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Plenary session 3 4 holly sutherland
1. What About STIK: How to Treat In-kind
Government Benefits at Micro- and Macro-
Levels
Discussant: Holly Sutherland, ISER, University of Essex
Session 3 IARIW General Conference 2014
Rotterdam, the Netherlands August 25th
2. 1. Household Income Disparity Comparisons Among
Countries with Various Levels of Redistribution,
Gyorgy Gyomai & Jennifer Ribarsky (OECD)
– “OECD paper”
2. Social Transfers in Kind (STIK) – Methodologies for
their Imputation, Impact on Economic Well-being
and a Comparison of the Treatment in Macro versus
Micro Data, Heather Burgess, Michael Smedes,
Andrew Tomadini & David Zago (Australian Bureau of
Statistics)
– “ABS paper”
3. Outline
• Introductory/contextual comments
• Common elements and differences
• OECD paper
– Summary
– Comments
• ABS paper
– Summary
– Comments
• Broader questions and reflections
4. Introduction/context
• From a micro (academic/policy) perspective
• AIM-AP project
– Cross-country comparisons of the distributional effects of
STiK (7 EU countries)
– Countries make different choices in the balance between
cash and non cash policies
• substitutes to some extent
• distributional consequences
• STiK in the context of cash redistribution
– Paulus, Sutherland and Tsakloglou (2010) JPAM
5. Common elements
• Concern with consistency between micro and macro
measures of STiK; (mostly) starting from the macro side
• Concepts and starting point from Canberra
• “Insurance approach” (modified) to measuring the incidence
of education, healthcare and “other” STiK, allocating producer
costs to relevant households in micro data
• STiK by income quintiles; effect on income inequality
• Reports of official studies
– OECD – Eurostat-OECD joint Expert Group on Disparities in
National Accounts (EG-DNA) → experimental calculations
– ABS – following from their contribution to EG-DNA;
ongoing work
6. Differences
• Geographical scope:
– OECD: calculations for 10 diverse countries - Australia, France, Italy, Korea,
Mexico, New Zealand, Slovenia, Sweden, Switzerland & the US
– ABS: Australia only
• Content
– OECD: Illustrative/experimental calculations from the EG-DNA; further
simulation work to show that measurement issues should not prevent
conclusions being drawn
– ABS: An account of how official statistics are constructed and
reconciled (micro-macro); effect of including STiK on measures of Low
Economic Resource (LER) headcounts
• Income ranking for quintiles:
– OECD: Equivalised cash disposable income, counting households
– ABS: Equivalised private income including IR, counting persons
7. OECD paper - methods
• National inputs into EG study, so not always fully comparable (or
documented?)
• STiK elements: Health, Education and “Other”
• Macro quantities adjusted for population scope of micro-data
– People in institutions/overseas, not in private households
– By cash income and STiK sub-component
• Benchmark sum of micro-level cash income variables to macro totals
(range of methods)
• Allocate macro quantity of STiK to households according to socio-demographic
characteristics (insurance approach, modified in places)
– Heath: age and a varying range of other characteristics (gender, region,
deprivation, long term disabled/in need of care)
– Education: for those participating in education by level (mostly not
distinguishing public/private)
– “Other” varies and may include social protection services, elderly or child-care
services, recreational and cultural services; methods vary
• Households ranked by equivalised cash household disposable income
(using the modified OECD scale) and divided into quintiles on this basis
– Nice discussion of the choice of ranking regime
8. OECD paper – selected results
• STiK distribution across income quintiles
• (Comparison for healthcare in Sweden with “actual use”
method)
• Impact on income distribution of net transfers as a whole
(cash benefits and taxes, plus STiK)
• Effect on Gini coefficient
9. Share of STiK by income quintile: health
– Differences across countries in distribution reflect differences in
• Household composition of quintiles
• Types of population targeted by STiK
• Content of what is classified as STiK by national accountants
15. OECD paper – extension
• STiK distribution is difficult to measure precisely so reliability
of estimates and comparisons across countries might easily be
questioned.
• What would happen if we allocated STiK randomly across
households?
• The simulation exercise shows that on this basis including STiK
would still reduce income inequality but by less than when
using the detailed methods of allocation.
16. OECD paper – conclusion
• The larger the starting levels of inequality the more likely any
random allocation of STiK will improve the Gini-coefficient.
• The larger the share of total STiK in adjusted disposable income the
greater impact STiK may have on the Gini coefficient.
• An imperfect allocation of STiK is better than not accounting for
STiK at all.
• A uniform allocation – corresponding to a situation where no
information is available to the NSO to model STiK allocation at a
micro level – already captures at least 2/3 of the “true” impact of
STiK on the value of the Gini-coefficient.
