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. 2023 Mar 15;22:101382. doi: 10.1016/j.ssmph.2023.101382

Subjective social status and mental health among adolescents in Ethiopia: Evidence from a panel study

Caroline Owens 1,∗, Craig Hadley 1
PMCID: PMC10041554  PMID: 36992716

Abstract

Numerous studies have found that a relationship between subjective status and measures of human health persists even after controlling for objective measures, including income, education, and assets. However, few studies have probed how status shapes health among adolescents, particularly those in low-and-middle-income settings. This study examines the relative effects of subjective and objective status on mental health among Ethiopian adolescents. Using data from two waves of the Jimma Longitudinal Family Survey of Youth (N = 1,045), this study uses a combination of linear regression and linear mixed-effects models to examine the relationships between objective social status, subjective social status, and mental well-being among adolescents in Ethiopia. Three measures of objective status, including household income, adolescent education, and a multidimensional measure of material wealth, were assessed. Social network and support variables were constructed using factor analysis. A community version of the 10-rung McArthur ladder was used to assess the subjective socioeconomic status of adolescents. The self-reporting questionnaire was used to assess mental well-being during both waves of the study. The significant effect of higher subjective status on reports of fewer non-specific psychological distress (−0.28; 95% CI: −0.43 to −0.14) was not mediated by objective status, material deprivation, or social support covariates. The observed relationship between status and mental well-being was consistent across successive study waves. Among a cohort of adolescents in Jimma, Ethiopia, several measures of objective status are associated with subjective status. However, akin to research among adults, the findings of our study suggest that the relationship between adolescent subjective social status and mental health persists above and beyond the effects of objective status. Future research is needed on the factors, environments, and experiences that inform adolescent perceptions of status and well-being over time.

Keywords: Subjective status, Objective status, Material wealth, Mental health, Adolescents, Ethiopia

Graphical abstract

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Highlights

  • •

    Socioeconomic status (SES) is an important determinant of health outcomes.

  • •

    Adolescence is a critical period for later-life health.

  • •

    This study examined the relationships SES and mental health among adolescents.

  • •

    Subjective status is associated with mental health among Ethiopian adolescents.

  • •

    This relationship is not mediated by measures of objective status or social support.

1. Introduction

A significant body of research has linked low socioeconomic status to adverse health outcomes, including depression and anxiety (Everson et al., 2002; Miech & Shanahan, 2000), diabetes (Connolly et al., 2000), cardiovascular disease (Clark et al., 2009, 2009d; de Mestral & Stringhini, 2017), and mortality (Bassuk et al., 2002). During the 1980s, the Whitehall mortality study found a graded risk in mortality with declining employment grade in British civil servants, despite those in the sample living at levels well above frank material deprivation. This finding challenged the prominent notion that the relationship between status and health was driven solely by the effects of material deprivation (Marmot et al., 1984) and illuminated the potential salience of subjective social standing. Subsequent research has elucidated that this graded effect between status and various health outcomes exists across the spectrum of socioeconomic status (SES) (Adler et al., 1994; Adler & Ostrove, 1999). The salience of subjective social status has been further supported by several meta-analyses, which have found that the effects of subjective status on health outcomes, including mental health, often persist after controlling for objective measures. These findings collectively support the idea that perceptions of relative position within society may often shape health above and beyond the effects of absolute position. The emerging importance of subjective social status as a determinant of health also raises critical questions about what factors shape subjective status beyond commonly used objective measures.

Objective SES measures the absolute level of a person's material resources by relying on indices such as income, educational attainment, occupation, material assets, or any combination thereof (Galobardes et al., 2007). Whereas objective SES describes factual reports of life circumstances with limited psychological influences, subjective SES measures a person's belief or perception about their position relative to others (Operario et al., 2004). Though objective SES provides a material basis for informing subjective status, studies have shown that the correlation between the two is only moderate, suggesting conceptual independence (Adler et al., 2000). One of the most widely used measures of subjective SES is the MacArthur Scale of Subjective Social Status (SSS), which assesses relative rank. Often referred to as the “social ladder,” the scale depicts ten rungs representing where individuals feel they stand relative to a given reference group. Typically, respondents rank themselves relative to those within their country or society; however, researchers have also applied the MacArthur ladder to assess rank relative to a community (Adler & Stewart, 2007), neighborhood (Wolff et al., 2010), or workplace (Akinola & Mendes, 2014) referent group. A recent meta-analysis comparing the effects of society and community ladders found that both were comparably and significantly associated with health outcomes, including mental health and self-rated health (Zell et al., 2018). Notably, the community and society ladders remained significantly associated with health outcomes after controlling for objective covariates. A separate meta-analysis found that, among a small sample of studies, the society ladder and school ladder had similar effects on health among adolescents (Quon & McGrath, 2014). Though scores on the scales were not perfectly correlated, this finding suggests that the mechanism linking SS to health may be invariant to the reference group.

