Over the past decades, life satisfaction has received an increasing amount of attention. Life satisfaction refers to the cognitive evaluation of the quality of one’s life as a whole (global life satisfaction), or of specific life domains (domain satisfaction) (Myers & Diener, 1995). Numerous studies have emphasized the links of life satisfaction with several benefits such as health, longevity, social relationships, prosociality and productivity (de Neve et al., 2013; Diener & Tay, 2012; Heintzelman & Tay, 2017; Lyubomirsky et al., 2005). Given its benefits, life satisfaction has become more and more relevant, both for individuals and policymakers (Maccagnan et al., 2019). It is, therefore, not surprising that over the past decades there has been an extraordinary amount of effort put in the assessment of life satisfaction, both on the national and the individual level. For instance, since 2005, the Gallup World Poll has collected data on life satisfaction of large samples in more than 160 countries in over 140 languages (Helliwell et al., 2019). This allows for between-country comparison on life satisfaction as well as analysis of change in life satisfaction within countries over time.

The positive effects of life satisfaction highlight the importance of improving our understanding of its antecedents and what can be done to improve it within society for psychology, public health and policy makers in these fields. One of the most interesting questions with regard to life satisfaction is the extent to which life satisfaction is influenced by external and material conditions versus personal factors (including the attitude towards these conditions). A more specific question is “What are the conditions for a satisfied life: to what extent needs life to be trouble-free?”. Available evidence shows that the average ratings of life satisfaction are lower in nations and people who are afflicted by serious hardship (e.g., war, violence personal adversity and loss) than in nations/people that are not. On the other hand, evidence obtained in various settings shows that people can be quite happy and can even thrive in spite of difficult circumstances (Veenhoven, 2005). These paradoxical findings make clear that there is more to learn about life satisfaction, in particular in people confronted with serious hardship.

The current study has been carried out in India, which is extreme in terms of population density and wealth inequality. Moreover, India is facing a large number of other social issues such as caste system, gender inequality, child labor, illiteracy, poverty, religious conflicts, and more. It represents, therefore, a particularly interesting setting for research on life satisfaction. This study focuses on life satisfaction of people living in a very low resource setting i.e., in urban slums in India where hardship is bounteous, which remains a vastly understudied group.

Growth and Inequality in India

India is one of the most culturally, linguistically, and genetically diverse countries in the world. It consists of 28 states and 9 union territories, has 22 official languages and over a thousand dialects, six major religions and over 4000 castes (Venkata Ratnam & Chandra, 1996). It is a country of opposites: on the one hand, India is known for its immense population and population growth, its pollution and poverty (Chandramouli, 2011; Khilnani & Tiwari, 2018; Thorat et al., 2017). On the other hand, however, it is a country of opportunities, where the economy and technological development are thriving at enormous speed: India has the second biggest annual GDP growth in the world and the national GDP grew about 260 percent from 2000 to 2019 (The World Bank, 2021). Regardless of the economic growth and newly achieved wealth in India, the wealth inequality in the country has increased rapidly since 1991. Where the wealth of Indian billionaires increased by almost 10 times over the last decade, the poorest half of the population saw their wealth rise by just 1 percent (Himanshu, 2018). Taking into consideration that about 22 percent—270 million people—of the total population in India is living below the poverty line (Reserve Bank of India, 2015), economic wealth is far out of sight for a significant number of people.

Throughout the twentieth century distress and poverty in the rural areas in India resulted in a huge influx of refugees and migrants into urban areas which has led cities to grow rapidly since the 1950s (Singh, 2016). The rapid urbanization resulted in lack of space and acceptable housing which culminated in the emergence of slum settlements in and outside the city (Ray, 2017). Although the living conditions in most slums are generally bad, there is significant variation between slums for example with regard to available facilities, identity groups (e.g., Hindu versus Muslim) and reported incomes (Lange, 2020). Another source of variability between slums is legal status. More than half of all slums in India are not recognized by the government (Nolan et al., 2017). These slums, also known as non-notified slums, are more deprived in access to basic services and living security than notified slums and people living in these slums live under a constant threat of eviction (Ray, 2017). This deprivation of living security may have negative consequences. Evidence from a systematic review revealed that threat of eviction is related to lower mental and physical health such as depression, anxiety, psychological distress, suicide, elevated blood pressure and child maltreatment (Vásquez-Vera et al., 2017). Even with significant variation in living security, facilities and ethical/religious demographics, slums are often deprived with respect to economic, social and living conditions (Ray, 2017) and most people would consider these places as unsuitable for living. When it comes to well-being, one might expect that a population of this enormous magnitude, living in these deprived circumstances, is less happy as their more privileged, Indian counterparts. But are the poor really as dissatisfied with life as one might expect them to be?

