Does improving vulnerable employment contribute to maternal and infant health: The mediating role of urbanization and gender gap analysis

Does improving vulnerable employment contribute to maternal and infant health: The mediating role of urbanization and gender gap analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Does improving vulnerable employment contribute to maternal and infant health: The mediating role of urbanization and gender gap analysis Chang Liu, Yuting Qin, Shundan Feng, Lijuan Cui, Weixue Yin, Dongxue Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9101763/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study uses transnational panel data from 166 countries between 1992 and 2019 to examine the relationship between the vulnerable employment rate, maternal mortality, infant mortality, and the mediating role of urbanization. Methods The World Bank database offers data on the vulnerable employment rate, maternal mortality rate, infant mortality rate, and urbanization levels. Panel regression is used to analyze the relationship between the vulnerable employment rate and maternal and infant mortality rates for each country, as well as the mediating effect of urbanization levels, while considering economic, demographic, and environmental factors. This paper also applies system GMM estimation and 2SLS estimation to handle potential endogeneity issues and determine causal links. Additionally, since male vulnerable employment mostly consists of own-account workers, whereas female vulnerable employment often involves contributing family workers, the study adjusts the regressions of male and female vulnerable employment rates on maternal and infant mortality rates to compare how gender disparities in vulnerable employment influence maternal and infant health. Finally, data are regressed based on each country's human development index level to examine heterogeneity. Results Overall, a significant positive correlation exists between the vulnerable employment rate and maternal and infant mortality rates (β = 3.116, 0.450, P < 0.01), with urbanization levels acting as a mediating factor (β = -2.643, -0.346, P < 0.01). Gender analysis shows that for every unit decrease in the male vulnerable employment rate, maternal mortality rate decreases by 0.357 units and infant mortality by 0.403 units. Conversely, for every unit decrease in the female vulnerable employment rate, maternal mortality rate decreases by 0.1 units and infant mortality rate by 0.243 units. Heterogeneity analysis reveals that in countries with low human development levels, there is a positive correlation between the vulnerable employment rate and infant mortality rate(β =0.574, P 0.5). Conclusions A decline in the vulnerable employment rate is conducive to reducing maternal and infant mortality rates. As the vulnerable employment rate decreases, urbanization levels increase, which in turn can effectively lower the maternal mortality rate and infant mortality rate. A gender analysis indicates that improving male vulnerable employment is more beneficial for maternal and infant health. In countries with low levels of human development, maternal mortality is associated with various other factors.This study advocates for paying attention to the impact of the gender gap in vulnerable employment on maternal and infant health and recommends increasing investment in healthcare systems in countries with low human development levels to achieve greater health benefits, thus ultimately fulfilling the United Nations Sustainable Development Goals. Vulnerable employment maternal mortality infant mortality urbanization gender gap Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Maternal and infant health is a major public health issue of global concern[1]. Since 2016, the decline in the global maternal mortality ratio (MMR) has significantly slowed, with approximately 260,000 women dying from pregnancy or childbirth-related complications in 2023. Similarly, the decrease in the infant mortality ratio (IMR) has markedly stagnated since 2015, with an estimated 2.3 million newborn deaths in 2022. At this current rate of progress, about four-fifths of countries will fail to reach the World Health Organization (WHO)’s maternal mortality reduction target by 2030, and roughly one-third will struggle to achieve the newborn mortality reduction goal. Notably, many of these deaths are preventable[2, 3]. As a result, maternal and neonatal health issues have consistently been a central concern in modern research endeavors. Employment is a significant factor affecting health[4]. Vulnerable employment, as a critical employment category, encompasses own-account workers and contributing family workers[5], embodying the most unstable and high-risk job types. Characteristics of vulnerable employment include income fluctuations and a lack of formal arrangements and social security[6]. Individuals in these roles are least likely to have statutory social insurance or access to social protection and safety nets. Research indicates that self-employment is detrimental to health[7]. However, research specifically examining the impact of vulnerable employment on MMR and IMR has yet to be found. Therefore, this paper focuses on the influence of vulnerable employment on maternal and infant health, exploring whether improvements in vulnerable employment can contribute to better maternal and infant health outcomes. Urbanization is the process whereby rural populations transition into an urban population[8], often accompanied by economic growth and industrial restructuring. In the context of vulnerable employment, a high proportion of own-account workers may indicate a large agricultural sector and slow growth in the formal economy. Conversely, a significant proportion of contributing family workers signals weakened economic development, limited employment growth, and a larger rural economy. Consequently, the reduction of vulnerable employment typically leads to a diminishment of the agricultural sector, with labor shifting from agriculture to industrial and service sectors. The concentration of industry and services further attracts population to urban areas, thereby driving urbanization[9, 10]. Studies have demonstrated correlations between urbanization levels and various health outcomes. Hence, this research hypothesizes that vulnerable employment may influence maternal and infant health through levels of urbanization. This paper aims to investigate the impact of vulnerable employment on maternal and infant health using long-term cross-national data, while exploring the mediating role of urbanization in controlling for complex factors such as economic conditions, population density, and environmental pollution. Additionally, the paper will explore whether gender differences in vulnerable employment lead to varying impacts on maternal and infant health. Finally, it will analyze how changes in employment among vulnerable groups influence maternal and infant health in countries with varying levels of human development. This research will raise global awareness of vulnerable employment, supporting the development of effective labor market and health policies to help reduce MMR and IMR to meet the United Nations Sustainable Development Goals (SDGs). Methods Study design The present study employs cross-national panel data to examine the relationship between the vulnerable employment rate and MMR and IMR across various countries from 1992 to 2019. Additionally, it investigates the mediating role of urbanization levels in this relationship. The data for this paper is sourced from the World Bank and the International Labour Organization (ILO). Countries and regions lacking sufficient data were excluded, leaving a sample of 166 nations and territories. For variables such as population density and GDP per capita with occasional missing values, backward filling was applied to complete the dataset. Additionally, all continuous variables are winsorized at the 1st and 99th percentiles to mitigate the impact of outliers, which may result from reporting errors, measurement inconsistencies, or specific socio-economic shocks, thereby enhancing the robustness and representativeness of the estimated coefficients. Index selection and data source Dependent variables This paper uses the maternal mortality rate to reflect maternal health. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. This paper uses the infant mortality rate to reflect maternal health. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year. The World Bank database provides data on MMR and IMR. Independent variable This paper employs the vulnerable employment rate to represent the status of such employment. Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment. Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment. The data is sourced from the ILO. Mediator variable The urbanization rate serves as a key indicator of the level of urbanization. This paper measures the urbanization rate by the urban population ratio to each country’s total population. The World Bank database provides this data. Covariates Given the complex array of factors influencing MMR and IMR, this paper, in conjunction with relevant studies, identifies that mortality rates are affected by economic, demographic, and environmental factors[11, 12]. As the study employs long-term cross-national data, we selected representative indicators from these three macro fields as control variables to prevent the substantial absence of micro-level factors from impacting the outcomes( Table 1 ). The data is sourced from the official World Bank database. This study selected GDP per capita as a representative indicator to measure economic status. A higher GDP per capita generally signifies a higher standard of living, encouraging greater investment in health, leading to reduced mortality rates[13]. At the same time, society has wider health coverage, allowing women and children to receive timely and effective diagnoses and treatments at well-equipped medical facilities within a comprehensive healthcare system, which can enhance maternal and child health[14]. This study applied a logarithmic transformation to the GDP per capita data. This study selected population density as the representative indicator of demographic status. Population density reflects the degree of population concentration in an area and the spatial distribution of medical resources. Population density has a significant interaction with sanitation; higher population density and poorer sanitary conditions lead to more severe health damage[15]. Some scholars also indicate that areas with higher population density typically have better access to healthcare services, particularly with a positive impact on maternal and infant health service coverage[16]. This study applied a logarithmic transformation to the population density data. PM2.5 air pollution has been selected as the representative indicator of environmental pollution for this study. Air pollution is a significant risk factor affecting population health[17], particularly as pregnant women and infants are most sensitive to its adverse effects. Overall evidence suggests that air pollution increases the risk of mortality for mothers and children. The strongest evidence indicates that air pollution escalates the risk of mortality among mothers and children[18] Hence, regulating environmental risk factors is crucial to this research. Table 1 Variable definitions and sources Indicator Definition Unit Source GDP per capita GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. current U.S. dollars World Bank Population density Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes. people per square kilometer of land area World Bank PM2.5 air pollution, mean annual exposure Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation’s population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas. micrograms per cubic meter World Bank Data analysis This study employs the statistical software Stata18.0 to conduct panel regression on the data, assuming constant control variables, and empirically examines the impact of the vulnerable employment rate on MMR and IMR. It compares the extent of impact of the gender gap in vulnerable employment on maternal and infant health using the standardized regression coefficient and investigates the role of urbanization as a mediator. Additionally, it checks for multicollinearity among the model's independent variables and conducts a VIF test. To estimate these relationships, the study adopts a two-way fixed effects model, including country fixed effects and time fixed effects, controlling for unobserved heterogeneity between countries and common shocks over time. The Hausman test (p = 0.000) confirms that the assumption of random effects does not hold in the data, indicating that the fixed effects method is indeed superior to random effects or mixed OLS models. To resolve potential endogeneity concerns, we employed system GMM estimation and 2SLS estimation on the dataset. Finally, heterogeneity analysis was performed using grouped regression