Abstract
The understanding of the distribution of social risks according to social classes can ensure more targeted social investment policies. This article aims to analyze the distribution of social risks according to the social classes in the three Baltic States of Estonia, Latvia, and Lithuania. The micro data used in this analysis are collected from the European Union Statistics on Income and Living Conditions (EU-SILC) data base of 2015 covering the three Baltic States: Lithuania, Latvia and Estonia. The findings of this analysis revealed that in many cases, the distribution of social risks is related to social class. However, the findings suggest there is a higher probability of certain social risks among members of the lower middle class than those who are employed in lower class elementary (basic skills) occupations.
Introduction
The social investment perspective focuses on investment in human capital. Investment in children’s human capital increases the individual’s capacities to participate in the labor market and have better life chances. Social investment as a policy was defined in the European Union’s Social Investment Package in 2013 where social investment was referred to as a preventive policy aimed at lowering social risks (unemployment, loss of life, poverty, illness, injury, etc.). Social investment is not cheap (Vandenbroucke & Vleminckx, 2011), and the targeting of social investment policy leads to better outcomes. Therefore, the analysis of social risk stratification can provide insights into where to direct investments and what kind of social investment is needed. The research for this article was inspired by the study of Pintelon et al. (2013), which was focused on the analysis of social risks according to social class. Pintelon et al. (2013) revealed the influence of social class on a relevant selection of social risks. This conclusion led to formulation of the following questions which guided our research. What are the effects of different social contexts on the distribution of risks? Do differences in social class influence the selection of social risks in the Baltic states where the class structure is different from that in most Western countries? The answers to these questions are important because there is a lack of scientific literature on social investment policy and social risk stratification in the Baltic States.
The answers to these questions will bridge this important gap in knowledge. Thus, the goal of the research behind this article is to evaluate the social risk distribution among the social classes in the Baltic States. The following objectives were set for this research: (a) to analyze the previous research studies on social investment and social class; (b) to analyze the academic literature on the class structure in the Baltic States; and (c) to evaluate the distribution of social risks according to the social classes in the three Baltic States of Lithuania, Latvia and Estonia.
Social Investment, Social Class, and Social Risks
According to Armingeon and Bonoli (2006), the transformation in the family raised the necessity for labor market institutions to develop new forms of social policy as a response to the social problems experienced by women and young and low-skilled employees who are exposed to more social risks than any other groups in the current welfare system. Bonoli (2006) found that the problems of family and work reconciliation, single parenthood, low skills, and insufficient social security coverage have given rise to new risks and a high probability of the risk of poverty.
Traditional welfare policies have not adapted to the new transformations in society. Therefore, new measures are required. As stated by Hemerijck (2013), the welfare effort should privilege the active phases of the life-course. This entails reallocating social expenditures away from pensions and social insurance toward social services for the new family, a more active labor market policy, early childhood education, vocational training, productivity improvement, and higher levels of employment for both men and women in the new more knowledge-based economy. In particular, the main goal of policy redirection is to prevent social disadvantages from developing in the early stages of the life-course. On other hand, Huber and Stephens (2006) have argued that “old” risks are no less important than the “new” risks because people will continue to retire, to get sick, or lose jobs, and this requires a transformation of the welfare state covering all the above-mentioned aspects and continuing to provide financial support for both “old” and “new” risks.
Cantillon (2011) stated that work-related social spending tends to be more beneficial to middle- or higher-income people. Such situation means that in an advanced welfare state, the middle-class tends to be the main beneficiary of social services and benefits (Vandenbroucke & Vleminckx, 2011). Thus, Vandenbroucke and Vleminckx (2011) refer to the “social investment approach,” which includes both social protection and social investment. The term “security” is not excluded from the social investment approach; rather, they are mutually reinforcing each other (Kvist, 2014). Hemerijck (2013) contends that adequate income protection is a critical precondition for an effective social investment strategy. According to Hemerijck (2013), at the heart of the social investment edifice lies the idea that each welfare system consists of three overlapping spheres of public policy: income support, capacitating regulation, and social services to address the new needs. Hemerijck (2013) states that the risk structure has shifted down the life cycle to young adults and families with children (especially single parents) as well as those with insufficient skills. The “old” social risks are related to old age, health problems, such as sickness and disability, and the financial burden of raising children (Vandenbroucke & Vleminckx, 2011).
Criticism has been directed at social investment strategies having too economic view of investments and returns, while neglecting gender, class, children’s well-being, and issues of the jobless poor (Cantillon, 2011; Lister, 2003; Nolan, 2013; Pintelon et al., 2013). European Union (EU) policies are criticized for being too much oriented toward higher-income groups and work-related (income-related) (Cantillon, 2011). In other words, social transfers are more income-and work-related leaving unemployed households disadvantaged. Thus, it can be argued that social investment policies have been too selective and not pro-poor.
The Class Structure in the Baltic States
The specific course of development of the Baltic States, particularly the change in their socioeconomic formation after their independence from the former Soviet Union, has had a huge impact on the socioeconomic life of these countries. Societies have undergone a change in their social stratification as well. However, academic discussions tend to focus on their inherited social structures, rather than the changes in the social stratification in the Baltic countries. Eglitis and Lace (2009) and Eglitis (2010) concludes that the socioeconomic stratification that has evolved is not voiced in terms of social class, but classes are “distinguished”. As stated by the same author (Eglitis, 2010), the new social order in these societies is highly stratified. According to Eglitis and Lace (2009), in post-Soviet Latvia, many members choose to “exit” as a strategy for coping with their dissatisfaction and entry into the transnational stratification order. There they may also occupy the ground rungs of the social economic hierarchy, and even those with higher education, generally work below their qualifications.
Defining the structure of society in post-Soviet countries, Whelan and Maître (2010) indicate seven classes, which include large employers; higher grade professional, administrative, and managerial occupations; intermediate occupations and lower supervisory and technician occupations; small employer and self-employed non-professional occupations; farmers; lower services, sales and clerical occupations; lower technical occupations; and routine occupations.
Morkevičius and Norkus (2012) argue that the factor of occupation is the best approach while seeking to analyze social stratification in post-Soviet societies. The above-mentioned scholars conclude that the Lithuanian class structure is different from the post-modern model. The class structure of Lithuania is similar to that of Latvia but different from the structure of Estonia. Morkevičius and Norkus (2012) state that the service class is relatively large in Lithuania as well as in other post-Soviet countries, and it is particularly enormous in Estonia. This class, which includes superiors, specialists, and professionals, was inherited from the Soviet period, which had a good higher education system. On other hand, a considerable proportion of educated people in society indicate hypertrophy of the service class. In some cases, the representatives of this class are frequently offered only the contract of manual workers. Such situation is one of the migration factors. The level of life satisfaction experienced by the lower part of service class does not differ from that of manual workers. Zickute (2013) employs the economic variable for class analysis in her study and defines the middle class as a group of individuals whose income exceeds the poverty threshold by two–five times. The same author (Zickute, 2013) states that the societies in Lithuania and Latvia consisted of 9% of middle class and even 90% of poor citizens, whereas the middle class in Estonia included about half population in 2011. A more comprehensive measurement of structure is offered by Matulionis (2014) who distinguishes the following social layers: highest, higher, middle, lower, and lowest classes. The criteria for social classification include power, wage, occupation, demographic characteristics, activity, and living environment. Self-assignment of the person to social class can be another criterion. Moreover, the migration process should also be taken into account in analyzing the social structure (Matulionis, 2014). Matulionis (2014) also elaborates on the structure of each social class. According to him, the highest class in Lithuania consists of top national politicians and officials, the most affluent Lithuanian entrepreneurs, chief executives of large international companies, elite scholars, and artists. The higher class includes ordinary politicians, bureaucrats, some entrepreneurs, as well as medical doctors and lawyers. Most of the specialists, clerks, and skilled workers can be assigned to the middle class. The lower class embraces the representatives of society whose income is very low and who are poverty-prone, that is, a big part of unemployed, retired, and disabled persons. Finally, the lowest class comprises older persons living in rural areas; retired people; and some marginal groups such as homeless people, prostitutes, and beggars.
