Archive for the ‘Social capital’ Category

Excerpts and Notes from Goldberg’s “Billions of Drops…”

December 23, 2015

Goldberg, S. H. (2009). Billions of drops in millions of buckets: Why philanthropy doesn’t advance social progress. New York: Wiley.

p. 8:
Transaction costs: “…nonprofit financial markets are highly disorganized, with considerable duplication of effort, resource diversion, and processes that ‘take a fair amount of time to review grant applications and to make funding decisions’ [citing Harvard Business School Case No. 9-391-096, p. 7, Note on Starting a Nonprofit Venture, 11 Sept 1992]. It would be a major understatement to describe the resulting capital market as inefficient.”

A McKinsey study found that nonprofits spend 2.5 to 12 times more raising capital than for-profits do. When administrative costs are factored in, nonprofits spend 5.5 to 21.5 times more.

For-profit and nonprofit funding efforts contrasted on pages 8 and 9.

p. 10:
Balanced scorecard rating criteria

p. 11:
“Even at double-digit annual growth rates, it will take many years for social entrepreneurs and their funders to address even 10% of the populations in need.”

p. 12:
Exhibit 1.5 shows that the percentages of various needs served by leading social enterprises are barely drops in the respective buckets; they range from 0.07% to 3.30%.

pp. 14-16:
Nonprofit funding is not tied to performance. Even when a nonprofit makes the effort to show measured improvement in impact, it does little or nothing to change their funding picture. It appears that there is some kind of funding ceiling implicitly imposed by funders, since nonprofit growth and success seems to persuade capital sources that their work there is done. Mediocre and low performing nonprofits seem to be able to continue drawing funds indefinitely from sympathetic donors who don’t require evidence of effective use of their money.

p. 34:
“…meaningful reductions in poverty, illiteracy, violence, and hopelessness will require a fundamental restructuring of nonprofit capital markets. Such a restructuring would need to make it much easier for philanthropists of all stripes–large and small, public and private, institutional and individual–to fund nonprofit organizations that maximize social impact.”

p. 54:
Exhibit 2.3 is a chart showing that fewer people rose from poverty, and more remained in it or fell deeper into it, in the period of 1988-98 compared with 1969-1979.

pp. 70-71:
Kotter’s (1996) change cycle.

p. 75:
McKinsey’s seven elements of nonprofit capacity and capacity assessment grid.

pp. 94-95:
Exhibits 3.1 and 3.2 contrast the way financial markets reward for-profit performance with the way nonprofit markets reward fund raising efforts.

Financial markets
1. Market aggregates and disseminates standardized data
2. Analysts publish rigorous research reports
3. Investors proactively search for strong performers
4. Investors penalize weak performers
5. Market promotes performance
6. Strong performers grow

Nonprofit markets
1. Social performance is difficult to measure
2. NPOs don’t have resources or expertise to report results
3. Investors can’t get reliable or standardized results data
4. Strong and weak NPOs spend 40 to 60% of time fundraising
5. Market promotes fundraising
6. Investors can’t fund performance; NPOs can’t scale

p. 95:
“…nonprofits can’t possibly raise enough money to achieve transformative social impact within the constraints of the existing fundraising system. I submit that significant social progress cannot be achieved without what I’m going to call ‘third-stage funding,’ that is, funding that doesn’t suffer from disabling fragmentation. The existing nonprofit capital market is not capable of [p. 97] providing third-stage funding. Such funding can arise only when investors are sufficiently well informed to make big bets at understandable and manageable levels of risk. Existing nonprofit capital markets neither provide investors with the kinds of information needed–actionable information about nonprofit performance–nor provide the kinds of intermediation–active oversight by knowledgeable professionals–needed to mitigate risk. Absent third-stage funding, nonprofit capital will remain irreducibly fragmented, preventing the marshaling of resources that nonprofit organizations need to make meaningful and enduring progress against $100 million problems.”

pp. 99-114:
Text and diagrams on innovation, market adoption, transformative impact.

p. 140:
Exhibit 4.2: Capital distribution of nonprofits, highlighting mid-caps

pages 192-3 make the case for the difference between a regular market and the current state of philanthropic, social capital markets.

p. 192:
“So financial markets provide information investors can use to compare alternative investment opportunities based on their performance, and they provide a dynamic mechanism for moving money away from weak performers and toward strong performers. Just as water seeks its own level, markets continuously recalibrate prices until they achieve a roughly optimal equilibrium at which most companies receive the ‘right’ amount of investment. In this way, good companies thrive and bad ones improve or die.
“The social sector should work the same way. .. But philanthropic capital doesn’t flow toward effective nonprofits and away from ineffective nonprofits for a simple reason: contributors can’t tell the difference between the two. That is, philanthropists just don’t [p. 193] know what various nonprofits actually accomplish. Instead, they only know what nonprofits are trying to accomplish, and they only know that based on what the nonprofits themselves tell them.”

p. 193:
“The signs that the lack of social progress is linked to capital market dysfunctions are unmistakable: fundraising remains the number-one [p. 194] challenge of the sector despite the fact that nonprofit leaders divert some 40 to 60% of their time from productive work to chasing after money; donations raised are almost always too small, too short, and too restricted to enhance productive capacity; most mid-caps are ensnared in the ‘social entrepreneur’s trap’ of focusing on today and neglecting tomorrow; and so on. So any meaningful progress we could make in the direction of helping the nonprofit capital market allocate funds as effectively as the private capital market does could translate into tremendous advances in extending social and economic opportunity.
“Indeed, enhancing nonprofit capital allocation is likely to improve people’s lives much more than, say, further increasing the total amount of donations. Why? Because capital allocation has a multiplier effect.”

“If we want to materially improve the performance and increase the impact of the nonprofit sector, we need to understand what’s preventing [p. 195] it from doing a better job of allocating philanthropic capital. And figuring out why nonprofit capital markets don’t work very well requires us to understand why the financial markets do such a better job.”

p. 197:
“When all is said and done, securities prices are nothing more than convenient approximations that market participants accept as a way of simplifying their economic interactions, with a full understanding that market prices are useful even when they are way off the mark, as they so often are. In fact, that’s the whole point of markets: to aggregate the imperfect and incomplete knowledge held by vast numbers of traders about much various securities are worth and still make allocation choices that are better than we could without markets.
“Philanthropists face precisely the same problem: how to make better use of limited information to maximize output, in this case, social impact. Considering the dearth of useful tools available to donors today, the solution doesn’t have to be perfect or even all that good, at least at first. It just needs to improve the status quo and get better over time.
“Much of the solution, I believe, lies in finding useful adaptations of market mechanisms that will mitigate the effects of the same lack of reliable and comprehensive information about social sector performance. I would even go so far as to say that social enterprises can’t hope to realize their ‘one day, all children’ visions without a funding allociation system that acts more like a market.
“We can, and indeed do, make incremental improvements in nonprofit funding without market mechanisms. But without markets, I don’t see how we can fix the fragmentation problem or produce transformative social impact, such as ensuring that every child in America has a good education. The problems we face are too big and have too many moving parts to ignore the self-organizing dynamics of market economics. As Thomas Friedman said about the need to impose a carbon tax at a time of falling oil prices, ‘I’ve wracked my brain trying to think of ways to retool America around clean-power technologies without a price signal–i.e., a tax–and there are no effective ones.”

p. 199:
“Prices enable financial markets to work the way nonprofit capital markets should–by sending informative signals about the most effective organizations so that money will flow to them naturally..”

p. 200:
[Quotes Kurtzman citing De Soto on the mystery of capital. Also see p. 209, below.]
“‘Solve the mystery of capital and you solve many seemingly intractable problems along with it.'”
[That’s from page 69 in Kurtzman, 2002.]

p. 201:
[Goldberg says he’s quoting Daniel Yankelovich here, but the footnote does not appear to have anything to do with this quote:]
“‘The first step is to measure what can easily be measured. The second is to disregard what can’t be measured, or give it an arbitrary quantitative value. This is artificial and misleading. The third step is to presume that what can’t be measured easily isn’t very important. This is blindness. The fourth step is to say that what can’t be easily measured really doesn’t exist. This is suicide.'”

Goldberg gives example here of $10,000 invested witha a 10% increase in value, compared with $10,000 put into a nonprofit. “But if the nonprofit makes good use of the money and, let’s say, brings the reading scores of 10 elementary school students up from below grade level to grade level, we can’t say how much my initial investment is ‘worth’ now. I could make the argument that the value has increased because the students have received a demonstrated educational benefit that is valuable to them. Since that’s the reason I made the donation, the achievement of higher scores must have value to me, as well.”

p. 202:
Goldberg wonders whether donations to nonprofits would be better conceived as purchases than investments.

p. 207:
Goldberg quotes Jon Gertner from the March 9, 2008, issue of the New York Times Magazine devoted to philanthropy:

“‘Why shouldn’t the world’s smartest capitalists be able to figure out more effective ways to give out money now? And why shouldn’t they want to make sure their philanthropy has significant social impact? If they can measure impact, couldn’t they get past the resistance that [Warren] Buffet highlighted and finally separate what works from what doesn’t?'”

p. 208:
“Once we abandon the false notions that financial markets are precision instruments for measuring unambiguous phenomena, and that the business and nonproft sectors are based in mutually exclusive principles of value, we can deconstruct the true nature of the problems we need to address and adapt market-like mechanisms that are suited to the particulars of the social sector.
“All of this is a long way (okay, a very long way) of saying that even ordinal rankings of nonprofit investments can have tremendous value in choosing among competing donation opportunities, especially when the choices are so numerous and varied. If I’m a social investor, I’d really like to know which nonprofits are likely to produce ‘more’ impact and which ones are likely to produce ‘less.'”

