Living conditions can negatively or positively impact physical and mental health, and good living conditions can help improve individual mental health (Barskova and Oesterreich, 2009; Evans et al., 2000). The World Health Organisation and the World Bank have called for improved self-rated health (SRH) among citizens through improved living conditions (WHO, 2015). Research on living conditions and subjective health has been well-established in the literature; for instance, Gu and Ming (2021) found that individuals living in high-quality houses reported better health more frequently than those living in ordinary homes. Olukolajo et al. (2013) found that poor housing conditions have a detrimental impact on human health. As such, the presence or absence of good living conditions may predict an individual’s subjective health. However, to what degree is that the case in an African context? Most research comes from the Global North. There is a lack of research exploring the direct link between material living conditions and subjective health in the Global South among Nigerians.

The literature on subjective wellbeing (SWB) generally views good subjective health as a crucial element of SWB (Diener, 2025; Dyar et al., 2022). According to Moore and Diener (2019) and Liebenberg and dos Santos (2018), subjective wellbeing, which is defined as life satisfaction with positive and negative effects, refers to ability to delve into life with positivity and have positive, enjoyable, and exciting experiences without suffering from distress, fear, or anxiety. Additionally, SWB is the degree to which a person thinks their life is going well (Diener et al., 2018). SWB can be situated on a continuum, ranging from very low to very high wellbeing states (Abdel-Khalek and Lester, 2013). Research on the link between material living conditions and subjective wellbeing (SWB) has recently gained attention among academics and policymakers (Ettema and Schekkerman, 2016; Weckroth et al., 2022). Growing evidence from the literature suggests that SWB and indicators of living conditions are indeed closely related. According to Aretz et al. (2019) and Levinson (2020), contextual factors, such as issues with public transportation, negatively impact wellbeing. Additionally, Rüger et al. (2023) found that the quality of housing and public goods is strongly associated with subjective wellbeing (SWB). Further studies (e.g., Ettema and Schekkerman, 2016; Ma et al., 2018; Weckroth et al., 2022) have found a strong link between the immediate living environment and physical health. Likewise, Ma et al. (2018), Balducci and Checchi (2009), and Dittmann and Goebel (2010) all found that crime, existential insecurity, and general Neighborhood problems have a negative impact both on mental health and SWB. Scholars such as Ettema and Schekkerman (2016) or Weckroth et al. (2022) stress the need for more investigation into the direct link between material living conditions and wellbeing, especially in developing countries. This study responds to this call by investigating the impact of material living conditions on subjective wellbeing in an African setting, highlighting the necessity for further research in this area.

The association between material living conditions and subjective health and wellbeing is captured in the bottom-up theory of SWB (Diener, 1984; Marans and Rodgers, 1975), which proposes that diverse factors in a life domain, such as living environment conditions, either unfavorable or favorable, determine an individual’s overall subjective health and SWB. Hence, a range of adverse living conditions, such as poor electricity, inadequate road networks, and unstable power supply, that people perceive can negatively predict subjective wellbeing and life satisfaction among the Nigerian population. This nexus has often been neglected in wealthy countries of the Global North.

Readers may wonder whether such subjective measures are not overly biased. In line with the classical Thomas Theorem (Thomas and Thomas, 1928), it is clear that only material living conditions that individuals perceive can impact people’s behavior. In other words, not the rain that fell, but the rain perceived as having fallen is essential.

Not all individuals who experience adverse material living conditions will report low levels of health and wellbeing. DeNeve and Cooper (1998) and Lucas (2018) asserted that the living conditions of people do not influence SWB, but rather their specific personal attributes that can underlie this association. Numerous researchers have agreed that individual characteristics are indeed related directly or indirectly to subjective wellbeing (Abdullahi et al., 2019; Easterlin, 2003; Easterlin et al., 2010; Helliwell et al., 2012; Myers and Diener, 1995), but the causal link has continued to be a subject of debate (Clark et al., 2008; Lyubomirsky et al., 2005). This present study considers specific personal attributes (such as gender, age, and education) that may explain the conditions or circumstances that may influence this association. These three personal attributes are further elaborated upon concerning material living conditions and subjective health and wellbeing.

The study utilized freely accessible data from the 2023 Afrobarometer,¹ which surveyed 1,600 Nigerian adults between March 5 and 31, 2023, across all 36 states in Nigeria, including the Federal Capital Territory (FCT). The 36 states and the FCT are subdivided into six geopolitical zones: North East, North Central, North West, South East, South West, and South South.

