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The impact of taxes on income mobility


This paper investigates how taxes affect relative mobility in the income distribution in the USA. Panel data for continuously married households drawn from the PSID between 1967 and 1996 are employed to analyse the relationship between marginal tax rates and the probability of staying in the same income decile. Exogenous variation in marginal tax rates is identified by using counterfactual rates based on legislated changes in the tax schedule. I find that higher marginal tax rates reduce income mobility. An increase in 1% point in marginal tax rates causes a decline of around 0.5% points in the probability of changing to a different income quintile. Tax reforms that reduce marginal rates by 7% points are estimated to account for around a tenth of the average movements in the income distribution in a year. Additional results suggest that the effect of taxes on income mobility differs according to the level of human capital and that it is particularly significant when considering mobility at the bottom of the distribution.

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  1. See Piketty and Saez (2003) for long-run trends in income inequality and Saez and Zucman (2016) or Quadrini and Ríos-Rull (2015) for the case of wealth.

  2. Piketty (2014) provides extensive evidence of income and wealth inequality around the world while Stiglitz (2012) highlights its consequences: “the impact of inequality on societies is now increasingly well understood -higher crime, health problems, and mental illness, lower educational achievements, social cohesion and life expectancy” (inside cover).

  3. See Gottschalk (1997).

  4. Kopczuk et al. (2010) argue for the need to study income inequality and mobility jointly. Income mobility is a determinant of inequality in the long run: when there is no mobility in the income distribution, short-run inequality perpetuates.

  5. See Piketty and Saez (2007) and Diamond and Saez (2011).

  6. The generality of this result should be assessed taking into account other factors, such as the heterogeneity in mobility uncovered for different subgroups of population, and the fact that this analysis focuses on married households.

  7. Saez et al. (2012) suggest a range of estimates from 0.12 to 0.40 for the ETI. The authors argue that responses for the top-earners can be substantially higher. For example, Slemrod (1996) finds that the Tax Revenue Act of 1986 explains to a large extent the increase in reported income of the top earners.

  8. I use variation in legislated taxes to address endogeneity following (Gruber and Saez 2002).

  9. Throughout this paper, I will refer to income mobility as intragenerational mobility. Jäntti and Jenkins (2015) also survey the literature on intergenerational or social mobility (the degree of association between parents’ and children’s income). There has been a recent increase in the research aiming to understand the degree of intergenerational mobility and its factors. For example, Chetty et al. (2014a) analyse the geographical differences of intergenerational mobility in the USA and Chetty et al. (2014b) explores its evolution over time, which has remained fairly constant despite rising inequality.

  10. Gottschalk (1997) notes that accounting for longer periods is not necessarily more appropriate than 1-year periods to analyse mobility and inequality, given the fact that low-income households are more likely to face borrowing constraints over longer horizons.

  11. Aghion et al. (2019) investigate the relationship between innovativeness and both top-income inequality and social mobility.

  12. Building on the same dataset, Saez and Zucman (2016) capitalise income to produce measures of wealth inequality, and find that this variables has substantially increased in the last few years.

  13. When we consider \(\tau _L=0.25\) (approximately the US average federal marginal tax rate on individual income during 1967–1996), \(w=10\) and productivity shocks representing \(5\%\) of the base wage w, then we have that the value of \(\tau\) such that \(n_{i,t}^{H^*}=n_{i,t}^{L^*}\) is \(\tau =0.07\), resulting in \(\tau ^L+\tau =0.32\).

  14. The survey contains data from 1967, since some of the variables asked (e.g. income) refer to the previous year.

  15. The PSID, as other surveys, is likely to contain measurement errors. However, Duncan and Hill (1985) use a validation study to document that the annual income variables (which are used throughout this manuscript) are amongst those most accurately reported.

  16. In the special case when vector \(s_{t-1}\) contains all the necessary information to predict \(s_{t}\), i.e. \(\hbox {Prob}(s_t | s_{t-1}, s_{t-2,}, \dots , s_{t-k}) = \hbox {Prob}(s_t | s_{t-1})\) \(\forall k \ge 1\) and t, the process \(s_t\) is said to be Markovian. \(P_t\) becomes the Markov matrix and transitions along the income distribution between k periods can be obtained from \(s_{t+k}=s_{t}P^k\).

  17. See Fields and Ok (1999b) or Jäntti and Jenkins (2015) for exhaustive reviews of the different tools available to measure income mobility.