“It isn’t necessary to replicate the complex working of the modern stock markets to fashion an intelligent and useful nonprofit capital allocation mechanism. All we’re looking for is some kind of functional indication that would (1) isolate promising nonprofit investments from among the confusing swarm of too many seemingly worthy social-purpose organizations and (2) roughly differentiate among them based on the likelihood of ‘more’ or ‘less’ impact. This is what I meant earlier by increasing [p. 209] signals and decreasing noise.”

p. 209:
Goldberg apparently didn’t read De Soto, as he says that the mystery of capital is posed by Kurtzman and says it is solved via the collective intelligence and wisdom of crowds. This completely misses the point of the crucial value that transparent representations of structural invariance hold in market functionality. Goldberg is apparently offering a loose kind of market for which there is an aggregate index of stocks for nonprofits that are built up from their various ordinal performance measures. I think I find a better way in my work, building more closely from De Soto (Fisher, 2002, 2003, 2005, 2007, 2009a, 2009b).

p. 231:
Goldberg quotes Harvard’s Allen Grossman (1999) on the cost-benefit boundaries of more effective nonprofit capital allocation:

“‘Is there a significant downside risk in restructuring some portion of the philanthropic capital markets to test the effectiveness of performance driven philanthropy? The short answer is, ‘No.’ The current reality is that most broad-based solutions to social problems have eluded the conventional and fragmented approaches to philanthropy. It is hard to imagine that experiments to change the system to a more performance driven and rational market would negatively impact the effectiveness of the current funding flows–and could have dramatic upside potential.'”

p. 232:
Quotes Douglas Hubbard’s How to Measure Anything book that Stenner endorsed, and Linacre and I didn’t.

p. 233:
Cites Stevens on the four levels of measurement and uses it to justify his position concerning ordinal rankings, recognizing that “we can’t add or subtract ordinals.”

pp. 233-5:
Justifies ordinal measures via example of Google’s PageRank algorithm. [I could connect from here using Mary Garner’s (2009) comparison of PageRank with Rasch.]

p. 236:
Goldberg tries to justify the use of ordinal measures by citing their widespread use in social science and health care. He conveniently ignores the fact that virtually all of the same problems and criticisms that apply to philanthropic capital markets also apply in these areas. In not grasping the fundamental value of De Soto’s concept of transferable and transparent representations, and in knowing nothing of Rasch measurement, he was unable to properly evaluate to potential of ordinal data’s role in the formation of philanthropic capital markets. Ordinal measures aren’t just not good enough, they represent a dangerous diversion of resources that will be put into systems that take on lives of their own, creating a new layer of dysfunctional relationships that will be hard to overcome.

p. 261 [Goldberg shows here his complete ignorance about measurement. He is apparently totally unaware of the work that is in fact most relevant to his cause, going back to Thurstone in 1920s, Rasch in the 1950s-1970s, and Wright in the 1960s to 2000. Both of the problems he identifies have long since been solved in theory and in practice in a wide range of domains in education, psychology, health care, etc.]:
“Having first studied performance evaluation some 30 years ago, I feel confident in saying that all the foundational work has been done. There won’t be a ‘eureka!’ breakthrough where someone finally figures out the one true way to guage nonprofit effectiveness.
“Indeed, I would venture to say that we know virtually everything there is to know about measuring the performance of nonprofit organizations with only two exceptions: (1) How can we compare nonprofits with different missions or approaches, and (2) how can we make actionable performance assessments common practice for growth-ready mid-caps and readily available to all prospective donors?”

p. 263:
“Why would a social entrepreneur divert limited resources to impact assessment if there were no prospects it would increase funding? How could an investor who wanted to maximize the impact of her giving possibly put more golden eggs in fewer impact-producing baskets if she had no way to distinguish one basket from another? The result: there’s no performance data to attract growth capital, and there’s no growth capital to induce performance measurement. Until we fix that Catch-22, performance evaluation will not become an integral part of social enterprise.”

pp. 264-5:
Long quotation from Ken Berger at Charity Navigator on their ongoing efforts at developing an outcome measurement system. [wpf, 8 Nov 2009: I read the passage quoted by Goldberg in Berger’s blog when it came out and have been watching and waiting ever since for the new system. wpf, 8 Feb 2012: The new system has been online for some time but still does not include anything on impacts or outcomes. It has expanded from a sole focus on financials to also include accountability and transparency. But it does not yet address Goldberg’s concerns as there still is no way to tell what works from what doesn’t.]

p. 265:
“The failure of the social sector to coordinate independent assets and create a whole that exceeds the sum of its parts results from an absence of.. platform leadership’: ‘the ability of a company to drive innovation around a particular platform technology at the broad industry level.’ The object is to multiply value by working together: ‘the more people who use the platform products, the more incentives there are for complement producers to introduce more complementary products, causing a virtuous cycle.'” [Quotes here from Cusumano & Gawer (2002). The concept of platform leadership speaks directly to the system of issues raised by Miller & O’Leary (2007) that must be addressed to form effective HSN capital markets.]

p. 266:
“…the nonprofit sector has a great deal of both money and innovation, but too little available information about too many organizations. The result is capital fragmentation that squelches growth. None of the stakeholders has enough horsepower on its own to impose order on this chaos, but some kind of realignment could release all of that pent-up potential energy. While command-and-control authority is neither feasible nor desirable, the conditions are ripe for platform leadership.”

“It is doubtful that the IMPEX could amass all of the resources internally needed to build and grow a virtual nonprofit stock market that could connect large numbers of growth-capital investors with large numbers of [p. 267] growth-ready mid-caps. But it might be able to convene a powerful coalition of complementary actors that could achieve a critical mass of support for performance-based philanthropy. The challenge would be to develop an organization focused on filling the gaps rather than encroaching on the turf of established firms whose participation and innovation would be required to build a platform for nurturing growth of social enterprise..”

p. 268-9:
Intermediated nonprofit capital market shifts fundraising burden from grantees to intermediaries.

p. 271:
“The surging growth of national donor-advised funds, which simplify and reduce the transaction costs of methodical giving, exemplifies the kind of financial innovation that is poised to leverage market-based investment guidance.” [President of Schwab Charitable quoted as wanting to make charitable giving information- and results-driven.]

p. 272:
Rating agencies and organizations: Charity Navigator, Guidestar, Wise Giving Alliance.
Online donor rankings: GlobalGiving, GreatNonprofits, SocialMarkets
Evaluation consultants: Mathematica

Google’s mission statement: “to organize the world’s information and make it universally accessible and useful.”

p. 273:
Exhibit 9.4 Impact Index Whole Product
Image of stakeholders circling IMPEX:
Trading engine
Listed nonprofits
Data producers and aggregators
Trading community
Researchers and analysts
Investors and advisors
Government and business supporters

p. 275:
“That’s the starting point for replication [of social innovations that work]: finding and funding; matching money with performance.”

[WPF bottom line: Because Goldberg misses De Soto’s point about transparent representations resolving the mystery of capital, he is unable to see his way toward making the nonprofit capital markets function more like financial capital markets, with the difference being the focus on the growth of human, social, and natural capital. Though Goldberg intuits good points about the wisdom of crowds, he doesn’t know enough about the flaws of ordinal measurement relative to interval measurement, or about the relatively easy access to interval measures that can be had, to do the job.]

References

Cusumano, M. A., & Gawer, A. (2002, Spring). The elements of platform leadership. MIT Sloan Management Review, 43(3), 58.

De Soto, H. (2000). The mystery of capital: Why capitalism triumphs in the West and fails everywhere else. New York: Basic Books.

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2003). Measurement and communities of inquiry. Rasch Measurement Transactions, 17(3), 936-8 [http://www.rasch.org/rmt/rmt173.pdf].

Fisher, W. P., Jr. (2005). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-9 [http://www.livingcapitalmetrics.com/images/FisherJAM05.pdf].

Fisher, W. P., Jr. (2007, Summer). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-3 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2009a). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In M. Wilson, K. Draney, N. Brown & B. Duckor (Eds.), Advances in Rasch Measurement, Vol. Two (p. in press [http://www.livingcapitalmetrics.com/images/BringingHSN_FisherARMII.pdf]). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr. (2009b, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement (Elsevier), 42(9), 1278-1287.

Garner, M. (2009, Autumn). Google’s PageRank algorithm and the Rasch measurement model. Rasch Measurement Transactions, 23(2), 1201-2 [http://www.rasch.org/rmt/rmt232.pdf].

Grossman, A. (1999). Philanthropic social capital markets: Performance driven philanthropy (Social Enterprise Series 12 No. 00-002). Harvard Business School Working Paper.

Kotter, J. (1996). Leading change. Cambridge, Massachusetts: Harvard Business School Press.

Kurtzman, J. (2002). How the markets really work. New York: Crown Business.

Miller, P., & O’Leary, T. (2007, October/November). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-34.

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Living Capital Metrics for Financial and Sustainability Accounting Standards

May 1, 2015

I was very happy a few days ago to come across Jane Gleeson-White’s new book, Six Capitals, or Can Accountants Save the Planet? Rethinking Capitalism for the 21st Century. The special value for me in this book comes in the form of an accessible update on what’s been going on in the world of financial accounting standards. Happily, there’s been a lot of activity (check out, for instance, Amato & White, 2013; Rogers & White, 2015). Less fortunately, the activity seems to be continuing to occur in the same measurement vacuum it always has, despite my efforts in this blog to broaden the conversation to include rigorous measurement theory and practice.

