Determinants of worker’s productivity in vietnamese sewing industry

Firm productivity is affected by many factors including opportunity gain by the business, technology, machinery, factory, and individual productivity as well.. Because of the differences

UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM

HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIE

VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

DETERMINANTS OF WORKER’S PRODUCTIVITY IN PROTRADE GARMENT

CO., TLD

BY

LE DINH HUY

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, NOVEMBER 2016

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM THE NETHERLANDS

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

DETERMINANTS OF WORKER’S PRODUCTIVITY IN PROTRADE GARMENT

CO., TLD

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

LE DINH HUY

Academic Supervisor:

DR TRUONG DANG THUY

HO CHI MINH CITY, SEPTEMBER 2016

DECLARATION

I declare that: “Determinants of worker’s productivity in Protrade Garment Co., Ltd.”

is my own work; it has not been submitted for any degree at other universities

I confirm that I have made all possible effort and applied all knowledge for finishing

this thesis to the best of my ability

Ho Chi Minh City, November 2016

Le Dinh Huy

ACKNOWLEDGEMENTS

I would like to express deepest special thanks to my academic supervisor, Dr Truong Dang Thuy who gives me helpful comments, excellent guidance His patience and caring brings the motivation for me He always gives me good advises whenever I got stuck, push me to finish the thesis, and always cares of my thesis process

I am also grateful to Prof Dr Nguyen Trong Hoai and all of Vietnam – Netherland staffs who always support me for the two-year of studying and more than 2 years

Last but not least, my sincerest thanks are for my family, my friends Without their frequent encouragement as well as spiritual support, I would not have been able to complete this thesis

