Dynastic cores and the borrowed time of newcomers. Wealth accumulation and the Norwegian one percent.

This paper explores the trajectories of Norwegians who, in their late-thirties, possessed financial assets, such as securities, company shares and stocks, qualifying them as the wealthiest one percent nationally. We describe the accumulation of financial wealth over a 25-year period in adulthood and study how different wealth sequences are linked to family origins and kinship ties. Although some Norwegians manage to build up large fortunes from relatively modest starting points over their life courses, we find that the value of the assets possessed by self-made individuals, and their ability to retain wealth over time, differ significantly to those based on dynastic lineage. Among the latter group, profound wealth early in adulthood and strategic positions in the economy add to propel exponential ownership of financial wealth from a young age and throughout adulthood. This chimes with C. Wright Mills' suggestion that the amassing of great fortunes is driven by two mechanisms of the big jump that enables initial asset build-up, and the accumulation of advantages that flows from advantageous economic and social ties. Kinship seems of key importance to ensure the efficacy of both mechanisms. Differences in the relationship of wealth accumulation and class origin seem to have little to do with educational strategies. We draw attention to direct wealth transfers and the institution of marriage as two little explored dimensions involved in dynastic closure.

INTRODUCTION

Recent concerns about increasing economic inequalities are often situated in terms of notions of the wealthiest ‘one percent.’ In studies of wealth concentration, the rich usually appear as a somewhat abstract category, with unclear demographic foundations and contradictory traits, such as being increasingly self‐made (Kaplan & Rauh, 2013), while relying increasingly on inheritance (Piketty, 2014). Thus, the lack of sociological knowledge about the wealthy—a concern repeatedly voiced by sociologists from the early (e.g., Sorokin, 1925) to the late phases of the 20th century (e.g., Giddens, 1976; Rubinstein, 1977; Scott, 1982)—still holds true. By addressing questions about the wealth trajectories, class origins and closure strategies of possessors of top‐level wealth, this research contributes by fleshing out the social characteristics of this abstract category and offering new insights into how privileges become accelerated over the life course.

In Scandinavian countries, there is a growing body of literature highlighting the centrality of inheritance in amassing great fortunes (Hansen & Wiborg, 2019) and the profound concentration of top wealth in those countries (Hansen, 2014; Pfeffer & Waitkus, 2021). Thus, notions of dynastic tendencies in Scandinavian countries have recently emerged (Björklund et al., 2012; Hansen, 2014; Sjögren, 2018). This gives rise to a fascinating paradox, given that these societies often receive attention for their alleged egalitarianism. Whereas housing wealth is important in stratifying wealth inequalities among the Norwegian population at large, a growing concentration of top wealth in recent years is driven by the accumulation of financial assets, such as stocks and securities (Aaberge & Stubhaug, 2018). This suggests that concepts such as ‘the one percent’ point to groups that are not merely distinct in society due to their ownership of vast wealth, but also due to positions of power that may enable them to control the means of production and the means of finance.

Curiously, the increasingly and enduringly powerful propertied classes are not at the forefront of current leading approaches to empirical class analysis (although some exceptions exist, see e.g., Flemmen, 2012; Savage et al., 2015; Wodtke, 2016). This is unfortunate, not only because capitalist property is immanently linked to capital accumulation in classical class theory (Savage, 2015), but theoretical concerns with strategies of reproduction and social closure offer important perspectives on the biographical experiences of the wealthiest (Bourdieu, 1996; Savage et al., 2005). In wealth scholarship, different types of assets are often treated uniformly (e.g., Adkins et al., 2020; Piketty, 2014). Conflating different sources of wealth, such as housing wealth and ownership of productive and financial assets, downplays the distinct social relationships that arise through the organization of private property in capitalist society (Wright, 2005, pp. 137–138).

Instead of studying great wealth by summarizing different types of assets, we focus on financial assets—such as company shares, stocks, and securities—which we believe are closer to the conventional interest in capitalist property ownership in advanced societies. We add to this literature by studying six Norwegian birth cohorts who, in their late‐thirties (in 2004), possessed financial assets qualifying them as the wealthiest one percent nationally. We ask whether these individuals enduringly or exponentially own top financial wealth over the life course and in what ways are different wealth trajectories shaped by class backgrounds and kinship ties? Our aim is to cross‐fertilize insights from both class analysis and scholarship on wealth inequalities and contribute to these research fields in two interrelated ways.

First, we offer a comprehensive map of how top financial wealth is accumulated over a period that covers most of adulthood. By using social sequence analysis, and studying a 25‐year panel, we construct a typology of the various pathways to the accumulation of financial wealth among individuals from their mid‐ to late‐twenties into their fifties. Our findings demonstrate how wealth tends to ‘monopolize new opportunities for getting “great wealth”’ (Mills, 2000[1956], p. 105). While some individuals exponentially accumulate wealth throughout adulthood, others seem to be living on borrowed time.

Second, we follow Piketty's (2014) central thesis of the return of patrimonial capitalism and explore class origin. Here, we use a detailed class scheme to separate a small, dominant category of managing directors, business leaders and top‐level business professionals—what we dub an economic upper class – from a category endowed with more cultural than economic capital, such as elite professionals, higher‐level civil servants, professors and architects. We link our typology of wealth sequences to class origin and find that the likelihood of owning top one percent financial wealth throughout adulthood is disproportionate to having parents with significant control and ownership of capital. Our findings offer little support to the notion that formal skills and educational credentials are the most important factors in mediating class privilege, even when measuring credentials in a rather granular way.

In order to dig more deeply into the kinship structures of these wealthy individuals, we turn to multiple correspondence analysis to study family resources as interrelated to, rather than as independent from, each other. Significantly, we include two underexplored dimensions of dynastic closure—extended family ties through parents‐in‐law and the role of homogamy, and the direct transfer of economic capital through inter‐vivos gifts and bequests. Our study reveals significant variation in the biographical experiences of the wealthy in Norway, most clearly demonstrated by a contrast between newcomers from modest family backgrounds and dynastic cores from backgrounds of profound privilege. These different family contexts are significantly associated with exponential growth of financial assets in adulthood. In closing, we draw attention to the wider implications these biographies of affluence may have for the social, political, and cultural dimensions to social inequality in general and in the Nordic welfare regime in particular.

