Rural poverty and fertility transitions in Sub-Saharan Africa: Understanding the link and pathways to stability

Rural poverty and fertility transitions in Sub-Saharan Africa: Understanding the link and pathways to stability | 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 Article Rural poverty and fertility transitions in Sub-Saharan Africa: Understanding the link and pathways to stability Aliyu Mohammed Isyaku, Jie Li, Olufemi Samuel Adegboyo, Asad Ur Rehman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7549782/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Fertility remains persistently high in many parts of Sub-Saharan Africa (SSA), particularly in rural areas where poverty is widespread and institutional support is limited. This study investigates the underexplored role of rural poverty in shaping fertility dynamics, focusing on both the direct effect of poverty on fertility and the distributional heterogeneity of this relationship across fertility levels. Using balanced panel data from 15 SSA countries covering the period 2010 to 2024, we employ the Pooled Mean Group (PMG) estimator, two-step System Generalized Method of Moments (GMM), and the Method of Moments Quantile Regression (MMQR) to systematically examine how rural poverty influences fertility decisions. Our results first reveal that rural poverty significantly associated with higher fertility in both the short and long term, with the effect becoming more pronounced in high-fertility contexts. Second, female labor participation and human development mitigate fertility, whereas unemployment and limited healthcare access reinforce it. Finally, the MMQR results confirm that the influence of rural poverty intensifies at higher levels of fertility, suggesting a deepening poverty–fertility relationship in the most vulnerable rural populations. These findings call for multifaceted interventions, including rural social protection programs, integrated family planning services, and gender-focused education and employment policies to disrupt the intergenerational cycle of poverty and high fertility in SSA. Earth and environmental sciences/Environmental social sciences Health sciences/Health care Rural poverty Fertility Fertility trap: Sub-Saharan African countries Figures Figure 1 Figure 2 1. Introduction Fertility remains persistently high in Sub-Saharan Africa (SSA), particularly in rural areas where poverty is widespread and institutional support is limited. High fertility places pressure on household resources, reduces investments in education and health, and reinforces cycles of deprivation across generations. In rural economies where children are often regarded as both labor resources and old-age security, poverty and fertility become mutually reinforcing, contributing to what is often described as a poverty–fertility trap. Addressing this challenge is critical, as high fertility not only slows socioeconomic development but also undermines progress toward sustainable development goals in SSA. Moreover, The relationship between fertility rates and poverty has long been a central topic of interest among researchers and policymakers. In developing countries, high fertility rates are frequently linked to persistent poverty, as larger households often face greater economic challenges. A robust body of empirical evidence supports this association, indicating that less affluent nations tend to exhibit higher population growth rates, with larger families typically experiencing poorer economic outcomes (Ainsworth, 1996 ; Gurmu & Mace, 2008 ; Oberndorfer et al., 2022 ). From a macroeconomic standpoint, neo-classical growth theory argues that rapid population growth diminishes capital accumulation, depresses wages, and reduces per capita income, thereby reinforcing the cycle of poverty (Sinding, 2009 ). High fertility, therefore, is frequently viewed as a major impediment to economic development, particularly in low-income nations (Eastwood & Lipton, 1999 ). In rural areas, especially those reliant on agriculture, fertility behavior is influenced not only by cultural norms but also by economic necessities. In many agrarian societies, children are considered economic assets, contributing to the household labor force essential for farming and manual tasks. Consequently, larger families are often preferred, especially where labor-saving technologies are lacking and physical labor remains a critical input. Moreover, in the absence of formal social security systems such as pensions, children often serve as a form of economic insurance for aging parents, incentivizing higher fertility (Oghenekevwe, 2024 ). These conditions highlight how fertility decisions in rural settings are driven by both survival strategies and socio-economic structures. However, the poverty-fertility nexus is not universally fixed and varies according to a range of contextual factors, including access to education, healthcare, and family planning services. These services are often limited in rural areas due to poor infrastructure and resource constraints. The lack of educational opportunities for women, in particular, is a critical driver of high fertility, as female education is widely recognized as a key determinant in reducing fertility rates (Eastwood & Lipton, 1999 ). At the same time, high fertility can further constrain access to education and healthcare for children, creating a self-reinforcing cycle where poverty leads to high fertility, and high fertility perpetuates poverty. Despite extensive research on the relationship between poverty and fertility, relatively few studies have focused explicitly on rural areas, where this dynamic is often most acute. Much of the existing literature relies on aggregate national-level data, thereby overlooking the distinct socio-economic and cultural conditions in rural regions. While both urban and rural poverty have declined globally, rural poverty remains significantly more prevalent, as illustrated in Fig. 1 . These persistent rural-urban disparities underscore the need for focused research on fertility behavior in rural contexts particularly in Sub-Saharan Africa (SSA), where fertility rates are among the highest in the world and where rural areas face especially severe development challenges. In SSA, the complexities of rural life, including limited access to health and family planning services, low levels of education, and traditional labor demands, shape unique fertility behaviors that differ considerably from those in urban areas. Understanding the rural poverty-fertility nexus is therefore critical for designing targeted policies and interventions that effectively address the specific needs of rural populations. This study seeks to fill this research gap by offering an in-depth analysis of the rural poverty-fertility relationship in SSA. Utilizing panel data and advanced econometric techniques, the study aims to capture both the short-term and long-term dynamics of fertility behavior in rural contexts. Specifically, the Pooled Mean Group (PMG) model is employed to analyze the dynamic relationship, as it allows for heterogeneous short-run effects while preserving a long-run equilibrium, a feature often neglected in previous studies. Earlier works, such as those by (Muhoza, 2019 ; Odimegwu & Adedini, 2017 ; Sasson & Weinreb, 2017 ), largely relied on cross-sectional or short-term models, including multilevel Poisson regressions and multinomial frameworks, which limit the understanding of long-term trends. In addition to the PMG approach, this study introduces a two-way System Generalized Method of Moments (GMM) estimator to address endogeneity concerns. This method corrects for potential reverse causality and unobserved heterogeneity, which are common issues when using secondary data in fertility studies. To further enhance the robustness of the analysis, MMQR is employed to explore how the relationship between fertility and rural poverty varies across different segments of the poverty distribution. Unlike traditional models that estimate average effects, quantile regression enables a deeper understanding of how fertility influences poverty among the poorest and relatively better-off rural households. This multi-method approach represents a significant contribution to the literature by providing a nuanced and context-specific analysis of fertility dynamics in rural SSA. The findings are expected to offer critical insights for policy design, particularly in identifying strategies to reduce fertility rates and break the cycle of rural poverty. By addressing both methodological and empirical gaps, this study advances our understanding of a complex and pressing development issue and offers practical pathways for improving the well-being of rural populations in Sub-Saharan Africa. Figure 2 illustrates fertility rates for each country regarding rural poverty. Values represent country means based on 2010–2024 average data. 2. Literature Review Rural poverty remains a critical challenge in Sub-Saharan Africa (SSA), where over 200 million people live in extreme poverty, with the majority residing in rural regions (Z. A. Otieno, 2008 ). This poverty is multidimensional, characterized by high unemployment, low agricultural productivity, and inadequate access to essential services such as education, healthcare, and infrastructure, which significantly hampers socioeconomic development (Agayı & Karakayacı, 2022 ). For instance, in Kenya, the rural poverty rate stands at 40.1%, compared to 29.4% in urban areas, highlighting the persistent rural-urban divide (Agayı & Karakayacı, 2022 ). SSA also exhibits one of the highest fertility rates globally, with a total fertility rate (TFR) of 4.6 children per woman, nearly double the global average of 2.4 (Shapiro, 2015 ). Poverty plays a substantial role in shaping fertility patterns. In contexts of deprivation, limited access to education, healthcare, and family planning services often results in higher fertility (V. Otieno et al., 2020 ). (Odimegwu & Adedini, 2017 ) emphasize that residence in rural and disadvantaged areas is strongly associated with higher fertility, pointing to the importance of community-level determinants beyond individual socio-demographic characteristics. The relationship between poverty and fertility has been examined through various theoretical frameworks. Economic theory suggests that in poorer households, larger family sizes may be seen as a form of economic security, particularly in rural agricultural economies where children contribute to household labor (Oberndorfer et al., 2022 ). Caldwell’s demographic transition theory further posits that as women gain greater economic independence and access to education, fertility tends to decline (Jeon et al., 2010 ). (Bongaarts, 2020 ) also underscores the pivotal role of women's education and labor market participation in reducing fertility rates, as education often delays marriage and childbearing. Another crucial factor influencing fertility behavior in rural settings is women’s autonomy. In many rural SSA communities, patriarchal norms continue to restrict women's decision-making power, particularly in reproductive matters. However, studies show that when women have greater agency in household decisions, they are more likely to opt for smaller families and delay childbirth (Do & Kurimoto, 2012 ). (Upadhyay et al., 2014 ) argue that enhancing women's reproductive autonomy may be more effective in reducing fertility than economic improvement alone. Access to reproductive health services is another determinant of fertility outcome in rural SSA. (Sedgh et al., 2016 ), report that the unmet need for contraception is significantly higher in rural areas, driven by factors such as supply chain constraints, long distances to health facilities, and sociocultural resistance. Poor transportation infrastructure and a shortage of trained health professionals further exacerbate these challenges, limiting rural women’s access to modern contraceptive methods. Empirical studies offer mixed findings on the poverty-fertility relationship. (Ainsworth, 1996 ) found that in many SSA countries, poverty is strongly associated with high fertility due to constraints in education and access to family planning. Similarly, (Bongaarts, 2020 ) confirms that lower levels of educational attainment are correlated with higher fertility. In Nigeria, fertility remains high, particularly in rural areas, with the Nigeria Demographic and Health Survey (Emmanuel et al., 2025 ) reporting an average TFR of 5.3, and over 6 births per woman in rural regions. Studies by (Adebowale et al., 2020 ) attribute these figures to early marriage, entrenched cultural norms, and inadequate access to contraceptives. On the other hand, some studies suggest that income growth alone does not necessarily lead to fertility decline. Research in Ghana and Kenya by (Cleland & Machiyama, 2017 ) indicates that improvements in income must be accompanied by broader social changes, particularly in education and gender norms, to significantly impact fertility behavior. The dynamics of fertility and poverty in SSA are further complicated by regional variation. Fertility rates are particularly high in Central and West Africa, whereas North African countries display relatively lower rates. This variation reflects differing cultural, economic, and institutional contexts (Dzivor et al., 2024 ). Moreover, urban areas consistently exhibit lower fertility rates than rural regions, largely due to better access to education, healthcare, and employment opportunities (Jeon et al., 2010 ). While both theoretical and empirical studies consistently highlight a strong and multifaceted relationship between poverty and fertility in Sub-Saharan Africa (SSA), the specific dynamics within rural contexts remain underexplored. High fertility is not only a consequence of poverty but also a reinforcing factor, perpetuating deprivation through increased dependency ratios, limited investments in human capital, and constrained opportunities for women (Odimegwu & Adedini, 2017 ). Despite this recognition, much of the existing literature relies on national-level or cross-sectional analyses such as (Oghenekevwe, 2024 ) for Nigeria and (Cleland & Machiyama, 2017 ) for Ghana and Kenya which often obscure the distinct socio-economic conditions shaping fertility behavior in rural areas. Moreover, these studies frequently lack a longitudinal approach, limiting their ability to capture the evolving and long-term nature of the poverty-fertility relationship. Research Gap While there is a vast literature on the relationship between fertility and poverty in Sub-Saharan Africa, much remains unknown. To begin with, the majority of past studies have been based on national-level or cross-sectional analysis, which tends to veil the unique socio-economic context of rural dwellers, where poverty and pressure on fertility are most pronounced (Adedini et al., 2014 ; Oghenekevwe, 2024 ). Furthermore, previous studies have rarely utilized longitudinal panel data that capture shifting fertility behavior patterns over time, limiting analysis of both long-term and short-term effects (Muhoza, 2019 ). Lastly, while benchmark models such as Poisson regressions and multinomial models have provided enlightening outcomes, they cannot capture heterogeneous effects across fertility levels. Few studies have combined advanced econometric techniques such as the Pooled Mean Group (PMG) estimator, two-step System GMM, and Method of Moments Quantile Regression (MMQR) to uncover distributional heterogeneities in the poverty–fertility relationship. There is a need to fill these knowledge gaps with an enhanced awareness of rural poverty drivers of fertility transformations in SSA. Theoretical Perspective To ground this inquiry in a solid conceptual foundation, the study adopts Becker’s Investment Model of (G. S. Becker & Tomes, 1976 ; G. Becker & Tomes, 1976 ). This theoretical framework conceptualizes children not merely as consumption goods but as investment goods, with the expectation that they provide economic support and security in contexts where formal social protection systems are weak or absent. The model is especially relevant to rural Sub-Saharan Africa, where agrarian livelihoods dominate, institutional safety nets are limited, and children contribute directly to household labor and future old-age security. By situating the analysis within this theoretical perspective, the study strengthens the linkage between rural poverty and fertility behavior, offering an explanatory lens through which empirical results can be better interpreted. This approach also provides a robust justification for the chosen methodological framework, as it highlights the intergenerational strategies rural households adopt in navigating poverty and demographic pressures. 