SKY PROJECTNAGOYA UNIVERSITY

Summary Report

SUMMARY REPORTOF THE TRAINING
NEEDS ASSESSMENT:EMPLOYABLE CAPACITYDEVELOPMENT FOR WOMENPROJECT IN PUNJAB, PAKISTAN

Prof. Dr. Shoko Yamada, Nagoya University, Japan (SKY Project Leader)Pimmada Charoensilp, Nagoya University, Japan
Emmanuel Estrellado, Nagoya University, Japan
Dr. Yuki Shimazu, Aichi Shukutoku University Japan© Skills and Knowledge for Youths ProjectGraduate School of International Development, Nagoya University
Furo-cho, Chikusa, Nagoya 464-8601, Japan
https://skills-for-development.com/

Table of Contents
Executive Summary
Acknowledgment
Introduction

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ii
01

01.
The profile of the textile and garment sector and skills

05

CONTENTS

development in Pakistan
1.1 TVET policies and structure in Punjab, Pakistan, and challenges faced by
TVET Institutions05

1.2 Skills demands for garment and textile industry in Punjab

06

1.3 Skills development as a means of women's empowerment in Punjab,
Pakistan

07

02.
Research design
2.1 Skills assessment module
2.2 Research participants and data collection

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08
10

03.
Comparison between home-based and factory workers

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3.1 Home-based workers are older and less paid, but their levels of skills are
not much different from factory workers, except for practical skills

15

04.
Factors affecting the variation of factory workers’

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practical skills
4.1 High-skill firms tend to have workers with more motivation, emotional
stability, and reading abilities

20

4.2 High-skill firms give more various types of OJT, and their managers
have more positive gender stereotype

21

4.3 A Model of the paths that workers’ soft skills and workplace conditions
contribute to improved practical skills

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4.4 Summary

2505.
Curriculum analysis
5.1 Overviewed curricula
5.2 Skills emphasized in the curricula
5.3 Personality traits and behavioral skills in the curricula

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26
27
29

06.
Conclusion
6.1 Conclusion
6.2 Policy implications and recommendations

References

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31
33
35

TABLES

List of Tables

TABLE 1.

Background information about participants by affiliation

TABLE 2.

Participants' percentage of attained education level by affiliation

TABLE 3.

Comparison across firms based on workers’ average skill level

TABLE 4.

List of skills mentioned in the curricula

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12
18
28

FIGURES

List of Figures

FIGURE 1.

Conceptual framework of skills assessment operation

FIGURE 2.

Participants’ work and OJT experience by affiliation

FIGURE 3.

Characteristics of participating firms

FIGURE 4.

Comparison of home-based and factory workers’ individual conditions

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12
13
15

and skills

FIGURE 5.

Standardized bias between home-based and factory workers’ individual

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conditions and skills

FIGURE 6.

Soft skills which workers in Low-skill and High-skill firms have

FIGURE 7.

Workplace factors that predict membership to Low- and High-skill firms

FIGURE 8.

A structural equation model of the contribution of workers’ soft skills

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and workplace conditions to practical skills

FIGURE 9.

Personality traits and behavioral skills written in the curricula30

ACRONYMS

FW

Factory Worker

GIZ

Deutsche Gesellschaft für Internationale Zusammenarbeit

HBW

Home-Based Worker

IFC

International Finance Cooperation

ILO

International Labor Organization

JICA

Japan International Cooperation Agency

LFPR

Labor Force Participation Rate

NAVTTC

National Vocational & Technical Training Commission

NSS

National Skills Strategy

NVQF

National Vocational Qualification Framework

OJT

On-the-Job Training

PCA

Principal Component Analysis

PCSW

Punjab Commission on the Status of Women

PPP

Public-Private-Partnership

P-SDC

Punjab Skill Development Council

P-TEVTA

Punjab Technical Education and Vocational Training Authority

PVTC

Punjab Vocational Training Council

SEM

Structural Equation Modelling

SKY

Skills and Knowledge for Youths Project

SMEDA

Small and Medium Enterprises Development Authority

TNA

Training Needs Assessment

TSP

Training Service Providers

TVET

Technical and Vocational Education and TrainingIn order to advance gender equity and to supply a skilled workforce for the formalindustrial sector, which contributes to the country’s economic growth, thegovernment of Punjab has prioritized Technical and Vocational Education andTraining (TVET) as a means to empower young female workers. With thetechnical and financial support from JICA, TVET authorities of Punjab are

EXECUTIVE SUMMARY

preparing a new project to improve TVET curricula to equip female students withknowledge and skills for enhanced employability, with a particular focus on soft
skills.
The Skills and Knowledge for Youths (SKY) research team of Nagoya University
was requested by JICA to diagnose necessary skills to be prioritized in the
curriculum reform through the investigation of work environment andrequirements in the formal sector companies, conditions of home-based workers,
the nature of skill mismatches between the industry’s workforce demand andworkers’ actual capacities, and the domains of curriculum which can be
improved to mitigate the skills mismatch. Using its unique skills assessment
module, which is composed of questionnaires for workers and stakeholders andwritten and practical tests for workers, the SKY research team has conducted asurvey from August to September 2023. It involved managers of 17 firms and 425
females who work in these firms and at home. A few TVET teachers also
answered a questionnaire.
In this paper, we highlight the outcomes of the training needs assessment andsuggest some factors which enable or impede women to learn new skills andachieve higher productivity. First, we describe the characteristics of skills ofworkers depending on their workplace, comparing among firms and between
those working in the formal sector and at home. Then, we will shed light onpeople with higher skills and their characteristics. Lastly, based on these findings,
we provide suggestions for improving the TVET curriculum and sensitizing
employers.
Our analysis tells, unlike the assumption of some people we interviewed in
Punjab, there is no significant difference in most of the skills tested, including
readingcomprehension,

calculation,

theoreticalknowledgeofgarment

production, and soft skills. We didn’t identify any significant differences between
home-based and formal sector workers, except for the practical skills, which were
determined by various factors.
While the differences in the workers’ abilities were not determined by theiremployment status, there are still significant variations across survey participantsin terms of their skills. We found that workers who demonstrated higher levels ofpractical skills were likely to have higher levels of reading skills. They are alsocharacterized by some types of soft skills which relate to their willingness to learn
new things, improvement in work, and emotional stability. Further, the likelihoodiof finding workers with higher practical skills is determined by the characteristics of theworkplace. Firms which provide more opportunities for on-the-job training have a higher
chance of having workers with good practical skills, while those with managers who adhere to
the old stereotype of women staying at home or in subordinate positions at work do not seemto encourage women to perform well.
These findings suggest the importance of soft skills for improving productivity among female
workers. At the same time, the nature of required soft skills is different depending on the overall
productivity levels of firms. Low-productivity firms tend to require workers who are open tolearn new things, while high-productivity ones are likely to have workers with a growth mindset
and emotional stability. Therefore, soft skills development should be done in a manner relevantto the context, based on a close examination of the skills gap in respective firms.
Also, practical skills are always important determinants of rewards for workers (i.e. salaries).
Therefore, firms’ support for the continuous upgrading of practical skills through training andadvice is highly recommended. Moreover, we suggest firms ensure positive workplaceenvironments by promoting gender equality and supporting workers’ initiatives in order toempower female workers and enhance their work productivity.

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ACKNOWLEDGEMENT

The financial and operational support fromfollowing organizations enabled theactivities reported in this paper:
Employable Capacity Development for
Women in Punjab Project, JICA
Pakistan, the Japan International
Cooperation Agency (JICA)
Punjab Technical Education and
Vocational Training Authority (P-TEVTA),
Government of the Punjab
Punjab Vocational Training Council(PVTC)
HomeNet PakistaniiEnsuring equitable opportunities for women to education and employment
would not only empower women with better income and well-being but alsosignificantly contribute to national economic growth. However, according to
the recent Global Gender Gap report by the World Economic Forum (2023),
Pakistan ranks fifth lowest in terms of gender parity, standing at the 142nd

INTRODUCTION

place out of 146 nations. The literacy rate of women in Punjab is worrisome,with the average at 58.4%, compared to that of 73.4% of males, according toPakistan Economic Survey 2022/23 (2023). Likewise, despite being nearly half(approximately 49%) of the total population of the province (Javed & Khan,
2018), women’s Labor Force Participation Rate (LFPR) is only 26.5%, comparedto 69.9% for men (Pakistan Bureau of Statistics, 2018). The low rate of
employment is a result of wide-ranging issues, including economic
restrictions, limited access to resources and education, and prevalent societal
values and norms regarding women. In the garment and textile sector, only
approximately 30% of workers are female (Hisam, 2017). A study conducted by
Khan (2010) reveals that women have fewer chances for skills development
and training, and employers’ biased perceptions of female workers caused
the minimal LFPR for women in this country. Under such a circumstance, socalled home-based workers (HBW), who work inside their own or others’
compounds as piece-rate workers, are prevalent. 72% of women aged
between 15 and 64 in Punjab are considered home-based or piece-rateworkers (PCSW, 2018). In addition, while the supply of young skilled workers ishighly critical, it is disheartening to note that 47% of women aged between 15
and 24 are neither in education nor training programs. Likewise, only half ofthe girls in primary education survive to the secondary level (Punjab
Commission for the Status for Women, n.d.).
To address the gender disparities and empower the skilled women
workforce, the Punjab government places a great priority on TVET. With thisemphasis, the Punjab Technical Education and Vocational Training Authority(P-TEVTA) and Punjab Vocational Training Council (PVTC) are aiming to
improve their vocational programs to empower women economicallythrough skills formation. Based on a longstanding relationship with the
Punjab government, the Japanese International Cooperation Agency (JICA)
embarked on a pilot project to provide technical support for the
aforementioned TVET authorities in advancing the curriculums for enhancing
capacity among the young female workforce.
The Skills and Knowledge for Youths (SKY) research team demonstrated
through multi-country case studies that the expectations of industrial
stakeholders for skills to be trained by educational institutions are often
unmet (Yamada, 2023; Yamada & Otchia, 2022). At the beginning stage of thecollaborative project between JICA and Punjab TVET authorities, the SKY
research team was involved in conducting the training needs assessment

