What is driving wealth inequality in the United States of America? The role of productivity, taxation and skills

Ernst, Ekkehard; Langot, François; Merola, Rossana; Tripier, Fabien Working Paper What is driving wealth inequality in the United States of America? The role of productivity, taxation and skills ILO Working Paper, No. 105 Provided in Cooperation with: International Labour Organization (ILO), Geneva Suggested Citation: Ernst, Ekkehard; Langot, François; Merola, Rossana; Tripier, Fabien (2024) : What is driving wealth inequality in the United States of America? The role of productivity, taxation and skills, ILO Working Paper, No. 105, ISBN 978-92-2-040491-1, International Labour Organization (ILO), Geneva, https://doi.org/10.54394/AHFP2990 This Version is available at: https://hdl.handle.net/10419/283546 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ XWhat Is Driving Wealth Inequality in the United States of America? The Role of Productivity, Taxation and Skills Authors / Ekkehard Ernst, François Langot, Rossana Merola, Fabien Tripier February / 2024 ILO Working Paper 105 Copyright © International Labour Organization 2024 This is an open access work distributed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). Users can reuse, share, adapt and build upon the original work, as detailed in the License. The ILO must be clearly credited as the owner of the original work. The use of the emblem of the ILO is not permitted in connection with users’ work. 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What Is Driving Wealth Inequality in the United States of America? : The Role of Productivity, Taxation and Skills, ILO Working Paper 105 (Geneva, ILO). https://doi.org/10.54394/AHFP2990 01 ILO Working Paper 105 Abstract Out of four major structural changes affecting the US economy – namely a rising share of skilled workers, skill-biased technological change, decreasing progressiveness of taxation and productivity slowdown – we show that the decline in productivity growth not only is the main driver of the widening wealth disparities observed in the United States of America over the past few decades, but is also the only mechanism that can explain inequalities both within and between skill groups. About the Authors Ekkehard Ernst: ILO, Research Department. E-mail: [email protected]g François Langot: Le Mans University; Institut Universitaire de France; Paris School of Economics; CEPREMAP. E-mail: fr[email protected] Rossana Merola: ILO, Research Department. E-mail: mer[email protected] Fabien Tripier: Université Paris Dauphine-PSL, Laboratoire d’ Économie de Dauphine (LEDa); CEPREMAP. E-mail:[email protected] 02 ILO Working Paper 105 Abstract 01 About the Authors 01 X1 Introduction 05 X2 Stylized Facts 08 2.1. Inequality between skill groups 08 2.2. Inequality within skill groups 09 2.3. Is there a "wealth polarization"? 11 X3 Model 13 3.1. Households 13 3.2. Production 15 3.3. Aggregation 16 X4 Calibration 17 4.1. Calibration of the constant parameters 17 4.2. Calibration of the structural changes in the US economy 18 X5 Identifying the sources of rising in wealth inequalities 20 X6 Extending the analysis: Changes in income risk 23 X7 Conclusions and policy implications 25 X8 Appendix: Detailed statistics from the Survey of Consumer Finances 26 References 34 Table of contents 03 ILO Working Paper 105 List of Figures Figure 1: Wealth and wage inequality between skills in the United States 10 Figure 2: Wealth inequality across and within skill groups in the United States, 1989–2016 11 Figure 3: Wealth inequality between skills in the United States, 1989-2016 12 Figure 4: Structural changes in the US economy 19 Figure 5: Changes in income risk in the US economy, skilled versus unskilled workers, 1968– 2010 23 04 ILO Working Paper 105 List of Tables Table 1: Wealth inequality across skill groups in the United States: Share of unskilled households in the total population and in the four quartiles of the wealth distribution, 1989– 2016 (percentage) 08 Table 2: Wealth inequality across skill groups in the United States: Mean value of wealth held by households, skilled versus unskilled, 1989–2016 (thousands of 2016 US dollars) 08 Table 3: Wealth inequality within skill groups in the United States, 1989–2016 09 Table 4: Wealth inequality between skill groups in the United States, 1989–2016 12 Table 5: Calibrated parameters 17 Table 6: Calibration of shocks 19 Table 7: Comparison between empirical data and model results 20 Table 8: Calibration of shocks 24 Table 9: Comparison between empirical data and modelling results (with income risk) 24 Table A1: Average earnings, income and wealth of US households, 1989 27 Table A2: Average earnings, income and wealth of US households, 1995 27 Table A3: Average earnings, income and wealth of US households, 2001 27 Table A4: Average earnings, income