Overall Life Satisfaction and Financial Well-Being: Revealing the Perceptions of the Beneficiaries of the Bolsa Família Program

Abstract

The study aims to analyze overall life satisfaction (OLS) and the financial well-being (FWB) of low income individuals by investigating 595 beneficiaries of the Bolsa Família Program through a questionnaire. The data analysis includes descriptive statistics, structural equation modeling, and clustering. The main results show that the people in this sample are satisfied overall with life, but are worried about and feel uncomfortable with the amount they owe, which lowers FWB. Confirming this result, the most representative cluster is made up of beneficiaries with high OLS and low FWB, who have precarious financial conditions, past due accounts and negative credit records as well as budget constraints that mean they cannot accumulate savings and cannot afford extra consumption.

Keywords:
overall life satisfaction; financial well-being; Bolsa Família Program; low income; financial management.

Resumo

O estudo teve como objetivo analisar a Satisfação Global de Vida (SGV) e o Bem-estar Financeiro (BEF) de indivíduos de baixa renda. Para isso, foram investigados 595 beneficiários do Programa Bolsa Família (PBF), mediante um questionário. A análise dos dados se deu por meio da estatística descritiva, modelagem de equações estruturais e cluster. Os principais resultados apontam que a amostra está satisfeita de maneira global com a vida, mas preocupa-se e sente-se desconfortável com a quantia que deve, o que minimiza o BEF. Ratificando esse resultado, o cluster de maior representatividade é formado pelos beneficiários com alta SGV e baixo BEF, os quais apresentam condições financeiras precárias, contas em atraso, nome vinculado a um cadastro negativo e restrição orçamentária que não permite a realização de poupança, nem o consumo de coisas extras.

Palavras-chave:
satisfação global de vida; bem-estar financeiro; Programa Bolsa Família; baixa renda; gestão financeira.

Resumen

El estudio tuvo como objetivo analizar la Satisfacción Global de Vida (SGV) y el Bienestar Financiero (BEF) de individuos de baja renta. Para eso, fueron investigados 595 beneficiarios del Programa Bolsa Familia (PBF) a través de un cuestionario. El análisis de los datos se realizó por medio de estadística descriptiva, modelaje de ecuaciones estructurales y cluster. Los principales resultados apuntan que la muestra está satisfecha de manera global con la vida, pero se preocupan y se sienten incómodos con el monto que deben, lo que minimiza el BEF. Ratificando este resultado, el cluster de mayor representatividad es formado por beneficiarios con alta SGV y baja BEF, los cuales presentan condiciones financieras precarias, cuentas atrasadas, nombre vinculado a un catastro negativo y restricción presupuestal que no permite la realización de ahorro ni el consumo de cosas extras.

Palabras clave:
satisfacción global de vida; bienestar financiero; Programa Bolsa Familia; baja renta; gestión financiera.

1. Introduction

To feel an overall satisfaction with life, individuals need to find support for this feeling from a series of life aspects, such as emphasizing the positive things in life, working in an area that one enjoys, taking care of one’s finances, having relationships with happy people, overcoming negative events and learning from them, and being involved in pleasurable activities that enhance one’s positive view of life and offer greater satisfaction (Kuppens, Realo and Diener, 2008). However, what constitutes overall satisfaction is different for each person and every life circumstance adds to this perception, which thus clarifies the multi-faceted and dynamic concept of Overall Life Satisfaction (OLS).

With this complexity in mind, Diener, Oishi and Lucas (2009) conceived of OLS as a positive or negative perception in terms of life events, which makes the individual more enthusiastic and euphoric or more discontent and unhappy. According to Giacomoni and Hutz (2008), this evaluation can be conducted in an overall manner, considering whether life as a whole satisfies one’s desires and aspirations, or in a more systematic manner, encompassing various facets of life such as family, education, working conditions, financial situation, or in other words, a series of evaluations that can be summed up as being satisfied or dissatisfied with the overall life.

Among the perspectives within the systematic evaluation, the income aspect has been increasingly debated. Cheung and Lucas (2014) clarify the interest in investigating this relationship between satisfaction and income based on the idea that money may predict the effects of other factors which can increase life satisfaction. Hagerty and Veenhoven (2003) justify this by showing that financial stability and the absence of worries in terms of monetary commitments result in better Financial Well-Being (FWB) and thus a more positive attitude towards life. In this sense, FWB is a feeling of being financially healthy and free from worries, based on subjective evaluations or perceptions of one’s financial situation in the present and future (Joo, 2008).