01(TNA) to provide the project’s stakeholders with insights by identifying skills gaps and mismatchamong workforce supply and demand sides of the garment and textile sector.
The SKY is an interdisciplinary research team at Nagoya University in Japan that has beenworking on skills development for the workforce in developing countries, particularly in Africa
and Asia. One of our academic expertise is creating and conducting a unique standardized skills
assessment module, which examines different skill types from various perspectives based on
the specificity of the study context. Our data and findings have been supplying fundamental
resources for governments, training institutions, and private entities in many countries forevidence-based decision-making. For the current project, we evaluated the skill requirements
based on local needs, drawing the perspective of female workers and stakeholders in thegarment and textile sector together through a survey using our skills assessment module. The
findings of TNA will be used for JICA and TVET authorities to upgrade vocational training
courses.
Recognizing the unique national conditions that make a majority of women work informally,
the TVET curriculum reform aims to equip marginalized female youth with employable
capacities either to become entrepreneurs or to get formal employment in firms. Reflecting thisoverall direction of the JICA-TVET collaboration project, the SKY team’s TNA tried to investigate
whether differences in skills exist between home-based and formal industrial workers. Not only
to examine the differences by employment status to provide insights for improving theworkforce’s productivity, we also aimed to discover factors that affect workers’ practical skills,
which are considered to represent their productivity at work, from various approaches. Further,
we analyzed the current TVET courses’ curriculum. Referring to the outcomes of our skills
assessment, we diagnosed the parts of the curriculum and teaching methods that would need
to be revised so that the courses will be more relevant to the situations and suited for upskillingand empowering female students and workers.
In sum, the fundamental questions that this report tries to provide answers to are as follows:
Do skills between home-based and factory workers completely differ from each other?
What causes the differences between workers who demonstrate high practical skills andthose who do not?
Out of the influential factors, which are related to workplace and environment, and which
are more related to individual abilities?
What kind of implications can we draw from this study to improve the skills development
programs and courses in the Punjab State of Pakistan?
The assessment module used in this study comprehensively captures the actual and perceived
abilities of workers. Different types of skills related to workers’ productivity were investigated,
including:02

Cognitive skills: Cognitive skills involve reading, mathematic proficiency, and integrated
skills, which refer to the ability to comprehend and apply reading and math skills to interprettables and graphs. The test items are designed to measure such skills in relation to theworkplace needs for garment production. Additionally, vocational knowledge, which
encompasses basic knowledge about garment production, is another essential component.
These cognitive skills are assessed through a written examination.
Soft skills: Soft (or non-cognitive) skills encompass personality traits such as grit, decisionmaking, and emotional stability, as well as workplace-related behavioral skills like tidinessand teamwork that enable individuals to apply their cognitive and practical skills andenhance their productivity at work. Psychometric questionnaires were employed to assess
this type of skill. There is no set of soft skills that is effective in all types of workers andworkplaces. Therefore, it is important to identify those that are appreciated and required intheir respective contexts. Based on the findings, this study indicates the types of soft skills
that stood out in both high- and low-skill firms.
Practical skills: Practical skills refer to the ability to apply cognitive knowledge and soft skills
to produce actual products effectively. This study measures workers’ practical skills by asking
them to produce a shirt pocket according to the sample, using the pattern and materials
provided. The practical test includes the production process, such as selecting materials,
understanding patterns, cutting, sewing, ironing, and finishing, all within a 20-minute time
constraint.
The three types of skills are interconnected and altogether contribute to workers’ productivity.
For instance, successfully completing a pocket within a limited 20 minutes requires quick andeffective decision-making, as well as maintaining a calm and stable emotion in a stressfulenvironment. Simultaneously, workers need to have adequate vocational knowledge tounderstand patterns and select the appropriate equipment and materials.
The TNA implementation consists of two phases: preliminary survey and assessment
administration. In the first phase, during May 2023, the team did the initial situation analysis
through observation and interviews in urban and rural areas of Lahore to gain a deep
understanding of female workers’ contexts, as well as social structures and values. This included
investigating the perspectives and requirements of stakeholders regarding women’s careers in
the garment sector, such as female workers, TVET instructors, factory managers, and authorities
in TVET and industrial sectors. Based on the initial findings, we added some question items andmodified the standardized skills assessment module to meet the unique needs of theparticipants and social contexts in Lahore.
In the second phase, the skills assessment was conducted during August and September 2023.
First, our team provided facilitation training for the assessment implementation staff andevaluators. Simultaneously, a pilot test was conducted, involving both home-based and factory
workers, to finalize the assessment module incorporating technical and linguistic suggestions
from Pakistani staff and the trial participants.

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In this report, we discuss Pakistan's industrial situation in the first section. It will be followed by achapter elaborating on the research design, including a discussion of our unique skills
assessment module and characteristics of the research participation. Chapter 3 presents thecomparison of different types of skills between home-based and industrial female workers.
Then, Chapter 4 will explain the factors that could account for the variations in the practicalabilities of female garment workers. Given that soft skills and conditions of firms are influential
factors to explain variations in practical skills, we further investigate in what ways soft skills andworkplace conditions affect. Thereafter, Chapter 5 presents the findings from the TVET
curriculum analysis, which includes both programs for industrial garment stitching anddomestic tailoring. Finally, the last section will conclude the report with policy implications andsuggestions from our TNA findings.

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1

THE PROFILE OF THE TEXTILE ANDGARMENT SECTOR AND SKILLSDEVELOPMENT IN PAKISTAN

The garment and textile production sector in Pakistan, which this study focuses on, is a vital
component of the country's economy. It contributes nearly a quarter of industrial value-added
products and provides employment to about 40% of the industrial workforce. Additionally, this
sector occupies an average share of about 60% of national exports (IFC, 2020). According to the
Pakistani government's national development plan titled Pakistan 2025, the country aspires toTips

achieve an inclusive and equitable economy and become an upper-middle-income country by2025 (Planning Commission, 2023). Considering that Pakistan is the 9th largest exporter ofgarments and the 5th largest producer of cotton in the world, there is no doubt that thedevelopment of this economic sector is crucial for the country to achieve this goal.
Regardless, women's participation in economic productive activities is quite limited in thiscountry. Despite increases in recent years, female labor force participation in Pakistan is 25%,
well below rates for countries with similar income levels (Asian Development Bank, 2016). While
the garment sector employs 40% of the Pakistani workforce as a whole, females occupy only
30% of it (GIZ, 2024). This ratio is extremely low, considering that the garment and textile
industry tends to be female-biased, with 60~80% of the global workforce in this sector being
women (ILO, 2023). As a means to mitigate the gender disparity in labor force participation, it
will be useful to identify and promote enabling factors for female workers in this sector, where
there lies high potential with their strengths to perform well. Such an effort would also
contribute to pushing up the productivity of this national flagship industry as a whole.
Based on these observations, this chapter will summarize key issues regarding female
participation in the labor market, not only in general but more specifically in the garment sector
in Punjab, Pakistan. Since skills development is the critical node of individual and industrial
development, the chapter also highlights the role of technical and vocational education andtraining (TVET) for the growth of the garment and textile sector and female empowerment.

1.1 TVET policies and structure in Punjab, Pakistan, and challenges faced by
TVET Institutions
After the long neglect of TVET's planning and operation since the 1960s, renewed attention has
been paid to this training sector in the last 20 years. National Vocational and Technical Training
Commission (NAVTTC) was established in 2005 to regulate & manage the TVET Sector in
Pakistan under the Ministry of Federal Education and Professional Training. Also, the National
Skills Strategy was developed in 2009, the country's first policy specific to the skills sector.
Several actions have already been taken on the recommendations contained in the NSS, which
include developing the National Vocational Qualification Framework (NVQF), national
qualifications system for teachers, code of conduct, accreditation system, human resources
development policy for TVET, skill standards, and curricula in priority areas (NAVTTC, 2024).

