer responses can be used to characterize,

or be provided by a variety of external sources in­

for the first time, the incidence and intensity of

cluding public and private training institutes, in­

in-service training in Malaysia: how much in­

dustry associations, foreign joint-venture partners,

service training goes on in Malaysia; where are

and buyers and suppliers.

employers training their workers, in company training programs or through external training

The content of training can vary, from machinery

providers; which external training sources are

operation to statistical process control to production

most in demand - public training institutions such

management. Training provided to different occupa­

as ITis or IKMs, skil l development centers

tional groups can differ, both in the numbers trained

(SDCs) or advanced training institutes, or other

and in the types and sources of training provided.

private sector providers? They will also allow us

Other dimensions of training- duration, intensity,

to identify which of the firms train and which

cost, and the quality of instruction- are also impor­

do not, and which groups of workers are being

tant, but are poorly measured in the MITP survey.

trained.

Box 1.1 Cross-National Enterprise Training Study

This study was based on five developing economies. Three countries-- Columbia, Indonesia, and Malaysia -- fielded surveys of manufacturing firms based on a World Bank survey instrument. A fourth country, Mexico, used a survey instrument developed jointly by the Secretariat of Labor and Social Welfare and the International Labor Organization (ILO), with input from the World Bank to ensure its comparability with the other surveys. Tawain, China was included in this sample because key training, technology and production information was elicted in its 1986 Census of Manufacturing. It was also attractive both for its large sample size and as a benchmark for the other developing economies. Table 1.2 presents some summary statistics on these economies. The five economies in the sample represent considerable diversity in the level of per capita income, stage of industrialization, and export performance. The World Development Report (1995) classifies Indonesia and Columbia as lower­ middle income economies. In1986, the year for which we have data, Taiwan, China would have been ranked as being higher-middle income by this classification system. These economies experienced strikingly different growth patterns over the 1980s and early 1990s, with stagnant or low groth of per capita GNP and manufacturing output in Mexico and Columbia, and rapid growth in Indonesia, Malaysia and Taiwan, China. Characteristics of Economies in the Enterprise Training Study

Developing Economy

GNP per Capita US$ 1993

GNP Growth 1980-93

Manufactures 1980-93

Export 1980-93

Indonesia

$740

4.2

11.8

6.7

Columbia

$1,400

1.5

3.5

11.0

Malaysia

$3,140

3.5

10.3

12.6

Mexico

$3,610

-0.5

2.1

5.4

Taiwain, China

$3,6878

7.6b

12.7b

6.2b

Notes: For Taiwan, China, a refers to 1986 and b refers to the 1980-86 period. Sources: World Development Report, 1995; Taiwan Statistical Yearbook, 1988. See Tan and Batra, Enterprise Training in Developing Countries, World Bank (1995)

INTRODUCTION

3

The survey included a comprehensive set of ques­

vey, this information provides an unprecedented

tions about the attributes of the enterprise. These

opportunity to explore the critical inter-dependen­

variables- total employment size, research and de­

cies that exist between key strategic variables, and

velopment spending, licensing of technology, for­

that ultimately determine the productivity levels

eign capital participation, exports, use of automatic

and competitiveness of firms in the economy.

equipment, quality control system, education and sex composition of the work force, and labor tum­

The Sampling Frame

over- are critical for understanding why firms train.

The design of the MITP sampling frame reflected several considerations. First, we wanted a large, na­

They allow us to address questions of how skill and

tionally representative sample of manufacturing en­

training requirements are influenced by firm size,

terprises.

by the technology and quality control system used,

representative of the composition of the manufac­

While the overall sample would be

by foreign capital participation either as joint ven­

turing sector, it would be stratified by three firm

tures or as wholly foreign-owned firms, and by the

sizes with larger firms being over-sampled relative

characteristics of its workforce. The survey elicited information on production and compensation, data critical to understanding the eco­ nomic motive for why firms train and how these

to their true weight in the population. A sample size of approximately 2,200 was thought to be ad­ equate for ensuring adequate representation in each industry-firm size cell.

investments in training affect firm-level productiv­

Second, we wanted to build in the potential for link­

ity and the wages paid to employees. Information

ing the MITP

survey

to the 1988 Malaysia

on production inputs and outputs allow us to esti­

LaborFlexibility Survey (MLFS). While its fo­

mate production functions and, after accounting for

cus was on labor market adjustment, the MLFS also

differences in capital, labor and other firm attributes,

elicited relevant information, such as skill com­

to relate investments in training to improvements

position of employees, and adoption of new tech­

in firm-level productivity. This ability to relate training to productivity out­ comes is important since different types and sources of training may have different effects on productiv­ ity, with implications for where and how policymakers and enterprises should allocate their

nology. To this end, two samples of firms were created- respondents of the 1988 MLFS still pre­ sumed to be in existence in 1994, the "survivors" sample; and firms not in the MLFS that began op­ eration between 1989 and 1994, the "new entrants" sample.

training resources. Similarly, the ability to relate

The MITP survey was carried out by Survey Re­

training to wages will allow us to address the issues

search Malaysia (SRM), using a sampling frame pro­

of how the productivity benefits of training are

vided by the Department of Statistics (DOS), and

shared with workers, and if the factors that shape

with participation of the local research team, the Eco­

training changes, (such as adoption of new technol­

nomic Planning Unit (EPU), and the World Bank.

ogy) what are the consequences for income distri­

The fieldwork involved several activities: track­

bution and inequality?

Finally, many variables elicited in the survey are also important in their own right. They represent key elements of private sector firms' innovation,

ing down firms in the DOS list, verifying the de­ mise or continued existence of firms and conducting pilot interviews to field-test and refine the MITP survey instrument.

human resource, organization, and marketing strat­

The survey enumeration was carried out over a pe­

egies as well as important areas of government

riod of four and a half months between December

policymaking. When brought together in one sur-

1994 and May 1995. Questionnaires were mailed to

4

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Table 1.1 Key Variables in the MITP Survey

Firm attributes Firm characteristics

Types of questions asked Single or multi-plant firm, age of enterprise Foreign capital by country of origin Principal product and exports by destination market

Markets and Technology

Prior growth and future growth expectations Capital stock-automation, vintage,

investment plans

R&D as% of sales, any technology licenses Quality control system, IS0-9000 certification Workforce and Compensation

Education and worker attributes by broad occupation Wages, fringe and statutory benefits by occupation Recruitment and labor turnover by occupation

Training system

Training facilities and training specialists Informal OJT vs formal, structured training Numbers trained in-house and mode by occupation Numbers trained by detailed external source Reasons for low investments in worker training

Production and Inputs

Value of output, capacity utilization rate Cost of intermediate inputs and energy

each firm that could be located, accompanied by a

sponse rates being in the Wilayah Persekutuan area.

letter from the EPU explaining the purpose of the

All the analyses in this report are based on a sample

survey, assuring them of confidentiality, and arrang­

of the first 2,200 firms that returned completed ques­

ing for a face-to-face interview after respondents had

tionnaires. In the analyses, no distinction is made

an opportunity to assemble all relevant data. A sec­

between the survivors and new entrants samples. 2

ond letter from the Human Resource Development Council was also sent out to emphasize the impor­

Analytic Approach

tance of responding to the MITP survey. Our analytic approach is motivated by an economic Table 12 . shows the fmal composition of the MITP

model in which firms develop technological capa­

sample and survey response rates by state. Out of

bilities through conscious investments in knowledge­

the 4,583 names provided by DOS, SRM verified

generating activities.

and mailed out or delivered questionnaires to a to­ tal of3,373 firms; of these, a total of2,318 firms

Our definition of technological capability follows

returned completed and usable questionnaires.

Bell and Pavitt (1992), who distinguish between "production capacity" and "technological capability."

The overall response rate-68 percent-is extremely

The former concept measures the capacity of firms

high, especially given the length and complexity of

to produce output at given levels of efficiency, with

the MITP questionnaire. Response rates were some­

existing inputs of capital, labor, and technology; the

what lower for the new entrant sample (66 percent)

latter incorporates the additional and distinct re­

as compared to the survivor sample (71percent), and

sources needed to generate and manage technologi­

varied considerably across states, with the lowest re-

cal change, including specialized managerial and

INTRODUCTION

5

technical skills, knowledge and experience, and in­

firms operating in the local markets \Westphal

ter-firm linkages. Employers with these technologi­

et al, 1984; Pack, 1992).

cal capabilities have a productivity advantage over

•

less capable firms.

ees. Whether importing foreign technology, or using, adapting and redesigning technol­

Technological capabilities can be developed in

ogy through deliberate investments in R&D,

several ways. •

firms can build technological capacity by in­ vesting in the skills and training of the

Firms can invest in their own R&D or pur­

workforce.

chase technology and know-how through li­ censing agreements with foreign firms. The

Several factors are at the heart of why education

evidence from developing countries suggests

and training are so critical to developing a firm's

that reverse engineering, imitation, and modi­

technological capabilities. First, we know that the

fication of foreign technology are often more

productivity advantage of new technology is only

critical to developing technological capabili­

attained through an intensive learning process. There

ties than investments in basic research and

is evidence from technology literature that much of

innovation (Pack, 1992). •

Firms can invest in the skills of their employ­

the productivity gains from introducing a new in­ novation comes from making cumulative small

Firms can acquire relevant and best-practice technology through their links with foreign buy­

modifications in it, essentially through an inten­

ers of exported products as well as from foreign

sive learning-by-doing process (Bell and Pavitt,

Table 1.2 The MITP Sample and Response Rates DOS Sample

Number Surveyed

Response Rate %

State

NE

s

Johor

340

331

247

276

84

79

90

93

83

88

Kelantan

34

45

26

90 45

Malacca

69 38

70

51

50

88 76

91 78

57

37

53

95

91 100

Kedah

Negri Sembi ian

NE

s

NE

s

80

Pahang

38

65

29

60

86

Penang

218

284

186

265

70

76

Perak

150

272

91

224

91

94

Perlis

5

4

4

3

100

100

S elangor

346

517

263

418

63

56

Wilayah Per.

601

358

327

249

14

37

23

37

19

34

100

94

251

249

143

110

71

91

2,450

2,133

1,615

1,757

66

71

Trengganu Sabah/Sarawak

TOTAL

Note: NE = new entrant sample, S =survivor sample. Source: 1995 MITP Survey

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

6

1992; Enos, 1962). The challenge for employers

which its graduates bring to the employer - will

is to motivate and provide workers with incen­

determine how cost effective it is for enterprises to

tives to learn about the new technology.

rely on outside training institutions rather than pro­ viding these skills in-house.

Second, innovative firms are more likely to use highly educated and skilled workers because they

The technology discussion suggests another set of

are more adept at critically evaluating new infor­

determining factors. If the productivity advantage

mation, and thus learn more. Being more efficient

of technology is revealed only through learning by

learners, they are more productive when exposed to

doing, then innovative firms have an incentive to

new and unfamiliar information.

train in-house to internalize the new technology in the skills of its workforce.

Microeconomic case srudies have identified the criti­ cal role of educated workers in the innovation pro­ cess (Setzer,

1974; Pack, 1992). There is a large body

of substantiating evidence for these views.

In contrast, outside training providers are typically not well-prepared to impart skills associated with the most recent, and still evolving, technologies. They play an increasingly important role (and their

Human capital studies, have shown that educated

training services are utilized more intensively by

farmers and workers are more productive in a rap­

firms), when technologies become standardized and

idly changing environment, and thus earn higher

their productive characteristics become well-under­

incomes (Welch,

1970; Tan, 1980; Mincer, 1989).

stood.

There is evidence from industrialized and devel­ oping countries that industries experiencing rapid

These perspectives-on the relative importance of

technological change are more likely to train their

in-house company training when firms are en­

workers, and that these training investments give rise to higher wages (Carnoy,

1990; Lillard and

gaged in innovation-are supported by the research of Lillard and

Tan(1992) and Tan et al (1992). In

1992; Tan et al, 1992). Finally, using frrm­

their study of the sources of worker training in

level data from Taiwan, Aw and Tan (1994) show

high- and low-technology industries in the U.S., they

that worker training has a large positive impact

find that in-house training programs are empha­

on firm-level productivity, and that this effect is

sized when employers are engaged in developing

larger when worker training is accompanied by

new technology.

Tan,

complementary investments in both R&D and for­ eign technology licenses.

These trends may be less pronounced in developing countries, such as Malaysia, where older, and more

To date, however, the literature has been relatively

standardized, technologies are in common use and

silent about the types of training that are most perti­

frrms have limited in-house training capabilities.

nent to technological change. Employers must make decisions not only about whether to train, but also what kinds of training to provide. They may pro­ vide training in-house, or rely on outside training providers, depending upon their in-house training capabilities, and the vocational education and train­ ing (VET) system in the country. The VET system- its ability to meet the skill re­ quirements of enterprises, the quality of technical training provided, and the job relevance of skills

Overview of the Report The report is divided into two broad sections. The

first section, which comprises Chapters Two through Four, focuses on the incidence and productivity out­ comes of employer-sponsored training and on gov­ ernment policies and incentives designed to encourage training by employers. The second sec­ tion, Chapters Five and Six, looks more broadly at

INTRODUCTrON

7

technology in firms, the use of quality control sys­

training are larger for small and medium size firms,

tems and IS0-9000 certification, and the skill re­

who do relatively little training; for firms investing

quirements associated with the use of new

in new technology, especially through technology

technologies and organizational change. The report

licensing; and for export-oriented firms and firms

concludes in Chapter Seven with a summary of

with some foreign capital participation.

findings and policy recommendations. The production function analyses also revealed Chapter Two uses the MITP survey to paint a

marked differences in the productivity effects of

broad brush picture of enterprise training in the

training provided to different groups of workers and

manufacturing sector of Malaysia. It reports sum­

training from different sources. The results show

mary statistics on the incidence of training across

that while skilled worker training leads to gains in

firms of different sizes and industries, and from in­

productivity, training provided to unskilled work­

ternal and external sources. The latter include

ers has no measurable productivity effects.

polytechnics, vocational schools, skill develop­ ment centers (SDCs), advanced training institutes

Among training sources, in-house company training

(ClAST), training institutions sponsored by dif­

is most strongly associated with productivity gains

ferent government ministries (ITis, IKMs, and

except in local firms where training capabilities are

YTCs), and various private training institutes, buy­

weak. The productivity effects of external training

ers and suppliers, joint venture partners, and train­

varies by source for different firms, with SDCs and ClAST being most important for local firms and

ing overseas.

other private training providers for foreign firms. The key finding is that most firms either meet their skill needs in-house or through largely private sec­

This chapter also analyzes the effects of training on

tor providers. With the exception of SDCs and

the average monthly wages of employees. The re­

ClAST, other public training institutions play a rela­

sults show that training leads to higher monthly

tively minor role in meeting the in-service training

wages. However, wage effects are smaller than pro­

needs of private sector firms Though they currently

ductivity effects, suggesting that employers share

.

play a greater role in providing pre-employment

part of the productivity gains from training with

training, in future they will need to become more

their employees. The pattern of wage effects from

demand driven and work closely with the private

training parallels the productivity results, namely, that the wage effects of training are larger in firms

sector.

that make complementary investments in new tech­ Analyses of the determinants of firm training also

nology, in foreign-owned firms, and to a lesser ex­

yielded other findings. They show that firms train­

tent in firms that export. Like the productivity

ing decisions are shaped primarily by firm size, by

results, training provided to skilled workers results

the educational, skill and sex mix of employees, by

in wage gains but not training to unskilled workers.

'

its technology level, whether it exports, foreign own­

Finally, it provides some evidence that firms can

ership, the type of equipment used and whether or

lower job turnover by the employees through high­

not employers emphasize quality control. Chapter 1hree analyzes the productivity impacts of formal, structured training provided by employers. Using a production function framework, it shows

wage policies. Productivity gains from increased training that comes from greater job retention of trained workers can offset higher wage costs. Chapter Four motivates the discussion of training

that training has a positive impact on raising the pro­

policies by reporting employer perspectives on why

ductivity levels of firms. Furthermore, it demon­

they do little or no training. This reveals that while

strates that the beneficial productivity impacts of

most firms do not train because of the low skill re-

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

8

quirements of relatively simple, standardized tech­

to invest in R&D and technology licenses than

nologies used, a large number of firms, small and

wholly foreign-owned firms of comparable size,

medium size employers in particular, also cited high

which may reflect the greater reliance of wholly­

labor turnover, lack of knowledge about how to train,

owned subsidiaries on the technology stock and

and limited resources, as reasons for not training.

R&D of the parent MNC.

These latter responses, coupled with evidence from

This chapter also touches on IS0-9000, a voluntary

previous chapters about the low incidence of train­

standard of the International Standards Organiza­

ing and high potential returns, suggest that many

tion that Malaysia has adopted. Over ten percent

Malaysian firms under-invest in training, and that

of firms in the MITP survey had some level of

several market failures pose important constraints

IS0-9000 certification, and 33 percent expected

on training. The chapter then presents the results of

to be certified within the next three years. How­

detailed analyses of two training policies designed

ever, IS0-9000 adoption will still be relatively

to encourage employers to train-the Double Deduc­

low in micro, small and medium firms, and should

tion Incentive for Training (DDIT) and the Human

be an important area of focus-both in terms of dis­

Resource Development Fund (HRDF).

semination a n d technical assista n c e - f o r policymakers. The analyses indicate that firms

It describes the limited use of the DDIT by firms

with IS0-9000 certification, or those actively

and the reasons why many firms did not use this

seeking it, are more successful in exporting to in­

training incentive. It reports some teething problems

dustrialized country markets.

with the HRDF, including what appears to be seri­ ous noncompliance to register and contribute to the

Chapter Six draws together the analyses of training

HRDF, and failure to take advantage of training re­

and technology by investigating firm-level tech­

imbursements. It is too early to judge the efficacy of

nical efficiency and its distribution. Using a fron­

HRDF, but there is some evidence that it has indeed

tier production function framework, it estimates

promoted training and skill upgrading among the

of how far each firm is from "best practice" tech­

sample of firms that have registered with the Hu­

nology, and what factors determine its level of

man Resource

Development Council.

efficiency. The overall results echo many of the main findings reported in previous chapters­

Chapter Five shifts the focus to use of new technol­

younger, export-oriented firms, firms that employ

ogy, quality control systems, and IS0-9000 certifi­

a more educated workforce, and those that pro­

cation in Malaysian firms, and their implications

vide training, skilled worker training in particu­

for changing skill requirements. It provides a broad

lar, are more efficient.

overview of research and development, technology licensing, use of testing equipment, automation, and

The efficiency estimates are used to characterize

equipment age among furns by size, local and foreign

the size distribution of efficiency in the MITP

ownership, and industry.

sample. The results show that SMis are not nec­

While the MITP survey reveals more private sector

efficient than many larger firms. Their low aver­

R&D than the 1992 MASTIC survey, its R&D esti­

age efficiency level, compared to larger firms, is

mates are still relatively low compared to other coun­

due to the fact that a high proportion of SMis have

essarily inefficient -some SMis actually are more

It shows marked differences in these

low efficiency and a high proportion of larger

technology indicators across firms, withjoint ven­

firms have high efficiency. If SMis are not in­

tries.

tures and wholly foreign-owned firms being more

herently inefficient, then it follows that their ef­

technologically advanced as compared to local furns.

ficiency levels can be improved through policy

It finds, however, that joint ventures are more likely

interventions.

