Can training close the gender wage gap? Evidence from Vietnamese SMEs

ABSTRACT

Firm-provided training is generally seen as an important tool for bridging the skills gap between the labour force and what the private sector demands. Little is known about how successful such training can be in closing the gender wage gap. We use a matched employer-employee panel dataset to assess why firms train and whether formal training affects wage outcomes in Vietnamese SMEs. Training is generally found to be firm-sponsored and specific in nature. We find that training is associated with higher wages for trained women as compared to both untrained women and men. However, we do not find a statistically significant wage difference between trained women and men. Furthermore, the wage increase is only associated with on-the-job training. Our findings indicate that, at least in Vietnam, firm-sponsored on-the-job training could help increase women’s labour productivity and thus contribute to closing the gender wage gap.

KEYWORDS:

• Training
• wage
• sme
• gender
• Vietnam

JEL CLASSIFICATION:

• J31
• J16
• M53
• O53

Acknowledgments

We would like to thank members of the Development Economics Research Group (DERG) at the University of Copenhagen for helpful comments and constructive feedback, especially John Rand and Finn Tarp. We are also grateful for productive and stimulating collaboration with the survey teams from the Vietnamese Institute of Labour Science and Social Affairs (ILSSA) and staff at Central Institute for Economic Management (CIEM). Financial support from Danida and UNU-WIDER is appreciated. An earlier version of this paper appeared as ‘Gender matters: Private sector training in Vietnamese SMEs’, WIDER Working Paper 2016/149, parts of which are reproduced here with the permission of UNU-WIDER.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Cross-sectional matched employer-employee data have been used in, for example, Görg, Strobl, and Walsh (2007) and Almeida and de Faria (2014).

2. Previous studies use matching methods to identify the average effect of training on wages (Almeida & de Faria, 2014; Rosholm et al., 2007), which controls only for selection for training based on observable characteristics. If selection into training is based partly on unobserved characteristics, such as worker ability or a firm’s training practices, the impact of training on individual wages could be over- or underestimated.

3. A randomized controlled trial (RCT) could address remaining endogeneity issues more efficiently. The literature evaluating various types of randomized labour market interventions is extensive and mainly focused on evaluations of labour market policies in the form of vocational training, wage subsidies, and job search assistance. The main conclusion from a couple of meta-analyses is that there is a relatively weak impact of such programs on employment and wages in developing countries (Card, Kluve, & Weber, 2018; McKenzie, 2017), while a targeted training increases the job search success (Abebe et al., 2019).

4. The questionnaire also includes information about informal training, but we do not base our key estimations on this variable, as it is likely to include substantial measurement error due to the vague definition of this training type.

5. In nominal terms, the average wage is in line with the average monthly earning of wage workers reported by the General Statistics Office, which were 3.8 million VND in 2012 and 4.5 million VND in 2014 (GSO, 2015).

6. Low value added sectors include: food and beverages, textiles, apparel, leather and recycling. Medium value added sectors include: wood, paper, publishing, rubber, petroleum, chemicals, non-metallic minerals, basic metals and fabricated metals. High value added sectors include: electronic machinery, vehicles, transport equipment and furniture.

7. The IV estimation procedure is described in the Online Appendix.

8. The results are robust t removing outliers in terms of wages (bottom and top 1%) and winsorizing, where the results of the positive effect of training on women’s wages remain significant at the 5% level. We do not show these results for space considerations, but they are available from authors upon request.

9. A direct comparison of the coefficient size with the OLS and fixed effects models is not straightforward. While OLS estimates show average treatment effect (ATE), the 2SLS regressions show a local average treatment effect (LATE), which is the effect of treatment for compliers – those whose treatment status is affected by the instrument (Angrist, Imbens, & Rubin, 1996).

Additional information

Notes on contributors

Benedikte Bjerge

Benedikte Bjerge is a Senior Research Economist at ROCKWOOL Foundation, where she evaluates social interventions aimed at children and young people. She holds a PhD in Economics from University of Copenhagen. Her research interests are at the intersection of development economics, applied econometrics and social networks. She works with education, agricultural household strategies, disasters and small and medium sized enterprises.

Nina Torm

Nina Torm is a Postdoctoral researcher at the Department of Social Sciences at Roskilde University. She is a development economist with extensive experience in survey design, data analysis, policy-oriented research and project coordination. Her research interests revolve around labour market issues with a particular focus on regulatory frameworks, employment promotion and informality in the global south. The geographical focus of her work is in East Asia (mostly Vietnam) and East Africa (Kenya and Tanzania).

Neda Trifkovic

Neda Trifkovic is an Assistant Professor at the Department of Economics, University of Copenhagen. Her research interests include development and agricultural economics, in particular certification, corporate social responsibility, contract farming, transformation of global value chains, labour and working conditions in small and medium enterprises, extreme weather events and violence against women. She has fieldwork experience from Vietnam, Myanmar, Tanzania and Morocco.