research objectives about poverty

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• Published: 05 September 2024

Navigating poverty in developing nations: unraveling the impact of political dynamics on sustainable well-being

• Yuda Kou 1 &
• Iftikhar Yasin   ORCID: orcid.org/0000-0001-6214-7989 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1143 ( 2024 ) Cite this article

Metrics details

Political instability, dismal governance, and corruption are among the factors that currently distress poverty. The primary objective of this research was to determine how political factors distress poverty in developing nations, which has perhaps not been investigated yet. The main objective was also to observe if the effect of political factors on poverty is a dilemma or a reality. Poverty (dependent variable) has been divided into two segments: income poverty index and Human Health Poverty Index; however, political factors (independent variables) studied were corruption, democracy, governance, and political globalization. Twenty-six years of data were taken from 1997 to 2022 of twenty-four developing nations. The poverty and institutional quality indices were constructed through Principal Component Analysis (PCA). The Fixed Effect, System GMM, and 2SLS approaches were used to determine the dynamic impact on poverty. Furthermore, this study incorporated fixed effects with Driscoll-Kraay standard errors to address cross-sectional dependence. The findings indicated that, although there was a significant connection between governance and income and human poverty. Democracy also showed a negative and significant relationship in the income poverty model but insignificant in the human poverty model. Political globalization showed negative and significant associations with poverty models (income and human poverty). Conversely, corruption showed a significant positive relationship in poverty models, i.e., income and human poverty.

Introduction

Poverty refers to a state or condition characterized by the absence of adequate financial resources and critical provisions necessary to meet the minimal requirements for a satisfactory standard of living, both at an individual and community level (Bununu, 2020 ; Wu et al., 2024 ). Poverty is a state characterized by a significant deficiency in meeting the fundamental needs of humans. It is currently an extreme challenge for the world and the foremost developmental objective in attaining equity in income distribution, leading to poverty reduction (Ogbeide and Agu, 2015 ). Moreover, It is the most critical problem hindering the progression of humanity as it happens to be a considerable phenomenon (Abbas et al., 2018 ). Poverty has additional harmful impacts on developing nations compared to developed nations. Notably, global developmental agendas such as the MDGs and SDGs, complete list of acronyms is given in Table S1 , Supplementary Information, have taken poverty, a developmental concern, into general consideration. MDGs focused on decreasing worldwide poverty severity between 1990 and 2015, whereas SDGs concentrate on ending poverty until 2030.

Poverty in developing countries is a significant issue, as the abstract indicates. The poorest regions on the planet are often represented by nations with many people living in extreme poverty (Hotez and Thompson, 2009 ). Figure 1 illustrates that in Honduras, 14.2% of the population resides in extreme poverty, with a poverty gap of 5.1%. In contrast, Georgia and Brazil exhibit lower rates, with 5.6% and 5.3% of their populations living in extreme poverty, respectively. Their poverty gaps also fall within the highest quantile, but they are nonetheless lower than those of Honduras. Here, Fig. 2 indicates that Pakistan faced infant mortality of 57.4 per 1000 live births, with child mortality being in the top quantile among the selected nations. Whereas the Dominican Republic exhibits the second-highest infant mortality rate at 28.6 per 1000 live births, positioning its child mortality under five within the uppermost quantile.

Poverty head count by poverty gap of selected Countries.

Infant mortality rate by mortality rate under 5 of selected Countries.

Moreover, different countries, provincial organizations, and Non-governmental Organizations have designed their agendas to decrease poverty or bring it to an end to defeat the problem according to their capability. However, the aspiration to eradicate poverty persists in numerous emerging countries (Deyshappria, 2018 ). Approximately 719 million people are predicted to live below the poverty level (World Band, 2023 ). Though it is also a familiar reality that the global poverty level has considerably decreased in the previous twenty years, much still needs to be done.

The topic of poverty alleviation is notable for developing nations’ development economists and economic plan builders. Efficiently, poverty begins the hypothesis that families’ welfare is essentially and absolutely tied to their capability to utilize commodities and services. More consumption results in better well-being (Wu et al., 2024 ). A household is poor if its spending capacity is low and it meets a few conditions (Satti et al., 2015 ). There are two ways to depict poverty. The first step is to quickly affect people with low incomes. This will break the poverty cycle. Another way to reduce poverty is to create a policy that boosts economic growth (Chani et al., 2011 ).

This study investigated factors distressing poverty in chosen developing nations, like democracy, corruption, governance, and political globalization. Democracies, as opposed to non-democracies, enhance the welfare of the deprived people. These arguments align with prominent political economy models that posit democracies as generators of numerous public goods and proponents of more significant income redistribution than non-democratic systems (Ross, 2006 ). It is supported by the redistribution theory, which argues that policies aiming at lowering income inequality via social programs, taxes, and wealth redistribution may be brought about by democratic institutions (Adserà et al., 2003 ; Besley and Persson, 2011 ).

Corruption disturbs the lives of poor people in several ways, such as distracting government expenses from socially priceless goods like education, distracting communal assets like health clinics through infrastructure investments, and increasing government expenditures on capital-abundant investments, which offer many opportunities for bribes like defense agreements (Ajisafe, 2016 ). Gupta et al. ( 2002 ) also claimed that corruption benefits the elite while depriving the rest. Political globalization has many complex effects on poverty in developing nations. Political globalization can boost trade, investment, jobs, and economic growth. However, political globalization can promote inequality and exploitation, worsening poverty. Globalization increases absolute poverty in the short- and long-term. According to Age’nor and Pierre-Richard ( 2004 ), globalization may immediately increase absolute poverty due to many causes. These factors include transaction costs, insufficient human capital, and inflation. It may also reduce poverty over time. Kawachi and Wamala ( 2007 ) indicated that openness may accelerate and expand transferable diseases like HIV Footnote 1 and H5N1, which can exacerbate poverty by reducing labor productivity and supply. This negative outcome can hurt the poor more than the rich, showing that openness may promote growth without reducing poverty. It may also affect societal norms and habits like eating and smoking (Yach et al., 2007 ), impacting health and efficiency.

The preceding discussion shows that hardly any updated study has probed the political factors of income and human health poverty in developing nations and performed a comparative analysis. Clarifying this relationship is the goal of this study. Our main goal is to quantify how corruption, governance, democracy, and political globalization directly affect income and human health poverty in developing nations. This novel approach examines how often these factors cause poverty, not just correlations. To better understand poverty’s multidimensionality, we create income and human health poverty indices. For robustness, we use advanced econometric methods like two-stage least squares (2SLS), system generalized method of moments (SGMM), and Driscoll-Kraay (DK) standard errors to address endogeneity and cross-sectional dependence (CD). This study examines political factors causing poverty in selected developing nations to fill the gap.