Poverty and Life Satisfaction in Urban Slum Settlements

The idea that material conditions (e.g., money) matter to happiness has inspired many scholars to explore the relationship between income and life satisfaction (e.g., Tay et al., 2018). The results of these studies show that income contributes to life satisfaction. For instance, the World Happiness Report 2019 found that higher national incomes are linked with higher life satisfaction of citizens, indicating that on average, those living in richer countries are happier than those living in poorer countries (Helliwell et al., 2019). The results from within-nation studies show however that the positive correlation between income and life satisfaction varies. The highest correlations between income and life satisfaction were found in low-income groups living in economically less developed countries. Yet, this relationship remains weak, which implies that income explains little of the variance in life satisfaction (Howell & Howell, 2008).

Despite the plethora of research on life satisfaction, few studies have focused on life satisfaction in people living in extreme poverty. Traditionally, poverty has been conceptualized as economic deprivation. Yet, since the 1980s, the definition of poverty has broadened from a monetary approach (measured by income only) to a multidimensional approach which takes into account other factors related to basic needs such as housing, sanitation, and education. Previous studies (Bag & Seth, 2018; Ki et al., 2005) showed that a multidimensional assessment of poverty more completely captures the phenomenon and it is now widely recognized that multidimensional poverty is a richer concept than the traditional unidimensional monetary approach (Asselin, 2009). The Multidimensional Poverty Index (MPI), constructed by (Alkire & Foster, 2011), is a methodology to measure poverty corresponding to the three dimensions: Health, Education and Standard of Living (Table 1). It gathers different household-level information with the use of ten indicators and captures whether households suffer deprivation according to the dimensions. Most of the research on poverty and life satisfaction has focused on the monetary approach solely, whereas the current research builds on this by including non-monetary indices of poverty.

Table 1 Dimensions and indicators of the Multidimensional Poverty Index (MPI)1

The Current Research

The purpose of this study is to examine life satisfaction and its predictors in the context of extreme poverty. The study is set in Kolkata which is one of the largest cities in India. Out of a total population of about 4.5 million people (Government of India, 2011), almost a third of its inhabitants lives in slums (Ray, 2017) showing the importance of carrying out research in this particular population.

The current study aims to replicate the work of (Biswas-Diener & Diener, 2001) who performed a study on life satisfaction among the poorest inhabitants of Kolkata in 2001. Their results showed that their sample, consisting of pavement dwellers, slum residents and sex workers, scored slightly negative on global life satisfaction. It is noteworthy, however, that the level of global life satisfaction in slum residents (which was significantly higher than in the other disadvantaged groups) almost matched the level of global life satisfaction in Indian students. Moreover, it was found that the participants were satisfied with most of the assessed life domains. These findings suggest that certain communities and cultures, although poor, may enjoy a relatively high level of life satisfaction.

Nearly 20 years have passed since then and during that time India has seen rapid changes. Economic growth has combined with actions by government agencies and Non-Governmental Organizations (NGOs) to address the newly revised target of the first Sustainable Development Goal (SDG): “to end poverty in all forms everywhere” (United Nations, 2015). Yet, despite these efforts, the gap between the rich and the poor has widened and still a substantial proportion of the population lives below the poverty line (World Bank Group, 2020). After 20 years of change it might be time to investigate the life satisfaction of the extreme poor again.