05

At the provincial level, Technical Education & Vocational Training Authorities (TEVTAs),
Vocational Training Council (VTC), and Skill Development Councils (SDCs) have taken charge ofplanning and executing training programs. TVET is also administered by provincial educationand labor departments, ensuring a multifaceted approach to skills development (Shah andKhan 2017).
According to its Skills Development Sector Plan 2018, the government of Punjab recognizes thatan important factor hampering the animation of the labor market is low skills and the lack ofeffective training for improving the capacities of working age population (15-64 years),

Tips young adults aged between 15 and 29 (Government of Punjab 2015). According toparticularly
the Labor Market Survey 2013, among working youths (15-29 years old) in the state of Punjab,
more than 40% have an education of less than 12 years (44% of males and 47% of females), outof which, population without any schooling occupies significant proportion (19% and 30% forrespective genders)(Pakistan Bureau of Statistics, 2015).
Given this generally low level of access to school, formal and nonformal TVET and on-the-job
skills development are of fundamental importance as a path towards developing humancapital. Regardless, only 12% of Punjab's working-age population (15-64 years) have acquired
skills training (Pakistan Bureau of Statistics, 2015).
Challenges persist, including a significant gap between skilled labor demand and supply,
highlighted by the annual demand for skilled workers far exceeding enrollment in TVET
institutions. As of 2015, the gross annual training capacity of TVET programs in Punjab wasreported to be about 164,000 individuals, far less than its target to reach 2.0 million(Government of Punjab, 2015). Efforts to address this gap face hurdles such as inadequate
inclusivity, particularly for marginalized groups like women, the poor, and the disabled.
Traditional skills often remain undervalued, hindering their formal training integration.
Regardless of its importance, training in the garment and textile sector also lags behind theneed for female upskilling and companies' labor demands.

1.2 Skills demands for garment and textile industry in Punjab
The garments sector in Punjab is primarily concentrated in Lahore, Faisalabad, Sialkot, andGujranwala, with each cluster specializing in specific products. Lahore, the regional capital,
focuses on denim products. Sialkot and Gujranwala excel in sports and technical wear, while
Faisalabad mainly produces hosiery items. Despite the predominance of small and mediumsized entities, the sector has grown over the past three decades, with an average growth rate of
29%. However, this growth rate has slightly declined in the last decade due to challenges suchas energy crises, security concerns, and global recession.
60% of entities that offer skills development programs related to garment and textile arelocated in Lahore, followed by Faisalabad, Islamabad, and Gujranwala, with limited
representation from Rawalpindi and Gujrat. 60% of the training providers are private, 28% arepublic, and 12% operate under Public-Private-Partnership (PPP). However, 52% of TSPs reportinsufficient infrastructure (Grant Thornton International, 2015).

06

As the majority of programs are ad-hoc and unregulated, their quality often fails to meetdemands. Also, with low funding, infrastructural conditions, and lack of expertise on the side oftraining providers, the range of skills taught in these programs is limited.

1.3 Skills development as a means of women's empowerment in Punjab,
Pakistan
Gender disparities in Pakistan are pervasive, as highlighted by the country's low rankings on the
Global Gender Gap Index (5th from the bottom in 2013). Despite comprising half of the workingage population,
women's participation in the labor force remains low, with only 22.5% actively
Tips
engaged. Urban areas exhibit significantly higher rates of female unemployment compared torural areas, exacerbating gender inequalities. However, there is growing recognition withingovernment and non-government sectors that this issue should be addressed.
The Technical and Vocational Education and Training (TVET) sector is recognized as a crucial
tool for reducing unemployment, empowering women, and alleviating poverty. However,gender disparities persist within the sector, with limited enrollment of women in trades such as
dressmaking, embroidery, and beauty services. Women's TVET institutes face numerous
challenges, including limited capacity, outdated facilities, and less relevant curricula.
Additionally, there is a considerable gender wage gap within the sector, further discouraging
women from entering the workforce (Rehman, 2020).
There is an urgent need to reform the TVET system to address these disparities and make theminclusive and women-friendly. At the same time, addressing gender disparities in the TVET
sector in Pakistan necessitates targeted interventions and concerted efforts from bothgovernment and non-government stakeholders.
In sum, the review of profiles of garment sector, skills development of the workforce particularly
for women in this sector, and relevant policies and literature suggest the necessity of accurate
diagnosis of the skills of women against the contexts in which they work. Unless we grasp thecomposition of skills the workers currently have and what hinders or enables them to learn new
skills, the chance that the government's efforts toward reforming TVET to upskill and empower
women will yield the desired results will be reduced. To contribute to the Government of Punjab
and related entities, including P-TEVTA, P-VTC, and P-SDC, the SKY research team has
conducted the survey, the outcomes of which are going to be presented in detail in thefollowing chapters. We hope this report will provide evidence for the stakeholders in Punjab tomake informed decisions.

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2

RESEARCH DESIGN

2.1 Skills assessment module
To capture workers’ essential skills and knowledge for garment production and grasp thenature of the skills mismatches between the industry’s stakeholders, the SKY research teamdeveloped a unique skills assessment module, which comprehensively captures workers’ skills
in all dimensions relevant to their daily work environment. The standardized skills assessment
model is comprised of three sections to evaluate different types of skills: (1) questionnaire, (2)written examination, and (3) practical skills examination. Likewise, the assessment module wasdesigned for both workers and employers to reflect on each other to measure the disparities inperceptions of skills between them.
The questionnaire distributed to workers consists of items about individual demographic
background, work and training experience, and scales to measure soft skills. The last section
measures workers’ soft skills through two psychometric scales. As mentioned in the
introduction, soft skills in our study involve personality traits and workplace-related skills. First,
personality traits consist of eight domains, including the Big Five, which is a well-known
taxonomy of personality: extraversion, conscientiousness, openness, emotional stability,
agreeableness, grit, decision-making, and hostile bias[1]. The scale for measuring participants’personality traits contains 26 items, asking participants to rate the frequency (from “Almost
never” to “Almost always”) with which they perform listed actions, such as “When doing a task,
are you very careful?” and “Do you forgive other people easily?.” The scale was adopted from thepersonality traits scale employed in the World Bank STEP Skills measurement survey conductedby Pierre et al. (2014). Each domain of personality traits was constituted by a group of question
items on the scale.
Additionally, workplace-related skills are captured to understand the differences between
workers’ and employers’ perceptions of attitudes at work. Similar to the scale for personality
traits, we use a Likert scale to measure workers’ perceptions of specific actions at the workplace.
Participants were asked to rate their level of agreement (from “Strongly agree” to “Strongly
disagree”) with listed work-related scenarios, such as “If any unusual situation occurs during
work, the workers share that with colleagues.”[1] Definition of each soft skill according to Pierre et al. (2014)
Extraversion: the sociability and dominance displayed in social situations.
Conscientiousness: the tendency to follow the rules, control their impulses, set goals, plan, and obey societal norms.
Openness: the enjoyment of learning and experiencing new things.
Emotional stability (or Neuroticism): the propensity to have negative emotions.
Agreeableness: the tendency to be friendly and helpful towards others and willing to work with them.
Grit: a propensity to persist in pursuing long-term goals.
Decision-making: the tendency to have consequential and alternative solution thinking when making a decision.
Hostile bias: the tendency to perceive other people's actions as harmful.

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Thereafter, the workers were given 90 minutes to complete reading, mathematics, integrated
skills, and vocational knowledge tests in the written examination section. All exam questions
were multiple-choice items. This section aims to capture their necessary cognitive skills forgarment production. Home-based and factory workers were given the same assessment
module. However, home-based workers were directed to skip questions about experiences in
the factory as they were self-employed. The participants filled in the questionnaire and written
examination using tablet devices.
After completion of the written examination, the assessment moved on to the practical skills
test section. The practical test aims to capture workers’ advanced garment-producing skills
comprehensively. The test asked each worker to complete a shirt pocket according to thesample pocket and pattern within 20 minutes. During the process, from selecting materials totidying up the workstations, each worker was evaluated by three graders who were experts
from both TVET and garment industrial sides, based on the SKY project’s standardized criteria.
The graders also assessed the quality of the completed pockets. The graders’ evaluation isrecorded along an 11-item Likert-type scale from 1 (could not do well) to 5 (excellent).

Participating
entitlesAssessment
spondents for questionna
As re
ire

ers for practical
xamin
test
As e

Industry

TVET

Participati
ng
entitlesProduction manager

Participating
entitlesFactory workers

Instructors

Participati
ng
entitlesQuestionnaire
Demographic background

Practical test
(Pocket making)Work & training
experience
Personality traits

Informal sector

Participati
ng
entitles
HBW

Participati
ng
entitlesWork-related behavioural

Participati
ng
entitlesskills(For managers)
Perceptions toward
women and workers’ skills

Mathematics, Reading,Integrated skills, andWritten test: Vocational knowledge

As participants for entire assessment

Figure 1. Conceptual framework of skills assessment operation09

To understand skills demand from the perspective of employers, the assessment also involves
factories’ production managers to share their expectations toward skills for workers in thegarment and textile sectors. The questionnaire for production managers includes questions
about their personal demography and professional backgrounds, desirable personality, andwork-related behaviors of workers. Besides, the SKY research team also developed a firm survey
for factories’ human resource managers to gather information about factory conditions,
including the composition of employees by age, gender, position and specialization, types andfrequency of training provided, and employment strategies. Figure 1 illustrates the constructs of
the SKY skills assessment module, their respective participants, and the ways they mutuallyrelate.
A key strength of our study lies in its methodology, which facilitates the comparison between
expectations from employers and TVET instructors and the actual skills possessed by workers.
We combine different instruments, such as questionnaires, cognitive skills tests, and practicalskills tests, to comprehensively analyze the gap between supply and demand at two distinct
levels: perception and actual skill. The questionnaires offer insights into stakeholders'
perspectives, encompassing self-assessments by workers and the expectations of production
managers. However, merely understanding perceptions is insufficient without examining how
these perceptions translate into real-world practical skills or productivity. Practical skills, thus,
need to be assessed by both employers and TVET sides. By involving stakeholders with different
standpoints and expectations on skills in evaluating the real performance of test takers
simultaneously, the influence of perceptions on a judgment can be contrasted.

