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Is income inequality a contributing factor to poverty and crime? Empirical evidence of the CIS nations

Published online by Cambridge University Press:  30 September 2025

Liudmila Filipava Open the ORCID record for Liudmila Filipava [Opens in a new window]
Liudmila Filipava*
Affiliation:
Department of International Economic Relations, Belarusian State University , Minsk, Belarus
*
Email: filippova.le30@gmail.com
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Abstract

This article addresses a critical research gap by investigating the link between social polarization and contemporary societal challenges, such as poverty and crime. Despite the existence of numerous studies describing the effects of income disparity, the role of social stratification in inducing delinquency and poorness in transitional economies has not received sufficient consideration. The author conducts an analysis of income inequality metrics (IIMs) across Commonwealth of Independent States (CIS) nations, examining dependencies that provide a precise depiction of the correlation between socioeconomic disparity and urgent society’s problems. The study disclosed that the lowest proportion of the impoverished was observed in countries with a minimal income disparity, such as Belarus and Kazakhstan; in contrast, nations that exhibit a significant degree of income differentiation, such as Kyrgyzstan, Moldova, and Armenia, demonstrate the highest proportion of the impoverished. Following a linear regression approach, the study revealed a strong positive correlation between IIMs and crime rate. By shedding light on these dependencies, the article provides fresh insights into the dynamic relationship between major social problems in the CIS countries.

Keywords

social stratificationincome inequality metrics (IIMs)povertycrimelinear regressionCommonwealth of Independent States (CISs)

Information

Type
Original Article
Information
Journal of International and Comparative Social Policy , Volume 41 , Issue 2 , July 2025 , pp. 93 - 115
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Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Social Policy Association

Introduction

In contemporary societies, income differentiation has become a crucial determinant of socio-economic advancement, as its uneven distribution fosters economic growth (Cingano, Reference Cingano2014; Cerra et al., Reference Cerra, Lama and Loayza2021). It appears that a society with rational differentiation becomes the most stable due to the prevailing middle class (Birdsall, Reference Birdsall2006); the prospect of social advancement and professional growth motivates individuals to acquire an education and engage in entrepreneurial endeavours. Nevertheless, excessive social differentiation may result in the inadequate utilization of resources, thereby exacerbating societal polarization. This phenomenon exhibits a cumulative nature (OECD, 2015), encompassing the preservation of wealth in one segment of the populace and the emergence of poverty in another (Piketty and Saez, Reference Piketty and Saez2001; Deaton, Reference Deaton2013). Therefore, in order to ensure economic stability, the regulation of social inequality appears to be an imperative component of state policy.

Throughout the socio-economic transformation, the Commonwealth of Independent States (CIS) has acquired a system of social protection that covered nearly all segments of the populace, encompassing provision of fundamental medical services, extensive food aid, professional skills training, and so forth, thereby circumventing the crucial issues that contribute to poverty in developing countries. Following market reforms since the mid-1990s, the CIS nations initiated a novel system of social protection, comprising social and medical insurance, pension systems, and state social assistance (Gupta, Reference Gupta1998; Heller, Reference Heller2001; Simai, Reference Simai2006). At present, the “ancient” and “new” social protection systems are observed to exist in varying proportions. This transitional state, in which the “old” has not yet been eradicated and the “new” has not yet been fully established, poses specific challenges and constraints.

In the present day, the issues of poverty and social inequality in transition economies are significantly underestimated by the scientific community and international organizations, as their focus is primarily directed towards developing countries (UBS, 2025). In light of the average statistics, the severity of these issues does not appear to be particularly pronounced. According to the World Inequality Report (Chancel at al, Reference Chancel, Piketty, Saez and Zucman2022), the Gini (GIN) value of certain CIS states, including Belarus, Kazakhstan, and Uzbekistan, is not inferior to that of the European Union (EU) countries. Additionally, the poverty rate in Central Asia and Europe is a mere 0.2% of the global average (UNDP, 2023). However, aggregated statistics only partially reflect the true picture, which is less optimistic when viewed in the context of individual countries. A significant number of CIS citizens (ranging from 30 to 60%, depending on the country) lack a safety net in savings (OECD, 2021). The situation is further complicated by the fact that the relatively high inflation rates in CIS states have a more substantial impact on the incomes of namely impoverished segments of the population (Easterly and Fischer, Reference Easterly and Fischer2001), and targeted payments are insufficient to alleviate widespread poverty.

The complexity of research in the CIS states is ascribed to data constraints. Since 2022, the Interstate Statistical Committee of the Commonwealth of Independent States (CIS Statcommittee) has discontinued the publication of the official data on poverty and socio-economic inequality. This decision might be attributed to the Russian invasion of Ukraine and the desire to conceal the rapidly deteriorating internal problems from the public. Data concealment is also apparent in the state statistical sources. Governments frequently withhold official data (as seen in Belarus), display outdated data (as in Moldova, where the most recent update was in 2012), or provide “convenient” figures that align with their political objectives. Nevertheless, the silence surrounding the problem only underscores its gravity and the significance of research on this topic.