Several aspects distinguish this study from existing research. Firstly, we move beyond establishing mere correlations and quantify the frequency with which political factors distress poverty. This novel approach provides valuable insights into the circumstances under which political factors most significantly impact poverty. Secondly, by taking into account both the economic and the health aspects of poverty, our dual poverty indices enable a more complex understanding of the multiple nature of poverty. Finally, using advanced econometric techniques strengthens the reliability and generalizability of our findings.

Furthermore, the period from 1997 to 2022, chosen for this study, aligns with several significant global economic events and trends that have impacted developing nations. This era witnessed the aftermath of the Asian financial crisis (1997–1998), the global financial crisis (2007–2008), and the rise of globalization, characterized by increased international trade and investment. These events have had profound effects on economic stability, governance structures, and poverty levels worldwide. Understanding the political and economic dynamics during this period provides essential context for analyzing how political factors distress poverty in developing nations.

Prior studies have investigated the possible correlation between political issues and poverty, although there remains a dearth of comprehension regarding the frequency and magnitude with which these elements directly contribute to poverty in developing countries. The objective of this study is to fill this gap by examining the following hypothesis:

H1: Political factors (democracy, governance, corruption, and political globalization) have a significant impact on poverty (income and human health) in developing nations.

This study aims to contribute valuable insights to the ongoing fight against poverty by investigating the prevalence of political factors distressing poverty. Our novel approach, detailed methodology, and robust results offer a deeper understanding of these complex dynamics, ultimately informing the development of more effective poverty reduction strategies for developing nations.

The next part illustrates the literature review. Theoretical framework and methodology have been provided in the third section. The results and discussions are briefly given in the fourth part. The last and final section brings about the conclusion and policy implications.

Literature review

Poverty, a complex issue with profound economic and social impacts, weighs heavily on developing nations. Although economic factors undeniably play a significant role, the influence of political forces on poverty outcomes is profound. This review thoroughly examines existing academic literature, analyzing the various ways in which governance, democracy, corruption, and political globalization interact with poverty in developing contexts.

Wu et al.’s ( 2024 ) study focused on the social determinants of poverty in a few developing nations that have received little prior research. According to the findings, there is a substantial and positive correlation between poverty and the age-dependence ratio, whereas there is a significant and negative correlation between poverty and social globalization. The income poverty model is unaffected by health and education, yet these factors have a negative and substantial association with human poverty. Similarly, population expansion significantly and favorably affected human poverty but had little effect on income poverty. While Wu et al. ( 2024 ) investigate the influence of social determinants, it is crucial to explore how political dynamics might interact with these factors to exacerbate or alleviate poverty.

In a study by Fambeu and Yomi ( 2023 ), an examination was carried out on 40 economies in Sub-Saharan Africa from 1999 to 2018. This study’s findings indicated no clear correlation between the presence of democracy and the reduction of poverty in these particular nations. In their study, Zang et al. ( 2023 ) analyzed data from 1992 to 2017, encompassing 117 economies. Their research examined the relationship between political globalization and national poverty, revealing a significant positive correlation. The exacerbation of national poverty resulting from political corruption was mitigated by implementing primary education and utilizing the Gini index as an intervening mechanism. However, the timeframe of these studies might not capture the most recent developments.

According to Salahuddin et al. ( 2020 ), their research indicates that globalization has reduced poverty and increased corruption in South Africa from 1991 to 2016. The research study focused on South Africa, a country classified as an upper-middle-income country, where poverty is prevalent in developing economies. Additionally, its time frame may not encompass the latest trends. Using PCSE and the SGMM model, Dossou et al. ( 2023 ) confirmed that governance superiority leads to poverty decline in 15 Latin American countries from 2003 to 2015. While Dossou et al. ( 2023 ) focus on Latin America, their time frame might not capture the recent trends. By applying data from five waves of China Family Panel Studies, Han et al. ( 2022 ) found that the anti-corruption campaign in China raises income and declines the poverty occurrence of the (possible) poor group. This study focused on China, so the results might not be generalized.

Corruption has a significant impact on poverty in developing countries. Poor people are more likely to be victims of corrupt behavior by government officials, as they heavily rely on government services (Olken and Pande, 2012 ). Corruption in developing nations represents regressive taxation that disproportionately affects low-income people and hampers development (Nwabuzor, 2005 ). Studies have shown that corruption is prevalent in many developing nations and forms a prominent feature of bureaucratic life (Justesen and Bjørnskov, 2014 ).

Ajisafe ( 2016 ) suggested that corruption affected poverty in the short run, but not in the long run, in Nigeria from 1986 to 2014. This study focused on Nigeria only, which might not be generalized to all developing economies. Aguilar ( 2017 ) selected poor democracies, rich democracies, poor non-democratic nations, and prosperous nations. The results suggested that in a democracy, circumstances, and citizens might affect the declining level of poverty. Cepparulo et al. ( 2017 ) examined whether financial and institutional development interrelates in poverty impacts. The results showed that financial development considerably and positively affected poverty alleviation. Although researchers used institutions, they did not categorize which types of institutions. Moreover, the timeframe of these studies might not capture the recent trends.

Yunan and Andini ( 2018 ) found that economic growth affected corruption considerably, and it also happened between poverty and corruption in ASEAN economies from 2002 to 2015. They used a small period; only the Granger causality test and random effect model were insufficient. Khan and Majeed ( 2018 ) depicted that economic and social globalization considerably alleviates overall poverty, whereas political globalization does not considerably alleviate poverty in 113 developing economies from 1980 to 2014. Besides globalization, other political factors affecting poverty were ignored. Aloui ( 2019 ) observed the governance impact on poverty in Sub-Saharan African nations and found that governance indicators positively and negatively influenced poverty alleviation from 1996 to 2016.

In their study, Gupta et al. ( 2002 ) examined the impact of corruption on poverty and income disparity. Their findings revealed that a one-standard-deviation rise in corruption was associated with an eleven-point increase in income inequality. Interestingly, individuals experiencing poverty observed a five-percentage point annual improvement in income growth.