methods based on levels of human development. Results Descriptive statistics Distribution analysis of vulnerable employment This survey includes 166 countries, categorized according to the WHO regions, with 44 countries in Europe (26.5%), 30 in the Americas (18.1%), 19 in the Western Pacific (11.4%), 19 in the Eastern Mediterranean (11.4%), 10 in South-East Asia (6.1%), and 44 in Africa (26.5%). Based on the analysis of the distribution of the vulnerable employment rate across regions from 1992 to 2019, the median value in the African region is at the highest level, with a relatively inconsistent data distribution. The European region has the lowest median, with a more concentrated data distribution, as shown in Figure 1 . Distribution analysis of maternal mortality and infant mortality From 1992 to 2019, the distribution and trends of MMR and IMR across regions were studied. The study results indicate that data distribution across Africa is inconsistent, with the median MMR and IMR at the highest levels. Compared with Europe, the data distribution is more concentrated, and the median MMR and IMR are at the lowest levels, as illustrated in Figure 2 . Distribution analysis of the relationship between vulnerable employment and maternal mortality and infant mortality We further grouped the 166 countries by WHO regions and levels of human development to analyze the relationship between vulnerable employment rate and maternal and infant mortality rates, as shown in Figure 3 and Figure 4 . The study shows that MMR and IMR are lower in European and American regions, and that the vulnerable employment rate is lower as well. In the African region, both MMR and IMR are higher, as is the vulnerable employment rate. In Southeast Asia, the Eastern Mediterranean, and the Americas, a higher MMR is often accompanied by a higher vulnerable employment rate. Countries with high and very high levels of human development have low MMR and IMR and a low vulnerable employment rate. Countries with low and medium levels of human development have high MMR and IMR, as well as a high vulnerable employment rate. Panel regression model results Firstly, to identify the best panel model, F-tests, LM tests, and Hausman tests were conducted on the study's variables, with the results shown in Table 2. The results suggest that employing a two-way fixed-effects model for panel regression is the most suitable, effectively controlling for time-invariant confounding factors at both the country and temporal levels. Additionally, the VIF test was conducted, revealing that the average of the variance inflation factors was below 10, indicating no multicollinearity. Table 2 Panel model category test results Maternal mortality Infant mortality statistics P-value statistics P-value F-test 120.78 <0.001 101.19 <0.001 LM-test 37482.70 <0.001 29246.17 <0.001 Hausman-test 341.08 <0.001 712.78 <0.001 Table 3 Fixed effect model vulnerable employment regression coefficient tablepresents the estimation results based on the two-way fixed-effects model, showing that a decrease in the vulnerable employment rate positively affects the reduction of MMR and IMR. The model indicates a positive correlation between the vulnerable employment rate and maternal and infant mortality rates (P<0.01). Specifically, for each unit decrease in the vulnerable employment rate, MMR drops by 3.116 units, while IMR declines by 0.450 units. Table 3 Fixed effect model vulnerable employment regression coefficient table Variables Maternal mortality Infant mortality Vulnerable employment 3.116*** 0.450*** (7.02) (11.39) PM2.5 air pollution, mean annual exposure -0.580* -0.098*** (-1.74) (-3.32) GDP per capita -22.056*** -6.248*** (-4.92) (-15.67) Density of population -260.573*** -30.816*** (-21.86) (-29.09) N 4648 4648 R 2 0.333 0.635 Adj. R 2 0.30 0.62 *** p<0.01, ** p<0.05, * p<0.1 Table 4 Female vulnerable employment standardized regression coefficient table Variable Maternal mortality Infant mortality Vulnerable employment， female 0.100 *** 0.243 *** (0.039) (0.033) PM2.5 air pollution, mean annual exposure -0.032 * -0.053 *** (0.019) (0.016) GDP per capita -0.151 *** -0.361 *** (0.024) (0.020) Density of Population -1.270 *** -1.458 *** (0.056) (0.048) N 4648.000 4648.000 r2 0.326 0.340 r2_a 0.297 0.311 *** p<0.01, ** p<0.05, * p<0.1 Subsequently, the study performed standardized regression analyses on male and female vulnerable employment rates with regard to both MMR and IMR. The standardization eliminated variations in units and magnitudes, thereby enabling a comparative analysis of the effects of male and female vulnerable employment on maternal and infant health. Results shown in Table 4 and Table 5 reveal that for each unit decrease in the male vulnerable employment rate, the MMR decreases by 0.357 units and the IMR by 0.403 units. Similarly, a one-unit decrease in the female vulnerable employment rate results in a reduction of 0.1 units in MMR and 0.243 units in IMR. Comparing the standardized regression coefficients, it is evident that a reduction in male vulnerable employment rate has a more significant impact on reducing MMR and IMR (β=0.357 > 0.1, 0.403 > 0.243). Table 5 Male vulnerable employment standardized regression coefficient table Variable Infant mortality Vulnerable employment， male 0.357 *** 0.403 *** (0.035) (0.030) PM2.5 air pollution, mean annual exposure -0.090 *** -0.302 *** (0.024) (0.021) GDP per capita -1.169 *** -1.344 *** (0.056) (0.048) Density of Population -248.730 *** -29.688 *** (11.960) (1.064) N 4648.000 4648.000 r2 0.340 0.639 r2_a 0.311 0.623 *** p<0.01, ** p<0.05, * p<0.1 Intermediate effect test Initially, this study utilized the stepwise regression coefficient method to investigate whether urbanization serves as a mediator between the vulnerable employment rate and both MMR and IMR. Model 1, Model 2, and Model 3 collectively form the mediation effect equation set. In Table 6 and Table 7 , Model 1 is a regression model of the vulnerable employment rate with MMR and IMR as dependent variables. Results indicate that a reduction in the vulnerable employment rate favors a decrease in maternal and infant mortality (β=3.116, 0.450, P<0.01). Model 2 has urbanization as the dependent variable for regression against the vulnerable employment rate. The results in the table show that reducing the vulnerable employment rate is conducive to higher urbanization levels (β = -0.177, P < 0.01). Model 3 verifies the mediating role of urbanization level when the vulnerable employment rate drops, leading to reductions in MMR and IMR, with negative regression coefficients (β = -2.643, -0.346, P < 0.01), indicating a significant mediating effect of the Human Development Index. Table 6 Intermediate effect analysis(1) Variables Model 1 Model 2 Model 3 Maternal mortality Urban population Maternal mortality Vulnerable employment 3.116 *** -0.177 *** 2.669 *** (0.444) (0.014) (0.451) PM2.5 air pollution, mean annual exposure -0.580 * -0.038 *** -0.675 ** (0.333) (0.010) (0.333) GDP per capita -22.056 *** 0.676 *** -20.352 *** (4.486) (0.137) (4.485) Density of population -260.573 *** 5.893 *** -245.715 *** (11.917) (0.363) (12.230) Urban population -2.643*** (-5.39) N 4648 4648 4648 R 2 0.333 0.545 0.337 Adj. R 2 0.303 0.525 0.307 *** p<0.01, ** p<0.05, * p<0.1 Table 7 Intermediate effect analysis(2) Variables Model 1 Model 2 Model Infant mortality Urban population Infant mortality Vulnerable employment 0.450 *** -0.177 *** 0.388 *** (0.039) (0.014) (0.040) PM2.5 air pollution, mean annual exposure -0.098 *** -0.038 *** -0.111 *** (0.030) (0.010) (0.029) GDP per capita -6.248 *** 0.676 *** -6.014 *** (0.399) (0.137) (0.397) Density of population -30.816 *** 5.893 *** -28.774 *** (1.059) (0.363) (1.083) Urban population -0.346 *** (0.043) N 4648 4648 4648 R 2 0.635 0.545 0.640 Adj. R 2 0.619 0.525 0.624 *** p<0.01, ** p<0.05, * p<0.1 Secondly, to address the issue of weaker effects in the regression coefficient test of mediation, this paper uses the Sobel-Goodman test and the Bootstrap test to assess the mediating role of urbanization in the relationship between vulnerable employment rate, MMR, and IMR. The Sobel test, based on the theory of normal distribution, judges the establishment of mediation effects by testing the significance of the product of the mediation path coefficients. The Bootstrap test derives the empirical distribution of the indirect impact through 1000 sampling iterations, establishing a 95% confidence interval; if this interval does not include 0, it indicates a significant mediation effect( Table 8 and Table 9 ). Results show that a decrease in the vulnerable employment rate leads to higher urbanization levels, which in turn contribute to reductions in MMR and IMR. Table 8 Sobel test Maternal mortality Infant mortality B S.E Z P-value B S.E Z P-value Indirect effect -1.6691 0.1000 -16.6855 <0.001 -0.1259 0.0079 -16.0283 <0.001 Direct effect -0.5336 0.2076 -2.5701 <0.001 -0.0156 0.0173 -0.8991 <0.001 Total effect -2.2027 0.2133 -10.326 <0.001 -0.1415 0.0176 -8.0578 <0.001 Table 9 Bootstrap text Maternal mortality Infant mortality B S.E Z P-value LLCI ULCI B S.E Z P-value LLCI ULCI Indirect effect 0.4464 0.1069 4.17 <0.001 0.2368 0.656 0.0613 0.0115 5.35 <0.001 0.0389 0.08 Direct effect 2.6694 0.3684 7.25 <0.001 1.9474 3.38 0.3882 0.0494 7.86 <0.001 0.2914 0.485 Robustness test 2SLS estimation This study uses the instrumental variable method with two-stage least squares regression to address endogeneity issues. The paper employs the first lag of the vulnerable employment rate as the instrument variable. Initially, a regression is performed with the vulnerable employment rate as the dependent variable and the lagged vulnerable employment rate as the independent variable, resulting in residuals. These residuals serve as explanatory variables, replacing the vulnerable employment rate in the first stage of the regression, as shown in Table 10 . A decrease in the vulnerable employment rate significantly reduces MMR and IMR, thereby improving health outcomes. Specifically, for every unit decrease in the vulnerable employment rate, MMR decreases by 2.885 units, and IMR drops by 0.429 units, aligning with expectations and confirming the reliability of the results. Table 10 2SLS estimation results Variable First stage Second stage Second stage Vulnerable employment Maternal mortality Infant mortality L.Vulnerable employment 0.965 *** (0.004) Vulnerable employment 2.885 *** (0.461) 0.429 *** (0.041) Other variables control control control Constant term 2.622 *** (0.661) Anderson canon. corr. LM 3956.23 *** Cragg-Donald Wald F 47208.57 N 4482.000 4482.000 4482.000 r2 0.939 0.326 0.633 *** p<0.01, ** p<0.05, * p<0.1 System GMM estimation Secondly, to make the results of this paper more convincing, this study further employs the system GMM method for estimation[19]. The system GMM method is suitable for handling dynamic panel data with lagged dependent variables, allowing precise capture of dynamic relationships between variables. This paper uses the one-period lagged MMR and IMR as instrumental variables, as shown in Table 11 . AR(1)0.1, indicating no second-order serial correlation in the regression equation, thus satisfying the assumptions for using the system GMM method. Furthermore, the Hansen test P>0.1, indicating that the instrumental variables are valid and effective. As shown in the table, a decrease in the vulnerable employment rate leads to reductions in both MMR and IMR. Table 11 System GMM estimation results Variables (1) (2) Maternal mortality Infant mortality Lagged Maternal Mortality 0.991*** (0.0433) Lagged. Infant mortality 0.855*** (0.158) Vulnerable employment -0.460 0.759 (1.981) (0.625) Other variables control control Observations 4,482 4,482 AR(1) p-value 0.0501 0.0501 AR(2) p-value 0.289 0.289 Hansen test p-value 0.207 0.207 *** p<0.01, ** p<0.05, * p<0.1 Heterogeneity analysis The Human Development Index (HDI) has been published by the United Nations Development Programme since 1990. It serves as a standard to measure the socio-economic development of countries, categorizing them into four levels: very high, high, medium, and low[20]. The HDI is calculated based on three indicators: average life expectancy at birth, years of education (including mean years of schooling and expected years of schooling), and GNI per capita. This allows for comparisons between countries[21, 22]. MMR and IMR reflect a country's overall health status and development level; thus, the HDI can explain much of the variation in these rates across nations. Each component of the HDI is highly correlated with MMR and IMR, and excluding life expectancy from the HDI does not reduce its ability to predict these mortality rates[23]. Therefore, it is more reasonable to group countries by HDI levels when analyzing the impact of vulnerable employment on MMR and IMR, rather than only grouping by income group. Accordingly, the sample is divided into four groups according to HDI levels: low human development level, medium human development level, high human development level, and very high human development