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Article

Causal Attribution for Poverty in Young People: Sociodemographic Characteristics, Religious and Political Beliefs

by
M. Carmen Terol-Cantero
1,
Maite Martín-Aragón Gelabert
1,
Borja Costa-López
2,
Javier Manchón López
1 and
Carolina Vázquez-Rodríguez
1,*
1
Department of Behavioral Sciences and Health, Miguel Hernández University, 03202 Elche, Spain
2
Department of Health Psychology, Faculty of Health Sciences, University of Alicante, 03690 Alicante, Spain
*
Author to whom correspondence should be addressed.
Soc. Sci. 2023, 12(5), 308; https://doi.org/10.3390/socsci12050308
Submission received: 6 March 2023 / Revised: 12 May 2023 / Accepted: 15 May 2023 / Published: 18 May 2023
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Abstract

:
Poverty is a multidimensional phenomenon that includes a lack of education, health or housing; it is a relevant factor of social vulnerability that could lead to a situation of social exclusion. According to studies, poverty can be explained by external/social, internal/individual or cultural fatalistic factors. The aim was to confirm the structure of causal attributions of poverty and their relationships with sociodemographic characteristics, as well as religious and political beliefs, in young people. This is a cross-sectional study with a non-probabilistic convenience sample of undergraduate students on different degree courses. A survey was administered to 278 participants (45.4% women) with an average age of 21.59. They were young people studying health science degrees (78.4%) who self-identified as belonging to the lower or middle–lower class (57.2%), without any religious beliefs (56.5%) and as left wing (37.8%; n = 94) or center-left (27.7%; n = 69) in their political orientation. Confirmatory factorial analysis and multiple regression analysis supported the results in previous literature (CFI = 0.90, SRMR = 0.07, RMSEA = 0.06), indicating that there are cultural (C), external (E-S) and internal (I) attribution factors of poverty. The results show moderate relationships between the cultural factor and internal or external factors. The findings show that political affiliation and sex are the most consistent predictors of attributions for poverty.

1. Introduction

1.1. Theoretical and Contextual Background

The causal attribution framework in social psychology and social sciences has become an area of special interest over the years, and, since the early 1950s, social psychologists have mainly identified an internal–external attribution to explain behaviors (Heider 1958; Collins et al. 1974; Ross 1977). The classic works by Heider (1958), Jones and Davis (1965) and Kelly (1967) were important contributions to the study of attribution in the field of social psychology. Specifically, Heider (1958) and Weiner’s (1985, 1995) later contributions are of special relevance to the theoretical background of attribution processes and our knowledge of them today (Malle 2022). Heider (1958) emphasized two dimensions: internal, referring to traits and abilities, and external, referring to situational forces and circumstances. Between these two dimensions, Heider (1958) introduced an internal–external dichotomy of causal attributions to explain outcomes and actions (Malle 2022). Likewise, Weiner‘s theory, one of the most used in the explanation of attributions, supports Heider’s proposal through one main dimension of causality with two poles: internal–external (Ljubotina et al. 2022). Thus, causality attributions basically consist of making inferences in order to somehow describe the “causes” of certain behaviors, environmental (external) or personal (internal), based on popular beliefs (Vázquez et al. 2018; Vázquez and Panadero 2022). This process reflects economy or simplicity in causal thinking, which is evident in the explanation of certain outcomes, such as success and failure and wealth and poverty (Yeboah and Kumi 2012; Reyna and Reparaz 2014; Castillo and Rivera-Gutiérrez 2018).
Poverty is a multi-dimensional phenomenon that encompasses the privation of education, health or housing (Bayón 2013); it is a relevant factor in social vulnerability, which can lead to a situation of exclusion that includes a lack of access to or participation in local or social decisions (Feagin 1972; Castañeda-Navarrete 2012; Abrahar-Ul-Haq et al. 2018a). According to reports from the UNO (SDG), 10% of the world population or the equivalent of 700 million people live in extreme poverty, suffering from severe difficulties in satisfying their most basic needs, such as health, education and access to water and sanitation. However, the definition of poverty is examined through its economic, political and social dimensions in a cross-cultural perspective, and its extent varies across geographical location (Abrahar-Ul-Haq et al. 2018b, 2019). In Spain, 27.8% of the population lives in poverty and social exclusion (European Anti-Poverty Network 2022). Spain’s unemployment rate rose to 12.87 percent in the fourth quarter of 2022 from 12.67 percent in the previous period, and young people were one of the most affected groups (INE 2022). This report on unemployment in Spain warns of an increase in poverty and vulnerability, which, in turn, affects how Spanish people explain it, shaping their perceptions and behaviors regarding social inequalities, justice, equity and social policy development (Bullock et al. 2003; Vázquez and Panadero 2007; Bastias et al. 2019; Contreras-Montero and Hidalgo-Mesa 2021; Davidai 2022). As attributions of wealth and poverty can vary due to contextual and cultural influences, a series of different investigations seem to reflect a certain consensus, based on at least three causality dimensions of poverty attributions and their main determinants or predictors, such as personal or sociodemographic characteristics and ideological beliefs (Davidai 2022; Ljubotina et al. 2022; Segretin et al. 2022; Vázquez and Panadero 2022).

