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Economic Growth, Inequality and Poverty in the EU

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Abstract

A country’s poverty rate is influenced by numerous factors, including economic growth and the distribution of its effects. This article aims to classify European Union (EU) member states in terms of their ability to handle the economic challenges of the past decade. A country’s ability to negotiate global challenges in conjunction with their respective social and economic growth, as well as that of the EU, represents a key classification attribute. In this article, classification is based on an analysis of changes in economic growth, inequality and poverty across all 28 EU member states. The classification emerges from monitoring trends in economic growth and inequality, and their interconnections with poverty across the different countries. In order to analyse these interactions, this investigates uses the Bourguignon model (Poverty-Growth-Inequality Triangle–PGI) and the Growth Incidence Curve. The article reveals that economic growth is connected with a decrease in poverty. However, as inequalities in income increase, poverty also increases. Nevertheless, rates of development differ across countries. Four broad categories of country sharing similar attributes are defined, and an additional, special category assigned to Greece owing to its distinctive attributes. These partial classifications facilitated the complex classification of the EU member states, by which different development tendencies across the countries in the period 2005–2015 might be deciphered. By analysing the relationships between gross domestic product, income distribution and poverty rates, and by developing a system by which to classify countries, essential information regarding individual countries’ economic and social development is revealed, with implications for their distinctive challenges in reducing inequality and poverty. The article also highlights considerable diversity in countries’ relative abilities to handle a range of unfavourable global trends, such as the recent global financial crisis. In general, countries with strong economies are better able to weather challenges such as inequality and poverty during a period of crisis.

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Fig. 1

Source: Bourguignon (2004)

Fig. 2

Source: Eurostat (2017)—Main GDP aggregates per capita; authors’ calculations. (Color figure online)

Fig. 3

Source: Eurostat (2017)—S80/S20 income quintile share ratio by sex and selected age group. (Color figure online)

Fig. 4

Source: Eurostat (2017) – At-risk-of-poverty rate. (Color figure online)

Fig. 5

Source: the authors. (Color figure online)

Fig. 6

Source: the authors. (Color figure online)

Fig. 7

Source: the authors. (Color figure online)

Fig. 8

Source: the authors. (Color figure online)

Fig. 9

Source: Eurostat (2017)—Distribution of income by quantiles; At-risk-of-poverty thresholds. (Color figure online)

Fig. 10

Source: Eurostat (2017)—Distribution of income by quantiles; At-risk-of-poverty thresholds. (Color figure online)

Fig. 11

Source: Eurostat (2017)—Distribution of income by quantiles; At-risk-of-poverty thresholds. (Color figure online)

Fig. 12

Source: Eurostat (2017)—At-risk-of-poverty rate; S80/S20 income quintile share ratio. (Color figure online)

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Notes

  1. A survey was conducted at the end of 2016 in 30 European countries to identify future directions for research in Europe. The topic of ‘Inequalities’ was deemed the second most important. Following its adoption by the European Commission it should become one of the priority research topics funded in the next stage of the Horizon 2020 programme (cf. Citizen and Multi-Actor Consultation on Horizon 2020, 2016).

  2. Furthermore, in the year before the study period (2004), the EU was enlarged by 10 countries, affecting its global development in the monitored period as well as economic development in its constituent countries.

  3. However, the EU also includes countries whose trends are in the opposite direction, i.e. levels of inequality are on the decrease; moreover, this might vary in terms of scale or time period.

  4. Many factors help explain the higher GDP growth rate in Central and Eastern European countries, including: high levels of foreign direct investment (FDI); increasing household consumption; improvements to labour markets; the possibility of accessing euro funds; increased exports; the high potential of debt stocks; development of company-based loans; increased public investments; stronger private investments for development such as highways, constructions and the property sector; improvements to economic structure; the development of services (Finweb 2016; Pravda 2017).

  5. This was the highest value recorded by the EU SILC survey, which began monitoring Romania in 2007. The value in 2007 reached 8.1 and has since tended to diminish; it is possible that the values surpassed 8.3 in 2005 and 2006 but those data are unavailable.

  6. The increase of both amount and share of EU inhabitants living below at-risk-of-poverty threshold was significantly caused by the EU enlargements of poor countries with a high share of the poor. The sixth enlargement (in 2007) involved both Romania and Bulgaria and during the seventh, in 2013, Croatia joined the EU.

  7. In some countries within the progressive model, the period of depression/stagnation lasted a little longer and eventually, a second growth slow-down might have occurred once the economy had restarted.

  8. The Index is then to referred as at-risk-of-poverty rate.

  9. Tourism has been one of the economic sectors most affected by the economic crisis, and so probably represents one of the reasons for rising inequality these countries.

  10. Our next intention is to correctly express this statement through the analysis of statistical connection.

  11. This fluctuation is typical for all three Baltic countries: Estonia, Latvia and Lithuania.

  12. Figure 10 displays the income distribution of Greece. The 2015 at-risk-of-poverty threshold lies on the left compared to the 2005 threshold. This is caused by the fact that the threshold fell from €471 per month in 2005 to €376 per month in 2015.

  13. The first group is represented by data for the 91st to 95th percentiles, the second for the 96th to 98th percentiles, and the third for the 99th and 100th percentiles. The main reason for this divide is the limited availability of data, as well as the relatively large share of top income decile on income distribution.

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Acknowledgements

The article was created with the support of the scientific project no. 2/0009/18, financed by the VEGA grant agency.

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Correspondence to Anton Michálek.

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Michálek, A., Výbošťok, J. Economic Growth, Inequality and Poverty in the EU. Soc Indic Res 141, 611–630 (2019). https://doi.org/10.1007/s11205-018-1858-7

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  • DOI: https://doi.org/10.1007/s11205-018-1858-7

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