Hrev_master Healthcare in Low-resource Settings 2023; volume 11(s2):12005 Urban and rural disparities: evaluating happiness levels in Indonesian women Sri Idaiani, Ika Saptarini Research Center for Preclinical and Clinical Medicine, National Research and Innovation Agency, Indonesia Abstract Grasping the underlying determinants of happiness has sig- nificant implications for societal growth and individual well- being. To this end, our investigation delved deep into the fac- tors enhancing happiness among Indonesian women, with a spotlight on the disparities evident in urban versus rural set- tings. From a robust sample size of 38,144 women, we employed logistic regression analysis (using a significance threshold of 0.05) and took advantage of Stata 17’s spmap command to meticulously outline happiness averages across provinces. Our analyses revealed a compelling trend: urban women consistently reported more elevated happiness scores (71.51; 95%CI 71.40-71.62) compared to their rural peers (70.19; 95%CI 70.08-70.29), with a significant p-value of 0.001. Parsing this data further, we recognized that across urban and rural landscapes, the nexus between higher education levels, younger age, and augmented household income remained a strong predictor of happiness elevation. Intriguingly, though, densely populated urban hubs did not always translate to heightened contentment. As a directive, policymakers should amplify efforts towards enriching educational and economic landscapes for women in high-density zones. Moreover, the study suggests a pivotal need to explore the idiosyncratic attributes of distant provinces, aiming to translate those lessons to enrich urban living conditions. Introduction The intricate relationship between happiness, prosperity, and health is well-established, yet it’s important to note that economic factors alone do not guarantee happiness. This is because happiness is a multifaceted construct influenced by a plethora of variables.1,2 Such determinants encompass genetics, education, socio-economic conditions, time management, activities, stress exposure, marital status, and intrinsic person- ality traits. Furthermore, elements like spirituality, religiosity, social support, as well as physical and mental health, have been observed to be closely tied to one’s happiness.3–7 Residency, whether urban or rural, plays a pivotal role in determining happiness. Generally, urban inhabitants report higher happiness levels, likely due to enhanced facilities in cityscapes. However, there are exceptions.8,9 Notably, in certain locales, rural populations have shown higher happiness scores than their urban counterparts.8 It’s essential to highlight that the determinants of happiness vary across demographic segments. For instance, factors contributing to the well-being of adoles- Correspondence: Sri Idaiani, Research Center for Preclinical and Clinical Medicine, National Research and Innovation Agency,Cibinong Science Center Jalan Raya Jakarta-Bogor Km. 46, Kecamatan. Cibinong, Kabupaten Bogor, Jawa Barat 16915, Indonesia. E-mail: sri.idaiani@brin.go.id Key words: Indonesian women; urban-rural divide; happiness deter- minants; societal well-being; provincial happiness levels. Conflict of interest: the authors declare no potential conflict of inter- est, and all authors confirm accuracy. Ethics approval: the Ethics Committee of the National Research and Innovation Agency (BRIN) adjudged this study as exempt from requiring an ethical clearance, as evidenced by Letter Number 129/KE.01/SK/7/2022. Availability of data and materials: this study used data from the Central Bureau Statistics (CBS) with permission from the Director of Statistic Dissemination. Data will be made available upon request at silastik@bps.go.id Conference presentation: part of this article was presented in Global Public Health Conference, February 23 rd 2023. Funding: the authors received no funding for the work reported in this article. Acknowledgements: the authors appreciate Dr. Harimat Hendarwan as the head of the Research Centre for Preclinical and Clinical Medicine and the head of Center for Data and Information National Research and Innovation Agency for facilitating the data request to CBS. Received: 19 October 2023. Accepted: 19 November 2023. Early access: 28 November 2023. This work is licensed under a Creative Commons Attribution 4.0 License (by-nc 4.0). ©Copyright: the Author(s), 2023 Licensee PAGEPress, Italy Healthcare in Low-resource Settings 2023; 11(s2):12005 doi:10.4081/hls.2023.12005 Publisher's note: all claims expressed in this article are solely those of the authors and do not necessarily represent those of their affili- ated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. [page 18] [Healthcare in Low-resource Settings 2023; 11(s2):12005] Non -co mmerc ial us e o nly cents, adults, or the elderly differ.10–12 Similarly, professionals or students might experience varied happiness sources, warrant- ing diverse measurement tools aligned with specific influencing factors.3,13–15 To cater to these varied