The current study focuses on the level and determinants of life satisfaction of people living in slums in Kolkata. In line with the study by Biswas-Diener and Diener (2001) the assessment of global life satisfaction will be complemented with measures of life-domain satisfaction (e.g., income satisfaction, health satisfaction). What is new is that the study does not solely focuses on monetary poverty as a predictor of life satisfaction, but also takes the explanatory power of multidimensional aspects of poverty into account. Last, as 59% of the slums in India are non-notified (Nolan et al., 2017), the role of fear of eviction as a predictor of global life satisfaction will be explored.

The specific aims of this study are:

  1. 1.

    To document the level of life satisfaction (global and domain-specific) of people living in urban slums in Kolkata, India.

  2. 2.

    To test whether there is a difference between the different domain satisfactions (social relationships, physical environment, physical health, psychological health and financial situation) in people living in urban slums in Kolkata, India.

  3. 3.

    To compare the level of global life satisfaction of slum residents with global life satisfaction measured in a representative sample from the general population of another large Indian city (Delhi) as measured by the Gallup Poll.

  4. 4.

    To examine age, gender, poverty indicators (monetary, multidimensional) and fear of eviction as predictors of global life satisfaction.


Participants and Procedure


The present study, conducted by Calcutta Rescue in 2019, is part of a larger cross-sectional research project on poverty in urban slums (Lange, 2020). Calcutta Rescue is a medium-sized NGO that focuses on supporting the slum communities in Kolkata which are most poorly served by the local and national government. Participants were eligible for this study if they were 18 years and older and resided in one of the six different slum settlements in the urban area of Kolkata in India (see Sect. Slum Selection and Sampling Method). Participation was voluntary and participants did not receive any financial compensation for their participation. The informed consent was read to (or by) all participants, dependent on whether the respondent was able to read and write. In case the respondent was illiterate, the informed consent was explained verbally, and a literate family member was asked to sign on behalf of the participant, or a thumbprint was obtained from the respondent.

Slum Selection and Sampling Method

A slum area is defined broadly in line with the 1997 Indian Compendium of Environment Statistics as groups of 25 or more poor-quality dwellings (Kundu, 2003). The slums to be sampled were all part of the operational area of Calcutta Rescue and were distributed across the city. Part of the slum settlements was unregistered and part was registered (legal). The slums varied in population size: based on the average household size and the number of households, the estimated population numbers vary between 132 (Baranagar) and 1107 (Local Bustee). Following Kundu’s (Kundu, 2003) study on slums in Kolkata, a systematic sampling method was used. According to this method, an equal distribution of 15 percent of the households is considered as a minimum sample size. The current study aimed at sampling/interviewing 20 percent of the slum or 30 households, whichever was bigger. In order to get an equal sample across the whole slum, the area was mapped and households were counted prior to data collection. Every fifth household (20 percent) was asked to participate. Data were collected during the day and the structured interviews lasted between 25 and 50 min. Interviews were conducted in the participant’s home with the researcher and a translator, who was fluent in English, Bengali and Hindi. The data were recorded on smartphones in (KoBoToolBox, 2019), an online data collection system for challenging environments.


Life Satisfaction

Global life satisfaction was assessed using Cantril’s ladder (Cantril, 1965). Respondents were asked to evaluate their satisfaction with their lives as a whole using the Ladder Scale; an illustration of a ladder which represents their life, 1 being their worst possible life and 8 being their best possible life. Participants’ domain satisfaction was measured with 5 single items assessing participants’ satisfaction with different life domains: social relationships, physical environment, physical health, psychological health and financial situation. The items were derived from the WHO questionnaire on Quality of Life (WHOQoL Group, 1994) (e.g., In general, how satisfied are you with your social relationships) using a 5-point Likert scale format. The one-to-five rating was depicted on a piece of paper ranging from an extreme frown (1) to an extreme smile (5), similar to earlier research by Biswas-Diener and Diener (Biswas-Diener & Diener, 2001). Higher scores reflect higher levels of life satisfaction.

Predictors of Life Satisfaction

Socio-Demographic Variables

Socio-demographic variables included age and gender (female = 0, male = 1).


Poverty was measured in two ways: using both monetary (income) and non-monetary approaches (MPI).


