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Inequality in OECD countries
Abstract
This article recalls the state of play of inequality levels and trends in OECD countries, with a special focus on Nordic countries. It sheds light on explaining the drivers of the rise in inequality and its economic consequences. It addresses in particular the issue of redistribution through taxes and transfers. It concludes with an overview of policy packages that should be considered to address the issue of rising inequalities.
... By taking a closer look at the influence of societal, political, and economic factors, one may be able to overall assess additional factors to the extent of the outbreak throughout different regions of the world. Indeed, this view is consistent with the result of previous studies (Mazzucchelli et al., 2020;Ojong, 2020;Quinn and Kumar, 2014;Thevenot, 2017) Herein, we tested the hypothesis that political, economic, and social factors might have impaired the ability of different countries to control the incidence and mortality produced by SARS-CoV-2 during the first seven months of the pandemic. For this purpose, we establish multiple correlation analyses between the number of new cases and deaths from different countries and multiple social, political, and economic indexes. ...
... Overall, multiple epidemiological studies have focused on explaining the course of this pandemic in terms of the biological properties of SARS-CoV-2 (Bhattacharya et al., 2020;Kumar et al., 2020;Leuzinger et al., 2020), analyses that are highly reasonable considering the unique clinical features manifested by infection with this virus (Sun et al., 2020;Wang et al., 2020). However, based not only on the complex epidemiological triad of SARS-CoV-2 (Shafer and Velazquez-Salinas, 2021), but also in the disparate levels of social inequality worldwide (Thevenot, 2017), we wanted to investigate the existence of potential political, social, and economic factors that might also be driving the course of this pandemic. ...
Purpose: In this study we propose to identify significant economic, political, and social factors that helped contribute to the incidence and mortality of SARS-CoV-2 around the world during the first seven months of the COVID-19 pandemic. Methods: Retrospective analyses were conducted using data acquired by the World Health Organization regarding the number of new cases and deaths from 76 countries from January through July 2020. Hierarchical cluster and principal component analyses were used to categorize different countries based on their ability to control the outbreak based on number of cases and deaths throughout these initial months of the pandemic. Furthermore, using non-parametrical analyses, different variables were correlated with multiple economic, political, and social indexes. Results: Multiple significant correlations were found, including the control of corruption, government effectiveness, and the human life indicator, with indexes containing the higher correlations demonstrating the capability of individual countries to help drive the course of this pandemic. Geographical hotspots were identified that required further attention to aid in outbreak control. Conclusion: Collectively, the findings in this study indicate that multiple factors beyond the biological should be considered in the implementation of measures to contain this pandemic
... Here we argue that an important factor characterizing socio-economic context and affecting WLB is economic inequality (henceforth inequality), defined as 'the unequal dispersion of resources across society' [14]. Inequality is increasing in most Organisation for Economic Co-operation and Development (OECD) countries [15] and is expected to increase even further given rising food and energy prices and a decline in wages, causing growing inequality between households [16]. Such inequality is linked to increased burnout and job insecurity [17] and to reduced well-being in general [18,19]. ...
International institutions' attention to work–life balance (WLB) demonstrates the global breadth of this issue. Yet the scientific community has thus far paid little attention to its structural underpinnings and to the interplay between these macro-level underpinnings and individual psychological factors. We examine the contextual role of economic inequality at the national level as a significant factor influencing working time and WLB perceptions using multiple empirical strategies. In the first set of studies (1a and 1b), we compared countries with different levels of inequality (Study 1a with 37 countries, Study 1b with longitudinal data from 34 countries, N = 254) and found increased working time and reduced WLB in highly unequal countries. In a pilot study (N = 81) and in the pre-registered Studies 2 (N = 338) and 3 (N = 499) we corroborated this evidence with an experimentally induced inequality perception, reporting an indirect effect of inequality on WLB (Studies 2 and 3) and working time (Study 3) through status anxiety and competitiveness. In Study 2, we manipulated socio-economic class in addition to economic inequality, showing that the detrimental effect of inequality on WLB is especially marked for participants assigned to a low-class condition. This research contributes to an integrated understanding of the impact of economic inequality and socio-economic class in shaping WLB and provides useful insights for organizations to develop context-specific policies to improve employees’ WLB that take both individual and structural factors into account.
... Within most OECD countries, income inequality has risen since the 1980s, though at differential rates (Thévenot 2017;Amaral, Yen, and Wang-Goodman 2019;Fine et al. 2019). However, the magnitude of this effect is less evident when one simply looks at the Gini index that emphasizes the middle of the income distribution. ...