segments, a myriad of hap- piness measurement instruments have been formulated. These tools, customized for distinct demographics like workers, care- givers, adolescents, or even cultural backgrounds, ensure nuanced assessments.3,16–18 Furthermore, these assessments often incorporate unique factors tailored for specific popula- tions, such as living arrangements or registrations.19,20 For urban youth, for instance, career progression prospects emerge as sig- nificant happiness determinants.21 Turning our gaze to Indonesia, the nation employs an extensive happiness assess- ment, anchored by the Central Bureau of Statistics. This evalu- ation, based on international standards, adopts frameworks delineated by the New Economic Foundation (NEF) and the Organization for Economic Co-operation and Development (OECD), mirroring Indonesia’s unique socio-cultural land- scape.22 Given the global economic ramifications triggered by the COVID-19 pandemic, evaluating happiness levels concern- ing urban versus rural residency gains paramount impor- tance.8,23–25 The factors influencing happiness evidently diverge across urban and rural landscapes. This disparity is pronounced in Indonesia, where 56% of the populace resides in the relative- ly advanced regions of Java and Bali.22 Yet, the characteristics delineating happiness across these settings remain underex- plored. Recognizing these distinct attributes could empower individuals to align their residence with their happiness deter- minants, thereby elevating their well-being. Women have a specialty, namely that based on the results of the 2021 happiness survey, their index is lower than men.22 Unlike men, women have hormonal influences, for example menopause, which causes discomfort. Besides that, the factors of living in urban or rural areas, work, social activities are dif- ferent between women and men therefore it is better to explain happiness for each gender, in this case for women.15,26 Thus, this research primarily seeks to discern characteristics fostering happiness among Indonesian women. Simultaneously, it delves into contrasting happiness determinants across urban and rural settings. Materials and Methods Design and Setting This study employed a survey-based design conducted in peri- od 1 July to 27 August 2021 by the Central Bureau of Statistics (CBS) across 34 provinces. While aiming for a sample size of 75,000 respondents, the survey yielded data from 74,684 partici- pants, reflecting a response rate of 99.5% with adequate provincial representation. The sampling unit is household. The survey employed a two-stage one-phase sampling strategy, namely the first stage was selecting census blocks. The second stage was household selection. Census blocks were selected by means of urban-rural stratification. In the first stage, it selected 7500 census blocks from 30,000 census blocks in probability proportional to size using urban rural stratification. In the second stage, it selected 10 households from 20 households in each selected census by tak- ing into account implicit stratification, including house floor vari- ables, lighting, drinking water, education and so on. That selection was using systematic random sampling. Data collection Data collection was carried out by CBS enumerators using questionnaires. The criteria for a household were an ordinary household, namely a group of people who live in one or part of a census building and eat from the same kitchen. Special house- holds, for example dormitories, were not included in the survey. The sampling unit is a household selected by CBS randomly. The primary respondent was the household head. In instances where the household head was unavailable, their spouse served as the rep- resentative respondent.22 Exclusion criteria were they refused, were not present until the specified time limit or they moved to dif- ferent census block. Enumerators may not replace households that were not found. For the purposes of this analysis, we narrowed our focus to female respondents, resulting in a final sample size of 38,144. It is noteworthy that this survey was conducted amidst the COVID-19 pandemic. Questionnaire The happiness level survey was initiated by CBS in 2012 and underwent a national-level pilot in 2014. It was crafted around the New Economic Foundation NEF framework. In 2017, it was aug- mented to incorporate elements from the Organization for Economic Co-operation and Development OECD framework. The methodology adopted is in line with the standardized Gallup World Pool method, which also serves as the foundation for the World Happiness Index. This consistency ensures that the derived data is compatible with other international happiness datasets. The happiness index encompasses 19 defining factors spread across three primary dimensions: life satisfaction, affect, and eudaimonia. The life satisfaction dimension probes into areas such as education, primary activities or employment, housing amenities, household income, health, familial harmony, availability of leisure time, neighborhood social