Has economic growth in Balkan Countries been pro-poor in the 2012-2017 period?

Zwierzchowski, Jan; Panek, Tomasz Article Has economic growth in Balkan Countries been pro-poor in the 2012-2017 period? Central European Economic Journal (CEEJ) Provided in Cooperation with: Faculty of Economic Sciences, University of Warsaw Suggested Citation: Zwierzchowski, Jan; Panek, Tomasz (2022) : Has economic growth in Balkan Countries been pro-poor in the 2012-2017 period?, Central European Economic Journal (CEEJ), ISSN 2543-6821, Sciendo, Warsaw, Vol. 9, Iss. 56, pp. 76-92, https://doi.org/10.2478/ceej-2022-0006 This Version is available at: https://hdl.handle.net/10419/324564 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Central European Economic Journal, 9(56), 76-92. DOI: 10.2478/ceej-2022-0006 To link to this article: https://doi.org/10.2478/ceej-2022-0006 Has Economic Growth in Balkan Countries Been Pro-Poor in the 2012-2017 period? Tomasz Panek, Jan Zwierzchowski Open Access. © 2022 T. Panek, J. Zwierzchowski, published by Sciendo. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. Jan Zwierzchowski Warsaw School of Economics, Collegium of Economic Analysis, Al. Niepodległości 162, 02-554 Warsaw, Poland corresponding author: [email protected] Tomasz Panek Warsaw School of Economics, Collegium of Economic Analysis, Al. Niepodległości 162, 02-554 Warsaw, Poland Has Economic Growth in Balkan Countries Been Pro-Poor in the 2012-2017 period? 1. Introduction During recent years, among many approaches to analyzing and combating poverty, the approach known as pro-poor growth, has gained popularity. This approach assumes that high economic growth may not be a sufficient condition for poverty reduction. Whether economic growth is favourable to the poor is determined by the participation of various groups in the generation and distribution of national income. Over a dozen years, the impact of economic growth on poverty reduction has been analysed and discussed in numerous theoretical and empirical papers (Araar et al., 2009; Bibi et al., 2012; Dollar & Kraay, 2002; Duclos, 2009; Essama-Nssah & Lambert, 2009; Grimm, 2007 Kakwani, et al., 2004; Kakwani & Pernia, 2000; Lo Bue & Palmisano, 2019; Ravallion, 1994; Ravallion & Chen, 2003; Son, 2004; Son & Kakwani, 2008; Tebaldi & Kim, 2015; Zeman & Shamsuddin, 2017; Panek & Zwierzchowski 2021). If economic growth leads to poverty reduction, then macroeconomic policy should focus on actions supporting growth while also possibly limiting funds for programs aimed at direct poor support. However, if economic growth does not reduce poverty, state policy should put more emphasis on direct financial support for the poor. The research conducted thus far does not give an unambiguous answer to the question of whether economic growth favours the poor. Results largely depend on the definition of pro-poor economic growth, the scope of the study, and the statistical methods used. Moreover, it is believed that the level of economic development and general welfare and pension regimes all play roles Abstract The study investigates whether economic growth in the Balkan countries was pro-poor in the most recent period. We also try to establish to what extent various measures of pro-poorness of economic growth produce consistent and comparable results. Firstly, concepts of pro-poor growth are defined and corresponding approaches toward measuring pro-poor growth are presented. We distinguish between measures based on a general class of pro-poor indices and a dominance-based techniques. In the empirical part of the study, we verified whether economic growth in six Balkan countries (Greece, Bulgaria, Romania, Slovenia, Croatia and Serbia) was pro-poor in the 2012-2017 period. The analyses is based on the latest available panel data of the European Union Survey on Income and Living Conditions (EU-SILC). Growth was pro-poor in Croatia, Romania and Slovenia during the whole analysed period. The growth pattern was non pro-poor in Bulgaria, Greece and Serbia in certain years, mainly during periods of economic downfall. Various measures of pro-poor growth patterns do not produce consistent results in all instances. The results of the conducted comparative analysis suggest that the level of social benefits does not directly influence the pro-poor nature of the economic growth. Keywords economic growth | poverty | inequality JEL Codes D31, D63, I32 CEEJ • 9(56) • 2022 • pp. 76-92 • ISSN 2543-6821 • DOI: 10.2478/ceej-2022-0006 78 in this process (Ashley, 2007; Dollar & Kraay, 2002; Harmáček, et al., 2017; Lo Blue & Palmisano, 2019; Kośny & Yalonetzky, 2015; Lopez 2006; Ruiz-Castillo, 2009Son & Kakwani, 2008; ). The aim of this article is