level. Twenty-two countries belong to the low human development group, thirty-nine countries to the medium human development group, forty-four countries to the high human development group, and sixty-one countries to the very high human development group. Firstly, the rate of vulnerable employment has a significant positive impact on MMR in medium, high, and very high human development level groups (β = 6.450, 1.315, 0.186, P < 0.01), achieving the greatest impact in medium human development level countries. However, in the low human development level group, this effect is not significant. The results are shown in Table 12 . Secondly, the vulnerable employment rate significantly affects IMR positively across all human development level groups (β = 0.574, 0.547, 0.192, 0.312, P < 0.01). Results are detailed in Table 13 , indicating overall that an increase in the vulnerable employment rate is associated with a rise in IMR, and that lowering the rate of vulnerable employment helps to decrease IMR. Table 12 Maternal mortality heterogeneous outcome Variables Maternal mortality Low human development Medium human development High human development Very high human development Vulnerable employment 0.933 6.450 *** 1.315 *** 0.186 *** (3.330) (1.340) (0.233) (0.060) PM2.5 air pollution, mean annual exposure -1.324 0.844 1.102 *** 0.115 * (1.510) (0.993) (0.167) (0.063) GDP per capita -4.079 -63.889 *** -14.686 *** -11.800 *** (18.317) (11.565) (3.167) (0.586) Density of population 387.223 *** -158.393 *** -52.278 *** -1.793 (110.134) (47.550) (13.111) (1.176) N 616 1092 1232 1708 r2 0.649 0.446 0.443 0.509 r2_a 0.617 0.408 0.407 0.481 *** p<0.01, ** p<0.05, * p<0.1 Table 13 Infant mortality heterogeneous outcome Variables Infant mortality Low human development Medium human development High human development Very high human development Vulnerable employment 0.574 *** 0.547 *** 0.192 *** 0.312 *** (0.220) (0.101) (0.040) (0.026) PM2.5 air pollution, mean annual exposure 0.087 -0.183 ** 0.142 *** -0.344 *** (0.100) (0.075) (0.028) (0.028) GDP per capita -2.341 * -5.511 *** -8.797 *** -5.014 *** (1.212) (0.869) (0.538) (0.256) Density of population -12.945 * -26.069 *** -15.757 *** 1.395 *** (7.287) (3.572) (2.228) (0.513) N 616.000 1092.000 1232.000 1708.000 r2 0.649 0.446 0.443 0.509 r2_a 0.617 0.408 0.407 0.481 *** p<0.01, ** p<0.05, * p<0.1 Discussion This study aims to analyze the relationship between the vulnerable employment rate and maternal and infant health, as well as whether urbanization serves as a mediating factor in this association. The study finds that a decline in the vulnerable employment rate overall has led to a reduction in MMR and IMR. Firstly, a decrease in the vulnerable employment rate can lead to an increase in stable income for this group. Numerous studies have indicated that income is a crucial determinant of infant survival, showing a significant correlation between income increases and reduced IMR[24, 25]. An increase in income can directly enhance a family's nutritional intake, housing, and sanitary conditions, thereby reducing direct fatal risks such as infant infections and malnutrition[26]. Secondly, a reduction in the vulnerable employment rate can expand social security coverage to more individuals, which has a timely and effective impact on lowering IMR. Zhuihui Li and other scholars argue that social security programs can directly alleviate economic pressures on families, improve infant growth environments, enhance accessibility to medical services, and make preventive and curative medical services more affordable[27], thereby decreasing IMR. Serván-Mori and other scholars in Mexico revealed that MMR is lower among women with social insurance compared to those without[28]. Other research has found that implementing health insurance in low and lower-middle-income countries, by increasing the use of maternal and reproductive health (MRH) services, especially antenatal care (ANC) and childbirth-related services, is directly linked to a reduction in MMR[29]. Furthermore, urbanization plays a mediating role in the relationship between the vulnerable employment rate and MMR and IMR. A lower vulnerable employment rate elevates urbanization levels, which can effectively reduce MMR and IMR. Vulnerable employment is concentrated in rural areas. Over three-quarters of rural employment worldwide is informal, and nearly half of informal workers are own-account workers[30]. A high proportion of vulnerable employment usually reflects an economy dominated by agriculture and informal sectors. The process of economic development essentially involves efficient formal sectors replacing informal sectors[31]; when vulnerable employment rates decrease, it reflects structural economic transformation, with employment concentrating in formal sectors. Given that the formal sectors are largely concentrated in cities[32, 33], employment shifts toward urban areas, driving rural populations to migrate and settle in cities, thereby promoting urbanization. Moreover, cities possess higher-quality medical facilities than rural areas, thereby improving maternal and infant health[34]. This study found that in countries with low human development levels, the vulnerable employment rate does not significantly reduce MMR, but significantly reduces IMR. This phenomenon arises from the fact that MMR is more dependent on a strong healthcare system, whereas IMR is further affected by various social determinants[3, 35]. Firstly, although the reduction of the vulnerable employment rate allows more people to be covered by social security, alleviating some poverty and increasing income, maternal health is perceived as secondary care[36], with the prevention and management of maternal mortality being more heavily linked to strong healthcare systems, quality emergency obstetric care, skilled medical personnel, hospital facilities, etc. According to a World Health Organization report, 95% of maternal mortality occurs in low-income and lower-middle-income countries[37], which are mostly countries with low human development levels. Maternal deaths often result from complications during pregnancy, childbirth, or postpartum periods, which can be treated in adequately staffed and equipped medical facilities[35, 38]. These countries lack high-quality medical facilities and obstetric care services, with a shortage of skilled health professionals[39]. The fragile health system prevents timely, high-quality prenatal and postnatal services, leading to treatment delays during maternal complications, significantly increasing avoidable maternal mortality[40, 41]. Especially in rural areas, the absence of fundamental health infrastructure and the considerable distance from the nearest hospital reduce access to healthcare services[42, 43]. Meanwhile, Mzembe, Tajvar, and other scholars indicate that expanding health service coverage and increasing maternal use of medical services can significantly reduce MMR in low human development countries[44, 45]. Secondly, although low human development level countries lack a strong healthcare system, infant mortality is not only related to the healthcare system but also to the social, economic, and environmental conditions of infants and their families, which influence infant survival and care conditions, thereby affecting infant mortality. A study in Africa demonstrates that enhancements in health facilities, in addition to health investments and economic growth, are closely related to declines in infant mortality[46]. A decrease in the vulnerable employment rate increases income, expands social security coverage, and improves the social, economic, and environmental conditions of infants, thus reducing infant mortality. Additionally, some research suggests that infant mortality reduction correlates with health expenditure per capita. Hence, although lowering the rate of vulnerable employment promotes income and increases health spending, it does not significantly impact maternal mortality[47, 48]. Yet, it can enhance conditions for infant survival and care, subsequently lowering infant mortality. Although the rate of vulnerable employment is quite similar between men and women, there is a significant gender gap in types of employment: men are more likely to be own-account workers, while women tend to be contributing family workers[6], Own-account workers generally have better circumstances than contributing family workers.[49]. Further analysis in this paper reveals that reductions in male vulnerable employment have a more noticeable impact on lowering MMR and IMR than comparable decreases among females. This can be explained by gender income differences and women's “double burden." Firstly, gender income inequality stands as a pervasive global issue, with women typically earning less than men for similar roles, even in many developed countries[50, 51]. Income from men's employment generally exerts a more direct influence on a family's overall economic capability. As a result, a decline in male vulnerable employment contributes to increased household income, which can be allocated to more prenatal check-ups and improved infant nutrition, improving access to quality healthcare and living conditions for mothers and infants, thus significantly reducing MMR and IMR. Secondly, when women transition from contributing family workers to formal employment, it does not necessarily lead to a decrease in domestic and childcare responsibilities. Instead, this transition often culminates in a "double burden," leading to greater stress and negatively impacting health[52, 53]. Therefore, although a decline in female vulnerable employment may help reduce MMR and IMR, the"double burden" of women undermines the positive impact that such reductions in female vulnerable employment could have on enhancing maternal and infant health. Based on the findings of this study, it is advised that governments optimize employment and social protection policies, broaden social security coverage, and enhance labor protection by thoroughly evaluating the circumstances of vulnerable employment, thus fostering more effective improvements in maternal and infant health. When formulating public policies in countries with low human development levels, it is essential to integrate interventions targeting vulnerable employment with efforts to improve the health system to better reduce MMR and IMR. Additionally, the study emphasizes that in efforts to decrease female vulnerable employment, the "double burden" faced by women in both career and domestic roles should be acknowledged. Strong policy support is essential for childcare services, infrastructure enhancements, and the promotion of equal opportunities in the labor market, helping women balance unpaid caregiving and paid work. It is also vital to develop suitable labor market policies to ensure women have sufficient time for breastfeeding. Finally, future research should focus on identifying the optimal balance paths between vulnerable employment and maternal and infant health across countries with varying levels of human development, aiming to significantly reduce MMR and IMR on a broad scale. Limitations There are several limitations inherent in this study. Firstly, the factors affecting MMR and IMR are very complex, this study does not thoroughly consider socio-cultural, legal, and political factors, apart from the economic, demographic, and environmental macro factors, which might also influence maternal and infant health outcomes. Secondly, data on the vulnerable employment rate is sourced from labor and household surveys, supplemented by official estimates and censuses in some countries. Due to differences in definitions and coverage across countries, there are some limitations in comparing cross-national and international data, which could impact the accuracy of the estimates. Conclusion The research demonstrates a statistically significant global correlation among vulnerable employment rate, MMR, and IMR, and the mediating effect of urbanization. As the vulnerable employment rate decreases, urbanization continues to rise, and MMR and IMR show a downward trend. This finding motivates countries to make efforts to further reduce vulnerable employment, particularly emphasizing sustained health system investments in countries with low human development levels, and paying attention to the varying impacts of male and female vulnerable employment on maternal and infant health, ultimately achieving a notable reduction in MMR and IMR, progress toward the SDGs for 2030. Abbreviations WHO: World Health Organization MMR: maternal mortality ratio IMR: infant mortality ratio SDGs: United Nations Sustainable Development Goals ILO: International Labour Organization Declarations Ethical approval and consent to participate We confirm that all methods were carried out according to relevant guidelines and regulations. We are not involved in experiments on humans and/or the use of human tissue samples. Ethics approval for the study protocol was obtained from the Ethics Committee of Harbin Medical University. Informed consent was obtained from all participants through online responses before the start of the survey. The Ethics Committee of Harbin Medical University approved the procedure for obtaining informed consent. Consent for publication Not applicable. Data availability The data is available in a public open access repository. The data used in the study is publicly available; These publicly available datasets may have been described in the manuscript. Competing interests The authors declare no competing interests. Funding This study was funded by the National Natural Science Foundation of China (72074064). The funding body had no influence on study design, data collection, data analysis, data interpretation or writing the manuscript. Author Contributions ZK took overall responsibility for the study design. CL, YQ, SF, LC were responsible for data analysis and manuscript writing. WX, WY, DW applied and obtained the research data. ZL, FX help with data interpretation and manuscript writing. LW made the charts and participated in the manuscript revision. All authors critically reviewed and revised the manuscript, and approved the final manuscript. Acknowledgements Thank you Professor Kang for your guidance on this article. Author details 1 Department of Social Medicine, School of Health Management, Harbin Medical University, Harbin, Heilongjiang Province 150081, China References WHO. World health statistics 2024: monitoring health for the SDGs, sustainable development goals. 