interactions, environmental quality, and safety measures. The affective domain delves into feelings of hap- piness/contentment/joy, serenity versus anxiety, and cheerfulness versus despondency. Lastly, eudaimonia addresses facets like autonomy, environmental mastery, personal growth, cultivating positive relationships, life’s purpose, and self-appreciation.22 A comprehensive list of these 19 questions is provided in Appendix 1. Responses are calibrated on a scale ranging from 0 (indicating deep dissatisfaction) to 10 (indicating supreme satisfac- tion, Table 1). Data analysis The CBS-sourced data was subjected to a multiple logistic regression analysis employing the Enter method. This analyti- cal method was selected owing to the non-normal distribution of the happiness dependent variable. A significance benchmark was established at 0.05. Variations in characteristics were eval- uated using independent sample T-tests and chi-square tests. All analytical procedures were conducted on STATA software (ver- sion 17). Moreover, an illustrative representation of average provincial happiness levels was generated using STATA’s spmap command. Ethical considerations Given our reliance on CBS’s secondary data, we sought an ethical review from the Ethics Committee of the National Research and Innovation Agency (BRIN). The committee adjudged this study as exempt from requiring an ethical clear- ance, as evidenced by Letter Number 129/KE.01/SK/7/2022. Article [Healthcare in Low-resource Settings 2023; 11(s2):12005] [page 19] Non -co mmerc ial us e o nly Results Sample Analyses were conducted on data from 38,144 respondents, with their demographic and other pertinent characteristics pre- sented in Table 2. Notably, urban areas reported a higher aver- age happiness score compared to their rural counterparts (71.51 vs. 70.19), with this disparity being statistically significant. Intriguingly, the mean age of respondents remains consistent across both urban and rural settings, and this consistency is sta- tistically significant. Additionally, the proportion of participants possessing higher education, positioned at the pinnacle eco- nomic bracket (level 1), and identifying as single, is notably lower than other categorized groups. In Table 4, both the crude and adjusted odds ratio (OR) cal- culations, a persistent theme emerges: tertiary education wields the most substantial influence over happiness levels in both urban and rural populations. Conversely, females who are either divorced, widowed, of advancing age, and situated in a lower economic stratum, depict an inverse relationship with happi- ness. After adjusting for age, marital status, and family income variables, the OR for respondents with higher education (mini- mum college graduates) registers at 3.409 (95% confidence interval (CI) = 3.026-3.839, p = 0.001) for urban regions, and 3.494 (95% CI = 3.013-4.051, p = 0.001) for rural localities. While household income certainly influences happiness levels, it’s the lowest income cohort (earning below IDR 1.7 million monthly) that manifests the most significant negative correlation. In the rural backdrop, this group’s OR is tabulated at 0.397 (95% CI = 0.342-0.462, p = 0.001). In stark contrast, their urban counterparts have an OR measured at 0.279 (95% CI = 0.245-0.319, p = 0.001). The average happiness metrics, segregated based on urban and rural demarcations per province, are elaborated in Table 3. These averages serve as a foundation for the graphical representations in Figures 1 and 2. Gleaning insights from these maps, one observes that provinces like Central Kalimantan, Central Sulawesi, Gorontalo, North Maluku, and Maluku consistently report ele- vated happiness levels, irrespective of urban or rural classifica- tion. A similar homogeneity in happiness scores, straddling urban and rural divides, is evident in provinces such as Aceh, West Sumatra, Lampung, East Java, East Nusa Tenggara, East Kalimantan, and Papua. Diverging from this trend, provinces like Jambi, South Sumatra, North Kalimantan, and North Sulawesi exhibit a pronounced urban bias, with city dwellers Article [page 20] [Healthcare in Low-resource Settings 2023; 11(s2):12005] Table 2. Characteristic respondents. Variables Urban (N=17873) % Rural (N=20271) % p Happiness (mean) 71.51; 95%CI 71.40-71.62 70.19; 95%CI 70.08-70.29 0.001* Age (mean) 46.22; 95%CI 46.02-46.41 46.21; 95%CI 46.02-46.41 0.483* Education - No school-Primary S 6370 35.64 12238 60.37 0.001** - Junior HS-Senior HS 8851 49.52 6844 33.76 - Diploma-University 2652 14.84 1189 5.87 Marital status - Unmarried 469 2.62 213 1.05 0.001** - Married 13238 74.07 14986 73.93 - Divorce/widowed 4166 23.31 5072 25.02 Household Income - Level1 2141 11.98 975 4.81 0.001** - Level2 2540 14.21 1531 7.55 - Level3 3952 22.11 3375 16.65 - Level4 5068 28.36 6895 34.01 - Level5 4172 23.34 7495 36.97 *Independent sample T test ** Chi square. Table 1. Variables. Variables Variable descriptions 1 Happiness Consisting of 19 questions. Each question is a self- reported measure of how satisfied the individual is with his/her health, all things considered, where 0 = very unsatisfied and 10 = very satisfied. 