Social Identity

The demographic impact of extended paid maternity leave in Bangladesh

  • Blog Post Date 14 June, 2017
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In March 2017, Indian Parliament passed the Maternity Benefit (Amendment) Bill, 2016 extending paid maternity leave to 26 weeks. This column analyses the impact of extension of paid maternity leave in Bangladesh in 2006 and 2010, on infant mortality, female labour force participation, and fertility rates.



Nearly all countries in South Asia have provided at least 12 weeks of paid maternity leave (ML) for decades. With the passage of the Labour Act of 2006, Bangladesh outperformed India and other South Asian countries in paid ML provisions by extending the period from 12 weeks to 16 weeks for the first two children. Expanded leave coverage resulted in higher costs for employers since all types of firms, particularly in the formal sector, are required by law to offer paid ML. The government of Bangladesh further extended the coverage to 24 weeks in December 2010, with effect from January 2011. However, the amended law is applicable only to civil servants, who are entitled to paid ML twice during their service. In March 2017, Indian Parliament passed the Maternity Benefit (Amendment) Bill, 2016, extending paid ML to 26 weeks1.

In this column, I analyse the demographic impacts of the expansion in paid ML coverage in Bangladesh: considerable improvement is seen in Infant Mortality Rates (IMR), female labour force participation (FLFP) and the Fertility Rate (FR) (Ahmed and Maitra 2015, Ahmed and McGillivray 2015, Ahmed and Ray 2016). I find evidence of strong association between paid ML length and Infant Mortality Rates (IMR) as well as evidence of paid ML policy having a positive impact on FLFP is found.

The impact of paid maternity leave

Economic theory suggests that various income and substitution effects embodied in these programmes should have predictable demographic impacts: paid ML provides payments to working mothers, directly raising their participation rates (income effect). This, in turn, should reduce the cost of raising children in more affluent households (substitution effect), and thus might improve the chances of infant survival.

It appears, however, that very little systematic investigation has been undertaken along these lines in low-income countries. Existing studies are mainly oriented to industrial countries with respect to child welfare, economic participation of women, and fertility (Winegarden and Bracy 1995).

Our study addresses the period between 1999 and 2013, in particular, data from Bangladesh before and after the scheme's announcement (that is, 2006 and 2010). We sourced data and related information from the World Banks' World Development Indicators on the FLFP rate of the 15-64 year age group, IMR and FR by different age groups, as well as the Bangladesh Demographic and Health Surveys (DHS).

Evidence on the following questions is provided:

  • How does the paid ML coverage affect IMR?
  • How does the paid ML coverage influence FLFP?
  • Does paid ML coverage affect the FR?
  • Did women change their sector of work in response to the scheme's extension in 2011?

a) Infant mortality rates

With respect to IMR, Winegarden and Bracy (1995) suggest that longer paid ML enhances infant survival by improving the quality of infant care (example, through prolonged breastfeeding). Moreover, there is a positive income effect on households with new-born infants as ML payments replace income that is otherwise lost.

The data from 1999-2013 suggests that IMR dropped without exception (see Figure 1). Eight extra weeks of paid ML reduces IMR by about 5%. As noted previously, this demographic effect seems to work primarily via the resources available for and the quality of infant care. The marked decline in IMR might also be driven by a highly successful child immunisation programme in Bangladesh.

b) Female labour force participation

What we see in Figure 1 is consistent with Sundstrom and Stafford (1991), who suggest that paid ML programmes induce more women to enter and remain in the labour market as paid ML increases life-time earnings of working women. This, in turn, should induce greater investment in the education and training of female workers, further increasing their earnings and, thus, their labour supply. However, this finding might be explained by other factors, such as finishing education or getting divorced.

We used the Bangladesh DHS data to look for evidence whether completion of education or divorce rates lead to an increase in FLFP. Women indeed have increased their educational credentials (example, completion of primary, secondary or higher degrees) in Bangladesh, but not during the period when paid ML length was extended. For example, the secondary or higher education completion rate of employed women aged 15-49 decreased remarkably between 2004 and 2011 (from 52.7% to 20.2%, but increased to 24.8% in 2014).

On the other hand, the divorce rate of employed women in a similar age group reduced considerably over the same period (from 85.7% to 36.6%) (National Institute of Population Research and Training (NIPORT), Mitra and Associates and ORC Macro 2005, NIPORT, Mitra and Associates and ICF International 2013, NIPORT 2015). Therefore, we can speculate that the increase in FLFP is not associated with completion of education or the divorce rate.

Figure 1. IMR and Female Participation Rate, 1999-2013

c) Fertility rate

Extended paid ML coverage is often assumed to induce women to have more births because paid ML reduces the cost of children (Becker 1991). This increased fertility exacerbates the financial burden on firms, as well as demographic pressures. The latter is often viewed as a social problem in many developing countries. Therefore, the liberalisation of paid ML coverage may produce a trade-off between IMR, FLFP, and FR, a relation that requires evidence.