3. Methodology This study will employ the Investment Model of Fertility, as developed by (G. S. Becker & Lewis, 1973 G. Becker & Tomes, 1976 ), as its theoretical framework. According to this model, children are considered investment goods, rather than consumption goods. In this view, parents "invest" in their children with the expectation that they will provide economic support when the parents grow old and retire. This framework is especially relevant in the context of rural poverty, where families often face limited access to social insurance or pension systems, making children an essential source of economic security in the future. The Investment Model emphasizes that parents’ value both current consumption and future consumption, which is provided by the children when parents are no longer in the workforce. This future consumption is expected to be a key benefit that parents gain from having children. In the context of rural poverty, families face significant economic constraints, which makes the investment model particularly applicable. When parents have fewer resources, they may be more likely to have larger families, perceiving the additional children as providing more economic support in the future. Given these circumstances, the study hypothesizes that rural poverty influences fertility rates by increasing the number of children born, as parents view children as a future form of insurance against old age. Building upon this theoretical framework and aligning with empirical findings from (Dzivor et al., 2024 ; V. Otieno et al., 2020 ), this study proposes the following baseline model: $$\:{FERT}_{i,t}=\:{\gamma\:}_{0}+{\gamma\:}_{1}{RPVT}_{i,t}+\sum\:_{k=2}^{3}{\gamma\:}_{k}{Control}_{i,t}+{\mu\:}_{i}+{\phi\:}_{t}+{\epsilon\:}_{i,t,}$$ 1 In this model, i represents countries, and t denotes the time from 2010 to 2023. The dependent variable, \(\:{\:FERT}_{i,t}\) represents the fertility rate. The independent variable, \(\:{RPVT}_{i,t}\:\) represents the level of poverty in rural. The model also includes several control variables, such as female labour participate rate, healthcare access, human development index and employment status, which may also affect fertility decisions. The fixed effects for countries \(\:{\mu\:}_{i}\) and time \(\:\:{\phi\:}_{t}\) are included to account for unobserved heterogeneity and common trends that affect all countries over time. The error term, \(\:{\epsilon\:}_{i,t,}\) captures any random variation in fertility rates not explained by the model. Data and Measurement of Variables This study employs a balanced panel dataset covering 15 Sub-Saharan African countries over the period 2010–2024. Table 1 provides a summary of the variables, their measurement, and data sources. The dependent variable, fertility rate (FTLT) , is measured as the total number of births per woman in a given year, obtained from the World Bank Development Indicators. The key independent variable, rural poverty (RPVT) , is defined as the proportion of the rural population living below the poverty line, measured using data from the World Poverty Clock. Control variables are included to account for socioeconomic and institutional factors influencing fertility decisions. Female labor participation (FLP) is measured as the percentage of females aged 15 and above participating in the labor force, based on International Labour Organization (ILO) estimates. Human Development Index (HDI) , sourced from the United Nations Development Programme, captures average achievements in education, health, and income. Unemployment rate (UMPR) reflects the share of the labor force without employment, as reported by the World Bank. Finally, access to healthcare (ATHC) is proxied by the number of physicians per 1,000 persons, also derived from World Bank data. Sample Size and Missing Data The dataset covers 15 countries observed over 15 years, resulting in 225 country-year observations. A balanced panel was constructed by cross-validating variables across multiple sources. Instances of missing data were minimal and addressed through linear interpolation using official World Bank and UNDP estimates to preserve data consistency. No country-year observation was dropped, ensuring a fully balanced sample across the study period. Table 1 Data Description Variable name Code Measurement/Definition Source Rural Poverty RPVT Population of rural poor (Percentage of the rural population living below the international poverty line USD 2.15/day). WPC Fertility Rate FTLT Total annual fertility rate (Total number of live births per woman in a given year) WB/WDI Human Development Index LERV average achievement in key dimensions of human development UNDP Unemployment Rate UMPR Share of the total labor force without employment (% of total labor force). WB Access to Healthcare ATHC Number of physicians per 1,000 persons (proxy for access to healthcare services). WB Female Labor Participation FLP Female labor force participation rate (% of women aged 15 years and above engaged in labor market activities). WB/ILO NB: WPC = World Poverty Clock, WB = World Bank, UNDP = United Nation Development Programme, ILO = International Labor Organization 4. Results 4.1.1 Descriptive Statistics Table 2 presents the descriptive statistics for the key variables in the analysis, offering insights into the distribution and variability of rural poverty, fertility, health, labor, and development indicators across the sample. The average rural poverty rate is 15.226, with a standard deviation of 1.571, suggesting a relatively high but moderately dispersed level of poverty in rural areas. The FTLT averages 4.510, indicating persistently high fertility across regions, with a standard deviation of 1.026 and a range from 2.261 to 6.623, reflecting meaningful cross-country differences in reproductive behavior. In contrast, ATHC is considerably low, with a mean of only 0.207 physicians per 1,000 persons and a standard deviation of 0.177, highlighting both a general shortage and unequal access to healthcare services. The UMPR shows the greatest variability among all indicators, with a wide range from 1.047 to 34.007, a mean of 7.228, and a very large standard deviation of 7.663, pointing to deep structural disparities in labor markets. On the other hand, FLP and the HDI demonstrate more stable distributions. FLP has a mean of 4.141 and a low standard deviation of 0.152, while HDI averages 3.953 with a standard deviation of 0.169, suggesting relative consistency in gender inclusion in the workforce and overall development outcomes across the sample. The variances reinforce these patterns, with the highest recorded for UMPR (58.718), followed by RPVT (2.467) and FTLT (1.052), indicating substantial dispersion in employment, poverty, and fertility, while the lowest variances in FLP (0.023) and HDI (0.029) imply greater uniformity. Table 2 Descriptive Statistics Mean Std. dev Min Max Variance RPVT 15.226 1.571 10.866 17.925 2.467 FTLT 4.510 1.026 2.261 6.623 1.052 ATHC 0.207 0.177 0 0.809 0.031 UMPR 7.228 7.663 1.047 34.007 58.718 FLP 4.141 0.152 3.823 4.419 0.023 HDI 3.953 0.169 3.664 4.304 0.029 Table 3 Correlation analysis Result FTLR RPVT FLP HDI UMPR ATHC FTLT 1.000 RPVT 0.177* (0.0710 1.000 FLP -0.121** (0.028) -0.574*** (0.000) 1.000 HDI -0.427*** (0.000) -0.128*** (0.019) 0.187* (0.056) 1.000 UMPR 0.594*** (0.000) 0.308*** (0.001) -0.423*** (0.000) -0.234*** (0.000) 1.000 ATHC -0.468*** (0.000) 0.133*** (0.017) 0.292*** (0.003) -0.464*** (0.000) -0.577*** (0.000) 1.000 Note: *,**,*** represent significant level at 10%, 5% and 1% respectively. 4.1.2 Correlation analysis Reliable estimation depends on understanding the relationships among variables, particularly to avoid multicollinearity, which typically arises when correlation coefficients exceed 0.8 (Gujarati, 2021 ). The correlation matrix among all variables is presented in Table 3 . The results indicate that all correlation coefficients are well below the threshold of 0.8, demonstrating no presence of multicollinearity or problematic interactions. This confirms the data quality and supports the stability of coefficient estimates, ensuring reliable and robust model results. 4.1.3. Cross-Dependence Test The cross-dependence (CD) test conducted to determine if the countries under study exhibits cross-sectional dependence among themselves. This study employs (Pesaran, 2021 ) CD test (P-CSD) and the result is presented in Table 4 . The result reveals that all the variables exhibit statistically significant cross-sectional dependence at the 1% level. This strong significance implies that the variables are interdependent across cross-sections, indicating the presence of common shocks or spillover effects in the panel data. Recognizing this dependence is crucial for selecting appropriate estimation techniques that account for cross-sectional correlation, thus ensuring consistent and efficient parameter estimates. Table 4 Result of the cross-dependence test Variables FTLR RPVT FLP HDI UMPR ATHC CD Test 20.986*** 9.360*** 14.223*** 10.346*** 7.548*** 10.182*** Note: *,**,*** represent significant level at 10%, 5% and 1% respectively. Table 5 Result of the Unit root test Variable CROSS SECTION IPS (CIPS) CROSS SECTION ADF (CADF) Level Diff. Level Diff. FTLR -3.001*** -5.729*** I(0) -3.347*** -5.385*** I(0) RPVT -2.043 -5.648**** I(1) -2.452** -4.829*** I(0) flp -3.658*** -6.768**** I(0) -2.356** -4.818*** I(0) Hdi -2.934*** -5.392*** I(0) -1.446 -3.460*** I(1) umpr -2.290* -4.814*** I(0) -2.474** -4.208*** I(0) ATHC -1899 -3.527*** 1(1) -2.377** -4.139*** I(0) Note: *,**,*** represent significant level at 10%, 5% and 1% respectively. 4.1.4 Stationarity Test The utilisation of the second-generation panel unit root tests, Cross-sectionally Augmented IPS (CIPS) and Cross-sectionally Augmented Dickey-Fuller (CADF) was necessitated by the presence of cross-sectional dependence (CSD), as confirmed by the CD test results. These tests account for potential interdependencies across units, providing more reliable stationarity diagnostics in panels with CSD. As shown in Table 5 , most variables including FTLR, FLP, HDI, and UMPR are stationary at level [I(0)] under both tests. However, RPVT and ATHC show mixed orders of integration, being non-stationary at level in the CIPS test but stationary under CADF. These findings justify the need for careful model specification and, where necessary, the application of estimation techniques robust to mixed integration orders and cross-sectional dependence. 4.1.7. Kao Residual Cointegration Test To assess the presence of a long-run equilibrium relationship among the variables, the (Kao, 1999 ) residual cointegration test was employed. As reported in Table 6 , the ADF t-statistic is statistically significant at the 1% level. This confirms the rejection of the null hypothesis of no cointegration, suggesting the existence of a stable long-run relationship among the variables in the panel. The low residual variance and HAC variance further support the robustness of the cointegration result. Table 6 Result of the Kao Residual Cointegration Test Test Statistic Value Prob. ADF t-Statistic -3.928 0.0001 Residual variance 0.031 HAC variance 0.037 Note: Null hypothesis of no cointegration is rejected at 1%. Table 7 Result of the baseline model Variables Result of the PMG Analysis Test Two-way System GMM (1) (2) (3) LagFTLR 0.784*** (4.67) RPVT 0.681*** (2.87) 0.467*** (3.93) 0.484*** (7.41) FLP -0.593** (2.33) -0.311** (2.02) -0.806*** (3.17) HDI -1.216*** (6.04) -0.765** (4.21) -0.923* (1.84) UMPR 0.827*** (5.15) 0.471*** (6.39) 0.632*** (10.26) ATHC 0.643** (2.42) 0.851*** (9.15) 0.289** (2.36) ECM -0.869*** (4.62) AR1 (p-value) 0.036** AR2 (p-value) 0.487 Hansen p-value (2-step weighting matrix) 0.632 Hansen p-value (2-step weighting matrix) 0.5111 Note: Column 1 displays the result of the PMG long run, while column 2 presents the short run. Column 3 presents the result of the two-way system GMM. t-statistics are in parentheses while *,**,*** represent significant level at 10%, 5% and 1% respectively. 