and wealth of US households, 2004 28 Table A5: Average earnings, income and wealth of US households, 2007 28 Table A6: Average earnings, income and wealth of US households, 2010 28 Table A7: Average earnings, income and wealth of US households, 2013 29 Table A8: Average earnings, income and wealth of US households, 2016 29 Table A9: Measures of inequality among US households, total and by skill group, 1989 31 Table A10: Measures of inequality among US households, total and by skill group, 1995 31 Table A11: Measures of inequality among US households, total and by skill group, 2001 31 Table A12: Measures of inequality among US households, total and by skill group, 2004 31 Table A13: Measures of inequality among US households, total and by skill group, 2007 32 Table A14: Measures of inequality among US households, total and by skill group, 2010 32 Table A15: Measures of inequality among US households, total and by skill group, 2013 32 Table A16: Measures of inequality among US households, total and by skill group, 2016 33 05 ILO Working Paper 105 X1 Introduction Over the past two decades, income inequality has increased in a large majority of member countries of the Organization for Economic Co-operation and Development. The average income gap between the top 10 per cent and the bottom 50 per cent of individuals within countries has almost doubled. If we look at wealth inequality, the wealth gap between upper-income families and middleand lower-income families is wider than the income gap and is growing more rapidly (Chancel et al. 2022). According to a study conducted by the Pew Research Center in 2020, "[t]he richest families in the U.S. have experienced greater gains in wealth than other families in recent decades, a trend that reinforces the growing concentration of financial resources at the top" (Horowitz, Igielnik, and Arditi 2020). Identifying the driving forces of inequality is a key challenge in designing effective policies to achieve inclusive and equitable growth and development. Existing studies have pointed to several structural changes that may be leading to greater inequality. We consider four of them in this paper: declining productivity, the growing share of skilled workers in the economy, skill-based technological change, and a trend whereby taxation is becoming less progressive. With regard to the first of these processes, that is, the slowdown in productivity growth, there is clearly a discrepancy between the level of investment in the information technology sector and national productivity levels. Back in 1993, Erik Brynjolfsson referred to this situation as the “productivity paradox”, which is encapsulated in the following quip: “You can see the computer age everywhere but in the productivity statistics” (Solow 1987).1 In the United States of America, the growth in labour productivity – real output per hour worked – slowed down markedly around 2004. Over the previous ten years, labour productivity in the business sector had risen at an annual average rate of more than 3 per cent. It slowed to about 2 per cent a year during 2004–10 before dropping to a paltry 0.5 per cent during 2010–16. More precisely, using data from Fernald (2014), we estimate that the growth rate of total factor productivity decreased significantly from 2.1 per cent before 1989 to 0.89 per cent in 2010. If the rate of return on capital, r, is much larger than the rate of growth, g, then existing capital grows faster than new capital created out of accumulated income and already rich owners of capital will become even richer. In contrast, a higher growth rate g implies higher income growth and enables those at the bottom of the distribution to accumulate new wealth, thereby reducing wealth concentration. As argued by Piketty (2014) and Piketty and Zucman (2015), a rate of return that exceeds economic growth (the r > g mechanism) is a key factor driving economic inequality. A second factor is related to the evolution of supply of, and demand for, skills. A rising share of skilled workers can lead to a reduction in inequalities as the relative scarcity of such workers decreases.2 A third driver is skill-biased technological change, which pushes the skill premium up, ultimately increasing inequalities between skill groups (Goldin and Katz 2009). The fact that the skill premium is rising suggests that the skills required to handle new technologies are in scarce supply (Acemoglu and Autor 2011). Moreover, it is generally agreed that skill-biased technological change led to a hollowing-out of the wage distribution in the 2000s, when many middle-wage cognitive routine occupations were automated (Goos, Manning, and Salomons 2014). The fourth driver of rising inequality has to do with the degree of progressiveness of the tax system. Over the past decade, several countries have implemented tax reforms making their tax structures much flatter (see Duncan and Sabirianova Peter 2012). The shift to flat taxes, or to a tax structure with lower levels of progressiveness, is consistent with a decline in the top income tax rates and has been associated with high levels of inequality. Gerber et al. (2018) find quite strong evidence of tax progressiveness having an impact on inequality. 