Recent studies examine the impact of income differentiation on diverse processes, encompassing economic growth (Stiglitz, Reference Stiglitz2016; Mdingi and Ho, Reference Mdingi and Ho2021; Ferreira et al., Reference Ferreira, Gisselquist and Tarp2022; Topuz, Reference Topuz2022), investments (Melki, Reference Melki2022), birth and death rates (Huynh et al., Reference Huynh, Parker, Harper, Pamuk and Schoendorf2005; Siddiqi et al., Reference Siddiqi, Jones and Erwin2015; Sanhueza et al., Reference Sanhueza, Cueva, Mujica, Soliz and Duran2024), life expectancy (Wilkinson and Pickett, Reference Wilkinson and Pickett2009), labour productivity (Delhey and Kohler, Reference Delhey and Kohler2011), demography (Butler et al., Reference Butler, Wildermuth, Thiede and Brown2020; Taresh et al., Reference Taresh, Sari and Purwono2021) and social mobility (Günther and Martorano, Reference Günther and Martorano2025), and political efficacy (Bouveta and King, Reference Bouveta and King2016) and economic stability (Murphy, Reference Murphy2015; Galbraith et al., Reference Galbraith, Priest and Purcell2006).

This research contributes to the growing literature by providing novel insights into the impact of high-income inequality on poverty and crime in transitional economies. The author extends the recent research of Amponsah et al. (Reference Amponsah, Agbola and Mahmood2023), Bergstrom (Reference Bergstrom2020), Beker (Reference Beker2020), Lechheb et al. (Reference Lechheb, Ouakil and Jouilil2019), Omar and Inaba (Reference Omar and Inaba2020), Hazra (Reference Hazra2020), Itskovich and Factor (Reference Itskovich and Factor2023), Sugiharti et al. (Reference Sugiharti, Purwono, Esquivias and Rohmawati2023), in two different ways. In lieu of the most frequently used GIN coefficient, the author employs a set of generalized income inequality metrics (IIMs) encompassing quintile (QNT) and decile (DEC) differentiation coefficients, as well as the GIN index, which are more precise indicators of nations’ sensitivity to the negative impact of income inequality. Second, the author examines the CIS states, which were underestimated by the scientific community in terms of their poverty and social stratification issues. The author seeks to answer two questions: (i) Does income inequality influence poverty and crime in CIS countries? and (ii) Does the correlation between IIMs and poverty (crime) display linear dependence? To answer these questions, the author analyses the IIMs, the Multidimensional Poverty Index (MPI), and the crime rate (CRM) variables within CIS states, setting Pearson correlation tests. Understanding these relationships would become imperative for policymakers and international organizations who strive to fight against poverty and crime.

The article is structured as follows: Section “Theoretical Background of the Income Inequality Problem and Hypothesis” provides a comprehensive overview of the subject matter, encompassing the hypothesis set. Section “Methodology” describes the methodology employed. Section “The Analysis of data by the CIS countries” presents the data for analysis. Section “Poverty and Crime as Possible Repercussions of Income Inequality” employs sufficient testing and discusses the empirical findings. Finally, Section “Concluding remarks” provides pivotal policy implications based on the achieved results.

Theoretical background of the income inequality problem and hypothesis

The study of diverse aspects of socio-economic inequality is devoted to both contemporary economic thought and related sciences, such as philosophy, psychology, and sociology. The intricate nature of global economic relations necessitated the utilization of an economic framework in studying this problem.

In the theory of social stratification (Weber, Reference Weber1947; Parsons, Reference Parsons1951; Barber, Reference Barber1963; Durkheim, Reference Durkheim1964), income inequality is primarily examined in the functional approach, which emphasizes the positive, “functional” nature of inequality and demonstrates its necessity. Davis and Moore (Reference Davis and Moore1945), American sociologists, argued that the stratification of society is a direct consequence of the division of labour. They posited that the unequal social functions of different groups of individuals objectively require correspondingly unequal remuneration. Society fosters the pursuit of essential, yet difficult and unpleasant, occupations through income and prestige inequality, which also motivates individuals who are more educated and talented. Parsons (Reference Parsons1951) concluded that the stratification factors of Western society are distinct from those employed by traditional societies. The “statuses” that an individual acquires are of greater significance than those that are innate and prescribed. Particular importance was given to the establishment of social consensus regarding the value of specific professions, determining differentiation in income. For instance, in American society, the primary social value is success in business and career, which is why scientists of technological specialties and plant directors are granted a higher status and income. In Europe, the concern is the preservation of “cultural patterns,” and as a result, intellectuals, humanists, clergymen, and university professors are accorded special recognition by society. However, the study was unable to offer a definitive response to the question of why the value systems of different societies are so dissimilar.

By today, sociology has not yet developed a comprehensive theory of stratification, as the various approaches (functional, hierarchical, and production) only illuminate individual aspects of inequality. In examining this issue, the theory of social welfare recognizes income inequality as a factor that impacts the efficiency of resource allocation and overall welfare. Two fundamental approaches elucidate the welfare state formation: economic, which emphasizes the economic reasons for the increased activity of the state in public life (as a response to unemployment, poverty, urbanization, the need to finance large-scale infrastructure projects, education, and health care) (Wilensky, Reference Wilensky1975); and political, which describes the development of the welfare state as a phenomenon of power (political and legal) relations (Korpi, Reference Korpi1983). In countries with an originally strong public sector and a developed civil society, the formation of the welfare state is primarily attributed to the political approach. Conversely, in nations with transition economies and emerging markets, the welfare state’s origins are rooted in the economic challenges and the objective incapacity to address them through market mechanisms.