Scholars have proposed various hypotheses to explain the mechanisms that undergird the relationship between subjective SES and health outcomes across contexts. Sometimes referred to as the “averaging hypotheses,” some scholars propose that subjective SES acts as a composite measure, accounting for multiple types of objective SES and SES over the life course, both of which may be obscured by commonly used objective measures (Singh-Manoux et al., 2005). By accounting for social comparisons, subjective SES indicators may be more relevant to exploring psychosocial and stress-related neurobiological mechanisms posited to explain socioeconomic gradients in health (Kawachi & Berkman, 2000; Pickett & Wilkinson, 2015; Wilkinson & Pickett, 2006). Research among non-human primates and humans underscores the psychosocial and neurobiological link between position in a social hierarchy and health outcomes (Akinola & Mendes, 2014; Sapolsky, 2005). These hypotheses point to the need for more research on the interplay between objective and subjective social status across the lifecourse.

Common method variance (also referred to as common method bias) has also been assessed as a plausible explanatory mechanism for the association between subjective SES and self-reported health outcomes, such as subjective well-being and mental health (Tan et al., 2020). Common method variance suggests that the association between constructs is driven by measurement similarity instead of conceptual similarity (Podsakoff et al., 2003). In a recent meta-analysis of subjective SES and subjective well-being, Tan and colleagues found that several proposed factors, including common method variance, social comparisons, population density, and social mobility, likely explain differences in associations across contexts (Tan et al., 2020). Finally, some studies suggest that the association between subjective SES and health outcomes may be reciprocal, such that health influences how people perceive their relative position within their society or community (Garbarski, 2010; Nobles et al., 2013). Our study incorporates longitudinal data as a mechanism for exploring how change in perceived status impacts perceived well-being over time. Similarly, by using household-level measures and adolescent measures, our analysis provides a preliminary exploration of potential bias associated with common method variance.

While social scientists generally hypothesize that rising wealth is linked with rising social status, the construct of wealth has been underdeveloped in the literature on subjective social status. Anthropologists, and others, have begun to problematize the very notion of wealth, elucidating the myriad ways one can be wealthy. For those in more agrarian contexts, ownership of livestock is a prominent example of wealth. Among pastoralist groups, cattle also likely form the basis for one's relative social status by providing livelihood security and improved standards of living (Nyima, 2014). In a different urban context, wealth in cattle or agricultural land might signal relative poverty or low social status. For instance, Hruschka and colleagues have demonstrated that these differing assessments of livelihood success, to what extent one is successful in the agricultural economy or cash economy, differentially predict health and wellbeing (Hruschka et al., 2017). We hypothesize that these differing wealth dimensions might differentially correlate with people's subjective social status. Similarly, a robust literature in the social and health sciences has focused on the importance of social capital and social networks (Ehsan et al., 2019; Kawachi & Berkman, n.d.; Lin, 1999). Given the documented associations between social capital, status, and health, we additionally hypothesize that individuals embedded in socially supportive networks may view themselves as having high subjective social status. Finally, people are likely sensitive to insecurity in daily resources, such as food and water. Therefore, we expect that living with food insecurity, a potential marker of material deprivation and psychosocial stress (Weaver & Hadley, 2009), would be associated with lower subjective social status.

Though the inverse gradient between subjective SES and health outcomes has been well-documented among adults, relatively few studies have explored this relationship among adolescents (Quon & McGrath, 2014). In contrast, considerable research has explored objective markers of status and adolescent health, particularly health behaviors (Hanson & Chen, 2007) and acute conditions, such as respiratory illness (Chen et al., 2006). Exploring how status shapes health is particularly crucial in adolescence, as it is a critical life phase for patterning future health behaviors and shaping later-life outcomes (Sawyer et al., 2012; Viner et al., 2012). Studies suggest that social determinants at the population level, including income inequality and wealth, significantly shape the health outcomes of young people across the globe (Viner et al., 2012). By adolescence, individuals have likely developed their own sense of status (Goodman et al., 2001), which may differ from their caregivers or family broadly. Young people might be sensitive to a very different set of factors in determining their subjective social status. As many young people are increasingly embedded in a global economy and are exposed to multiple forms of wealth and status markers, their beliefs about their relative position may shift. Underscoring this idea, scholars of adolescent mental health argue that globalization has produced “consequent changes in values, culture, and attitudes [that] have contributed to increased expectations by young people” (Patton et al., 2016). Therefore, examining adolescents’ subjective SES is likely to provide valuable insight into the development of the status-health gradient during the lifecourse, as well as insights into which forms of wealth are particularly meaningful to young people.

Of studies that have explored subjective SES and adolescent health, Quon and McGrath found that, as with adults, the magnitude of the effect varied by health outcome (Quon & McGrath, 2014) but was not significantly attenuated with the inclusion of objective measures. More specifically, larger effects were observed for self-reported outcomes such as mental health, self-rated health, and general symptoms than for biomarkers of health and health behaviors (Quon & McGrath, 2014). Notably, Quon and McGrath (2014) found between-group differences for geographical regions of study, suggesting that the relationship between subjective SES and health among adolescents may not be universal. Given the variability in how SES may manifest across countries with diverse policies, income inequality, sociocultural values and influences, and economic livelihoods, research on how subjective and objective SES affects adolescent health across countries is much needed. Furthermore, research that explores potential confounds of this relationship, including comprehensive measures of objective status, is required to uncover mediating and moderating factors between SES and health outcomes.