1. Less happy (< mean score) 2. Happy (≥ mean score) 2 Age 1.< 30 years; 2. 30-59 years; 3. ≥ 60 years. 3 Marital status 1. Unmarried, 2. Married, 3. Divorced/widowed 4 Education 1. No School-Primary finished, 2. Secondary-High School, 3. D1-University 5 Living 1. Urban, 2. Rural 6 Household Income Level 1 (> 7.2 million/month), Level 2 (> 4.8 -7.2 million/month), Level 3 (> 3.0-4.8 million/month), Level 4 (> 1-1.8-3.0 million/month), Level 5 (< 1.8 million/month) Non -co mmerc ial us e o nly Article Table 3. The mean of urban and rural happiness level by province.Variables Urban Rural No Province Mean No Province Mean No Province Mean No Province Mean 1 Aceh 72.50 20 West 72.35 1 Aceh 70.10 20 West 70.25 Kalimantan Kalimantan 2 North Sumatera 70.71 21 Central Kalimantan 73.43 2 North Sumatera 68.47 21 Central Kalimantan 71.44 3 West Sumatera 71.13 22 South Kalimantan 72.52 3 West Sumatera 69.69 22 South Kalimantan 68.73 4 Riau 70.77 23 East Kalimantan 73.08 4 Riau 71,16 23 East Kalimantan 70.78 5 Jambi 73.96 24 North Kalimantan 74.10 5 Jambi 69,54 24 North Kalimantan 70.85 6 South Sumatera 71.91 25 North Sulawesi 75.55 6 South Sumatera 70,06 25 North Sulawesi 75,07 7 Bengkulu 70.36 26 Central Sulawesi 75.20 7 Bengkulu 67.28 26 Central Sulawesi 73.56 8 Lampung 71.91 27 South Sulawesi 72.35 8 Lampung 69.70 27 South Sulawesi 70.07 9 Babel 71.37 28 South East Sulawesi 72.45 9 Babel 70.52 28 South East Sulawesi 72.04 10 Riau Island 72.50 29 Gorontalo 73.74 10 Riau Island 71.14 29 Gorontalo 73.54 11 Spec Region Jakarta 71.18 30 West Sulawesi 72.51 11 Spec Region Jakarta 30 West Sulawesi 72.59 12 West Java 70.71 31 Maluku 75.26 12 West Java 68.54 31 Maluku 75.15 13 Central Java 69.81 32 North Maluku 78.33 13 Central Java 69.40 32 North Maluku 74.83 14 Spec Region Jogjakarta 33 Spec Region Jogjakarta 70.51 Papua Barat 72.98 14 67.73 33 Papua Barat 72.46 15 East Java 71.04 34 Papua 73.04 15 East Java 69.74 34 Papua 69.79 16 Banten 69.29 35 Indonesia 71.51 16 Banten 68.28 35 Indonesia 70.19 17 Bali 70.23 17 Bali 67.57 18 West Nusa Tenggara 70.66 18 West Nusa Tenggara 69.19 19 East Nusa Tenggara 72.15 19 East Nusa Tenggara 69.27 [Healthcare in Low-resource Settings 2023; 11(s2):12005] [page 21] Table 4. Logistic regression model characteristics influenced to Happiness. Non -co mmerc ial us e o nly Article Figure 1. Urban happiness. Figure 1. Rural happiness. [page 22] [Healthcare in Low-resource Settings 2023; 11(s2):12005] Non -co mmerc ial us e o nly expressing heightened happiness. In stark contrast, provinces like Central Java, West Sulawesi, South East Sulawesi, and West Papua spotlight rural regions as happiness hotspots. A concluding observation is the comparatively depressed happi- ness scores in provinces like North Sumatra, Riau, Bengkulu, Banten, West Java, Bali, and West Nusa Tenggara, spanning both urban and rural sectors. Discussion Our findings indicate a notable trend: on average, urbanites in Indonesia revel in greater happiness compared to their rural coun- terparts. These findings resonate with similar investigations across the globe, suggesting a pervasive urban-rural happiness divide.8,24 However, it’s pivotal to acknowledge the deviations seen in some developed nations where rural inhabitants, empowered by a pletho- ra of amenities, often report enhanced happiness levels.24 A remarkable highlight from our analysis was the preeminent role of education in governing happiness across urban and rural landscapes. Nonetheless, contrasting literature suggests that the real elixirs of joy might be well-paying jobs and robust income streams, rather than educational milestones.27 Gender emerges as another pivotal determinant, particularly pronounced in rural settings. Rural men seem to grapple with hap- piness more than their urban peers.15 And while gender’s influence on happiness appears fairly balanced across urban and rural arenas, life events such as menopause can usher in profound psychological perturbations for women.26 Hence, urban locales, brimming with a plethora of engagements ranging from academia to politics, might appeal more to this demographic.28 Age undeniably modulates happiness. While youth often radi- ates exuberance, the multifaceted ingredients of happiness morph across the age spectrum. Older demographics often report an inverse relationship between age and happiness, with familial bonds and interactions with kin playing a cardinal role in their emotional well-being.11,12,19 Socio-economic stature, underlined by family income, remains a stalwart indicator of happiness. Such factors consistently sculpt the happiness landscape across urban and rural arenas. However, post-factorial adjustments spotlight the pronounced weight of socio-economic influences in rural settings compared to urban ones.2,22 This is intriguing, especially when contemplating the broader, macro-economic perspectives and their interplay with individual happiness. Our spatial analysis, via comprehensive