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House Price Cycles, Wealth Inequality and Portfolio Reshuffling∗
Clara Martínez-Toledano January 1st, 2020
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Business cycle dynamics can shape the wealth distribution through asset price changes, saving responses, or a combination of both. This paper studies the implications of housing booms and busts for wealth inequality, examining two episodes over the last four decades in Spain. I combine fiscal data with household surveys and national accounts to reconstruct the entire wealth distribution and develop a new asset-specific decomposition of wealth accumulation to disentangle the main forces behind wealth inequality dynamics (e.g., capital gains, saving rates). I find that the top 10% wealth share drops during housing booms, but the decreasing pattern reverts during busts. Differences in capital gains across wealth groups appear to be the main drivers of the decline in wealth concentration during booms. In contrast, persistent differences in saving rates across wealth groups and portfolio reshuffling towards financial assets among top wealth holders are the main explanatory forces behind the reverting evolution during housing busts. I show that the heterogeneity in saving responses is largely driven by differences in portfolio adjustment frictions across wealth groups and that tax incentives can exacerbate this differential behavior. Using a novel personal income and wealth tax panel, I explore the role of tax incentives exploiting quasi-experimental variation created by a large capital income tax reform in a differencesin-differences setting. I find that capital income tax cuts, largely benefiting top wealth holders, explain on average 60% of the increase in the top 10% wealth share during the recent housing bust. These results provide novel empirical evidence to enrich macroeconomic theories of wealth inequality over the business cycle.
JEL: D31, H31, G51
∗Contact information for the author: Clara Martínez-Toledano, Paris School of Economics, 48 Boulevard Jourdan (Office R5-70), 75014 Paris, France; email: [email protected]; phone: +33 665 975 874. I want to especially thank Thomas Piketty for his guidance and encouragement. I am also very grateful to Facundo Alvaredo, Miguel Artola, Lydia Assouad, Laurent Bach, Luis Bauluz, Thomas Blanchet, Olympia Bover, Laura Castillo-Martínez, Gabrielle Fack, Axelle Ferriere, Martín Fernández-Sánchez, Jonathan Goupille, Laura Hospido, Camille Landais, Juliana Londoño-Vélez, Benjamin Moll, José Montalbán, Jorge Onrubia, José-Víctor Ríos-Rull, Kilian Russ, Emmanuel Saez, Moritz Schularick, Daniel Waldenström and Gabriel Zucman for helpful discussions, as well as participants at the CEPR European Conference on Household Finance in Rhodes, the 2019 EEA Congress, the 2019 IIPF Congress, the 2019 SEM Conference, the Warwick Workshop on the Micro and Macro of Inequality, the 1st EAYE Workshop on Housing and Macroeconomics, the 5th ECB Conference on Household Finance and Consumption, the 15th LAGV International Conference in Public Economics, the NY ECINEQ Meeting, the Lisbon Spring Meeting of Young Economists, the Barcelona GSE EDP Jamboree, and seminars at Paris School of Economics, Bank of Spain, University of Barcelona, University of Oxford, London School of Economics, University College London, University of Bonn, IE Business School, University of Valencia and University of Zaragoza. I acknowledge financial support from Fundación Ramón Areces, Bank of Spain and Fundación Rafael del Pino at different stages of the project. All errors are my own.

I Introduction
The evolution and determinants of wealth inequality are currently at the center of the academic and political debate. This renewed interest is largely motivated by two well-established empirical facts. First, household wealth has grown faster than national income in the last four decades, with similar levels and trends across advanced economies (Piketty and Zucman [2014]). Second, wealth concentration trends have diverged over the same period of time, rising, for instance, much faster in the US than in continental Europe (Alvaredo et al. [2018b]). Despite this recent progress, little is known on the complex interaction between the evolution of aggregate household wealth and its distribution. These interactions are of particular importance during asset booms and busts. Wealth levels and portfolio composition along the distribution might significantly change—either mechanically through asset price changes, saving responses, or a combination of both—and consequently, trends in medium to long-term wealth inequality could revert. Wealth inequality matters in the determination of aggregates such as consumption (Carroll et al. [2014], Krueger et al. [2016]). Thus, understanding the determinants of wealth inequality dynamics at different phases of the economic cycle is of interest to gauge the risks of business cycles and set appropriate stabilization policies. The extent to which these dynamics are purely mechanical or respond to changes in saving behavior is still an open question.