1The lack of diffusion of new technologies can be viewed as a major factor behind the productivity paradox (Ernst 2022). 2It is worth noting that this effect may be ambiguous. Aziz and Cortes (2021) argue that, in societies where the share of skilled workers is below 50 per cent, a rise in educational attainment increases inequality between skill groups. Conversely, in societies where that share is above 50 per cent, an increase in educational attainment puts downward pressure on the skill premium and ultimately reduces inequality between skill groups. 12 ILO Working Paper 105 XTable 4: Wealth inequality between skill groups in the United States, 1989–2016 All Skilled Medium Unskilled Ratio skilled to medium-skilled Ratio skilled to unskilled 1989 353.33 765.69 344.84 186.50 2.22 4.11 2016 689.51 1504.17 340.09 219.21 4.42 6.86 Annual growth rate 2.51 2.53 -0.05 0.60 2.59 1.92 Note: Source: Authors’ calculations based on data from Survey of Consumer Finances. XFigure 3: Wealth inequality between skills in the United States, 1989-2016 Note: Unskilled people are defined as those having at best a high-school diploma; medium-skilled people are those having at best a bachelor’s degree; skilled people are those having higher than a bachelor’s degree. Source: Authors’ calculations based on data from the Survey of Consumer Finances. 13 ILO Working Paper 105 X3 Model We develop a general equilibrium model with incomplete financial markets and idiosyncratic productivity shocks à la Achdou et al. (2021). The model is extended to include labour supply as in Chang and Kim (2006), heterogeneous agents (skilled and unskilled workers, as in Ahn et al. 2018), the non-linear fiscal system à la Heathcote, Storesletten, and Violante (2017) and skill-biased technological change. Variables with a dot on top denote growth rates, while variables with a tilde on top are in level. 3.1. Households Agents can be skilled (s) or unskilled (u) workers, q є {u, s}. Within each skill group, there exist patient (p) and impatient (i) agents characterized by their discount factor, ω є {p, i}. We denote the state vector ς = (q, ω). The utility function of the household with skill level q is where c ~t , and h,t denote respectively consumption and hours worked and they both depend on the household’s skills and patience and his/her labour market history. The parameter χ measures labour supply elasticity, γ is the risk aversion parameter and ρ is the discount factor for each type of household. The budget constraint of this household is where  a t, is the financial assets accumulated by households on which they receive the interest rate rt at time t. Financial markets are imperfect: there is a borrowing constraint which limits household debt to a − . At each time t, when they supply work h t,, households q receive wage  wqt , , depending on his/her skill level, which is subject to a labour income shock zqt ,. The function describing taxes T (·) is given by where τ is the tax rate on financial income, λ measures the progressiveness of the taxation scheme and g is the long-run technological progress. A fall in λ makes the fiscal system less progressive. For λ = 0 (no progressiveness), the budget constraint becomes 14 ILO Working Paper 105 The process of labour income shock z Z∈ qt q,, with z −=1 q, is where θ q governs the persistence of the labour income shock. We will later interpret an increase in σ q as a more risky labour market for workers. The joint distributions of z q and wealth aq are denoted () gat, qq, for q su ={ ,} . Following Achdou et al. (2021), the Hamilton-Jacobi-Bellman equation in stationary variables xex = ~ gt is: where  ρρ γg =− (1− ). The long-run technological progress g modifies the stationary discount factor (for γ≠1 ) and the law of motion of the stationary wealth a t, because  aaegae ˙=˙ tt gt t gt ,, , . The two terms vz and v zz represent respectively the first derivative and the second derivative of the value functions with respect to the labour income shocks zqt ,. First-order conditions with respect to consumption c t and labour supply l t are respectively The labour supply solves Piketty effect (r–g) in the model. We consider the case without financial constraints to highlight how the (r–g) effect intervenes in the model. We omit indices for the sake of simplicity. The optimization problem yields: 15 ILO Working Paper 105 where the marginal return of an asset R(a) in the stationary economy is It declines with wealth and labour market earnings when