Is gender pay gap low in plants with more female managers evidence from small and medium entreprises of vietnam 2007

Tran Thi Hieu Master’s Thesis VNP20-20 15 UNIVERSITY OF ECONOMICS, HO CHI MINH CITY VIET NAM – NETHERLANDS PROJECT FOR M.A PROGRAM IN DEVELOPMENT ECONOMICS IS GENDER PAY GAP LOW IN PLANTS WITH MORE FEMALE MANAGERS? EVIDENCE FROM SMALL AND MEDIUM ENTREPRISES OF VIETNAM 2007 o0o - A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By Tran Thi Hieu Supervisor Dr Pham Dinh Long Nov – 2015 Tran Thi Hieu Master’s Thesis VNP20-2015 ABSTRACT The goal of gender equality in income is not only an important matter of human rights but also basic requirement for development of fair and efficiency Therefore, study on the state of gender inequality in income has the significant implications in moving toward the equality in society and enhancing the efficiency of economic and social growth Actually, there have been many previous studies related to the issues of gender income difference, particularly the factors impact on reducing the gender gap in earnings Inheriting these previous researches, my dissertation attempts to investigate whether the gender gap in earnings is low in the establishments with more female managers Using a sample size of 1043 employees and 2492 enterprises were surveyed in the Small and Medium Enterprises (SME) in Vietnam in the year of 2007, matching employer and employee data and distinguishing occupation the study ends up 345 job–cells In addition, ordinary least squares modeling and ordinary least square with job-cell fixed effects are applied to explore the effect on male – female income difference of proportion of female manager Various explanatory variables for characteristics of workers and plants are used as control variables Result reveals that there is a negative relationship between the female share in managers and gender pay gap, and the education has a significant statistic and strong impact on the wage of the labors Key words: Gender pay gap, matched employee – employer data Tran Thi Hieu Master’s Thesis VNP20-2015 ACKNOWLEDGEMENTS This thesis is completed with not only painful process but also enjoyable experience Fortunately, throughout the process I always got a lot of helps and supports from many people in order to make thesis possible On the same occasion, I would like to express my gratitude to all of them Foremost, I would like to give my sincere gratitude and special appreciation to my academic supervisor Dr Pham Dinh Long, for his patience, motivation, enthusiasm, and immense knowledge His guidance helped me in all the time of research and writing of this thesis Similarly, I sincerely thank to the Scientific Committee and staffs of Vietnam-Netherland Program for their willingness to provide information during the last time Last but not the least, I would like to thank my family and classmate at VNP20 for their backing and helping so that I can complete this thesis TABLE OF CONTENTS CHAPTER 1: INTRODUCTION .4 1.1 Problem statement 1.2 Research objective 1.3 Structure of research CHAPTER 2: LITERATURE REVIEW 10 2.1 The definitions 10 2.1.1 Gender 10 2.1.2 Gender equality 10 2.1.3 Gender gap in earnings 11 2.2 Cause of gender wage gap and the factors impact on gender wage gap 12 2.2.1 Cause of gender wage gap 12 2.2.2 The factors impact on gender wage gap 16 2.2.2.1 Non-economic factor 16 2.2.2.2 Economic factors 16 2.2.2.2.1 Characteristics of employee 16 2.2.2.2.2 Education 17 2.2.2.2.3 Employment 17 2.2.2.2.4 Geographic factor 18 2.3 Measurement of gender pay gap 18 2.4 The impact on economic - social development of gender wage gap 19 2.5 The impact on male – female income difference of female manager .20 CHAPTER 3: OVERVIEW OF GENDER WAGE GAP IN VIETNAM 25 3.1 Overview the status of gender gap in earnings in Vietnam 25 3.2 The factors impact on male – female income difference 31 3.2.1 The age of employee 31 3.2.2 Education 32 3.2.3 Employment 32 3.3.4 Geography 32 CHAPTER 4: METHODOLOGY 34 4.1 The data 34 4.2 The variables 35 4.2.1 Education 35 4.2.2 Seniority 36 4.2.3 Age of employee 36 4.2.4 Size and Industry 36 4.3 The Econometrics Models and Results 37 CHAPTER 5: CONCLUSION, POLICY IMPLICATIONS AND LIMITATIONS .42 5.1 Conclusion 42 5.2 Policy implications 42 5.3 Limitations 43 REFERENCES 44 Bibliography Error! Bookmark not defined LIST OF TABLES Table1: Human development index and Human development index in Southeast Asia Area 25 Table2: The share of women’s labor force participation 26 Table 3: The share of female in plants 27 Table 4: Summary statistics 38 Table 5: Wages and female shares in managers 38 Table 6: Wages regression 39 LIST OF APPENDICES Appendix 1: Correlation of variables 47 Appendix 2: Statistics variables 47 Appendix 3: OLS wage regression 48 Appendix 4: OLS wage regression with job – cell fixed effect 49 Appendix 5: Test for heteroskedasticity 49 Appendix 6: Test for multicollinearity 50 Appendix 7: The individual categories of ISIC 50 ABREVIATIONS ILO International Labor Organization VGCL The General Labor Confederation of Vietnam SMEs Small and medium enterprises DWC Division of Workers’ Compensation CEDAW The Convention on the Elimination of all forms of Discrimination against women OECD Organization for economic co-operation and development STEM Science, Technology, Engineering and Mathematics WB World Bank GDP Gross domestic product HLM Hierarchical linear modeling OLS Ordinary least square EEOC Equal employment opportunity commission HDI Human Development Index GDI Gender Development Index of Vietnam UNDP United Nations Development Programme WEF World Economic Forum GSO The General Statistics Office of Vietnam VHLSS Vietnam Household Living Standards Survey CHAPTER 1: INTRODUCTION 1.1 Problem statement Gender pay gap is a global problem Indeed, according to Nguyet and Binh (2007), male – female income difference is not only one of the root causes of poverty but also major factor that distributes to hinder the development process The greater gender gap in earnings are, the more poverty it has to pay dearly for that as well as more malnutrition status, illness and other hardships In addition, economic growth will bring more effective for the reduction of poverty level in the country which has a higher level of gender equality Moreover, inequalities in income between women and men prevent the equal development and this makes the use of resources in society inefficient In fact, the situation of gender inequality in earnings occurred in many countries, especially in the developing countries It might be said that the cause of this condition primarily rooted traditional perspectives and preconceived ideas in the social about the male-supremacy in many countries Thereby, these lead to the restriction of opportunities for women to access the education and training, the choice of professions, an opportunity to improve professional qualifications The distribution of labor between men and women in different occupations, employment arrangements and job positions in the same business line is also obvious differences, which greatly affected on the difference in gender income Furthermore, women also have fewer opportunities to access to these services as well as other basic resources such as water, transportation and marketing, capital, etc These things certainly impact on their improvement of the condition and economic status In Vietnam, as reported by the International Labor Organization (ILO) on 7th March, 2013, the gap of income between men and women in Vietnam was continuously increasing while the proportion of women in work force was higher than other countries in the world Approximately 72% of women participated in the labor force in Vietnam and this ratio was higher than that in most of other countries worldwide Nevertheless, Vietnam was one of the few countries where the gender wage gap was increasing in contrast with the trend in most of other countries in the 2008-2011 period compared with the period 1999-2007 Moreover, according to the 2012-2013 Global Wages Report of the ILO, gender income gap of Vietnam increased by 2% in the recent period Statistical data of the General Department of Statistics in 2011 indicated that women's income was lower than men’s income, approximately 13% Also, the General Labor Confederation of Vietnam (VGCL) conducted a survey of employees’ salary in enterprises in 2012 This organization saw that wage of female workers was less, only 70-80% of their male colleagues Besides, Labor Survey Report published in 2012 stated the average monthly income of women was less than men’s in all economic sectors, state, non-state and foreign investment Even in occupations which primarily recruited focus on women such as health care, social work and sales, women still had a lower salary than male fellowship More specifically, the VGCL survey found that women often did the normal work whereas male undertook the management positions Due to the important of this field, there were thus a lot of researches about gender pay gap conducted in the past indicating the determinants of difference in wage of women and men as well as providing solutions to reduce this gap such as Anderson, Tracy, et al (2001), Hultin and Szulkin (2003), Manning (2006), Blau and Lawrence (2007), Cohen and Huffman (2007), Becker (2010), Cardoso and Winter-Ebmer (2010), Spencer (2015) In Vietnam, studies about gender gap in earnings there are Liu (2002), Pham