to evaluate whether economic growth was pro-poor in the Balkan countries between 2012 and 2017. Moreover, we investigate how different definitions of pro-poor growth affect conclusions drawn from an empirical analysis and to what extent results obtained for various measures remain comparable. We included Bulgaria, Croatia, Greece, Romania, Slovenia, and Serbia in the empirical analysis. All six countries analysed experienced an overall increase in GDP in the 2012-2017 period. A temporary decrease in GDP per capita took place only in Greece, Croatia and Slovenia in 2013 due to the financial crisis. The mean personal income also increased in almost all countries in the analysed time frame (except for Bulgaria and Greece in 2014 and 2015). The question arises: how the GDP growth and general increase in mean personal incomes translate into the financial situation of impoverished individuals? The paper attempts to answer whether the positive (negative) economic growth in these countries stimulates a decrease (increase) in poverty and whether it was favourable to the poor according to the various definitions of ‘being favourable’ introduced in the literature (more on this in Section 2.1.). In the theoretical part of the study, various approaches to the analysis of the growth patterns and basic measures of pro-poor growth are presented. Next, theoretical foundations for the construction of these measures are defined, and their basic advantages and limitations are discussed. We propose certain modifications of these measures. In the empirical part of the study, we try to verify whether the economic growth in the Balkan countries between 2012 and 2017 was favourable to the poor. The empirical analysis is based on the latest available panel data taken from the European Union Survey on Income and Living Conditions (EU-SILC) and Eurostat data on GDP growth and inflation. The outline of the paper is as follows. Section 2 is devoted to the conceptual framework. Section 3 presents various approaches to the analysis of the growth pattern. Section 4 provides statistical sources and the assumptions of the study. Section 5 contains the empirical part of the study. Section 6 discusses the empirical results. Section 7 concludes the paper. 2. Concepts of Pro-Poor Economic Growth International institutions (United Nations [UN], 2000; Organisation for Economic Co-operation and Development [OECD], 2007 define pro-poor growth as growth that benefits the poor and enables them to improve their economic situation. This definition is very vague and imprecise and therefore provides little guidance to its measurement or to formulate propoor policies. In recent years, there have been many proposals for a more specific definition of pro-poor growth (Essama & Lambert, 2009; Kakwani, et al., 2004; Klasen, 2008; Kraay, 2006; Ravallion & Chen, 2003; Son & Kakwani, 2008). The proposed definitions can be classified under two basic approaches to pro-poor growth: namely, absolute and relative. The distinction is related to the general concept of measuring poverty and inequality. According to the absolute approach, the process of growth is considered favourable to the poor if the wealth (measured by incomes) of the poor increases (Klasen, 2008). This approach does not compare the distribution of benefits of growth between the poor and the non-poor. Furthermore, Klasen distinguishes between ‘strong’ and ‘weak’ absolute growth favoring the poor. The strong absolute growth favouring the poor occurs when the growth income gains of the poor are larger than the income gains of the non-poor. The weak absolute growth favouring the poor occurs when the incomes of the poor increase in absolute terms; however, the incomes of the non-poor increase even more (growth rate of the poor’s incomes is greater than 0). The weak absolute pro-poor growth implies that growth is pro-poor if it reduces poverty (Ravallion & Chen, 2003). Most of the growth processes can be classified as weakly pro-poor in absolute terms. Duclos (2009) argues that the absolute approach should be applied in underdeveloped countries, where a significant part of the population obtains incomes below the subsistence level. In these countries, the income redistribution policy should focus on poverty reduction in absolute terms to provide for the most basic needs. The relative approach focuses on distribution of growth benefits between the poor and the non-poor population. Within the relative approach, growth is considered to be favourable to the poor if the wealth of the poor grows faster than wealth of the non-poor (Klasen, 2008), i.e., when economic growth reduces CEEJ • 9(56) • 2022 • pp. 76-92 • ISSN 2543-6821 • DOI: 10.2478/ceej-2022-0006 