In their study, N’Zue and N’Guessan ( 2005 ) examined the relationship between corruption, poverty, and economic growth. Their research revealed a complex interplay between these variables in 18 African economies from 1996 to 2001. Specifically, the authors identified multiple dimensions of this relationship. Firstly, they observed that the condition of economic growth can lead to both corruption and inequality. Secondly, they found that inequality serves as a causal factor for corruption. Thirdly, the authors noted that corruption and poverty jointly impact economic growth. Additionally, they discovered that poverty and growth simultaneously influence corruption. Lastly, N’Zue and N’Guessan ( 2005 ) found that inequality and growth affect corruption. They have undoubtedly used panel data from 18 African countries, but the time was minimal, only five years.

Governance, political globalization, and corruption significantly impact poverty in developing countries. Good governance, including effective government, control of corruption, and a stable political system, can promote economic growth, minimize income distribution conflicts, and reduce poverty (Hassan et al., 2020 ). On the other hand, poor governance, characterized by corruption, ineffective governments, and political instability, not only hampers income levels through market inefficiencies but also increases poverty incidence through income inequality (Tebaldi and Mohan, 2010 ). Additionally, the study suggests that economic liberalization in countries with high levels of corruption can lead to faster economic growth but does not improve distributive justice, resulting in increased poverty and unchanged inequality levels (Hanlon, 2012 ). Furthermore, the relationship between good governance and poverty is beneficial for middle-income countries but not low-income countries, indicating that governance reforms alone may not be sufficient to reduce poverty in all countries (Choi and Woo, 2011 ).

A review study undertaken by Resnick and Birner ( 2006 ) found that indicators of governance that describe a healthy decision-making environment for investment and policy achievement. political stability and the rule of law, are linked by growth; however, they give diverse consequences concerning poverty alleviation. Ross ( 2006 ) confirmed that democracy had a slight or no impact on poverty variables.

According to Hasan et al. ( 2006 ), the measurement of good governance, which includes factors such as a solid commitment to the rule of law, significantly impacts poverty reduction primarily due to its influence on economic growth. Tebaldi and Mohan ( 2010 ) examined the detrimental effects of corruption, poor governance, and political instability on income levels. The incorporated timeframes in the above studies might not capture the recent trends.

In a study by Nwankwo ( 2014 ), the author examined the impact of corruption on Nigeria’s economic growth. A significant correlation between corruption and economic growth over an extended period has been identified. This study is limited to Nigeria, which might not be generalized to all developing economies. According to Dzhumashev’s ( 2014 ) recommendation, the impact of corruption on public expenditures is influenced by the correlation between corruption and governance, which in turn influences economic growth. According to the findings of Goryakin et al. ( 2015 ), there is a significant association between globalization and the increasing prevalence of overweight among women. Surprisingly, the phenomenon of social and political globalization gives rise to the impact of economic considerations. Although the study used 56 low- and middle-income countries, the time was limited.

While existing literature has shed light on the complex relationship between political factors and poverty in developing nations, crucial gaps remain. Many of the studies’ timeframes are old enough, so they might not capture the recent trends in poverty. Notably, only a few studies have quantified the frequency with which specific political factors directly affect income and human health poverty across various contexts in developing economies. The delicate interplay between these factors and poverty dimensions, like human health, requires further exploration. Hence, this study aims to bridge these gaps by utilizing advanced econometric techniques to systematically investigate the prevalence of political factors affecting various facets of poverty in selected developing nations. By filling these critical knowledge gaps, our research aspires to inform the development of more targeted and effective poverty reduction strategies for those nations most burdened by this persistent challenge.

Theoretical framework and methodology

This research study examines the political factors influencing poverty in twenty-four developing countries. According to the inclusive institutions theory given by Daron Acemoglu and Robinson ( 2013 ), broad access to economic opportunities and property rights are protected by inclusive political and economic institutions, which are associated with increased economic success and decreased poverty. The redistribution theory highlights that policies aiming at lowering income inequality via social programs, taxes, and wealth redistribution may be brought about by democratic institutions. A more equitable allocation of resources is thought to reduce poverty (Adserà et al., 2003 ; Besley and Persson, 2011 ). Gupta et al. ( 2002 ) argued that corruption causes income inequalities as the elite benefit from corruption while the rest of the population stays in poverty. At the same time, greater income inequality is linked to greater poverty levels. Hence, following Acemoglu and Robinson ( 2013 ), Gupta et al. ( 2002 ), and Hanmer et al. ( 2003 ), we developed the following model:

In the above equation, Poverty, CORRUP, and DEMOC capture poverty, democracy, and corruption. Panel data covering the twenty-six-year period from 1997 to 2022 was employed. Twenty-four developing nations from lower-, middle-, and upper-middle-income countries were chosen for this study. The nations were selected based on data availability for poverty variables. The list of nations is detailed in Table S1 , Supplementary Information. The empirical dataset utilized in this research comprises key indicators reflecting poverty metrics, including poverty headcount and poverty gap, alongside vital health indices such as infant and child mortality rates, sourced from the World Development Indicators database, a repository maintained by the World Bank.

Additionally, metrics indicative of political dimensions encompassing government effectiveness, control of corruption, voice and accountability, absence of violence, political stability, regulatory quality, and rule of law were acquired from the Worldwide Governance Indicators dataset, also sourced from the World Bank. The data about democracy metrics were obtained from the Freedom House Data, whereas indices such as the Corruption Perception Index and Political Globalization were sourced from the Transparency International and KOF Globalization datasets. These diverse and meticulously acquired datasets collectively underpin the empirical foundation for this study’s comprehensive analysis of the interplay between poverty dynamics and multifaceted political determinants. Data was investigated by applying E-views 13 along with STATA 17.

The proposed model of the impact of political factors on poverty, presented in the above equation, has been extended as follows:

The subscript “i” denotes countries, which are 1–24, and “t” indicates the period. The continuous and some poverty level estimates are shown by \({\alpha }_{0}\) and \({\beta }_{0}\) . IPI = Income Poverty Index generated through the combination of poverty headcount and poverty gap. IPIit-1 = Income Poverty Index lag, HHPI = Human Health Poverty Index generated through the combination of child and infant mortality rates. HHPIit-1 = Human Health Poverty Index lag, LNCORRUP = Log of Corruption, LNDEMOC = Log of Democracy, GOV = Governance as measured by developing an index of government effectiveness, control of corruption, voice, and accountability, absence of violence, political stability, regulatory quality and rule of law, LNPGLOB = Log of Political Globalization.