λ>0 , with the level of taxation τ and with the long-run trend of the economy g. Therefore, a fall in long-run growth stimulates saving in the economy where γ determines the magnitude of the effect. Hence, wealth inequality will be high when individuals accumulate wealth rapidly. If we assume for simplicity that λ=0 , which implies that R(a) =(1−τ)r−g∀a , a strong capitalization effect (large r − g) magnifies inequalities by facilitating wealth accumulation. The decline of the growth rate g observed since the 1990s can then rise wealth inequalities. Conversely, a decline in the interest rate, consistent with the wage premium increase, reduces wealth inequality within skill groups. It should also be noted that an increase in the tax rate on financial income τ will decrease (1−τ)r , and thus R(a) when λ=0 and ultimately will reduce wealth inequalities (weakening of the capitalization effect). This argument is the basis of Piketty’s proposal to levy taxes on capital income to reduce inequalities (see Piketty 2014). On the other hand, an objection to using capital income taxes to tackle widening inequalities is that higher taxation on capital will simply lead to capital outflows since capital is internationally mobile. 3.2. Production The domestic production technology is based on Krusell et al. (2000) where y ~ t denotes the final output, st , and ut , the skilled and unskilled labour inputs, k ~ t the physical capital input. The parameters µζ {,} determine the income shares of production factors. The parameter σl is the elasticity of substitution between unskilled labour and capital or skilled labour 16 ILO Working Paper 105 and σh is the elasticity of substitution between skilled labour and capital. As shown by Krusell et al. (2000), capital-skill complementarities assumes that σ σ> lh . We consider two sources of technological change. The first one is the technological progress e X ≡ tgt , which is common to skilled and unskilled workers (a drop in g, which can be interpreted as a global productivity slowdown). The second source is the skill-biased biased technological progress, s, defined such that a rise in s increases the demand for skilled workers and decreases the demand for unskilled workers. 3.3. Aggregation In the financial market, the interest rate adjusts until assets held by households equal the amount of physical capital used in the production sector In the two labour markets, skilled and unskilled q ∈ {u, s}, aggregate supply of labour equal aggregate demand for labour: Since the size of the total population is normalized to one (in the absence of demographic growth), we impose NN+=1 us and interpret an increase in N s as the outcome of higher education policy. The level of taxation τ is adjusted to finance public spending G. 17 ILO Working Paper 105 X4 Calibration To study the drivers of wealth inequality in the US economy, we proceed in three steps. First, the model is calibrated for the year 1989, which is the first wave of the SCF. We compute indicators of wealth inequality between and within skill groups. Second, we use external sources (e.g. observed data between 1989 and 2010) as well as the literature to calibrate the size of the shocks that may widen wealth inequalities. Third, we compute the new post-shock steady state and compare it with observed data for 2010. 4.1. Calibration of the constant parameters XTable 5: Calibrated parameters Category Symbol Values Sources and targets (for 1989) Population N s 0.24 Data (SCF 1989) Preferences γ 2Attanasio and Low (2004) χ 3Target: hs/hu = 1.4 Θ 1Normalization ρsp , 0.0180 Target: p[25; 50]p = 0.045 ρsi , 0.0535 Target: p[75; 100]p = 0.823 ρup , 0.0245 Target: p[25; 50]i = 0.032 ρui , 0.0484 Target: p[75; 100]i = 0.830 Share sp , 1/4 Target: Capital-Output Ratio = 2.3 Share up , 1/5 Target: Wealths/Wealthu = 3.35 18 ILO Working Paper 105 Category Symbol Values Sources and targets (for 1989) Technology μ0.52 Krusell et al. (2000) ζ0.86 " σi1.67 " σp0.67 " s3Target: ws/wu = 2.42 g0.021 Fernald (2014) δ0.08 Prescott (2004) A1Normalization Earning shocks θi− log (0.9859) Hong, Seok, and You (2019) θp− log (0.9834) " σ i 2 0.0086 " σ p 2 0.0171 " Fiscal system λ0.16 Ferrière and Navarro (2018) Table 5 reports the calibrated parameter values for the initial steady state. The calibration relies on external sources and targets which are based on data available for 1989. The share of skilled workers Ns is calibrated so as to match the share of this population group in the SCF data in 1989. As for the parameters in the utility function, the parameter measuring agents’ risk aversion ( γ ) takes the values estimated by Attanasio and Low (2004), while the elasticity of hours worked to wages (χ) is calibrated so that the model reproduces the average difference in hours worked between skilled and unskilled workers observed in the SFC for 