‘No Place to Be Sick’: Cooptation and Convergence in the US Hospital Care Sector

The health care sector in the US is characterized by a complex entanglement of government, not-for-profit (NFP) and for-profit (FP) organizations. Curiously, while most other aspects of health care are dominated by large FP firms (e.g., pharmaceutical production), general hospital care provision is dominated by NFP organizations.¹ Because of this, the question of the role(s) of not-for-profit organizations in capitalist organized health care is a central one for understanding the political economic dynamics of the sector. According to neoclassical economists, NFP organizations should be viewed as an alternative to government services, arising from the government’s inability to provide satisfactory “collective-consumption goods” (Weisbrod 1975, 181–82). These goods (e.g., health care) are assumed to be driven by ‘social’, rather than individual utility, and thus private firms are deemed ‘socially inefficient’ at providing them.² Consequently, “when the government is able to provide these services in forms and amounts that voters want, little role exists for nonprofits” (Weisbrod 2000, 3). However, “when populations are very diverse, services that satisfy the majority may leave many people severely under satisfied [sic]” and NFP organizations become appealing “as an alternative mechanism for providing public-type services” (3). Due to ‘voter-consumers’ dissatisfaction with the ‘government market failure’ of publicly organized services, individuals seek to procure these collective goods from the private, not-for-profit market (Weisbrod 1975, 182). Another way of putting this claim (in neoclassical terms) is that FP firms are concerned with ‘efficiency’, whereas governments and NFP firms are concerned with ‘equity’ (Rosenthal and Newbrander 1996, 207). According to this account, NFP hospitals are situated somewhere between these two spheres, ‘filling the gap’ between meeting opposing equity and efficiency concerns. Echoing this logic, the OECD defines NFP organizations as a “third sector” lying “between state and market, fulfilling both economic and social missions” (OECD 2003, 10).

Several issues challenge this theory in the case of the US hospital sector. First, it is incomplete. While the neoclassical explanation may explain why NFP organizations dominate in certain sectors, it does not explain why the prevalence of NFP organizations like hospitals differs from state to state. In some states, like Delaware, not-for-profit hospitals own nearly 100% of hospital beds. In others, like Alabama, NFPs own less than 20%, government organizations own 50%, and for-profit hospitals own the rest. This account must therefore offer a further explanation for why voter-consumers organize collective goods in such diverse configurations, with most states containing all three types of organization. Second, there is the question of whether the hospital landscape can be considered a ‘market’ at all. Hospital care in the US is strictly regulated by the federal government—which is also overwhelmingly the largest health care payer. Hospital systems operated by state and municipal governments cannot be assumed to behave in the same manner as profit-maximizing firms, further ‘distorting’ their interactions. In addition, most states’ hospital landscapes are dominated by a few large hospital systems, and most populations live geographically close to only a handful of hospitals (and some are not close to any), giving many hospitals significant pricing power (Guardado 2021). As a result, the hospital landscape can hardly be described as competitive in the neoclassical sense of the term. Third, given the ‘distorting’ effects of large, concentrated and government regulated institutions, the assumption that health care organizations are shaped by individual consumer choice is implausible. Individuals have little power to affect the health care system as stand-alone actors. It is only through collective mobilization and organization that health care users can hope to shape the hospital landscape. Finally, as we show below, the prevalence of government and FP hospitals are positively associated, complicating the conceptual relationship between public and NFP organization and market failure. In some cases, large NFP hospitals are even more profitable than their FP counterparts—which would indicate that they are more (neoclassically) ‘efficient’ than FPs!

To sum up, the diversity of ownership and concentration across state hospital landscapes; the lack of a ‘perfectly competitive’ market; the implausibility of health care consumer ‘sovereignty’; as well as empirical evidence against the market failure hypothesis, all suggest the need for an alternative approach to understanding the sector.