But to back up a bit, recent events around sustainability metric standards don’t seem to be connected to previous controversies around financial standards and economic modeling, which were more academically oriented to problems of defining and expressing value. Gleeson-White doesn’t cite any of the extensive literature in those areas (for instance, Anielski, 2007; Baxter, 1979; Economist, 2010; Ekins, 1992, 1999; Ekins, Dresner, & Dahlstrom, 2008; Ekins, Hillman, & Hutchins, 1992; Ekins & Voituriez, 2009; Fisher, 2009b, 2009c, 2011; Young & Williams, 2010). Valuation is still a problem, of course, as is the analogy between accounting standards and scientific standards (Baxter, 1979). But much of the sensitivity of the older academic debate over accounting standards seems to have been lost in the mad, though well-intentioned, rush to devise metrics for the traditionally externalized nontraditional forms of capital.

Before addressing the thousands of metrics in circulation and the science that needs to be brought to bear on them (the ongoing theme of posts in this blog), some attention to terminology is important. Gleeson-White refers to six capitals (manufactured, liquid, intellectual, human, social, and natural), in contrast with Ekins (1992; Ekins, et al., 2008), who describes four (manufactured, human, social, and natural). Gleeson-White’s liquid capital is cash money, which can be invested in capital (a means of producing value via ongoing services) and which can be extracted as a return on capital, but is not itself capital, as is shown by the repeated historical experience in many countries of printing money without stimulating economic growth and producing value. Of her remaining five forms of capital, intellectual capital is a form of social capital that can satisfactorily be categorized alongside the other forms of organization-level properties and systems involving credibility and trust.

On pages 209-227, Gleeson-White takes up questions relevant to the measurement and information quality topics of this blog. The context here is informed by the International Integrated Reporting Council’s (IIRC) December 2013 framework for accounting reports integrating all forms of capital (Amato & White, 2013), and by related efforts of the Sustainability Accounting Standards Board (SASB) (Rogers & White, 2015). Following the IIRC, Gleeson-White asserts that

“Not all the new capitals can be quantified, yet or perhaps ever–for example, intellectual, human and social capital, much of natural capital–and so integrated reports are not expected to provide quantitative measures of each of the capitals.”

Of course, this opinion flies in the face of established evidence and theory accepted by both metrologists (weights and measures standards engineers and physicists) and psychometricians as to the viability of rigorous measurement standards for the outcomes of education, health care, social services, natural resource management, etc. (Fisher, 2009b, 2011, 2012a, 2012b; Fisher & Stenner, 2011a, 2013, 2015; Fisher & Wilson, 2015; Mari & Wilson, 2013; Pendrill, 2014; Pendrill & Fisher, 2013, 2015; Wilson, 2013; Wilson, Mari, Maul, & Torres Irribarra, 2015). Pendrill (2014, p. 26), an engineer, physicist, and past president of the European Association of National Metrology Institutes, for instance, states that “The Rasch approach…is not simply a mathematical or statistical approach, but instead [is] a specifically metrological approach to human-based measurement.” As is repeatedly shown in this blog, access to scientific measures sets the stage for a dramatic transformation of the potential for succeeding in the goal of rethinking capitalism.

Next, Gleeson-White’s references to several of the six capitals as the “living” capitals (p. 193) is a literal reference to the fact that human, social, and natural capital are all carried by people, organizations/communities, and ecosystems. The distinction between dead and living capital elaborated by De Soto (2000) and Fisher (2002, 2007, 2010b, 2011), which involves making any form of capital fungible by representing it in abstract forms negotiable in banks and courts of law, is not taken into account, though this would seem to be a basic requirement that must be fulfilled before the rethinking of capitalism could said to have been accomplished.

Gleeson-White raises the pointed question as to exactly how integrated reporting is supposed to provoke positive growth in the nontraditional forms of capital. The concept of an economic framework integrating all forms of capital relative to the profit motive, as described in Ekins’ work, for instance, and as is elaborated elsewhere in this blog, seems just over the horizon, though repeated mention is made of natural capitalism (Hawken, Lovins, & Lovins, 1999). The posing of the questions provided by Gleeson-White (pp. 216-217) is priceless, however:

“…given integrated reporting’s purported promise to contribute to sustainable development by encouraging more efficient resource allocation, how might it actually achieve this for natural and social capitals on their own terms? It seems integrated reporting does nothing to address a larger question of resource allocation….”

“To me the fact that integrated reporting cannot address such questions suggests that as with the example of human capital, its promise to foster efficient resource allocation pertains only to financial capital and not to the other capitals. If we accept that the only way to save our societies and planet is to reconceive them in terms of capital, surely the efficient valuing and allocation of all six capitals must lie at the heart of any economics and accounting for the planet’s scarce resources in the twenty-first century.
“There is a logical inconsistency here: integrated reporting might be the beginning of a new accounting paradigm, but for the moment it is being practiced by an old-paradigm corporation: essentially, one obliged to make a return on financial capital at the cost of the other capitals.”

The goal requires all forms of capital to be integrated into the financial bottom line. Where accounting for manufactured capital alone burns living capital resources for profit, a comprehensive capital accounting framework defines profit in terms of reduced waste. This is a powerful basis for economics, as waste is the common root cause of human suffering, social discontent and environmental degradation (Hawken, Lovins, & Lovins, 1999).

Multiple bottom lines are counter-productive, as they allow managers the option of choosing which stakeholder group to satisfy, often at the expense of the financial viability of the firm (Jensen, 2001; Fisher, 2010a). Economic sustainability requires that profits be legally, morally, and scientifically contingent on a balance of powers distributed across all forms of capital. Though the devil will no doubt lurk in the details, there is increasing evidence that such a balance of powers can be negotiated.

A key point here not brought up by Gleeson-White concerns the fact that markets are not created by exchange activity, but rather by institutionalized rules, roles, and responsibilities (Miller & O’Leary, 2007) codified in laws, mores, technologies, and expectations. Translating historical market-making activities as they have played out relative to manufactured capital in the new domains of human, social, and natural capital faces a number of significant challenges, adapting to a new way of thinking about tests, assessments, and surveys foremost among them (Fisher & Stenner, 2011b).

One of the most important contributions advanced measurement theory and practice (Rasch, 1960; Wright, 1977; Andrich, 1988, 2004; Fisher & Wright, 1994; Wright & Stone, 1999; Bond & Fox, 2007; Wilson, 2005; Engelhard, 2012; Stenner, Fisher, Stone, & Burdick, 2013) can make to the process of rethinking capitalism involves the sorting out of the myriad metrics that have erupted in the last several years. Gleeson-White (p. 223) reports, for instance, that the Bloomberg financial information network now has over 750 ESG (Environmental, Social, Governance) data fields, which were extracted from reports provided by over 5,000 companies in 52 countries.  Similarly, Rogers and White (2015) say that

“…today there are more than 100 organizations offering more than 400 corporate sustainability ratings products that assess some 50,000 companies on more than 8,000 metrics of environmental, social and governance (ESG) performance.”

As is also the case with the UN Millennium Development Goals (Fisher, 2011b), the typical use of these metrics as single-item “quantities” is based in counts of relevant events. This procedure misses the basic point that counts of concrete things in the world are not measures. Is it not obvious that I can have ten rocks to your two, and you can still have more rock than I do? The same thing applies to any kind of performance ratings, survey responses, or test scores. We assign the same numeric increase to every addition of one more count, but hardly anyone experimentally tests the hypothesis that the counts all work together to measure the same thing. Those who think there’s no need for precision science in this context are ignoring the decades of successful and widespread technical work in this area, at their own risk.

The repetition of history here is fascinating. As Ashworth (2004, p. 1,314) put it, historically, “The requirements of increased trade and the fiscal demands of the state fuelled the march toward a regular form of metrology.” For instance, in 1875 it was noted that “the existence of quantitative correlations between the various forms of energy, imposes upon men of science the duty of bringing all kinds of physical quantity to one common scale of comparison” (Everett, 1875, p. 9). The moral and economic  value of common scales was recognized during the French revolution, when, Alder (2002, p. 32) documents, it was asked:

“Ought not a single nation have a uniform set of measures, just as a soldier fought for a single patrie? Had not the Revolution promised equality and fraternity, not just for France, but for all the people of the world? By the same token, should not all of the world’s people use a single set of weights and measures to encourage peaceable commerce, mutual understanding, and the exchange of knowledge? That was the purpose of measuring the world.”

The value of rigorously measuring human, social and natural capital includes meaningfully integrating qualitative substance with quantitative convenience, reduced data volume, augmenting measures with uncertainty and consistency indexes, and the capacity to take missing data into account (making possible instrument equating, item banking, etc.)  In contrast with the usual methods, rigorous science demands that experiments determine which indicators cohere to measure the same thing by repeatedly giving the same values across samples, over time and space, and across subsets of indicators. Beyond such data-based results, advanced theory makes it possible to arrive at explanatory, predictive methods that add a whole new layer of efficiency to the generation of indicators (de Boeck & Wilson, 2004; Stenner, et al., 2013).