79 income inequality. Within the relative approach Kakwani and Son (2008) recognize two kinds of the relative pro-poor growth: a relatively pro-poor growth and an absolutely pro-poor growth. Both approaches verify whether the distribution of the benefits of growth favours the poor as compared to the non-poor, However, growth relatively pro-poor leads to a decline in a relative inequality, whereas growth absolutely pro-poor leads to a decline in an absolute inequality (Grosse, et al., 2008). The relative approach should be applied as a supplement to the absolute approach in developed countries (Layard et al., 2010). Although the income redistribution policies should always be mainly focused on ensuring the physical existence of the poorest groups of the society, in the case of developed countries, their secondary goal should focus on preventing too much income inequality. Within the relative approach, changes in the poverty sphere are analysed on the basis of both growth and distribution of incomes among the poor and the nonpoor. Consequently, growth is described as pro-poor in relative terms only if it leads to reduction of both poverty and income inequality. 3. Analysis of the Growth Pattern Kakwani, et al. (2004) provide classification of the growth pattern analysis methods distinguishing partial and full methods. Within the partial approach, the analysis does not require defining any poverty indices or poverty lines. Analysis of the nature of growth is based on stochastic domination curves (Panek, 2011). When stochastic dominance conditions are not met, the pro-poorness of growth cannot be assessed; hence, the approach is called ‘partial’. The full approach needs to be based on poverty measures. As a result, it allows for the relevant assessment in every situation. 3.1 Assessing the Growth Pattern under the Full Approach The growth pattern indicators under the full approach are based on the elasticity of poverty measures with respect to economic growth. Kakwani & Subarrao (1990) proposed decomposing the changes in poverty into growth and inequality components. The poverty elasticity is estimated using the Lorenz curve. Similarly, Kakwani & Pernia (2000) proposed comparing the changes in poverty indices resulting from changes in income inequality with hypothetical changes, which would occur if the shape of income distribution remained constant and only the mean income changed. Poverty indices can be characterized by the poverty line (z), the mean income of individuals (μ) and the Lorenz function (L(q)): 𝑃𝑃𝑃𝑃(𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇,𝐿𝐿𝐿𝐿(𝑞𝑞𝑞𝑞)) . (1) (1) Changes in a poverty index between the initial period t = 1 and the final period t = 2 can be described using two components: - the growth component (G12) – changes resulting from the change in the mean income, - the inequality component (I12) – changes resulting from the change in the inequality of incomes. The change in poverty index (P12) can be presented as 𝑃𝑃𝑃𝑃12 =𝑃𝑃𝑃𝑃2−𝑃𝑃𝑃𝑃1=𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿[𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)�], (2) 𝑃𝑃𝑃𝑃12 =𝑃𝑃𝑃𝑃2−𝑃𝑃𝑃𝑃1=𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿[𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)�], (2) (2) and furthermore, decomposed into growth and inequality components: 𝑃𝑃𝑃𝑃12 =𝐺𝐺𝐺𝐺12 +𝐼𝐼𝐼𝐼12. (3) (3) Kakwani (2000) defined the two components as follows: 𝐺𝐺𝐺𝐺12 =1 2�𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)��+𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)���, (4) 𝐺𝐺𝐺𝐺12 =1 2�𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)��+𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��� , (4) (4) and 𝐼𝐼𝐼𝐼12 =1 2�𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)��+𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)���. (5), 𝐼𝐼𝐼𝐼12 =1 2�𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇1,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)��+𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,𝐿𝐿𝐿𝐿2(𝑞𝑞𝑞𝑞)��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃𝑃𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇2,𝐿𝐿𝐿𝐿1(𝑞𝑞𝑞𝑞)��� . (5), (5) CEEJ • 9(56) • 2022 • pp. 76-92 • ISSN 2543-6821 • DOI: 10.2478/ceej-2022-0006 80 where P(z, μ2, L1 (q))- poverty index at the level of income from the final period and the distribution of income from the initial period, P(z, μ1, L2 (q))– poverty index at the level of income from the initial period and the distribution of income from the final period. The total growth elasticity of poverty is defined as the ratio of the proportional change in poverty to the proportional change in the mean income. We can estimate it as the total differential