INTRODUCTION

9

Potentially important areas for policy are sug­

Finally, Chapter Six reports some preliminary analy­

gested by the profile of efficient firms by size.

ses of efficiency spillovers to local firms from linkages

Highly efficient firms tend to have technology li­

with joint ventures and foreign firms. The results in­

censes but not necessarily R&D; they export and/

dicate that a higher foreign presence is associated with

or; have some foreign capital equity; they pro­

efficiency improvements for local firms and that part

vide formal structured training to both skilled

of these gains come from the R&D done by joint ven­

and unskilled workers, and do not rely only on

tures, and part comes from the training that wholly for­

informal OJT. Efficient firms emphasize qual­

eign-owned firms give their employees.

,

ity, especially statistical process control; they use precision measuring instruments and do not rely

The report concludes with Chapter Seven. It sum­

on visual inspection and are more likely to be

marizes the main findings and draws out their policy

seeking IS0-9000 certification. Highly efficient

implications in five areas: (i) collection and dissemi­

firms have formed work organizations that seek

nation of training information; (ii) expanded role

to reduce job turnover, using high-wage policies

of education and training institutions; (iii) more ef­

and compensation that includes severance pay,

fective training policies; (iv) technology diffusion

profit-sharing and bonuses to attract and retain

and promotion; and (v) better coordinated and pro­

workers.

active SMI policies.

CHAPTER Two: OVERVIEW oF TRAINING In this chapter, the MITP Survey is used to paint a

training nor formal training; those that rely exclu­

broad picture of enterprise training in the manufac­

sively on informal on-the-job training from co-work­

turing sector. We describe the incidence of training

ers and supervisors; and those that provide formal

by firm size and industry. We present estimates on

training, either in-house or from external sources.

training provided by employers and by a variety of external training institutions, both in terms of the

The figures on training are adjusted using sampling

proportions of employers using each training source

weights constructed from the 1988 industrial survey

and in terms of the number of workers trained. We

whenever aggregate figures are reported for the

use employer responses to gain insights into why a

manufacturing sector as a whole or by industry .1 The

substantial proportion of firms provide little or no

data are not weighted when figures are reported by

formal training to their employees. Finally, we esti­

size since the MITP survey is already stratified by

mate regression models to identify the important fac­

size. For the purposes of this report, we define four

tors which shape company decisions to train

firm size categories-micro firms (with 15 or fewer

different groups of workers and to rely on in-house

workers), small finns (with 16-100 workers), medium

versus external training providers.

firms (101-250 workers) and large firms (with more than 250 workers).

Incidence of Training

Table 2.1 shows the incidence of enterprise-spon­ sored formal training for the manufacturing sector as

The MITP Survey elicited a wealth of information

a whole and by firm size. Two points stand out. First,

on training. It asked a limited number of questions

a very high fraction of firms either provide their

about informal on-the-job training provided by co­

workers with no training (32 percent), or they rely

workers and supervisors, and detailed questions

exclusively on informal, on-the-job training (48 per­

about formal, structured training-the number of work­

cent). Only 2 1 percent of all employers provide

ers getting formal training over the past year, by

their workers with any formal, structured training.

broad occupational group and by source of training.

Secondly, there are very marked differences in the

It distinguished between formal training provided

incidence of training by firm size. The proportion

in-house by the employer, and formal training ob­

of firms that do not provide any training is highest

tained from a variety of external training institutions,

among the micro finns (34 percent) and lowest among

both public and private. The public training institu­

the largest size firms (four percent). Conversely,

tions included polytechnics, vocational and techni­

formal training is most common among the large finns

cal schools, advanced skills training institutes

(71 percent) and lowest among the smallest firms (10

(ClAST), Industrial Training Institutes (ITI), Insti­

percent). Most firms which provide formal training

tute Kemahiran Mara (IKM), Youth Training Cen­

also have informal on-the-job training, a point that is

ters (YTC), Skill Development Centers (SDC), and

apparent from a comparison of the last two rows of

other government institutes. The private training

Table2.1.

sources include buyers and material suppliers, joint­ venture partners, and private sector training institutes.

Table 2.2 presents the corresponding estimates of

We can broadly characterize training incidence by

reveal considerable cross-industry variation in the

classifying firms into three groups: thosethat provide

proportion of firms that do no training and those that

no training of any kind, neither informal on-the-job

provide formal training.

training incidence by 16 industrial sectors. They

OVERVIEW OF TRAINING

11

First, consider the industries where large numbers

electrical machinery, iron and basic metals, trans­

of firms do no training. These include such tradi­

port equipment, textiles, apparel, and rubber indus­

tional domestic-oriented industries as wood and fur­

tries are relatively training-intensive, with over 35

niture, paper and printing, glass and pottery,

percent reporting formal training.

fabricated metals, machinery, and food products where only 10-25 percent of firms provide formal

The high proportion of firms providing formal

training to their employees. On the other hand, the

training in electrical machinery, transport equip-

Table 2.1 Incidence of Training in Manufacturing and by Firm Size Mean Characteristics

Overall

Micro

Small

2,200

247

959

%Firms not training

31.8

33.6

14.8

5.2

3.7

% Firms with only informal training

47.6

56.3

58.7

43.6

25.6

% Firms doing formal training

20.7

10.1

26.5

51.2

70.7

% Firms formal & informal training

17.0

6.9

24.5

48.4

66.5

Number of firms with training data

Notes:

Medium

Large

535

454

Overall estimates are weighted; estimates by firm size are not weighted micro

=

15 or fewer workers;

small= 16-100 workers; medium = 101-250 workers;

large= over 250 workers. Source: 1995 MITP Survey

Table 2.2 Incidence of Training by Industry Industry

All Industries

#Firms

%Firms

with Training

not

only Informal

Data

Training

Training

%Firms

%Firms with Formal Training

2,195

31.8

47.6

20.7

265

34.2

40.4

25.4

Beverages & tobacco

152

30.0

68.5

1.5

Textiles

107

23.6

17.7

58.7

Food

Apparel

116

13.8

49.2

37.0

Wood & Furniture

306

58.1

31.1

10.7

Paper & Printing

126

55.5

26.8

17.6

90

16.9

57.5

25.6 35.1

Chemicals Rubber

131

32.1

32.8

Plastics

133

10.4

77.5

12.1

Glass & Pottery

143

36.4

42.2

21.4

Basic Metals Fabricated Metals Machinery

71

6.1

30.9

63.0

110

43.3

38.8

86

38.8

45.9

17.9 15.3

213

1.8

50.2

Transport equipment

78

9.8

41.1

48.0 49.1

Other Industries

73

23.9

68.1

7.9

Electric Machinery

Note: Estimates by industry are weighted using 1988 Industrial Survey weights. Source: 1995 MITP Survey

12

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

ment, iron and basic metals is not surprising since these capital-intensive industries tend to be quite technology-intensive.2 Electrical machinery, along with rubber and apparel are also major export­ oriented industries, and we speculate that export­ ers have greater incentives to train so as to produce high-quality products for international markets.3 In summary, these data appear to substantiate con­

ventional beliefs about training in Malaysia, namely, that the larger firms are more likely to train their employees than smaller employers, and that enter­ prise training is related to capital intensity, technol­ ogy and export-orientation of industries. However, what is especially striking is the presence of large numbers of firms without any system of worker train­ ing at all, formal or informal. This shouldbe of con­ cern to Malaysian policymakers, given the critical role that skills play in technology acquisition and de­ velopment, and their presumed beneficial effects on productivity and wages. (These links are quantified in Chapters Three and Five.) Also worrisome is the high proportion of employers (48 percent) that rely exclusively on informal on­ the-job training (OIT). Informal OJT, while an inte­ gral part of the skill acquisition process, typically involves fairly basic skills such as familiarizing new hires with the firm's equipment and operating pro­ cedures-the "how to" -rather than the "why. " It excludes the higher-level problem-solving skills that can come from structured training courses grounded in theory. Both kinds of skills are needed; indeed, as noted earlier, most firms that provide formal training also train informally. What is of concern is that firms which rely only on informal training develop few of the critical problem-solving skills needed to acquire and master new technologies and improve productiv­ ity. This fact, coupled with evidence indicating that informal OJT has no measurable impact on wages or fum-level productivity,4leads us to focus on formal structured training in the remainder of this report.

Sources of Enterprise Training

Table 2.3 shows the different ways in which firms provide formal in-service training. It distinguishes between formal in-house company training and external sources of training, both public and pri­ vate. Of the 21 percent of employers that train formally, about an equal proportion of them ( 13 percent) use in-house resources as external train­ ing providers. The bottom panel of Table 2.3 shows the relative importance of each external training source as re­ ported by enterprises. Conditional on the employer providing external training, the most commonly cited external sources are private training insti­ tutes (34.9 percent), followed by Skills Develop­ ment Centers (25. 8 percent), A dvanced Skills Training Institutes (21.3 percent), and their buy­ ers and material suppliers (11 percent).

Table 2.3 Internal and External Sources of Training Percentage of Firms

Source of Training• %Any Formal Training

b

20.7

% Internal Formal Training

12.6

% External Formal Training

13.0

External Sources of Training c Polytechnics Vocationalffechnical Schools

4.0

Advanced Skills Training Institutes

3.2 21.3

Skills Development Centers (SDC)

25.8

Institute Kemahiran Mara (IKM)

1.2

Industrial Training Institute (ITI)

5.3

Youth Training Centers (YTC)

0.5

Other Government Institutes

8.2

Joint Venture Partners Buyers/Material Suppliers Private Training Institutes Overseas Training

3.6 11.0 34.9 4.6

The numbers are weighted using 1988 Industrial Survey weights. Includes firms that train formally either inside the firm or from external sources. Conditional on doing external training. Source: 1995 MITP Survey

OvERVIEW oF TRAINING

13

It is plausible that these are external providers with

skills, not for the intermediate or advanced-level skills

capabilities to flexibly provide higher-level skills

that are needed after entering employment.

training to firms. The high proportion of firms that report using skill development centers (SDCs) is strik­

For policymakers, the issue is whether these public

ing, especially since most of them (other than the

institutions should continue to limit their training ac­

Penang SDC) were only established in the past three

tivities to pre-employment training, or whether they

years. The least commonly cited external sources

also have a role to play in post-employment skills

of training are government-run training institu­

upgrading. One aspect of this issue-the limited in­

tions-theYouth Training Centers (0.5 percent), IKM

service training provided by these institutions-can

institutes

be studied (see C hapter Three); however, the

(1.2 percent), vocational and technical schools (3.2 percent), and other government insti­ tutes (8.2 percent).

broader issue can only be addressed by a different study and is beyond the scope of this report.

The relatively small role of government training in­

Table 2.4 disaggregates the different sources of train­

stitutes reflec� their focus on pre-employment train­

ing by firm size. The top panel shows the propor­

ing, not in-service training that is the subject of the

tions of firms that provide formal training in-house

survey. The exceptions are the public agencies in

and externally. In general, the use of both training

the "other" category, such as SIRIM and NPC which

sources rises with firm size, with a higher proportion

provide a variety of training and other services di­

of small and medium firms training in-house than us­

rectly to the private sector. 5 This orientation towards

ing external training providers.

pre-employment training is borne out by data on National Vocational Training Council (NVTC) ad­

The bottom panel shows, for the firms that train ex­

ministered trade tests taken by graduates from dif­

ternally, the proportion of employers citing each

ferent public training institutes. Most YTC, m, and

external source of training. (Note that figures for the

IKM graduates are tested for competencies in basic

micro firm size group are not reliable since less than

Table 2.4 Sources of Training by Firm Size Source of Training

Micro

Small

9.1 5.2 5.2

18.2 13.5 7.6

% Firms training formally % Firms training in-house % Firms training externally

Medium

Large

44.7 31.7 27.0

70.6 53.6 51.4

5.1 3.1 6.3

9.3 4.2 19.9

14.9

28.8

2.3 11.0 1.2* 22.7 9.8 25.1 44.3 12.9

5.1 18.2 2.1 27.1 11.9 25.0 53.0 21.2

External Sources of Training•

Advanced Skills Training Institutes

12.5* 12.5* 12.5*

Skills Development Centers

25.0*

Polytechnics Vocationai!Technical Schools

2.0* 0.0 8.2* 10.2

Institute Kemahiran Mara (IKM)

0.0

4.1*

Industrial Training Institute (ITI)

12.5* 0.0 0.0 0.0 25.0* 25.0* 0.0

0.0 2.0* 20.4 10.2 24.5 28.6 8.2

Youth Training Centers Other Government Institutes Joint Venture Partners Buyers/Material Suppliers Private Training Institutes Overseas Training Conditional on doing external training. *

very small sample sizes (3 or less observations).

Source: 1995 MITP Survey

14

ENTERPRISE TRAJNJNG, TECHNOLOGY AND PRODUCTIVITY

Table 2.5 Workers Trained, Overall and by Firm Size SourceofTraining

NumberofWorkers Trained Overall

Micro•

Small

Medium

Large

Any formal training

195,8 94

35,08 4

13,917

34,5 4 9

112,343

Internal formal training

167,6 14

28,716

12,396

27,286

99,2 14

28,279

6,367

1,52 0

7,262

13,128

External formal training

% workers with formal training

21.7

8.9

10.4

13.2

29.5

% workers with internal training

18.6

7.2

9.2

10.5

26.1

% workers with external training

3.1

1.7

1.1

2.8

3.4

External Training Sources Polytechnics

647

154

24

121

Vocational schools

477

154

0

92

230

Advanced Skills Training Institutes

2,197

1,255

39

198

703

Skill Development Centers

347

7,611

3,844

278

488

3,000

ITIs

833

154

0

232

446

IKMs

275

0

84

113

77

96

0

18

34

43

Other government institutes

1,605

0

160

697

747

Buyers & suppliers

1,792

22

312

548

909

Joint venture partner firms

1,508

0

213

355

938

10,359

782

321

3,972

5,283

872

0

67

405

399

YTCs

Private training institutes Overseas training

Estimates not reliable because of small sample size. Note:

Estimates of numbers trained are weighted using 19881ndustrial Survey weights.

Source: 1995 MITP Survey

five percent of them rely on external training pro­

We estimate there figures by using the firm's responses

viders.) The table clearly shows variation in the use

about the numbers of workers trained from each

of different external sources by firms of different

source, and inflating them using size-based weights

size. Training provided by private institutes contin­

constructed from the

1988 Industrial Survey. 6

ues to be the single most commonly cited external train­ ing source.

We caution that these are rough estimates, given changes since 1988 not only in the number of firms

Among the other sources, both small and medium

but also their composition. The estimates for micro

firms are most likely to cite training from buyers­

enterprises are likely to be quite imprecise, given

materials suppliers and from other government insti­

their small numbers in our sample (153 firms) and

tutes. Large firms are most likely to cite SDCs, other

correspondingly large weights assigned to them.

government institutes, buyers and suppliers, ad­

We are much more confident of the estimates for

vanced skills training institutes, and to a growing ex­

the small, medium, and large firms where our

tent, ms as well.

sample sizes are larger. We note that this proce­ dure yields an estimate of the manufacturing workforce of just under one million

Workers Getting Training by Source

which is to be expected since

(900,493), 1988 sample weights

are used. The number of workers trained provides another perspective on the relative importance of the differ­

Table 2.5 presents estimates of the number of work­

ent in-house and external sources of formal training.

ers receiving formal training by source in the manu-

OVERVIEW OF TRAINING

15

facturing sector, and separately by four firm size

firms or on numbers of workers trained. Both mea­

categories.

sures point to the dominant role of private training

First, consider the overall estimates. They suggest

that 196,<XX> workers received fonnal training in 1993, of which 168,000 were trained in-house and just

28,CXX> were trained by external providers. As a share of the total workforce, these represent 21.7 percent for any formal training, 18.6 percent for in-house training, and 3 .1 percent for external training.

institutes, SDCs, and advanced skills training insti­ tutes which provided training for 10,359 workers, 7,611 workers, and 2,197 workers, respectively. The numbers of workers trained by buyers and materials suppliers and partner firms are as large as the numbers trained by "other government training institutes," and considerably larger than the indi­ vidual contributions of lTis, IKMs, YTCs, polytech­

The overall results are comparable to those based on the proportion of finns that train, but the mix of in­ house and external training differs widely. While an equal proportion of firms report using in-house and external training sources (13 percent), the. esti­ mates based on workers trained suggest that fums are giving in-house training to a significantly larger number of employees than they are sending outside for training.

nics, and public vocational and technical institutes. Table 2.5 also presents separate estimates of the num­ ber of workers trained by fum size. The estimates for micro fums are likely to be unreliable, and will not be emphasized in the following discussion. For the other firm sizes, these worker-based estimates reinforce the points made earlier using utilization rates of finns . For small firms, training provided by private training institutes, buyers and materials sup­

The relative importance of each external training source is broadly comparable irrespective of whether estimates are based on utilization rates of

pliers, and SDCs are of roughly equal importance. For medium and large firms private training insti­ ,

tutes have by far the most significant role in external

Table 2.6 Number of Workers Trained by Industrial Sector Number of Workers Trained Industry

Percent of Workforce

Any

Internal

External

Any

Internal

Formal

Formal

Formal

Formal

Formal

Formal

Training

Training

Training

Training

Training

Training

External

Food

6,331

4,348

1,982

2.9

2.0

0.9

Beverages & tobacco

1,661

1,307

1.0

11,807

11 '180

353 626

0.7 9.9

0.2 0.5 0.1

Textiles

10.4

Apparel

8,549

8,395

153

3.8

3.8

Wood & Furniture

9,773

8,809

964

22.8

20.6

2.2

Paper & Printing

4,139

3,259

880

1.9

0.5

1.5

0.8

3.0

1.2

Chemicals Rubber

4,157

2,705

1,451

2.4 2.3

10,055

7,251

2,803

4.2

7,779

5,871

1,907

8.0

6.0

2.0

Glass & Pottery

10,653

9,358

1,294

33.2

29.2

4.0

Iron & Basic Metals

32,082

25,683

6,399

73.4

58.8

14.6

6,694

6,020

673

3.2

2.9

0.3

Machinery

11 '129

10,193

936

6.8

6.2

0.6

Electric Machinery

Plastics

Fabricated Metals

58,730

52,701

6028

38.8

34.8

4.0

Transportation

5,301

4,319

982

4.7

3.8

0.9

Other industries

7,046

6,207

839

3.5

3.1

0.4

Note: Estimates are weighted using 1988 Industrial Survey weights. Source: 1995 MITP Survey

16

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Table 2. 7 Workers Getting Formalin-House Training by Skill Group Occupational Group

Number

Total Number

Percentage

Trained

of employees

Trained

Supervisors

17,109

67,713

25.3

Technicians

15,105

47,396

31.9

Skilled Production Workers

76,074

462,855

16.4

Unskilled Production Workers

59,327

443,051

13.4

Note: Estimates weighted using 19881ndustrial Survey weights Source: 1995 MITP Survey

training, though SDCs, buyers and suppliers, part­

They suggest that, on average, a higher proportion of

ner firms, and other government institutes are also

technicians (32 percent) and supervisors (25 percent)

responsible for training a sizeable number of work­

are trained as compared to production workers (13-16

ers. Particularly striking is the heavy use of SDCs

percent); however, skilled production workers are

by the largest furns which sent about 3,000 workers

more likely to be trained (16 percent) than unskilled

for training in SDCs and 5,283 workers for training

production workers (13 percent).

in private training institutes. Though not reported in Table 2.7, the data indicate that In Table 2.6, we report more aggregated statistics on

production workers are also less likely to get external

the number of workers trained by industrial sector,

training (14 percent) as compared to non-production

as well as their share of the workforce in each indus­

workers (28 percent).

try. The latter measure is particularly significant given the recommendation of the Industrial Master Plan

In Table 2.8, we report the numbers trained by ex­

(IMP) that employers provide training to 10 percent of their work force (Mill, Review ofthe IMP, 1994)_7

production workers, as well as the proportion getting

ternal source of training for production and non­ training in each occupation. The figures show that

By this yardstick, it appears that the target of 10 per­

private training institutes and SDCs are the most im­

cent training has only been achieved in five out of

portant external sources of training for both groups.

the 16 industrial sectors under consideration-iron and

However a higher proportion of non-production

basic metals (73 percent), electric machinery (39 per­

workers get training from private training institutes

cent), glass and pottery (33 percent), wood and fur­

(52 percent) than from SDCs (18 percent), while pro­

niture (23 percent), and textiles (lOpercent). In the

duction workers are more likely to get training at

other industrial sectors, the proportion of the work force

SDCs (31 percent) than at private training institutes

getting training is considerably lower. The indus­

(26 percent).

tries with the lowest figures (less than three percent

trained) include food products, beverages and tobacco, paper and printing, and chemicals.