WEALTH ACCUMULATION AND DYNASTIC WEALTH

There are intergenerational aspects to Piketty's (2014) interdisciplinary intervention. Capital in the 21st century, he writes, is driven by the return of ‘patrimonial capitalism’ where inherited, rather than self‐made, wealth is the key to gaining top wealth. But how important is inherited capital among today's capitalists? Scholars are divided on the matter. On the one hand, some studies conclude that self‐made wealth has become more common and that the proportion of inheritors among the super‐rich has declined over time (Edlund & Kopczuk, 2009; Khan, 2012). According to Kaplan and Rauh (2013), rather than being wealthy heirs, today's super‐rich have accessed higher education and used their skills in profitable and booming industries. Indeed, education, net of income differences, has been shown to propel the accumulation of wealth in terms of both pace and size (Killewald et al., 2017, see also Keister, 2005).

On the other hand, Keister and Lee (2014) have shown that top income and wealth distributions only partly overlap and that the importance of inherited wealth is greatest among those at the top of both distributions. A number of Scandinavian studies has also demonstrated the strong reproduction of top wealth between parents and children (Boserup et al., 2018; Gustavsson & Melldahl, 2018; Hansen, 2014; Melldahl, 2018) and the influx of the upwardly mobile into top wealth categories has declined in recent decades (Hansen, 2014). A recent study even suggests that family origins not only help attain the largest fortunes but also affect the likelihood of successfully retaining such wealth over time. By focusing on the super‐rich Forbes lists in the USA, Korom et al. (2017) have found that inheritors are more likely to be relisted over time compared to the self‐made super‐rich.

According to C. Wright Mills, two crucial mechanisms enable the ‘appropriation of big money’ and the amassing of great fortunes over an individual's economic career. First, he points to the centrality of a big jump, which entails coming ‘into command of a strategic position which allows him [they] the chance to appropriate big money.’ This is usually dependent on having secured a big enough sum of money for top‐wealth accumulation. Once the big jump has been achieved, the amassing of further wealth is secured through a second mechanism, the accumulation of advantages. The more that is achieved in the ‘big jump’ the easier it is to accumulate wealth: ‘the more he [they] has, and the more strategic his [their] economic position, the greater and the surer are his [their] chances to gain more’ (Mills, 2000[1956], pp. 110–111). The accumulation of advantages is contingent upon both ‘psychological readiness’ and ‘objective opportunities’, and is facilitated by advantages associated with positions in the economy, social networks and membership to ‘important cliques’, ‘inside information’ and so on.

Of course, Mills emphasized that both mechanisms are often ensured through the privileges that flow from coming from a wealthy family. Inheritors may take advantage of big jumps made by their predecessors. Illustrative of this phenomenon in present‐day Norway is the fact that three out of the 10 richest (dollar) billionaires under the age of 30 in the world are Norwegian heirs. Research has shown that wealthy Norwegian parents transfer wealth to their children at a young age, helping propel wealth accumulation in adulthood (Hansen & Wiborg, 2019). Here, we explore the role of bequests and inter‐vivos transfers among the super‐rich. To our knowledge, this has not been previously studied.

Yet, the impact of class background on wealth accumulation may also be mediated by educational credentials. Selective education may ease access to the highest‐paying jobs, with lucrative opportunities for savings and easy access to advantageous credit (Dwyer, 2018; Fourcade & Healy, 2013); or selective educational backgrounds may forge network ties with educational alumni that prove beneficial to subsequent business and investment opportunities (Pinçon & Pinçon‐Charlot, 1999, p. 222). Indeed, educational qualifications account for approximately one‐quarter of the intergenerational reproduction of wealth (Pfeffer & Killewald, 2015). On the other hand, evidence from Sweden suggests that reproduction via the education system is less persistent among the wealthiest family dynasties, probably reflecting the vast ‘freedom from economic necessity’ that flows from large fortunes (Melldahl, 2018, p. 444).

In addition, class origins may channel a host of social and cultural mechanisms beneficial to the accumulation of wealth and the likelihood of experiencing the accumulation of advantage (Pinçon & Pinçon‐Charlot, 1999). Children who grow up in wealthy families with business backgrounds may develop field‐specific dispositions, or a ‘psychological readiness’, to use Mills' term, that help shape their inclinations to engage successfully in the accumulation of profit (Hartmann, 2000; Kuusela, 2018; Neely, 2018). They may benefit by developing ‘financial literacy’ (Lusardi & Mitchell, 2014), and gaining access to professional consulting and management services (Glucksberg & Burrows, 2016; Harrington, 2016; Herlin‐Giret, 2021), which help them to secure profitable returns on assets. Moreover, kinship ties may provide ample opportunities through social networks and career opportunities in general, or through family‐owned businesses in particular (Allen, 1987; Carney & Nason, 2018; Korom et al., 2017; Mills, 2000[1956]). Family‐owned businesses are a large, important part of the Norwegian economy and family representatives are often involved in managing company boards (Berzins et al., 2018). Research also suggests that originating from a wealthy family provides a safety net, mitigating the potentially adverse repercussions of making risky investments while bolstering the pursuit of elite careers (Pfeffer & Hällsten, 2012; Toft & Friedman, 2021).

Familial ties beyond parents may also help propel wealth accumulation. Not only can spouses and in‐laws benefit those who marry into wealthy families, but wealthy families may join forces through marriage. Marriage strategies may, as such, constitute strategies of reproduction (Bourdieu, 1976, 2014) and serve as distinct sources of elite closure (Kocka, 1984; Mills, 2000[1956]). Recent Scandinavian evidence suggests that the wealthiest families are increasingly marrying homogamously (Wagner et al., 2020) and that the likelihood of upper‐class homogamy is more persistent for the children of the upper class than for upwardly mobile ‘newcomers’ (Toft & Jarness, 2021). Attending to these dimensions, our study comprehensively maps the role of parents‐in‐law and the institution of marriage in stratifying the life courses and biographical experiences of those who amass great fortunes in Norway.