The present segment includes the description of the variable incorporated in this study. The variables are chosen because of their comparative significance on a theoretical and empirical basis. The definitions of these selected variables are given below in Table 1 :

Limitations of conventional econometric methods, like fixed and random effects, can lead to unreliable results due to heteroskedasticity, endogeneity, and serial correlation. This study uses the 2SLS methodology to address these concerns. Developed by Cumby et al. ( 1983 ), 2SLS offers an advantage over Ordinary econometric methods by relaxing the assumption of no correlation between regressors and the error term. This assumption violation can lead to biased estimates and undermine the homogeneity hypothesis (Pesaran and Yamagata, 2008 ). To address the endogeneity issue, 2SLS replaces potentially endogenous regressors with instrumental variables, mitigating the bias and providing more reliable estimates. Recognizing these advantages, we opt for 2SLS as our analytical tool, offering a robust alternative to conventional methods.

While the 2SLS method represents a significant advancement in econometrics, it has limitations compared to the Generalized Method of Moments (GMM) for panel analysis (Maydeu-Olivares, Shi, & Rosseel, 2019 ). Specifically, Arellano and Bond’s ( 1991 ) GMM estimation addresses issues like endogeneity, serial correlation, and heteroskedasticity more effectively than 2SLS. This is because GMM uses lagged instrumental variables, mitigating potential endogeneity concerns, and allows for flexible assumptions regarding error structures, accommodating serial correlation and heteroskedasticity. Given these advantages, this study utilizes the GMM estimator for its robustness in handling the challenges mentioned above in panel data analysis. Hence, the model recommended in this study is the GMM Footnote 2 proposed by Arellano and Bond ( 1991 ). The selection of GMM over alternative models was based on several factors. The reasons mentioned above encompass the following. According to Roodman ( 2006 ), the use of the GMM is advantageous in cases when the number of years (T) is smaller than the number of countries (N). In the present study, the number of years (T) is 22, which is indeed less than the number of countries (N), which is 24. (ii) The technique of constructing instrumental variables addresses potential endogeneity concerns in the regressors (Omri and Chaibi, 2014 ). (iii) This technique does not eliminate the presence of cross-country idiosyncrasies. (iv) SGMM captures the cross-country heterogeneity (Gregoriou and Ghosh, 2009 ). Standard estimate approaches, such as most minor square regressions, may be susceptible to dynamic panel bias, facilitating the elimination of country-specific heterogeneities. Finally, the inclusion of a lagged independent variable (namely, one lag of income and the HHPI) as a regressor variable in the model enhances the proficiency of the GMM estimator, enabling it to provide unbiased and trustworthy estimation.

Cross-sectional dependency might provide estimations that are not reliable. We thus used the fixed effect (FE) models with DK standard errors for our regression analysis to allay this concern. Driscoll and Kraay ( 1998 ) developed the DK technique, which was used to address problems with serial correlation, cross-sectional variability, and panel data reliance. This technique works well with missing values and can also be used for balanced and imbalanced datasets. Additionally, it provides robust standard errors and has proven accurate and consistent in handling CD difficulties (Baloch et al., 2019 ).

Before analyzing the panel data, it is crucial to assess the presence of CD. CD arises when there is a reciprocal influence between two or more cross-sectional units (Liu et al., 2021 ; Yasin et al., 2023 ; Yasin et al., 2024 ). This phenomenon emerges due to factors like deep financial and economic integration and exposure to global trade and commerce, all of which render these units susceptible to the effects of global economic shocks. Consequently, these interdependencies between nations can affect panel data from cross-sectional countries. Following the assessment of CD, the homogeneity of slope coefficients is evaluated using the Pesaran and Yamagata ( 2008 ) slope heterogeneity test. This evaluation is necessary because differences in the economic, social, and demographic contexts of 24 developing nations can potentially impact the reliability of panel estimators. To account for such dependencies and potential heterogeneity across variables, second-generation unit root tests (CIPS) are employed.

This study investigates the relationship between income poverty, human health poverty, corruption, globalization, democracy, and governance in 24 developing countries. Before estimating long-term parameters, establishing the co-integration of the underlying variables is crucial. Therefore, this study employs the Pedroni ( 2004 ) co-integration analysis to examine the presence of co-integration among the variables.

Results and discussion

The descriptive statistics of the sample data utilized in the research for political factors have been presented in Table 2 .

Income poverty index

This index is created through two proxy variables, such as poverty gap and headcount, as the highest correlation has been found in both variables. The Principal Component Analysis (PCA) results for constructing the comprehensive index for chosen developing nations are presented in Table 3 . As shown in Fig. 3 , which follows the scree diagram criterion and Kaiser ( 1974 ), only one component is kept that is allocated to hold specifically those factors having eigenvalues above 1. Table 3 brings out just one component having an eigenvalue of 1.90281 above 1. Overall, Kaiser–Meyer–Olkin (KMO) statistics are 0.620, and Kaiser ( 1974 ) argues that 0.5 or above 0.5 is good enough to describe the sample satisfactorily enough to take forward the investigation.

Scree plot of Eigenvalues after PCA for Income Poverty Index.

Human Health Poverty Index

Infant and child mortality rates are vital in determining human health deprivation (Hanmer et al., 2003 ). Hence, this study incorporated the Infant Mortality Rate and Child Mortality Rate to capture the health poverty of humans. This index is created through two proxy variables, including child and infant mortality rates, as the highest correlation has been found in both variables. Table 4 contains the PCA results from which the comprehensive index for chosen developing nations will be derived. According to the scree plot criterion presented in Fig. 4 , one component has been retained. Furthermore, only 1 component has eigenvalues above 1, which is 1.99696. Overall, the statistics for KMO are 0.670, and Kaiser ( 1974 ) pursues 0.5 or greater than 0.5, which is sufficient to confirm sample adequacy for the investigation.

Scree plot of eigenvalues after PCA for Human Poverty Index.

For governance (independent variable of political factors), the institutional quality index is constructed by combining the data of six variables, i.e., government effectiveness, control of corruption, voice and accountability, absence of violence, political stability, regulatory quality, and the rule of law (Apergis and Ozturk, 2015 ; Yasin et al., 2019 ).

Table 5 shows the findings of the PCA analysis used to create the comprehensive index for a subset of developing nations. The table below shows that only one element comprising eigenvalue 3.73263 is above 1. The scree plot, exhibited in Fig. 5 , also indicates the retention of only one component. Overall, the statistics for KMO are 0.7620, which shows that the data is large enough to estimate.

Scree plot of eigenvalues for governance (institutional quality index) after PCA.

