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Article

Who Are the People at Socio-Economic Risk? Socio-Demographical Analysis of the Czech People in Specific Economical Situations Recognized in Value-Based Risk Prediction Model

by
Jiří Pospíšil
,
Ivana Olecká
*,
Nataša Matulayová
,
Helena Pospíšilová
,
Pavlína Jurníčková
and
Pavla Macháčková
Department of Christian Social Work, Sts Cyril and Methodius Faculty of Theology, Palacký University Olmouc, 779 00 Olomouc, Czech Republic
*
Author to whom correspondence should be addressed.
Soc. Sci. 2022, 11(5), 211; https://doi.org/10.3390/socsci11050211
Submission received: 12 April 2022 / Revised: 7 May 2022 / Accepted: 9 May 2022 / Published: 12 May 2022
(This article belongs to the Section Social Stratification and Inequality)
Browse Figure
Versions Notes

Abstract

:
The aim of this study was to analyze the dependence of personal economic situation on gender, age, education, occupational status, field of work or study, family situation, and number of children in the family. The research was designed as cross-sectional ex post facto. The survey examined data collected through a structured questionnaire completed and returned by a total of 5175 respondents aged 15+ selected from the general population of the Czech Republic. The statistical significance of hypotheses was tested using χ2 statistics, and the adjusted residuals z in each cell were calculated. The impacts of socio-demographic factors on specific economic situation were recognized and statistically confirmed. Compared to women, men show higher financial knowledge, which benefits them significantly. Economic situation worsens with increasing age. As education increases, the level of vulnerability decreases, and the individual’s economic situation improves. Protective factors include cohabitation with a partner and having a complete family. People without children are in the best financial situation. The situation gradually worsens with the number of children, and families with five or more children are typically in a negative economic situation with a high level of vulnerability. The specific contribution of our research lies in the inclusion of potential financial risks in the identification of people at risk. We recognized a high-risk group of people—women in a positive economic situation with a high level of risk.

1. Introduction

Human decision making is influenced by personal values, personality types, and life experiences, together with financial literacy and actual financial situation, to varying degrees. However, it can be assumed that socio-demographic factors such as gender, age, education, occupational status, the field of work or study, family situation, and number of children in the family also have an impact. In our previous research, we developed the value-based financial risk prediction model (Pospíšil et al. 2021), and we proved that values and economic behavior influence each other significantly. The presented model serves several purposes: it can be used as a source for the development of a diagnostic tool in helping professions; it is applicable as a source for the development of an auto-diagnostic tool; and finally, it can be developed into other research instruments, leading to various hypotheses and the enhancement of theories concerning economic behavior. One of the model’s actual applications is to research how socio-demographic factors influence potential financial risks, and how all of this information can be helpful in the process of identifying people at risk. This motivated us to translate our knowledge of the groups at socio-economic risk into practice and to fill the gap between theory and practice in terms of preventing a worsening of quality of life for those at the highest risk.
This paper aims to identify the dependence of personal economic situation on gender, age, education, occupational status, field of work or study, family situation, and number of children in the family. We wanted to analyze who was at socio-economic risk, as we understood that the level of socio-economic risk is dependent on socio-demographic factors and it can differ. While doing a literature review for this paper, we realized that there was a research gap in the field of diagnostics of various socio-economic risks and identifying the people at the highest level of socio-economic risk in the Czech Republic. The Introduction and Literature Review in our paper serve as a theoretical background for formulating the hypotheses and one of the possible applications of the value-based financial risk prediction model developed by our team (Pospíšil et al. 2021). The socio-demographical analysis of the Czech people in specific economic situations presented in this paper is a part of our long-term research, as illustrated in the research flowchart in Section 3. Findings relevant to the stated hypotheses are presented in Section 4 and interpreted in Section 5. Limitations of the study are briefly mentioned in a separate section before Section 7.

