For Good Measure Read online

  In light of the massive fall of the pre-tax incomes of the bottom 50% in the United States, our findings also suggest that policy discussions about rising global inequality should focus on how to equalize the distribution of primary assets—including human capital, financial capital, and bargaining power—rather than merely discussing ex-post redistribution through taxes and transfers. Policies that could raise the pre-tax incomes of the bottom 50% include improved education and access to skills, which may require major changes in the system of education finance and admission; reforms of labor market institutions, including minimum wage, corporate governance, and workers’ bargaining power through unions and representation in the board of directors; and steeply progressive taxation, which can affect pay determination and pre-tax distribution, particularly at the top end (Piketty, Saez, and Stantcheva, 2014; Piketty, 2014).

  The comparison between the United States, China, and France illustrates how DINA can be used to analyze the distribution of economic growth across income groups. As shown in Table 6.1, national income per adult increased in the three countries between 1978 and 2015: by 811% in China, by 59% in the United States, and by 39% in France. Nevertheless, performance has been very different across the distribution. There has been a clear pattern of rising inequality: top income groups enjoyed higher growth. In China, people at the top experienced very high growth rates of their income, but average growth was so large that the average income of the bottom 50% also grew markedly, by 401%. This is likely to make rising inequality more acceptable. In contrast, there was no growth at all for the bottom 50% in the United States (-1%). France illustrates another type of situation: people at the very top of the distribution experienced above-average income growth, but this pattern of rising inequality happened only for very high and numerically relatively negligible groups, so that it had limited consequences for the majority of the population. In effect, the bottom 50% income group enjoyed the same income growth as average growth (39%).

  Table 6.1. Real Income Growth Across the Distribution, 1978–2015 Percentages

  Note: Distribution of pre-tax national income (before taxes and transfers, except pensions and unemployment insurance benefits) among adults. Corrected estimates combining survey, tax, wealth, and national accounts data. Equal-split-adult series (income of married couples divided by two).

  Sources: US: Piketty, T., E. Saez, and G. Zucman (2016), “Distributional national accounts: Methods and estimates for the United States,” NBER Working Paper, No. 22945; France: Garbinti, B., J. Goupille-Lebret, and T. Piketty (2017), “Income inequality in France, 1900–2014: Evidence from Distributional National Accounts (DINA),” WID.world Working Paper, No. 2017/4; China: Piketty, T., L. Yang, and G. Zucman (2017), “Capital accumulation, private property and rising inequality in China 1978–2015,” WID.world Working Paper, No. 2017/6. StatLink 2 http://dx.doi.org/10.1787/888933839658.

  Private and Public Wealth-to-Income Ratios: The United States, China, France, the United Kingdom, Japan, Norway, and Germany

  Next, we present findings on the evolution of aggregate wealth. We observe a general rise of the ratio between net private wealth and national income in nearly all countries in recent decades. It is striking to see that this phenomenon was largely unaffected by the 2008 financial crisis. The unusually large rise of the ratio for China is notable: net private wealth was a little above 100% of national income in 1978, while it was above 450% in 2015. The private wealth-to-income ratio in China is now approaching the levels observed in the United States (500%), the United Kingdom, and France (550–600%).

  The structural rise of private wealth-to-income ratios in recent decades is due to a combination of factors, which can decomposed into: (1) volume factors (high saving rates, which can themselves be due to aging and/or rising inequality, with differing relative importance across countries, combined with growth slowdown); (2) relative asset prices; and (3) institutional factors, including the increase of real estate prices (which can be due to housing portfolio bias, the gradual lift of rent controls, and lower technical progress in construction and transportation technologies as compared to other sectors) and of stock prices (which can reflect higher power of shareholders leading to the observed increase in Tobin’s Q ratios—i.e., the ratio between market and book value of corporations).

  Figure 6.3. The Decline of Public Property and the Rise of Sovereign Funds

  Note: Share of net public wealth (public assets minus public debt) in net national wealth (private plus public).