1.2. Research of Causal Attributions for Poverty

Attributions for poverty has become a significant area of research, which, over the years, has aimed to understand the processes of attribution toward the poorest social groups in individuals and society, and their impact on policies and social relations (Niemelä 2011; Vázquez and Panadero 2007; Bastias et al. 2019). The results from these studies have been oriented toward promoting or designing public social policies for disadvantaged sectors of the population (poverty or social exclusion), as well as increasing the general population’s social empathy toward poor people (Feagin 1972; Bullock 1999; Vázquez et al. 2018; Bastias et al. 2019). Most of the studies about this subject classify the causal attributes of poverty into three groups: (1) internal or individual causes, where responsibility lies with oneself (e.g., a lack of effort, laziness, low in intelligence, being on drugs); (2) external causes or sources external to the people who are in that situation (e.g., being a victim of discrimination, low wages, disadvantages compared to rich people; or (3) fatalistic causes (factors beyond people’s control that are related to fate, where individuals and society have little control—bad luck, disability or mental difficulties) (Feagin 1972; Kluegel and Smith 1986; Cozzarelli et al. 2001; Weiss and Gal 2007; Reyna and Reparaz 2014; Osborne and Weiner 2015; Bastias et al. 2019; Yúdica et al. 2021; Sainz et al. 2022). These causal explanations have been reported in different studies that have conducted exploratory and confirmatory factorial analyses of the attributions for poverty to primarily determine their internal and external dimensions, while also obtaining different factorial structures, including social, structural, fatalistic or cultural explanations, and mixed categories (Bullock 1999; Kreidl 2000; Cozzarelli et al. 2001; Bullock et al. 2003; Hunt 2004; Nasser et al. 2005; Weiss and Gal 2007; Ljubotina and Ljubotina 2007; Halik et al. 2012; Bobbio et al. 2010; Reyna and Reparaz 2014; Bergmann and Todd 2019; Sainz et al. 2022; Segretin et al. 2022). Although this tripartite division has been empirically supported, there is a serious discussion about whether causes can be placed in one of three mutually exclusive categories. Thus, the literature refers to the role of an individual explanation for the causes of poverty, and points out their interaction with other structural factors and explanations (Cozzarelli et al. 2001; Small et al. 2010; Weiner et al. 2011; Norcia et al. 2010; Segretin et al. 2022).

1.3. Determinants of Causal Attributions for Poverty

All studies focusing on poverty issues also emphasize some aspects or antecedents that have an important role as predictors of causal attributions, such as sociodemographic characteristics and values or beliefs (Lepianka et al. 2009). Sociodemographic characteristics such as age, sex or gender, educational level, social class, nationality and country of residence, among others, can affect the causal attribution for poverty (Cozzarelli et al. 2001; Norcia et al. 2010; Vázquez et al. 2018; Bastias et al. 2019). Some of the studies show that women are more likely to carry out external attribution or to think that poverty is caused by extrinsic problems (J.D. Jones 1994; Bullock 1999; Norcia et al. 2010; Bastias et al. 2019). Social class and socioeconomic status are also usually considered as relevant predictors of attributions of the causes of poverty (Feagin 1972; Kluegel and Smith 1986; Hunt 1996). These studies indicate that people who belong to a low social class and/or a low socioeconomic status show a greater tendency toward structural or external attributions for poverty in contrast to an upper social class or a higher socioeconomic status, which relates to internal or individualistic poverty attributions (Bullock 1999; Cozzarelli et al. 2001; Norcia et al. 2010; Mickelson and Hazlett 2014; Bastias et al. 2019; Yúdica et al. 2021). With regard to other sociodemographic features, such as age, there are studies where older people tend to attribute poverty to personal or individual factors compared to younger people (da Costa and Dias 2015; Vázquez et al. 2018). At the same time, other authors report that older people tend to attribute poverty to cultural or less internal/individualistic causes (Cozzarelli et al. 2001; Nasser et al. 2005).
With respect to the values that are found to influence attributions for poverty, the studies mention political orientation or ideology, work ethics (Protestant), post-materialism, feelings about social justice, and religious tradition or beliefs. Political orientation, often operationally defined as voting preference or party affiliation (Kluegel and Smith 1986; Bullock 1999) and/or self-ascribed, ranges from liberalism to conservatism or left to right wing (Lee et al. 1992; Appelbaum 2001). Most of the research points out that those with a more conservative (right and center-right) or traditional political orientation frequently tend toward individual or internal attributions for poverty explanations, whereas others with a more liberal or progressive approach (left or center-left) emphasize external and structural or social attributions of poverty and positive attitudes toward welfare policy (Bullock 1999; Cozzarelli et al. 2001; Ljubotina and Ljubotina 2007; Vázquez and Panadero 2007; Lepianka et al. 2009; Bobbio et al. 2010; Vázquez et al. 2017; Boeh 2018; Bergmann and Todd 2019; Toft and Calhoun 2021; Contreras-Montero and Hidalgo-Mesa 2021; Vázquez and Panadero 2022). However, the role of religion in attributions for the causes of poverty presents unclear results in the different studies. Vázquez et al. (2017) could not corroborate that the population of countries with a strong Catholic tradition tends toward attributing poverty to external reasons as Lepianka et al. (2009) show. Meanwhile, other studies found that religious beliefs are associated to internal poverty attributions (Boeh 2018; Bergmann and Todd 2019).