2. Literature Review

We reviewed the existing literature to examine the relationship between socio-demographic factors (such as age, gender, family status, education, occupational status) and their components, describing three key aspects of the actual economic situation of an individual (financial knowledge, financial responsibility, and financial well-being), as defined in the value-based financial risk prediction model (Pospíšil et al. 2021).
Financial knowledge is defined as a central component of financial literacy and is used as the basis for its measurement (Hung et al. 2009; Nejad and Javid 2018; Williams and Satchell 2011; Mudzingiri et al. 2018; Ouachani et al. 2021; Remund 2010; Vural and Beichar 2020; Kadoya and Khan 2020). Financial literacy is a construct that covers knowledge of financial concepts, ability to communicate about financial concepts, aptitude in managing personal finances, skill in making appropriate financial decisions, and confidence in planning effectively for future financial needs (Remund 2010; Lusardi 2012). Whatever definition of financial literacy is chosen, there are three essential and interrelated factors—knowledge, experience, and attitude (Hogarth and Hilgert 2002). In high-income countries, as classified by the World Bank (World Bank Country and Lending Groups 2022), surveys and studies show that financial literacy is correlated with retirement planning, which is associated with more sophisticated investment behavior and affects debt and mortgage outcomes for individuals (Xu and Zia 2012; Nicolini and Haupt 2019; Swiecka et al. 2020). Financial knowledge is connected with borrowing behavior (Huston 2012; Sevim et al. 2012) its extent varies by gender and education (Borden et al. 2008; Chen and Volpe 1998). Financially literate people have knowledge and the ability to apply that knowledge. This is where education enters into the equation as one of the most important variables (Tang et al. 2015). Financial literacy level is generally lower in women than in men, and it follows an inverted-U shape with respect to age (Xu and Zia 2012). Financial knowledge improves with age, and some authors suggest that financial knowledge increases until a person reaches 65 years and then declines (Alhenawi and Elkhal 2013; Lusardi et al. 2010). Another important factor is marital status. According to some studies, married individuals are more financially literate, even though married/coupled women seem to have a lower level of financial literacy than married/coupled men (Aguiar-Díaz and Zagalaz-Jiménez 2021; Yao and Hanna 2005; Ronald et al. 2002). When considering financial knowledge, there is a significant influence of the employment status of an individual. Self-employed people surpass traditional employees in managing finances, as it is an important skill set for them (Struckell et al. 2022). The results reveal that financial literacy positively affects the probability of being self-employed. As financial literacy is acquirable, findings suggest that entrepreneurial activities may be raised by enhanced financial knowledge (Ćumurović and Hyll 2019). Another key aspect of the actual economic situation of an individual is financial responsibility, which is not strictly defined. In general, people who are able to consider their income and determine how much of that income would be appropriate to allocate to expenses are considered financially responsible (van Raaij 2016; Pospíšil et al. 2021). Another critical component, which determines personal and household financial decisions, including investment in risky assets, is risk tolerance (Xiao et al. 2001). Risk tolerance is a person’s attitude towards accepting risk (Hallahan et al. 2004). Women are more conservative in the case of investing than men, which makes them less likely to take risks (Fisher 2010; Bajtelsmit and Bernasek 1997). Risk tolerance can be described as the level of loss a person is prepared to handle. Financial risk tolerance is higher for single than married people, and risk tolerance generally increases with education and income (Yao and Hanna 2005; Fisher and Yao 2017). Age plays a key role in financial responsibility and financial behavior in general, mainly because of young adults’ transition from financial dependence to independence (Arnett 2011). People who find it difficult it to live on their current income experience economic strain (Whelan et al. 2001). Silinskas et al. (2021) described several ways that people behave during hardship. The most common way of reacting to financial difficulties is cutting current expenses and using emergency savings. Other types of reaction include borrowing from either family or friends, taking out loans, or increasing income by working more or selling personal possessions. The last option may be trying one’s luck in various lotteries and games, such as betting, gambling, and slot machines. Different studies have focused on gender differences in personal financial responsibility, but these differences are influenced by marital status. Women, even if they are the primary bread winner, are much more likely than men to have the major financial responsibility of handling the household finances. With regard to financial decision making, there is a greater propensity to share responsibility equally, and income ranking is more important than gender in defining household roles, with higher earners more likely to have a larger share of responsibility (Hitczenko 2016). Responsible financial behavior is determined by three levels of influences: the cognitive level (e.g., financial knowledge), social level (e.g., parental influence), and psychological level (e.g., self-discipline and thoroughness). At the social level, theories of human behavior assume that individuals exist within, and are influenced by, a social environment, which includes family members, co-workers, friends, helping professionals, and others. Social influences and psychological factors are key influences on their financial behavior and financial well-being (Tang et al. 2015). The actual economic situation of an individual is related to their financial well-being. Financial knowledge as a component of financial literacy, because of its relevance to people’s financial decisions, can be a pathway to improved financial well-being (Kadoya and Khan 2020). Financial well-being is a concept that describes the perception of being able to sustain current and anticipated desired living standards and financial freedom (Brüggen et al. 2017). Financial well-being can be described as people’s own perspectives on their financial situation; it relates to health and psychological well-being (Strömbäck et al. 2017; Kim et al. 2021; Arber et al. 2014; Netemeyer et al. 2018). Job satisfaction, either in entrepreneurship or traditional employment, is one of the factors closely linked to a person’s perception of their own financial well-being (Lanivich et al. 2021). Joo and Grable (2004) have studied the relationship between age and financial satisfaction. This study found a positive relationship between age and financial well-being. The relationship between gender and financial well-being has been the subject of many studies. According to Falahati and Sabri (2015), financial knowledge is a greater predictor of financial well-being in the case of female students than in male students. Women perceive a lower level of financial well-being as they age, since most of them have been naive about financial issues such as investments and insurance and are dependent on their husbands (Goldscheider 1990; Keith 1993; Yin-Fah et al. 2010). Other authors have shown that the financial security of women is influenced by financial status, while the financial satisfaction of men is influenced by financial knowledge (Tahir and Ahmed 2021).