4.1 PMG Analysis This study utilizes the PMG estimation method to investigate the impact of RPVT on fertility rates in Sub-Saharan Africa. The PMG model is chosen for its ability to account for both cross-sectional dependence and parameter heterogeneity across countries, while still estimating a common long-run relationship. The results are presented in Table 7 , with column 1 showing long-run estimates and column 2 reflecting short-run dynamics. The findings reveal that RPVT has a positive and statistically significant association to fertility rates in both the long run and short run. This suggests that higher levels of rural poverty are consistently associated with increased fertility rates over time. These results are consistent with previous studies by (Odimegwu & Adedini, 2017 ; V. Otieno et al., 2020 ). This relationship can be interpreted through the lens of economic rationality and social structure in underdeveloped rural settings. In poor, agriculture-based communities, children are frequently seen as both a source of labor and a form of economic security, especially in the absence of formal pension or social protection systems. As a result, higher fertility may reflect a survival strategy families choose to have more children to mitigate income instability, offset high child mortality risks, and provide old-age support. Additionally, rural poverty is closely tied to low educational attainment, particularly for women, and limited access to reproductive healthcare, both of which are well-established drivers of high fertility. In such settings, cultural norms favoring large families, low female autonomy in reproductive decision-making, and inadequate family planning services create a feedback loop in which poverty sustains high fertility, and high fertility deepens poverty. In line with theoretical expectations, FLP exhibits a negative and statistically significant effect on fertility in both the short and long run. This finding aligns with prior studies by (Behrman & Gonalons-Pons, 2020 ; Irfan Chani et al., 2024 ; Mishra & Smyth, 2010 ), and is consistent with demographic transition theory. Increased participation of women in the labor force typically delays marriage and childbearing, raises the opportunity cost of motherhood, and enhances exposure to family planning resources all of which contribute to reduced fertility. Similarly, HDI has a strong negative relationship with fertility. Higher levels of education, income, and life expectancy are associated with lower fertility rates, likely due to increased access to healthcare services, better awareness of reproductive choices, and changing societal values. These results support findings by (Götmark & Andersson, 2020 ; Hafner & Mayer-Foulkes, 2013 ; Harttgen & Vollmer, 2014 ). UMPR is found to have a positive and significant link to fertility, suggesting that in economically insecure environments, especially traditional or rural ones, families may respond by having more children. This behavior may stem from the role of children in household labor or reflect limited reproductive autonomy and constrained access to contraception. This outcome aligns with (Andersen & Özcan, 2021 ), though it contrasts with studies in developed countries, such as (Miladinov, 2021 ) on Turkey and Greece, (Cavallini, 2024 ) on Italy, and (Di Nallo & Lipps, 2023 ) on the UK and Germany, which generally find a negative fertility response to unemployment. Interestingly, ATHC shows a positive association with fertility. This may initially seem counterintuitive; however, it could reflect a situation where improved maternal and child healthcare reduces child mortality, which may encourage the persistence of traditional fertility preferences. Alternatively, if improvements in general healthcare access are not matched by expanded family planning services, the net effect may still support higher fertility. Finally, the ECM is negative and highly significant, indicating the presence of a stable long-run equilibrium. The coefficient suggests that approximately 87% of short-term deviations from the long-run equilibrium are corrected within one period, implying a rapid speed of adjustment toward long-run stability. 4.2 Robustness Test Two-Way System GMM While the PMG estimator is well-suited for analyzing long-run relationships in panel data, it comes with certain limitations. Specifically, it assumes no endogeneity among regressors and imposes homogenous long-run coefficients across cross-sectional units. These assumptions may result in biased and inconsistent estimates, particularly in the presence of simultaneity, unobserved heterogeneity, or dynamic relationships, where past values of the dependent or independent variables may influence current outcomes. To address these limitations, the study adopts the two-way System GMM approach. This method corrects for endogeneity by using lagged levels and differences of the variables as internal instruments, thereby ensuring consistent estimates even when regressors are endogen by incorporating both individual and time fixed effects, offering more robust estimates in the presence of interdependencies across countries. We begin by evaluating the validity of the System GMM estimator, with results presented in Table 7 , column 3. The Arellano-Bond test for AR(1) is significant, indicating the expected presence of first-order serial correlation. However, the AR(2) test is not significant, suggesting no second-order autocorrelation, which supports the appropriateness of the model specification. Furthermore, the Hansen test of overidentifying restrictions is not significant for both the two-step weighting matrix and the second iteration, confirming that the instruments used are valid and the model is not over-identified. With model validity confirmed, we proceed to interpret the results. The lagged fertility rate (LagFTLT) is found to be positive and highly significant, indicating strong persistence in fertility behavior over time. This result supports the idea of fertility inertia, where past fertility patterns strongly influence current decisions. In many traditional or rural settings, established fertility norms are reinforced by cultural expectations, economic roles of children, and intergenerational behaviors, contributing to a self-reinforcing cycle. Consistent with the findings from the PMG model, RPVT remains a positive and significant determinant of fertility. This reinforces the conclusion that rural poverty consistently drives higher fertility rates, regardless of whether a static or dynamic model is applied. Other control variables also show results that align closely with the PMG estimates, further supporting the robustness of the main findings. 4.3 Quantile Regression Analysis Table 8 Result of the quantile regression Variable (1) (2) (3) (4) (5) 0.2 0.4 0.6 0.8 0.95 RPVT 0.194** (2.14) 0.303*** (5.57) 0.384*** (6.77) 0.454** (2.77) 0.547*** (2.62) FLP -0.487*** (-3.76) -0.573*** (-5.34) -0.625** (-2.31) -0.649*** (-3.79) -0.702*** (-8.69) HDI -0.507** (-2.29) -0.581* (1.91) -0.616** (-2.35) -0.705** (-2.85) -0.874*** (-4.05) UMPR 0.414*** (2.79) 0.467*** (6.35) 0.512** (2.19) 0.484** (2.56 0.554** (2.20) ATHC 0.183* (1.71) 0.326** (2.32) 0.372*** (3.21) 0.414** (2.42) 0.492*** (3.26) Note: t-statistics are in parentheses, while *,**,*** represent significant level at 10%, 5% and 1% respectively. Having found that rural poverty contributes to fertility rate using PMG, which uses mean effect, we went further to examine this relationship using quantile regression. This study specifically employs Method of Moments Quantile Regression. The motive for this is to provide a more comprehensive understanding of the conditional distribution of the dependent variable, rather than focusing solely on the mean effect, which PMG provides. Also, PMG assume homogeneity in error variance and may overlook important distributional differences, however, MMQR captures heterogeneity in the impact of independent variables across different points (quantiles) of the outcome distribution (Adegboyo et al., 2025 ; Bui et al., 2021 ), which is especially useful when the relationship between variables varies at different levels of the dependent variable, such as in cases involving poverty. The results is presented in Table 8 and it reveals that RPVT positively influences fertility across all quantiles, suggesting that the effect of rural poverty on fertility is consistent, but its strength increases at higher fertility levels. This result is similar to the result of the PMG and the findings of (Odimegwu & Adedini, 2017 ; V. Otieno et al., 2020 ) but provide a deeper understanding of how rural poverty affects fertility rate across different quantile. The result implies that while RPVT raises fertility even at lower levels, its influence grows stronger and becomes significantly more pronounced at the higher fertility quantiles. The quantile regression results show that the impact of rural poverty on fertility is more pronounced at higher fertility levels, suggesting that in regions already experiencing high fertility, the economic pressures of poverty exacerbate the desire for larger families. This reflects the fact that, in poverty-stricken areas, children are often seen as economic assets helping with household labor and ensuring financial stability. At lower fertility levels, RPVT still has a positive impact, but this effect is less pronounced, indicating that while rural poverty remains a key driver of fertility, its impact is more significant in high-fertility regions. Across all quantiles, female labor force participation (FLP) shows a negative and significant effect, with the impact becoming more substantial at higher fertility levels. This supports the idea that as women increasingly engage in the workforce, the opportunity cost of childbearing rises, leading to fertility decline particularly in regions where fertility is traditionally high. These findings align with demographic transition theory and echo the results of PMG. HDI also demonstrates a negative effect on fertility across all quantiles, with a stronger relationship at the upper end of the fertility distribution. This suggests that improvements in human development, through better education, healthcare, and income contribute to fertility decline, especially in high-fertility settings where developmental gains have the most transformative potential. Similarly, UMPR shows a positive association with fertility across all quantiles, with the effect amplified at higher quantiles. This indicates that in contexts of economic instability, particularly in high-fertility areas, households may respond by having more children as a form of economic security. This behavior may reflect traditional social expectations or a lack of alternative coping mechanisms in the absence of stable employment. Lastly, the availability of trained healthcare personnel exhibits a positive effect on fertility, and this relationship becomes more pronounced at higher fertility levels. While improved healthcare reduces child mortality and enhances maternal well-being, in areas where access to family planning services remains limited, this may inadvertently encourage higher fertility. Families may continue to adhere to traditional reproductive norms in the absence of sufficient reproductive health education and services. 5. Discussion The findings of this study demonstrate that rural poverty is a key driver of high fertility in Sub-Saharan Africa, reinforcing the notion that children are viewed as both labor resources and economic security in the absence of strong social protection systems. This outcome is consistent with earlier work (Odimegwu & Adedini, 2017 ; Otieno et al., 2020 ), and it lends supports to poverty–fertility relationship in rural areas where deprivation is most severe. Beyond poverty itself, socioeconomic conditions strongly shape fertility behavior. Female labor participation and higher levels of human development are associated with reduced fertility, highlighting the importance of women’s economic empowerment, education, and improved living standards in shifting demographic outcomes (Andersen & Özcan, 2021 ; Behrman & Gonalons-Pons, 2020 ) Conversely, unemployment is positively associated with fertility, which contrasts with findings from developed (Di Nallo & Lipps, 2023 ; Miladinov, 2021 ). This suggests that in rural SSA, economic insecurity encourages larger families as a coping strategy rather than discouraging childbearing. Access to healthcare also shows a positive relationship with fertility, likely reflecting reductions in child mortality without parallel expansion of family planning services, reinforcing the need to integrate reproductive health into rural health systems. The quantile regression analysis further enriches these insights by showing that the impact of rural poverty is not uniform but intensifies at higher fertility levels. This nonlinear relationship indicates that interventions targeting the poorest and most high-fertility regions may yield the strongest demographic effects. By combining PMG, GMM, and MMQR approaches, the study not only confirms the robustness of the poverty-fertility link but also uncovers distributional dynamics often missed in prior research. Taken together, the results validate Becker’s Investment Model of Fertility in rural SSA and emphasize that reducing fertility requires more than economic growth. Comprehensive strategies that combine poverty alleviation, women’s empowerment, education, and integrated family planning are essential for breaking the cycle of rural poverty and persistently high fertility. 6. Conclusion This research investigates the impact of rural poverty on fertility rates across 15 Sub-Saharan African countries over the period 2010–2024. The study employs the Pooled Mean Group estimator, two-step System Generalized Method of Moments, and MMQR to comprehensively analyze both average and distributional effects. The main conclusions of this study are as follows: First, rural poverty is consistently associated with higher fertility rates in both the short and long term, as evidenced by both PMG and GMM estimates. This finding confirms the existence of a poverty-fertility relationship, particularly in rural SSA, where children serve as economic security in the absence of formal welfare systems. Second, quantile regression results reveal that the positive relationship between rural poverty and fertility intensifies at higher levels of fertility. This suggests that in regions already experiencing high fertility, rural poverty exacerbates the problem more severely, indicating a nonlinear and asymmetric effect. This finding emphasizes the need for regionally targeted interventions, as poverty-driven fertility behaviors are most intense in areas with already elevated fertility levels. Third, female labor participation and the human development index are negatively associated with fertility across all estimation techniques. In particular, increased FLP and improvements in HDI through education, healthcare access, and income growth are found to significantly reduce fertility rates, underscoring the importance of women’s economic empowerment and human development in fertility reduction strategies. Fourthly, unemployment and access to healthcare positively influence fertility. Unemployment appears to be associated with higher fertility as a household coping strategy in the face of economic insecurity. Meanwhile, increased healthcare access, while reducing child mortality, may also unintentionally reinforce existing fertility preferences, especially in areas lacking adequate family planning services. Finally, these findings highlight that tackling rural poverty is not just a matter of improving livelihoods but also a necessary condition for managing fertility dynamics and achieving demographic transition in SSA. Any effective fertility-reduction strategy must therefore be grounded in sustained poverty alleviation targeted at rural populations. 