Changes in maternity leave coverage: Implications for fertility, labour force participation and child mortality

Social Science & Medicine 241 (2019) 112573

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Changes in maternity leave coverage: Implications for fertility, labour force participation and child mortality

T

Salma Ahmeda,∗, David Fieldingb,c a

Alfred Deakin Institute for Citizenship and Globalisation, Geelong Waurn Ponds Campus, 75 Pigdons Road, Deakin University, VIC, 3220, Australia Department of Economics, University of Otago, PO Box 56, Dunedin, 9054, New Zealand c Global Development Institute, Manchester University, Oxford Road, Manchester, M13 9PL, UK b

ARTICLE INFO

ABSTRACT

Keywords: Africa Asia Maternity leave Child health Fertility Employment

Analysing macro-panel data from 18 African and Asian countries over the period 1995–2016, this article investigates the effects of the level and duration of paid maternity leave on three dimensions of human development: fertility, female formal-sector employment and infant mortality. There is some evidence that, on average, extending the duration of leave leads to reductions in infant mortality and employment. However, there is no conclusive evidence that leave duration has a direct effect on fertility. In contrast, there is some evidence that higher maternity leave payments lead to higher fertility, but no evidence that payment levels have any effect on infant mortality or employment.

1. Introduction There is now substantial evidence regarding the beneficial consequences of paid maternity leave (ML) in industrialised countries. These consequences include longer periods of breastfeeding (Berger et al., 2005; Baker and Milligan, 2008; Khanam et al., 2016; Albagli and Rau, 2018), lower mortality rates among infants and young children (Winegarden and Bracy, 1995; Ruhm, 2000; Tanaka, 2005; Fallon et al., 2017; Heymann et al., 2017), higher childhood vaccination rates (Daku et al., 2012; Khanam et al., 2016; Heymann et al., 2017) and, for mothers, better post-partum physical and mental health (Aitken et al., 2015, and literature cited therein). Research also suggests that ML has a positive effect on fertility (Winegarden and Bracy, 1995; Averett and Whittington, 2001; Risse, 2006; Luci-Greulich and Thévenon, 2013). However, there is relatively little evidence linking ML with child and maternal well-being in low-income countries. There are a small number of non-statistical studies (Chang, 2004; İlkkaracan, 2012), but only Nandi et al. (2016) and Fallon et al. (2017) present detailed statistical evidence. Nandi et al. (2016) apply a difference-in-differences model to data from the Demographic and Health Surveys in 20 low- and middle-income countries for 2000–2008, but they caution that their findings may not be generalisable to other countries. Fallon et al. (2017) apply a cross-sectional time-series model to data from 121 lowand middle-income countries for 1999–2012. Against this background, we assess the relationship between ML and

∗

key demographic indicators for women and children in 18 countries in Africa and Asia. In these countries, as in most others, there are two key dimensions to ML: the duration of the leave and the level of payment. The latter is usually expressed as a percentage of earnings, that is, as the wage replacement rate (WRR). Nearly all of the countries in our sample have had at least 12 weeks of ML for several decades, but many of them have increased the level or duration of payment in recent years (see Table 1). As we will see, substantial progress in terms of lower infant mortality rates (IMRs) and total fertility rates (TFRs) has also been seen during this time period, but there has been little or no increase in the rates of female formal-sector employment (FFSE). The reasons for these differences in trends are not well understood. Moreover, there is still little recent evidence on how changes in ML coverage are related to the development outcomes. This article updates earlier evidence by focusing on a period of substantial changes in IMRs and TFRs, examining the extent to which ML has contributed to these changes and suggesting explanations for the absence of any corresponding change in FFSE. We note as a caveat that our sample includes only a subset of countries in Asia and a small fraction of countries in Africa. Our results are relevant to policies in these countries, but we do not claim that they are relevant to the whole of Africa and Asia. This article contributes to the literature in several ways. First, our sample includes a number of countries that have been absent from any previous data analysis (e.g., our Asian sample includes Afghanistan and the Maldives). Second, our statistical model allows for changes in laws

Corresponding author. E-mail addresses: [email protected] (S. Ahmed), [email protected] (D. Fielding).

https://doi.org/10.1016/j.socscimed.2019.112573 Received 23 November 2018; Received in revised form 29 August 2019; Accepted 24 September 2019 Available online 26 September 2019 0277-9536/ © 2019 Elsevier Ltd. All rights reserved.

90

112

56

84

90

105

84

60

98

98

52

84

84

112

84

182

120

Bangladesh

Bhutan

India

Kenya

Laos

Lesotho

Maldives

Morocco

Myanmar

Nepal

Pakistan

Sri Lanka

Turkey

Uganda

Vietnam

Zambia

Total

2

60

56

14

42

42

49

42

60

42

56

30

Pre

122

56

70

42

56

49

60

42

45

42

56

56

60

Post

Days of paid leave

Afghanistan

Country

Not identified

Mandatory

Mandatory

Mandatory

Mandatory

Mandatory

Not identified

Mandatory

Mandatory

Not identified

Mandatory

Mandatory

Mandatory

Mandatory

Not identified

Mandatory

Not identified

Type of Leave

100%

100%

100%

67%

85%

100%

100%

70%

100%

100%

100%

100%

100% with minimum

100% with minimum

100% with minimum

100% with minimum

100% with minimum

Wage Replacement rate

Table 1 Maternity Leave and abortion policies in sample countries in 2016.

Employer

Social security

Employer

Social security

Employer

Employer

Employer

Social security

Social security

Employer

Unclear

Social security, Employer

Employer

Employer

Employer

Employer

Employer

Funding

Liberal

Liberal

Liberal

Liberal

Restricted

Restricted

Liberal

Restricted

Restricted

Restricted

Liberal

Restricted

Liberal

Liberal

Restricted

Restricted

Restricted

Abortion Policy

(continued on next page)