Finally, Gleeson-White (pp. 220-221) reports that “In July 2011, the SASB [Sustainability Accounting Standards Board] was launched in the United States to create standardized measures for the new capitals.” “Founded by environmental engineer and sustainability expert Jean Rogers in San Francisco, SASB is creating a full set of industry-specific standards for sustainability accounting, with the aim of making this information more consistent and comparable.” As of May 2014, the SASB vice chair is Mary Schapiro, former SEC chair, and the chairman of SASB is Michael Bloomfield, former mayor of NYC and founder of the financial information empire. The “SASB is developing nonfinancial standards for eighty-nine industries grouped in ten different sectors and aims to have completed this grueling task by February 2015. It is releasing each set of metrics as they are completed.”

Like the SASB and other groups, Gleeson-White (p. 222) reports, Bloomberg

“aims to use its metrics to start ‘standardizing the discourse around sustainability, so we’re all talking about the same things in the same way,’ as Bloomberg’s senior sustainability strategist Andrew Park put it. What companies ‘desperately want,’ he says, is ‘a legitimate voice’ to tell them: ‘This is what you need to do. You exist in this particular sector. Here are the metrics that you need to be reporting out on. So SASB will provide that. And we think that’s important, because that will help clean up the metrics that ultimately the finance community will start using.’
“Bloomberg wants to price environmental, social and governance externalities to legitimize them in the eyes of financial capital.”

Gleeson-White (p. 225) continues, saying

“Bloomberg wants to do more generally what Trucost did for Puma’s natural capital inputs: create standardized measures for the new capitals–such as ecosystem services and social impacts–so that this information can be aggregated and used by investors. Park and Ravenel call the failure to value clean air, water, stable coastlines and other environmental goods ‘as much a failure to measure as it is a market failure per se–one that could be addressed in part by providing these ‘unpriced’ resources with quantitative parameters that would enable their incorporation into market mechanisms. Such mechanisms could then appropriately ‘regulate’ the consumption of those resources.'”

Integrating well-measured living capitals into the context of appropriately configured institutional rules, roles, and responsibilities for efficient markets (Fisher, 2010b) should indeed involve a capacity to price these resources quantitatively, though this capacity alone would likely prove insufficient to the task of creating the markets (Miller & O’Leary, 2007; Williamson, 1981, 1991, 2005). Rasch’s (1960, pp. 110-115) deliberate patterning of his measurement models on the form of Maxwell’s equations for Newton’s Second Law provides a mathematical basis for connecting psychometrics with both geometry and natural laws, as well as with the law of supply and demand (Fisher, 2010c, 2015; Fisher & Stenner, 2013a).

This perspective on measurement is informed by an unmodern or amodern, post-positivist philosophy (Dewey, 2012; Latour, 1990, 1993), as opposed to a modern and positivist, or postmodern and anti-positivist, philosophy (Galison, 1997). The essential difference is that neither a universalist nor a relativist perspective is necessary to the adoption of practices of traceability to metrological standards. Rather, focusing on local, situated, human relationships, as described by Wilson (2004) in education, for instance, offers a way of resolving the false dilemma of that dichotomous contrast. As Golinski (2012, p. 35) puts it, “Practices of translation, replication, and metrology have taken the place of the universality that used to be assumed as an attribute of singular science.” Haraway (1996, pp. 439-440) harmonizes, saying “…embedded relationality is the prophylaxis for both relativism and transcendance.” Latour (2005, pp. 228-229) elaborates, saying:

“Standards and metrology solve practically the question of relativity that seems to intimidate so many people: Can we obtain some sort of universal agreement? Of course we can! Provided you find a way to hook up your local instrument to one of the many metrological chains whose material network can be fully described, and whose cost can be fully determined. Provided there is also no interruption, no break, no gap, and no uncertainty along any point of the transmission. Indeed, traceability is precisely what the whole of metrology is about! No discontinuity allowed, which is just what ANT [Actor Network Theory] needs for tracing social topography. Ours is the social theory that has taken metrology as the paramount example of what it is to expand locally everywhere, all while bypassing the local as well as the universal. The practical conditions for the expansion of universality have been opened to empirical inquiries. It’s not by accident that so much work has been done by historians of science into the situated and material extension of universals. Given how much modernizers have invested into universality, this is no small feat.
“As soon as you take the example of scientific metrology and standardization as your benchmark to follow the circulation of universals, you can do the same operation for other less traceable, less materialized circulations: most coordination among agents is achieved through the dissemination of quasi-standards.”

As Rasch (1980: xx) understood, “this is a huge challenge, but once the problem has been formulated it does seem possible to meet it.” Though some metrologically informed traceability networks have begun to emerge in education and health care (for instance, Fisher & Stenner, 2013, 2015; Stenner & Fisher, 2013), virtually everything remains to be done to make the coordination across stakeholders as fully elaborated as the standards in the natural sciences.

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Another Take on the Emerging Paradigm Shift

November 8, 2014

Over the course of human history, people have usually been able to rely on some stable source of authority and control in their lives, be it religion, the king or queen, or the social order itself. However benevolent or malevolent a regime might be, usually there have been clear lines along which blame or credit can be assigned.

So, even though the complexity and scale of success and failure in today’s world provide ample evidence that no one exerts centralized control over events, it is not surprising that many people today still find it comforting to think some individuals or groups must be manipulating others to their own ends. There is, however, an alternative point of view that may provide a more productive path toward effective action.

After all, efforts to date that have focused on the removal and replacement of any given group that appears to be in control have simply resulted in an alteration of the system, and not the institution of a fundamentally new system. Thus, socialist and communist governments have failed in large part because they were unable to manage resources as effectively as capitalist systems do (which is, of course, not all that well). That is, despite the appearance of having put in place a radically different system of priorities, the constraints of socioeconomics themselves did not change in the context of socialist and communist regimes.

The individual incumbents of social and economic positions have nothing whatsoever to do with the creation of the socioeconomic system’s likelihoods of success and failure, and if they had not accepted their roles in that system, others would have. Changing the system is much more difficult, both conceptually and practically, than merely assigning blame and replacing an individual or group with another individual or group. To the extent the system remains the same, changing the occupants within it makes little difference.

The idea is much the same as was realized in industry when it shifted from quality control’s “tail-chopping” methods to continuous quality improvement’s “curve-shifting” methods. In the former, a certain ratio of acceptable to malformed parts is dictated by the system’s materials and processes. Quality control simply removes the bad parts from the production line and does nothing to change the system. Since quality is often normally distributed, taking the statistical shape of a bell curve, it is accordingly inevitable that cutting off the bad end of that distribution (tail-chopping) only results in it being filled in again in the next production cycle.

Continuous quality improvement methods, in contrast, focus on changing the system and on reducing the likelihood of producing bad parts. Efforts of these kind move the entire quality distribution up the scale so that no parts fall in the previous distribution’s bad tail at all. Of course, the outcomes of our socioeconomic system’s processes are very different from the manufacturing of machine parts. The point of this simple illustration is only that there is remarkable value in thinking less about removing undesired individuals from a process and in thinking more about changing the process itself.

There is no denying that those who seem to be in control benefit disproportionately from others’ efforts. But even though they have had little or nothing to do with creating the system that confers these benefits on them, they certainly do have a vested interest in maintaining that system. This fact reveals another important aspect of any solution that will prove truly viable: the new system must provide benefits not available under the old one. The shift from old to new cannot be a matter of mere will power or organizational efficiency. It must come about as a result of the attractions offered by the new system, which motivate behavior changes universally with little or no persuasion. Qualitatively different classes of opportunities and rewards can come about only by integrating into the system features of the environment that were excluded from the previous system. The central problem of life today is how to provoke this kind of shift and its new integrations.

We can begin to frame this problem in its proper context when we situate it horizontally as an ecological problem and vertically as an evolutionary one. In the same way that ecological niches define the evolutionary opportunities available to species of plants and animals, historical and cultural factors set up varying circumstances to which human societies must adapt. Biological and social adaptations both become increasingly complex over time, systematically exhibiting characteristic patterns in the ways matter, energy, and information are functionally integrated.

The present form of contemporary global society has evolved largely in terms of the Western European principles of modern science, capitalism, and democracy. These principles hinge on the distinction between a concrete, solid, and objective world and an impressionistic, intuitive, and subjective mind. For instance, science and economics focus traditionally on measuring and managing material things and processes, like volts, meters, kilograms, barrels, degrees Celsius, liters, speed, flows, etc. Human, social, and environmental issues are treated statistically, not in terms of standardized metric units, and they are economically regarded as “externalities” excluded from profit and loss calculations.

So, if qualitatively different classes of opportunities and rewards can come about only by integrating into the system features of the environment that were excluded from the previous system, what can we do to integrate the subjective with the objective, and to also then incorporate standardized metric units for the externalities of human, social, and environmental capital into science and economics? The question demands recognition of a) a new system of ecological niches with their own unique configurations of horizontal relationships, and b) the evolution of new species capable of adapting to life in these niches.

The problem is compounded by the complexity of seeing the new system of niches as emerging from the existing system of ecological relationships. Economically speaking, today’s cost centers will be tomorrow’s profit drivers. Scientifically speaking, sources of new repeatable and stable phenomena will have to be identified in what are today assumed to be unrepeatable and unstable phenomena, and will then have to be embodied in instrumental ensembles.

The immediate assumption, which we will have to strive to overcome, is that any such possibilities for new economic and scientific opportunities could hardly be present in the world today and not be widely known and understood. A culturally ingrained presupposition we all share to some extent is that objective facts are immediately accessible and become universally adopted for their advantages as soon as they are recognized. Claims to the contrary can safely be ignored, even if, or perhaps especially if, they represent a truly original potential for system change.