of the expression, 𝜂𝜂𝜂𝜂=𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑃𝑃𝑃𝑃(𝑧𝑧𝑧𝑧,𝜇𝜇𝜇𝜇,𝑑𝑑𝑑𝑑(𝑞𝑞𝑞𝑞)) 𝑔𝑔𝑔𝑔12 , (6) (6) where g12=dLn(μ)=Ln(μ2)-Ln(μ1) – growth rate of mean income, and g12 (q) – growth rate of the income at the q-th quantile of income distribution, while g12=dLn(y(q))=Ln(y2 (q))-Ln(y1 (q)), 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿(𝑦𝑦𝑦𝑦2(𝑞𝑞𝑞𝑞)) −𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿(𝑦𝑦𝑦𝑦1(𝑞𝑞𝑞𝑞 ) ) , (7) 𝜂𝜂𝜂𝜂=𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔+𝜂𝜂𝜂𝜂𝑖𝑖𝑖𝑖. (7) where y1 (q), (y2 (q) – q-th quantiles of income distribution in the initial and final periods. The decrease in the poverty index is influenced by both the increase in mean income and the decrease in the inequality. Hence, the total growth elasticity of poverty can be presented as the sum of the relative growth elasticity of poverty (hg) and the relative inequality elasticity of poverty (hi) (Kakwani & Son, 2008): 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿(𝑦𝑦𝑦𝑦2(𝑞𝑞𝑞𝑞)) −𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿(𝑦𝑦𝑦𝑦1(𝑞𝑞𝑞𝑞)) , (7) 𝜂𝜂𝜂𝜂=𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔+𝜂𝜂𝜂𝜂𝑖𝑖𝑖𝑖. (8) The components of Equation (8) can be expressed as 𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔=𝐺𝐺𝐺𝐺12 𝑔𝑔𝑔𝑔12 , (9) (9) and . and 𝜂𝜂𝜂𝜂𝑖𝑖𝑖𝑖=𝐼𝐼𝐼𝐼12 𝑔𝑔𝑔𝑔12. (10) (10) Generally, the total growth elasticity of poverty ( η ) in the relative sense is neutral if the increase in income of individuals is proportionally the same for the poor and the non-poor. The growth elasticity of poverty (hg) describes the proportional change in the poverty index as a result of a 1% increase in mean income, assuming that relative income inequality does not change. The growth elasticity of poverty (hg) is generally negative – mean income should reduce poverty given constant income distribution. On the other hand, changes in income inequality resulting from economic growth may have both a negative and positive impact on poverty changes. Ultimately, when growth is pro-poor (not propoor) in relative terms, the total growth elasticity of poverty is lower (greater) than the neutral growth elasticity of poverty. Based on the decomposition of the poverty index, Kakwani and Pernia (2000) defined the pro-poor growth index (PPGI), in the relative sense, as the ratio of the total growth elasticity of poverty to the relative growth elasticity of poverty: 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 =𝜂𝜂𝜂𝜂 𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔. . (11) (11) When the PPGI is greater than 1, the inequality elasticity of poverty is negative ( η i<0) and both poverty and inequality decrease because of the increase in mean income. Growth is called relatively pro-poor (it is also called strictly relatively pro-poor), as the poor benefit proportionally more than the non-poor. If the PPGI is less than 0 (i.e., if η i>0 and | η i|>| η g|), growth is nonpro-poor, as it leads to both increased poverty and inequality (it is also called immiserizing growth). Finally, when 0<PPGI<1 (i. e., if η i>0 and | η i|<| η g|), poverty decreases due to an increase in mean income. However, the decrease is mitigated by an increase in income inequality. This type of growth is classified as trickle-down growth favouring the poor. However, while the average income of the poor grows, the nonpoor benefit proportionally more. During a recession, the mean income growth rate is negative (g12<0) and poverty usually increases, as both P12 and G12 are negative. If income inequality does not change, a recession is called pro-poor if P12<G12 and favorable to the non-poor if P12>G12. In this case, PPGI is defined as (Kakwani & Pernia, 2000) 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 =𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔 𝜂𝜂𝜂𝜂. . (12) (12) The recession will be described as favouring the poor when PPGI >1 and not favouring the poor when PPGI <1. CEEJ • 9(56) • 2022 • pp. 76-92 • ISSN 2543-6821 • DOI: 10.2478/ceej-2022-0006 81 For assessing whether growth is absolutely propoor (sometimes called strong absolute pro-poor growth) Kakwani and Son (2008) proposed the absolute pro-poor growth index (PPGI*). The absolute growth elasticity of poverty ( η g * ) is described as neutral if the increase in mean income leads to equal absolute income growth for both poor and non-poor individuals. The absolute PPGI is given by 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺∗=𝜂𝜂𝜂𝜂 𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔 ∗ . (13) (13) In order to assess the extent to which growth reduces poverty, Kakwani et al. (2004) proposed a modified measure that includes the actual incomes growth rate. They defined