Other key external sources for both groups of work­ ers are buyers and suppliers-who provide the train­ ing to meet their product requirements or to use

Which workers are getting training? Table 2.7 pre­

their equipment-and advanced skills training insti­

sents estimates of the numbers trained in four

tutes. As before, few workers get training at ITis,

broad occupational groups-supervisors, techni­

IKMs, youth training centers and vocational schools,

cians, and skilled and unskilled production work­

reflecting the primary orientation of these public

ers-as a proportion of the total number of employees

training institutions to pre-employment training in

in the relevant occupation.

basic skills.

OvERVIEW OF TRAINING

17

labor, and whether the firm is unionized. Two-digit

Factors Shaping Training Decisions of Firms

industry dummy variables control for other industry differences.

With this overview of training as background, we

In the discussion that follows, we summarize the ef­

now turn to a more formal analysis of the factors that shape firms' decisions to provide their employees

fects of the most important regressors on the likeli­

with formal structured training, and whether the de­

hood of the employer providing any formal training,

terminants of training differ by skill group and by

by skill group, and by training source. The coeffi­

training source. To address these issues, we esti­

cients estimated by the probit model (these are re­

mate separate probit regression models for any for­

ported in Annex Table 2.1) provide insights into the

mal training, training for production workers and

statistical significance of each variable and the direc­

non-production workers, and in-house versus ex­

tion of its effects on training. However, they cannot

ternal training.

be interpreted as marginal effects because of the non-linear nature of the probit model.

The likelihood of an employer providing each type of training is hypothesized to depend on the relative

To facilitate interpretation, we report instead the

costs and benefits. It equals one if the present value

marginal effects of the probit model evaluated at the

of training exceeds its cost, and equals zero other­

sample means of each variable. The marginal ef­

wise. The net benefits of training (benefits minus

fects from different probit models are presented to­

costs) are not directly observed, but are thought to

gether in Table

be related to a set of observable attributes of the

regressors across the different training measures.

2.9 to facilitate comparisons of

employer. These firm attributes include firm size; worker characteristics such as educational attainment

Firm Size

and skill mix; its level of technology as reflected in

Table 2. 9 confirms that training probability is strongly

its R&D expenditures and its purchases of know­

related to firm size. Relative to micro firms (the omit­

how; exporting, and foreign capital participation; or­

ted size category), small, medium and large firms are

ganizational factors such as the degree of automation,

14, 35 and 53 percent more likely to provide any

use of quality control methods, employment of female

formal training. The importance of firm size, con-

Table 2.8 Workers Trained from External Sources by Occupation Production Workers External Source of Training

Polytechnics Vocational/Technical Schools Advanced Skills Training Institutes Skills Development Centers Institute Kemahiran Mara (IKM) Industrial Training Institute (ITI) Youth Training Centers Other Government Institutes Joint Venture Partners Buyers/Material Suppliers Private Training Institutes Overseas training Note: Source:

Estimates weighted using

1995 MITP Survey

Non-Production Workers

Number

Proportion

Number

Proportion

Trained

Trained

Trained

Trained

2.4 2.1 9.6 31.3 1.2 3.4 0.5 4.1 6.9 10.0 25.7 2.6

219 99 503 2,065 56 228 2 876 294 644 5,810 406

1.9 0.9 4.5 18.4 0.5 2.0 3.6 7.8 2.6 5.7 51.8 3.6

429 379 1,649 5,546 219 605 95 726 1,214 1,767 4,550 466

1988 Industrial Survey weights

18

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

trolling for the other correlates of training (includ-

The effects of firm size on training probability dif-

ing level of technology), may reflect scale econo-

fer by skill group: compared to micro firms, the

mies in training provision, the greater access of large

likelihood of training for skilled workers in large

firms to resources for training, and unobserved em-

firms rises to 61 percent as compared to only 43 per-

player attributes associated with improved manage-

cent for unskilled workers, a trend evident in the

ment and training capabilities.

simple tabulations reported earlier. Larger firms are Table2.9 Marginal Effects on the Likelihood of

Formal Training Estimated from a Probit Model

Independent Variable

Any

In-house

External

Skilled

Formal

Formal

Training

Worker

Worker

Training

Training

Training

Training

0.138 IJI

Small Firm Size (16-100 workers)

(0.067) 0.348�1

Medium Firm Size

(0.065)

(1 01-250 workers)

0.529�

Large Firm Size (over 250 workers)

(0.070)

Mean education

0.024�

of the workforce

(0.007) 0.006 �

Percent of skilled workers

(0.001) 0.140al

Invests in R&D

(0.030) 0.071 a!

Foreign capital participation Exports

Proportion of female

0.026 a! (0.006)

0.002 �

0.005 a!

0.003 a!

0.006 �

(0.0008)

(0.0007)

(0.0008)

(0.0008)

0.135-'!1 (0.026) 0.080 a!

0.095 � (0.023)

0.112 � (0.026)

-0.007

0.019

(0.020)

(0.023)

0.151 a! (0.027) 0.080 �

0.008

0.031

0.001

(0.024)

(0.022)

(0.024)

(0.026)

(0.025)

0.001

0.001 �

0.001 �

0.004

(0.001)

(0.0002)

(0.0003)

(0.003)

0.101 a! (0.023)

0.0431J/

0.128 a!

(0.020)

(0.023)

0.084 a! (0.024)

0.024

0.008

0.013

-0.051

0.005

(0.047)

(0.041)

(0.037)

(0.042)

(0.043)

(0.031) -1133.40

Log (likelihood)

0.025 a! (0.081)

0.430 � (0.071)

-0.004

0.060..£1

Unionization

0.016 a! (0.005)

0.606 � (0.094)

0.224 � (0.060)

0.010

(0.026)

workers

0.028 � (0.006)

0.488 � (0.080)

0.395 � (0.081)

0.085 (0.061)

(0.027)

0.103 a!

Use of quality control methods

0.426 � (0.074)

0.253 � (0.062)

0.168 cJ (0.086)

(0.024)

(0.0003)

automatic machinery

0.261 � (0.063)

0.059 (0.063)

(0.027)

0.001

%Value of

0.129 IJI (0.065)

Unskilled

0.027 (0.026) -1090.86

0.059 � (0.024) -918.23

0.0661Ji

0.020

(0.027)

(0.028)

-970.59

-1102.68

a= Significant at 1% b=

Significant at 5%

c =Significant

Note:

at 10% level

Numbers in parantheses are standard errors. Omitted firm size is micro enterprises with 15 or fewer workers. Age of firm and multi-plant status included but were not statistically significant.

Source: Annex Table 2.1.

OvERVIEW

OF

TRAINING

19

also more likely to use both in-house and external

both skill groups. Thus, unskilled workers enjoy an

training sources than their smaller counterparts.

externality by working in a workplace with a high proportion of skilled workers.

Education and Skill Mix The training effects of education stand out. The

The Firm's Technology

results indicate that employers are more likely to

The results provide strong evidence that skill and

provide formal training for all groups and from all

training requirements are shaped by the firm's tech­

sources the more educated are their workers. A

nology. Firms that invest in research and develop­

one year increase in the education of the workforce

ment (R&D) are about 10-15 percent more likely to

(the mean in the MITP sample is 8. 7 years of school­

train formally than firms without R&D.

ing) is associated with a two to three percent higher probability of training. The significant positive rela­

The results, by skill group, suggest that while R&D

tionship is strong evidence that the two kinds of hu­

firms are more likely to train both production and

man capital-education and training-are highly

non-production workers than firms not doing R&D,

complementary. Educated workers are better

the likelihood of their training production workers is

learners and thus benefit more than less educated

actually higher.

workers from training. A higher level of work force education also raises the probability that the firm will train in-house relative to sending workers for external training, a result evident from the rela­ tively larger estimated effects of education for in­ house training.

Workers typically require little formal instruction, beyond some informal OJT by co-workers, to oper­ ate mature well-established technologies. When new technologies are being introduced, however, pro­ duction is no longer routinized. Under these new and challenging circumstances, formal structured

Firms with a more skilled workforce are more likely

training for all workers-both production and non­

to train. Skill mix is measured as the percentage

production-becomes critical if unanticipated prob­

share of managers, engineers, technicians, supervi­

lems are to be detected and fixed, and the

sors, and skilled production workers in the total work

productivity advantage of using new technologies

force of the firm. Controlling for education (and

over mature technologies are to be realized.8

other factors), a one percent increase in the skill mix is associated with roughly half a percent increase in the probability of training.

Doing R&D has different effects on where employ­ ers train their workers. The marginal effects of do­ ing R&D on training probability are larger for

The results also indicate that skill mix of the work

in-house programs (13.5 percent) than for training

force is a more important determinant of external

from external sources (9.5 percent).

training than of in-house training. To the extent that skilled worker training tends to be highly technical and specialized, employers may fmd it more eco­ nomical to send non-production workers to external training providers than to develop these programs

These results-that R&D finns are more likely to train their workers in-house-are consistent with the hy­ pothesis that the use of advanced technologies is associated with a greater reliance on training in­

in-house.

house than on external. 9 In part, this is because

There is also evidence that a more highly skilled

train in new technologies when extant knowledge is

external training providers are not well-equipped to

workforce is associated with a greater probability

so limited; and in part, because in-house experience

of training for both skilled workers and unskilled

working with, and adapting, new technology devel­

workers. The skill mix variable is positive for

ops the firm's technological capabilities.

20

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Exports and Foreign Ownership

technology. These results suggest that automation

We hypothesize that finns can acquire relevant and

will require greater efforts on the part of employers

best-practice technology through their links with

to tram non-production workers and to send them

foreign buyers and foreign firms operatirig locally,

for external training.

and are therefore more likely to trairi their employ­ ees. However, the results suggest that exportirig is

On average, employers that emphasize quality con­

not associated with trainirig. The weak result may

trol methods are between four and

be reflecting the high correlation between exports

likely to train than those frrrns without quality con­

and other firm attributes, such as foreign capital

13 percent more

trol. This result is significant for training provided to

participation, which are already included in the

all groups of workers and for trairiiri g from both ill­

regression.

house and external sources.

Foreign firms are in general about seven to eight

A second result is suggested by comparing the rela­

percent more likely to provide trairiirig for their em­ ployees as compared to local firms. Note that this

tive size of the estimated margirial effects on trairiirig for each skill group and for each training source.

marginal effect persists even after controlling for

These comparisons iridicate that employers using

other factors, many beirig characteristics of multina­

quality control methods are more likely to trairi skilled

tionals such as R&D, exports, and firm size.

workers (13 percent) than unskilled workers (eight

Foreign firms are eight percent more likely to tram

opposed to sending them offsite for trairiirig (four

ill-house than local firms, but not when it comes to

percent).

percent), and to tram them ill-house

(10 percent) as

external trairiirig. This may reflect well-developed ill-house trairiirig capabilities, sirice many are large

Use of Female Labor and Unionization

multinationals irivolved iri technology intensive semi­

We iriclude two other variables to characterize work

conductor and electronics production and assem­

organization in the firm-the use of female labor and

bly. Finally, while foreign firms are no more likely to

unions. Use of large numbers of female workers

train skilled workers than local firms, they are signifi­

may reflect forms of organization built around simple

cantly more likely to trairi their unskilled employees.

assembly, manual dexterity, seasonal work, and rela­

Automation and Quality Control

support for this hypothesis in Malaysia.

tively low skills. However, there appears to be little The model included two variables-the degree of equipment automation and use of quality control

Controlling for mean education and skill composi­

methods-to irivestigate the trairiirig effects of mod­

tion, a workforce with a higher proportion of female

em modes of production organization. Automation

workers is not associated with a lower likelihood of

can either lead to the "dumbirig down" of skills, as

training. This is important in the context of tight labor

some have argued, or to iricreased skill requirements

markets in Malaysia for it suggests that iricreased use

to operate and maintairi iricreasirigly sophisticated

of female workers to meet iridustry's labor needs is

equipment. The results suggest that the probability

unlikely to have deleterious effects on firm-level

of formal training is higher the greater is the per­

productivity, provided women are similarly educated

centage share of the firm's machinery and equip­

and given the same formal trairiing as their male coun­

ment that is semi- or fully automatic.

terparts.

Malaysian policymakers have stressed the need for

In theory, unions are thought to reduce the likeli­

iridustry to become more automated, both to con­

hood of training by negotiating higher levels of

serve on increasingly scarce labor and to deepen

wages and reducirig the ability of employers to lower

OVERVIEW OF TRAINING

wages to finance firm-specific training through a

21

With the exception of SDCs and ClAST, two in­

training wage. However, when statistically signifi­

stitutions that are either demand-driven or that cater

cant union effects on training are found, they are

to higher-level skills training, the other public

invariably positive and about six to seven percent

training institutions-IDs, IKMs, YTCs, polytech­

higher as compared to non-unionized firms. Simi­

nics, and vocational and technical schools-play a

lar results have been reported in several industri­

very minor role in meeting the in-service training

alized countries (see Lillard and Tan, 1992, Tan et

needs of the manufacturing sector. Their primary

al, 1992).

focus thus far has been on pre-employment train­ ing in basic and intermediate-level technical skills.

The union effect is strongest in increasing training

Given their limited role in in-service training, it is

from external sources (six percent) and training for

clear is that the private sector will have to take on

skilled workers (seven percent). Unions may have

greater responsibilities for meeting its growing skill

this beneficial effect on training by giving workers

requirements.

an alternative to job turnover. By establishing griev­ ance and arbitration procedures, unions promote

The Government can, and is, helping facilitate in­

greaterjob stability and increase incentives for finns

creased private sector-led training through the Hu­

to invest in training.

man Resource Development Fund, through seed-grants to set up private sector-managed SDCs in the different states, and through subsidized credit,

Findings and Policy Implications

training, and technical assistance for the population of small and medium-scale firms that are most likely

Manufacturingfirms in Malaysia under-invest in

not to train or to tely on informal on-the-job training

the training of their employees. This is based on

(these policies are assessed subsequently).

our estimates that about 80 percent of all firms ei­ ther do no training or rely exclusively on infor­

However, the design and implementation of these

mal training from co-workers and supervisors, and

training and related policies are rarely accompanied

that only 21 percent of firms provide formal training.

by adequate monitoring of their take-up, or by pro­ gram impact analyses, both of which require a sys­

This conclusion is bolstered by the responses of

tematic data collection effort.

employers (reported later in Chapter Four) about why they provide little or no training. Most cite

The Government's existing system for training data

the use of mature technology as the principal rea­

collection and analysis isfragmented and should be

son for doing little training. While this is not a

strengthened. Data on public training institutions

market failure per se, a sizeable number of other

are typically maintained by each responsible minis­

employers, smaller firms in particular, cite other

try but seldom reported, on a systematic basis to­

training constraints that are-free ridership from

gether with detailed cost data, to a central coordinating

high labor turnover, lack of knowledge about train­

agency for planning and policy analysis.

ing methods, and limited resources for training. Likewise, information on private-sector training in­

Finns that train meet their skill needs in-house or

stitutions is only collected on an ad hoc basis. Few

through a variety of external training sources. Of

evaluation studies of training programs-based on

the external training sources, firms rely most

tracer surveys of graduates, comparisons with a con­

heavily on private providers-private training in­

trol group, and cost-benefit calculations-have been

stitutes, buyers and equipment suppliers, joint­

conducted; evaluations comparing different public

venture partners, a n d overseas training

training institutions are even rarer. The National

institutions.

Vocational Training Council (NVTC) was designated

22

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

as the institution to coordinate both public and pri­

provide DOS with the necessary resources and in­

vate vocational training programs, and the Govern­

centives to implement and speedily process the aug­

ment should give NVTC the necessary legal standing,

mented surveys on a periodic basis.

resources, and capabilities to play this role more ef­ fectively.

Several determinants of enterprise training stand out. First, smaller firms are much less likely to train than

Least well developed is in formation on in-service

larger firms suggesting that this groups will require

training. Existing industrial and household surveys

special attention from policymakers. Second, em­

fielded by the Department of Statistics (DOS) are

ployers are more likely to train when its workforce

,

potentially potent, but currently under-exploited,

is better educated and more technically skilled since

vehicles for developing these training data bases.

they benefit more from training. As such, firm incen­ tives to train should increase as education policies to

A great deal of demographic and employer infonna­

promote higher school retention rates and more tech­

tion is already elicited in these surveys. The addi­

nical education are implemented.

tion of a short training module to each survey thus provides nationally representative estimates of train­

Investments in new technology, automated equip­

ing at the level of the enterprise and at the level of

ment, and quality control are associated with in­

the individual. Once institutionalized, these aug­

creased training, a fact that reinforces the need for

mented firm- and worker-level surveys will yield

continuous skills upgrading if firms are to adopt

time-series data needed for policymakers to monitor

more technology-intensive production. Finally,

and analyze training trends. The Government

local firms are in general much less likely to train

should set up a committee to design, fund, and coor­

relative to foreign firms, reflecting both their weak

dinate analyses using these training modules, and

training capabilities and lack of a training culture.

OVERVIEW OF TRAINING

23

Annex 2.1 The following table reports the parameter estimates of probit models for different training measures, where the dependent variable is one if an employer invests in that source or type of training, and zero otherwise.

While these estimates provide insights into the significance of explanatory variables and the

direction of their impact on the training outcome, they are not readily interpreted because of the non-linear nature of the dependent variable which is constrained to lie between 0 and 1.

However, the marginal

effects of explanatory variables can be calculated and they are reported in the text.