3. Materials and Methods

The research was designed as a cross-sectional ex post facto study. This approach is used to measure and analyze influences of social factors on specific phenomena (Clark et al. 2021; Black 1999). The investigation was conducted nationwide.

3.1. Measurement of Variables, Hypotheses, and Statistical Procedures

Age of respondent was measured as a number, which was categorized into four clusters: young adulthood (aged 15–24), middle adulthood (aged 25–44), older adulthood (aged 45–59), and seniors (over 60). Gender of respondent was measured using a trinomial scale (male, female, other), where the category ‘other’ was omitted from the following analyses due to very low numbers (only 14).
The respondents’ education was measured by using a categorical closed scale consisting of the following categories adapted from International Standard Classification of Education 2011 categories (levels 1, 2, 3, 6–8) (UNESCO Institute for Statistics 2012): lower secondary or primary only, upper secondary without direct access to tertiary education, upper secondary with direct access to tertiary education, and tertiary education.
Occupational status was measured using a categorical closed scale consisting of the following categories: pupil/student, employee, self-employed or other, pensioner (including invalid) or maternity leave, and unemployed.
The next socio-demographic factorial variable used in our research was the field of work or study. It was measured by using a categorical closed scale consisting of the following categories: management and control, technical and technology, medicine and health, education and training, business and economics (including accounting), engineering and technology, medicine and health, public administration (clerk/official), information and communication technology, legal, social or cultural, auxiliary and unskilled labor, service sector, security forces, and agriculture and livestock.
Family situation as a socio-demographic factorial categorized variable was measured using a scale containing the following options: living without own family with parents in a complete family, living without own family in an incomplete or stepfamily, living alone (or otherwise), living with wife/husband or partner, living with own complete family with children, and living with own incomplete family with children.
The last socio-demographic factorial variable was number of children, measured using the number and consequently clustered into four categories (none, one, two, more than two).
The economic situation was measured and clustered using three indexes (responsibility Ifr, well-being Ifwb, and knowledge Ifk). The method and process of economic situation clustering and assigning respondents to clusters can be found in our previous paper Value-based risk prediction model (Pospíšil et al. 2021). The economic situation recognized and calculated in the model can be used both for stratification and for calculation of economic risk level of each recognized group. In the model, we recognized four economic clusters, which allowed us to describe four specific economic situations with attached level of risk (numbers in brackets represent level of risk calculated in the cited model):
  • ES1—Neutral economic situation with higher level of risk (59.7%);
  • ES2—Positive economic situation with high level of risk (65.71%);
  • ES3—Negative economic situation with higher level of risk (58.82%);
  • ES4—Positive economic situation with low level of risk (5.66%).
The risk level of each ES could be interpreted as the percentage of risk that the person will have financial problems if their life and/or social conditions unexpectedly change.
The stated hypotheses have the same dependent variable—personal economic situation. Hence, the hypotheses we proposed are the following:
Hypothesis 1 (H1).
Gender is a significant factor influencing personal economic situation.
Hypothesis 2 (H2).
Age is a significant factor influencing personal economic situation.
Hypothesis 3 (H3).
Achieved education degree is a significant factor influencing personal economic situation.
Hypothesis 4 (H4).
Occupational status is a significant factor influencing personal economic situation.
Hypothesis 5 (H5).
Field of work or study is a significant factor influencing personal economic situation.
Hypothesis 6 (H6).
Family situation significantly influences personal economic situation.
Hypothesis 7 (H7).
Number of children in the family is a significant factor influencing personal economic situation.
Statistical significance of hypotheses were tested using χ2 statistics, for two-way (C × R) contingency tables (Sheskin 2011; Azen and Walker 2021). For better interpretation of the results, the adjusted residuals z in each cell were calculated. The degree of statistical dependence is expressed in tabled results by the asterisks (* p < 0.05, ** p < 0.01, *** p < 0.001).

