1. (co-author: Santiago Sanchez) LIS/LWS Users Conference
Estimating market and disposable top income shares with national accounts data
Thomas Goda
(co-author: Santiago Sanchez)
LIS/LWS Users Conference
Medellín,

2. Agenda
Introduction
Issues with HS data and tax adjusted top income shares
Methodology
Results
Conclusions

3. The recent increase in income inequality is among the biggest global challenges
Income inequality within countries demonstrated a sharp upward trend from onwards (globally and especially in OECD countries)
seven out of 10 people live in countries in which the gap between rich and poor is greater than it was 30 years ago (Oxfam, 2014, pg.8).
Recent research suggest that this increase in inequality has had adverse effects on
social cohesion (Wilkinson & Pickett, 2011; Atkinson, 2015)
financial stability (Kumhof et al., 2015; Goda et al., 2017)
economic growth (Herzer & Vollmer, 2012; 2013; Halter et al., 2014)
democratic decision processes (Crotty, 2012; Gilens & Page, 2014

4. The “true” extent of inequality most likely is underestimated
Typically, income inequality is measured via household surveys
However, surveys tend to underestimate the income at the very top of the distribution (Atkinson & Brandolini, 2001; Atkinson & Piketty, 2007; Atkinson et al., 2011)
Hence, an increasing number of studies has tried to adjust the top income data, with:
fiscal data (Atkinson & Piketty, 2007; 2010; Atkinson et al., 2011)
national accounts data (Lakner & Milanovic, 2013; Campos Vázquez & Chavez, 2016)
fiscal and national accounts data (Alvaredo et al., 2017b)
West’s conditional likelihood estimator (Ruiz & Woloszko, 2016)

5. This study adjust market and disposable top income shares for a sample 40 countries
We adjust the latest household survey data from LIS with the respective household income data from the System of National Accounts (SNA)
The major novelty of our study is to explicitly distinguish between capital and labour income during the adjustment process
We expect that most of the adjustment will take place due to capital income on the grounds that wealth is highly concentrated at the top (see Piketty, 2015; Goda, 2017)

6. Agenda
Introduction
Issues with HS data and tax adjusted top income shares
Methodology
Results
Conclusions

7. Household surveys often under-represent low income and top income households
Very poor households often lack a registered address and very rich households are not easily accessible
Moreover, especially for rich households non-responses and misreporting are prevalent —deliberately or due to ignorance
Many household surveys oversample top income households; however this strategy is not 100% successful
household survey data underestimate the degree of income at the very top (Groves & Couper, 1998; Deaton, 2005; Alvaredo, 2010; Atkinson and Piketty, 2007)

8. The usage of tax data to adjust household survey data has some important drawbacks
To adjust the household survey top income shares often tax data are used (Atkinson & Piketty, 2007; 2010; Atkinson et al., 2011)
Fiscal data has two major disadvantages (Leigh, 2007; Lakner and Milanovic, 2013)
coverage, definition, and valuation of income depend on each nation’s tax laws
the very rich part of the population often (successfully) tries to avoid and evade tax payments (hidden offshore asset estimates range from $4.4 to $21 trillion)
The newest estimates from the World Wealth & Income Database (WID) try to account for this underreporting in tax reports (see Alvaredo et al., 2017a)
A further disadvantage of tax data is that they are only available for a relative small number of countries

9. Agenda
Introduction
Issues with HS data and tax adjusted top income shares
Methodology
Results
Conclusions

10. This study uses national accounts data to adjust LIS income data
Following Lakner & Milanovic (2013) and Campos Vázquez & Chavez (2016) we use SNA data instead of tax data to adjust the household survey data from LIS
National accounts data have the advantages that (Deaton, 2005; Lakner & Milanovic, 2013)
they are readily available for most countries
the data reporting is standardize across countries
they track all money flows within an economy, including top incomes
We account for two different income concepts:
market income
disposable income

11. LIS data
All household survey data are readily available in the LIS database and are retrieved for income percentiles 𝑃 1 … 𝑃 (multiplied by the one percent of total households)
LIS labour income (il) includes all “monetary payments and value of non-monetary goods and services received from dependent employment” and “profits/losses and value of goods for own consumption from self-employment”
LIS capital income (ic) relates to “monetary payments from property and capital (including financial and non-financial assets)
LIS disposable income (dhi) is the sum of labour income (il), capital income (ic) and transfer income (it), minus income taxes (xiti) and social security contributions (xits)