The dynamics of wealth inequality are even more relevant during housing booms and busts. Housing is the main asset in most individual portfolios (Saez and Zucman [2016], Garbinti et al. [2018a]) and it forms the lion’s share of total return on aggregate wealth (Jordà et al. [2019]). Moreover, the recent rise in household wealth to national income ratios has been mainly driven by capital gains on housing (Piketty and Zucman [2014], Artola Blanco et al. [2019]). Analyzing the implications of house price cycles for wealth inequality is, however, an empirical challenge. This is likely due to the difficulty of finding settings with multiple housing ups and downs episodes, that make it possible to generalize the results, and with sufficiently rich data sources. Evidence on the interaction between large house price fluctuations and wealth inequality has thus so far been elusive.
This paper breaks new grounds on these issues by studying how housing booms and busts shape the wealth distribution. I examine the Spanish context, an ideal laboratory since the country has experienced two housing booms (1985-1991, 1998-2007) and busts (1992-1995, 20082014) in the last forty years and it has reliable statistics on individual asset ownership going back to the 1980s. I combine individual tax returns, with household surveys and national accounts to reconstruct the entire wealth distribution. I then develop a novel asset-specific decomposition of wealth accumulation that I use to identify the key forces (e.g., capital gains, saving rates) behind the observed wealth inequality dynamics. This new decomposition is critical to better understand saving responses, which have attracted much less scrutiny than asset prices in the

analysis of wealth inequality dynamics over the business cycle (Kuhn et al. [2018]). Lastly, I examine several candidate explanations behind the observed saving dynamics: heterogeneity in portfolio adjustment frictions, real estate market dynamics and tax incentives. I explore the latter in more depth exploiting a novel personal income and wealth tax panel and quasiexperimental variation created by a large reform in the Spanish personal income tax during the recent house price cycle. In conjunction, these analyses provide novel ingredients to generate realistic wealth dynamics in quantitative models of wealth inequality (Achdou et al. [2017], Benhabib and Bisin [2018], De Nardi and Fella [2017], Gomez [2019], Hubmer et al. [2019]).
The backbone of this study is the measurement of the wealth distribution. In Spain, wealth tax returns only cover the very top of the wealth distribution and wealth surveys are only available since the 2000s. I thus rely on the capitalization method—recently used by Saez and Zucman [2016] to reconstruct the US wealth distribution—to recover the entire wealth distribution going back to the 1980s. This approach involves the application of a capitalization factor to the distribution of capital income from tax records to arrive at an estimate of the wealth distribution. Capitalization factors are computed for each asset in such a way as to map the total flow of taxable income to total wealth recorded in national accounts. To ensure full consistency with national accounts, I then account for assets and individuals that do not generate taxable income flows by means of household surveys, following the mixed capitalizationsurvey method recently developed by Garbinti et al. [2018a]. Wealth distribution series have been found to be sensitive to the assumption of constant capitalization factors by asset class in the US context (Smith et al. [2019]). I perform numerous robustness checks with wealth tax returns and household surveys to make sure that the mixed capitalization-survey method derives credible estimates in terms of levels, asset composition and trends of the Spanish wealth distribution. Overall, this series constitutes an ideal basis to understand the dynamics of wealth inequality during housing booms and busts.
The new wealth distribution series shows that the top 10% wealth share declines during housing booms—to the benefit of the bottom 50% wealth group and even more of the middle 40% wealth group—but the decreasing pattern reverts during housing busts. These findings hold in both episodes (1985-1995, 1998-2014). I also show that these results apply to the house price cycle of the early 2000s in France and the US using the wealth distribution series of Garbinti et al. [2018a] and Saez and Zucman [2016], respectively. The international resemblance in the dynamics is because of similar asset composition along the distribution. As in France and the US, bottom deciles in Spain own mostly financial assets in the form of cash and deposits, whereas primary residence is the main form of wealth for the middle of the distribution. As we move toward the top 10% and the top 1% of the distribution, unincorporated business assets, other owner-occupied and tenant-occupied housing gain importance, and financial assets—mainly equities—gradually

become the dominant form of wealth. I develop a new asset-specific decomposition of wealth accumulation that I use in combi-
nation with the wealth distribution series to run simulation exercises and analyze whether the observed dynamics are purely mechanical—due to differences in asset prices—or driven by other forces. This is an extension of the standard wealth accumulation decomposition used by Saez and Zucman [2016] in which the three forces driving wealth inequality dynamics are differences in labor income, rate of return and saving rates across the distribution.1 The novelty of this decomposition is that it breaks down the composition of savings by asset class (i.e., housing, unincorporated business assets, financial assets), making it possible to improve our understanding of saving dynamics across wealth groups, especially during asset booms and busts.
