1. Long-run trends in the concentration of income and wealth Daniel Waldenström (IFN, Stockholm) Presentation at 3rd GLOBALEURONET Summer School, Paris School of Economics, July 10, 2008

2. THE ISSUES A.What are the long-run cross-country trends in the concentration of income and wealth? B.The role of industrialization? C.The role of globalization? D.Other determinants?

3. THIS TALK  Part I: Wealth –Long-run trends and revisit impact of industrialization –Sample: 7 countries (old and new data) –Time period: From mid-18th century to present day  Part II: Income –Long-run determinants of top income shares –Role of some economic variables –Sample: 16 countries –Time period: 1900-2000  Conclusions & some unresolved issues

4. Part I: The long-run concentration of wealth: An overview of recent findings

5. Outline of Part I 1.Starting point 2.Wealth concepts and definitions 3.Country results:  France  Switzerland  UK  US  Denmark  Norway  Sweden 4.Cross-country comparison 5.Conclusions

6. 1. Starting point Issues of interest:  Common vs. specific trends  Heterogeneity within the top  Did wealth inequality increase in the initial phase of industrialization? (Kuznets hypothesis)  Role of wars, taxes, globalization of the 20th century  The role of the Scandinavian Welfare State?

7. Starting point (2)  Historical data on wealth inequality: –France, 1807-1994 (Piketty et al, 2006) –Switzerland, 1913-1997 (Dell et al, 2005) –United Kingdom, 1740-2003 (several authors) –United States, 1774-2002 (several authors) –Denmark, 1789-1996 (various sources) –Norway, 1789-2002 (various sources) –Sweden, 1800-2006 (various sources)  Variation across these countries: –Timing of industrialization –Participation in World Wars –Level of wealth taxation

8. 2. Wealth concepts and definitions  A mix of sources –Estate tax data –Wealth tax data –Survey data  Wealth concept –Net worth = real and financial assets less debts –Does (typically) not include: art & jewelry, TV:s etc, pension wealth, human capital, public goods  A mix of observational units –Households (wealth tax-based, survey sources) –Individuals (deceased, estate-tax based sources)

9. Concepts (cont’d)  Computation of top wealth shares: –Estimate share of total net worth that goes to the top 10, 5, 1, 0.1, etc % of all potential wealth holders. –Reference total wealth: All personal wealth (not only taxed wealth) estimated from tax records or national accounts –Reference total for the population: All potential tax units (not just those who file tax returns)  Problems with tax-based data –Evasion, avoidance etc. –Importance grows with systematic differences across distribution and over time –We lack compositional information (except for France)

10. 3. French wealth concentration, 1807-1994

11. Swiss wealth concentration, 1913-1997

12. U.K. wealth concentration, 1774-2001 Lindert (2000) Atkinson et al IRS (2006)

13. U.S. wealth concentration, 1774-2001 Shammas (1993) Lindert (2000) Kopzcuk & Saez (2004) Wolff (1987,...)

14. Danish wealth concentration, 1789-1996

15. Norwegian wealth concentration, 1789-2002

16. Swedish wealth concentration, 1870-2006 Industrialization Crises, Home ownership, Welfare State Globalization ? Bottom 90% Top 10-1% Top 1%

17. What happened in Sweden after 1980? … and why does it not show up in the official statistics?  Unique Swedish combination of the 1980s & 90s: –High taxes on wealth, inheritance and capital income –Financial market boom –Liberalized capital account (after 1989)  Effect: Large fortunes ”disappear” –Private wealth (and its holders) leave Sweden –Capital in Sweden transfered to closely held companies What does this do to the distribution of wealth?

18. Swedish wealth concentration 1950-2005 (official series) Bottom 90% Top 1% Wealth share (%)

19. Swedish wealth concentration 1950-2005 (our new series) Bottom 90% Top 1% Wealth share (%)

20. How do we estimate the wealth of the rich?  We add fortunes to the wealth of the richest percentile in the domestic population Three additions: 1.Foreign household wealth –Net errors and omissions in the Balance of Payments –”Unexplained savings” in the Financial Accounts 2.Family-firm wealth of rich Swedes in Sweden –Listings of super rich Swedes since 1983 3.Wealth of rich Swedes abroad –Listings of super rich Swedes since 1983

21. Net errors and omissions (N.E.O.) Accumulated net errors and omissions over GDP

22. Net errors and omissions (N.E.O.) Accumulated net errors and omissions over GDP

23. Top 1% - Sweden’s official series Wealth share (%) SCB official

24. Effect by adding foreign household wealth Wealth share (%) SCB official + N.E.O.

25. Effect by adding family-firm wealth of rich Swedes living in Sweden Wealth share (%) SCB official + N.E.O. + Rich in Sweden

