A growing portion of household income is going to the top of the distribution, according to research using data from the Income Tax Administration (Atkinson et al. 2011). This has called into question the reliance of many inequality studies and official observations on family surveys, as it may fail to adequately capture those top incomes. Eunzan et al. (2022), for example, report a significant and growing gap for the United States in the top 1% share from the current population survey vs. tax record.
If the amount of this interval varies in different countries and over time, neither the comparison of the country nor the trend of time from the survey will be reliable. To see if this is a significant problem, one must look across a wide set of countries over a significant period of time in the survey that are themselves as comparable as possible.
Scale assessment of problems across European countries
This is what we do at Carranza et al. (2022) EU-Statistics on Income and Social Conditions (EU-SILC) using microdata from a national survey conducted in a general framework, the EU’s main source for inequality and social inclusion statistics. We take advantage of the fact that adjustment mechanisms have been developed for the ‘missing top’ and applied to EU-SILC data in the context of the Distributive National Accounts (WID DINA) (Blanchet et al. 2019, 2021, 2022) of the Global Inequality Database. , Aligned with estimated top shares from tax data.
WID DINA output is mostly related to adults not related to family, income related to ‘not equivalent’ considering differences in family size and structure and income concepts that are separate from the standard survey-based measure of total and disposable income. This means that no one can see from the abundant DINA output what effect their top income adjustments have on measuring conventional inequality. We go back to the EU-SILC microdata, apply those top-income corrections (an adapted version of it) and solve this by creating ‘corrected’ / consistent statistics for the inequalities commonly measured in surveys.
Impact of Adjusting for ‘Missing Top’ in EU Statistics Income and Social Conditions (EU-SILC)
Our results show that, firstly, adjusting the top of the revenue distribution to match estimates from tax data makes a significant difference across the 26 EU-SILC countries and external tax-based top income estimates for the year. The average, disposable income for the country-year is expressed in terms of the Gini coefficient percentage, which is usually between 30-35 in the survey, an increase of 2.3 ‘Guinea points’. The share of income going to the top 1% – usually 5-6% in the survey – increases by an average of two percentage points. These are fairly average effects.
What is more interesting is that consistency has far more impact for some countries than for others. Belgium, Iceland, Germany, Poland, Romania, Switzerland and the UK saw an average increase of 4-6 genie points and a 3.5-5.5 percentage point increase in the top 1% shares. In contrast, some more countries – including Denmark, Greece, Ireland, Italy and Sweden – have seen almost no change in Ginny or the top 1% share in the years covered.
For some countries the impact of adjustment has also changed a lot over time. To illustrate, Figure 1 shows the effect on the Gini coefficient for Austria and Germany and Figure 2 shows the effect for Iceland and Norway. For Germany, consistency always increases the genie very significantly from six to eight genie points with no obvious tendency. The impact for Austria is low but still substantial, initially in the order of 4-5 genie points, but has dropped to almost two points in recent years. The impact for Iceland is mostly 2-4 genie points but much higher in the vicinity of the financial crisis. The effect for Norway is usually in the range of 2-4 but doubles in a given year.
Figure 1 Impact of top income adjustment on Gini coefficient for gross and equivalent disposable income, EU-SILC for Austria and Germany
Figure 2 Impact of top income adjustment on Gini coefficient for gross and equivalent disposable income, EU-SILC for Iceland and Norway
These patterns first point to an important aspect of the national survey in the EU-SILC and more generally: the amount they link and draw with the administrative data on income tax and social security systems varies greatly and this capacity is increasing for some countries over time. . One would expect that for such a survey, the top income adjustment would have less of an impact, and that is what we find on average. Countries such as Germany, Poland, and Portugal, which have little or no access to administrative data, have the most significant impact on coordination, while some countries that have high levels of this power from the beginning, such as Denmark, see very little impact. It is also possible that in some countries – including Austria – the increasing use of administrative data over time may contribute to a reduction in the impact of top coordination. However, while some countries are making extensive use of administrative data, which applies to both Iceland and Norway, there is still considerable impact of adjustment in some or all of the years covered.
The sharp fluctuations there in particular also highlight the sensitivity of tax-based estimates in situations such as Iceland’s dramatic financial upheaval and a certain change in dividend tax for Norway. The latter echoes the conclusion of Yonjan et al. (2022) that changes in the U.S. tax code were a significant contributor to widening the gap between survey and tax-based estimates there. The nature of tax statistics, what they do and do not capture and how they change over time, needs to be kept in mind.
Taken together with other recent top-income studies in the EU-SILC by Hlasny and Verme (2018) and Bartels and Metzing (2019), the adjustment of top-income based on external data without relying on sample characteristics seems to be more important than the technical approach.
Intrinsicity
These results highlight that the ‘missing top’ problem in household income surveys is significant to an extent that also varies across developed economies across surveys conducted within a holistic and general framework. This significantly complicates the observation, comparison and analysis of the dimensions and trends of income inequality and the understanding of the underlying causal processes in the workplace.
From an EU perspective, a two-track strategy seems certain. On the one hand, the link between EU-SILC surveys with administrative data needs to be further encouraged and facilitated, to improve the ‘capture’ of top income and to exploit the potential of this connection to limit the need for post-survey coordination. Valuable though, it doesn’t seem to be a ‘magic bullet’ that solves the problem, as it still depends on the original population surveyed and may struggle to capture the full income, especially from capital. At the same time, a ‘consistent’ summary inequality system can be created for countries with current inconsistent indicators where available tax figures provide a suitable basis for that adjustment. In doing so, a wide range of tax-based statistics from survey methods to measure income and the nature and limitations of those tax-based measures need to be fully considered.
More broadly, responding to the challenges that surveys face in capturing adequately at the top of revenue distribution means exploiting administrative resources as extremely valuable complementary information. In each particular national case a deeper understanding of the strengths and limitations of both sources is needed as they are combined to better represent the distribution of income.
References
Atkinson, AB, T. Piketty and E. Says (2011), “Top Rewards in the Long Run in History”, Journal of Economic Literature 49 (1): 3-71.
Bartels, C and M Metzing (2019), “An integrated approach to top-revised income distribution”, Journal of Economic Inequality 17 (2): 125-143.
Blanchett, T., L. Chancellor and A. Gethin (2019), “Forty Years of Inequality in Europe”, VoxEU.org, 22 April.
Blanchett, T. L. Chancellor and A. Gethin (2021), “Why is Europe more equal than the United States?”, American Economic Journal: Applied EconomyUpcoming
Blanchet, T, I Flores and M Morgan (2022), “Weight of the Rich: Improving Survey Using Tax Data”, Journal of Economic Inequality 20: 119-150.
Carranza, R, M Morgan and B Nolan (2022), “Top Income Coordination and Inequality: An EU-SILC Inquiry”, Review of income and assets.
Hlasny, V and P Verme (2018), “Measuring Top Income and Inequality: A Comparative Analysis of Correction Methods Using EU SILC Data”, Econometrics 6 (2): 1-21.
Yonzan, N, B Milanovic, S Morelli and J Gornick (2021), “Mind the Gap: Income measured between inequality survey and tax data”, VoxEU.org, 05 November.
Yonzan, N, B Milanovic, S Morelli and J Gornick (2022), “Draw a Line: Comparison of Top Income Estimates Between Tax Data and Household Survey Data”, Journal of Economic Inequalityy 20: 67-95.