7. Policy Recommendations Based on the findings of this study, the following policy recommendations are proposed: First, in high-fertility rural regions, where poverty significantly drives fertility decisions, governments should prioritize integrated rural development programs that combine income-generating activities with reproductive health education. These programs should address the root causes of high fertility by reducing rural poverty and economic insecurity. Second, expand access to quality and affordable reproductive health services in rural areas. This includes not only increasing the availability of contraceptives and family planning education but also ensuring that healthcare systems in rural communities integrate fertility management into primary healthcare delivery. Third, promote female labor force participation through targeted rural employment programs, vocational training, and microcredit access for women. As the study shows, FLP is a powerful determinant of reduced fertility. Policies that create economic opportunities for rural women can delay childbearing and reduce desired family size. Fourth, invest in rural education infrastructure, particularly for girls. Secondary school completion should be incentivized through conditional cash transfer schemes and school feeding programs. Education is a key long-term lever for lowering fertility rates and improving overall human development. Fifth, in areas where healthcare access is increasing but fertility remains high, integrate family planning into all maternal and child health services. Governments and health agencies should ensure that gains in healthcare access are complemented by family planning counseling and services. Sixth, given the diverse effects of poverty on fertility across different quantiles, interventions should be geographically targeted. Regions with both high rural poverty and high fertility should receive prioritized support, including enhanced family planning campaigns, infrastructure investment, and gender empowerment initiatives. Seventh, governments should prioritize poverty reduction strategies tailored to rural settings, focusing on income diversification, agricultural productivity, and rural infrastructure. Programs such as rural cash transfers, microcredit schemes, and climate-resilient agriculture support can reduce economic dependence on large family sizes. Finally, increase investment in rural social protection systems, including non-contributory pensions and child benefit programs. These can reduce the economic rationale for high fertility by providing alternative forms of old-age and income security. 8. Contribution and limitations This study makes several contributions to the literature on fertility and poverty in Sub-Saharan Africa. Theoretically, it extends Becker’s Investment Model of Fertility by demonstrating its relevance in rural contexts where children continue to serve as economic security in the absence of formal social protection systems. Methodologically, it applies a combination of PMG, System GMM, and MMQR approaches, providing robust evidence while also capturing distributional dynamics that are often overlooked in prior research. Empirically, by focusing specifically on rural populations across 15 Sub-Saharan African countries, the study highlights the persistence of a poverty–fertility trap and shows that its effects are strongest in high-fertility contexts. From a policy perspective, the findings underscore the need for integrated strategies that combine poverty alleviation, women’s empowerment, and family planning services to break the intergenerational cycle of deprivation and high fertility. At the same time, several limitations must be acknowledged. The analysis is based on secondary data from international sources, which may be subject to reporting inconsistencies and measurement errors. The focus on 15 countries in Sub-Saharan Africa, while analytically manageable, restricts the generalizability of results to the broader SSA region. Although advanced econometric methods were employed to mitigate endogeneity and heterogeneity concerns, these approaches still rely on assumptions that may not fully capture the complex causal mechanisms shaping fertility behavior. Finally, the study emphasizes quantifiable socioeconomic variables, leaving out cultural, religious, and household-level gender dynamics that are also crucial in fertility decisions. Future research could build on this work by incorporating richer, country-specific data and adopting mixed-method approaches to provide deeper insights into the poverty–fertility nexus. Declarations Funding Statement: No funding, grants, or other support were received throughout this research. Author Contribution AM: overall title idea, conceptualization, method, data, analysis, and entire draft of the paper. JL: Supervised, revised the analysis, validated the data, formulated the draft, and corrected the entire idea. 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09:12:18","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135571,"visible":true,"origin":"","legend":"","description":"","filename":"41753941efbe42e6a72b372d46b87a971structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7549782/v1/41b49334e107567b31ac989f.xml"},{"id":92841534,"identity":"cc25789a-081d-4763-afec-48c3aafa725a","added_by":"auto","created_at":"2025-10-06 09:04:18","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141584,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7549782/v1/08f8346e39aff2c42ea9cbcc.html"},{"id":92841525,"identity":"6d4befbc-1ece-4ee7-8d00-3ba4136c675b","added_by":"auto","created_at":"2025-10-06 09:04:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTrend of Rural and Urban Poverty (\u003c/em\u003eThe Demographic Profile of the Global Poor\u003cem\u003e, 2024)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7549782/v1/6af12902a88b19535bff4806.png"},{"id":92841533,"identity":"00aa8e87-0dae-41e6-9831-e0725ceb0b28","added_by":"auto","created_at":"2025-10-06 09:04:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFertility Rate vs Rural Poverty\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7549782/v1/cc6acffff0dafb4cc02ab185.png"},{"id":92843853,"identity":"3e8930f1-a7af-410b-9f00-4c8d0692d6ad","added_by":"auto","created_at":"2025-10-06 09:20:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":968384,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7549782/v1/67010024-2919-4a16-813f-a1631d4b1806.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rural poverty and fertility transitions in Sub-Saharan Africa: Understanding the link and pathways to stability","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFertility remains persistently high in Sub-Saharan Africa (SSA), particularly in rural areas where poverty is widespread and institutional support is limited. High fertility places pressure on household resources, reduces investments in education and health, and reinforces cycles of deprivation across generations. In rural economies where children are often regarded as both labor resources and old-age security, poverty and fertility become mutually reinforcing, contributing to what is often described as a poverty\u0026ndash;fertility trap. Addressing this challenge is critical, as high fertility not only slows socioeconomic development but also undermines progress toward sustainable development goals in SSA. Moreover, The relationship between fertility rates and poverty has long been a central topic of interest among researchers and policymakers. In developing countries, high fertility rates are frequently linked to persistent poverty, as larger households often face greater economic challenges. A robust body of empirical evidence supports this association, indicating that less affluent nations tend to exhibit higher population growth rates, with larger families typically experiencing poorer economic outcomes (Ainsworth, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Gurmu \u0026amp; Mace, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Oberndorfer et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From a macroeconomic standpoint, neo-classical growth theory argues that rapid population growth diminishes capital accumulation, depresses wages, and reduces per capita income, thereby reinforcing the cycle of poverty (Sinding, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). High fertility, therefore, is frequently viewed as a major impediment to economic development, particularly in low-income nations (Eastwood \u0026amp; Lipton, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In rural areas, especially those reliant on agriculture, fertility behavior is influenced not only by cultural norms but also by economic necessities. In many agrarian societies, children are considered economic assets, contributing to the household labor force essential for farming and manual tasks. Consequently, larger families are often preferred, especially where labor-saving technologies are lacking and physical labor remains a critical input. Moreover, in the absence of formal social security systems such as pensions, children often serve as a form of economic insurance for aging parents, incentivizing higher fertility (Oghenekevwe, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These conditions highlight how fertility decisions in rural settings are driven by both survival strategies and socio-economic structures.\u003c/p\u003e\u003cp\u003eHowever, the poverty-fertility nexus is not universally fixed and varies according to a range of contextual factors, including access to education, healthcare, and family planning services. These services are often limited in rural areas due to poor infrastructure and resource constraints. The lack of educational opportunities for women, in particular, is a critical driver of high fertility, as female education is widely recognized as a key determinant in reducing fertility rates (Eastwood \u0026amp; Lipton, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). At the same time, high fertility can further constrain access to education and healthcare for children, creating a self-reinforcing cycle where poverty leads to high fertility, and high fertility perpetuates poverty. Despite extensive research on the relationship between poverty and fertility, relatively few studies have focused explicitly on rural areas, where this dynamic is often most acute. Much of the existing literature relies on aggregate national-level data, thereby overlooking the distinct socio-economic and cultural conditions in rural regions. While both urban and rural poverty have declined globally, rural poverty remains significantly more prevalent, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These persistent rural-urban disparities underscore the need for focused research on fertility behavior in rural contexts particularly in Sub-Saharan Africa (SSA), where fertility rates are among the highest in the world and where rural areas face especially severe development challenges.\u003c/p\u003e\u003cp\u003eIn SSA, the complexities of rural life, including limited access to health and family planning services, low levels of education, and traditional labor demands, shape unique fertility behaviors that differ considerably from those in urban areas. Understanding the rural poverty-fertility nexus is therefore critical for designing targeted policies and interventions that effectively address the specific needs of rural populations.\u003c/p\u003e\u003cp\u003eThis study seeks to fill this research gap by offering an in-depth analysis of the rural poverty-fertility relationship in SSA. Utilizing panel data and advanced econometric techniques, the study aims to capture both the short-term and long-term dynamics of fertility behavior in rural contexts. Specifically, the Pooled Mean Group (PMG) model is employed to analyze the dynamic relationship, as it allows for heterogeneous short-run effects while preserving a long-run equilibrium, a feature often neglected in previous studies. Earlier works, such as those by (Muhoza, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Odimegwu \u0026amp; Adedini, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sasson \u0026amp; Weinreb, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), largely relied on cross-sectional or short-term models, including multilevel Poisson regressions and multinomial frameworks, which limit the understanding of long-term trends. In addition to the PMG approach, this study introduces a two-way System Generalized Method of Moments (GMM) estimator to address endogeneity concerns. This method corrects for potential reverse causality and unobserved heterogeneity, which are common issues when using secondary data in fertility studies. To further enhance the robustness of the analysis, MMQR is employed to explore how the relationship between fertility and rural poverty varies across different segments of the poverty distribution. Unlike traditional models that estimate average effects, quantile regression enables a deeper understanding of how fertility influences poverty among the poorest and relatively better-off rural households. This multi-method approach represents a significant contribution to the literature by providing a nuanced and context-specific analysis of fertility dynamics in rural SSA. The findings are expected to offer critical insights for policy design, particularly in identifying strategies to reduce fertility rates and break the cycle of rural poverty. By addressing both methodological and empirical gaps, this study advances our understanding of a complex and pressing development issue and offers practical pathways for improving the well-being of rural populations in Sub-Saharan Africa.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates fertility rates for each country regarding rural poverty. Values represent country means based on 2010\u0026ndash;2024 average data.