Helemae and Saar (2015) stated that there is a growing consensus among social scientists that social stratification in Estonia largely overlaps with ethnic lines. Estonia has a liberal market economy and a widely accepted “transition culture” leading to a sharp differentiation of life chances, and the importance of status of groups (especially “Estonian” vs. “Russian-speaking” ethnicity), and that status group membership itself becomes a form of market capacity.
Menshikov (2016) applies the economic criteria for class analysis in Latvia. This author (Menshikov, 2016) also states that a decrease in the middle class and the reduction of its progressive role take place almost everywhere either due to the growing crisis of a social state or insufficient economic and political resources to create such a state.
Taljūnaitė and Sviklas (2016) claimed that the middle class consists of employees who receive a salary. On the other hand, the income should be enough to satisfy the primary needs and provide for leisure, holidays, and savings. The findings of Taljūnaitė and Sviklas (2016) show that the higher their financial status, the more respondents identify themselves as middle class. However, along with financial criteria, education and age are additional criteria used in self-attribution of social class.
To summarize, the Baltic States are highly stratified societies. The class structure based on occupational groups suggests there is a large service class, but if financial criteria are used for social class determination, a different picture of the social structure in the Baltic States emerges. Based on financial criteria alone, the middle class is small in the Baltic States. In future research on this question in the Baltic States, it would be advisable to use both occupational and financial criteria.
Data and Methods
The analysis reported in this article was based on EU-SILC micro data form 2015 for the three Baltic States of Lithuania, Latvia, and Estonia. The analysis focused separately on children (persons under 18), young people (aged 18–24), persons aged 25–64 years, and people aged 65 years and over. In accordance with Pintelon et al. (2013), the analysis focused on five social risks for adults aged 18–24 and 25–64 years. These five risk categories were unemployment, ill health, jobless households, single parenthood, and low income. Only the factors like ill health and low income were analyzed within the group of retired persons (aged 65 and older). The focus of analysis in the 0–17 age group was on the social risks of children living in jobless households, with a single parent, with two parents but where at least one of them received low income (low pay) and children living with parents where at least one of them was unemployed (unemployment). Ill health can be considered as one of the most important risks for children, but data on children’s health were not available.
To assess the impact of social class and to determine the specifics within the Baltic States, different logistic regressions were used for each country. Predictors of age and sex, which can be related to social risk, were also considered. Persons of age 18–64 years were considered to be unemployed if they indicated that they were “unemployed.” A respondent was considered as being in ill health if the person reported their health status as “bad” or “very bad.” This risk was considered only for respondents who were 18–65 years and over. A jobless household was defined as a household with less than 0.2 amount of work. Single parents were defined as persons living with dependent children younger than 18. Low income was defined as earnings which were two-thirds of the median gross earnings of all full-time employees over a full year.
The respondents’ occupations were used as the main indicators of their social class (Morkevičius & Norkus, 2012; Pintelon et al., 2013), and for unemployed persons, the former occupation was used. On the basis of the ISCO-08 codes, the following occupational categories were used as the basis for social classes: high-skilled non-manual occupations (ISCO-08 11–35), low-skilled non-manual occupations (ISCO-08 41–54), skilled manual occupations (ISCO-08 61–83), and elementary occupations (which involve the performance of simple and routine tasks that may require the use of hand-held tools and considerable physical effort; ISCO-08 91–96). In the case of children (age 0–18), fathers’ and/or mothers’ social classes were used.
Findings
Based on our classification of social classes and the data we analyzed in our research, the class structure in the three Baltic states is quite similar (see Figure 1). Most notable is the finding that the high-skilled non-manual class appears to be the largest class in all three of these states, followed by the skilled manual class, which is the second largest class of all three. Conversely, the number of individuals with elementary occupations is the smallest class in each of these three countries. The low-skilled non-manual class in our research is not much larger than the elementary occupations class.