Negative evaluations in regard to these aspects can lead to a low perception of FWB. This is why, among the social classes, those who are most affected by financial tensions are low income families, who survive on restricted budgets and usually do not practice consistent financial management in terms of their basic needs, making it more difficult for them to achieve FWB and OLS (Lyons and Yilmazert, 2005). Therefore, this group is of interest for studies related to this theme, because they are more vulnerable. Thus, in seeking to identify individuals with similar life conditions and extremely restricted budgets, we have chosen to investigate the beneficiaries of the Bolsa Família Program (BFP).

The BFP is a federal government initiative whose main objective is to improve the quality of life for poor and extremely poor families in Brazil (MDS and Senarc, 2014). To do this, the government uses a Conditional Income Transfer Program, through which the public sector provides a fixed monthly support payment to provide short-term relief from poverty, and also assumes the commitment to broaden access to public services that constitute social rights such as the areas of health and education for these beneficiaries (MDS and Senarc, 2014).

Based on this context, the main goal of this study is to analyze the OLS and FWB of the BFP beneficiaries. It also has several specific objectives: i) to validate a measurement model for OLS and FWB factors for the low income population, ii) to divide the sample into sub-groups with similar OLS and FWB profiles; and iii) to characterize these groups through variables linked to management and their financial situation and behavior.

In terms of contributions, this study stands out due to: its focus on the beneficiaries of the BFP, the simultaneous evaluation of OLS and FWB constructs and the characterization of financial management profiles according to clusters. It should be noted that most studies of OLS have been conducted with the elderly (Niedzwiedz et al., 2014) and adolescents (Hutz, 2014). In addition, Brazilian studies in this group have not been dedicated to evaluating OLS and FWB individually or together. Also, we have not identified works that relate the different OLS and FWB profiles to financial management variables.

The importance of this study is directly related to the management of the BFP, because the results presented here can contribute to the adoption of strategies by the federal government that can improve the BFP, especially regarding the way in which beneficiaries receive their money and its impact on OLS and FWB. In addition, one of the program’s purposes is to support the development of these families in terms of improved well-being (MDS and Senarc, 2014). Thus, evaluating the OLS and FWB of these families is a way to analyze whether this goal has been achieved. Finally, we would like to emphasize the relevance of investigating the relationship between OLS and FWB, mainly for the low income population, given that many argue that “money brings happiness” (Diener et al., 2010; Becchetti and Rossetti, 2009), but little evidence of this has been linked to low income Brazilians.

2. Overall Life Satisfaction and Financial Well-Being

Studies related to OLS have gained greater notoriety over the past 20 years with the broadening of perspectives in terms of life satisfaction and the insertion of various factors as individual facets of life which can be summed up to determine overall satisfaction (Kuppens, Realo and Diener, 2008). Based on this evolution, the concept of OLS today is described by Hutz (2014) as the “contentment that someone feels in thinking about his or her life overall.” In an analogous way, Cheung and Lucas (2015) point to this as a subjective evaluation that the individual makes in relation to his or her experiences which have led to satisfaction or dissatisfaction with life.

Based on these concepts, OLS is influenced by various factors and for this reason it should be measured in material aspects as well as psychological ones. Thus, it is understood that for individuals to be satisfied with life, they need to evaluate their emotional and rational relationships positively and only draw lessons from their negative experiences (Diener, 2000; Kuppens, Realo and Diener, 2008).

Given these perceptions, the chances of deriving greater satisfaction from life increases. In cases where this holds true, the individual is happier and develops better social relations, health, infrastructure and leisure (Comis and Pinto, 2014). In contrast to this, people who are dissatisfied with their conditions are more likely to have low self-esteem, anxiety, fear and frustration (Lipovetsky, 2007), because this unease is responsible for developing psychosomatic alterations that lead to physical and emotional dysfunction. Physical problems are related to higher indices of obesity or anorexia, hypertension, headaches, insomnia and low immunity; psychological problems include increased irritability, impatience, apathy, emotional distance, loss of professional enthusiasm and, as a result, financial problems (Nunes, 2014).