  18. A broader definition of income would include other sources within the family (e.g. children or other relatives). However this would require making assumptions on how to identify tax units within the household and limit the availability of data. Section 5 explores the robustness of the results to different definitions of income.

  19. Moffitt and Gottschalk (2002) and Moffitt and Gottschalk (2012) document a marked increase in the transitory volatility of male earners for that period.

  20. See Table 20.

  21. See Feenberg and Coutts (1993) for an introduction to the TAXSIM program.

  22. Since mortgage interests are not available in the PSID for all the time horizon, I follow Aaronson and French (2009) and assume that \(80\%\) of mortgage payments go to interest to impute this variable.

  23. See Fig. 6 for the evolution of the average tax rate during the same period.

  24. Barro and Redlick (2011) uses data from a random sample of actual tax files and computes the average marginal tax rate with TAXSIM.

  25. Note that while alterations of the EITC and other provisions have increased the average marginal rate of the bottom deciles, the tax pressure of this group (as measured by the average tax rate shown in Fig. 6) has lowered since 1986. Note that our computation of the marginal tax rates excludes the phase-out of government transfers that do not count towards taxable income and might be relevant for households at the bottom deciles, such as the Supplemental Nutrition Assistance Program (SNAP or food stamps) or the Aid to Families with Dependent Children (AFDC), later replaced by the Temporary Assistance for Needy Families (TANF) program.

  26. The PSID provided an estimation of the marginal tax rate on federal income during 1976–1991 based on question in the survey regarding exemptions, filling status, etc. The correlation with my marginal tax rate computed through TAXSIM is above 90%. Butrica and Burkhauser (1997) explore the differences between the PSID simulations and TAXSIM.

  27. Labour supplied by the spouse is an important factor to take into account in this analysis since married female workers have a more dispersed distribution of hours worked and are, therefore, more likely to be able to adjust their workload. See Blundell et al. (1998) for an investigation on the effects of tax reforms on female labour supply.

  28. See French (2005) for an investigation on how health affects labour supply and retirement decisions.

  29. See Appendix A for a definition of the income variables.

  30. Throughout this paper, models that estimate a binary outcome report estimates that can be interpreted as changes in probability. For example, an estimate of \(-\) 0.391 represents a reduction of success of the dependent variable of 0.391% points.

  31. This strategy has been also employed in the income elasticity literature: see Gruber and Saez (2002) for an example and Saez et al. (2012) for a review of this literature and its identification approaches.

  32. A systematic correlation between income and changes in tax legislation would threaten the validity of \(\Delta \tau _{i,t}^{t-1}\) as an exogenous instrument. Including a long panel where tax reforms are the result of different ideological positions mitigates this problem. Section 5 checks the robustness of the results to including lag income (see Gruber and Saez (2002) for a discussion).

  33. The F-statistic from the first-stage regressions shows a very high value above 1500 for all the specifications, indicating that the instrument is relevant. Some specifications reduce considerably this value, although it always remains well above 10.

  34. Average tax rates are constructed by dividing federal income liabilities by income. Figure 6 plots the evolution of these tax rates in the USA between 1967 and 1996, averaged across income deciles.

  35. With the exception of specification of column 3 (using deciles of post-transfers income) where the estimated coefficient is close to zero but slightly positive.

  36. Interestingly, the estimated coefficient on the dummy variable for working wife in the household becomes larger and more significant than in other specifications.

  37. In our sample, the ratio of asset income to labour income is relatively low (between 1 and 10% depending on the deciles), what may explain the limited effect of changes in the tax rates on mobility based on asset income. The estimated coefficient is also small but statistically significant when considering changes across deciles of income (column 5 in Table 11).

  38. This measure of income is divided by the square root of the number of people living in the family to adjust it for family size. See Jäntti and Jenkins (2015).

  39. PSID data only includes snapshots of wealth for years 1984, 1989 and 1994, and since 1999 onwards.

  40. Information on household equity is included yearly in the PSID, with the exception of years 1973 and 1974. Fairlie and Krashinsky (2012) reports that net home equity represents 60% of the average homeowner wealth (64% for the median homeowner).

  41. Tables 12 and  13 reproduce the robustness tests of this sections for the case of deciles of income.

  42. As an additional robustness test, Table 14 includes as controls the average marginal tax rate for households at a similar point in the income distribution, finding similar results to the benchmark estimations in Table 3.

  43. FICA marginal tax rate has averaged 0.03% between 1967 and 1978. Its standard deviation between 1967 and 1996 is half of the federal income tax rates, and about a third of it during 1967–1997. See Barro and Redlick (2011).