Bangladesh, like many other developing countries, is a high-fertility country. However, the country has seen a reduction in the average number of children per woman (total FR). This figure was around 3% from 1999 to 2006, and has since fallen consistently; reaching 2.2% in 2013, which is the lowest FR in South Asia (see Figure 2). Similarly, fertility of ages 15-19 fell considerably between 1999 and 2006 (from 112% to 96% per thousand women). This decrease was even more pronounced when paid ML was extended to eight weeks in 2010.

Therefore, the liberalisation of paid ML coverage does not appear to increase the FR. This does not seem an unreasonable finding given the rising trend in FLFP and low IMR (see also, Winegarden and Bracy 1995). A falling FR might also be attributable to a minimum term of employment before becoming fully vested in maternity benefits (see Labour Act, 2006). It is tempting to conclude that a downtrend in the FR is also a cumulative effect of the expansion in access to health, family planning, and nutrition services for the poor.

Figure 2. Total Fertility Rate, 1999-2013

d) Sector switching and participation decisions

Women could be anticipated to change their sector of work, or enter or leave the labour market, in response to the 2010 paid ML announcement for civil servants. For example, private sector workers with high fertility preferences could be incentivised to get public sector jobs. Similarly, it could induce non-working women to enter the labour force and disproportionately choose public sectors jobs.

However, neither is apparent over the period of analysis (see Figure 3). There is little or no movement at all in public sector employment during the period when paid ML length was extended to 24 weeks (that is, between 2010 and 2013). A number of other significant changes have taken place over time: the percentage of women working in the private sector has risen by 4% while it has risen by only 1% in the informal sector. So, the female workers entering the labour force are more likely to participate in the private sector despite the shorter duration of paid ML. These findings may well be expected given a downward trend in fertility desires over time. This possibility clearly indicates the need for additional studies on the demographic effects of paid ML, especially in the public sector.

Figure 3. Percentage of Women in Public, Private and Informal Sectors, 1999-2013

Conclusion

Seeing a plausible connection between paid ML policies and demographics does not necessarily suggest acceptable documentation of a direct causal connection. Causality is obscured when social policies expand gradually and in step with local/macro conditions, and when policies in turn are revised in response to demographic behaviour. For example, the rising rate of FLFP might operate through a political process to bring about extensions to paid ML. On the other hand, rising fertility might negatively influence paid ML policies. These necessarily indicate a reverse causation between paid ML and demographic variables.

An investigation of a possible reverse causality is outside the scope of the present study. One should thus interpret the findings as evidence of association rather than causation. Nevertheless, the strong association between paid ML length and IMR is one with considerable policy significance. Moreover, the study shows evidence of paid ML policy having a positive impact on FLFP.

This column first appeared on IGC Blog: http://www.theigc.org/blog/demographic-impact-extended-paid-maternity-leave-bangladesh/.

Note:

  1. The new law will apply to all establishments employing 10 or more people and the entitlement will be for only up to first two children. For the third child, the entitlement will be for only 12 weeks.

Further Reading

  • Ahmed, Salma and Pushkar Maitra (2015), "A Distributional Analysis of the Gender Wage Gap in Bangladesh", The Journal of Development Studies, 51(11):1444-1458.
  • Ahmed, Salma and Mark McGillivray (2015), "Human Capital, Discrimination, and the Gender Wage Gap in Bangladesh", World Development, 67:506-524.
  • Ahmed, S and R Ray (2016), 'Quality of Governance and Welfare Outcomes', Ideas for India, 27 April 2016.
  • Becker, G (1991), A Treatise on the Family, Harvard University Press, Cambridge.
  • Bangladesh Employers' Federation (2009), 'A Handbook on the Bangladesh Labour Act 2006'. Available here.
  • National Institute of Population Research and Training (NIPORT) (2016), 'Bangladesh Demographic and Health Survey 2014', NIPORT, Mitra and Associates and ICF International.
  • NIPORT (2013), 'Bangladesh Demographic and Health Survey 2011', NIPORT, Mitra and Associates and ICF International.
  • NIPORT (2005), 'Bangladesh Demographic and Health Survey 2004', NIPORT, Mitra and Associates and ORC Macro.
  • Sundstrom, M and F Stafford (1991), 'Female Labour Force Participation, Fertility, and Public Policy', Stockholm Research Reports in Demography, Stockholm University.
  • Ministry of Labour and Employment (2016), 'The Maternity Benefit (Amendment) Bill (2016)'. Available here.
  • Winegarden, Calman Robert and Paula M Bracy (1995), "Demographic Consequences of Maternal-Leave Programs in Industrial Countries: Evidence from Fixed-Effects Models", Southern Economic Journal, 61(4):1020-1035.
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