Financial difficulties include higher levels of debt and the inability to pay bills and a lack of monetary control, all of which lower FWB. This is conceived of by Arber, Fenn and Meadows (2014) as a classification that depends on whether the individual’s income can satisfy a person’s present and future needs in general (Chan, 2012). Thus, good financial prospects for the future as well as good conditions in the present together with monetary security maximize FWB, which offers a better quality of life. This assertion is confirmed by Binder and Coad (2014), who clarify that people with greater financial stability, job security, good financial management and larger monthly income are more satisfied with their lives. Falahati, Sabri and Paim (2012) corroborate this, signaling that financial satisfaction acts as a determinant factor of an individual’s satisfaction with life in general.

As justifications for this relationship, Kim and Tech (1999) point out that sentiments of satisfaction or dissatisfaction in relation to financial affairs influence various aspects of an individual’s life, such as family and work. Hagerty and Veenhoven (2003) reveal that stability, equilibrium and the absence of worries about being late in the payment of bills, maximizes the chances of achieving dreams and meeting needs and desires, thus increasing happiness in the long term. In contrast, people with murky financial situations, who have greater difficulty making it through the month with their income, and have less control over their spending, may have a lower perception of overall satisfaction (Kahana et al., 1995).

This lower overall life satisfaction is often linked to the availability of financial resources, or in other words, poorer people do not have sufficient resources to pay for investments that would bring greater meaning to their lives, which thus leads to low levels of OLS, with individuals living in extreme poverty being the most vulnerable to feeling dissatisfied with life (Meyer and Dunga, 2014). On the other hand, increasing income can lead to changes, mainly in OLS, because it offers greater buying power, optimism and financial satisfaction (Diener, Tay and Oishi, 2013).

However, the effects of increasing income are distinct for each social class. Easterlin and Angelescu (2009) point out that individuals with fewer financial resources derive a greater increase in OLS through financial assistance than wealthier classes do. The authors arrived at this argument by verifying the comparative behavior noted in rich and poor countries. According to their findings, residents in wealthier countries were less affected by an increase in income in terms of OLS than people who live in worse situations in poorer countries.

Conversely, in some cases, even an increase in salary may not lead to greater satisfaction, as is the case with people with marital problems, the unemployed, the sick and those with social difficulties, because these problems are not improved with money (Becchetti and Rossetti, 2009). Observing this, Neve and Oswald (2012) highlight the double-sided relationship between satisfaction and income, and satisfaction with life can lead a person to greater economic dynamism and therefore better results.

Contradicting the results which show, to some extent, the strong relationship between satisfaction and increased income, Cheung and Lucas (2015) find a weak relationship between these two constructs. In the same way, Becchetti, Corrado and Rossetti’s study (2008) shows a modest improvement in satisfaction with life after the income of the Britons interviewed increased.

These studies indicate that there is no unanimity in terms of the relationship between income and overall life satisfaction, which signifies that different contexts can lead to different relationships. Thus, investigating this theme in terms of the beneficiaries of the BFP can help the federal government understand whether the focus on monetary aspects is appropriate for this scope.

3. Methodology

The target population of the study consists of the BFP beneficiaries who live in the Central-West region of the Brazilian State of Rio Grande do Sul, which is made up of 31 municipalities. To identify this population, we used the data available in the Transparency Portal during the month of October 2014. Thus, the data was collected based on the number of beneficiaries in each of the 31 municipalities identified. For a sample error of 4%, with 95% confidence and a finite population of 31,671 (Transparency Portal, 2015), the minimum sample investigated had to be 590 families who are beneficiaries of the BFP, and we were able to obtain a total of 595 interviewees by the end of the study.

Given the specificity of the target audience, the large number of cities and the difficulty of access to the beneficiaries, the sample was selected due to two types of convenience: initially, we tried to enlist the help of the municipal governments in pointing out establishments like the Social Assistance Reference Centers (CRAS) or health posts that serve a large number of the beneficiaries. Given that we did not achieve success with this approach, the interviewers went to the neighborhoods and asked people on the street if they were beneficiaries of the BFP and then whether they wanted to participate in our study. Those who did, answered a questionnaire individually in the format of an interview. The researcher read each question and the interviewee answered them one by one. We opted for this more direct form of contact to guarantee a greater understanding of the questions and more authenticity in the responses.