  44. See Table 15. Figure 8 plots the variation across individuals in total marginal tax rates during the period 1978–1996.

  45. Table 16 shows these robustness tests for the case of mobility across income deciles.

  46. These thresholds are often considered to determine the prime age for labour market engagement. See for example Keane (2011).

  47. A dummy for heads who are employed is added to these specifications.

  48. PSID usually assigns the role of the head of the household to a male when he is present. But in some occasions this role corresponds to the wife (e.g. when the female prefers to be designated as the head).

  49. See Tables 17 and 18 for deciles and quintiles of income, respectively, which include the marginal tax rate sequentially lagged up to three periods. When the three lags are included at the same time, only the first coefficient remains significant, with an estimated value of 0.72 (and standard error of 0.30) for the specification with the pre-tax definition of income (and a distribution split in quintiles).

  50. The size of these coefficients falls within the range of earlier estimates of labour supply elasticities. See for example Keane (2011) for a survey on the effects of taxes on labour supply.

  51. See Table 19, which complements the evidence shown in Table 1. The estimations of Table 19 employ measures of absolute income described in Fields and Ok (1999b) and Jäntti and Jenkins (2015) and known as individual income growth. The results are qualitatively similar when employing alternative measures of absolute income mobility such as the fraction of people that report higher pre-tax income between two periods as in Chetty et al. (2017) (adapted to the intragenerational case).

  52. The estimated coefficients for the mobility variables are related by \(\beta ^{\hbox {move}}=\beta ^{\hbox {up}}+\beta ^{\hbox {down}}\), where \(\beta ^{\hbox {move}}=-\beta ^{\hbox {stay}}\). In the regressions shown in panels A and B of Tables 9 we have that \(\beta ^{\hbox {up}}+\beta ^{\hbox {stay}} + \beta ^{\hbox {down}}\) is not usually 0. This is due to differences in the samples used: specifications for variable \(\hbox {up}_{i,t}\) (columns 1 and 2) exclude households with an income in time \(t-1\) belonging to the top quintile, while specifications for variable \(\hbox {down}_{i,t}\) (columns 5 and 6) exclude households with an income in time \(t-1\) belonging to the bottom quintile. This sample adjustment is done to account for the fact that households in the top (bottom) quintile cannot experience further upward (downward) movements in the income distribution.

  53. Interestingly, across both educational groups, a working spouse increases the likelihood of upward mobility and reduces the probability of downward mobility. Tables 22 and 23 show all the estimated coefficients when doing a cross-decile analysis for the no-college and college-educated samples.

  54. The elasticities for low-educated and highly-educated are 0.24 and 0.12, respectively, when assuming log-quadratic utility and 0.31 versus 0.16 when considering a Box-Cox utility function. The literature on labour supply elasticities by education is not, however, settled on unambiguous findings. Other studies such as Showalter and Thurston (1997) and Goolsbee (2000) find higher elasticities for those in college-related occupations such as physicians or executives, respectively, than many estimates for other groups.

  55. A parallel analysis for income deciles can be found in Table 24.

  56. See Zidar (2019). Kline and Tartari (2016) study the short-term impact of a welfare reform experiment on women’s labour supply and find that intensive margin responses are nontrivial.

  57. See also Butrica and Burkhauser (1997) for further details.


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I am indebted to Morten Ravn for invaluable guidance. I also thank Andrew Chesher, Mariacristina De Nardi, Eric French, Raffaella Giacomini, Valérie Lechene, Attila Lindner, Austin Nichols, Fabien Postel-Vinay, Vincent Sterk, two anonymous referees and conference and seminar participants at UCL, University of Essex, Bank of England, Bank of Spain, CREST, Universitat Autónoma de Barcelona, Queen Mary University of London, University of Manchester, Nazarbayev University, Uppsala University, the 2017 Royal Economic Society Annual Conference, Halle Institute for Economic Research, the 16th Journées Louis-André Gérard-Varet and IIPF 2019.

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Appendix A: Definition of income variables

The benchmark definition of income used in the main text is that of Adjusted Gross Income (AGI), which also represents our measure of pre-tax income. This is calculated by TAXSIM by subtracting some adjustments (often called “above the line deductions”) to gross income.