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eRural poverty remains a critical challenge in Sub-Saharan Africa (SSA), where over 200\u0026nbsp;million people live in extreme poverty, with the majority residing in rural regions (Z. A. Otieno, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This poverty is multidimensional, characterized by high unemployment, low agricultural productivity, and inadequate access to essential services such as education, healthcare, and infrastructure, which significantly hampers socioeconomic development (Agayı \u0026amp; Karakayacı, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, in Kenya, the rural poverty rate stands at 40.1%, compared to 29.4% in urban areas, highlighting the persistent rural-urban divide (Agayı \u0026amp; Karakayacı, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). SSA also exhibits one of the highest fertility rates globally, with a total fertility rate (TFR) of 4.6 children per woman, nearly double the global average of 2.4 (Shapiro, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Poverty plays a substantial role in shaping fertility patterns. In contexts of deprivation, limited access to education, healthcare, and family planning services often results in higher fertility (V. Otieno et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). (Odimegwu \u0026amp; Adedini, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) emphasize that residence in rural and disadvantaged areas is strongly associated with higher fertility, pointing to the importance of community-level determinants beyond individual socio-demographic characteristics.\u003c/p\u003e\u003cp\u003eThe relationship between poverty and fertility has been examined through various theoretical frameworks. Economic theory suggests that in poorer households, larger family sizes may be seen as a form of economic security, particularly in rural agricultural economies where children contribute to household labor (Oberndorfer et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Caldwell\u0026rsquo;s demographic transition theory further posits that as women gain greater economic independence and access to education, fertility tends to decline (Jeon et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). (Bongaarts, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also underscores the pivotal role of women's education and labor market participation in reducing fertility rates, as education often delays marriage and childbearing. Another crucial factor influencing fertility behavior in rural settings is women\u0026rsquo;s autonomy. In many rural SSA communities, patriarchal norms continue to restrict women's decision-making power, particularly in reproductive matters. However, studies show that when women have greater agency in household decisions, they are more likely to opt for smaller families and delay childbirth (Do \u0026amp; Kurimoto, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). (Upadhyay et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) argue that enhancing women's reproductive autonomy may be more effective in reducing fertility than economic improvement alone. Access to reproductive health services is another determinant of fertility outcome in rural SSA. (Sedgh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), report that the unmet need for contraception is significantly higher in rural areas, driven by factors such as supply chain constraints, long distances to health facilities, and sociocultural resistance. Poor transportation infrastructure and a shortage of trained health professionals further exacerbate these challenges, limiting rural women\u0026rsquo;s access to modern contraceptive methods.\u003c/p\u003e\u003cp\u003eEmpirical studies offer mixed findings on the poverty-fertility relationship. (Ainsworth, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) found that in many SSA countries, poverty is strongly associated with high fertility due to constraints in education and access to family planning. Similarly, (Bongaarts, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) confirms that lower levels of educational attainment are correlated with higher fertility. In Nigeria, fertility remains high, particularly in rural areas, with the Nigeria Demographic and Health Survey (Emmanuel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reporting an average TFR of 5.3, and over 6 births per woman in rural regions. Studies by (Adebowale et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) attribute these figures to early marriage, entrenched cultural norms, and inadequate access to contraceptives. On the other hand, some studies suggest that income growth alone does not necessarily lead to fertility decline. Research in Ghana and Kenya by (Cleland \u0026amp; Machiyama, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) indicates that improvements in income must be accompanied by broader social changes, particularly in education and gender norms, to significantly impact fertility behavior. The dynamics of fertility and poverty in SSA are further complicated by regional variation. Fertility rates are particularly high in Central and West Africa, whereas North African countries display relatively lower rates. This variation reflects differing cultural, economic, and institutional contexts (Dzivor et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, urban areas consistently exhibit lower fertility rates than rural regions, largely due to better access to education, healthcare, and employment opportunities (Jeon et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile both theoretical and empirical studies consistently highlight a strong and multifaceted relationship between poverty and fertility in Sub-Saharan Africa (SSA), the specific dynamics within rural contexts remain underexplored. High fertility is not only a consequence of poverty but also a reinforcing factor, perpetuating deprivation through increased dependency ratios, limited investments in human capital, and constrained opportunities for women (Odimegwu \u0026amp; Adedini, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite this recognition, much of the existing literature relies on national-level or cross-sectional analyses such as (Oghenekevwe, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for Nigeria and (Cleland \u0026amp; Machiyama, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for Ghana and Kenya which often obscure the distinct socio-economic conditions shaping fertility behavior in rural areas. Moreover, these studies frequently lack a longitudinal approach, limiting their ability to capture the evolving and long-term nature of the poverty-fertility relationship.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Gap\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile there is a vast literature on the relationship between fertility and poverty in Sub-Saharan Africa, much remains unknown. To begin with, the majority of past studies have been based on national-level or cross-sectional analysis, which tends to veil the unique socio-economic context of rural dwellers, where poverty and pressure on fertility are most pronounced (Adedini et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Oghenekevwe, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, previous studies have rarely utilized longitudinal panel data that capture shifting fertility behavior patterns over time, limiting analysis of both long-term and short-term effects (Muhoza, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Lastly, while benchmark models such as Poisson regressions and multinomial models have provided enlightening outcomes, they cannot capture heterogeneous effects across fertility levels. Few studies have combined advanced econometric techniques such as the Pooled Mean Group (PMG) estimator, two-step System GMM, and Method of Moments Quantile Regression (MMQR) to uncover distributional heterogeneities in the poverty\u0026ndash;fertility relationship. There is a need to fill these knowledge gaps with an enhanced awareness of rural poverty drivers of fertility transformations in SSA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheoretical Perspective\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo ground this inquiry in a solid conceptual foundation, the study adopts Becker\u0026rsquo;s Investment Model of (G. S. Becker \u0026amp; Tomes, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; G. Becker \u0026amp; Tomes, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). This theoretical framework conceptualizes children not merely as consumption goods but as investment goods, with the expectation that they provide economic support and security in contexts where formal social protection systems are weak or absent. The model is especially relevant to rural Sub-Saharan Africa, where agrarian livelihoods dominate, institutional safety nets are limited, and children contribute directly to household labor and future old-age security. By situating the analysis within this theoretical perspective, the study strengthens the linkage between rural poverty and fertility behavior, offering an explanatory lens through which empirical results can be better interpreted. This approach also provides a robust justification for the chosen methodological framework, as it highlights the intergenerational strategies rural households adopt in navigating poverty and demographic pressures.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study will employ the Investment Model of Fertility, as developed by (G. S. Becker \u0026amp; Lewis, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1973\u003c/span\u003e G. Becker \u0026amp; Tomes, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), as its theoretical framework. According to this model, children are considered investment goods, rather than consumption goods. In this view, parents \"invest\" in their children with the expectation that they will provide economic support when the parents grow old and retire. This framework is especially relevant in the context of rural poverty, where families often face limited access to social insurance or pension systems, making children an essential source of economic security in the future. The Investment Model emphasizes that parents\u0026rsquo; value both current consumption and future consumption, which is provided by the children when parents are no longer in the workforce. This future consumption is expected to be a key benefit that parents gain from having children.\u003c/p\u003e\u003cp\u003eIn the context of rural poverty, families face significant economic constraints, which makes the investment model particularly applicable. When parents have fewer resources, they may be more likely to have larger families, perceiving the additional children as providing more economic support in the future. Given these circumstances, the study hypothesizes that rural poverty influences fertility rates by increasing the number of children born, as parents view children as a future form of insurance against old age.\u003c/p\u003e\u003cp\u003eBuilding upon this theoretical framework and aligning with empirical findings from (Dzivor et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; V. Otieno et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this study proposes the following baseline model:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{FERT}_{i,t}=\\:{\\gamma\\:}_{0}+{\\gamma\\:}_{1}{RPVT}_{i,t}+\\sum\\:_{k=2}^{3}{\\gamma\\:}_{k}{Control}_{i,t}+{\\mu\\:}_{i}+{\\phi\\:}_{t}+{\\epsilon\\:}_{i,t,}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this model, \u003cem\u003ei\u003c/em\u003e represents countries, and t denotes the time from 2010 to 2023. The dependent variable,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:FERT}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e represents the fertility rate. The independent variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{RPVT}_{i,t}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the level of poverty in rural. The model also includes several control variables, such as female labour participate rate, healthcare access, human development index and employment status, which may also affect fertility decisions. The fixed effects for countries \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e and time\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\phi\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e are included to account for unobserved heterogeneity and common trends that affect all countries over time. The error term, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i,t,}\\)\u003c/span\u003e\u003c/span\u003ecaptures any random variation in fertility rates not explained by the model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData and Measurement of Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study employs a balanced panel dataset covering 15 Sub-Saharan African countries over the period 2010\u0026ndash;2024. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a summary of the variables, their measurement, and data sources. The dependent variable, \u003cem\u003efertility rate (FTLT)\u003c/em\u003e, is measured as the total number of births per woman in a given year, obtained from the World Bank Development Indicators. The key independent variable, \u003cem\u003erural poverty (RPVT)\u003c/em\u003e, is defined as the proportion of the rural population living below the poverty line, measured using data from the World Poverty Clock.\u003c/p\u003e\u003cp\u003eControl variables are included to account for socioeconomic and institutional factors influencing fertility decisions. \u003cem\u003eFemale labor participation (FLP)\u003c/em\u003e is measured as the percentage of females aged 15 and above participating in the labor force, based on International Labour Organization (ILO) estimates. \u003cem\u003eHuman Development Index (HDI)\u003c/em\u003e, sourced from the United Nations Development Programme, captures average achievements in education, health, and income. \u003cem\u003eUnemployment rate (UMPR)\u003c/em\u003e reflects the share of the labor force without employment, as reported by the World Bank. Finally, \u003cem\u003eaccess to healthcare (ATHC)\u003c/em\u003e is proxied by the number of physicians per 1,000 persons, also derived from World Bank data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Size and Missing Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe dataset covers 15 countries observed over 15 years, resulting in 225 country-year observations. A balanced panel was constructed by cross-validating variables across multiple sources. Instances of missing data were minimal and addressed through linear interpolation using official World Bank and UNDP estimates to preserve data consistency. No country-year observation was dropped, ensuring a fully balanced sample across the study period.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData Description\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeasurement/Definition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural Poverty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRPVT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation of rural poor (Percentage of the rural population living below the international poverty line USD 2.15/day).