This assumption is an instance of what behavioral economists like Simon and Kahnemann refer to as bounded rationality, which is the idea that language and culture prethink things for us in ways we are usually unaware of. Research has shown that many decisions in daily life are tinged with emotion, such that a certain kind of irrationality takes an irrefutable place in how we think. Examples include choices involving various combinations of favorable and unfavorable odds of profiting from some exchange. Small but sure profits are often ignored in favor of larger and less sure profits, or mistaken calculations are assumed correct, to the disadvantage of the decision maker. There is surely method in the madness, but the pure rationality of an ideal thought process can no longer be accommodated.

Given the phenomenon of bounded rationality, and the complexity of the metasystematic shift that’s needed, how is change to be effected? As Einstein put it, problems of a certain kind cannot be solved from within the same framework that gave rise to them. As long as we continue to think in terms of marshalling resources to apply to the solution of a problem we have failed in conceiving the proper magnitude and scope of the problem we face.

We must instead think in terms of problem-solution units that themselves embody a new evolutionary species functioning within a new system of ecological niches. And these species-niche combinations must be born fully functional and viable, like birds from lizard eggs, caught up in the flow and play of their matter, energy and information streams from the moment of their arrival.

A vitally important aspect of this evolutionary leap is that the new system emerge of its own accord, seemingly with a will of its own. But it will not take shape as a result of individuals or groups deliberately executing a comprehensive design. There will be no grand master architect, though the co-incidence of multiple coordinations and alignments will seem so well planned that many may assume one exists.

It may be, however, that a new spontaneously self-organizing culture might be grown from a few well-placed spores or seeds. The seeds themselves need to be viable in terms of their growth potential and the characteristics of the particular species involved. But equally important are the characteristics of the environment in which the seeds are planted. Bernstein (2004) describes four conditions necessary to the birth of plenty in the modern world:

  1. Property rights: those who might create new forms of value need to own the fruits of their labors.
  2. Scientific rationalism: innovation requires a particular set of conceptual tools and a moral environment in which change agents need not fear retribution.
  3. Capital markets: investors must be able to identify entrepreneurs and provide them with the funds they need to pursue their visions.
  4. Transportation/communications: new products and the information needed to produce and market them must have efficient channels in which to move.

If we take the new emerging culture as unmodern, nonmodern, or amodern, might a new paradigm of plenty similarly take shape as these four conditions are applied not just to manufactured capital, land, and labor, but to human capital (abilities, health, performance), social capital (trust, honesty, dependability, etc.), and natural capital (the environmental services of watersheds, fisheries, estuaries, forests, etc.)? Should not we own legal title to defined shares of each form of capital? Should not science be systematically employed in research on each form of capital? Should not investments in each form of capital be accountable? Should not each form of capital be mobile and fungible within established networks? Should not there be common languages serving as common currencies for the exchange of each form of capital? Instead of assuming the answers to these questions are uniformly “No,” should not we at least entertain them long enough to firmly establish why they cannot be “Yes”?

An Entrepreneurial Investment Model Alternative to Picketty’s Taxation Approach to Eliminating Wealth Disparities

May 14, 2014

Is taxation the only or the best solution to inequality? The way discussions of wealth disparities inevitably focus on variations in how, whom or what to tax, it is easy to assume there are no viable alternatives to taxation. But if the point is to invest in those with the most potential for making significant gains in productivity, so as to maximize the returns we realize, do we not wrongly constrain the domain of possible solutions when we misconceive an entrepreneurial problem in welfare terms?

Why can’t we require minimum levels of investment in social capital stocks and bonds offered by schools, hospitals, NGOs, etc? In human capital instruments offered by individuals? Why should not we expect those investments to be used to create new value? What supposed law of nature says it is impossible to associate new human, social and environmental value with stable and meaningful prices? And if there is such a law (such as Kenneth Arrow (1963) proposed), how can we break it? Why can’t we reconceive human and social capital stocks and flows in new ways?

There is one very good reason why we cannot now make such requirements, and it is the same reason why liberals (including me) had better become accustomed to accepting the failure of their agenda. That reason is this: social and environmental externalities. Inequality is inevitable only as long as we do not change the ways we deal with externalities. They can no longer be measured and managed in the same ways. They must be put on the books, brought into the models, measured scientifically, and traded in efficient markets. We have to invent accountability and accounting systems that harness the energy of the profit motive for the greater good—that actually grow authentic wealth and not mere money—and we have to do this far more effectively than has ever been done before.

It’s a tall order. But there are resources available to us that have not yet been introduced into the larger conversation. There are options to consider that need close study and creative experimentation. Proceeding toward the twin futilities of premature despair or unrealistic taxation will only set up another round of self-fulfilling prophecies inexorably grinding to yet another unforeseen but fully foretold disaster. Conversations about how to shape the roles, rules and institutions that make markets what they are (Miller and O’Leary, 2007) need to take place for human, social, and natural capital (Fisher and Stenner, 2011b). Indeed, those conversations are already well underway, as can be seen in the prior entries in this blog and in the sources listed below.

Arrow, K. J. (1963). Uncertainty and the welfare economics of medical care. American Economic Review, 53, 941-973.

Fisher, W. P., Jr. (2007). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-1093 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2009a). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 42(9), 1278-1287.

Fisher, W. P., Jr. (2009b). NIST Critical national need idea White Paper: Metrological infrastructure for human, social, and natural capital (http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology (11 pages).

Fisher, W. P., Jr. (2010a, 22 November). Meaningfulness, measurement, value seeking, and the corporate objective function: An introduction to new possibilities. Sausalito, California: LivingCapitalMetrics.com (http://ssrn.com/abstract=1713467).

Fisher, W. P. J. (2010b). Measurement, reduced transaction costs, and the ethics of efficient markets for human, social, and natural capital (http://ssrn.com/abstract=2340674). Bridge to Business Postdoctoral Certification, Freeman School of Business: Tulane University.

Fisher, W. P., Jr. (2010c, June 13-16). Rasch, Maxwell’s method of analogy, and the Chicago tradition. In G. Cooper (Ed.), https://conference.cbs.dk/index.php/rasch/Rasch2010/paper/view/824. Probabilistic models for measurement in education, psychology, social science and health: Celebrating 50 years since the publication of Rasch’s Probabilistic Models. FUHU Conference Centre, Copenhagen, Denmark: University of Copenhagen School of Business.

Fisher, W. P., Jr. (2011a). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 12(1), 49-66.

Fisher, W. P., Jr. (2011b, Thursday, September 1). Measurement, metrology and the coordination of sociotechnical networks. In S. Bercea (Ed.), New Education and Training Methods. International Measurement Confederation (IMEKO). Jena, Germany: http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24491/ilm1-2011imeko-017.pdf.

Fisher, W. P., Jr. (2012a). Measure and manage: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (pp. 43-63). New York: Palgrave Macmillan.

Fisher, W. P., Jr. (2012b, May/June). What the world needs now: A bold plan for new standards [Third place, 2011 NIST/SES World Standards Day paper competition]. Standards Engineering, 64(3), 1 & 3-5 [http://ssrn.com/abstract=2083975].

Fisher, W. P., Jr., & Stenner, A. J. (2011a, January). Metrology for the social, behavioral, and economic sciences. http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36.

Fisher, W. P., Jr., & Stenner, A. J. (2011b, August 31 to September 2). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium. Jena, Germany: http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf.

Fisher, W. P., Jr., & Stenner, A. J. (2013a). On the potential for improved measurement in the human and social sciences. In Q. Zhang & H. Yang (Eds.), Pacific Rim Objective Measurement Symposium 2012 Conference Proceedings (pp. 1-11). Berlin, Germany: Springer-Verlag.

Fisher, W. P., Jr., & Stenner, A. J. (2013b). Overcoming the invisibility of metrology: A reading measurement network for education and the social sciences. Journal of Physics: Conference Series, 459(012024), http://iopscience.iop.org/1742-6596/459/1/012024.

Miller, P., & O’Leary, T. (2007, October/November). Mediating instruments and making markets: Capital budgeting, science and the economy. Accounting, Organizations, and Society, 32(7-8), 701-734.

Professional capital as product of human, social, and decisional capitals

April 18, 2014

Leslie Pendrill gave me a tip on a very interesting book, Professional Capital, by Michael Fullan. The author’s distinction between business capital and professional capital is somewhat akin to my distinction (Fisher, 2011) between dead and living capital. The primary point of contact between Fullan’s sense of capital and mine stems from his inclusion of social and decisional capital as crucial enhancements of human capital.

Of course, defining human capital as talent, as Fullan does, is not going to go very far toward supporting generalized management of it. Efficient markets require that capital be represented in transparent and universally available instruments (common currencies or metrics). Transparent, systematic representation makes it possible to act on capital abstractly, in laboratories, courts, and banks, without having to do anything at all with the physical resource itself. (Contrast this with socialism’s focus on controlling the actual concrete resources, and the resulting empty store shelves, unfulfilled five-year plans, pogroms and purges, and overall failure.) Universally accessible transparent representations make capital additive (amounts can be accrued), divisible (it can be divided into shares), and mobile (it can be moved around in networks accepting the currency/metric). (See references below for more information.)

Fullan cites research by Carrie Leanna at the U of Pittsburgh showing that teachers with high social capital increased their students math scores by 5.7% more than teachers with low social capital. The teachers with the highest skill levels (most human capital) and high social capital did the overall best. Low-ability teachers in schools with high social capital did as well as average teachers.