3.2. Collected Data

The data used for the construction and validation of the model were collected in 2018 and 2019 from 5175 respondents across the Czech Republic. The data was not affected by the COVID-19 crisis and describes the pre-pandemic situation. The research was designed as cross-sectional ex post facto. The constructed model was not locally dependent, as nationality/regional aspects are not included in the indexes or employed in the model. The research flowchart is displayed in Figure 1.
The sample consisted of 2204 male (42.59%) and 2957 female (57.14%) respondents aged over 14, with an average age of 37.5. Only 14 people categorized themselves as the other gender. The data were collected from September 2018 to June 2019 as part of a research project examining values, fundamental worldview, leisure, economic situation, and proficiency in ICT. The survey was delivered both electronically and using a hard paper copy. In the case of respondents who were not able to fill out the questionnaire online, in-person interviews or assisted completion of the questionnaire was utilized. The respondents were selected from across the country using a stratified selection with stratification criteria of gender, age, and size of the municipality. Inside the stratified groups, the questionnaire was widely and randomly spread across the population thanks to more than 200 volunteers who helped by delivering the survey.
The sample was clustered into four age categories: there are 1397 young adults (aged 15–24; 27.05%), 2108 people in middle adulthood (aged 25–44; 40.81%), 1098 people in older adulthood (aged 45–59; 21.26%), and 562 seniors (aged 60 or more; 10.88%). We also included the size of the population of the village/town/city in which the respondents live. Aside from age and gender, this was the third stratification criterion supporting the sample to be representative. In our sample, there are 1190 respondents from small villages (<2000 habitants; 23.04%), 663 from bigger villages (2001–5000 habitants; 12.84%), 849 from small towns (5001–15,000 habitants; 16.44%), 1175 from towns (15,000–60,000 habitants; 22.75%), 596 from cities (60,001–150,000 habitants; 11.54%), and 692 from big cities (>150,000 habitants; 13.40%).
The collected data can be considered representative with regard to the gender and age of respondents and size of the municipality—in most of the stratification criteria, the difference between population and samples was less than 10%.

4. Results

In the following, a confirmation of the initial hypotheses will be presented.

4.1. Gender

Hypothesis H1 (personal economic situation depends on gender) was clearly confirmed (p < 0.001). It is evident from the data presented in Table 1 that the difference is most evident in the positive economic situation. Compared to women, men show higher financial knowledge, which benefits them significantly. Women, even if they have a positive economic situation and a good economic background, are significantly at risk due to their lack of financial knowledge, especially in the case of negative changes in the household’s economic situation.

4.2. Age

Hypothesis H2 (personal economic situation depends on age) was clearly confirmed (p < 0.001). Economic situation worsens with increasing age. The risk factor of low levels of financial knowledge contributes to this. This aspect is particularly evident in the group of seniors (see Table 2).

4.3. Education

Hypothesis H3 (personal economic situation depends on education) was clearly confirmed (p < 0.001). Table 3 shows that as education increases, the level of vulnerability decreases, and the individual’s economic situation improves.

4.4. Occupation

Hypothesis H4 (personal economic situation depends on occupation) was clearly confirmed (p < 0.001). The data suggest an association with the age variable, with economic situation worsening in old age. Employees are in the best and least risky situation. On the other hand, retired people are in the most risky situation (see Table 4).

4.5. Field of Work or Study

Hypothesis H5 (personal economic situation depends on field of work or study) was clearly confirmed (p < 0.001). Primarily, people whose professions are closely related to the fields of economics, financial management, and IT (management and control, business and economics, information and communication technologies) are in a positive economic situation with a very low level of vulnerability. Table 5 shows that professionals in the medical and health care sector are in a positive economic situation with high level of risk due to low financial knowledge. The worst financial situation is for people whose occupation is operating machinery or doing auxiliary or unskilled work.

4.6. Family Situation

Hypothesis H6 (personal economic situation depends on family situation) was clearly confirmed (p < 0.001). In this case, the protective factors include cohabitation with partners and a complete family. On the contrary, risk was identified for an incomplete family (see Table 6).

