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Sufficient Statistics for Nonlinear Tax Systems with General Across-Income Heterogeneity. (2022). Taubinsky, Dmitry ; Lockwood, Benjamin B ; Ferey, Antoine.
In: CESifo Working Paper Series.
RePEc:ces:ceswps:_9958.

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  1. (2019). Panel (b) plots the implied weighted average savings tax rate in each bin. See Appendix D.1.2 for details. D.1.3 Measures of s0 inc A key input for our sufficient statistics is the marginal propensity to save out of earned income, s0 inc (z) := ∂s(z) ∂z θ=θ(z) , which relates changes in the amount of net-of-tax savings at the time of retirement to changes in the amount of pre-tax earnings z. We draw from two sources of empirical data to calibrate our marginal propensities to consume (or save), translated into measures of s0 inc (z). These results are plotted in Figure I.
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  15. Because our survey question asked about a change in pre-tax income, we do not need to multiply by 1 − T0 1(z) as in equation (233); we just divide by 1 1+r +T0 s(s(z)) to reach our measure of s0 inc(z). This results in an estimate somewhat higher than that obtained by Fagereng et al. (2021) for Norway, plotted in Figure I. We use this as the baseline measure of s0 inc(z) for our simulations, and the difference between the cross-sectional slope s0 (z) and s0 inc(z) provides our estimate of the key statistic for preference heterogeneity, s0 het(z), which is also plotted in Figure I.
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  21. Change in savings per dollar of earned income Cross-sectional profile s'(z) Survey estimate of causal s'inc (z) Fagereng et al. (2019) estimate of causal s'inc (z) Implied s' het (z) from survey Notes: This figure reports the slope of the cross-sectional profile of savings s0 (z) (blue), as well as our calibrations of s0 inc(z) based on causal income effects, derived from Fagereng et al. (2021) and from a new nationally representative survey. See Section 6 and Appendix D.1 for details.
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  24. Comparison to Golosov et al. (2013). Golosov et al. (2013) also study preference heterogeneity, providing a useful point of comparison. In their baseline calibration, they assume individuals’ preferences are Constant-RelativeRisk -Aversion U(c, s, l) = α (w) 1 + α (w) ln c + 1 1 + α (w) ln s − 1 σ (l) σ , where l is the labor supply of an individual with hourly wage w such that earnings are given by z = wl. The riskaversion parameter is set to γ = 1, the isoelastic disulity from labor effort is such that σ = 3, and the taste parameter is given by α (w) = 1.0526 (w) −0.0036 .
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  26. Country Wealth Capital Gains Property Pensions Inheritance Australia – Other SL, SN SL – Austria – Other SL, SN SN – Canada – Other SL SN – Denmark – SN SL, SN SL, SN SN France – Other Other SL, SN SN Germany – Other SL SN SN Ireland – SN SL, SN SN SN Israel – Other Other SN – Italy SL, SN SL SL SL SL, SN Japan – SL, SN SN SN SN Netherlands SN SL SL, SN SN SN New Zealand – Other SN SL, LED – Norway SN SL SL SN – Portugal – SL Other SN SL Singapore – Other SN SN – South Korea – SN SN SN SN Spain SN SN SL, SN SN SN Switzerland SN SN SL, SN SN SN Taiwan – SL, SN SL, SN SN SN United Kingdom – Other SN SN SN United States – LED SL SN SN Notes: This table classifies tax systems applied to different savings vehicles across countries in 2020 according to the types of simple tax systems we consider.