Our study focuses on adolescent mental health, the leading cause of health-related disability in this age group (Kieling et al., 2011). Research on these issues in low-income and middle-income countries, where access to traditional and biomedical care often remains low, is especially needed (Lee et al., 2014). Globally, diagnoses of depression, anxiety, and behavioral disorders are increasing among children and adolescents (Kieling et al., 2011). Recent epidemiological research estimates that mental health disorders impact approximately 18% of adults and 15% of children in Ethiopia (Sathiyasusuman, 2011). Institutional-based studies conducted at universities across Ethiopia have reported an even higher prevalence of nonspecific psychological distress and common mental disorders, affecting 40.9% among undergraduates at the University of Gondar, located in the Northwest part of the country (Dachew et al., 2015). Furthermore, as a separate study also conducted in Jimma illustrates, parents may not always recognize internalizing symptoms of distress among children and adolescents and express preferences toward traditional explanatory models and treatment options (Abera et al., 2015).

The present study aims to address these gaps in the literature by assessing the relationship between subjective SES and a suite of more objective SES indicators among adolescents in Ethiopia. Our analysis relies on longitudinal data on adolescents from two waves of the Jimma Longitudinal Family Survey of Youth conducted from 2009-to 2010 and 2012-to 2014. Our primary objectives in this study are to (1) explore the association between objective and subjective markers of status among adolescents; (2) assess associations between objective and subjective status and mental well-being among adolescents and explore potential mediators; and (3) examine how subjective status changes over time and how these changes subsequently impact changes in mental well-being. The novel data include adolescents' self-reported subjective status in a low-income country and a multidimensional marker of objective status using locally derived assets that are more sensitive to the variable livelihood pathways in Ethiopia. Additionally, by using longitudinal data, this study also explores whether and to what extent adolescents’ perceptions of status, much like more objective markers, are mutable over time.

Though we did not have explicit hypotheses about how covariates may alter these associations, we conducted stratified analyses by gender, place of residence, household income quintile, and educational attainment. Research using the Ethiopia Demographic and Health Survey (EDHS) data highlights gender disparities in socioeconomic indicators (Lailulo et al., 2015). Gender disparities among Ethiopian adolescents may influence mental well-being indirectly through perceptions of status and limited social mobility and directly through lower access to material resources and services. Gender disparities may also manifest in educational attainment or differences in educational and livelihood aspiration, as documented previously among adolescents in Ethiopia (Favara, 2017). As a result, it is conceivable that adolescent females will perceive themselves as having less control over their livelihoods, which may exacerbate perceptions of stress and mental disorders.

Place of residence may similarly capture differences in resources and services, particularly those related to mental health. Previous studies among rural communities in Ethiopia have documented a high prevalence of mental distress driven, in part, by stressful life events and a lack of mental health care integration into primary care practices (Fekadu et al., 2014, 2015; Hanlon et al., 2014). Place of residence may also inform perceptions of materials required for a sufficient lifestyle, potentially explaining covariation between material asset indices and subjective status. We also assess differences by level of education and household income, as researchers commonly use these measures as proxies for objective socioeconomic status.

2. Methods

Data for this study were obtained from the Jimma Longitudinal Family Survey of Youth (JLFSY), which followed an initial sample of 3,695 randomly sampled households and 2,084 randomly selected adolescents aged 13–17 years living in Jimma, Ethiopia. Baseline surveys were completed between 2005 and 2006 (Wave 1), with subsequent adolescent-based survey rounds in 2006–2007, 2009–2010, and 2012–2014, respectively.

This study uses data from Wave 3 (both household-level and adolescent-level surveys) and Wave 4. These survey waves were selected because questionnaires during these periods contained the variables of interest. A multi-stage stratified cluster sample was used in Jimma Town, and stratification (rural, urban) was used in the areas outside of Jimma Town. In the small towns and rural areas, the household registration lists maintained by the local political authorities were used as sampling frames. The use of stratification in Jimma Town ensured that the sample included neighborhoods from each of the major areas of the city, and the selection of neighborhoods reduced by approximately two-thirds the area of the city for which sampling frames needed to be constructed. The JLFSY is based on a sample of households and youth drawn from a major urban area (Jimma Town), three nearby towns, and the rural areas adjacent to the towns. The locations were purposely selected to provide a range of development levels (urban, semi-urban, and rural), with a random selection of households and youth within the selected communities. Structured household and adolescent-level questionnaires were used to collect data. Questionnaires were administered in Amharic and Oromoifa. Household-level information included demographic information on all current household members, including household assets and food security.

IRB pre-approval was obtained by the PIs' academic institutions [Details omitted for double-blind reviewing] and Jimma University in Ethiopia. Prior to participating, researchers explained the study's aims and procedures to all study participants, who then gave their oral informed consent. All respondents were made aware of services in their area. Because this study was an analysis of secondary data with no identifying information, it was exempted by the Institutional Review Board.