4.7. Number of Children in the Family

Hypothesis H7 (personal economic situation depends on number of children in the family) was clearly confirmed (p < 0.001). People without children are in the best financial situation. The situation gradually worsens with number of children, and families with five or more children are typically in a negative economic situation with a high level of vulnerability (see Table 7).

5. Discussion

Sociodemographic characteristics affect the economic situation of the individual and thus the household. As seen in the results, some of the characteristics strongly determine only some of the respondents’ economic situations, while others determine economic situations comprehensively. This influence takes on very different values, as presented above.
The first variable discussed is gender. The gender difference is most evident in the positive economic situation, where it is evident that men show higher financial knowledge compared to women (Aguiar-Díaz and Zagalaz-Jiménez 2021; Kim and Garman 2003; Singh and Kumar 2017). This may be due to the fact that they are more concerned with financial issues, as reported by Kim and Garman (2003). Women, even if they have a positive economic situation and have a good economic background, do not have sufficient financial knowledge, which is a significant threat to them in case of negative changes in the household economic situation. This is consistent with the findings of Woodyard and Robb (Woodyard and Robb 2012), who identified differences in the level of objective financial knowledge between men and women, which were strongest in the 18–34 age group and the 55+ age group. The results of the analysis clearly identify a group of older women who appear to be the most vulnerable to financial problems in the future. Respondents who are both women and over 55 years of age exhibit lower objective and subjective financial knowledge and are less likely to engage in recommended financial practices. Another of the findings, which also supports the results of the presented study, is that while significant differences were found in knowledge, this was not the case for financial satisfaction.
In the area of life stages by age, the data clearly confirms the trend that economic situation deteriorates at the older ages. As reported in the data from the Czech Statistical Office (2021), the proportion of people with an income below the poverty line was 16.6% in 2019 in the group of seniors over 65. The amount of households at risk of income poverty among individuals aged 65 and over was already 41.2% in 2019. According to our research, the risk factor of lower levels of financial knowledge contributes to this. Unless these individuals’ job focus has been tied to the financial sector or they have educated themselves, they are currently unprepared to function effectively and use financial market instruments in a rapidly changing environment. These conclusions are not consistent with the results of studies by Henager and Cude (2016), Taft et al. (2013), Cude (2010), Lusardi et al. (2010), Kindle (2010), or Xiao et al. (2014), which report increasing financial knowledge in relation to age and experience. This may be influenced, for example, by a different socio-political context (OECD 2019), which implies a different level of involvement of the individual in financial decision making in old age. However, the link between age and gender, outlined above, is important. The combination of these two variables makes it possible to say that older women are among the most vulnerable groups. Similar results were reached by Taft et al. (2013), as well as in research by Aguiar-Díaz and Zagalaz-Jiménez (2021), whose results report that married women have lower levels of financial literacy than married men. According to them, this may be due to the fact that men often make decisions about family finances, while women are in charge of other household chores. This may have important implications for the financial independence of women of all ages, particularly as they age. The second specific group is young people. As with the elderly, there are significantly fewer of them in the good financial situation and low risk group, but significantly more in the neutral financial situation and low risk group. This means that young people have relatively good financial knowledge, but their financial situation is still influenced by the situation of their parents. The influence of family background in this group was also seen by Henager and Cude (2016), who suggest that this group sees their security in their family background rather than in their own resources.
Education and job orientation are two other variables that have a complex influence on economic circumstances. As education increases, the level of vulnerability decreases, and an individual’s economic situation improves (Cude 2010; Malone et al. 2010; Al Tamimi and Kalli 2009), as does their financial well-being, and this leads to greater economic prosperity (Taft et al. 2013). At the same time, it can be said that higher financial well-being leads to lower financial worry (Taft et al. 2013). Another key aspect for the distribution of economic well-being with respect to work and study orientation appears to be the area of qualification training. In the group with positive economic situation, respondents who use financial skills as part of their job (e.g., managers, ICT, etc.) are statistically significantly more prevalent. It can be assumed that they then use this knowledge in their personal life, and it becomes an important part of their financial decision making. On the contrary, in non-financial sectors (e.g., healthcare or manual work), there are people with a riskier economic situation. This fact offers an opportunity for further research that would focus on the impact of study and job content on the sub-aspects of economic situation as presented in the text (Pospíšil et al. 2021).
As confirmed by the above results, economic situation deteriorates in old age. This fact is also reflected in the personal situation variable, where the vulnerable groups include retired people (here including disability). This is also confirmed by the above-mentioned data from the Czech Statistical Office (Czech Statistical Office 2021) on the risk of income poverty. Furthermore, the results identify a group of respondents that emerged only in relation to the personal situation. This is the group of parents on maternity/parental leave. This group is likely to be in a positive economic situation with high levels of vulnerability, which will be the subject of further investigation. As can be seen in the data above, partner cohabitation and a complete family are protective factors against a negative economic situation and the risk of vulnerability. In contrast, incomplete families are at risk. In this context, we can point to two studies that show a positive relationship between financial literacy and marital status. Studies are worthy that married and married people have higher financial literacy (Taft et al. 2013; Chen and Volpe 1998). However, the latter study did not show a relationship between marital status and financial well-being and financial worry (Taft et al. 2013). Children can also put a strain on a household’s economic situation. However, whether they only burden or overburden it is determined by their number, and according to the data, having two children seems to be the limit. This is confirmed by the study of Van Winkle and Leopold (Van Winkle and Leopold 2021), who, although only working with women, also perceive a break in the relationship with economic well-being with two children.