maps, unveils consis- tent happiness zeniths across provinces like Central Kalimantan and Central Sulawesi. Conversely, stark urban-rural happiness dis- parities are evident in provinces like Jambi and Central Java. Alarmingly, traditionally dense and developmentally advanced provinces such as Java and Bali manifest suboptimal happiness metrics across both urban and rural domains. The results of this study show that Indonesia is not yet like developed countries, for example Denmark, where the level of happiness is higher for those living in rural areas.8 The fact that urban areas on the island of Java are densely populated turns out to provide less happiness for its residents. This needs to be researched more deeply socioculturally to find out what actually happens to residents in densely populated areas in relation to their happiness. Yet, our study isn’t devoid of limitations. Notably absent is the inclusion of professional vocations, largely due to data unavailabil- ity. Also, our respondent demographic, primarily household heads or their surrogates, might induce certain sampling biases, possibly skewing happiness insights towards mature adults. This age restricting could be possible overgeneralizing happiness among Indonesian women. Another limitation are that self-reported happi- ness measurements have the potential for subjectivity. The social support, cultural factors, access to healthcare and other variable might contribute to happiness were not available as well.29 The temporal context of our research, conducted amidst the 2021 COVID-19 pandemic, cannot be overlooked. This global health crisis, with its far-reaching psychological reverberations, undeni- ably imprinted upon our findings.30 A commendable strength of our study lies in its vast, nationally representative sampling. Bolstered by a standardized happiness assessment tool, our results are ripe for juxtaposition with global studies. However, a potential limitation arises from the singular respondent model, restricting the age profile of our respondents and possibly overgeneralizing happiness insights among Indonesian women. Our findings beckon attention from policy architects, health- care professionals, and local authorities. Intriguingly, densely pop- ulated and economically advanced provinces register lackluster happiness metrics, in stark contrast to fledgling provinces like Maluku, which bask in contentment across urban and rural spec- trums. In conclusion, The trinity of higher education, robust income, and youthfulness seems instrumental in paving pathways to happiness, irrespective of urban or rural habitats. The implication of the results of this research for the future is that to increase happiness evenly, it is necessary to increase educa- tion and income. Increased income will also be in line with the increased availability of living facilities. In this way, it is hoped that there will be no disparity in the happiness of residents whether they live in the city or in the village. Conclusions Factors contributing to heightened happiness among women include advanced education, youth, and high household income. Surprisingly, in densely populated and developed provinces, hap- piness levels remain lower. In contrast, in provinces distant from the capital—both in urban and rural settings—happiness levels are notably higher. Governments and policymakers should prioritize investments in education and economic opportunities for women, particularly in densely populated areas. Additionally, studying the unique attributes of newer provinces distant from the capital could provide insights for improving well-being in more developed regions. amplify efforts towards enriching educational and eco- nomic landscapes for women in high-density zones. Moreover, the study suggests a pivotal need to explore the idiosyncratic attributes of distant provinces, aiming to translate those lessons to enrich urban living conditions. References 1. Halbreich U. Pursuit of Happiness, Prosperity and Health (P- HPH). Int J Soc Psychiatry 2018;64:307–308. 2. Sasaki Y, Shobugawa Y, Nozaki I, et al. 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Asian Nurs Res (Korean Soc Nurs Sci) 2021;15:96–104. 27. van Aardt CJ, de Clercq B, Meiring J. The stochastic determi- nants of happiness in South Africa: A micro-economic model- ling approach. J Econ Financial Sci 2019;12:1–15. 28. He L, Wang K, Liu T, et al. Does political participation help improve the life satisfaction of urban residents: Empirical evi- dence from China. PLoS ONE 2022;17:1–24. 29. Şahin F, Sahin Altun Ö. Concept of happiness in schozo- phrenic. Psikiyatride Guncel Yaklasimlar - Current Approaches in Psychiatry 2022;14:291–298. 30. Lin X, Lin Y, Hu Z, et al. Practice of New Normal Lifestyles, Economic and Social Disruption, and Level of Happiness Among General Public in China in the Post-COVID-19 Era. Risk Manag Healthc Policy 2021;14:3383–3393. Article [page 24] [Healthcare in Low-resource Settings 2023; 11(s2):12005] Online supplementary materials Appendix. Questions Non -co mmerc ial us e o nly