1989. Regarding the discount factors, (ρq,w, for q=s, u and ω = p, i), their values and their distribution in the population of skilled and unskilled workers are calibrated so as to reproduce the values observed in the SFC for the capital ratio and other statistics summarizing the characteristics of the distribution of wealth in the United States. The parameters of the constant-elasticity-of-substitution (CES) production function take the values estimated by Krusell et al. (2000). The growth rate of the economy and the depreciation rate are set following Fernald (2014) and Prescott (2004). The earning shocks are calibrated following the estimations of Hong, Seok, and You (2019). Finally, the parameter measuring the progressiveness of the US tax system, λ, is calibrated in accordance with Ferrière and Navarro (2018). 4.2. Calibration of the structural changes in the US economy Panel A of figure 4 shows the increase in the share of skilled workers in the United States between 1989 to 2016, based on SCF data. This share rises from 24 per cent to 31 per cent between 1989 and 2010 (see Table 6). 19 ILO Working Paper 105 XFigure 4: Structural changes in the US economy Panel B in figure 4 shows the historical path of the growth rate of the US economy using data from Fernald (2014). On average, the growth rate has dropped significantly from 2.1 per cent before 1989 to 0.89 per cent in 2010 (see table 6). Based on Ferrière and Navarro (2018), the parameter λ has dropped significantly from 0.18 to 0.13 after 1989, reflecting a tax system which is becoming less progressive (see table 6). XTable 6: Calibration of shocks Category Symbol Values (1989) Values (2010) Source SBTC s3.2 8.5 SCF (wage premium) Growth rate g0.021 0.0089 Fernald (2014) Tax λ0.18 0.13 Ferrière and Navarro (2018) Skills Ns0.24 0.31 SCF (share of skilled households) Note: SBTC = skill-biased technological change; SCF = Survey of Consumer Finances. 20 ILO Working Paper 105 X5 Identifying the sources of rising in wealth inequalities To solve and simulate the model, we employ the methodology proposed by Achdou et al. (2021). Results are reported in table 7. The upper part of table 7 compares empirical data with the model simulations based on the combination of the four shocks. XTable 7: Comparison between empirical data and model results Wage premium Wealth premium Share of total wealth held by top 25% ( skilled workers) Share of total wealth held by top 25% (unskilled workers) Gini index Data 1989 2.43 3.35 0.82 0.83 0.79 Model 1989 2.44 3.33 0.80 0.86 0.69 Data 2010 3.34 5.11 0.86 0.87 0.85 Model 2010 3.28 9.12 0.79 0.86 0.79 Model 2010: only Skills 2.03 3.21 0.81 0.83 0.69 Model 2010: only SBTC 3.29 4.09 0.79 0.86 0.71 Model 2010: only Tax 2.68 4.78 0.79 0.85 0.71 Model 2010: only Growth 2.82 6.95 0.87 0.88 0.81 We calibrate the model to reproduce almost perfectly the target moments in 1989. The projections for 2010 simultaneously take into account changes in the composition of labour supply, skill-biased technical change, progressiveness of taxation and the rate of growth of the economy (see table 6). The model predicts an increase in inequality relatively close to the actual increase observed in the United States between 1989 and 2010. The Gini coefficient increases by 10 percentage points (from 0.69 to 0.79), which is very close to the 6 percentage point increase (from 0.79 to 0.85) in the empirical data. With regard to inequalities within skill groups, the share of wealth held by the richest 25 per cent remains stable in the model both for skilled and unskilled workers, while it increases very slightly in the data (from 0.82 to 0.86 for skilled workers and from 0.83 to 0.87 for unskilled workers). As for inequality between skill groups, the wealth premium – defined as the ratio of the average wealth held by skilled workers to the average wealth held by unskilled ones – increases from 3.33 to 9.12 (+5.79) in the model, while in the data the increase is more moderate, from 3.35 to 5.11 (+1.76). We calibrate our model so as to reproduce very accurately the increase in the wage premium observed in the data. The model predicts an increase in the wage premium from 2.44 in 1989 to 3.28 in 2010 (+0.84), while in the data it increases from 2.43 to 3.34 (+0.91). Therefore, we may conclude that the combination of the four structural changes is able to explain the observed evolution of inequalities in the United States from 1989 to 2010.9 Both the data and the model indicate that, since 1989, the US economy has witnessed a rise in both wealth and income inequality, although wealth seems to be more unequally distributed than wages and income, as argued by Piketty (2014). Wealth inequality has increased to a greater extent between skill groups than within them. 9However, our model generates a too high elasticity of wealth premium. 