2024. https://www.who.int/publications/i/item/9789240094703. Accessed 29 Jan 2026. WHO. Trends in maternal mortality 2000 to 2023: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2025. WHO. Newborn mortality. 2024. https://www.who.int/news-room/fact-sheets/detail/newborn-mortality. Accessed 7 Dec 2025. Marmot M, Friel S, Bell R, Taylor S. 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Global maternal mortality projections by urban/rural location and education level: a simulation-based analysis. eClinicalMedicine. 2024;72:102653. https://doi.org/10.1016/j.eclinm.2024.102653. WHO. Maternal mortality. 2025. https://www.who.int/news-room/fact-sheets/detail/maternal-mortality. Accessed 20 Dec 2025. Bokhari FAS, Gai Y, Gottret P. Government health expenditures and health outcomes. Health Econ. 2007;16:257–73. https://doi.org/10.1002/hec.1157. WHO. Trends in Maternal Mortality 2000 to 2020:estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization; 2023. UNFPA. Maternal health | united nations population fund. https://www.unfpa.org/maternal-health. Accessed 7 Jan 2026. WHO. Most countries make progress towards universal health coverage, but major challenges remain, WHO–world bank report finds. https://www.who.int/news/item/06-12-2025-most-countries-make-progress-towards-universal-health-coverage-but-major-challenges-remain-who-world-bank-report-finds. Accessed 30 Jan 2026. Mlotshwa PR, Sibiya MN. Pregnant women’s views regarding maternity facility-based delivery at primary health care facilities in the province of KwaZulu-natal in South Africa. Int J Environ Res Public Health. 2023;20:6535. https://doi.org/10.3390/ijerph20156535. Gunawardena N, Bishwajit G, Yaya S. Facility-based maternal death in western Africa: a systematic review. Front Public Health. 2018;6. https://doi.org/10.3389/fpubh.2018.00048. Tanou M, Kishida T, Kamiya Y. The effects of geographical accessibility to health facilities on antenatal care and delivery services utilization in Benin: a cross-sectional study. Reprod Health. 2021;18:205. https://doi.org/10.1186/s12978-021-01249-x. Essendi H, Johnson FA, Madise N, Matthews Z, Falkingham J, Bahaj AS, et al. Infrastructural challenges to better health in maternity facilities in rural Kenya: community and healthworker perceptions. Reprod Health. 2015;12:103. https://doi.org/10.1186/s12978-015-0078-8. Mzembe T, Chikwapulo V, Kamninga TM, Vellemu R, Mohamed S, Nthakomwa L, et al. Interventions to enhance healthcare utilisation among pregnant women to reduce maternal mortality in low- and middle-income countries: a review of systematic reviews. BMC Public Health. 2023;23:1734. https://doi.org/10.1186/s12889-023-16558-y. Tajvar M, Hajizadeh A, Zalvand R. A systematic review of individual and ecological determinants of maternal mortality in the world based on the income level of countries. BMC Public Health. 2022;22:2354. https://doi.org/10.1186/s12889-022-14686-5. Alemu AM. To what extent does access to improved sanitation explain the observed differences in infant mortality in Africa? Afr J Prim HEALTH Care Fam Med. 2017;9:1370. https://doi.org/10.4102/phcfm.v9i1.1370. Rana RH, Alam K, Gow J. Health expenditure, child and maternal mortality nexus: a comparative global analysis. BMC Int Health Hum Rights. 2018;18:29. https://doi.org/10.1186/s12914-018-0167-1. Nicholas A, Edward N-A, Bernardin S. The effect of health expenditure on selected maternal and child health outcomes in Sub-Saharan Africa. IJSE. 2016;43:1386–99. https://doi.org/10.1108/IJSE-08-2015-0199. Lo Bue MC, Le TTN, Santos Silva M, Sen K. Gender and vulnerable employment in the developing world: Evidence from global microdata. World Development. 2022;159:106010. https://doi.org/10.1016/j.worlddev.2022.106010. OECD. Gender equality in a changing world: taking stock and moving forward. OECD Publishing; 2025. https://doi.org/10.1787/e808086f-en. Blau FD, Kahn LM. The Gender Wage Gap: Extent, Trends, and Explanations. Journal of Economic Literature. 2017;55:789–865. https://doi.org/10.1257/jel.20160995. Bird CE. Gender, household labor, and psychological distress: the impact of the amount and division of housework. J Health Soc Behav. 1999;40:32–45. https://doi.org/10.2307/2676377. Killewald A, Gough M. Money isn’t everything: wives’ earnings and housework time. Soc Sci Res. 2010;39:987–1003. https://doi.org/10.1016/j.ssresearch.2010.08.005. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9101763","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611300660,"identity":"45c74ed2-af93-4b55-810f-b8f794d441ef","order_by":0,"name":"Chang Liu","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Liu","suffix":""},{"id":611300661,"identity":"96c0e892-4baf-44e1-95b4-53ab70165dfc","order_by":1,"name":"Yuting Qin","email":"","orcid":"","institution":"Harbin Medical 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2","display":"","copyAsset":false,"role":"figure","size":317779,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of maternal mortality and infant mortality rates by region, 1992–2019\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9101763/v1/1054878b307d127e86b1d59b.png"},{"id":105477179,"identity":"3a1285de-139c-46db-941d-e18436f15a01","added_by":"auto","created_at":"2026-03-26 13:06:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":922760,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between vulnerable employment rate and maternal mortality and infant mortality rates (by regional grouping)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9101763/v1/3990881234fb446c900c00bf.png"},{"id":105566198,"identity":"9ba07e58-f0c4-420c-afc9-278cecade0b4","added_by":"auto","created_at":"2026-03-27 12:55:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":866978,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between vulnerable employment rate and maternal mortality and infant mortality rates (by human development level)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9101763/v1/795dca0e5489c03d65826fa1.png"},{"id":107704589,"identity":"ddccc588-ab7e-4434-8aca-3a984b041823","added_by":"auto","created_at":"2026-04-24 08:50:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2937967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9101763/v1/ff1b1a27-cf6e-4b31-8a1d-1c998b2ad198.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does improving vulnerable employment contribute to maternal and infant health: The mediating role of urbanization and gender gap analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eMaternal and infant health is a major public health issue of global concern[1]. Since 2016, the decline in the global maternal mortality ratio (MMR) has significantly slowed, with approximately 260,000 women dying from pregnancy or childbirth-related complications in 2023. Similarly, the decrease in the infant mortality ratio (IMR) has markedly stagnated since 2015, with an estimated 2.3 million newborn deaths in 2022. At this current rate of progress, about four-fifths of countries will fail to reach the World Health Organization (WHO)’s maternal mortality reduction target by 2030, and roughly one-third will struggle to achieve the newborn mortality reduction goal. Notably, many of these deaths are preventable[2, 3]. As a result, maternal and neonatal health issues have consistently been a central concern in modern research endeavors.\u003c/p\u003e\n\u003cp\u003eEmployment is a significant factor affecting health[4]. Vulnerable employment, as a critical employment category, encompasses own-account workers and contributing family workers[5], embodying the most unstable and high-risk job types. Characteristics of vulnerable employment include income fluctuations and a lack of formal arrangements and social security[6]. Individuals in these roles are least likely to have statutory social insurance or access to social protection and safety nets. Research indicates that self-employment is detrimental to health[7]. However, research specifically examining the impact of vulnerable employment on MMR and IMR has yet to be found. Therefore, this paper focuses on the influence of vulnerable employment on maternal and infant health, exploring whether improvements in vulnerable employment can contribute to better maternal and infant health outcomes.\u003c/p\u003e\n\u003cp\u003eUrbanization is the process whereby rural populations transition into an urban population[8], often accompanied by economic growth and industrial restructuring. In the context of vulnerable employment, a high proportion of own-account workers may indicate a large agricultural sector and slow growth in the formal economy. Conversely, a significant proportion of contributing family workers signals weakened economic development, limited employment growth, and a larger rural economy. Consequently, the reduction of vulnerable employment typically leads to a diminishment of the agricultural sector, with labor shifting from agriculture to industrial and service sectors. The concentration of industry and services further attracts population to urban areas, thereby driving urbanization[9, 10]. Studies have demonstrated correlations between urbanization levels and various health outcomes. Hence, this research hypothesizes that vulnerable employment may influence maternal and infant health through levels of urbanization.\u003c/p\u003e\n\u003cp\u003eThis paper aims to investigate the impact of vulnerable employment on maternal and infant health using long-term cross-national data, while exploring the mediating role of urbanization in controlling for complex factors such as economic conditions, population density, and environmental pollution. Additionally, the paper will explore whether gender differences in vulnerable employment lead to varying impacts on maternal and infant health. Finally, it will analyze how changes in employment among vulnerable groups influence maternal and infant health in countries with varying levels of human development. This research will raise global awareness of vulnerable employment, supporting the development of effective labor market and health policies to help reduce MMR and IMR to meet the United Nations Sustainable Development Goals (SDGs).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design\u003c/p\u003e\n\u003cp\u003eThe present study employs cross-national panel data to examine the relationship between the vulnerable employment rate and MMR and IMR across various countries from 1992 to 2019. Additionally, it investigates the mediating role of urbanization levels in this relationship. The data for this paper is sourced from the World Bank and the International Labour Organization (ILO). Countries and regions lacking sufficient data were excluded, leaving a sample of 166 nations and territories. For variables such as population density and GDP per capita with occasional missing values, backward filling was applied to complete the dataset. Additionally, all continuous variables are winsorized at the 1st and 99th percentiles to mitigate the impact of outliers, which may result from reporting errors, measurement inconsistencies, or specific socio-economic shocks, thereby enhancing the robustness and representativeness of the estimated coefficients.\u003c/p\u003e\n\u003cp\u003eIndex selection and data source\u003c/p\u003e\n\u003cp\u003eDependent variables\u003c/p\u003e\n\u003cp\u003eThis paper uses the maternal mortality rate to reflect maternal health. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.\u003c/p\u003e\n\u003cp\u003eThis paper uses the infant mortality rate to reflect maternal health. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.\u0026nbsp;The World Bank database provides data on MMR and IMR.