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  35. Figure I: Decomposition of Cross-Sectional Savings Profile 0 50 100 150 200 250 300 Income ($1000s) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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  59. Online Appendix Ferey, Lockwood, and Taubinsky Figure A2: Calibration of Savings Tax Rates Across Incomes in the U.S. (a) Decomposition of Savings Types: Bricker et al. (2019) 0 20 40 60 80 100 Share of savings (%) Income percentile <25 25-50 50-60 60-70 70-80 80-90 90-99 Financial (market) Nonfinancial (business) Financial (transaction) Financial (retirement) Nonfinancial (residences) (b) Calibrated Savings Tax Rates in the United States, by Income Percentile 0 .02 .04 .06 .08 .1 Savings tax rate <25 25-50 50-60 60-70 70-80 80-90 90-99 Income percentile Notes: This figure illustrates the calibration of savings tax rates in the U.S. across the income distribution. Panel (a) plots the composition of asset types in individuals’ portfolios across the income distribution, reported by Bricker et al.
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  70. Since our survey question asks about consumption and spending within each year, we interpret these estimates as short-run responses. Fagereng et al. (2021) show that positive income shocks are followed by consumption responses that can last up to 5 years. We use their impulse-response profile to convert these 1-year MPS into a 5-year MPS, which we interpret as a total effect on savings before returns. To do so, we use the fact that they report a 1-year MPC of 0.52 and a 5-year MPC of 0.90; we therefore compute our long run marginal propensity to save as MPS5y = MPS1y 1 − 0.90 1 − 0.52 = 0.60 0.208 = 0.125.
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  77. These results are reported in the bottom two panels of Figure III, which display schedules of LED and SN savings tax rates computed under the assumption that (i) individuals with different income levels differ in their private rates of return, and that (ii) the savings tax is levied in period-2 dollars. We compute the tax schedules that satisfy the equations for the optimal tax conditions in Proposition 7. As in the case of multidimensional heterogeneity, we hold fixed the schedule of marginal social welfare weights g(z) proportional to those which rationalize the status quo income tax in our baseline inverse optimum calculation. Building on the findings of Fagereng et al. (2020), we follow Gerritsen et al.
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  78. This gives a constant s0 inc = 1−0.3 1/2.1+1.042∗(1/2.1+0.01) = 0.71, which is much higher than our estimate. Leveraging the fact that s0 inc is constant, we can also infer that at an annual income of $125, 000, the annual amount of savings available for consumption in period 2 (including compounded interest) is approximately equal to s = s0 inc ∗ $125, 000 = 0.71∗125, 000 = $88, 750. Thus, s0 het = 1/2.1+0.02 1/2.1+1.042∗(1/2.1+0.02)+0.02 ∗(34∗10−9 )∗88, 750 = 0.0015.58 These values for s0 inc and s0 het imply that in the calibration of Golosov et al. (2013), preference heterogeneity is substantially smaller than our estimate of across-income heterogeneity, as it only explains s0 het s0 het+s0 inc = 0.0015 0.71+0.0015 = 0.2% of the variation in savings between individuals earning $100, 000 annually and those earning $150, 000.
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  79. To obtain an approximation of s0 het(z) in their setting, we use the fact that Golosov et al. (2013) report in their simulation results that individuals with an annual income z = $100, 000 have an hourly wage w = $40 while those with an annual income z = $150, 000 have an hourly wage w = $62.5. We can thus approximate dα dz = α(62.5)−α(40) 150,000−100,000 = 1.0370−1.0387 50,000 = −34 ∗ 10−9 . For T 0 z , we assume a linear income tax rate τz = 0.3, for R = 2.1 we use our real interest rate of 3.8% compounded over 20 years), and for T 0 s we assume a linear income tax rate τs = 0.01 which we show below (see equation (235)) to be consistent with a linear tax of 4% on capital gains (the approximate average in Figure A2b).
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  80. WERNING, I. (2010): “Nonlinear Capital Taxation,” working paper.
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  28. Market heterogeneity and the distributional incidence of soft-drink taxes: evidence from France. (2021). Boizot-Szantai, Christine ; Lecocq, Sebastien ; Etile, Fabrice.
    In: PSE-Ecole d'économie de Paris (Postprint).
    RePEc:hal:pseptp:hal-03553704.