The concept and typologies of the welfare state were initially examined in the renowned work “Three Worlds of Welfare Capitalism” (Esping-Andersen, Reference Esping-Andersen1990), which served as a foundation for subsequent research in this scope. Utilizing a diverse array of critical indicators (e.g., residuality, stratification, and decommodification), he distinguished three welfare state models: liberal, conservative, and social democratic. Subsequently, the scientist introduced a novel classification that rested on the welfare state model’s criteria and the family’s role within it (Esping-Andersen, Reference Esping-Andersen1996, Reference Esping-Andersen1999). Its primary distinction from the preceding typology was the allocation of Scandinavian countries to a distinct group and the inclusion of southern European countries in the typology. Other classifications of welfare state models employ alternative criteria. The typology of Bonoli (Reference Bonoli1997) and Ferrera (Reference Ferrera1996) is based on the quantification of public expenditure in relation to Gross domestic product (GDP) and social expenditure, both in absolute and relative terms. The scientists have identified four welfare state models: Anglo-Saxon, Bismarckian, Scandinavian, and Southern European. Kautto (Reference Kautto2002), in a similar vein, conducted an analysis of the extent of social assistance expenditure, distinguishing between states where social assistance is primarily provided in kind, cash, or a mixed form.

However, these approaches have since been the subject of criticisms (Gough, Reference Gough2001; Wildeboer Schut et al., Reference Wildeboer Schut, Vrooman and Beer2001; Bambra, Reference Bambra2007; Jensen, Reference Jensen2008), as welfare state models were examined statically, without considering the qualitative changes that have occurred over the past 30 years in relation to globalization, demographic changes, transformation of the household structure and labour market, and slowdown in economic growth. Additionally, the models were restricted to a small number of countries, primarily Western European, and the classification criteria did not account for gender and family policies (Orloff, Reference Orloff1993; Daly and Lewis, Reference Daly and Lewis1998), as well as inequality in the primary distribution of income (Castles, Reference Castles1993). Deacon (Reference Deacon2007), Arcanjo (Reference Arcanjo2009), Castles et al. (Reference Castles, Leibfried, Lewis, Obinger and Pierson2010), and Korpi (Reference Korpi2003) proposed that the welfare state models be disseminated to countries in Asia, Africa, North and South America, and Eastern Europe. Further, the qualitative criteria for classifying welfare states are evolving, with a greater emphasis on institutional changes than redistribution processes.

Neoliberal economists broadened their analysis by interpreting inequality and poverty through the lens of globalization and economic growth. Remarkably, empirical studies can yield conflicting results for advanced and emerging countries, as well as for internal and external economic inequality. Initially, the disparity between developing and wealthier economies is decreasing as communications and transportation become more efficient and “cost-effective,” and countries are becoming more integrated into a single global production and trade network (Milanovic, Reference Milanovic2002; Firebaugh and Goesling, Reference Firebaugh and Goesling2004; Sala-i-Martin, Reference Sala-i-Martin2006). Conversely, economic discrimination is considerably more prevalent in developing nations. Enderwick (Reference Enderwick2006) estimated that a mere 5% increase in the proportion of exports generated by developing countries would lead to an annual increase of $350 billion, which is more than seven times the current level of financial assistance they receive. As for the social inequality, the situation is frequently less impressive. Globalization drivers, such as the expansion of multinational corporations (Feenstra and Hanson, Reference Feenstra and Hanson1996; Meschi and Vivarelli, Reference Meschi and Vivarelli2007; Brewer, Reference Brewer2011), the development of information and communication technologies (Brown and Campbell, Reference Brown and Campbell2002; Mills, Reference Mills2009; Baek and Shi, Reference Baek and Shi2016), population growth, and migration (Borjas, Reference Borjas2000), have a substantial impact on the wage gap in developing countries, thereby exacerbating poverty (Ravallion, Reference Ravallion2003) and increasing social stratification (Cornia, Reference Cornia2004; Dreher and Gaston, Reference Dreher and Gaston2008). Therefore, the relationship between inequality, growth, and poverty strikes a delicate balance (UN, 2013), necessitating an effective social policy to reduce poverty rather than solely prioritizing economic growth.

Today, the issue of poverty is often reflected in the “green” Keynesianism, which claims that developed nations should reorient towards a moderate level of consumption and efficiency in income redistribution (Custers, Reference Custers2010; Cömert, Reference Cömert2019). Economic progress must be oriented towards enhancing social and cultural life, which assumes the distribution of services that encompass human capital, for example, education and healthcare (Harris, Reference Harris2013).

In order to further investigate the issues of poverty (crime) and income polarization, the author proposes the following hypotheses:

H1: Poverty is associated with income inequality in the CIS countries, and

H2: The increase in crime could be attributed to income inequality.

To validate these hypotheses, the author conducts an analysis of income inequality indicators across nine CIS nations, examining dependencies that provide a precise depiction of the correlation between income inequality and contemporary society’s problems, such as poverty and crime.

Methodology

To evaluate the impact of income inequality on the expansion of poverty and crime, we employed IIMs and the CRM.

IIMs, such as the GIN, QNT, and DEC differentiation coefficients, are determined through the Lorenz curve (Lorenz, Reference Lorenz1905). It is defined by the points (p, L(p)), where p is the cumulative proportion of income-receiving units sorted from the poorest to the richest and L(p) is the cumulative proportion of income. Gastwirth (Reference Gastwirth1971) proposed a general definition of the Lorenz curve as if X were a random variable with a cumulative distribution function F(x), a quantile function F–1(x), and a mean ʃ xdF(x). Then the Lorenz curve is the mapping

(1) $$ p\to L(p)=\frac{1}{\mu }{\int}_0^p{F}^{-1}(t) dt $$

A Lorenz curve will have some expected properties: (1) if p = 0 then L(p) = 0, (2) if p = 1 then L(p) = 1, (3) L(p) ≤ p, and (4) L(p) is continuous and differentiable, and the slope of the curve increases monotonically (Belz, Reference Belz2019).