2.1. Sample

For this analysis, only adolescents who had completed waves three and four of JLFSY were included. Previous waves were excluded, as mental health was only assessed during these two survey rounds. To be eligible in waves three and four, participants must have completed previous waves of JLFSY, which began in 2005. There were no additional exclusion criteria in waves three and four of surveying. In the initial wave, household heads or spouses of the household head completed a questionnaire to glean household-level information. In households with one adolescent male or female ages 13–17, the adolescent was automatically eligible for surveying. In households with two or more eligible adolescents, a Kish table was used to randomly select an eligible adolescent. Adolescent data were merged with household data from previous survey waves to estimate household-level wealth and food insecurity.

1,528 adolescents completed the survey during wave 3. During wave 4, 1,211 adolescents completed the survey; of the 317 adolescents lost to follow-up, 27 refused to participate, and the remaining 290 adolescents were unable to be contacted at home after three attempts. In total, complete household and adolescent survey data across waves were available for 1,047 adolescents. Given that JLFSY focused on youth and young adults, individuals sampled during wave four ranged in age from 19-to 26.5 years old.

3. Measures

3.1. Dependent variables

In this study, we assess two dependent variables, the first of which is subjective social status. This measure is based on a 10-rung community ladder instrument that asked adolescents to rank the perceived wealth of their household compared to other households in their kebele (an administrative subunit roughly equivalent to “community” or neighborhood). We then assess whether subjective social status is associated with mental well-being using the self-reporting questionnaire (SRQ), a validated measure of non-specific psychological distress developed for use in low-and-middle-income countries (Beusenberg et al., 2021). The modified version of the SRQ contained 23 items, including, “In the last 30 days, do you find it difficult to enjoy your daily activities?” and “In the last 30 days, was your appetite poor?” which assess non-specific psychological distress using binary response options of yes or no. The complete SRQ instrument used in this study is available in Supplementary Table 1.

3.2. Independent variables

To address our primary objective, we used several commonly-used measures of objective social status: (1) monthly household income, (2) adolescent education level, and (3) a multidimensional measure of material asset-based wealth.

Following Hruschka et al. (2017), our assessment of household wealth used a multidimensional approach determined through multiple correspondence analysis. The multiple correspondence analysis included a range of household assets, including household construction materials, water and sanitation access, access to services such as electricity, material and consumer goods, and agricultural assets. Possession of assets was dummy coded as binary variables (0–1). Assets with counts, such as the number of cows or chickens, were bracketed into categories to facilitate dummy coding. To determine what the dimensions through the multiple correspondence analysis best represent, we examined the correlations between each of the first two dimensions and several key assets assessed in the survey, including land, cows, chickens, urban residence, having a bank account, owning a computer, and owning a television. By anchoring dimensions with diverse assets, the degree to which each dimension represents different livelihoods or engagements in different economies can be approximated. For instance, urban residence, education, and owning a TV are associated more strongly with dimension one than dimension two. Conversely, dimension two is more strongly associated with markers of agricultural accumulation (for instance, if the households own any land, cows, or chickens), while dimension 1 exhibits negative associations with these variables. Based on these associations, we interpret dimension 1 as reflecting achievement in a ‘cash economy, and dimension 2 as reflecting achievement in the agricultural economy. To further assess our interpretation, we estimated weights for agricultural wealth by running a linear regression predicting the agricultural wealth index described by Hackman and colleagues (Hackman et al., 2021)) by the list of assets collected in the Jimma study (r2 = 0.95). This regression procedure was repeated for material wealth to generate DHS-derived weights for each asset (r2 = 0.88). Additional assessment using DHS-derived weights found strong correlations between our measure of cash economy wealth and DHS estimates (r = 0.96) and our measure of agricultural economy wealth and DHS estimates (r = 0.82).

We standardized the household income and multidimensional material wealth scores to ease the interpretation of coefficients. Place of residence was classified as urban (Jimma City), semi-urban (Towns), and rural areas. As an additional covariate likely correlated with a lack of material assets, we included a variable for household-level economic stress experienced. Household survey participants were also asked whether any of the following occurred in the past three months: a member of the household had to increase the number of income-generating activities to meet their needs, a member of the household had to sell some of their personal belongings in order to meet their needs, or any students in the household had to miss school because they needed to help with income- or food-generating tasks? Households who answered “yes” to any of the events were treated as under economic stress in the previous three months.

To address our secondary objective, we also investigated whether an observed relationship between subjective social status and mental well-being was mediated by household-level or adolescent-level food insecurity and a series of social support indices. To measure perceived social support, household-level participants ranked each item on a Likert scale from 1 to 4, representing perceived ability to receive support from “very easy” to “very difficult.” Support given and support exchanged were measured based on binary responses to whether support was needed and if it was attained. In total, participants were asked about difficulty receiving support on 14 items, including “Find someone to watch your children”, “Get help with a heavy task,” and “Borrow a small amount of salt or coffee.” Support received and exchanged assessed the same domains but framed questions as “Needed help with a heavy task” and “Got help with a heavy task.” Factor analysis was used to construct three indices of social support: expectations of ability to receive support, support given, and support exchanged. For each of these indices, higher values correspond to greater levels of support.

4. Results

4.1. Descriptive characteristics

Of the 1,528 survey respondents during wave three, 57% were male, and the average age of respondents was 19 years old (Table 1). Approximately 43% of young adults had received or were receiving a secondary education or higher, whereas only 2% reported receiving no education. Most young adults resided in outlying towns surrounding Jimma center (29%) or more rural areas adjacent to the towns (36%), whereas 35% resided in the urban area of Jimma (Table 1).