the poverty equivalent growth rate (PEGR) as a hypothetical growth rate of mean income (g12 *), which would affect the level of poverty in the same way as the actual growth rate (g12), given constant relative income inequality. The proportional reduction in poverty is equal to η g12. If the changes in income distribution were neutral in the relative sense, then an increase in mean income g12 * would cause a proportional reduction in poverty equal to η gg12 *, which should be equal to η g12. The PEGR in the relative sense is defined as 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝑃𝑃𝑃𝑃 =𝜂𝜂𝜂𝜂 𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔12 =𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺∗𝑔𝑔𝑔𝑔12 . (14) (14) A positive PEGR value implies a decrease in corresponding poverty index, and larger values indicate even stronger reduction. Growth is relatively pro-poor (strictly relatively pro-poor) when PEGR is greater than the mean income growth rate (PEGR>g12). If PEGR is greater than zero but less than the rate of growth of mean income (0<PEGR<g12) poverty is still reduced; however, the inequality increases (trickledown growth). It is also possible that an increase in mean income is accompanied by an increase in poverty (PEGR<0) as increasing inequality outweighs economic growth (immiserizing growth). During recession (g12<0), poverty generally increases. However, a strong income inequality decline may still lead to poverty reduction. Such a recession is called relatively strictly pro-poor and corresponds to PEGR>0. On the other hand, when g12<PEGR<0, the recession will favour the poor, as they lose proportionally less than the non-poor; however, the relevant poverty index will increase. Recession will be unfavourable to the poor when PEGR<g12<0. In this case, poverty grows, and the poor lose proportionally more than the non-poor (Kakwani et al., 2004). To determine whether growth is pro-poor in the relative sense, we can rewrite PEGR as 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝑃𝑃𝑃𝑃 = g12 +(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺−1)𝑔𝑔𝑔𝑔12. (15) (15) Growth is pro-poor (strictly pro-poor) in the relative terms if g12>0 and PPGI>1 or when g12<0 and PPGI<1. Therefore, the second element of the righthand side of Equation (15) is positive. It follows that growth will be relatively pro-poor if PEGR>g12. PEGR in the absolute sense (PEGR*) is defined similarly to PEGR in the relative sense using PPGI* (12). Therefore, Equation (15) can be rewritten as (Kakwani & Son, 2008) 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝑃𝑃𝑃𝑃∗=𝑔𝑔𝑔𝑔12[1 + (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺−𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺∗)] +[𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺∗−1]𝑔𝑔𝑔𝑔12. (16) 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝑃𝑃𝑃𝑃∗=𝑔𝑔𝑔𝑔12[1 + (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺−𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺∗)] +[𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺∗−1]𝑔𝑔𝑔𝑔12 . (16) (16) Growth is pro-poor in the absolute terms (strictly absolutely pro-poor) if g12>0 and PPGI*>1 (or recession is strictly pro-poor if g12<0 and PPGI*<1). In that case, the second element of the right-hand side of Equation (16) is positive. Thus, growth will favour the poor in absolute terms (growth is strictly absolutely propoor) if PEGR*>g12, with higher values indicating faster poverty reduction. Both categories introduced by Kakwani (growth relatively pro-poor, growth absolutely pro-poor) compare the distribution of income growth among the poor and the non-poor, the only difference being that comparisons are conducted using relative or absolute differences. The directions of changes in the values of the PEGR and related poverty measures should be consistent. However, for these relations to hold, the poverty indices should satisfy the monotonicity axiom. The monotonicity axiom states that, holding all else constant, when the income of a poor individual who is below the poverty line increases, the poverty index should decrease. This axiom is not met by the poverty headcount ratio1, which is the basic measure of poverty and, consequently, the PEGR changes may not 1 The headcount ratio, which is a share of individuals with incomes falling below the poverty line, measures poverty incidence. CEEJ • 9(56) • 2022 • pp. 76-92 • ISSN 2543-6821 • DOI: 10.2478/ceej-2022-0006 82 be consistent with the direction of poverty incidence changes in some situations (Subramanian, 2004; Zheng, 1997). On the other hand, the monotonicity axiom is fulfilled by the poverty gap index2 and the Watts poverty index3. 