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IAOS 2018 - The recent role of government in decreasing harmful inequality, I. Cserhati, T. Kereszteli, I. Ritzlné Kazimir
1. THE RECENT ROLE OF GOVERNMENT IN
DECREASING “HARMFUL INEQUALITY”
ILONA CSERHÁTI – TIBOR KERESZTÉLY – ILDIKÓ RITZLNÉ KAZIMIR
EFOP-3.6.2-16-2017-00017
Sustainable, intelligent and inclusive regional and
city models
2. Problem
statement
• Trends and measuring inequality
• The inequality – growth relationship
• „Inequality is a multidimensional concept” (Kornai, 2015)
„Harmful
inequality”
• New concept
• Traditional tools vs. social income in kind
• The role of the government
Empirical
analysis
• Potential drivers
• Principal component analysis
• Cluster analysis
Results
• Different paths in terms of the government’s role and main objectives
• Conclusion and further research plans
AGENDA
4. • Ambigous effect
• Different stages of economic development
(Kuznets, 1955)
• Labour and capital income
• Meta analysis of income inequality and
economic growth (Neves, Afonso, Tavares
Silva, 2016)
inequality is a multidimensional problem
ECONOMIC GROWTH AND INEQUALITY
Inequality
Income per capita
5. • Income inequality has widened
in a majority of OECD
countries over the past decades
(by almost 10% on averages).
• Inequality of market incomes
increased more rapidly than that
for net disposable incomes
(including taxes and cash
benefits)
• Governments were not able to
stop the process
• Wealth has become even more
unequally distributed than
income
• well-being
inequalities
have also
widened.
INEQUALITIES OF MARKET AND DISPOSABLE INCOME
IN THE PAST DECADES
Traditional government tools (taxation and monetary transfers)
are not sufficiant
6. • Income inequality remains at very high level in spite of
the recovery
• Across OECD countries average income inequality is
marginally higher now then it was in 2007
Possible impact of the recovery:
• Improving labor markets – more job opportunities – less
inequalities
• Recovery - more capital incomes - higher inequalities
Increased cash transfers did not stop the widening process…
INEQUALITY TRENDS AFTER THE FINANCIAL CRISIS
Gini (disposable income, post taxes and
transfers)
Year 2007 2014 2015 Change
Austria 0,28 0,28 0,28 97,9%
Belgium 0,28 0,27 0,27 97,5%
Canada 0,32 0,32 0,32 100,6%
Chile .. .. 0,45 93,6%
Czech Republic 0,25 0,26 0,26 101,2%
Finland 0,27 0,26 0,27 98,9%
Germany .. 0,29 .. 102,1%
Greece 0,33 0,35 0,35 107,9%
Hungary 0,28 0,28 .. 101,4%
Iceland 0,28 0,24 .. 86,2%
Ireland 0,30 0,30 .. 101,7%
Italy 0,31 0,33 .. 106,8%
Latvia 0,36 0,34 0,34 94,7%
Luxembourg 0,28 0,29 .. 102,5%
Norway .. 0,27 0,28 110,5%
Poland 0,32 0,30 0,30 92,8%
Portugal 0,35 0,34 0,33 94,4%
Slovak Republic 0,24 0,24 0,25 105,0%
Slovenia 0,23 0,25 0,25 106,0%
Spain 0,31 0,35 0,35 111,8%
United Kingdom 0,37 0,36 0,36 97,6%
100.0
100.3
102.3
103.1
103.3
103.6 103.6
100.0 100.2 100.3 100.1
100.7 100.9
101.5
99
100
101
102
103
104
2007 2008 2009 2010 2011 2012 2013
Inequality before and after redistribution
(OECD average, 2007=100%)
Market income inequality Disposable income inequality
9. WHAT DO WE MEASURE EXACTLY?
• Inequalities:
• in income,
• in wealth,
• in opportunities (education, health care, labor market),
• in multidimensional living standards,
• in well-being, etc.
HOW DO WE MEASURE IT?
