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BY-NC-ND 3.0 license Open Access Published by De Gruyter September 29, 2018

Evaluating a Nonprofit Health Index as a Policy Tool

Roland J. Kushner EMAIL logo
From the journal Nonprofit Policy Forum

Abstract

This paper examines possible ways to reduce and synthesize information regarding the health of the U.S. nonprofit sector, exploring how a national “Nonprofit Health Index” might inform policymakers and the public by illuminating key indications of the continuing sustainability of the sector. The paper draws on the experiences of producing the National Arts Index. The paper examines the overall construct of nonprofit health, suggests an impact model based on the Balanced Scorecard, and explores the appropriate level of analysis. The challenges in resolving the nature of the nonprofit health construct make a national index impractical, but policy indexes targeted at locales and industries are more likely to benefit policymakers and inform the public.

1 Introduction

Policymakers charged with making decisions regarding regulatory action, program development, or fiscal policy will typically want such decisions to be fully informed with up-to-date information about target areas of action. However, the process of winnowing vast amounts of data to a level of tractable yet informative knowledge resource is difficult. For example, O'Grady (2006) describes the need for historical data on how many Americans had no health insurance. This information was critical for developing federal health policy. The statistic was elusive because of the wide-ranging but minimally coordinated missions of diverse federal data-gathering bureaus.

Trend data can inform multiple stages of the policy process, looking to the future when policymakers set agendas and identify problems amenable to policy solutions, then retrospectively as they evaluate implemented policies. Another driver for trend data in a policy process is interest in how past trends may or may not continue into the future. When regulators and tax authorities score policy options, their future scenarios can be informed by past trends.

Without being gathered into a story, individual data series, even tracked over multiple time periods, cannot satisfy the entirety of a policy-maker's information needs. Those that attract public attention are primarily macroeconomic: financial market performance, inflation, interest rates, unemployment, and the like. For smaller aggregations, like the nonprofit sector, getting from data to useful information, through distillation and synthesis, is work that consumes scarce time in policymaking processes. It can speed the process to have available and validated syntheses of data to highlight the information that can best inform policy.

This paper concerns such a synthesis for the nonprofit sector. Policymakers with interests in nonprofit action may be especially interested in trends relating to the condition of the nonprofit sector overall, and also of subsets of nonprofits. Scholars and practitioners have similar interests. The 2017 Independent Sector/ARNOVA Public Policy Symposium addressed the question, “How healthy is the U.S. nonprofit sector?,” and asked for analysis of a possible “Nonprofit Health Index.” Such a measure fits the definition of as a “policy index,” which Kushner & Cohen (2017) previously defined as a curated data set, compiled as one or more concentrated measures, regularly presented over time, and designed to inform and influence public discourse and policy. Emerging from the Symposium, this paper addresses three focal questions:

  1. What are the underlying theoretical and empirical structures of a prospective Nonprofit Health Index (NHI)?

  2. What are the levels and units of analysis, and temporal frame for a prospective NHI?

  3. What is the case for creating and maintaining NHI for the U.S. nonprofit sector?

In addressing these questions, this paper draws on the experiences of an arts-focused policy index, the National Arts Index (NAI) and Local Arts Index (LAI) projects of Americans for the Arts, a private-sector advocacy organization. Funded by Rockefeller and Kresge Foundations and others, these projects and their outputs were designed in an advocacy context, to inform policy and public discussion processes by aggregating and distilling data on arts and culture at a national level (NAI), and then later at a county level (LAI). While their arts and culture focus extended beyond the nonprofit sector, they provide many salient lessons for development of a policy index about nonprofits in general. The following sections of this paper address those three questions, concluding that the need for a policy index focusing on nonprofit health is much stronger at regional and industry levels than it is for the US nonprofit sector as a whole.