\u003c/p\u003e\n\u003cp\u003eIndependent variable\u003c/p\u003e\n\u003cp\u003eThis paper employs the vulnerable employment rate to represent the status of such employment. Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment. Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment. The data is sourced from the ILO.\u003c/p\u003e\n\u003cp\u003eMediator variable\u003c/p\u003e\n\u003cp\u003eThe urbanization rate serves as a key indicator of the level of urbanization. This paper measures the urbanization rate by the urban population ratio to each country’s total population. The World Bank database provides this data.\u003c/p\u003e\n\u003cp\u003eCovariates\u003c/p\u003e\n\u003cp\u003eGiven the complex array of factors influencing MMR and IMR, this paper, in conjunction with relevant studies, identifies that mortality rates are affected by economic, demographic, and environmental factors[11, 12]. As the study employs long-term cross-national data, we selected representative indicators from these three macro fields as control variables to prevent the substantial absence of micro-level factors from impacting the outcomes(\u003cstrong\u003eTable 1\u003c/strong\u003e). The data is sourced from the official World Bank database.\u003c/p\u003e\n\u003cp\u003eThis study selected GDP per capita as a representative indicator to measure economic status. A higher GDP per capita generally signifies a higher standard of living, encouraging greater investment in health, leading to reduced mortality rates[13]. At the same time, society has wider health coverage, allowing women and children to receive timely and effective diagnoses and treatments at well-equipped medical facilities within a comprehensive healthcare system, which can enhance maternal and child health[14]. This study applied a logarithmic transformation to the GDP per capita data.\u003c/p\u003e\n\u003cp\u003eThis study selected population density as the representative indicator of demographic status. Population density reflects the degree of population concentration in an area and the spatial distribution of medical resources. Population density has a significant interaction with sanitation; higher population density and poorer sanitary conditions lead to more severe health damage[15]. Some scholars also indicate that areas with higher population density typically have better access to healthcare services, particularly with a positive impact on maternal and infant health service coverage[16]. This study applied a logarithmic transformation to the population density data.\u003c/p\u003e\n\u003cp\u003ePM2.5 air pollution has been selected as the representative indicator of environmental pollution for this study. Air pollution is a significant risk factor affecting population health[17], particularly as pregnant women and infants are most sensitive to its adverse effects. Overall evidence suggests that air pollution increases the risk of mortality for mothers and children. The strongest evidence indicates that air pollution escalates the risk of mortality among mothers and children[18] Hence, regulating environmental risk factors is crucial to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e Variable definitions and sources\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGDP per capita is gross domestic product divided by midyear population.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGDP is the sum of gross value added by\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eall resident producers in the economy\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eplus any product taxes and minus any\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esubsidies not included in the value of\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ethe products. It is calculated without\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emaking deductions for depreciation of\u0026nbsp;\u003c/p\u003e\n \u003cp\u003efabricated assets or for depletion and\u0026nbsp;\u003c/p\u003e\n \u003cp\u003edegradation of natural resources.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecurrent\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eU.S.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003edollars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePopulation density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePopulation density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epeople per square kilometer of land area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePM2.5 air\u003c/p\u003e\n \u003cp\u003epollution, mean\u003c/p\u003e\n \u003cp\u003eannual exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePopulation-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation’s\u003c/p\u003e\n \u003cp\u003epopulation to concentrations of suspended particles measuring less than\u003c/p\u003e\n \u003cp\u003e2.5 microns in aerodynamic diameter,\u003c/p\u003e\n \u003cp\u003ewhich are capable of penetrating deep\u003c/p\u003e\n \u003cp\u003einto the respiratory tract and causing\u003c/p\u003e\n \u003cp\u003esevere health damage. Exposure is\u003c/p\u003e\n \u003cp\u003ecalculated by weighting mean annual\u003c/p\u003e\n \u003cp\u003econcentrations of PM2.5 by population\u003c/p\u003e\n \u003cp\u003ein both urban and rural areas.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emicrograms per cubic meter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorld Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData analysis\u003c/p\u003e\n\u003cp\u003eThis study employs the statistical software Stata18.0 to conduct panel regression on the data, assuming constant control variables, and empirically examines the impact of the vulnerable employment rate on MMR and IMR. It compares the extent of impact of the gender gap in vulnerable employment on maternal and infant health using the standardized regression coefficient and investigates the role of urbanization as a mediator. Additionally, it checks for multicollinearity among the model's independent variables and conducts a VIF test. To estimate these relationships, the study adopts a two-way fixed effects model, including country fixed effects and time fixed effects, controlling for unobserved heterogeneity between countries and common shocks over time. The Hausman test (p = 0.000) confirms that the assumption of random effects does not hold in the data, indicating that the fixed effects method is indeed superior to random effects or mixed OLS models. To resolve potential endogeneity concerns, we employed system GMM estimation and 2SLS estimation on the dataset. Finally, heterogeneity analysis was performed using grouped regression methods based on levels of human development.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics\u003c/p\u003e\n\u003cp\u003eDistribution analysis of vulnerable employment\u003c/p\u003e\n\u003cp\u003eThis survey includes 166 countries, categorized according to the WHO regions, with 44 countries in Europe (26.5%), 30 in the Americas (18.1%), 19 in the Western Pacific (11.4%), 19 in the Eastern Mediterranean (11.4%), 10 in South-East Asia (6.1%), and 44 in Africa (26.5%). Based on the analysis of the distribution of the vulnerable employment rate across regions from 1992 to 2019, the median value in the African region is at the highest level, with a relatively inconsistent data distribution. The European region has the lowest median, with a more concentrated data distribution, as shown in \u003cstrong\u003eFigure\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eDistribution analysis of maternal mortality and infant mortality\u003c/p\u003e\n\u003cp\u003eFrom 1992 to 2019, the distribution and trends of MMR and IMR across regions were studied. The study results indicate that data distribution across Africa is inconsistent, with the median MMR and IMR at the highest levels. Compared with Europe, the data distribution is more concentrated, and the median MMR and IMR are at the lowest levels, as illustrated in \u003cstrong\u003eFigure\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eDistribution analysis of the relationship between vulnerable employment and maternal mortality and infant mortality\u003c/p\u003e\n\u003cp\u003eWe further grouped the 166 countries by WHO regions and levels of human development to analyze the relationship between vulnerable employment rate and maternal and infant mortality rates, as shown in \u003cstrong\u003eFigure 3\u003c/strong\u003e and \u003cstrong\u003eFigure 4\u003c/strong\u003e. The study shows that MMR and IMR are lower in European and American regions, and that the vulnerable employment rate is lower as well. In the African region, both MMR and IMR are higher, as is the vulnerable employment rate. In Southeast Asia, the Eastern Mediterranean, and the Americas, a higher MMR is often accompanied by a higher vulnerable employment rate. Countries with high and very high levels of human development have low MMR and IMR and a low vulnerable employment rate. Countries with low and medium levels of human development have high MMR and IMR, as well as a high vulnerable employment rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel regression model results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, to identify the best panel model, F-tests, LM tests, and Hausman tests were conducted on the study\u0026apos;s variables, with the results shown in Table 2. The results suggest that employing a two-way fixed-effects model for panel regression is the most suitable, effectively controlling for time-invariant confounding factors at both the country and temporal levels. Additionally, the VIF test was conducted, revealing that the average of the variance inflation factors was below 10, indicating no multicollinearity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e Panel model category test results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMaternal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003estatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003estatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eF-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e120.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e101.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eLM-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e37482.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e29246.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eHausman-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e341.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e712.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Fixed effect model vulnerable employment regression coefficient tablepresents the estimation results based on the two-way fixed-effects model, showing that a decrease in the vulnerable employment rate positively affects the reduction of MMR and IMR. The model indicates a positive correlation between the vulnerable employment rate and maternal and infant mortality rates (P\u0026lt;0.01). Specifically, for each unit decrease in the vulnerable employment rate, MMR drops by 3.116 units, while IMR declines by 0.450 units.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e Fixed effect model vulnerable employment regression coefficient table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003eMaternal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eVulnerable employment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e3.116***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.450***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e(7.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e(11.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003ePM2.5 air\u003c/p\u003e\n \u003cp\u003epollution, mean\u003c/p\u003e\n \u003cp\u003eannual exposure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e-0.580*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e-0.098***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e(-1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e(-3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e-22.056***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e-6.248***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e(-4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e(-15.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eDensity of population\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e-260.573***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e-30.816***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e(-21.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e(-29.