Pro bit Estimates of the Likelihood of Formal Training Independent Variable

Any Formal Training

Small Firm Size

0.362 �/

(16-100 workers)

(0.176)

Medium Firm Size

0.939 �

(101-250 workers)

(0.176)

Large Firm Size

1.446 �

(over 250 workers)

(0.193)

Mean education of

0.064 �

the workforce

(0.019)

Percent skilled

0.0174 �

workers

(0.0023)

Invests in R&D

0.365 � (0.078)

Foreign capital

0.188 �

participation Exports

%Value of automatic machinery Use of quality

Proportion of

Unionization

Log (likelihood) •

Note:

0.801 � (0.192) 1.182 � (0.207) 0.088 � (0.019) 0.0063 � (0.003) 0.395-£1 (0.076) 0.243 �

Unskilled

0.221 (0.232) 0.924 � (0.228) 1.477 � (0.084) 0.062 � (0.021) 0.0194 � (0.003) 0.334 � (0.080)

0.518'"' (0.264) 1.265 � (0.259) 1.748 � (0.271) 0.081� (0.020) 0.0110 � (0.003) 0.344 � (0.079)

-0.029 �

0.064

(0.079)

(0.075)

0.246 (0.177) 0.657 � (0.176) 1.172 � (0.192) 0.076 � (0.019) 0.0167 � (0.002) 0.424 � (0.076) 0.233 �

0.027

-0.012

0.030

0.101

0.002

(0.074)

(0.077)

(0.084)

(0.081)

(0.076)

0.002

0.001

(0.001)

(0.001) 0.309 � (0.071)

0.004 � (0.001) 0.160 �/

0.004 � (0.001) 0.403 �

(0.076)

(0.073)

(0.073)

0.001 (0.001) 0.245 � (0.071)

0.065

0.025

0.048

-0.167

0.016

(0.127)

(0.128)

(0.141)

(0.137)

(0.128)

0.158.l0'

Constant term

0.385 � (0.193)

Skilled

Worker Training Worker Training

(0.073)

(0.070)

female workers

External

(0.072)

0.272 �

control methods

In-house

Formal Training Training

0.082

0.215 �

0.207 �/

0.059

(0.083)

(0.083)

(0.086)

(0.085)

(0.083)

-1.808 �

-2.305 �

-2.331 �

-2.514 �

-2.214 �

(0.326)

(0.336)

(0.374)

(0.387)

(0.329)

-1090.86

-918.23

-970.59

-1102.68

-1133.40

= Significant at 1%

b

= Significant at 5%

c

=Significant at 10% level.

Numbers in parantheses are standard errors. Omitted firm size is micro enterprises with 15 or fewer workers. Age of firm and multi-plant status were also included but were not statistically significant

Source: 1995 MITP Survey

CHAPTER THREE: PRODUCTIVITY

AND

wAGE OUTCOMES

In this chapter, we tum to an empirical analysis of the

rizes the complex engineering relationships be­

outcomes of enterprise training-both on firm-level

tween the firm's output and the inputs used to pro­

productivity and on the wages of workers. We are

duce that output- plant and equipment, labor,

interested in finding out whether employer investments

intermediate inputs and energy.

in formal training are associated with higher firm-level productivity. Other issues of interest are whether

It can be specified in many ways, but the specifi­

there are productivity differences in the training pro­

cation that we will use is the Cobb-Douglas pro­

vided to different groups of employees, for example,

duction function.1 The firm's output is measured

skilled or unskilled workers, and which source of

as the natural logarithm of value-added, that is

training-in-plant training programs or training pro­

gross output less the value of intermediate inputs

vided by external institutions--has the largest im­

and energy used, and this is related to the firm's

pact on firm-level productivity.

use of the two major factors of production - capital

We also examine the relationship between training

labor (total employment), both-also expressed in

and monthly wages paid by employers. The issue is

logarithms.

(book value of physical plant and equipment) and

whether the productivity gains from training are shared with workers in the form of higher pay and, if

In this Cobb-Douglas functional form, the coeffi­

so, what kinds of training have the largest wage ef­

cients of capital and labor represent their relative

fects and which groups of workers benefit most. 1his

contribution to output, and they typically sum to

analysis of the productivity and wage outcomes of train­

one, or roughly constant returns to scale. The pro­

ing has ramifications for employers, workers and

duction function that we estimate is augmented to

policymakers. Insights into the effects of training on

include different training measures and a set of con­

firm-level productivity are important for employers

trol variables.

who must make decisions about whether to train, who to train, and what kinds of training to sponsor.

The training measures range from simple indicator

For workers, these wage gains, if any, represent an

training-to more complex ones, such as training pro­

incentive for them to undertake training and a moti­

vided to different groups of workers, training by

vation for them to develop long-term job attachment

source (in-house versus external training), and type

to the firm. This is important since high job turnover

of external training providers. These training mea­

variables-whether employers provide any formal

reduces employer incentive to invest in workers'

sures allow us to ask whether training investments

skills. The training outcomes are also of interest to

are associated with higher firm-level productivity,

policymakers concerned with issues of economic

controlling for inputs of capital and labor, and for the

performance, resource allocation, design of training

influences of other contemporaneous variables that

policies, and income distribution.

also affect productivity. The latter include the average educational attain­ ment of the firm's workforce, indicator variables for

Estimating the Productivity Impact of Training

firm characteristics such as whether it exports or in­

We analyze the productivity outcomes of employer

licensing agreements), and two-digit industiy dummy

investments in fonnal training within a production func­

variables to control for productivity differences

tion framework. The production function summa-

across industries.

vests in technology (measured by R&D or technology

PRODUCTIVITY AND WAGE OUTCOMES

25

Our production function approach takes into account

function. Using the predicted, rather than the actual

the possibility of" selectivity bias" in estimating train­

value of the training variable in the production func­

ing outcomes. Tills bias may arise if firms have very

tion allows us to get unbiased estimates of the pro­

different underlying productivity endowments, and

ductivity effects of training.

the firms that choose to train differ systematically from non-training firms in both their observed and unob­ served productivity attributes. To the extent that we

Productivity Effects of Training for

cannot fully control for these unmeasured differ­

Different Firms

ences, the production function may over or under­ state (bias) our estimates of the productivity impact of

We begin by presenting estimates of the productiv­

training. We use an instrumental variable approach

ity effects of formal training provided by different

to correct for this potential "selectivity bias."

groups of employers. In defining these different groups, we rely on the findings in Chapter Two,

In Chapter Two, we report the results of estimating

namely, that the likelihood of training is greater in

probit models for the firm's decision to train. Here,

larger firms in firms using new technology, in ex­

we use those probit results to construct a predicted

porting firms, and in foreign-owned firms. If the

value for the training variable that, by its construc­

higher incidence of training in these firms is indica­

,

tion, is uncorrelated with the unmeasured produc­

tive of the relative profitability of investments in

tivity attributes (the error term) in the production

worker training, we should also expect to find rela-

Table 3.1 Production Function Estimates by Firm Size Independent Variable

Overall

Small

Medium

Large

Log (labor)

0.576• (0.039)

0.583• (0.065)

0.401• (0.201)

0.578• (0.090)

0.267• (0.019)

0.256• (0.026)

0.274• (0.042)

0.304• (0.039)

Invests in technology

-0.104 (0.071)

-0.107 (0.118)

-0.139 (0.115)

0.024 (0.113)

Exports

0.034 (0.069)

0.187b (0.091)

-0.138 (0.124)

-0.387 (0.170)

Age of firm

0.007• (0.002)

0.006b (0.003)

0.002 (0.004)

0.009• (0.003)

Education of workers

0.029 (0.020)

0.025 (0.029)

0.019 (0.034)

0.053 (0.039)

Predicted Training

0.325• (0.080)

0.323• (0.104)

0.297b (0.144)

0.125 (0.151)

Constant

7.614" (0.403)

8.130" (0.505)

8.905" (1.131)

7.179" (0.767)

Log

(capital)

a=

b Note:

=

Significant at 1%.

Significant at 5%.

Numbers in parentheses are standard errors. Industry dummy variables included but their estimates are not reported here.

26

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

tively high productivity outcomes from training

The firm's age is positive, suggesting that the older firms tend on average to be more productive, re­

among these groups of firms.

flecting their accumulation of production experience. In interpreting the training results, note that output

The other explanatory variables-the mean educa­

or value-added is expressed in natural logarithms.

tional attainment of the workforce, technology, and

This allows us to interpret the coefficient of the train­

exports-never attain statistical significance in the pro­

ing indicator variable (or its predicted value) as the

duction function estimates though, as seen in Chap­

percentage change in output ofbeing a training firm

ter Two, they are very important determinants of the

rather than a non-training firm, controlling for the

firm's decision to train.

productivity effects of other variables. For the MITP sample as a whole, Table 3.1 indicates Productivity Effects by Firm Size

that training has a positive and statistically signifi­

Table 3.1 reports the production function estimates

cant impact on firm-level productivity. The esti­

for the MITP sample as a whole and separately by

mated training coefficient is 0.325, suggesting that

three firm sizes. Before turning to the training esti­

training firms are, on average, about 32 percent more

mates, we note that both labor and capital coefficients

productive than firms that do not train, controlling

are positive and significant, and that their magnitudes

for all other factors that also influence productivity

of approximately two-thirds and one-third, respec­

in firms. Training effects of this magnitude are not

tively, are broadly consistent with the shares of la­

unusual and, in fact, are broadly similar to those esti­

bor and capital in the economy.

mated for other developing countries (see Box 3 .1).

Box 3.1 Enterprise Training and Productivity in Developing Countries

Tan and Batra (1995) used a common production function model to estimate the firm-level produc­ tivity effects of training in the manufacturing sector of Indonesia, Colombia, Malaysia, and Mexico. In all four countries, they found evidence that enterprise training is associated with higher firm-level productivity. Their findings also indicated that the productivity effects of training, especially training provided skilled workers, are larger in lower-income economies (Colombia and Indonesia) as compared to the higher-income countries in their sample (Malaysia and Mexico), possibly reflecting the relative scarcity of skills in these lower income countries. The implication is that economic development is strongly tied to workforce skills development, and that policies to encourage increased enterprise training will have large productivity gains for the economy. Country (year of survey) Indonesia (1992) Colombia (1992) Malaysia (1994) Mexico (1992)

GNP per capita US$ $ 670 $1,330 $3,140 $3,470

Productivity Effects Any Training 0.711 0.266 0.282 0.444

Productivity Effects Skilled Training 1.430 0.386 0.252 0.204

Source: Tan and Batra, Enterprise Training in Developing Countries, Private Sector Development Department, World Bank, 1995.

PRODUCTIVITY AND WAGE OUTCOMES

Do the productivity effects of training vary by firm

firms, the productivity effects of training are much

size? To address this question, separate production

smaller-12 percent-and these effects are not statis­

functions are estimated for three firm size groups­

tically significant.

27

small firms (up to 100 employees), medium-size finns (101-250 employees), and large firms (over 250 em­

The fact (shown in Chapter Two) that relatively

ployees)-and the results reported in columns two

few smaller firms train despite this evidence of

through four ofTable 3 1

large potential gains in productivity from doing

.

.

so, leads us to conclude that small and medium The estimated training coefficients are 0.32, 0.29 and

firms under-invest in training. Such wide discrep­

0.12 in small, medium and large firms. These results

ancies in returns could not persist in a perfectly

indicate that worker training has large productivity

competitive market since firms would train (in­

benefits among small and medium-size firms-32 and

crease the supply of trained workers) to equalize

29 percent-productivity increases that are statisti­

the returns to training across markets. The fact

cally significant at the five percent level. For large

that they do not suggests that market failures are

Box 3.2 Technology Raises the Productivity of Training in Taiwan, China. Using microdata from the 1986 Taiwan Census of Manufactures, Aw and Tan (1994) investigated the effects of training on firm-level productivity in seven industries. They were interested in whether the productivity effects of training varied with the firm's technology level, as measured by in-house

R&D or purchases of technology.

For each industry, they estimated separate production functions

for firms that invested in technology, termed "high-tech," and for firms that made no such technology investments, or "low-tech," correcting for potential selectivity bias in firms' technology decisions. They found clear evidence that technology had an impact on the productivity outcomes of training. First, within each industry, training provision was associated with a larger impact on firm-level productivity when training was accompanied by firm investments in R&D or purchased technology. Second, looking across industries, the differential impact of training in high-tech and low-tech firms is more pronounced in the technology-intensive industries such as electronics, chemicals, and plastics than in the more traditional industries like textiles and apparel. Thus, both within and across industries, the evidence indicates that the returns to training rises with technological change.

--- -

---- ----�-

P 1

ro d

.

B

0

6

0

4

0

--------

ctivity

Effe

-

---

cts

of

Tra in

in

g

·-

T aiw

a n

1986

2

I' 1 0

u

em H ig h - T e c h

-Lo w -Te ch L-.--------�

2 0

-�--------

---�-

- -

---

----

__j

28

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

present, especially among small and medium firms

Productivity Effects by Technology

(SMis).

The level of technology used in firms may also af­ fect the productivity outcomes of worker training

This conclusion is bolstered by employer responses

(Lillard and Tan, 1992). The characteristics of new

about why they provide employees with little or no

technologies are often poorly understood, and their

training. The evidence, which will be presented in

productivity advantages over the older technolo­

Chapter Four, suggests that several market failures­

gies that they replace are seldom manifested without

from lack of information about how to develop and

significant employer investments in learning-by-do­

manage their training programs, high job turnover

ing and training. In this environment, the productiv­

which makes it difficult for firms to recoup their train­

ity gains from worker training can be quite

ing investments, and limited access to fmance for

substantial.

training-are important reasons for why some employ­ ers invest very little in training. More significantly,

In contrast, older and more established technologies

their responses also suggest that these factors pose

require less training since their specific characteris­

particularly severe constraints for mar�y SMis.

tics are well-known; consequently, the productiv-

Table 3.2 Production Function Estimates by Technology Level DoTec=O Log (labor)

Log (capital)

DoTec=1

Age of Firm

Education of workforce

Predicted Training

Constant

R square

0.604"

0.531"

0.624"

0.391"

(0.058)

(0.041)

(0.084)

0.259"

0.294"

0.261"

0.287"

(0.022)

(0.038)

(0.020)

(0.062)

-0.139

-0.218

(0.083)

(0.155)

0.093

-0.132

0.063

-0.084

(0.079)

(0.126)

(0.070)

(0.213)

0.005b

0.009"

0.006"

0.019"

(0.002)

(0.003)

(0.002)

(0.007)

0.028

0.032

0.029

0.004

(0.024)

(0.034)

(0.021)

(0.055)

0.281"

0.282"

0.233"

0.554"

(0.095)

(0.120)

(0.082)

(0.181)

7.954"

7.768"

7.813"

8.798"

(0.421)

(0.643)

(0.375)

(1.035)

0.619

0.635

0.636

0.626

a= Significant at 1%. b = Significant at 5%. Note:

Numbers in parentheses are standard errors. Industry dummy variables included but their estimates are not reported here.

Do Tee= invest in R&D or has technology license. HasTL =has technology licensing agreement(s). Source: 1995 MITP Survey

HasTL=1

(0.047)

Conducts R&D

Exports

HasTL=O

PRODUCTIVITY AND WAGE OUTCOMES

ity gains from training to use older technologies are

29

To summarize, these production function estimates

also likely to be limited. In Chapter Two, we found

show that firms can make potentially large produc­

strong evidence that firms were more likely to train

tivity gains of over 55 percent when new technolo­

their workers if they were also investing in R&D.

gies acquired through licensing agreements are

These perspectives lead us to formulate the follow­

complemented with investments in training. In con­

ing tests of the link between the firm's technology

trast, R&D has limited impact either on overall pro­

level and the productivity outcomes of training. We

ductivity levels, or on productivity of worker

split the MITP sample into two groups of firms by

training. We interpret this limited impact of R&D

level of technology, and compare the productivity

as reflecting the relatively weak R&D capabilities

effects of training in the high and low technology

of Malaysian firms; MNCs are widely believed to

groups. Such an analysis can also be done for indi­

have greater capabilities in conducting R&D but they

vidual industries when data on large samples of

do little in Malaysia.

firms are available (see Box 3.2). Productivity Effects by Export Orientation Two defmitions of teclmology are used. First, we

and Ownership

define an indicator variable, DOTEC, which takes

Two other attributes of firms-export orientation and

on a value of one if the firms invests in R&D or has

foreign ownership-may affect the productivity out­

technology licensing agreements with other firms,

comes of training through the mediating role of tech­

and zero otherwise. Second, we define an indicator

nology and links with external markets.

variable, HASTL, to distinguish between firms with and without technology licenses.

The level of teclmology in exporting firms may be higher for two reasons: (a) a firm's export-orienta­

This second definition recognizes that when in­

tion may simply reflect its underlying teclmological

house R&D capabilities are weak, as is true in many

capabilities and international competitiveness; (b) ex­

Malaysian firms, licensing agreements can be an im­

porting may also raise technological capabilities by

portant means of accessing relatively sophisticated

giving firms access to technologies and know-how

technologies from abroad, even if the firm does no

from abroad and, through interactions with foreign

in-house R&D. The production function estimates

buyers, information about new markets and product

corresponding to these two teclmology measures

specifications as well.

are reported in Table 3.2. When the broad defini­ tion of technology, DOTEC, is used, the productiv­

Foreign firms-defmed here as firms with over 50

ity effects of training-about 28 percent increase in

percent foreign capital participation-are thought to

value-added-are virtually indistinguishable in the

embody relatively high levels of teclmology, know­

high teclmology and low technology firm samples.

how, and managerial expertise as compared to do­

To see this, note that almost similar training coeffi­

mestic firms. Their level of technological capabilities

cients of0.28 are reported in columns one and two.

is not well reflected by indicators such as local R&D spending or technology licenses, since they are able

The second defmition, HASTL, which is based on

to draw on the MNC parent's stock of technology

whether firms have technology licensing agree­

and R&D.2 These are not typically located in de­

ments, does a better job of discriminating between

veloping countries where there is often a short sup­

the high and low teclmology firms, controlling for

ply of experienced R&D scientists and engineers.

their R&D investments. Not only are average pro­ ductivity levels (reflected in the constant term) much

Table 3.3 reports production function estimates for

higher in firms with teclmology licenses, the pro­

groups of firms differing in their export-orientation

ductivity effects of training in these firms are over

and ownership. Columns one and two indicate that

twice as big as those for firms without technology

the productivity effects of training are higher in firms

licenses-55 versus about 23 percent.

30

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Table 3.3 Production Function Estimates by Export Orientation and Ownership

Exports=O

Exports>O

Foreign=O

Log {labor)

0.678• (0.068)

0.515• (0.046)

0.571• (0.048)

0.578• (0.069)

Log (capital)

0.251• (0.029)

0.266• (0.027)

0.285• (0.022)

0.207• (0.044)

Invest in Technology

0.071 (0.151)

-0.135 (0.075)

-0.096 (0.087)

-0.099 (0.119)

0.041 (0.078)

-0.419 (0.215)

Exports

Foreign=1

Age of Firm

0.004 (0.003)

0.01oa (0.003)

0.007" (0.002)

0.018" (0.006)

Education of workforce

0.036 (0.034)

0.022 (0.023)

0.025 (0.024)

0.099b (0.042)

Predic ted Training

0.270b (0.133)

0.333• (0.095)

0.283• (0.098)

0.327b (0.158)

Constant

7.751• (0.579)

7.784• (0.506)

7.319• (0.487)

7.843• (0.776)

R square

0.583

0.608

0.625

0.673

a= Significant at b

Note:

= Significant at

1%. 5%.

Numbers in parentheses are standard errors. Industry dummy variables included but their estimates are not reported here.

Source:

1995 MITP Survey

that export-about 33 percent-as compared with those that do not-about 27 percent. Columns three and four show a similar result, that the productivity outcomes of training are higher in foreign-owned firms (33 percent) than in domestic firms (28 per­ cent). It is also noteworthy that overall levels of productivity (as reflected in the constant term) are much higher in the sample of foreign-owned firms (7 .84) than in the domestic firm sample (7 .32).

of domestic firms. As shown in Chapter Two, firms with these characteristics are more likely to invest in the training of their workers. The pro­ duction function results reported here confirm that the productivity effects of this increased training are significantly higher among firms that export, have technology licensing agreements, and some foreign capital participation.