TABLE OF CONTENTS

CHAPTER 1 INTRODUCTION 8

1.1 Problem statement 8

1.2 Research objectives 11

1.3 Scope of study 11

1.4 Structure of the thesis 12

CHAPTER 2 LITERATURE REVIEW 13

2.1 Concepts and theories 13

2.1.1 Individual productivity 13

2.1.2 Factors effect to individual productivity 14

2.2 Empirical evidences 19

2.2.1 Age, experiences related to productivity 19

2.2.2 Level of worker, technology related to productivity 21

2.2.3 Gender, work environment related to productivity 23

CHAPTER 3 RESEARCH METHODOLOGY 24

3.1 Conceptual frame work: 24

3.2 General Analytical Model 26

3.3 Data source and description 27

3.3.1 Data source 27

3.3.2 Definition of productivity 27

3.3.3 The description of variables 30

3.4 Model Estimation and Hypothesis Testing 32

CHAPER 4 EMPIRICAL RESULTS 33

4.1 Data descriptive: 33

4.2 The OLS result 37

CHAPTER 5 CONCLUSIONS 45

5.1 Main findings: 45

5.2 Policy implications 46

5.3 Limitations and further research 46

REFERENCES: 46

LIST OF TABLES

Table 1: Comparison of four factories in general figures 10

Table 2: Variables definition and expected sign 27

Table 3: How to calculate efficiency 29

Table 4: Product type and organization differences between 4 factories 30

Table 5: Management differences between 4 factories 31

Table 6: Customers and pressure differences between 4 factories 31

Table 7: Variables definition and expected sign 31

Table 8: Summary of the variables 32

Table 9: Gender in each factory (discrete variable) 35

Table 10: Worker's level in each factory (discrete variable) 35

Table 11: Data descriptions of continuous variables 36

Table 12: Summarized Estimation Results 37

Table 13: Worker’s level & number of operations in each factory 40

Table 14: Worker’s level & number of operations in each factory 40

Table 15: Results of Regression: Eff Exp Gender Operation 42

Table 16: Number of worker and average worker level for male and female 44

LIST OF FIGURES

Figure 1: Export of Vietnamese industries 2000-2012 (Unit: 1000USD/year) 9

Figure 2: Effort and performance (Ruth, 1987) 14

Figure 4: Age and Effort-Performance 15

Figure 3: Experience and Effort-Performance 15

Figure 5: Hypothetical Performance - Utility function as a function of Age 16

Figure 6: Hypothetical Effort -Utility function as a function of Age 16

Figure 7: Management factors affects to individual performance 17

Figure 8: The technical change and the aggregate 18

Figure 9: Relation between individual effort and performance 19

Figure 10: Effort-performance differences, depends on the experience 20

Figure 11: Effort-performance differences, depends on the ages 21

Figure 12: The productivities of textile & sewing in period 1949 -> 1999 22

Figure 13: The conceptual frame work 25

Figure 14: How to calculate efficiency 28

Figure 15: Age and Efficiency 33

Figure 16: Relation between efficiency and experience 34

Figure 17: Operations and efficiency 34

Figure 18: Distributions of experience 38

Figure 19: Experience and efficiency 38

Figure 20: Experience and predicted productivity 39

Figure 21: Distribution of operations 41

Figure 22: Correlation between worker level and number of operation 41

Figure 23: Correlation between efficiency and number of operations 42

Figure 24: The age distributions 43

Figure 25: Age and predicted productivity 43

In the labor intensive industries, when the investment in capital such as machinery or factory or technology becomes to be saturated, the labor productivity is the vital factor to stimulate the profit for firms Therefore, the more the firm can increase the worker productivity, the more that firm can earn profit and maintain its competitiveness in that industry

In Vietnam, garment production is one of the most important industries with the export value growing rapidly from 1.9 billion USD in year 2000 to be more than 15 billion USD in year 2012, as in below Figure 1 Garment became a top industry of Vietnam in term of export

in year 2012, this is the result came from the investments in physical capital in factory and machinery as well as the improving in labor productivity during this period

(Source: General Statistic Office - http://www.gso.gov.vn/)

The problem with Vietnamese sewing firms currently is that how can they maintain the growth and their competitiveness with emerging firms in Laos, Myanmar or Bangladesh where they can have lower labor cost What Vietnamese firms can do when the investment in machinery or factory become to be saturated, in this labor intensive industry? The only way to survive for Vietnamese sewing firm is that they need to improve the labor productivity of the worker

Firm productivity and individual productivity are totally different Firm productivity is affected by many factors including opportunity gain by the business, technology, machinery, factory, and individual productivity as well However, when technology is unchanged, firm also cannot invest to build up another better factory with better machinery, the worker productivity is the most importance The company Protrade Garment Co Ltd has total four good factories which well equipped machinery and the same technology This is a kind of firm which cannot invest more in capital or technology to increase firm productivity The only way

to improve company performance is that they need to improve worker productivity in production The workers should produce as many products as possible in their working time to earn money The more products a worker made during his or her working time, the more productivity or efficiency that worker has In general, individual productivity is the efficiency

Figure 1: Export of Vietnamese industries 2000-2012 (Unit: 1000USD/year)

of workers using the production time to produce as many products as possible The company, Protrade, has a system to calculate the standard time for each process and the system to monitor the real time which each worker really produced in their working time Base on collected information, we can calculate the worker productivity in percentage of ratio of standard time and actual production time of each worker

Protrade Garment Co Ltd has about 2000 workers in four factories with the same technology and physical conditions The different between the four factories are they produce different kind of garment: Factory 1 produces shirt, Factory 2 produces light outwear or sport wear, Factory 3 produces jeans trousers in traditional lines with normal machine, and Factory

4 also produces jeans in new layout lines with modern machines and less workers Because of the differences in product types, the production layout, machines and management in each factory is different, the figure 2 below shows the comparison of four factories in term of product types and management:

Table 1: Comparison of four factories in general figures

Factory

Products

Range of Standard Allowed Minute per product (SAM)

Production layout (Lines or Group)