2 Learning from the Arts Index Project

The National Arts Index (NAI) was an annual measure of the vitality of arts and culture in the U.S., published by the advocacy group Americans for the Arts. The NAI distillation and synthesis of arts data was documented in reports from 2010 to 2016 (Kushner & Cohen (2016) at https://www.americansforthearts.org/by-program/reports-and-data/research-studies-publications/national-arts-index-reports-download-center), Kushner & Cohen (2011), and Kushner & Cohen (2017). In its development and implementation stages, the NAI confronted essentially the same questions and others, and came to answers that can inform a prospective NHI. The underlying area of interest was arts and culture, and the principal construct of interest was “vitality,” which we defined as “ … the health of the arts industries—and their ability to sustain themselves over time and deliver these valuable benefits.” We posited an overall model to reduce a large number of data indicators to a small number of dimensions. The initial design of NAI was informed by policy index projects in other domains (Conference Board, Institute for Supply Management, University of Michigan Survey Research Center, Annie E. Casey Foundation, United Way of America). We also took note of other long-running policy index reports such as the Newseum's State of the First Amendment, American Lung Association “State of the Air,” and Gallup's long-running Key Findings on Religion in the U.S.

These were design stage considerations. Implementing the NAI design required data acquisition, data reduction (distillation) and data synthesis. We then populated the work with real-world data, maintained hundreds of multi-year data points at the national level to be distilled and presented in annual reports, published over a thousand pages of reports, and supported a web site with tens of thousands of visitors. Data in the NAI reports spanned the years 1998 to 2013, providing both a compendium of detailed data and a highlighted single number that was the overall NAI score, along with a time-series representation of its path over the years, illustrated in Figure 1 (Kushner & Cohen 2017).

Figure 1: National Arts Index, 2000-2013, 2003=100.0
Figure 1:

National Arts Index, 2000-2013, 2003=100.0

The level of analysis for the NAI was national, and (with one interruption) it was issued annually. There was no single “unit of analysis,” but rather 81 separate measures combined in a summary measure. On its release in 2010, a common response to the NAI came from arts organizers and advocates in regions around the country, seeking a comparable local version. In 2011, Americans for the Arts began a Local Arts Index (LAI) web site with county-level data on many comparable elements of arts and culture. This was designed as a cross-sectional effort for site users to evaluate the arts and culture measures for multiple counties. The LAI data are accessible at https://www.icpsr.umich.edu/icpsrweb/NADAC/studies/36984.

Both projects ended in 2016. Overall, as a policy index, NAI had some success in raising the level of discourse regarding the arts and had a limited but distinct impact on key national policy issues for the arts (author and co-author 2017). The NAI was a private sector venture, rather than a government activity. In the years leading up to the NAI, governments in other developed nations frequently measured the arts and interacted through UNESCO and other bodies on global standards for cultural measurement. During most of the 2000s, the U.S. was largely absent from those interactions. NAI helped catalyze similar tracking of arts and culture by the Bureau of Economic Analysis in its Arts & Culture Production Satellite Accounts since 2015. National-level arts index projects in the United Kingdom and the Netherlands explicitly cited NAI as catalysts and models. Additional lessons from the NAI experience emerge in later sections of this paper in responding to the three focus questions.

3 Nonprofit Health: The Construct Challenge

For a prospective NHI, articulating the nature of nonprofit health to facilitate measurement has multiple challenges– semantic, ontological, epistemological, and more. In NAI, the notion of arts “vitality” was the underlying construct of interest. Seeking to inform the policy process and be a resource for policymakers, the NAI had a wide vision of arts and culture, and found ample data describing arts engagement far beyond traditional nonprofit arts institutions. Data came from numerous federal, industry, commercial, and academic data sites.

