Property and power: lessons from Piketty and new insights from the HFCS

Rehm, Miriam; Schnetzer, Matthias Article Property and power: lessons from Piketty and new insights from the HFCS European Journal of Economics and Economic Policies: Intervention (EJEEP) Provided in Cooperation with: Edward Elgar Publishing Suggested Citation: Rehm, Miriam; Schnetzer, Matthias (2015) : Property and power: lessons from Piketty and new insights from the HFCS, European Journal of Economics and Economic Policies: Intervention (EJEEP), ISSN 2052-7772, Edward Elgar Publishing, Cheltenham, Vol. 12, Iss. 2, pp. 204-219, https://doi.org/10.4337/ejeep.2015.02.06 This Version is available at: https://hdl.handle.net/10419/277330 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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/ Property and power: lessons from Piketty and new insights from the HFCS Miriam Rehm Federal Chamber of Labour Vienna and University of Vienna, Austria Matthias Schnetzer Federal Chamber of Labour Vienna and Vienna University of Economics, Austria This paper argues that the cumulative causation processes between wealth and power risk leading to an escalation of wealth inequality. Piketty’s historical description of this development from administrative data for individual countries is corroborated with new survey data for the eurozone, the Household Finance and Consumption Survey (HFCS). Wealth is extremely unequally distributed in the eurozone –much more so than income. Furthermore, we provide a multi-faceted picture of wealth distribution in Europe using the socio-economic characteristics available in the HFCS, and we show that inheritances are the single most important factor for wealth inequality. The structural power to shape economic and political institutions is thus ever more concentrated. Finally, we discuss three channels through which the unequal distribution of private assets may affect power relations and economic activity. Keywords: wealth inequality, power, Piketty, Household Finance and Consumption Survey JEL codes: D31, E12, P16 1 INTRODUCTION Thomas Piketty’s work has placed the debate on distribution center-stage both in mainstream neoclassical economics and in the public. Previously, the focus on distribution in the economics profession was largely a unique selling point of some heterodox schools of thinking, most notably (post-)Keynesianism and Marxism. Piketty has blazed a trail for research on distribution by showing that key premises of neoclassical economic theory regarding distribution, such as the lifecycle income hypothesis or marginal productivitybased remuneration, do not hold up to empirical scrutiny. 1 Piketty has also revived economic research by refocusing distribution analysis from income to wealth. Important progress in stock-flow consistent modelling within the post-Keynesian school notwithstanding, a focus on flows rather than stocks remains prevalent both in mainstream and in heterodox theoretical approaches. It is likely that the lack of reliable empirical data has played a key role in directing research towards income, and away from wealth. A new data set published by the European Central Bank now provides a basis for the analysis of wealth in 17 European countries for the first time. This has widened the scope for research on wealth in Europe significantly, and consequently set off a first wave of scientific publications on the topic. In particular, the detailed socio-economic information 1. Piketty’s theoretical framework remains firmly rooted in neoclassical economics, and is thus open to criticism from heterodox economics (see for example Taylor 2014). European Journal of Economics and Economic Policies: Intervention, Vol. 12 No. 2, 2015, pp. 204–219 © 2015 The Author Journal compilation © 2015 Edward Elgar Publishing Ltd The Lypiatts, 15 Lansdown Road, Cheltenham, Glos GL50 2JA, UK and The William Pratt House, 9 Dewey Court, Northampton MA 01060-3815, USA available makes it possible to paint a richer picture of the wealth distribution than Piketty’s data allow him to do. While time series, of course, are not available for Europe, investigating differences will be possible with the release of the second wave in 2016. The Household Finance and Consumption Survey (HFCS) thus allows for an optimistic perspective for future inequality research in Europe. From a progressive point of view, it is imperative that research on inequality is not confined to income differentials based on functional or personal income distribution. The question of power, largely neglected in neoclassical mainstream economics, cannot be broached satisfactorily in economics without an understanding of the distribution of the command over resources; that is, in a first and simplifying approximation, wealth. The ownership of wealth affects power relations and economic activity by conferring disproportionate influence on the democratic process to a very small group of actors. The political sphere, in turn, has an impact on the design of the market framework within which economic actions occur, leading to a process of cumulative causation. Piketty shares this point of view; his scientific analysis is borne by his concern for democracy. Finally, a caveat is in order: this paper is limited to inequality within individual countries and within the group of European countries as a whole. It is beyond its purview to discuss wealth inequality and power relations between countries within Europe or, especially, worldwide. The structure of the paper is as follows. In Section 2, we discuss the differences between Piketty’s data analysis and the HFCS 2010. We then present the first empirical results available for the distribution of wealth in Europe (Section 3), including differences in asset holding between wealth groups, socio-economic characteristics and the gender wealth gap (Section 4), and inheritances (Section 5). Section 6 discusses the implications of this wealth distribution for democratic processes. It briefly highlights the limitations of mainstream economic theory before turning to a discussion of the connection between wealth inequality and democratic power, the concentration of corporate power, and the question of private versus public wealth. 