The duration of standard paid leave was 90 days throughout 1995–2016. The Labour Law of 2007 provides for a 14-day extension after abnormal or multiple births. Pre-partum and post-partum leave were clarified in 2009. Covered: civil servants, workers and contractors. Abortion is only permitted to save a woman's life. The duration of standard paid leave was 84 days until 2006, when it was extended to 112 days. In 2011, it was extended to 182 days for mothers in the public service. Covered: employees in the formal sector. Abortion is only permitted to save a woman's life. The duration of standard paid leave was 56 days throughout 1995–2016. An extension to 30 days was introduced in cases of miscarriage (2006) and multiple births (2012). Covered: public employees. Until 2004, abortion was permitted only to save a woman's life. It is now permitted on mental health grounds. The duration of standard paid leave was 84 days throughout 1995–2016. In 2008, this was extended to 182 days for mothers in the Central Government Service. Covered: factories, mines and plantation workers. Abortion is permitted on almost all grounds. Abortion law was enhanced to improve access to safe abortion facilities in 2002. The duration of standard paid leave was 60 days until 2008, when it was extended to 90 days. There is no information on pre-partum or post-partum leave. Covered: employees in the formal sector. Abortion has been permitted on almost all grounds since 2010. The duration of standard paid leave was 90 days until 2013, when it was extended to 105 days. Covered: employees in the formal sector. Abortion is only permitted to save a woman's life and to preserve her physical health. Abortion law eligibility criteria were amended in 2016. The duration of standard paid leave was 14 days until 2007, when it was extended to 42 days; it was extended to 84 days in 2009. There is no obligation for an employer to cover maternity pay unless stipulated in the employment contract. Covered: public and private sector employees. Abortion permitted on almost all grounds as of 2012. The duration of standard paid leave was 60 days throughout 1995–2016. In 2008, this was extended to 28 days in cases of maternal or child post-natal illness. Covered: employees in the formal sector. Abortion is permitted to save a woman's life and to preserve her physical health. The duration of standard paid leave was 84 days until 2004, when it was extended to 98 days. Covered: employees in the formal sector. Until 2013, abortion was permitted only to save a woman's life or to preserve her physical health. Abortion on mental health grounds has been legal since 2013. The duration of standard paid leave was 84 days until 2012, when it was extended to 98 days. Covered: employees in the formal sector. Abortion is permitted only to save a woman's life. The duration of standard paid leave was 52 days for the first birth throughout 1995–2016. In 2009, coverage was extended to a second birth. There is paid leave of 60 days for public employees. There is no information on prepartum and post-partum leave. Covered: employees in the formal sector. Abortion has been permitted on almost all grounds since 2002. The duration of standard paid leave was 84 days throughout 1995–2016. Covered: all establishments. Abortion is permitted to save a woman's life or to preserve her physical and mental health. The duration of standard paid leave was 84 days throughout 1995–2016. Covered: all workers except employees in shops or offices where the nature of the work is casual. Abortion is only permitted to save a woman's life. The duration of standard paid leave was 84 days until 2003, when it was extended to 112 days. Covered: all employees. Abortion is permitted on almost all grounds. The duration of standard paid leave was 30 days until 2006, when it was extended to 84 days. There is no information on pre-partum or post-partum leave. Covered: employees in the formal sector. Abortion has been permitted on almost all grounds since 2006. The duration of standard paid leave was 120 days until 2013, when it was extended to 182 days. Covered: employees in the formal sector except civil servants, who are covered by other legislation. Abortion is permitted on almost all grounds. Abortion law was amended to address unsafe abortion in 2004. The duration of standard paid leave was 84 days until 2002, when it was extended to 90 days; it was extended to 120 days in 2006. Covered: public and private sector workers. Abortion is permitted on almost all grounds.

Notes:

S. Ahmed and D. Fielding

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Restricted Employer 100% 53 45 98 Zimbabwe

Total

Pre

Post

Mandatory

Type of Leave Days of paid leave Country

Table 1 (continued)

Notes: “Standard paid leave” means leave in the absence of exceptional circumstances such as post-partum illness. Recently, the Indian Parliament passed the Maternity Benefit (Amendment) Bill, 2016, extending paid ML to 182 days. This new law will apply to all establishments employing ten or more people, but the entitlement will only be for the first two children. For the third child, the entitlement will be for 84 days. The proportion of the population employed in the formal sector varies in this sample of countries from under 6%–65% (ILO, 2018). Although these proportions are lower than in industrialised countries (particularly in Bangladesh, India, Nepal, Laos PDR, Lesotho and Uganda), the analysis is still valuable, given the enormous variation in maternity leave policy in developing countries. If abortion is permitted on almost all grounds, abortion policy is said to be liberal. Source: authors' estimates from World Policy Analysis Centre (2019). See also Centre for Reproductive Rights (2014).

Abortion Policy Wage Replacement rate

Funding

Notes:

The duration of standard paid leave was 90 days until 2006, when it was extended to 98 days. Covered: public and private sector workers. Abortion is permitted to save a woman's life and to preserve her physical health, or in cases of physical abuse.

S. Ahmed and D. Fielding

relating to abortion. These changes reflect policy innovation in an area that still attracts a large amount of social stigma but has substantial heterogeneity across countries. In India, for example, abortion has been legal in a wide range of situations since the 1950s, while in Nepal it was not legalised until 2002 (Table 1). In many other countries, legal abortion is restricted to cases in which there is a serious threat to the mother's physical or mental health. To date, there has been no statistical study analysing the effects of both abortion policy and ML on IMR and TFR. Third, we follow the empirical design of Fallon et al. (2017) but improve on previous research by modelling IMR, TFR and FFSE simultaneously using three-stage least squares (3SLS) and allowing for interactions between the three development outcomes (see Winegarden and Bracy, 1995). We apply our model to a relatively lengthy series and control for a relatively wide set of institutional factors. Several different versions of the model are fitted to the data in order to assess the robustness of the results. We present evidence that increasing the duration of ML leads to lower IMR and FFSE. This second result may reflect a negative effect on labour demand that exceeds any positive effect on labour supply. There is no robust evidence that ML duration has an effect on fertility. In contrast, there is some evidence that increases in WRR lead to higher TFR but no evidence that WRR has an effect on IMR or FFSE. There are some large and statistically significant interactions between the three development outcomes, which helps explain the persistently low rates of female employment. There are no statistically significant effects due to changes in abortion policy, on average. The next section outlines the expected relationships between the variables of interest, based on the existing literature. Subsequent sections present the data and statistical results. 2. Associations between maternity leave (ML) and development outcomes 2.1. Maternity leave (ML) and the infant mortality rate (IMR) For a given level of fertility, higher ML payments are likely to reduce the IMR through an income effect (those households with mothers taking leave have more resources to spend on the child) and a substitution effect (there is a lower opportunity cost of taking ML to devote more care to the child). These beneficial effects could be offset by the fact that women normally need to be at work during early pregnancy in order to qualify for ML, and this might adversely affect child health (Frisbie et al., 1996; Ruhm, 2000). However, existing empirical studies in other parts of the world indicate a positive effect on child health and its correlates (e.g., the duration of breastfeeding: see Winegarden and Bracy, 1995; Ruhm, 2000; Batal et al., 2006; Victora et al., 2011). Our first hypothesis is therefore as follows: Hypothesis 1. A higher duration of ML and higher WRR lead to lower IMR. 2.2. Maternity leave (ML) and female formal-sector employment (FFSE) Sundstrom and Stafford (1991) suggest that ML programmes raise the lifetime earnings of working women and, therefore, encourage women to enter the labour market. However, as noted by Fallon et al. (2017), poverty forces most women in developing countries into the labour market, and variation in FFSE figures is likely to be driven mainly by movement between the formal and informal sectors. ML programmes may make FFSE more attractive to women, but at the same time, such programmes may make formal-sector activity less attractive to employers, either because employers bear some of the cost or because ML has a negative effect on human capital (see, e.g., Summers, 1989). Existing studies suggest that the net effect of ML on FFSE is negative in both industrialised countries (see, e.g., Albrecht et al., 2003) and developing countries (see Chang, 2004). Our second 3

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S. Ahmed and D. Fielding

hypothesis is therefore as follows:

three dependent variables in our main model (IMR, FFSE and TFR) and the two key explanatory variables: WRR and the number of days of paid ML (DYSMLV). Table A1 of the appendix provides more information about trends in the dependent variables for individual countries. Data on WRR and DYSMLV were taken from the World Policy Centre's PROSPERED project (World Policy Analysis Centre, 2019). Note that these measures do not include family leave, adoption leave or child-rearing leave. Very few developing countries have adopted policies with respect to these other types of leave, so there is very little information about them in our sample, and they do not appear in our analysis. Data on IMR, measured as the number of deaths below the age of one year per 1000 live births, were taken from the World Bank's Development Indicators (WDI) 2017 database (World Bank, 2017). This is also the source for TFR, measured as the ratio of live births to the number of women aged 15–49. Data on FFSE, measured as the fraction of women over the age of 14 in formal-sector employment, were taken from the International Labour Organization's (ILO, 2016) ILOSTAT database. In the appendix, we explore the sensitivity of the results to alternative ways of measuring child health and female employment. Data on abortion policy were taken from the Centre for Reproductive Rights (2014). Data on other explanatory variables were taken from the WDI 2018 database (World Bank, 2018), the World Health Organisation (2017) database (WHO, 2017) and various Demographic and Health Survey reports (Inner City Fund, 1995–2016). Missing values for some explanatory variables were interpolated using the two methods in Ruhm (2000) and Tanaka (2005): either (a) the missing observation was assumed to take the same value as in the previous year, or (b) the missing observation(s) were interpolated using a linear trend. More details appear in the appendix. There were no missing values for any of the dependent variables. When the model is fitted to a sample excluding all country-years with missing data, the results would be substantially the same as those reported in section 4.

Hypothesis 2. A higher duration of ML and higher WRR lead to lower FFSE. 2.3. Maternity leave (ML) and the total fertility rate (TFR) Economic theory suggests that ML will increase TFR by reducing the opportunity cost of childbearing (Becker, 1960). This effect will be strengthened by any work-life balance policies that reduce the indirect costs of having children, and there is evidence that such policies are associated with higher fertility in industrialised countries (Gauthier and Hatzius, 1997; Hilgeman and Butts, 2009; Luci-Greulich and Thévenon, 2013). Our third hypothesis is therefore as follows: Hypothesis 3. A higher duration of ML and a higher WRR lead to a higher TFR. Note, however, that work-life balance policies are rare in developing countries. Moreover, ML may raise female job security (Fallon et al., 2017), and this may encourage women to delay childbirth, leading to lower TFR in the long run. These effects tend to reduce any positive association between ML and TFR. 2.4. Interdependence among the three development outcomes When estimating the effect of ML duration and WRR on the three outcomes (IMR, FFSE, TFR), it will also be informative to estimate the extent to which the outcomes affect each other. Identifying causal effects among the three outcomes will require a structural model, which is discussed in Section 4. Here, we note that there is evidence for a positive association between TFR and IMR in Rosenzweig and Wolpin (1988). One explanation for this association is that higher fertility is a cause of poor child health, but Cigno (1998) also notes that an understanding of this effect may influence parents’ fertility choices. Higher expected mortality will then lead to higher fertility; therefore, the association could be a function of causal effects in both directions (see Rosenzweig and Schultz, 1985; Chowdhury, 1988; Schultz, 1993). Higher female employment rates may lead to lower TFR through an increase in the opportunity cost of maternal time (Winegarden and Bracy, 1995; Ruhm, 2000; Luci-Greulich and Thévenon, 2013). However, this substitution effect could be offset by an income effect; better employment opportunities could be associated with higher income levels that facilitate larger families (Luci-Greulich and Thévenon, 2013). If mothers are mainly responsible for childcare, then external factors leading to a higher TFR may prevent women from going to work. However, if other family members (e.g., grandparents) are involved in childcare, then a higher TFR may motivate the mother to go to work to pay for the cost of the extra mouths to feed (see Cramer, 1980). Female employment could also have a direct effect on child health and IMR. For example, higher income through female employment could enhance child health. However, if working mothers have less time to spend on childcare, then their employment may be associated with poor child health (Ruhm, 2000). Yet, there is no reason to suppose that IMR has a large direct effect on female employment; therefore, our employment equation does not contain an IMR term.

3.2. Patterns in maternity leave (ML) and abortion policies and in development outcomes Historically, there has been substantial heterogeneity across the 18 countries in terms of ML, abortion policies and development outcomes. Contemporary policies are summarised in Table 1. In 2000, the ILO's Convention on Maternity Protection recommended paid ML of at least 14 weeks, with at least six weeks of leave in the post-partum period. However, there is still substantial variation in the length of ML. Five of the countries in the sample (Bangladesh, Laos, Turkey, Vietnam and Zambia) now have ML of over 14 weeks, and two (Morocco and Myanmar) have exactly 14 weeks. However, the Maldives only has 60 days of ML, and Nepal only has 52. In Afghanistan, the ILO standard is met for some mothers, for example, those giving birth to twins or those miscarrying. All countries except Myanmar, Sri Lanka and Turkey now have a WRR of 100%, although this has not always been the case. There is also substantial variation in abortion policies, and this is not strongly correlated with the variation in ML policies. In some countries (Afghanistan, Bangladesh, Myanmar and Sri Lanka), abortion is still permitted only in cases of a serious threat to the mother's life. In other countries (Bhutan, Laos, the Maldives, Morocco, Pakistan and Zimbabwe), the range of cases in which abortion is permitted has become somewhat wider, including serious (but not necessarily lifethreatening) medical conditions. The remaining countries in our sample (India, Kenya, Lesotho, Nepal, Turkey, Uganda, Vietnam and Zambia) now permit abortion in an even wider range of circumstances, although this was not always the case at the beginning of the sample period. In the model discussed below, this final group is taken to have “liberal” abortion policies. This paper is concerned with the effects of changes in policy rather than the effects of historical cross-country differences. Fig. 1 shows the years and countries in our sample in which there was a major reform to

3. Data and variables used 3.1. Data sources Our macro panel comprises annual data for 1995–2016 for 18 developing countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan and Sri Lanka (collectively “South Asia”); Laos, Myanmar, Turkey and Vietnam (collectively “Other Asia”); and Kenya, Lesotho, Morocco, Uganda, Zambia and Zimbabwe (collectively “Africa”). Table 2 summarises the definitions and data sources for the 4

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Table 2 Summary statistics and data sources. Dependent variables

Symbol

N

Mean

S.D.

Source

Infant mortality rate (deaths under one year/1000 live births) Percentage of women in formal-sector employment (aged over 14) Total fertility rate (births per woman under 50)

IMR FFSE TFR

396 396 396

51.82 49.95 3.64

24.71 22.39 1.46

World Bank (2017) ILO (2016) World Bank (2017)

Exposure variables

Symbol

N

Mean

S.D.

Source

Maternity leave duration (days) Wage replacement rate (percent)

DYSMLV WRR

396 396

82.31 94.99

25.59 11.04

World Policy Analysis Centre (2019) World Policy Analysis Centre (2019)

Control variables

Symbol

N

Mean

S.D.

Source

Mean age at childbirth Square of mean age at childbirth GDP per capita (constant 2010 dollars) Total health expenditure as a percentage of GDP Divorce rate (women aged 15–49) Population aged over 64 as a percentage of population aged 15-64 Ratio of female unemployment rate to male unemployment rate Abortion policy of the government (liberal = 1; not liberal = 0)

MEANAGE MEANAGESQ GDPPC SPENDING DIV DEP UNEMP ABORT

396 396 396 396 396 396 396 396

28.39 807.76 1853 5.39 1.94 7.19 1.36 0.32

1.23 69.03 2390 2.60 1.86 1.91 0.50 0.47

World Bank (2017) World Bank (2017) World Bank (2017) World Bank (2018) DHS (ICF, 1995–2016) World Bank (2017) UNDP (2017) Centre for Reproductive Rights (2014)

3.3. Control variables

ML. All of these reforms led to extensions in ML coverage, although details regarding eligibility and the proportion of leave available before childbirth vary from one country to another. In contrast, there has only been moderate variation over time in the level of ML benefits in each country. These benefits are mostly paid by employers, although Indian benefits were financed through the social security system until 2008. Figure A1 of the appendix provides more detail about the trends in ML coverage in individual countries. For each of the three regions, Fig. 1 also shows the trend in the cross-country average value of IMR over time, along with the trend in DYSMLV. Figs. 2–3 show the corresponding trends in TFR and FFSE. There is an upward trend in the DYSMLV averages in all three regions, although this is less marked in South Asia than in Other Asia and Africa. There are also downward trends in IMR and TFR in all three regions, and these are slightly more marked in South Asia than elsewhere. In fact, both IMR and TFR have fallen in every country in the sample, although, as shown in Table A1 of the appendix, the initial levels vary greatly. Afghanistan, Uganda and Zambia began with IMR levels of over 100 per thousand, and these have fallen to around 40 or 50. At the other extreme, Sri Lanka, Turkey and Vietnam began with IMR levels of under 50 per thousand, and these have fallen to under 20. Correspondingly, Afghanistan, Uganda and Zambia are the three countries starting with a TFR level of over six births per woman, while Sri Lanka, Turkey and Vietnam are the three countries starting with a TFR level of under three births per woman. Note that this paper presents the results from a fixed-effects panel model; this model does not attempt to explain differences in initial conditions (which are captured by country-fixed effects) but does uncover some of the reasons for cross-country differences in the rate of progress. In individual countries, there has sometimes been a marked fall in IMR after a reform that increases the length of ML. For example, Afghan IMR fell from around 91 per thousand in the years prior to the reform of 2009 to under 60 per thousand thereafter; this corresponds to an equally marked fall in TFR. It remains to be seen whether this is a statistically robust characteristic of the dataset as a whole and whether the trend in ML is one cause of the trends in IMR and TFR. However, the most striking feature of Fig. 3 is that there is no noticeable trend in female employment rates in any of the three regions. Despite progress in other dimensions of human development, female employment remains low. This feature of the cross-country average reflects increasing female participation rates in some countries (e.g., the Maldives, Turkey and Uganda) combined with decreasing rates elsewhere (e.g., Bangladesh and India). Possible explanations for the absence of any overall growth in FFSE are discussed after the presentation of our results.

The additional explanatory variables in the model are as follows. See Table 2 for data sources and descriptive statistics. Note that some of these variables are excluded from some of the development outcome equations (IMR, FFSE and TFR); these exclusions identify the structural model described below. i Real per capita GDP (GDPPC). Higher income levels are likely to be associated with better nutrition and healthcare and, hence, lower IMR. For a given ML policy, higher income could either increase TFR through better maternal health or reduce TFR through a higher opportunity cost of mothers' time. The effect of income on FFSE will depend on whether women's time at home is a normal good or an inferior good. This variable appears in all three equations. ii Mean age of mothers at childbirth (MEANAGE) and mean age squared (MEANAGESQ). There is a positive association between teenage motherhood and IMR (Pampel and Pillai, 1986). However, the relationship between IMR and age is unlikely to be linear; only very young mothers risk bearing underweight infants with poor survival chances. We therefore include mean age and mean age squared in the IMR equation. These variables do not appear in the other two equations. iii Healthcare expenditure as a percentage of GDP (SPENDING). This variable captures the total level of health spending in a country, which is likely to be positively associated with child health and negatively associated with IMR (Ruhm, 2000; Tanaka, 2005; Fallon et al., 2017). To the extent that better healthcare represents a positive income effect, it may also influence fertility and employment choices; therefore, this variable appears in all three equations. iv The divorce rate among women aged 15–49 (DIV). The incidence of divorce reflects marital instability, which could be positively associated with female employment if the insecurity persuades women to enter the workforce. Marital instability could also dissuade parents from having children (Winegarden and Bracy, 1995) or reduce the quality of parental childcare; therefore, this variable appears in all three equations. v The ratio of the population aged over 64 to the population aged 15–64 (DEP). As noted by Winegarden and Bracy (1995), a higher dependency ratio may be associated with a greater need for working-age women to enter the labour market. However, younger women may be required to stay at home to care for the elderly; therefore, the effect of DEP on FFSE is ambiguous. Care of the elderly could reduce the amount of time available for childcare 5

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Fig. 1. Infant mortality rates (left-hand scale) and paid maternity leave (right-hand scale) by region Notes: South Asia (SA): Afghanistan (AFG); Bangladesh (BDG); Bhutan (BTN); India (IND); Maldives (MDV); Nepal (NDL); Pakistan (PAK); Sri Lanka (LKA). Other Asia: Laos (LAO); Myanmar (MYN); Turkey (TUR); Vietnam (VIET). Africa: Kenya (KEN); Lesotho (LESO); Morocco (MOR); Uganda (UGA); Zambia (ZAM); Zimbabwe (ZIM).

6

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Fig. 2. Fertility rates (left-hand scale) and paid maternity leave (right-hand scale) by region Notes: South Asia (SA): Afghanistan (AFG); Bangladesh (BDG); Bhutan (BTN); India (IND); Maldives (MDV); Nepal (NDL); Pakistan (PAK); Sri Lanka (LKA). Other Asia: Laos (LAO); Myanmar (MYN); Turkey (TUR); Vietnam (VIET). Africa: Kenya (KEN); Lesotho (LESO); Morocco (MOR); Uganda (UGA); Zambia (ZAM); Zimbabwe (ZIM).

7

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Fig. 3. Female formal-sector employment (left-hand scale) and paid maternity leave (right-hand scale) by region Notes: South Asia (SA): Afghanistan (AFG); Bangladesh (BDG); Bhutan (BTN); India (IND); Maldives (MDV); Nepal (NDL); Pakistan (PAK); Sri Lanka (LKA). Other Asia: Laos (LAO); Myanmar (MYN); Turkey (TUR); Vietnam (VIET). Africa: Kenya (KEN); Lesotho (LESO); Morocco (MOR); Uganda (UGA); Zambia (ZAM); Zimbabwe (ZIM).

(Teitelbaum, 1985), but grandparents could help with childcare. Therefore, the effect of DEP on TFR is also ambiguous. DEP does not appear in the IMR equation. vi The ratio of the female unemployment rate to the male

unemployment rate (UNEMP). This variable reflects the extent of gender bias in employment conditions; a higher level of UNEMP should be associated with a lower level of FFSE. The model allows for this gender bias variable to be associated with IMR, but it is 8

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excluded from the TFR equation. vii Abortion policy (ABORT). As noted by Winegarden and Bracy (1995), liberal abortion policies may reduce TFR by decreasing the number of unwanted pregnancies carried to term. ABORT is constructed as a binary variable equal to one in the case of liberal policies and equal to zero otherwise; coding of this variable is based on the information about abortion policy listed in Table 1. ABORT is excluded from the IMR and FFSE equations. Abortion policy may affect IMR and FFSE through a change in the fertility rate but is assumed to have no direct effect.

other words, we assume that fertility choices are influenced by labour market conditions only through the effect of these conditions on female employment or possibly through their effect on child health. We do not rule out the possibility that labour market conditions directly affect child health. For example, changes in the relative earning power of men and women could affect women's bargaining power in the home, and this could affect the proportion of resources devoted to childcare. One restriction implicit in systems (1–2) is that there are no country-specific time trends in any of the three development outcomes. In the results discussed below, two sets of estimates are reported: one including such trends and one excluding them. Breusch-Pagan LM tests indicate that there is significant heteroscedasticity in the model (p < 0.01 in all cases); therefore, standard errors that allow for heteroscedasticity are computed using a bootstrap. The residuals in the model are statistically significantly correlated with each other (p < 0.02 in all cases); therefore, the 3SLS estimator is preferred to the two-stage least squares. In most cases, the null that the endogenous regressors are in fact exogenous can also be rejected at the 1% level using a Hausman test; therefore, an ordinary least squares (OLS) estimator is inappropriate. Note also that the number of exclusion restrictions in systems (1–2) exceeds the number of parameters on endogenous regressors; the equations for IMR and TFR are over-identified, while the equation for FFSE is just-identified. It is therefore possible to compute Hansen tests for the validity of the over-identifying restrictions. The null of validity cannot be rejected at the 10% level in any version of the model. One potential concern with the estimation of systems (1–2) is that DYSMLV and WRR may be correlated with the error terms μ, and this would lead to inconsistent parameter estimates. In order to test for such correlation, we follow the procedure proposed by Winegarden and Bracy (1995). That is, we regress DYSMLV and WRR on all of the control variables in system (1) using OLS and then add the regression residuals to the three equations in the system. The parameters in this augmented model are then estimated using OLS to facilitate tests of the null that the parameters on the generated residuals are equal to zero. In no case can the null be rejected at the 10% level, which gives us some confidence that DYSMLV and WRR are orthogonal to the error terms μ. This method is also used to test for the correlation of ABORT with the error terms, and again, the null cannot be rejected in any case. A second concern is reverse causality, that is, the possibility that ML or abortion policies could respond to changes in the development outcomes. For example, reduced fertility may lead to extensions of ML at a later date. To test this, we re-estimated the system with 10-year leads (or alternatively, five-year or three-year leads) of the policy variables DYSMLV, WRR and ABORT on the right-hand side of the equations in system (1). If the parameters on the leading variables were significantly different from zero and had the opposite sign of the parameters on the contemporaneous variables, then there would be evidence for reverse causality. The results of this exercise are discussed in the appendix; generally, there is no evidence that reverse causality affects the results discussed below. A third concern is that changes in ML and abortion policy could be correlated with other factors influencing the development outcomes. Such factors could include the prevalence of breastfeeding, the quality of prenatal care, rates of immunization, civil conflict and the prevalence of HIV. In the next section, we report results from an augmented model that includes additional control variables to allow for such effects. Finally, it will be informative to discover whether there is any substantial heterogeneity in the effect of ML policies across countries. One way of identifying such heterogeneity is to augment the model with interaction terms for DYSMLV (or WRR) and the country-specific fixed effects. The next section also reports the results of such an exercise. In the appendix, we also explore whether the South Asian group of countries as a whole is significantly different from the other countries in the sample.

4. The model of development outcomes The empirical model comprises a three-equation system fitted by 3SLS. Henceforth, the acronyms denoting variables in the model will be italicised; the three dependent variables are IMR, FFSE and TFR. There are two versions of the basic model, as follows:

IMRjt = a1 Hj + a2 Tt + a3 DYSMLVjt + a4 TFRjt + a5 FSEjt +

i = 11 i = 6 ai

X ijt + µ1 jt FFSEjt = b1 Hj + b2 Tt + b3 DYSMLVjt + b4 TFRjt +

i=9 i i = 5 bi Y jt

TFRjt = c1 Hj + c2 Tt + c3 DYSMLVjt + c4 IMRjt + c5 FSEjt +

+ µ2 jt i = 10 i = 6 ci

Z ijt + µ3 jt (1)

IMRjt = a1 Hj + a2 Tt + a3 WRRjt + a4 TFRjt + a5 FSEjt + X ijt

+ µ1 jt

FFSEjt = b1 Hj + b2 Tt + b3 WRRjt + b4 TFRjt +

i=9 i i = 5 bi Y jt

TFRjt = c1 Hj + c2 Tt + c3 WRRjt + c4 IMRjt + c5 FSEjt + Z ijt

i = 11 i = 6 ai

+ µ3 jt

+ µ2 jt i = 10 i = 6 ci

(2)

Here, j indexes the 18 different countries in the panel; t indexes the 22 different years; H is a country-fixed effect; T is a time-fixed effect; the μ terms are structural residuals; and the a, b and c terms are parameters to be estimated. In system (1), DYSMLV appears in all three equations, while in system (2), WRR appears in all three equations. The two ML policy variables are correlated with each other, and with a sample of only about 400 observations, we do not attempt to estimate the effects of both together in a single model. Following the discussion in Section 3.3, X i ∈ {GDPPC, MEANAGE, MEANAGESQ, SPENDING, DIV, UNEMP}, Y i ∈ {GDPPC, SPENDING, DIV, DEP, UNEMP} and Z i ∈ {GDPPC, SPENDING, DIV, DEP, ABORT} denote the control variables in the model. As discussed in Section 2.4, IMR appears in the TFR equation but not in the FFSE equation; FFSE appears in both the IMR and TFR equations; and TFR appears in both the IMR and FFSE equations. The effect of TFR in the IMR equation is identified by the exclusion of DEP and ABORT from X i, and the effect of FFSE in the IMR equation is identified by the exclusion of DEP from X i. In other words, we assume that the old-age dependency ratio affects child health only through its effects on fertility and female employment. We contend that the burden of elderly relatives is largely a financial burden. Families might cope with this burden by having fewer children or by allocating female labour time to paid employment rather than to childcare, but these are the main channels through which the burden is likely to influence child health. We also assume that abortion policy influences the other development outcomes only through fertility choices, and the effect of TFR in the FFSE equation is identified by the exclusion of ABORT from Y i . The effect of IMR in the TFR equation is identified by the exclusion of MEANAGE, MEANAGESQ and UNEMP from Z i, and the effect of FFSE in the TFR equation is identified by the exclusion of UNEMP from Z i. In 9

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Table 3 Effects of paid maternity leave on fertility, female employment and infant mortality: results from 3SLS models. Source: authors' estimates based on data described in Table 2. (1)

(2)

(3)

System (1)

DYSMLV WRR

TFR GDPPC MEANAGE MEANAGESQ SPENDING DIV DEP UNEMP ABORT Constant R2 Hansen j test Hausman test (p-value) Breusch-Pagan test (p-value) N Country FE Year FE Time trends

(5)

(6)

(7)

System (1) with trends FFSE

TFR

IMR

FFSE

TFR

−0.005*** (0.002)

−0.144*** (0.045)

0.011*** (0.004)

−0.002* (0.001)

−0.119*** (0.037)

0.007 (0.005)

−0.012 (0.009) 0.849*** (0.196) −0.025*** (0.005) 4.898*** (1.594) −0.091*** (0.030) 0.015 (0.013) 0.157** (0.071) 0.095 (0.071) −67.035*** (21.892) 0.888 p = 0.568 41.81 (0.000) 51.15 (0.000) 396 Yes Yes No

10.405*** (3.736) 0.072 (0.074)

0.129 (0.297) 1.911 (1.529) −3.637*** (0.626) −2.009 (1.272)

−0.036 (0.506) 0.048 (0.032) 0.003 (0.013)

−0.009 (0.024) −0.201** (0.086) 0.221* (0.113)

−33.723 (24.500) 0.958 – 5.72 (0.017)

−0.133 (0.125) 5.158*** (1.952) 0.951 p = 0.112 9.56 (0.002)

396 Yes Yes No

396 Yes Yes No

−0.006 (0.009) 0.735*** (0.161) −0.023*** (0.005) 2.657* (1.470) −0.049* (0.027) 0.009 (0.012) 0.120** (0.060) 0.063 (0.074) −37.078* (20.608) 0.928 p = 0.667 37.91 (0.000) 56.97 (0.000) 396 Yes Yes Yes

9.464** (4.109) 0.116 (0.075)

0.087 (0.276) 1.438 (1.335) −3.407*** (0.633) −2.235 (1.425)

(8)

(9)

System (2)

IMR

IMR FFSE

(4)

0.576 (0.693) 0.032 (0.042) 0.013 (0.020)

−0.005 (0.022) −0.170* (0.090) 0.142 (0.162)

−35.400 (32.039) 0.963 – 1.43 (0.232)

−0.109 (0.094) 3.947* (2.087) 0.969 p = 0.268 5.21 (0.023)

396 Yes Yes Yes

396 Yes Yes Yes

(10)

(11)

(12)

System (2) with trends

IMR

FFSE

TFR

IMR

FFSE

TFR

−0.032 (0.127)

0.106 (0.222)

0.025** (0.010) 0.092 (0.577) 0.017 (0.022)

−0.013 (0.013)

0.288 (0.202)

0.018** (0.009) 1.037 (0.686) 0.005 (0.023)

−0.010 (0.182) 1.308 (5.085) −0.037 (0.026) 4.296 (20.258) −0.078 (0.404) −0.019 (0.144) 0.185 (0.743) 0.087 (0.579) −60.855 (267.820) 0.726 p = 0.100 20.59 (0.000) 63.31 (0.000) 396 Yes Yes No

10.844** (4.648) 0.086 (0.108)

−0.254 (0.355) 1.261 (1.188) −2.441*** (0.571) −3.970*** (1.185)

0.014 (0.013)

0.028 (0.021) −0.130* (0.070) 0.070 (0.074)

−57.062*** (19.065) 0.953 – 10.29 (0.000)

−0.090 (0.101) 3.710 (2.388) 0.964 p = 0.366 9.25 (0.003)

396 Yes Yes No

396 Yes Yes No

−0.008 (0.020) 0.843** (0.406) −0.025*** (0.007) 1.886 (2.450) −0.035 (0.045) −0.006 (0.017) 0.104 (0.074) 0.004 (0.100) −26.370 (34.634) 0.907 p = 0.100 18.48 (0.000) 67.15 (0.000) 396 Yes Yes Yes

6.552 (5.105) 0.194** (0.092)

−0.109 (0.346) 0.529 (1.083) −2.240*** (0.528) −4.417*** (1.204)

0.028* (0.016)

0.013 (0.019) −0.125* (0.069) 0.023 (0.076)

−51.129** (24.027) 0.969 – 11.56 (0.000)

−0.029 (0.104) 1.338 (2.848) 0.977 p = 0.187 0.46 (0.499)

396 Yes Yes Yes

396 Yes Yes Yes

Standard errors are in parentheses and are based on 200 bootstrap replications. *p < 0.10, **p < 0.05, ***p < 0.01.

5. Results

days of ML are associated with a reduction in the infant mortality rate of 1 in 10,000. This effect may seem relatively small, but in a country such as India, with around 18 million children born each year, a reduction in IMR of 1 in 10,000 equates to 1800 fewer infant deaths. The estimated coefficient of −0.119 in column 5 implies that an extra 50 days of ML are associated with a reduction in FFSE of just under six percentage points. This effect is large relative to the mean value of FFSE (22%), and it is one part of the explanation for the absence of any upward trend in female employment in Fig. 3. The results in Table 3 imply that, in the absence of the upward trend in DYSMLV, some rise in the female employment rate could be expected. However, any tendency towards a higher employment rate has been offset by the extension of ML. As noted in Section 2.2, one possibility is that, in the regulatory environments prevalent in most of the countries in the sample, extensions in ML discourage formal-sector employers from hiring women. The first row of results in columns 7–12 indicates that the only statistical effect of WRR is on TFR. Again, the estimated size of this effect is smaller when the model allows for country-specific trends. In the column 12 results (with such trends), the estimated coefficient of 0.018 implies that an increase in the wage replacement rate of 25 percentage points (i.e., just over two standard deviations of this variable) could be expected to raise fertility by 0.45 children per mother on average. It is worth noting that this result contrasts markedly with

5.1. How does paid maternity leave (ML) affect development outcomes? Table 3 includes 12 columns of results. Columns 1–6 relate to system (1) with DYSMLV, while columns 7–12 relate to system (2) with WRR. Columns 1–3 and 7–9 report parameter estimates from the model, excluding country-specific trends, while columns 4–6 and 10–12 report parameter estimates from the model, including these trends. Each individual column relates to parameter estimates in the equation for one particular development outcome (IMR, FFSE or TFR). The signs of the estimated parameters are generally the same in the with-trend results and without-trend results, but the trends are jointly statistically significant; therefore, the former are probably more reliable. The first row of results in columns 1–6 indicates that higher levels of DYSMLV (that is, extensions to the length of ML) are associated with lower IMR, lower FFSE and higher TFR. These effects are consistent with the hypotheses presented in Section 2. However, the estimated size of all three effects is smaller when country-specific trends are included in the model (columns 4–6), and the TFR effect in column 6 is not significantly different from zero at the 10% level. Therefore, we do not claim to have found robust evidence for an effect of DYSMLV on TFR. In column 4, the estimated coefficient of −0.002 implies that an extra 50 10

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results reported in Fallon et al. (2017): in Fallon et al.’s sample of countries, WRR has a significantly negative effect on TFR. One possible explanation for this difference is that the sample distribution of WRR in our study is very different from that in Fallon et al.’s. In our sample, WRR has a mean value of 95% and a minimum value of 67%; the standard deviation is 11%. In Fallon et al.’s sample, WRR has a mean value of 85% and a minimum value of zero; the standard deviation is 25%. Fallon et al.’s sample includes countries with very low levels of WRR that are excluded from our sample, and it is possible that the effect of WRR on TFR is non-monotonic. At relatively low initial levels of WRR, raising WRR reduces TFR, perhaps because the main effect of a higher WRR in this range is to increase job security (see the discussion in section 2.3). However, as WRR approaches 100%, the association becomes positive, perhaps because the main effect of a higher WRR in this range is to reduce the opportunity cost of childbearing. With a sample of countries larger than ours, and with more variation in WRR, it would be possible to test this hypothesis by fitting a quadratic function of WRR. However, the variation of WRR in our sample is too small to generate robust results from a non-linear model. Another striking feature of Table 3 is that the estimated effect of liberal abortion policies on fertility is small and insignificantly different from zero. The estimated effect varies across the different models but is typically close to −0.1, with a standard deviation of about 0.1. This implies that even at the lower 95% confidence interval, the effect is only about −0.3; that is, the countries with liberal abortion policies have a fertility rate that is lower by 0.3 children per woman on average. In the appendix, we show that there is some evidence for a significantly negative effect of liberal abortion policy on fertility in the South Asian group of countries, but this is a small subsample of countries in our sample, and we do not claim that the result is generalisable to other countries in the global south. Of all the interactions between the three dependent variables, only two effects are significantly different from zero: a higher level of TFR leads to higher levels of both IMR and FFSE. In column 4, the estimated coefficient of 0.735 implies that a reduction in the fertility rate of one child per woman can be expected to reduce the infant mortality rate by 0.735 children per thousand. In column 5, the estimated coefficient of 9.46 implies that a reduction in the fertility rate of one woman per child can be expected to reduce the female formal sector employment rate by 9.46 percentage points. This positive association between fertility and employment is another part of the explanation for the absence of any upward trend in employment in Fig. 3. The results imply that, without any reduction in fertility, there would be an upward trend in female employment. In the sample of countries in this study, one consequence of the success in reducing fertility rates (see Fig. 2) seems to have been a fall in female employment. One possible explanation is that, with lower fertility rates and smaller households, the reduced economic burden means that fewer families feel the need for women to work.

Table 4 includes summary statistics and data sources for the additional development outcomes: the prevalence of breastfeeding under six months, the percentage of women receiving prenatal care, DPT 1 and measles 1 immunization coverage, the number of deaths per year due to civil conflict or war, and the prevalence of HIV. Each of these outcomes is correlated with DYSMLV and WRR (more details available on request), and one concern is that the effects attributed to DYSMLV and WRR in Table 3 are in fact the consequence of effects of one or more of the additional variables. In order to explore this possibility, an augmented version of systems (1–2) was fitted to the data, including all of the additional variables in the IMR equation and battle-related deaths and HIV prevalence in the other two equations. Since breastfeeding prevalence, prenatal care and immunization are unlikely to affect female employment and fertility except through their effect on child health, these variables appear only in the IMR equation. Table 5 shows the resulting parameter estimates on DYSMLV, WRR and the additional variables; parameter estimates for the other control variables in the model are available on request. As might be expected, civil conflict leads to a significantly higher IMR and a significantly lower TFR. Conflict also raises FFSE; when men go off to fight, women replace them in the labour force. HIV prevalence is associated with significantly lower FFSE, but not with any statistically significant change in IMR or TFR. None of the other additional variables have a statistically significant effect on IMR. Conditional on these effects, DYSMLV's estimated effect on IMR and FFSE is still negative, and the estimated effect on TFR is still positive. The effect on FFSE is still significant at the 1% level, but this is not true of the effect on IMR. This should be noted as a caveat to our conclusions about the effect of ML duration on infant mortality, but note that the augmented IMR equation includes a large number of additional control variables related to child and maternal health, none of which have any statistically significant effect on infant mortality. The estimated signs of WRR's effects on IMR, FFSE and TFR in Table 5 are the same as those in Table 3. The TFR effect is still significant at the 5% level, and (unlike in Table 3) the FFSE effect is also statistically significant. A 10-percentage-point increase in WRR is estimated to raise FFSE by 5.46 percentage points. However, some caution should be attached to this result, as the significance of the effect depends on which control variables are included in the model. 5.3. Exploring cross-country heterogeneity in the effects The results in Table 3 show the association between changes in ML policy and changes in development outcomes on average across all of the countries in the sample. Table 6 shows the results from an augmented model in which the ML policy variables (DYSMLV and WRR) interact with country-fixed effects. The table shows only the estimated parameters on these interaction terms; other parameter estimates are available on request. It can be seen that none of DYSMLV's effects on IMR or TFR are significantly different from zero. Although DYSMLV has a statistically significant effect on IMR across all countries on average in Table 3, the sample is too small to identify any country-specific effects. In contrast, many of the country-specific effects that DYSMLV has on FFSE are significantly different from zero; some are positive and some are negative. In other words, the overall negative effect of DYSMLV on

5.2. Robustness checks In this section, we explore the robustness of the results in terms of inclusion of a wider range of development outcomes in the model, including breastfeeding, the quality of prenatal care, rates of immunization, civil conflict and the prevalence of HIV. Table 4 Summary statistics for additional variables.

Prevalence of breastfeeding under 6 months (% of children) Prenatal care (% of pregnant women) DPT1 immunization coverage for infants under 6 weeks (%) Measles1 immunization coverage for children under 1 year (%) Battle-related deaths per year Prevalence of HIV (% of women over 14)

N

Mean

S.D.

Source

396 396 396 396 396 396

41.03 73.64 88.31 79.64 712.13 32.39

19.83 22.86 11.37 17.12 2012.6 21.51

World Bank (2017) World Bank (2017) WHO (2017) WHO (2017) World Bank (2017) World Bank (2018)

11

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Table 5 Effects of paid maternity leave on fertility, female employment and infant mortality: results from extended 3SLS models. Source: authors' estimates based on data described in Tables 2 and 4 (1)

(2)

(3)

System (1) with trends

DYSMLV WRR Prevalence of breastfeeding Prenatal care DPT1 immunization coverage Measles 1 coverage Battle-related deaths Prevalence of HIV Constant R2 N Country Year Time trends

(4)

(5)

(6)

IMR

FFSE

TFR

−0.005 (0.007) 0.002 (0.001) 0.003 (0.004) 0.001 (0.002) 0.002 (0.002) 0.00002* (0.00001) −0.003 (0.009) −10.209 (20.438) 0.958 396 Yes Yes Yes

0.546*** (0.098)

0.020** (0.009)

0.00021* (0.00012) −0.292*** (0.074) −5.881 (14.695) 0.982 396 Yes Yes Yes

−0.00003* (0.00002) −0.002 (0.010) −2.856 (2.480) 0.970 396 Yes Yes Yes

System (2) with trends

IMR

FFSE

TFR

−0.001 (0.001)

−0.100*** (0.024)

0.003 (0.003)

0.001 (0.001) 0.001 (0.002) 0.001 (0.001) 0.002 (0.001) 0.00003*** (0.00001) −0.003 (0.006) −23.475 (16.971) 0.958 396 Yes Yes Yes

0.00037** (0.00017) −0.505*** (0.123) 10.655 (22.137) 0.977 396 Yes Yes Yes

−0.00005** (0.00002) 0.015 (0.013) −0.728 (2.116) 0.971 396 Yes Yes Yes

Standard errors are in parentheses and are based on 200 bootstrapped replications. *p < 0.10, **p < 0.05, *** p < 0.01. Notes: the models used to produce these results also include the variables in Table 3: GDPPC, MEANAGE, MEANAGESQ, SPENDING, DIV, DEP, UNEMP, ABORT, IMR, FFSE, and TFR.

FFSE in Table 3 masks considerable cross-country heterogeneity, and Hypothesis 2 holds for only a subset of countries. The countries with a large negative effect are Afghanistan, India, Laos, the Maldives, Nepal, Pakistan and Turkey. The countries with a large positive effect are Lesotho, Myanmar, Sri Lanka, Uganda and Vietnam. As seen in Table 1 and Table A1 of the appendix, both groups include countries with relatively high levels of FFSE (e.g., Laos PDR and Uganda) and high levels of DYSMLV (e.g., Turkey and Vietnam). Both groups also include countries with relatively low levels of FFSE (e.g., Afghanistan and Sri Lanka) and low levels of DYSMLV (e.g., Nepal and Lesotho). The reasons for the heterogeneity in the effects that DYSMLV has on FFSE are unclear and deserve further research. None of the country-specific effects that WRR have on IMR are statistically significant. There are some large and significantly positive effects on FFSE, but there are also some large and significantly negative effects, which reinforces the observations in the previous paragraph. All of the estimated effects on TFR are positive or very close to zero, and some are statistically significant. Most (but not all) of the largest effects are in African countries. Given the small sample size, there is not enough evidence to conclude that there is any substantial cross-country heterogeneity in the positive effect that WRR has on TFR.

absence of any overall increase in female formal-sector employment rates across the 18 countries, despite strong trends in other human development indicators. Indeed, the declining fertility rate observed across all countries in the sample may have contributed to the absence of any improvement in female formal-sector employment rates, as data analysis suggests that reductions in fertility are associated with reductions in employment, on average. One explanation for this effect is that a reduction in family size entails a lower financial burden on the household; therefore, there is less need for a mother to work. Although there is no robust evidence regarding the effect that the length of ML would have on fertility, there is evidence that increasing the level of leave payments leads to higher fertility rates. This effect is consistent with results in industrialised countries, where higher fertility rates might be viewed as beneficial and a reflection of women's choice to combine work with motherhood when adequate ML coverage is available. In developing countries, where fertility rates are well above two children per woman and populations continue to expand, the effect of leave payments on fertility may be more of a concern. The analysis reveals no substantial cross-country heterogeneity in the effects ML has on infant mortality or fertility, although the sample size is not large enough to conclude that such heterogeneity does not exist. There is evidence of substantial heterogeneity in the effects ML has on female formal-sector employment; despite the negative overall effect, there are positive effects in some individual countries. One possible explanation for these differences is that negative labour-demand effects dominate in some countries, while positive labour-supply effects dominate in others. However, the group of countries with positive effects is somewhat heterogeneous and includes both African and Asian countries at different levels of human development. The same is true of the group with negative effects. Discovering the conditions under which ML promotes female employment should be a focus for future research.

6. Conclusion Analysis of panel data from 18 African and Asian countries indicates that increases in the length of paid ML have been associated with reductions in infant mortality rates and in female formal-sector employment rates. The first of these results is encouraging (although the magnitude of the effect is moderate); the second is a cause for concern. One possible explanation for the negative association with employment is that the provision of ML reduces demand for female labour in the formal sector. In the absence of other policies encouraging employment of female workers, ML may have negative effects on their participation in the labour force. The negative effect is part of the explanation for the 12

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Table 6 Effects of paid maternity leave on fertility, female employment and infant mortality: country-specific results from 3SLS models. Source: authors' estimates based on data described in Table 2. (1)

(2)

(3)

(4)

System (1) without trends: DYSMLV effects

AFG BDG BTN IND KEN LAO LESO MDV MOR MYN NPL PAK LKA TUR UGA VIET ZAM ZIM Constant R2 N Country FE Year FE Time trends

(5)

(6)

System (2) without trends: WRR effects

IMR

FFSE

TFR

IMR

FFSE

TFR

0.000 (0.891) 0.001 (0.318) −0.003 (0.407) 0.004 (0.613) −0.013 (0.032) 0.002 (0.297) −0.007 (0.564) 0.004 (0.621) −0.006 (0.031) −0.001 (0.398) 0.007 (0.269) 0.000 (0.222) −0.004 (0.843) 0.016 (0.875) −0.009 (0.241) −0.007 (0.290) −0.001 (0.240) −0.002 (0.256) −7.979 (350.086) 0.964 396 No Yes No

−0.554*** (0.065) −0.053 (0.042) 0.007 (0.080) −0.237*** (0.050) −0.113* (0.066) −0.468*** (0.138) 0.529*** (0.071) −0.398*** (0.038) −0.121*** (0.045) 0.192*** (0.033) −0.198*** (0.048) −0.335*** (0.058) 0.252*** (0.056) −0.410*** (0.087) 0.189** (0.084) 0.199*** (0.057) −0.105* (0.058) 0.052 (0.046) 64.659*** (15.171) 0.961 396 No Yes No

0.020 (0.077) 0.000 (0.018) −0.005 (0.015) −0.002 (0.030) 0.031 (0.052) −0.016 (0.098) 0.018 (0.082) −0.004 (0.057) 0.023 (0.024) −0.004 (0.026) −0.025 (0.032) −0.004 (0.060) −0.006 (0.026) −0.040 (0.051) 0.039 (0.036) 0.014 (0.026) 0.016 (0.017) −0.001 (0.013) −10.091 (18.170) 0.939 396 No Yes No

−0.059 (0.337) −0.013 (0.135) −0.012 (0.086) −0.015 (0.177) −0.017 (0.060) −0.010 (0.071) −0.015 (0.095) −0.029 (0.203) −0.039 (0.160) −0.021 (0.089) −0.020 (0.157) −0.011 (0.059) −0.012 (0.126) 0.021 (0.244) −0.055 (0.223) −0.009 (0.048) −0.053 (0.206) −0.033 (0.130) −60.546 (360.151) 0.713 396 No Yes No

−0.707** (0.288) 0.033 (0.142) 0.094 (0.152) −0.127 (0.140) 0.064 (0.137) −0.062 (0.120) 0.306** (0.153) −0.374* (0.196) −0.144 (0.226) 0.185 (0.179) −0.103 (0.162) −0.192 (0.127) 0.464** (0.217) −0.210 (0.164) −0.073 (0.289) 0.418*** (0.114) −0.245 (0.292) 0.078 (0.212) 22.972* (13.477) 0.952 396 No Yes No

0.054*** (0.012) 0.012 (0.007) 0.011 (0.008) 0.015* (0.009) 0.006 (0.008) 0.010 (0.006) 0.010 (0.012) 0.030** (0.012) 0.032*** (0.008) 0.017* (0.010) 0.020** (0.009) 0.015** (0.007) 0.012 (0.015) −0.002 (0.021) 0.043*** (0.011) −0.001 (0.012) 0.046*** (0.009) 0.025** (0.010) 0.797 (1.762) 0.963 396 No Yes No

Standard errors are in parentheses and are based on 200 bootstrapped replications. *p < 0.10, **p < 0.05, ***p < 0.01. The models used to produce these results also include the variables in Table 3: GDPPC, MEANAGE, MEANAGESQ, SPENDING, DIV, DEP, UNEMP, ABORT, IMR, FFSE, and TFR. AFG: Afghanistan; BDG: Bangladesh; BTN: Bhutan; IND: India; MDV: Maldives; NPL: Nepal; PAK: Pakistan; LKA: Sri Lanka LAO: Laos; MYN: Myanmar; TUR: Turkey; VIET: Vietnam KEN: Kenya; LESO: Lesotho; MOR: Morocco; UGA: Uganda; ZAM: Zambia; ZIM: Zimbabwe.

Appendix A. Supplementary data

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