Number of workers per line / group

Factory #2 Junior jeans trousers 17-19 Group

Group front panels: 45 workers

Group back panels: 30 workers

Group assembly: 105 workers

Factory #3 Men jeans trousers 40 8 lines 35-40 workers per line

Meantime, there is also a big gap in worker efficiency among factories, and also among the workers with differences age, experience and gender The question is that what is the

significant factors effect to individual productivity between working conditions, age, experience or gender? There were many previous studies to find the determinants effect to individual productivity, in labor intensive industry Briefly, the study of Ruth (1987) found the age and experience have effect to individual productivity, or Trond (2005) found the effect of gender on individual productivity or Susan (2008) also found the environment had effect to individual productivity

1.2 Research objectives

Coming from the facts of Vietnamese sewing industry as mentioned above, the objective of this paper is to find out the effective determinants of individual productivity in Protrade Garment Co., Ltd Then, the findings can help firms with the similar structure with this company to improve their productivity by applying recruitment or training strategies or the firms are aware of that they should improve the working conditions such as management or change the type of products can stimulate production

1.3 Scope of study

The study uses the panel data from a sewing company, named Protrade Garment Co Ltd This company has four factories producing different kind of garment products with about 2,000 workers This company pays the worker base on the quality of semi-products which each worker made every month Therefore, it has a system to calculate the standard time of any process in production, and it also has a good IT system which can record all the data concern about worker productivity monthly

There are 2,787 observations were taken in to record monthly in a time period from Jan/2014 to May/2014) in four different factories The study collected the information about the individual workers such as age, experience, gender, monthly productivity And we also compared among four factories to find whether the different working conditions affect to individual productivity t The weakness of the paper is that it is only analyze in general which working conditions (which factory) can stimulate productivity but it is not show the particular factors in working conditions can affect to productivity

1.4 Structure of the thesis

The outline of this study is as follow Chapter 1 is introducing the objectives of the research, the research question and scope Secondly, Chapter 2 presents the literature about individual productivity and its determinants Thirdly, Chapter 3 describes the model specifications, research methodology and data description Next, the descriptive of each variable and empirical result will be presented in Chapter 4 Finally, Chapter 5 will summary the conclusions then gives some policy implications and suggestions for future research

CHAPTER 2 LITERATURE REVIEW

2.1 Concepts and theories

2.1.1 Individual productivity

Productivity is the relationship between the quantity of output and the quantity of input used to generate that output It is the ratio of output and combined inputs which used to create output:

it will not be easy to know where having problems In term of management, a company managers need to analyze the productivities in many aspects, such as the efficiency of human (individual), machine, investment decisions, management And from that analysis, the

managers know where the strength is and where the weakness is to improve or focus in

Inside a company, output is measured in two ways: physical quantities and financial value Physical quantities method is conducted when output are homogeneous and present that number of products were made with accepted quality Financial value method access to production’s value such that sales, value added Input contains all the resources such as labor, capital, technology and other factors that are used to produce output (Singapore Spring, 2011) Depends on the typology of each company, different ways of measuring productivities are applied In a financial company where most of the profit or turn-over comes from investment decisions, that company will measure the productivity on each decision base on the gain divide the cost The reason is that with this kind of measurement, the managers can analyze the good and the bad of each decision and continuous improve for future decisions In the other hand, in

a production company where most of the profits are came from the output of workers that company will choose to measure individual productivity The reason is that with this type of measurement, it will be able to analyze workers inefficiency and how to improve individual efficiency then increase the output

The individual productivity is measured by the number of products or added value which that worker made in their working time, in the same working place or in the same working conditions In another word, in the same working conditions, the more value which an individual made per hour the more productivity that individual achieved (Douglas, 1994)

2.1.2 Factors effect to individual productivity

Ruth Kanfer in many years build up many theories supposed that each individual allocates personal resources (e.g., effort, energy, skills) to a particular job by the effort-performance function It showed that there are relationships between Effort, Age and

experience or skills with individual productivity

In 1987, the paper given by Ruth Kanfer proved the relationship between motivation (or effort) with individual performance Figure 2 shows in general for a specific of work, the

more effort will lead to higher individual performance (Ruth, 1987)