17. OECD paper – comments (1/2)
• On the simulation exercise and the conclusion
– Almost axiomatic;
– Not clear that random allocation (equal shares) is the natural or only
appropriate counterfactual for STiK (or public spending generally):
proportional to voting power or cash income?
– STiK is created through political decision making. There are policy
implications of assuming random or equal shares.
• On the EG-DNA method: broadly standard/fine but
– Treatment of those who opt out of public provision?
– Treatment of non-compulsory (e.g. tertiary) education?
– Appropriateness of the equivalence scale for all income concepts?
– The “other” category is large/variable enough to be problematic
– Demonstrates the challenges of cross-country comparability and
suggests that an analysis based on collection of national studies may
not be fully adequate
18. OECD paper – comments (2/2)
• On the analysis of results
– It looks like the inequality results are calculated on quintile averages (5
numbers) rather than the micro-level distributions (e.g. Fig 3): if so a
huge and unnecessary loss of information.
– “Differences across countries in distribution reflect differences in
• Household composition of quintiles
• Types of population targeted by STiK
• Content of what is classified as STiK by national accountants”
• (Actual resources going into STiK)
– Suggestion: why not try and distinguish these effects?
19. ABS paper - methods
• Similar method to the OECD paper, more detail about Australian specifics
provided, including the “other” category, which is disaggregated
– Rent subsidies, electricity concessions, child care and other care
• STiK by equivalised “private income quintile” incl IR. [Before taxes and
cash transfers? What e-scale?]
• Small un-reconciled differences in SNA and micro-data based (Survey of
Income and Housing- SIH) estimates of total STiK due to difficulties in
aligning the transactions that are included, and reduced population scope
in micro-data.
– E.g. SNA includes spending on national parks, wildlife and cultural facilities
and services; micro-data based estimates do not
• Confronting micro- and macro- estimates usefully reveals different
(“subjective”) treatments by context especially in relation to
– Deciding which expenditures are individual and which collective
– Omission of the return on capital in non-market production, leading to STiK
being under-valued.
20. ABS paper – selected results
• STiK (3 main components) by private income
quintile
• Income shares by quintile + Gini coefficients
using 3 income concepts:
21. STiK in $ (3 main components) by private
income quintile
1 2 3 4 5
300
200
100
Source: ABS publications 6523.0 and 6537.0.
Equivilised private income (incl. IR) quintile
Average weekly benefit ($)
0
Education benefits
Health benefits
Social security and welfare benefits
22. Shares of income by income concept, by quintile
+ Gini coefficients
23. ABS paper – low economic resource
(LER) households/people
• LER – in the bottom 40% of equivalised disposable
income including IR and in the bottom 40% by
equivalised net worth
• Adding STiK to the former changes the composition
of those considered to be with LER
– Fewer couples/singles over age 65, those with children,
those with a disability
– More couples/singles below age 65 without children
• BUT LER is an indicator of economic hardship, not
the level of resources received: so STiK not
necessarily relevant.
24. ABS paper - conclusions
• STiK are a key mechanism for redistribution; the appropriate
measurement is vital in order that statistics (both micro and
macro) correctly reflect the economic circumstances of
households
• Contribution to quality assurance
• Contribution to developing international standards
25. ABS paper - comments
• On the method:
– Health benefit STiK is allocated to households on the basis of age, sex
and state/territory of residence taking the “insurance approach”
– But those with a disability or a long term health condition have a
higher utilisation rate applied, based on data on doctor visits.
– This would seem fine if it were balanced by an adjustment to the
equivalence scale to take account of the greater needs of these
people.
– But it is not, meaning that these people appear to be better resourced
than their equivalents without disability or long term health problems.
– The evidence in the OECD paper on Sweden suggests that this may not
have a big effect on quintile means. But …
• It might in Australia
• It probably would at the individual level
• On the analysis of results:
– We need to know how people are ranked
26. General reflections (1/2)
• Details matter. These papers are generally pretty good at providing the
necessary ones – but some are still missing (What is the ranking variable?
Who is being counted? What is the equivalence scale?)
• Results could be sensitivity tested to key assumptions to provide bounds
around the true value
• This could also aid country comparisons; these are very challenging using
OECD-style national inputs; better done centrally, in dialogue with national
experts?
• What drives cross-country differences? Can they be decomposed?
• More thought needs to be given to the appropriate equivalence scale to
use (unclear that there are economies of scale associated with most STiK)
– cf. Section 2B yesterday
• STiK in the context of cash redistribution (taxes and benefits); they are
partial substitutes
27. General reflections (2/2)
• The distribution of STiK is a policy/political issue; a key question we need
to be able to address is if public spending is cut, how are the losses
distributed across the population? Can our methods address that
robustly?
• (No?)
• That is why we need to keep working on improving concepts, precision
of measurement, comparability across countries and through time and
making sure the implications of our assumptions are transparent.