The Changing Education Distribution and Income Inequality in Great Britain

• Published date: 01 September 2021
• Author: Iva Valentinova Tasseva
• Date: 01 September 2021
• DOI: http://doi.org/10.1111/roiw.12486

© 2020 The Authors. Review of Income and Wealth published by John Wiley & Sons Ltd on behalf of

International Association for Research in Income and Wealth

659

THE CHANGING EDUCATION DISTRIBUTION AND INCOME

INEQUALITY IN GREAT BRITAIN

by Iva valentInova tasseva*

Institute for Social and Economic Research,University of Essex

Over the past years, education attainment has increased at an unprecedented rate in Great Britain.

We analyze how the education expansion affected inequality in household net incomes since the early

2000s. We show that, all else being equal, education composition changes led to higher living standards

mostly through higher wages. As education expansion led to larger income gains in the middle and top

than at the bottom of the distribution, income inequality increased. Despite the increasing share of

high-educated workers, we find limited evidence of a “compression” effect on inequality, as the higher

education wage premium remained broadly unchanged.

JEL Codes: D31, I24, I26

Keywords: education expansion, income distribution, decomposition

1. IntroductIon

Over the past years, there has been an unprecedented increase in education

attainment in Great Britain. The share of individuals completing full-time educa-

tion aged 16 fell by 37 percent between 2001 and 2017; whilst the share of individ-

uals completing education aged 17–19 (beyond compulsory education) increased

by 47 percent and the share of those completing education aged 20+ (in Higher

Education) increased for women by 66 percent and for men by 49 percent (Table1).

These large structural changes in education have important consequences for

income inequality. For developing countries in particular, it has been noted that

the returns to education are convex and hence, an equally distributed expansion of

education among low- and high-skilled can lead to a rise in inequality (Battistón

et al., 2014). Bourguignon et al. (2004) refer to this link between education and

Note: I would like to thank Mike Brewer, Paola De Agostini, Paul Fisher, Kitty Stewart, Holly

Sutherland, Philippe Van Kerm, the editor and three anonymous referees for useful comments. I also

gratefully acknowledge the contribution of all past and current members of the EUROMOD consor-

tium. This research was supported by the Economic and Social Research Council (ESRC) through the

Research Centre on Micro-Social Change (MiSoC) at the University of Essex (grant number ES/

L009153/1) and NORFACE ERA-NET Welfare State Futures Programme (grant number 462-14-010).

The results presented here are based on EUROMOD version I1.112. The process of extending and

updating EUROMOD is financially supported by the European Union Programme for Employment

and Social Innovation ‘Easi’ (2014–20). I make use of micro-data from the Family Resources Survey

made available by the Department of Work and Pensions via the UK Data Service. The results and their

interpretation are my own responsibility.

*Correspondence to: Iva Valentinova Tasseva, Institute for Social and Economic Research,

University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK (itasseva@essex.ac.uk).

Review of Income and Wealth

Series 67, Number 3, September 2021

DOI: 10.1111/roiw.12486

This is an open access article under the terms of the Creative Commons Attribution License, which

permits use, distribution and reproduction in any medium, provided the original work is properly

cited.

bs_bs_banner

Review of Income and Wealth, Series 67, Number 3, September 2021

660

© 2020 The Authors. Review of Income and Wealth published by John Wiley & Sons Ltd on behalf of

International Association for Research in Income and Wealth

inequality as the “paradox of progress.” The literature on the effect of education

on income inequality emphasizes the “composition” and “compression” effects of

education expansion (Knight and Sabot, 1983; Gregorio and Lee, 2002, Rehme,

2007, Teulings and van Rens, 2008). As the relative size of the high-education

group increases—i.e. the “composition” effect—inequality goes up initially; even-

tually inequality goes down as fewer low-educated people remain. As the share of

educated workers increases, the higher education (HE) wage premium goes down

which in turn lowers inequality—i.e. the “compression” effect.

The link between the recent education trends and household net income

inequality in Great Britain is not well understood and the aim of this paper is to

provide an in-depth account of this relationship for the period 2001–17. Our paper

contributes to the literature by studying changes along the distribution of income

and covering the recent period of education expansion.

We contribute directly to the literature on education and income inequality.

Coady and Dizioli (2018) study the relationship between inequality of disposable

income and education in many different countries between 1980 and 2010. They

find that income inequality increases with inequality in education and average years

of schooling. Eika et al. (2019) study the impact of education changes on inequal-

ity of earnings among couples in the US, UK, Denmark, Germany and Norway.

Focusing on changes to educational assortative mating, they find that these matter

little for explaining trends in inequality. Brewer and Wren-Lewis (2015) decompose

changes to income inequality in Great Britain, by income source and household

characteristics. For education, they find that between 1991 and 2008–09 education

quantity effects (changes to the distribution of education) raised inequality, while

price effects (changes to the returns to education) mitigated this increase. Overall,

the relationship between the recent education changes and inequality of household

net income in Great Britain remains relatively understudied.

TABLE 1

educatIon shares by age at WhIch completed Full-tIme educatIon (In %)

2001 2007 2011 2017

Men

Aged 16 or less 60.1 53.3 45.7 39.5

Aged 17–19 19.1 21.7 26.9 29.3

Aged 20+ 20.9 25.0 27.4 31.2

Women

Aged 16 or less 57.1 50.1 42.5 34.7

Aged 17–19 23.4 25.8 31.3 33.0

Aged 20+ 19.5 24.1 26.1 32.3

All

Aged 16 or less 58.6 51.7 44.1 37.1

Aged 17–19 21.3 23.8 29.1 31.2

Aged 20+ 20.2 24.5 26.8 31.8

Notes: Sample includes individuals aged 15 to 64. Individuals are classified into the three categories

using information on age completed full-time education (Family Resources Survey variable “tea”) or, if

missing, using information on their age and whether presently in full-time education (variable “fted”).

Source: Author’s calculations using the Family Resources Survey for 2001/02 (2001), 2007/08

(2007), 2011/12 (2011) and 2017/18 (2017).