1.4. Causal Attribution of Poverty in Young People

Young people are the social and economic future in a country’s development, and their beliefs will influence political actions. Thus, in most studies, groups and undergraduate students have been asked how they perceive the causes of poverty and about the characteristics and beliefs that could be related to their perceptions (Ljubotina and Ljubotina 2007; Weiss and Gal 2007; Nasser 2007; Bobbio et al. 2010; Halik et al. 2012; Bastias et al. 2019; Bergmann and Todd 2019; Alcañiz-Colomer et al. 2022). As outlined above, the explanation about the causes of poverty differs according to some sociodemographic variables and ideological/religious values or beliefs, but the literature also indicates how professional areas or types of undergraduate education in young people can also have an influence. With regard to educational level in general, some studies indicate that those with a higher educational level tend to attribute poverty to personal or individual factors compared to those with a lower level of education, who indicate social and structural causes as the origin of poverty (da Costa and Dias 2015; Vázquez et al. 2018). Among university students, research in different countries found that undergraduate students accorded more importance to external–structural explanations of poverty (Nasser et al. 2005; Weiss and Gal 2007; Bobbio et al. 2010; Nasser 2007; Halik et al. 2012; Bastias et al. 2019; Alcañiz-Colomer et al. 2022). Other studies compare students’ perspectives at different stages of their education and among groups from different university degrees (Schwartz and Robinson 1991; Nasser et al. 2005; Ljubotina and Ljubotina 2007; Bastias et al. 2019; Isla-Esquivel 2021; Stephen et al. 2021). In general, students from social work programs or sciences (sociology and psychology, among others) consider the individual or internal factor less important than other students (economics, agriculture, business management and so on) and show a preference for the external or social and structural factors of the causes of poverty (Weiss and Gal 2007; Ljubotina and Ljubotina 2007; Bastias et al. 2019; Stephen et al. 2021). There are not so many studies on health profession students, but the results are heterogeneous, showing both internal and external causes of poverty. However, these students did achieve more positive attitudes or attributions to poverty and social justice through educational programs in poverty (Stewart and Schommer-Aikins 2016; Richey et al. 2016; Delavega et al. 2017; Scheffer et al. 2019).

1.5. Current Research

There are two questions in this study. Previous research suggested some discrepancies as to whether causes can be placed in one of three mutually exclusive categories (Cozzarelli et al. 2001; Small et al. 2010; Weiner et al. 2011; Norcia et al. 2010; Segretin et al. 2022). Previous studies also indicate that attribution causes of poverty are influenced by certain determinants, such as demographic characteristics (sex, age, social class), education or professional areas or types of undergraduate degrees and ideological/religious values or beliefs. Following the interest in these questions about the discrepancies regarding the tripartite division or dimensions of the causes of poverty and the determinants that consistently explain poverty attributions in the literature, this study was designed to add new information and further develop our understanding of attributions for poverty in young undergraduate students.
Therefore, this study will aim, firstly, to explore the structure of different causal attributions of poverty and, secondly, their relationships with different sociodemographic characteristics (sex, social class), the type of undergraduate degree and religious and political beliefs in a sample of young people in Spain. Based on the most consolidated results of the above mentioned research, the hypothesis in this research is defined as follows: young undergraduate students will show significantly more external or structural attributions for poverty. The following sociodemographic and belief variables were studied: being a woman, belonging to a lower–middle social class and having a left-wing political orientation.

2. Method

2.1. Participants

The Spanish sample of young undergraduates consisted of 278 participants: 124 men (44.6%) and 154 women, with an average age of 21.59 (standard deviation (SD) = 5.3 years). They were studying health science degrees: 29.5% (n = 68) of the participants were studying psychology, 48.9% (n = 136), pharmacy or physiotherapy, whereas the rest of the participants were studying food sciences and technology or engineering. In this group, 42.8% (n = 119) self-identified as belonging to the upper or middle–upper class and 57.2% (n = 159) to the lower or middle–lower class. Regarding religious beliefs, 56.5% (n = 157) did not mention any, 35.2% (n = 98) were catholic and 8.3% (n = 23) indicated other religious beliefs. Finally, in political beliefs, most participants defined themselves as left wing (37.8%; n = 94) or center-left (27.7%; n = 69). The other group considered themselves as center-right (19.7%; n = 49) or right wing (14.9%; n = 37).

2.2. Instruments

Sociodemographic Characteristics. Information about age, sex, self-perceived social class (upper or upper–middle class and lower–middle or lower class) and degree was collected.
Religious and Political Beliefs. They were measured with two ad hoc items formulated as follows: “According to your religious or political beliefs, which do you identify with?”. Participants were asked to choose from four options. For religious beliefs: None, Catholicism, Islamism, Other (in this case, they mentioned which one); and, for political beliefs: they indicated their position on the political spectrum, such as the left, center-left, center-right and the right.
Attributions toward poverty. This construct was measured by the Attributions for Poverty Scale (Cozzarelli et al. 2001; adapted by Reyna and Reparaz 2014). Participants were asked to rate the importance of each of the 23 items as causes of poverty on a Likert response scale ranging from 1 (not at all important as a cause of poverty) to 5 (extremely important as a cause of poverty). It includes items for individualistic (e.g., “lack of effort or laziness”, “alcohol and drug abuse”, “lack of thrift and proper money management”), societal (e.g., “prejudice and discrimination in promotion and wages”, “having to attend bad schools”, “being taken advantage of by the rich”) and fatalistic (e.g., “just bad luck”, “illness or physical disabilities”) attributions. A principal component analysis, as carried out by Cozzarelli et al. (2001) and Reyna and Reparaz (2014), revealed three factors defined as external, internal, and cultural attributions. High scores in each subscale or factor indicate higher levels of this type of attribution toward poverty. The internal consistency for all subscales was higher than 0.70 (Reyna and Reparaz 2014).