My findings suggest that differences in capital gains are the main drivers of wealth inequality dynamics during housing booms, while differences in saving behavior are the main forces during housing busts. I show that capital gains contribute to reducing wealth concentration levels during booms for two main reasons. First, middle and bottom wealth groups have a larger share of housing in their portfolio. Second, capital gains on housing are higher on average than on financial assets. However, differences in capital gains do not seem to explain why top wealth concentration patterns revert, given that rates of capital gain almost fully converge across wealth groups during housing busts. Instead, persistent differences in saving rates across wealth groups and portfolio reshuffling towards financial assets among top wealth holders appear to be the main explanatory forces behind the reverting pattern in wealth concentration during housing busts.2 The results hold for both house price cycle episodes (1985-1995, 1998-2014). Using wealth surveys, I document that large changes in the composition of savings among top wealth holders during housing busts are not only due to channeling new saving towards financial assets, but also due to dissaving in housing (i.e., tenant-occupied housing). I perform the same assetspecific decomposition with the French (Garbinti et al. [2018a]) and US wealth distribution series (Saez and Zucman [2016]) and show that these findings also apply to the house price cycle of the early 2000s in France and the US. Hence, these results are not specific to the Spanish context and seem to generally hold for housing booms and busts episodes.
Lastly, I explore potential mechanisms behind the heterogeneity in saving behavior along the wealth distribution during housing busts. I focus on three main candidate explanations: differences in portfolio adjustment frictions, real estate market dynamics and tax incentives. Contrary to middle and bottom wealth holders, I show that it is easier for top wealth holders
1Note that the rate of return is the sum of the flow return and the rate of capital gain. 2Persistent differences in flow rates of return across the whole distribution perpetuate the high levels of long-run wealth concentration. Nonetheless, because trends are quite similar across wealth groups, they do not seem to be the main drivers of wealth inequality dynamics during housing booms and busts. Labor income inequality does not strike as an important factor either, since labor income shares remain quite stable along the wealth distribution.

to reshuffle their portfolio towards financial assets because they are subject to fewer broadly defined portfolio adjustment frictions. First, top wealth holders have higher savings, so that they have fewer difficulties to incur in transaction costs (e.g., capital gains taxes) associated to selling real estate. Second, top wealth holders have lower indebtedness attached to real estate. Consequently, when it comes to sell, they are less constrained by the evolution of the value of their property relative to the value of their mortgage. Third, top wealth holders have much larger holdings of real estate for investment purposes (i.e., tenant-occupied housing). Contrary to housing for consumption purposes (i.e., primary residence), housing for investment is not subject to additional transaction costs such as those concerning moving to another property. Hence, top wealth holders can liquidate these types of properties more easily.
Real estate market dynamics could be a competing explanation for the larger portfolio reshuffling among top wealth holders during housing busts. Both housing demand and housing prices could evolve differently across time and space affecting wealth groups in an heterogeneous manner. If the dynamics of the real estate market are such that there is a higher demand for the type of properties owned by top wealth holders during the housing bust, this could explain why they managed to dissave more in real estate. Using wealth surveys, I document that indeed primary residences and other properties owned by bottom and middle wealth holders have different characteristics (e.g., value, size) than properties owned by the top. However, using information (e.g., number of listings, number of contacts received by listing, offer price) on the universe of 2009 property listings from the largest Spanish commercial real estate website, I find that the demand for housing was not significantly different in districts with the highest average house price versus the rest of districts.3 Furthermore, top wealth holders might have decided to dissave relatively more in housing than middle and bottom wealth holders if the value of their properties had not declined or had declined less. Nonetheless, I show that top wealth holders live in municipalities whose average house price has experienced a similar evolution to municipalities in which bottom and middle wealth holders reside. This evidence suggests that real estate market dynamics are not driving the differential saving behavior across wealth groups during housing busts.
I also document that institutional factors such as tax incentives can exacerbate differences in saving behavior along the wealth distribution. In particular, I examine a large reform introduced in 2007 on the Spanish personal income tax aimed at incentivizing saving on financial assets. Financial income (i.e., interest, dividends, short-term capital gains) that used to be taxed under a progressive tax schedule with the rest of income components, started to be taxed at a flat rate of 18%. The reform implied substantial tax variation across individuals, largely benefiting top
3The demand index I use is directly elaborated by the commercial real estate company (El Idealista). It is based on the number of e-mails received by listing normalized by a factor, to make it comparable across space and time.