Our data collection used an instrument with four blocks of questions. The first dealt with OLS using a scale adapted from Hutz (2014). The original scale was made up of 10 variables, but after content analysis we opted to use 7 questions using a five point likert¹ type scale. Content analysis was used, according to Malhotra (2011), to select questions which contribute to the collection of the desired information. If the question did not contribute, it was excluded. This process was conducted by three specialists who assumed that seven questions to evaluate overall life satisfaction would already be satisfactory. In terms of the interpretation of the responses, the higher the degree of agreement with the statements corresponds to the individual’s greater satisfaction with life.

The second block deals with the FWB factor, originally developed by Norvilitis, Szablicki and Wilson (2003), and it consists of eight questions. However, to place it within the same perspective as the OLS factor, we used just four variables which deal with feelings related to life at the moment of the questionnaire, or in other words, the present. In terms of the interpretation of the responses, the higher the degree of agreement with the alternatives corresponds to the individual’s lower FWB. The third part deals with personal and family financial decisions, practices and experiences. These questions were structured based on an adaptation of the models already applied by Shockey (2002), the OECD (2013), the Ministry of Development and the Fight Against Hunger and the Secretariat of Information Management Evaluation (MDS and Sagi, 2014), Flores and Vieira (2014) and Brazil (2009). The last section consists of questions regarding the respondents’ profile.

To analyze the data collected, we used descriptive statistics and multivariate analysis techniques, for which we used SPSS 20.0® and Amos™ software. We initially used Confirmatory Factor Analysis (CFA) to validate our constructs. In this context, Byrne (2013) considers it necessary to analyze various indices of adjustment to evaluate the appropriateness of the proposed model for the data sample. For the purposes of this study, the validity of the measurement model was evaluated through the verification of convergent validity, reliability and unidimensionality for each construct, in accordance with the recommendation of Hair and partners (2010).

The convergent validity of each construct was analyzed by observing the magnitude and statistical significance of the standard coefficients and by the indices of absolute adjustments: the chi-squared test (χ²), the root mean square residual (RMR), the root mean square error of approximation (RMSEA), and the goodness-of-fit index (GFI) and the indices of comparative adjustments: the comparative fit index (CFI), the normed fit index (NFI) and the Tucker-Lewis index (TLI). There is no consensus in the literature in terms of acceptable values for these indices. For chi-squared/degrees of freedom the recommendations vary from less than five to less than two. For CFI, GFI, NFI and TLI, values greater than 0.95 are suggested, and the RMR and the RMSEA should be below 0.05 and 0.08 respectively (Hooper, Coughlan and Mullen, 2008).

To measure the reliability of the constructs we used Cronbach’s reliability index and alpha, in which values superior to 0.6 have been considered acceptable for studies of an exploratory nature (Hair et al., 2010). The verification of construct unidimensionality was performed through an evaluation of the standard residuals, and absolute values less than 2.58 indicate that there are no problems (Hair et al., 2010). In addition, all the models were estimated using a maximum likelihood bootstrap, with a sample size of 1,000 as suggested by Cheung and Lau (2008).

After performing the CFA, the OLS and FWB indicators were formed, and the Pearson correlation and a cluster analysis were applied to them. The Pearson correlation indicates the associative force between any two variables (Pestana and Gageiro, 2008). Cluster analysis aggregates objects based on their characteristics (Hair et al., 2010). According to Malhotra (2011), the clusters obtained should present internal homogeneity as well as external homogeneity, distinguishing them from the rest of the objects. The Ward method was used for the cluster analysis, which is also known as the variance method. The Ward method was selected because it is one of the most consistent for interval scales (Hair et al., 2010). This study uses three clusters, because we perceived that the groupings identified with this composition are more appropriate to explain the behavior of our sample group.

After identifying each individual’s cluster, we calculated descriptive statistics for the constructs within each cluster to find the OLS and FWB levels of each formed group. In addition, to verify whether there is a significant difference between the groups, we applied a t-test. To improve the characterization of these clusters, we performed descriptive statistics of variables related to aspects of personal and family financial decisions, practices and experiences within the different groups of our study sample.

