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Tax Evasion and Inequality Online Appendix⇤ Tax Evasion and Inequality Online Appendix⇤ Annette Alstadsæter (Norwegian University of Life Sciences) Niels Johannesen (University of Copenhagen) Gabriel Zucman (UC Berkeley and NBER) July 31, 2018 Abstract This Appendix supplements our paper “Tax Evasion and Inequality.” ⇤Annette Alstadsæter: [email protected]; Niels Johannesen: [email protected]; Gabriel Zucman: [email protected]. The paper, appendix, programs and data files are all available on- line at http://gabriel-zucman.eu/leaks. We thank the Scandinavian tax administrations (Skatteetaten, Skatteverket, and SKAT), Statistics Sweden, and SVT Uppdrag granskning for their goodwill and cooperation; Sigurd Bjørnestad, Joachim Dyfvermark, Linda Larsson Kakuli, Fredrik Laurin, Petter Lundberg, Søren Ped- ersen, Gard Thomassen, and UiO Services for Sensitive Data (TSD) for exceptionally valuable assistance; Alan Auerbach, Brooke Harrington, Send Jonas, Patrick Kline, Adair Morse, Daniel Reck, Emmanuel Saez, Joel Slem- rod, Daniel Waldenstr¨omand numerous seminar and conference participants for helpful comments and reactions. We are grateful for financial support from the Nordic Tax Research Council and the FRIPRO-program of the Research Council of Norway. Johannesen gratefully acknowledges financial support from the Danish Council for Independent Research and the Danish National Research Foundation. Zucman gratefully acknowledges financial support from the Laura and John Arnold Foundation. Contents A Household Wealth and its Distribution in Scandinavia 3 A.1 General methodological principles .......................... 3 A.2 Aggregate wealth and income in Scandinavia .................... 4 A.3 The distribution of wealth in Scandinavia ...................... 4 B Wealth in Norway: Data sources and Methods 5 B.1 Methodological issues and sources specific to Norway ............... 5 B.2 Norwegian micro data sources ............................ 8 B.3 Norwegian data construction ............................. 9 C Wealth in Sweden: Data Sources and Methods 11 C.1 Swedish data sources ................................. 11 C.2 Swedish data construction .............................. 12 D Wealth in Denmark: Data Sources and Methods 12 E The HSBC leak 13 E.1 Background information ............................... 13 E.2 Representativity of HSBC .............................. 16 F The Panama Papers sample 17 F.1 Norway ........................................ 17 F.2 Sweden ......................................... 18 G Samples of Amnesty Participants 18 G.1 Norwegian amnesty .................................. 19 G.2 Swedish amnesty ................................... 19 H Additional details on Danish random audits 20 H.1 Deliberate vs. non-deliberate evasion ........................ 20 H.2 Construction of standard errors ........................... 20 I Macro estimates of o↵shore wealth 21 J Distributional tax gaps: additional details and robustness 22 1 K A Model of Tax Evasion and Inequality 22 K.1 Baseline Model .................................... 22 K.2 Proof of proposition 2 ................................ 26 K.3 Competition in the supply of tax evasion services ................. 27 2 This Appendix supplements our paper “Tax Evasion and Inequality.” It is organized as follows. Sections A to J contains detailed description of the data we use and present robustness checks. Each of these sections is supplemented by an Excel file containing a large number of supplementary results, all posted online at http://gabriel-zucman.eu/leaks.SectionK provides extension of the model of tax evasion presented in the main paper and omitted proofs. A Household Wealth and its Distribution in Scandinavia In this Section we describe how we compute homogenous estimates of household wealth and its distribution in Scandinavia defined as the aggregate of Norway, Sweden, and Denmark. We start by laying out a number of general methodological principles that we apply in each of the three Scandinavian countries, before describing the way that we combine the three countries, and discussing the results. Country-specific methodological details and sources are discussed in Sections B (Norway), C (Sweden), and D (Denmark). A.1 General methodological principles A.1.1 Definition of household wealth We are interested in computing the distribution of total household wealth at market value, using the same concepts and definitions as those used by the World Wealth and Income Database (http://WID.world) so as to obtain wealth levels and shares for Scandinavia that are directly comparable to those estimated in the United States and other countries available on http: //WID.world. A general discussion of the methods involved is provided in Alvaredo et al. (2017). The starting point involves