This document discusses two papers about measuring and accounting for in-kind government benefits (social transfers in kind or STIK) at micro and macro levels. The OECD paper presents illustrative calculations of STIK distribution across income quintiles for 10 countries. The ABS paper focuses on how the Australian Bureau of Statistics constructs official statistics on STIK and reconciles micro and macro measures. Both papers use an "insurance approach" to allocate STIK by characteristics like age, education level, and health status. The discussant comments on strengths and limitations of the methods and calls for more work to improve cross-country comparisons and understand what drives differences in STIK distribution.

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

10. Share of STiK by income quintile: education

11. Share of STiK by income quintile: “other”

12. Share of STiK by income quintile: health in Sweden

13. Impact of net transfers (cash and STiK)
on the ratio of income in Q5 to Q1

14. Gini coefficients “across equivalized disposable
income quintiles”

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.