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  29. Changes in beverage purchases following the announcement and implementation of South Africas Health Promotion Levy: an observational study. (2021). Ng, Shu Wen ; Popkin, Barry ; Swart, Elizabeth C ; Hofman, Karen ; Edoka, Ijeoma ; Stacey, Nicholas.
    In: LSE Research Online Documents on Economics.
    RePEc:ehl:lserod:109878.

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  30. The effectiveness of sin food taxes: Evidence from Mexico. (2021). Gutierrez, Emilio ; Seira, Enrique ; Aguilar, Arturo.
    In: Journal of Health Economics.
    RePEc:eee:jhecon:v:77:y:2021:i:c:s0167629621000400.

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  31. Avoidance behaviors circumventing the sugar-sweetened beverages tax. (2021). McCluskey, Jill ; Brady, Michael P ; Gallardo, Karina R ; Zhang, QI.
    In: Food Policy.
    RePEc:eee:jfpoli:v:105:y:2021:i:c:s0306919221001469.

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  32. Inequality, information failures, and air pollution. (2021). Hausman, Catherine ; Stolper, Samuel.
    In: Journal of Environmental Economics and Management.
    RePEc:eee:jeeman:v:110:y:2021:i:c:s009506962100108x.

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  33. Evaluating Marginal Internalities: A New Approach. (2021). Kalamov, Zarko.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_9476.

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  34. Food Price Elasticities for Policy Interventions: Estimates from a Virtual Supermarket Experiment in a Multistage Demand Analysis with (Expert) Prior Information. (2021). Hassan, Andres Ramirez ; Nghiem, Nhung ; Jacobi, Liana ; Blakely, Tony ; Ramirezhassan, Andres.
    In: The Economic Record.
    RePEc:bla:ecorec:v:97:y:2021:i:319:p:457-490.

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  35. The impact of sweet food tax on producers and household spending—Evidence from Hungary. (2021). Bíró, Anikó.
    In: Agricultural Economics.
    RePEc:bla:agecon:v:52:y:2021:i:4:p:545-559.

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  36. The Feasibility of Plastic Bag Usage for Newly Established South African Small, Medium and Micro Enterprises Amidst Increases in Sin tax: An Online Desktop Study. (2021). Witbooi, Nadine ; Williams, Edlin ; Coulter, Ingrid ; Charles, Courtney ; Brown, Timeeka ; Mabesele, Lindiwe ; Bruwer, Juan-Pierre.
    In: International Journal of Business Research and Management (IJBRM).
    RePEc:aml:intbrm:v:12:y:2021:i:4:p:175-190.

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  39. The welfare effects of persuasion and taxation: Theory and evidence from the field. (2020). Löschel, Andreas ; Loschel, Andreas ; Rodemeier, Matthias.
    In: ZEW Discussion Papers.
    RePEc:zbw:zewdip:20019.

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  40. The welfare effects of persuasion and taxation: Theory and evidence from the field. (2020). Löschel, Andreas ; Loschel, Andreas ; Rodemeier, Matthias.
    In: CAWM Discussion Papers.
    RePEc:zbw:cawmdp:112.

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  41. Informality, Consumption Taxes and Redistribution. (2020). Gadenne, Lucie ; Bachas, Pierre ; Jensen, Anders.
    In: The Warwick Economics Research Paper Series (TWERPS).
    RePEc:wrk:warwec:1277.

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  42. Whats on the Menu? Policies to Reduce Young Peoples Sugar Consumption. (2020). Griffith, Rachel ; Stroud, Rebekah ; Smith, Kate ; O'Connell, Martin.
    In: Fiscal Studies.
    RePEc:wly:fistud:v:41:y:2020:i:1:p:165-197.

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  43. Reformulation and taxes for healthier consumption: Empirical evidence in the French Dessert market. (2020). Bonnet, Celine ; Allais, Oliver ; Requillart, Vincent ; Spiteri, Marine .
    In: TSE Working Papers.
    RePEc:tse:wpaper:124156.

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  44. A Method to Estimate Discrete Choice Models that is Robust to Consumer Search. (2020). Abaluck, Jason ; Compiani, Giovanni.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:26849.