The Afrobarometer is an African, non-partisan research network that surveys public opinion on governance, democracy, wellbeing, economic conditions, and other related issues in over 42 African countries, including Nigeria. As a population-based survey of participants, the Afrobarometer consists of large cross-sectional and some longitudinal samples drawn using national probability sampling, thereby securing a nationally representative, randomly stratified database. The survey network often employs the stratified random sampling method, which involves dividing populations into various strata (such as regions, urban versus rural areas, etc.) to ensure that all relevant groups are represented. This stratification ensures that the sample is diverse and reflects Nigeria’s demographic composition. For the sample size, the Afrobarometer survey typically targets a 95% confidence level; hence, it often uses a desired margin of error of ± 2–3%. This smaller margin of error requires a larger sample size. This is why a sample size of N = 1,600 was used for the survey. Afrobarometer survey data have been well utilized by scholars in academic papers and doctoral theses (Isbell, 2022; Diallo, 2022; Alabi and Olajide, 2023). Sampling was done using multi-step random selection methods and proportionate probability for persons aged 18 and above in Nigeria. Gender-balanced interviewers conducted all interviews face-to-face, with men interviewing male respondents and women interviewing female respondents. For more details on sampling procedures, please visit the Afrobarometer website at https://www.afrobarometer.org/surveys-and-methods/sampling.

All variables in the study were adopted from the 2023 Afrobarometer data set. Due to the partial reliance on self-report measures, self-report biases, including social desirability and response set biases, have a relatively high potential. These biases may skew the findings in a particular direction or increase errors in the study.

All analyses were conducted using IBM SPSS Statistics for Windows, Version 27, and IBM AMOS Version 24 software (Armonk, NY, United States; Arbuckle, 2016). Descriptive and inferential statistics were employed to analyze the collected data. Descriptive statistics were calculated for participants’ sociodemographic characteristics, including frequencies, means, and standard deviations. Pearson’s bivariate correlations were used to assess the associations between variables. Structural equation modeling with maximum likelihood estimation was used to test the direct relationship between material living conditions and subjective health and wellbeing. Several different coefficients were used to assess the goodness-of-fit of the SEM model, which includes the goodness-of-fit index (GFI), Tucker-Lewis index (TLI), comparative fit index (CFI), incremental fit index (IFI), root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). The goodness-of-fit is acceptable if the GFI, TLI, CFI, and IFI values are higher than 0.90, and excellent if they are higher than 0.95. Values of RMSEA and SRMR should be lower than 0.08 (Browne and Cudeck, 1992; Hu and Bentler, 1999) for well-fitting models. Furthermore, a multigroup SEM analysis was employed to investigate the moderating effect of personal attributes (gender, age, and education) on the relationship between material living conditions and subjective health and wellbeing. The χ² score was compared between the unconstrained and constrained models, where path coefficients were fixed to equality. If the unconstrained model exhibits a significantly better fit when comparing the χ² scores of both models, it is concluded that there is some form of moderation effect in the structural model. To assume a moderation between two groups, Δχ², the difference between the χ² scores of the unconstrained and constrained models, must be 3.84 or 6.63 to produce a significant difference at p < 0.05 and p < 0.01, respectively.

The dataset contains 1,600 participants, who were checked for accuracy and completeness. Due to the secondary nature of the dataset, missing data were excluded before uploading the dataset from the Afrobarometer website. A careful observation of the data downloaded by the researchers reveals indeed no missing values in the dataset. The univariate normality test was done using the skewness and kurtosis indices. The analysis showed that the skewness values for the variables ranged from –0.07 to –1.97, while the kurtosis values varied from –1.19 to 3.11. These values are within Byrne (2010) and Kline (2011) suggested range of ± 3 for skewness and only minutely outside these boundaries for kurtosis.

Multicollinearity was checked using the Tolerance Value and Variance Inflator Factor (VIF). The analysis also revealed that tolerance values were between 0.98 and 0.99, which are within the threshold of lower than 0.1, whereas Variance Inflator Factor (VIF) values were between 1.00 and 1.01, also within the range of less than 10 (Hair et al., 2010; Tabachnick and Fidell, 2006).

Descriptive statistics (Table 1) were used to document the respondents’ socio-demographic characteristics. It was observed that the majority, 830 (51.9%), were male, whereas 770 (48.1%) were female. The age distribution revealed that 827 (51.7%) were between the ages of 18 and 32 years, 773 (48.3%) were between the ages of 33 and 97 years, and the median age for the distribution was 32.00 years. Additionally, most participants, 1,128 (70.5%), were educated, while 472 (29.5%) had no education.