3.2. Measuring the Growth Pattern under a Partial Approach The partial approach allows for determining whether growth patterns reduce poverty in the absence of formal poverty measures. Ravallion and Chen (2003) introduced a framework for measuring pro-poorness of growth using growth incidence curves (GICs). The GIC is a graphical tool that visualizes the rate of income growth for each percentile of the nondecreasing income distribution. The income y of an individual corresponding to the qth quantile in the income distribution can be presented as the inverse of the cumulative distribution function of income F(y): 𝑦𝑦𝑦𝑦(𝑞𝑞𝑞𝑞)=𝐹𝐹𝐹𝐹−1(𝑞𝑞𝑞𝑞)=𝐿𝐿𝐿𝐿′(𝑞𝑞𝑞𝑞)𝜇𝜇𝜇𝜇. (17) (17) Letting q vary from 0 to 1, we get the so-called ‘quantile function’ (Moyes, 1999), which is a version of the Pen’s parade (Pen, 1971). The quantile growth rate (g12(q)) traces out the GIC and shows how the increase in incomes is distributed among the quantiles ranked by income. It follows from Equations (7) and (17) that 𝑔𝑔𝑔𝑔12(𝑞𝑞𝑞𝑞)=𝐹𝐹𝐹𝐹2 −1(𝑞𝑞𝑞𝑞)−𝐹𝐹𝐹𝐹1 −1(𝑞𝑞𝑞𝑞) 𝐹𝐹𝐹𝐹1 −1(𝑞𝑞𝑞𝑞)=𝐿𝐿𝐿𝐿2′(𝑞𝑞𝑞𝑞) 𝐿𝐿𝐿𝐿1′(𝑞𝑞𝑞𝑞)(𝑔𝑔𝑔𝑔′12 + 1) −1, (18) (18) where F1 -1(q),F2 -1(q) is the inverse of the cumulative distribution function at the qth quantile of income distribution in the initial and final periods: 𝑔𝑔𝑔𝑔′12 =𝜇𝜇𝜇𝜇2−𝜇𝜇𝜇𝜇1 𝜇𝜇𝜇𝜇1 . (19) (19) 2 The poverty gap index measures poverty depth and is defined as an average shortfall of the total population from the poverty line. 3 The Watts index measures poverty severity, taking into account together poverty incidence, poverty depth, and income inequality between the poor. Identification of the growth pattern based on the GIC uses the concept of the first-order stochastic dominance (Atkinson, 1987; Foster & Shorrocks, 1988; Panek, 2011; Ravallion, 1994). Let F1(y) and F2(y) be the cumulative distribution functions of incomes in the two analysed periods. The first-order dominance of F2 over F1 can be defined as 𝐹𝐹𝐹𝐹2𝐹𝐹𝐹𝐹1 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 ⇔ ∀𝑦𝑦𝑦𝑦𝐹𝐹𝐹𝐹2(𝑦𝑦𝑦𝑦)≤ 𝐹𝐹𝐹𝐹1(𝑦𝑦𝑦𝑦). (20) (20) When the quantile growth rates for the entire population are monotonically decreasing, the growth is favourable to the poor in the relative sense, regardless of whether it is positive or negative. It follows from Equation (18) that if the Lorenz curve does not change, then g12(q)=g12 * for all q. Moreover, g12(q)=g12 * only when (y2(q))/μ2 increases in the analysed period. If g12(q) is a decreasing (increasing) function for all q, income inequalities (measured by inequality measures satisfying the Pigou–Dalton transfer axiom4) falls (rises). If the GIC is situated strictly above zero (g12(q)>0 for all q), then the first-order dominance occurs, i.e., income increased for each quantile in the analysed period. However, usually GIC has a different sign for various values of y, and it does not identify the nature of the growth pattern. Therefore, Ravallion and Chen (2003) introduced a measure called rate of pro-poor growth (RPPG). The RPPG is equal to the normalized area under the GIC curve from q=0 to the value of the poverty headcount ratio5 at the initial period (H1)6, i.e., the area under the GIC curve for the poor in the initial period: 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 =1 𝐻𝐻𝐻𝐻1∫𝑔𝑔𝑔𝑔12(𝑞𝑞𝑞𝑞)𝑑𝑑𝑑𝑑𝑞𝑞𝑞𝑞 ≅ 1 𝐻𝐻𝐻𝐻1𝑄𝑄𝑄𝑄∑𝑔𝑔𝑔𝑔12(𝑞𝑞𝑞𝑞) 𝑞𝑞𝑞𝑞𝐻𝐻𝐻𝐻1 𝑞𝑞𝑞𝑞=1 𝐻𝐻𝐻𝐻1 0 , (21) (21) where qH1 is the quantile corresponding to the percentage of the poor for the initial period and Q is the number of quantiles. 4 This axiom states that the transfer of income from a poorer individual to a richer individual should increase income inequality. 5 Headcount ratio (H1) is a proportion of the poor in the total population. 6 The RPPG is functionally related to the Watts poverty index, i.e. RPPG=-dW, where 𝑊𝑊𝑊𝑊=1 𝑛𝑛𝑛𝑛�ln ( 𝑧𝑧𝑧𝑧 𝑦𝑦𝑦𝑦𝑖𝑖𝑖𝑖) 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 𝑖𝑖𝑖𝑖=1 . This index meets the monotonicity axiom. (Panek, 2011; Subramanian, 2012). CEEJ • 9(56) • 2022 • pp. 76-92 • ISSN 2543-6821 • DOI: 10.2478/ceej-2022-0006 83 The RPPG can be used to assess the pattern of growth both in absolute and relative terms. If RPPG > 0 growth is pro-poor in the weak sense (poverty reducing pro-poor growth). If RPPG < 0 the growth is not poverty-reducing. In relative terms, the growth is pro-poor if RPPG>g12 * (strictly pro-poor) and not propoor if PG<g12 *. In the empirical research, the RPPG is often compared with the growth rate of mean income (cf. Grimm, 2007; Harmáček, et al., 2017). This is a certain inconsistency, as the RPPG is the average of the quantile growth rates for all quantiles up to qH1 (for the poor). Thus, a more appropriate solution is to compare its value with the mean of quantile growth rates for all quantiles (for the entire population), i.e. with the value 𝑔𝑔𝑔𝑔12(𝑞𝑞𝑞𝑞) � � � � � � � � � (q = 1,...,n) rather than growth rate of mean income. 