This is great, but the real cream of Fullan’s argument concerns the importance of what he calls decisional capital. I don’t think this will likely work out to be entirely separate from human capital, but his point is well taken: the capacity to consistently engage with students with competence, good judgment, insight, inspiration, creative improvisation, and openness to feedback in a context of shared responsibility is vital. All of this is quite consistent with recent work on collective intelligence (Fischer, Giaccardi, Eden, et al., 2005; Hutchins, 2010; Magnus, 2007; Nersessian, 2006; Woolley, Chabris, Pentland, et al., 2010; Woolley and Fuchs, 2011).

And, of course, you can see this coming: decisional capital is precisely what better measurement provides. Integrated formative and summative assessment informs decision making at the individual level in ways that are otherwise impossible. When those assessments are expressed in uniformly interpretable and applicable units of measurement, collective intelligence and social capital are boosted in the ways documented by Leanna as enhancing teacher performance and boosting student outcomes.

Anyway, just wanted to share that. It fits right in with the trading zone concept I presented at IOMW (the slides are available on my LinkedIn page).

Fischer, G., Giaccardi, E., Eden, H., Sugimoto, M., & Ye, Y. (2005). Beyond binary choices: Integrating individual and social creativity. International Journal of Human-Computer Studies, 63, 482-512.

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2003). Measurement and communities of inquiry. Rasch Measurement Transactions, 17(3), 936-938 [http://www.rasch.org/rmt/rmt173.pdf].

Fisher, W. P., Jr. (2004a, Thursday, January 22). Bringing capital to life via measurement: A contribution to the new economics. In R. Smith (Chair), Session 3.3B. Rasch Models in Economics and Marketing. Second International Conference on Measurement. Perth, Western Australia:  Murdoch University.

Fisher, W. P., Jr. (2004b, Friday, July 2). Relational networks and trust in the measurement of social capital. Twelfth International Objective Measurement Workshops. Cairns, Queensland, Australia: James Cook University.

Fisher, W. P., Jr. (2005a). Daredevil barnstorming to the tipping point: New aspirations for the human sciences. Journal of Applied Measurement, 6(3), 173-179.

Fisher, W. P., Jr. (2005b, August 1-3). Data standards for living human, social, and natural capital. In Session G: Concluding Discussion, Future Plans, Policy, etc. Conference on Entrepreneurship and Human Rights. Pope Auditorium, Lowenstein Bldg, Fordham University.

Fisher, W. P., Jr. (2007, Summer). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-1093 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2008a, 3-5 September). New metrological horizons: Invariant reference standards for instruments measuring human, social, and natural capital. 12th IMEKO TC1-TC7 Joint Symposium on Man, Science, and Measurement. Annecy, France: University of Savoie.

Fisher, W. P., Jr. (2008b, March 28). Rasch, Frisch, two Fishers and the prehistory of the Separability Theorem. In J. William P. Fisher (Ed.), Session 67.056. Reading Rasch Closely: The History and Future of Measurement. American Educational Research Association. New York City [Paper available at SSRN: http://ssrn.com/abstract=1698919%5D: Rasch Measurement SIG.

Fisher, W. P., Jr. (2009a, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 42(9), 1278-1287.

Fisher, W. P., Jr. (2009b). NIST Critical national need idea White Paper: Metrological infrastructure for human, social, and natural capital (http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology (11 pages).

Fisher, W. P., Jr. (2010a, 22 November). Meaningfulness, measurement, value seeking, and the corporate objective function: An introduction to new possibilities. Sausalito, California: LivingCapitalMetrics.com (http://ssrn.com/abstract=1713467).

Fisher, W. P. J. (2010b). Measurement, reduced transaction costs, and the ethics of efficient markets for human, social, and natural capital (p. http://ssrn.com/abstract=2340674). Bridge to Business Postdoctoral Certification, Freeman School of Business: Tulane University.

Fisher, W. P., Jr. (2010c). The standard model in the history of the natural sciences, econometrics, and the social sciences. Journal of Physics: Conference Series, 238(1), http://iopscience.iop.org/1742-6596/238/1/012016/pdf/1742-6596_238_1_012016.pdf.

Fisher, W. P., Jr. (2011a). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In N. Brown, B. Duckor, K. Draney & M. Wilson (Eds.), Advances in Rasch Measurement, Vol. 2 (pp. 1-27). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr. (2011b). Measuring genuine progress by scaling economic indicators to think global & act local: An example from the UN Millennium Development Goals project. LivingCapitalMetrics.com [Online]. Available: http://ssrn.com/abstract=1739386 (Accessed 18 January 2011).

Fisher, W. P., Jr. (2012). Measure and manage: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (pp. 43-63). New York: Palgrave Macmillan.

Fisher, W. P., Jr., & Stenner, A. J. (2005, Tuesday, April 12). Creating a common market for the liberation of literacy capital. In R. E. Schumacker (Ed.), Rasch Measurement: Philosophical, Biological and Attitudinal Impacts. American Educational Research Association. Montreal, Canada: Rasch Measurement SIG.

Fisher, W. P., Jr., & Stenner, A. J. (2011a, January). Metrology for the social, behavioral, and economic sciences. Available: http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36 (Accessed 12 January 2014).

Fisher, W. P., Jr., & Stenner, A. J. (2011b, August 31 to September 2). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium. Jena, Germany: http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf.

Hutchins, E. (2010). Cognitive ecology. Topics in Cognitive Science, 2, 705-715.

Magnus, P. D. (2007). Distributed cognition and the task of science. Social Studies of Science, 37(2), 297-310.

Nersessian, N. J. (2006, December). Model-based reasoning in distributed cognitive systems. Philosophy of Science, pp. 699-709.

Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010, 29 October). Evidence for a collective intelligence factor in the performance of human groups. Science, pp. 686-688.

Woolley, A. W., & Fuchs, E. (2011, September-October). Collective intelligence in the organization of science. Organization Science, pp. 1359-1367.

On the Criterion Institute’s Leaders Shaping Markets initiative

November 14, 2013

The Criterion Institute’s Leaders Shaping Markets initiative is an encouraging development in large part because of its focus on systems level change. As the Institute recognizes, the questions being raised and the resources being invested are essential to overcoming recurrent problems of fragmentation and marginalization in efforts being made in more piecemeal fashion across a number of other arenas.

Of particular interest from the Institute’s second roundtable session is Joy Anderson’s list of Strategies for Shaping Market Systems. Anderson presents five strategies:

  1. reframing the issues, problems, and boundaries of the system;
  2. engaging systems of power, elegantly;
  3. continuously identifying leverage points in the system;
  4. building structures and leadership for sustained systems-level disruption; and
  5. attending to change over time and across context.

Reframing is the right place to start. As I’ve said elsewhere in this blog, the problem is the problem. At this level of complexity, problems cannot be solved from within the same paradigm they were born from. Conceiving ways of redefining problems that truly reframe the issues and boundaries of a system is hard enough, but implementing them is even harder.

From my point of view, philosophically, the central problem that makes everything so difficult has to do with our deeply ingrained Western habits of thought around not viewing problems and solutions as of a piece, as wholes in which each implies the other. As long as we keep defining problems and solutions in ways that separate them, as though the solution is in no way involved in perpetuating the problem, we are hopelessly stuck.

So we restrict our options for solving problems by the way we frame the issues. And when we misidentify the problem, as when we fail to properly frame it, then we will likely not only not solve it, we will make it worse. That seems to be exactly what’s been going on in the struggle for economic and social justice for decades and centuries.

So if we reframe the problem of shaping markets around the mutual implication of problems and solutions, how do we move to the next step, to engaging systems of power, elegantly? There are a lot of deep and complex philosophical concepts involved here, but we can cut to the chase and note that our language and tools embody problem-solution unities. Social ecologies of relationships define the meanings and uses of things and ideas.

One way of engaging systems of power elegantly to shape markets might then be to harness the power driving those markets in new, more efficient and meaningful ways. The question that then immediately arises concerns the next of Anderson’s five points: where do we find the leverage in the system that would enable the harnessing of its power?

There is likely no greater concentration of power in markets than the profit motive. How might it become the primary lever for engaging the power of the market? We might, for instance, deploy tools and ideas that co-opt the interests of the systems of power by enhancing the predictability of market forces and sustainability of profits. Concentrating now on dwelling within the problem-solution unity of how to shape markets, we can tap into a key factor that makes markets efficient: we manage what we measure, and management is facilitated when we can measure quality and quantity cheaply and easily.

Common currencies for the exchange of value are essential not just to trade and commerce, but also take shape as the standard metrics employed in science, engineering, music, and as the signs and symbols of basic communication. Money is such an easy to manage measure of value that the problems we are addressing here stem in large part from using it too exclusively as a proxy for the authentic wealth we really want. Engaging with systems of power elegantly also then requires us to think in terms of extending the power of standard units of measurement into the new domains of intangible assets: human, social, and natural capital.

This is where we arrive at the structures for sustained system-level disruption. Current economic models and financial spreadsheets focus on the three classic forms of capital: land, labor, and manufactured tools/commodities. (Money, as liquid capital, is fungible relative to all three.) Of these three, we have a metric system for measuring and managing only property and manufactured tools/commodities.

Green economics offers an alternative four-capitals model that adds social capital and reframes land as natural capital and labor as human capital. Both of the latter are found to be far more complex and valuable than their usual reductions to a piece of ground or “hands” would suggest. Human capital involves health, abilities, and motivations; natural capital includes the earth’s air and water purification systems, and food supplies. The addition of social capital is justified on the grounds that, without it, markets are impossible.