THE ECONOMIC UPPER CLASS IN SOCIAL DEMOCRATIC COUNTRIES

In Norway, the concentrated affluence and intergenerational reproduction of wealth at the top end of the distribution is found to be similar to that of most European countries (Hansen, 2014; see also, Pfeffer & Waitkus, 2021). Generally, financial wealth is unequally distributed to a greater extent than composite wealth that includes fixed assets, such as home ownership (Hansen, 2012). As in the USA, Norway has witnessed a u‐shaped pattern over time, with a recent intensification of high‐end inequality (e.g. Alstadsæter et al., 2018, Table A10). This seems to suggest the position of a thriving class of capitalist owners has been strengthened in recent years.

This may seem surprising, in light of widespread notions of Scandinavian egalitarianism. The Nordic model entails comparatively generous public services, such as education and health services, active labor market policies, insurance schemes in case of sickness or unemployment, and centralized wage bargaining. The Nordic countries have generally been found to have high‐fluidity with regard to class mobility and wage elasticity (Corak, 2013, but see Bukodi et al., 2020). Although the Nordic model strongly regulates employees' working lives, it does not strongly regulate capital.

A number of scholars have linked this u‐shaped pattern of high‐end inequality to policy changes starting in the 1980s, including the deregulation of credit markets, the easing of restrictions on financial markets and a reduction in capital gains tax. Most recently, inheritance tax has been abolished and wealth tax reduced (Aaberge et al., 2018). Moreover, Norway has experienced rapid economic growth since the early 1990s, with a booming oil industry and surging property values, in an environment that has provided ample opportunities for wealth accumulation. Since the 1990s, wealth inequalities have also been increasingly stratified by class origin, suggesting that wealth‐based opportunity hoarding is on the rise in Norway (Hansen & Toft, 2021).

In sum, even though Norwegian society has comparatively high fluidity rates, compressed wage distribution and safety nets provided by a comprehensive welfare state, these coexist with concentrated affluence at the top of society and, possibly, the development of wealthy dynasties enduring over generations (Björklund et al., 2012; Hansen, 2014).

DATA AND METHODS

Data

The panel structure of the registry data allows us to study wealth accumulation at an individual level over a 25‐year period, covering important periods in the working lives of 1963–1968 birth cohorts. We study individuals in these birth cohorts who owned financial assets in 2004 that placed them in the national one percent, i.e., at ages 36–41. This population is chosen because we can study individual‐level wealth sequences during the majority of adulthood and pay attention to the period before and the period after the one‐percent threshold. It should be noted that those who own wealth at this level in their late thirties are not representative of the national one percent, who tend to be older. Among those who were older in 2004, however, our ability to study inter‐vivos transfers from early ages is limited. Moreover, we find that the class origins of these younger age groups among the one percenters are not particularly distinct in comparison to those of older ages. We chose 2004 because it permits the study of wealth accumulation during important moments of adulthood (from ages 25–49 for the 1968 birth cohort and 30–54 for the 1963 birth cohort). This particular year does not seem to offer any specific challenges with respect to period effects. As seen in Figure 1, the share of financial wealth owned by the wealthiest one percent grew steadily in that period and 2004 does not seem to have been a deviant year. Notably, we see that, by 2017, the wealthiest one percent owned half of all financial wealth among the working‐age population.

FIGURE 1

Percent of total financial wealth in the working‐age population (ages 25–60) that is owned by the wealthiest one percent

Variables

We study the accumulation of financial capital, including liquid assets of various types, such as shares in stock funds, bonds and money market funds, bank deposits, listed shares, stocks, and holdings from other securities. There are significant benefits to the use of these data. They are not self‐reported, nor are they restricted to the population whose wealth exceeds a certain tax limit, which is often a limitation to the use of wealth data from other sources. However, an important limitation is that wealth hidden to evade tax could not be recorded. As tax evasion is more common among the super‐rich (Alstadsæter et al., 2018), this may lead to an underestimation of class inequalities in wealth attainment. In addition, real estate, land and ownership in unincorporated businesses were included as real capital in the registers.

For each year, we divide the distribution of financial wealth among the working‐age population (ages 25–60) into categories by inserting cut points at percentiles 50, 75, 90, 95, 99, and 99.9. These categories serve as the different states in the sequence analysis that we employ to analyze trajectories of wealth accumulation over a 25‐year period.

The majority of those who own financial assets at the national one percent level qualify as capitalists in most class schema. Yet, in order to understand the class situations within this group, we construct a detailed categorization to differentiate between those who were self‐employed/proprietors/rentiers (SPRs), executives, business professionals, and non‐business employees in 2004. SPRs are defined as those whose capital income exceeds their labor income, or those without occupational affiliations; they are differentiated from top‐level executives who are corporate heads of either small or large enterprises. Business professionals are those who are occupationally active in business, such as the financial industries, but who do not have executive positions. The aim of this category is to capture the growing importance of ‘financial intermediaries’ and auxiliary professionals in advanced financialized economies (Folkman et al., 2007). Finally, we separate individuals who hold assets at the level of the national one percent but who are occupationally active in non‐business activities, such as the cultural industries or state administration (Gustavsson & Melldahl, 2018).

The class position of parents, siblings (including step‐ and half‐siblings), partners and parents‐in‐law are based on the Oslo Registry Data Class (ORDC) scheme (Hansen et al., 2009), shown in Figure 2. The scheme explicitly aims to capture the upper class and to recognize divisions within this group according to the Bourdieusian principle of capital composition, where economic capital is differentiated from cultural capital. In our analyses, we collapse this division within the lower‐middle class, as our main interest is to contrast the most privileged class backgrounds with those who possess the fewest resources (the unskilled working class). The economic upper class consists of positions assumed to possess considerable control and ownership of capital, such as business leaders, executives, and top‐level business professionals. The cultural fraction reflects occupations thought to possess power in terms of national cultural expressions and representation, such as academics, museum directors or architects, while the balanced fraction typically includes the elite professions (e.g., lawyers, civil engineers) or top‐level bureaucrats and state functionaries (e.g., secretaries of state, department heads).