2.3. Procedure

This was a cross-sectional study with non-probabilistic convenience sampling. Once permission was obtained to access a standard public university classroom, participants were selected among undergraduate students on different degree courses. Students were informed about the aim of this project, informed consent, confidentiality and anonymity by a researcher. They were invited to participate voluntarily and signed a “commitment to participate”. The questionnaires were completed in a self-administered paper version or recorded version via Google Form provided by a member of the research group. The sample was calculated following the minimum criteria required for the different programmed statistical analyses. This means: (1) for CFA (Confirmatory Factor Analysis) with factor loading greater than 0.40, a sample of 200 is adequate (MacCallum et al. 1999; Lloret-Segura et al. 2014); (2) in the multiple regression analysis, 10–20 cases by number of predictors is satisfactory (Montero Granados 2016); and (3) in the analysis of differences (t-Student) between groups, a sample > 72 individuals by independent group is sufficient (according to p = 0.05, with d = 0.50 and 1-β ≥ 0.95) (Cohen 1988; Ledesma et al. 2008).

2.4. Statistical Analyses

All data were analyzed using SPSS Statistical software package, IBM Statistics v25, New York, USA. Means and frequencies were carried out for the descriptive analyses. Pearson’s correlation coefficients and Student’s t-test were used for relationships between variables and mean differences between groups. Interpretation of the effect size values for non-parametric differences showed: small effect size (0.20), medium effect size (0.50) and large effect size (0.80) (Cohen 1992). According to scientific literature, previous exploratory factor analyses were carried out by original authors (Cozzarelli et al. 2001; and Reyna and Reparaz 2014) to develop the likely factor structure for the instrument (Floyd and Widaman 1995; Carretero-Dios and Pérez 2005). Therefore, in order to reflect the structure of the attributions toward poverty, a confirmatory factor analysis (CFA) was performed to verify the goodness of fit model with factor solution. According to the literature, the model’s goodness of fit was: chi-square > 0.05, root mean square error of approximation (RMSEA) ≤ 0.06, comparative fit index (CFI) ≥ 0.90 and standardized root mean square residual (SMRS), ≤0.08 (Hooper et al. 2008; Kline 2010; Brown 2015). In addition, analyses of the scales’ internal consistency (Cronbach’s alpha) were performed to establish their validity. A criterion of 0.70 is universally used (Nunnally and Bernstein 1994). Finally, a hierarchical regression analysis (stepwise method) and multicollinearity tests (VIFs < 10; Kutner et al. 2004) were conducted to determine sociodemographic variables and religious and political beliefs as predictors of different attributions toward poverty. Sociodemographic variables (sex, age, social class) were entered as predictor variables in the first step of the regression, and the rest of the variables were introduced in the second step.

3. Results

3.1. Confirmatory Factorial Analysis

In the preliminary analyses of the subscales, the correlation coefficients of all items were ≥0.20 except for items 5 (Only bad luck) and 23 (Being unemployed), which were excluded, concurring with the results obtained by Cozzarelli et al. (2001). Then, a CFA was conducted, where the results are given in Table 1. The initial factorial solution showed unacceptable results, as none of the criteria (CFI, SRMR and RMSEA) were met. As item 4 showed a factor loading greater than 0.40 in factors 1 and 3, an alternative model with item 4 in factor 3 was tested, but the indices did not improve. By removing this item, the model fit noticeably improved. For model 3, item 20 was removed. Modification indices suggested that all three factors explained its variance; however, the best result was produced when removing the item. Finally, since items 16 and 17 shared factor loading in more than one of the factors, the same procedure used for items 4 and 20 was followed, and they were removed. By excluding all four items that performed well in more than one of the factors, the goodness of fit improved and a satisfactory factorial solution was obtained (CFI = 0.90, SRMR = 0.07, RMSEA = 0.06) (see Table 1). The final factor loading can be found in Table 2. All factor loads were statistically significant, and their communalities were higher than 0.40 (Table 2). The final factorial structure and internal consistency were established as follows: Factor 1: cultural attributions include nine items (13, 14, 15, 18, 19, 21) (α = 0.687; ω = 0.713); Factor 2: internal attributions with six items (1, 2, 6, 9, 10, 11) (α = 0.734; ω = 0.745); and Factor 3: external attributions described by six items (3, 7, 8, 12, 22) (α = 0.769; ω = 0.784) (see Figure 1).

3.2. Correlations, Differences and Regression Analysis

In this section, the results are analyzed according to the CFA solution. Internal and external causal attributions for poverty were significantly related to cultural attributions (r = 0.37; r = 0.28, respectively; p = 0.001). All the factors showed significant differences between each other (see Table 3).
The attributional causes considered most important in a sample of undergraduate students in Spain were external–structural causes (M = 3.79 ± 0.75) followed by internal and cultural causes (M = 2.89 ± 0.70; M = 2.60 ± 0.71, respectively). The participants from a lower social class (−2.08, p = 0.038), not studying a health sciences degree (t = −3.08, p = 0.002), with religious beliefs (t = −3.08; p = 0.002) and with a right-wing political orientation (t = 3.31; p = 0.001) showed significantly more internal attributions for poverty. With respect to external–structural attributions, we found significantly higher mean scores in the groups of women (t = −2.96; p = 0.003), students from health sciences degrees (t = 2.43; p = 0.016), those without religious beliefs (t = −2.70; p = 0.006) and those with a left-wing political orientation (t = −6.25; p = 0.001). All these significant differences showed an effect size index very close to a medium value (>0.40), whereas the differences in external–structural attributions by left-wing political orientation reached a large effect size index (>0.80). However, there was not a significant effect size index for differences by social class (see Table 4).
The regression analysis showed that cultural attributions were positively predicted by age (β = 0.16; t = 2.45; p = 0.015), and internal attributions were explained by having religious beliefs (β = −0.22; t = −3.45; p ≤ 0.001) and not studying health sciences degrees (β = 0.16; t = 2.59; p = 0.010). Finally, being a woman (β = 0.18; t = 2.97; p = 0.003) and having left-wing political beliefs (β = 0.36; t = 6.38; p ≤ 0.001) accounted for external–structural attributions (see Table 5).