The GIN index (G) is determined in terms of the Lorenz curve, corresponding to twice the area between L(p) and the egalitarian line as

(2) $$ G=2{\int}_0^1\left(p-L(p)\right) dp $$

The methodology for its assessment (Gini, Reference Gini1914) uses a population-weighted national income per capita, which varies from 0 to 1: the greater the value deviates from zero, the greater the degree of income concentration among individual population groups. Generally, the GIN coefficient should not exceed 0.4, as excessive income inequality can adversely impact the rate of economic growth and create a poverty trap.

QNT and DEC differentiation coefficients are based on a method for ranking households according to their income. QNT groups refer to the distribution of households into five subgroups of 20% of families. The first QNT group is 20% of families with the lowest income, and the fifth is 20% of families with the highest income. Thus, the QNT differentiation coefficient is a ratio between the income level of the fifth QNT group, which has the highest income level, and that of the first QNT group, which has the lowest income level. The DEC differentiation coefficient is calculated in a similar manner. Higher values indicate a higher inequality.

Applying the standard Pearson correlation (Pearson, Reference Pearson1904), the relationship between IIMs and CRM as a result of social stratification will be represented as follows:

(3) $$ corr\left( IIM, CRM\right)=\frac{\sum \left({CRM}_i-\overline{CRM}\right)\left({IIM}_i-\overline{IIM}\right)}{\sqrt{\sum {\left({CRM}_i-\overline{CRM}\right)}^2\sum {\left(I{IM}_i-\overline{IIM}\right)}^2}} $$

where IIMi is the value of the income inequality indicator, CRMi is the value of the crime rate, $ \overline{\mathrm{IIM}} $ is the arithmetic mean for the income inequality indicator, and $ \overline{\mathrm{CRM}} $ is the arithmetic mean for the crime rate.

A similar approach is employed to assess the correlation between IIMs and MPI.

The analysis of data by the CIS countries

The sources of income

In the CIS countries, wages constitute the primary source of total income, accounting for between 26.5% (Tajikistan) and 64.0% (Kazakhstan). A significant portion of income also comes from self-employment and entrepreneurship (Figure 1).

Figure 1. The sources of household income, 2010 and 2022.

Note. Upper graph: 2010 data; lower graph: 2022 data.

Source: Composed by the author based on the Interstate Statistical Committee of the Commonwealth of Independent States data.

Although the income structure has not significantly changed over the past decade, overall, the CIS countries have experienced a positive trend. According to Table 1, the highest increase in average monthly nominal wages was observed in Moldova (up by 131% since 2010); the lowest income growth was observed in Uzbekistan (down by 10% since 2015), which can be attributed to the fact that the economy of Uzbekistan was the most vulnerable to the coronavirus disease 2019 pandemic (CIS Statcommittee, 2023a), resulting in a significant decline in primary financial revenues. In 2015, Russia saw a decline in average incomes due to the Ruble’s dependence on oil prices. Its exports experienced a decline of 32% compared to 2014 levels, and according to the Russian Public Opinion Research Center, every fourth Russian rated their financial situation as “poor.”

Table 1. The average monthly nominal wages, US dollars

Source: Composed by the author based of the CIS Statcommitte (, 2023b) data.

In 2022, nominal wages experienced an increase in all CIS countries (Table 2), with the highest increase occurring in Kyrgyzstan (137%). The real wages experienced a rise too, except Belarus and Moldova (98 and 90% of the 2021 value, respectively). The highest reported average wage in Russia demonstrates a sixfold discrepancy with Tajikistan. (Compared to other countries, this disparity ranges from 0.99 with Kazakhstan to 4.36 with Kyrgyzstan.)

Table 2. The monthly growth in average wages compared to the previous year, 2022

Source: Composed by the author based on the CIS Statcommittee (2023c) data.

An examination of economic activity within the CIS countries revealed that the number of individuals employed in low-paid industries was significantly higher than that employed in high-paid industries. For instance, in Tajikistan, 64.28% of the population is employed in agriculture, one of the lowest-paid sectors of the economy; and only 2% of the employed population works in such industries as information and communications, finance and insurance, and sports and entertainment (Figure 2). A similar situation was observed in Azerbaijan, with 51.42 and 2.73%, respectively. In contrast, in Russia, only 9% of the populace is employed in low-paying industries, whereas 6.99% are employed in high-paying industries, such as information and communications (1.83%), mining (2.34%), and finance and insurance (2.24%).

Figure 2. The percentage of the workforce employed, 2022.

Source: Composed by the author based on the CIS Statcommittee (2023c) data.

The indicators of income inequality

Over the past decade, a trend towards decreasing social inequality has been observed in the CIS countries (Figure 3). The exception is Kyrgyzstan, where the GIN coefficient reached 0.408 in 2022, surpassing Russia, which has historically demonstrated the highest values. The most notable achievements in reducing income disparities were observed in Moldova (37%); the highest increase was observed in Kyrgyzstan (by 25%). Belarus and Kazakhstan have not undergone any changes; however, in Armenia, Uzbekistan, Russia, and Azerbaijan, the ratio has experienced a decline of 11–16%

Figure 3. The Gini Index values.

Source: Composed by the author based on the World Population Review official website.