6. Limitations of the Study

The study might be limited by using the newly defined value-based financial risk prediction model, and therefore the results of this model application are rather indicative. Completion of the validation process of the model is the aim of future research.

7. Conclusions

Based on the value-based financial risk prediction model (Pospíšil et al. 2021), we recognized the specific conditions of people at socio-economic risk. In this paper, we stated hypotheses concerning the relationship between several socio-demographic factors and the specific economic situations derived from the model. We found a significant influence of gender (H1), age (H2), education (H3), occupational status (H4), field of work or study (H5), family situation (H6), and number of children in the family (H7).
Many of the findings presented in this paper confirm the results from previous research, especially in the high-income countries. The specific contribution of our research lies in including the potential financial risks in the identification of people at risk. Therefore, we were able to recognize a high-risk group of people—women in a positive economic situation with a high level of risk—who had not been identified in the relevant research and/or theory yet. Future research should be directed towards revalidation and confirmation of the model, and specifically designing and implementing interventions in various helping professions for women in positive economic situations with a high level of risk. The main focus should be on various forms of prevention—for example, promotion of importance of financial literacy and financially responsible decision making, educational programs, interventions aimed at stabilizing a person’s financial situation, and so on.

Author Contributions

Conceptualization, J.P. and P.J.; methodology, J.P. and I.O.; software, J.P.; validation, J.P. and I.O.; formal analysis, I.O.; investigation, N.M. and P.M.; resources, N.M. and P.M.; data curation, J.P. and I.O.; writing—original draft preparation, N.M., P.M., J.P., I.O., H.P., P.J.; writing—review and editing, I.O. and H.P.; visualization, I.O.; supervision, J.P.; project administration, N.M.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by IGA_CMTF_2022_005 Values context of social functioning II.

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to informed consent obtained from all subjects involved in the study.

Informed Consent Statement

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

Data Availability Statement

Research data are available from the authors upon request.

Conflicts of Interest

The authors declare no conflict of interest.