Taken together, these results and the findings re­ ported in Table 3.2 provide support for the view that employer investments in technology and train� ing are complementary in that investments in one enhance the productivity of the other. Given cur­ rent weak local R&D capabilities, the results sug­ gest that export-orientation, foreign technology licensing and joint-ventures may offer the great­ est potential for improving the technology levels

Productivity Outcomes by Skill Group and Training Source

Thus far, we have treated all forms of training as if they had the same productivity outcomes. We now consider several potential variations in the produc­ tivity outcomes of training across different worker groups and sources of training. As before, we con­ trol for selectivity bias by including the predicted

PRODUCTIVITY AND WAGE OUTCOMES

values of training from pro bit models of training

pervisors, engineers, technicians and skilled produc­

for different skill groups and for different training

tion workers; and unskilled production workers.

sources.

For each skill group, we begin by estimating sepa­

We also test a specification where training is pre­ dicted using a tobit model. The tobit specification is a mix of a probit model and a regression model in that it incorporates information both on the prob­ ability of an event and, conditional on that event taking place, the distribution of a continuous vari­

31

rate probit models of whether employers provide in-house or external training, including the group­ specific measures of skill mix and female workers as the identifying variables. The estimated parameters are then used to calculate predicted training mea­ sures for each group in the production function.

able. This tobit specification allows us to estimate

A corresponding set of tobit training models is also

the productivity effects of "training intensity," that

estimated to predict training intensity of in-house and

is, the proportion of workers in a specific group

external training. Production function estimates us­

getting training, while taking into account the de­

ing these alternative probit and tobit training mea­

cisions of some firms not to train.

sures are reported in Table 3 .4. Both the probit and

Estimating the separate effects of each type and source of training is complicated by the high correlation that exists between different training measures. The correlation arises in large part because firms that provide training tend to rely on all sources of training while employers that do little training rarely use more than one source. This means that our probit or tobit predicted train­ ing measures will be correlated, unless identify­ ing variables can be found to explain why employers might choose one training source over another. Given the paucity of identifying vari­ ables for each source, we can only address this identification issue in a limited way. For training by skill groups, we rely on variations in the proportion of skilled occupations and un­ skilled workers to identify what types of training are provided. For training by source, we include the presence of a training center and training staff to identify decisions to provide in-house training, and the use of joint training programs with other firms for external training. The limited number of identifying variables precludes a more disaggre­ gated analysis of training for each detailed occu­ pational group or every training source.

tobit measures are consistent in showing that training of skilled workers has a positive and statistically sig­ nificant impact on firm-level productivity while the training of unskilled workers does not. The training estimates for the latter group never attain statistical significance, and are thus interpreted as having zero impact on productivity. What about training skilled workers? Using the probit measure, the estimated coefficient of0.38 in­ dicates that provision of skilled worker training is associated, on average, with a 38 percent increase in productivity. A reasonable way of interpreting the tobit measure is to evaluate its coefficient-1.22-at the sample mean of the training variable. The propor­ tion of skilled workers trained was0.175 or 17.5 per­ cent. This implies that a 10 percent increase in the proportion of skilled workers trained (i.e. 0.017 5) is associated with a 2 .1 percent increase (1.22 x 0.0175 x 100) in productivity. The differential impact of training by skill group is not surprising once it is recognized that education is the foundation of subsequent learning, and to the extent that skilled workers are more efficient learn­ ers, they benefit more from training. Employers ap­ pear to recognize these differences in the learning capabilities of different worker groups. In Chapter

Productivity Effects by Skill Group

Two, the analyses indicated that firms were more

We consider the productivity effects of training for

likely to provide all kinds of training to their skilled

two worker groups: skilled workers, including su-

employees than to their unskilled workers.

32

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

We note that these results are not unique to Malay­

ing from all outside sources combined. As before,

sia. Similar patterns of training outcomes, not only

the probability and intensity of in-house and exter­

on the productivity but also the wages of skilled and

nal training measures are predicted using probit and

unskilled workers, are found in other developing

tobit

countries such as Mexico, Colombia and Indonesia

tify the employer's choice of each training mode by

(Tan and Batra, 1995). If the experiences of these

the occupational and sex mix of its workforce.

models, respectively. In these models, we iden­

countries are any guide, the differential productiv­ ity impact of training is likely to result in growing

The production function results with the alternative

wage differentials between skilled and unskilled

training measures are reported in Table 3.5, sepa­

workers in the absence of training policies to up­

rately for local firms (columns one and two) and for

grade unskilled workers to skilled status.

foreign firms (columns three and four). Table 3.5 clearly shows that productivity outcomes by training

Productivity Effects by Training Source

source are quite different depending upon owner­

Next, we compare the productivity effects of in­

ship status of the firm. 3

house company training programs and external trainTable 3.4 Production Function Estimates with Predicted Training by Worker Groups

Independent Variable Log (labor)

Log (capital)

Invests in Technology

Exports

Age of Firm

Probit

Tobit

Prediction

Prediction

0.5773

0.558•

(0.040)

(0.042)

0.2793

0.266•

(0.019)

(0.019)

-0.102

-0.107

(0.065)

(0.071)

0.010

0.021

(0.064)

(0.069)

0.0063

0.0063

(0.002)

(0.002)

First, consider the productivity effects of internal and external training when training measures are pre­ dicted by a probit model (columns one and three). For the sample of domestic firms, only externally provided training has a positive and significant im­ pact on productivity, averaging about 26 percent; no statistically significant impact of in-house training is evident. In the case of foreign firms, the results indicate that both in-house and external training have a positive and significant effect on firm productivity13 percent for in-house training and 33 percent for external. These results are striking-on one hand, they point to the strong in-house training capabilities of foreign firms; on the other hand, they highlight the weak in­ house training capabilities of local firms and the po­

0.035

0.029

(0.023)

(0.022)

0.383b

1.220b

Worker Training

(0.165)

(0.571)

Predicted Unskilled

-0.151

-0.680

Worker Training

(0.248)

(0.723)

7. 5333

7.7323

ence is that external training intensity for foreign

(0.448)

(0.425)

firms is no longer statistically significant.

Education of workforce

Predicted Skilled

Constant

a=

providers can play in meeting their training needs. The qualitative results using the tobit training mea­ sures are broadly similar in showing the importance of external training sources for local firms and in­ house training for foreign firms. The only differ­

Significant at 5%. Numbers in parentheses are standard

What are the effects of training more intensely, at

errors. Industry dummy variables included but

are statistically significant? For local firms, the

b

Note:

Significant at 1%

tentially important role that external training

=

their estimates are not reported here. Source: 1995 MITP Survey

least for those external sources of training that proportion of workers getting in-house and ex­ ternal training is 0.094 and 0.026, respectively.

PRODUCTIVITY AND WAGE OUTCOMES

33

Table 3.5 Production Function Estimates: In-house versus External Training

Local Firms

Independent Variable

Probit prediction

prediction

Foreign Firms Probit prediction

Tobit prediction

0.627"

0.589"

0.559"

0.637"

(0.049)

(0.042)

(0.077)

(0.058)

Log (labor)

0.273 a

0.290"

0.201"

0.209"

(0.022)

(0.022)

(0.044)

(0.042)

-0.083

-0.029

-0.120

-0.144

(0.088)

(0.088)

(0.118)

(0.116)

0.101

0.051

-0.442b

-0.331

(0.079)

(0.076)

(0.213)

(0.206)

Log (capital)

Invests in Technology

Tobit

Exports

0.005b

0.005b

0.017"

0.015"

(0.002)

(0.002)

(0.006)

(0.006)

Age of Firm

0.026

0.015

0.089b

0.092b

workforce

(0.023)

(0.025)

(0.042)

(0.042)

Predicted Internal

-0.038

-0.063

Training

(0.050)

(0.169)

Education of

0.400b (0.214)

0.256a

1.751a

0.329b

1.297

(0.096)

(0.691)

(0.151)

(1.021)

Predicted External Training

0.13b (0.068)

Constant

7.555a

7.735a

8.494a

8.364a

(0.512)

(0.437)

(0.847)

(0.747)

a= Significant at 1% Note:

b = Significant at 5% Numbers in parentheses are standard errors. Industry dummy variables included but their estimates are not reported here.

Source: 1995 MITP Survey

Evaluated at these means, the coefficient of 1. 754

eral external sources-poly technics, vocational

on external training suggests that a 10 percent in­

training institutes,

crease in the proportion of workers getting external

Skills Training Institutes (e.g. ClAST and GMI),

IKM, ITI, SDCs, Advanced

training (0.0026) will lead to a 0.5 percent increase

buyers and suppliers, other private firms, and over­

in productivity. For foreign firms, the correspond­

seas (presumably by foreign partners). The pre­

ing proportions are 0.191 for in-house training and 0.032 for external training. Using the in-house train­ ing coefficient of0.40, a 10 percent increase in train­ ing (0.019) is associated with a produc tivity improvement of about 0. 8 percent. 4

likely to be most important for improving produc­

Productivity Effects of Different External

fective for domestic firms as for foreign firms?

vious findings raise the following questions: Which of these external training providers are tivity and, given differences in in-house training capabilities by ownership status, are the same ex­ ternal training providers likely to be equally ef­

Sources of Training The MITP survey elicited information from firms

We begin to address these questions by combin­

about the number of employees trained in sev-

ing the different external training providers into

34

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Table 3.6:

Production Function Estimates: Training from External Sources

Independent Variable

Domestic Firms Tobit

Probit prediction Log {labor )

Log (capital)

Invests in Technology

Exports

Age of Firm

Education of workforce

Predicted Internal Training

prediction 0.588"

0.546"

0.589"

(0.049)

(0.078)

(0.076)

0.269"

0.284"

0.220"

0.208"

(0.022)

(0.022)

(0.043)

(0.043)

-0.077

-0.083

-0.109

-0.095

(0.088)

(0.089)

(0.118)

(0.117)

0.158C

0.002

-0.427c

-0.457b

(0.088)

(0.085)

(0.223)

(0.236)

0.002

0.008"

0.024"

0.015b

(0.003)

(0.003)

(0.007)

(0.006)

0.011

0.005

0.077c

0.105b

(0.026)

(0.029)

(0.046)

(0.051)

-0.031

-0.132

0.147b

0.515b

(0.050)

(0.172)

(0.069)

(0.232)

-12.516b

1.466b

24.396"

(5.376)

(0.602)

(9.129)

-0.339 (0.350) 0.632b

39.099b

(0.258)

(16.767)

Predicted Training in

0.019

11.346

Government Institutes

(0.262)

Constant

a=

b Note:

=

Tobit prediction

0.65 "

Predicted Training in

SDCs & Adv. Train. Ins.

prediction

(0.057)

Private Institutes Predicted Training by

Foreign Firms Probit

(8.341)

-0.741

-31.238

(0.456)

(38.148)

-0.406

-33.619

(0.441)

(17.425)

8.508"

8.158"

7.325"

7.557"

(0.712)

(0.700)

(1.196)

(1.343)

Significant at 1% Significant at 5%

Numbers in parentheses are standard errors. Industry dummy variables included but their estimates are not reported here.

Source: 1995 MITP Survey

three groups5: (1) government-run training in­

of external training. The production function re­

stitutions, including ITis, IKMs, YTCs, voca­

sults with predicted training measures are re­

tional and technical institutes, and polytechnics;

ported in Table 3. 6. These results should be

(2) advanced skills training centers and SDCs

treated with caution since we do not have sepa­

providing high-level skills training with input from

rate identifying variables for each external train­

the private sector; and (3) all other private sec­

ing source.

tor training providers, including private training institutes, foreign partners, buyers and sellers,

Table 3. 6 shows that external training providers have

and overseas training sponsored by employers.

very different productivity effects depending upon

We are motivated to aggregate training into

ownership. For local firms, in-house training contin­

three broad categories because of the high de­

ues to be statistically insignificant. Among external

gree of correlation among the different sources

training sources, the results indicate that only the

PRODUCTIVITY AND WAGE OUTCOMES

35

training provided by SDCs and advanced skills

vanced skills training centers is associated with a

training centers to local firms has a positive and sta­

1.2 percent gain in productivity. For foreign firms

tistically significant productivity impact, irrespec­

the corresponding productivity gains from increas­

,

tive of whether probit or tobit training measures are

ing the intensity of in-house training is one percent,

used. The probit measure indicates that this pro­

that from private training institutes and overseas

ductivity impact is large, averaging about 63 per­

training is 5. 6 percent.

cent. Tr aining provided by other private providers is actually associated with lower pro­

The different patterns of training outcomes in for­

ductivity in domestic firms.

eign and domestic firms suggest the following in­ terpretation. Foreign firms have well developed

For foreign firms, two sources of training are as­

in-house training capabilities, and therefore may

sociated with significant productivity gains-in­

need to rely less on SDCs and advanced skills

house training, and training from private training

training centers for training their workers. We

institutes-these include local providers as well as

speculate, but cannot confirm, that the relative im­

overseas training. However, the latter source of

portance of training from other private providers

training has an implausibly large productivity im­

may reflect their ability to send their workers

pact of 146 percent, based on the probit measure.

abroad to the parent company for training. In

In both groups of firms, there are no significant

contrast, domestic firms have relatively weak in­

productivity gains from training in government

house training capabilities so that SDCs and ad­

institutes.

vanced skills training centers are important

The productivity effects of increased training in­

sources of higher-level skills training for them.

tensity are reported in Table 3. 7 for statistically

Firm-Level Wages Outcomes of Training

significant sources of training. For each group of firms, the table reports the sample means of train­ ing intensity, and the gains in productivity from

We now tum to a second outcome of training-its ef­

increasing training intensity by 10 percent.

fects on the monthly wages of employees. Several

For local firms, a 10 percent increase in the propor­

tivity gains from training passed on to workers in the

tion of workers getting training in SDCs and ad-

form of higher pay? In which types of firms are the

issues are of interest: to what extent are the produc­

Table 3.7: Productivity Effects of Increased Training Intensity Local Firms Training source

Foreign Firms

Fraction

Productivity

Fraction

getting

impact of

getting

impact of

training

10% change

training

10% change

Productivity

In-house training

0.094

not sig.

0.191

1.0

Private training institutes

0.017

not sig.

0.023

5.6

SDCs & Adv. train. ins.

0.003

0.004

not sig.

Government training institutes

0.005

0.005

not sig.

Note:

1.2 not sig.

The productivity impact is calculated for a 10 percent change in mean training intensity; statistically insignificant impacts denoted as "not sig."

Source: Coefficients of tobit training measures taken from Table 3.6, means of training intensity variables calculated from the MITP data.

36

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

wage payoffs to training highest? And, which groups

experience (age), and in firms that invest in new

of workers benefit most from training?

technology. Firms with a workforce that is more highly educated also tend to pay higher wages, while

We are interested in these issues for several rea­

those that rely heavily on female workers pay lower

sons. First, how the productivity gains from training

wages.

are shared has implications not only for worker in­ centives to undertake training, but also for employer

Both training measures indicate a positive and sig­

incentives to sponsor and pay for training which may

nificant effect on monthly wages. In column one,

not be able to be recouped because of job turnover.

the training indicator variable has an estimated

Second, to the extent that higher wage payments are

wage effect of 0.04 (four percent); in column two,

feasible only when justified by productivity gains

the estimated wage effect of the predicted train­

from training, these wage analyses-by firm charac­

ing variable is0.06 (six percent). Even given this

teristics and by worker groups-provide a way of in­

range of estimates, what is striking is that the wage

dependently verifying the productivity outcomes of

effects of training are smaller than the productiv­

training identified earlier.

ity gains from training of0.32 (32 percent) esti­ mated in a production function model (see Table

To get estimates of the wage effects of training, we

3.1, column one).

regressed the logarithm of monthly wages on a mea­ sure of worker training and other control variables.

A comparison of these estimates suggests that roughly

The other explanatory variables are similar to those

one-eighth to one-fifth of the productivity gains from

used in the production function model, and include a

training are passed on to workers in the form of

quadratic measure of firm size (logarithm of employ­

higher wages. By implication, the remaining seven­

ment), age of the firm indicator variables for whether

eights to four-fifths of the productivity gains accrue

the firm exports or invests in technology, mean

to the employer as the returns to his (share of) in­

schooling of the workforce, the proportions of non­

vestments in training.

,

production and female workers, and a set of two­ digit industry dummy variables. We also estimated

This evidence of firm-worker sharing of productiv­

separate wage models for four occupational groups

ity gains from training is of some policy interest, given

to determine if the wage effects of training differ for

proposed guidelines on linking wages to productiv­

skilled and unskilled workers.

ity growth currently being drafted in Malaysia by a tripartite group representing employers, unions and

Overall Wage Effects of Training

the government. However, we caution that this evi­

In Table 3. 8, we report the results of two wage model

dence is cross-sectional, when what is required to

specifications, one where training is measured by an

inform the deliberations of this tripartite groups is

indicator variable (column one), a second where we

evidence on how gains in productivity over time are

include the predicted value of training obtained from

passed through to wages increases. This will re­

a pro bit model to account for potential selectivity

quire rigorous time-series analyses of productivity

bias (column two). As noted in previous sections, the

and wage growth, which is beyond the scope of this

wage effects of training may be biased (either up or

report.

down) if the firms that train differ systematically from those that do not.

Do the wage effects of training vary systematically across different groups of firms? The previous pro­

Before presenting the training results, we note that

duction function results revealed a pattern of pro­

mean pay levels tend to rise with firm size (employ­

ductivity gains from training that was higher in firms

ment) up to a point, but decline in the very large

that invested in technology, that exported, or had

firms; they are higher in firms with more production

foreign capital participation. To determine if the wage

PRODUCTIVITY AND WAGE OUTCOMES

Table 3.8 Wage Model Estimates with Training Indicator and Predicted Values

Independent Variable

Training Indicator Predicted Training Specification Specification

Log (labor)

0.0988

0. 0738

cant impact on wages in firms that do not invest in technology, or in domestic firms . These weak wage effects may reflect, in part, the much smaller produc­ tivity gains from training in these latter groups of

firms . Thus, we conclude that the training-wage re­ sults by firm characteristics are broadly consistent

(0.027)

(0.029)

Log

-0.0138

-0.0128

with the patterns of productivity gains from training

(labor squared)

(0.003)

(0.003)

found earlier.

Invest in Technology Exports

0.032°

0.031°

(0.018)

(0.019)

Wage Effects of Training by Occupation

-0.023

-0.023

The wage effects of training can also be analyzed

(0.018)

(0.018)

for four occupational groups-supervisors, tech­

nicians, skilled production workers, and unskilled

0.0048

0.004a

(0.001)

(0.001)

0.023a

0.0188

(0.004)

(0.005)

0.038b

0.062b

Training

(0.017)

(0.027)

Proportion

-0.034a

-0.046a

tion received in-house or external training, and

Female Workers

(0.013)

(0.014)

then using their corresponding training intensity

Age of Firm

Education of Workforce Any Formal

Proportion of Nonproduction Labor Constant

1.039a

0.943a

(0.060)

(0.079)

production workers. The MITP survey elicited detailed occupation-specific information on wages as well as numbers getting training by source. We exploit this rich detail by estimating separate wage models for each occupation, first using indicator vari­ ables for whether workers in that specific occupa­

measures.

5.779a

5.9538

Table 3.9 Wage Effects ofTraining by Level of

(0.089)

(0.119)

Technology, Exports, and Ownership

a= Significant at 1%

Estimated Training Coefficient

b = Significant at 5% c

Note:

= Significant at 10%

Numbers in parentheses are standard

No investment in technology

errors.