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e4648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e4648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 187px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e Female vulnerable employment standardized regression coefficient table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eMaternal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eVulnerable employment， female\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.100\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.243\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003ePM2.5 air\u003c/p\u003e\n \u003cp\u003epollution, mean\u003c/p\u003e\n \u003cp\u003eannual exposure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.032\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.151\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.361\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eDensity of\u003c/p\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-1.270\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-1.458\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e4648.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e4648.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003er2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003eSubsequently, the study performed standardized regression analyses on male and female vulnerable employment rates with regard to both MMR and IMR. The standardization eliminated variations in units and magnitudes, thereby enabling a comparative analysis of the effects of male and female vulnerable employment on maternal and infant health. Results shown in \u003cstrong\u003eTable 4\u003c/strong\u003e and \u003cstrong\u003eTable 5\u003c/strong\u003ereveal that for each unit decrease in the male vulnerable employment rate, the MMR decreases by 0.357 units and the IMR by 0.403 units. Similarly, a one-unit decrease in the female vulnerable employment rate results in a reduction of 0.1 units in MMR and 0.243 units in IMR. Comparing the standardized regression coefficients, it is evident that a reduction in male vulnerable employment rate has a more significant impact on reducing MMR and IMR (\u0026beta;=0.357 \u0026gt; 0.1, 0.403 \u0026gt; 0.243).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e Male vulnerable employment standardized regression coefficient table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eVulnerable employment， male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.357\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.403\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003ePM2.5 air\u003c/p\u003e\n \u003cp\u003epollution, mean\u003c/p\u003e\n \u003cp\u003eannual exposure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.090\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-0.302\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-1.169\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-1.344\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eDensity of\u003c/p\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-248.730\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e-29.688\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(11.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e(1.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e4648.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e4648.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003er2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003eIntermediate effect test\u003c/p\u003e\n\u003cp\u003eInitially, this study utilized the stepwise regression coefficient method to investigate whether urbanization serves as a mediator between the vulnerable employment rate and both MMR and IMR. Model 1, Model 2, and Model 3 collectively form the mediation effect equation set. In \u003cstrong\u003eTable 6\u003c/strong\u003e and \u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e7\u003c/strong\u003e, Model 1 is a regression model of the vulnerable employment rate with MMR and IMR as dependent variables. Results indicate that a reduction in the vulnerable employment rate favors a decrease in maternal and infant mortality (\u0026beta;=3.116, 0.450, P\u0026lt;0.01).\u0026nbsp;Model 2 has urbanization as the dependent variable for regression against the vulnerable employment rate. The results in the table show that reducing the vulnerable employment rate is conducive to higher urbanization levels (\u0026beta; = -0.177, P \u0026lt; 0.01). Model 3 verifies the mediating role of urbanization level when the vulnerable employment rate drops, leading to reductions in MMR and IMR, with negative regression coefficients (\u0026beta; = -2.643, -0.346, P \u0026lt; 0.01), indicating a significant mediating effect of the Human Development Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e Intermediate effect analysis(1)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eMaternal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eUrban population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eMaternal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eVulnerable employment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.116\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.669\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.444)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.451)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003ePM2.5 air\u003c/p\u003e\n \u003cp\u003epollution, mean\u003c/p\u003e\n \u003cp\u003eannual exposure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.580\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.038\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.675\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-22.056\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.676\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-20.352\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(4.486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(4.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eDensity of population\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-260.573\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e5.893\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-245.715\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(11.917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(12.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eUrban population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-2.643***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(-5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e7\u003c/strong\u003e Intermediate effect analysis(2)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eModel\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eUrban population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eVulnerable employment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.450\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.177\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.388\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003ePM2.5 air\u003c/p\u003e\n \u003cp\u003epollution, mean\u003c/p\u003e\n \u003cp\u003eannual exposure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.098\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.038\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.111\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-6.248\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.676\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-6.014\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.397)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eDensity of\u003c/p\u003e\n \u003cp\u003epopulation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-30.816\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e5.893\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-28.774\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(1.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(1.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eUrban population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e-0.346\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003eSecondly, to address the issue of weaker effects in the regression coefficient test of mediation, this paper uses the Sobel-Goodman test and the Bootstrap test to assess the mediating role of urbanization in the relationship between vulnerable employment rate, MMR, and IMR. The Sobel test, based on the theory of normal distribution, judges the establishment of mediation effects by testing the significance of the product of the mediation path coefficients. The Bootstrap test derives the empirical distribution of the indirect impact through 1000 sampling iterations, establishing a 95% confidence interval; if this interval does not include 0, it indicates a significant mediation effect(\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e8\u003c/strong\u003e and \u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e9\u003c/strong\u003e). Results show that a decrease in the vulnerable employment rate leads to higher urbanization levels, which in turn contribute to reductions in MMR and IMR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e8\u003c/strong\u003e Sobel test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 275px;\"\u003eMaternal mortality\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 284px;\"\u003eInfant mortality\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.6691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-16.6855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-16.0283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.5336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-2.5701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.8991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eTotal effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.2027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-10.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-8.0578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e9\u003c/strong\u003e Bootstrap text\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"794\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 331px;\"\u003eMaternal mortality\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 359px;\"\u003eInfant mortality\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eIndirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.4464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.1069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2.6694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.3684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.9474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.3882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.0494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRobustness test\u003c/p\u003e\n\u003cp\u003e2SLS estimation\u003c/p\u003e\n\u003cp\u003eThis study uses the instrumental variable method with two-stage least squares regression to address endogeneity issues. The paper employs the first lag of the vulnerable employment rate as the instrument variable. Initially, a regression is performed with the vulnerable employment rate as the dependent variable and the lagged vulnerable employment rate as the independent variable, resulting in residuals. These residuals serve as explanatory variables, replacing the vulnerable employment rate in the first stage of the regression, as shown in \u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e10\u003c/strong\u003e. A decrease in the vulnerable employment rate significantly reduces MMR and IMR, thereby improving health outcomes. Specifically, for every unit decrease in the vulnerable employment rate, MMR decreases by 2.885 units, and IMR drops by 0.429 units, aligning with expectations and confirming the reliability of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e10\u003c/strong\u003e 2SLS estimation results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eFirst stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSecond stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSecond stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eVulnerable employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eMaternal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eL.Vulnerable employment\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.965\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eVulnerable employment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.885\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.429\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eOther variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eConstant term\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.622\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(0.661)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eAnderson canon. corr. LM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3956.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eCragg-Donald Wald F\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e47208.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4482.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4482.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4482.