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Female employment in MENA’s manufacturing sector: the implications of firm-related and national factors

Economic Change and Restructuring volume 48, pages 37–69 (2015)Cite this article

Abstract

The Middle East and North Africa (MENA) region has realized significant advances toward improving women’s well-being and social status over the last few decades. However, women’s employment rate in the MENA region remains one of the lowest in the world. This paper examines the implications of firm-related and national factors for female employment in manufacturing firms located in the MENA region. The empirical analysis is implemented for firm-level data derived from the World Bank’s Enterprise Surveys database. It uses fractional logit and other econometric models to perform the estimations for female overall employment, female non-production employment, and female employment in managerial positions. The results reveal significant implications of firm-related factors, such as private foreign ownership, exporting activities, firm size, and labour composition, for female employment. They also show that national factors, such as economic development and gender equality, promote female employment. There are considerable differences in the estimated marginal effects across female employment categories. This paper provides policy-makers with directions to design strategies aiming at enhancing women’s economic opportunities and employment rates.

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Notes

  1. Female employment rate is commonly determined as the proportion of women employed in the private and public sectors (Stevenson 2010; The World Bank 2013a).

  2. Several countries in the MENA region have dedicated significant resources to women’s education over the past few decades. For example, since the 1990s, MENA countries have enjoyed substantial growth in female enrolment in primary and secondary education, and have benefited from some progress in female enrolment in tertiary education (Morrison et al. 2008).

  3. Female labour force (workforce) participation rate is usually measured as the proportion of women aged 15 years and older who are economically active (i.e., employed or looking for jobs) (Stevenson 2010; World Bank 2013a). Hence, female employment rates and female labour force participation rates are linked, but they are not interchangeably equivalent. Note that lower rates of women participation in the labour market are typically associated with lower female employment rates. According to the World Bank (2013a), employment includes the contribution in all market production (paid work) in addition to certain types of non-market production (unpaid work). Thus, it covers the proportion of women that are currently working.

  4. Also, Chamlou et al. (2011) find that traditional social norms reduce women’s participation in the labour market in Jordan.

  5. The survey year/fiscal year are: 2002/2001 and 2007/2006 for Algeria’s firms, 2007/2005 and 2008/2007 for Egypt’s firms, 2006/2006 for Jordan’s firms, 2009/2008 for Lebanon’s firms, 2007/2005 for Morocco’s firms, 2003/2002 for Oman’s firms, 2003/2002 and 2009/2008 for Syria’s firms, and 2010/2009 for Yemen’s firms.

  6. It is worth noting that sample attrition is not a pertinent issue through the empirical analysis. Sample attrition is a common incident in panel data collected over two or more points in time. It occurs when some cross-sectional units (e.g., some firms) are dropped out from the original set over time. Attrition would generate bias if the dropped-out cross-sectional units are systematically different from those which remain in the sample. The final dataset used in this paper is characterized as a cross-section where each firm corresponds to only one observation without regard to differences in time.

  7. The World Bank’s Enterprise Surveys database includes information on the number of part-time workers. However, data covering female part-time employment are inadequate to carry out an empirical analysis through this paper. Women normally demand more part-time arrangements than men to balance between family responsibilities and work tasks. However, women on part-time jobs usually receive fewer benefits than regular full-time workers (e.g., Zeytinoglu et al. 2010). Accordingly, firm characteristics are expected to have various effects on part-time female employment.

  8. The UNDP’s GII is designed for the measurement of gender disparity. It covers female disadvantages through three dimensions: reproductive health, empowerment, and economic activities. It ranges from zero (full gender equality) to one (extreme gender inequality).

  9. The WDI’s ease of doing business indicator is a ranking score of countries according to the quality of regulatory environment in enhancing business. We inversely rank the MENA countries in our dataset, giving higher scores to more business-conducive regulatory environments. Hence, a positive coefficient on this variable would indicate a positive effect of business freedom on the dependent female employment variable.

  10. Larger firms tend to pay higher wages because they are normally expected to earn higher profits and to be more innovative and capital-intensive compared to smaller firms. They also tend to pay higher wages to compensate for disutilities in the working atmosphere (Masters 1969; Schmidt and Zimmermann 1991).

  11. Cavalcanti and Tavares (2008) show that technological progress in the household sector (through durable goods) contribute in liberating women from domestic work. This outcome eventually leads to increases in women participation in the labour market. They also find a positive relationship between GDPC and female workforce participation.

  12. Increases in female employment and workforce participation rates per se may not be necessarily a sign of a decline in gender inequality (Standing 1999; Klasen and Pieters 2012).