Table 1.

Demographic characteristics of participants and key variables included in the analysis of wave three by study site.

Wave 3
Jimma City (N = 540) Town (N = 439) Rural (N = 549)
Age
 Mean (SD) 19 (±1.4) 19 (±1.4) 19 (±1.3)
 Missing 1 (0.2%) 0 (0%) 0 (0%)
Sex
 Female 247 (46%) 186 (42%) 220 (40%)
 Male 293 (54%) 253 (58%) 329 (60%)
Education
 No School 1 (0%) 2 (0%) 33 (6%)
 Primary 175 (32%) 188 (43%) 413 (75%)
 Secondary 316 (59%) 237 (54%) 103 (19%)
 Higher 48 (9%) 12 (3%) 0 (0%)
Subjective Status
 Mean (SD) 4.0 (±1.6) 4.1 (±1.7) 4.0 (±1.4)
 Missing 1 (0.2%) 0 (0%) 3 (0.5%)
Cash Economy Wealth
 Mean (SD) 0.82 (±0.69) 0.25 (±0.63) −1.1 (±0.45)
Agricultural Economy Wealth
 Mean (SD) −0.22 (±1.0) −0.28 (±0.86) 0.51 (±0.88)
Household Income
 Mean (SD) 160 (±230) 120 (±200) 37 (±45)
Household-Level Food Insecurity
 Mean (SD) 3.7 (±2.0) 3.5 (±1.8) 4.1 (±1.4)
 Missing 19 (3.5%) 22 (5.0%) 36 (6.6%)
Household-Level Economic Stress
 None 363 (67%) 337 (77%) 406 (74%)
 Experienced Stress 161 (30%) 86 (20%) 125 (23%)
 Missing 16 (3.0%) 16 (3.6%) 18 (3.3%)
Index of Support Expected
 Mean (SD) −0.31 (±1.0) 0.21 (±1.0) 0.41 (±0.85)
Index of Support Given
 Mean (SD) 0.12 (±1.2) 0.023 (±1.0) 0.12 (±0.84)
Index of Support Exchanged
 Mean (SD) −0.13 (±1.0) 0.14 (±1.1) 0.33 (±0.91)
Self-Report Questionnaire Score
 Mean (SD) 5.6 (±4.1) 5.2 (±4.3) 4.0 (±3.7)
 Missing 8 (1.5%) 4 (0.9%) 12 (2.2%)
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Household-level food insecurity scores were similar in Jimma Town and outlying towns (an average score of 3.7 and 3.5, respectively) but were higher among those residing in rural areas (4.1). The market economy wealth measure was lowest among those residing in rural areas (−1.0) and highest among those living in urban areas (0.84). Conversely, agricultural wealth was highest among those residing in rural areas (0.51) compared to those in urban areas (−0.25) or towns (−0.32). Household income also varied by place, with households in urban areas reporting the highest average monthly income of 160 birr. Those in towns, on average, reported a marginally lower income of 115 birr per month, while rural residents, on average, reported monthly incomes of 37 birr, on average. Indices of social support were highest across all domains among those residing in rural areas. In other words, households in rural areas expected to receive, give, and exchange more support and assistance with others compared to those in towns or cities. For both support expected and support exchanged, increasing rurality was associated with increases in average index scores; however, expected support given to others was, on average, comparable in cities and rural areas. Adolescents living in rural areas reported the lowest mean scores (4.0) on the Self Reporting Questionnaire (SRQ) compared to those in towns or urban areas (5.2 and 5.6, respectively).

Household-level variables were not collected again between waves three and four; therefore, material assets, household income, economic stress, and indices of social support did not vary between study waves for those who remained enrolled. Of those enrolled in wave three, 1,045 adolescents responded to the survey during wave four. Comparing wave three participants to wave four participants, relatively more males and more rural adolescents were lost to follow up (Table 2). Additionally, all social support indices were higher among those lost to follow-up, indicating greater perceived support given, expected, and exchanged. By wave four, most respondents reported having finished secondary school or higher (62%). Compared to wave three, the average subjective status was marginally higher (4.2 compared to 4.0). Average subjective status was consistent across time in both towns (0.1-point change) and rural areas (no change) but increased by 0.7 points for those in cities (Table 2). Average scores on the SRQ also increased between waves from 4.8 to 5.3 among respondents, indicating increases in non-specific psychological distress over time. Changes over time were not uniform across study sites. Specifically, adolescents residing in cities reported the highest average increase of non-specific psychological distress (0.6 points higher), while those in cities and rural areas reported more marginal increases (0.1 and 0.2 points higher, respectively).

Table 2.

Demographic characteristics of participants and key variables included in the analyses by study wave and follow-up status.

Wave 3
 Wave 4
Respondent (N = 1045) Lost to Follow-Up (N = 483) Respondent (N = 1045)
Age
 Mean (SD) 19 (±1.4) 19 (±1.4) 22 (±1.5)
 Missing 0 (0%) 1 (0.2%) 2 (0.2%)
Sex
 Female 389 (37%) 264 (55%) 389 (37%)
 Male 656 (63%) 219 (45%) 656 (63%)
Education
 No School 24 (2%) 12 (2%) 21 (2%)
 Primary 548 (52%) 228 (47%) 376 (36%)
 Secondary 432 (41%) 224 (46%) 471 (45%)
 Higher 41 (4%) 19 (4%) 177 (17%)
Subjective Status
 Mean (SD) 4.0 (±1.6) 4.0 (±1.5) 4.2 (±1.4)
 Missing 2 (0.2%) 2 (0.4%) 3 (0.3%)
Type of Place
 Jimma City 398 (38%) 142 (29%) 398 (38%)
 Town 300 (29%) 139 (29%) 300 (29%)
 Rural 347 (33%) 202 (42%) 347 (33%)
Cash Economy Wealth
 Mean (SD) 0.028 (±0.99) −0.12 (±1.1) 0.028 (±0.99)
Agricultural Economy Wealth
 Mean (SD) −0.032 (±1.0) 0.14 (±0.95) −0.032 (±1.0)
Household-Level Food Insecurity
 Mean (SD) 3.8 (±1.8) 3.7 (±1.8) 3.8 (±1.8)
 Missing 49 (4.7%) 28 (5.8%) 49 (4.7%)
Household-Level Economic Stress
 None 759 (73%) 347 (72%) 759 (73%)
 Experienced Stress 246 (24%) 126 (26%) 246 (24%)
 Missing 40 (3.8%) 10 (2.1%) 40 (3.8%)
Index of Support Expected
 Mean (SD) 0.056 (±1.0) 0.19 (±1.0) 0.056 (±1.0)
Index of Support Given
 Mean (SD) 0.081 (±1.0) 0.12 (±1.0) 0.081 (±1.0)
Index of Support Exchanged
 Mean (SD) 0.079 (±1.0) 0.19 (±1.0) 0.079 (±1.0)
Self-Report Questionnaire Score
 Mean (SD) 4.8 (±4.0) 5.0 (±4.3) 5.3 (±4.6)
 Missing 14 (1.3%) 10 (2.1%) 59 (5.6%)
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5. Objective 1: Modeled associations between objective and subjective status

Model 1 shows significant associations between our dependent outcome (adolescent subjective status) and several objective status markers at both the household and individual levels. Household-level material assets in both the cash economy and agricultural economy domains were both significantly associated with higher subjective status among adolescents, though the effect of agricultural wealth was approximately two times higher than the effect of cash economy wealth (Table 3). While an increase of one standard deviation (SD) in cash economy wealth was associated with a 0.22 rung increase (95% CI 0.13 to 0.32), the same increase in agricultural wealth was associated with a 0.41 rung increase (95% CI: 0.34 to 0.49) on the subjective status ladder. As expected, educational attainment was also significantly associated with higher subjective status compared to the reference group (no education). Higher educational attainment was also associated with a greater magnitude of effect (primary education: 0.71 [0.21–1.21]); secondary education: 0.91 [0.40–1.43]; higher education: 0.92 [0.26–1.58]). In contrast to expectation, household-level income did not have an effect on subjective status. Furthermore, no statistically significant associations were found between household-level indices of social support and adolescent mental health. At the individual level, sex was also significantly associated with subjective status, with males reporting lower status (−0.20; [−0.36-0.05]). In sex-stratified analyses, higher educational attainment was associated with significantly higher subjective status rankings among females but not among males.

Table 3.

Linear regression model of objective status measures and subjective status among adolescent respondents during wave three.

 Subjective Status
Predictors Estimates 95% CI p
Age 0.00 −0.05 – 0.06 0.890
Male −0.20 −0.36 – -0.05 0.009
Cash Economy Wealth 0.22 0.13–0.32 <0.001
Agricultural Economy Wealth 0.41 0.34–0.49 <0.001
Household Income 0.00 0.00–0.00 0.001
Primary Education 0.71 0.21–1.21 0.006
Secondary Education 0.91 0.40–1.43 0.001
Higher Education 0.92 0.26–1.58 0.006
Index of Support Expected 0.06 −0.73 – 0.85 0.884
Index of Support Given −0.08 −0.73 – 0.58 0.822
Index of Support Exchanged 0.18 −1.06 – 1.41 0.780
(Intercept) 3.19 2.00–4.37 <0.001
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Note: 1.5% of the sample (n = 1,528) were excluded in the analysis in Table 3 because they lacked values for outcomes or covariates. Linear Regression in R/R-squared = 0.14. N = 1505.

6. Objective 2: Modeled associations between status and mental well-being

Model 2 reveals an association between subjective status and adolescent reports of non-specific psychological distress. Importantly, this relationship is not mediated or moderated by the addition of objective status markers. Specifically, each increase of 1 unit (rung) on the ladder was associated with a decrease of 0.3 points (95% CI: −0.43 to −0.14) in SRQ score (Table 4). Sex-stratified analyses reveal that this significant association exists only among males in the sample. Rural residence provided a significant protective effect compared to the reference of urban residence (−1.23; 95% CI: −2.23 to −0.24). No statistically significant associations were observed between objective status markers and SRQ scores among adolescents during wave three. Though material assets were not significantly associated with non-specific psychological distress, the two domains exhibited differential effects, such that each SD increase in cash economy wealth was associated with higher SRQ scores (0.96, 95% CI: −0.21 to 2.12), or more non-specific psychological distress symptoms, whereas each one SD increase in agricultural economy wealth was associated with lower SRQ scores (−0.48; 95% CI: −1.64 to 0.68).