What we do not have is a metric system for three of the four forms of capital. Nor do we have the legal and financial systems needed to bring these forms of capital to life in efficient markets, to make them recognized and accepted in banks and courts of law. We further also do not have leaders aware of the need for these structures, and of the established basis in scientific research that makes them viable.

The science is complex and technical, but it brings to bear practical capacities for meaningful, individual level, qualitatively informative and quantitatively rigorous measurement. There is considerable elegance in this method of approaching engagement with the systems of power. There is mathematical beauty in the symmetry and harmony of instruments tuned to the same scales. There is exquisite grace in the way the program for shaping markets grows organically from the seeds of existing markets. The human value of enabling the realization of heretofore unreachable degrees of individual potentials would be enormous, as would be the social value of being able to make returns on investments in education, health care, social services, and the environment accountable.

Successful new markets harnessing the profit motive in the name of socially responsible and sustainable economies well ought to provoke a new cultural renaissance as the proven relationships between higher rates of educational attainment and health, community relations, and environmental quality are born out. The challenges are huge, but properly framing the problems and their solutions will unify our energies in common purpose like never before, bringing joy to the effort.

For further reading along these lines, see:

Fisher, W. P., Jr., & Stenner, A. J. (2011, August 31 to September 2). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium, http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf, Jena, Germany.

Fisher, W. P., Jr. (2009). NIST Critical national need idea White Paper: metrological infrastructure for human, social, and natural capital. Washington, DC: National Institute for Standards and Technology, http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf

Fisher, W. P., Jr., & Stenner, A. J. (2011, January). Metrology for the social, behavioral, and economic sciences (Social, Behavioral, and Economic Sciences White Paper Series). Washington, DC: National Science Foundation, http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36

Fisher, W. P., Jr. (2012, May/June). What the world needs now: A bold plan for new standards [Third place, 2011 NIST/SES World Standards Day paper competition]. Standards Engineering, 64(3), 1 & 3-5, http://ses-standards.org/associations/3698/files/2011WSDthirdplacepaper.pdf or http://ssrn.com/abstract=2083975

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 42(9), 1278-1287, http://doi:10.1016/j.measurement.2009.03.014

Fisher, W. P., Jr. (2011). Bringing human, social, and natural capital to life: Practical consequences and opportunities. Journal of Applied Measurement, 12(1), 49-66, http://ssrn.com/abstract=1698867

Fisher, W. P. J. (2010). Measurement, reduced transaction costs, and the ethics of efficient markets for human, social, and natural capital. Qualifying Paper, Bridge to Business Postdoctoral Certification, Freeman School of Business, Tulane University, http://ssrn.com/abstract=2340674

https://livingcapitalmetrics.wordpress.com/2010/01/13/reinventing-capitalism/

Dispelling Myths about Measurement in Psychology and the Social Sciences

August 27, 2013

Seven common assumptions about measurement and method in psychology and the social sciences stand as inconsistent anomalies in the experience of those who have taken the trouble to challenge them. As evidence, theory, and instrumentation accumulate, will we see a revolutionary break and disruptive change across multiple social and economic levels and areas as a result? Will there be a slower, more gradual transition to a new paradigm? Or will the status quo simply roll on, oblivious to the potential for new questions and new directions? We shall see.

1. Myth: Qualitative data and methods cannot really be integrated with quantitative data and methods because of opposing philosophical assumptions.

Fact: Qualitative methods incorporate a critique of quantitative methods that leads to a more scientific theory and practice of measurement.

2. Myth: Statistics is the logic of measurement.

Fact: Statistics did not emerge as a discipline until the 19th century, while measurement, of course, has been around for millennia. Measurement is modeled at the individual level within a single variable whereas statistics model at the population level between variables. Data are fit to prescriptive measurement models using the Garbage-In, Garbage-Out (GIGO) Principle, while descriptive statistical models are fit to data.

3. Myth: Linear measurement from ordinal test and survey data is impossible.

Fact: Ordinal data have been used as a basis for invariant linear measures for decades.

4. Myth: Scientific laws like Newton’s laws of motion cannot be successfully formulated, tested, or validated in psychology and the social sciences.

Fact: Mathematical laws of human behavior and cognition in the same form as Newton’s laws are formulated, tested, and validated in numerous Rasch model applications.

5. Myth: Experimental manipulations of psychological and social phenomena are inherently impossible or unethical.

Fact: Decades of research across multiple fields have successfully shown how theory-informed interventions on items/indicators/questions can result in predictable, consistent, and substantively meaningful quantitative changes.

6. Myth: “Real” measurement is impossible in psychology and the social sciences.

Fact: Success in predictive theory, instrument calibration, and in maintaining stable units of comparison over time are all evidence supporting the viability of meaningful uniform units of measurement in psychology and the social sciences.

7. Myth: Efficient economic markets can incorporate only manufactured and liquid capital, and property. Human, social, and natural capital, being intangible, have permanent status as market externalities as they cannot be measured well enough to enable accountability, pricing, or transferable representations (common currency instruments).

Fact: The theory and methods necessary for establishing an Intangible Assets Metric System are in hand. What’s missing is the awareness of the scientific, human, social, and economic value that would be returned from the admittedly very large investments that would be required.

References and examples are available in other posts in this blog, in my publications, or on request.

What the Economy Needs?

September 5, 2012

Expanding on remarks made by Thomas Friedman in the course of an interview with Charlie Rose broadcast on August 31, 2012…

Friedman broke the problem down to three key points. We have to have 1) a plan, 2) a fair tax contribution from the rich, and 3) aspirations for improving the overall quality of life, economically and  democratically.

The plan outlined from various points of view in this blog is to create a scientific and market infrastructure for intangible assets (human, social and natural capital), assets amounting to at least 90%of the capital under management.

The plan is fair in its advancement of equal opportunity to invest in and realize returns from one’s skills, motivations, health and trustworthiness. Everyone will be able to invest in, and receive their share of the profits from, the human, social, and natural capital stocks of individuals, communities, schools, hospitals, social service agencies, firms, etc. The rich will then both contribute to the advancement of the greater good at the same time they are able to profit from the growth in the authentic wealth created by improvements to human, community, and environmental value.

The plan aspires to great accomplishments in the depth and breadth of the innovation it will facilitate, its fulfillment of democratic principles, and the new economic growth it promises.

And so I would now like to raise a couple of sets of questions. What if all the money put into Medicare, Medicaid, education, HUD, food stamps, the EPA, etc. was instead invested in an infrastructure for intangible assets metrology and HSN capital stocks (individual, organizational–school, hospital, nonprofit, NGO, firm–and community)? Usually, talk of letting the market solve social and environmental problems is nothing but a self-serving excuse for allowing greed to rule at the expense of the greater good. Those so-called market solutions do nothing to actually shape the institutions, rules, and roles by which markets are created, and so the end result would be catastrophic. But there is an essential and unnoticed inconsistency in previously proposed approaches that involves the double standards used in defining and actualizing the various forms of capital.

As previous posts (like this one or this one) in this blog, and several of my publications, have argued, manufactured capital and property have long since been brought to life by transferable representations (titles, deeds, precision quantity measures, etc.) and the various legal, financial, educational, and scientific institutions built up around them. Human, social, and natural capital have not been brought to life and so we remain unable to take proper possession of our own properties, the ones that we most value and on which life, liberty, and happiness are most dependent.

But what if we created the needed market institutions, rules, and roles? What if everyone knew how many shares of community capital they owned, and what the current price of those shares in the market was? What if tuition for an advanced degree was denominated in the shares of literacy capital one obtained, as evident in the increased literacy measures achieved? What if taxes were abolished and minimum investments in human, social, and natural capital stocks were required? What if real, efficient, functional markets in intangible assets were created, and the associated governmental programs and departments were abolished? How much would the federal budget decrease? How much would government shrink? How much might the economy grow if that much money was invested in human, social, and natural capital stocks paying even a minimal reasonable profit?

Another round of questions asks whether we have the optimal social safety net in the current institutional context, or if perhaps that safety net could be significantly improved by following through on the concepts of impact investing and outcome-based budgeting to create a truly sustainable and socially responsible economic system? What if everyone held known numbers of tradable shares of their intangible assets (their skills, motivation, health, trust)? What if the value of those shares was common public knowledge? What if the investment paths to increasing the number and value of shares held were all well known? What if monetary profit could be derived–and could only be derived–by increasing the value of human, social, and natural capital shares? What if groups of people joined together in various kinds of organizations (schools, hospitals, businesses) to collectively grow the value of their authentic wealth? What if lean thinking was applied to the 90% of the capital under management (the human, social, and natural capital) that is currently nearly unmanageable because it is not measured in universally uniform scientific units?

The balance scale is a common symbol of justice. We do not usually aspire to take that symbol as seriously as we could. We ought to have a plan for economic justice that does not have to coerce anyone to acknowledge, pay back, and re-invest in the broad support they received en route to becoming successful. And we ought to have a plan that reinvigorates the aspirations for equal opportunity and freedom that have become a model for people all over the world. Friedman got the broad strokes right. Now’s the time to start filling in the details.

Measuring/Managing Social Value

August 28, 2012

From my December 1, 2008 personal journal, written not long after the October 2008 SoCap conference. I’ve updated a few things that have changed in the intervening years.