Among 18–24-year-olds, elementary occupations and high-skilled non-manual classes were much smaller in Lithuania than in the other age groups in that country. The high-skilled non-manual class was also smaller in Estonia and Latvia in this group of 18–24-year-olds, but the low-skilled non-manual class was significantly larger in this age group in all the Baltic States compared to the other age groups. Among persons 65 and older, the class of elementary occupations was larger in all the Baltic States than in the other age groups. The high-skilled non-manual class was smaller in this age group than other age groups in all three Baltic States.
Distribution of Social Risks by Class for Mothers and Fathers
In almost all cases, the unemployment risk was lower among fathers and mothers belonging to the higher social classes compared with those in the elementary occupations class (see Table 1). There were two exceptions. The unemployment risk was higher for skilled manual fathers than for those holding elementary occupations in Estonia. Also, the unemployment risk was higher for low-skilled manual mothers than for mothers in elementary occupations in Latvia. In addition, the risk to be unemployed was higher for low-skilled mothers compared with skilled manual work mothers in Lithuania and Latvia (see Table 1).
A slightly different situation exists in the case of the social risk of those in a jobless household. If we compare the probability for individuals with elementary occupations to become jobless, we can observe a consistent lower probability within a higher social class only in Lithuania. The higher risk to be in a jobless household exists for low-skilled non-manual mothers in Lithuania, but we can see that risk to be in a jobless household was high for low-skilled non-manual fathers compared to skilled manual fathers in Latvia (see Table 1). The risk to be in jobless household was higher for low-skilled manual mothers than for skilled manual mothers in Latvia and Lithuania.
The risk to receive low pay was low in the elementary occupations for fathers in Lithuania and Latvia and for mothers in Lithuania and Estonia. However, the risk to be low paid was higher for skilled manual and high-skilled non-manual fathers in Estonia and skilled manual mothers in Latvia compared with their counterparts in the elementary occupations. Additionally, the risk to be low paid was higher for low-skilled non-manual mothers compared with skilled manual mothers in Lithuania (see Table 1).
Social Risks by Class for Age Group 0–17
Social Risks by Class for Persons Aged 18–64
In the group of 18–24-year-old respondents, the risk to be unemployed is higher for low-skilled non-manual occupations in Lithuania and for skilled manual workers in Latvia and Estonia compared with those in elementary occupations (see Table 2). The risk of living in a jobless household is more for the high-skilled non-manual and low-skilled non-manual classes compared to the class of elementary occupations in Latvia. It is interesting to point out that the risk to be low paid is higher for high-skilled non-manual 18–24-year-old individuals than for respondents with elementary occupations in Latvia (see Table 2). Similarly, the risk to be low paid was higher for low-skilled non-manual 18–24-year-old individuals than for skilled manual workers in Lithuania. The unemployment risk is also higher for Lithuanian low-skilled non-manual individuals in the group of 18–24-year-olds than for skilled manual workers (see Table 2).
Almost all social risks are lower for higher social class members compared to individuals holding elementary occupations among the 25–64-year-old respondents in the Baltic States (see Table 2). However, there are some exceptions. The higher risk to be a single parent was identified for skilled manual class members who are 25–64 years old in Latvia compared to individuals with elementary occupations. Similarly, the same risk was higher for all higher classes compared to the elementary occupations in Estonia.
The risk to live in a jobless household is greater for low-skilled non-manual persons aged 25–64 compared to members of the skilled manual class in Latvia. In Lithuania, individuals from high-skilled non-manual and low-skilled non-manual classes have a higher risk of being a single parent. The risk to be a single parent was higher for low-skilled non-manual respondents compared with skilled manual ones in the age group of 25–64-year-old persons in Estonia.
Social Risks by Class for Age Group 18–64
Social Risks by Class for Persons Aged 65+
The probability for ill health risk is lower for the 65 and older age category compared to the members of the elementary occupations class, which is used as a reference for a high risk of ill health, except in Lithuania. As Table 3 indicates, in Latvia, the ill health risk is higher for skilled manual class members who are 65 and older than the members of this age category in the elementary occupations class and for the low-skilled non-manual and skilled manual class members who are 65 and older in Estonia. As might be expected, the risk of receiving low pay in the age group of 65 and older was lower in all the higher classes than the 65 and older members of the elementary occupations class in all three Baltic States (see Table 3). The risk of low pay for persons aged 65 and over was higher among high-skilled non-manual workers than low-skilled manual workers in Latvia and for high- and low-skilled manual workers in Estonia.
