Determinants of worker’s productivity in vietnamese sewing industry

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIE THE NETHERLANDS 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 HO CHIMINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE 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 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 INTRODUCTION 1.1 Problem statement 1.2 Research objectives 11 1.3 Scope of study 11 1.4 Structure of the thesis 12 CHAPTER 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 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 EMPIRICAL RESULTS 33 4.1 Data descriptive: 33 4.2 The OLS result 37 CHAPTER 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 factories 30 Table 5: Management differences between factories 31 Table 6: Customers and pressure differences between 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) 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 CHAPTER INTRODUCTION 1.1 Problem statement Productivity is very important either for a country or for a company point of view At a country level, productivity stimulates economic growth in short-run as well as in long-run At a firm or industry level, productivity could contribute the better wages and conditions for the workforce, higher profit for company, lower price for customer, improvement of environmental protection and it contributes higher tax revenue for government (Dean, 2011) 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 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 Figure 1: Export of Vietnamese industries 2000-2012 (Unit: 1000USD/year) (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 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 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 produces shirt, Factory produces light outwear or sport wear, Factory produces jeans trousers in traditional lines with normal machine, and Factory 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 below shows the comparison of four factories in term of product types and management: Table 1: Comparison of four factories in general figures Kind of products Range of Standard Factory Allowed Products Minute per product (SAM) Factory #1 Shirt 22-24 Factory #2 Junior jeans trousers Factory #3 Men jeans trousers Capri pants Men jeans trousers Factory #4 Capri pants 17-19 40 60-65 Management Production layout (Lines or Group) lines Group 45 workers per line Group front panels: 45 workers Group back panels: 30 workers Group assembly: 105 workers lines 35-40 workers per line Group Group front panels: 50 workers Group back panels: 70 workers Group assembly: 80 workers 32 60-65 Number of workers per line / group 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 10 Experience and Efficiency: data in general also shows that the efficiency positive correlation with each other Figure 16 shows that the efficiency is increasing with more experienced worker It is the same with expected sign shown in previous chapter % (year) Figure 16: Relation between efficiency and experience Number of operations per worker and Efficiency: Logically, the more operations workers produced in month, the less efficiency for that worker The figure 17 shows that in the data of this thesis, the negative relation between efficiency and the number of operations of the workers % Number of operations per worker Figure 17: Operations and efficiency 34 Gender: This is a dummy variable which “1” is stand of male and “0” is stand for female Total 3190 observations, there are 42% population is male and 58% are female The ratio between two genders in each factory is descriptive as in table below Factory and factory are mostly the same population between male and female, while factory and factory are quite difference in term of gender with 40% male and 60% female Table 9: Gender in each factory (discrete variable) Factory # Workers Factory Ratio (%) Workers Factory Ratio (%) Workers Factory Ratio (%) Workers Factory Ratio (%) Workers Total Ratio (%) Male Female 334 499 40% 60% 280 295 49% 51% 365 515 41% 59% 270 229 54% 46% 1249 1538 42% 58% Total 833 100% 575 100% 880 100% 499 100% 2787 100% Working condition and worker level: in different working condition (factories), the number of workers for each worker’s level are different, and it is described in table 10 Table 10: Worker's level in each factory (discrete variable) Worker level Factory Number of worker Percent of each level Factory Number of worker Percent of each level Factory Number of worker Percent of each level Factory Number of worker Percent of each level Total workers Global percent of each level l1 439 52.7% 298 51.8% 273 31% 189 37.8% 1199 43.0% l2 119 14.3% 14 2.4% 63 7.2% 23 4.6% 219 7.9% 35 l3 50 6% 137 23.8% 280 31.8% 182 36.4% 649 23.3% l4 103 12.4% 94 16.3% 261 29.7% 90 18.0% 548 19.7% l5 45 5.4% 24 4.2% 0.3% 10 2.0% 82 2.9% l6 77 9.2% 1.4% 0% 1.2% 91 3.3% TOTAL 833 100% 575 100% 880 100% 500 100% 2787 100% Beside the discrete variables as mentioned above: Gender, worker’s level and working condition, I summary the data of all continuous variables based on the collected data in Table 11 The efficiency level of the company is quite high with Mean and Median over 80%., but there is a big gap between the minimum (less than 1%) and the maximum 229% The standard deviation of efficiency is 30.29% The workers age is variance from 18 years old as a youngest to 55 years old as an oldest The average age is about 25 years old with mean 26.74 and Median 25.50 The most experienced worker is almost 22 years working for the company, and the Median of experience is 2.75 years In this, the workers from level to level is taking the most populations with Exp Mean equal to 2.41 The deviation of Age and Exp are 6.27 years and 4.04 years, respectively In average, each worker did more than operations monthly, fluctuation in the scope from Min operation until Max 34 operations Table 11: Data descriptions of continuous variables Variables Mean Median Maximum Minimum Std Dev Skewness Kurtosis Jarque-Bera Probability Sum Sum Sq Dev Observations Eff (%) Exp (year) Age (year) No.of Operation (number) 87.659 3.938 26.740 8.118 84.786 229.606 0.111 30.290 0.643 3.880 282.588 0.000 244307.3 2556241 2787 2.756 21.909 0.000 4.046 1.764 6.628 2974.408 0.000 10976.13 45616.95 2787 25.501 55.021 18.016 6.278 1.136 4.476 853.216 0.000 74525.33 109810.2 2787 7.000 34.000 1.000 5.463 0.943 3.757 480.017 0.000 22626.00 83158.93 2787 36 4.2 The OLS result From the collected data, I would run totally three OLS models as below Table 10: Table 12: Summarized Estimation Results Variables Model lnIM Age (year) 1.126* (0.598) -0.018* (0.009) 1.970*** (0.370) -0.085*** (0.023) 4.658*** (0.995) -0.035 (0.109) 15.362*** (1.552) 1.162 (1.567) -24.226*** (1.514) -4.158** (1.850) -4.986*** (1.268) -8.386*** (1.328) -8.867*** (2.870) -4.718** (2.826) 71.390*** (8.584) 2787 0.334 c.Age#c.Age Exp (year) c.Exp#c.Exp Gender (Male/Female) Operation (number) Fac1 Fac2 Fac3 L2 L3 L4 L5 L6 Constant Observations R-squared Model Without Age Model Without Exp 2.429*** (0.501) -0.035*** (0.008) 2.302*** (0.311) -0.110*** (0.019) 4.765*** (0.993) 0.039 (0.109) 15.143*** (1.543) 1.116 (1.566) -24.235*** (1.513) -4.225** (1.850) -5.004*** (1.263) -8.394*** (1.322) -8.927*** (2.869) -4.161 (2.803) 86.733*** (1.727) 2787 0.333 Standard errors in parentheses *** p