Over the last month, I’ve been digesting what I learned at the Social Capital Markets conference at Fort Mason in San Francisco, and at the conference I attended just afterward, Bioneers, in Marin county. Bioneers (www.Bioneers.org) could be called Natural Capital Markets. It was quite like the Social Capital Markets conference with only a slight shift in emphasis, and lots of discussion of social value.

The main thing that impressed me at both of these conferences, apart from what I already knew about the caring passion I share with so many, is the huge contrast between that passion and the quality of the data that so many are basing major decisions on. Seeing this made me step back and think harder about how to shape my message.

First, though it may not seem like it initially, there is incredible practical value to be gained from taking the trouble to construct good measures. We do indeed manage what we measure. So whatever we measure becomes what we manage. If we’re not measuring anything that has anything to do with our mission, vision, or values, then what we’re managing won’t have anything to do with those, either. And when the numbers we use as measures do not actually represent a constant unit amount that adds up the way the numbers do, then we don’t have a clue what we’re measuring and we could be managing just about anything.

This is not the way to proceed. First take-away: ask for more from your data. Don’t let it mislead you with superficial appearances. Dig deeper.

Second, to put it a little differently, percentages, scores, and counts per capita, etc. are not measures that have the same meaning or quality that measures of height, weight, time, temperature, or volts have. However, for over 50 years, we have been constructing measures mathematically equivalent to physical measures from ability tests, surveys, assessments, checklists, etc. The technical literature on this is widely available. The methods have been mainstream at ETS, ACT, state and national departments of education globally, etc for decades.

Second take-away: did I say you should ask for more from your data? You can get it. A lot of people already are, though I don’t think they’re asking for nearly as much as they could get.

Third, though the massive numbers of percentages, scores, and counts per capita are not the measures we seek, they are indeed exactly the right place to start. I have seen over and over again, in education, health care, sociology, human resource management, and most recently in the UN Millennium Development Goals data, that people do know exactly what data will form a proper basis for the measurement systems they need.

Third take-away: (one more time!) ask for more from your data. It may conceal a wealth beyond what you ever guessed.

So what are we talking about? There are methods for creating measures that give you numbers that verifiably stand for a substantive unit amount that adds up in the same way one-inch blocks do (probabilistically, and within a range of error). If the instrument is properly calibrated and administered, the unit size and meaning will not change across individuals or samples measured. You can reduce data volume dramatically, not only with no loss of information but also with false appearances of information either indicated as error or flagged for further attention. You can calibrate a continuum of less to more that is reliably and reproducibly associated with, annotated by, and interpreted through your own indicators. You can equate different collections of indicators that measure the same thing so that they do so in the same unit.

Different agencies using the same, different, or mixed collections of indicators in different countries or regions could assess their measures for comparability, and if they are of satisfactory quality, equate them so they measure in the same unit. That is, well-designed instruments written and administered in different languages routinely have their items calibrate in the same order and positions, giving the same meaning to the same unit of measurement. For instance, see the recent issue of the Journal of Applied Measurement ([link]) devoted to reports on the OECD’s Programme for International Student Assessment.

This is not a data analysis strategy. It is an instrument calibration strategy. Once calibrated, the instrument can be deployed. We need to monitor its structure, but the point is to create a tool people can take out into the world and use like a thermometer or clock.

I’ve just been looking at the Charity Navigator (for instance, [link]) and the UN’s Millenium Development Goals ([link]), and the databases that have been assembled as measures of progress toward these goals ([link]). I would suppose these web sites show data in forms that people are generally familiar with, so I’m working up analyses to use as teaching tools from the UN data.

You don’t have to take any of this at my word. It’s been documented ad nauseum in the academic literature for decades. Those interested can find out more than they ever wanted to know at http://www.Rasch.org, in the Wikipedia Rasch entry, in the articles and books at JAMPress.com, or in dozens of academic journals and hundreds of books. Though I’ve done my share of it, I’m less interested in continuing to add to that than I am in making a tangible contribution to improving people’s lives.

Sorry to go on like this. I meant to keep this short. Anyway, there it is.

PS, for real geeks: For those of you serious about learning about measurement as it is rigorously and mathematically defined, look into taking Everett Smith’s measurement course at Statistics.com ([link]) or David Andrich’s academic units at the University of Western Australia ([link]). Available software includes Mike Linacre’s Winsteps, Andrich’s RUMM, and Mark Wilson’s, at UC Berkeley, Conquest.

The methods Ev, Mike, David, and Mark teach have repeatedly been proven, both in mathematical theory and in real life, to be both necessary and sufficient in the construction of meaningful, practical measurement. Any number of ways of defining objectivity in measurement have been shown to reduce to the mathematical models they use. Why all the Chicago stuff? Because of Ben Wright. I’m helping (again) to organize a conference in his honor, to be held in Chicago next March. His work won him a Career Achievement Award from the Association of Test Publishers, and the coming conference will celebrate his foundational contributions to computerized measurement in health care.

As a final note, for those of you fearing reductionistic meaninglessness, look into my philosophical work.  But enough…

Review of “Advancing Social Impact Investments Through Measurement”

August 24, 2012

Over the last few days, I have been reading several of the most recent issues of the Community Development Investment Review, especially volume 7, number 2, edited by David Erickson of the Federal Reserve Bank of San Francisco, reporting the proceedings of the March 21, 2011 conference in Washington, DC on advancing social impact investments through measurement. I am so excited to see this work that I am (truly) fairly trembling with excitement. I feel as though I’ve finally made my way home. There are so many points of contact, it’s hard to know where to start. After several days of concentrated deep breathing and close study of the CDIR, it’s now possible to formulate some coherent thoughts to share.

The CDIR papers start to sort out the complex issues involved in clarifying how measurement might contribute to the integration of impact investing and community development finance. I am heartened by the statement that “The goal of the Review is to bridge the gap between theory and practice and to enlist as many viewpoints as possible—government, nonprofits, financial institutions, and beneficiaries.” On the other hand, the omission of measurement scientists from that list of viewpoints adds another question to my long list of questions as to why measurement science is so routinely ignored by the very people who proclaim its importance. The situation is quite analogous to demanding more frequent conversational interactions from colleagues while ignoring the invention of the telephone and not providing them with the tools and network connections.

The aims shared by the CDIR contributors and myself are evident in the fact that David Erickson opens his summary of the March 21, 2011 conference with the same quote from Robert Kennedy that I placed at the end of my 2009 article in Measurement (see references below; all papers referenced are available by request if they are not already online). In that 2009 paper, in others I’ve published over the last several years, in presentations I’ve made to my measurement colleagues abroad and at home, and in various entries in my blog, I take up virtually all of the major themes that arose in the DC conference: how better measurement can attract capital to needed areas, how the cost of measurement repels many investors, how government can help by means of standard setting and regulation, how diverse and ambiguous investor and stakeholder interests can be reconciled and/or clarified, etc.

The difference, of course, is that I present these issues from the technical perspective of measurement and cannot speak authoritatively or specifically from the perspectives represented by the community development finance and impact investing fields. The bottom line take-away message for these fields from my perspective is this: unexamined assumptions may unnecessarily restrict assessments of problems and their potential solutions. As Salamon put it in his remarks in the CDIR proceedings from the Washington meeting (p. 43), “uncoordinated innovation not guided by a clear strategic concept can do more than lose its way: it can do actual harm.”

A clear strategic concept capable of coordinating innovations in social impact measurement is readily available. Multiple, highly valuable, and eminently practical measurement technologies have proven themselves in real world applications over the last 50 years. These technologies are well documented in the educational, psychological, sociological, and health care research literatures, as well as in the practical experience of high stakes testing for professional licensure and certification, for graduation, and for admissions.

Numerous reports show how to approach problems of quantification and standards with new degrees of rigor, transparency, meaningfulness, and flexibility. When measurement problems are not defined in terms of these technologies, solutions that may offer highly advantageous features are not considered. When the area of application is as far reaching and fundamental as social impact measurement, not taking new technologies into account is nothing short of tragic. I describe some of the new opportunities for you in a Technical Postscript, below.

In his Foreword to the CDIR proceedings issue, John Moon mentions having been at the 2009 SoCap event bringing together stakeholders from across the various social capital markets arenas. I was at the 2008 SoCap, and I came away from it with much the same impression as Moon, feeling that the palpable excitement in the air was more than tempered by the evident fact that people were often speaking at cross purposes, and that there did not seem to be a common object to the conversation. Moon, Erickson, and their colleagues have been in one position to sort out the issues involved, and I have been in another, but we are plainly on converging courses.

Though the science is in place and has been for decades, it will not and cannot amount to anything until the people who can best make use of it do so. The community development finance and impact investing fields are those people. Anyone interested in getting together for an informal conversation on topics of mutual interest should feel free to contact me.

Technical Postscript

There are at least six areas in efforts to advance social impact investments via measurement that will be most affected by contemporary methods. The first has to do with scale quality. I won’t go into the technical details, but numbers do not automatically stand for something that adds up the way they do. Mapping a substantive construct onto a number line requires specific technical expertise; there is no evidence of that expertise in any of the literature I’ve seen on social impact investing, or on measuring intangible assets. This is not an arbitrary bit of philosophical esoterica or technical nicety. This is one of those areas where the practical value of scientific rigor and precision comes into its own. It makes all the difference in being able to realize goals for measurement, investment, and redefining profit in terms of social impacts.

A second area in which thinking on social impact measurement will be profoundly altered by current scaling methods concerns the capacity to reduce data volume with no loss of information. In current systems, each indicator has its own separate metric. Data volume quickly multiplies when tracking separate organizations for each of several time periods in various locales. