Socioeconomic Status

Excerpt taken from my research proposal, pages 96-103.

*Minor adjustments have been made for formatting and readability purposes.

Socioeconomic Status (SES): SES is an indication of the social and economic capital an individual possesses and can be measured by a variety of factors (Antonoplis, 2023).

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SES is a key component in research studies in the human services field. At the individual level, SES reflects a person’s relative social and economic standing, whereas at the national level, it functions as a measure of a country’s global standing and its position in the world-system (Rehuher et al., 2021).

To further explain, Rehuher et al. (2021) used the World Health Organization’s (WHO) (2010) Social Determinants of Health (SDOH) model specifically, which describes socioeconomic environment as defined by governance, financial systems, and social policies and values while one’s socioeconomic class within their environment is based on gender, education, occupation, and income. This framework is additionally supported by Nishan et al.’s (2025) study on IPV where age, residence, wealth, education, work status, and education were variables considered to represent SES.

In recent decades, SES has become a global priority, evident by the United Nation’s (UN) seventeen SDGs which focus on ending poverty, improving health and education, reducing inequalities, promoting economic growth, and preserving the earth’s natural environment (UN, 2025). The WHO’s SDOH model and the UN SDGs provide the framework for establishing SES and determining its role in research.

SES has been used in many studies to examine various aspects of underdeveloped regions of the world. For example, Amir-ud-Din et al. (2024) studied how sociodemographic gaps in age, education, and income impacted intimate parnter violence (IPV) in twenty-nine developing countries in Asia, Africa, and the Middle East. More relevant to my research topic, however, is the study conducted by Ashraf et al. (2022) on countries in the Pacific region that had been classified by the UN Economic and Social Commission for Asia and the Pacific (UNESCAP) as being preemptively subjected to socioeconomic barriers that hinder them from meeting Sustainable Development Goal (SDG) 2030 targets.

Or Gillani’s (2021) study of one-hundred, seventy countries which showed that higher levels of national dependency on external support contributes to higher levels of poverty. And finally, Kandemir’s (2022) study which tested the economic effects of the multidimensional poverty index, inequality in income, and the GNI per capita on the education level of women in eighty-two developing countries. As demonstrated by these examples, SES is a clear indicator of a country’s financial health and developmental progress.

Many social scientists have attempted to explain the persistent disparities between developed and underdeveloped nations. Some researchers have argued that external forces driven by capitalism impede a country’s development (Gillani, 2021; Hunt, 2024; John et al., 2023; Yende & Ntini, 2022), while others have claimed that a lack of communal support for democratic, liberal values and policies are ultimately the cause (Gurney, 2022; Nguyen-Phung, 2024; Toklo, 2024).

Nevertheless, researchers agree that poverty remains a significant concern for humans around the world, (Adeleke et al., 2022; Ashraf et al., 2022; Gillani, 2021; Gurney, 2022). And while researchers equally agree that gender inequalities exist everywhere today, there are a number of studies which have shown that gender-balanced communities actually increase national productivity and promote overall economic growth (Ashraf et al., 2022; Girón et al., 2024; Ozili, 2024; Pervaiz et al., 2023; Shittu et al., 2024; Wani et al, 2024).

Collectively, these examples demonstrate the versatility of SES in scientific research as well as the ability to draw valid conclusions about poverty levels and social statuses across diverse cultures and backgrounds as discussed in greater detail below.

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Education

There is a direct link between poverty and education, especially for women. For example, Rehuher et al. (2021) found that in the Micronesian region where the FAS are located, socioeconomic inequalities in academics and job markets have continuously reinforced women’s poverty levels. In comparison, Kandemir (2022) concluded that in eighty-two low- to middle-income countries (LMICs), as gross national income (GNI) per capita increased, so did the number of women twenty-five or older with at least a secondary education.

Similarly, as Adeleke et al. (2022) explained, women living in poor, rural areas of Africa have less access to basic services and are oftentimes unable to pursue academic opportunities. This was supported by their study’s results which showed the highest number of internet users were between the ages of fifteen and nineteen, from higher income households in urban areas, with at least a secondary education. According to these authors, there is a strong connection between the poorest households and deficiencies in education.

Gender inequalities further contribute to poverty and women’s education. In Rehuher et al.’s (2021) study of the Pacific Islands, only fifty-two percent of women could read, only thirty-five percent of households achieved a primary-level education, and most children in the RMI did not attend school after age fourteen. Similarly, Nishan et al. (2025) found that IPV against pregnant women was higher in lower-educated couples in a study that included data from thirty LMICs across five continents. While many women are expected to fulfill unpaid roles in the home, societal constraints and familial pressures oftentimes deter women from pursuing education (Rehuher et al, 2021).

However, in a study of Kenyan women specifically, Nguyen-Phung and Nthenya (2024) determined that even one additional year of education betters the lives of women by prolonging childbirth, reducing IPV, and increasing household decision-making abilities. Furthermore, in two different studies, researchers found higher levels of gender equality in education, economic activity, and employment to have a positive effect on a country’s financial status (Girón & Kazemikhasragh, 2022; Girón et al., 2024).

Likewise, Ashraf et al. (2024) discovered that investments in education had a positive impact on fiscal development. These studies not only show the impact of education on SES but also establish the importance of gender parity in academic settings.

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Gender Equality and Women’s Empowerment

Women in LMICs have had a substantial impact on their communities. Over time, women’s groups around the world have driven progress and transformed gender roles through collective, grass-roots actions that prioritize social issues (Smith & Katzman, 2020). While Brulé (2023) found women to be historically instrumental in challenging oppressive political and economic establishments in the Global South, Gurney (2022) determined that women in the Pacific serve as important change agents who encourage economic and social advancements that promote communal health today.