FIGURE 2

The Oslo Register Data Class Scheme (ORDC) with example occupations

Parental occupation information is taken from the 1970 and 1980 censuses. We define class origin based on the parent who occupied the highest vertical class position. If both parents occupied positions at the same vertical level, but in different class fractions, we prioritize the economic fraction over the cultural fraction or ‘balanced’ fraction. The same procedure is followed for the class positions of parents‐in‐law. Extended‐kinship variables are analyzed using multiple correspondence analysis in order to study them as interrelated, rather than independent of each other (as described below). Here, we are mainly interested in kinship ties associated with positions of power and ownership. The variables and the categories are shown in Table 1.

TABLE 1

Descriptive statistics for the 1963–1968 birth cohorts, by one percent‐status in year 2004. Variables for the specific multiple correspondence analysis to the right. Missing variables are deemed to be ‘passive’ (p) in the analysis, meaning they do not affect the results (Hjellbrekke, 2018)

	Analytical sample	Remaining pop	Variables for the multiple correspondence analysis
				Analytical sample	Remaining pop
♀ (%)	21.63	49.29	Class origin (%)
Number of children in year 2003 (%)			Other uppper class	11.14	5.97
No children	11.91	14.90	Economic upper class	13.13	1.14
One child	11.10	14.32	Other upper‐middle class	9.66	10.43
Two children	39.73	38.58	Economic upper‐middle class	20.31	6.95
Three children	28.85	23.48	Lower‐middle class	15.53	17.54
Four or more children	8.41	8.71	Skilled working class	7.35	13.40
Parental net worth at age 36			Unskilled working class	14.09	27.92
Mean	15,900,000	2,620,946	Farmers/fishery/forestry	6.84	5.63
Standard deviation	42,200,000	6,611,500	Missing (p)	1.96	11.01
Mean (€)	1,560,704	257,265	Parental wealth (%)
Wealth transfers, 1995–2013 (mean)			<p25	7.61	20.65
Inter‐vivos transfers and bequests	2,553,307	658,001	p25/p50	8.86	21.88
Standard deviation	5,961,126	1,289,439	p50/p75	13.64	21.87
Mean (€)	250,626	64,588	p75/p90	15.24	13.01
Educational length (%)			p90/p99	29.24	8.47
Obligatory education	10.53	22.63	Top 1%	20.60	0.70
High school	40.63	43.09	Missing (p)	4.81	13.42
Bachelor's	35.04	23.55	In‐law wealth (%)
Master's or higher	13.67	7.55	<p25	11.46	15.01
Missing	0.13	3.18	p25/p50	14.38	16.46
Class situation in year 2004 (%)			p50/p75	17.65	16.27
SPRs	47.69	0.41	p75/p95	21.66	14.30
Executives	29.11	4.66	Top 5%	11.33	3.73
Business professionals	5.26	9.07	Missing/no partner (p)	23.52	34.23
Non‐business employees	15.69	61.37	Siblings' class position (%)
Outside of the labour force	0.00	13.17	None in the economic upper class	56.58	87.31
Missing	2.25	11.32	1 in the economic upper class	26.41	7.79
Has a partner (%)			>=2 in the economic upper class	12.13	0.75
No	12.74	19.41	Missing/no sibling (p)	4.88	4.16
Yes	87.26	80.59	Partner's class destination (%)
			Other uppper class	8.28	6.73
			Economic upper class	11.91	5.28
			Cultural middle class (up+low)	6.48	5.38
			Balanced upper‐middle class	7.61	8.49
			Economic upper‐middle class	5.71	4.17
			Balanced lower‐middle class	12.80	7.68
			Economic lower‐middle class	9.34	6.83
			Skilled working class	5.91	12.86
			Unskilled working class	9.63	13.96
			Outside of the labour market	9.60	9.21
			Missing/no partner (p)	12.74	19.41
			In‐law class position (%)
			Upper class	12.20	5.46
			Other upper‐middle class	10.01	7.81
			Economic upper‐middle class	9.72	5.39
			Lower‐middle class	15.05	13.50
			Skilled working class	9.40	10.22
			Unskilled working class	16.66	21.18
			Farmers/fishery/forestry	4.59	4.40
			Missing/no partner (p)	22.37	32.05
			Bequests and inter‐vivos gifting, 1995–2013 (%)
			Not recieved	44.83	71.44
			Median and less	14.96	14.38
			p50/p75	11.33	7.16
			p75/p95	15.08	5.68
			p95/p99	7.32	1.10
			Top 1%	6.48	0.24
			Timing of first wealth transfer (%)
			Not recieved (p)	44.83	71.44
			1993/1997	15.56	4.70
			1998/2000	12.52	5.15
			2001/2007	18.87	10.99
			2008/2013	8.22	7.73
			N	3,116	409,992
			%	0.75	99.25

Parental wealth is the sum of the net worth of the mother and father ((finance capital + real capital) − debt) when their offspring was 36 years of age. We follow the same procedure for in‐laws. Bequests and inter‐vivos transfers capture the total sum transferred from 1995–2013. As direct monetary transfers may trigger exponential capital accumulation over time, we also use a categorical variable to study the timing of the first transfer, as outlined in Table 1. For net worth, bequests and inter‐vivos transfers, we calculate the percentile distribution for the total birth cohorts and insert cut‐points, as shown in Table 1. For the cut‐points of in‐laws, we use the values of the distribution of parental net worth to provide comparable categories.

To assess the importance of educational credentials for mediating class origins, we start our analysis by exploring how the relationship between class origin and wealth sequences is affected by a control for educational qualifications consisting of 74 unique combinations of educational field and educational level. To provide an indication of the educational backgrounds of the Norwegian one‐percenters, Table 1 outlines the highest education level attained.

Table 1 illustrates the selectivity of our analytical sample in comparison to the complete 1963–1968 birth cohorts. The individuals who possessed financial assets that qualified them as the wealthiest one percent nationally in their late‐thirties were disproportionately from very wealthy and privileged families, on average they received large amounts of direct wealth transfers, and a significant proportion of them had one or more siblings in economic upper‐class positions. Compared to the population at large, they tended to have higher levels of educational attainment and were married to partners from disproportionately well‐resourced backgrounds and social classes. Importantly, the Norwegian one‐percenters are overwhelmingly men.