4. Discussion

This study explored, firstly, the structure of different causes of poverty as perceived by young undergraduate students in Spain and, secondly, the relationships with their sociodemographic characteristics, type of degree and political and religious beliefs.
The first part of the discussion focuses on the structure of different causes of poverty. This was measured with the Attributions for Poverty Scale (Cozzarelli et al. 2001; adapted by Reyna and Reparaz 2014). It was applied to young undergraduate students in Spain and the factorial solutions obtained by the different authors were also tested. Items 5 and 23 were excluded, concurring with Cozzarelli et al. (2001), and when the CFA was conducted, the goodness of fit improved after four items were excluded. Three of these items—4 (Illness or physical disability), 17 (The types of jobs that the poor can get are often low paying) and 20 (Lack of thrift and proper money management)—were also excluded by either of the previous authors (Cozzarelli et al. 2001; or Reyna and Reparaz 2014), whereas item 16 (The breakdown in the nuclear family) was maintained by both (Cozzarelli et al. 2001; Reyna and Reparaz 2014). Certainly, the mixed classification of these items could partly explain their exclusion when the construct validity is analyzed. In this sense, item 17 (The types of jobs that the poor can get are often low paying), classified as a cultural attribution according to Cozzarelli et al. (2001), could also be classified as external (e.g., bad luck) or due to societal causes (economic opportunities). In the same way, item 20 (Lack of thrift and proper money management) could be explained as an individual cause or responsibility, but it can also be understood as a result of socialization processes and beliefs about the “culture of poverty” (Bullock 1999; Cozzarelli et al. 2001). In addition, in the context of illness or physical disability (item 4), different processes or dimensions (control, external and/or fatalistic) reflecting the diversity of circumstances and models of illness could involve a more complex or interdependent causal attribution (Sensky 1997). This complexity in the classification of attribution processes also depends on causal dimensions applied to a particular context, perceived distance from poor people and even the relationships between causal beliefs and emotions (Weiner 1985; Weiner et al. 2011). In fact, the reviewed literature shows a mixed causal classification for some items and a certain shared content between cultural attributions and internal or external attributions. This suggests that it could be more useful to consider them as complementary or coexistent instead of mutually exclusive categories or contradictory beliefs (Hunt 1996; Cozzarelli et al. 2001; Bullock and Waugh 2005; Bobbio et al. 2010; Weiner et al. 2011; Small et al. 2010; Norcia et al. 2010; Sainz et al. 2022; Segretin et al. 2022). In this way, and according to previous studies, this study confirmed (1) three factors for poverty attributions: internal (items 1, 2, 6, 9, 10, 11), external–structural (items 3, 7, 8, 12, 22) and cultural (items 13, 14, 15, 18, 19, 21) in line with most of the results from different research studies (Cozzarelli et al. 2001; Reyna and Reparaz 2014; Segretin et al. 2022); (2) significant differences between the three factors but also moderately significant relationships between the cultural factor and internal or external–structural factors (Halik et al. 2012; Reyna and Reparaz 2014; Sainz et al. 2022); and (3) a stronger alpha coefficient for internal and external attributions for poverty versus the third factor (which, in this study, is cultural, whereas it is fatalistic/structural in other studies) (Cozzarelli et al. 2001; Bullock et al. 2003; Ljubotina and Ljubotina 2007; Halik et al. 2012; Bastias et al. 2019; Sainz et al. 2022).
The second part of the discussion focuses on the results for frequency in the causes of poverty and their relationships with sociodemographic characteristics (type of degree and other beliefs). The whole group of undergraduate students in Spain showed a higher frequency in external–structural attributions followed by internal and cultural attributions. These findings are similar to those found in different countries with different samples of students, mostly from social or health sciences and humanistic degrees (psychology, social sciences or social work) (Nasser et al. 2005; Weiss and Gal 2007; Ljubotina and Ljubotina 2007; Halik et al. 2012; Bobbio et al. 2010; Stewart and Schommer-Aikins 2016; Vázquez et al. 2017; Bastias et al. 2019; Scheffer et al. 2019; Isla-Esquivel 2021; Stephen et al. 2021). This study includes a majority of psychology and physiotherapy students (health sciences group) who have to maintain less “social distance” or “closer relationships” with their patients and their needs, including a certain social sensitivity toward exclusion groups. In addition, the degree subjects and professional competencies involved in physiotherapy are also oriented toward understanding psychosocial and illness contexts and developing some abilities for “counseling”. In this sense, the health sciences group showed significant differences, with a higher mean in external–structural attributions compared to the other group (nutrition, technology or engineering), who showed significantly more internal attributions to explain poverty. In Spain, in the study by Bastias et al. (2019) on young undergraduate students, the business management, accounting or human resources group also reached a higher mean for individual causes of attribution of poverty. With respect to religious beliefs, young undergraduate students in Spain with religious beliefs refer significantly more to internal attributions for poverty, whereas the other group without religious beliefs indicated more external–structural attributions. The regression analysis confirmed that religious beliefs and not studying health science degrees were related to internal causes (F = 10.3; p = 0.000). In general, the literature showed tendencies related to both internal attributions (Boeh 2018; Bergmann and Todd 2019) and external attributions (Lepianka et al. 2009). These attributions have also been found to have an unclear role, suggesting indirect effects due to other values and more complex measures, including religiosity or spiritual practices, within different religious traditions (Vázquez et al. 2017; Bergmann and Todd 2019). In the sample of young undergraduates in Spain, the participants’ religious beliefs are mainly traditionally Catholic, and, in line with some research, this can suggest that more conservative or traditional religious beliefs could promote a sense of responsibility or individuality and “blame” in terms of sentences such as “each one has what they deserve” (Bergmann and Todd 2019). On the basis of this dissimilarity in results, Brechon (1999) underlines that the impact of religion on attributions of poverty should be studied by taking into account other possible indirect effects caused by a more complex combination of variables or values.