2.2 Research participants and data collection
The assessment was conducted between August and September of 2023, involving 220 female
home-based workers and 205 factory workers from 17 firms in Lahore, the capital city of Punjab.
Also, one human resource and one production manager from each firm were invited to answer
the questionnaire for this study.
Table 1 summarizes the demographic information of the participants. Home-based and factory
workers’ characteristics are different in a few significant aspects. Home-based workers arearound five years older than factory workers on average and earn 40% of the average monthly
income of factory workers. In contrast, factory workers work approximately 2.5 hours more thanhome-based workers per day. In terms of the family’s background, both groups share similar
characteristics in household size and the economic level of the family. Both groups of workers’
families consist of around 6 members. The economic level of the family was constituted bysumming scores of the household’s ownership of a DVD/CD player, TV, cell phone, computer,internet, bank account, car, motorcycle, bicycle, and refrigerator/freezer. Factory and homebased worker groups similarly rated their family’s economic level at approximately 3.2 points.
Additionally, both groups of workers share a similar community type in which they reside. The
variable community type depicts the categorization of participants’ residing areas: rural andsemi-urban areas were coded as 0, while urban area was coded as 1. The average score of bothgroups was around .8, signifying that most participants live in urban areas.

10Figure 2 illustrates the comparison between factory and home-based workers regarding
participants’ experiences in work and training. Women in Lahore generally start their first workaround the age of 19 (see Table 1). However, the proportion of factory workers who had work
experience before their current job is slightly higher than that of home-based workers. We
discovered that 46.1% of factory workers have work experience prior to their current job, while
only 31.4% of home-based workers had worked before their current job. In contrast, both factoryand piece-rate workers have similar characteristics in engagement in the OJT opportunity.
Based on the findings, 37.1% of factory workers and 33.8% of home-based workers reported
having OJT experience.
In terms of educational background, as shown in Table 2, there is no apparent difference
between home-based workers and factory workers. Both groups have a variety of academic
backgrounds. In general, while around a quarter of female workers did not attend formal
schooling, more than 30% completed at least 10 years of education.
Table 1 Background information about participants by affiliation
Home-based
worker (n=220)

Factory worker(n=205)

Gender (Female = 1) [a]

1.000

1.000

Age (year)

33.85328.532

Number of family member

6.384

6.483

Family member in garment industry

3.170

2.710

Community type [b]

.830

.785

Economic level of household [c]

3.205

3.200

Age at first work (year)

18.93119.813

8,277.778

20,679.350

Work hours/day

5.440

8.055

Work days/week

5.399

5.972

Variable

Average monthly income (PKR)

Source: Data from Skills and Knowledge for Youth Program assessmenta. 0 = male, 1 = female.
b. The community where participants reside in: 0 = Rural and semi-urban, 1 = Urban.
c. Aggregate factor scores of DVD/CD player, TV, cell phone, computer, internet, bank account, car,motorcycle, bicycle, and refrigerator/freezer.

11

Work experience before the current job

Factory
workers

46.1% of factory workers had work experience.

Home-based
workers
31.4% of home-based worker had work experience.
On-the-job-training (OJT) experience

Factory
workers

37.1% of factory workers had OJT experience.

Home-based
workers

33.8% of home-based workers had OJT experience.
Figure 2. Participants’ work and OJT experience by affiliation

Table 2. Participants' percentage of attained education level by affiliation
Education level

Home-based
worker (%)

Factory worker(%)

No formal schooling23

28

Primary education (Grade 1 - 5)

23

16

Lower secondary education(Grade 6 - 8)1616High school/Matric (Grade 9 - 10)2027Intermediate/ F.A./ F.Sc

10

9

TVET

1

0

Tertiary (Diploma, BA/BSC,
MA/MC, Ph.D.)

43

Other [a]20Source: Data from Skills and Knowledge for Youth Program assessment
a. [Other] refers to certification which cannot be categorized under the national education system.

12

The characteristics of participating firms. The firms consist of 8 smalls, 4 mediums, and 5 large
firms. The firm size is categorized based on the number of employees defined by the Small andMedium Enterprises Development Authority (SMEDA) of Pakistan [2]. We found that more than
90% of firms provide On-Job-Training (OJT) to workers. The proportion of female employees in
the firms enormously varies between 4% and 100%. On average, 4 out of 10 workers are female
on average. Only 35% of companies reported that they collaborate with TVET institutions when
they employ workers.

Participant Firms Consists of 8 Small,
4 Medium, and 5 Large Firms

9 out of 10 Companies Offers OJT

35% of Companies Collaborate with
TVET Institutions

On Average, 4 out of 10 Workers
are Female

Figure 3. Characteristics of participating firms[2] The definition of firm size based on the number of employees (SMEDA, 2018, as cited in Raza et al.,2018)
Small: Up to 35 people
Medium: 36-250 people
Large: More than 251 people

13

3

COMPARISON BETWEEN HOMEBASED AND FACTORY WORKERSThis section features a comparative analysis of workers who work at home and those who areemployed in factories. From the time the SKY team was requested to conduct the survey, there
was a shared assumption that there should be significant differences in the skills between
home-based and factory workers. Therefore, the first step of our work was to check if thisassumption is supported with evidence or not.
For this objective, we conducted a logistic analysis using a dummy variable that classifies theparticipants according to their employment status as the dependent variable. Logistic
regression is a statistical technique employed to forecast the likelihood of a binary outcome
based on one or more predictor variables. It is frequently used when the dependent variable iscategorical and has only two potential outcomes, often referred to as 0 and 1 (Gudivada et al.,2016). In the case of our analysis here, factory workers are coded 1, and home-based workers arecoded 0. The results of the logistic analysis are visually presented with the illustration in Figure 3.
Figure 4 contrasts home-based and factory workers according to their demography, job-related
conditions, and the various types of skills they possess. The items listed at the left end of the
figure are the predictor variables whose statistical relationship with the dependent variable(home-based worker or factory worker) is examined.
The bars stretching toward the left from the central point (with a negative value) indicate theprobability that the increase of the concerned predictor variable by one unit happens withhome-based workers. In contrast, the positive value in the figure signifies the probability that,when we find one unit of the concerned variable increases, the person is a factory worker. For
example, if we randomly draw a person out of all the research participants, the probability that
she is a home-based worker rises by 63.5% as her age increases by one year. Similarly, the longer
the person’s work hour is, the chance that a person is a factory worker becomes higher. An
increase of one hour of work raises the chance that she is a factory worker by 40.5%. The lengthsof the horizontal bars indicate the magnitude that respective independent variables covariate
with the dependent variable (home-based vs. factory worker). An asterisk beside the value ofeach horizontal bar represents the significance level of a predictor variable's contribution topredicting the outcome. The number of asterisks (*), whether one, two, or three, indicatesdifferent levels of statistical significance. Specifically, one asterisk (*) signifies less than a 5%chance that the observed relationship between the concerned predictor and dependent
variables is due to random chance, a two asterisk (**) signifies less than a 1% chance, and a threeasterisk (***) signifies less than a 0.1% chance. The lower the chance that the relationshipbetween variables is random, the likelihood that the variables are related to each otheraccording to a consistent pattern. In other words, these asterisks suggest that we canconfidently accept the existence of the relationship between the predictor and dependent
variables at these levels of statistical significance.

143.1 Home-based workers are older and less paid, but their levels of skills are
not much different from factory workers, except for practical skills
The outcomes of the logistic regression presented in Figure 4 show that individuals who are
older tend to work at home, while those who earn a higher monthly income and work longer
hours per day tend to work in factories. The findings are consistent with the study of Mahnaz
Hassan & Azman (2014), which states that payments Pakistani female home-based workers
receive are minimal, often lower than the official minimum wage standard, as labor laws are notapplied to informal workers. Specifically, for every year of age, there is a 63.5% increase in theprobability of being a home-based worker. Similarly, for every unit increase in monthly income,there is a roughly 29% increase in the probability of working in a factory. Lastly, for every
additional hour worked per day, there is an approximately 40.5% increase in the probability of
being a factory worker.
The shaded parts in Figure 4 are variables representing the skills of workers. The green-shadedpart is practical skills, the grey-shaded part covers a variety of cognitive skills, while the blue
shade indicates noncognitive (soft) skills.