Methods

Sequence analysis offers a way to study trajectories in an exploratory and holistic manner. Most techniques for dealing with traits that vary over time emphasize one temporal feature, such as a specific transition or the duration of a state. Sequence analysis is holistic in that it searches for similarities between trajectories with respect to the entire list of states in a timeline.

First, dissimilarities are calculated by producing a pair‐wise dissimilarity score between all the sequences in the data. This offers a quantification of the degree of dissimilarity between each sequence pair and scores are then mobilized to group similar sequences into a typology. The aim is to provide a simplified depiction of how ‘ideal‐typical sequences’ evolve over time (Abbott & Hrycak, 1990).

To calculate the pair‐wise dissimilarities between sequences, we employ the widely used optimal matching algorithm. The degree of dissimilarity between sequences amounts to the efforts required to turn one sequence into another through three elementary operations: insertion, deletion, and substitution. Each operation is assigned a cost that is modeled by the researcher. Different strategies can guide the costs assigned to the substitution of one state for another or the cost for inserting and deleting states (indel) in a sequence. We generate substitution costs by calculating the absolute difference in the mean levels of wealth between two states, making substitutions involving states that are far apart in the wealth distribution more costly than transitions that are closer in worth. Indel operations are less sensitive to the timing of states in a sequence and since we believe timing to be of great theoretical importance when modeling wealth accumulation, we follow the common practice of setting the indel costs to half the maximum substitution cost (Lesnard, 2014).

We then subject the resultant dissimilarity matrix to hierarchical agglomerative clustering using the Ward procedure in combination with PAM‐clustering (partitioning around the medoids), as suggested by Studer (2013). We decide on the number of clusters to include in our typology by gauging different measures of the quality of each partitioning, as well as stressing the sociological interpretability of the types produced (see Appendix A for quality statistics for alternative solutions with different numbers of groups).

In order to study the relationship between wealth sequences and class origin, we first use multinomial logistic regression, where the sequence typology serves as the dependent variable. After establishing this initial relationship and assessing the educational mediation of this association, we turn to multiple correspondence analysis. This allows us to analyze multiple indicators of dynastic privilege, providing a deeper understanding of the biographical divisions among the wealthy. Based on a range of indicators for each individual, the technique searches for the fewest dimensions that capture variance in the data. Geometric representations of these oppositions offer both an account of which individuals are closely related to each other based on shared attributes, and which attributes tend to co‐occur for individuals. The key task for the researcher is to sociologically interpret the dimensions. After we reveal the main oppositions in the data, we explore how these divisions relate to wealth sequence and gender differences. This is achieved by projecting our wealth sequence typology and gender variables onto the kinship space (Hjellbrekke, 2018). This addresses the question of whether one‐percenters who resemble one another in their origins and kinship ties also resemble one another in their wealth sequences and gender.

RESULTS

Wealth accumulation and the life course

Figure 3 shows four typical trajectories of wealth accumulation retrieved from our cluster analysis. We visualize the sequence typology with both state distribution plots (the left column) and index plots (the right column). The colors indicate the level of wealth owned at different points in time. The state distribution plots show the typicality of each state for each year along the x‐axis, while the index plot illustrates how each individual wealth sequence unfolds over time. In these plots, one line represents one sequence. We have ordered the index plots by silhouette widths so that the most characteristic sequences within a cluster are ordered at the top (those closest to the center of the cluster and those furthest from the closest cluster) (Studer, 2013, p. 15). The cluster statistics suggest a somewhat weak structuring (see Appendix A), but given that we study social phenomena over a 25‐year period, this could be expected. Arguably, the typology signifies sociologically meaningful differences during this period of time. We have labeled the plots to emphasize their most defining features.

FIGURE 3

Four different types of wealth accumulation. State distribution plot in the left column and sequence index plot, ordered by silhouette widths in the right column. The most characteristic sequences within a cluster are ordered at the top in the index plots

The first cluster is one of extreme privilege. This cluster groups individuals who, almost exclusively, are not only in the top one percent, but are the top 0.1% of the wealthiest individuals in Norway from their late twenties into their fifties. The levels of wealth amassed early on in adulthood suggest that the position of appropriation of great wealth occurs from early in the life course and we name this cluster head start and extreme wealth. In the first year of observation, almost 75% owned financial assets at the national one percent, yet more than 50% owned wealth at the national 0.1%. By 2004, 96% owned financial assets at the national 0.1%, setting them apart from their peers within the national one percent. The amassing of top finanical wealth persisted throughout the period of observation and thus into their fifties. Among Norwegians who own finanical assets at the national one percent in their late‐thirties, this type of wealth sequence is atypical. Only five percent of the analytical population experienced this wealth trajectory throughout adulthood and the remaining clusters feature periods of owning fewer assets.

In contrast to the first cluster, the individuals with other types of wealth accumulation are characterized by starting with less wealth in their late twenties. Therefore, these involve a ‘climb’ in one sense or another, and only a very small proportion started at the top of the wealth distribution. The wealth trajectories of the first period of study resemble those of the second and third cluster. Around the turn of the millennium, however, when these individuals were in their mid‐thirties, the second cluster started amassing great wealth at a higher pace than the third cluster. By 2004, almost one third of the second cluster owned wealth at the national 0.1% and this share increased throughout their forties and fifties. The second cluster, therefore, seemed to climb onto a big jump, while the climb for the third cluster mostly arrived above the one percent threshold. Albeit never reaching the upper echelons of the 0.1%, most successfully retained their wealth at the national one percent throughout adulthood. Amounting to 50% of the wealth sequences in our sub‐population, this trajectory, the climb into the one percent, is the most typical for the birth cohorts under investigation.

The final cluster, which we name climb and fall from grace, is more distinct than the other typologies due to longer periods of owning relatively less wealth. This cluster, amounting to 36%, not only had a comparatively longer climb into the top one percent but also experienced a greater loss of wealth during their forties and fifties. More than 40% started their accumulation cycle in their mid‐twenties with assets worth less than the 75th percentile and, despite reaching the top one percent ten years later, by their fifties, only 12% had retained levels of wealth at the national one percent and 27% owned assets below the top ten percent.