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  45. Inequality, Information Failures, and Air Pollution. (2020). Hausman, Catherine ; Stolper, Samuel.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:26682.

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  46. Informality, Consumption Taxes and Redistribution. (2020). Jensen, Anders ; Gadenne, Lucie ; Bachas, Pierre.
    In: IFS Working Papers.
    RePEc:ifs:ifsewp:20/14.

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  47. Brown sugar, how come you taste so good? The impact of a soda tax on prices and consumption. (2020). Gonçalves, Judite ; Dos, Joo Pereira ; Gonalves, Judite.
    In: Social Science & Medicine.
    RePEc:eee:socmed:v:264:y:2020:i:c:s0277953620305517.

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  48. The use of financial incentives to prevent unhealthy behaviors: A review. (2020). de Walque, Damien.
    In: Social Science & Medicine.
    RePEc:eee:socmed:v:261:y:2020:i:c:s027795362030455x.

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  49. Temperature and temperament: Evidence from Twitter. (2020). Baylis, Patrick.
    In: Journal of Public Economics.
    RePEc:eee:pubeco:v:184:y:2020:i:c:s0047272720300256.

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  50. Ethnicity and tax filing behavior. (2020). Giebe, Thomas ; Bastani, Spencer ; Miao, Chizheng.
    In: Journal of Urban Economics.
    RePEc:eee:juecon:v:116:y:2020:i:c:s0094119019300920.

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  51. The effectiveness of taxes in decreasing candy purchases. (2020). Wrenn, Douglas H ; Hoy, Kyle A.
    In: Food Policy.
    RePEc:eee:jfpoli:v:97:y:2020:i:c:s0306919220301639.

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  52. A reconsideration of the sugar sweetened beverage tax in a household production model. (2020). bordignon, massimo ; Zhan, Lue ; Xiang, DI.
    In: Food Policy.
    RePEc:eee:jfpoli:v:95:y:2020:i:c:s0306919220301378.

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  53. When environmental and redistribution concerns collide: The case of electricity pricing. (2020). Radulescu, Doina ; Feger, Fabian.
    In: Energy Economics.
    RePEc:eee:eneeco:v:90:y:2020:i:c:s0140988320301687.

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  54. The health and financial impacts of a sugary drink tax across different income groups in Canada. (2020). Paulden, Mike ; Ohinmaa, Arto ; Jones, Amanda C ; Kao, Kai-Erh.
    In: Economics & Human Biology.
    RePEc:eee:ehbiol:v:38:y:2020:i:c:s1570677x19302746.

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  55. Sin Taxes and Self-Control. (2020). Schmacker, Renke ; Smed, Sinne.
    In: Discussion Papers of DIW Berlin.
    RePEc:diw:diwwpp:dp1881.

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  56. The Welfare Effects of Persuasion and Taxation: Theory and Evidence from the Field. (2020). Löschel, Andreas ; Loschel, Andreas ; Rodemeier, Matthias.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8259.

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  57. Lifestyle Taxes in the Presence of Profit Shifting. (2020). LEVAGGI, ROSELLA ; Marchiori, Carmen ; Panteghini, Paolo.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8138.

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  58. A Macroeconomic Model of an Epidemic with Silent Transmission and Endogenous Self-isolation. (2020). Diez de los Rios, Antonio.
    In: Staff Working Papers.
    RePEc:bca:bocawp:20-50.

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  59. Should We Tax Sugar-Sweetened Beverages? An Overview of Theory and Evidence. (2019). Taubinsky, Dmitry ; Lockwood, Benjamin B ; Allcott, Hunt.
    In: Journal of Economic Perspectives.
    RePEc:aea:jecper:v:33:y:2019:i:3:p:202-27.

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  60. Taxation and Market Power in the Legal Marijuana Industry. (2018). Hollenbeck, Brett ; Uetake, Kosuke.
    In: MPRA Paper.
    RePEc:pra:mprapa:90085.

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