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The Political Economy of Neo-modernisation pp 85–117Cite as

Palgrave Macmillan

Stylised Fact of the Changing Inequality-Growth Landscape

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Abstract

The causal relationships between country-level economic development and inequality remain a matter of considerable debate and policy interest. This chapter contributes to the literature on this topic by presenting the results of short-run and long-run Granger causality tests in both directions. Countries are classified according to the combination of positive and negative one-way and two-way causal relations found to be statistically significant. This challenges universal theories by distinguishing between 11 clusters of countries according to the Inequality-Growth causal nexus they display over time. The empirical findings on the existence of distinctive features of each of the regional groups can be consistent with the main arguments of the varieties of (welfare) capitalism in a way that each regional group may have largely been institutionalised by stronger (or weaker) (re)distribution systems and/or by inequality enabling (or restraining) growth potential. The findings are also consistent with the theory of an ‘N’-shaped relation between national income per person and inequality: in other words, it confirms that for many high-income countries growth is associated with rising inequality.

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Notes

  1. 1.

    For example, Friedman (1953), Rebelo (1991), Deaton and Paxson (1997), Galor and Tsiddon (1997), Partridge (1997), Li and Zou (1998), Forbes (2000), and Bell and Freeman (2001).

  2. 2.

    For example, Stiglitz (1969), Feldstein and Horioka (1980), Alesina and Rodrik (1994), Persson and Tabellini (1994), Clarke (1995), Alesina and Perotti (1996), Benabou (1996), Benhabib and Rustichini (1996), Perotti (1996), De La Croix and Doepke (2003), Wilkinson and Pickett (2010), Razmi and Ashrafzadeh (2012), Berg and Ostry (2013), and Lee and Son (2016).

  3. 3.

    For example, Psacharopoulos et al. (1995), Aghion and Bolton (1997), Bruno et al. 1998), De Janvry and Sadoulet (2000), Kakwani and Pernia (2000), Bourguignon (2003), Krongkaew and Kakwani (2003), Adams (Adams 2004), Heltberg (2004), Ravallion (2007), Basu and Mallick (2008), Ogun (2010), Ostry et al. 2014, and Akinci (2017).

  4. 4.

    For example, Cutler and Katz (1991), Blank and Card (1993), Weriemmi and Ehrhart (2008), and Rubin and Segal (2015).

  5. 5.

    For example, Deininger and Squire (1996), Barro (2000), Chen (2003), Galbraith and Kum (2005), Castelló-Climent (2010), and Jihène and Ghazi (2013).

  6. 6.

    The time period used between countries differ, which could provoke some econometric controversy with the extent to which Granger causality tests can be compared like-for-like (i.e., a structural break might appear in one series and not the other and hence affects the causality relationship differently in the results). Nonetheless, more than four-fifth of the studied countries have convergence of the period of 1985–2010. Furthermore, this econometric analysis covers 110 sample countries (out of 143 data available) that exceed 105 recommended number of the countries that can be appropriate to represent the world views (stylised facts) based on 5 per cent margin of error and 95 per cent confidence level so that the difference of the time period can be ignored.

  7. 7.

    Selection of ARG is unbiased. ARG is selected to describe the results simply because ARG is the first one both that is presented in Appendix 2 and that is confirmed with both long- and short-run associationships.

  8. 8.

    Note: 1 represents the 7 countries characterised for negative associationship between INE and PCGDP with GC cluster (KEN KHM TUN), GE cluster (MLI NPL TJK), and GCE cluster (BFA); 2 represents the 10 countries characterised for both positive and negative associationship between INE and PCGDP with GIE cluster (ARM LKA NGA), GUE cluster (HND KGZ SLE), GX cluster (EGY VNM), GIC cluster (SEN), and GCU cluster (LAO); 3 represents the 16 countries characterised for positive associationship between INE and PCGDP with GU cluster (BDI BGD GTM IDN IND MAR RWA UGA UKR ZMB), GI cluster (CIV GHA LSO UZB), and GIU cluster (PAK TZA); 4 represents the 21 countries characterised for negative associationship between INE and PCGDP with GE cluster (AGO CHN COL DOM MEX MNE MYS NAM RUS SRB TUR VEN), GC cluster (AZE BRA BWA ECU GEO KAZ PAN PRY), and GCE cluster (ARG); 5 represents the 8 countries characterised for both positive and negative associationship between INE and PCGDP with GCU cluster (JAM MKD), GUE cluster (THA ZAF), GX cluster (CRI PER), GIC cluster (TKM), and GIE cluster (JOR); 6 represents the 3 countries characterised for positive associationship between INE and PCGDP with GI cluster (BGR ROU) and GU cluster (BLR); 7 represents the 7 countries characterised for negative associationship between INE and PCGDP with GC cluster (FRA HUN IRL SWE) and GE cluster (HRV LVA SVK); 8 represents the 12 countries characterised for both positive and negative associationship between INE and PCGDP with GIE cluster (EST ISL KOR NZL PRT), GIC cluster (CAN CHL GBR), GCU cluster (ESP ISR), and GUE (CZE HKG); and 9 represents the 20 countries characterised for positive associationship between INE and PCGDP with GU cluster (AUS CHE CYP ITA JPN LUX MLT POL SGP TTO USA), GI cluster (BEL DEU DNK GRC NOR PRI SVN), and GIU cluster (AUT NLD)).

  9. 9.

    Six countries in Middle East and North Africa comprise GU (33.3 per cent), GC (16.7 per cent), GIE (16.7 per cent), GUC (16.7 per cent), and GX (16.7 per cent); 13 countries in East Asia and Pacific comprise GU (30.8 per cent), GC (77 per cent), GE (15.4 per cent), GIE (15.4 per cent), GUC (7.7 per cent), GUE (15.4 per cent), and GX (7.7 per cent); six countries in South Asia comprise GU (40 per cent), GIU (20 per cent), the (20 per cent), and GIE (20 per cent); 17 countries in Latin America and the Caribbean comprise GI (5.9 per cent), GU (11.8 per cent), GC (23.5 per cent), GE (23.5 per cent), GCE (5.9 per cent), GIC (59 per cent), GUC (5.9 per cent), GUE (5.9 per cent), and GX (11.8 per cent); 18 countries in sub-Saharan Africa comprise GI (16.7 per cent), GU (22.2 per cent), GIU (5.6 per cent), GC (11.1 per cent), GE (16.7 per cent), GCE (5.6 per cent), GIC (5.6 per cent), GIE (5.6 per cent), and GUE (11.1 per cent); 43 countries in Europe and Central Asia comprise GI (20.9 per cent), GU (16.3 per cent), GIU (4.7 per cent), GC (16.3 per cent), GE (18.6 per cent), GIC (4.7 per cent), GIE (9.3 per cent), GUC (4.7 per cent), and GUE (4.7 per cent); two countries in North America comprise GU (50 per cent) and GIC (50 per cent)).

  10. 10.

    For a more detailed discussion, see Chap. 2.

  11. 11.

    For a more detailed discussion, see Chap. 3.

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Baek, S.J. (2018). Stylised Fact of the Changing Inequality-Growth Landscape. In: The Political Economy of Neo-modernisation. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-91394-0_4

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