4. Data Source and Assumptions The empirical analyses are based on the data from the EU-SILC. EU-SILC started in 2003 and was fully implemented in all European Union (EU) countries by 2005. Serbia joined the research in 2012. Therefore, we use EU-SILC data for six Balkan countries from 2012 to 2017. EU-SILC is conducted using a rotational panel method in a four-year cycle. In every country, an initial sample is divided into four subsamples with the same size and structure. Starting from the second year, one of the four subsamples is removed and another subsample with the same size and structure is drawn. Ultimately, each subsample is meant to last four years. The survey results are weighted to represent the size and structure of the entire population for each EU member state. The sample size differs across countries, as it can be equal to as low as 4,000 households or as high as 20,000 households. Missing data on incomes is imputed using various methods of data imputation in different countries. The assessment of the growth pattern based within the axiom of anonymity does not require observation of the same individuals in two analysed periods. Nevertheless, we used a sequence of twoyear panels from the 2012–2017 period. This allows for mitigation of the sampling error, which is higher for cross-sectional data. Since we based our analyses on panel data, the RPPG index was applied under the stochastic dominance approach. The empirical analysis is based on individuals’ equivalent incomes. Income is defined as yearly household equivalent disposable income in the last year preceding the survey. All incomes were adjusted using relevant CPI indices with 2012 as the base. The equivalent disposable incomes were calculated by dividing disposable household income by the OECD modified equivalence scale. The modified OECD scale assigns a value of 1 to the first household member, 0.5 to every additional household adult member, and 0.3 to each child. The disposable income is defined as a sum of net monetary income gained by all households’ members. It does not take into account any fringe benefits (with exception of the company car) and other non-monetary incomes. Each individual is assigned a value of his household’s equivalent income. Negative incomes were changed to zero. In our empirical analysis, some modification of the PPGI and PEGR measures have been introduced. They involved the application of various methods of calculating the inequality elasticity of poverty in the relative and absolute approaches (see Appendix). Furthermore, in the empirical analyses of the growth pattern, the RPPG estimates were compared with the mean of quantile growth rates for the entire population ( 𝑔𝑔𝑔𝑔12(𝑞𝑞𝑞𝑞) � � � � � � � � � ) instead of comparing them with the growth rate of mean income (g12 *). Standard errors were calculated using bootstrapping. To identify the impoverished and calculate poverty indices, poverty lines need to be defined. The national poverty lines were calculated for 2012 as 60% of the national median equivalent income. This corresponds to the poverty lines’ definition implemented by Eurostat. However, for the following years we used the same 2012 poverty lines. Poverty indices used in the study focus on the three basic poverty aspects, e. g. on its incidence (headcount ratio7), depth (poverty gap index), and severity (Watts index). 7 The headcount ratio was applied. although the PEGR changes may not be consistent with the direction of poverty incidence changes in some situations, as it is the basic measure of poverty. CEEJ • 9(56) • 2022 • pp. 76-92 • ISSN 2543-6821 • DOI: 10.2478/ceej-2022-0006 90 7. Conclusion Designers of policies aimed at combating poverty should consider the relationship between economic growth and income distribution. Particularly, the impact of economic growth on the incomes of the poor should be investigated and understood so that the social policies facilitate the poor to participate in the fruits of economic growth. This study presents and implements a set of statistical measures that are designed to inform policymakers on whether the economic growth favour the impoverished. The theoretical part of the study provides the definition of the pro-poor growth and distinguishes between pro-poor growth in absolute and in relative terms. Moreover, the theoretical foundations of the construction of pro-poor growth measures were presented, and their basic advantages, limitations, and potential comparability were discussed. In order to answer our research question of whether growth in the Balkan countries was pro-poor, we calculated and analysed