From this first theory, we can simply understand that with one person, the more effort that he put in his task would make a better result However, the theory came with the assumption that he has same skill, knowledge and intelligence and he’s never old from time to time We all can see that in reality, the case is more complicated For example, with the same person, he can learn and improve his skill and knowledge from studying or have experience from his work And, there are thousands people who have different skill, intelligence and they are doing various kind of works We cannot compare how “only efforts” effects to individual

Figure 2: Effort and performance (Ruth, 1987)

performance, but we should consider the effect of a combination of efforts, skill, intelligence, kind of work and other individual characteristicsto individual performance

In 2004, a paper of Ruth Kanfer and Phillip L.Ackerman gave more development from the first theory in 1987 In this research, Kanfer models added the efforts, age and experience into a link with the individual performance, showing in figure 3 and 4

In the job required crystallized intelligence, which required skillfulness and knowledge such as teaching or sewing technician, Figure 3 shows the person with more experience on the work will have more advantages with non-experience ones This point is similar to the theory

“learning curve”, that the more you practice the more efficiency you will archive in that kind

of repeating work “Crystallized intelligence” normally started at the age from twenty to twenty five, and growth personal skill or knowledge after school or from practicing in the job The crystallized intelligence do not have a decline with the age, it depends on the motivation of each person

On the other hand, in the job required fluid intelligence, such as studying which everything is new and need to have fast decision or fast catch up, Figure 4 shows that the person with younger age will have better performance than the older one In the other words, with the same basic of education and experience, the young person will have more efficiency than the older one The “fluid intelligence” started from age of six then gets peak at about twenty and then decline after twenty five

(Source: Ruth & Phillip, 2004)

For example, for a person when he was young the tasks which he can do the best is studying, easily to catch up and understand the lesson than the older one who is more than

Figure 4: Experience and

Effort-Performance

Figure 3: Age and Performance

Effort-twenty five And, he is suitable with the tasks who request fast reflection such as playing sport

or doing fast mathematic, we call “fluid intelligence” In reverse, the person who was graduated, he can use his knowledge to apply on his job which the younger person does not have And, he can learn and more and more experience to make his work more efficient, we call “crystallized intelligence”

Kanfer’s model also shows the function of age with the performance-utility and utility function “Performance-utility and effort –utility functions describe the individual’s perceptions of the form of the relationship between the attractiveness of different levels of performance and effort, respectively (as Figure 5 and 6) It means that with different age, the motivation of person will be different with the attractiveness of different level of performance

effort-or effeffort-ort, this relationship is link with behavieffort-or effort-or motivation of employee Feffort-or example with a salesman job, the company gave different bonus for the salesman who achieve different turnover With the high bonus, a 20-years-old-employee will have more motivation than a 40-years-old employee

In some circumstances, experience and age may have correlation with each other (Donal P., 1976) or maybe not, which we need to take them into account For example in a sewing factory, normally older person has longer working time in sewing compared to the young person, then he or she can achieve higher performance Even when we see the worker profile which shows that the older and young workers just start to work in the factory at the same time, but we are not sure whether the older one already has experience of sewing at home

Or, in other case that the younger worker has a talent or better skill than older worker even he

or she is lacked of experience, there is still a possibility to achieve better performance even with lack of experience The individual skillfulness of is also one of the important factors that

Figure 5: Hypothetical Performance -

Utility function as a function of Age

Figure 6: Hypothetical Effort -Utility function as a function of Age

effect to performance or productivity, but it was not mentioned clearly where it is in this paper and how important it effect to performance

To be sum up, until the Ruth’s second paper, we saw the relationship between effort, age and experience with individual productivity However, the level of skillfulness or intelligent which is important to individual productivity but we have not yet known clearly how important it is And, the paper also mentioned the motivation differences depends on the age and the attractiveness of performance The attractiveness is more linked to the company policy

or working environment which we can call that they are external factors which effect to individual performance The working environment, management or leadership need to be involved to individual productivity to help the parameters to be more clear and measureable instead of talking about “utility” or “attractiveness”

In 2007, a group of authors including Gilad Chen, Bradlley Kirkman, Don Allen and