Monthly income per capita was calculated based on the monthly household income divided by the number of household members.

Multidimensional Poverty

Table 1 illustrates the assessment of multidimensional poverty based on the Multidimensional Poverty Index (Alkire & Santos, 2014; UNDP, 2020). The MPI identifies deprivations at the household and individual level across three dimensions and 10 indicators: Health (child mortality, nutrition), Education (years of schooling and school enrollment) and Standard of Living (water, sanitation, electricity, cooking fuel, floor, assets). As shown in the table each of the three dimensions is equally weighted (one third each), though the individual indicators receive different weights. Weights are thus applied to each of the indicators, which are then summed up to a total MPI score. The total MPI score for each person lies between 0 and 1. A higher score represents a higher level of multidimensional deprivation.

Fear of Eviction

In addition to the above-mentioned MPI-indicators, information was gathered about whether the participant experienced a fear of being evicted (0 = no, 1 = yes).

Statistical Analysis

An a priori power analysis was conducted through G power (Faul et al., 2009) (α = 0.05, power = 0.80, medium effect sizes) to calculate the required sample size. Descriptive statistics (medians, means, standard deviations, percentages) were used to describe the data and to address the first research aim. Repeated measures analysis of variance (ANOVA) was used to test the significance of the mean differences between the five life domains (research aim 2). The Greenhouse–Geisser adjustment was used to correct for violation of assumption of sphericity, which is common in ANOVA within-subject analyses. Effect sizes (ES) were based on Cohen’s η2 (ES: 0.01 = small, 0.06 = medium, 0.16 or larger = large) (Draper, 2011). To address the third research aim we compared the sample mean with the mean life satisfaction score of a representative sample of the general population in Delhi as measured by the Gallup Poll (De Neve & Krekel, 2020). We first homogenized the responses for the Cantril’s ladder of our study (measured on a 1–8 response format) with those obtained by the Gallup Poll (the Cantril’s ladder in the Gallup Poll uses a 0–10 response format) using the linear stretch method (de Jonge et al., 2014). The one sample t test was used to determine whether the Kolkata slum sample mean significantly differed from the Delhi general population mean. Effect sizes were based on Cohen’s d (ES: 0.2 = small, 0.5 = medium, and 0.8 or larger = large; Draper, 2011). The fourth research aim was tested by applying hierarchical multiple regression analysis (method enter) in which age and gender were entered in the first step, income per capita in the second step and the MPI and living security in the third step. Effect sizes were based on R2 (1% small, 9% medium, 25% large; Draper, 2011). Statistical significance (alpha) was assessed at the 0.05 level.


Sample Characteristics

The characteristics of the sample (N = 164) are described in Table 2. As shown in the table, the sample was predominantly female (90.9%). Almost two-thirds of the sample were literate and from a Hindu background. The participants were long-term residents who had, themselves or their families, lived in the slum settlement for decades (not shown in the table). Almost two-third of the households was deprived in living security (64.6%). Most participants were able to meet daily needs. The results showed that the participants were most deprived in terms of housing, assets and living security.

Table 2 Sample characteristics (n = 164)

The Level of Life Satisfaction in Kolkata Slum Residents

The descriptive statistics for global and domain-specific life satisfaction (research aim 1) are presented in Table 3. With regard to research aim 2, the results of the repeated measures ANOVA with a Greenhouse–Geisser correction demonstrated a significant difference between the mean satisfaction levels across the different life domains (F(3.61, 580.69) = 21.83, p = 0.000, partial η2 = 0.12). A Bonferroni-adjusted post hoc analysis revealed that the participants reported significantly lower satisfaction in the financial domain than in the other life domains, whereas their ratings of satisfaction in the social domain were significantly higher than those of the other life domains (all p < 0.05). The satisfaction levels of the living environment domain and health domains (physical and psychological) did not significantly differ from each other (all p’s > 0.05).

Table 3 Descriptive statistics for life satisfaction

Life Satisfaction: Kolkata Slum Residents vs. the General Population

Regarding research aim 3, the results of the one sample t test showed the mean global life satisfaction score measured in Kolkata’s slum population (M = 4.27, SD = 3.19) did not significantly differ from the average global life satisfaction score of 4.01 of people living in Delhi (De Neve & Krekel, 2020). The difference, 0.26, 95% CI [-0.24 to 0.75], t(160) = 1.02, p = 0.31, represented an effect size of d = 0.08.

Prediction of Life Satisfaction

Table 4 shows the relationships between (non)monetary indices of poverty, fear of eviction and global life satisfaction (controlled for age and gender) (research aim 4). The results of the bivariate analyses (presented in the second column of the table) revealed that lower age, higher income, lower levels of multidimensional deprivation (MPI scores) and lower scores on fear of eviction were associated with higher levels of global life satisfaction. When entered in the multivariate model (presented in columns 3–5 of the table) the association between fear of eviction and global life satisfaction was no longer significant. The MPI (reflecting the non-monetary approach to poverty) accounted for additional variance (above income). The full model explained 15.4% of the variance (F(5, 150) = 5.46; p = 0.00) in global life satisfaction.

Table 4 Hierarchical multiple regression analysis of factors contributing to life satisfaction


This study investigated the level and predictors of life satisfaction of people living in slums in Kolkata, India. In line with previous research, it was found that slum residents were less dissatisfied with their lives than one would have held given the dire living conditions of these people. For the prediction of global life satisfaction, income (monetary poverty) was complemented with the Multidimensional Poverty Index (non-monetary poverty) and fear of eviction. The results showed that not only income but also non-monetary indices such as education, living standards and fear of eviction are important correlates of life satisfaction of people living in slums.

The level of global life satisfaction observed in this study was comparable to those measured in a representative sample from Delhi, another large metropole in India. Although counterintuitive, our finding of a relatively high level of life satisfaction in a marginalized group is not new. For example, in a study among the poorest of the poor in South Africa, it was found that landfill waste pickers scored higher on life satisfaction than the national average (Blaauw et al., 2020). The same study found that there was a significant group of waste pickers who were very satisfied with their lives. Our findings also resemble those reported by Biswas-Diener and Diener (2001) and Cox (2012) who found slightly positive global life satisfaction in urban slum residents and dump dwellers in Kolkata, India and Managua, Nicaragua, respectively.

With regard to domain satisfaction, the slum residents were fairly satisfied with three of the five life domains assessed in this study i.e., their social relationships and health (physical and psychological). They were least satisfied with their financial situation and physical environment. Similar findings have been reported in previous studies addressing domain satisfaction in poor populations (Biswas-Diener & Diener, 2001; Cox, 2012; Sharma et al., 2019). Various scholars have emphasized the importance of social ties for well-being (Diener & Seligman, 2004), especially in poor populations (Boswell & Stack, 1975; Domínguez & Watkins, 2003; Henly, 2007). Social connectedness has been associated with access to various forms of social support and cognitive processes associated with subjective well-being such as life satisfaction, enhanced self-esteem, self-worth, purpose and meaning in life (Thoits, 2011). Social ties may serve as a private safety net, a poor family can fall back on in times of need (Edin & Lein, 1997).

In terms of prediction, higher levels of life satisfaction were related to age, income and deprivation. Due to shared variance with the MPI, fear of eviction did not explain unique variance in life satisfaction. Specifically, younger residents and those with higher incomes and lower scores on the MPI reported higher levels of global life satisfaction. Our findings regarding the relationship between age and global life satisfaction related to those reported by (Cox, 2012) who examined age as a predictor of life satisfaction in poor populations in Nicaragua and data from the Gallup World Poll (Fortin et al., 2015). Our results are in line with previous work which emphasized the role of income in life satisfaction (Whitaker & Moss, 1976). Moreover, the income-life satisfaction relationship in this study was comparable to the average r effect size of 0.28 computed for low-income samples in developing countries in Howell and Howell’s (2008) meta-analysis. The current study also confirms the results of research reporting a negative relationship between the MPI and life satisfaction in people living in the poorest districts of Peru (Mateu et al., 2020) and India (Strotmann & Volkert, 2018).

Overall, data from several studies suggest that slum residents in developing countries, such as India, are more satisfied with their lives than one would expect based on their living conditions. This contradicts the common-sense belief that poor people are unhappy by definition. Such judgment is, however, an illustration of the “focusing illusion” (Schkade & Kahneman, 1998) which has received a lot of attention in the literature on life satisfaction. The “focusing illusion” takes place when individuals exaggerate the importance of a single factor (e.g., living circumstances or material wealth) on well-being. Going beyond the stereotype that poverty equates unhappiness may provide a different picture. Research suggests that people living in poverty may consider different aspects of life important for their well-being than people from a more affluent background. For example, extremely poor Nicaraguan garbage dump dwellers in the study by (Vásquez-Vera et al., 2017) reported that their happiness did not emerge from job status or income, but rather from meaningful interactions and relationships with others.

Moreover, the explanatory power of objective poverty (as measured by income and the MPI) for life satisfaction was limited. This is in line with a vast array of research showing that objective life conditions do explain only a minor part of inter-individual differences in life satisfaction (Argyle, 2013; Diener & Biswas-Diener, 2002). How hardship is perceived on the other hand, may be of much bigger importance for the appraisal of one's life (Veenhoven, 2005). Poverty is a subjective feeling, which means that people defined as poor by objective standards do not necessarily have to feel poor. Indeed, results from a recent meta-analysis (Tan et al., 2020) indicate that life satisfaction has a stronger link to subjective socio-economic status than objectively measured income or education.

Our findings could be interpreted in the light of the human capacity to adapt to environmental demands. Adaptability is a self-regulatory resource which allows individuals to adjust to good and bad phenomena by altering their standards, thoughts, behaviors and emotions to the requirements of situations at hand. Adaptability can help prevent or mitigate the negative impact of challenge and adversity on well-being (Carver & Scheier, 2001). Following the multiple discrepancies theory (Michalos, 1985), life satisfaction relates to the discrepancy between what one has and what one wants (desire discrepancy) and what relevant others have (social comparison discrepancy) (Brown et al., 2009). Perceived negative discrepancies between one’s standards and one’s actual situation have a negative impact on life satisfaction. In the context of slums, perceived discrepancies between what one has (slum dwelling) and what one wants (a decent house), or what one has (no income) and what relevant others have (improvement in daily wage) could be a source of dissatisfaction with life. Effects of perceived negative discrepancies can be counterbalanced, however, by self-regulatory discrepancy reducing processes such as choosing a relevant reference group for social comparison and lowering aspirations (Carver & Scheier, 2001).

Regarding social comparison, it has been found that people have a natural tendency to compare themselves with others (Festinger, 1954), in particular with relevant reference groups such as people with a similar ethnicity, background or occupation (Khaptsova & Schwartz, 2016). In the case of low status or minority groups, several studies found that exposure to a successful referent from a low-status group is more pleasant and meaningful than exposure to a referent from a high-status group (Blanton et al., 2000; Leach & Smith, 2006; Mussweiler et al., 2000). This highlights the value of identifying local champions (e.g., former classmates who have excelled in school or sports) to serve as role models for young people living in low resource settings (Kearney & Levine, 2020).

Lowering aspirations is another discrepancy reducing mechanism. This has been observed in deprived neighborhoods including two Kenyan urban slums (Kabiru et al., 2013) where the constraints of the environment had a leveling effect on young people’s occupational and educational aspirations. Similar findings have been reported for youth in disadvantaged neighborhoods in the US and Scotland (Furlong et al., 1996; Stewart et al., 2007). In the case of Kolkata, it is possible that slum residents compare themselves mostly to people within their community and set their aspirations and goals accordingly. Indeed, research has found that expectations of life and oneself are influenced by one’s relative position and social norms within one’s community (Knight et al., 2009). Both social comparison and lowering aspirations are self-protective strategies that may help to ensure subjective well-being in situations in which the remediation of disadvantage is beyond the scope of personal control (Blanton et al., 2000; Leach & Smith, 2006; Mussweiler et al., 2000). Unfortunately, such strategies may also lead to aspiration traps where people under-aspire in occupational and educational goals, thereby contributing to the intergenerational transmission of poverty (Flechtner, 2014).