2 CAPITAL OR WEALTH? PIKETTY AND THE HFCS DATA The natural starting point for a comparison of Piketty’s work and the HFCS are the bases of their respective data. Piketty’s long-term analysis of wealth relies on a plethora of data sources, owing to the breadth of his investigation. In the first part of the book, he focuses mostly on aggregate data from the System of National Accounts (SNA) to trace the development of the ratio between the capital stock and national income net of depreciation (Piketty 2014: 17). For the calculation of differential returns on financial investments resulting from the size of the endowment, Piketty draws on information from trusts of large universities in the United States (ibid.: 447f). Regarding the distribution of wealth, he makes use of wealth tax data, inheritance registers, and probates (ibid.: 18f). Due to the local specificity of these data sources, it is only possible for Piketty to draw conclusions regarding some countries, including France, Great Britain, and the United States. For these countries, Piketty calculates the shares of the top 10 and top 1 per cent in total wealth over 2 centuries. The HFCS data complement Piketty’s analysis by providing a glimpse of the wealth distribution in 17 euro area countries. 2 The HFCS survey was modeled on the US 2. The countries included are Austria, Belgium, Cyprus, Finland, Germany, Greece, France, Italy, Luxembourg, Malta, the Netherlands, Portugal, Slovakia, Slovenia, and Spain. For simplicity, these are referred to in this article as ‘the eurozone’despite missing Estonia and Ireland. Property and power: lessons from Piketty and new insights from the HFCS 205 © 2015 The Author Journal compilation © 2015 Edward Elgar Publishing Ltd Survey of Consumer Finances (SCF). As wealth –especially at the upper tail of the distribution –is notoriously difficult to capture adequately in surveys, the ECB went to great lengths to ensure good data quality. This included largely harmonizing the questionnaire for all countries, conducting personal interviews (rather than telephone or online interviews), collecting large amounts of metadata, carefully training interviewers and monitoring computer-assisted interviews in real time, and documenting data processing in detail (ECB 2013a). Missing values are multiply imputed using meta-data to take into consideration the statistical uncertainty associated with imputation procedures. All estimations in this paper thus apply Rubin’s rule (Little/Rubin 2002). As a consequence, and despite the indisputable drawbacks of survey data on wealth, the HFCS provides the fullest picture of wealth distribution in the eurozone available to date. The HFCS explicitly surveys private wealth of households, which contrasts with Piketty’s claim to investigate capital; a term used by Piketty interchangeably with wealth. This terminology has rightly irritated (post-)Keynesian economists who understood the capital controversy as proof of the circular definition of capital (Galbraith 2014), as well as Marxist economists who see capital as a means of production since Smith and Ricardo, and as a relation of power since Marx (Duménil/Lévy 2014; Harvey 2014). 3 A term less prone to misunderstandingsisthusprobably‘wealth’rather than ‘capital.’ The HFCS adopts –like Piketty –a quite narrow, accounting-based definition of wealth. In this sense, wealth consists of economic goods which can generate returns. It must be possible to assign them a monetary value, to use them as collateral, to transfer them, to liquidate them, and, in the case of the HFCS, to attribute them to natural persons. Both Piketty and the HFCS therefore exclude wealth categories such as social wealth (including pay-as-you-go pension systems, unemployment insurance, or public health insurance), environmental wealth (for example, clean air and water, or the lack of noise pollution), and human capital (the innate human capability of producing returns). In contrast to income, these definitions of wealth are not universally accepted. 4 In particular, the potential future claims on public pension systems are typically found to significantly reduce inequality in wealth. Piketty’s data and the HFCS’s differ regarding the sectors whose wealth is accounted for. On the one hand, the HFCS is limited to private households; it thus indirectly includes the corporate sector which is typically owned by households. Wealth that is ultimately owned by legal persons such as certain trusts or associations is, however, excluded. Piketty, on the other hand, uses all sectors of the SNA, at least in parts of his analysis. That is, he includes non-profit institutions serving households and the public sector when looking at the capital stock as a multiple of national income (Piketty 2014: 113f). The HFCS data are categorized into household balance sheets following an accountingbased wealth definition. Assets include tangible assets, which comprise for instance the main residence, other real estate property, vehicles, and shares in self-employment businesses, as well as financial assets, which contain deposits, shares, bonds, and mutual funds as well as money that is owed by others to the household. Liabilities consist of collateralized and non-collateralized debt. 3. The (sparse) theoretical sequences of Piketty’s book are squarely rooted in standard neoclassical theory –yet almost invariably followed by an empirical refutation of the resulting conclusions. 