1. Joint analysis of Income, Consumption and Wealth
The experience of Insee

2. ICW joint distribution (micro)
Beyond aggregates, what about the saving rate and its variation across households ? Two approaches
Social Statistics
National Accounts
Coherence between NA and surveys
+ Decomposition by HH groups C
HBS I
SILC
Adm.data W
HWS
DNA
ICW joint distribution (micro)
Estimation of the distribution, in the population, of NA aggregates for the Households sector is a topic which have been actively analyzed for some years now. OECD has launched several initiatives (at the end of the 2010, then in 2013, still ongoing), and more recently with Estat (2017). Insee has been working on the subject since at least mid Among the objectives : how does the saving rate vary across HH?
Two approaches :
1 – The disaggregation of each line in the HH (national) account according HH groups (age, occupation, family type,…)  EG-DNA OECD ; Insee has published results in 2009 and after (for the HH wealth account as well). NA unit and Social surveys unit at Insee work together
2 – the estimation at the microeconomic level of the joint distribution of Income, Wealth and Consumption. Insee started experiments in 2006, obtaining first results in First published results in 2014 (Garbinti, Lamarche, Insee-Références).
This presentation is about this latter approach
DSS - Luxemburg 1-2 March 2018

3. First approach : national accounts decomposition+
Microdata sources
(SILC, HBS, Adm. data,…) : distribution
by HH groups =…
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4. First approach : national accounts decomposition (2)=…
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5. What DNA can bring… 1 – A measure of disparities behind the NA totals…
2 - … more comprehensive than the surveys-based measures
3 – A rigorous measure of quality of surveys
ex : Discrepancy HBS – NA on 2011 household consumption total:
Apparent: NA > HBS by 31% - Real: NA > HBS by 16%
… But it brings only tables by subgroups and not a complete micro joint I,C,W distribution
DSS - Luxemburg 1-2 March 2018

6. ICW joint distribution : 1st method – Statistical matching between surveys via a group of core variables
CORE V.
I (SILC, adm. data)
CORE V.
W (HWS)
CORE V.
C (HBS)
CORE V.
I+C+W
To estimate the (micro-economic) joint distribution of I, C, W, 2 techniques :
1 - Matching the relevant surveys via a group of Core variables (gender, age, income class, social level, housing status,…)
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7.  No « real » information on the couple (W,C)
ICW joint distribution : 1st method – When surveys are linked to income registers
CORE V.
W (HWS)
CORE V.
C (HBS)
I (adm. data)
CORE V.
C (HBS) +I
CORE V.
I+C+W
CORE V.
W (HWS) + I
Note : in France, surveys are, as a rule, linked to administrative income register income is known for each hh.
Then :
in HBS, we have the true distribution (I, C)
in HWS, we have the true distribution (I,W)
Matching gives (I,C,W).
Drawback : actually, we have no real information on the couple (W,C). The correlation is unknown (known only conditionally to the Core variables + Income).
 No « real » information on the couple (W,C)
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8. ICW joint distribution : 2nd method – Including short modules in surveys : principles
HBS +I(adm)
SILC + I (adm)
HWS + I (adm)
HWS + I
+ mini HBS
HBS + I
+ mini HWS
SILC I
+ mini HWS
+ mini HBS
2nd technique : inserting a HBS in the HWS, or a HWS in the HBS, or even a HWS and a HBS in the income survey (say SILC).
But a full-fledged HBS in the HWS (or the other way round) seems not an option from the response burden point of view (see, however, the Hungarian SILC).
 The challenge : how to devise a short module able to give a good estimate of the HH total consumption (resp. total wealth)?
C + I + W
C + I + W
C + I + W
The challenge : how to devise a short module able to give a good estimate
of the HH total consumption (resp. total wealth)?
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9. ICW estimation : 2 – Including short modules, French experimentation
HWS + I
SILC + I
HWS + I
+ mini HBS
SILC + I
+ mini HWS
In France, we experimented a mini HBS in HWS
(we did a mini HWS in SILC as well  but does not give the complete (I,C,W) we are looking for).
I + W
C + I + W
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10. Mini – HBS : what is it and how does it work?
The idea : (Browning, Crossley, Weber, Ec. Journal, 2003, « Asking consumption questions in general purpose surveys »)
1 - in the WHS : include a few questions on expenses on items HH most easily recall:
food at home/outside + housing costs (rents, electricity, heating) + phone, Internet + transportation costs,…
(basically : «  expenditures regularly billed »)  rather well evaluated
2 - in the (full-fledged) HBS : estimate the link F
CHBStotal = F (foodH,foodO, housing, phone, etc.)
3 - in the WHS : impute
CHWStotal = F (foodH,foodO, housing, phone, etc.)
The idea proposed by Browning, Crossley, Weber in the beginning of the 2000s :
1 - Ask the HH a few questions about the « easiest » parts of its consumption (= the most regular expenses).  HH has no trouble to give rather good estimates of these items.
2 – But recall that in a real full fledged HBS, we can estimate the relationship (or « model ») between those items and the HH total consumption
3 – Then we are done! Applying the model to the items estimates in the WHS, we deduce the total consumption for each WHS HH
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11. Mini – HBS : what is it and how does it work?
Mini HBS module was introduced in the French WHS (Enquête Patrimoine) : in 2010, 2014, 2017
1 – replicates rather well the consumption and the saving rate at the household level 
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12. The saving rate distribution in WHS +mini HBS matches closely the one in HBS
The saving rates that can be estimated for each HH in the WHS thanks to the included mini HBS distribute very much like the saving rates in the HBS.
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13. Mini – HBS : what are the results ?
Mini HBS module was introduced in the French WHS (Enquête Patrimoine) : in 2010, 2014, 2017
1 – replicates well the consumption at the household level
2 – leads to interesting insights : all things being equal
- couples with children save less than those without
- low wealth HH tend to save a bit more than high wealth HH
- saving rates display high dispersion within as well as between income classes (more in low income groups) 
Having the joint distribution (I,C,W) in an unique source, we were able to produce original studies. See e.g. Lamarche, Garbinti (2014) :
« Qui épargne, qui désépargne », Insee-Référence RPM 2014
«  les hauts revenus épargnent-ils davantage ? », Economie et statistique, n° , 2014.
It helps to appreciate how much the saving rate is dispersed and the need to go beyond the traditional unique agreggate figure
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14. The distribution of the saving rate varies across the income classes
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15. Future work
Continue collaboration with other countries, in the OECD-Eurostat EG framework, in order to define a fully harmonized DNA method and publish the results of a new annual DNA exercise
revisit (time and resources permitting) the decomposition of the household wealth account, using the 2014 HWS
- analyse and publish the new 2017 joint data collection (short consumption module in 2017 HWS).
DSS - Luxemburg 1-2 March 2018

16. Thank you for your attention !