This study is one of the few examining life satisfaction in people living in a very low resource setting such as an urban slum in India. Other strengths are the relatively large sample size and the inclusion of non-monetary indicators of objective poverty as predictors of life satisfaction. The use of non-monetary poverty indices such as the MPI in life satisfaction research is relatively new. This approach is in line with new perspectives on measuring the material situation (combining income with a direct measure such as a deprivation index) (Christoph, 2010). Our results (showing an incremental contribution by the MPI) suggest the added value of combining monetary- (income) and non-monetary measures (the MPI) when analyzing the relationship between the material situation and life satisfaction.

Nevertheless, some limitations merit attention. First of all, this study only included objective measures of poverty. The addition of subjective measures of poverty (the individual’s perception of his/her financial and material situation) could have offered a more complete picture of the poverty-life satisfaction relationship. Secondly, the cross-sectional design of this study failed to establish causality. Thirdly, because the interviews were conducted in person and in the participants’ homes, which gave the possibility onlookers or family members meandering in earshot of the survey being asked, the research design could have been prone to social desirability bias (Tourangeau et al., 2000). Finally, the fact that the sample was predominantly female was most likely caused by the fact that interviews were conducted during the day when women were more typically at home. This may limit the generalization of the results. However, a recent meta-analysis of 281 samples (Batz-Barbarich et al., 2018) did not show significant gender differences in life satisfaction. In addition, the study of Biswas-Diener and Diener (2001) which was conducted in a comparable sample in Kolkata showed no significant differences in life satisfaction between men and women. This gives us no reason to believe that the unequal sample sizes in gender influence outcomes in life satisfaction in the current study.

The results of the present study highlight the need for further research. A mixed methods design adding qualitative approaches to the assessment of life satisfaction could illuminate a more holistic and contextual understanding of slum residents’ perceptions and experiences in daily life (Camfield et al., 2009). Secondly, in addition to measures of objective poverty, further research should also include subjective indices of poverty as this accounts for a better prediction of life satisfaction compared to objective poverty measures (Tan et al., 2020). Lastly, it would be valuable to learn more in-depth about psychological processes underlying life satisfaction of people living in slums such as social comparison and aspirations.

In terms of clinical practice, practical assistance such as slum upgrading should be complemented with efforts to improve the life satisfaction of slum residents. Research highlights the benefits of a positive mindset including a less pronounced stress response (Smyth et al., 2017), better role functioning (Moskowitz et al., 2012) and more efficient decision making (Isen, 2000). This has been explained by research showing that a positive mental state helps building coping resources by broadening the individual’s attention and action repertoire (Fredrickson, 2004). Other research has shown that the presence of a positive mindset buffers against the negative psychological impact of adversity (Suldo & Huebner, 2004; Veenhoven, 2008). Psychological interventions aimed at improving the mental health of people living in slums should thus not exclusively focus on the reduction in problems but also on the enhancement of positive mental states. The few studies that have examined the effect of individual and group-based positive psychology interventions in disadvantaged populations in developing countries show promising results, including a large increase in life satisfaction, positive affect, positive thoughts, generalized self-efficacy and reductions in self-reported symptoms of depression and negative affect (Ghosal et al., 2013; Sundar et al., 2016). Efforts to improve the life satisfaction of the slum residents may thus be worthwhile to consider, as it may help them deal with the harsh reality of life.


The common belief that poor people are unhappy by definition is challenged by the results of this study on life satisfaction in urban slums in India. The findings of the study show that the slum residents in Kolkata scored comparable to the general population in terms of global life satisfaction (evaluation of the quality of life as a whole) and that they found satisfaction in other life domains than finances and their living environment. Moreover, in terms of prediction, objective poverty indicators explained only a minor part of the variance in life satisfaction. This suggests that it is not correct to determine a person’s life satisfaction on the basis of income and other socioeconomic variables alone and that other factors such as the appraisal of one’s life should be taken into account when examining life satisfaction. A suggestion for future studies is to include measures of subjective poverty and other personal individual difference factors when measuring the impact of poverty on life satisfaction.