For a prospective NHI, “nonprofit health” is the central construct, and it needs clarity in concepts, labels, and classification schemes used for both “nonprofit” and for “health.” Both are defined and constrained by statutory and industry systems. This organizational population has considerable scale and even wider scope and variation. 501(c)(3) status is inadequate when it comes to defining the nonprofits sector; as it does not capture the full range of philanthropy, trusteeship, and voluntarism that inhabit civil society. This surfaces an ontological challenge, to explain the underlying nature of the attribute in question. In practice, this means overlaying and combining semantic meanings of what we might call “nonprofitness” and health onto the very real world of 501(c)(3) and other 501cx organizations, as well as other nonprofit-characterized actors. From a policy perspective, the epistemology articulates how and why knowledge is acquired, what is measured, and what is communicated. What knowledge might emerge, and then, who might make decisions with that knowledge? Epistemologically, linking to private or public policy implies at least awareness, if not adoption, of normative standards driving policy agendas; the views of nonprofit health in different professions and disciplines; and the range of policy options facing relevant actors. Rural health nonprofits have different concerns than urban educational organizations, and so do the policymakers that influence them most.

Health is an analogy to a similar construct for organisms, signifying vitality, well-being, the ability to continue. An organism's health is in part a consequence of its environment as described in population ecology theory (Pettijohn and Boris 2018; Hannan and Freeman 1984; this volume). A nonprofit health index would benefit from a clear definition of what makes for a munificent environment. Outside of the construct of nonprofit health, but weighing heavily on its meaning, is the environmental context faced by any population of nonprofit entities. For individual organizations conducting strategic planning, various situational analysis tools might assess industry competition or the “PESTEL” characteristics of the environment (political, economic, social, technological, ecological, legal). No single element illuminates the conditions faced by the whole sector, given the scope of organization types, places, industries, and more. It is not enough, for example, to cite private sector philanthropy, because that primarily benefits public charities – but not the whole nonprofit sector. The environment for nonprofit action reflects not just philanthropy, but also other normative ideals and values that are embodied by sector organizations: mercy, justice, beauty, peace, equity, safety, service, heritage, growth, devotion. If these ideals can be characterized consistently over time (i. e., in a policy index), assessing the health of entities and actors whose health depends on them becomes easier. And some environmental forces affecting nonprofit sector health are more challenging; since the mid-2010s, the political environment for nonprofits has become a more pressing environmental force. Thus, the “nonprofit health” construct has no unitary measure, but like most normative constructs of superior performance, is multidimensional.

For the NAI, we used an “impact model” as part of developing the policy index. An impact model for a policy index describes the dimensions of the phenomenon of interest, how they are related, and how they may inform users. An impact model bridges between concepts and data, and establishes a structure for a policy index: what it explains and how it is curated, compiled, and produced from available data. It articulates how diverse elements of data contribute to the impact the model is intended to achieve. We identified four dimensions of an impact model describing how the elements of the arts and culture ecology contributed to arts vitality. Our approach was informed by the advocacy interest and by our arts industry and cultural policy experience. We proposed an “Arts and Culture Balanced Scorecard,” emulating Kaplan and Norton (1996) formulation. The four comparable normative dimensions for the entire arts sector were Financial Flows, Capacity, Arts Participation, and Competitiveness. All four were positively scaled, reflecting our normative interest that the more of each, the better for the arts. Operationally, the dimensions were the “buckets” that gathered data series. They were mutually exclusive, so each of the 81indicator series was uniquely associated with one dimension. This model is shown in Figure 2 and described in detail in the full NAI reports. They showed how “vitality” comprised four distilled themes within the single synthesized overall NAI. The balanced dimensions facilitated communications and understanding, helping us convey the arts as a complex and widespread societal ecology with multiple attributes to recognize and consider.

Figure 2: Arts & culture balanced scorecard.
Figure 2:

Arts & culture balanced scorecard.

In Kushner & Cohen 2017, we noted that of these dimensions, two that captured attention from researchers and colleagues were those describing Capacity and Competitiveness. The first, which captured data on financial, organization, and workforce, illustrated the national scale and scope of the arts and culture ecology. The second showed ways in which the arts were (or were not) thriving, using measures of market share, returns on investment, and comparative performance against other industry sectors.

