This document discusses the need for more and better data on wealth inequality. It outlines some key findings from existing wealth distribution studies and data sources. It argues that collecting more household-level balance sheet data, microdata from surveys, and information on pensions, the very wealthy, and combining register and survey approaches would help provide a more accurate picture of global wealth inequality. Improving how wealth at the top of the distribution is estimated is especially important given sampling and non-sampling issues with current survey approaches.

1. Workshop on Measuring Inequalities of
Income and Wealth
Distribution of Wealth:
The Need for More and Better Data
James Davies
Berlin, September 16, 2015

2. Outline
I. Background: Credit Suisse Global Wealth
Report and Databook
II. Need for more data
a) Household balance sheets
b) Micro statistics
III. Need for better data
a) Pensions
b) Upper tail
c) Combining register and survey data

3. I. Background: Estimating world wealth
distribution
James Davies, Susanna Sandström, Tony Shorrocks, Ed Wolff
• “The world distribution of household wealth” in J.B. Davies (ed.)
Personal Wealth from a Global Perspective (OUP, 2008)
• “The level and distribution of global household wealth”, Economic
Journal, March 2011
Tony Shorrocks, James Davies, Rodrigo Lluberas
• Credit Suisse Global Wealth Report & Databook 2010, 2011, 2012,
2013, 2014, 2015 (forthcoming).
http://economics.uwo.ca/people/davies_docs/credit-suisse-global-wealth-report-
2014.pdf
http://economics.uwo.ca/people/davies_docs/global-wealth-databook-2014-v2.pdf

4. Special topics in CS global wealth
reports/databooks
2010: Gender dimensions Wealth Composition
2011: Long-run trends in levels Wealth & Age
2012: Debt Inheritance
2013: Mobility Weath in the Eurozone
2014: Inequality trends since 2000
2015: Global middle class

5. Global wealth distribution 2014, adults
Decile Share (%) Decile Share (%)
1 -0.3 8 2.6
2 0.1 9 7.1
3 0.1 10 87.4
4 0.3 Top 5% 75.7
5 0.5 Top 1% 48.2
6 0.8 Gini 0.911
7 1.4 Mean $51,634

6. II. Need for more data: household balance sheets
• Part of national accounts
– Estimated by national statistical organizations, central
banks or ministries of finance
• Counterparty data for most financial assets
• Surveys, perpetual inventories for non-financial
assets
• Data available in 2014:
– 17 countries complete - - 14 OECD, plus Singapore,
South Africa and Taiwan
– 30 countries only financial assets & debts - - all in
OECD or Transition except Brazil, Colombia, Cyprus,
Malta, and Thailand

7. II. Need for more data: micro statistics
• 28 countries currently with survey data
• 25 OECD:
– 15 EU countries with HFCS
– 5 “Anglo-Saxon” countries: Australia, Canada, New
Zealand, UK, US
– Chile, Japan, Korea
– Denmark, Norway: survey/register data
• China 2002, 2012; India 2012 & every 10 years,
Thailand 2011
• Others soon: Ireland, Uruguay

8. III. Need for better data: include pensions?
• Employer-based pensions regularly in surveys:
– Australia: 2003-04, 2005-06, 2009-10, 2011-12
– Canada, SFS: 1999, 2005, 2012
– UK, WAS: 2006-08, 2008-10, 2010-12
– US, SCF, only defined-contribution pensions: every 3
years since 1983
• Tax sheltered retirement saving plans only:
– 15 EU countries with HFCS surveys
– Others?

9. Wealth inequality with and without
employer-based pensions , no “social security wealth”
Country Gini Coefficient Share of top 10% Share of top 20%
W/O With W/O With W/O With
Australia,
SIH 2012
60.7% 61.2%
Canada,
SFS 2012
0.689 0.655 53.2% 46.4% 69.9 64.8
UK, Estate
Tax 1994
0.67 0.59 52
43
UK, WAS
2006-08
0.581 0.606 40.7 43.4 58.5 61.6
US, SCF
2007/2010
0.860 0.809*
*Takes into account effect of DC pensions in 2010 SCF microdata plus effect of DB
pensions estimated by Wolff (2011) for 2007.

10. Wealth inequality with and without
pensions including “social security wealth”
Country Gini Coefficient Share of top 10% Share of top 20%
W/O With W/O With W/O With
Australia,
SIH 2012
60.7% 60.8%
Finland,
2005
0.497 0.336
Germany,
SOEP 2007
0.799 0.637
UK, Estate
Tax 1994
0.67 0.49 52% 36%
US, SCF*
2007/2010
0.860 0.688*
*Takes into account effect of DC pensions in 2010 SCF microdata plus effect of DB
pensions & social security estimated by Wolff (2011).