and Barry (2007) and so on Interestingly, related to the solutions given by the researchers in order to reduce gender income differences, there was the relationship between female share in managers and gender wage gap Particularly, in the research of Hultin and Szulkin (2003) namely “Mechanisms of inequality unequal access to organizational power and the gender wage gap”, the authors used the multilevel models in order to analyze the dataset of Swedish which combined the information on a large number of private – sector enterprises and all their employees The finding showed that gender gap in income in plants were wider while there were more male representations among managers and supervisors in these enterprises and low relative wage of female worker in plants in which there were no or only a few women in managerial position Another notable research, Hirsch’s paper (2013) is one of these above studies, through the evidences from linked employer – employee data for Germany, Hirsch found that gender income difference decreased by 0.5 log point when increased the female share in first level management by 10% points Therefore, follows the cornerstones of Hirsch’s research and applies for the case of Vietnam in order to investigate the relationship of female share managers and gender pay gap, this study is conducted particularly in small and medium enterprises (SMEs) in Vietnam in the year of 2007 1.2 Research objective The objective of the study is an analysis to find out whether or not the women have an important role in the case of reducing the gender income difference when they stand in the assembly line of managers in small and medium plants in Viet Nam This research is also conducted to answer the main question that is gender pay gap low in plants with more female managers? And for the scope of study, it is undertaken in SMEs in Vietnam in the year of 2007 4.3 The Econometrics Models and Results In order to answer the main question whether or not gender pay gap is low in plants with more female among managers, the study will be undertaken based on the method of Hirsch (2013) in which the augmented Mincer wage regressions The baseline specification is a fully interacted model Lnwi = β1femalei + β2femshmani + β3femalei×femshmani + x’i (γ + femalei × δ ) + ui Lnwi: log daily gross wage of employee i femalei: female dummy femshmani: the female share among managers femalei x femshmani: the interaction of these two xi: a vector of control variables, xi includes variables capturing the individual’s human capital endowment and plant characteristics, such as age, agesq, education, seniority, lnsize, industry variable In this study, β1 is as the average unexplained gender pay gap in the sample β3 is expected to be positive in order to indicate that the unexplained gender income difference is lower if the female share in management is higher Ordinary least squares (OLS) regression is a technique of statistical estimation This technique is commonly used in the linear regression model The aim of the method is from discrete samples, determine the function which approximately represents the distribution of these samples, and after that estimate the value which can not be measured Moreover, the assumptions of this model related to homoscedastic, linearity and the impact of outliers are not difficult to check (Craven and Sardar, 2011) Besides, in order to control for segregation effects, the study adds job-cell fixed effects to arrive at the unexplained within job male – female income difference that address unobserved plant and job and thus employment segregation The model is estimated by carring out in Stata Summary statistics for all explanatory variables are given in the table as bellows: Table 4: Summary statistics Variables Mean Age 32.6869 Agesq 1161.2260 Seniority 4.9333 Education 14.7768 lnwage 3.8153 Lnsize 3.4514 Industry 0.9913 Female 0.6086 Female share in managers 0.5580 (Source: Author’s calculation) Std Dev Min Max 9.6466 712.2256 4.9130 5.2351 0.4708 16 256 0 0.9808 64 4096 30 21 5.4524 1.1590 0.0929 0.4887 0.6931 0 7.0535 1 0.2350 0.1470 Table 5: Wages and female shares in managers Variables Women Men Mean Std.dev Mean Std.dev Gross daily wage (in 1000 vnd) 45.9795 21.4490 57.1753 27.5101 Log gross daily wage 3.7358 0.4300 3.9391 0.5053 Female share in management 0.5733 0.2392 0.5342 0.2272 Observations 454 454 (Source: Author’s calculation) The table presents some sample statistics Therein, male workers has the average of daily wage is higher than female worker, the difference is approximately 11.1958 The raw gender pay gap amounts to 20.33 log points This indicates that there is discrimination about income between male and female workers Table 6: Wages regression Variables Coefficient Standard error Female - 0.2882*** 0.1198 Female share in managers -0.1422 0.1663 managers 0.2076 0.2013 Age 0.0052* 0.0032 Agesq -0.0004* 0.0002 Seniority -0.0043 0.0053 Education 0.0356*** 0.0045 Lnsize 0.0249 0.0226 Industry 0.0856 0.3031 OLS regression Female x Female share in OLS wage regression with job – cell fixed effect Female - 0.1767** 0.0892 0.0620 0.1330 Age 0.0060* 0.0032 Agesq -0.0003* 0.0002 Female x Female share in managers Seniority -0.0058 0.0053 Education 0.0331*** 0.0069 Lnsize 0.0343 0.0222 Ioccupation_2 -0.0245 0.0921 Ioccupation_3 -0.0707 0.1105 Ioccupation_4 0.1499* 0.0830 Ioccupation_5 -0.0545 0.0761 Observations 345 345 ***significant at percent, **significant at percent, * significant at 10 percent (Source: Author’s calculation) The table shows the key results of the wage regression for sample including observations from the plants which are surveyed An average unexplained gender gap in earnings is 28.82 log points Although the interaction effect of the female dummy and the female share in managers has insignificant statistic, but the positive sign is the same with expectation Age, agesq and education variable have a significant statistics at 10 percentage and percentage level Particular in education variable, it has a strong effect on the wage of labor, the higher the level of education the labors have, the more wage they receive When control segregation by adding job – cell fixed effects, both unexplained gender income difference and the interaction impact of female and female share in managers are reduced For the case of control or noncontrol job-cell effect, the interaction impact on gender pay gap of the female dummy and the proportion of female share managers is insignificant statistic, 40 but there is the negative relationship among two variables Furthermore, the results are in the line with earlier findings by Hirsch (2013) and Hultin and Szulkin (2003), the gender pay gap is lower in plants with more female managers 41 CHAPTER 5: CONCLUSION, POLICY IMPLICATIONS AND LIMITATIONS 5.1 Conclusion Follows the empirical studies have scrutinized in which ways gender differentiated representation in manager effect on gender pay gap, when these studies supposed the gender gap in earnings can be explained in the terms of discrimination Therefore, the main assumption in this research is that the gender pay gap is lower in enterprise with more female manager participation By using matched employer and employee data for SMEs in the year of 2007 and controlling for employment segregation by including job – cell fixed effects, the study finds that the impact on male – female income difference of female share manager has insignificant statistic, but it has a negative relationship between the two Thus, the paper can not conclude that the gender pay gap reduces in the plants with more female managers However, the education variable always has significant statistic, and it implies that the wage of the labors will rise if they have a good education level, especially for women workers In addition, the negative relationship between the number of female on managers and gender pay gap are consistent with the previous studies in this field 5.2 Policy implications Discrimination in income of women stems from variety of causes, possibly due to incorrect views about gender roles in society, traditional ideas or prejudice these factors may be eliminated through educational activities Hence, the government should implement measures about education synchronously and systematically as well as carry out the policies of socioeconomic development towards gender equality goals so that the people change the view on the role of gender in society, such as policy to encourage enterprise to build the fair competitive environment, create equal opportunities for both women and men worker In addition, in this study the numbers of women involved in manager position have the impact on reducing discrimination in income between men and women, for that reason businesses need to adopt policies to encourage women participation in manager as a measure to reduce the income gap Besides, the results also shows that variable of education has strong statistical significant to the income of workers, so for worker particular in female, they need to improve their knowledge and skills more and more in order to shorten the gap in income compared with male 5.3 Limitations The matched data for enterprises and employees which participate in SMEs in sample is small, so it can not fully express the population of small and medium enterprise of Vietnamese Furthermore, due to the lack of data, only for the year of 2007, there is not shown the effect of time variable on gender pay gap as well as reflected the impact on current time Moreover, the literature shows that determinants of wage come from variety of characteristics of employee and plant (Hirsch, 2013) However, in my research, I focus on a few of these variables There is another model can be applied in order to investigate the impact of gender of manager position on gender pay gap, such as multilevel modeling – the appropriate model for handling the nested data But, due to the limitation of time and data, the study has not used this model Mentioned are four limitations in my study that will provide strong basics for further research REFERENCES Anderson, Tracy, et al "The gender pay gap." London: Women and Equality Unit (Cabinet Office) (2001) Becker, Gary S The economics of discrimination University of Chicago press, 2010 Blau, Francine D., and Lawrence M Kahn "The gender pay gap." The Economists' Voice 4.4 (2007) Bojas , George J (2005),Labor Economics, McGraw-Hill, Third Edition Bryk, Anthony S., and Stephen W Raudenbush Hierarchical linear models: applications and data analysis methods Sage Publications, Inc, 1992 Cardoso, Ana Rute, and Rudolf Winter-Ebmer "Female-led firms and gender wage policies." Industrial & Labor Relations Review 64.1 (2010): 143-163 Cardoso, Ana Rute, and Rudolf Winter-Ebmer "Female-led firms and gender wage policies." Industrial & Labor Relations Review 64.1 (2010): 143-163 Cohen, Philip N., and Matt L Huffman "Working for the woman? Female managers and the gender wage gap." American Sociological Review 72.5 (2007): 681-704 Cohen, Philip N., and Matt L Huffman "Working for the woman? Female managers and the gender wage gap." American Sociological Review 72.5 (2007): 681-704 Craven, B D., and Sardar MN Islam Ordinary least-squares regression SAGE Publications, 2011 Gelman, Andrew "Multilevel (hierarchical) modeling: what it can and cannot do." Technometrics (2012) Haas, Sherri "Economic development and the gender wage gap." The Fifth Annual Carroll Round (2007): 126 Hamermesh, Daniel "Fun with matched firm-employee data: Progress and road maps." Labour Economics 15.4 (2008): 662-672 Hedija, Veronika "The Effect of Female Managers on Gender Wage Differences." Prague Economic Papers 2015.1 (2015): 38-59 Hirsch, Boris "The impact of female managers on the gender pay gap: Evidence from linked employer–employee data for Germany." Economics Letters 119.3 (2013): 348-350 Hultin, Mia, and Ryszard Szulkin "Mechanisms of inequality Unequal access to organizational power and the gender wage gap." European Sociological Review 19.2 (2003): 143-159 Hultin, Mia, and Ryszard Szulkin "Mechanisms of inequality Unequal access to organizational power and the gender wage gap." European Sociological Review 19.2 (2003): 143-159 Hultin, Mia, and Ryszard Szulkin "Wages and unequal access to organizational power: An empirical test of gender discrimination."Administrative Science Quarterly 44.3 (1999): 453-472 Jensen, Paul H "Exploring the uses of matched employer–employee datasets." Australian Economic Review 43.2 (2010): 209-216 Larsen, Anna Folke, John Rand, and Nina Torm "Do recruitment ties affect wages? An analysis using matched employer–employee data from Vietnam."Review of development economics 15.3 (2011): 541-555 Liu, Amy YC "Gender wage gap in transition in Vietnam." (2002) Manning, Alan The gender pay gap No 200 Centre for Economic Performance, LSE, 2006 Nguye t, Nguye n Thi , and Le Thi An B nh Ba t b nh a ng gi i ve thu nha p cu a ng i lao o ng Vie t Nam va mo t so g i y gia i pha p ch nh sa ch Tạp chí Quản Lý Kinh tế 13 (2007): 33-45 Pham, Thai-Hung, and Barry Reilly "The gender pay gap in Vietnam, 1993– 2002: A quantile regression approach." Journal of Asian Economics 18.5 (2007): 775-808 Shin, Taekjin "The Gender Gap in Executive Compensation The Role of Female Directors and Chief Executive Officers." The Annals of the American Academy of Political and Social Science 639.1 (2012): 258-278 Spencer, Neville "Gender pay gap widens." (2015) Tajfel, Henri "Social psychology of intergroup relations." Annual review of psychology 33.1 (1982): 1-39 Torm, Nina The union wage gap among Vietnamese SMEs Working Paper, Department of Economics, University of Copenhagen, 2011 APPENDIX Appendix 1: Correlation of variables corr cen_age1 cen_age1sq seniority education lnwage lnsize femshman industry female (obs=345) cen_age1 cen_ag~q senior~y educat~n lnwage lnsize femshman industry female cen_age1 cen_age1sq seniority education lnwage lnsize femshman industry female 1.0000 0.5814 0.4729 -0.0975 -0.0011 -0.0378 -0.0374 0.0164 -0.0741 1.0000 0.2316 -0.1475 -0.1375 -0.0997 0.0583 0.0250 0.0968 1.0000 -0.1186 -0.0521 -0.0685 -0.1019 0.0242 -0.0702 1.0000 