wealth holders. Using a novel personal income and wealth tax panel, I exploit quasi-experimental variation created by the reform to estimate behavioral responses to the Spanish personal income tax in a differences-in-differences setting. I compare the evolution of reported interest income for individuals who experience a tax cut (treatment group) with individuals who experience a slight tax increase (control group) after the reform.4 I find that interest income increased on average 76% more for individuals who experienced a tax cut relative to those who experience a slightly tax increase. The effect is increasing with the size of the tax cut. Counterfactual simulations with the wealth distribution series reveal that the capital income tax reform explains on average 60% of the growth rate in the top 10% wealth share during the recent housing bust. In conjuction, these analyses suggest that portfolio adjustment frictions appear to be the most plausible explanation for the differential saving behavior across wealth groups during housing busts and that behavioral responses to tax incentives can exacerbate this behavior.5
This paper contributes to four main literatures. First, there is a nascent theoretical and empirical literature analyzing the determinants of wealth inequality dynamics (Bach et al. [2018a], Bach et al. [2018b], Fagereng et al. [2019a], Fagereng et al. [2019b], Gomez [2019], Hubmer et al. [2019], Kuhn et al. [2018]). While these studies have mainly focused on the implications of asset prices and rates of return for wealth inequality, my results reveal that behavioral components, and in particular saving responses, are also important factors behind wealth inequality dynamics. To my knowledge, this is the first study documenting how changes in the composition of savings across wealth groups shape the wealth distribution over the business cycle. Moreover, these studies have barely documented or explained why saving rates change in the way they do. This paper moves one step forward and uses quasi-experimental evidence from a large Spanish reform to quantify for the first time by how much capital income tax cuts contribute to changes in saving behavior and wealth concentration.
Second, this work also relates to the literature measuring wealth distributions (Alvaredo et al. [2018a], Garbinti et al. [2018a], Kopczuk and Saez [2004], Kuhn et al. [2018], Roine
4I focus on interest because dividends and capital gains are quite volatile and even more so during the crisis, so that any type of saving response is very hard to identify. 5I also briefly discuss other candidate explanations: differences in risk aversion, financial literacy, financial advisory and expectations on house prices. First, using Spanish wealth surveys I show that the fraction of households reporting not to be willing to take any financial risk is significantly lower for the top 10% wealth group relative to the middle 40% wealth group and even lower relative to the bottom 50% wealth group. Second, using a Spanish survey of financial competences I document that both financial knowledge and independent financial advising are positively correlated with economic outcomes, such as income. Thus, top wealth holders might have reshuffled their portfolio more during the housing bust because they were less risk averse or more financially informed. Nonetheless, differences in risk aversion, financial knowledge and financial advising seem to only explain why bottom and middle wealth holders did not invest as much as top wealth holders in risky financial assets (i.e., stocks), but not why they did not invest as much on safe financial assets (i.e., deposits). Little financial knowledge or advice is needed to invest in safe financial assets, especially deposits. Third, top wealth holders could have also dissaved more in housing if they had more pessimistic expectations about the future evolution of house prices. However, Bover [2015] finds using survey data no significant association of such beliefs with wealth during the recent housing bust.

and Waldenström [2009], Saez and Zucman [2016], Smith et al. [2019]). These studies have documented long-term wealth inequality trends, but abstracting from cyclical effects. This paper is the first to provide comprehensive long-term evidence on how housing booms and busts shape the wealth distribution. Kuhn et al. [2018] have recently shown that housing booms lead to substantial wealth gains for leveraged middle-class households in the US. However, the extent to which this pattern persists or not throughout housing busts has received much less attention so far. In Spain, the wealth distribution has been analyzed in the past using wealth tax records (Alvaredo and Saez [2009]) and wealth survey data (Anghel et al. [2018]), but the coverage in terms of distribution and time span was limited. The new wealth distribution series constructed in this paper covers the full distribution over the period 1984-2015 and provides complete long-run evidence on the evolution of wealth inequality over the last four decades in Spain.