Ruth Kanfer continuously developed external factors linked to individual performance, the

management factors have affect to team and individual performance (Ruth Kanfer et al,

2007)

Figure 7: Management factors affects to individual performance

There were many hypotheses to show the relationship between Leadership climate, team empowerment, individual empowerment, team performance and individual performance

The paper used the empowerment as parameters to measure the leadership climate Good leadership will give team and individual empowerment, and it will help to facilitate to increase team performance Once team performance is good, then the individual performance would be good In short, leadership climate has positive relation with individual performance To open wide the discussion, we understand that working environment which are included all the external factors have effects to individual productivity To be simple, whenever the employee feel more autonomy or free, they have chance to exchange with leaders or the working conditions are good with less stress, then their productivity will be higher or we can say they have higher performance With the same purpose, Susan & Raymond in 2008 also raised the importance of the working environment such as working place or working conditions also effect to individual productivity The more employees feel comfortable, the more productivity they contribute in their jobs

How about technology, does it impact to the productivity? A famous neo-classical economic growth models said “yes” (Sollow, 1957) In the Figure 8, with the same level of technology the production function is positive related with the capital Once the technology change, it will make the production function shift to another curve (from t=1 to t=2) Sollow paper also proved that output per man hour in long term increased by the technical change, increase of capital and increase of productivity

Figure 8: The technical change and the aggregate

However, Sollow could not measure the technology change (A) in direct way, but he calculate by the combination between the production function today and production functions

at least forty years ago, then he divide by years Sollow assumed that the technology A is a constant in year by year, and the shift of production function only happened in long term, not

in short-term How technology effect to productivity in short term, there’s still a question and for now we can consider that in short term technology does not affect to individual productivity

For example, “productivity paradox of information technology”

2.2 Empirical evidences

To summarize, individual productivity is affected by many factors including: effort, experience, age, skill, gender, working environment and technology We will go more into details from previous studies to explain how each factors effect to productivity in theory

2.2.1 Age, experiences related to productivity

McEvoy & Cascio (1989) examining about 100 studies illustrate that it is ambiguous

relationship between the age of the worker with productivity (work output, supervisor ratings)

Hypothetical Kanfer’s model (Ruth K., 1987) supposed that each individual allocates personal resources (e.g., effort, energy, skills) to a particular job by the effort-performance function The effort-performance function illustrates the figure for each individual across a range of effort levels and the predictably performance ability which was associated with levels

of effort In an accurate perceived higher or lower effort-performance can occur for the individual who has an erroneous self-concept that performance for a particular level of effort,

or the individual has insufficient information the relation of his or her effort on the level of performance

Figure 9: Relation between individual effort and performance

This function relates to either actual or perceived relationship between effort and performance There is a direct trend for growth in degree of effort with performance However,

the ratio of increasing performance with efforts will be different depends on each individual experience or ages The

Figure 10: Effort-performance differences, depends on the experience

In Figure 10, it shows that with the same percentage of efforts but the performance will

be different depends of the individual experience, and the slope of trend will be also different Changes in effort-performance functions with number of working year, the increasing in relevant experience promotes relatively high levels of performance, even in the high levels of effort With the experienced employee, he can get the high performance with less effort because

he already has experienced and master his job Then, if when he puts more and more efforts in his job but the performance will not increase sharply On the other hands, with the less experienced employee, he will get very low performance at the beginning even he already tried more effort than experienced ones Later, if he pushes more and more efforts in his job, the performance would be increased sharply Come back with the experienced worker, it will be more efficient if he spent less efforts in his experienced operations then use his balance efforts for another operations It means that, if the management can develop the workers with more and more multi skills, then the production will be more efficient if have the factory have good organizations

Figure 11: Effort-performance differences, depends on the ages

Figure 11 shows that with the same percentage of efforts but the performance will be different depends of the individual ages, but the slope of trend will be different but the difference of slope is not so big gap compare to figure 6b Changes in effort-performance relationship with age, as levels of age decline primarily associated with increasing age, the slope of the function decreases so that even maximal effort leads to a higher level of performance being possible at younger ages The younger workers will be have more productivity than the older ones in the assumption that they spend the same efforts in the same job