  Source: China: Piketty, T., L. Yang, and G. Zucman (2017), “Capital accumulation, private property and rising inequality in China 1978–2015,” WID.world Working Paper, No. 2017/6; other countries: Piketty, T. and G. Zucman (2014), “Capital is back: Wealth-income ratios in rich countries, 1700–2010,” Quarterly Journal of Economics, Vol. 129(3), pp. 1255–1310, and WID.world updates. StatLink 2 http://dx.doi.org/10.1787/888933839715.

  Another key institutional factor driving the rise of private wealth-to-income ratios is the gradual transfer from public wealth to private wealth. This is particularly spectacular in the case of China, where the share of public wealth in national wealth dropped from about 70% in 1978 to 35% by 2015, as shown in Figure 6.3. The corresponding rise of private property has important consequences for the levels and dynamics of inequality. Net public wealth has become negative in the United States, Japan, and the United Kingdom, and is only slightly positive in Germany and France. This arguably limits government’s ability to redistribute income. The only exceptions to the general decline in public property are oil-rich countries with large public sovereign funds, such as Norway.

  Wealth Inequality Dynamics: The United States, China, France, and the United Kingdom

  Finally, we present findings on wealth inequality in Figure 6.4. We stress that currently available statistics on the distribution of wealth are highly imperfect. More transparency and better access to administrative and banking data sources are sorely needed if we want to gain knowledge of the underlying evolutions. In WID. world, we combine different sources and methods to reach robust conclusions: the income capitalization method (using income tax returns), the estate multiplier method (using inheritance and estate tax returns), wealth surveys, national accounts, and “rich lists.” Nevertheless, our series should still be viewed as imperfect, provisional, and subject to revision. We provide full access to our data files and computer codes so that everybody can use them and contribute to improving the data collection.12

  We observe a large rise of top wealth shares in the United States and China in recent decades, and a more moderate rise in France and the United Kingdom. A combination of factors explains these trends. First, higher-income inequality and severe bottom-income stagnation explain higher wealth inequality in the United States. Next, the very unequal process of privatization and access by Chinese households to quoted and unquoted equity probably played an important role in the very fast rise of wealth concentration in China. The potentially large mitigating impact of high real estate prices should also be taken into account; this effect, which benefited the middle class, is likely to have been particularly strong in France and the United Kingdom, where housing prices have increased significantly relative to stock prices.

  Figure 6.4. Top 1% Wealth Share in China, the United States, France, and the United Kingdom, 1890–2015

  Note: Distribution of net personal wealth among adults. Corrected estimates (combining survey, fiscal, wealth, and national accounts data). For China, US, and France, equal-split-adult series (wealth of married couples divided by two); for UK, adult series.

  Source: US: Saez, E. and G. Zucman (2016), “Wealth inequality in the United States since 1913: Evidence from capitalized income tax data,” Quarterly Journal of Economics, Vol. 131(2), pp. 519–578; UK: Alvaredo, F., A.B. Atkinson, and S. Morelli (2018), “Top wealth shares in the UK over more than a century,” forthcoming, Journal of Public Economics, and Alvaredo, F., A.B. Atkinson, and S. Morelli (2017), “Top weal
th shares in the UK over more than a century,” CEPR Discussion Paper, No. 11759; France: Garbinti, B., J. Goupille-Lebret, and T. Piketty (2016), “Accounting for wealth inequality dynamics: Methods, estimates and simulations for France (1800–2014),” WID.world Working Paper, No. 2016/5; China: Piketty, T., L. Yang, and G. Zucman (2017), “Capital accumulation, private property and rising inequality in China 1978–2015,” WID.world Working Paper, No. 2017/6. StatLink 2 http://dx.doi.org/10.1787/888933839734.

  Given all these factors, it is not easy to predict whether the observed trend of rising concentration of wealth will continue. In the long run, steady-state wealth inequality depends on the inequality of saving rates across income and wealth groups, inequality of labor incomes and of rates of returns to wealth, and the progressivity of income and wealth taxes. Numerical simulations show that the response of steady-state wealth inequality to relatively small changes in these structural parameters can be large (Saez and Zucman, 2016; Garbinti, Goupille-Lebret, and Piketty, 2016). This instability reinforces the need for increased democratic transparency about the dynamics of income and wealth.