In this review essay, I explore the consequences of growing economic inequality within advanced democracies. My aim is not to provide novel empirical evidence but to stitch together disparate threads of existing literature to make the case that growing economic inequality should be viewed as one of the most significant problems afflicting industrialized democratic societies today. I begin by defining the concept of economic inequality and by outlining the methods for measuring this phenomenon empirically. In this section, I encourage scholars to shift away from complex indices in favor of decile ratios that are more legible to the public. Next, I summarize trends in income and wealth inequality within advanced democracies to illustrate the growing scale of the problem in recent decades. Subsequently, I make the case that rising economic inequality should be viewed as a concern of utmost importance for political economists by reviewing the literature in three key issue areas that may be adversely impacted by rising levels of inequality: political equality, far-right populism, and economic growth. In scientific terms, economic inequality is conceptualized as an independent variable, and I examine its impact on relevant dependent variables. I conclude by making a call to action with the hope of mobilizing scholars across countries and academic disciplines to study the determinants of rising economic inequality, and to advocate for policy measures to combat the problem.
... In Kuznets' hypothesis [51], inequality must rise in the early stages of economic development, but falls after a certain point of economic development. However, the pattern has changed in recent decades, with income inequality not only in developing countries but also in OECD countries [52]. us, we employ GDP per capita (pgdp) to reflect the urban economic growth, which describes an area's economic and financial resources and how efficiently they are allocated [53,54], and detect its impact on urban IG. ...
In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inclusive growth efficiency (IGE). Furthermore, a nest of machine learning (ML) algorithms are introduced to explore the drivers of urban IGE, which overcomes the defects of endogeneity and multicollinearity of traditional econometric methods. We find for the overall sample that entrepreneurship and innovation contribute the most to IGE, accounting for about 35%, respectively, and they are the most critical drivers, while the heterogeneity test results reveal that the contribution of influencing factors has changed for different regions such as the eastern region, the central region, and the western region. Based on the experimental results of the ML model, we provide some policy suggestions for China and similar developing countries and emerging economies to promote IG.
... Because the benefits of economic growth tend to disproportionately accrue to the upper two quintiles of the income distribution, the official poverty rate has been relatively stagnant while household income inequality has been increasing in recent decades (Brady and Jäntti 2016;Economic Policy Institute 2021;Iceland 2006). In 2019, the Gini coefficient for household income inequality in the United States was 0.484 (Semega et al. 2020), which is higher than other developed nations (Thevenot 2017). ...
Building upon prior research on intergenerational income mobility, we assess class effects versus racial effects on the probability of becoming a poor adult, broken down by gender. We define the class effect (for each race-and-gender group) as the difference between the probability that a person who was born into the lowest income quintile becomes poor and the probability that a person who was born into the highest income quintile becomes poor. For each minority-by-gender group, using Whites as the baseline, the racial effect is defined as the average racial differential in the probability of becoming a poor adult, irrespective of class origins. The results indicate that, for all minority-by-gender groups, the class effect is larger than the racial effect. Our findings underscore the continuing significance of the comparatively large effects of class origins, which have not been adequately acknowledged in recent research.
... A Gini coefficient with a value of 0 represents a situation in which everyone has the same income, whereas a Gini coefficient of 1 represents a situation in which one household or family possesses all the income in a society. The mean Gini coefficient for OECD countries in 2014 was 0.35, and the Gini coefficient for Norway was significantly lower, at 0.25 (Thévenot 2017). The calculation of the Gini coefficient used in the present analyses was based on information on gross family income for all households in all the 15 city districts of Oslo in 2008. ...
During the last decade, Norway has experienced a major increase in permanent poverty among children. Although income inequality among Norwegian families is ranked as the lowest in Europe, the capital city of Oslo is characterized by significant differences in income across and within city districts. This paper analyses the degree to which utilization of specialized mental health services among children and adolescents is influenced by low-income and geographical-income inequality. In particular, do these factors affect immigrant children differently than they do native children? The data include the total population of children and adolescents with a native or non-Western background residing in Oslo (N = 121,449), in addition to register data covering all contacts with specialized mental healthcare institutions during the years 2008–2011 (N = 446,440). Multivariate logistic regression and Poisson regression were applied to analyse variations in rates of mental health care utilization. Non-Western children had significantly lower rates of utilization than their native peers, with the exception of a small minority of children residing in city districts with high levels of income inequality. For native children, there appears to be an inverse relationship between living in an area of income inequality and mental healthcare utilization: living in city districts with high income inequality to some extent lowers the probability of having contact with mental healthcare services. The results are discussed, involving factors such as underutilization, ethnic density effects, and selection processes that influence who becomes poor.