Home-based workers

Factory
workers
Percentage point

Age-63.5***
3.8

Education
Father's edu

-1.4
5.4

Household size
Household’s economic level

-2.1

Urban

-6.1
29***

Monthly income

40.5***

Work hours/day

15.2

Cognitive skills

Wage increase
Practical skills

45.3***

Reading-0.7

Mathematics-4.6**-1.2

Integrated skills-10*

Vocational knowledge
Extraversion-0.1

Soft skills

Conscientiousness

0.8-13.1*

Openness-1.7

Emotional stability

1.3

Agreeableness
Grit

10.9

Decision making

0.5
-1.3

Hostile bias
-80

-60

-40

-20

0

20

40

60

Figure 4. Comparison of home-based and factory workers’ individual conditions and skills

15

Out of thirteen skills, most cognitive and soft skills are not significantly different between homebased and factory workers, except for practical skills, mathematics, vocational knowledge, andopenness. Individuals with strong practical skills are more likely to be found among people whowork in factories, while those with a solid foundation in mathematics, vocational knowledge,
and a willingness to learn new things are more likely to work at home. Holding all othervariables constant, each additional point in practical skills raises the probability of being afactory worker by roughly 43.5 percentage points. Conversely, each additional point inmathematics and vocational knowledge raises the probability of being a home-based worker byapproximately 4.6 and 10 percentage points, respectively. Additionally, an increase of one point
in openness raises the probability of being a home-based worker by approximately 13.1
percentage points. Regardless, compared to the clear contrasts identified regarding age,monthly income, and work hours, the differences in the skills composition between the twogroups of workers are far less overt.
In order to verify our findings, we also conducted a standardized bias test using the propensity
score. Standardized bias is a statistical method used to measure the difference in the means of
the covariate between participant groups. It is often used to compare the magnitude ofcovariate imbalances across different covariates. The propensity score using logistic regression
helps to determine whether or not the conditions of two participant groups are different byaddressing balanced covariates between the groups (Stuart, 2010).
Figure 5 displays the standardized differences across covariates of respective predictor variables
with the dependent variable (home-based vs. factory workers). A recommended cutoff for theacceptable absolute standardized bias is set at the level of 0.25. Larger standardized biases
indicate that two groups of workers are too dissimilar (Stuart et al., 2013). A positive scoreindicates that the concerned predictor variable is more likely to describe the characteristics offactory workers, while a negative score signifies a higher association of the variable with homebased workers.
According to Figure 5, the predictors within the bias range of -25% and 25% (highlighted inyellow) show no difference between home-based and factory workers. The indicators which are
dotted outside of this yellow box are few, which are age, monthly income, work hours, andexperience of wage increase. In sum, these five factors can be considered to make home-based
and factory workers different. The findings confirmed that among all types of skills, only
practical skills demonstrate the difference between home-based workers and factory workers.
Therefore, in general, there is no significant difference in most types of skills if we categorize
workers by their employment status.
In academic studies, when the direct measure of workers’ productivity is difficult, the indicatorsof practical skills are often used as their proxy. As the SKY research team has identified indifferent national contexts, the determinants of the skills that lead to higher productivity andlabor market outcomes are highly context-dependent. Also, the investigation of such contextual
determinants of workers’ skills and productivity gives us nuanced pictures of the interaction

16

between workers’ individual characteristics and their work environment (Kondo et al., 2021;
Yamada, 2023). The analysis of this chapter shows that the practical skills, a proxy of productivity,
are significantly different between home-based and factory workers. Such a difference inpractical skills would also relate to the differences in salary and work hours. As we confirm thatthe workers’ productivity seems to be determined more by the work environment than theircapacities, we will now move to examine what aspect of the work environment matters for theworkers’ productivity.
To explain the variation of practical skills in more detail caused by the work environment, thenext chapter will compare formal sector workers’ practical skills across firms. One of the reasonsfor this focus on factory workers is that there are rich firm-provided data to be matched withthose of their workers. As for home-based workers, no data from employers can be matched,which limits the possibility of a deeper analysis of them. As we confirmed that little difference
exists between home-based and factory workers in terms of their inherent capacities, theinvestigation of factors affecting the performance of formal sector workers would give us
insights that can also be applied to understanding the determinants of performance among
home-based workers.

Figure 5. Standardized bias between home-based and factory workers’
individual conditions and skills

17

4

FACTORS AFFECTING THE VARIATIONOF FACTORY WORKERS’ PRACTICALSKILLSIn this section, we examine the factors that would explain the variations in the practical skills of
female garment workers in Punjab, Pakistan, focusing on factory-based workers. Our analysis
revealed significant differences in the practical skills among female garment workers
depending on the firm they work in. Table 3 compares firms by the average skill levels of theirworkers. We categorized firms into three groups based on the ranking of their workers’ averagepractical test performance (low, moderate, and high-skill) and conducted an ordered logistic
regression to determine the factors that would most likely predict membership into thosegroups. Low-skill firms are in the bottom five, while the high-skill ones are in the top five firms interms of workers’ average practical skills. Out of 17 firms, thus, 7 firms were classified asmoderate-skill firms. Low-skill firms have significantly lower average practical skill scores thanthe mean. In comparison, high-skill firms have average practical skill scores significantly higher
than the overall mean. Since our study doesn't seek to single out firms with low-skilled workers
and criticize them, the firms classified into respective groups will be kept anonymous. The
names of the firms are not necessary for our aim to identify the factors that drive the increase inworker productivity and related skills.

Table 3. Comparison across firms based on workers’ average skill level

18

Table 3 illustrates the marginal effects of each independent variable on the probability of beingin each type of firm: low-skilled, moderate-skilled, or high-skilled firms. Unlike the coefficients inan ordered logistic model, the marginal effects are easier and more direct to interpret as they
represent the probability of the targeted outcome for a unit change in the independent
variable. For instance, if the marginal effect of “Practical skill” is .155 in the high-skilled firmcolumn, it can be interpreted that for each additional practical skill score, the probability ofworkers being in a high-skilled firm increases by 15.5 percentage points, holding all predictors
constant. Then, the statistical significance levels, one (*), two (**), and three asterisks (***), areincluded to respectively express that there is less than 5%, 1%, and 0.1% probability that theeffect is due to random chance.
Table 3 lists independent variables of roughly two types. One is the items incorporated from theworkers’ questionnaire and represent characteristics of individual workers in three categories offirms. Workers’ age (“Age, in logs”), monthly income (“Wage, in logs”), and hours they work in aday (“Work hours per day, in logs”) are the results of converting original data using the logtransformation to improve the normality of data distribution. We also include variables
depicting whether participants graduated above the 9th grade (“Education”) and whether theyreceive advice from supervisors or colleagues (“Advice”). As for participants’ skills, practical skills,
four domains of cognitive skills (“Mathematics”, “Reading”, “Integrated skills”, and “Vocational
knowledge”), and four types of soft skills (“Openness”, “Emotional stability”, “Grit”, and “Growthmindset”) were included in the analysis. The first three soft skills were personality traits wemeasured by a specialized psychometric scale, as explained in Chapter 2, while the growthmindset was constituted by a multiple-answer question asking participants’ reasons forchoosing their current jobs, using the statistical method called principal component analysis(PCA) [3]. The answer options capturing participants’ eagerness to have self-confidence andreceive skill development opportunities from working were grouped into a variable called“Growth mindset”, which refers to one’s belief that skills can be improved and aspiration to
make an effort for improvement.
Regarding the workers’ individual characteristics, putting aside the practical skills according towhich the firms are classified and, thus, naturally indicates significance to the opposite
direction, a soft skill to desire and make an effort for improvement (“Growth mindset”), cognitive
abilities to read, soft skills to be open to learn new things (“Openness”) and to be emotionallystable (“Stability”) also showed the similar contrasts.
Another type of independent variable is the items from the firm managers’ questionnaire,
which provide information about the firms’ business, provision of training for workers, and theirperceptions about the necessary skills of workers to be effective and productive in their firms.
The managers’ expectations regarding workers’ work-related soft skills are transformed into
four variables: “Diff: Punctuality”, “Diff: Customer relations”, “Diff: Initiative”, and “Tidiness,
Punctuality, & Collaboration.” The last variable, "Tidiness, Punctuality, & Collaboration", groups a
set of questions that measure managers' perceptions of the importance of noncognitive skills,[3] Principal component analysis (PCA) is a linear dimensionality reduction statistical method, which identifies patterns
and commonalities of variables to reduce dimension in multivariate data (Shlens, 2014).

19such as tidiness, punctuality, and teamwork skills, based on the results of PCA. On the other
hand, the variables starting with “Diff” represent the difference in perceptions of specific workrelated soft skills between the employees and managers. Positive values determine thatmanagers value the skill more than workers.
For this particular survey in Punjab, Pakistan, the SKY research team added some question
items about managers’ perceptions of women and their involvement in productive work. Thisset of questions about managers’ (employers’) perceptions of women’s strengths andweaknesses is clustered into the last variable in the table named “women stereotype”, whose
effects are opposite to low-skill and high-skill firms (For both categories, this variable
demonstrates statistical significance with two asterisks but the covariance value for high-skill
firms is negative and that for low skill firms is positive.
As for firm characteristics, it is not only with the “women stereotype” variable that theindependent variables demonstrate significance to the opposition directions between high-skill
and low-skill firms. The number of employees (“Firm size”), whether the firm exports the
products or not (“Export firm”), the firm’s variety of on-the-job training opportunities for workers(“OJT offering”), whether the firm collaborates with TVET institutions when they employ newworkers (“TVET collaboration”), and whether the managers appreciate workers to take initiative
at work (“Diff: Initiative”) are significant but in contrasting ways between low-skilled and highskilled firms.
In the following sections, we will discuss more in detail factors characterizing the workplaces
which have higher average levels of workers’ practical skills in comparison to those with lower
average practical skills. Section 4.1 will focus on the individual workers’ characteristics,
particularly their cognitive and noncognitive skills. Section 4.2 will highlight the workplace
conditions that either enable or discourage female workers from improving their skills andproductivity.