In summary, the determination of effort-performance by task demands is in the sphere

of an individual’s cognitive ages, abilities, knowledge, and skills However, effect on the effort performance function by ages or experience must be considered by the natural demands of the work or job role For example, with the kind of job which doesn’t require more skills or knowledge then the performance gap between experienced and non-experienced workers may not so huge

2.2.2 Level of worker, technology related to productivity

The role of technology in the productivity was emphasized by Nicolas B., Remy L., & Tristan-Pierre M (2004) This research conclude growth rate of labor productivity depend on investment in technology and level of human capital (level of worker) Furthermore, two

variables have positive significant impact to productivity

In decades of 90s, apply the econometrics model in studying productivity in the textile industry of Christoffersen S., Malhotra D.K., & Anusua D help investor and the following

researcher understand the importance of practicing employer, enhancing level of worker In textile industry, level of worker have the significant certainty role to firms gain high productivity and profit There were also the comparison of productivity growth between textile industry and apparel industry Because apparel industry is more labor intensity than textile industry, so that is why the productivity growth of textile industry is more tremendous than sewing industry

Figure 12: The productivities of textile & sewing in period 1949 -> 1999

(Source: Christoffersen S., Malhotra D.K., & Anusua D (2001) Optimal Investment

Strategies for Enhanced Productivity in The Textile Industry)

The contribution of technology could help to increase the productivity at the beginning However, there was a slowdown in productivity growth from 1970s due to the diminishing returns to technology Therefore, even though technology had a positive relationship with productivity but this relationship is in the diminishing horizon (Griliches, 1998)

2.2.3 Gender, work environment related to productivity

Gender variable is a significant determinant of worker’s productivity Women are expected to have productivity advantage in some jobs while men have productivity advantage

in other Almost researches on productivity in manufacturing section in Europe state that occupations which require manipulative skill, women are more efficient than men and women are more suitable than men for some kind of job especially textile industry In addition, married status of worker also affect to productivity Virtual studies argued that women who are married have less productivity but men are contrary In summary, difference in gender concern difference in productivity and women are slightly less efficient than men that emphasized by Trond, P., Vemund, S., & Eva M (2005)

Rolf B (2001) suggest that technology, human skill, attribute of job, working conditions are influential factors of productivity and assumed that productivity function is able

to set up: P = f (T+H+Wp+Wc) This investigation is conducted in truck hauling of tree section where work environment have more influence on productivity of worker

Ann B., Casey I., & Kathryn S (2003) stated the important of gathering right data to study and find out determinants of productivity of companies from firms in the steel industry Besides, they stated that omission of relevant variables in model will be biased in result

Susan L., Raymond B (2008) investigated the productivity in nursing job and concluded that beside age and total year of working, the working environment is the most important factors which effect productivity In this paper, the working environment were measured through mainly 03 proxies such as quality of care provided, job stress score, having had a job injury

CHAPTER 3 RESEARCH METHODOLOGY

This chapter is designed to expose the research methodology of this thesis Firstly, the conceptual frame work which this thesis is built up taken from literature review and previous empirical studies Secondly, analytical models with the independent variables and dependent variables are introduced with their expected signs The last section will present the data sources and descriptions of each variable

3.1 Conceptual frame work:

From the theories, the method to calculate productivity in macro view is the ratio between Output / Input There are many aspects to calculate Output and Input, from Capital, multi-factors or labor As sewing production is a labor intensity industry (Chrstofferent, 2001) then the good way to measure productivity is that we should focus on worker efficiency Taken from many previous empirical studies, the factors which affect to labor productivity with expected sign as following: Age, Experience, Worker’s level(skills), Technology, Gender, Working environment However, the Technology is considered as constants in this thesis because the study took the data in 1 company which can be understood that there is the same technology in its four factories The conceptual frame work is drawn as in figure 13 below