Social Risks by Class for Age Group 65+
Discussion and Conclusions
The social investment strategy focuses on investments in human capital in order to prevent social risks and pays specific attention to social risks such as single parenthood, low skills, insufficient health insurance coverage, or reconciliation of family with work. The effective use of social investment policy is related to knowledge of how to allocate resources and how social risks are distributed among the population. The findings of the research conducted by Pintelon et al. (2013) show the influence of social class on a variety of social risks. The class structure of the Baltic States is different from most Western societies where occupation or economic status is used as the main criterion for determining class. In the Baltic States, there is a large class of service workers based on occupational criteria and a small middle class based on economic criteria.
This study reveals that the distribution of social risks by social class in the Baltic States depends on one’s age group, gender, and the type of social risk. The influence of social class (based on occupation) clearly affects the distribution of social risks. As a result, the findings of our study confirm the findings and conclusions of Pintelon et al. (2013) in the context of the Baltic States. However, the findings also reveal the higher probability of experiencing social risks among the respondents from the lower middle social class (low skilled non manual and skilled manual, (Matulionis, 2014) compared to the ones in elementary occupations. Differences are also identified among the Baltic States. These results relate to the findings of Morkevičius and Norkus (2012) who found that the lower part of service class and manual workers experience a similar level of satisfaction in life.
Differences have also been identified among the three Baltic States. The risks of unemployment and low pay are higher for lower middle-class fathers in Estonia than in the other two Baltic States, while the risks of unemployment, jobless households, and low pay are higher for lower middle class mothers in Latvia and Lithuania.
The influence of social class on the probability of respondents within the age group of 18–24 years experiencing a social risk also varies among the Baltic States and among the different kinds of social risks as well. Thus, the risk of being unemployed is higher for lower middle-class individuals aged 18–24 in Latvia and Lithuania than in Estonia. Furthermore, in the age group of 25–64-year-olds, in most cases, the higher social class members of this age group face a lower risk compared to the 25–64-year-olds who hold elementary occupations.
There is also a higher risk to be a single parent among the higher social class in Latvia and Estonia. Older individuals (aged over 65) face the risk of ill health and low pay differently in each country. In Lithuania. the higher social class faces lower risks. However, the situation in Latvia and Estonia is slightly different and being a member of a higher social class does not guarantee lower risks for ill health or low pay.
Our research has also revealed that in the Baltic States, the differences between the higher social classes are not always significant, and this is also seen in the low-skilled non-manual class (mainly consisting of clerks and service workers) and the skilled manual class. In the Baltic States, the social risk of receiving low pay can be directly related to the small size of the middle class if class is based on economic criteria. The data also reveal that higher middle class members in the age group of 18–24-years are at a greater risk of being low paid in Lithuania, whereas females aged 18–24 years face a higher risk of being low paid in all the Baltic States. The higher social class has lower odds of being low paid in the group of working age people (age 25–64). This finding confirms the thesis that social investment leads to better life chances through investment in human capital.
The data reveal women and older persons face a higher risk of low pay in general. In Estonia, there is a higher risk to be low paid among the higher social class seniors (aged over 65). The research data suggest that in some cases, the lower layers of the middle class in the Baltic States have a higher probability of being exposed to social risks than individuals in elementary (low skill) occupations. In other words, higher investments in education do not always guarantee a low probability of low income, jobless households, and ill health in the Baltic States. This can be related to the structure of the labor market and the policy on wages for the large class of service workers in the Baltic States. As Cantillon (2011) has argued, the main objectives of social investment policy are not only to provide more education but also well-paid jobs.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research for this article was funded by a grant (No. Ger-009/2017) from the Research Council of Lithuania.