4. While standards for the statistical gathering of income data have been harmonized by the UN at least since the 1970s, the OECD presented an internationally agreed set of guidelines for micro statistics on household wealth for the first time in 2013 (OECD 2013). 206 European Journal of Economics and Economic Policies: Intervention, Vol. 12 No. 2 © 2015 The Author Journal compilation © 2015 Edward Elgar Publishing Ltd 3 WEALTH CONCENTRATION IN EUROPE Owing to the ex-ante harmonization of the HFCS survey, it is possible to compare the available data across countries. However, looking at absolute wealth levels is not particularly meaningful. This is especially the case for median wealth, but also for the mean. The reason lies in the institutional differences of national welfare systems, which affect the necessity of private households to accumulate wealth (Fessler et al. 2012). Countries with a well-functioning social housing policy or pay-as-you-go public pension scheme thus tend to have lower private wealth. A more promising approach is thus the cross-country comparison of distributional aspects, for which the data are very well suited. Figure 1 lists the countries covered by the HFCS according to their Gini coefficient of net wealth. It shows that Austria, Germany, Cyprus, and France have the highest inequality. At Gini values of up to 0.77, inequality in wealth is thus much higher than income inequality for all countries as well as for the eurozone as a whole. The wealthiest 10 percent of households in the eurozone own more than 50 percent of total net worth in the HFCS data. In contrast, regarding income flows, the 10 percent of households with the highest income receive roughly 31 percent of total income (ECB 2013b: 96). Sierminska/Medgyesi (2013) calculate the contribution of the individual asset classes to this total wealth inequality in the countries of the HFCS. The picture that emerges is far from homogenous. While real estate property is the main contributor to wealth inequality in countries like Luxembourg, Greece, and Slovakia, inequality is mostly due to financial assets in Belgium. The unequal distribution of business ownership drives wealth inequality in Germany, Austria, France, and Portugal. This finding on the one hand might not be surprising given the fungibility of capital. On the other hand, it underscores the importance of investigating country-specific aspects in research of wealth inequality –atask which is left to future research here. The high concentration of wealth found in the HFCS is nonetheless likely to be an underestimation of the actual wealth inequality. Both the willingness to participate in voluntary wealth surveys such as the HFCS at all (response rate), and to answer individual questions on wealth levels (item non-response) declines at the two tails of the wealth 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 Slovkia Slovenia Greece Spain Malta Belgium Italy Netherlands Luxembourg Finland Portugal France Cyprus Germany Austria Gini index Source: Sierminska/Medgyesi (2013), HFCS (ECB 2010). Figure 1 Gini index of wealth by country Property and power: lessons from Piketty and new insights from the HFCS 207 © 2015 The Author Journal compilation © 2015 Edward Elgar Publishing Ltd distribution. However, as the amount of wealth owned collectively at the lower end of the wealth distribution is negligibly small, the main cause of the distortion lies in the very high wealth levels at the top end of the distribution. 5 This discrepancy between survey information and actual wealth is made palpable by a comparison between the HFCS data and the Forbes list of the richest people in each country (Vermeulen 2014). 6 In Germany, the household with the highest wealth in the HFCS survey owns €76 million, while the household with the lowest wealth out of the 52 Germans covered by the Forbes list has €818 million. In Austria, the gap is even more dramatic. It spans from €22 million 7 (HFCS) to €1560 million (Forbes). For this reason, Piketty prefers administrative data on wealth, usually gathered through estate or wealth taxation. These capture the entire distribution of wealth much better, despite problems relating to tax avoidance and evasion, as well as exceptions in the legal definition of the tax base, which often differs from an economic or accountingbased definition of wealth. Piketty’s administrative sources also suggest the underreporting of large fortunes in the HFCS. According to Piketty’s data, the top 1 percent in France owns roughly 24 percent of total wealth (Piketty 2014: 340), whereas the HFCS data yield 18 percent for that value. It is thus very likely that the inequality registered in the HFCS underestimates the actual level of wealth inequality. This should be borne in mind in the interpretation of results found from survey information on wealth, and the HFCS data in particular. This distortion can be corrected for by exploiting the statistical regularity that top wealth levels are distributed according to a Pareto distribution. Vermeulen (2014) exploits this to estimate ranges for the inequality of wealth in selected eurozone countries. Whereas some countries’raw data lie within the band presented by Vermeulen, others –such as Germany, Italy, Belgium, and Austria –seem to underestimate actual inequality to varying degrees. In Germany, for instance, the share of the top 1 percent owns 24 percent of total net household wealth according to the HFCS data. Correcting for underreporting, this share is between 26 and 33 percent. However, even these estimates may not fully capture the extent to which wealth inequality is understated by the HFCS data: even at the upper limit of the range given by Vermeulen’s estimates, the share of France’s top 1 percent does not reach the 24 percent reported in Piketty’s administrative data. Survey data such as the HFCS’s thus clearly underestimate actual wealth inequality and total wealth levels due to underreporting of the top tail of the distribution. This can be partially corrected for, which exacerbates the high inequality observed in wealth as compared to income. On the whole, the findings from the first wave of the HFCS on wealth inequality clearly support Piketty’s main conclusion for Germany, France, and the UK: wealth is extremely strongly concentrated in European high-income countries. The HFCS data allow this finding to be extended to 15 further countries of the eurozone. 5. Some countries that participated in the HFCS attempted to correct for this underestimation through oversampling; however, this was not possible in all cases. The results suggest that countries that oversample achieve a partial correction of non-response bias and the low probability of very wealthy households to be drawn into the sample. 6. It should be noted here that the quality of rich lists does not conform to the most basic academic data standards. Nevertheless, it is the only information available at this point. 7. This number quoted by Vermeulen (2014) refers to a single imputation; taking the multiply imputed nature of the data into account, the value for the wealthiest household would be around €14 million. 208 European Journal of Economics and Economic Policies: Intervention, Vol. 12 No. 2 © 2015 The Author Journal compilation © 2015 Edward Elgar Publishing Ltd 4 SOCIO-ECONOMIC CHARACTERISTICS OF HOUSEHOLDS IN THE WEALTH DISTRIBUTION The drawbacks of survey data, most notably their underestimation of wealth inequality, are counterbalanced by their advantage of providing an abundance of additional socioeconomic characteristics. These allow the drawing of a more multi-faceted picture of the households owning different levels of wealth than is possible using administrative data. This section offers a first glimpse by showing the participation of the different wealth groups in various asset classes in the eurozone. This is followed by coloring in some details regarding the effects of education and employment status, and a quick brushstroke across the composition of millionaire households. For the latter aspects, results are only available for Austria at this stage. A first rough sketch of the households making up different wealth groups can be drawn from an overview of their participation in various asset classes. The groups in Table 1 follow Piketty’s categorization into the bottom 50 percent, the ‘affluent’of the next 45 percent, and the ‘rich,’the top 5 percent (Piketty 2014: 246f ). In Europe, the bottom 50 percent owns mainly motor vehicles in real assets, while ownership of the main residence gains importance for the ‘affluent.’Other real estate property and shares in self-employment businesses, however, are pivotal only in the top 5 percent group of the ‘rich.’ Regarding financial assets, virtually all groups own deposits in sight and savings accounts. Publicly traded shares and mutual funds play a more important role for the ‘affluent,’and bonds and other financial assets are more widespread among the ‘rich.’ Collateralized debt, on the other hand, is most prevalent among the affluent if secured by the main residence, and among the rich if secured by other properties. Non-collateralized Table 1 Participation in asset classes by wealth group in the eurozone a <50 51–95 >95 Vehicles 61.7 84.7 91.3 Main residence 28.0 92.1 94.1 Other valuables b 36.9 50.7 62.4 Properties (other than main residence) 8.0 35.4 78.4 Self-employment business 6.5 14.3 49.7 Sight accounts 92.4 96.7 99.1 Savings accounts 56.6 63.4 67.2 Money owed to the household 8.5 6.4 10.2 Bonds 1.5 7.9 19.6 Publicly traded shares 3.9 14.1 35.0 Mutual funds 6.3 15.5 31.9 Other financial assets c 1.6 2.4 7.6 Mortgage (main residence) 13.1 26.1 20.0 Mortgage (other properties) 2.2 7.5 18.9 Other loans 13.5 6.3 4.7 Outstanding balance on credit cards 3.1 3.4 3.0 Non-collateralized loans 26.1 18.8 16.4 Notes: a. Excluding Estonia and Ireland. b. Other valuables comprise jewelry or paintings. c. Other financial assets are, for instance, options, futures, precious metals, index certificates, etc. Source: HFCS (ECB 2010), own calculations. Property and power: lessons from Piketty and new insights from the HFCS 209 © 2015 The Author Journal compilation © 2015 Edward Elgar Publishing Ltd liabilities, on the other hand, are most common among the bottom 50 percent; their prevalence tends to decrease among higher wealth groups. The form that debt takes is thus linked to the ownership of assets. While Piketty emphasizes the importance of, for instance, education in the analysis of distributions, his data permit a more detailed investigation only in the chapter on income (Piketty 2014: 304f). The HFCS provides rich information on socio-economic parameters; however, it documents wealth at the household level and socio-economic characteristics at the person level. Here, the so-called financially knowledgeable person, who