4.1 High-skill firms tend to have workers with more motivation, emotional
stability, and reading abilities
According to Table 3, certain factors contrast the three groups, particularly between the low-skill
and high-skill firms. For the individual-level variables, both groups of firms demonstrate
opposite tendencies in workers’ practical skills, growth mindset, openness, emotional stability,
and reading skills. These variables are with the asterisks (*, **, or ***), indicating the level ofstatistical significance.
According to Figure 6, visualization of skills predicting the membership to low- or high-skill
firms, our analysis identified the key characteristics of high-skill firms, which have more workers
with high practical skills and a desire to learn and improve (“Growth mindset”). We found thatevery one-point increase in the workers’ growth mindset raises the probability that the firm she
works for will be categorized into the high-skill group by 4.8%. Workers who enter the firm witha growth mindset believe that their practical skills can be developed through hard work,
perseverance, and feedback from others (Dweck, 2016). Having such a desire for growth and20commitment motivates workers to do better at work, which in turn contributes to enhancing
their productivity (Ng, 2018; Rhew et al., 2018). With regards to soft skills, firms whose workers
are generally more emotionally stable (represented by the variable “Stability”) tend to be foundin the high-skill group. On the other hand, businesses that hire individuals with more open
minds to learn new things (represented by variable “Openness”) are less likely to fall into thisgroup. Our research outcomes indicate that openness is the preferred soft skill for firms withlow-skilled workers because eagerness to learn new things is a desirable trait for people whoneed upskilling (Bing and Lounsbury, 2000). As firms transit to high-skill firm status, emotional
stability becomes the desired trait for workers, with openness taking a secondary role. A metaanalysis conducted by Judge and Bono (2001) shows that emotional stability (low tendency toneuroticism) is significantly correlated with job performance. Workers who possess highemotional stability remain calm, composed, and confident in stressful situations and are able toquickly adapt to changing situations in the workplace.

Low-Skill Firm

Openness

High-Skill Firm

Growth Mindset

Reading Ability

Emotional
Stability

Figure 6. Soft skills which workers in Low-skill and High-skill firms have
Aside from soft skills, in firms where workers with higher practical skills are found, the workers
also demonstrate higher reading abilities. Interestingly, even a small increase in a worker's
reading ability (as measured by a point increase in their score) can boost the likelihood that
their firm will be classified in the high-skill group by 2.3%. According to a study conducted by
Lazar et al. (1998), there is a correlation between significant improvements in basic cognitive
skills like reading and numeracy, and positive changes in workers' attitudes toward their jobs,
self-esteem, and performance. Also, workers with strong reading skills are better equipped withthe means to digest instructions in written forms and follow them. For example, in garment
firms, stitching specifications and quality control guidelines are provided in writing, whose
understanding will directly lead to effective production with fewer errors and defects.

4.2 High-skill firms give more various types of OJT, and their managers
have more positive gender stereotype
Now, let us turn to the workplace-related variables that demonstrate statistical significance in
explaining the firms’ average levels of workers’ practical skills in Table 3. At the firm level, firmsize, exportation, provision of OJT, TVET collaboration, managers’ appreciation of workers’
initiative (“Diff: Initiative”) and stereotypes toward women’s characteristics establish acontrasting propensity between low- and high-skill firms.
Our analysis has yielded insightful results regarding workplace conditions that can affect boththe practical and soft skills of female garment workers in Punjab, as visualized in Figure 7.
Specifically, the variety of on-the-job training (OJT) offered, and the employer's perception of21

female workers were identified as key predictors of high-skill firms. Companies often use OJT asa way to train their workers in new competencies. This approach involves workplace training
where a supervisor, manager, or experienced employee provides guidance to the trainee. OJTs
are designed to provide practical experience to workers and are highly contextualized as they
are tailored to the specific needs of the firm.
According to our findings, each additional type of OJT opportunity is associated with a 3.9%
increase in the likelihood of being a high-skill firm. Our study's findings are consistent with priorresearch that has established a positive correlation between on-the-job training (OJT) and job
performance (Andri and Mandataris, 2023; Lin and Hsu, 2017; Thevanes and Dirojan, 2018;
Toumahuw, 2022). This suggests that investing in a greater variety of OJT initiatives can yieldsignificant benefits for both employees and employers, including improved job performanceand outcomes. These results underscore the importance of continued investment in employee
development and training programs to enhance workforce skills and productivity.

Low-Skill Firm

Larger
Factories

TVET
Collaboration

High-Skill Firm

Exporter
Variety of OJT

Women
SterotypeManagers’ High
expectation ofWorkers’ Initiative

Figure 7. Workplace factors that predict membership to Low- and High-skill firms

In addition, as for the firm’s conditions, we found that the firm size and whether they export
their product (represented by the variable “Export firm”) significantly predict the membership to
low-skill or high-skill firms. Every level (including small, medium, and large) decrease in the firm
size enhances the probability that the participant works in a high-skill firm by 8.2%. Likewise, theresults indicate that being a domestic-oriented firm increases the likelihood of being a highskilled firm by 20.1%. In SME firms, the production of a garment, such as a dress, is typicallyhandled by one or a few workers. As a result, those employed at smaller firms tend to have morehands-on experience with a wider range of practical skills. In contrast, larger export-oriented
factories often establish systematic production lines in which workers are assigned to specificsections and tasks, resulting in skill development in only one or a few practical abilities. With thisinsight, larger firms, especially export-oriented ones, could consider offering skill development
programs that allow workers to have a broader range of practical skills.
Results of our analysis have also shown that the perspective of managers on women plays acrucial role in a company's productivity. Having managers who are biased can reduce thelikelihood of their company being classified as a high-skill group by 4.0%. If managers hold onto

22

outdated and biased beliefs about women, such as assuming they are only suitable for parttime work or staying at home, it can negatively impact the development of a skilled workforce.
The presence of workplace stereotypes often leads to employees experiencing anxiety related totheir performance, which in turn hampers their ability to perform optimally and achieve theirfull potential (Kramer and Harris, 2016; Lavaysse and Probst, 2019). This result highlights thesignificance of creating a positive and inclusive workplace to improve the productivity of female
garment workers.
Our research discovered that the difference in appreciation of proactive behaviors at theworkplace between managers and workers significantly contributes to the likelihood of theparticipant being classified to work in high-skill firms. The marginal effect of the variable “Diff:
Initiative” is -0.075, signifying that for every one-unit increase in the difference between
managers’ and workers’ perception of workers’ taking initiatives, the probability that the firm
will be classified as a high-skill one decreases by

7.5%. In other words,

if the managers’

appreciation of workers’ taking initiatives is consistently lower than that of their workers, thelikelihood of the firm being categorized as a high-skill group increases. This means that thealignment between managers and female workers regarding proactive attitudes and behaviors
at work is positively associated with firms’ productivity. Rather than leaning on managers’ highexpectations of workers’ taking initiatives, firms need to make sure that workers share acommon appreciation of the skill.
To reduce the gap between workers and managers, firms may inspire and motivate theiremployees to see the importance of proactive behaviors and practice daily at the workplace.
This could be done through the provision of training and the formation of an encouraging
workplace environment with a culture of appreciating workers’ initiatives. By fostering workers’
initiatives, their problem-solving abilities, flexibility, communication, collaboration, andadaptability can be enhanced. For instance, employees who are encouraged to take theinitiative and work collaboratively with their colleagues are more likely to develop better
problem-solving skills. Additionally, employees who are empowered to make decisions are more
likely to be adaptable and flexible in their approach to work. Ultimately, by addressing theseareas, firms can improve the alignment between managers and workers regarding skill
perceptions, ultimately enhancing the firm’s productivity.

4.3 A Model of the Paths that Workers’ Soft Skills and Workplace
Conditions Contribute to Improved Practical Skills
As we identified that some types of soft skills and workplace conditions predict the high-skill
firm membership, we have developed a model to delve deeper into the relationship between
these factors. The goal is to determine how these factors improve the practical skills andproductivity of female garment workers in Pakistan. Our findings are presented in Figure 8,
showcasing a structural equation model (SEM) that illustrates how workplace conditions andsoft skills impact practical skills. SEM is a statistical method which establishes a structuralmodel, displaying causal relations and correlation between variables by using the covariancematrix of the relationship between variables (Byrne, 2016). Single-direction arrows demonstrate
the causal relations of the variables ‘Openness’ and ‘OJT offering’ on ‘Practical skills,’ which is23the dependent variable. The value shown above the single-direction arrows shows thecoefficient size. A negative value, such as the effect of firm size on practical skills, depicts thatfirm size inversely predicts workers’ practical skills. On the other hand, double-headed curved
arrows represent the correlation between variables, such as between ‘Grit’ and ‘Emotional
stability.’