Results and discussion in the income poverty model

This research investigates the impact of corruption, governance (specifically Political Institutional Quality), and democracy on income and human health poverty in 24 developing countries from 1997 to 2022. To assess the presence of cross-sectional dependency within the series, we initially employed the CD test proposed by Pesaran ( 2021 ). This is necessary as the first generation’s conventional panel unit root methods may yield unreliable results when confronted with CD, particularly when its magnitude is low. This study employed Pesaran CD tests. Table 6 below demonstrates cross-dependence in this panel. Khan ( 2019 ) also investigated similar results. We use the technique developed by Pesaran and Yamagata ( 2008 ) to verify the slope homogeneity. As observed by Table 7 , the results validate the presence of a heterogeneous slope in model 1 and reject the null hypothesis of a homogeneous slope. The covariances of Model 1 (IPI) are presented in Table S3 , Supplementary Information. The findings suggest a positive association between corruption and governance with the IPI, while a negative association is observed between democracy and political globalization with the index mentioned above.

The utilization of the second-generation CIPS panel unit root test, as reported by Pesaran ( 2007 ), has been motivated by the existence of CD inside the panel dataset. Table 8 presents the findings, indicating that all variables exhibit stationarity at their levels and after being differenced once.

Table 9 contains Pedroni’s panel co-integration test results. The outcome rejects the null hypothesis of no co-integration and asserts that the series has a long-run association. Long-run associations may be recommended to subsist among the variables, meaning they travel collectively to a steady stability phase. The results are the same as those of Dursun and Ogunleye ( 2016 ).

This study employs multiple estimation techniques to explore the identified associations between income poverty and its potential determinants. These techniques include FE, supported by the Hausman and Heterogeneity tests, SGMM, DK, and 2SLS estimators. The results in Table 10 reveal a positive and statistically significant association between past and present poverty levels in our sample of developing countries. The lagged poverty coefficient estimated using the SGMM method is 0.5521, while the 2SLS estimate is 1.1248. These findings indicate that higher poverty levels in the preceding year contributed to increased poverty in the current year. This means that countries with higher poverty rates in the past are more likely to have higher poverty rates in the present, creating a persistent cycle of poverty, which might be due to the poverty trap. Individuals and communities trapped in poverty face various disadvantages, like limited access to education, healthcare, and productive resources. These disadvantages perpetuate poverty by hindering people’s ability to rise above their circumstances and find better opportunities (Banerjee and Duflo, 2011 ). Our analysis further reveals a statistically significant and positive association between corruption and income poverty across several estimation methods, including FE, DK, SGMM, and 2SLS. These findings align with the previous work of Ajisafe ( 2016 ), who also identified a positive relationship between these variables.

Furthermore, the assertion made by Gupta et al. ( 2002 ) is reinforced by this finding, suggesting that corruption contributes to the perpetuation of income inequalities. This is because the privileged few reap the benefits of corrupt practices while most of the population remains impoverished. Corruption diverts public resources for poverty alleviation programs and social services towards private gain. This misallocation deprives the poor of vital resources like healthcare, education, and infrastructure, hindering their ability to escape poverty (Rose-Ackerman, 1997 ). Furthermore, widespread corruption can erode trust in government institutions and weaken social capital. This lack of trust and cooperation hinders collective action and community development, making it difficult for low-income people to advocate for their rights and improve their circumstances collectively (Putnam et al., 1992 ).

The findings further reveal a robust and statistically significant negative association between income poverty and democracy across various estimation methods, including FE, DK, SGMM, and 2SLS. These results support the notion put forth by Ross ( 2006 ) that democracies are more effective in alleviating poverty compared to non-democracies within developing countries. Democratic governments are more likely to prioritize investments in public goods and services that benefit everyone, including the poor, such as healthcare, education, and infrastructure. This can improve human capital and productivity and ultimately reduce poverty Acemoglu and Robinson ( 2013 ). This finding is also supported by the redistribution theory, which argues that policies aiming at lowering income inequality via social programs, taxes, and wealth redistribution may be brought about by democratic institutions (Adserà et al., 2003 ; Besley and Persson, 2011 ).

The estimated coefficient for the institutional quality index, reflecting governance quality in developing countries, exhibits mixed statistical significance across various estimation methods. Despite remaining positive across all methods (FE, DK, and SGMM), its significance varies from less significant in SGMM and FE to highly significant in DK and 2SLS. This finding suggests that while governance may positively impact poverty reduction, the current level of institutional quality in developing countries may not be robust enough to exert a statistically significant effect consistently. The same findings are suggested by Karim et al. ( 2013 ). Furthermore, Jindra and Vaz ( 2019 ) argued that the beneficial effect of good governance on poverty reduction is more pronounced in middle-income countries than in low-income countries.

Interestingly, the study by Ochi et al. ( 2023 ) explores a similar question but focuses on South Asian and Sub-Saharan African countries. Their findings suggest a non-linear relationship, where governance quality starts to decrease poverty only above a certain threshold. Furthermore, these results align with Perera and Lee ( 2013 ), who found that institutional quality, such as bureaucratic quality, increases poverty in developing countries. These differing results show how complex governance and poverty associations are. While our findings suggest a potential negative association in some contexts, the referenced study and other research emphasize the potential benefits of good governance for poverty reduction. Further research considering regional variations, non-linear effects, and different poverty definitions is necessary to fully understand this intricate dynamic.

Political globalization is substantial (1%) and has negative coefficients for both FE, DK, and SGMM. Due to political globalization, poverty is significantly declining in these developing nations. Bergh and Nilsson ( 2014 ) and Salahuddin et al. ( 2020 ) also provide similar suggestions on these results. The GMM estimators are subject to efficiency and validity tests, namely the autoregressive coefficient (AR) (2) test for second-order autocorrelation and the Sargan test for over-identifying restrictions. The AR value in Table 10 displayed above is 0.196, exceeding the threshold of 0.05 and lacking statistical significance. The observed result invalidates the presence of second-order autocorrelation in the model. The Sargen test yielded a value of 0.823 in the case of SGMM and 0.6851 in the case of 2SLS, which was deemed statistically insignificant since it was above the threshold of 0.10. This suggests that the instruments employed in the SGMM and 2SLS estimations are valid and that the overidentifying limitations are not violated.

Results and poverty in the model of human health poverty model

Table 6 indicates the cross-dependence occurrence in this panel. The findings stated are identical to Yasin et al. ( 2019 ). Table 11 illustrates how the data negate the null hypothesis of a homogeneous slope and supports the existence of a heterogeneous slope in model 2. The covariances related to Model 2 (HHPI) are displayed in Table S4 , Supplementary Information. The results indicate a positive correlation between corruption and governance with the HHPI, while an inverse correlation is noted between democracy and political globalization with the mentioned index.