21 ILO Working Paper 105 In order to understand the impact of each of these four structural changes on the evolution of US wealth inequalities, we simulate the model by varying only one of the relevant parameters at a time. Specifically, we simulate one by one the effect of (a) an increase in the share of skilled workers; (b) technological change biased in favour of skilled workers; (c) a tax reform reducing the degree of tax progressiveness; and (d) a global productivity slowdown. These counterfactual simulations allow to isolate the impact of each factor (last four rows in Table 7). The impact of the labour force composition The US economy has experienced a huge increase in the share of educated workers. The "only Skill" row in table 7 shows that, according to our model, the increase in the labour supply of skilled workers relative to unskilled workers should reduce the skill wage premium from 2.44 in 1989 to 2.03 in 2010. The model predicts a fall in the skill wealth premium as well (from 3.33 to 3.21) – leaving wealth inequality within skill groups unchanged. The race between technological change and education, as argued by Goldin and Katz (2009), is the most popular explanation for growing inequality despite rising levels of education. The change in labour force composition is not able to explain the rise in inequality observed in the data, neither between nor within skill groups. On the contrary, an increase in the share of skilled workers seems to moderate the rise in wage and wealth premium. This result is consistent with the observation in Aziz and Cortes (2021) that in societies where the share of skilled workers is above 50 per cent, an increase in educational attainment puts downward pressures on the skill premium and ultimately reduces inequality between skill groups. The US economy crossed the 50 per cent threshold in the 1980s, which explains why an increase in the share of skilled workers has had the effect of reducing between skill inequality. The impact of skill-biased technological change According to a commonly held view, technological change that is biased in favour of skilled workers explains the rise in the skill wage premium (see Krusell et al. 2000). The "only SBTC" row in table 7 shows that this shock can explain the rise in both wage and wealth premiums between skill groups. According to the empirical data, between 1989 and 2010 the skill wealth premium increased from 3.35 to 5.11 (+1.76), while the wage premium increased from 2.43 to 3.34 (+0.91). The model with an "only SBTC" shock predicts a +0.76 point increase in the skill wealth premium, from 3.33 to 4.09, and a +0.85 point increase in the skill wage premium, from 2.44 to 3.29, which means that it underestimates the increase in both wage and wealth inequality between skill groups. However, while the estimated increase in wage inequality is quite close to the empirical value, the estimated increase in wealth inequality is less than half of the value observed in the data. Moreover, with an “only SBTC” shock, our model predicts a negligible change in inequality within skill groups. Therefore, the "only SBTC" shock is unable to explain either inequality within skill groups or the observed increase in wealth inequality between skill groups. The impact of the tax reform One of the drivers of widening inequality is the decreasing progressiveness of taxation, as demonstrated by Piketty and Saez (2007) among others. The "only Tax" row in table 7 shows that a fiscal reform reducing the progressiveness of the tax system leads to an increase of both the wage and wealth skill premium. On the one hand, the model with the only tax shock is the able to explain both wage and wealth inequality between skill groups, although it slightly underestimates the increase in both premiums: according to the model, the wage premium should have increased by 0.24 points, whereas the empirically observed increase is 0.91 points; the model predicts a 1.45 point increase in the wealth premium, which is quite close to the increase observed in the data (1.76). In that respect, our results are close to the findings of Kaymak and Poschke (2016). On the other hand, a less progressive tax system is unable to explain the widening of inequalities within skill groups. The impact of growth We finally analyse the effect of a global productivity slowdown which has been disclosed by Piketty (2014) and Piketty and Zucman (2015) as the r − g mechanism. The "only g" row in table 7 demonstrates the attractive nature of this mechanism: it alone is able to account for an increase in wealth inequality both between and within skill groups, as observed in the data. The model with an "only Growth" shock predicts an increase of 0.05 points in within-skill 28 ILO Working Paper 105 XTable A4: Average earnings, income and wealth of US households, 2004 B 0-1 B 1-5 B 5-10 1st Q 2nd Q 3rd Q 4th Q 5th Q