On the other hand, in the regression analysis, being a woman and having left-wing political beliefs predicted external attributions in the group of undergraduate students in Spain (F = 25.9; p = 0.001). As in this study, results from different research studies show that women use external attribution or socio-structural causes more frequently to explain poverty; they view this problem as beyond an individual circumstance and have more positive attitudes toward social welfare issues (J.D. Jones 1994; Bullock 1999; Norcia et al. 2010; Bastias et al. 2019). With regard to political orientation, most studies consistently demonstrate the importance of political orientation as antecedent to the cause of poverty (Zucker and Weiner 1993; Weiner et al. 2011). According to these results, political orientation was the strongest predictor of their external–structural poverty attributions in this study, showing significant differences with a large effect size index (0.84). In line with previous studies, our results pointed out that people with left or center-left political orientation tend to consider the causes of poverty as predominantly external (Vázquez and Panadero 2007; Vázquez et al. 2017; Stephen et al. 2021; Toft and Calhoun 2021; Vázquez and Panadero 2022). In contrast, the group of young undergraduate students with a political orientation toward the right use internal explanations for the causes of poverty. From the literature reviewed, it is possible to infer consistently positive relationships of right-wing or conservative political orientation and traditional values or ideology with internal attributions for poverty. In this sense, policies of inequality, privilege or restrictions are promoted, with greater emphasis on individual responsibility rather than on society as a whole. However, on the other side of the political spectrum, regarding progressive ideologies, social justice and humanistic values are associated with external or structural attributions and welfare spending (Bullock 1999; Cozzarelli et al. 2001; Bullock et al. 2003; Ljubotina and Ljubotina 2007; Norcia et al. 2010; Lepianka et al. 2009; Bobbio et al. 2010; Boeh 2018; Yúdica et al. 2021; Contreras-Montero and Hidalgo-Mesa 2021; Vázquez and Panadero 2022).
Finally, in this study, cultural attributions for poverty were positively predicted by age as in the studies by Cozzarelli et al. (2001) and Nasser et al. (2005), where younger participants endorse more internal attributions whereas older participants display more cultural causes. However, other relationships expected according to the determinant of a different social class were not found. The lower–middle social class group displayed a significantly higher mean in internal attribution but they did not show a significant effect size index of difference and consequently did not predict any causal attribution for poverty. These results are similar to other studies (Nasser et al. 2005; Nasser 2007; Vázquez et al. 2017) but contrast with those studies that point out the importance of social class, socioeconomic status or income attributions as an explanatory antecedent for poverty attributions (Bullock 1999; Cozzarelli et al. 2001; Norcia et al. 2010; Lepianka et al. 2009; Mickelson and Hazlett 2014; da Costa and Dias 2015; Bastias et al. 2019; Yúdica et al. 2021; Dunga and Dunga 2022). One possible interpretation of our results could be related to using a perceived or subjective measure, where, in addition, the middle class is more representative in both groups than the lower and upper social class in each group. However, according to other authors, it is important to consider the type of variables (income level, socio-economic status, different arbitrary groups or objective/subjective or perceived categories) that should be used to compare social classes, as well as the effect of other personal characteristics or individual values that interact in the relationships between the social class, values and attributions for poverty (Lepianka et al. 2009; Boeh 2018). Therefore, the hypothesis stating that young undergraduate students will show significantly more external or structural attributions for poverty has been confirmed. Moreover, the relationships between external–structural attributions and characteristics such as being a woman and having a left-wing political orientation have been corroborated as determinants. However, belonging to a lower social class did not verify its influence as a determinant of external–structural attributions

4.1. Theoretical and Practical Implications

Firstly, from a theoretical perspective, future research should pay attention to the complexity of attribution processes and how to measure them. It will be important to be able to encompass all variables that may be involved in explaining differences between groups from different countries, as well as the personal experiences that moderate and interact with the predictive characteristics most often assessed in poverty research. Secondly, there is a certain heterogeneity among authors who have tried to replicate the factorial structure of attribution scales in general and specifically with respect to the scale used in this study. Therefore, the different causes that explain poverty attributions and the scales used to measure them should be carefully reviewed. In order to achieve this, the aspect explained by each factor and overlapping content or mixed classification should be analyzed. In this sense, the operational identification and definition of all the factors and causes of poverty attribution, as well as their possible predictors, are a key issue for understanding this construct and its relationships with other variables. All this should be approached with a practical view and a common goal, which is to change different beliefs and attitudes toward some groups of the population, designing public policies and specific strategies in order to bring about changes in this attitude and to improve equality, social justice and social development. Moreover, further research should include groups of students at different stages of their education, such as secondary school, university or professional training. In this way, it would be possible to test the consequences of humanistic values and attitudes transmitted through educational levels and professional skills training. Furthermore, these students are the generation that will have an influential role in the fulfilment of the 2030 agenda for sustainable development and the eradication of poverty.