  13. For instance, consider estimating an equation characterized by wage as the dependent variable and schooling level as the regressor. We only observe wages of working individuals. Hence, a two-step estimation method is warranted in this case. The first step determines the probability of working, and the second step explains the wage equation after taking into account the probability of working. A corresponding two-step approach would be conceptually more difficult to adapt for the empirical analysis of female employment rates through this study.

  14. The regressions can be alternatively implemented with a binary variable that equals one for firms using E-mail as a form of business communication and zero otherwise. The corresponding results are similar to those obtained through the benchmark regressions.

  15. The percentage of female workers in total employment for manufacturing firms located in Algeria has a mean of 22.9 % and a standard deviation of 28.0 % in our dataset. The corresponding statistics for manufacturing firms located in Yemen show a considerably lower mean of 5.4 % with a standard deviation of 6.8 %.

  16. We note that the estimation of fixed effect models through non-linear regressions would give rise to incidental parameters problems in panel datasets when T is fixed and N approaches infinity (Wooldridge 2002; Li 2011). In this case, the estimates obtained from fixed effect models would be inconsistent. The dataset used in this paper corresponds to a cross-section of firm observations located in few MENA countries. The specific-effects used in our model are not firm-specific effects. They consist of a limited (finite) set of industry dummy variables that equal one when the observed firm belongs to a given industry and zero otherwise, and a limited set of country dummy variables that equal one when the observed firm is located in a given MENA country and zero otherwise. The use of industry dummy and country dummy variables does not raise identification issues such as incidental parameters because the sample size is determined by the number of firms.

  17. We note that the estimations are also implemented using the alternative fractional probit model. The corresponding marginal effects are presented in columns (1) and (2) of Table 8 of the “Appendix”. They are found to be equivalent to the results obtained through the fractional logit model.

  18. The correlation coefficient between the GII and the GDPC variables is around −0.9. Consequently, these variables are not included together in the same regression due to multicollinearity. We note that positive relationships are commonly documented in the literature between economic development and national gender equality (e.g., Weiss et al. 1976; Clark et al. 1991; Abu Ghaida and Klasen 2002; Klasen and Lamanna 2009; Cuberes and Teignier-Baqué 2011).

  19. Some indicators do not exhibit variations across MENA countries covered in the dataset. For example, the Constitution of all MENA countries covered in the dataset guarantee equality between genders before the law. Also, there are some indicators that cannot be distinguished from country-specific effects. For example, Morocco is the only MENA country in the dataset where personal law is not recognized as a valid source of law under the Constitution.

  20. The national inward FDI index is derived from the United Nations Conference on Trade and Development (UNCTAD) database, and is determined as the ratio of total inward FDI stock to gross fixed capital formation.

  21. We note that the marginal effects from estimating fractional probit models are displayed in columns (3) and (4) of Table 8 of the “Appendix”. They are found to be similar to the estimates obtained from the fractional logit models.

  22. This is commonly depicted through the glass-ceiling hypothesis which states that it is more difficult for women than for men to be promoted to top managerial positions in the workplace (Oakley 2000; Eagly and Karau 2002).

  23. The results from estimating linear probability models are presented in columns (5) through (8) of Table 8 of the “Appendix”. They are comparable to those obtained from the probit model.

  24. The empirical analysis for disaggregated manufacturing industries would entail more observations through the industries. It would also require more variations in the composition of female workers and in different firm characteristics across firms within each industry. This dimension could be explored through an extended dataset which includes observations on firms located in other countries across industries.

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Acknowledgments

The authors are grateful to two anonymous reviewers and to the editor, George Hondroyiannis, for comments and suggestions. The authors wish to thank Ragui Assaad, İpek İlkkaracan, Lars Vilhuber, and Philipp vom Berge, and the participants at the 47th Annual Conference of the Canadian Economics Association (2013), 34th Annual Meeting of the Middle East Economic Association (2014), and 20th Annual Conference of the Economic Research Forum (2014) for comments and discussions.

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Authors and Affiliations

  1. Department of Economics, School of Business, Lebanese American University, P.O. Box 13-5053, Beirut, Lebanon

    Ali Fakih

  2. Department of Economics, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada

    Pascal L. Ghazalian

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  1. Ali Fakih
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  2. Pascal L. Ghazalian
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Correspondence to Pascal L. Ghazalian.

Appendix

Appendix

See Table 8.

Table 8 Results from alternative empirical models
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Fakih, A., Ghazalian, P.L. Female employment in MENA’s manufacturing sector: the implications of firm-related and national factors. Econ Change Restruct 48, 37–69 (2015). https://doi.org/10.1007/s10644-014-9155-1

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Keywords

  • Female employment
  • Fractional logit model
  • Manufacturing firms
  • MENA region

JEL Classification

  • J16
  • J21
  • J23
  • J82