Individual’s soft skills

Grit
production manager’s perceptions.2

Emotional
stability.19

Firm size
Womenstereotype

Openness

.26

-.31
.66

Manager’s expectation
on tidiness, punctuality,
and teamwork

Workplace’s
conditions

OJT offering.09

Practical skills

.32

.23

Figure 8. A Structural equation model of the contribution of workers’ soft skills andworkplace conditions to practical skills
As we previously discussed, emotional stability and openness are essential components thatpredict a company's overall skill level. It's important to recognize that these soft skills areinterdependent, and the lack of one can adversely affect the effectiveness of others. Our SEM
model has shown that workers' productivity is not determined by a single soft skill alone.
Instead, a combination of soft skills is required to improve workers’ practical skills. These results
highlight the importance of comprehensive soft skills training to workers as different soft skills
exhibit strong associations with each other.
In Section 4.2, we explore how workplace conditions impact a firm's skill level. The previous
finding has shown that companies that offer more variaty of on-the-job training (OJT) tend tohave highly skilled workers. Now, our SEM model has revealed that the perceptions andexpectations of managers towards female employees play a significant role in the provision of
OJT. Firms with managers who perceive women’s characteristics more positively tend toprovide more OJT opportunities. Managers invest more in skill development and training forfemale workers when they believe in women’s abilities as equally as that of male workers.
Likewise, although managers’ expectations toward workers’ tidiness, punctuality, and teamwork
skills did not predict the membership of low- or high-productivity firms, it significantly affects
the firms’ provision of OJT. When managers highly perceive specific workplace-relevant soft
skills, they tend to prioritize offering skill development programs for workers.
It is, therefore, essential to promote a positive stereotype of women among managers to ensurethat female employees equally receive opportunities for training and development as their malecounterparts. This will ultimately benefit the organization as a whole. Interestingly, we have24

found that smaller firms are likely to provide more variety of OJT opportunities to theiremployees. Our results suggest that larger companies should follow suit and offer more OJT
opportunities to improve their overall performance.

4.4 Summary
Key findings in this chapter highlight the influence of workers' soft skills and reading abilities on
the probability of membership in high-productivity firms. At the firm level, the provision of OJTemerged as a significant predictor of high-skill firms, underlining the importance of consistentinvestment in a variety of OJT opportunities to enhance the firm’s productivity. Moreover,production managers' perceptions of women played a critical role in promoting workers’
practical skills. The managers’ negative stereotypes are associated with firms classified as lowskill. This signifies the need for a gender-inclusive workplace environment to improve theproductivity of female garment workers. Likewise, the alignment between managers' andworkers' perceptions of proactive initiatives also is a driver of firm productivity, suggesting theimportance of fostering positive mindsets toward initiative-taking behaviors among workers.
The study further investigates the relationship between soft skills and workplace conditions. The
results reveal that workers' practical skills are influenced by workers’ openness to learn newthings and firms’ provision of OJT. Soft skills such as grit and emotional stability areinterconnected with openness, highlighting the need for comprehensive soft skills training.
Additionally, the provision of OJT is influenced by managers' perceptions, including stereotypes
toward women and expectations for worker behaviour, particularly tidiness, punctuality, andteamwork. Lastly, smaller firms are found to offer more variety of OJT opportunities, resulting inenhanced productivity. Large firms, thus, are encouraged to increase investments in OJT toenhance workforce productivity.

25

5

CURRICULUM ANALYSIS

5.1 Overviewed Curricula
In this section, we provide an overview of the curricula prepared by the Punjab Technical
Education and Vocational Training Authority (P-TEVTA) and the Punjab Vocational Training
Council (PVTC) to analyze which skills are emphasized during the training at TVET institutions.
The curricula employed by the TVET institutions under P-TEVTA are titled Domestic Tailoring
and Industrial Stitching Machine Operator. Both of them are six-month courses. In addition,
we have looked at two curricula used at the institutions under PVTC, which are Dress Making
and Industrial Garment Expert. They are also six-month courses and are classified at Level 2
within the Pakistan National Vocational Qualification Framework (NVQF). NVQF is a national
system of technical and vocational qualifications which was launched in March 2015. The criteria
for NVQF Level 2 are as follows. The criteria show that NVQF Level 2 qualified worker has thebasic knowledge and skills to work under indirect supervision and adapt his/her own abilities
when solving problems.

IKnowledge and understanding
Basic knowledge of readily available facts, processes and general theory of an area ofwork or study.
SkillsBasic practical skills required to complete tasks and solve problems by selecting andapplying basic methods, tools, materials and information.
Responsibility
Take responsibility for prioritising and completing tasks in work or study under indirectsupervision with some autonomy and adapt own abilities when solving problems.
(Source: NAVTTC, 2015 underlines added by the author)
Although the above four curricula are implemented by different TVET institutions,commonalities are observed among the industrial human resources they seek to develop. The
Industrial Garment Expert curriculum prepared by PVTC aims to produce employable garment
experts capable of operating various machines used in the garment industry according tonational and international standards. The curriculum also expects the learners to go to higher
levels of NVQF to become managers in the future. Similar objectives are outlined in the
Industrial Stitching Machine Operator curriculum developed by P-TEVTA, designed forparticipants aspiring to work in the garment industry as stitchers or industrial stitching machine
operators. Both curricula aim to cultivate garment machine operators in factories withspecialized high-level sewing skills.

26

On the other hand, the Dress Making curriculum aims to provide skilled manpower to enhancethe existing garment industry, enabling them to find employment as cutters, tailors, or checkers
in factories or become self-employed. Similarly, the Domestic Tailoring curriculum also aims toprepare trainees for employment in garment factories, boutiques, or home-based settings.
These two curricula place more emphasis on developing individuals with broader and basic
garment knowledge and skills.

5.2 Skills emphasized in the curriculaThis part examines the emphasized skills within each curriculum. Table 4 displays the skills
outlined in the four curricula. A double-circle ( ) indicates the presence of specific sections
dedicated to learning the skills, incorporating both theoretical understanding and practicalapplication. There are some exceptional skills—calculation skills, for instance. Although there isno dedicated section for improving the calculation skills in the Dress Making curriculum, theskills are referenced in various other sections, such as “calculate fabric length and width” in the
body measurement section and “estimate and calculate labor costs” in the stock management
section. A circle ( 〇 ) denotes skills not explicitly addressed in separate sections but seemingly
integrated within others. For example, cutting, ironing, and calculations are not individually
outlined in the Domestic Tailoring curriculum, yet they appear to be encompassed within
sewing sections.
i) Practical skills
Regarding practical skills, the Dress Making and Domestic Tailoring curricula encompass awider array of competencies, albeit more foundational and general in nature. Conversely, theIndustrial Garment Expert and Industrial Stitching Machine Operator curricula prioritize
possessing a higher level of “sewing” skills essential for operating various industrial garment
machines effectively. These practical skills components appear tailored to align closely with therespective curricular objectives.
ii) Soft skills
There is a notable discrepancy in the treatment of soft skills and cognitive skills acrossinstitutions. PVTC places a greater emphasis on soft skills. For instance, within the Dress
Making curriculum, there’s a module titled “Develop Professionalism,” which delves into aspects
such as work ethic, punctuality, networking, and communication skills. Here, communication
skills focus on fostering polite and transparent interactions with clients. Similarly, the Industrial
Garment Expert curriculum includes a section on “Demonstrating Communication Skills,”
which emphasizes active listening, effective verbal and non-verbal communication, andconfidence building. Notably, this curriculum underscores the importance of a friendly work
environment in enhancing productivity, a concept uniquely highlighted. The curriculum also
repeatedly mentions the importance of cleaning to maintain a safe workplace in each sewing
machine operation section. The P-TEVTA’s curricula also include “cleaning.” However, it pertains
not to overall workplace cleanliness and safety but rather to cleaning sewing machines toprevent malfunctions.

27

Table 4. List of skills mentioned in the curricula◎: There are specific sections on how to learn the skills.
〇: There is no specific section for the skills, but it seems to be included in the others.

28iii) Cognitive skills
P-TEVTA’s curricula feature modules that foster cognitive skills, particularly in IT and English
language proficiency. Both the Domestic Tailoring and Industrial Stitching Machine Operator
curricula have the same modules for those skills. The sections dedicated to IT skills are tailoredfor individuals to be able to use desktop PCs. Its content centers on guiding users through tasks
such as document creation using Microsoft Word, setting up email accounts for computerbased communication, attaching documents to emails, and providing tips for effective internet
searches. The sections for the English language skills cover grammar, writing, reading, speaking,and listening. Although the curriculum suggests using a textbook titled “High School English
Grammar,” the sections are more likely to be related to the workplace. For instance, the writing
part of the section has the skit of “submitting a report of the performance of the team oftechnicians” and “Job application/C.V.” PVTC’s curricula do not have English sections. Still, themedium of instruction for the Industrial Garment Expert curriculum can be English, unlike theDress Making curriculum, which is required to teach either Urdu or the local language.

5.3 Personality traits and behavioral skills in the curricula
Based on our team’s findings in the previous sections, personality traits (openness, emotional
stability, and grit) and behavioral skills (tidiness, punctuality, and teamwork) influence workers’
practical skills both directly and indirectly. By simply observing the four curricula, it appears tolack dedicated sections for enhancing personality traits or acquiring behavioral skills. However,certain segments seem capable of fostering their development. This part gives some examples.
i) Personality traits
Personality traits encompass patterns of thoughts, feelings, and behaviors unique to individuals.
Our team’s analysis identified emotional stability, grit, and openness as pivotal traits enhancing
workers’ practical skills.
Within the "Develop Professionalism" segment of the Dress Making curriculum, stressmanagement is highlighted. It potentially aids learners in bolstering their emotional stability.
Similarly, the "Demonstrate Communication Skills" component in the Industrial Garment
Expert curriculum focuses on fostering skills for creating a friendly workplace atmosphere,
which can contribute to nurturing openness among participants. However, as these initiatives
only offer the potential to enhance personality traits, instructors must be dedicated to fostering
the improvement of students’ abilities.
P-TEVTA's curricula appear to allocate ample individual project time, fostering grit bynecessitating learners to initiate projects from scratch and crafting entire clothing pieces
independently. What should be emphasized here is that the instructors must effectively drawout trainees' motivation and guide them through task completion. The English language
learning module within P-TEVTA's curriculum advocates for discussions, interviews, and roleplay
during classes. If instructors prioritize improving communication skills over learning language
acquisition, this section could also serve to enhance various aspects of learners' personality
traits.