Instead of explaining the diversity of ownership and profitability among US hospitals by reference to consumer preferences or market dynamics, we propose to map the US hospital landscape from the perspective of organized power. Organized power, in this context, refers to the relative control of competing social groups over the provisioning of and benefit from hospital-based care. By hospital landscape, we mean the distribution of capacity between different ownership types, the relative sizes of hospital groups and the terms on which these organizations interact with the underlying population. From our perspective, the role of different organizational forms within the hospital care sector is an open theoretical question. We do not start with the assumption that different types of owners—government, NFP and FP—denote distinct, opposing, or even permanently fixed interests and goals. Rather, we start from the assumption that capitalist society is driven by the conflictual struggle for and against hierarchical social power, and that as such, each of these institutions must be examined in relation to the more general dynamics of capitalist accumulation, including this conflictual struggle. Our tentative hypothesis is two-fold. First, rather than an ahistorically defined functional division between private and public logics of health care organization, FP, NFP and government hospitals are becoming “increasingly intertwined organs of the same capitalist mode of power” (Bichler and Nitzan 2021). Second, this process is not occurring equally across US states but is shaped by the existence of organized resistance.

It may seem odd to speak about organized resistance to capitalized care in the US, given the extent to which the health care sector is dominated by capitalist ideology. However, we argue that, while certainly fragmented, marginalized and lacking a visible presence on a national scale, organized resistance to capitalist control of health care continues to shape the hospital landscape. To illustrate this shaping, we measure the relationship between two quantitative measures, as rough proxies for the level of organized resistance at the state level. These variables —popular and worker-organized resistance to capitalist control of health care—are meant only as rough proxies for a hypothetical causal mechanism, and we do not mean to suggest that they, and organized resistance more generally, are necessarily the sole force determining the hospital landscape. The absence of strong democratic political institutions in the US suggests the opposite: namely that the overriding factors shaping health care are the changing interests of dominant capital and the differential struggle for power between institutions, including for-profit and not-for-profit hospital groups; doctor’s associations; and municipal, state and federal government agencies – all of which may conflict or coordinate on different issues and at different times. Nevertheless, the empirical evidence shows that the extent to which the hospital landscape is dominated by capitalized care is closely related to the existence of popular and worker-organized resistance. We believe this interaction between power and resistance is especially important for untangling the roles of government and NFP hospitals in the current landscape of hospital care.

We focus on union density as our first measure of organized resistance because of the historical significance of the labor movement in the expansion of public health capacity and access in the US (Klein 2014). While the emergence of large scale, private NFP healthcare rose in part from a coalition of physicians attempting to shield their profit-making from government control, these large and powerful physician groups were in turn checked by worker-organized health care, during a time when worker power in the US was at or near its 20th century peak (Hendricks 1991; Klein 2014). To be sure, NFP health care was in many ways a compromise between workers and owners of capital, as far as US capitalists have successfully prevented the creation of a universal public health care system. Henry Kaiser, for instance, sought to coopt worker-organized health care plans in the 1930s and 1940s—conducting anti-communist purges and marginalizing union-backed doctors—at the same time as seeking a cheaper alternative to fee-for-service physician care for maintaining a ‘healthy’ (read: productive) workforce (Hendricks 1991). Similarly, although the NFP model, as well as major expansions in public health spending like the Medicare Act, were largely designed to promote the private organization of hospital care, these changes occurred in response to massive public pressure for public health care (Klein 2014). Based off this historical legacy and the ongoing power of unions to marshal political resources in support of popular demands, we hypothesize that the persistence of organized labor in the US contributes to the public orientation of the health care landscape by acting as a limiting force of resistance to the subjugation of care in pursuit of profit.

We chose a state’s adoption date for the Affordable Care Act’s (ACA) Medicaid expansion program as a second measure of organized resistance because the Medicaid expansion represents a widely popular, major redistribution of public resources to the poorest and most vulnerable. While the Affordable Care Act fell short in many ways, the Medicaid expansion had an unquestionable effect of increasing public insurance coverage by 13 million and had significant financial effects for low-income and younger adults (Manchikanti et al. 2017; Hamilton 2024). This expansion of public funding created a more egalitarian distribution of resources and thus we assume that it occurred sooner in states that have a stronger political resistance to capitalized care. By contrast, we would expect states with little or no organized political resistance to capitalized care to act to preserve or even increase the vulnerability of the poor and uninsured to predation.

In Figures 1a and 1b, we use these two variables to sort states into high and low resistance groups and then compare the distribution of revenue and number of beds by ownership type. The comparison reveals a significant difference, with high union density and early Medicaid expansion (the dark blue observations) associated with a higher proportion of NFP beds and revenue and a lower proportion of both FP and government beds and revenue. Conversely, states with lower union density that delayed or refused the Medicaid expansion (the orange observations) show a significantly higher proportion of FP and government beds and revenue, with a lower proportion of NFP beds and revenue.