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Socsci 11 00211 g001 550
Figure 1. The research flowchart.
Figure 1. The research flowchart.
Socsci 11 00211 g001
Table
Table 1. Economic situation’s dependence on gender.
Table 1. Economic situation’s dependence on gender.
Economic Situation
GenderES1ES2ES3ES4Total
Male7245122277412204
14.03%9.92%4.40%14.36%42.70%
z: 0.34z: −4.66 ***z: −1.73z: 5.34 ***
Female9588583507912957
18.56%16.62%6.78%15.33%57.30%
z: −0.34z: 4.66 ***z: 1.73z: −5.34 ***
Total1682137057715325161
32.59%26.55%11.18%29.68%100.00%
ChiSquare: χ2(df=3) = 38.7508, *** p < 0.001, n = 5161. Significant results are colored red.
Table
Table 2. Economic situation’s dependence on age.
Table 2. Economic situation’s dependence on age.
Economic Situation
AgeES1ES2ES3ES4Total
Young adulthood5853391673061397
11.33%6.56%3.23%5.92%27.05%
z: 8.66 ***z: −2.24 *z: 1.06z: −7.45 ***
Middle adulthood6125761557652108
11.85%11.15%3.00%14.81%40.81%
z: −4.55 ***z: 1.08z: −7.27 ***z: 8.63 ***
Older adulthood3432841273441098
6.64%5.50%2.46%6.66%21.26%
z: −1.09z: −0.56z: 0.45z: 1.35
Seniors144171129118562
2.79%3.31%2.50%2.28%10.88%
z: −3.74 ***z: 2.22 *z: 9.37 ***z: −4.77 ***
Total1684137057815335165
32.60%26.52%11.19%29.68%100.00%
ChiSquare: χ2(df=9) = 233.5876, * p < 0.05, *** p < 0.001, n = 5165. Significant results are colored red.
Table
Table 3. Economic situation’s dependence on education.
Table 3. Economic situation’s dependence on education.
Economic Situation
EducationES1ES2ES3ES4Total
Lower secondary or primary only23313813247550
4.51%2.67%2.56%0.91%10.65%
z: 5.17 ***z: −0.81z: 10.08 ***z: −11.48 ***
Upper secondary without direct access to tertiary education145260183128716
2.81%5.03%3.54%2.48%13.86%
z: −7.60 ***z: 6.39 ***z: 13.14 ***z: −7.45 ***
Upper secondary with direct access to tertiary education7286051966572186
14.09%11.71%3.79%12.72%42.32%
z: 0.92z: 1.61z: −4.34 ***z: 0.50
Tertiary/higher education578367677011713
11.19%7.11%1.30%13.57%33.17%
z: 1.23z: −5.85 ***z: −11.69 ***z: 12.46 ***
Total1684137057815335165
32.60%26.52%11.19%29.68%100.00%
ChiSquare: χ2(df=9) = 587.6952, *** p < 0.001, n = 5165. Significant results are colored red.
Table
Table 4. Economic situation’s dependence on occupational status.
Table 4. Economic situation’s dependence on occupational status.
Economic Situation
Occupational StatusES1ES2ES3ES4Total
Pupil/student5222271212151085
10.11%4.39%2.34%4.16%21.01%
z: 12.26 ***z: −4.70 ***z: −0.05z: −8.00 ***
Employee86981223910042924
16.82%15.72%4.63%19.44%56.61%
z: −5.05 ***z: 2.32 *z: −7.86 ***z: 8.37 ***
Self-employed or others 1249234138388
2.40%1.78%0.66%2.67%7.51%
z: −0.28z: −1.31z: −1.58z: 2.64 **
Pensioner, invalid pensioner, or maternity leave 159229167164719
3.08%4.43%3.23%3.18%13.92%
z: −6.47 ***z: 3.49 ***z: 11.03 ***z: −4.35 ***
Unemployed1010171249
0.19%0.19%0.33%0.23%0.95%
z: −1.83z: −0.97z: 5.24 ***z: −0.80
Total1684137057815335165
32.60%26.52%11.19%29.68%100.00%
ChiSquare: χ2(df=12) = 354.5525, * p < 0.05, ** p < 0.01, *** p < 0.001, n = 5165. Significant results are colored red.
Table
Table 5. Economic situation’s dependence on field of work or study.
Table 5. Economic situation’s dependence on field of work or study.
Economic Situation
Field of Work or StudyES1ES2ES3ES4Total
Management1266423152365
2.45%1.24%0.45%2.95%7.09%
z: 0.83z: −4.04 ***z: −3.08 **z: 5.18 ***
Technical/technology20013836184558
3.88%2.68%0.70%3.57%10.83%
z: 1.75z: −1.02z: −3.77 ***z: 1.79
Medicine/health care1751454695461
3.40%2.81%0.89%1.84%8.95%
z: 2.60 **z: 2.50 *z: −0.87z: −4.48 ***
Education31521480178787
6.12%4.15%1.55%3.46%15.28%
z: 4.86 ***z: 0.45z: −1.00z: −4.73 ***
Business and economics (incl. accounting)21316747246673
4.14%3.24%0.91%4.78%13.07%
z: −0.54z: −1.09z: −3.72 ***z: 4.17 ***
Public administration (clerk)14911628137430
2.89%2.25%0.54%2.66%8.35%
z: 0.97z: 0.21z: −3.22 **z: 1.02