The correlation matrix (Table 2) was produced to ascertain associations among personal attributes, perceived material living conditions, and subjective health and wellbeing. The results showed that age and subjective health and wellbeing scores were correlated at r = –0.05, p < 0.05. High wellbeing scores indicate challenging conditions, suggesting that subjective health is evaluated minutely more favorably in the older age group. Gender was unrelated to subjective health and wellbeing (r = –0.01, p > 0.05). For education and subjective wellbeing, the correlation was r = –0.11, p < 0.05, suggesting that educated study participants rated their subjective wellbeing better than uneducated study participants. The correlation between material living conditions and subjective health and wellbeing was r = –0.17, p < 0.05, indicating that better living conditions are associated with fewer subjective health and wellbeing issues.

The measurement model ascertains the model fit of the sample data; it is illustrated in Figure 2. The model comprises two latent constructs (material living conditions and subjective health and wellbeing) and ten manifest variables following remodification. An assessment of the fit indices revealed that the measurement model achieved a satisfactory fit: χ²(30, N = 1,600) = 141.91, p < 0.001, CMIN/DF = 4.731, CFI = 0.98, TLI = 0.98, NFI = 0.98, RMSEA = 0.05. All the indicators loaded significantly at p < 0.001 on their respective latent constructs. The measurement model’s construct validity, convergent validity, and discriminant validity were also assessed. It was found that the composite reliabilities for the measures of material living conditions (0.93) and subjective health and wellbeing (0.90) were higher than the recommended cut-off of 0.60 (Bagozzi and Yi, 1988). The convergent validity, assessed through Average Variance Extraction (AVE), was 0.76 for material living conditions and 0.65 for subjective health and wellbeing, both exceeding the cutoff point of 0.50 (Fornell and Larcker, 1981). A check on the two measures also shows that they achieved discriminant validity, as the square roots of the average variance extracted (AVE) were larger than the inter-construct correlations.

The structural model helps ascertain the direct and indirect effects of the study variables in Figure 2 and Table 3. It revealed that material living conditions negatively predict subjective health and wellbeing (β = –0.16, CR = –5.77, p < 0.001). The material living conditions indicate a small effect size (f² = 0.03). Considering that the Afrobarometer asks about the frequency of experiencing health hazards in the last 12 months when assessing subjective health and wellbeing, this result aligns with our first hypothesis, H1, that favorable material living conditions are associated with fewer health hazards.

The study variables, material living conditions, and level of education have emerged as essential constructs among Nigerians as they are associated with subjective health and wellbeing. First, evidence has shown that poor material living conditions reduce subjective health and wellbeing. Second, the study helps further understand the moderating role of education as a personal attribute in the negative association between material living conditions and subjective health and wellbeing, with educated Nigerians experiencing this negative association. Thus, the findings of this study have implications for practitioners, specifically psychologists and health experts, to develop support programs where people come together to offer mutual support or infrastructure development interventions aimed at improving the living conditions of Nigerians, which can ultimately enhance the health and wellbeing of Nigerians. At the policy level, our study calls on the government to provide robust and well-coordinated basic social living amenities, including access to affordable housing, uninterrupted power supply, shelter and clothing, clean water, and a sound healthcare system. It is crucial for educated Nigerians to develop a sense that their extra effort to invest in education yields a payoff. Theoretically, the study has strengthened the scientific knowledge base of the existing literature on the relationship between material living conditions, personal attributes, subjective health, and wellbeing. Additionally, the direction of the interaction effect of education as an individual attribute on the relationship between material living conditions and subjective health and wellbeing is novel to this field of study. It can serve as a springboard for future research endevors.

Despite the valuable insights of the study, there are still some limitations that future research should address. The study employed interviewer data to assess the quality of material living conditions. To address this limitation, future research should incorporate multiple objective data sources, such as peer assessment and behavioral observations. Ideally, future research should involve a longitudinal study to better understand the association between material living conditions, personal attributes, and wellbeing. Furthermore, another limitation is the study’s exclusive reliance on quantitative instruments alone to measure the variables. Due to the complex and multifaceted nature of health and wellbeing, a deeper understanding can be gained through qualitative approaches, such as interviews and focus group discussions, which can provide valuable insights for future researchers. Future research is called to explore whether these findings may be generalized to other African or Global South contexts. These collective research efforts are geared toward developing effective strategies and hope to improve the health and wellbeing of the Nigerian and African population.