... Ten years since the crisis began, unemployment remains high for several of the 28 member states of the European Union (EU) and particularly in Spain and Greece [3], even though economic growth has recovered somewhat in recent years [4]. Income inequalities (which reached record high levels prior to the crisis in 2008) have continued to rise during the recession [5]. ...
Aims:
The so-called 'Great Recession' in Europe triggered widespread concerns about population health, as reflected by an upsurge in empirical research on the health impacts of the economic crisis. A growing body of empirical studies has also been devoted to socioeconomic inequalities in health during the Great Recession. The aim of the current study is to summarise this health inequality literature by means of a scoping review.
Methods:
We have performed a scoping review of the research literature (English language) published in the years 2012-2017. Only empirical papers with (a) health status measured on the individual level, (b) information on socioeconomic position (i.e. employment status, educational level, income/wealth, and/or occupational class), and (c) data from European countries in both pre- and post-crisis years were considered relevant. In total, 49 empirical studies fulfilled these inclusion criteria.
Results:
The empirical findings in the 49 included studies predominantly show that socioeconomic inequalities in health either increased or remained stable from pre- to post-crisis years. Two-thirds (65%) of the studies found evidence of either increasing or partially increasing health inequalities. Thus, people in lower socioeconomic strata fared worse overall in terms of health during the Great Recession, compared to people with higher socioeconomic status.
Conclusions:
The Great Recession in Europe tends to be followed by increasing socioeconomic inequalities in health. Policymakers should take note of this finding. Widening socioeconomic inequalities in health is a major cause of concern, in particular if health deterioration among 'vulnerable groups' is caused by accelerating cumulative disadvantages.
... In recent years, extant evidence suggests that health status is conditioned by the unequal distribution of social determinants [7,8]. For instance, place of birth and socioeconomic position may explain inequalities in risks associated with health and/or disease, as a result of the different opportunities and resources people may have to achieve good health [9]. ...
This study examined obesity inequalities according to place of birth and educational attainment in men and in women in Spain. A cross-sectional study was conducted using data from the Spanish National Health Survey 2011–2012 and from the European Health Survey in Spain 2014. We used data for 27,720 adults aged 18–64 years of whom 2431 were immigrants. We used log-binomial regression to quantify the association of place of birth with obesity before and after adjusting for the selected characteristics in women and in men. We found a greater probability of obesity in immigrant women (PR: 1.42; 95% CI: 1.22–1.64) and a lower probability of obesity in immigrant men (PR: 0.73; 95% CI: 0.59–0.89) relative to natives after adjustment. Significant heterogeneity was observed for the association of place of birth and obesity according to education in men (p-interactions = 0.002): Men with lower educational levels (PR: 0.47; 95% CI: 0.26–0.83) have a protective effect against obesity compared with their native counterparts. This study suggests that place of birth may affect obesity in women and in men. However, this effect may be compounded with education differently for women and men.
This chapter sets the stage for the rest of the book by examining the rising economic inequalities in many Western liberal democracies. It traces these inequalities back to the economic reforms of the 1980s, which promoted economic globalisation and deregulation. The chapter then defines neoliberal ideology as involving not only an economic doctrine but also through its constitutive socio-cultural politics. While the former is concerned with economic relations, the latter primarily involves the question of subjecthood and the types of subjectivity most conducive to sustaining social relations within hyper-capitalist societies. The discussions in this chapter draw on existing reports on economic inequalities, historical analyses of inequality regimes, and post-structural sociological theories of subjectification.
From unemployment to Brexit to climate change, capitalism is in trouble and ill-prepared to cope with the challenges of the coming decades. How did we get here? While contemporary economists and policymakers tend to ignore the political and social dimensions of capitalism, some of the great economists of the past - Adam Smith, Friedrich List, John Maynard Keynes, Joseph Schumpeter, Karl Polanyi and Albert Hirschman - did not make the same mistake. Leveraging their insights, sociologists John L. Campbell and John A. Hall trace the historical development of capitalism as a social, political, and economic system throughout the twentieth and early twenty-first centuries. They draw comparisons across eras and around the globe to show that there is no inevitable logic of capitalism. Rather, capitalism's performance depends on the strength of nation-states, the social cohesion of capitalist societies, and the stability of the international system - three things that are in short supply today.