Invest in technology

0.023

0.073b

(0.022)

(0.030)

Industry dummy variables included but Source: 1995 MITP Survey

effects of training also exhibited this pattern of variation, we estimated separate wage models for dif­

technology license(s) 0.026

0.106b (0.049)

Does not export

Exports

The training effects for each group of firms are sum­

Like the productivity results, they show positive and

0.064a

(0.033)

(0.020) Foreign-owned Firm

0.022

0.070b

(0.022)

(0.028)

a= Significant at 1%

statistically significant wage effects of training in the

b = Significant at 5%

firms that invest in technology, that have technology contrast, there is little evidence (except in non-ex­

0.061°

Domestic firm

marized in Table 3.9.

porting firms) that training has a statistically signifi-

technology license(s)

(0.019)

ferent groups of firms defined in an analogous way.

licenses, that export, and that are foreign-owned. In

Has

No

not reported here.

c

Note:

= Significant at 10% level

Numbers in parentheses are standard errors.

Source: 1995 MITP Survey

37

38

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Table 3.10 Occupation-Specific Wage Effects of Training Training Measures

Supervisors

Technicians

Skilled

Unskilled

Production

Production

Workers

Workers

Training Indicators Internal Training Indicator

External Training Indicator

0.063•

0.014

0.072•

0.029

(0.024)

(0.024)

(0.024)

(0.020)

0.001

-0.019

o.osoc

(0.025)

(0.025)

(0.026)

(0.022)

-0.001

Training lntensi!Y Proportion trained internally

Proportion trained externally

Note:

a=

Significant at 1%

b=

Significant at 5%

c=

Significant at 10%.

0.095•

0.019

0.079•

-0.007

(0.030)

(0.031)

(0.034)

(0.027)

-0.052

0.005

0.110
250workers)

454

145

60

41.4

Tobacco

52

27

51.9

Textiles

74 97

14 25

18.9

food, beverages and tobacco, wood and furniture,

25.8

glass and pottery industries (one-third to one-half of firms), than the electrical machinery, chemicals,

Small (50-100workers) Medium

the MITP sample is not completely representative.

Industry Food Other Food, Beverages,

Apparel Wood & Furniture

208

104

50.0

Paper & Printing

89

22

24.7

Chemicals

62

7

11.3

21

18.6

to be in non-compliance (49 percent) than large

firms (eight percent). Across industries, non-compliance is higher in the

and textile industries where rates of non-compliance rates are lower (10 to 20 percent). As such, it ap­

Rubber

113

Plastics

87

23

26.4

pears that non-compliance is concentrated among

Glass & Pottery

80

27

33.8

Iron & Basic Metals

42

9

21.4

small firms and firms operating in the traditional,

Fabricated Metals 71

16

22.5

domestic-oriented industries.

Machinery

39

8

20.5

Electrical Machinery

198

21

10.6

48

10

20.8

probit model to identify the factors associated with

8

17.8

non-compliance. Underlying this analysis is an eco­

Transport Other Industries

45

Source: 1995 MITP Survey

To look at this issue in greater depth, we estimate a

nomic model in which firms make cost-benefit cal­ culations, weighing the probability and cost of being

Wedefine "eligible fume:;" asthoo:::employing50ormore

caught in non-compliance against the benefits of not

workers, broadly following the 1995 Guidelines from

registering with the HRDF.

HRDF, Human Resource Developrrent Couocil.10 On the basis of this rough definition, 402 firms (about 27.7

We hypothesize that the probability of appre­

percent) out of the total of 1,450 eligible reported that

hension is lower for smaller firms, who are less

they were not registered with the HRDF.

visible, and for firms that are located in more re­ mote areas. Benefits of non-compliance are two­

Of those that were registered with the HRDF, 45

fold: the firm avoids payment of payroll levies,

percent claimed reimbursements under ATP, 47 per­

and the potentially high fixed cost of setting up a

cent under SBL, and less than 10 percent under the

formal training program if one does not already

PLTscheme. However, 34.5 percent of registered

exist.

firms reported that they did not claim reimburse­ ments under any of the three schemes.

The results of the pro bit analysis are reported in

Non-Compliance with HRDF

First, compared to small :finns, medium size and large

Table 4. 7, and they suggest the following points.

The Human Resource Development Act of 1992

firms are less likely to be in non-compliance, possi­

made it mandatory for eligible firms to register with

bly because they believe that the probability of their

the HRDC. The 27 percent non-compliance with

being caught is high, given their higher profile.

TRAINING

Table 4.7 Probit Estimates of Non­

PouciES

The HRDC is aware of the non-compliance issue but it has few resources to devote to enforcement.

Compliance with HRDF

55

It

currently has a skeleton team-an administrative of­ Independent Variable

Estimate

Standard error

Medium size firms (1 01-250 workers)

0.086

-i .323a

0.114

It has established a panel of lawyers but their au­

0.254a

0.083

0.759a

0.148

ment of levies by registered firms, not prosecution

-0.767a

0.141

Only internal informal training No training Region: Western

corridor states Trengganu, Kelantan

0.254

0.184

Region: Perlis and Kedah

-0.421 a

0.187

-0.241 a

0.081

Increased production Constant Log likelihood

thority is limited to civil cases regarding non-pay­ of non-registered firms. Only legal officers can pursue the latter cases, and the HRDC is seeking to fill several of these positions. Until such time

Region: Pahang,

over the last 3 years

underlying population of firms that are eligible but not registered with the HRDC.

-0.532a

Large size firms (>250 workers)

ficer and a clerk-working on developing lists of the

0.420a -684.46

0.153

as the HRDC develops the capability to identify and prosecute non-registered firms, the threat of prosecution will not be credible and the non-com­ pliance problem will persist.

-684.46

The following steps should be taken to address this a= Statistically significant at 1% level. The omitted re­ gion is Sabah and Sarawak; the omitted size is small firms with 50-100 workers; the omitted train­ ing group is firms providing formal training. Source: 1995 MITP Survey

Second, firms that do not train or that rely only on informal training, are significantly more likely to be in non-compliance as compared to those provid­ ing formal training. This is consistent with the pres­ ence of high-fixed costs of developing and setting up training programs, and incorporating the new skills into existing production. Third, there are systematic regional variations, with non-compliance being much higher in the eastern states of Pahang, Kelantan and Trengganu, and in East Malaysia as compared to the west coast states. Finally, we attempted to control for the firm's growth experience in the past three years, on the grounds that firms that are not growing are more likely to fail and thus have little incentive to reg­

issue. First, the Government should expeditiously provide HRDC with the necessary manpower and legal resources to identify and prosecute firms in non-compliance, and to recover back levies and other penalties. Second, with additional resources in hand, the

HRDC should also mount an information campaign, on television and in newspapers, to encourage eli­ gible firms to register with HRDC. It should an­ nounce its intention to vigorously enforce compliance with the HRDF Law and, to ensure that this threat is credible, it should publicize its in­ creased enforcement capabilities as well as its pros­ ecutions of selected firms. Finally, this campaign should be accompanied by a time-limited amnesty program for firms to come for­ ward, register with the HRDC, and pay their back levies without civil or criminal penalties. Similar amnesty programs have been effectively used in the United States.

Claims under HRDF

ister with the HRDF. The estimated results-that

A second issue is that a sizable number (362) of reg­

firms with stagnant or declining sales are less

istered firms do not claim reimbursements for train­

likely to register-is consistent with this hypothesis.

ing expenditures despite contributions of payroll

56

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

levies to the HRDF. Their claims for training under

line-to June 1995-granted by the HRDC.12 This is

any of the schemes are a crude indicator of how ef­

supported by HRDC data showing an increase in

fective HRD F has been in encouraging firms to be­

the ratio of funds approved to levy collected, from

gin or increase training.

64.7 percent in 1994 (the time period covered by the MITP survey) to 88.9 percent in 1995.

Tabulations suggest that small firms are less likely to claim as compared to their larger counterparts-

We estimate probit models to get insights into why

50.2 percent for small firms with 50-100 employ­

these registered firms did not implement training

ees, 41.3 percent for medium size finns with 101-250

program s and claim reimbu r sements. The depen­

employees, and 19.4 percent for large firms with over

dent variable is an indicator variable with a value

250 workers. This is a key issue since firms paying

of one if the firm does not claim, and zero other­

into the system, but not claiming reimbursements,

wise.

in effect pay a tax of one percent of payroll without getting any tangible benefits from doing so.

Two models specifications are considered. In the

first model, we include several measures of firm size, Who are these non-claimant firms and why are they

reported training status, an indicator variable for

not training? Table 4.8 reports the distribution of

whether they have a training plan, and industry

registered firms that do not claim according to

dummy variables. In the second specification, we

theirtraining status-no training for workers, only

replace the actual training variables with the fmns'

informal training on-the-job, and training formally.

own responses about why they provided little or no training. Table 4.9 reports the estimated probit re­

Table 4.8 Registered Firms Not Claiming from HRDF by Training Status Training Status

Registered

Distribution

firms not claiming

by training status

22

6.1

Firms training informally only

196

54.1

Firms training formally

144

39.8

Firms not training

Source: 1995 MITP Survey

sults for these two models. The results of the first model indicates that the firms least likely to claim from HRD F are small firms, and firms providing no training or only informal train­ ing. This result was already evident in the simple tabulations. Having a training plan, however, is as­

sociated with a greater likelihood of a claim to HRDF, though not necessarily through the PLT (annual training plan) scheme.13 This is not surprising. A training plan is indicative

Interestingly, only about six percent of these non­

of an awareness, on thepartoftheemployer, of its

claimants do no training. The majority of firms (54

skill needs, as well as a commitment to a strategy of

percent) are those that only provide informal OJT

systematically training employees to meet these skill

from co-workers and supervisors.11 Thus, about 60

needs either in-house or through services of exter­

percent of these firms are not eligible for reimburse­

nal training providers.

ments because they either provide no training or are only training informally. The remaining 40 percent report providing formal training yet do not claim reimbursements for these expenditures.

The results of the second model provide insights into why firms contribute but do not claim. The statisti­ cally significant responses are that employers have limited resources for trainin g, they use mature tech­

It is possible that some (unknown) fraction of these

nology with low skill requirements which are

latter firms submitted claims subsequent to the

readily met by school graduates, and skilled work­

MITP survey, based upon an extension of the dead-

ers are readily hired from other firms .

TRAINING PoLICIEs

57

Table 4.9 Probit Estimates of Not Claiming from HRDF Independent Variable

Model1

Standard

Model2

Estimates

Errors

Estimates

Standard Errors

Medium firms (101-250 workers)

-0.123

0.107

-0.196

0.105

Large firms (>250 workers)

-0.630 a

0.114

-0.836•

0.110

Limited resources for training

0.242b

0.115

No knowledge about training

0.067

0.099

Mature technology

0.147°

0.084

Get skilled workers from others

0.285b

0.119

Skeptical about training benefits

0.097

0.139

0.283b

0.124

Skills from schools are adequate 0.463•

0.091

Firm does not train

0.511•

0.216

Firm has a training plan

-0.493•

0.089

Firm only trains informally

-0.248b

Constant

-623.06

Log Likelihood a=

0.104 -623.06

Significant at1%

b = Significant at 5% c=

Significant at 10% level Note: The omitted size category is small firms with 51-100 workers; and the omitted training category in model1 is firms that provide formal training. Industry dummy variables included but are not statistically significant.

Source: 1995 MITP Survey

These responses are consistent with the weak

In the JTS scheme, groups of small firms reap

training and technological capabilities of small

scale economies by banding together to engage

and local firms shown in Chapter Two, as well as

training providers who run specific training pro­

the financial constraint on funding training iden­

grams for them on a joint basis. In addition to

tified in earlier sections of this chapter. They

lower per trainee cost, the ITS provides an added

explain why most of them do little or no formal

incentive for the firm that organizes the joint

training on their own despite the financial incen­

training.

tive of the HRDF levy, relying instead on the edu­ cational system or on trained workers hired from

In the GTS scheme, now being implemented on a

other firms.

pilot basis, 14 employer associations are encour­ aged to take the initiative in providing training

Recent HRDF Initiatives

to members, with HRDF providing funds to set

The HRDC recognizes the funding and training

up training facilities, and paying the salary of a

difficulties faced by small local firms, and it has

training coordinator for three years. The coordi­

introduced several schemes and initiatives to ad­

nator conducts a skill needs survey, and organizes

dress these constraints. One set of initiatives seeks

training program for its members.

to assist both large and small firms, in develop­ ing a training infrastructure-the JURUPLAN for

In this section, we provide insights into these ini­

developing a training plan, and a scheme to pur­

tiatives using data reported in the MITP survey.

chase training aids and set up training rooms. The

We report the incidence among firms of in-house

second set of initiatives-the Joint Training Scheme

training centers, training plans and how they were

(JTS) and Group Training Scheme (GTS)-is de­

developed, and the different types of joint train­

signed to encourage group training for smaller

ing programs, focusing in particular on differences

fums.

in these measures by firm size.

58

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Table 4.10 Training Centers and Training Plans in MITP by Firm Size

Conditional on Having a Training Plan

Training System Firm Size

% firms

% firms

with

% firms with

Training Plan

Training Plan

Training

Training

developed at

developed by

funded by

Centers

Plans

own cost

consultants

JURUPLAN 7.6

% firms

% firms Training Plan

Small

3.1

10.9

83.2

23.7

Medium

9.7

42.4

76.7

33.5

7.1

19.2

69.2

79.3

35.7

8.6

Large

Source: 1995 MITP Survey Table 4.11 Joint Training Programs in MITP by Firm Size

Firm Size

Source of Joint Training Programs Small % Firms with joint training programs

3.9

Medium 9.9

Large 18.9

Conditional on ajoint training program: Through Industry associations

23.4

30.2

37.2

Organized by government institutions

27.7

24.5

30.2

Ad hoc programs with other firms

34.0

49.1

52.3

Through specialized companies

21.3

35.9

40.7

Organized by suppliers

31.9

34.0

17.0

Organized by buyers

14.9

17.0

27.9

Source: 1995 MITP Survey

Table 4.10 reports the distribution of firms with

How were these training plans fmanced? Columns

training centers and training plans by three firm sizes.

three through five report the sources of their fund­

Small, medium and large sizes are defined as em­

ing to develop training plans, conditional on having

ployers with less than 100 employees, 101-250 em­

one. Multiple responses are permitted so that the

ployees, and over 250 employees, respectively.

numbers do not sum to 100.

Column one shows that firm size is an important

Two points emerge. First, there do not appear to be

determinant of whether an employer has a training

major differences by size in how employers develop

center-only three percent of small firms have train­

training plans. Second, the figures reveal that most

ing facilities as compared to 19 percent of large firms.

training plans (over 70 percent) are developed by

As such, considerable potential exists for HRDC to

employers at their own cost, followed next by train­

extend the training aids scheme to small firms.

ing plans developed with the assistance of consult­ ants. The proportion of training plans funded by

Column two shows the proportion of firms with a

the JURUPLAN scheme is low, about seven to

training plan. As before, the presence of a training

eightpercent. HRDC should ascertain the reasons

plan is highly correlated with size, though a much

for why employers prefer to fund their own train­

higher number of firms of all sizes report having a

ing plans when an alternative source of fmance is

training plan as compared to training facilities. To

available through the JURUPLAN scheme.

the extent that employers can send workers outside for training, it reduces the necessity for them to have

Table 4.11 shows the incidence and types of joint

a training facility in-house.

training reported by employers in the MITP survey.

TRAINING POLICIES

59

Firms were asked whether they had joint

Such ad hoc arrangements arise when firms,

trainingprograms with other firms to provide work­

which otherwise compete with each other, see

ers with training, and if so, how these training pro­

acollective interest in jointly investing in a com­

grams were organized. Finns indicated one or more

mon good, in this case, worker training. The im­

of six types of programs organized through:

portance of other types of joint programs varies markedly by firm size.

•

industry or professional associations

•

government or public institutions

For small firms, programs organized by suppliers

•

ad hoc arrangements with other firms

and government agencies are cited most often after

•

specialized training companies

ad hoc arrangements. Unfortunately, the govern­

•

programs organized by suppliers

ment and public agencies involved are not identi­

•

programs organized by buyers

fied. For medium size firms, suppliers are also important as are specialized training companies. For

The first row of Table 4.11 indicates that joint train­

large firms, most commonly cited after ad hoc joint

ing programs are relatively rare in Malaysian indus­

programs are specialized training companies and

try. When they occur, these programs are most

employer or professional associations. The latter's

commonly found among large firms (19 percent)

focus on large employers would be reoriented to­

rather than among smaller firms (four percent) who,

wards supporting smaller firms under theHRDF's

it may be argued, need them most. Unlike larger

new GTS scheme.

firms, individual small employers are often unable to assemble a large enough group of employees to

Has HRDF Increased Training by Firms?

warrant the fixed costs of hiring an outside provider

It is too early to make judgments about the efficacy

to deliver a t ailored training program. Joint train­

ofHRDF in promoting training and skill upgrad­

ing programs, such as those envisaged by the

ing. Additional years of accumulated information

JTSscheme, would allow groups of small firms to

(panel data) will be needed to do that. However, a

reap the economies of scale.

crude test is possible using retrospective responses

Given the obvious benefits of such programs, espe­

has changed-iocreared, stayed the sarre, ordecreased­

cially for smaller firms, it is unclear why more joint

over the past three years, a period spanning the year

from employers about how their level of training

programs are not found among them. Is it due sim­

prior to the introduction ofHRDF in 1993, to the

ply to the low skill requirements of small firms,

present (1995). We will do this by comparing the

or are there collective failures-no tradition of col­

training experiences of two groups of firms: those

laboration among small firms, or absence of em­

registered with theHRDF, and those who were eli­

ployer associations to represent the collective

gible but did not register. In principle, the regis­

interests of small business-which prevent them from

tered group would have increased incentives to train

working together? This issue should be studied by

so as to recover their payroll levy contributions. In

HRDC to determine if incentives alone are suffi­

contrast, the non-registered group would not face

cient to encourage joint training.

these same incentives since they do not contribute toHRDF. We recognize that these two groups of

The remaining columns ofTable 4.10 show how ex­

firms are different, not only in terms of their mea­

isting joint training programs are organized. When

sured characteristics but also in terms of their unob­

firms have one, the single most important type of

served (to us) productivity attributes.

joint training program for all firm sizes is through ad hoc arrangements with other firms-the fractions citing this range from 34 percent for small firms to 52 percent for large firms.

Table 4.12 compares the training experiences of these two groups of firms. Of those registered with the HRDF, about 50 percent said that they had increased

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

60

Table 4.12: Probit Estimates of Increased Training Under HRDF Independent Variable

Combined

Purely

Firms with

Interactions Between Size

Sample of

Domestic

Foreign

Firms

and HRDF Small Firm HRDF

0.160 (0.122)

Medium Firm HRDF

Firms

capital

.141

0.179

(0.151)

(0.215)

0.312"

0.171

0.435•

(0.101)

(0.129)

(0.179)

Large Firm HRDF

0.788"

0.839"

0.753"

(0.102)

(0.147)

(0.175)

Introduced new technology

0.428"

0.499"

0.365"

in last 3 years

(0.076)

(0.103)

(0.114)

Constant

-0.574"

-0.616"

-0.516"

(0.083)

(0.099)

(0.160)

•=

Significant at 1% level

Note:

Industry dummies were included but were not statistically significant.