Table 4.

Linear regression model of non-specific psychological distress among adolescent respondents during wave three.

 Self-Report Questionnaire Score
Predictors Estimates CI p
Subjective Status −0.28 −0.43 – -0.14 <0.001
Age 0.09 −0.07 – 0.25 0.254
Sex (Male) 0.24 −0.19 – 0.67 0.269
Primary Education 0.07 −1.40 – 1.53 0.930
Secondary Education −0.47 −1.99 – 1.05 0.544
Higher Education −1.46 −3.35 – 0.43 0.130
Place of Residence (Town) −0.21 −0.82 – 0.40 0.494
Place of Residence (Rural) −1.23 −2.23 – -0.24 0.015
Cash Economy Wealth 0.96 −0.21 – 2.12 0.107
Agricultural Economy Wealth −0.48 −1.64 – 0.68 0.420
Household Food Insecurity 0.03 −0.10 – 0.17 0.617
Support Exchanged 0.69 −2.89 – 4.26 0.706
Support Expected −0.47 −2.75 – 1.80 0.684
Support Given −0.45 −2.35 – 1.44 0.640
Economic Stress (Experienced Stress) −0.12 −0.63 – 0.40 0.652
(Intercept) 4.79 1.34–8.24 0.007
Observations 1405
R2/R2 adjusted 0.056/0.045
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7. Objective 3: Modeled associations between status and mental well-being across time

Given the observed relationship between subjective status and adolescent mental well-being during wave three, we sought to explore how change in subjective status over time is associated with change in mental well-being. Since repeated measurements of mental health taken from each subject are correlated over time, a mixed-effect linear model was used to examine the relationship between status and mental health fit with a random effects term for the individual. In these models, random effects describe the individual variability in mental health scores and changes over time. Our analysis applied a restricted maximum likelihood estimation. Once again, each rung change in subjective status was significantly associated with lower non-specific psychological distress symptoms (−0.19; 95% CI: −0.30 to −0.08) (Table 5). As with the cross-sectional analysis of wave three, being male was associated with higher SRQ scores (0.69, 95% CI: 0.30 to 1.06). Again, more rural places of residence provided protective effects against non-specific psychological distress. Specifically, living in towns and more outlying rural areas was significantly associated with lower SRQ scores compared with Jimma City (−0.70; 95% CI: −1.16 to −0.24 and −2.19; 95% CI -2.67 to −1.70, respectively).

Table 5.

Mixed-effect regression model of non-specific psychological distress across waves three and four.

 Self-Report Questionnaire
Score
Predictors Estimates CI p
Subjective Status −0.19 −0.30 – -0.08 0.001
Age 0.13 0.05–0.22 0.002
Sex (Male) 0.68 0.30–1.06 <0.001
Primary Education −0.08 −1.33 – 1.16 0.896
Secondary Education −0.51 −1.79 – 0.77 0.434
Higher Education −1.14 −2.54 – 0.26 0.110
Place of Residence (Town) −0.21 −0.82 – 0.40 0.494
Place of Residence (Rural) −1.23 −2.23 – -0.24 0.015
(Intercept) 4.09 2.00–6.17 <0.001
Random Effects
σ2 11.97
τ00personid 5.46
ICC 0.31
N personid 1497
Observations 2452
Marginal R2/Conditional R2 0.052/0.349
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8. Discussion

In this study, we investigated associations between objective and subjective socioeconomic status and non-specific psychological distress symptoms among adolescents in Ethiopia. We found that several objective indicators of status are significantly associated with subjective measures. Specifically, both cash-economy and agricultural-economy wealth exhibited strong positive associations with subjective social status. The arguably counter-intuitive finding that agricultural wealth has a greater effect on subjective social status echoes the work of Lachaud and colleagues, which stresses the need to account for locally-valued forms of wealth in health research (Lachaud et al., 2020). However, subjective status was more robustly associated with mental well-being in our sample. This finding is akin to previous research that documents the often comparatively stronger predictive power of subjective as opposed to objective socioeconomic status on health outcomes (Singh-Manoux et al., 2005). Furthermore, this association persisted across study waves, as changes in subjective status were significantly associated with changes in symptom scores.