29ii) Behavioural skills
As the previous section elucidated, managers’ expectations regarding workers' tidiness,
punctuality, and teamwork skills significantly influence the provision of on-the-job training (OJT)
in firms, thereby enhancing workers’ practical skills.
Several sections of the curricula underscore the importance of tidiness. It is evident in curricula
such as Industrial Stitching Machine Operator and Industrial Garment Expert, which stressthe necessity of cleaning, oiling, and conducting basic maintenance on sewing machines.
Additionally, PVTC's curricula contain sections titled “Follow Environmental, Health, and Safety
Rules” and “Identify Hazards at Workplace,” which can aid learners in improving their tidiness byemphasizing the importance of maintaining cleanliness throughout the workplace, not justfocusing on machines.
Furthermore, teamwork is a recurrent theme across various sections of the curricula. For
instance, the Industrial Garment Expert curriculum includes a segment titled “Demonstrate
Communication Skills,” designed to cultivate learners' abilities to foster a positive workplaceatmosphere. However, due to the absence of specific measurement criteria and communication
guidelines within the curriculum, instructors must rely on their discretion to teachcommunication skills effectively.
While the curricula don't have a dedicated section emphasizing punctuality, learners may still
acquire time management skills by completing their individual projects or assignments by thedeadlines. To meet deadlines, students need to create detailed work plans, thus requiring
assistance from instructors. Moreover, the instructors' consistent adherence to class schedules
can also contribute to this development.

Discussions,interviews,
and roleplay during
the classes(English classes)

Stress management
to develop a
professional
attitudesonality tr
ait
Per
s
Emotional
stability

Grit
Individual projects tomake clothes fromscratchOpenness

ioural ski
hav
lls
e
B
Punctuality

Tidiness

Teamwork

Clean and maintainsewing machines(machine operator’s curriculum)

Clean workplace
for safety andworkers’ health
Learn various
communication methods
useful at workplace

Create a friendly
workplace to increase
productivity

Figure 9. Personality traits and behavioral skills written in the curricula

30

6

CONCLUSION

6.1 Conclusion
In conclusion, the comparative study between home-based and factory workers throughlogistic regression analysis and a standardized bias test indicates the differences in individual
conditions and skills between the two groups. For individual backgrounds, the age of homebased workers is likely higher than that of factory workers. In contrast, higher monthly income
and longer work hours tend to be more associated with factory workers. In terms of skills, mostcognitive and soft skills show no significant disparities between the two groups. Practical skills
are the only skills that exhibited differences between home-based and factory workers.
Practical skills, often regarded as a measure of the workforce’s productivity, vary between thetwo groups. Nevertheless, to understand the determinants of practical skills, we need amultifaceted analysis drawing the involvement of workplace and environmental factors.
Therefore, the more in-depth analysis focuses on investigating factors contributing to variationsin practical skills through comparisons across firms. Given the similarity in skills between homebased and factory workers, the subsequent analysis focuses solely on factory workers due to thericher availability of data at both individual and firm levels.
Based on an ordered logistic regression, the firms are categorized into three groups based on
the average practical skills scores of their workforce: low, moderate, and high-skill firms. The
high-skill firms exhibit markedly higher average practical skill scores than the overall mean,
while low-skill firms display average scores significantly below the mean. The findings identify
that workers’ growth mindset, reading abilities, and emotional stability are the key factors
encouraging membership in high-productivity firms. For the firm-level factor, the provision ofOn-the-Job Training (OJT) emerges as a significant predictor of high-skill firms, signifying theimportance of regular investment in various types of OJT to improve the workforce’s
productivity.
Additionally, the perceptions of production managers toward women employees also play a
crucial role in the organization’s productivity. Managers’ negative stereotypes toward women
increase the likelihood of firms being classified as low-skill. This highlights the necessity ofensuring a gender-inclusive workplace environment to improve the productivity of female
garment workers. Likewise, the alignment of managers’ and workers’ perceptions of workers’
proactive initiatives at the workplace can advance the productivity of the firm. By addressing
the perception gap through the cultivation of a positive mindset toward initiative-taking
behaviors among workers and the creation of encouraging and supportive workplace’ cultures,the firm average practical skills can be improved.

31

Furthermore, after understanding their contribution to workers' practical skills, we explore therelationship between soft skills and workplace conditions using structural equation modelling.
Workers’ practical skills are influenced by their openness and OJT. Openness is, at the sametime, affected by other types of soft skills like grit and emotional stability, which are also closely
linked. The findings indicate that soft skills training should be comprehensive due to theinterconnection of different types of soft skills. Likewise, the provision of OJT significantly
impacts practical skills; however, it is influenced by managers' perceptions, including
stereotypes towards women and expectations for workers' behaviour. Promoting a positive
impression of women among managers is vital to ensure equal opportunities for female
employees to receive training and fair treatment. Lastly, we have found that smaller firms
provide more variety of OJT opportunities, leading to better productivity. Thus, large firms arerecommended to invest in OJT in more diverse types for the better productivity of theirworkforce.
The current TNA project discovers similarities in skills between home-based and factory workers,
as well as the factors influencing the productivity of female garment workers in Punjab, drawing
the importance of the firm context. By addressing these factors, firms can enhance their overall
workforce's skills and productivity, contributing to the economic expansion and sustainability of
the firm. At the same time, we offer suggestions for TVET curriculum improvement to fill theskill gaps and mismatches between the workforce demander and supplier sides.

32

6.2 Policy Implications and Recommendations
Based on the findings, three pillars of policy implications are compiled along withrecommendations for enhancing the workplace-related skills of female garment workers in
Punjab.

01

The importance of soft skills development isconfirmed but contextual relevance is the key.

”Comprehensive Soft Skills Training Programs: Emphasizing theinterconnection between different types of soft skills and implementing
soft skills training programs that cover a wide range of skills are crucial.
This can include encouraging workers to develop attitude with openness,
teamwork, emotional stability, and more.
In low-skill firms, openness to learning new things is crucial for skill
acquisition.
Emotional stability becomes attractive as firms advance their workers
to high-skill status.

Fostering a Growth Mindset: Encouraging employees to develop a
growth mindset can lead to increased productivity and innovation. The
skill development can be achieved through offering regular On-the-JobTraining (OJT) with constructive feedback and recognizing workers’
achievements in an environment where learning and development arecherished.
Contextualized Soft Skills Training: Tailoring soft skills training programs
according to the conditions, specific needs, and weaknesses/strengths of
the firms can assist the progress of skills transfer and application inworkers’ daily lives at the workplace. The training can employ real-lifescenarios, simulations, and case studies, which reflect the actual contextsof specific firms and industries.

02

Practical Training is as important as soft skills forbetter productivity.

”

We suggest emphasizing the importance of practical training alongside soft
skills for enhanced productivity.
Targeted Programs for Low-Skill Firms: Implementing focused skills
development programs to improve workers’ practical and reading skills.
Regular Upskilling for High-Skill Firms: Continuously Investing in
upskilling programs to ensure the maintenance and improvement of
current levels of practical skills among their workforce.
Partnerships between firms and TVET: Providing contextually relevant
practical training opportunities for future workers through thesepartnerships.

33

03

A Positive Work Environment Enhances Female
Workers’ Productivity.
Sensitizing Employers to Take Approaches for Promoting”

GenderEquality and Diversity:
Firms should not only address existing disparities but also actively copewith gender stereotypes and biases that may exist in the workplace.
Initiatives such as awareness campaigns and gender sensitivity training
programs for workers in managerial roles can help combat unconscious
bias and create a more inclusive and comfortable environment wherefemale employees feel safe, valued, and respected.
Adopting

gender-neutral

policiesandpractices,

such

as

equal

opportunities for advancement and equal treatment in promotion
processes, can facilitate better support for gender equality within thefirm.
Fostering Worker Initiative: Firms and managers should empower
workers to take initiative. This can have significant benefits for a firm’s
productivity. When employees are provided with the independence tomake decisions and take ownership of their work, they become moreengaged, motivated, and committed to achieving organizational goals. It
also enhances workers’ soft skills, such as problem-solving, adaptability,
flexibility, communication and collaboration among colleagues.

By implementing these recommended policies, stakeholders can form a work environment andskills development that not only cultivates the workforce’s productivity but also encourages
non-cognitive abilities, including efficiency and innovation, throughout the organization.
Consequently, this leads to an enhancement in organization-wide productivity and facilitates
economic growth at both firm and national levels. By placing a focus on creating a positive work
environment, advocating for gender equality and inclusivity, and promoting employee
initiatives, firms can position themselves for sustainable prosperity in today's highly competitive
business world.

34

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Skills and Knowledge for Youths
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Nagoya, Japan 464-8601
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