According to the income inequality by GIN index, the CIS countries can be divided into two groups, with less and more critical income differentiation. The initial group comprises Belarus, with a GIN value of 0.268, Uzbekistan (0.283), and Kazakhstan (0.285). The second group comprises Moldova (0.347), Armenia (0.352), Russia (0.395), and Kyrgyzstan (0.422). Although the GIN index is not specified for Azerbaijan, the values of the QNT and DEC coefficients allow us to assign it to the first group. The CIS nations belonging to the first contingent group are not inferior to the EU countries in terms of the GIN value (Figure A1), whereas in the second group, particularly in Russia and Kyrgyzstan, income inequality is significantly more evident.

In 2022, the QNT ratio in Kyrgyzstan complied with 8.5, indicating that the wealthiest 20% of households concentrated approximately half of all resources; simultaneously, the 20% of Azerbaijan’s population that is least affluent possesses twice as many resources as the corresponding group in Kyrgyzstan (Figure 4).

Figure 4. The household income ranking by quintile groups, 2022.

Source: Composed by the author based on the CIS Statcommittee (2023a, 2023b) data.

The DEC coefficient reveals similar dynamics. According to Table 3, there was no significant difference in income in Kazakhstan and Belarus; in contrast, in Azerbaijan and Kyrgyzstan, the disparity between the 10% of the most and least affluent households was 23%. In Russia, Uzbekistan, and Moldova, this gap has narrowed (by 17, 18, and 49%, respectively). Simultaneously, in Moldova, Armenia, Kyrgyzstan, and Russia, the social disparities exceeded the threshold value. Therefore, the values of the QNT and DEC coefficients confirm the division based on the GIN index, as previously stated.

Table 3. The quintile and decile ratios, 2010 and 2022

Source: Composed by the author based on the CIS Statcommittee (2023a, 2023b) data.

The factors of income inequality

Territorial affiliation (Lessmann, Reference Lessmann2014; Remington, Reference Remington2015; Wang et al., Reference Wang, Shao and Li2019), education (Afonso et al., Reference Afonso, Schuknecht and Tanzi2010; Artige and Cavenaile, Reference Artige and Cavenaile2023), gender pay gap (Oyenubi and Mosomi, Reference Oyenubi and Mosomi2024), and population aging (Fields and Yoo, Reference Fields and Yoo2000; Dolls et al., Reference Dolls, Doorley, Paulus, Schneider and Sommer?2019; Thaning, Reference Thaning2021; Kim and Kim, Reference Kim and Kim2024) have a significant impact on income differentiation.

Large cities play a crucial role in the region’s development, attracting significant foreign investment. Simultaneously, the heightened burden on cities indicates uneven settlement (Li et al., Reference Li, Jia, Song and Cheng2024), thereby escalating the burden on social, engineering, and transportation infrastructures. Today, capital cities of the CIS countries account for 36.4% of the urban populace in Armenia, 22.8% in Azerbaijan, 22% in Moldova, 21.7% in Belarus, 16.3% in Kyrgyzstan, 12.2% in Tajikistan, 8.9% in Russia, 6.9% in Uzbekistan, and 6.6% in Kazakhstan (CIS Statcommittee, 2023c). The number of impoverished individuals indicates a significant inequality between urban and rural areas (Table 4), with the largest disparity level in Belarus (8.89 times), Moldova (7.84 times), and Kazakhstan (6.01 times).

Table 4. The proportion of the impoverished populace as a percentage of the overall population, 2019

Note: *2018 data.

Source: Composed by the author based on the CIS Statcommittee (2021) data.

Education accessibility varies significantly among the CIS nations. In Belarus, the educational standard surpasses the global average. In contrast, in Azerbaijan and Tajikistan, the availability of higher education is significantly restricted; as for Uzbekistan, only 10% of the youth have the opportunity to obtain a higher education. According to CIS Statcommittee (2024), there are 51–60 university students per 1,000 people in Belarus, Kazakhstan, Kyrgyzstan, and Russia; 34 and 41 in Armenia and Moldova, respectively; and 27 and 23 in Tajikistan and Azerbaijan, respectively. The ratio of female students in secondary and higher educational institutions varies from 57 to 58% in Belarus, Kazakhstan, and Moldova; 49–55% in Azerbaijan, Armenia, Kyrgyzstan, and Russia; and 32% in Tajikistan (CIS Statcommittee, 2023d). As a result, limited access to education leads to a decrease in human capital (Erosa et al., Reference Erosa, Koreshkova and Restuccia2010) and a slowdown in labour productivity growth (Magableh et al., Reference Magableh, Alalawneh and Al-qalawi2022), consequently increasing social disparity.

Gender pay gap is another factor of income inequality. According to the report on Monitoring of Sustainable Development Goals Indicators in the CIS region by CIS Statcommittee (2024), in 2022, the average monthly salary of women was 27.7% lower than that of men (Table 5). The most significant disparity was observed in Armenia (39%). The recent years have shown a tendency towards a reduction in the gender gap in the CIS countries, which, nevertheless, remains significant.

Table 5. The gender gap in women’s wages as a percentage of men’s average monthly wages

Source: Composed by the author based on the CIS Statcommittee (2024) data.

In 2023, the number of pensioners in the CIS nations surpassed 42 million, accounting for 17% of the entire population. According to forecasts (CIS Statcommittee, 2023b), in CIS countries, the number of individuals over the age of 60 years will double by 2050. It is an overall trend that a significant proportion of elderly people remain in their employment after reaching retirement age. The highest proportion of employed pensioners is observed in Moldova (20%), Russia (12%), and Armenia (8%).