T 90-95 T 95-99 T 99-100 All 0-100 Skilled E27.16 46.16 31.43 44.79 72.61 89.69 106.65 274.15 211.79 408.06 1244.48 117.56 I32.97 49.03 36.08 48.96 77.95 102.38 128.67 366.34 281.90 545.45 1854.26 144.84 W-162.06 -29.97 -0.64 1.23 138.04 376.57 891.51 5475.11 3380.01 8891.17 38911.66 1375.86 Unskilled E45.60 28.10 15.69 20.50 30.46 41.33 45.63 70.25 71.96 58.34 286.48 41.66 I49.85 33.82 20.50 26.05 36.04 48.90 58.36 98.81 91.83 90.67 449.15 53.67 W-73.78 -10.16 -0.55 -4.47 20.16 91.51 249.80 1376.79 971.83 1816.38 9957.02 347.73 All E39.88 34.95 18.71 24.34 41.44 53.46 69.10 158.98 119.46 214.96 829.76 69.45 I44.23 39.30 23.61 29.58 46.97 62.13 83.35 213.32 150.26 293.53 1211.37 87.05 W-111.56 -16.33 -0.75 -6.59 40.98 157.73 426.66 3003.60 1750.45 4391.43 23950.10 724.07 XTable A5: Average earnings, income and wealth of US households, 2007 B 0-1 B 1-5 B 5-10 1st Q 2nd Q 3rd Q 4th Q 5th Q T 90-95 T 95-99 T 99-100 All 0-100 Skilled E44.21 49.97 37.40 46.12 73.28 95.54 99.26 305.01 288.20 462.22 1415.72 123.83 I45.27 55.06 44.24 51.61 80.52 105.72 120.68 442.35 355.60 701.57 2457.92 160.17 W-110.42 -28.96 1.18 3.72 149.78 387.06 857.99 5956.26 4086.98 10055.75 40267.84 1471.03 Unskilled E33.19 28.13 16.21 19.92 31.57 38.67 45.03 81.88 73.09 83.11 343.55 43.40 I39.19 34.70 22.22 26.31 37.83 48.53 58.68 118.52 95.36 120.96 635.24 57.95 W-95.67 -13.05 -1.29 -6.90 19.32 95.49 264.85 1381.40 884.51 1711.94 10947.21 350.47 All E38.81 36.06 17.91 24.88 38.44 53.69 69.44 172.23 117.66 282.50 875.49 71.76 I42.12 42.68 24.74 30.92 45.38 63.85 83.27 246.36 155.09 385.69 1509.85 93.99 W-107.69 -18.35 -1.29 -7.09 39.98 166.29 418.91 3106.47 1650.20 4984.13 25040.12 745.57 XTable A6: Average earnings, income and wealth of US households, 2010 B 0-1 B 1-5 B 5-10 1st Q 2nd Q 3rd Q 4th Q 5th Q T 90-95 T 95-99 T 99-100 All 0-100 Skilled E64.93 59.67 42.77 45.44 62.37 80.04 95.04 280.47 248.53 471.98 982.94 112.79 I72.13 65.31 48.21 51.68 69.15 91.69 116.94 356.70 300.16 599.34 1377.89 137.39 W-245.65 -70.79 -14.00 -25.51 71.81 244.93 691.27 4826.92 3287.17 7684.50 31898.93 1164.72 Unskilled E51.26 37.26 25.73 22.92 25.42 38.07 42.75 65.12 57.95 88.35 242.92 38.87 I59.10 44.81 32.92 30.59 32.48 46.48 56.19 93.41 81.75 125.08 345.99 51.85 W-143.19 -32.89 -5.96 -15.16 10.64 55.22 174.33 1050.43 667.46 1474.98 8051.88 255.47 All E60.42 46.40 30.27 27.87 35.53 48.19 58.25 161.79 136.45 260.55 725.57 66.43 I68.51 53.02 36.98 35.19 42.05 58.03 73.51 209.24 170.96 319.87 1025.27 83.74 W-192.15 -46.59 -7.94 -20.02 20.96 97.54 294.14 2568.44 1604.80 3982.72 20324.84 594.43 29 ILO Working Paper 105 XTable A7: Average earnings, income and wealth of US households, 2013 B 0-1 B 1-5 B 5-10 1st Q 2nd Q 3rd Q 4th Q 5th Q T 90-95 T 95-99 T 99-100 All 0-100 Skilled E44.47 44.82 38.55 37.39 59.63 76.28 96.52 273.76 227.34 399.47 1277.04 108.69 I70.77 50.13 44.86 44.23 67.67 90.02 121.10 396.80 296.94 571.96 2294.97 143.92 W-188.79 -61.00 -16.66 -21.77 64.62 236.87 624.33 4410.79 2984.98 7055.77 31757.32 1062.61 Unskilled E40.15 31.24 20.80 19.38 24.79 33.19 37.87 62.54 50.57 83.38 254.23 35.56 I47.15 38.53 28.82 27.46 32.07 42.78 54.46 93.16 73.91 120.67 394.48 50.01 W-153.46 -29.95 -5.57 -14.98 9.29 47.18 151.67 954.63 578.53 1234.96 8105.64 230.00 All E47.20 34.10 29.77 24.44 32.19 44.16 56.68 162.30 130.18 254.17 771.50 63.93 I60.41 41.18 36.36 32.20 39.60 55.19 74.97 230.38 170.34 348.23 1294.15 86.43 W-172.65 -41.58 -8.76 -18.63 17.95 89.28 271.00 2406.87 1337.00 3772.93 19600.45 552.93 XTable A8: Average earnings, income and wealth of US households, 2016 B 0-1 B 1-5 B 5-10 1st Q 2nd Q 3rd Q 4th Q 5th Q T 90-95 T 95-99 T 99-100 All 0-100 Skilled E43.37 53.60 46.50 46.39 66.59 81.25 116.57 350.46 268.50 623.34 1658.54 132.45 I47.27 57.84 51.88 52.31 77.50 101.47 153.89 526.28 373.53 877.23 3012.74 182.62 W-207.90 -64.26 -17.16 -20.17 95.62 297.52 821.07 6165.24 3821.76 9970.48 45762.50 1476.57 Unskilled E40.66 32.57 26.56 23.27 27.71 38.70 45.64 70.79 56.63 80.25 352.87 41.19 I48.62 39.47 33.70 31.05 35.02 48.62 63.17 110.71 87.03 136.57 514.54 57.67 W-97.01 -24.19 -4.79 -10.44 12.16 59.86 174.66 1135.61 653.86 1454.14 9808.87 273.68 All E45.96 43.39 30.38 26.57 33.78 48.30 58.69 190.48 136.35 281.44 1118.24 71.55 I51.46 48.26 37.17 33.53 41.48 59.71 80.17 279.62 195.62 400.56 1799.70 98.88 W-149.97 -38.51 -7.20 -16.36 20.99 99.02 292.37 2990.30 1615.18 4504.90 26296.39 676.86 30 ILO Working Paper 105 31 ILO Working Paper 105 XTable A9: Measures of inequality among US households, total and by skill group, 1989 Skilled Unskilled All E I W E I W E I W Coefficient of variation 4.51 5.04 3.84 1.17 1.47 6.28 3.85 4.60 5.06 Varriance of logs 0.87 0.82 3.76 1.08 0.99 4.16 1.09 1.05 4.27 Gini index 0.48 0.51 0.76 0.51 0.47 0.78 0.53 0.52 0.79 Location of mean 68.00 74.00 79.00 61.00 64.00 77.00 65.00 69.00 80.00 99-50 ratio 7.13 8.64 36.51 