constructing the aggregate amount of wealth, which we distribute to the entire adult population (and which we use when computing top shares). Following international standards codified in the System of National Accounts (United Nations, 2009), we include in wealth all the non-financial and financial assets over which ownership rights can be enforced and that provide economic benefits to their owners. This definition of wealth includes all funded pension wealth, but excludes all promises of future government transfers such as the present value of future Social Security income. As in other http://WID.world countries, we disregard human capital—which contrary to non-human wealth cannot be sold on markets—, the wealth of nonprofit institutions and of the government,1 consumer durables (about 10% of 1It would be interesting to compute distributions of national wealth in Norway, where the government owns a large amount of public assets in a sovereign wealth fund, but the allocation of public wealth to households 3 household wealth), and valuables. A.1.2 Unit of observation Our unit of analysis is the household, as in Saez and Zucman (2016). A household is either a single person aged 20 or above or a married couple, in both cases with children dependents if any. We define fractiles relative to the total number of households in the population, including those who do not have to pay any wealth tax. In 2006 (our benchmark year for the computation of our distributional tax gaps), there were 10.6 million households in Scandinavia, covering the full population of 19.2 million Scandinavian residents. The top 0.1% of the distribution, therefore, includes about 10,600 households, and the top 0.01% about 1,060 households. We have access to population-wide micro data for each of the three Scandinavian country (see below). Because children’s assets are typically included with one of their parent’s tax return, they are properly accounted for in our data. A.2 Aggregate wealth and income in Scandinavia Detailed statistics on aggregate income and wealth in Scandinavia and each of the Scandina- vian country individually are presented in Appendix Tables A.0 and A.1, and Appendix Figures A.1 to A.15, see Online Appendix A data files. These figures show that Norway, Sweden, and Denmark are usually very similar in terms of their average income, average wealth, wealth composition, wealth distribution, and income distribution. The main di↵erence is that Nor- way has a somewhat lower amount of aggregate private wealth (relative to national income), maybe because it has more public wealth (as it has a large sovereign wealth fund, financed by oil revenues). PPP-adjusted rates slightly reduce the weight of Norway (where the price level is relatively high) in the Scandinavian aggregate (e.g., Norway accounts for 24% of total Scan- dinavian wealth using market exchange rates, vs. 22% using PPP-adjusted rates in 2014, see Appendix Tables A1d and A1e), but using PPP vs. market exchange rates does not significantly a↵ect any of the main results of the paper. A.3 The distribution of wealth in Scandinavia To aggregate Norway, Sweden, and Denmark into a single Scandinavian “country”, we use the generalized Pareto-interpolation techniques recently developed by Blanchet, Fournier et Piketty (2017). We proceed in two steps. First, we collapse the population-wide files or each raises complex conceptual questions that we leave for future research. 4 Scandinavian countries by generalized percentiles, or g-percentiles. There are 127 g-percentiles: 99 for the bottom 99 percentiles, 9 for the bottom 9 tenth-of-percentiles of the top percentile, 9 for the bottom 9 one-hundredth-of-percentiles of the top tenth-of- percentile, and 10 for the 10 one-thousandth-of-percentile of the top one-hundredth- of-percentile. For each g-percentile, we compute the minimum wealth, average wealth, and number of households. Wealth is converted to US$ using current market exchange rates. Second, we generate a synthetic Scandinavian population-wide file using the g-pinter tool available at http://wid.world/gpinter/,andtheoption“interpolateandmergecountries”. In practice, this tool first generates country-specific full-size datasets using the generalized Pareto-interpolation techniques of Blanchet, Fournier and Piketty (2017), and then appends the resulting files. We refer to http://wid.world/gpinter/ and to Blanchet, Fournier and Piketty (2017) for complete methodological details. Detailed statistics for the distribution of wealth in Scandinavia in 2006 are presented in Appendix Table J.8 and J.9, see Online Appendix J.Scandinaviaismuchmoreequalthanthe United States: strikingly, although both economies have the same average wealth per adult ($290,000 in 2014), the bottom 90% is twice richer in Scandinavia,
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