Information and communication technologies874817135287
1.69%0.93%0.33%2.62%5.57%
z: −0.83z: −3.87 ***z: −2.92 **z: 6.61 ***
Law—social or cultural 14613142145464
2.83%2.54%0.82%2.81%9.01%
z: −0.53z: 0.87z: −1.54z: 0.76
Machinery/equipment operation751098875347
1.46%2.12%1.71%1.46%6.74%
z: −4.50 ***z: 2.13 *z: 8.66 ***z: −3.41 ***
Helping/Unqualified jobs24495424151
0.47%0.95%1.05%0.47%2.93%
z: −4.44 ***z: 1.67z: 9.71 ***z: −3.77 ***
Private and public services (craftsmen, police, fire brigade, gardeners, etc.) 167186116159628
3.24%3.61%2.25%3.09%12.19%
z: −3.40 ***z: 1.87z: 6.16 ***z: −2.57 *
Total1677136757715305151
32.56%26.54%11.20%29.70%100.00%
ChiSquare: χ2(df=30) = 418.4179, * p < 0.05, ** p < 0.01, *** p < 0.001, n = 5151. Significant results are colored red.
Table
Table 6. Economic situation’s dependence on family situation.
Table 6. Economic situation’s dependence on family situation.
Economic Situation
Family SituationES1ES2ES3ES4Total
Living without own family with parents in a complete family 45722899201985
8.85%4.41%1.92%3.89%19.07%
z: 10.26 ***z: −2.67 **z: −1.26z: −7.08 ***
Living without own family in an incomplete or stepfamily93866367309
1.80%1.67%1.22%1.30%5.98%
z: −0.97z: 0.54z: 5.29 ***z: −3.17 **
Living alone (or otherwise)153196140209698
2.96%3.79%2.71%4.05%13.51%
z: −6.48 ***z: 1.00z: 7.99 ***z: 0.16
Living with wife/husband or partner5715081555841818
11.06%9.84%3.00%11.31%35.20%
z: −1.35z: 1.70z: −4.48 ***z: 2.83 **
Living with own complete family with children370271894261156
7.16%5.25%1.72%8.25%22.38%
z: −0.49z: −2.69 **z: −4.27 ***z: 6.06 ***
Living with own incomplete family with children40813246199
0.77%1.57%0.62%0.89%3.85%
z: −3.84 ***z: 4.62 ***z: 2.23 *z: −2.07 *
Total1684137057815335165
32.60%26.52%11.19%29.68%100.00%
ChiSquare: χ2(df=15) = 282.3521, * p < 0.05, ** p < 0.01, *** p < 0.001, n = 5165. Significant results are colored red.
Table
Table 7. Economic situation’s dependence on number of children in the family.
Table 7. Economic situation’s dependence on number of children in the family.
Economic Situation
Number of Children in the FamilyES1ES2ES3ES4Total
None8866112397132449
17.16%11.83%4.63%13.81%47.43%
z: 5.19 ***z: −2.45 *z: −3.07 **z: −0.83
One child20221085238735
3.91%4.07%1.65%4.61%14.24%
z: −3.21 **z: 1.35z: 0.36z: 1.74
Two children4734081504201451
9.16%7.90%2.91%8.13%28.10%
z: −0.02z: 1.61z: −1.19z: −0.72
More than two children123141103161528
2.38%2.73%1.99%3.12%10.23%
z: −4.82 ***z: 0.09z: 6.41 ***z: 0.44
Total1684137057715325163
32.62%26.53%11.18%29.67%100.00%
ChiSquare: χ2(df=9) = 75.0338, * p < 0.05, ** p < 0.01, *** p < 0.001, n = 5163. Significant results are colored red.
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Pospíšil, J.; Olecká, I.; Matulayová, N.; Pospíšilová, H.; Jurníčková, P.; Macháčková, P. Who Are the People at Socio-Economic Risk? Socio-Demographical Analysis of the Czech People in Specific Economical Situations Recognized in Value-Based Risk Prediction Model. Soc. Sci. 2022, 11, 211. https://doi.org/10.3390/socsci11050211

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Pospíšil J, Olecká I, Matulayová N, Pospíšilová H, Jurníčková P, Macháčková P. Who Are the People at Socio-Economic Risk? Socio-Demographical Analysis of the Czech People in Specific Economical Situations Recognized in Value-Based Risk Prediction Model. Social Sciences. 2022; 11(5):211. https://doi.org/10.3390/socsci11050211

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Pospíšil, Jiří, Ivana Olecká, Nataša Matulayová, Helena Pospíšilová, Pavlína Jurníčková, and Pavla Macháčková. 2022. "Who Are the People at Socio-Economic Risk? Socio-Demographical Analysis of the Czech People in Specific Economical Situations Recognized in Value-Based Risk Prediction Model" Social Sciences 11, no. 5: 211. https://doi.org/10.3390/socsci11050211

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