Figure 1a. Hospital Landscape in States Grouped by High and Low Union Density
Proportion of number of beds and net patient revenue for Not-for-Profit (NFP), For-Profit (FP) and Government (GVT) hospital systems in each state. Horizontal bars indicate 95% Bootstrap confidence intervals for the mean proportion in states with union density above the 40th percentile and for states with union density below the 40th percentile. Data for the year 2021, data for other years can be viewed at https://casp-lab.shinyapps.io/no_place_to_be_sick/

Figure 1b. Hospital Landscape in States Grouped by Early vs Delayed Medicaid Expansion
Proportion of number of beds and net patient revenue for Not-for-Profit (NFP), For-Profit (FP) and Government (GVT) hospital systems. Horizontal bars indicate 95% Bootstrap confidence intervals for the mean proportion in states that adopted Medicaid expansion early (in 2014) and for states that did not adopt Medicaid expansion early (adopted in 2015 or later, or never adopted). Data for the year 2021, data for other years can be viewed at https://casp-lab.shinyapps.io/no_place_to_be_sick/

What is particularly notable about this divide is the fact that relative government and FP hospital prevalence are positively correlated, highlighting the analytical difficulty in viewing ownership distribution through the lens of neoclassical economics. From a neoclassical perspective, the prevalence of government services denotes a ‘market failure’. However, FP hospitals tend to do better in states with a large government hospital presence, suggesting the existence of public hospitals is contributing to the ‘market success’ of these firms. Not only do the FP hospitals have a higher proportion of beds and revenue, they also enjoy a higher profitability in states with a large government prevalence. In addition, higher government hospital prevalence is paradoxically associated with low interest in publicly funded care among elected officials, represented by the delay or refusal of the Medicaid expansion.

This apparent ambiguity suggests two things. First, a political economic analysis of the hospital landscape should not assume that different ownership types can be analyzed individually as fulfilling pre-defined functions (e.g., fixing a market failure, expressing consumer choice) or having autonomous interests (e.g., government hospitals serve an ‘equity’ function). Rather, there seems to be a complex relationship between different types of hospitals that is not immediately self-evident. Second, organized resistance to capitalized care clearly still plays a role in the sector at the state level, with greater worker organization and political support for public health funding favoring NFP hospitals.

To explore more fully the relationship between the hospital landscape and the dynamics of capitalized care, we also look at how the above organized resistance variables shape the relative power and profitability of large hospitals. To examine this relation, we measure hospital markup and the ‘social cost’ of capitalized care, indicated by the prevalence of people with high medical costs.

Markups are an important measure of social power, as higher markups denote greater capacity to increase prices over costs. There are many ways to measure markup, each with different advantages and disadvantages. For instance, because the accounting practices and regulations differ between FP, NFP and government firms, measures of the profit margins in the hospital sector are not meaningfully comparable. A more appropriate measure of markup is charges as percent of costs (CPC).³ The CPC measures the relative price a hospital charges for a given procedure over the Medicare-allowable cost. CPCs are a more reliable way to compare markup between different ownership types both because they are used by all types of hospitals, and because there are fewer regulations around how they are used (Bai and Anderson 2015, 2). NFP hospitals, unlike FP hospitals, are restricted in how they account for profit in order to maintain their tax-exempt charitable status, making conventional profitability measures (like profit margin) less meaningful. Because there are no such restrictions on CPC, this measure is more directly comparable between FP, NFP and government hospitals.

There is some debate about the significance of this internal markup measure. Hospital CEOs tend to argue that they are meaningless because most institutional payers, whether government or private, can negotiate lower prices in practice (Bai and Anderson 2016, 2). While the range of “actual” prices paid in practice are unknown, several studies reveal consistent patterns in the distribution of cost to charge differences, suggesting that these internal markups matter. For instance, one study of hospitals charging the highest markups found that almost all the hospitals in the sample are run by the largest and most profitable FP hospitals systems, Community Health Systems and HCA (Bai and Anderson 2015, 3). Individual hospitals in these hospital systems charge as much as 12.6 times the Medicare-allowable costs (4). In another study the authors found that “a one-unit increase [in the CPC] was associated with $64 higher patient care revenue per adjusted discharge” (Bai and Anderson 2016, 1). A 2024 study found that hospitals tend to raise CPCs after switching to FP ownership, and that on average, FP hospitals “exhibit [CPCs] approximately 1.61 times or 161% higher than governmental hospitals and nearly 0.997 times or 99.7% higher than non-profit (NFP) hospitals” (Beniwal and Shakya 2024, 4). These extra charges, moreover, show no relation to patient outcomes (Zander et al. 2024).

Following this literature, we use CPC as a measure of the ability of hospitals systems to inflate prices relative to their own costs for a given procedure. However, in engaging with the capital as power tradition, our focus is on the most profitable hospital systems in a state, and in particular, their power. Power is inherently a relative quantity, and therefore it is not enough to examine the CPC of the largest hospital systems by themselves. We compare these to the CPC of the other hospital systems (all hospital systems except for the most profitable), and use this differential CPC as our measure of power – the ability of the largest hospital systems in a state to inflate its prices relative to ability of the rest of the hospital systems in that state to do the same. I.e., we take the differential CPC to be a proxy to the power of the most profitable hospital systems, where an increase in the differential CPC in a state is interpreted as an increase in the power of the most profitable hospital systems in state (see captions for Figures 2a, 2b and 3 and the data appendix for detailed explanations about the computation of the differential CPC).

In addition, to make the social cost of price inflation more visible, we measure the percentage of people in each state with high levels of medical cost. This variable highlights the conflictual character of capitalized care and the distributional effects of rising prices for medical procedures. When organizations compete to raise prices higher and faster than their corporate rivals, this dynamic produces and is produced by unequal power relations and results in higher overall costs for health care patients. Moreover, uninsured patients bear the full cost of these markups, whereas large insurers (including Medicaid) negotiate lower prices. This gives private hospitals a direct financial and political interest in restricting (within limits) the expansion of public health insurance. As hospitals grow larger and more profitable, all else remaining the same, more and more of the broader resources of society are siphoned off and diverted toward augmenting the differential power of these organizations, rather than expanding health care capacity and access. Highlighting the connection between organized power and price inflation reveals the direct connection between capitalized care in the health care sector and the exploding levels of private medical costs and debt across the US, which in turn are causing widespread harm: increasing the severity of financial insecurity and of food insecurity; contributing to higher mortality and premature death rates; and compounding ongoing mental health crises (Himmelstein et al. 2022; Han et al. 2024; Moon et al. 2025; Treffeisen et al. 2025).

Figures 2a and 2b compare our state clusters against these two resistance variables. We find that, in states with low organized resistance to capitalized care, there is a statistically significant higher prevalence of people that are burdened with high medical costs. Secondly, low resistance is associated with higher relative markups of the top 5% most profitable hospital systems.

Figure 2a. Social Cost in States Grouped by High and Low Union Density
Top: Proportion of people with high medical costs. Horizontal bars indicate 95% Bootstrap confidence intervals for the mean proportion in states with union density above the 40th percentile and for states with union density below the 40th percentile. Bottom: Differential charges as percent of costs computed as the difference between mean charges as percent of costs for the top 5% of hospital systems in each state (ranked by net income) and the mean charges as percent of costs for the rest of the hospital systems in each state. Horizontal bars indicate 95% Bootstrap confidence intervals for the median difference in states with union density above the 40th percentile and for states with union density below the 40th percentile. Data for the year 2021, data for other years can be viewed at https://casp-lab.shinyapps.io/no_place_to_be_sick/

Figure 2b. Social Cost in States Grouped by Early vs Delayed Medicaid Expansion
Top: Proportion of people with high medical costs. Horizontal bars indicate 90% Bootstrap confidence intervals for the mean difference in states that adopted Medicaid expansion early (in 2014) and for states that did not adopt Medicaid expansion early (adopted in 2015 or later, or never adopted). Bottom: Differential charges as percent of costs computed as the difference between mean charges as percent of costs for the top 5% of hospital systems in each state (ranked by net income) and the mean charges as percent of costs for the rest of the hospital systems in each state. Horizontal bars indicate 90% Bootstrap confidence intervals for the median difference in states that adopted Medicaid expansion early (in 2014) and for states that did not adopt Medicaid expansion early (adopted in 2015 or later, or never adopted). Data for the year 2021, data for other years can be viewed at https://casp-lab.shinyapps.io/no_place_to_be_sick/

It is again worth noting here the contradictory role of government in low resistance states. Higher government hospital prevalence is associated with a substantively more powerful FP sector, characterized by huge and hugely profitable hospital systems. These states systematically delayed or refused federal assistance for the most vulnerable in the form of Medicaid expansion, despite also contending with higher relative levels of medical costs. Below, we attempt to explain this puzzle from the perspective of capital as power theory. Briefly, from our perspective, far from denoting a more equitable or socially beneficial hospital landscape, a larger government hospital presence denotes a health care system that is more deeply subjugated to capitalist accumulation.

What of NFP hospitals? The correlation between organized resistance and the prevalence of NFP hospitals does not necessarily imply that all NFP hospitals are uniquely or permanently oriented toward the goal of equitable health care provision. As Stevens (1989) documents in detail, NFP hospitals have done a brilliant job marketing themselves as institutions fundamentally guided by public service, even as they retain many of the core features of a private business. To investigate further, we examine whether larger NFP hospitals also engage in price inflation, which would indicate a greater orientation toward – though perhaps not the actual practice of – capital accumulation. To do so, we measure the differential — that is, relative to the average — markups for the most profitable NFP hospitals in relation to a state’s NFP hospital concentration in terms of number of beds.

Figure 3 illustrates the relationship between the markup of top NFP hospital systems and all other hospital systems in 2021 for the high NFP concentration vs low NFP concentration states. There is a clear differential, where NFP hospitals in high concentration states charge higher relative prices. Furthermore, during the period 2011-2022, top NFP hospital markups in the group of states with high concentration of NFP hospital systems were rising relative to the average across all hospital types at a statistically significant annual rate of 0.04%, building to an increase of about 0.5% in the differential CPC over the entire period.⁴ By contrast, during that period, there was no statistically significant trend in the group of states with low concentration of NFP hospital systems.

This evidence suggests that despite the association between organized resistance and the prevalence of large NFP hospitals, these organizations are not necessarily or consequently free from the logic of accumulation. Unlike government hospitals, which seem to either underpin or follow the expansion of FP hospitals, these large NFP hospitals have instead emulated the tactics of FP care. While playing a potentially rivalrous role, such emulation, even if successful, marks the increasing integration of NFP hospital systems into the logic of capitalized care, rather than their success in providing a meaningful alternative.

Figure 3. Cost in States Grouped by High and Low NFP Hospital System Concentration
Differential charges as percent of costs computed as the difference between mean charges as percent of costs for the top 10% of Not-for-Profit (NFP) hospital systems in each state (ranked by net income) and the mean charges as percent of costs for the rest of the hospital systems in each state (of all ownership types). Values for each state and 95% Bootstrap confidence intervals for the mean difference (indicated by horizontal bars) in states for which the NFP sector is more concentrated (where the Gini coefficient for the bed size of all NFP hospital systems is above its median) and in states for which the NFP sector is less concentrated (where the Gini coefficient for the bed size of all NFP hospital systems is below its median). Data for the year 2021, data for other years can be viewed at https://casp-lab.shinyapps.io/no_place_to_be_sick/

To return to our opening research question, how can we synthesize the empirical evidence into an explanation for the significant prevalence of public and NFP hospitals within the development of the broader capitalist social order? At this stage, we can only offer a sketch – one which draws on the insights of other capital as power researchers into the changing role of government in the evolutionary development of the capitalist mode of power.

In “Dominant capital and government” Jonathan Nitzan and Shimshon Bichler note the seeming paradox that during the era of ‘neoliberal capitalism’, which is supposedly characterized by a shrinking role of governments, the size of government spending has only increased (2021). They argue that “as capitalism develops, governments and large corporations become increasingly intertwined organs of the same capitalist mode of power,” and as such, suggest that this paradox may be resolved by distinguishing government ‘size’ from government autonomy.⁵ They point out that the rise in government expenditures across capitalist countries has been underpinned by a shift from direct provision and organization of social goods to “reactive transfers,” meaning usually financial transfers that leave the provision of services increasingly to the private sector (2021).⁶ The cost of leaving things to the private sector, of course, is that capitalist power tends to undermine the social foundations upon which it ultimately depends. And this, for Nitzan and Bichler, is exactly “where government spending comes in as a mitigating force.” From their viewpoint, “bigger government – particularly its sprawling social programs and transfer payments – mirrors not the failure of neoliberalism, but it’s very success,” and the “apparent largesse” of expanding government “indicates not greater power but subjugation” (2021).

This logic might help explain the notable correlation between capitalized care and the prevalence of government hospitals. Research investigating the degree to which public hospitals subsidize private health care by serving a disproportion of poor, uninsured, and/or underinsured patients, suggests this is the case. For instance, Hsia, Kellermann, and Shen (2011) found that public hospitals are significantly less likely to close their emergency rooms (the primary site of care for unprofitable patients) in the face of financial strain. Similarly, a study of hospitals in South Florida found that public hospitals tend to take a much higher portion of uncompensated care than either NFP or FP hospitals (Jackson and Beatty 2004). In states with a low organized resistance to capitalized care—of which Florida is perhaps the exemplar—the enduring section of government operated hospitals increasingly acts as a ‘reactive’ transfer to serve only the most severely underserved, leaving the vast majority of the population subject to the depredations of the capitalized hospital sector.

This ‘large yet subjugated’ dynamic of public institutions also characterizes the health care sector in the US more broadly. In addition to rising private medical debt, rising costs are borne disproportionately by the government even as a larger share of government spending goes into private hands. As Figure 4 below shows, government spending makes up an increasing proportion of total health care spending, though without a simultaneous increase in the direct provisioning of government health care. Interestingly, this trend predates the ‘neoliberal’ era. Gaffney, Woolhandler, and Himmelstein (2023) find that the trend in government subsidization of the private health care sector extends as far back as 1923. Since then, “the tax-financed share of health care spending has risen from 9% in 1923 to 69% in 2020” (325). Moreover, Gaffney et al estimate that as much as 25% of government spending is accounted for by public subsidization of private expenditures (for instance, by paying private health insurers) (340).

Figure 4. Government Spending on Healthcare vs Direct Government Procurement of Healthcare
Domestic general government health expenditure (% of current health expenditure) vs proportion of government beds (US).

Returning again to the case of NFP hospitals, the above analysis has shown that these organizations are far from immune to the logic of accumulation. Far from promoting a public and universal system of equitable care, the NFP system has historically served as a highly organized political force against the public provision of hospital care (Stevens 1989). NFP hospitals’ historical origins and continuing association with organized resistance to capitalized care suggests that their existence and importance rests on a precarious politics of compromise and cooptation, particularly where large NFP hospitals wear the mantle of public service while prioritizing private interests. Gaffney et al note that among the different forms of health care provision, the community hospital is the one area of health care provisioning that has remained largely NFP (Gaffney, Woolhandler, and Himmelstein 2023, 341). However, the clear and growing hierarchy within the sector suggests that many of the biggest NFP hospitals have internalized the logic and strategies of FP corporations (Mouré and Gorsky 2023). The supposed ‘dominance’ of private NFP hospitals appears to rest on an increasingly flimsy pretense of public-service orientation and a substantive emulation of the accumulation strategies of capitalized care.

In the above, we have tried to move beyond some of the conventional analytical distinctions of health care political economy while unpacking some of the salient features and dynamics of the US hospital care landscape. Despite the lack of a powerful or coherent national movement for universal health care, we found that historical and ongoing organized resistance to capitalized care continues to play an important, if generally overlooked role in the state configuration of hospital care. The predominance of NFP community hospitals in the US as the lone exception to an otherwise widely FP health care system is a striking testament to that resistance, as are lower prices and lower medical costs in high resistance states. However, the connection between resistance to capitalized care and NFP hospitals is only part of the story. The diversity of NFP institutions includes many large hospital systems whose orientation toward the accumulation of power and profit is similar to if not indistinguishable from large FP firms. Government hospitals also appear to support, rather than compete with capitalized care.

In terms of further study, we suggest that political economic studies of the hospital sector should not take the distinction between types of hospitals as functionally or analytically self-evident. The intertwined functions and orientations of different hospital types, as well as their historically contingent development also calls into question the analytical usefulness of the ‘public/private’ distinction. Moreover, the neoclassical dichotomy between FP firms seeking ‘efficiency’ and public and private NFP firms seeking ‘equity’ inadequately explains the dynamic evolution of capitalist health care in the US. Our concept of ‘organized resistance’ came in part from this effort to find a new conceptual ‘outside’ from which to define the function and orientation of institutions where the public/private distinction seems to lose much of its meaning. Under modern capitalism, ostensibly public organizations have been coopted and reshaped in support of, rather than defense against private abuses. As a result, it is likely that a new conceptual language is needed both to analyze these dynamics and as well as to imagine radical alternatives.

References

Data Appendix

Our source for data on hospitals and hospital systems is the National Academy for State Health Policy’s (NASHP) Hospital Cost Tool, downloaded from tool.nashp.org on May 14, 2024. The data set covers the years 2011-2022. Most of the hospital level variables in this data set are drawn from the Centers for Medicare & Medicaid Services (CMS) Healthcare Cost Report Information System (HCRIS). Hospital system identifiers in it are drawn from the Agency for Healthcare Research and Quality (AHRQ) compendium of U.S. Health Systems database.

Union Density estimates by state for 1977-2021 were downloaded from www.unionstats.com on July 29, 2024.

Data about Medicaid implementation by state was downloaded from KFF’s Status of State Medicaid Expansion Decisions at www.kff.org on May 30, 2023.

Estimates on percent of people with a high medical cost burden from 2010-2023 were downloaded from the State Health Access Data Assistance Center (SHADAC) program of the School of Public Health at the University of Minnesota at statehealthcompare.shadac.org on January 8, 2025.

Estimates on domestic general government health expenditure as percent of current health expenditure for the US are from the World Development Indicators of the World Bank, series SH.XPD.GHED.CH.ZS, downloaded May 21, 2025.

Preprocessing

Our theoretical focus in this paper is on hospital systems as business units, and on the “top” hospital systems in each US state. This focus involves several challenges: determining ownership type of a hospital system, computing financial information for hospital systems that span multiple states in each state separately, and identifying “top” hospital systems and computing measures that compare “top” hospital systems in a state with the rest of the hospital systems in that state.

Determining the hospital system ownership type is not a trivial task: FP entities may own NFP subsidiaries and vice versa. In addition, the definition of a hospital system as provided by the Compendium of U.S. Health Systems counts hospitals in the same hospital system through common ownership or joint management, which allows hospitals of different ownership types to be counted in a single system. Our approach for determining the ownership type of a hospital system is based on computing the proportions of NFP/FP/GVT hospitals in a system and classifying a system to one of these ownership types to the proportion that is above 70%. If no ownership type proportion is above 70%, we classify a system as “Hybrid”. Another complication is that when hospitals change ownership on a date which is not their fiscal year end, they appear twice (or more) in an annual cost report. To solve the issue of changes in ownership mid-fiscal year we only count the ownership type of a hospital after the change of ownership.

Financial information on individual hospitals is readily available from the CMS Cost Reports. Some data on hospital systems at a national level is available from sources like the American Hospital Association. However, no information is available regarding the financial performance of hospital systems that span multiple states in each state separately. For example, for a given hospital system that owns hospitals in several states, we are interested in being able to determine what is the net patient revenue from these hospitals in each of the states. To answer this and related questions we choose a bottom-up approach: using the hospital system identifier we group together hospitals in a state (independent hospitals are considered as a one-hospital hospital system). We sum all the additive variables (patient revenue, net income, number of hospital beds etc.) for all hospitals in a state that are in the same hospital system and consider these sums to be the values of the same variables for a hospital system in a state. Variables that are computed as ratios (charges as percent of costs, markup etc.) are then computed as ratios of the respective summed variables. Variables that are not additive or computed as ratios of additive variables are omitted from our data set.

Once we have determined ownership type and computed financial sizes for hospital systems (or portions thereof) in each state, we continue to compute additional differential variables that compare “top” hospital systems in a state with those that are not considered “top.” There are several ways to conceptualize and compute such differential variables. In this manuscript, we use two such measures.

For the first, used in Figures 2a and 2b, we compare the mean value of a variable for the group of the top 5% hospital systems in a state ranked by net income regardless of their ownership type to the mean value of all other hospital systems in a state.

For the second, used for the trend analysis and in Figure 3, we compare the mean value of a variable for the group of the top 10% NFP hospital systems in a state ranked by net income to the mean value of all other hospital systems in a state regardless of their ownership type.