Figure 4 shows the evolution of wealth for each of the sequence types. We plot the percentage increase from median financial wealth in the population for each year. This figure demonstrates large differences in asset ownership among those in their late thirties who are among the top one percent nationally. In particular, we see the persistence of the accumulation of advantages that is often associated with great fortunes (Mills, 2000[1956], p. 110f). Ownership of financial assets at the high‐end of the top one percentile—from early on in the first cluster and obtained mid‐career for the second cluster—yielded cumulative advantage and exponential wealth accumulation over time. This is particularly evident with the first cluster. Figure 4 shows that while the analytical sub‐population owned financial assets at the national one percent in the middle of their careers (2004 is marked in grey), they are simply not the same types of one‐percenters. Their starting points were vastly different and the amounts of wealth amassed by their mid‐thirties vary greatly. The opportunities for exponential growth intensify over the life course, and this seems particularly true for those lucky few who are offered a head start. For the head start and extreme wealth cluster, there seems to have been some muted interference from the economic crisis of 2008; however, the cluster's superior levels of worth were noteworthy throughout the period.

FIGURE 4

Average wealth of each accumulation type over time. Percent higher than the population median. Two‐year averages

The typology thus separates meaningful variation with regard to pathways to great wealth, as well as the various pathways after the one‐percent status in 2004. Measuring the one percent at one point in time, therefore, risks obfuscating the fragility of percentile bins in a distribution, such as the one percent. While some individuals accumulated wealth that has endured and grown exponentially, others seem to be living on borrowed time.

Table 2 provides an overview of the descriptive statistics for each cluster in 2004, the year when every type was among the top one percent of the financially wealthy in Norway. To further understand the different dynamics that enable or constrain possibilities for wealth accumulation, we turn to the different economic positions that defined each cluster in 2004. As noted by Mills (2000[1956], p. 111), for some, the big jump may be secured through bequests and gifting within wealthy families but, in order to ‘parlay considerable money into the truly big money, he [they] must be in a position to benefit from the accumulation of advantages.’ It is through positions within the economic structure and within institutions that opportunities for amassing large fortunes often arise.

TABLE 2

Descriptive statistics for year 2004 (when in the top one percent)

	Head start and extreme wealth	Climb onto a big jump	Climb into the one percent	Climb and fall from grace	Total
Class situation (%)
SPRs	64.52	49.82	49.42	45.34	48.79
Executives	30.97	43.01	32.92	21.64	29.78
Business professionals	3.23	2.15	5.19	6.81	5.38
Non‐business employees	1.29	5.02	12.47	26.21	16.05
Company sector a (%)
Private business	89.47	92.83	86.32	76.27	83.71
Finance and banking	3.16	3.59	5.25	5.46	5.08
State and municipal	7.37	3.59	8.43	18.27	11.21
Company size a
Mean	441	295	534	1,029	673
♀ (%)	34.62	16.49	21.11	21.83	21.63
Mean finance capital (2019 NOK)	100,660,866	18,363,081	7,534,792	5,190,593	12,329,293
Mean (€)	9,663,443	1,762,856	723,340	498,297	1,183,612
Cluster statistics
Max (state, %)	p99.9‐100, 82%	p99.9‐100, 40%	p99‐99.9, 67%	p96‐99, 39%
Min (state, %)	p‐50, 0.26%	p‐50, 1.86%	p‐50, 1.85%	p99.9‐100, 0.40%
Average Silhouette Width (ASW)	0.53	0.34	0.15	0.50
N	156	279	1,568	1,113	3,116
%	5.01	8.95	50.32	35.72	100.00

Valid percent for employees only.

Table 2 offers some indication of this. While more than a quarter of the climb and fall from grace‐type were non‐business employees in 2004, the head start and extreme wealth and the climb onto a big jump clusters were significantly more likely to hold positions as SPRs (with no occupational affiliation) or executives in their mid‐thirties. In particular, the profile of the climb onto a big jump cluster suggests the centrality of executive positions for securing a big jump mid‐career and the subsequent accumulation of advantages. The head start and extreme wealth cluster, on the other hand, was the most likely to be self‐employed or hold a position as proprietor or rentier (SPRs) and, at this point in their life course, those in this cluster were already firmly enjoying the benefits of a previously obtained big jump and its associated accumulation of advantage. In 2004, both economic position and company traits varied between the different wealth sequences. In particular, the climb onto a big jump was more likely to be employed within relatively small‐sized private businesses, while public sector employment within large companies was more common for the climb and fall from grace type.

An interesting gendered dynamic also distinguishes the two most privileged clusters. While the head start and extreme wealth cluster is distinct from the remaining sub‐population in its greater share of women, the climb onto a big jump cluster disproportionately consists of men. The two remaining clusters are no different from the female share in the total sub‐population, although the glaring underrepresentation of women among the national one percent, as seen in Table 1, merits attention in its own right.

Table 2 also quantifies the vast discrepancies in the value of assets owned in 2004, as already evident in Figure 4. Clearly, these clusters were not equally ‘one percenters’ in their mid‐thirties. At that age, the head start and extreme wealth cluster already owned assets worth a mean sum of 100 million NOK, while the climb and fall from grace cluster—those unable to retain their wealth in the following years—owned assets that were worth a mere 5 percent of this. For those who climbed their way onto the big jump, their mean worth of 18 million NOK hints that a big jump was already in play by their mid‐thirties.

Class origin, educational mediation, and wealth accumulation

So far, our sequence analysis has revealed meaningful variation among the one‐percenters; not only have we shown different pathways to top‐level wealth but also important differences in the level of success in remaining at that level over the life course. Next, we explore whether the different wealth sequences are structured by class origin. In Figure 5, we employ multinomial logistic regression and outline the average marginal effects for each sequence type when comparing different class origins to unskilled working‐class backgrounds (see Appendix B for the tables). The squared points denote class‐origin estimates when controlling only for demographic traits (gender, children (and an interaction term between the two) and birth cohort), while the triangular points also include controls for granular educational dummies. We have marked significant estimates in black and insignificant estimates in gray.