  Conclusions

  We have very briefly described the basic concepts, sources, and methods that we apply in the World Inequality Database (WID.world) and in the development of the DINA project. We should stress again that these methods are fragile, exploratory, and subject to revision. As more countries join the database, new lessons will be learned, and the methods will be refined and updated. Accordingly, new updated versions of the DINA Guidelines will be regularly released on WID.world.

  We have also presented selected results on income and wealth inequality dynamics based on the DINA project. Global inequality dynamics involve strong and contradictory forces. We observe rising top income and wealth shares in nearly all countries in recent decades. But the magnitude of rising inequality varies substantially across countries, suggesting that different country-specific policies and institutions matter considerably. High GDP growth rates in emerging countries reduce between-country inequality, but this in itself does not guarantee acceptable within-country inequality levels and ensure the social sustainability of globalization. Access to more and better data (administrative records, surveys, more detailed national accounts, etc.) is critical to monitor global inequality dynamics, as this is a key building block to properly understand the present as well as the forces that will dominate in the future, and to design appropriate policy responses.

  Notes

  1. See also Piketty (2014) for an interpretative historical synthesis on the basis of this new material and of the top income shares time series.

  2. Social Accounts Matrices are a related precedent.

  3. Kuznets (1953) was preceded by ten years in this by Frankel and Herzfeld (1943), who made estimates of the European income distribution in South Africa based on income tax returns, making use of control totals from the census of population and from the national accounts.

  4. Results of these comparisons are already available for France (Garbinti, Goupille-Lebret, and Piketty, 2017) and the United States (Piketty, Saez, and Zucman, 2016).

  5. As new DINA series become available, we will systematically compare the inequality trends obtained in the old and the new series and analyze the sources of biases.

  6. We also keep the fiscal income definition associated with the first top income share series (Atkinson and Piketty, 2007, 2010; Alvaredo et al., 2011–15).

  7. Readers are referred to the DINA Guidelines Appendix, where we provide an Excel file with the formulas linking the income and wealth definitions to the SNA 2008 classification codes.

  8. WID.world provides estimates of the consumption of fixed capital in countries where these series are not available in SNA series. WID.world also estimates missing income from tax havens to correct net foreign income flows (see Blanchet and Chancel [2016] for a discussion of methods). While these imputations are far from fully satisfactory, they increase the level of comparability of national income aggregates across countries.

  9. The DINA Guidelines also discuss how the initial WTID time series, based on a fiscal income concept, can be corrected so as to be more directly comparable to new DINA series. In order to construct DINA/WID.world series for countries and time periods with limited data, we strongly recommend using the “Generalized Pareto interpolation” (gpinter) web interface available on-line (http://WID.world/gpinter). See Blanchet, Fournier, and Piketty (2017) for full technical details on Pareto curves and the corresponding interpolation techniques.

  10. The series for China make use of the data recently released by the tax administration on high-income taxpayers and include a conservative adjustment for the undistributed profit of privately owned corporations.

  11. These series refer to pre-tax, pre-transfer inequality. Post-tax, post-transfer series (in progress) are likely to reinforce these conclusions, at least regarding the US–France comparison.

  12. We refer to the country-specific papers for detailed discussions: Saez and Zucman, 2016; Alvaredo, Atkinson, and Morelli, 2017, 2018; Garbinti, Goupille-Lebret, and Piketty, 2016; and Piketty, Yang, and Zucman, 2017.

  References

  Alvaredo, F., A.B. Atkinson, and S. Morelli (2018), “Top wealth shares in the UK over more than a century,” forthcoming, Journal of Public Economics.

  Alvaredo, F., A.B. Atkinson, and S. Morelli (2017), “Top wealth shares in the UK over more than a century,” CEPR Discussion Paper, No. 11759.

  Alvaredo, F. et al. (2017), “Global inequality dynamics: New findings from WID.world,” American Economic Review Papers & Proceedings, Vol. 107(5), pp. 404–409.

  Alvaredo, F. et al. (2016), “Distributional National Accounts (DINA) guidelines: Concepts and methods used in the World Wealth and Income Database,” WID.world Working Paper, No. 2016/2.

  Alvaredo, F. et al. (2011–2015), The World Top Incomes Database, online between January 2011 and December 2015.

  Alvaredo, F. et al. (2013), “The top 1% in international and historical perspective,” Journal of Economic Perspectives, Vol. 27(3), pp. 3–20.

  Atkinson, A.B. (2007), “Measuring top incomes: Methodological issues,” in Atkinson, A.B. and T. Piketty (eds.), Top Incomes over the 20th Century: A Contrast Between Continental European and English-Speaking Countries, Oxford University Press, Oxford, pp. 18–43.

  Atkinson, A.B. (2005), “Top incomes in the U.K. over the 20th Century,” Journal of the Royal Statistical Society, Vol. 168(2), pp. 325–343.

  Atkinson, A.B. and A.J. Harrison (1978), Distribution of Personal Wealth in Britain, Cambridge University Press, Cambridge.

  Atkinson, A.B. and T. Piketty (2010), Top Incomes: A Global Perspective, Oxford University Press, Oxford.

  Atkinson, A.B. and T. Piketty (2007), Top Incomes over the 20th Century: A Contrast Between Continental European and English-Speaking Countries, Oxford University Press, Oxford.

  Atkinson, A.B., T. Piketty, and E. Saez (2011), “Top incomes in the long run of history,” Journal of Economic Literature, Vol. 49(1), pp. 3–71.

  Blanchet, T. and L. Chancel (2016), “National Accounts series methodology,” WID.world Working Paper, No. 2016/1.

  Blanchet, T., J. Fournier, and T. Piketty (2017), “Generalized Pareto curves: Theory and applications to income and wealth tax data for France and the United States, 1800–2014,” WID.world Working Paper, No. 2017/3.

  Bourguignon, F. (2015), “Appraising income inequality databases in Latin America,” Journal of Economic Inequality, Vol. 13(4), pp. 557–578.

  Deaton, A. (2005), “Measuring poverty in a growing world (or Measuring growth in a poor world),” Review of Economics and Statistics, Vol. 87(1), pp. 1–19.

  Fesseau, M. and M. Mattonetti (2013), “Distributional measures across household groups in a national accounts framework: Results from an experimental cross-country exercise on household income, consumption and saving,” OECD Statistics Working Papers, No. 2013/04, OECD Publishing, Paris, http://dx.doi.org/10.1787/k3wdjqr775f
-en.

  Frankel, S.H. and H. Herzfeld (1943), “European income distribution in the Union of South Africa and the effect thereon of income taxation,” South African Journal of Economics, Vol. 11(2), pp. 121–136.

  Garbinti, B., J. Goupille-Lebret, and T. Piketty (2017), “Income inequality in France, 1900–2014: Evidence from Distributional National Accounts (DINA),” WID.world Working Paper, No. 2017/4.

  Garbinti, B., J. Goupille-Lebret, and T. Piketty (2016), “Accounting for wealth inequality dynamics: Methods, estimates and simulations for France (1800–2014),” WID.world Working Paper, No. 2016/5.

  Kuznets, S. (1953), Shares of Upper Income Groups in Income and Savings, National Bureau of Economic Research.

  Nolan, B., M. Roser, and S. Thewissen (2016), “GDP per capita versus median household income: What gives rise to divergence over time?,” INET Oxford Working Paper, No. 2016-03.

  Piketty, T. (2014), Capital in the 21st Century, Harvard University Press, Cambridge, MA.

  Piketty, T. (2003), “Income inequality in France, 1901–1998,” Journal of Political Economy, Vol. 111, pp. 1004–1042.

  Piketty, T. (2001), Les hauts revenus en France au 20e siècle: Inégalités et redistributions, 1901–1998, Grasset, Paris.

  Piketty, T. and E. Saez (2003), “Income inequality in the United States, 1913–1998,” Quarterly Journal of Economics, Vol. 118(1), pp. 1–39.

  Piketty, T., E. Saez, and S. Stantcheva (2014), “Optimal taxation of top labor incomes,” American Economic Journal: Economic Policy, Vol. 6(1), pp. 230–271.

  Piketty, T., E. Saez, and G. Zucman (2016), “Distributional national accounts: Methods and estimates for the United States,” NBER Working Paper, No. 22945.

  Piketty, T., L. Yang, and G. Zucman (2017), “Capital accumulation, private property and rising inequality in China 1978–2015,” WID.world Working Paper, No. 2017/6.