It is noteworthy that the ratio of the minimum pension to the average monthly wage exceeds 20% in countries with comparatively low-income inequality, such as Azerbaijan, Belarus, Kazakhstan, and Moldova; conversely, in countries with higher social polarization (Uzbekistan, Tajikistan, Kyrgyzstan, Russia, and Armenia), this ratio is significantly below 20% (Table 6). Hence, the magnitude of pension provision is correlated with income differentiation. These findings are consistent with existing empirical studies by Li et al. (Reference Li, Wang, Xu and Yuan2020) and Andersen et al. (Reference Andersen, Bhattacharya, Grodecka-Messi and Mann2024). Hence, social policy plays a pivotal role in addressing the issue of income inequality.

Table 6. The ratio of minimum pension to average monthly wage, 2022

Source: Composed by the author based on the CIS Statcommittee (2023c) data.

Poverty and crime as possible repercussions of income inequality

Data analysis

Social inequality and poverty are measured by comparing the ratio of per capita cash income to the subsistence minimum budget. According to Table 7, Kazakhstan and Belarus have the lowest income differentiation, with 3.9 and 5.2, respectively. In contrast, Kyrgyzstan, Moldova, and Armenia exhibit the highest degree of income disparity, with more than 20% of the populace being impoverished.

Table 7. The proportion of the populace with an income below the minimum subsistence level

Source: Composed by the author based on the CIS Statcommittee (2023b) data.

According to Table 8, a significant proportion of individuals who live on less than $5.5 per day reside in Moldova, Kyrgyzstan, and Armenia. As for the CIS Statcommittee data, 13.5% of the population of Moldova, or every 10th resident, lives below the extreme poverty line (Table 9). However, the methodology employed for defining and evaluating extreme poverty varies depending on the country, allowing for the distinction between food insecurity (Armenia), a minimum expenditure basket (Kazakhstan), or extreme poverty (Moldova).

Table 8. The percentage of the populace living on less than $5.50 a day

Source: Composed by the author based on the World Bank data.

Table 9. The percentage of the populace living below the extreme poverty line

Source: Composed by the author based on the Interstate Statistical Committee of the Commonwealth of Independent States data.

The Global MPI was developed in 2010 by the Oxford Poverty & Human Development Initiative and the United Nations Development Program (UNDP) to evaluate the prevalence and severity of poverty in over 100 developing nations. The global MPI promotes SDG 1 – ending poverty in all its forms everywhere – and measures interconnected deprivations across indicators related to SDGs 1, 2, 3, 4, 6, 7, and 11.

The MPI comprises 10 indicators that span three dimensions, namely health (nutrition and child mortality), education (years of schooling and school attendance), and living standards (cooking fuel, sanitation, drinking water, electricity, housing, and assets). All indicators are equally weighted within the same dimension. A person’s deprivation score is the sum of all the weighted deprivations she/he experience. The MPI indicates that individuals are multidimensionally impoverished if their deprivation score exceeds 1/3; a higher score indicates a greater degree of impoverishment (UNDP, 2023).

Table 10 illustrates the country’s poverty score. It is anticipated that most CIS countries, except for Tajikistan, exhibit a lower poverty score that does not exceed 0.02.

Table 10. The MPI values, 2022

Source: Composed by the author based on the Global Multidimensional Poverty Index website.

To analyse the extent of organized crime, we employed the Global Organized Crime Index (GOCI). The index considers aspects, such as the presence of criminal groups, the degree of corruption, drug trafficking, smuggling, terrorist activity, and other related factors. According to the GOCI data, Russia holds the highest level of organized crime among the CIS nations, followed by Belarus, Moldova, and Tajikistan (Table 11). Therefore, organized crime remains a serious problem in the majority of the CIS countries.

Table 11. The GOCI ranking, 2023

Source: Composed by the author based on the Global Organized Crime Index official website.

Preliminary tests

To examine the impact of income inequality on poverty and crime growth, the author employs several variables (Appendix, Table A1). Before performing the linear regression, the data were checked for normality distribution. After analysing the skewness, kurtosis, Kolmogorov–Smirnov (Kolmogorov, Reference Kolmogorov1933; Smirnov, Reference Smirnov1948), and Shapiro–Wilk (Shapiro and Wilk, Reference Shapiro and Wilk1965) tests (Table 12), the results reject the null hypothesis for the CRM variables, indicating a normal distribution of the data. Regarding the QNT, DEC, GIN, and MPI variables, tests have revealed that the data are not normally distributed. Nevertheless, normal distribution of the data is not a necessary prerequisite for using it in linear regression. The assumption frequently pertains to the error term, which can be ascertained by analysing residuals rather than data.

Table 12. Normal distribution tests

Source: Composed by the author.

Pearson correlation tests

Figure 5 illustrates the linear relationship between the MPI and income inequality variables (QNT, DEC, and GIN); Figure 6 illustrates the linear relationship between CRM and IIMs. The corresponding correlation statistics are presented in Tables 13 and 14.

Figure 5. The Pearson correlation scatter plots for the dependence of the IIMs and the Multidimensional Poverty Index.

Source: Composed by the author.

Figure 6. The Pearson correlation scatter plots for the dependence of the IIMs and the crime rate.

Source: Composed by the author.

Table 13. The regression statistics for the dependence of the IIMs and the Multidimensional Poverty Index

Source: Composed by the author.

Table 14. The regression statistics for the dependence of the IIMs and the crime rate

Source: Composed by the author.