FIGURE 5

Average marginal effects. The relationship between class origins and the accumulation sequences. Controls for demographic variables in squared points, additional controls for granular education dummies in triangular points. Significant estimates from unskilled working‐class origins in black, insignificant estimates in grey

We find significant class‐origin differences in the likelihood of experiencing the various accumulation trajectories. In particular, we find the largest associations when comparing children of the unskilled working class to children of the economic upper class: the probability of experiencing the head start and extreme wealth sequence is 17 percentage points higher, whereas the probability of experiencing the climb and fall from grace trajectory is 31 percentage points lower. Indeed, the likelihood of retaining one's wealth above the one percent threshold, as captured by the first three clusters, was significantly more likely for children of the economic upper class in comparison to children of the unskilled working class. As noted by Mills (2000[1956], p. 115), ‘It is difficult to climb to the top, and many who try fall by the way. It is easier and much safer to be born there.’

Strikingly, and consistent with Melldahl (2018), we find that such class‐origin differences are unlikely to reflect mediation via the educational system. Our granular control for educational credentials (the triangular points) is barely distinguishable from our baseline estimates. In order to provide a deeper understanding of the social divide between those who became wealthy in their mid‐thirties, we turn to multiple correspondence analysis which allows us to more systematically unpack the significance of kinship among the wealthy.

Kinship structures: parents, siblings, in‐laws, and partners

To dig more deeply into the differences between the biographical divisions of the wealthy, we turn to multiple correspondence analysis. This allows us to study a range of family resources interdependently, such as the class positions of parents, partners, siblings, in‐laws, the net worth of in‐laws and parents, and the timing and volume of direct intergenerational transfers of economic assets (see Appendix D for the coordinates and contributions for the dimensions and Appendix E for the cloud of individuals).

We retain two dimensions for our analysis as, in combination, they capture 85 percent of the modified rates, although the first dimension is highly dominant in the space. Figure 6 depicts, horizontally, the attributes that define this first division. On the one hand, it identifies individuals whose parents typically hold positions in the economic upper class and who are among the super‐rich, whose siblings (plural) and partner have reached positions in the economic upper‐class and those who had typically received the highest amounts of bequests and inter‐vivos transfers by their late twenties/early thirties. These profiles are defined in contrast to the left‐hand side of the space, where we typically find those one‐percenters whose parents and siblings are not affiliated with positions of power, where parents tend to be less wealthy and are less likely to transfer economic assets directly. Thus, the first—and most crucial—dimension dividing the Norwegian one‐percenters can be found between what we might term dynastic cores and newcomers to financial wealth.

FIGURE 6

Attributes that contribute above averagely in the construction of the first dimension

The second dimension, while less divisive, highlights the institution of marriage in strengthening privilege. Figure 7 depicts, vertically, the attributes that most clearly define the second dimension. In the top segments are marriages in which both in‐laws and partners occupy upper‐class positions and, while in‐laws tend to own considerable wealth, their class positions are not exclusively linked to the economic realm. These one‐percenters tend to originate in the cultural and balanced upper‐class or upper‐middle class fractions and are also distinct in not having received direct transfers. These profiles, in turn, are contrasted with those with in‐laws and partners in the working classes and who possess considerably less wealth. The second dimension contrasts the one‐percenters who, through the institution of marriage, have wealthy in‐laws and partners in positions of power (often beyond the economic sphere) and those who do not.

FIGURE 7

Attributes that contribute above averagely in the construction of the second dimension

Do individuals with similar kinship and family ties also have similar wealth sequences? To assess this relationship, we cut the space into different regions, differentiating between north, south, east, and west. Within a radius of 0.5 standard deviations from the barycenter, we identify a center category with profiles regarded as insufficiently distinct from the average profiles of the group in total. A chi‐square test of the regions of the space and our sequence typology suggests a significant association and Figure 8 shows the association for each cell in this contingency table using Pearson's (standardized) residuals (see Appendix F for further details).

FIGURE 8

Mosaic plot of standardized residuals. The association between the sequence clusters and the different regions of the kinship space

In the plot, each region is represented by a bar and the size of each bar reflects how many positions each region encompasses in the space. Each bar is divided into four according to the sequence typology. Blue indicates that the occurrence of the sequence type is greater than might be expected if it were random and red indicates that the occurrence is lower than might be expected if it were random. The plot reveals that the associations are strongest for wealth sequences characterized by a head start and extreme wealth and for the most unfavorable trajectory that experiences a (relative) loss of wealth over time. The head start and extreme wealth type is clearly associated with the eastern regions of the space, including both the south‐east of dynastic origins and the north‐east, where resourceful origins go hand‐in‐hand with upper‐class marriage. Those who climb onto a big jump do not display a similar tendency for upper‐class marriage, although they are equally overrepresented in the south‐eastern regions of dynastic origins.

In general, the head start and extreme wealth cluster is located in regions that are inversely associated with the climb and fall from grace type. Those who experience a relative loss of wealth in adulthood are more strongly associated with the western regions of the space, characterized by modest family origins, highlighting their position as ‘newcomers’ to top financial wealth. The positive relationship between this wealth sequence and the north‐western regions may also suggest that marriage may constitute a mobility strategy, as well as being a strategy of reproduction (immobility) among the established upper class.

Importantly, the associations shown in Figure 8 should not be exaggerated; the regional cut‐points of the space force our attention to the most distinct positions. The center category of ‘average profiles’ is fairly large (approximately one‐third of our one‐percenters) and the location of the average position of our sequence type is visualized in Figure 9 to provide a better account of the ‘typical’ location of the wealth trajectories. In addition, we visualize the mean points of male and female one‐percenters.

FIGURE 9

Mean position of each supplementary category

The greatest differences are visible along the first dimension, highlighting the importance of dynastic lineage. Women are located to the right of the space, suggesting their relative inclination for being ‘inheritors’. There are ‘notable’ distances between the genders (0.68 standard deviations) and ‘large’ distances [i.e. above 1 standard deviation (Le Roux & Rouanet, 2010, p. 59)] between the head start and extreme wealth type and those who fall from grace after their climb (1.46 standard deviations), as well as those who climb into the one percent (1.02 standard deviations). Interestingly, we also find ‘notable’ distances between the head start and extreme wealth and the climb onto the big jump (0.84 standard deviations). The latter type is also notably distant (0.62 standard deviations) from the climb and fall from grace type. The mean points thus corroborate the firm association between the first dimension of dynastic lineage and the likelihood of exponentially accumulating wealth throughout adulthood.