Third, I also contribute to the literature studying how inequality evolves over the business cycle (Barlevy and Tsiddon [2006], Bonhomme and Hospido [2017], Castañeda et al. [1998], Heathcote et al. [2010], Kuznets and Jenks [1953], Storesletten et al. [2004]). These studies find that income inequality is countercyclical—with some exceptions at the top of the income distribution—but they do not analyze the implications of cyclical effects for wealth inequality.6 This paper shows that wealth inequality is also countercyclical in the context of housing booms and busts.
Finally, this study contributes to the literature on housing and portfolio choice (Campbell [2006], Chetty et al. [2017], Cocco [2004], Guiso et al. [2002]). These studies analyze the role played by housing in the portfolio decisions of households, but they abstract from the implications of these decisions for wealth inequality. The results of this paper emphasize the importance of portfolio choice and in particular, differences in portfolio rebalancing across wealth groups, in shaping wealth inequality dynamics.
The layout of the paper is as follows. Section II discusses the concepts, data and methodology used to construct the wealth distribution series. In Section III, I first present the main patterns in real house prices and aggregate wealth and I then analyze wealth inequality dynamics during housing booms and busts. Lastly, I develop a new asset-specific decomposition of wealth accumulation and carry some simulation exercises to understand the key drivers of the dynamics of wealth inequality during housing booms and busts. In Section IV, I propose and explore several candidate explanations for the observed asset-specific saving responses. In Section V, I reconcile and test the methodology used with other sources. Finally, Section VI concludes.
6Fawaz et al. [2012] find that the relationship is procyclical in some developing countries.

II Concepts, Data and Methodology
This section describes the concepts, data and methodology used to construct the Spanish wealth distribution series over the period 1984-2015, which will then be used to study the implications of housing booms and busts for wealth inequality. Further methodological details of the Spanish specific data sources and computations can be found in the appendix at the end of the paper and all detailed calculations in the companion data appendix.
II.I Aggregate Wealth: Concept and Data Sources
The wealth concept used is based upon national accounts and it is restricted to net household wealth, that is, the current market value of all financial and non-financial assets owned by the household sector net of all debts. For net financial wealth, that is, for financial assets net of liabilities, I rely on the latest and previous financial accounts (European System of Accounts (ESA) 2010 and 1995, Bank of Spain) for the period 1996-2015 and 1984-1995, respectively. Financial accounts report wealth quarterly and I use mid-year values.
Households’ financial assets include equities (stocks, investment funds and financial derivatives), debt assets, cash, deposits, life insurance and pensions. Households’ financial liabilities are composed of loans and other debts. It is important to mention that pension wealth excludes Social Security pensions, since they are promises of future government transfers. As stated in Saez and Zucman [2016], including them in wealth would thus call for including the present value of future health care benefits, future government education spending for one’s children, etc., net of future taxes. Hence, it would not be clear where to stop.
The wealth concept used only considers the household sector (code S14, according to the System of National Accounts (SNA)) and excludes non-profit institutions serving households (NPISH, code S15). There are three reasons which explain this decision. First, due to lack of data, non-profit wealth is not easy attributable to individuals. Second, income from NPISH is not reported in personal income tax returns. Third, non-profit financial wealth amounts to approximately 1-3% of household financial wealth between 1995 and 2017 in Spain (Table A1). Hence, it is a negligible part of wealth and excluding it should not alter the results.
Spanish financial accounts report financial wealth for the household and NPISH sector and also for both households and NPISH isolated as separate sectors. However, the level of disaggregation of the balance sheets in the latter case is lower than in the case in which households and NPISH are considered as one single sector. For instance, whereas the balance sheet of the sector of households and NPISH distinguishes among wealth held in investment funds and wealth held in stocks, the balance sheet of the household sector only provides an aggregate value with the sum of wealth held in these two assets. In order to have one value for household wealth held in investment funds and one value for household wealth held in stocks, I assume that they are

proportional to the values of households’ investment funds and stocks in the balance sheet of households and NPISH.