There were 28 variables, which contemplate the third part of the data collection, which questions how long it takes to spend the BFP funds, whether they have debts in general, store debts, have credit cards, whether they are within their monthly budget, who makes the family’s spending decisions, what their financial situation is, whether they control their monthly expenses, whether they are satisfied financially, whether they are managing to save money, how they spend their BFP funds, what they use it to pay for as soon as they receive it, if any of this benefit remains by the end of the month and what is done with it, what they do when a need arises, how they choose where to shop, whether their name has already been on a bad credit record list, whether they lend their name or have already lent their name for someone else’s purchase, and whether they have a bank account.

4. Analysis and discussion of the results

Of the 595 people surveyed, only 14 were men. This characteristic can be justified by the fact that the federal government prioritizes the granting of this benefit to women, who are viewed as more cautious and responsible (MDS and Senarc, 2014). This act by the federal government can be seen as being part of the concept of positive discrimination as a necessity, as pointed out by Souza (2015), in the sense that it benefits the most vulnerable portions of the population in order to diminish inequalities, in this case poor women (the profile of the beneficiaries of the BFP). However, the same author reveals that the effects of the BFP in terms of empowering women do not diminish the differences between the sexes and, on the contrary, reinforce the stereotype of their being the ones responsible for maintaining the family’s well-being.

In terms of age, the sample is quite heterogeneous and all the age groups are represented. The other profile variables are homogeneous, with 48.3% of those interviewed being married, (95.6% of them having children, with an average of three per family), with 67.1% having their own residence, and 76.7% having an 8th grade education or better. In terms of their occupations, 21.6% work in the informal job market (mostly as maids), 21.6% are housewives with a monthly family income of R$ 200.00 to R$ 500.00 (27.1%), with part of this sum coming from the BFP which transfers an average of R$ 173.62 to each beneficiary. In other words, these benefits represent a sizeable portion of their income.

Through these characteristics, we can identify a very specific profile of individuals with a defined family structure, with a spouse and children (even those who are not married have children), their own home (in most cases, provided by the municipal governments to low income families), a low level of education and seasonal employment, which are linked to low levels of income and greater vulnerability and more difficulties in maintaining a minimum standard of living. These are the characteristics which increase the importance of investigating aspects linked to the OLS and FWB for this portion of the population. Thus below we present descriptive statistics for these two constructs (Table 1).

TABLE 1
Descriptive, average and frequency statistics for the responses relating to the Overall Life Satisfaction and Financial Well-Being constructs

• Source: Study results (2015).

In terms of OLS, the sample shows an average positive perception of 3.69. Examining the data in a more detailed manner, we can see that there was more agreement with variables Q2, Q4, and Q6 which indicates that the beneficiaries like and are happy with their lives and feel well the way they are. However, when asked whether they have all that they need, 46% of the respondents disagree, or in other words, they are satisfied and happy, but they do not have all they need. This result enables us to conjecture that material aspects are not that relevant for Overall Life Satisfaction for this portion of the population. Ng and Diener (2014) corroborate this, making it clear that material wealth is more important to the rich in terms of their feeling of satisfaction with life than it is to the poor.

A study of 400 families of the same profile (with budgetary restrictions) in Africa, found that this portion of the population had a relatively low feeling of satisfaction with life, but income levels, professional situations, conditions of poverty and government services can influence this result and they were not examined in the African study (Meyer and Dunga, 2014). Observing these gaps gives one a better impression of the Brazilian government’s initiative with the BFP, which has significantly improved the quality of life of its beneficiaries (Fonteles et al., 2011), which may be leading to this positive perception of OLS.

Investigating FWB, we can see that the average overall perception was 3.52, revealing that the respondents did not present an elevated level of FWB, given that higher values indicate more unfavorable perceptions on the part of these individuals. We can see that the highest averages (Q9 and Q10) are for the variables concerning constant worrying and thinking about their debts, which minimizes the feeling of financial well-being. In addition, the beneficiaries related that they feel uncomfortable with the size of their debts. Contrary to these variables is the fact that in general the respondents have not had discussions with other people about their debts, which indicates that this is not an everyday occurrence for these families.