The county-level LAI had a similar impact model, a “Community Arts Vitality Model,” with three similar normative dimensions of Arts Activity, Resources and Capacity, and a complementary dimension of Local Cultural Character. Each arts-related data series was placed in only one of the dimensions according to our experience and informed judgement. The LAI and drew on similar data series (when they were coded at the county level) into local, community, and regional measures sought by local arts advocates in private nonprofit, commercial, and government settings (e. g., per capita National Endowment for the Arts or state arts grant funding). The LAI data were mainly from 2009–2014, helping local actors compare to their neighbors in real time. For them, LAI data constituted intelligence that could be used for collaboration, competition, future planning, and retrospective evaluation. Significantly, the LAI did not incorporate a single summary score for each county. Custom multi-county data sets were available with population-weighted measures. The project ended before longer local-level trends could be observed in the data.

4 An Impact Model of Nonprofit Health

A policy index emerges in iterative fashion, bouncing among competing/conflicting desires, data availability, and financial constraints, while pursuing clarity, reliability, ease, etc. A key question is the extent to which nonprofit health is seen as analogous to organizational health. Aggregating nonprofits into “civil society” and “third sector” is not organization-specific, though both terms assume a population of nonprofit entities as one of their elements. There is no a priori reason to assume that what makes an organization healthy uniformly corresponds to what makes a group of organizations healthy. Not every performance characteristic is scalable, and measures of the whole population subsume competition between members, which is arguably a component of health (Porter 1980). Resource-based value theory (Barney 1991) suggests that some successful organizations have rare resources that are not accessible to their competitors.

On the other hand, there are conveniences attached to using tools and concepts developed for organizational entities to explore groups of organizations. Cameron and Whetten (1983) called organizational effectiveness “the ultimate dependent variable in organizational research,” and the organization studies literature in nonprofit and business studies offers many performance models.

Balanced scorecard (Kaplan and Norton 1996), social construction (Herman and Renz 1997), systems model (Kushner and Poole 1996), resource-based value (Barney 1991), theory of change, multiple bottom line, economic efficiency, social return on investment (Richmond, Mook & Quarter 2003), and logic model (Kellogg Foundation 2006) approaches are widespread throughout the sector. These follow earlier theoretical models including competing values (Quinn and Rohrbaugh 1983), multiple constituency satisfaction (Zammuto 1984; resource mobilization (Yuchtman and Seashore 1967), and others are already available. Lecy, Schmitz, and Swedlund (2012) describe scholarship on organizational effectiveness since 1990. So, the shakiness of the analogy has to be counterbalanced against the efficiency of if not using, then at least taking advantage of existing models to assess individual organizations.

Possible approaches to measuring nonprofit health deriving from this body of resources include a systems approach and a “balanced scorecard” variant– similar to what was used in the NAI – as an appropriate start for a new NHI. Doing so favors the health of the members of sector as the object of analysis, as opposed to the munificence or scarcity of resources and conditions in the nonprofit environment that affect the sector's health. It is important not to lose sight of the environmental conditions as they embody ceteris paribus, the assumption that all else stays constant in the presence of a focal measure. But the question “how healthy is the environment for nonprofits?” is different from “how healthy are nonprofits?” The organizational performance analogues below are oriented to the second of those questions.

In attempting to establish a multi-dimensional civil society index for international comparisons, Salamon and Sokolowski (2003 and 2014) established three overall factors: capacity, sustainability and impact. Their capacity measures include workforce, civil society share of GDP, and diversification. Sustainability measure address diversification of income, government income, volunteering, and the rule of law. Impact includes sector wages and imputed volunteer wages along with evidence of advocacy. Data for 34 countries was gathered in a snapshot view, though it described years from 1995 to 2002. The chapter reports in their 2014 book described conditions in different nations but lacked the longitudinal nature of a real policy index.