4.2. Conclusions and Recommendations

This study confirmed three factors for poverty attributions: internal, external–structural and cultural, as in most studies in the literature reviewed. However, the complexity of classifying attribution processes and the possibility of a mixed causal classification of causality dimensions should be studied more thoroughly in order to reach a homogeneous proposal. In accordance with the suggestions made by other authors (Bobbio et al. 2010; Segretin et al. 2022), the results of this research corroborate that internal and external–structural attributions emerge as the main defining factors that explain causal attributions for poverty and their relationships with the variables assessed. In line with Cozzarelli et al. (2001), sociodemographic characteristics and religious beliefs are generally inadequate for predicting attributions of poverty, whereas political affiliation and sex are the most consistent predictors. The results of previous studies with undergraduate groups are generally based on social work students and fail to report differences between groups of students from different degrees. However, different types of degrees, such as health sciences in this study, and professional areas also predict causal attributions of poverty. In view of the results, it seems necessary to extend the range of variables for explaining the causes of poverty, studying their interactions with individualistic and collectivist cultural beliefs learned across several societal contexts and socialization processes. This means considering how people are influenced by their country’s cultural issues, different ideological and professional values, history and policies, all of which play an important role in their opinion and attributions of poverty.
Finally, future research should also consider that poverty eradication policies should be formulated in the context of an empowerment approach, where skills, knowledge, employment and gender equality are a possibility for people’s participation and inclusion. This process also requires that people themselves participate in bringing about change, as poverty is a complex issue that requires the full involvement of all (Abrahar-Ul-Haq et al. 2018b, 2019).

5. Limitations

The main limitation of this study is recruitment and sample homogeneity and the possible bias of cross-sectional study design. It is a non-probabilistic convenience sample, and the participants are students from a public university. However, with respect to selection bias, although a random and larger sample selection would be beneficial, Jager et al. (2017) consider that homogeneous convenience samples (sociodemographic or other factors of the general population) can be a positive alternative. In this sense, the sample features in this study are similar to those found in other studies with groups of undergraduate students in Spain (Vázquez and Panadero 2007; Vázquez et al. 2017; Bastias et al. 2019). Nevertheless, as university students are seen to be the social and economic future in a country’s development, it would still be useful to compare these results with different sectors of the population over a wider range of young and adult people who will coexist as a future generation involved in social changes. On the other hand, in relation to information or measure bias, by using standardized and validated tools and designing the study in terms of association variables or predictors rather than from a causal effect, it is possible to minimize inaccuracies or biases (Levin 2006). With respect to predictor variables, although the R-square is very low, these results are similar to Nasser et al. (2005) and Bergmann and Todd (2019). An important issue is to specify the role of social class as a predictor of poverty in relation to other stronger predictors, such as political orientation. In this sense, groups that perfectly represent (upper, middle and lower) social classes should be recruited so that objective and subjective indices of social class can be compared.

Author Contributions

Conceptualization, M.M.-A.G. and M.C.T.-C.; methodology, M.C.T.-C. and J.M.L.; formal analysis, J.M.L. and B.C.-L.; data curation, M.C.T.-C.; writing—original draft preparation, B.C.-L., C.V.-R.; writing—review and editing, C.V.-R. and M.C.T.-C.; visualization, C.V.-R.; supervision, M.C.T.-C.; project administration, M.M.-A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The APC was funded by Miguel Hernández University (Open access publishing grants (OPEN ACCESS).

Institutional Review Board Statement

This study has the approval of Ethical Committee (O.I.R: Research Office Registered: 191204114702; 2019.546.E.OIR; Reference: DCC.MMG.01.19) of Miguel Hernández University.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Due to the sensitive nature of the attitudinal questions asked in this study, survey respondents were assured raw data would remain confidential. Data from this study may be available upon request from the corresponding author, however personal characteristics cannot be used for association with attitudinal responses.

Conflicts of Interest

The authors declare no conflict of interest.