In general, the beneficiaries of the Bolsa Família Program do not have a high level of FWB. However, this is not a result that is observed just within this sample. Gutter and Copur (2011), in their study of students at 15 universities in the United States, and Diniz et al (2014) in their study of the population of the Brazilian state of Maranhão, also found a low level of FWB. Thus, it should be pointed out that even samples involving portions of the population with better financial conditions do not demonstrate positive levels of FWB, especially since this perception is based on whether individuals feel their incomes are enough to satisfy their overall needs (Arber, Fenn and Meadows, 2014).

Analyzing these two constructs together, we can see that our respondents display a high level of OLS, but a low level of FWB. Similar results have been presented by Biswas-Diener, Vittersø and Diener (2005). Looking at these initial results, we can suggest that even though our interviewees do not have sufficient financial resources to make them feel secure, they do feel satisfied with their lives, probably because other factors that affect OLS are being met such as food, shelter (more than half have their own home) and basic education, as can be seen in the descriptive statistics.

In the next step we used the CFA to validate our OLS and FWB constructs. This was done to confirm whether the models are appropriate by analyzing the adjustment indices which are presented in Table 2.

TABLE 2
Adjustment indices for the Overall Life Satisfaction and Financial Well-Being constructs

• Source: Study results (2015).

Verifying the initial OLS model, we can see that the values are outside of the pre-established standards. Two main measures were taken in order to achieve a more appropriate measurement model: the variables with a coefficient below 0.5 were removed (Hair et al., 2010) and correlations between variable errors were inserted as was suggested by the software and which makes sense theoretically. Thus, variable Q1 was excluded because it presented a coefficient of 0.349 and a correlation between variables Q2 and Q6 (value 36.040) was inserted. After these adjustments, all the adjustment, reliability and unidimensionality indices presented satisfactory values.

In terms of FWB, most of the adjustment indices presented values within the pre-established standards. However, the reliability index was below 0.5. Thus variable Q11 was excluded, and its coefficient of 0.043 was not significant. After this adjustment, the model displayed appropriate results. Figure 1 illustrates the initial model and the final validated model for OLS and FWB.

FIGURE 1
Initial and final measurement models for the Overall Life Satisfaction and Financial Well-Being constructs

The construct OLS, originally composed of seven questions, was validated with six variables, and variable Q5, which asked whether individuals were satisfied with their lives, presented the highest coefficient, indicating it had the greatest impact on OLS, followed by question Q7. The higher coefficients identified with these two questions supports the influence of the perception of satisfaction and personal realization on the level of OLS. Question Q3 had the lowest coefficient, which is evidence of the lesser relevance of material aspects in the formation of a positive perception of OLS.

For FWB we found that the variable that had the greatest influence was Q10, which questioned whether those interviewed think a lot about their debts, followed by question Q9. Question Q8 was the one which contributed least to the formation of the model and it is related to individuals feeling uncomfortable with the amount of debt that they owe. This result indicates that the fact that people think and worry about their debts is more relevant to the formation of FWB than being uncomfortable with finding themselves in this situation.

Based on the validated models, the constructs OLS and FWB were formed for the following phases of analysis. The formation of each construct used the coefficients for each of the variables in the validated models. Thus, 100% of the impact of each question was weighed in the formation of each construct and then the OLS and FWB factors were formed based on the sum of the variables multiplied by their respective weightings. The OLS factor was determined based on the equation {0.15*Q2+0.13*Q3+0.17*Q4+0.20*Q5+0.16*Q5+0,18*Q6}. In the same manner, the FWB factor was determined based on the equation {0.23*Q8 +0.37*Q9 + 0.38*Q10}.

With these two standard factors in hand, we first applied the Pearson correlation to verify the degree of association between the constructs OLS and FWB. The result of the test was not significant (value -0.063 and sig. 0.127), revealing that there is no association between OLS and FWB for the population we studied. This finding can justify the fact that our sample group presents an elevated OLS and a reduced FWB, or in other words, the financial worries and difficulties faced by these beneficiaries do not interfere with their perception of OLS, and other aspects may be interfering with this factor, such as the possibility of maintaining the minimum standard of living provided by the BFP. A study performed by the OECD has already reported similar results. According to the authors, income is the aspect that least affects the well-being of Brazilians. In the same vein, Cheung and Lucas (2015) found a very low association between these two factors.

