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HLEG thematic workshop on Measuring Inequalities of Income and Wealth, Peter Van de Ven
- 1. BRIDGING THE GAPS BETWEEN MICRO SURVEYS AND NATIONAL ACCOUNTS: BRINGING TWO WORLDS TOGETHER Workshop on Measuring Inequalities of Income and Wealth (Berlin, 15-16 September 2015) Peter van de Ven, OECD
- 2. Introduction Main goal of the project: To arrive at (timely) distributional information for households, consistent with national accounts • Background information • Main methodologies • The magnitude of the problem: differences between micro and macro • First results • Why it matters • Way forward 2
- 3. Background information: start • OECD-Eurostat Expert Group to measure Disparities in a National Account framework (EG DNA) – Feasibility study on how to introduce distributional information from existing micro sources in national accounts – 25 NSOs, Luxembourg Income Study, ECB, Eurostat • Comparison of micro and macro sources for household income (20 countries), consumption (21), and wealth (7) • Experimental disparity indicators, consistent with national accounts for income, consumption and savings (16) • Recap provided under the following link: http://www.oecd.org/std/na/Measuring-inequality-in- income-and-consumption-in-a-national-accounts- framework.pdf 3
- 4. Background information: follow-up • OECD Expert Group on Distributional Information on Income, Consumption and Saving within the SNA (EG DNA) • 22 NSOs, ECB, Eurostat • Objectives: – Refine methodology on combining micro and macro sources, with focus on consistency of income and consumption – Compile data consistent with national accounts for a more recent year – Consider the possible development of a methodology to compile more timely estimates of levels and changes • First results available • To be finalised in 2016 4
- 5. 5 Basic information on methodology Step 1 – Adjust national accounts totals Step 2 – Determine relevant variables from micro data sources in relation to the national accounts variables Step 3 – Impute for missing elements and scale the micro data to the adjusted national accounts totals Step 4 – Clustering households Step 5 – Derive relevant indicators for the household groups
- 6. 6 Optimal use of available information AUT CHE FRA GBR ISR JPN MEX NLD PRT SVN USA B2 Operating surplus X X X X X X X X Owner occupied dwellings X X X X X X X Leasing of dwellings X X X X X X B3 Mixed income X X X X X X Own account production X X X X Underground production X X X Mixed income excluding underground and own account production X X X X X X D1R Compensation of employees X X X X X X X X X D11R Wages and salaries X X X X X X X X X X X D121R Employers'’actual social contributions X X X X X X X X X X D122R Employers’' imputed social contributions X X X X X D4R-D4P-FISIM Net property income received X X X X D41'R Interest (not adjusted for FISIM) X X X X X X X X X X D42R Distributed income of corporations X X X X X X X D44R Investment income disbursements X X X D441R Investment income attributable to insurance policy holders X X X X D441AR Prop. income received attr. to non-life insurance policy holders X D441BR Prop. income received attr. to life insurance policy holders X D442R Investment income payable on pension entitlements X X D443R Investment income attr. to collective investment funds share holders X X D45R Rent X X X -D41'P Interest (not adjusted for FISIM) X X X X X X X X X -D45P Rent X X Adjustment for FISIM (-/-) X B5 Balance of primary incomes X X X X X X X Optional cells Income Micro information
- 7. 7 Optimal use of available information AUT CHE FRA GBR ISR JPN MEX NLD PRT SVN USA B5 Balance of primary incomes X X X X X X X -D5P Current taxes on income and wealth X X X X X X X X X X X -D61P Net social contributions X X X X X X X -D611P Employers'’actual social contributions X X X X X X X X -D612P Employers' imputed social contributions X X X -D613P+D614P Households' social contributions (actual and supplements) X X X X X X X D62R Social benefits other than STiK X X X X X X X X X D7R-D7P Other current transfers (net) X X X X X X D72R-D71P Net non-life insurance claims minus premiums X X X X -D71P Non-life insurance premiums X X X X D72R Non-life insurance claims X X X D75R Net miscellaneous current transfers received - paid X X X X X D75R Miscellaneous current transfers received X X X -D75P Miscellaneous current transfers paid X X X of which transfers between resident households (SNA 93 8.95) X X B6 Disposable income X X X X X X X D63R STiK X X X X X X X Education X X X X X Health X X X X X Other X X X X B7 Adjusted disposable income X X X X X X X Optional cells Income Micro information
- 8. 8 Optimal use of available information AUT CHE FRA GBR ISR JPN MEX NLD PRT SVN CP010 Food and non-alcoholic beverages X X X X X X X X X X CP020 Alcoholic beverages, tobacco and narcotics X X X X X X X X X X CP030 Clothing and footwear X X X X X X X X X X CP040 Housing, water, electricity, gas and other fuels X X X X X X X X X CP041 Actual rentals on housing X X X X X X X X X CP042 Imputed rentals on housing X X X X X X X X CP043 Maintenance and repair of dwellings X X X X X X X X CP044 Water supply and miscellaneous X X X X X X X X X CP045 Electricity, gas and other fuels X X X X X X X X X CP050 Furnishings, hh equipment and routine maint. of the house X X X X X X X X X CP060 Health X X X X X X X X CP061 Medical products, appliances and equipment X X X X X X X X X CP062 Out-patient services X X X X X X X X X CP063 Hospital services X X X X X X X X X CP070 Transport X X X X X X X X CP071 Purchases of vehicles X X X X X X X X X CP072 Operation of personal transport equipment X X X X X X X X X CP073 Transports services X X X X X X X X X CP080 Communications X X X X X X X X X CP090 Recreation and culture X X X X X X X X X CP100 Education X X X X X X X X X CP110 Restaurants and hotels X X X X X X X X X CP120 Miscellaneous goods and services X X X X X X X CP12x Miscellaneous (less FISIM, less insurance) X X X X X X X CP1261 FISIM X CP125 Insurances expenditures (life and non-life) X X X X X X X X P31DC Final domestic consumption expenditure X X X X X P33 Final consumption expenditure of resident households abroad X X X X P31NC Final national consumption expenditure X X X X X X X X X D63R STiK X X X X X P4 Actual final consumption X X X X X X Consumption Micro information
- 9. 9 But also … need to impute information Example: Distribution of Social Transfers in Kind on health
- 10. 10 Grossing up micro data: income Adjustment coefficient for the main income components, based on Method A Adjusted national accounts estimate over micro source aggregate most recent year second most recent year most recent year second most recent year most recent year second most recent year most recent year second most recent year B2 Operating surplus 4 2.06 1.12 2.06 1.12 1.68 1.12 2.43 1.12 B3 Mixed income 4 1.71 1.79 1.40 1.79 1.30 1.67 2.43 1.91 D1R Compensation of employees 4 1.19 … 1.20 … 1.16 … 1.20 … D41R Interest (not adjusted for FISIM) 7 4.14 3.84 1.77 1.06 0.66 0.72 16.21 12.52 D42R Distributed income of corporations 5 5.71 14.08 1.85 14.08 0.70 3.74 18.41 24.41 D41P Interest (not adjusted for FISIM) 7 2.58 2.49 2.40 1.50 1.02 1.01 4.67 4.74 D5P Current taxes on income and wealth 7 1.31 1.40 1.21 1.23 0.87 1.10 2.14 2.00 D61P Net social contributions 2 1.25 1.54 1.25 1.54 1.25 1.28 1.25 1.80 D62R Social benefits other than STiK 7 1.26 1.35 1.26 1.34 0.97 0.98 1.55 1.67 D63R1 STiKs on Education 2 0.96 0.87 0.96 0.87 0.79 0.75 1.13 0.98 D63R2 STiKs on Health 2 1.45 1.13 1.45 1.13 1.18 0.99 1.72 1.27 Maximum Code Instrument Number of countries Average Median Minimum
- 11. 11 Grossing up micro data: income Coverage rates by country for the main income components(1) 0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200% AUT 2012 FRA 2011 ISR 2012 MEX 2012 NLD 2011 PRT 2011 SVN 2012 SWI 2011 UK 2012
- 12. 12 Grossing up micro data: consumption Adjustment coefficient for the main expenditure components, based on Method A Adjusted national accounts estimate over micro source aggregate most recent year second most recent year most recent year second most recent year most recent year second most recent year most recent year second most recent year CP010 Food and non-alcoholic beverages 9 1.48 1.55 1.29 1.36 1.01 0.95 2.86 2.81 CP020 Alcoholic beverages, tobacco and narcotics 8 3.88 5.18 2.45 2.41 1.68 1.13 12.92 17.79 CP030 Clothing and footwear 9 1.43 1.47 1.26 1.25 0.98 1.00 2.13 2.44 CP040 Housing, water, electricity, gas and other fuels 7 1.78 1.41 1.33 1.15 0.84 0.87 4.56 2.49 CP050 Furnishings, households equipment & house maint. 9 1.59 1.67 1.29 1.25 1.15 0.96 2.93 2.98 CP060 Health 7 2.67 2.84 2.24 2.71 1.17 1.15 4.78 4.81 CP070 Transport 7 1.80 1.90 1.29 1.35 0.93 0.94 4.48 3.95 CP080 Communications 9 1.34 1.64 1.28 1.69 0.89 1.07 2.28 2.54 CP090 Recreation and culture 9 1.61 1.66 1.36 1.14 0.98 1.01 3.27 3.04 CP100 Education 9 1.15 0.77 1.01 0.99 0.19 0.13 1.90 1.08 CP110 Restaurants and hotels 9 1.57 1.24 1.45 1.11 0.82 0.85 2.98 1.69 CP120 Miscellaneous goods and services 6 2.04 2.11 2.42 2.47 1.02 1.00 2.63 2.85 Code Instrument Number of countries Average Median Minimum Maximum
- 13. 13 Grossing up micro data: consumption Coverage rates by country for detailed consumption components 0% 20% 40% 60% 80% 100% 120% 140% 160% AUT 2012 FRA 2011 ISR 2012 MEX 2012 NLD 2011 PRT 2011 SVN 2012 SWI 2011 UK 2012
- 14. 14 First results: income Relative position of each household group compared to the average, by Equivalized Disposable Income quintile Adjusted disposable income per consumption unit for each group to the average adjusted disposable income per consumption unit in the country 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Q1 Q2 Q3 Q4 Q5 FRA 2011 USA 2012 MEX 2012 SVN 2012 NLD 2011 PRT 2011
- 15. 15 First results: income Relative position of the 20% highest to the 20% lowest income households Adjusted disposable income per consumption unit for the fifth quintile to the adjusted disposable income for the first quintile 2003 2008 2009 2006 2008 2010 0.0 2.0 4.0 6.0 8.0 10.0 12.0
- 16. 16 First results: impact of secondary income distribution Impact of net transfers on the relative position of each household group compared to the average, by quintile Adjusted disposable income per consumption unit for each group to the adjusted disposable income per consumption unit average minus primary income per consumption unit to the primary income per consumption unit average -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 Q1 Q2 Q3 Q4 Q5 AUS 2011 AUT 2012 FRA 2011 ISR 2012 MEX 2012 NLD 2011 CHE 2011 PRT 2011 SVN 2012 GBR 2012 USA 2012
- 17. 17 First results: consumption Relative position of each household group compared to the average, by Equivalized Disposable Income quintile Actual final consumption per consumption unit for each group to the average actual final consumption per consumption unit in the country 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Q1 Q2 Q3 Q4 Q5 AUS 2011 AUT 2012 FRA 2011 ISR 2012 MEX 2012 NLD 2011 PRT 2011 SVN 2012 GBR 2012
- 18. 18 First results: consumption Relative position of the 20% highest income households to the 20% lowest income households Final consumption expenditure and Actual final consumption, per consumption unit 1.5 1.7 1.8 1.8 1.9 2.0 2.0 2.3 4.1 1.8 2.4 2.6 2.3 2.7 1.9 2.6 2.8 5.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 SVN 2012 AUS 2011 ISR 2012 GBR 2012 FRA 2011 AUT 2012 NLD 2011 PRT 2011 MEX 2012 Actual Consumption Final Consumption
- 19. 19 First results: savings Saving as a percentage of adjusted disposable income by Equivalized Disposable Income quintile Mexico France
- 20. 20 First results: income, breakdown by age
- 21. 21 First results: income, breakdown by labour market status
- 22. Why it matters Ratio of richest to poorest (Q5/Q1): comparison between the EG DNA results and the OECD micro database (IDD) Note: The legend indicates the extent to which the results from the EG DNA and the IDD are comparable. A star indicates similar micro sources. A year is indicated in case IDD and EG relate to the same year. Note: Micro measures are based on a grouping by individuals, whereas the EG is based on households. 0 5 10 15 20 25 30 Income Distribution Database (IDD) EG - Adjusted disposable income per consumption unit EG - Adjusted disposable income per consumption unit excluding "National Accounts concepts"
- 23. • Impact on inequality measures • Providing more opportunities to link distributional information to macro-economic policy issues • Improving consistency between income, consumption, savings (and wealth) • Potential to improve both micro surveys and national accounts estimates • Potential to compile time series data and to improve timeliness 23 Why it matters
- 24. • More countries compiling (consistent time series of) integrated data on income, consumption and savings • Improving methodology, e.g. on methodologies to close the gaps between micro and macro • Further research into the validity of the results for savings • Research into methodologies to extrapolate distributional information, using more timely national accounts estimates and other timely data sources (LFS?) • Including wealth? 24 Way forward
- 25. Thank you for your attention 25 For more information please contact: Peter.VANDEVEN@oecd.org Jorrit.ZWIJNENBURG@oecd.org
Editor's Notes
- The second step in the procedure is to determine the relevant components from the micro data sources, as compared to the NA variables for income and consumption. Ideally, for all NA items, micro data can be found. However, as some items are specific to the system of NA (such as FISIM and investment income attributed to policy holders), full coverage is not possible. The table presents an overview of data coverage for the main income items in the exercise. This shows that micro data coverage is rather good for the main components. Coverage only turns out to be poor for Investment income disbursements. As a consequence coverage is also low for Net property income. When the coverage is reviewed on a more detailed level, the picture is more mixed. Looking at how countries score: the availability of micro data seems specifically low in Austria, the Netherlands, Slovenia and Switzerland, whereas coverage looks particularly high in France, Mexico, the UK and the US.
- Looking at the re-distributional income transactions, coverage turns out to be poor for other social transfers in kind (STiK). Also for underlying components of D7 Other current tran.sfers
- Note, that no data were available for the US, so not shown in the figure. For the consumption items the coverage generally seems to be better than for the income components. Most countries have micro data available for all items, with the exception of Japan and the Netherlands, for which data is lacking for a couple of them. Furthermore, for some countries data on the final consumption expenditure of resident households abroad is lacking. Looking at how countries score overall: the availability of micro data seems specifically low in Austria, the Netherlands, Slovenia and Switzerland, whereas coverage looks particularly high in France, Mexico, the UK and the US.
- In the case of components for which no distributional information is available in micro sources, national experts have to make imputations at the micro level. The next couple of sheets will focus on the imputations for the various components of STiK. Eight out the eleven countries provided data on STiK. For STiK on Health countries use different methods for imputations: some use the actual value approach, others use the insurance value approach (most of them) and also other methods are used, for instance by Austria and the US. For four of the eight countries (AUS, ISR, MEX, SVN) the concentration of STiK on Health results to be highest in the lower income groups. On the other hand, for the other three countries (AUT, FRA, USA) STiK is almost flat across quintiles.
- With regard to method A, it is also interesting to see how large the adjustment coefficients are for the various countries and for the various transactions. The table shows the adjustment coefficients for the main income components. Distributed income of corporations turns out to have the highest adjustment coefficients on average (5.71), followed by income received and paid (4.14 and 2.58), and operating surplus (2.06). Also, the outliers are very large for some items, such as distributed income (18.41). It would be interesting to discuss the main reasons for these gaps. As these results underlie the ultimate results, decisions that are made at this stage have large implications for the results. Therefore, simply applying the proportional attribution of the gap to the micro results may not lead to the best results. It may be better to manually attribute parts of the gaps (for instance due to missing information on the ‘Mick Jagger’s’ or specific importance of the underground economy in specific higher or lower income quintiles. Manual attribution of these results may lead to better results than simply applying a proportional attribution. However, we would like to hear your views on this!
- Another way to display the adjustment coefficients is to calculate the coverage rate of the totals according to the micro data source as a percentage of the adjusted NA totals. The figure shows the coverage rates of the micro sources for the main income components. When the coverage rates are within an interval of 80% and 120% this indicates good alignment between the two. In the figure this is indicated by two solid lines. Mexico and the UK show low rates for all components. On the other hand, Austria, France and the Netherlands are within the interval for most of the components. Furthermore, it can be observed from the figure that none of the countries in the exercise show a coverage rate in the interval for interest received and distributed income of corporations.
- The same analysis was conducted for the consumption components. For consumption, smaller differences across components are observed compared to income. For all the components the average coefficients are between 1.15 and 2.67 in the most recent year, except for item Alcoholic beverages, tobacco and narcotics. However, the average seems to have been highly influenced by the maximum values.
- Another way to display the adjustment coefficients is to calculate the coverage rate of the totals according to the micro data source as a percentage of the adjusted NA totals. The figure shows the coverage rates of the micro sources for the main consumption components. Half of the rates are within the 80% and 120% interval which indicates good alignment. Austria and Switzerland show the best rates as they are inside the interval for almost all their components. Looking at all the components, Alcohol and tobacco shows a bad coverage as no country reports a rate within the interval. Restaurant and hotels is also a category that does not show good alignment. The best results are recorded for Education and for Housing.
- The figure shows the results for six countries for income. It shows the relative position of each household compared to the average on the basis of the adjusted disposable income per consumption unit. It can be observed that the ratio to average for the highest income quintile is the highest in Mexico and the United States. It is lowest in Slovenia. The income of the fifth quintile is 1.5 times the average income in Slovenia versus 2.6 times in Mexico. On the other hand, Mexico and the US record the lowest ratios for the first quintile. Whereas the ratio for the lowest income quintile is 62% for France and 67% for Slovenia, the first quintile represents only 27% of the average in the United States and 32% for Mexico. The figure also shows that the median income is below average in all the countries (although close for the Netherlands). The median income is approximated by the average income of the third quintile. In Slovenia and Portugal, the ratio is even less than 1.0 for the fourth quintile, which means that in these two countries the average income of the fourth quintile is still below average of the population as a whole. However, the overall picture is very different in these two countries. Slovenia shows a particularly flat distribution from Q2 to Q4 (from 0.86 to 0.99), whereas Portugal shows a steeper slope (from 0.67 to 0.99), which means that the income differences between these two quintiles are smaller in Slovenia than in Portugal.
- As it can be seen from the figure, the highest ratio is observable in the US and Mexico. This is in line with the results that were presented for the ratio to average. In the United States, on average, the households with the highest income receive an adjusted disposable income that is 8.6 times higher than the one received by the ones with the lowest income. In Mexico this is 8.3 times. The lowest income disparities are observable in Slovenia, where the ratio is only 2.3. Also for France, the Netherlands, the UK and Australia this ratio is relatively low. This is in line with the results shown before, where these countries showed a relatively flat line for the ratio to average. In comparison with the second most recent year the ratio has dropped in the Netherlands, France and Mexico. For Switzerland the ratio increases, as well as for Austria and Portugal, although to a lesser extent.
- The relative position of each household group compared to the average is different when measured for primary income, i.e. before deducting any income taxes and social contributions paid and adding transfers in cash and in kind. Net current transfers, mainly related to the intervention of general government and pension insurance, bring some household groups closer to the average. The figure presents these changes for the various countries. As can be observed from the figure, the first three quintiles mostly benefit from the net current transfers. Also, the distributional effects seem to be highest in the Netherlands and the UK, whereas the impact in Switzerland and Mexico seems to be the lowest. For Switzerland, also the fifth quintile seems to benefit slightly from the re-distributional transactions, but this is due the imputation technique used by the Secretariat to impute for missing quintile data.
- The figure shows the relative position of each household group compared to the average with regard to consumption expenditure. The distribution over the quintiles is quite similar. Mexico, Portugal and Slovenia are the only exceptions. For all the other countries, the consumption of the lowest income quintile varies from 69 to 77% of the average, whereas the consumption of the highest income quintile ranges between 1.3 to 1.4 times the average. For the other quintiles, the differences between the countries are also small. For Mexico, the differences are much bigger as households in the first quintile consume just above 50% of the average, whereas the households in the fifth quintile consume 1.8 times the average. To a lower extent, the same picture applies to Portugal where the distribution goes from 68% of the average for the first quintile to 159% for the fifth quintile. The Slovenian distribution is also diverging from the other countries, by showing a quasi-equal percentage of the average for the fourth and the fifth quintile of 1.2 times the average. Even with a higher income, households in the fifth quintile consume approximately the same amount as those in the fourth quintile. This implies that the savings component is expected to be higher for the households in the fifth quintile.
- The figure presents the relative position of the households with the highest income versus the households with the lowest income for consumption expenditure. It shows actual consumption and final consumption (excluding social transfers in kind). Mexico records the highest ratio. Households in the fifth quintile show a level of actual consumption (the consumption expenditure by households including social transfers in kind) that is 4.1 times higher than the level of consumption of the lowest income households. Slovenia reports the smallest difference with 1.5 times. These results are in line with the earlier observations. Excluding social transfers in kind would lead to higher consumption inequalities. When looking at the data for final consumption (which excludes social transfers in kind), especially Mexico, Israel and France show a higher inequality. Austria is the only country for which the exclusion of social transfers in kind leads to a lower inequality. For four countries, the ratios could be compared with the results for the second most recent year. It turns out that for three of the countries the results are almost the same. Only for Portugal a decrease can be observed from 2006 to 2011.
- The last figure presents the results on savings. Savings are calculated as the difference between adjusted disposable income and actual consumption plus the change in net equity of households in pension funds. The figure presents the savings per quintile as a percentage of their adjusted disposable income. The figure shows negative saving rates for the lowest income quintile in all countries, with a particularly high negative rate in Mexico (-45%). Savings are still negative in the second quintile for half of the countries. Mexico and Portugal even record negative savings in the third (and Portugal even in the fourth) quintile. Furthermore, a sharp increase in savings can be observed in Slovenia from the fourth to the fifth quintile. This is in line with the observations in the earlier figures. Furthermore, a small decrease can be observed for the Netherlands from the third to the fourth quintile. In that regard, the Netherlands is the only country that records a drop in the savings ratio when going up the quintile rank.
- For some countries the quintile composition of each age group is presented. It turns out that in Israel younger persons tend to have the lowest income, with income increasing with age groups (with a slight deceleration for the oldest age group). In the Netherlands, the population with the highest income (Q4 and Q5) seems to be concentrated in the middle age groups (25-64), while persons in the youngest age groups (0-14 and 15-24) are highly concentrated in the low income quintiles (Q1 and Q2). Individuals grouped in the 65+ age group tend to be concentrated in the second income quintile. Looking at the drop of the percentage of the 65+ group in comparison with the age group 45-64 in the 4th and the 5th quintile, this may imply that the passage to the retirement age has a negative and substantial impact on individuals’ income levels. In Slovenia the age group composition by income quintile appears to be almost homogenous in the first five age groups (from the youngest, 0-14, to the oldest, 45-64), while an increased concentration of the oldest age group can be observed in the lower income quintile. Finally, in the United States, the picture is much more varied, with a relatively flat distribution in the middle age groups (i.e. 25-34 and 35-44) and a larger concentration of the youngest (i.e. age groups 0-14 and 15-24) in the lower income quintiles, and of the oldest individuals (i.e. age groups 45-64 and 65+) in the higher income quintiles.
- It is interesting to have a closer look at the resident population's labour market status decomposed by income quintile for a couple of countries. For France, it is evident that the Employer category (and secondly the Own-account worker category) is more than average represented in the highest income quintile (Q5) and, as expected, the Unemployed category is concentrated in the low income quintile. In Mexico, the Unpaid family worker category includes a relevant portion of low income persons, as the category name itself implies, while the Retired seem to receive relatively high incomes. I In the Netherlands, similarly to the case of France, the Employers are relatively well represented in the high income quintile (although less pronounced, compared to France), while the Unemployed category show a relatively flat distribution across quintiles. Finally, in Slovenia, it is interesting to see that high income individuals represent the highest portion observed in the Employee category, on a larger magnitude compared to the share recorded in the Employer category.