has the best insight into the financial situation of the household and serves as a respondent in the HFCS interview, is selected as a reference person (Humer et al. 2014). With regard to education, net wealth increases with each of the four levels of education captured in the Austrian HFCS data. It rises from an average of about €60000 if the highest level achieved is primary education, to €380000 at tertiary level (a university degree). Another socio-economic aspect clearly linked to wealth is employment status; the self-employed own about 5 times as much wealth as employees. While employee households have a net wealth of about €180000 on average, self-employed households have €930000. The difference is mostly due to the ownership of self-employment businesses, but also real-estate property other than the main residence. This is also reflected in the socio-economic composition of the millionaire households in Austria. The self-employed are significantly overrepresented among the households with a net wealth over €1 million. Their share is about 5 times higher than their share in the total population (Humer et al. 2014). The results presented in this section so far illustrate that the HFCS data can be used to put a face on the wealth concentration described by Piketty. It is of course not possible to deduct conclusions regarding other countries of the eurozone from the findings for Austria; in particular cross-country comparisons are left to future research. The person-level data contained in the HFCS allow an investigation of the gender wealth gap in the eurozone, in analogy to the gender wage gap. However, this is again hampered by the fact that wealth is available only on the household level. Schneebaum et al. (2014) thus conducted the analysis for male and female (single) households in the eurozone. These households comprise both one-person households and households whose reference person is living without a partner but with children or grandchildren in the household. The data showa substantial gender wealth gap, which is attributedmostly to a ‘wealth glass ceiling’in the descriptive analysis. Male households have a higher net wealth than female households in the top 30 percent, but especially in the top 10 percent (see Figure 2). The multivariate analysis encompasses a number of possible determinants of differences in net wealth. These include personal information such as age, education, relationship status, age of children, and information on inheritances, as well as labor-market related data including income, employment status, the level of work autonomy, and work history. These labor-market related characteristics, especially income, self-employment, and the lifetime spent in employment, are strongly correlated and in fact largely explain the wealth difference between men and women. That is, in the eurozone countries of the HFCS, there is a gender wealth gap after quantifiable explanatory effects have been taken into account when standard econometric methods are applied. Labor-market related factors, however, are a very large explanatory factor for the wealth difference between male and female households. On the whole, the HFCS data thus paint a more colorful picture of the wealth distribution. Broadly speaking, the bottom half of the wealth distribution has mainly cars, deposit accounts, and is often indebted, whereas the affluent typically also own an –often unmortgaged –main residence and bonds. The top 5 percent possess other real-estate property, 210 European Journal of Economics and Economic Policies: Intervention, Vol. 12 No. 2 © 2015 The Author Journal compilation © 2015 Edward Elgar Publishing Ltd more risky financial assets, and business ownership on top of that. Wealth rises with education, and the self-employed own substantially higher wealth. Finally, male households own substantially more wealth than female ones. The next section looks at explanatory factors for the observed inequality in wealth. 5 INHERITANCES One of Piketty’s main concerns is the role of inheritances in the rising concentration of wealth. He argues that, at the beginning of the twenty-first century, large fortunes are much more likely to arise from inheritance than from work. This is comparable to the situation in the nineteenth century, when substantial inheritances paved the way to a much higher living standard than high labor income. Piketty’s conclusions regarding inheritances are based on data gathered painstakingly from French administrative archives on estate taxes and probate records established in the wake of the French revolution. Unfortunately, this also means that the results are not easily replicable for other European countries. Here, again, the HFCS can fill in the picture, albeit at the cost of underestimating inequality as survey information on past events such as inheritances is particularly prone to underreporting. The HFCS can be used to estimate the effect of different explanatory factors on wealth inequality, as a first approach to disentangling the two possible sources of this inequality: savings out of income and transfers (that is, inheritances and bequests). Leitner (2015) applies the Shapley value approach to decomposition for selected HFCS countries. Inheritances make up roughly a third of the predicted inequality of gross wealth, which is by far the largest single factor, on average for all the countries covered (see Figure 3; results for net wealth and real assets are qualitatively similar). In contrast, the contribution from differences in gross household income amounts to about 10 percent. 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