Source: 1995 MITP Survey

Table 4.13 Changes in Training Levels Over the Past Three Years: Firms Registered with HRDF and Unregistered Firms Registration Status Eligible Registered Firms

Eligible Unregistered Firms

Increased TrainingTraining is the Same

Decreased Training

522

412

12

(49.8)

(39.3)

(1.2)

109 (27.1)

190 (47.3)

21 (5.2)

Note: The percentages do not sum to 100. Close to 10 percent of registered firms and 20 percent of unregistered firms said they did not know. Source: 1995 MITP Survey

training over the last three years , 39 percent firms

creased training over the past three years in regis­

said that their training had remained the same, and

tered HRDF firms versus non-registered firms. The

only one percent said that their training had de­

effects of HRDF are allowed to vary by firm size

creased. In contrast, of the eligible firms not regis­

using a set of interaction terms between firm size

tered with the HRDF, 27 percent said that their

dummies and an indicator variable for being regis­

training had increased, 47 percent firms said that

tered with HRDF.

their training had remained unchanged, and five percent said that their training had decreased over

The model includes a set of industry dummy vari­

the last three years. Thus, it appears thatHRDFmay have played a role in increasing training provision among registered firms.

ables to control for possible differences in industrycomposition of registered and non-regis­ tered firms. We include a measure of whether the

firm introduced new technologies over the past three We test this hypothesis formally using a probit

years. The intent was to net out the confounding

model. This model compares the likelihood of in-

effects of increased training due to new technology

TRAINING POLICIES

61

that is independent of HRD F. Finally, separate

sponding increase in demand for training among

models are estimated for pure domestic firms and

such firms.

firms with foreign capital to see if the training ef­ fects of HRDF varies by foreign ownership. Table 4.13 reports the results of this exercise. They demonstrate that HRDF has had a significant role in increasing training among medium and large firms registered with the HRDF, but not small firms. This result continues to hold for the sample of firms with some foreign capital participation. Among purely domestic firms, HRDF has only been effective in increasing the training of large firms with over 250 employees; the HRDF incentive was not effective in increasing training among small and medium-size local firms. These results were not affected by dif­ ferences in industrial composition of the two groups, which we control for using industry dummies. However, whether or not firms had introduced new technology in the recent past made a difference. In­ creases in training and introduction of. new technol­ ogy over the past three years are significantly correlated, a result consistent with that fmdings in Chapter Two that technological change is accompa­ nied by higher skill requirements.

Findings and Policy Implications

The DDJI' incentive scheme has generally not proved effective in inducingfirms to train. It has been used primarily by MNCs,joint-ventures,and larger firms who, arguably, were training already. For these firms, the DDIT scheme has meant sizable wind­ fall gains; for the firms that provided little or no training, the DDIT scheme has failed to induce them to begin, or to increase provision of train­ ing. Lack of awareness about DDIT, and its re­ quirements, has been the principal reason for its limited use. A second factor was the heavy re­ quirements of applying for DDIT, and the corre­ sponding high rates of rejection, both of which reduced interest in using the incentive. The key lesson for policymakers is that any policy or in­ centive, whether in training or in other areas, is unlikely to be fully effective if targeted benefi­ ciaries are unaware of or inadequately familiar­ ized with the program. Another lesson is that,where feasible, filing requirements should be streamlined to improve take-up of incentives.

The DDJI' incentive is currently restricted to small firms with less than 50 employees; all other firms are covered by HRDF. The Government should eliminate the remaining DDIT coverage entirely

Markets are generally well functioning in Malay­

on several grounds. First, it is likely that few

sia, but there is evidence that marketfailures pose

small firms are using the incentive today. Sec­

important constraints on trainingfor many employ­ ers, especially the small and medium-size compa­

ond, bringing all firms under the HRDF umbrella greatly simplifies administration, since universal

nies. These include high labor turnover, which

coverage of all firms would searnlessly accommo­

prevent employers from recouping investments in

date the growth or shrinkage of firms above or

training; poor information on training methods,

below the 50 employee cutoff. Finally, HRDF is

especipecially how to train or what kinds of training

developing new schemes to support the training

to provide; and inadequate finance for training, es­

activities of SMis, and the 50 employee cutoff

pecially among SMis. These market failures justify

would arbitrarily restrict access of small firms to

government intervention. While not a market failure

these programs. The issue of payroll contributions

per se, the use of mature technologies with low skill

for these smaller firms needs to be resolved. The

needs was the principal reason for little or no training

government might consider a waiver of the payroll

both among local firms and SMis. Increased take-up

levy for small firms, and provide HRDF with a block

of incentives to adopt new technology or improve qual­

grant from general revenues to cover their use of

ity, such as ITAF schemes, should lead to a corre-

training services.

62

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Non-compliance in HRDF appears to be significant.

deadline granted by HRDC, it is likely that the un­

The MITP survey indicates that as many as 27 per­

derlying problem remains, especially for smaller

cent of eligible firms with 50 or more employees are

firms. About 60 percent of them provide no train­

not registered with, and contributing to, the HRDF.

ing or only unstructured, informal, on-the-job train­

I t is concentrated among smaller firms, firms in tra­

ing that is not eligible for reimbursement under the

ditional and domestic-oriented industries, firms in

HRDF. Some of their constraints-poor knowledge

the states on the east coast and in East Malaysia, and

about training, not having a training plan, or inad­

among firms providing little or no structured train­

equate training facilities-are being addressed through

ing. While there may be good reasons to downplay

HRDF'sTNA workshops, theJURUPLANscheme

enforcement in the early gestation period, policymak­

to develop training plans, and schemes to fund pur­

ers will eventually have to make a strong effort to

chase of training aids. Other factors which limit

address the issue of non-compliance. HRDC cur­

demand for training, such as use of mature technol­

rently has few personnel or legal officers to devote

ogy, are under the purview of other government

to enforcement. The Government should expedi­

agencies, and policies to address them are discussed

tiously provide HRDC with the necessary man­

in Chapters Five and Six.

power and legal resources to identify and prosecute HRDC has introduced two new schemes-ITS and

eligible but non-registered firms .

GTS-to encourage group trainingfor smaller em­ The HRDC should also llWUnt an infonnation cam­

ployers, either initiated by groups of small firms

paign, on television and in newspapers, to encour­

themselves, or organized by employer associations.

age eligiblefirms to register with HRDC. It should

The MITP survey indicates that such joint training

announce its intention to vigorously enforce com­

programs between firms are rare in Malaysia. They

pliance with the HRDF Law and, to ensure that this

are commonly found not among SMis, but among

threat is credible, it should publicize its increased

large firms. When they occur, most are ad hoc ar­

enforcement capabilities as well as its prosecutions

rangements. Joint training programs organized by

of selected firms . This campaign should be accom­

suppliers and by government agencies are more im­

panied by a time-limited amnesty program for firms

portant for small fim1s; joint programs organized

to come forward, register with the HRDC, and pay

by specialized companies are cited by many medium

their back levies without civil or criminal penal­

and large fimls; industry associations are also cited,

ties. Similar time-limited amnesty programs have

but primarily by large firms. These industry as­

been used effectively in several states in the U.S. to

sociations will have the responsibility, under the

improve compliance.

pilot GTS scheme, for organizing training for SMis. These group-oriented approaches are po­

As ofyear-end 1994, over one-third of registered

tentially potent policy instruments for fostering

}inns had not claimed any reimbursementsfor train­

training among SMis. Variants of both policies

ing through the HRDF. This figure was especially

have been used, with some success, in a number

pronounced for small and medium size firms about

of developing countries (see Chapter Six) and the

,

half of whom did not claim. While claims have risen

progress of these initiatives in Malaysia should

since them, in large part due to an extension of the

be carefully monitored.

CHAPTER FivE: TEcHNOLOGY, QuALITY

AND

SKILLS

Malaysian policymakers have identified low levels

past three years, and its consequences for changing

of technology and product quality as a bottleneck to

skill needs and employment.

sustained growth and competitiveness. They are actively encouraging firms-through fiscal incentives

This wealth of firm-level data, from such a large

and the activities of technology support institutions:

sample of firms, provides an unprecedented oppor­ tunity to analyze enterprise decisions to invest in

•

to increase firms ' spending on research and de­

technology, to examine the quality control efforts of

velopment,

firms, to identify the effects of introducing new tech­

•

to accelerate technology transfer from the MNCs

nology on future skill requirements and productiv­

to domestic firms, to invest in automated pro­

ity, and to draw out their implications for

duction technologies to economize on scarce

policymakers.

labor, •

•

to modernize small and medium-scale industries

(SMis), to adopt quality control systems and raise prod­ uct quality to meet exacting international stan­ dards for exports (MID, IMP Review, 1994).

The 1992 National Survey ofResearch and Devel­ opment provides some information on industrial train­

Technological Characteristics of Firms We begin by using the MITP survey to character­ ize the technology level of firms in Malaysia, then their efforts to improve quality. Employers can develop their technological capabili­ ties in several different ways. First, they can de­

ing in Malaysia. 1 It indicates that overall the research

velop technology in-house through investments in

and development efforts in Malaysia-about 0.37 per­

research and development. Since few local enter­

cent of GDP-are lower than projected in the 1990

prises have the requisite scientific, engineering and

Action Plan for Industrial Technology Development

technical capabilities to conduct cutting-edgeR&D,

(APITD), while private sectorR&D spending---0.17

much of this expenditure may reflect relatively mod­

percent ofGDP-is higher than projected. However,

est engineering and productR&D activities.

the scale of private sector industrial R&D is Malay­ sia is relatively modest by international standards

Second, when in-house R&D capabilities are lim­

(WorldBank, 19%).2 Much ofR&D is concentrated

ited, technology transfer is an alternative way for

in the electrical and electronics industry and among

local firms to acquire new technology, either through

MNCs, and private R&D spending in Malaysia total

licensing and know-how agreements with other

RM 125.4 million spread over just 97 firms.

firms, or through joint-ventures with foreign firms . Finally, firms can acquire new production technol­

In this chapter, we use the MITP survey to provide

ogy embodied in new vintages of capital, through

additional insights on the technology level, quality

investments in automatic machinery, computer-as­

control systems, and associated skill needs of enter­

sisted production, and testing and quality control

prises. The survey elicited detailed firm-level infor­

equipment.

mation on R&D expenditures as a percentage of sales; technology and know-how licensing agree­

Table 5.1 begins by providing a broad overview of

ments; investments in automation and quality control

the incidence of these technology indicators by four

equipment, as well as the vintage (age) of machin­

firm size categories aiXi by ownership--domestic firms,

ery; quality control methods; IS0-9000 certification;

joint-ventures, and wholly foreign-owned firms.

whether new technology was introduced over the

Three broad sets of indicators are considered:

64

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

•

Research and development -- whether the

most half of this R&D (RM 4.812 million) is concen­

firm does R&D, and R&D expenditures as a

trated in the electronics industry.

percentage of sales •

•

Technology transfer -- does the firm have

When these sample estimates are multiplied by the

any technology or know-how licensing agree­

population weights, total R&D spending almost

ments, from foreign or domestic sources

doubles--toRM 1,908 million in 1994. Thus, com­

Sophistication of machinery and equip­

pared to the 1992 R&D survey, the 1994 MITP sur­

ment-- percent of equipment that is fully auto­

vey finds four times as many firms reporting R&D

matic, whether the firm has quality control or

expendirures, higher per firm R&D spending, and if

testing equipment, and percent of equipment

the weighted estimates are to be believed, almost 15

that is more than 10 years old.

times as much private sector R&D spending as re­ ported in the 1992 R&D. Some part of this gap is

First, consider the R&D expenditures reported by

undoubtedly due to differences between surveys in

firms in the MITP survey. Out of a sample of2,200

the definition of R&D, and to the two years separat­

firms 435 firms or 19.8 percent had positive R&D

ing the surveys. The important point to note is that

expenditures in 1994. Based on reported R&D to

even with this more expansive R&D measure, lev­

sales figures, we estimate that these firms spent a

els of private R&D in Malaysian industry are still

total ofRM 1,030 million on research and develop­

relatively low in comparison to other Asian NICs

ment, or approximatelyRM 2.4 million per firm. AI-

and industrialized countries.

,

Table 5.1 Technology Characteristics by Firm Size and Ownership Mean Values

Percent of Firms Ownership Type and Firm Size

Do R&D

HaveQC

R&D

Equipment

Automatic

Technology

& Testing

%of

over 10 yrs

equipment

license(s)

Equipment

sales

Have

%

%

Domestic Firms Micro

4.4

1.5

4.4

1.31

39.7

3.1

Small

9.2

2.8

17.1

0.08

34.2

11.6 20.6 27.3

Medium

23.9

5.6

38.4

1.40

25.1

Large

31.4

12.8

50.0

0.38

22.0

Joint-Ventures Small

16.7

10.0

36.7

0.27

28.2

17.4

Medium

23.4

22.5

44.0

0.93

20.9

22.3

Large

38.8

33.8

60.4

1.64

19.3

32.9

100% Foreign Small

15.4

7.7

46.2

0.49

11.4

23.4

Medium

20.7

15.7

47.9

0.43

25.9

Large

26.9

23.1

67.5

2.09

7.2 7.5

Notes:

Micro= less than 16 workers, Small= 16-100 workers,

38.3

Medium= 101-250 workers

Large= over 250 workers. Micro firms not reported for joint-ventures or 100% foreign firms because of small sample sizes. Source: 1995 MITPSurvey

TECHNOLOGY, QUALITY AND SKILLS

65

Table 5.1 shows a striking positive relationship be­

Finally, Table 5.1 reveals striking differences in the

tween firm size and the likelihood of R&D. Among

types and vintage of capital equipment by firm size

local firms just over four percent of micro firms re­

and ownership. Compared to larger firms, micro,

,

port R&D spending; this figure rises to 24 percent

small and medium size firms are less likely to have

for medium firms and to over30 percent for large

quality control and testing equipment, a smaller frac­

finns . A similar size-R&D trend is found amongjoint­

tion of their equipment is made up of numerically

ventures and wholly foreign-owned firms. Except

controlled automatic machinery, and a higher frac­

for local firms, R&D spending as a ratio of sales gen­

tion of their capital equipment is over 10years old.

erally rises with firm size especially among large

Furthermore, for any given finn size, the table shows

firms with foreign capital.

that a progression to more intensive use of testing

A second, intriguing result are the differences by

tages of equipment as the fraction of foreign equity

ownership status. For any givenfirm size, medium

increases in the firm.

equipment, greater automation, and younger vin­

and large local firms and joint-ventures are more likely to report R&D spending than wholly foreign­

Table 5.2 reports these technology indicators by two­

owned firms For example, among large firms with

digit industrial sector. The figures reveal consider­

over 1,000employees, over31 and39percent of

able cross-industry variation by foreign equity, by

local firms andjoint-ventures reported R&D spend­

capital intensity, and by export orientation. Indus­

ing, respectively, as compared to just 27 percent of

tries with high levels of foreign direct investment

wholly foreign-owned finns . Plausibly, the latter firms

(FDI) and joint ventures, such as electrical machin­

have few incentives to conduct R&D locally since

ery and chemicals, are more likely to have high pro­

.

they can draw on the parent MNC's stock of tech­

portions of finns with R&D and technology licenses,

nology and R&D laboratories; these typically are

using quality control and testing equipment in pro­

not located in developing economies.

duction. Capital intensive industries with heavy do­ mestic ownership, such as iron and basic metals and

A similar pattern of technology licenses by firm size

transport equipment, are also relatively technology­

and ownership is also apparent. In general, small

intensive. A high proportion of firms in iron and

firms irrespective of their ownership are less likely

basic metals have technology licenses and quality

to report technology licenses than larger firms, re­

control equipment, while many finns in the transport

flecting lower levels of technological capabilities in

sector (primarily Proton) report R&D spending. Elec­

SMis. Joint ventures are more likely to report tech­

trical machinery, along with plastics, rubber and ap­

nology licenses than comparably-sized wholly for­

parel are also export-oriented industries, and a high

eign firms For example, among small firms, lOpercent

proportion of these firms have quality control and

of joint ventures have technology licenses versus

testing equipment to produce for export markets.

.

eight percent of foreign firms; the differential wid­

The remaining industries -- food products, bever­

ens among large firms with34 percent of joint ven­

ages and tobacco, textiles and apparel, and general

,

tures and 27 percent of foreign firms reporting

machinery -- generally show low overall levels of

technology licenses.

the technology indicators.

This pattern of licensing by ownership status may

Quality Control and Precision in

reflect a conscious strategy by MNCs to recover the

Production

costs of developing new technologies from its joint

To become competitive in world markets, Malaysian

ventures. This incentive to license its technology is

firms will need to produce more and better products

diminished when the enterprise in question is a

that meet international standards for price and qual­

wholly-owned subsidiary.

ity. It is not adequate merely to introduce new tech-

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

66

Table 5.2 Technology Characteristics by Industry Percent of Firms Industrial Sector

R&D

Mean Values

Have

HaveQC

R&D

Equipment

Automatic

Technology

& Testing

%of

over10

equipment

equipment

sales

years%

Do

license(s)

%

Food

3.7

1.2

7.9

0.99

19.2

7.0

Beverages & tobacco

1.9

0.3

2.3

0.09

46.3

2.2

Textiles

5.3

0.6

10.2

0.15

12.5

5.9

Apparel

8.1

0.5

11.2

0.51

23.2

4.8

Wood & furniture

4.6

1.4

5.6

0.74

20.3

3.6

Paper & Publishing

4.8

0.7

6.5

0.45

66.9

10.0

Chemicals

30.4

27.0

42.4

1.11

23.1

6.5

Rubber

11.8

5.1

33.0

0.79

29.6

16.0

Plastics Glass & Pottery Iron & Basic Metals Fabricated Metals Machinery

8.3

11.4

14.4

0.08

15.2

16.5

11.1

4.8

17.7

0.30

28.2

7.5

3.8

59.5

66.3

0.02

56.4

5.1

11.6

3.3

19.4

0.90

18.7

5.7

3.7

3.4

18.7

0.80

51.4

6.3

Electrical Machinery

18.9

11.8

76.1

3.23

19.6

27.1

Transport

50.7

2.9

11.9

1.24

82.7

2.2

Other

52.8

2.5

3 .0

0.77

11.3

2.5

Source: 1995 MITP Survey

nologies to reduce production costs. Enterprises will

process control, quality control circles and precision

also need to introduce new fonns of work organiza­

testing instruments in production, and only 16 per­

tion that emphasize product quality, precision in

cent rely on visual inspection to verify accuracy in

production, consistency of quality, and continuous

production. These figures stand in sharp contrast to

quality improvement. Such organizational features

those for SMis. Less than a quarter of micro and

include the introduction of quality control circles

small firms report using either statistical process con­

(QCC), the use of statistical process control (SPC),

trol or quality control circles. Less than one-fifth of

and reliance on quality control and testing equip­

them use precision measuring equipment to verify

ment rather than visual inspection to meet the high

accuracy in production; in fact, 64 percent of micro

levels of quality demanded by increasing sophisti­

finns and 43 percent of small finns rely exclusively

cated users and consumers.

on visual inspection to verify accuracy. This sharp size differential in quality highlights an area of pri­

Table 5.3 reports the incidence, by firm size and

ority for policymakers. There is a clear need for

ownership, of several variables that reflect firms' em­

policies to instill quality consciousness among SMis

phasis on product and process quality:

through information dissemination, subsidized QC training, and incentives to use precision measuring

•

whether it relies on statistical process control

•

whether it has quality control circles

•

how it verifies accuracy in production

•

whether it provides training in quality control.

and testing equipment. Second, local finns are less likely than joint ventures and wholly foreign-owned firms to have a quality control system. Between 20 and 25 percent of local

First, firm size is an important determinant of whether

firms use SPC or QCC techniques, and about five

an enterprise has a quality control system in place.

percent provide QC training. The comparable fig­

Among large finns , about 50 percent use statistical

ures for joint ventures are 31-46 percent for use of

TECHNOLOGY, QUALITY AND SKILLS

67

Table 5.3 Quality Control and Precision in Production Quality Control System Firm Size and

Statistical Quality

Ownership Type

Process Control

Control Circles

Verifying Accuracy in Production

Quality

Precision

Control

Measuring

Measuring

Training

Equipment

Devices

Simple

Visual Inspection

Firm Size

8.1

4.1

1.6

8.1

23.7

63.8

Small

16.5

25.1

4.7

19.9

31.6

42.8

Medium

30.8

41.7

9.2

34.8

29.9

26.4

Large

49.6

53.5

13.7

52.9

24.5

16.3

Micro

Ownership

20.0

25. 4

5.5

20. 5

30.0

42.9

Joint-Ventures

31.5

46.0

9.9

43.0

28.9

22.3

1 00% Foreign

44.7

47.9

12.3

49.2

25.6

19.4

Domestic

Source: 1995 MITP Survey

these QC techniques and 10 percent of Ns provide

control training variable was constructed from in­

QC training. The incidence of QC and QC training

formation provided by employers on the main types

is highest among wholly foreign-owned firms

of training provided to different occupational

.

groups-technicians, supervisors, skilled production While part of this result reflects differences in size

workers and unskilled production workers. A firm

composition by ownership, it is also consistent with

was coded as providing QC training if any one of

the notion that the use of new technology requires

these four occupational groups reported QC training

new fonns of work organization and quality control.

as being the most important training type provided. 3

Are local firms less likely to have quality control systems than joint ventures or foreign firms, once

It very likely understates the incidence of quality

account is taken of size? The answer appears to

control training, since it excludes QC training from

be yes.

external sources and QC training that was a second­

Figure 5 .1 graphically shows the incidence of these

provided QC training to one or more occupational

quality control indicators by firm size and foreign

groups. Not surprising, the industries with high pro­

ownership. The vertical bars represent the percent

portions of finns providing training in quality con­

ary area of training. By this defmition, 160 firms

of local firms, joint ventures (Ns), and wholly for­

trol-electrical machinery, plastics and chemicals-were

eign owned firms reporting each QC indicator. The

also the industries where QC methods are common.

four panels clearly show that controlling for firm size, a higher proportion of joint ventures and wholly for­

To summarize , the MITP data show that the scope of

eign firms use statistical process control, quality con­

private R&D in Malaysia is relatively low by interna­

trol circles and precision measuring instruments to

tional standards. Furthermore, there are large dif­

verify accuracy in production as compared with do­

ferences in technological capabilities-as measured

mestic firms. Across all size categories, a much higher

by R&D, technology licensing, sophistication of ma­

proportion of domestic firms rely on visual inspec­

chinery, and quality control systems-between local

tion to verify accuracy in production as compared

and foreign-owned firms, and between SMis and

with foreign firms.

large finns. These size and ownership differences in technological capabilities mirror those involving

Finally, the table confinns that the introduction of

training, which is not surprising, given the strong

quality control systems increases the requirement for

linkages between training and technology revealed

training in quality control techniques. This quality

by the analyses in previous chapters.

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

68

Figure 5.1 Quality Control Systems by Firm Size and Ownership

A. Statistical Process Control

B. Quality Control Circles

70 60

50

50

40

40

30

30

20

20

10

10

0 +-"""'"'---+ro E

70

60

60

50

50

40

40

30

30

20

20

10

10

u

.E

12' .!!!

E

D. Visual Inspection Only

70

0 +-'-----t-

QJ

::J

�

Ul

C. Precision Measuring Equipmt.

e

E

ro E

Ul

�------....,

0 ro E Ul

E

::J 15 QJ

E

QJ

1111 Local

If finns with weak technological capabilities are also

E ::J 15 QJ E

ro E Ul

12'

.!!!

0

QJ

12' .!!!

Foreign

First, the overwhelming demand from firms is for

the same ones with weak training capabilities, it im­

diffusion services for known technologies rather

plies that policies should be designed to address both

than support to develop wholly new technologies.

sets of finn-level weaknesses and constraints since

For Malaysia, this means focusing support on tech­

the target population is the same. A recent study of

nology transfer, licensing agreements, dissemi­

technology-support institutions in six countries sug­

nation of information, standards and testing, and

gests some broad directions for the design of tech­

skills training rather then public R&D or R&D

nology policies to address these firm size and

ince n t i v e s f o r f i r m s .

ownership differences in capabilities (see Box 5 .1).

prioritization o f technology diffusion, rather than

Su p p o r t f o r t h e

TECHNOLOGY, QUALITY AND SKILLS

69

Box 5.1 Use of External Sources of Technical Support by Firms A recent World Bank study looked at six economies--Japan, Korea, China, India, Mexico and Taiwan--to examine the key characteristics of technology support institutions (Tis) and their use by industrial firms (Goldman, 1 995). Tis were broadly defined to include all public and private sources of technology and training support used by firms, including (i) national technology and standards institutes, industry associations, and productivity cen­ ters; (ii) private sources such as foreign technology licensors and contract laboratories; and (iii) technical assistance from suppliers and buyers.

Over two thousand firms were

interviewed, including both small and large firms and covering six sectors. Several of the principal findings and conclusions are summarized below. The overwhelming demand by firms, both large and small, is for services related to technology diffusion, i.e. the transfer and application of known technology.

Firms most

commonly use basic services related to acquisition of information, standards and test­ ing, trouble shooting, and technology-related training. And when firms use R&D services of public Tis, they tend to contract for answers to specific technology questions, rather than for development of new technologies. While larger firms tend to use Tis more intensively than smaller firms, use is also shaped by whether firms have in-house laboratories or technical departments. This is especially pronounced among small firms, where those with in-house resources use Tis at nearly twice the rate of other small firms. This highlights the difficulty of reaching and serving small firms, particularly those without internal technological capabilities. Large firms are also three times more likely than the overall sample to have received grants, tax incen­ tives or soft loans for technology; only assistance directed at technology diffusion--such as help in developing standards, or subsidies for training--seem to be taken up by small as well as by larger firms. A high proportion of firms reported using a public Tl at least once, though long-term customers, followed by suppliers, were the most commonly used external sources of technology.

The survey found that small firms require special Tis dedicated to them;

otherwise, they obtain little or no support. Tis focusing on small firms need to work proactively to expose them to the benefits of change if demand is to be generated for technology improvement and assistance. The Japanese approach--support directed at industry clusters in a region and focusing on technology diffusion--is quite effective in reaching a large number of small firms. The Taiwanese approach--productivity centers which develop generic expertise with applicability to small firms in a wide range of indus­ tries--is also effective, but reaches a lower fraction of the target population.

development, was provided in Chapter Three

ized support institutions working actively to de­

which showed licensing to have greater produc­

liver technology support services to hard-to-reach

tivity effects than R&D.

SMis, especially those with limited in-house ca­ pabilities, to expose them to the benefits of change

Second, larger firms use support services more in­

and create demand for technology improvement

tensively, and their take-up of technology incen­

and assistance. For Malaysia, this means restruc­

tives is more common, than smaller firms. SMis

turing the way public institutions deliver support

have special needs, and these are seldom met by

to firms, SMis in particular-from one that relies

broad-based institutions. They require special-

on firms to take-up incentives, to one in which

70

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

technology support services are delivered

systems and the structured training programs needed

proactively to firms. These policies are discussed

to attain those standards.4 The Brazilian experience

in greater length in Chapter Six.

indicates that adoption ofiS0-9000 certification and total quality management standards (TQM) in pro­

150-9000 and Quality Assurance It is increasingly recognized that standards and me­

duction has led to productivity and quality gains (see Box 5.2). IS0-9000 certification also provides a strong signal to clients that a firm is prepared to

trology can be an important policy instrument for

attain and maintain high and exacting quality stan­

improving and diffusing modem production meth­

dards (Frischtak, 1995). As such, it has become the

ods and quality control systems (Dahlman, 1992), and

prerequisite for doing business in many sectors of

upgrading product quality to meet the exacting in­

the European Community (EC) and, increasingly,

ternational standards for export markets. One such

is being extended to the East Asian Trade area around

voluntary standard, the IS0-9000 series introduced

Japan.

in 1987 by the International Standards Organization, represents the international consensus on how best

The MITP survey provides insights into the adop­

to operate and assess quality management systems.

tion of IS0-9000 standards in Malaysia. There is

The publication of IS0-9000 standards provides

Malaysian firms. The Standards and Industrial Re­

growing interest in implementing IS0-9000 among firms with a benchmark of what constitutes best

search Institute of Malaysia (SIRIM), which is re­

practice in their specific area, and thus incentives to

sponsible for metrology and standards, is the

put in place both the quality control and assurance

registration body for IS0-9000. It has awarded 700

Box 5.2 Diffusion and Impact of IS0-9000 in Brazil Many observers attribute the recent productivity and quality gains in Brazilian industry to producers' commitment to total quality management (TQM) and adherence to international quality standards of the International Standards Organization (ISO) 9000 series.

The diffusion of IS0-9000 among in­

dustrial firms has been rapid--between 1990 and 1994, the number of certified firms increased from 18 to 577, an average annual growth rate of 138 percent. The industries with the greatest number of certified firms are electrical equipment and instruments, chemicals, basic metals and fabricated products, and general machinery.

A number of factors were responsible for the rapid diffusion of TQM and IS0-9000 certification-­ industrial restructuring during the 1990-92 recession, major trade reforms beginning in 1990, and growing awareness of the increasing importance of quality control to meet client needs, reduce costs, and improve competitiveness viz. a viz. international producers. The Government has played an active diffusion role through the Brazilian Program for Quality and Productivity (PBQP). The PBQP provides (1) analysis of the market environment, (2) assessments of systemic and internal con­ straints to competitive behavior and the diffusion of TQM (3) establishment of sectoral and global benchmarks in terms of productivity and quality indicators, (4) dissemination of information on TQM and provision of TQM training, and (5) subsidizing adoption of TQM practices.

A recent survey of 93 major Brazilian enterprises indicates that adoption of new managerial methods for quality control, IS0-9000 certification in particular, has had beneficial effects on the firm--55 percent cited increases in productivity, 35 percent improved standardization of processes, 31 per­ cent increased employee participation in quality control, 25 percent in product quality improvement, and over 20 percent cited increases in client satisfaction (Frischtak, 1995).

TECHNOLOGY, QUALITY AND SKILLS

71

Table 5.4 IS0-9000 Status and Quality Control Systems Systems of Quality Control and Verification of Accuracy

With IS0-9000

Seeking IS0-9000

Certification

Certification

No IS0-9000 plans

Firms

%

Firms

%

Firms

%

229

10.4

731

33.2

1,240

56.4

Statistical Process Control

130

56.8

268

36.7

171

13.8

Quality Control Circles

123

53.7

329

45.0

268

21.6

141

61.6

299

41.0

198

16.0

47

20.5

216

29.6

370

29.9

22

9.6

154

21.1

609

49.1

Total Sample of Firms Quality Control System

Verifying Accuracy in Production Precision Instruments Simple measuring devices Visual Inspection Source: 1995 MITP Survey

foreign and local finns with some level ofiS0-9000

and implemented systems of quality control and

certification, and is reportedly in the process of as­

quality assurance. A much higher proportion of

sessing another 600 firms.

IS0-9000 certified firms use QCC and SPC to en­ sure quality in production and precision instruments

The survey elicited information from firms about

to ensure accuracy in production, followed by finns

whether they had any IS0-9000 series certification,

seeking certification within the next three years.

and if they did not, whether they expected to gain

Firms with no plans for IS0-9000 certification are

IS0-9000 certification within the next three years.

much less likely to report use of QCC or SPC, and

This second question was designed to identify finns

are significantly more likely to rely on visual in­

that were preparing for certification, a process that

spection to verify accuracy in production.

can take as long as three years. Firms that did not currently have IS0-9000 certification, or were not

Table 5.5 reports the distribution ofiS0-9000 sta­

expecting it within three years, were classified as

tus by firm size and ownership. It indicates that

having no plans for IS0-9000 certification.

while the number of IS0-9000 certified firms is small, interest is growing. Currently, over 30 per­

Table 5.4 reports the number of firms with IS0-

cent of large finns are certified, but the proportion

9000 certification, those seeking certification and

of micro, small and medium firms with IS0-9000

those without certification. Out of 2,200 firms in

certification is relatively low-less than one percent

the MITP survey, 229 firms (10.4 percent) had

among micro firms four percent among small firms

IS0-9000 certification; 731 firms (33.2 percent) ex­

and eight percent among medium-size firms. The

,

,

pected IS0-9000 certification within the next three

trend in the number of firms expecting certification

years, and 1,240 firms (56.4 percent) did not have

is more optimistic, with 27 percent of small firms

certification and did not have any plans to become

and 48 percent of medium firms expecting IS0-9000

certified. Industries with the highest fraction of firms

certification within the next three years. This trend

with IS0-9000 included the most technology-inten­

implies that within three years, over three-quarters

sive industries such as electrical machinery and

of large firms will have IS0-9000 certification.

chemicals as well as the export-oriented industries such as rubber and plastics.

Nonetheless, the total coverage ofiS0-9000 among micro firms will still be below seven percent at the

The bottom panel of Table 5.4 show the distribu­

lowest end of the size spectrum. This highlights a

tion of quality control systems by firms' IS0-9000

potentially important area of focus for Malaysian

status. IS0-9000 certifies firms to have documented

policymakers. Another important area of policy fo-

72

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Table 5.5 150-9000 by Firm Size and Ownership Firm Size and Ownership Type

%Firms With

%Firms Seeking

%Firms With No

IS0-9000

IS0-9000

IS0-9000

Certification

Certification

Plans

Firm Size Micro

0.8

6.5

92.7

Small

4.2

26.9

68.9

8.2

48.2

43.5

31.3

43.6

25.1

Medium Large Ownership Type

5.6

29.6

64.8

Joint-Ventures

14.3

43.9

41.8

100%Foreign

27.8

35.3

36.9

Domestic

Source: 1995 MITP Survey

cus should be local firms. Compared to joint ven­ tures (14 percent) and foreign-owned firms (28 per­ cent), only about five percent of local fums currently haveIS0-9000 certification. H owever, this appears to be changing. A growing number of local fmns appear to realize the importance of total quality man­ agement and quality standards for improving com­ petitiveness and meeting the increasingly high standards demanded in international markets--thirty percent of them expect to get IS0-9000 certification within the next three years.

IS0-9000. According toSIRIM, the SMI section has provided QIP consultancies to a cumulative to­ tal of 162SMis by the end of 1995.

SIRIM can play a greater role in disseminating international best practices in production and qual­ ity control to employers and, through their adop­ tion of IS0-9000 standards, improve the competitiveness of local firms. The recent corporatization ofSIRIM in 1996, and the reorga­ nization of the institution that is now in progress, should allow it to respond more flexibly to the dramatic growth in private sector demand forIS09000 certification. 5

QIPs developedjointly bySIRIM and leadingMNCs in specific sub-sectors, are a potentially powerful policy instrument for assisting groups ofSMis to upgrade their quality and to foster increased link­ ages withMNCs and other larger firms. For many MNCs, a major obstacle to developing supplier rela­ tions with localSMis is the low and uneven quality of their products(Fong, 1991).SMis may notknow what quality standards are required to become part sup­ pliers, so that few are willing to invest the necessary resources to upgrade quality practices on the chance ofbecoming a subcontractor. To the extent that QIPs can establish clear, and certifiable, quality standards acceptable to leading firms in a given sub-sector, they provide tangible incentives not only forSMis to improve and upgrade their quality control prac­ tices, but also forMNCs and other larger employers to accept QIP-certified SMis as part suppliers.

Not all firms,SMis in particular, can afford the high cost and time required (about three years on aver­ age) to meet IS0-9000 standards.SIRIM can play an expanded role in helpingSMis to improve quality control by building on its existing, but thus far lim­ ited, consultancies on Quality I mprovement Prac­ tices (QIP), which are less expensive to attain than

S ub-sectoral QIPs, when developed, are amenable to group provision of assistance toSMis in terms of consultancies, training, finance, as well as technical assistance from leading firms in the industry. SIRIM should pursue this program in collaboration with other goveriUIY;!nt agencies-such as the National Productivity Corporation (NPC), HRDC, and

TECHNOLOGY, QUALITY AND SKILLS

MID's SMI agency-and with leading private sector

73

countries, Australia, and New Zealand. All other countries, primarily those in ASEAN and the

finns.

Middle East, are included in the developing mar­ ket category.

150-9000 and Export Orientation Table 5. 6 shows the proportion of firms exporting There is considerable anecdotal evidence linking

to different markets by their I S0-9000 status.

quality certification to producers' efforts to penetrate

They suggest two points. First, among firms with­

developed markets in the US, the EEC, and Japan

out plans for IS0-9000 certification, a lower pro­

(see Frischtak, 1995). In Malaysia, we also observe a

portion export to industrialized markets (20

strong correlation between IS0-9000 certification

percent) as compared to developing country mar­

and the export status of firms. About 82 percent of

kets (24 percent). Second, firms with IS0-9000,

IS0-9000 certified firms currently export, while

or in the process of certification, are more likely

export-orientation is 69 percent among firms seek­

to export to industrialized country markets-47 and

ing certification within the next three years, and

36 percent, respectively-than to developing coun­

just 44 percent among those without certification

try markets, where the corresponding fractions

and not planning to do so in the near future. While

of exporting firms is 36 and 33 percent. The third

it is difficult to establish a causal relationship be­

column, which is conditioned on exporting, rein­

t ween getting IS0-9000 certification and in­

forces these points, namely, that the relative impor­

creased exports, these figures suggest that the

tance of exports to industrialized markets increases

firms which already have IS0-9000 certification

with firms' efforts to get IS0-9000 certification.

.•

or those in the process of being certified, are bet­ Figure 5. 2 shows the proportion of firms exporting

ter able to compete in export markets.

to each market type in each of 16 industries where Exporting is clearly not precluded for firms without

industries are sorted in ascending order (from left to

certification. However, it may be more difficult with­

right) by the share of firms with IS0-9000 certifica­

out IS0-9000 certification to break into industrial­

tion. The percentage share of certified firms in each

ized country markets, where quality requirements

industry is represented by the heights of bars. The

tend to be higher, than it is to export to developing

dark shaded area shows the percent of firms export­

countries. To determine ifiS0-9000 certification

ing to industrialized country markets, the light shaded

makes a difference, we distinguished between in­

area the corresponding figure for exports to devel­

dustrialized country markets and developing

oping country markets.

country markets on the basis of firms reported

primary export market. The industrialized mar­

In all industries, a higher proportion of firms ex­

kets include the United States, Japan, the EEC

port to developing country markets than to indus­ trialized markets, as is evident by the light shaded

Table 5.6 IS0-9000 and Export Orientation

% Firms that Export IS0-9000 Status

To

To

Exporting Firms % exporting to

industrialized

developing

industrialized

countries

countries

countries

No certification plans

20.3

23.5

46.6

Seeking IS0-9000 certification

36.0

33.3

51.8

With IS0-9000 certification

46.5

36.0

56.3

Source: 1995 MITP Survey

74

ENTERPRISE TRAINING, TECHNOLOGY AND PRODUCTIVITY

Figure 5.2

150-9000 and

Exports

90 80 70 60 50 40 30

1

20 1 o 0

-e "'

(l_

(l_