Analysis of the results

In the current research, the author was unable to determine whether a greater risk of income inequality was associated with greater poverty. Regarding the impact of social stratification on poverty, linear regression exhibits a weak dependence on GIN-MPI (Pearson coefficient 0.10732) and even a reverse dependence on QNT-MPI and DEC-MPI (0.18566 and 0.18434, respectively). At the same time, during the main data examination, the lowest proportion of the impoverished was observed in countries with a minimal income disparity, such as Belarus and Kazakhstan; in contrast, nations that exhibit a significant degree of income differentiation, such as Kyrgyzstan, Moldova, and Armenia, demonstrate the highest proportion of the impoverished. The sole exception is Russia, where poverty is primarily caused by the oligarchy’s strengthening and the critically disproportionate distribution of financial assets (Novokmet et al., Reference Novokmet, Piketty and Zucman2018), as well as high interregional and intersectoral differentiation.

Current empirical studies that examine the correlation between poverty and income inequality exhibit both positive and negative dependencies. On the one hand, income inequities encourage entrepreneurial activity, financial inclusion, and capital inflow, thereby propelling economic growth, leading to a rise in incomes and a reduction in poverty (Beker, Reference Beker2020; Bergstrom, Reference Bergstrom2020; Omar and Inaba, Reference Omar and Inaba2020). On the other hand, substantial income inequality, exhibited by low-income economies, may have the opposite effect, causing economic stagnation and therefore increasing poverty (Lechheb et al., Reference Lechheb, Ouakil and Jouilil2019; Amponsah et al., Reference Amponsah, Agbola and Mahmood2023).

Similar to developing countries, transition economies are marked by polarization. Therefore, a group of impoverished nations exists, with over half (Tajikistan), one-sixth (Kyrgyzstan), and one-seventh of the population (Armenia) living in conditions of severe deprivation. Despite the substantial progress that these countries have made in poverty reduction since 2010, it is expected that the significant disparity between them and other CIS nations will continue for an extended period. The increasing living standards of all socio-economic groups in Russia are resulting in a gradual decrease in poverty and a concurrent increase in income inequality, albeit at a varying pace. A notably high DEC ratio suggests that the affluent’s income growth exceeds the impoverished rate of improvement in their material circumstances. This discrepancy leads to the official poverty line failing to accurately reflect the actual state of need.

As of the present, Belarus is the sole CIS nation to have eradicated poverty as a socio-economic phenomenon. Its presence in other countries of this economic group (Kazakhstan and Azerbaijan), albeit in minimal amounts, indicates the insufficient efforts of governments to provide the entire population with state minimum guarantees. And also serves as evidence of the immense distance that even the most promising post-Soviet nations have from the European Universal Basic Income model.

The author observes a positive correlation between IIMs and the CRM. (The correlation coefficient stands at 0.51730 for the QNT–CRM, 0.52694 for the DEC–CRM, and 0.40369 for the GIN–CRM dependence.) The results are consistent with the existing research by Hazra (Reference Hazra2020), Sugiharti et al. (Reference Sugiharti, Purwono, Esquivias and Rohmawati2023), and Itskovich and Factor (Reference Itskovich and Factor2023), and allow us to confirm the significance of income inequality in the context of poverty and criminality rise for the CIS economies.

Concluding remarks

In the majority of CIS countries, the income differentiation appears below the global average level. However, regardless of how critical it may be, the results obtained allowed us to state the importance of this problem in the context of escalating poverty and crime.

Although the author was unable to establish a strong correlation between IIMs and MPI through linear regression tests, the analysis of countries’ data revealed that CIS states with a higher degree of income differentiation have a higher prevalence of poverty. Additionally, we found a positive correlation between IIMs and the CRM. In this regard, an efficient social policy of the CIS states aimed at reducing income inequality may indirectly contribute to the reduction of poverty and the number of criminal acts.

An essential step in this direction would become the implementation of progressive taxation, which appears to be a fundamental component of civilized distribution relations in advanced economies., Without additional budgetary costs, a progressive tax scale would enable (i) the augmentation of the minimum amount for all types of pensions to the subsistence minimum, (ii) the augmentation of accrued wages of public sector employment by one and a half times, (iii) the introduction of a guaranteed per capita monetary income for complete and incomplete nuclear families with children at the level of 50% of the median per capita monetary income, as well as an enhancement of the monthly child allowance for other families with children, and (iv) the augmentation of the minimum wage to the subsistence minimum (Farhi and Werning, Reference Farhi and Werning2005). Additionally, a combination of income tax and a progressive property tax could serve as an optimal mechanism for implementing social planning (Makdissi and Mussard, Reference Makdissi and Mussard2008).

Further, a redistribution of income would provide an incentive to improve the demographic situation through the construction of affordable housing, compensatory benefits, and so forth. In the CIS countries, the magnitude of the impact of excessive inequality and poverty on the economic and demographic dynamics is considerable. Reducing excess inequality will not only halt depopulation but also ensure significantly higher rates of economic growth. Thus, the indicators of inequality and relative poverty should become a key benchmark for coordinated economic, social, and demographic policies.

One of the measures should be directed towards supporting the agricultural sector. In Tajikistan and Kyrgyzstan, a significant portion of the workforce is employed in the agricultural sector, which makes the issue of low wages particularly important. In the longer term, enhancing the standard of living of rural residents will alleviate the burden on cities, thereby reducing the degree of inequity within society. This will necessitate the implementation of reforms aimed at enhancing labour productivity, infrastructure, and social services in rural regions.