12. SNA data
The SNA household income data were taken from UNdata, OECD.stat, and Eurostat
Total LIS market income should be equivalent to the following SNA data (S14 primary resources account):
𝑀𝑎𝑟𝑘𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒 𝑆𝑁𝐴 𝑖 = 𝐷1 𝑖 +0.7∗ 𝐵3𝑛 𝑖 +( 𝐷4 𝑖 +0.3∗ 𝐵3𝑛 𝑖 )
where 𝐷1 is compensation of employees, 𝐵3𝑛 is net mixed income and 𝐷4 is property income.
Total LIS disposable income should be equivalent to the following SNA data (S14 secondary distribution of income account):
𝐷𝑖𝑠𝑝𝑜𝑠𝑎𝑏𝑙𝑒 𝐼𝑛𝑐𝑜𝑚𝑒 𝑆𝑁𝐴 𝑖 = 𝑀𝑎𝑟𝑘𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒 𝑆𝑁𝐴 𝑖 +𝐷62+𝐷7−𝐷61−𝐷5
where 𝐷62 are social benefits other than social transfers in kind, 𝐷7 are other current transfers, 𝐷61 are net social contributions, and 𝐷5 are current taxes on income and wealth.

13. Adjustment of LIS top income shares
𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙=𝑆𝑁𝐴 𝑑𝑎𝑡𝑎 −𝐿𝐼𝑆 𝑑𝑎𝑡𝑎𝑡𝑜𝑡𝑎𝑙
To estimate the Pareto coefficient we use the Top 10% and Top 20% income shares
Relative income shares of the Pareto distribution can be expressed as follows (see Atkinson, 2007):
𝑆 𝑖 𝑆 𝑗 = 𝐻 𝑖 𝐻 𝑗 𝛼−1 𝛼
where 𝑆 𝑖 and 𝑆 𝑗 are the LIS top income share adjusted by the residual, and 𝐻 𝑖 and 𝐻 𝑗 are the respective proportions of the population that hold these income shares.
Given that 𝑆 𝑖 , 𝑆 𝑗 , 𝐻 𝑖 and 𝐻 𝑗 are know, the Pareto coefficient (𝛼) can be estimated as follows:
𝛼 = 1 1−( ln 𝑆 𝑖 − ln 𝑆 𝑗 ln 𝐻 𝑖 − ln 𝐻 𝑗 )

14. Adjustment of LIS top income shares
We adjust capital and labour income separately, i.e. we calculate individual residuals for labour income and capital income
We assign:
95% of the capital income residual to 𝑆 10 and 5% to 𝑆 20 − 𝑆 10
75% of the labour income residual to 𝑆 10 and 25% to 𝑆 20 − 𝑆 10
80% of the disposable income residual to 𝑆 10 and 20% to 𝑆 20 − 𝑆 10

15. Agenda
Introduction
Issues with HS data and tax adjusted top income shares
Methodology
Results
Conclusions

16. Top 10% market income shares

17. Top 10% capital income as a percentage of total capital income

18. Top 10% labour income as a percentage of total labour income

19. Top 1% market income shares

20. Top 1% capital income as a percentage of total capital income

21. Top 1% labour income as a percentage of total labour income

22. Top 10% disposable income shares

23. Top 1% disposable income shares

24. Obtained Pareto coefficients vs. reference Pareto coefficients

25. Obtained top income shares vs. WID top income shares

26. Obtained Gini coefficients

27. Obtained Gini coefficients

28. Agenda
Introduction
Issues with HS data and tax adjusted top income shares
Methodology
Results
Conclusions

29. The results suggest that inequality is significantly higher than suggested by LIS data
We estimate top market and disposable income shares for a larger number of countries than previously done
The mayor novelty of our paper is the distinction between capital and disposable market income
Our results suggest that in most countries top income shares are significantly higher than those suggested by LIS household survey data
We find that most of adjustment is due to the capital income residual
capital income share on total Top 10% (Top 1%) income increases to 25% (45%)
especially for the Top 1% this figure seems realistic
capital income share on total capital income is 84% (65%) after the adjustment
especially for the Top 10% this figure seems very high (but same % as in Sweden’s LIS data)

30. The results present some novel insights but should be treated with caution
In many countries the overall adjustment very strong, but they are moderate for Nordic countries (especially Sweden) and in line with previous studies in the case of Canada, Japan, Switzerland and the US
The presented results should be treated with caution; our methodology does not yield a consistent upward bias but the large differences
could indicate that the quality of the SNA data is questionable
suggest that it seems important to account for differences between countries
however, given the limits of the SNA data, it is not quite clear how
One possibility: one could assume that large values indicate that the residual needs to be distributed over a larger population share (according to the distribution indicated by LIS data)
In any case, our results present some novel and valuable insights and show the urgent need for more research on the topic and the availability of better data