There is a striking variation in adolescents’ place of residence and SRQ scores. Compared to urban residents, residing in towns and rural areas appears to have a protective effect on non-specific psychological distress among adolescents. While we postulated that place-based and status effects may be due to material deprivation or social capital, when included in the models, neither food insecurity nor social support indices modified this relationship. This finding aligns with some research in the United States that, despite lack of access to material resources and healthcare (Summers-Gabr, 2020), rural residence is not a risk factor for psychological distress (Rohrer et al., 2005). Studies of wealth and mental health in low-and-middle-income countries have documented similarly protective effects of rurality (Lachaud et al., 2020; Lund et al., 2010). In our sample, we are left to speculate about the persistent place-based effect on subjective status among adolescents. It may be the case that, in some settings, less income inequality in rural areas provides a buffer against the development of non-specific psychological distress. It is possible that adolescents living in urban areas experience more exposure to opportunities or lifestyles that are not accessible. Supporting this hypothesis, previous anthropological research using cultural consonance (Dressler et al., 2005), lifestyle incongruity (Sorensen et al., 2009), and perceived lifestyle discrepancy (Garcia et al., 2017) has documented the adverse effects of the inability to meet culturally-defined sufficient lifestyles. Alternatively, it may be that the types of stress experienced in urban versus rural areas differentially impact mental well-being.

Strengths of this study include the use of multiple measures of wealth and socioeconomic status, including household income, material wealth (cash economy and agricultural), access to resources (food insecurity), and human and social capital (education and social support networks). All measures were either culturally adapted or validated, adding further rigor to the study. Finally, our use of longitudinal data sheds novel preliminary insight into how perceptions of status shift over time among adolescents. While the relationship between status and health outcomes has been well-documented among adults, far fewer studies have assessed these dynamics among youth and adolescents, especially those in low or middle-income countries. In a noteworthy exception, Amir and colleagues have investigated the applicability of the MacArthur Ladder in non-Western populations and how early in ontogeny subjective status can be measured (Amir et al., 2019). Elucidating the factors that informed status perceptions, Amir and colleagues found that money was the strongest predictor of ladder placement in the US, India, and cross-Cutucú, while food and things predicted status in Argentina and Upano Valley Ecuador (2019). Combined with this recent research, our findings demonstrate that contextually-sensitive frameworks are crucial for understanding poverty, perceptions of status, and how those perceptions subsequently shape mental health outcomes (Kaiser & Weaver, 2019; Weaver & Kaiser, 2022). As Weaver and Kaiser argue, attunement to local expressions of distress and forms of wealth are of paramount importance for understanding the complex, and potentially syndemic, relationships between poverty, mental distress, and other health outcomes across the lifecourse.

Our study also has several noteworthy limitations. Although many of the key variables, including subjective status, educational attainment, and SRQ scores, were collected at multiple time points, household-level data were only available at a cross-section of the study. Therefore, this study was limited in its ability to detect cause-effect relationships between changes in different types of status and non-specific psychological distress over time. The difference in data collection timing between households and adolescents may have also affected results. Second, while the study sought to draw upon a representative sample of the population in and around Jimma, our findings among adolescents in this region may not be generalizable to a larger population. Further, our measures of household-level social support may not fully capture the broader social network dynamics of household heads and adolescents. Finally, we cannot discount that the strong relationship between subjective status and adolescent mental health may be a product of common method variance.

9. Conclusion

Socioeconomic status is a major driver of human well-being across the lifecourse and is increasingly recognized as a determinant of mental health. Examinations of how conditions of poverty, power, and status will be paramount to advancing the study of mental health-a relatively under-examined component of human biology and development (Kohrt & Mendenhall, 2016; Lachaud et al., 2020). This study provides preliminary insight into the relationships between objective status and perceptions of status among adolescents in Ethiopia. As expected, household-level material assets and investment in education are associated with subjective status. Our findings also support the hypothesis that perceptions of status are more strongly associated with non-specific psychological distress than objective markers of stress in this sample. Though more research is needed, our study suggests that adolescents’ subjective status may be more inclusive, informed by broader environmental conditions and lived experiences, than objective status. The robust associations between place of residence and non-specific psychological distress after accounting for measures of social and material capital requires further investigation. Our analysis also provides insight into shifts in status across a critical period of lifecourse development: adolescence. As income inequality continues to rise across many developing regions, shifting livelihood conditions and opportunities are likely to alter status perceptions and aspirations, particularly among adolescents. Adolescence is a vital period for the acquisition and development of physical, emotional, social, and economic resources that not only shape life-span well-being but also lay the foundations for future generations (Patton et al., 2016).

Broadly, this study adds to a growing body of research documenting that perceptions of status often associate more robustly with health outcomes than objective markers (Adler et al., 2000; García et al., 2017; Singh-Manoux et al., 2005). In their study in Utila, Honduras, García and colleagues provide preliminary evidence for the role of hypothalamic-pituitary activation in shaping these dynamics. Further research on the mechanisms by which perceived status impacts health, especially among young people, is needed.

Financial disclosure statement

The authors declare that they have no relevant or material financial interests that relate to the research described in this manuscript.

CRediT author statement

Caroline E. Owens: Conceptualization, Methodology, Software, Formal Analysis, Writing- Original draft preparation.

Craig Hadley: Investigation, Conceptualization, Methodology, Data Curation, Software, Formal Analysis, Writing- Reviewing and Editing, Funding Acquisition.

Declarations of competing interest

None.

Acknowledgment

The Jimma Longitudinal Family Survey of Youth was funded by the Packard Foundation, Campton Foundation, National Institute of Health and National Science Foundation.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ssmph.2023.101382.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.doc (56KB, doc)

Data availability

The authors do not have permission to share data.

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