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003er2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003eSystem GMM estimation\u003c/p\u003e\n\u003cp\u003eSecondly, to make the results of this paper more convincing, this study further employs the system GMM method for estimation[19]. The system GMM method is suitable for handling dynamic panel data with lagged dependent variables, allowing precise capture of dynamic relationships between variables. This paper uses the one-period lagged MMR and IMR as instrumental variables, as shown in \u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e11\u003c/strong\u003e. AR(1)\u0026lt;0.1, AR(2)\u0026gt;0.1, indicating no second-order serial correlation in the regression equation, thus satisfying the assumptions for using the system GMM method. Furthermore, the Hansen test P\u0026gt;0.1, indicating that the instrumental variables are valid and effective.\u0026nbsp;As shown in the table, a decrease in the vulnerable employment rate leads to reductions in both MMR and IMR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e11\u003c/strong\u003e System GMM estimation results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eMaternal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eLagged Maternal Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.991***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.0433)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eLagged. Infant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.855***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eVulnerable employment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(1.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.625)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eOther variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4,482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e4,482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eAR(1) p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.0501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.0501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eAR(2) p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eHansen test p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003eHeterogeneity analysis\u003c/p\u003e\n\u003cp\u003eThe Human Development Index (HDI) has been published by the United Nations Development Programme since 1990. It serves as a standard to measure the socio-economic development of countries, categorizing them into four levels: very high, high, medium, and low[20]. The HDI is calculated based on three indicators: average life expectancy at birth, years of education (including mean years of schooling and expected years of schooling), and GNI per capita. This allows for comparisons between countries[21, 22]. MMR and IMR reflect a country\u0026apos;s overall health status and development level; thus, the HDI can explain much of the variation in these rates across nations. Each component of the HDI is highly correlated with MMR and IMR, and excluding life expectancy from the HDI does not reduce its ability to predict these mortality rates[23]. Therefore, it is more reasonable to group countries by HDI levels when analyzing the impact of vulnerable employment on MMR and IMR, rather than only grouping by income group. Accordingly, the sample is divided into four groups according to HDI levels: low human development level, medium human development level, high human development level, and very high human development level. Twenty-two countries belong to the low human development group, thirty-nine countries to the medium human development group, forty-four countries to the high human development group, and sixty-one countries to the very high human development group.\u003c/p\u003e\n\u003cp\u003eFirstly, the rate of vulnerable employment has a significant positive impact on MMR in medium, high, and very high human development level groups (\u0026beta; = 6.450, 1.315, 0.186, P \u0026lt; 0.01), achieving the greatest impact in medium human development level countries. However, in the low human development level group, this effect is not significant. The results are shown in \u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e12\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eSecondly, the vulnerable employment rate significantly affects IMR positively across all human development level groups (\u0026beta; = 0.574, 0.547, 0.192, 0.312, P \u0026lt; 0.01). Results are detailed in \u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e13\u003c/strong\u003e, indicating overall that an increase in the vulnerable employment rate is associated with a rise in IMR, and that lowering the rate of vulnerable employment helps to decrease IMR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e12\u003c/strong\u003e Maternal mortality heterogeneous outcome\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 442px;\"\u003e\n \u003cp\u003eMaternal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eLow human development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eMedium human development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eHigh human development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eVery high human development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eVulnerable employment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e6.450\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.315\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.186\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(3.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(1.340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.233)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePM2.5 air\u003c/p\u003e\n \u003cp\u003epollution, mean\u003c/p\u003e\n \u003cp\u003eannual exposure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-1.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.102\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.115\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(1.510)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-4.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-63.889\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-14.686\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-11.800\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(18.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(11.565)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(3.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.586)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eDensity of\u003c/p\u003e\n \u003cp\u003epopulation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e387.223\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-158.393\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-52.278\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-1.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(110.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(47.550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(13.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(1.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003er2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e \u003cstrong\u003e13\u003c/strong\u003e Infant mortality heterogeneous outcome\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 442px;\"\u003e\n \u003cp\u003eInfant mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eLow human development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eMedium human development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eHigh human development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003eVery high human development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eVulnerable employment\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.574\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.547\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.192\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.312\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.026)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ePM2.5 air\u003c/p\u003e\n \u003cp\u003epollution, mean\u003c/p\u003e\n \u003cp\u003eannual exposure\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.183\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.142\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-0.344\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-2.341\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-5.511\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-8.797\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-5.014\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(1.212)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eDensity of\u003c/p\u003e\n \u003cp\u003epopulation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-12.945\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-26.069\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e-15.757\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1.395\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(7.287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(3.572)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(2.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e(0.513)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e616.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1092.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1232.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e1708.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003er2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003er2_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aims to analyze the relationship between the vulnerable employment rate and maternal and infant health, as well as whether urbanization serves as a mediating factor in this association.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study finds that a decline in the vulnerable employment rate overall has led to a reduction in MMR and IMR. Firstly, a decrease in the vulnerable employment rate can lead to an increase in stable income for this group. Numerous studies have indicated that income is a crucial determinant of infant survival, showing a significant correlation between income increases and reduced IMR[24, 25]. An increase in income can directly enhance a family's nutritional intake, housing, and sanitary conditions, thereby reducing direct fatal risks such as infant infections and malnutrition[26]. Secondly, a reduction in the vulnerable employment rate can expand social security coverage to more individuals, which has a timely and effective impact on lowering IMR. Zhuihui Li and other scholars argue that social security programs can directly alleviate economic pressures on families, improve infant growth environments, enhance accessibility to medical services, and make preventive and curative medical services more affordable[27], thereby decreasing IMR. Serván-Mori and other scholars in Mexico revealed that MMR is lower among women with social insurance compared to those without[28]. Other research has found that implementing health insurance in low and lower-middle-income countries, by increasing the use of maternal and reproductive health (MRH) services, especially antenatal care (ANC) and childbirth-related services, is directly linked to a reduction in MMR[29].\u003c/p\u003e\n\u003cp\u003eFurthermore, urbanization plays a mediating role in the relationship between the vulnerable employment rate and MMR and IMR. A lower vulnerable employment rate elevates urbanization levels, which can effectively reduce MMR and IMR. Vulnerable employment is concentrated in rural areas. Over three-quarters of rural employment worldwide is informal, and nearly half of informal workers are own-account workers[30]. A high proportion of vulnerable employment usually reflects an economy dominated by agriculture and informal sectors. The process of economic development essentially involves efficient formal sectors replacing informal sectors[31]; when vulnerable employment rates decrease, it reflects structural economic transformation, with employment concentrating in formal sectors. Given that the formal sectors are largely concentrated in cities[32, 33], employment shifts toward urban areas, driving rural populations to migrate and settle in cities, thereby promoting urbanization. Moreover, cities possess higher-quality medical facilities than rural areas, thereby improving maternal and infant health[34].