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWPC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFertility Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFTLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal annual fertility rate (Total number of live births per woman in a given year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWB/WDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman Development Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLERV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eaverage achievement in key dimensions of human development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUNDP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployment Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUMPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShare of the total labor force without employment (% of total labor force).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to Healthcare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eATHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of physicians per 1,000 persons (proxy for access to healthcare services).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWB\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale Labor Participation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFLP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemale labor force participation rate (% of women aged 15 years and above engaged in labor market activities).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWB/ILO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNB: WPC\u0026thinsp;=\u0026thinsp;World Poverty Clock, WB\u0026thinsp;=\u0026thinsp;World Bank, UNDP\u0026thinsp;=\u0026thinsp;United Nation Development Programme, ILO\u0026thinsp;=\u0026thinsp;International Labor Organization\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.1 Descriptive Statistics\u003c/div\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics for the key variables in the analysis, offering insights into the distribution and variability of rural poverty, fertility, health, labor, and development indicators across the sample. The average rural poverty rate is 15.226, with a standard deviation of 1.571, suggesting a relatively high but moderately dispersed level of poverty in rural areas. The FTLT averages 4.510, indicating persistently high fertility across regions, with a standard deviation of 1.026 and a range from 2.261 to 6.623, reflecting meaningful cross-country differences in reproductive behavior. In contrast, ATHC is considerably low, with a mean of only 0.207 physicians per 1,000 persons and a standard deviation of 0.177, highlighting both a general shortage and unequal access to healthcare services. The UMPR shows the greatest variability among all indicators, with a wide range from 1.047 to 34.007, a mean of 7.228, and a very large standard deviation of 7.663, pointing to deep structural disparities in labor markets. On the other hand, FLP and the HDI demonstrate more stable distributions. FLP has a mean of 4.141 and a low standard deviation of 0.152, while HDI averages 3.953 with a standard deviation of 0.169, suggesting relative consistency in gender inclusion in the workforce and overall development outcomes across the sample. The variances reinforce these patterns, with the highest recorded for UMPR (58.718), followed by RPVT (2.467) and FTLT (1.052), indicating substantial dispersion in employment, poverty, and fertility, while the lowest variances in FLP (0.023) and HDI (0.029) imply greater uniformity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. dev\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPVT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.467\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFTLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUMPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e58.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation analysis Result\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFTLR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRPVT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFLP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHDI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUMPR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eATHC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFTLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPVT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.177*\u003c/p\u003e\u003cp\u003e(0.0710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.121**\u003c/p\u003e\u003cp\u003e(0.028)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.574***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.427***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.128***\u003c/p\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187*\u003c/p\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUMPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.594***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.308***\u003c/p\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.423***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.234***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.468***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.133***\u003c/p\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.292***\u003c/p\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.464***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.577***\u003c/p\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *,**,*** represent significant level at 10%, 5% and 1% respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.2 Correlation analysis\u003c/div\u003e\u003cp\u003eReliable estimation depends on understanding the relationships among variables, particularly to avoid multicollinearity, which typically arises when correlation coefficients exceed 0.8 (Gujarati, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The correlation matrix among all variables is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results indicate that all correlation coefficients are well below the threshold of 0.8, demonstrating no presence of multicollinearity or problematic interactions. This confirms the data quality and supports the stability of coefficient estimates, ensuring reliable and robust model results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.3. Cross-Dependence Test\u003c/div\u003e\u003cp\u003eThe cross-dependence (CD) test conducted to determine if the countries under study exhibits cross-sectional dependence among themselves. This study employs (Pesaran, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) CD test (P-CSD) and the result is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The result reveals that all the variables exhibit statistically significant cross-sectional dependence at the 1% level. This strong significance implies that the variables are interdependent across cross-sections, indicating the presence of common shocks or spillover effects in the panel data. Recognizing this dependence is crucial for selecting appropriate estimation techniques that account for cross-sectional correlation, thus ensuring consistent and efficient parameter estimates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResult of the cross-dependence test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFTLR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRPVT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFLP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHDI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUMPR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eATHC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.986***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.360***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.223***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.346***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.548***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.182***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *,**,*** represent significant level at 10%, 5% and 1% respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResult of the Unit root test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCROSS SECTION IPS (CIPS)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eCROSS SECTION ADF (CADF)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDiff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDiff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFTLR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.729***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.347***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-5.385***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRPVT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.648****\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.452**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-4.829***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eflp\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.658***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.768****\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.356**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-4.818***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHdi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.934***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-5.392***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.460***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eI(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eumpr\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.290*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.814***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.474**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-4.208***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eATHC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.527***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.377**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-4.139***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eI(0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *,**,*** represent significant level at 10%, 5% and 1% respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.4 Stationarity Test\u003c/div\u003e\u003cp\u003eThe utilisation of the second-generation panel unit root tests, Cross-sectionally Augmented IPS (CIPS) and Cross-sectionally Augmented Dickey-Fuller (CADF) was necessitated by the presence of cross-sectional dependence (CSD), as confirmed by the CD test results. These tests account for potential interdependencies across units, providing more reliable stationarity diagnostics in panels with CSD. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, most variables including FTLR, FLP, HDI, and UMPR are stationary at level [I(0)] under both tests. However, RPVT and ATHC show mixed orders of integration, being non-stationary at level in the CIPS test but stationary under CADF. These findings justify the need for careful model specification and, where necessary, the application of estimation techniques robust to mixed integration orders and cross-sectional dependence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e4.1.7. Kao Residual Cointegration Test\u003c/div\u003e\u003cp\u003eTo assess the presence of a long-run equilibrium relationship among the variables, the (Kao, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) residual cointegration test was employed. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the ADF t-statistic is statistically significant at the 1% level. This confirms the rejection of the null hypothesis of no cointegration, suggesting the existence of a stable long-run relationship among the variables in the panel. The low residual variance and HAC variance further support the robustness of the cointegration result.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResult of the Kao Residual Cointegration Test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProb.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADF t-Statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAC variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Null hypothesis of no cointegration is rejected at 1%.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResult of the baseline model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eResult of the PMG Analysis Test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTwo-way System GMM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLagFTLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.784***\u003c/p\u003e\u003cp\u003e(4.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPVT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.681***\u003c/p\u003e\u003cp\u003e(2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.467***\u003c/p\u003e\u003cp\u003e(3.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.484***\u003c/p\u003e\u003cp\u003e(7.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.593**\u003c/p\u003e\u003cp\u003e(2.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.311**\u003c/p\u003e\u003cp\u003e(2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.806***\u003c/p\u003e\u003cp\u003e(3.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.216***\u003c/p\u003e\u003cp\u003e(6.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.765**\u003c/p\u003e\u003cp\u003e(4.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.923*\u003c/p\u003e\u003cp\u003e(1.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUMPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.827***\u003c/p\u003e\u003cp\u003e(5.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.471***\u003c/p\u003e\u003cp\u003e(6.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.632***\u003c/p\u003e\u003cp\u003e(10.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.643**\u003c/p\u003e\u003cp\u003e(2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.851***\u003c/p\u003e\u003cp\u003e(9.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.289**\u003c/p\u003e\u003cp\u003e(2.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.869***\u003c/p\u003e\u003cp\u003e(4.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR1 (p-value)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR2 (p-value)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHansen p-value (2-step weighting matrix)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHansen p-value (2-step weighting matrix)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Column 1 displays the result of the PMG long run, while column 2 presents the short run. Column 3 presents the result of the two-way system GMM. t-statistics are in parentheses while *,**,*** represent significant level at 10%, 5% and 1% respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 PMG Analysis\u003c/h2\u003e\u003cp\u003eThis study utilizes the PMG estimation method to investigate the impact of RPVT on fertility rates in Sub-Saharan Africa. The PMG model is chosen for its ability to account for both cross-sectional dependence and parameter heterogeneity across countries, while still estimating a common long-run relationship. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, with column 1 showing long-run estimates and column 2 reflecting short-run dynamics. The findings reveal that RPVT has a positive and statistically significant association to fertility rates in both the long run and short run. This suggests that higher levels of rural poverty are consistently associated with increased fertility rates over time. These results are consistent with previous studies by (Odimegwu \u0026amp; Adedini, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; V. Otieno et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This relationship can be interpreted through the lens of economic rationality and social structure in underdeveloped rural settings. In poor, agriculture-based communities, children are frequently seen as both a source of labor and a form of economic security, especially in the absence of formal pension or social protection systems. As a result, higher fertility may reflect a survival strategy families choose to have more children to mitigate income instability, offset high child mortality risks, and provide old-age support. Additionally, rural poverty is closely tied to low educational attainment, particularly for women, and limited access to reproductive healthcare, both of which are well-established drivers of high fertility. In such settings, cultural norms favoring large families, low female autonomy in reproductive decision-making, and inadequate family planning services create a feedback loop in which poverty sustains high fertility, and high fertility deepens poverty.\u003c/p\u003e\u003cp\u003eIn line with theoretical expectations, FLP exhibits a negative and statistically significant effect on fertility in both the short and long run. This finding aligns with prior studies by (Behrman \u0026amp; Gonalons-Pons, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Irfan Chani et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mishra \u0026amp; Smyth, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and is consistent with demographic transition theory. Increased participation of women in the labor force typically delays marriage and childbearing, raises the opportunity cost of motherhood, and enhances exposure to family planning resources all of which contribute to reduced fertility. Similarly, HDI has a strong negative relationship with fertility. Higher levels of education, income, and life expectancy are associated with lower fertility rates, likely due to increased access to healthcare services, better awareness of reproductive choices, and changing societal values. These results support findings by (G\u0026ouml;tmark \u0026amp; Andersson, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hafner \u0026amp; Mayer-Foulkes, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Harttgen \u0026amp; Vollmer, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). UMPR is found to have a positive and significant link to fertility, suggesting that in economically insecure environments, especially traditional or rural ones, families may respond by having more children. This behavior may stem from the role of children in household labor or reflect limited reproductive autonomy and constrained access to contraception. This outcome aligns with (Andersen \u0026amp; \u0026Ouml;zcan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), though it contrasts with studies in developed countries, such as (Miladinov, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on Turkey and Greece, (Cavallini, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on Italy, and (Di Nallo \u0026amp; Lipps, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) on the UK and Germany, which generally find a negative fertility response to unemployment.\u003c/p\u003e\u003cp\u003eInterestingly, ATHC shows a positive association with fertility. This may initially seem counterintuitive; however, it could reflect a situation where improved maternal and child healthcare reduces child mortality, which may encourage the persistence of traditional fertility preferences. Alternatively, if improvements in general healthcare access are not matched by expanded family planning services, the net effect may still support higher fertility. Finally, the ECM is negative and highly significant, indicating the presence of a stable long-run equilibrium. The coefficient suggests that approximately 87% of short-term deviations from the long-run equilibrium are corrected within one period, implying a rapid speed of adjustment toward long-run stability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Robustness Test\u003c/h2\u003e\u003cp\u003e\u003cb\u003eTwo-Way System GMM\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile the PMG estimator is well-suited for analyzing long-run relationships in panel data, it comes with certain limitations. Specifically, it assumes no endogeneity among regressors and imposes homogenous long-run coefficients across cross-sectional units. These assumptions may result in biased and inconsistent estimates, particularly in the presence of simultaneity, unobserved heterogeneity, or dynamic relationships, where past values of the dependent or independent variables may influence current outcomes. To address these limitations, the study adopts the two-way System GMM approach. This method corrects for endogeneity by using lagged levels and differences of the variables as internal instruments, thereby ensuring consistent estimates even when regressors are endogen by incorporating both individual and time fixed effects, offering more robust estimates in the presence of interdependencies across countries.\u003c/p\u003e\u003cp\u003eWe begin by evaluating the validity of the System GMM estimator, with results presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, column 3. The Arellano-Bond test for AR(1) is significant, indicating the expected presence of first-order serial correlation. However, the AR(2) test is not significant, suggesting no second-order autocorrelation, which supports the appropriateness of the model specification. Furthermore, the Hansen test of overidentifying restrictions is not significant for both the two-step weighting matrix and the second iteration, confirming that the instruments used are valid and the model is not over-identified. With model validity confirmed, we proceed to interpret the results. The lagged fertility rate (LagFTLT) is found to be positive and highly significant, indicating strong persistence in fertility behavior over time. This result supports the idea of fertility inertia, where past fertility patterns strongly influence current decisions. In many traditional or rural settings, established fertility norms are reinforced by cultural expectations, economic roles of children, and intergenerational behaviors, contributing to a self-reinforcing cycle. Consistent with the findings from the PMG model, RPVT remains a positive and significant determinant of fertility. This reinforces the conclusion that rural poverty consistently drives higher fertility rates, regardless of whether a static or dynamic model is applied. Other control variables also show results that align closely with the PMG estimates, further supporting the robustness of the main findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Quantile Regression Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResult of the quantile regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRPVT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.194**\u003c/p\u003e\u003cp\u003e(2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.303***\u003c/p\u003e\u003cp\u003e(5.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.384***\u003c/p\u003e\u003cp\u003e(6.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.454**\u003c/p\u003e\u003cp\u003e(2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.547***\u003c/p\u003e\u003cp\u003e(2.62)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFLP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.487***\u003c/p\u003e\u003cp\u003e(-3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.573***\u003c/p\u003e\u003cp\u003e(-5.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.625**\u003c/p\u003e\u003cp\u003e(-2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.649***\u003c/p\u003e\u003cp\u003e(-3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.702***\u003c/p\u003e\u003cp\u003e(-8.69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.507**\u003c/p\u003e\u003cp\u003e(-2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.581*\u003c/p\u003e\u003cp\u003e(1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.616**\u003c/p\u003e\u003cp\u003e(-2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.705**\u003c/p\u003e\u003cp\u003e(-2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.874***\u003c/p\u003e\u003cp\u003e(-4.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUMPR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.414***\u003c/p\u003e\u003cp\u003e(2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.467***\u003c/p\u003e\u003cp\u003e(6.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.512**\u003c/p\u003e\u003cp\u003e(2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.484**\u003c/p\u003e\u003cp\u003e(2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.554**\u003c/p\u003e\u003cp\u003e(2.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATHC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.183*\u003c/p\u003e\u003cp\u003e(1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.326**\u003c/p\u003e\u003cp\u003e(2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.372***\u003c/p\u003e\u003cp\u003e(3.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.414**\u003c/p\u003e\u003cp\u003e(2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.492***\u003c/p\u003e\u003cp\u003e(3.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: t-statistics are in parentheses, while *,**,*** represent significant level at 10%, 5% and 1% respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHaving found that rural poverty contributes to fertility rate using PMG, which uses mean effect, we went further to examine this relationship using quantile regression. This study specifically employs Method of Moments Quantile Regression. The motive for this is to provide a more comprehensive understanding of the conditional distribution of the dependent variable, rather than focusing solely on the mean effect, which PMG provides. Also, PMG assume homogeneity in error variance and may overlook important distributional differences, however, MMQR captures heterogeneity in the impact of independent variables across different points (quantiles) of the outcome distribution (Adegboyo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bui et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which is especially useful when the relationship between variables varies at different levels of the dependent variable, such as in cases involving poverty.\u003c/p\u003e\u003cp\u003eThe results is presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and it reveals that RPVT positively influences fertility across all quantiles, suggesting that the effect of rural poverty on fertility is consistent, but its strength increases at higher fertility levels. This result is similar to the result of the PMG and the findings of (Odimegwu \u0026amp; Adedini, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; V. Otieno et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but provide a deeper understanding of how rural poverty affects fertility rate across different quantile. The result implies that while RPVT raises fertility even at lower levels, its influence grows stronger and becomes significantly more pronounced at the higher fertility quantiles. The quantile regression results show that the impact of rural poverty on fertility is more pronounced at higher fertility levels, suggesting that in regions already experiencing high fertility, the economic pressures of poverty exacerbate the desire for larger families. This reflects the fact that, in poverty-stricken areas, children are often seen as economic assets helping with household labor and ensuring financial stability. At lower fertility levels, RPVT still has a positive impact, but this effect is less pronounced, indicating that while rural poverty remains a key driver of fertility, its impact is more significant in high-fertility regions.\u003c/p\u003e\u003cp\u003eAcross all quantiles, female labor force participation (FLP) shows a negative and significant effect, with the impact becoming more substantial at higher fertility levels. This supports the idea that as women increasingly engage in the workforce, the opportunity cost of childbearing rises, leading to fertility decline particularly in regions where fertility is traditionally high. These findings align with demographic transition theory and echo the results of PMG. HDI also demonstrates a negative effect on fertility across all quantiles, with a stronger relationship at the upper end of the fertility distribution. This suggests that improvements in human development, through better education, healthcare, and income contribute to fertility decline, especially in high-fertility settings where developmental gains have the most transformative potential. Similarly, UMPR shows a positive association with fertility across all quantiles, with the effect amplified at higher quantiles. This indicates that in contexts of economic instability, particularly in high-fertility areas, households may respond by having more children as a form of economic security. This behavior may reflect traditional social expectations or a lack of alternative coping mechanisms in the absence of stable employment. Lastly, the availability of trained healthcare personnel exhibits a positive effect on fertility, and this relationship becomes more pronounced at higher fertility levels. While improved healthcare reduces child mortality and enhances maternal well-being, in areas where access to family planning services remains limited, this may inadvertently encourage higher fertility. Families may continue to adhere to traditional reproductive norms in the absence of sufficient reproductive health education and services.