The development of a country is not related to only its economic growth, once its sustainable development should consider social, economic and environmental aspects. This work intends to measure and analyze the efficiency of sustainability in Brazil comparing to other countries of OECD (Organization for Economic and Cooperation Development), understanding the current ambition of Brazil to compose the OECD. This analysis used a mathematical programming method called Data Envelopment Analysis (DEA), which enabled, from the SBM model and the window analysis, to evaluate the ability of countries to reduce the imputs (CO2 emission, energy consumption, unemployment rate and Gini index), as well as to increase the outputs (life expectancy, GDP, sustainable energy, sanitation of quality). The comparison between the countries was made using the sustainable efficiency ranking and the results of this study indicated that the Latvia and Chile is the most eficiente and in the fourth place there is Brazil. Moreover, relative slacks were analyzed and it was concluded that, although Brazil is not a benchmark in any variable, its slacks are relatively low. The index measured is important to contribute to the discussions related to evaluating the countries sustainability, helping to identify those with the best practices with regard to social, economic and environmental aspects; and guide policy decisions regarding government incentives to promote the development of countries in search of more sustainable production.
A prominent issue in current debates is the coincidence between growing within-country disparities in the labor income distribution and countries’ increasing exposure to globalization. This article contributes to such discussions by providing an overview of the main theoretical and empirical literature on the link between globalization and a component of income inequality: wage inequality. Specific attention is paid to the role of trade, in the form of exports and imports, and global value chains (GVCs). This review emphasizes that in-depth consideration of several intertwined mechanisms is necessary in order to accurately address trade-induced inequality concerns.
JEL Classification: F11, F12, F14, F16, F66, J31
This is the eighth edition of Society at a Glance, the biennial OECD overview of social indicators. This report addresses the growing demand for quantitative evidence on social well-being and its trends. It updates some indicators included in the previous editions published since 2001 and introduces several new ones, with 25 indicators in total. It includes data for the 35 OECD member countries and where available data for key partners (Brazil, China, India, Indonesia, Russia and South Africa); other G20 countries (Argentina and Saudi Arabia) are also included. The report features a special chapter on the NEET challenge and what can be done for jobless and disengaged youth.
In the three decades to the recent economic downturn, wage gaps widened and household income inequality as measured by GINI increased in a large majority of OECD countries. This occurred even when countries were going through a period of sustained economic and employment growth. This report analyses the major underlying forces behind these developments. It examines to which extent economic globalisation, skill-biased technological progress and institutional and regulatory reforms have had an impact on the distribution of earnings. The report further provides evidence of how changes in family formation and household structures have altered household earnings and income inequality. And it documents how tax and benefit systems have changed in the ways they redistribute household incomes. The report discusses which policies are most promising to counter increases in inequalities and how the policy mix can be adjusted when public budgets are under strain. "Analyses rely on simple statistical techniques that are accessible to a large readership... the graphic and charts are of great help to gain a quick visual grasp of the various issues addressed."
This paper takes advantage of a recently-compiled cross-country dataset that distinguishes market inequality from net inequality and allows us to calculate redistributive transfers for a large number of country-year observations. Our main findings are: 1. More unequal societies tend to redistribute more. 2. Lower net inequality is robustly correlated with faster and more durable growth, for a given level of redistribution. 3. R edistribution appears generally benign in terms of its impact on growth; only in extreme cases is there some evidence that it may have direct negative effects on growth. Thus the combined direct and indirect effects of redistribution -including the growth effects of the resulting lower inequality- are on average pro-growth. While we should be cognizant of the inherent limitations of the data set and of cross-country regression analysis more generally, we should be careful not to assume that there is a big tradeoff between redistribution and growth. The best available macroeconomic data do not support that conclusion.
A recent literature has constructed top income shares time series over the long run for more than twenty countries using income tax statistics. Top incomes represent a small share of the population but a very significant share of total income and total taxes paid. Hence, aggregate economic growth per capita and Gini inequality indexes are sensitive to excluding or including top incomes. We discuss the estimation methods and issues that arise when constructing top income share series, including income definition and comparability over time and across countries, tax avoidance, and tax evasion. We provide a summary of the key empirical findings. Most countries experience a dramatic drop in top income shares in the first part of the twentieth century in general due to shocks to top capital incomes during the wars and depression shocks. Top income shares do not recover in the immediate postwar decades. However, over the last thirty years, top income shares have increased substantially in English speaking countries and in India and China but not in continental European countries or Japan. This increase is due in part to an unprecedented surge in top wage incomes. As a result, wage income comprises a larger fraction of top incomes than in the past. Finally, we discuss the theoretical and empirical models that have been proposed to account for the facts and the main questions that remain open. (JEL D31, D63, H26, N30)
Redistribution, inequality, and growth. IMF Staff Discussion Note
- J Ostry
- A Berg
- C Tsangarides
Ostry J, Berg A and Tsangarides C. Redistribution, inequality, and growth. IMF Staff Discussion Note, February 2014.