For non-financial wealth, it is not possible to rely on non-financial accounts based on the SNA. Even though there are some countries that have these accounts, such as France and United Kingdom, no institution has constructed these type of statistics for Spain yet. I need to use other statistics instead. My definition of household non-financial wealth consists of housing and unincorporated business assets and I rely on the series elaborated by Artola Blanco et al. [2019]. Housing wealth is derived based on residential units and average surface from census data on the one hand, and average market prices from property appraisals, on the other hand.7 Unincorporated business assets have been constructed using the five waves of the Survey of Household Finances (2002, 2005, 2008, 2011, 2014) elaborated by the Bank of Spain and extrapolated backwards using the series of non-financial assets held by non-financial corporations also constructed by the Bank of Spain.8
I exclude collectibles since they amount to less than 1% of total household wealth and they are not subject to the personal income tax. Furthermore, consumer durables, which amount to approximately 10% of total household wealth, are also excluded, because they are not included in the definition of wealth by the SNA and there are no statistics about consumer durables owned by Spanish households for the period prior to 2002.9
II.II Distribution of Wealth: The Mixed Capitalization-Survey Approach
The wealth distribution series are constructed by allocating the total household wealth as defined in the previous subsection to the various groups of the distribution. I proceed with the following three steps. First, the distribution of taxable capital income is calculated. Second, the taxable capital income is capitalized. Third, I account for wealth that does not generate taxable income. This is a mixed method and not the pure capitalization technique, because income and wealth surveys are used in order to account for both income at the bottom of the distribution and assets that do not generate taxable income.
7Net housing wealth is the result of deducting real estate debt from household real estate wealth. Note that real estate debt is approximated by total household liabilities. This a quite reasonable approximation since as Table A2 in appendix shows, real estate property debt accounts for 80-88% of total household debt over the period 2002-2014 according to the Survey of Household Finances. 8A detailed explanation of the sources and methodology used in order to construct these two series can be found in the appendix of Artola Blanco et al. [2019]. 9The shares of both collectibles and consumer durables over total household wealth are obtained using the Survey of Household Finances developed by the Bank of Spain. See Table A3 in appendix.

II.II.I The Distribution of Taxable Capital Income
The starting point is the taxable capital income reported on personal income tax returns. I use micro-files of personal income tax returns constructed by the Spanish Institute of Fiscal Studies (Instituto de Estudios Fiscales (IEF)) in collaboration with the State Agency of Fiscal Administration (Agencia Estatal de Administración Tributaria (AEAT)). Three different databases are available: two personal income tax panels that range from 1982-1998 and 1999-2014, respectively, and personal income tax samples for 2002-2015. For the benchmark series, I use the first income tax panel for 1984-1998, the second panel for 1999-2001 and all income tax samples for 2002-201510. I also use the full second panel 1999-2014 to carry robustness checks. The micro-files provide information for a large sample of taxpayers11, with detailed income categories and an oversampling of the top. The income categories I use are interest, dividends, effective and imputed housing rents, as well as the profits of sole proprietorships.12 The micro-files are drawn from 15 of the 17 autonomous communities of Spain, in addition to the two autonomous cities, Ceuta and Melilla. Two autonomous regions, Basque Country and Navarre, are excluded, as they do not belong to the Common Fiscal Regime (Régimen Fiscal Común), because they manage their income taxes directly. Combined these two regions represent about 6-7% and 8% of Spain in terms of population and gross domestic product, respectively (Tables A4 and A5).
The unit of analysis used is the adult individual (aged 20 or above), rather than the tax unit. Splitting the data into individual units has on the one hand the advantage of increasing comparability as across units since individuals in a couple with income for example at the 90th percentile is not as well off as an individual with the same level of income. On the other hand, it is also more advantageous for making international comparisons, given that in some countries individual filing is possible (i.e. Spain, Italy) and in others (i.e. France, US) not. Since in personal income tax returns the reporting unit is the tax unit, I need to transform it into an individual unit. A tax unit in Spain is defined as a married couple (with or without dependent children aged less than 18 or aged more than 18 if they are disabled) living together, or a single adult (with or without dependent children aged less than 18 or aged more than 18 if they are disabled). Hence, only the units for which the tax return has been jointly made by a married couple need to be transformed. For each of these units I split the joint tax returns into two separate individual returns and assign half of the jointly reported capital income to each
10Even though the first panel is available since 1982, I decided to start using it from 1984 since I found some inconsistencies between the files for 1982 and 1983 and subsequent years.
11Personal income tax samples are more exhaustive (i.e. 2,700,593 tax units in 2015) than the panels (i.e. 390,613 tax units in 1999). This is the reason why I rely on the tax samples for constructing the benchmark series.
12Note that imputed housing rents exclude primary residence from the period 1999-2015. I explain the way in which I account for primary residence in the following subsection. Moreover, profits of sole proprietorships are considered as a mixed income, so that I assume as it is commonly done in the literature that 70% of profits are labor income and 30% capital income.

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