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Socsci 12 00308 g001 550
Figure 1. Final factorial structure.
Figure 1. Final factorial structure.
Socsci 12 00308 g001
Table
Table 1. Confirmatory factor analysis. Goodness of fit criteria.
Table 1. Confirmatory factor analysis. Goodness of fit criteria.
Χ2Χ2/dfCFISRMRRMSEAChanges
Model 1a362.18 *2.140.8410.0830.067I4 in F1
Model 1b374.81 *2.210.8310.0870.069I4 in F3
Model 2291.29 *1.920.8760.0790.060I4 removed
Model 3275.53 *1.970.8720.0770.062I20 removed
Model 4197.59 *1.800.9040.0700.056I16 and I17 removed
Notes: * = p < 0.001.
Table
Table 2. Factors and final factor loading (CFA).
Table 2. Factors and final factor loading (CFA).
Factors
123
Item 15: Having many children0.60
Item 14: Being born with a low IQ0.63
Item 18: Having to attend a bad school0.57
Item 13: Being born into poverty0.34
Item 19: Not inheriting money from relatives0.56
Item 21: Not having the right contacts to help find jobs0.45
Item 2: No attempts at self-improvements 0.58
Item 1: Lack of effort and laziness by the poor 0.55
Item 11: Loose morals among poor people 0.53
Item 10: A lack of motivation caused by being on welfare 0.56
Item 9: Lack of skills or talent 0.63
Item 6: Alcohol and drug abuse 0.43
Item 12: Prejudice and discrimination in promotion and wages 0.77
Item 8: Prejudice and discrimination in hiring 0.71
Item 3: Being taken advantage of by the rich 0.55
Item 7: Failure of industry to provide enough jobs 0.54
Item 22: A government which is insensitive to the plight of the poor 0.45
IQ: Intelligent quotient.
Table
Table 3. Correlations and differences. Poverty attributions.
Table 3. Correlations and differences. Poverty attributions.
F1: CulturalF2: InternalF3: External–Structural
F1: r = 0.37 (p = 0.001)r = 0.28 (p = 0.001)
F2:t = 5.27 (p = 0.001)
F3:t = 21.70 (p = 0.001)t = −14.10 (p = 0.001)
Table
Table 4. Differences by sociodemographic, religious and political beliefs.
Table 4. Differences by sociodemographic, religious and political beliefs.
Sociodemographic CharacteristicsBeliefs
Sex (M ± sd)t (p)Religious (M ± sd)t (p)
MaleFemale YesNo
F1: Cultural2.7 ± 0.682.5 ± 0.73 2.7 ± 0.602.7 ± 0.66
F2: Internal2.9 ± 0.712.8 ± 0.68 3.1 ± 0.642.8 ± 0.723.61 (0.001) d: 0.440
F3: External-S3.6 ± 0.753.9 ± 0.73−2.96 (0.003); d: 0.4053.6 ± 0.793.9 ± 0.64−2.70 (0.006) d: 0.417
Social Class (M ± sd) Political (M ± sd)
HighLow RightLeft
F1: Cultural2.5 ± 0.662.6 ± 0.75 2.6 ± 0.592.6 ± 0.76
F2: Internal2.8 ± 0.652.9 ± 0.72−2.08 (0.038) d: 0.1463.1 ± 0.612.8 ± 0.713.31 (0.001) d: 0.453
F3: External-S3.7 ± 0.803.9 ± 0.71 3.4 ± 0.774.0 ± 0.65−6.25 (0.001) d: 0.842
Health Sciences Total Sample (M ± sd)
YesNo
F1: Cultural2.6 ± 0.682.7 ± 0.77 2.60 ± 0.71
F2: Internal2.8 ± 0.683.1 ± 0.73−3.08 (0.002) d: 0.425 2.89 ± 0.70
F3: External-S3.9 ± 0.733.6 ± 0.812.43 (0.016) d: 0.389 3.79 ± 0.75
M = mean; sd = standard deviation; σ = effect size.
Table
Table 5. Regression analysis predicting poverty attributions.
Table 5. Regression analysis predicting poverty attributions.
F1: Cultural AttributionsR2Βtp
Age0.0250.1592.450.015
%V.E.: 2.1; F = 6.1 (p = 0.015) VIFs < 1
F2: Internal Attributions
Religious Beliefs0.054−0.22−3.45≤0.001
No Health Sciences0.0800.162.590.010
%V.E.: 7.3; F = 10.2 (p = 0.001); VIFs < 1
F3: External–Structural Attributions
Sex0.0390.182.970.003
Left Political Beliefs0.1800.366.38≤0.001
%V.E.: 17.3; F = 25.9 (p = 0.001); VIFs < 1.
Predictors: sex, age, social class, health sciences, religious and political beliefs
V.E.: Explained variance based on adjusted R2; VIFs = variance inflation factors.
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Terol-Cantero, M.C.; Martín-Aragón Gelabert, M.; Costa-López, B.; Manchón López, J.; Vázquez-Rodríguez, C. Causal Attribution for Poverty in Young People: Sociodemographic Characteristics, Religious and Political Beliefs. Soc. Sci. 2023, 12, 308. https://doi.org/10.3390/socsci12050308

AMA Style

Terol-Cantero MC, Martín-Aragón Gelabert M, Costa-López B, Manchón López J, Vázquez-Rodríguez C. Causal Attribution for Poverty in Young People: Sociodemographic Characteristics, Religious and Political Beliefs. Social Sciences. 2023; 12(5):308. https://doi.org/10.3390/socsci12050308

Chicago/Turabian Style

Terol-Cantero, M. Carmen, Maite Martín-Aragón Gelabert, Borja Costa-López, Javier Manchón López, and Carolina Vázquez-Rodríguez. 2023. "Causal Attribution for Poverty in Young People: Sociodemographic Characteristics, Religious and Political Beliefs" Social Sciences 12, no. 5: 308. https://doi.org/10.3390/socsci12050308

APA Style

Terol-Cantero, M. C., Martín-Aragón Gelabert, M., Costa-López, B., Manchón López, J., & Vázquez-Rodríguez, C. (2023). Causal Attribution for Poverty in Young People: Sociodemographic Characteristics, Religious and Political Beliefs. Social Sciences, 12(5), 308. https://doi.org/10.3390/socsci12050308

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