Finally, we performed a cluster analysis to classify individuals according to their OLS and FWB. Table 3 presents the descriptive statistics of the standard constructs according to the distribution of our three formed clusters.

TABLE 3
Descriptive statistics of the constructs according to the cluster distribution

• Source: Study results (2015).

In analyzing the descriptive statistics of the constructs that form each of these clusters, we can observe that the individuals with high OLS and low FWB make up Cluster 1, which has the largest proportion of respondents (64.61%). This composition is an intriguing combination to the extent that it indicates that the beneficiaries of the BFP are satisfied with their lives, but have low FWB. This result matches the evidence identified by the descriptive statistics of these constructs, and this structure of responses was already verified by that analysis. To justify this fact we can point to the OECD study (2014) that found that income is relatively unimportant to the well-being of Brazilians.

Cluster 2 contains individuals with intermediate Overall Life Satisfaction (average 2.74), close to the indifferent option, and low Financial Well-Being (average 1.58). Thus, the satisfaction with life is not completely dominant, and a lack of financial well-being could be one of the factors leading to a more unfavorable view of life within this specific group, because this behavior was not observed in Cluster 1. Analyzing Cluster 1 and Cluster 2 together, we can see that they represent roughly 85% of those interviewed, with a low level of FWB in common and high and intermediate levels of OLS respectively. In terms of OLS, it is possible that the availability of this monthly financial resource has a greater effect on their standards of living, especially because their greatest dilemma is the seasonality of their monthly income (Collins et al., 2009).

For most of the beneficiaries, the amount received through the BFP is their only fixed monthly income (Sugiyama and Hunter, 2013). This monetary amount offers a better quality of life to these families, because their basic needs are better met (Souza and Lamounier, 2010). In seeking to understand this relationship, previous studies have pointed to the possibility that meeting the basic needs of these low income individuals already makes them more satisfied with life (Howell and Howell, 2008). This perspective takes Maslow’s theory into consideration, which reveals that individuals are motivated by their needs (Maslow, 1943). In this way, to low income individuals, the fact that the base of the pyramid is being contemplated (physiological needs) already increases their satisfaction with life (Diener et al., 2010). Given that, before the BFP, not even these needs were supplied, which would have made them more dissatisfied and would have generated negative emotions. Clark, Frijters and Shields (2006) report that poorer people adapt to the reality that is imposed on them, which makes them more satisfied with life even when their financial conditions are precarious.

Thus we can say that from a psychological point of view that these families have the perception that life improved once they became beneficiaries of the BFP (Santos et al., 2014; Brazil, 2009), which positively affects OLS. But, on the other hand, the value provided is not sufficient to generate FWB and eliminate the worries inherent in having debts and financial instability. The Brazilian government provides an average sum of R$ 173.62 to our sample group, which 36.97% describe as low and 25.04% describe as sufficient. Santos and partners (2014) confirm this, clarifying that the beneficiaries feel more satisfaction with life due to the increase in income that they receive from this social program; however, this is not enough to provide their families with financial emancipation.

Finally, in contrast to the behavior of the rest of our sample, Cluster 3 is made up of individuals with high OLS and high FWB. Our results in terms of this group in particular confirm the findings of Falahati, Sabri and Paim (2012), who emphasize that financial satisfaction is fundamental to a person’s overall satisfaction with life. This is because, according to Kim and Tech (1999), feelings of satisfaction or dissatisfaction are linked to financial aspects which influence various spheres of life.

To characterize each of these clusters we used the financial management variables listed in Table 4. We wish to point out that the variables presented were only those which presented at least a 5% level of statistical significance between the clusters and the variables.

TABLE 4
Percentage of responses for each variable per cluster and the Chi² value with its value and significance

• Note: Cluster 1 (High OLS and Low FWB); Cluster 2 (Avg. OLS and Low FWB) and Cluster 3 (High OLS and High FWB). Source: Elaborated by the authors.