the wide range of pro-poor growth measures using the most up-to-date panel data sets available. These were the poverty equivalent growth rates (PEGR and PEGR*) for various poverty indices and the RPPG. The results were compared across the measures for six Balkan countries, and considerable differences were observed. This was due to various assumptions adopted in the applied measures, the construction of which is furthermore derived from different definitions of pro-poor growth. Generally, growth was significantly povertyreducing only in Greece and Romania in 2015–2017, Croatia in 2014–2016, Slovenia in 2013–2017, and Serbia in 2016–2017. The growth pattern was significantly non-poverty-reducing only in Bulgaria in 2014–2015. Different indicators of growth patterns yielded similar results; however, in the case of Greece in 2012–2013, values of RPPG and PEGR contradict each other, as they measure slightly different aspects of poverty. Throughout all analysed countries, growth patterns tend to be more poverty-reducing or propoor in times of faster economic growth. It was also shown that the level of social benefits to the poor does not directly influence the pro-poor nature of the economic growth. The reasons for which countries differ with respect to the pro-poor nature of economic growth require further theoretical and empirical research. Appendix Pro-Poor Growth Indices Estimation The PEGR and the PEGR* indices were estimated according to Equation (13), using the relative and the absolute PPGI, respectively. To estimate the PPGI, we used the growth and inequality decomposition of poverty index (Equations 4 and 5). However, the poverty indices P(z,μ2,L1(q))and P(z,μ1,L2(q)) were estimated in this survey by adjusting the poverty line instead of adjusting the mean income, as was proposed by Kakwani and Son (2008). When estimating the poverty index, P(z,μ1,L2(q)), we use the distribution of household income from the final period, adjusting the poverty line appropriately. Similarly, when estimating P(z,μ2,L1(q)), we use the distribution of household incomes from the initial period and adjust the poverty line accordingly. This adjustment takes different forms, depending on whether we estimate the PPGI in the relative sense or in the absolute sense. PPGI in the relative sense is estimated as the ratio of the total growth elasticity of poverty ( η ) to the neutral growth elasticity of poverty ( η g)9: 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 =𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔+𝜂𝜂𝜂𝜂𝑖𝑖𝑖𝑖 𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔, (A.1) (A.1) The estimation of the growth poverty elasticity and inequality poverty elasticity when calculating PPGI in the relative sense was made as follows: 𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔=�𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧𝜇𝜇𝜇𝜇1/𝜇𝜇𝜇𝜇2,𝑦𝑦𝑦𝑦1��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝑦𝑦𝑦𝑦1��+𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝑦𝑦𝑦𝑦2��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧𝜇𝜇𝜇𝜇2/𝜇𝜇𝜇𝜇1,𝑦𝑦𝑦𝑦2��� 2𝑔𝑔𝑔𝑔12 , (A.2) (A.2) 𝜂𝜂𝜂𝜂𝑖𝑖𝑖𝑖=�𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧𝜇𝜇𝜇𝜇2/𝜇𝜇𝜇𝜇1,𝑦𝑦𝑦𝑦2��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝑦𝑦𝑦𝑦1��+𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝑦𝑦𝑦𝑦2��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧𝜇𝜇𝜇𝜇1/𝜇𝜇𝜇𝜇2,𝑦𝑦𝑦𝑦1��� 2𝑔𝑔𝑔𝑔12 . (A.3) (A.3) When calculating PPGI* (PPGI in the absolute sense) the growth poverty elasticity and inequality poverty elasticity were estimated as 𝜂𝜂𝜂𝜂𝑔𝑔𝑔𝑔 ∗=�𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧𝑧𝜇𝜇𝜇𝜇1+𝜇𝜇𝜇𝜇2,𝑦𝑦𝑦𝑦1��𝑧𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝑦𝑦𝑦𝑦1��+𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝑦𝑦𝑦𝑦2��𝑧𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧+𝜇𝜇𝜇𝜇2𝑧𝜇𝜇𝜇𝜇1,𝑦𝑦𝑦𝑦2��� 2𝑔𝑔𝑔𝑔12 , (A.4) (A.4) 𝜂𝜂𝜂𝜂𝑖𝑖𝑖𝑖 ∗=�𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧+𝜇𝜇𝜇𝜇2−𝜇𝜇𝜇𝜇1,𝑦𝑦𝑦𝑦2��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝑦𝑦𝑦𝑦1��+𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧,𝑦𝑦𝑦𝑦2��−𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿�𝑃𝑃𝑃𝑃�𝑧𝑧𝑧𝑧−𝜇𝜇𝜇𝜇1+𝜇𝜇𝜇𝜇2,𝑦𝑦𝑦𝑦1��� 2𝑔𝑔𝑔𝑔12 . (A.5 ) (A.5 ) CEEJ • 9(56) • 2022 • pp. 76-92 • ISSN 2543-6821 • DOI: 10.2478/ceej-2022-0006 91 In every instance, statistical significance was assessed based on standard errors estimated using bootstrapping for 200 subsamples. For each indicator, a z-type statistic was calculated using estimated standard errors and statistical significance was assessed considering a difference of the estimated value from 0. References Araar, A., Duclos, J-Y., Audet, M., & Makdissi, P. (2009). 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