11. III. Need for better data: more attention to the
upper tail is needed
• Upper tail hard to estimate in surveys:
– Sampling error
• Prob of finding anyone in top .01% by random sampling =
63.2%; of finding at least one billionaire in U.S. = 2.2%
• Partial solution: try to over-sample upper tail
– Non-sampling error
• Under-reporting - - worse for financial assets
• Differential response rates
– Partial solution: weight by characteristics correlated with
wealth (income in tax records; geographic area...)
– Better solution: weight using a “list sample” based on
predicting wealth of people in tax records (as in U.S.)

12. III. Need for better data: pay more attention to the
upper tail (continued)
• Don’t restrict attention to surveys
– Estate and wealth tax records
– Investment income multiplier method
– Forbes and other rich lists
• Don’t randomly sample to determine the upper tail
of the distribution of star brightness. Go outside
and look up!

13. Top Wealth Shares in Survey Data
Country Year Unit Share of top
25% 20% 10% 5% 2% 1.0% 0.5% 0.10%
Australia 2010 household 61.8
Austria 2010 household 77.1 61.7
Belgium 2010 household 61.2 44.1
Canada 2012 family 67.2 47.7
Chile 2011 household 56.4 37.6
China 2002 person 59.3 41.4
Cyprus 2010 household 72.4 56.8
Denmark 2009 family 92.8 69.3
Finland 2010 household 64.9 45.0
France 2010 household 67.5 50.0
Germany 2011 household 76.3 59.2
Greece 2009 household 56.7 38.8
India 2002 household 69.9 52.9 38.3 15.7
Indonesia 1997 household 78.9 65.4 56.0 28.7
Italy 2010 household 68.9 62.6 45.7 32.9 21.0 14.8
Japan 2009 household 62.8 55.3 34.3 19.3

14. Top Wealth Shares in Survey Data (continued)
Country Year Unit Share of top
25% 20% 10% 5% 2% 1.0% 0.5% 0.10%
Korea, Rep. 2011 household 63.9
Luxembourg 2010 household 66.7 51.3
Malta 2010 household 62.0 46.9
Netherlands 2009 household 61.3 40.2
New Zealand 2001 tax unit 67.0 48.0
Norway 2004 household 80.1 65.3
Portugal 2010 household 67.9 52.7
Slovakia 2010 household 48.9 32.8
Slovenia 2010 household 54.3 36.2
Spain 2008 household 67.3 61.3 45.0 32.6 21.7 16.5
Sweden 2007 adult 67.0 49.0 24.0
Switzerland 1997 family 71.3 58.0 34.8 27.6 16.0
Thailand 2006 household 69.5
UK 2008 adult 62.8 44.3 30.5 12.5
USA 2010 family 90.3 86.7 74.4 60.9 44.8 34.1

15. Pareto Top Tail
1
10
100
1,000
10,000
100,000
1,000,000
100000 1000000 10000000 100000000 1E+09
Thousands of adults above wealth level
(logarithmic scale)
Weallth level (USD, Logarithmic scale)
Unadjusted wealth estimates Fitted Pareto

16. Number of Forbes Billionaires by Country of Citizenship
Country 2001 2015 Country 2001 2015
1. USA 269 535 15. Singapore 6 19
2. Japan 29 24 16. Israel 5 17
3. Germany 28 103 17. Sweden 5 23
4. Italy 18 39 18. Taiwan 5 33
5. Canada 16 39 19. Turkey 5 32
6. Switzerland 16 29 20. Argentina 4 5
7. France 15 47 21. India 4 92
8. Hong Kong 14 55 22. Malaysia 4 12
9. Mexico 13 16 23. Australia 3 27
10.UK 12 53 24. Greece 3 3
11.Russia 8 88 25. Philippines 3 11
12. S. Arabia 8 10 26. Chile 2 12
13. Spain 8 21 27. Denmark 2 0
14. Brazil 6 54 28. Indonesia 2 22