6.42 6.57 44.04 7.88 8.37 50.56 90-50 ratio 2.53 2.48 6.62 2.56 2.34 8.31 2.63 2.61 8.50 Mean-to-median ratio 1.39 1.53 3.27 1.28 1.30 4.04 1.38 1.43 4.21 50-30 ratio 1.45 1.47 3.07 1.91 1.74 6.24 1.74 1.66 4.93 XTable A10: Measures of inequality among US households, total and by skill group, 1995 Skilled Unskilled All E I W E I W E I W Coefficient of variation 3.17 4.22 5.08 1.53 2.78 5.87 2.92 4.14 5.89 Varriance of logs 0.82 0.84 3.00 1.02 1.10 3.44 1.04 1.13 3.48 Gini index 0.52 0.52 0.78 0.52 0.48 0.78 0.54 0.52 0.80 Location of mean 72.00 75.00 81.00 61.00 64.00 77.00 67.00 71.00 82.00 99-50 ratio 8.54 9.34 47.69 7.53 7.35 33.17 8.86 9.09 44.00 90-50 ratio 2.67 2.80 7.41 2.67 2.51 6.73 2.64 2.57 7.14 Mean-to-median ratio 1.48 1.61 4.04 1.32 1.33 3.52 1.44 1.48 3.89 50-30 ratio 1.55 1.44 2.33 2.06 1.64 4.64 1.75 1.66 3.92 XTable A11: Measures of inequality among US households, total and by skill group, 2001 Skilled Unskilled All E I W E I W E I W Coefficient of variation 2.15 3.18 3.94 2.01 2.06 5.03 2.34 3.40 4.94 Varriance of logs 0.96 0.94 3.15 0.95 0.92 4.03 1.08 1.08 4.23 Gini index 0.53 0.57 0.77 0.50 0.46 0.79 0.55 0.56 0.82 Location of mean 75.00 79.00 81.00 64.00 64.00 78.00 69.00 73.00 81.00 99-50 ratio 12.87 14.61 50.83 5.33 5.41 46.21 12.20 12.57 80.49 90-50 ratio 2.62 2.84 6.85 2.58 2.46 10.02 2.86 2.79 8.91 Mean-to-median ratio 1.54 1.76 3.71 1.33 1.34 4.64 1.59 1.69 5.05 50-30 ratio 1.54 1.51 2.57 1.72 1.56 4.25 1.67 1.64 4.23 XTable A12: Measures of inequality among US households, total and by skill group, 2004 Skilled Unskilled All E I W E I W E I W Coefficient of variation 2.45 2.78 4.03 1.56 2.05 5.33 2.53 2.99 5.11 Varriance of logs 0.98 0.90 3.45 0.83 0.81 4.22 1.01 1.00 4.53 Gini index 0.52 0.52 0.78 0.48 0.44 0.79 0.55 0.53 0.82 Location of mean 70.00 73.00 81.00 62.00 63.00 78.00 68.00 70.00 81.00 32 ILO Working Paper 105 Skilled Unskilled All 99-50 ratio 9.52 10.33 52.01 5.59 5.20 40.25 9.49 10.20 80.30 90-50 ratio 2.56 2.60 7.03 2.64 2.53 10.34 2.94 2.87 9.94 Mean-to-median ratio 1.46 1.55 3.94 1.27 1.31 4.34 1.54 1.59 5.41 50-30 ratio 1.59 1.58 2.92 1.76 1.52 5.23 1.70 1.62 4.67 XTable A13: Measures of inequality among US households, total and by skill group, 2007 Skilled Unskilled All E I W E I W E I W Coefficient of variation 2.42 3.44 4.30 3.32 4.28 5.97 2.90 4.05 5.44 Varriance of logs 0.97 0.91 3.28 0.93 0.75 4.47 1.08 0.95 4.58 Gini index 0.54 0.56 0.79 0.51 0.46 0.80 0.57 0.55 0.82 Location of mean 74.00 78.00 83.00 62.00 66.00 77.00 70.00 73.00 82.00 99-50 ratio 11.83 13.80 51.36 6.48 6.67 48.08 11.33 12.99 78.35 90-50 ratio 2.72 2.83 7.34 2.83 2.70 9.67 2.85 2.87 8.55 Mean-to-median ratio 1.60 1.74 3.81 1.35 1.40 4.47 1.56 1.69 5.20 50-30 ratio 1.49 1.46 2.88 1.79 1.53 6.01 1.76 1.64 5.10 XTable A14: Measures of inequality among US households, total and by skill group, 2010 Skilled Unskilled All E I W E I W E I W Coefficient of variation 2.59 3.24 4.55 1.84 1.96 6.72 2.74 3.41 5.88 Varriance of logs 1.06 0.98 3.79 1.03 0.69 4.43 1.19 0.94 4.81 Gini index 0.57 0.55 0.82 0.52 0.44 0.85 0.59 0.55 0.87 Location of mean 73.00 75.00 81.00 61.00 64.00 79.00 71.00 73.00 83.00 99-50 ratio 13.08 13.39 69.33 6.20 5.70 63.88 13.19 12.91 115.59 90-50 ratio 3.23 3.19 12.27 2.84 2.60 11.91 3.06 2.96 14.20 Mean-to-median ratio 1.67 1.73 5.51 1.38 1.37 5.86 1.61 1.65 7.73 50-30 ratio 1.61 1.52 3.56 1.96 1.54 5.03 1.87 1.67 5.43 XTable A15: Measures of inequality among US households, total and by skill group, 2013 Skilled Unskilled All E I W E I W E I W Coefficient of variation 3.00 3.89 5.43 1.33 1.48 4.78 3.09 4.06 6.62 Varriance of logs 1.20 1.01 3.45 1.07 0.73 4.77 1.27 0.99 4.97 Gini index 0.58 0.57 0.82 0.53 0.45 0.87 0.60 0.56 0.87 Location of mean 73.00 76.00 81.00 62.00 66.00 80.00 70.00 73.00 83.00 99-50 ratio 13.63 14.24 61.29 6.80 6.36 87.15 12.96 13.62 131.06 90-50 ratio 3.04 3.01 9.30 3.01 2.74 15.45 3.28 3.17 14.91 Mean-to-median ratio 1.67 1.78 4.97 1.38 1.41 7.71 1.68 1.79 8.15 50-30 ratio 1.69 1.59 4.24 1.98 1.55 4.41 1.93 1.66 5.48 33 ILO Working Paper 105 XTable A16: Measures of inequality among US households, total and by skill group, 2016 Skilled Unskilled All E I W E I W E I W Coefficient of variation 2.49 3.46 5.30 2.08 2.29 10.01 2.75 3.87 7.30 Varriance of logs 1.23 1.10 3.50 0.98 0.78 4.97 1.24 1.08 5.39 Gini index 0.60 0.61 0.84 0.51 0.46 0.85 0.61 0.60 0.88 Location of mean 75.00 79.00 83.00 63.00 66.00 82.00 72.00 77.00 85.00 99-50 ratio 17.84 22.12 81.12 6.17 6.56 74.23 15.72 16.09 146.47 90-50 ratio 3.14 3.36 10.35 2.74 2.47 11.27 3.39 3.22 14.09 Mean-to-median ratio 1.81 2.05 5.83 1.36 1.37 6.25 1.79 1.87 8.57 50-30 ratio 1.70 1.53 3.45 1.86 1.65 5.47 1.86 1.70 5.82 34 ILO Working Paper 105 References Acemoglu, Daron and David Autor, 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings”. 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