From the Chi-Squared figure we can tell that there is a statistically significant association of at least 5% between the clusters and the variables used. Observing the results for Cluster 1, which consists of people with high OLS and low FWB, it may be noted that more of the responses clearly characterize people in an unfavorable financial situation, with debts that reach such high levels that their names appear on bad credit lists and tight budgets that do not allow them to save money or buy extra things. But even with all of these problems, they state that they are satisfied with their financial situation.

These characteristics show that for this portion of the sample, their current monetary adversity has not affected their perception of their financial situation. It is possible that they have been in worse situations before and the simple fact that they are able to meet their basic needs more than satisfies them, and since the low income population has lower material aspirations, they are irrelevant to the improving of their overall satisfaction with life (Diener et al., 2010). In this way, this portion of the population may be considering other aspects to consolidate their perceptions, like eating well and having good social relationships as well as good relationships with their spouses (Becchetti and Rossetti, 2009).

Compared to Cluster 1, we can see that the beneficiaries of Cluster 2 (indifferent in terms of OLS with low FWB) are in an even worse situation financially, because besides their debts and budgetary restrictions, they still cannot pay their monthly bills, are dissatisfied with their financial situation, have already lent their name for someone else’s purchase and have a bank account. Total financial instability and limitations on their consumption may be the reasons why this specific group is indifferent in terms of OLS, because it is possible that their basic needs are not being completely met.

Finally, Cluster 3 consists of beneficiaries with high OLS and high FWB who do not have debts, are managing to handle their responsibilities, are satisfied with their financial situation, have never had a bad credit record, and thus find themselves in preferable financial conditions. This characterization makes this group the one with the highest levels of satisfaction with life and perceptions of FWB.

5. Final considerations

The descriptive statistics of the OLS factor reveal that, on average, those interviewed are satisfied with their lives, they like and feel happy with the life they are living, but say they do not have everything they need. In the validation of the construct, this fact was confirmed and the material aspects presented a lower coefficient. In relation to FWB, a negative perception was confirmed, justified by a heightened worrying and constant thinking about debts, which were the variables that presented the greatest relevance. The inexistence of a correlation between these two factors reveals that low FWB does not affect OLS. Thus, we conjecture that other aspects are making our interviewees perceive their lives in a positive manner.

This context is confirmed by the cluster results which point to the largest group of respondents belonging to Cluster 1, which consists of people with high OLS and low FWB. In characterizing the financial profile of these individuals, it may be perceived that their financial situations are quite adverse. As a justification for this context, we can point to the fact that these are beneficiaries of the BFP, and based on this alone they perceive life in a more favorable light despite their precarious financial situation, because their basic needs are now being better met since they have become recipients of aid from the BFP.

In terms of this study’s contributions, we pointed out earlier that one of the goals of the federal government in developing the BFP has been met, at least in the region investigated, since the families show an elevated satisfaction with life. On the other hand, it is clear that the size of the benefit is not enough to meet these families’ financial needs, which implies a low FWB. This indicates that there is a need for greater investment by the federal government in the BFP and mainly in the delineating of structural policy to generate work and income.

In this sense, we can also emphasize the possibility of the federal government developing complementary actions, because it is evident that material aspects, such as the money that comes from the program, may not be sufficient to consolidate these people’s satisfaction with life. This may stimulate the managers of the BFP to focus on other factors besides transferring income, such as employment, food and shelter, thus addressing the multidimensional aspects of poverty.

This study also makes academic contributions, mainly through an enhanced understanding of low income families, and an examination of poverty in terms of its psychological aspects such as OLS and FWB. In addition, it has enhanced the validation of these factors through structural equation modeling, which offers future studies our scales, which have already been validated for the low income population.

Further studies can use a broader sample size and can also identify other factors which may be associated with OLS, and can also seek a deeper understanding of the relationship between FWB and OLS. In addition, we suggest the conducting of other similar studies to verify whether the elevated OLS and low FWB found here are unique characteristics of this sample or are common to other low income contexts.

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• 1
  The use of the five point likert scale has the advantage of a neutral point and it usually presents an appropriate level of reliability, adjusting to respondents of different ability levels. Its disadvantages are that it does not offer the discrimination of a seven point scale and is longer than a three point scale (Dalmoro and Vieira, 2013).

Publication Dates

• Publication in this collection
  Mar-Apr 2017

History

• Received
  02 Nov 2015
• Accepted
  20 Sept 2016