As revealed during the research, countries with minimal income differentiation exhibit higher social payments compared to those with significant income inequality. Nevertheless, all CIS nations are undergoing a significant transformation in pension and social insurance systems, identifying optimal financial models for pensions and social benefits that adhere to international norms and standards. Kazakhstan has made notable progress in this direction among the other Commonwealth nations. The new Social Code, which entered into force on July 1, 2023, is intended to establish an equitable society based on the fundamental principle of inclusion, where all citizens are afforded “equal opportunities and have equal access to social services.” The document contains numerous provisions that aim to improve work environments, attract individuals with disabilities, establish job quotas, collaborate with non-governmental organizations, and increase the amount of social benefits by 23%.

Kazakhstan’s experience is instructive and may serve as a model for other CIS nations to improve their social programmes. However, given the internal processes occurring within the CIS states, it is imperative to develop a comprehensive regional strategy as well as individual country strategies that will take into account the specifics and exigencies of each country. Due to the plethora of economic, political, and social factors that have an impact on the CIS nations, it is imperative to explore novel approaches to attain an optimal balance between the economic and social components of society’s advancement, thereby preventing any potential conflict between economic priorities and the principles of social justice.

Limitations

Models constructed with a variety of measures and variables have the potential to yield divergent and occasionally diametrically opposed outcomes. This can be attributed to disparities in data collection systems across various nations, as well as disparities in the translation of national monetary measures into a comparable currency, thereby complicating cross-country comparisons. Furthermore, the CIS Statcommittee publishes wage statistics for individuals who are employed in large and medium-sized enterprises, omitting self-employed individuals who are not included in the official registers.

The author found that the MPI, which was used to determine the relationship between income inequality and poverty, was not appropriate for the CIS nations. For some countries, low poverty results were attributed to methodological limitations, for example, missing indicators on nutrition (Uzbekistan), child death (Kazakhstan), cooking fuel (Turkmenistan), and so forth. In this regard, the author’s subsequent research will necessitate a more meticulous selection of poverty metrics.

Additionally, the CIS countries employ diverse methodologies for determining poverty, which posed an obstacle for the empirical data collection. For instance, in Russia, the poverty threshold is determined by monetary income, while in Belarus, it is consumption expenditure. Such an absolute approach, applied to income evaluation, does not permit the assessment of individuals who are on the brink of poverty and those who can be classified as middle-income. Moreover, it is important to acknowledge that governments frequently conceal official data (such as in Belarus), furnish outdated data (such as in Moldova, which was last updated in 2012), or provide “convenient” figures based on political objectives. These aspects suggest the need for further in-depth research using more sophisticated methodological approaches applied to various levels and aspects of the topic.

Future research

The investigation of “income inequality–poverty” dependence will require a more meticulous selection of poverty metrics, as the MPI was found to be inappropriate for the CIS nations. Future research should employ panel quantile regression (Koenker, Reference Koenker2004), setting macroeconomic conditions of IIMs (e.g., urbanization, education, and employment) as control variables to provide a more precise representation of the correlation between income inequality and poverty.

Data availability statement

Data will be available on request.

Funding statement

The author did not receive support from any organization for the submitted work.

Disclosure statement

The author declares none.

Compliance with ethical standards

Ethical approval was not sought for the present study.

Informed consent

This article does not contain any studies with human participants performed by the author.

Appendix

Table A1. The mean of variables

Figure A1. The Gini index values for the EU and CIS countries, 2022.

Source: composed by the author based on the World Population Review official website.

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Figure 0

Figure 1. The sources of household income, 2010 and 2022.Note. Upper graph: 2010 data; lower graph: 2022 data.Source: Composed by the author based on the Interstate Statistical Committee of the Commonwealth of Independent States data.

Figure 1

Table 1. The average monthly nominal wages, US dollars

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Table 2. The monthly growth in average wages compared to the previous year, 2022

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Figure 2. The percentage of the workforce employed, 2022.Source: Composed by the author based on the CIS Statcommittee (2023c) data.

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Figure 3. The Gini Index values.Source: Composed by the author based on the World Population Review official website.

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Figure 4. The household income ranking by quintile groups, 2022.Source: Composed by the author based on the CIS Statcommittee (2023a, 2023b) data.

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Table 3. The quintile and decile ratios, 2010 and 2022

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Table 4. The proportion of the impoverished populace as a percentage of the overall population, 2019

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Table 5. The gender gap in women’s wages as a percentage of men’s average monthly wages

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Table 6. The ratio of minimum pension to average monthly wage, 2022

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Table 7. The proportion of the populace with an income below the minimum subsistence level

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Table 8. The percentage of the populace living on less than $5.50 a day

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Table 9. The percentage of the populace living below the extreme poverty line

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Table 10. The MPI values, 2022

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Table 11. The GOCI ranking, 2023

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Table 12. Normal distribution tests

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Figure 5. The Pearson correlation scatter plots for the dependence of the IIMs and the Multidimensional Poverty Index.Source: Composed by the author.

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Figure 6. The Pearson correlation scatter plots for the dependence of the IIMs and the crime rate.Source: Composed by the author.

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Table 13. The regression statistics for the dependence of the IIMs and the Multidimensional Poverty Index

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Table 14. The regression statistics for the dependence of the IIMs and the crime rate

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Table A1. The mean of variables

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Figure A1. The Gini index values for the EU and CIS countries, 2022.Source: composed by the author based on the World Population Review official website.