For instance, although Foudjo and Keneck-Massil (2024) determined that climate vulnerability had a negative effect on children’s health, they also found that women’s political empowerment (WPE) and women’s education mitigated those effects. In the same way, Le and Nguyen (2024) found that across fifty-two LMICs, maternal education decreased child mortality. In recent years, scholars have increasingly examined how gender equality and women’s empowerment intersects with the broader analyses of social and fiscal developments. 

Gender equality and economic equality have been widely studied together. In a bibliometric analysis of two-hundred, eight journal publications conducted by Fauzi et al. (2023), the researchers showed that promoting financial inclusion and empowering entrepreneurial women through microfinance programs, lowered poverty rates. In addition, Girón and Kazemikhasragh (2022) investigated the relationship between gender inequality and fiscal growth in LMICs and results indicated a negative and significant relationship between the Gender Inequality Index and economic development.

In fact, multiple studies of LMICs show gender equality in institutions, programs, and laws were proven to have a significant positive effect on a nation’s fiscal strength and stability (Girón et al., 2024; Ozili, 2024; Pervaiz et al., 2023; Wani et al., 2024).

Other illustrations include Tadesse et al. (2024), who studied gender-related aid in one-hundred, eighteen LMICs over the course of twenty-three years and concluded that significant funding designed to decrease inequalities were, in fact, extremely effective. This is further supported by Dim (2024) who concluded that female leaders of developed countries allocated more women-focused aid to LMICs than males and Tusalem (2022) who found that a minimal number of women in official decision-making roles can have a significant influence on public policies, respectively. These studies show how important women are in ensuring social and economic progress.

However, despite all of this, women still face significant obstacles to achieving equality. In a recent study, researchers found that gender norms and societal practices lead to an increased risk of GBV for women in rural and agricultural areas of LMICs, which ultimately limits their academic potential and their ability to contribute to their communities (O’Mullan et al., 2024). In addition, Johnston et al. (2023) noted an increase in gender-based violence (GBV) paired with a decrease in services for abused women in the Asia Pacific region since COVID-19. Results of another study that included over one-hundred, fifty-thousand women from five different areas of the world, showed Papau New Guinea, an island nation in the Oceania region, to have the highest rate of IPV against pregnant women (Nishan et al., 2025).

Ultimately, authors agree that patriarchal values and the traditional division of labor have reinforced structural gender inequalities, creating environments where women still experience considerable discrimination and marginalization today (Johnston et al., 2023; Nishan et al., 2025; Roy & Xiaoling, 2022; Shittu et al., 2024). As I have shown, SES has been used in a variety of studies to examine numerous social, political, and economic aspects on local, national, and global levels.            

There are many quantitative studies incorporating SES which utilize a cross-sectional design and/or regression analysis. As examples, Amir-ud-Din et al. (2024), Kandemir (2022), Nishan et al. (2025), Tusalem (2022), and Wani et al. (2024) all used a cross-sectional design and regression analysis to study various SES factors including education, age, wealth status, urbanity, and economic growth. Gillani (2021) employed linear regression to analyze the effect of external economic dependency on poverty, and in a study conducted by Ashraf et al. (2024), regression analysis was used to explain how indicators for SDG1, SDG2, SDG3, SDG4, and SDG5 impacted sustainable development, which was measured by GDP.

In another example, Girón et al. (2024) used ordinary least-squares (OLS), fixed-effect, and random-effect regressions to examine the relationship between independent variables that represented gender equality and the overall financial health of thirty-four countries, measured by GDP and FDI. These quantitative studies have provided robust data and contributed to academic discussions about the influences on and implications of SES.

There are inherent strengths and weaknesses to quantitative research involving SES and LMICs. For example, collecting data in LMICs can be challenging because of insufficient infrastructure and outdated technology that can impede an entity’s ability to accurately gather relevant information (Orhan & Guajardo, 2022; Shittu et al., 2022). Furthermore, in regions where education is limited, many groups and institutions do not always have qualified workers who can perform adequate analyses of the data that is collected. O’Mullan et al. (2024) also noted that gathering relevant and reliable data in LMICs is challenging and that underreporting remains a concern.

Additionally, although some studies generated greater insight on a single country or a small number of countries in a single region, generalizability of the studies’ results were limited in these cases (Adeleke et al., 2022; Dim, 2024; Nguyen-Phung & Nthenya, 2024). Even when dozens of countries were incorporated in studies, researchers noted that expanding further studies to include more countries would generate more vigorous and comprehensive data (Toklo, 2024; Wani et al., 2024). While valid and appropriate numbers from LMICs can be inadequate in some circumstances, there is still a plethora of existing information for researchers to draw sufficient conclusions from.

Relying on secondary data can also be problematic, however, as researchers are unable to control the data collection process and parameters. For example, Nguyen-Phung and Nthenya (2024) relied on data from the Kenya Demographic Health Survey (KDHS) from six different years and Dim (2024) collected data for Australia, Canada, Finland, and Germany between 2002 and 2016 from the Organization for Economic Cooperation and Development (OECD) Wizard. Girón et al. (2024) also used secondary data collected by International Labor Organization Statistics (ILOSTAT) and the World Bank between 2000 and 2019. This was also the case in other studies which may have had an influence on the research results (Amir-ud-Din et al., 2024;Ashraf et al., 2024;Nishan et al., 2025; Tusalem, 2022).

Nonetheless, as noted by Le and Nguyen (2024), when information is collected from multiple countries over a range of time and space, estimations are more likely to be considered externally valid. In this section, I have shown how SES has been used to study numerous aspects of communities both small and large, and I provided examples that substantiated the use of a quantitative methodology to conduct said studies.

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