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings of this study demonstrate that rural poverty is a key driver of high fertility in Sub-Saharan Africa, reinforcing the notion that children are viewed as both labor resources and economic security in the absence of strong social protection systems. This outcome is consistent with earlier work (Odimegwu \u0026amp; Adedini, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Otieno et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and it lends supports to poverty\u0026ndash;fertility relationship in rural areas where deprivation is most severe.\u003c/p\u003e\u003cp\u003eBeyond poverty itself, socioeconomic conditions strongly shape fertility behavior. Female labor participation and higher levels of human development are associated with reduced fertility, highlighting the importance of women\u0026rsquo;s economic empowerment, education, and improved living standards in shifting demographic outcomes (Andersen \u0026amp; \u0026Ouml;zcan, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Behrman \u0026amp; Gonalons-Pons, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Conversely, unemployment is positively associated with fertility, which contrasts with findings from developed (Di Nallo \u0026amp; Lipps, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Miladinov, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that in rural SSA, economic insecurity encourages larger families as a coping strategy rather than discouraging childbearing. Access to healthcare also shows a positive relationship with fertility, likely reflecting reductions in child mortality without parallel expansion of family planning services, reinforcing the need to integrate reproductive health into rural health systems. The quantile regression analysis further enriches these insights by showing that the impact of rural poverty is not uniform but intensifies at higher fertility levels. This nonlinear relationship indicates that interventions targeting the poorest and most high-fertility regions may yield the strongest demographic effects. By combining PMG, GMM, and MMQR approaches, the study not only confirms the robustness of the poverty-fertility link but also uncovers distributional dynamics often missed in prior research.\u003c/p\u003e\u003cp\u003eTaken together, the results validate Becker\u0026rsquo;s Investment Model of Fertility in rural SSA and emphasize that reducing fertility requires more than economic growth. Comprehensive strategies that combine poverty alleviation, women\u0026rsquo;s empowerment, education, and integrated family planning are essential for breaking the cycle of rural poverty and persistently high fertility.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis research investigates the impact of rural poverty on fertility rates across 15 Sub-Saharan African countries over the period 2010\u0026ndash;2024. The study employs the Pooled Mean Group estimator, two-step System Generalized Method of Moments, and MMQR to comprehensively analyze both average and distributional effects. The main conclusions of this study are as follows: First, rural poverty is consistently associated with higher fertility rates in both the short and long term, as evidenced by both PMG and GMM estimates. This finding confirms the existence of a poverty-fertility relationship, particularly in rural SSA, where children serve as economic security in the absence of formal welfare systems. Second, quantile regression results reveal that the positive relationship between rural poverty and fertility intensifies at higher levels of fertility. This suggests that in regions already experiencing high fertility, rural poverty exacerbates the problem more severely, indicating a nonlinear and asymmetric effect. This finding emphasizes the need for regionally targeted interventions, as poverty-driven fertility behaviors are most intense in areas with already elevated fertility levels. Third, female labor participation and the human development index are negatively associated with fertility across all estimation techniques. In particular, increased FLP and improvements in HDI through education, healthcare access, and income growth are found to significantly reduce fertility rates, underscoring the importance of women\u0026rsquo;s economic empowerment and human development in fertility reduction strategies. Fourthly, unemployment and access to healthcare positively influence fertility. Unemployment appears to be associated with higher fertility as a household coping strategy in the face of economic insecurity. Meanwhile, increased healthcare access, while reducing child mortality, may also unintentionally reinforce existing fertility preferences, especially in areas lacking adequate family planning services. Finally, these findings highlight that tackling rural poverty is not just a matter of improving livelihoods but also a necessary condition for managing fertility dynamics and achieving demographic transition in SSA. Any effective fertility-reduction strategy must therefore be grounded in sustained poverty alleviation targeted at rural populations.\u003c/p\u003e"},{"header":"7. Policy Recommendations","content":"\u003cp\u003eBased on the findings of this study, the following policy recommendations are proposed: First, in high-fertility rural regions, where poverty significantly drives fertility decisions, governments should prioritize integrated rural development programs that combine income-generating activities with reproductive health education. These programs should address the root causes of high fertility by reducing rural poverty and economic insecurity. Second, expand access to quality and affordable reproductive health services in rural areas. This includes not only increasing the availability of contraceptives and family planning education but also ensuring that healthcare systems in rural communities integrate fertility management into primary healthcare delivery. Third, promote female labor force participation through targeted rural employment programs, vocational training, and microcredit access for women. As the study shows, FLP is a powerful determinant of reduced fertility. Policies that create economic opportunities for rural women can delay childbearing and reduce desired family size. Fourth, invest in rural education infrastructure, particularly for girls. Secondary school completion should be incentivized through conditional cash transfer schemes and school feeding programs. Education is a key long-term lever for lowering fertility rates and improving overall human development. Fifth, in areas where healthcare access is increasing but fertility remains high, integrate family planning into all maternal and child health services. Governments and health agencies should ensure that gains in healthcare access are complemented by family planning counseling and services. Sixth, given the diverse effects of poverty on fertility across different quantiles, interventions should be geographically targeted. Regions with both high rural poverty and high fertility should receive prioritized support, including enhanced family planning campaigns, infrastructure investment, and gender empowerment initiatives. Seventh, governments should prioritize poverty reduction strategies tailored to rural settings, focusing on income diversification, agricultural productivity, and rural infrastructure. Programs such as rural cash transfers, microcredit schemes, and climate-resilient agriculture support can reduce economic dependence on large family sizes. Finally, increase investment in rural social protection systems, including non-contributory pensions and child benefit programs. These can reduce the economic rationale for high fertility by providing alternative forms of old-age and income security.\u003c/p\u003e"},{"header":"8. Contribution and limitations","content":"\u003cp\u003eThis study makes several contributions to the literature on fertility and poverty in Sub-Saharan Africa. Theoretically, it extends Becker\u0026rsquo;s Investment Model of Fertility by demonstrating its relevance in rural contexts where children continue to serve as economic security in the absence of formal social protection systems. Methodologically, it applies a combination of PMG, System GMM, and MMQR approaches, providing robust evidence while also capturing distributional dynamics that are often overlooked in prior research. Empirically, by focusing specifically on rural populations across 15 Sub-Saharan African countries, the study highlights the persistence of a poverty\u0026ndash;fertility trap and shows that its effects are strongest in high-fertility contexts. From a policy perspective, the findings underscore the need for integrated strategies that combine poverty alleviation, women\u0026rsquo;s empowerment, and family planning services to break the intergenerational cycle of deprivation and high fertility.\u003c/p\u003e\u003cp\u003eAt the same time, several limitations must be acknowledged. The analysis is based on secondary data from international sources, which may be subject to reporting inconsistencies and measurement errors. The focus on 15 countries in Sub-Saharan Africa, while analytically manageable, restricts the generalizability of results to the broader SSA region. Although advanced econometric methods were employed to mitigate endogeneity and heterogeneity concerns, these approaches still rely on assumptions that may not fully capture the complex causal mechanisms shaping fertility behavior. Finally, the study emphasizes quantifiable socioeconomic variables, leaving out cultural, religious, and household-level gender dynamics that are also crucial in fertility decisions. Future research could build on this work by incorporating richer, country-specific data and adopting mixed-method approaches to provide deeper insights into the poverty\u0026ndash;fertility nexus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Statement:\u003c/h2\u003e\u003cp\u003eNo funding, grants, or other support were received throughout this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAM: overall title idea, conceptualization, method, data, analysis, and entire draft of the paper. JL: Supervised, revised the analysis, validated the data, formulated the draft, and corrected the entire idea. OS: Reviewed the analysis, formulated, and structured the draft. AR: review the theoretical perspective, proofread, and formatted.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdebowale, A. S., Fagbamigbe, A. F., Akinyemi, J. O., Olowolafe, T., Onwusaka, O., Adewole, D., Sadiku, S., \u0026amp; Palamuleni, M. (2020). Dynamics of poverty-related dissimilarities in fertility in Nigeria: 2003-2018. \u003cem\u003eScientific African\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, e00468. https://doi.org/10.1016/J.SCIAF.2020.E00468\u003c/li\u003e\n\u003cli\u003eAdegboyo, O. S., Yang, L., de Dieu Ndayambaje, J., \u0026amp; Gakuru, E. (2025). 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Women\u0026rsquo;s empowerment and fertility: A review of the literature. \u003cem\u003eSocial Science \u0026amp; Medicine\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e, 111\u0026ndash;120. https://doi.org/10.1016/j.socscimed.2014.06.014\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rural poverty, Fertility, Fertility trap: Sub-Saharan African countries","lastPublishedDoi":"10.21203/rs.3.rs-7549782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7549782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFertility remains persistently high in many parts of Sub-Saharan Africa (SSA), particularly in rural areas where poverty is widespread and institutional support is limited. This study investigates the underexplored role of rural poverty in shaping fertility dynamics, focusing on both the direct effect of poverty on fertility and the distributional heterogeneity of this relationship across fertility levels. Using balanced panel data from 15 SSA countries covering the period 2010 to 2024, we employ the Pooled Mean Group (PMG) estimator, two-step System Generalized Method of Moments (GMM), and the Method of Moments Quantile Regression (MMQR) to systematically examine how rural poverty influences fertility decisions. Our results first reveal that rural poverty significantly associated with higher fertility in both the short and long term, with the effect becoming more pronounced in high-fertility contexts. Second, female labor participation and human development mitigate fertility, whereas unemployment and limited healthcare access reinforce it. Finally, the MMQR results confirm that the influence of rural poverty intensifies at higher levels of fertility, suggesting a deepening poverty\u0026ndash;fertility relationship in the most vulnerable rural populations. These findings call for multifaceted interventions, including rural social protection programs, integrated family planning services, and gender-focused education and employment policies to disrupt the intergenerational cycle of poverty and high fertility in SSA.\u003c/p\u003e","manuscriptTitle":"Rural poverty and fertility transitions in Sub-Saharan Africa: Understanding the link and pathways to stability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 09:04:12","doi":"10.21203/rs.3.rs-7549782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T20:08:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T05:56:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252477423902677681562411244020175957063","date":"2026-01-10T14:36:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16841060822468441253210051892201731633","date":"2026-01-10T08:40:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157734326894125806585844977478930566535","date":"2026-01-05T03:33:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16695610360990990953693592774592423552","date":"2025-10-25T09:46:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129469934043550823217307295698197130005","date":"2025-10-23T14:52:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T14:28:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60567928603431629243771724197974533414","date":"2025-10-03T09:26:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T06:56:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T06:52:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-24T06:16:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T09:51:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-11T09:22:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9f6c82a5-7c42-4682-a272-af11e6e5cf02","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":55759069,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":55759070,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-03-04T20:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 09:04:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7549782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7549782","identity":"rs-7549782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}