0.4259 0.2870 -0.2046 -0.0398 -0.0183 1.0000 0.2056 -0.1364 -0.0087 -0.2110 Appendix 2: Statistics variables tabstat wage lnwage femshman, by(gender) s(mean sd) Summary statistics: mean, sd by categories of: gender gender wage lnwage femshman Nam 57.17531 3.939105 5342071 27.5101 5053168 2272161 N÷ 45.97952 3.735837 5733195 21.449 4300014 2392863 Total 50.36048 3.815377 5580147 24.58165 4708226 2350819 1.0000 -0.4531 1.0000 -0.0323 -0.0908 1.0000 -0.1247 0.0813 0.0528 1.0000 Appendix 3: OLS wage regression reg lnwage female femshman fexfesh cen_age1 cen_age1sq seniority education lnsize industry Source SS df MS Model Residual 18.1938474 2.0215386 58.061992 335 173319379 Total 76.2558393 344 221673952 lnwage female femshman fexfesh cen_age1 cen_age1sq seniority education lnsize industry _cons Coef Std Err -.2882251 -.1421975 2076764 0052352 -.0004123 -.0042775 0356548 0249139 0856529 3.359286 1198081 1662681 2013309 0031958 0002224 0052853 004555 0226346 2442587 3031156 Number of obs =F( 9, 335) = Prob > F = R-squared = Adj R-squared = Root MSE = 345 11.66 0.0000 0.2386 0.2181 41632 t P>|t| [95% Conf Interval] -2.41 -0.86 1.03 1.64 -1.85 -0.81 7.83 1.10 0.35 11.08 0.017 0.393 0.303 0.102 0.065 0.419 0.000 0.272 0.726 0.000 -.5238961 -.0525541 -.4692586 1848636 -.1883556 6037084 -.0010513 0115216 -.0008497 0000252 -.014674 006119 0266948 0446148 -.0196098 0694377 -.3948212 566127 2.763036 3.955536 Appendix 4: OLS wage regression with job – cell fixed xi: reg lnwage female fexfesh cen_age1 cen_age1sq seniority education lnsize i.occupation i.occupation _Ioccupatio_1-5 (_Ioccupatio_1 for occ~n==L§ nghiƯp vô omitted) Source SS df MS Model Residual 19.5891683 11 1.78083349 56.666671 333 170170183 Total 76.2558393 344 221673952 lnwage female fexfesh cen_age1 cen_age1sq seniority education lnsize _Ioccupatio_2 _Ioccupatio_3 _Ioccupatio_4 _Ioccupatio_5 _cons Coef Std Err -.1767059 0620072 0060819 -.0003806 -.0058067 0331172 0343101 -.0245227 -.0707198 149934 -.0544924 3.357736 0892215 1330774 0032461 0002209 0053335 0069202 0222388 0921884 1105858 0830412 0761862 1763228 Number of obs =F( 11, 333) = Prob > F = R-squared = Adj R-squared = Root MSE = t P>|t| -1.98 0.47 1.87 -1.72 -1.09 4.79 1.54 -0.27 -0.64 1.81 -0.72 19.04 0.048 0.642 0.062 0.086 0.277 0.000 0.124 0.790 0.523 0.072 0.475 0.000 345 10.47 0.0000 0.2569 0.2323 41252 [95% Conf Interval] -.3522148 -.0011971 -.199771 3237855 -.0003036 0124673 -.000815 0000539 -.0162982 0046849 0195043 04673 -.0094361 0780563 -.2058677 1568224 -.2882546 1468149 -.0134174 3132854 -.2043593 0953745 3.010889 3.704583 Appendix 5: Test for heteroskedasticity estat hettest Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of lnwage chi2(1) = 2.51 Prob > chi2 = 0.1134 effect Appendix 6: Test for multicollinearity vif Variable VIF 1/VIF fexfesh female femshman cen_age1 cen_age1sq lnsize seniority education industry 9.12 6.81 3.03 1.89 1.59 1.37 1.34 1.13 1.02 0.109703 0.146940 0.329786 0.530105 0.628816 0.732004 0.747229 0.886052 0.976830 Mean VIF 3.03 Appendix 7: The individual categories of ISIC Section Divisions Description A 01–03 Agriculture, forestry and fishing B 05–09 Mining and quarrying C 10–33 Manufacturing D 35 Electricity, gas, steam and air conditioning supply E 36–39 F 41–43 Construction G 45–47 Wholesale and retail trade; repair of motor vehicles Water supply; sewerage, waste management and remediation activities 50 and motorcycles H 49–53 Transportation and storage I 55–56 Accommodation and food service activities J 58–63 Information and communication K 64–66 Financial and insurance activities L 68 Real estate activities M 69–75 Professional, scientific and technical activities N 77–82 Administrative and support service activities O 84 P 85 Education Q 86–88 Human health and social work activities R 90–93 Arts, entertainment and recreation S 94–96 Other service activities Public administration 97–98 compulsory social security Activities T and defense; of undifferentiated households goods-and as employers; services-producing activities of households for own use U 99 Activities of extraterritorial organizations and bodies 51 ... stand in the assembly line of managers in small and medium plants in Viet Nam This research is also conducted to answer the main question that is gender pay gap low in plants with more female managers? ... supposed the gender gap in earnings can be explained in the terms of discrimination Therefore, the main assumption in this research is that the gender pay gap is lower in enterprise with more female. .. the gender gap in earnings is low in the establishments with more female managers Using a sample size of 1043 employees and 2492 enterprises were surveyed in the Small and Medium Enterprises