An organizational performance model with prospects as a foundation for NHI is the Balanced Scorecard, for similar reasons as obtained for the NAI. Kaplan and Norton (1996) argued that financial success was a necessary but not a sufficient condition for business success; if the ultimate impact was vision, strategy, and performance, then customer satisfaction, learning and growth, and internal process elements were just as necessary. Their model has gained traction in multiple industry and sector settings, and its terms (“KPI” or Key Performance Indicator measuring an individual characteristic within the four dimensions) are especially widely used. The “balance” in the balanced scorecard refers to a rough equality of significance of all the dimensions; not only is each necessary and all of them sufficient, but each is associated with about 25% of the system's overall performance. We found this helpful in the Arts Index projects, for reasons that might emerge in preparing a new NHI. Figure 3 below, shows the original Balanced Scorecard model.

Figure 3: Kaplan and Norton's balanced scorecard.
Figure 3:

Kaplan and Norton's balanced scorecard.

5 What Could a Nonprofit Health Balanced Scorecard Look Like?

Numerous values contend in forming an impact model: parsimony, comprehensive coverage of the field being studied, clarity in communications, and the ability to support a measurement system that delivers clear answers. The NAI used a version of the Balanced Scorecard (Figure 2) designed to accommodate these values. Many of those circumstances apply to a prospective NHI, suggesting a “Nonprofit Health Balanced Scorecard,” with similar structural elements, shown in Figure 4. The dimensions largely mirror the NAI impact model, addressing economic vigor, scale, relative sustainability, and degree of social engagement of the sector, which over time contribute to nonprofit sector health.

Figure 4: Nonprofit health balanced scorecard.
Figure 4:

Nonprofit health balanced scorecard.

Table 1 suggests areas for measurement implied by such a model. Data for Table 1 could come from Urban Institute, and Bureaus of Labor Statistics, Census Bureau, and Economic Analysis. Other national-level data series are maintained by individual industry trade associations. Some data series can be synthesized from multiple sources, e. g. per capita measures.

Table 1:

Measurement implications of the balanced scorecard approach to nonprofit health.

Nonprofit health dimensionCould measure …
Financial flowsEarned, contributed, investment, event, and UBIT-classified revenues
Capacity and infrastructureNumbers of organizations, labor force size, asset base
Societal engagementConsumption of nonprofit service, provision of volunteer labor, rate of growth of the sector
Nonprofit competitivenessShare of the economic pie against other sectors, economic profitability, success relative to population, success compared to other sectors (government, market, household)

6 Level and Unit of Analysis Challenges for Nonprofit Health

Policymakers will be able to make better use of a prospective NHI if they have a clear sense of its range and domain. That clarity should include explication of the unit and level of analysis, the temporal frame, and the unitary vs. cross-sectional nature of the data.

The health of “the nonprofit sector” writ large aggregates in some ways the health of all organizations in it, without accounting for variation among them. Even before approaching a measurement model, it is important to know what falls within and what is outside the nonprofitness whose health is being indexed. A single multi-dimensional model of nonprofit health cannot apply equally to every location on the spectrum of nonprofitness. At a smaller level of aggregation are various subsets of nonprofit organizations (e. g., public charities, foundations, 501cx nonprofits that are tax exempt but not charitable). But “organizations” are not the sole inhabitants of the nonprofit ecology; as David Smith and colleagues have pointed out (Smith and Shen 2002), civil society has many unincorporated and collaborative entities. The body of nonprofit organizations can also be segmented by “industry” or area of service.