The unit root tests are presented in Table 8 , employing the level and the first difference approaches. Based on the results obtained from the CIPS test, it can be observed that all variables exhibit stationarity when differenced once, but only a limited number of variables demonstrate stationarity at the original level.

Pedroni’s panel co-integration test in Table 12 describes the results. The test statistics indicate the incident of long-run association amid the series. It may be recommended that associations subsist among the variables in the long run. This means they travel collectively toward a steady equilibrium phase, and Dursun and Ogunleye ( 2016 ) have the same findings.

Table 13 displays the estimated coefficients for the human health poverty variable. Across our panel of developing countries, the lagged human health poverty coefficient exhibits positive and highly significant values (at the 1% level) in both SGMM and 2SLS estimations. This finding indicates a substantial and statistically significant direct impact of the previous year’s poverty levels on current poverty, highlighting the persistence of human health poverty. Human health poverty and corruption also have a positive and substantial association, confirming the findings of Ajisafe ( 2016 ). This outcome is also supported by Gupta et al. ( 2002 ) supposition that corruption causes income inequalities as the elite benefit from corruption while the rest of the population stays in poverty, deteriorating human health. The analysis reveals a negative association between human health poverty and democracy. This finding suggests that democratic institutions in developing nations might contribute to mitigating poverty levels.

The coefficient of governance (Political Institutional Quality Index), an index of six indicators of institutional quality, is highly significant and positive in the case of DK and 2SLS but comparatively less significant in the case of FE and SGMM, indicating that governance, instead of decreasing, significantly increases poverty. The main reason for this outcome is poor and adverse governance in developing countries. Another reason may be that the institutions distinguished through political instability create hindrances for growth instruments, and the capabilities of a nation are restricted. The same findings are suggested by Karim et al. ( 2013 ). Political globalization has a very significant and negative coefficient. This indicates that poverty is declining considerably in these developing nations due to political globalization. Bergh and Nilsson ( 2014 ) and Salahuddin et al. ( 2020 ) also provide recommendations on these results. This outcome might be because stronger international relations facilitate better policy coordination and the exchange of information and resources, which may support initiatives to reduce poverty (Dreher, 2006 ; Dollar and Kraay, 2004 ).

For the GMM estimators, the efficiency and validity tests are AR (2), a test for second-order autocorrelation, and a Sargan test for over-identifying limitation. The AR (2) value in Table 13 above is 0.386, more than 0.05 and not statistically significant. This outcome rejects the incidence of second-order autocorrelation in the model. The Sargan test statistic yields p-values of 0.419 and 0.6109 for the SGMM and 2SLS estimations, respectively. As both values exceed the conventional 0.10 significance level, these results offer no evidence of instrument invalidity or violation of overidentifying restrictions in the SGMM and 2SLS estimations.

Policy recommendations

Our research question focused on understanding how political factors contribute to poverty in developing nations. This study employed a panel data analysis for 24 developing countries from 1997 to 2022, investigating the impact of corruption, governance (political institutional quality), democracy, and political globalization on income and human health poverty.

Our study provides compelling evidence that political factors influence poverty levels in developing countries. The findings provide robust evidence for the significant influence of political factors on poverty levels. Corruption exhibits a positive and statistically significant association with income and human health poverty, highlighting its detrimental effects. Conversely, democracy presents a negative and significant association with poverty, suggesting its potential to alleviate poverty. The relationship between governance and poverty seems more complex, with varying degrees of relevance across various estimating techniques. Political globalization, on the other hand, demonstrates a strong negative association with both income and human health poverty, indicating its potential for fostering poverty reduction.

These findings offer valuable insights for policymakers addressing poverty in developing nations. The detrimental effects of corruption on poverty necessitate robust anti-corruption measures. Empowering anti-corruption agencies, increasing transparency in government spending, and strengthening legal frameworks to deter corruption are crucial steps.

The positive association between democracy and poverty reduction underscores the importance of fostering solid democratic institutions. This might involve supporting initiatives that promote free and fair elections, protect freedom of speech and assembly, and enhance citizen participation in governance.

The positive impact of political globalization suggests that international cooperation can play a vital role in tackling poverty. This includes fostering collaboration among developing countries to share best practices, promoting fair trade agreements, and encouraging international investments contributing to sustainable development.

Real-world examples further illustrate the effectiveness of these policy recommendations. Initiatives like Rwanda’s successful anti-corruption efforts and India’s focus on empowering local governments through democratic processes offer valuable insights. China’s focus on strengthening anti-corruption measures through targeted campaigns and institutional reforms offers a compelling example. This highlights the importance of a comprehensive approach to tackling corruption. Additionally, regional trade agreements like the African Continental Free Trade Area demonstrate the potential of international cooperation in promoting economic growth and poverty reduction.

Conclusions

The primary aim of this study was to assess the influence of political factors on poverty levels within a specific set of developing countries. The dependent variables utilized in this study were the income and HHPI, whereas explanatory variables were selected based on political issues. PCA was employed to create poverty indicators and an institutional quality index. Subsequently, CD tests were utilized to verify the presence of cross-dependency in the panel data. Following the confirmation of cross-sectional dependency, the analysis employed CIPS methodology to investigate stationary variables. The study employed the Pedroni co-integration test to analyze the enduring association between the variables. The SGMM methodology was employed to ascertain the dynamic impact on poverty since it is deemed more appropriate to employ GMM in cases where there are contemporaneous correlations among cross-sections.

Corruption has increased income and caused human poverty in these developing countries. It is a significant economic disaster that harms society’s growth and development and the economy in general. However, democracy showed that it had decreased income and human poverty but with statistically illustrated insignificant results. In democratic societies, openness and freedom should also be able to give weightage to the capacity and capability of each person, group or firm participating in economic activities.

Governance in these selected developing countries had verified constructive, considerable, and insignificant links between income and human poverty. Overall governance, if appropriate, casts a positive effect on all sectors of human life. The economy is one essential part of an individual’s life. The selection of the governance team is the most essential thing in running a country. The governance team ought to include a few members who have at least basic knowledge of the economy and what governance elements economic policies need in their implementation.

Political globalization showed a negative and highly significant relationship between poverty and both kinds of poverty i.e., poverty in terms of income and human health. Countries should work more on political globalization because they have a highly significant relationship with reducing income and human poverty. The focus should be on sharing information, results bearing effective government policies, and further establishing, if already existing, links for flows of goods, capital, and services, international trade, and investment, where both sides of countries can benefit mutually.