17. Country 2001 2015 Country 2001 2015
29. Ireland 2 5 44. N. Zealand 1 2
30. Netherlands 2 9 45. UAE 1 4
31. Norway 2 10 46. Austria 0 7
32. Portugal 2 0 47. Peru 0 6
33. S. Africa 2 7 48. Finland 0 5
34. S. Korea 2 30 49. Kazakhstan 0 5
35. Thailand 2 17 50. Kuwait 0 5
36. Venezuela 2 3 51. Nigeria 0 5
37. Belgium 1 3 52. Poland 0 5
38. Bermuda 1 0 53. Monaco 0 3
39. China 1 213 54. Morocco 0 3
40. Colombia 1 0 55. Oman 0 2
41. Egypt 1 0
42. Lebanon 1 7 56 – 65: Angola, Algeria, Georgia,
Guatemala, Guernsey, Iceland,Lithuania
Nepal, St. Kitts & N, Vietnam - - all 0, 1
43. Lichtenstein 1 0

18. Survey data versus our estimates– top 20%
Survey Data Our Estimates
Country Share of top
20% 10% 1% 20% 10% 1%
Australia 61.8 66.0
Canada 67.2 47.7 73.2 57.0 24.4
Chile 56.4 37.6 79.7 68.9
China 59.3 41.4 75.2 64.0
Denmark 92.8 69.3 85.5 67.5
Finland 64.9 45.0 71.1 54.5
France 67.5 50.0 69.5 53.1
Germany 76.3 59.2 77.7 61.7
India 69.9 52.9 15.7 83.4 74.0 49.0
Indonesia 78.9 65.4 28.7 86.1 77.2 50.3
Italy 62.6 45.7 14.8 66.6 51.5 21.7
Japan 55.3 34.3 65.4 48.5

19. Survey data versus our estimates– top 20%
Survey Data Our Estimates
Country Share of top
20% 10% 1% 20% 10% 1%
Netherlands 61.3 40.2 71.1 54.8
New Zealand 67.0 48.0 72.9 57.0
Norway 80.1 65.3 80.4 65.8
Spain 61.3 45.0 16.5 68.8 55.6 27.0
Sweden 67.0 24.0 68.6 30.8
Switzerland 71.3 34.8 71.9 30.9
Thailand 69.5 83.9
UK 62.8 44.3 12.5 69.5 54.1 23.3
USA 86.7 74.4 34.1 86.7 74.6 38.4
Average 68.5 52.8 22.6 75.4 62.1 33.9

20. III. Need for better data: combining register and
survey data
• Registers:
– Advantages: complete coverage
– Disadvantages:
• Household definition - - tax definition, too many singles
• Undervaluation
• Excluded assets
• Surveys:
– Advantages: can cover all assets; can use economic definition of
households
– Disadvantages: nonsampling errors

21. Nordic wealth shares - - most recent estimates
Denmark,
2009
Finland,
2010
Norway,
2013
Sweden,
2007
1st quintile -18.9% -1.1% -5.4%
2nd quintile -1.3 2.2 2.2
3rd quintile 5.2 10.8 11.2
4th quintile 22.2 23.2 23.4
9th decile 23.5 19.9 19.1
10th decile 69.3 45.0 49.5 67.0
Top 5% 35.8 49.0
Top 1% 18.3 24.0
Data Type Register-
based
HFCS
survey
Register-
based
Register-
based

22. Norway: Old (2004) and New Register-based (2010, 2013)
Wealth Share Estimates
2004 2010 2013
1st quintile 0.2% -6.3% -5.4%
2nd quintile 1.7 1.9 2.2
3rd quintile 5.0 11.1 11.3
4th quintile 13.0 23.4 23.3
9th decile 14.8 19 19.1
10th decile 65.3 51 49.5
Top 5% 37.5 35.8
Top 1% 20.1 18.3
Top 0.1% 9.8 8.3

23. Norway: Features of new register-based data:
• Households: identified using household register rather
than survey (as in 2004), but attempt to make household
definition correspond to 2004 definition
• House values: estimated using a model derived from
Building Register data
– house values from tax records = 20% of market value
• Student loans from State Educational Loan Fund
• Students: counted with household in which they actually
live, not with parents.
– But excluded from the data shown here
• Life insurance and pension wealth excluded

25. Extra Slides: Nordic wealth data sources
• Denmark: Economic Council of the Labour Movement,
Fordeling & Levevilkår 2009, Øget polariseirng i
Danmark, http://www.ae.dk/publikationer
• Finland: microdata, but see also ECB Household
Finance and Consumption Network
http://www.ecb.europa.eu/pub/economic-
research/research-
networks/html/researcher_hfcn.en.html
• Norway, Sweden: “Under construction”