Comparing rationales for different models at the national vis-à-vis state, local, or global levels is instructive for a new NHI and its construct challenge. Nonprofits are chartered and/or located in one or another political jurisdiction. Is nonprofit health the same thing in Michigan as in Pennsylvania (Pettijohn and Boris, this volume)? Within, or between states, is it the same thing in rural, suburban, and urban settings? The LAI examined variation between regions related to arts and culture. Local planners and actors wanted to assess their arts vitality against the next town or county more than they wanted to compare the national state of the arts in 2012 to 2009. Some data series related to arts and culture showed variation in amount of arts content (e. g., arts industry employment), while others varied in type (e. g., balance of newer vs. older arts organizations). One LAI measure was of state arts grants per capita, of interest to municipal actors. Adjacent counties in two different states could have very different values on this indicator because of variation in state arts funding policies.

Because a policy index is a summary of trend data, the temporal frame of NHI is another part of the design challenge. Necessarily, a policy index is historical when it is computed, and the backwards look can inform agenda setting and identification of policy alternatives. But a time series can be extrapolated into the future. With enough data points, forecasting models based on a policy index can be tested with historical data for predictive or correlational links to future related phenomena. Another time dimension is the frequency of reporting. The more often a policy index report is issued, the more it can be used (assuming reliability). The NAI design was to report annually.

7 Making the Case for a Nonprofit Health Index

Given all of the above, what is the overall feasibility of the NHI as originally articulated by ARNOVA and Independent Sector? One remaining question is how it could impact policy. Events in 2017 suggest a realized NHI might create little or no actual impact at the national level. Legislative positioning leading up to the Tax Cuts and Jobs Act included public debate on two federal policy areas, changes in deductibility that were anticipated to reduce charitable giving, and overturning the Johnson Amendment limiting nonprofits’ political speech activities. Arguably, these were the most significant national policy issues affecting the nonprofit sector in many years. Elected officials and policy advocates strove mightily to score points and make arguments for or against either move. What is instructive for a potential NHI is that the past health of the nonprofit sector played little or no role in legislative action. The debate was carried out on rhetorical and normative grounds, driven (for the majority) by a policy intent to reduce taxes for segments of the tax base. NHI, if it existed, would have been unlikely to make a difference. Politically, other factors drive the policy environment for nonprofits. Perhaps actors with sector-wide interests, like national funders, research organizations like ARNOVA, or advocacy groups like Independent Sector, might benefit from such a compilation, but that is a relatively small, albeit influential audience.

More generally, using sector-wide aggregates, medians, and averages rests in part on the erroneous assumption of homogeneity in the nonprofit population, a construct problem for NHI for semantic, ontological, and epistemological reasons. With many visions of what the constructs are and how they matter, there are many ways to look – even using an impact model. While those sector wide aggregates and measures of central tendency are informative from a macroeconomic standpoint, they do not convey the normative content inherent in “health.” Combined with this construct challenge is the reality of the cost, financing, technical, and administrative burdens of maintaining a policy index. The NAI experience included many such burdens and could not endure past six years. Many of those same problems could be anticipated for a prospective NHI.

8 The Logic of Scale, Industry, and Locale Indexing of Nonprofit Health

A nonprofit policy index supporting policy development can be made more useful by narrowing its scope to subgroups of all nonprofit entities. Populations of nonprofits vary in strata, overall industry scale, product-market or industry identity, and location. Numerous historical trend data sets (as much as one or two years lagged) are available using standardized codes for industry (NAICS), nonprofit type (NTEE), location (FIPS, sometimes zip, sometimes census tract). Building policy indexes that exploit these dimensions instead of sector aggregate would be more productive for the development and conduct of policy, both private and public. This is the route followed by nonprofit industry groups who publish policy index reports with ongoing compendiums of operating data on their particular sector.

Example from the arts industries include Theatre Communications Group's annual Theatre Facts report, all of whose subjects are nonprofits, and the League of American Orchestras and its annual Orchestra Statistical Report for members, stratifying study subjects into eight tiers from grand big name metropolitan orchestras to amateur ensembles in small communities. In 2018, the National Center for Arts Research makes a county-level Cultural Vibrancy Index available on the web, a similar venture to the LAI. Further research beyond this paper could identify similar industry-specific associations, advocacy organizations, and professional societies in other areas of nonprofit enterprise – social justice, environment, education, health, etc.

Drilled-down subsets of nonprofit activity benefit policymakers by enabling comparison to other relevant aggregates of nonprofit actors. These subsets provide situational context for stratified, industry-specific and/or local analysis and comparison. Local policy actors seek to compare themselves to neighbors, peers, and perceived rivals for funding, recognition, and regulatory peace. Location quotients and percentile grading help to put local data into those contexts. Some customized Local Arts Index reports showed in which decile a particular county lay on multiple data series. Readers could compare that county to others that were similar in their arts offerings and also their exogenous geographical, socioeconomic, and demographic characteristics.

9 Conclusions

This paper has addressed three questions regarding a prospective Nonprofit Health Index: What would it measure, what is the right scale, and is it a practical idea to help policymakers? Generally, its answers are that nonprofit health is a variable construct, that national is not the right scale compared to industry or other segments of the nonprofit population, and that there is not a compelling case to initiate such an index. A national-level, longitudinal policy index measuring overall health of the nonprofit sector is an unlikely scenario.

Policymakers will be more likely to benefit from measuring nonprofit action in smaller groups that can be compared to national norms, which show relative performance but do not reach the aspirational target of “nonprofit sector health.” Having a higher score on a measure than the median actor means exactly that; it does not reveal the health of the median or even the highest actor. Salamon and Sokolowski (2003 and 2014) scaled their measures from minimum to maximum. Even the subset approach outlined above cannot solve the normative and semantic challenge of being limited by the range of any data series. Porter (2008) argued that similar forces affect all industry competitors and that in the long run, certain industries were more profitable (e. g., pharmaceuticals) than others (e. g., airlines). The same logic obtains in nonprofit studies. Nonprofit social service agencies serving indigent populations struggle for financial solvency. Maintaining positive cash flow when margins are thin may reveal a “going” concern, but not necessarily make a “healthy” one. The NAI showed about one in three arts organizations regularly run deficits. Large universities have the opposite problem of large asset bases in their endowments generating cash flow to support operations. In a turbulent environment for health care financing, the condition of nonprofit hospitals does little to inform broad knowledge of the condition of nonprofit organizations in general.

A policy index measuring nonprofit health over time should be match the level where policy actions can have an impact in the future. This guidance applies as much to private policymakers – advocates, social entrepreneurs, and prospective collaborators – as to government ones. Policymakers need to know not just past trends, but what present capacity for future action resides in the population of nonprofits. Each nonprofit population subgroup has individual capacity within organizations and collective capacity among them. Ultimately, how a particular slice of the nonprofit sector performed in the past can be assessed not only against standards of health, but also as a point for evaluating its potential for positive change. Policymakers should benefit from index projects targeting areas of concern more than macro-level generalizations.

References

Barney, Jay. 1991. “Firm Resources and Sustained Competitive Advantage.” Journal of Management 17 (1): 99–120.10.1016/S0742-3322(00)17018-4Search in Google Scholar

Cameron, K. S., and D. A. Whetten. 1983. Organizational Effectiveness. A Comparison of Multiple Models, New York: Academic Press10.1016/B978-0-12-157180-1.50006-9Search in Google Scholar

Hannan, M. T., and J. Freeman. 1984. “Structural Inertia and Organizational Change.” American Sociological Review 49 (2): 149–164. DOI: 10.2307/2095567.Search in Google Scholar

Herman, R. D., and D. O. Renz. 1997. “Multiple Constituencies and the Social Construction of Nonprofit Organization Effectiveness.” Nonprofit and Voluntary Sector Quarterly 26 (2): 185–206.10.1177/0899764097262006Search in Google Scholar

Kaplan, R. S., and D. P. Norton. 1996. “Using the Balanced Scorecard as a Strategic Management System.” Harvard Business Review 75–87.Search in Google Scholar

Kellogg Foundation, W.K. 2006. W.K. Kellogg Foundation Logic Model Development Guide. Accessed October 25, 2017. https://www.wkkf.org:443/resource-directory/resource/2006/02/wk-kellogg-foundation-logic-model-development-guideSearch in Google Scholar

Kushner, Roland J., and Randy Cohen. 2011 (March). “Measuring National-Level Cultural Capacity with the National Arts Index.” International Journal of Arts Management 13 (3): 20–40.Search in Google Scholar

Kushner, Roland J., and Randy Cohen. 2016. National Arts Index 2016. An Annual Measure of the Vitality of Arts and Culture in the U.S., Washington, DC: Americans for the Arts. Accessed January, 28 2018.Search in Google Scholar

Kushner, R. J., and R. Cohen. 2017. “Creating a Policy Index for the Arts.” Stanford Social Innovation Review 15 (4): 48–53.Search in Google Scholar

Kushner, R. J., and P. P. Poole. 1996. “Exploring Structure-Effectiveness Relationships in Nonprofit Arts Organizations.” Nonprofit Management and Leadership 7 (2): 119–136.10.1002/nml.4130070203Search in Google Scholar

Lecy, J. D., H. P. Schmitz, and H. Swedlund. 2012. “Non-Governmental and Not-for-Profit Organizational Effectiveness: A Modern Synthesis.” VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations 23 (2): 434–457.10.1007/s11266-011-9204-6Search in Google Scholar

O'Grady, M. J. 2006. “Commentary—Improving Federal Health Data: The Essential Partnership between Researcher and Policy Maker.” Health Services Research 41 (3p1): 984–989.10.1111/j.1475-6773.2006.00556.xSearch in Google Scholar

Pettijohn, S., and E. Boris. 2018. State Nonprofit Culture: Assessing the Impact of State Regulation on the Government-Nonprofit Relationship. [This volume].Search in Google Scholar

Porter, M. E. 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York: Free Press.Search in Google Scholar

Porter, M. E. 2008. “The Five Competitive Forces That Shape Strategy.” Harvard Business Review 95 (1): 78.Search in Google Scholar

Quinn, R. E., and J. Rohrbaugh. 1983. “A Spatial Model of Effectiveness Criteria: Towards A Competing Values Approach to Organizational Analysis.” Management Science 29 (3): 363–377.10.1287/mnsc.29.3.363Search in Google Scholar

Richmond, B. J., L. Mook, and Q. Jack. 2003. “Social Accounting for Nonprofits: Two Models.” Nonprofit Management and Leadership 13 (4): 308–324.10.1002/nml.2Search in Google Scholar

Salamon, L. M., and S. W. Sokolowski. 2003. Global Civil Society: Dimensions of the Nonprofit Sector. Bloomfield, UNITED STATES: Kumarian Press.Search in Google Scholar

Salamon, L. M., and S. W. Sokolowski. 2014. Global Civil Society. Vol. 2, Dimensions of the Nonprofit Sector. Boulder: Kumarian.Search in Google Scholar

Smith, D. H., and C. Shen. 2002. “The Roots of Civil Society: A Model of Voluntary Association Prevalence Applied to Data on Larger Contemporary Nations.” International Journal of Comparative Sociology 43 (2): 93–133. DOI: 10.1177/002071520204300201.Search in Google Scholar

Yuchtman, E., and S. E. Seashore. 1967. “A System Resource Approach to Organizational Effectiveness.” American Sociological Review 32 (6): 891–903.10.2307/2092843Search in Google Scholar

Zammuto, R. F. 1984. “A Comparison of Multiple Constituency Models of Organizational Effectiveness.” Academy of Management Review 9 (4): 606–616.10.5465/amr.1984.4277358Search in Google Scholar

Published Online: 2018-09-29

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