DISCUSSION AND CONCLUSION

This paper has offered important insights to the trajectories of top‐wealth owners in Norway by following a select group of individuals who, in their mid‐thirties, owned financial wealth at the national top one percent. Four different types of accumulation have been identified. A highly privileged minority, whose wealth has grown exponentially throughout adulthood, was disproportionately recruited from capitalist origins and owned significantly more wealth than the remaining one‐percenters at all periods under observation. Their high levels of wealth early on in adulthood have permitted them to exponentially accumulate wealth over time; both a Millsian big jump and the related accumulation of advantages seem to have operated in full force to bolster their fortunes throughout adulthood. Those showing more unstable trajectories, such as those who climbed up but failed to retain top financial wealth over time, are less likely to be of upper‐class origin; they also owned less wealth and were less likely to be in positions of control and ownership in the economy. In short, the ability to retain one's assets beyond the one percent threshold over time (as captured by three of the clusters) is significantly less probable for the long‐range upwardly mobile compared to the children of the economic upper class. Thus, while there are ‘self‐made’ one‐percenters in Norway, these individuals are more likely to experience (relatively speaking) a ‘fall from grace’ and, as such, seem to be living on borrowed time. Tellingly, and corroborating the findings of Melldahl (2018), such class‐origin differences among the wealthy are unlikely to reflect inequalities within the education system, as shown by our granular measure of educational field and the length thereof.

For some, great fortunes had already been amassed in early adulthood while, for others, the big jump of great asset build‐up, and the unleashing of the advantages that cumulate with the ownership of top financial wealth, occurred later in adulthood. Of those who climbed onto a big jump, a disproportionate number were the sons of economically privileged families with executive functions in small‐ and medium‐sized private firms. The role of family‐owned businesses and the organizational underpinnings to these pathways to great wealth merit further attention (see also Carney & Nason, 2018).

Moving from a class‐origin estimate to a multiple correspondence analysis, we explored the consolidation of economic power by visualizing how parental wealth, the class affiliations of siblings and parents, the strategies of direct monetary transfer and the institution of marriage interact in producing vastly different biographies. We found that the most decisive contrast among the one‐percenters is that between newcomers from modest origins and individuals from dynastic cores. Our findings correspond to Piketty's (2014) emphasis on patrimony in contemporary society. Among the one‐percenters, a specific fraction originates from very wealthy family contexts, where parents, siblings, and partners tend to enjoy vast amounts of economic control and ownership. Individuals in this fraction also typically received large bequests and inter‐vivos transfers early in adulthood. Such dynastic cores are in contrast to less wealthy, working‐class family contexts where siblings are not in positions of control and ownership of capital. A second principle of division reveals the role of the institution of marriage in strengthening privileges. Although this opposition is less salient, our analysis adds to recent findings that highlight the marriage market as a strategic arena for the preservation of top wealth and dynastic formation (Wagner et al., 2020). It also highlights the ways that the institution of marriage may enable social mobility.

Intra‐class divisions between the one‐percenters are statistically associated with different pathways to great wealth, suggesting that wealth accumulation is embedded in the social structure of kinship ties. The clearest crystallization of this association is found in the likelihood of accumulating enduring top‐level wealth throughout adulthood and originating from families of dynastic wealth, and conversely, the association between a newcomer's position and the inability to retain financial assets at the level of the national one percent over time.

In short, and corresponding to the findings of Korom et al. (2017), family contexts characterized by multiple indicators of significant ownership and control of economic assets seem to foster advantages that help propel children's abilities to exponentially amass large fortunes throughout adulthood. Bearing in mind that, as Table 1 clearly shows, these individuals constitute a highly select group from the outset. Such differences are fascinating and testify to the enduring influence of parental and kinship resources, not only in structuring the likelihood of attaining elite positions, but in accelerating the degree of privilege the super‐rich enjoy over time (cf. Friedman & Laurison, 2019; Korom et al., 2017; Toft, 2019).

The profound privilege that characterizes the dynastic core has important societal implications. Over their life courses, members of such dynastic cores enjoy economic leeway and ample opportunities for consumption that enhance their well‐being and scope for action, but liquid assets, such as bonds, shares in companies and so on, are assets that imply strategic control of the economy.

Considering that the lived experiences of these dynastic cores are unimaginable for most of the population, whose lives are likely to be influenced by the power they wield, our biographical accounts have wider ramifications for class relations in contemporary society. In contrast to the newcomers—whose level of wealth sets them apart from the life chances of their family members—the experiences and life situations of the dynastic cores resemble those of their kin; siblings, parents and partners often enjoy positions of profound advantage, much like the one‐percenters themselves. To the extent that such conditions shape their subjective beliefs, outlooks and frames of reference, their own privileges might become normalized, taken for granted and thus naturalized (Bourdieu, 1990). This seems even more likely if such biographies also entail limited contextual and spatial impulses (Toft, 2018) and homophilous social circles.

These trends might be particularly worrisome within the context of the Nordic model, core elements of which include universal welfare benefits and a high degree of societal trust. These are pressing matters for future research but, as witnessed in Finland—another Nordic welfare state—intensified economic inequality has probably fostered ‘new imaginaries’ among the corporate super‐rich, where egalitarian policies may be resented and welfare recipients and the unemployed may be seen as undeserving, envious, and lazy (Kantola, 2020; Kuusela, 2020). The pulling‐away of the increasingly rich—economically, but perhaps also spatially, culturally, and socially—may, therefore, preclude super‐wealthy ‘heirs’ from understanding the social realities of the dominated. To understand these phenomena better, class analysts might be wise to follow the lead of those economists who suggest a ‘return to capital’, while sharpening their sociological tools to decipher the cultural, social and biographical dimensions involved in the concentration of top financial wealth and the preservation of large fortunes.

CONFLICT OF INTEREST

There is no conflict of interest.

Supplementary Material

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