• Problems:
• What can questionnaires measure exactly (top 1% is
missing, poorests are missing)
• Do we have sufficient information? (long panel data
bases, micro level information on intergenerational
issues)
MULTIDIMENSIONAL INEQUALITY
10. 1. „Harmful inequalities”: drivers for the long term economic performance
• Inequalities in access to (high quality) education
• Inequality in access to (high quality) health care
• Intergenerational labor market mobility
• (In)effectiveness of government
2. Esimating the relationship between „harmful inequality” and growth
3. New role of the government: change distribution of social transfers in kind
A NEW MEASUREMENTS: „HARMFUL INEQUALITY” OR
INEQUALITIES IN OPPORTUNITIES
11. Potential drivers (explaining variables):
• ECS Strength: Percentage of variance in student performance in science explained by
socio-economic status (data source: PISA 2015)
• ECS Rate: Ratio of science performance of students in the top quarter and students in
the bottom quarter of socio-economic status (data source: PISA 2015)
• Self-perceived Health by socio-economic status: ratio of answers good/very good in
quintile5, divided by the ratio of answers good/very good in quintile1 (data source:
OECD)
• Self-perceived Health by Education: ratio of answers good/very good in ISCED5-8,
divided by the ratio of answers good/very good in ISCED0-2 (data source: OECD)
• Gov Eff: Government Effectiveness (data source: Worldwide Governance Indicators
(WGI), World Bank)
• Reg Qual: Regulatory Quality (data source: Worldwide Governance Indicators (WGI),
World Bank)
• IGE: Intergenerational Elasticity (relationship between wages of fathers and sons), 3
categories: low, medium, high (data source: Miles Corak (2013), own estimation)
POTENTIAL DRIVERS
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
12. Dependent variables:
• Low perform rate: Percentage of students performing below
Level 2 in science
source: PISA 2015
• Science Score (unadjusted)
source: PISA 2015
• Years Lost: Years lost, /100 000 population, aged 0-69 years old
source: OECD
• GDP per capita: constant prices, PPP
source: OECD
• GINI coefficient
source: World Bank
DEPENDENT VARIABLES
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
13. How to measure the cause-effect relationship?
• Dataset: cross-sectional, 35 OECD members, year
2015
• First idea: multiple regression models
o Too many estimated parameters regarding to the number of observations
o Time inconsistency: lack of information for a usable panel data
• Our solution
o Cluster analysis using the explaining variables
o Computing the means of the dependent variables for the clusters
o ANOVA
METHODOLOGY
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
14. • Dataset: 8 variables
o 6 quantitave
o 2 dummies from 1 qualitative (IGE)
• Problem: high collinearity, similar content is some cases
• Solution: principle components
o Used only 7 variables (using both dummies caused hardly interpretable
parameters), the dummy „medium” of IGE was left
CLUSTER ANALYSIS
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
15. PRINCIPLE COMPONENTS
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
• Factor1: Governance (Gov Eff, Reg Qual)
• Factor2: Access to education (ECS
strength, ECS Rate)
• Factor3: Acces to health care (Self-
percevied health by education and SCES)
and social mobility (DUM_H)
16. CLUSTER ANALYSIS
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
• Cluster1: bad governance, big differences in access to education, medium differences in access to health care
• Cluster2: good governance, small differences in access to education and to health care
• Cluster3: medium governance, small differences in access to education, big differences in access to health
care
17. CLUSTER ANALYSIS
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
Years lost and GINI are not influenced significantly by the cluster memberships.
18. CLUSTER ANALYSIS
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
Our cause-effect analysis is not perfect, so it could be informative to see, what happens if use
all the varaibales is one cluster analysis.
19. • Inequality of opportunity can affect long-term growth.
• Inequality should be measured multidimensionally.
• The most important dimension is the harmful inequality: it has a
cause-effect story with the GDP.
• Taxation is not enough.
• Social transfers in kind are the key point: the government should
concentrate on this issue.
The poor also has to get chance to access the high-level education
and health-care!
Intergenerational mobility is the key element of the progress.
Implementation of panel surveys (for OECD countries) would be a
great advancement.
CONLUSIONS AND
FURTHER RESEARCH PLANS
EFOP-3.6.2-16-2017-00017 – Sustainable,
intelligent and inclusive regional and city models