In particular, gross income includes the following sources: labour income of head and wife, asset income, Social Security Income and taxable pension income. Note that in the PSID the variable “taxable income” is adjusted for alimony paid, retirement income (exclusive of Social Security and Veterans Administration pensions) received, unemployment compensation, and the taxable portion of Social Security benefits.Footnote 57 Hence, asset income is obtained by subtracting labour income of head and wife from the variable “taxable income of head and wife”. In the PSID capital gains and losses are only included in the special “wealth modules” started in 1985 and published every 5 years until 1999. Hence, these are omitted from the study. Lastly, gross income is adjusted of alimony paid to arrive at AGI.

Post-tax income is defined as Adjusted Gross Income minus federal income taxes as computed by TAXSIM (in the robustness section, post-tax income also includes payroll tax liabilities (FICA) and state-level tax liabilities). Post-tax and post-transfers income incorporates transfers to the precious definition of income. Transfers include both non-taxable public income (aid to dependent children, Supplemental Security Income received and child support) and income transferred from other sources (mainly, transfers received from relatives). During the period considered, the PSID does not offer exact information on public transfers alone (SSI is reported, but others are not) with yearly frequency. However, the percentage of non-public income in the transfers variable considered here was only about 0.4% in 1980, on average.

The robustness section includes alternative definitions of income. Taxable income includes the joint labour and asset income of head and wife, including net profit from farms or businesses. Labour income is directly measured in the PSID and obtained from the sum of wages and salaries of head and wife, which includes wages, bonuses, overtime, tips and commissions. Asset income is defined as described above and hence includes interest income, dividend income, rental income, income from trust funds and net profits from farms or businesses.

Appendix B

See Figs. 5, 6, 7 and 8 and Tables 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and 24

Fig. 5
figure 5

Evolution of the probability of transition matrix (1967–1996). Note Evolution of indices of mobility at the bottom and top deciles between 1967 and 1996. Panel A shows the evolution of the probability that a households leave the first decile of income [(i.e. \(1-P^{1,1}\) in Eq. (2)]. Panel B shows the evolution of the probability that a households moves down from the top decile of income [(i.e. \(1-P^{10,10}\) in Eq. (2)]. The distribution is computed using both pre-tax and post-tax income

Fig. 6
figure 6

Variation in Average Tax Rates (1967–1996). Note Evolution of the average tax rates between 1967 and 1968. The figure displays the average ratio of total federal income tax liabilities to Adjusted Gross Income (AGI) for each income decile. Tax liabilities are computed using TAXSIM and data from PSID

Fig. 7
figure 7

Variations in Average Tax Rates due to legislated tax changes (1967–1996). Note Evolution between 1967 and 1996 of the instrument \(\Delta \bar{\tau _{i,t}^{t-1}} = \tau _{i,t}^{t}-\tau _{i,t}^{t-1}\) (difference between the actual average tax rate and a counterfactual average tax rate computed using TAXSIM). Grey bars represent the narrative measure of legislated tax changes (as percentage of nominal GDP) from Romer and Romer (2010). These are classified as endogenous tax changes (related to the current state of the economy, in light grey) and exogenous tax changes (unrelated to the state of economy, in dark grey) (Color figure online)

Fig. 8
figure 8

Variation in total marginal tax rates (1977–1996). Note Relationship between total marginal tax rates and real Adjusted Gross Income (1996 US dollars). Total marginal tax rates include the federal marginal rates on individual income, payroll and Social Security liabilities and State marginal tax rates for each household and year in the PSID before and after the 1986 tax reform (in red and blue, respectively) (Color figure online)

Table 11 Robustness to alternative definitions of income (deciles)
Table 12 Robustness to further controls (deciles)
Table 13 Robustness to income controls (deciles)
Table 14 IV estimations (including average marginal tax rates for each group)
Table 15 IV Estimation with State and Payroll Tax rates
Table 16 Robustness to sample selection (deciles)
Table 17 Robustness to different lags of the marginal tax rate (deciles of income)
Table 18 Robustness to different lags of the marginal tax rate (quintiles of income)
Table 19 The impact of taxes on measures of absolute income mobility
Table 20 IV estimation using a reduced sample (1967–1990)
Table 21 Nonlinear estimates around selected tax reforms
Table 22 IV estimates—households without college education (deciles of income)
Table 23 IV estimates—households with college education (deciles of income)
Table 24 IV estimates—households in bottom and top deciles

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Alloza, M. The impact of taxes on income mobility. Int Tax Public Finance 28, 794–854 (2021).

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  • Income mobility
  • Inequality
  • Marginal tax rate

JEL classification:

  • E24
  • E62
  • D31
  • D63
  • H24
  • H31