
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13031-9
10.1016/j.heliyon.2024.e37000
e37000
Research Article
Gender wage gap and child malnutrition in Ethiopia: A probit instrumental variable method☆
Lyu Wenyi lwy_columbia@sjtu.edu.cn
⁎
Yu Leng yuleng@sjtu.edu.cn

Lv Haihong lvhh@lzu.edu.cn

⁎ Corresponding author. lwy_columbia@sjtu.edu.cn
30 8 2024
15 9 2024
30 8 2024
10 17 e3700019 3 2024
22 8 2024
26 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Objective: Child malnutrition is a widespread concern in Sub-Saharan Africa. Previous studies mainly focus on the association between women's employment status and child malnutrition, however, the aim of this study is to examine the causal effect of household gender wage gap on child malnutrition in Ethiopia.

Methods: This study relies on a data set consisting of 2066 children under 5 years of age using 2018/19 Living Standards Measurement Study data for Ethiopia. A probit instrumental variable (IV) method is applied to determine the causal effect.

Results: Persistent gender wage gap of approximately 35% has been observed across various sectors in Ethiopia. Estimated results show that the decrease in household gender wage gap significantly enhances child growth outcomes, especially for younger girls and children in households with limited access to market. Specifically, one percentage point increase in gender wage gap is associated with a 0.74% (p<0.05) increase in the probability of stunting and a 0.42% (p<0.05) increase in the likelihood of wasting. Three mechanisms have been identified as contributing factors: more allocation of health resources to children, improved dietary diversity in the household, and increased household income.

Conclusions: Policy interventions aiming at improving the children nutrition status in Ethiopia are expected to narrow down gender wage inequality accordingly. Further research is needed to explore the association using reliable and large-scale data source in other countries.

Keywords

Household gender wage gap
Child malnutrition
Probit IV
Developing countries
Ethiopia
==== Body
pmc1 Introduction

Child malnutrition remains a critical issue in low and middle countries (LMICs), with a particular emphasis on the sub-Saharan African region [1], [2], [3]. The 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) revealed alarming statistics: approximately 37% of children under five years old were stunted, while around 7% were wasting. The economic implications of malnutrition are severe, as it undermines a country's economic prospects [4], [5] and diminishes household welfare by escalating child mortality rates and perpetuating inter-generational disadvantages [6], [7], [8], [9], [10]. Therefore, reducing the child malnutrition is of great significance for eliminating poverty and promoting economic growth. The determinants that could potentially influence child malnutrition include sanitation and hygiene practices, breastfeeding, socio-economic factors, cultural norms, public policies [11], [12], [13], [14]. Among these factors, persistent gender wage differences within households are a key factor that has not been adequately addressed in the literature.1

This paper fills the research gap by identifying the impact of gender wage gap on child malnutrition in Ethiopia, and elucidating several mechanisms that may link gender wage disparities to child nutritional conditions. Our work adds to the strand of the literature that relates to the relationship between child malnutrition and maternal employment, which has yielded mixed results dependent on the context. Evidence from Nepal suggests that improving women's empowerment is a pathway to better long-term child nutrition status in communities with limited production diversity [17]. Conversely, some scholars find negative correlation between maternal employment and child nutritional outcomes. They emphasize that working mothers, especially in low-paying or time-intensive jobs, would face constraints in providing optimal childcare, which exacerbates malnutrition risks among children [18], [19], [20].2 However, multivariate analyses of child malnutrition determinants in Bangladesh and Pakistan indicate that the maternal employment does not play a significant role in child malnutrition [21], [22].

The observed variations in the direction and magnitude of the association between maternal employment and child nutrition may result from the opposing decomposed effects of maternal employment: namely, income effects and substitution effects. Income effects refer to the potential benefits derived from a mother's earnings, which can enhance children's welfare through the purchase of nutritious foods, hiring of childcare providers, and fostering positive human development outcomes [23]. On the other hand, substitution effects denote the potential reduction in a mother's caregiving time for her family, which may in turn decrease breastfeeding duration, compromise the quantity and quality of hygienic foods, and reduce quality time dedicated to children's cognitive development [14]. If the substitution effect outweighs the income effect, a negative effects of maternal employment on children's nutritional conditions would have a higher probability of being observed. However, previous studies, which define maternal employment status solely based on whether women in the household are employed, do not provide clear evidence as to when the income effects might surpass the substitution effects. These studies may not accurately assess the overall effects of maternal employment on child nutrition outcomes, especially given the persistent gender wage differences even if women have participated in the labor market.

This study contributes to the related literature by considering the intrahousehold gender wage gap while investigating the relationship between child malnutrition and maternal employment in Ethiopia. Our findings suggest that the substitution effects could be compensated by enhancing income effects originated from less gender wage gap. This could potentially mitigate the negative impacts, or even result in positive effects, of maternal employment on children's nutritional status. In addition, most of the previous studies fail to deal with the potential endogeneity problem, which is fully addressed in this study.3 Moreover, this study supplements the series of empirical evidence on the association between maternal employment and child malnutrition, an area of research that has been particularly sparse in the context of Ethiopia.

2 Literature review and hypothesis

The relationship between child malnutrition and women's empowerment has been extensively studied by scholars across various domains of women's empowerment [24], [25], [26], [27], such as social support networks, workload and time availability, autonomy and control of household resources. Each of these aspects is characterized by unique indicators and measurement approaches [28], [12]. However, a prominent element of women's empowerment, gender wage gap, has received little attention in research exploring the impact of women's empowerment on child nutrition. Previous studies which delve into the implications of gender wage gap generally concentrate on how changes in the gap influence macro-level economic outcomes [29], [30], [31]. While a few studies have examined the impact of gender wage gap on household-level outcomes, they mainly concentrate on women's welfare and household welfare [32], [33]. This paper indicates that the benefits of changes in gender wage gap are not only limited to women's welfare but also have significant implications for children's welfare.4 Next, we propose three mechanisms which are tested subsequently in the empirical analysis based on existing literature.

The first mechanism pertains to parents' heterogeneous weights of care for children. Studies have shown that mothers often value children's healthcare more than fathers do. Therefore, as the gender wage gap narrows and women gain greater control over household resources, they are able to allocate more healthcare resources to children. This, in turn, can lead to an improvement in children's nutritional status [35], [36].

Hypothesis 1 More resource allocations to children in response to decreased gender wage gap improve children's nutrition status.

The second mechanism is related to the diet diversity within the household. On one hand, women may increase the diversification of food groups within the households when they own more decision-making power associated with decreasing gender wage gap [37], [38], [39], [40]. On the other hand, women with higher incomes are often better educated and possess more nutritional knowledge, which equips them to make healthful food choices and diversify food consumption within the households [41], [26], [42].

Hypothesis 2 Greater diet diversity in response to decreased gender wage gap improves children's nutrition status.

The last mechanism is associated with the household income. An increase in total household income driven by higher earnings from women enables households to purchase necessary resources from the market, thereby ensuring the nutritional wellbeing of children. [43], [14].

Hypothesis 3 Higher household income in response to decreased gender wage gap improves children's nutrition status.

3 Methods and materials

3.1 Data and variables

ESS Data and sample. The study relies on the secondary data from Ethiopia Socioeconomic Survey (ESS) administered under the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) of World Bank in collaboration with the Central Statistical Agency of Ethiopia.5 The survey collects data on household over the period 2011–2019 in four waves (2011/12, 2013/14, 2015/16 and 2018/19). The primary sampling unit is an enumeration area (EA), which is the particular region of Ethiopia in the charge of one or more census representatives (known as enumerators).6 The first wave of data collection in 2011/12 includes only rural and small town areas, whereas the sample is expanded to all urban areas since the second wave of data collection.7

The study primarily utilizes data from wave 2018/19 for the main regression, while data from wave 2015/16 is used for robustness checks. The survey data collected encompasses all the information we need for estimating the impact of gender wage gap within households on child malnutrition in the empirical analysis. Detailed information collected includes household demographics, children's anthropometric measurements, housing conditions, food and non-food consumption expenditure, and wealth conditions, etc. The survey also solicits community-level information on access to services, such as infrastructure and markets.

Child nutrition measures. Child malnutrition or growth conditions are measured using height-for-age (HAZ) and weight-for-height (WHZ) z-scores, constructed from anthropometric measures of children under five years of age obtained from the ESS data. These z-score indicators describe the number of standard deviations from 2006 WHO child growth standard, which allow us to compare individual data to the growth standard among children of different ages and genders [44]. In order to identify stunting (a long-term indicator of child nutritional status) and wasting (a short-term indicator of acute malnutrition) in children, WHO recommends a z-score cut-off point of -2. This recommendation is based on extensive epidemiological research, which indicates that children with z-scores below -2 standard deviations face significantly higher health risks, including increased morbidity and mortality rates [45], [46], [47]. A child with a height-for-age z-score less than -2 is classified as stunted, while one with a weight-for-height z-score less than -2 is defined as wasted. Table A1 presents the summary statistics for child growth outcomes. The average values of height-for-age z-score and weight-for-height z-score are -1.65 and -0.32, respectively, which indicate the prevalence of stunting and wasting growth among children under 5 years of age in Ethiopia until 2018.

Gender wage gap. This paper follows the approach developed by [48] to measure gender wage gaps within households. Accordingly, gender wage gaps are derived by determining the difference between the average earnings of male and female adult household members, with this difference then expressed as a percentage of the average earnings of male adult household members. The corresponding formula is as follows:(1) wage_gap=mean_earningsmale−mean_earningsfemalemean_earningsmale

The household member's income includes the total earnings from all types of work, such as working for employers, self-employment and agricultural activities.8 However, the household member's agricultural income can not be directly acquired from the data set when the household member engages in agricultural activities.

Inspired by [49], the net production of a certain crop type within a household is estimated by subtracting the total crop consumption of the household from the total crop production for that crop type. The income generated by the household from this specific crop type is then determined by multiplying the net crop production by the average local market price of the corresponding crop9 [50]. The total agricultural income of the household is ultimately derived from the summation of the household's income from all types of planted crops [51], [52].10 To allocate the income among household members based on their labor contributions, the total agricultural income is multiplied by the proportion of hours each household member dedicates to agricultural work.

Individuals surveyed may engage in various types of work simultaneously, such as full-time, part-time and temporary labor. The individual's total income accounts for the earnings from all types of work, and we do not discriminate between income derived from primary work or other work when developing our main regression results. Considering that primary work generally serves as a more stable and reliable income source for household members, we propose an alternative approach to calculating the gender wage gap. The approach is based on the household member's primary work type, determined by the survey question: “What is the main activity of the business where [NAME] works?”. By focusing on the main work type, we aim to provide a more precise measurement of the gender wage gap.

Independent variables. The variable of concern in our study is gender wage gap within households.11 The complex interactions between gender and wage dynamics are central to understanding the comprehensive effects of maternal employment and wage inequality within the household on child health outcomes. The choice of control variables relies on the previous theory and empirical literature based on data availability, including household characteristics and child characteristics. Child characteristics control for child age, child sex and parental education.12 Household characteristics contain basic household demographics and composition (household size, sex of head, region and urban), wealth indicators (land size, owned asset), housing features (available rooms, access to improved water resources and electricity), proximity to services (roads, market) [21], [54], [55], [56]. The summary statistics of these variables are reported in Table A1.

3.2 Probit method

Model specification. Considering the binary outcome variables of child nutrition status (stunting or wasting), linear probability models (LPM) are commonly used to estimate nonlinear response models [14]. An important concern is that households gender wage gap might be quite similar over time. If this gap remains relatively unchanged, it could lead to insufficient variation in our data, thereby resulting in statistically insignificant outcomes. Therefore, we apply the probit model to estimate the nonlinear relationship between gender wage gap and child malnutrition. The model is assumed to take the form:(2) Yijz={1,βGjz+γXijz+μz+ϵijz>00, otherwise 

where the idiosyncratic error term is normally distributed ϵijz∼i.i.dN(0,1), Yijz represents the indicator of whether the child i in household j of enumeration area z is stunting (wasting) or not. Gjz provides measures of gender wage gap within households and Xijz is a vector of control variables, such as household characteristics and child characteristics. μz is the dummy variable to control for the enumeration area fixed effects, and potentially time invariant variables including regional aggregated shocks, labor demand changes and spatial differences in infrastructure and policy. Under this specification, β characterizes the impact of gender wage gap on child malnutrition.

Potential endogeniety. The estimated impact of gender wage gap within households on child malnutrition might suffer from unobserved endogeneity for the following reasons. First, unobserved household characteristics, such as women's skills and healthcare access, could potentially introduce a selection bias in gender wage gap within households. Second, child malnutrition may affect gender wage gap due to reverse causality. Ignorance of these econometric issues could either overestimate or underestimate the real results of gender wage gap. Most of previous studies utilize the Ordinary Least Squares (OLS) method to estimate the impact of maternal employment on child health outcomes. While this method can yield unbiased estimations under the Gauss-Markov assumption, it can result in biased estimations if suffering from endogeneity issues. In such instances, a consistent estimation can be achieved using the Probit Instrumental Variable (IV) model with a large sample size dataset—a condition that is easily met in our study. Therefore, our paper adopts the Probit IV model to investigate the relationship between the gender wage gap and child malnutrition.

Previous studies have suggested several potential instruments for women's employment status, such as the local unemployment rate [57], and the cluster average of women's employment [58]. Women living in the same region, and facing the similar demographics, institutions and economic structures are likely to possess comparable human capital. As a result, they enter the same industries and earn similar wages [59], [60]. Specifically, women living in neighborhoods with less gender discrimination may have easier access to higher-paying job opportunities. Thus, we are able to employ the average village gender wage gap as the instrumental variable for gender wage gaps using the available data in LSMS.13 Table A4 shows the first stage regression results, and F-statistics are large enough to reject the weak instruments hypothesis.14

4 Results

4.1 Gender wage gap in the economic sectors of Ethiopia

As described in Table A1, the average gender wage gap for the sample data is around 35%, which implies that women earn 35% less than men on average. Fig. 1 shows the presence of little variation in gender wage gap across the four waves of LSMS by comparing average hourly wage of male with female in wage employment. Fig. 2 provides detailed information on the wage ratio of women to men by different sectors, which indicates that agriculture sector produces the largest wage gap, and the smallest gap remains in the public sector.Figure 1 Mean hourly wage of male and female.

Figure 1

Figure 2 Wage ratio and employment share, by industries.

Figure 2

To figure out the potential relationship between gender wage gap and child nutrition outcomes, we provide the scatter plot between the average gender wage gap and the child nutritional status in seven regions and two city administrations (Addis Ababa and Dire-Dawa) in Fig. 3. Fig. 3(a) shows the relationship between gender wage gap and the proportion of child stunting, while Fig. 3(b) provides the scatter plot between gender wage gap and the proportion of child wasting. A pattern of the positive correlation between average gender wage gap and child malnutrition is apparently observed with upward trends. However, more rigorous regression analysis is needed to substantiate the effect of gender wage gap on child malnutrition in the following sections.Figure 3 Mean gender wage gap and the proportion of malnutrition: (a) Stunt and (b) waste.

Figure 3

4.2 Gender wage gap and child malnutrition

In order to differentiate the impact of gender wage gap on child malnutrition across different working types, we employ a subsample that excludes households with members engaged in agricultural work or self-employment, in addition to the whole sample. Table 1 presents the estimated effects of gender wage gap on child malnutrition outcomes, obtained from the whole sample in panel A and the subsample in panel B. The results show that all coefficients are positive and statistically significant.15 For the whole sample, one percentage increase in the relative gender wage gap within the households is associated with 0.75% and 0.42% increase in the probability of child stunting and wasting, respectively. The results are still robust when we turn to the subsample.Table 1 Estimated impact of gender wage gap on child growth.

Table 1	Stunt	Waste	
(1)	(2)	(3)	(4)	
Panel A. Whole sample					
Gender wage gap	0.747** (0.303)	0.730** (0.301)	0.419** (0.213)	0.358* (0.190)	
Enumeration area FE	Yes	No	Yes	No	
Observations	2066	2066	2066	2066	


	
Panel B. Exclude agricultural work and self-employment	
Gender wage gap	0.734*** (0.264)	0.679** (0.339)	0.383*** (0.143)	0.360* (0.202)	
Enumeration area FE	Yes	No	Yes	No	
Observations	1845	1845	1845	1845	
Note: Regression analysis is presented in this table. All regressions include child and household characteristics. Panel A reports the estimated effects using the whole sample and Panel B reports the estimated effects using the sample excluding the households engaged in agricultural work and self-employment. Standard errors are clustered at the individual level. *p<0.10, **p<0.05, ***p<0.01. “Enumeration area FE” refers to the enumeration area fixed effects μz in equation (2).

Since probit IV model does not allow us to utilize time dimension information in our regression,16 we restrict our analysis to cross-sectional data from the 2018/19 wave. To ensure that our conclusion is not driven by any idiosyncratic shocks or policy changes in one particular year during the sample period, we re-estimate our model using LSMS-ISA data from wave 2015/16. The estimated results are shown in columns (3)-(4) of Table A3. The estimated coefficients are all significant, and the magnitude of the coefficients is similar to our baseline results using data from wave 2018/19, which provides evidence that the results are not driven by any time-varying idiosyncratic shocks affecting child nutrition status or gender wage gap.

4.3 Contextual factors in gender wage gap and malnutrition

Next, we discuss the heterogeneous effects of household gender wage gap on child malnutrition in terms of age and sex of the child, and access to market. To identify the heterogeneity, we establish the interaction terms between household gender wage gap and various contextual factors, such as age and sex of the child, and access to market. These interaction terms are then incorporated as independent variables into equation (2). Table 2 presents the estimated results.Table 2 Heterogeneous impacts of gender wage gap on child growth, by child's demographics and access to market.

Table 2Dependent variable	Stunt	Waste	
(1a)	(2a)	(3a)	(4a)	
Panel A: Sex and age of child					
Gender wage gap	0.067**	
(0.031)	

	0.174	
(0.305)	

	0.088*	
(0.045)	

	0.130	
(0.112)	

	
Gender wage gap⁎Girl	0.024*	
(0.014)	

	0.060*	
(0.035)	

	0.009*	
(0.005)	

	0.054	
(0.051)	

	
Gender wage gap⁎Children < 3years	0.075**	
(0.036)	

	0.019*	
(0.010)	

	0.041***	
(0.002)	

	0.089*	
(0.050)	

	
Enumeration area FE	Yes	No	Yes	No	
Observations	2066	2066	2066	2066	


	
	(1b)	(2b)	(3b)	(4b)	
Panel B: Access to market					
Gender wage gap	0.046**	
(0.021)	

	0.158*	
(0.085)	

	0.105**	
(0.040)	

	0.078**	
(0.039)	

	
Gender wage gap⁎DistancetoMarket	0.009**	
(0.004)	

	0.021	
(0.033)	

	0.013	
(0.065)	

	0.021	
(0.058)	

	
Enumeration area FE	Yes	No	Yes	No	
Observations	2066	2066	2066	2066	
Note: Regression analysis is presented in this table. All regressions include child and household characteristics. Panel A presents the heterogeneous impacts of gender wage gap on child growth by sex and age of child and panel B represents the heterogeneous impacts by access to market. Standard errors are clustered at the individual level. *p<0.10, **p<0.05, ***p<0.01. “Enumeration area FE” refers to the enumeration area fixed effects μz in equation (2).

Demographics. Panel A of Table 2 shows that the marginal effects of household gender wage gap on girls' nutrition conditions are more pronounced than those on boys. One percentage increase in the gender wage gap results in an additional 0.024% and 0.009% increase in the probability of stunting and wasting in girls compared to boys. However, previous studies find that Chinese parents tend to invest more in sons rather than daughters when faced with a narrowing gender wage gap. One potential explanation for the differential impact of gender wage gap based on child sex is the expectation of greater returns from investments in their sons' education by Chinese parents in terms of future remittances and old-age support [34]. The results in panel A further indicate that the impact of increase in household gender wage gap on the risk of stunting and wasting is significantly larger for children under 3 years old compared to those under 5 years old. By investigating age group of participating children, our results supplement [19]'s finding that timing within the child's first five years plays an important role in the potential malnutrition related with maternal employment.

Access to market. The study tests whether the impact of gender wage gap on child growth differs based on the household's access to market, determined by the survey question regarding the household's distance to the nearest market measured by road. The results in panel B of Table 2 reveal that decreasing household gender wage gap has a larger positive and significant effect on reducing child stunting and wasting among households that live away from the market. Combined with [9]'s and [61]'s findings which demonstrate that the effect of crop diversification on child malnutrition is stronger for children in households with limited market access, we may naturally infer that crop diversification and dietary diversity is a possible pathway through which gender wage gap makes a difference in improving child nutrition status. We further investigate this potential mechanism in the next section.

5 Discussion

To test the three hypotheses through which gender wage gap influences child malnutrition, we estimate equation (2) by replacing dependent variables using the appropriate variables.

The estimated results of Table 3 verify Hypothesis 1. The results imply that the health resources allocated to children are significantly and negatively associated with the household gender wage gap.17 One percentage increase in the relative household gender wage gap will result in 0.811% decrease in the probability of healthcare consultation and 0.464% decrease in the probability of child's enrolling into health insurance. Our results echo with [62]'s findings that children whose mothers have medium decision-making power are more likely to seek complete immunization for their children compared to those with low decision-making power using cross-sectional data from 26 countries in sub-Saharan Africa. To determine whether larger allocation of health resources to children resulting from decreasing gender wage gap has transformed into lower probability of stunting or wasting, we add the measurements of child health resources as independent variables into equation (2) and estimate the equation. The coefficients of the corresponding variables are all significant and negative, which substantiates that more allocation of health resources to children is one of the channels through which decreased gender wage gap contributes to less probability of child malnutrition.Table 3 Mechanisms I: allocation of health resources.

Table 3	Healthcare consultation	Health insurance	Stunt	Waste	
(1)	(2)	(3)	(4)	(5)	(6)	
Gender wage gap	-0.811*** (0.041)	-0.464*** (0.031)	0.620* (0.355)	0.633* (0.329)	0.838** (0.403)	0.781** (0.373)	
Healthcare consultation			-0.943* (0.483)		-0.864* (0.487)		
Health insurance				-0.422*** (0.135)		-0.229* (0.121)	
Enumeration area FE	Yes	Yes	Yes	Yes	Yes	Yes	
Observations	1483	1569	1483	1569	1483	1569	
Note: Regression analysis is presented in this table. The dependent variables in columns (1)-(2) are healthcare consultation and health insurance, respectively, and the dependent variables in columns (3)-(6) are the dummy variables of stunting and wasting. Standard errors are clustered at the household level. *p<0.10, **p<0.05, ***p<0.01. “Enumeration area FE” refers to the enumeration area fixed effects μz in equation (2).

To test Hypothesis 2, we adopt three variables to measure dietary diversity in the households, i.e., crop diversity, food diversity, and the times of meals outside.18 The results show a significantly positive relationship between reduction in gender wage gap and dietary diversity within the households measured by the three dimensions. Specifically, the regression result shows that one percentage increase in gender wage gap is associated with a decrease in food diversity by 0.942 percentage points. The results are consistent with previous studies that suggest empowered mothers are positively correlated with children's dietary diversity [63], [64].19 The significance of the coefficients in columns (4)-(9) of Table 4 further elucidates that the increased household's dietary diversity has enabled children to get access to more balanced dietary and necessary nutrition.Table 4 Mechanisms II: dietary diversity.

Table 4	Food diversity	Crop diversity	Meal outside	Stunt	Waste	
(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	
Gender wage gap	-0.942*** (0.082)	-0.874*** (0.081)	-0.053*** (0.014)	0.396*** (0.129)	0.399*** (0.129)	0.045** (0.019)	0.393*** (0.147)	0.239* (0.135)	0.241** (0.120)	
Food diversity				-0.144* (0.074)			-0.109*** (0.419)			
Crop diversity					-0.418*** (0.135)			-0.211** (0.095)		
Meal outside						-0.018* (0.010)			-0.111* (0.059)	
Enumeration area FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	
Observations	2066	1054	2038	2066	1054	2038	2066	1054	2038	
Note: Regression analysis is presented in this table. The dependent variables in columns (1)-(3) are food diversity, crop diversity and meal outside, respectively, and the dependent variables in columns (3)-(9) are the dummy variables of stunting and wasting. Standard errors are clustered at the household level. *p<0.10, **p<0.05, ***p<0.01. “Enumeration area FE” refers to the enumeration area fixed effects μz in equation (2).

The results in Table 5 show that households with less gender wage gap are typically accompanied with higher household income, which validates Hypothesis 3. One percentage increase in gender wage gap is associated with 0.218 percentage points decrease in household income, and one percentage increase in household income will thus reduce child stunting and wasting by 0.041 and 0.017 percentage points, respectively. These results coincide with the evidence from Canada, which suggests lower height percentiles for youth from low-income households [23].Table 5 Mechanisms III: household income.

Table 5	Household income	Stunt	Waste	
(1)	(2)	(3)	
Gender wage gap	-0.218*** (0.084)	0.071* (0.042)	0.057** (0.026)	
Household income		-0.041* (0.021)	-0.017 (0.053)	
Enumeration area FE	Yes	Yes	Yes	
Observations	2066	2066	2066	
Note: Regression analysis is presented in this table. The dependent variable in column (1) is household income, and the dependent variables in columns (2)-(3) are the dummy variables of stunting and wasting. Standard errors are clustered at the household level. *p<0.10, **p<0.05, ***p<0.01. “Enumeration area FE” refers to the enumeration area fixed effects μz in equation (2).

Most of previous empirical analysis finds that maternal employment status exhibits a higher substitution effect than the income effect on children's nutritional conditions in developing countries, which leads to higher probability of child malnutrition risk after women enter into labor market. The findings of this paper that narrowing gender wage gap may contribute to decreasing child malnutrition provide a new insight to understand the relationship between maternal employment and child nutrition status. The mechanisms discussed above provide clear evidence of increased income effects of maternal employment with narrowing gender wage gap, including more allocation of health resources to children, more dietary diversity, and greater household income. The substitution effect may be compensated by the enhanced income effects, leading to higher women's and children's welfare.

6 Policy implications

The findings of this study underscore the need for policy interventions that target gender wage equality as a strategic approach to reduce child malnutrition. Policymakers are encouraged to consider developing comprehensive strategies that not only address wage disparities but also support women in balancing employment and caregiving responsibilities, when designing various social protection measures to improve the nutritional outcomes for children, such as productive safety net programs. The analysis highlights the significance of eliminating women's constraints to accessing to decent work. Policymakers should provide specific skill training opportunities to women, and enhance their prospects for higher education. It is equally important and necessary for the governments to raise gender equality awareness among employers and eradicate gender discriminatory across employment opportunities. Moreover, the heterogeneous results imply the significance of infrastructure and local market development for the policymakers aiming to improve the overall child growth in local communities.

7 Conclusion

This study reveals that gender wage gap plays a significant role in determining the relative magnitude of income effects to substitution effects when assessing the impact of maternal employment on child growth. The estimated results also examine three mechanisms through which gender wage gap impacts child nutrition status, including increasing allocation of health resource to children, more dietary diversity in the household and higher household income. One concern about our findings is that the results are mainly estimated based on self-reported survey data, which may suffer from the subjectivity of surveyed individuals. Another limitation of the study is that the empirical results on the association between maternal employment and child malnutrition in Ehiopia may not necessarily imply the same situations in other developing countries. More work should be done to examine the generalizability of findings using data from other countries. Further research is required to verify the child nutrition enhancing effects of an actual policy intervention aiming at decreasing gender wage gap, and compare the nutrition impacts of decreasing gender wage gap with other policies and interventions, which would help to identify complementary strategies that synergy with the policy of reducing gender wage gap to contribute to the child growth.

CRediT authorship contribution statement

Wenyi Lyu: Writing – original draft, Visualization, Software, Methodology, Formal analysis, Data curation, Conceptualization. Leng Yu: Writing – review & editing, Supervision, Project administration. Haihong Lv: Resources, Investigation, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary material

The following is the Supplementary material related to this article.MMC

Appendix A contains additional tables of the article. Appendix B includes the robustness checks, and Appendix C provides the method of estimating vulnerability rate.

MMC

Data availability

Data have been publicly deposited at https://microdata.worldbank.org/index.php/catalog/3823/data-dictionary.

☆ Funding: This work was supported by the Gansu Province Joint Research Fund [grant numbers 23JRRA1499 ]; the Gansu Province Health Industry Research Project [grant numbers GSWSKY2023-13 ].

Appendix A Supplementary material related to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e37000.

1 The incomes of female are, on average, no more than two-thirds of that of male in Ethiopia and the gender wage gaps are mainly underpinned by unequal access to high-paying sectors [15], [16].

2 The underweight risk of children due to maternal employment may vary based on different population characteristics, such as employment sectors, timing within the child's first five years [19], poverty level of communities [18], birth intervals, and maternal educations [20].

3 Endogeneity problem is a common econometric issue in the scope of economics. Details about the reasons for endogeneity and the potential solutions are provided in Section 3.2.

4 Although [34] examine the association between gender wage gap and expenditure on children's education in China, they primarily pay attention to educational investment. In contrast, our paper focuses on the nutritional impact of gender wage gap on children, and suggests how decreased gender wage gap may affect child malnutrition.

5 Data source: https://microdata.worldbank.org/index.php/catalog/3823/study-description.

6 Wave 2018/19 covers all nine regional states (including Tigray, Afar, Amhara, Oromia, Somali, Benishangul Gumuz, Snnp, Gambela, Harar) and two administrative cities (Addis Ababa and Dire Dawa).

7 The sampling method is a two-stage stratified probability sample. Initially, enumeration areas are selected using simple random sampling for rural areas and systematically with probability proportional to size for urban areas; subsequently, households are chosen within these areas through systematic random sampling, targeting 10 agricultural and 2 non-agricultural households per rural EA.

8 We include all the households where both men and women have reported their income or their income could be estimated in the whole sample.

9 Multiplying the net crop production by the local market average price level of the corresponding crop converts the physical quantity of the crop into a monetary value [50].

10 Aggregating income from all types of crops ensures that the total agricultural income accurately reflects the household's diverse agricultural activities. This holistic approach is crucial because it accounts for the impact of variations in seasons and crop types on the overall financial health of the household [51], [52].

11 The variable reflects one particular domain of women's empowerment, providing a more nuanced view of maternal employment status by comparing women's earnings relative to men's within households [53].

12 The relationship between child malnutrition and child age may be nonlinear. More specific age period indicators are needed if we want to know the effects for each age period. However, our main concerned variable is gender wage gap, and we only need to include the continuous child age as control variable in our study.

13 We use alternative instruments as robustness checks in Appendix B.

14 The first stage equation involved in the probit IV model takes the form: Gjz=ϕZijz+μz+vijz, where Gjz is a measure of gender wage gap; Zijz is a vector of control variables including household characteristics, child characteristics as well as instrumental variables for gender wage gap. μz is the enumeration area fixed effects. vijz is the error term.

15 Columns (5) and (6) of Table A3 suggest that employing alternative definition of gender wage gap that only focuses on the individual's primary working type does not change our results. The details of other robustness checks, including alternative specification, measures of malnutrition, control of vulnerability rate, and migration are shown in the Appendix B.

16 Unlike the linear model, eliminating the unobserved fixed effects is more complex with respect to the nonlinear probit model, which is still pending resolution.

17 If the child in the household has checked up or consulted for other preventive care or the women in the household have prenatal checkups, healthcare consultation is equal to 1, otherwise it is 0. Health insurance equals to 1 if the child is enrolled in any type of health insurance, and 0 otherwise.

18 Previous literature documented that the diversity of crops planted by the rural households in Ethiopia is positively correlated with the food diversity in the households, thereby contributing to better child nutrition status [61].

19 [63] suggest that empowerment in its binary form, group membership, and ownership of assets are the three indicators positively and moderately related to children's dietary diversity in Timor-Leste (β = 0.3; P = 0.044), (β = 0.2; P = 0.040), and (β = 0.1; P = 0.047), respectively. Based on [64]'s review of 167 findings for 24 unique nutrition outcomes in South Asia, 40 (24%) are reported with positive/significant findings.
==== Refs
References

1 Gillespie Stuart van den Bold Mara Agriculture, food systems, and nutrition: meeting the challenge Glob. Chall. 2056-6646 1 3 2017 1600002
2 Nguyen Cuong Viet The effect of preschool attendance on children's health: evidence from a lower middle-income country Health Econ. 31 8 2022 1558 1589 35484690
3 Galvin Lauren Verissimo Cristiana K. Ambikapathi Ramya Gunaratna Nilupa S. Rudnicka Paula Sunseri Amy Jeong Joshua O'Malley Savannah Froese Yousafzai Aisha K. Sando Mary Mwanyika Effects of engaging fathers and bundling nutrition and parenting interventions on household gender equality and women's empowerment in rural Tanzania: results from effects, a five-arm cluster-randomized controlled trial Soc. Sci. Med. 324 2023 115869
4 Heltberg Rasmus Malnutrition, poverty, and economic growth Health Econ. 18 S1 2009 S77 S88 19294635
5 Nugent Rachel Levin Carol Hale Jessica Hutchinson Brian Economic effects of the double burden of malnutrition Lancet 395 10218 2020 156 164 31852601
6 Hoddinott John Maluccio John Behrman Jere R. Martorell Reynaldo Melgar Paul Quisumbing Agnes R. Ramirez-Zea Manuel Stein Aryeh D. Yount Kathryn M. The consequences of early childhood growth failure over the life course International Food Policy Research Institute Discussion Paper, Washington, DC 2011 1073
7 Black R.E. Victora C.G. Walker S.P. Bhutta Z.A. Christian P. de Onis M. Global nutrition report 2016: from promise to impact ending malnutrition by 2030 JSTOR 2016
8 Asfaw Abraham The inter-generational health effect of early malnutrition: Evidence from the 1983-85 Ethiopian famine Available at SSRN 2972977 2016
9 Lovo Stefania Veronesi Marcella Crop diversification and child health: empirical evidence from Tanzania Ecol. Econ. 0921-8009 158 April 2019 168 179
10 Ramírez-Luzuriaga María J. DiGirolamo Ann M. Martorell Reynaldo Ramírez-Zea Manuel Waford Rachel Stein Aryeh D. Influence of enhanced nutrition and psychosocial stimulation in early childhood on cognitive functioning and psychological well-being in Guatemalan adults Soc. Sci. Med. 275 2021 113810
11 Story Mary Kaphingst Karen M. Robinson-O'Brien Ramona Glanz Karen Creating healthy food and eating environments: policy and environmental approaches Annu. Rev. Public Health 29 2008 253 272 18031223
12 Cunningham Kenda Ruel Marie Ferguson Elaine Uauy Ricardo Women's empowerment and child nutritional status in South Asia: a synthesis of the literature Matern. Child Nutr. 1740-8709 11 1 2015 1 19
13 Victora Cesar G. Bahl Rajiv Barros Aluísio J.D. França Giovanny V.A. Horton Susan Krasevec Julia Murch Simon Sankar Mari Jeeva Walker Neff Rollins Nigel C. Breastfeeding in the 21st century: epidemiology, mechanisms, and lifelong effect Lancet 387 10017 2016 475 490 26869575
14 Hosen Md. Zobraj Pulok Mohammad Habibullah Hajizadeh Mohammad Effects of maternal employment on child malnutrition in South Asia: an instrumental variable approach Nutrition 105 2023 111851
15 Buehren Niklas Goldstein Markus Gonzalez Paula Hagos Adiam Kirkwood Daniel Paskov Patricia Poulin Michelle Raja Chandni Africa Gender Innovation Lab Ethiopia Gender Diagnostic: Building the Evidence Base to Address Gender Inequality in Ethiopia 2019 World Bank Washington, DC
16 Buehren Niklas Gonzalez Paula Copley Amy What Are the Economic Costs of Gender Gaps in Ethiopia? 2019 World Bank Washington, DC
17 Malapit Hazel Jean L. Kadiyala Suneetha Quisumbing Agnes R. Cunningham Kenda Tyagi Parul Women's empowerment mitigates the negative effects of low production diversity on maternal and child nutrition in Nepal J. Dev. Stud. 51 8 2015 1097 1123
18 Rastogi Saumya Dwivedi Laxmi Kant Child nutritional status in metropolitan cities of India: does maternal employment matter? Soc. Change 44 3 2014 355 370
19 Brauner-Otto Sarah Baird Sarah Ghimire Dirgha Maternal employment and child health in Nepal: the importance of job type and timing across the child's first five years Soc. Sci. Med. 224 2019 94 105 30771663
20 Naz Lubna Patel Kamalesh Kumar Uzoma Ifeoma Evan The prevalence of undernutrition and associated factors among preschool children: evidence from Pakistan demographic and health survey 2017–18 Child. Youth Serv. Rev. 119 2020 105579
21 Pulok Mohammad Habibullah Sabah Md Nasim-Us Enemark Ulrika Socioeconomic inequalities of child malnutrition in Bangladesh Int. J. Soc. Econ. 43 12 2016 1439 1459
22 Khan Sadaf Zaheer Sidra Safdar Nilofer Fatimi Determinants of stunting, underweight and wasting among children < 5 years of age: evidence from 2012-2013 Pakistan demographic and health survey BMC Public Health 19 2019 1 15 30606151
23 Mark Sean Lambert Marie O'Loughlin Jennifer Gray-Donald Katherine Household income, food insecurity and nutrition in Canadian youth Can. J. Public Health 103 2012 94 99 22530529
24 Santoso Marianne V. Kerr Rachel Bezner Hoddinott John Garigipati Priya Olmos Sophia Young Sera L. Role of women's empowerment in child nutrition outcomes: a systematic review Adv. Nutrition 10 6 2019 1138 1151
25 Heckert Jessica Olney Deanna K. Ruel Marie T. Is women's empowerment a pathway to improving child nutrition outcomes in a nutrition-sensitive agriculture program? Evidence from a randomized controlled trial in Burkina Faso Soc. Sci. Med. 233 2019 93 102 31195195
26 Melesse Mequanint B. The effect of women's nutrition knowledge and empowerment on child nutrition outcomes in rural Ethiopia J. Agric. Econ. 52 6 2021 883 899
27 Noghanibehambari Hamid Noghani Farzaneh Long-run intergenerational health benefits of women empowerment: evidence from suffrage movements in the US Health Econ. 2023
28 Branisa Boris Klasen Stephan Ziegler Maria Gender inequality in social institutions and gendered development outcomes World Dev. 45 2013 252 268
29 Aizer Anna The gender wage gap and domestic violence Am. Econ. Rev. 100 4 2010 1847 1859 25110354
30 Fogli Alessandra Veldkamp Laura Nature or nurture? Learning and the geography of female labor force participation Econometrica 79 4 2011 1103 1138
31 Cuberes David Teignier Marc Aggregate effects of gender gaps in the labor market: a quantitative estimate J. Hum. Cap. 10 1 2016 1 32
32 Krueger Dirk Perri Fabrizio On the welfare consequences of the increase in inequality in the United States NBER Macroecon. Annu. 18 2003 83 121
33 Danquah Michael Iddrisu Abdul Malik Boakye Ernest Owusu Owusu Solomon Do gender wage differences within households influence women's empowerment and welfare? Evidence from Ghana J. Econ. Behav. Organ. 0167-2681 188 August 2021 916 932
34 Wang Haining Cheng Zhiming Mama loves you: the gender wage gap and expenditure on children's education in China J. Econ. Behav. Organ. 188 2021 1015 1034
35 Sayer Liana C. Bianchi Suzanne M. Robinson John P. Are parents investing less in children? Trends in mothers' and fathers' time with children Am. J. Sociol. 110 1 2004 1 43
36 Björkman Nyqvist Martina Jayachandran Seema Mothers care more, but fathers decide: educating parents about child health in Uganda Am. Econ. Rev. 107 5 2017 496 500 29553628
37 Amugsi Dickson A. Lartey Anna Kimani-Murage Elizabeth Mberu Blessing U. Women's participation in household decision-making and higher dietary diversity: findings from nationally representative data from Ghana J. Health Popul. Nutr. 35 2016 1 8 26825275
38 Kassie Menale Fisher Monica Muricho Geoffrey Diiro Gracious Women's empowerment boosts the gains in dietary diversity from agricultural technology adoption in rural Kenya Food Policy 95 2020 101957
39 Baye Kaleab Laillou Arnaud Chitekwe Stanley Empowering women can improve child dietary diversity in Ethiopia Matern. Child Nutr. 2021 e13285
40 Bonis-Profumo Gianna Stacey Natasha Brimblecombe Julie Measuring women's empowerment in agriculture, food production, and child and maternal dietary diversity in timor-leste Food Policy 102 2021 102102
41 Abuya Benta A. Ciera James Kimani-Murage Elizabeth Effect of mother's education on child's nutritional status in the slums of Nairobi BMC Pediatr. 12 1 2012 1 10 22208358
42 Zheng Hongyun Ma Wanglin Guo Yanzhi Does nutrition knowledge training improve dietary diversity and nutrition intake? Insights from rural China Agribusiness 2023
43 Akee Randall Copeland William Costello E. Jane Simeonova Emilia How does household income affect child personality traits and behaviors? Am. Econ. Rev. 108 3 2018 775 827 29568124
44 WHO WHO global database on child growth and malnutrition https://platform.who.int/nutrition/malnutrition-database 2006
45 Karra Mahesh Subramanian S.V. Fink Günther Height in healthy children in low-and middle-income countries: an assessment Am. J. Clin. Nutr. 105 1 2017 121 126 28049661
46 Perumal Nandita Bassani Diego G. Roth Daniel E. Use and misuse of stunting as a measure of child health J. Nutr. 148 3 2018 311 315 29546307
47 Heemann Markus Kim Rockli Vollmer Sebastian Subramanian S.V. Assessment of undernutrition among children in 55 low-and middle-income countries using dietary and anthropometric measures JAMA Netw. Open 4 8 2021 e2120627 34383059
48 OECD Gender wage gap https://data.oecd.org/earnwage/gender-wage-gap.htm 2021
49 Singh R.B. Kumar Praduman Woodhead T. Smallholder farmers in India: Food security and agricultural policy 2002
50 Castagnini Raffaella Menon Martina Perali Federico Extended and full incomes at the household and individual level: an application to farm households Am. J. Agric. Econ. 86 3 2004 730 736
51 Ellis Frank Rural Livelihoods and Diversity in Developing Countries 2000 Oxford University Press
52 Reardon Thomas Berdegué Julio Barrett Christopher B. Stamoulis Kostas Household Income Diversification into Rural Nonfarm Activities 2007 Johns Hopkins University Baltimore, Maryland, USA
53 Blau Francine D. Kahn Lawrence M. The gender wage gap: extent, trends, and explanations J. Econ. Lit. 55 3 2017 789 865
54 Kim Rockli Mejia-Guevara Ivan Corsi Daniel J. Aguayo Víctor M. Subramanian S.V. Relative importance of 13 correlates of child stunting in South Asia: insights from nationally representative data from Afghanistan, Bangladesh, India, Nepal, and Pakistan Soc. Sci. Med. 187 2017 144 154 28686964
55 Rashad Ahmed Shoukry Sharaf Mesbah Fathy Does maternal employment affect child nutrition status? New evidence from Egypt Oxf. Dev. Stud. 47 1 2019 48 62
56 Katoch Om Raj Determinants of malnutrition among children: a systematic review Nutrition 96 2022 111565
57 Anderson Patricia M. Butcher Kristin F. Levine Phillip B. Maternal employment and overweight children J. Health Econ. 22 3 2003 477 504 12683963
58 Lenze Jana Klasen Stephan Does women's labor force participation reduce domestic violence? Evidence from Jordan Fem. Econ. 23 1 2017 1 29
59 Kandpal Eeshani Baylis Kathy The social lives of married women: peer effects in female autonomy and investments in children J. Dev. Econ. 140 2019 26 43
60 Bandiera Oriana Buehren Niklas Burgess Robin Goldstein Markus Gulesci Selim Rasul Imran Sulaiman Munshi Women's empowerment in action: evidence from a randomized control trial in Africa Am. Econ. J. Appl. Econ. 12 1 2020 210 259
61 Tesfaye Wondimagegn Crop diversification and child malnutrition in rural Ethiopia: impacts and pathways Food Policy 113 2022 102336
62 Seidu Abdul-Aziz Ahinkorah Bright Opoku Ameyaw Edward Kwabena Budu Eugene Yaya Sanni Women empowerment indicators and uptake of child health services in sub-Saharan Africa: a multilevel analysis using cross-sectional data from 26 countries J. Public Health 44 4 2022 740 752
63 Obisesan Adekemi Awolala David Crop diversification, productivity and dietary diversity: a gender perspective Rev. Agric. Appl. Econ. 24 1 2021 98 108
64 Kumar Neha Scott Samuel Menon Purnima Kannan Samyuktha Cunningham Kenda Tyagi Parul Wable Gargi Raghunathan Kalyani Quisumbing Agnes Pathways from women's group-based programs to nutrition change in South Asia: a conceptual framework and literature review Glob. Food Secur. 17 2018 172 185