Furthermore, fighting corruption and strengthening democracy are crucial to reducing poverty in developing countries. This means empowering anti-corruption agencies, clearing government spending, and protecting free speech. Additionally, developing countries should collaborate with others to share ideas and create fair trade deals. These steps and investments in education and healthcare can help build a brighter future.

Data availability

The data analyzed in this study are available at https://databank.worldbank.org and https://kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html .

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Subjective Perceptions of Poverty and Objective Economic Conditions: Czechia and Slovakia a Quarter Century After the Dissolution of Czechoslovakia

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• Martina Mysíková   ORCID: orcid.org/0000-0002-6340-4753 1 ,
• Tomáš Želinský   ORCID: orcid.org/0000-0001-7198-0278 2 ,
• Thesia I. Garner 3 &
• Jiří Večerník   ORCID: orcid.org/0000-0002-0535-3118 1  

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Studies into the relation between subjective perceptions of individuals and objective economic conditions have usually resulted in ambiguous empirical findings. Whilst most studies perceive subjective welfare as being operationalized by indicators of happiness or life satisfaction, we narrow the approach to an economic domain of subjective well-being—perceptions of poverty. We argue that our approach better reflects the economic dimension, as the former may include numerous non-economic domains. We use a case study of two countries—Czechia and Slovakia—which underwent early economic transition as a common state in 1989–1992, then became independent states in 1993, after the dissolution of Czechoslovakia. We base our findings on three historical data sets covering a period from around the end of the communist era to the early years after the split, and recent data from EU Statistics on Income and Living Conditions (2005–2016). Despite initially small differences in subjective poverty levels in socialist Czechoslovakia, a considerably larger drop in economic performance during the transition period in Slovakia than in Czechia resulted in a sharp widening of the subjective poverty gap. The recent data suggests that, despite a high degree of actual economic convergence of Slovakia and Czechia, the gap in subjective perceptions of poverty is declining at a remarkably slower pace. We argue that relatively fast economic growth is not necessarily associated with a commensurate decline in subjective poverty perceptions. Our results thus support the Easterlin Paradox, although we substitute happiness by an economic dimension of subjective well-being.

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Source : SOCO 1995, EU-SILC 2005–2016. Own computations

Source : The period 1990–2009 is based on the CZSO (2012) data, the period 2010–2017 is based on Eurostat data (variable nama_10_pc)

Source : Social Stratification in Eastern Europe 1993. Own computations

Source : EEA W3–W6. Own computations

Source : Social Consequences of Transition data (1995). Own computations

Source : EU-SILC 2005–2016. Own computations

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See Van den Bosch ( 1993 ) for a brief review of the two model-based approaches and Deleeck et al.’s ( 1992 ) approach.

Household Questionnaire, Section 17: “Concerning your family's food consumption over the past 1 month, which of the following is true?” with the following responses: “1: It was less than adequate for your family’s needs; 2: It was just adequate for your family’s needs; 3: It was more than adequate for your family’s needs; 4: Not applicable”. “Adequate” is defined as: “no more nor less than what the respondent considers to be the minimum consumption needs of the family”.

Variable YINC-7990 with the following question: “Which of the following best describes [yours/yours and your spouse's/yours and your partner's] financial condition?” and the corresponding categories: “1—very comfortable and secure; 2—able to make ends meet without much difficulty; 3—occasionally have some difficulty making ends meet; 4—tough to make ends meet but keeping your head above water; 5—in over your head”.

Even today, the subjective approach is regarded as neglected within the welfare concepts, though it remains a conceptually appealing way to define poverty (Ravallion 2014 ). Although economists have long been skeptical about subjective variables (Bertrand and Mullainathan 2001 ), their skepticism about subjective data seems, to some extent, to have been overcome today (Deaton and Stone 2013 ).

Most of the countries joined the survey in 2005.

While a targeted sociological survey on the poor population, ready to be launched by the mid-1980s, was not allowed during the communist era, poverty was opened as a research issue at the beginning of the economic transition.

The first wave did not contain the subjective variables of our interest. The second wave collected information on household disposable income using a single question (as opposed to the next waves) which makes our indicator highly incomparable. The last wave collected data on income only at intervals, which hinders construction of our indicator.

Households reporting that their actual income was absolutely sufficient to get along, consisting of 23% of Czech and 6% of Slovak households, were thus not asked the MIQ. For the rest, the reported minimum income was always higher than or equal to the actual income. Thus, if the respondents’ income was not absolutely sufficient, they were assumed to have reported a higher minimum income needed.

Note that the number of categories of the scale-evaluated questions differs across the data sets used, from four in EEA to six in EU-SILC. This raises comparability issues, which are partly offset by focusing on Slovak–Czech ratios instead on the levels of “feeling poor” or “inability” incidence across the surveys.

EU-SILC variable HY020—as actual income corresponds to annual income, one twelfth of the reported value is taken into account. EU-SILC is usually conducted in spring in Czechia and Slovakia, and the income reference period corresponds to the previous calendar year, while the subjective questions are related to the current situation. We are aware of possible inconsistencies between the current and previous year reference periods. However, the income reference period is considered to provide the best approximation of current income, as suggested by Eurostat ( 2010a ), and it is also used in this sense in official statistics.

The reason for the arbitrarily chosen threshold of 75% was that approximately the same share of the Czech population reported “insufficient income” as the share of persons designated as “at risk of poverty”—the official indicator—in 2015 (documented by Večerník and Mysíková 2016 ).

Regarding the possible limitations of the “individual” method and the arbitrarily stated poverty line, note that we cannot utilize any model-based estimation of the poverty line (SPL) because a part of the SOCO 1995 survey respondents were not asked the MIQ (almost a quarter of respondents in CZ).

Note that the scale differs in SOCO and in EU-SILC.

The categories are derived based on the population size and density of the municipality. The definition of the degree of urbanization has changed slightly over time in EU-SILC definitions (compare Eurostat 2010b , 2016 ).

The regression results mostly show a statistically insignificant difference between those paying a mortgage and those paying rent; however, if the coefficient is statistically significant, those paying a mortgage are less likely to feel poor.

Our definition of the work intensity indicator is different from the one applied by Eurostat in official statistics (see Ward and Ozdemir 2013 ). Here we simply add up the number of months worked during the year by all household members aged 16+ and divide it by 12 times the number of household members aged 16+. Our purpose is to control for the share of household members who actively contribute to the household budget, while the Eurostat definition is aimed to identify socially excluded persons.

The NUTS2 level includes 8 Czech and 4 Slovak regions.

Employment rate for population aged 15–64, Eurostat database (variable lfst_r_lfe2emprt).

Eurostat database (variable nama_10r_2gdp).

Data on expenditures stem from Household Budget Surveys, provided by the Czech and Slovak Statistical Offices.

For the sake of space, we do not provide full results of the logistic regressions models or comment on them, but they are available upon request.

As with the official statistics, all computations based on EU-SILC are weighted by the individual weights provided in the datasets.

Indeed, the variance of the two indicators of subjective poverty defined above differs, especially in Slovakia: while the variance of the insufficient income indicator ranges from 0.07 to 0.14 in CZ and from 0.16 to 0.25 in SK throughout the period 2005–2016, the respective figures are 0.06–0.09 and 0.09–0.11 for the inability indicator.

Slovakia suffered from weaker economic performance than Czechia for decades, followed by a rapid convergence with Czechia through the 1980s. Slovakia attained 61% of the Czech GDP in 1948, whereas its GDP reached 88% of that of Czechia at the end of the communist era in 1989 (see, e.g., Vintrová 2008 , 2009 for further details).

The consistency of the data on actual household disposable income over survey waves is rather low as the income questions varied in each wave. The reason was to improve the validity of the variable by dividing one original summary question into several asking about individual sources of income. However, as a consequence, the indicator of insufficient income is unstable over time.

The SK dummy shows a relative outcome, meaning that the perceived subjective poverty might have increased in Slovakia, decreased in Czechia, or any combination that might have led to the wider SK–CZ gap in Model B compared to Model A. In order to shed more light on what was happening in each country, we ran a model with pooled data for all years and included year dummies together with their interaction terms with the SK dummy. The results showed that both the Czechs and Slovaks are more likely to perceive their household income as insufficient once the economic conditions are controlled for. However, this difference is substantially greater in Slovakia than in Czechia. Further, this difference is decreasing over time in both countries. Finally, the results of the pooled model with economic controls (Model B) indicate that, in Czechia, the likelihood an individual will perceive his/her income as insufficient (with the last year as a reference point) was decreasing gradually (from relatively low likelihood at the beginning). In Slovakia, the relatively high likelihood at the beginning was sharply decreasing up to 2010.

The 2007 jump appears when the regional GDP is added to the regressors. Without the regional GDP, the odds ratio for SK accounts for 10.4 in 2007, and the peak occurs in 2006 (19.5), similarly to Model A.

Finally, we conclude that the fact that the subjective questions are responded to by one household member and attributed to all other household members has only a negligible impact on the results (compare Fig.  6 and the left panel of Fig.  12 for insufficient income, and Fig.  7 and the right panel of Fig.  12 for inability). Apart from a loss in statistical significance in some years, the qualitative results and trends remain the same.

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Acknowledgements

This work was supported by the European Regional Development Fund—Project “CSDA Research” (No. CZ.02.1.01/0.0/0.0/16_013/0001796) and by the Slovak Scientific Grant Agency (VEGA 2/0002/19). Tomáš Želinský further acknowledges support for visiting the Institute of Sociology of the Czech Academy of Sciences financed within the Protocol on cooperation in the field of education, youth and sports between the Ministry of Education, Science, Research and Sports of the Slovak Republic and the Ministry of Education, Youth and Sports of the Czech Republic. The EU-SILC datasets were made available on the basis of Contract No. 265/14 between the European Commission, Eurostat, and the Institute of Sociology of the Czech Academy of Sciences. Thanks for additional data information go to the Statistical Office of the Slovak Republic and the Czech Statistical Office. The authors especially wish to thank Andrew Clark, who provided valuable comments and ideas as a discussant of this paper at the IARIW 2018 General Conference. The authors would also like to thank the anonymous reviewers for their helpful and constructive comments which substantially contributed to the overall readability of the final version of this paper. Responsibility for all conclusions drawn from the data lies entirely with the authors.

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Regression analysis of different definitions of insufficient income indicator as the dependent variable, SK dummy (odds ratio/coefficients), 2005–2016. Notes : Respondents aged 16+. All coefficients of the SK dummy are statistically significant at the 1% level. Model A controls for demographic characteristics. Model B additionally controls for socioeconomic and regional macroeconomic characteristics (see Sect.  3 on the list of control variables). GDP is missing in 2016. (Robust standard errors were used.)

Logistic regression of inability (1–2)—SK dummy (odds ratio), 2005–2016. Notes : Inability indicator (1–2): the dependent variable equals 1 if the answer was “great difficulty” or “difficulty”. Respondents aged 16+. Coefficients in Model A are statistically significant at the 1% level; in Model B, empty marks represent coefficients which are not statistically significant at the 10% level, other coefficients of the SK dummy are statistically significant at least at the 10% level. Model A controls for demographic characteristics. Model B additionally controls for socioeconomic and reginal macroeconomic characteristics (see Sect.  3 on the list of control variables). GDP is missing in 2016. (Robust standard errors were used.)

Logistic regression of insufficient income and inability—SK dummy (odds ratio), subsample of respondents answering household questionnaire, 2005–2016. Notes : Only respondents aged 16+ responsible for answering household questionnaire are included. Empty marks represent coefficients which are not statistically significant at the 10% level; all other coefficients of the SK dummy are statistically significant at the 5% level. Model A controls for demographic characteristics. Model B additionally controls for socioeconomic and reginal macroeconomic characteristics (see Sect.  3 on the list of control variables). GDP is missing in 2016. (Robust standard errors were used.)

Source : EU-SILC 2015 and 2016 (own computations); Eurostat database for GDP (current prices, Euro per capita—variable nama_10_pc)

Spatial distribution of mean wage, GDP, insufficient income and inability (NUTS2 regions), 2015–2016.  Notes : Maps were created using (c) EuroGeographics for the administrative boundaries “NUTS 2013” shapefiles.

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Mysíková, M., Želinský, T., Garner, T.I. et al. Subjective Perceptions of Poverty and Objective Economic Conditions: Czechia and Slovakia a Quarter Century After the Dissolution of Czechoslovakia. Soc Indic Res 145 , 523–550 (2019). https://doi.org/10.1007/s11205-019-02102-2

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Accepted : 11 March 2019

Published : 12 April 2019

Issue Date : 01 September 2019

DOI : https://doi.org/10.1007/s11205-019-02102-2

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