26. Effect by adding wealth of rich Swedes abroad Wealth share (%) SCB official + N.E.O. + Rich in Sweden + Rich abroad

27. Comparing Sweden and the U.S. (SCF) SCB official + N.E.O. + Rich in Sweden + Rich abroad USA + N.E.O, rich in USA and abroad Wealth share (%)

28. 4. Cross-country P99-100, 1774-2003

29. Cross-country P95-99, 1774-2003

30. Overview of pre-1914 trends Period:≈1780-1914 Fractile:P99-100P95-99 FranceIncreaseFlat Switzerland-- UKIncreaseDecrease USIncreaseFlat? DenmarkDecreaseFlat NorwayDecreaseIncrease SwedenFlatFlat

31. Overview of long-run trends Period:≈1780-19141914-2000 Fractile:P99-100P95-99P99-100P95-99 FranceIncreaseFlatDecreaseFlat Switzerland--FlatFlat UKIncreaseDecreaseDecreaseFlat USIncreaseFlat?DecreaseFlat? DenmarkDecreaseFlatDecreaseFlat NorwayDecreaseIncreaseDecreaseDecrease SwedenFlatFlatDecreaseDecrease

32. Summarizing Part I  Industrialization’s impact on wealth mixed  20th century sees massive wealth equalization –Owner-occupied housing –Wars, crises and progressive taxation –Role of government mixed (public provision of shooling, health, pensions, increase inequality of net worth)  What about the Kuznets inverse-U theory? –No clear increase in inequality during industrialization, but a clear decrease thereafter –That is, rather an inverse-J curve...  International capital flows may imply that national wealth concentration is underestimated

33. Part II: The long-run determinants of inequality: What can we learn from top income data?

34. Starting point  New database on long-run income inequality: –Top income shares  General dissatisfaction with available inequality data –scattered –short time periods –different across countries making comparisons difficult  A solution: use tax data –available since the early 20th C.  Long-run series –available in most countries  cross-country comparisons –before WWII, primarily top incomes observed –focus on the rich important for analyzing driving factors

35. Income inequality data  Top income data: –Main concept: gross total income before taxes/transfers –Tax units: individuals or households –Composition: labor, capital, business income included –Realized capital gains not included*  Computation of top income shares: –Share of total income of the top 10, etc % of all potential income earners. –Reference income not only taxed income –Reference population not just those who file tax returns

36. France  Piketty, 2003, Journal of Political Economy

37. United States  Piketty and Saez, 2003, Quarterly J of Econ.

38. Sweden  Roine and Waldenström, 2008, J of Public Ec.

39. Other countries... –Canada (Saez and Veall, 2005, AER) –United kingdom (Atkinson, 2005, J Roy Stat Soc) –Switzerland & Germany (Dell, 2005, JEEA) –Netherlands (Atkinson and Salverda 2005, JEEA) –Australia (Atkinson and Leigh, 2006) –New Zealand (Atkinson and Leigh, 2006, RevIncWealth) –India (Banerjee and Piketty, 2005, WBER) –Japan (Moriguchi and Saez, 2006, ReStat) –Finland (Riihilä et al, 2005) –Spain (Alvaredo and Saez, 2006) –Argentina (Alvaredo, 2006) –Ireland (Nolan, 2007) –China (Piketty and Qian, 2006) –Indonesia (Leigh and van der Eng, 2006) –Norway (Aaberge and Atkinson, 2008) –Underway: Portugal, Denmark, South Africa, African colonies  New OUP volumes edited by Atkinson & Piketty

40. Main findings in top income literature  Long-run top income trends strikingly similar Up to 1980, income inequality decreasesUp to 1980, income inequality decreases  After 1980, some divergence seem to arise Anglo-Saxon countries experience surge in top sharesAnglo-Saxon countries experience surge in top shares Continental European countries remain on low levelsContinental European countries remain on low levels  Differences within the top: Top precentile has large share of capital incomeTop precentile has large share of capital income Rest of top decile mainly highly paid wage earnersRest of top decile mainly highly paid wage earners  Suggested causes (based on country cases): Shocks to capital reduces before WWIIShocks to capital reduces before WWII Progressive taxation holds back increase after WWIIProgressive taxation holds back increase after WWII After 1980: many candidates...After 1980: many candidates...

41. This analysis  Use new panel with long-term top income shares  Divide the income distribution into three groups: –The Rich (Top 1 percentile) –The Upper Middle Class (Top10–1 percentiles) –The Rest (Bottom 90 percentiles)  Try to relate their income shares to other variables: –Economic growth, Trade openness, Financial development, Growth of government  Allow effects to differ between –Levels of economic development (low/medium/high) –Anglo-Saxon countries and ”rest of the world” –Bank-oriented vs market-oriented financial systems

42. Potential determinants of inequality  Economic growth –Top incomes are more closely tied to the economy (bonuses, incentive contracts)  Trade openness –Standard: Capitalists gain in capital abundant countries –”Superstars” in global labor markets (Rosen, 1980; Gersbach & Schmutzler, 2007)  Financial development –Typically seen as pro-poor Reduces credit constraints, pools resources (Beck et al. 2007)Reduces credit constraints, pools resources (Beck et al. 2007) –When is finance pro-rich? When the rich have control over politics and financeWhen the rich have control over politics and finance At early stages of development (Greenwood & Jovanovic 1990)At early stages of development (Greenwood & Jovanovic 1990)

43. Potential determinants of inequality  Marginal income taxation –Two potential effects from higher top marginal tax: Lowers pre-tax income through reduced incentives to workLowers pre-tax income through reduced incentives to work Raises pre-tax income to compensate for tax increaseRaises pre-tax income to compensate for tax increase Altogether: Theory provides conflicting answers

44. Data (cont’d)  Other variables: –GDP/capita and Population Source: MaddisonSource: Maddison –Financial development: Bank deposits + Stock market Sources: Mitchell, IFS, FSD, Bordo, Rajan & ZingalesSources: Mitchell, IFS, FSD, Bordo, Rajan & Zingales –Trade openness: (Exports+Imports)/GDP Sources: Mitchell, López-Córdoba & Meissner, BordoSources: Mitchell, López-Córdoba & Meissner, Bordo –Central government spending Sources: Mitchell, Rousseau & Sylla, Bordo, IFS, FSDSources: Mitchell, Rousseau & Sylla, Bordo, IFS, FSD –Top marginal tax rates Sources: Top inc studies, OECD, Rydqvist et al., Bach et al,Sources: Top inc studies, OECD, Rydqvist et al., Bach et al,

45. First look at the data  Several common chocks clearly visible –Great depression –WWII  Effects from globalization can be common to the countries in our sample –This makes them hard to trace statistically

46. Top 1% - ”The rich”

47. Top 10-1% - ”The upper middle class”

48. Variable plots GDP/cap Openness Total capitalization Gov. spending

49. Variable plots Marginal tax rate (Preferred) (rate at inc= 5xGDP/cap, statutory) Marginal tax rate 2 (statutory)

50. Econometric method  We model top income shares as being function of: –financial development, trade openness, government spending, tax progressivity, economic growth  Obviously, we cannot claim to establish causality.  We use five-year period averages in analysis  The panel dataset is long and narrow –Fixed effects model (de-meaning) not optimal –Measurement errors likely to be serially correlated  Use first-differenced model (Bound & Krueger, 1991)

51. Econometric method (2) Error terms also serially correlated. We use two approaches to cope with this: 1.First-difference GLS (FDGLS - presented here) Δy it = ΔX' it b 1 + γ t + μ i + ε it (ε ~ AR(1)) 2.Dynamic first-difference (DFD - in Appendix) Δy it = b 0 Δy it–1 + ΔX' it b 1 + γ t + μ i + ε it

52. Baseline results (FDGLS) ΔTop1ΔTop1ΔTop10-1ΔTop10-1ΔBot90ΔBot90 ΔGDPpc5.806***6.562***–8.816***–7.017***5.527**–1.654 ΔPop–4.362–12.59**–0.519–12.019.70322.83* ΔOpenness–8.799–2.312–0.1990.3463.15–0.322 ΔFindev0.983***1.270***0.1720.184–0.533–1.874*** ΔGovspend5.9763.619–16.51***–24.05***22.52***23.94*** ΔMargtax1–4.390***–3.181**10.18*** Obs1269299779977 Cntry trends YesYesYesYesYesYes Time effects YesYesYesYesYesYes N countries 141212101210

53. Interacting with level of development ΔTop1ΔTop1ΔTop10-1ΔTop10-1ΔBot90ΔBot90 ΔGDPpc5.437***–8.571***4.896* ΔPop–4.690–5.584–2.7024.239.6824.945 ΔGovspend3.6015.941–18.03***–19.14***24.01***23.50*** ΔFindev1.045***0.219–0.555 ΔOpenness–9.105***–8.468***–0.269–0.7163.6183.831 ΔGDP×Low 5.067***–9.031***4.53 ΔGDP×Med 6.406***–7.342***5.982 ΔGDP×High 2.617–9.841***8.262* ΔFin×Low 1.647*–3.263**2.081 ΔFin×Med 0.883*0.334–1.015 ΔFin×High 0.862*0.401–0.875 F: Low=Med 0.310.450.520.020.740.18 F: Low=High 0.250.420.80.010.450.18 F: Med=High 0.070.980.340.940.590.9 Obs12612699999999 N countries 141412121212

54. Are Anglo-Saxon countries different? ΔTop1ΔTop1ΔTop10-1ΔTop10-1ΔBot90ΔBot90 ΔGDPpc5.652***5.537***–9.511***–9.260***7.492**6.776** ΔPop–4.528–4.177–0.2511.868.0860.364 ΔGovspend6.0755.847–15.83***–17.07***20.52**24.09*** ΔFin0.995***0.982***0.1940.2–0.597–0.438 ΔOpenness–8.799***–9.870***0.472–1.5080.9675.987 ΔGDP×AS 0.4362.036–6.619* ΔOpen×AS 3.0916.033–16.50*** Obs12612699999999 Cntry trends YesYesYesYesYesYes Time effects YesYesYesYesYesYes N countries 141412121212

55. The role of financial system? ΔTop1ΔTop1ΔTop10-1ΔTop10-1ΔBot90ΔBot90 ΔGDPpc4.606***5.605***–9.221***–8.631***6.132***5.502** ΔPop1.166–4.5533.403–1.063–6.9979.477 ΔGovspend3.0544.63–15.59***–16.21***19.28**23.32*** ΔOpenness–1.922–8.268***–0.341–0.2822.3343.061 ΔBankdep.2.979***0.279–3.367** ΔMarketcap.0.872**0.351–0.691 Obs168128129101129101 Cntry trends YesYesYesYesYesYes Time effects YesYesYesYesYesYes N countries 161513131313

56. Extensions and robustness  We also run analysis on top shares within the top. –Top1/Top10 and Top0.1/Top1 –Additional dimension of inequality (e.g., Superstar theory) –Removes reference income total (robustness) –Results are in line with main analysis  Robustness: Our results are robust to: –Restrict analysis to postwar period Rules out influence from Great DepressionRules out influence from Great Depression –Dropping Japan from sample Japan has no top decile data, hence we have used top 5%.Japan has no top decile data, hence we have used top 5%. –Replacing Margtax with Margtax2 Margtax2 consists of only statutory top rates. More homogenous but also more measurement errorMargtax2 consists of only statutory top rates. More homogenous but also more measurement error

57. Summarizing Part II Analyzing new long-run income inequality panel 1.Finance appears to be strongly pro-rich –Bank- and market-based systems alike 2.Trade openness: no clear impact on inequality 3.Economic growth is pro-rich while income shares of the upper middle class decrease –Extends Dew-Becker & Gordon (2005, 2007) –No support for ”global labor market” for elites 4.Government growth accompanies lower inequality

58. CONCLUSION OF TALK A.Trends in wealth concentration  Up to 1914: Top wealth shares increase in France, US and UK (top 1%). They seem to decrease in Scandinavia  After 1900: Significant equalization in all countries (possibly with reversal after 1980s)  Shares of moderately rich (P95-99) fairly stable over time Trends in income concentration  1900–1980: Top income shares reduced in all countries  1980– : Increased inequality in Anglo-Saxon countries, but still low inequality in Continental Europe, Japan.  Again, shares of upper middle class much more stable

59. MAIN CONCLUSIONS (cont’d) B.Role of industrialization?  No clear increase in wealth concentration during industrialization, but clear decrease thereafter  Suggests inverse-J rather than Kuznets’ inverse-U curve C.Role of globalization?  Trade globalization?? Financial globalization seems to benefit capital owners significantly D.Other contributing factors?  Taxation/growth of government hampers top shares  House ownership reduces wealth concentration  Economic growth seems to be pro-rich over the long run  Financial development seems to benefit the top

60. Unresolved issues...  The rich - who are they? –More evidence on professions, industries etc needed  Composition of wealth and income? –Crucial for understanding impact of structural change  Static vs. dynamic inequality? –Few studies of long-run income and wealth mobility –In particular, intergenerational mobility of interest –Requires high-quality micro-level data –Kopczuk, Saez & Song on U.S. within carreer mobility  Gender issues? –Edlund & Kopzcuk: Men make wealth - women inherit it