\u003c/p\u003e\n\u003cp\u003eThis study found that in countries with low human development levels, the vulnerable employment rate does not significantly reduce MMR, but significantly reduces IMR. This phenomenon arises from the fact that MMR is more dependent on a strong healthcare system, whereas IMR is further affected by various social determinants[3, 35]. Firstly, although the reduction of the vulnerable employment rate allows more people to be covered by social security, alleviating some poverty and increasing income, maternal health is perceived as secondary care[36], with the prevention and management of maternal mortality being more heavily linked to strong healthcare systems, quality emergency obstetric care, skilled medical personnel, hospital facilities, etc. According to a World Health Organization report, 95% of maternal mortality occurs in low-income and lower-middle-income countries[37], which are mostly countries with low human development levels. Maternal deaths often result from complications during pregnancy, childbirth, or postpartum periods, which can be treated in adequately staffed and equipped medical facilities[35, 38]. These countries lack high-quality medical facilities and obstetric care services, with a shortage of skilled health professionals[39]. The fragile health system prevents timely, high-quality prenatal and postnatal services, leading to treatment delays during maternal complications, significantly increasing avoidable maternal mortality[40, 41]. Especially in rural areas, the absence of fundamental health infrastructure and the considerable distance from the nearest hospital reduce access to healthcare services[42, 43]. Meanwhile, Mzembe, Tajvar, and other scholars indicate that expanding health service coverage and increasing maternal use of medical services can significantly reduce MMR in low human development countries[44, 45]. Secondly, although low human development level countries lack a strong healthcare system, infant mortality is not only related to the healthcare system but also to the social, economic, and environmental conditions of infants and their families, which influence infant survival and care conditions, thereby affecting infant mortality. A study in Africa demonstrates that enhancements in health facilities, in addition to health investments and economic growth, are closely related to declines in infant mortality[46]. A decrease in the vulnerable employment rate increases income, expands social security coverage, and improves the social, economic, and environmental conditions of infants, thus reducing infant mortality. Additionally, some research suggests that infant mortality reduction correlates with health expenditure per capita. Hence, although lowering the rate of vulnerable employment promotes income and increases health spending, it does not significantly impact maternal mortality[47, 48]. Yet, it can enhance conditions for infant survival and care, subsequently lowering infant mortality.\u003c/p\u003e\n\u003cp\u003eAlthough the rate of vulnerable employment is quite similar between men and women, there is a significant gender gap in types of employment: men are more likely to be own-account workers, while women tend to be contributing family workers[6], Own-account workers generally have better circumstances than contributing family workers.[49]. Further analysis in this paper reveals that reductions in male vulnerable employment have a more noticeable impact on lowering MMR and IMR than comparable decreases among females. This can be explained by gender income differences and women's “double burden.\" Firstly, gender income inequality stands as a pervasive global issue, with women typically earning less than men for similar roles, even in many developed countries[50, 51]. Income from men's employment generally exerts a more direct influence on a family's overall economic capability. As a result, a decline in male vulnerable employment contributes to increased household income, which can be allocated to more prenatal check-ups and improved infant nutrition, improving access to quality healthcare and living conditions for mothers and infants, thus significantly reducing MMR and IMR. Secondly, when women transition from contributing family workers to formal employment, it does not necessarily lead to a decrease in domestic and childcare responsibilities. Instead, this transition often culminates in a \"double burden,\" leading to greater stress and negatively impacting health[52, 53]. Therefore, although a decline in female vulnerable employment may help reduce MMR and IMR, the\"double burden\" of women undermines the positive impact that such reductions in female vulnerable employment could have on enhancing maternal and infant health.\u003c/p\u003e\n\u003cp\u003eBased on the findings of this study, it is advised that governments optimize employment and social protection policies, broaden social security coverage, and enhance labor protection by thoroughly evaluating the circumstances of vulnerable employment, thus fostering more effective improvements in maternal and infant health. When formulating public policies in countries with low human development levels, it is essential to integrate interventions targeting vulnerable employment with efforts to improve the health system to better reduce MMR and IMR. Additionally, the study emphasizes that in efforts to decrease female vulnerable employment, the \"double burden\" faced by women in both career and domestic roles should be acknowledged. Strong policy support is essential for childcare services, infrastructure enhancements, and the promotion of equal opportunities in the labor market, helping women balance unpaid caregiving and paid work. It is also vital to develop suitable labor market policies to ensure women have sufficient time for breastfeeding. Finally, future research should focus on identifying the optimal balance paths between vulnerable employment and maternal and infant health across countries with varying levels of human development, aiming to significantly reduce MMR and IMR on a broad scale.\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eThere are several limitations inherent in this study. Firstly, the factors affecting MMR and IMR are very complex, this study does not thoroughly consider socio-cultural, legal, and political factors, apart from the economic, demographic, and environmental macro factors, which might also influence maternal and infant health outcomes. Secondly, data on the vulnerable employment rate is sourced from labor and household surveys, supplemented by official estimates and censuses in some countries. Due to differences in definitions and coverage across countries, there are some limitations in comparing cross-national and international data, which could impact the accuracy of the estimates.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research demonstrates a statistically significant global correlation among vulnerable employment rate, MMR, and IMR, and the mediating effect of urbanization. As the vulnerable employment rate decreases, urbanization continues to rise, and MMR and IMR show a downward trend. This finding motivates countries to make efforts to further reduce vulnerable employment, particularly emphasizing sustained health system investments in countries with low human development levels, and paying attention to the varying impacts of male and female vulnerable employment on maternal and infant health, ultimately achieving a notable reduction in MMR and IMR, progress toward the SDGs for 2030.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWHO: World Health Organization\u003c/p\u003e\n\u003cp\u003eMMR: maternal mortality ratio\u003c/p\u003e\n\u003cp\u003eIMR: infant mortality ratio\u003c/p\u003e\n\u003cp\u003eSDGs: United Nations Sustainable Development Goals\u003c/p\u003e\n\u003cp\u003eILO: International Labour Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that all methods were carried out according to relevant guidelines and regulations. We are not involved in experiments on humans and/or the use of human tissue samples. Ethics approval for the study protocol was obtained from the Ethics Committee of Harbin Medical University. Informed consent was obtained from all participants through online responses before the start of the survey. The Ethics Committee of Harbin Medical University approved the procedure for obtaining informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data is available in a public open access repository. The data used in the study is publicly available; These publicly available datasets may have been described in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (72074064). The funding body had no influence on study design, data collection, data analysis, data interpretation or writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZK took overall responsibility for the study design. CL, YQ, SF, LC were responsible for data analysis and manuscript writing. WX, WY, DW applied and obtained the research data. ZL, FX help with data interpretation and manuscript writing. LW made the charts and participated in the manuscript revision. All authors critically reviewed and revised the manuscript, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank you Professor Kang for your guidance on this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Social Medicine, School of Health Management, Harbin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedical University, Harbin, Heilongjiang Province 150081, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. World health statistics 2024: monitoring health for the SDGs, sustainable development goals. 2024. https://www.who.int/publications/i/item/9789240094703. Accessed 29 Jan 2026.\u003c/li\u003e\n\u003cli\u003eWHO. 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Soc Sci Res. 2010;39:987\u0026ndash;1003. https://doi.org/10.1016/j.ssresearch.2010.08.005.\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vulnerable employment, maternal mortality, infant mortality, urbanization, gender gap","lastPublishedDoi":"10.21203/rs.3.rs-9101763/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9101763/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study uses transnational panel data from 166 countries between 1992 and 2019 to examine the relationship between the vulnerable employment rate, maternal mortality, infant mortality, and the mediating role of urbanization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe World Bank database offers data on the vulnerable employment rate, maternal mortality rate, infant mortality rate, and urbanization levels. Panel regression is used to analyze the relationship between the vulnerable employment rate and maternal and infant mortality rates for each country, as well as the mediating effect of urbanization levels, while considering economic, demographic, and environmental factors. This paper also applies system GMM estimation and 2SLS estimation to handle potential endogeneity issues and determine causal links. Additionally, since male vulnerable employment mostly consists of own-account workers, whereas female vulnerable employment often involves contributing family workers, the study adjusts the regressions of male and female vulnerable employment rates on maternal and infant mortality rates to compare how gender disparities in vulnerable employment influence maternal and infant health. Finally, data are regressed based on each country's human development index level to examine heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, a significant positive correlation exists between the vulnerable employment rate and maternal and infant mortality rates (β = 3.116, 0.450, P \u0026lt; 0.01), with urbanization levels acting as a mediating factor (β = -2.643, -0.346, P \u0026lt; 0.01). Gender analysis shows that for every unit decrease in the male vulnerable employment rate, maternal mortality rate decreases by 0.357 units and infant mortality by 0.403 units. Conversely, for every unit decrease in the female vulnerable employment rate, maternal mortality rate decreases by 0.1 units and infant mortality rate by 0.243 units. Heterogeneity analysis reveals that in countries with low human development levels, there is a positive correlation between the vulnerable employment rate and infant mortality rate(β =0.574, P \u0026lt; 0.01), while the relationship with maternal mortality rate is not significant(β =0.933, P \u0026gt; 0.5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA decline in the vulnerable employment rate is conducive to reducing maternal and infant mortality rates. As the vulnerable employment rate decreases, urbanization levels increase, which in turn can effectively lower the maternal mortality rate and infant mortality rate. A gender analysis indicates that improving male vulnerable employment is more beneficial for maternal and infant health. In countries with low levels of human development, maternal mortality is associated with various other factors.This study advocates for paying attention to the impact of the gender gap in vulnerable employment on maternal and infant health and recommends increasing investment in healthcare systems in countries with low human development levels to achieve greater health benefits, thus ultimately fulfilling the United Nations Sustainable Development Goals.\u003c/p\u003e","manuscriptTitle":"Does improving vulnerable employment contribute to maternal and infant health: The mediating role of urbanization and gender gap analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 13:06:13","doi":"10.21203/rs.3.rs-9101763/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e86bf146-44b5-4729-aac1-2bc1420f10a4","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T13:42:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 13:06:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9101763","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9101763","identity":"rs-9101763","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}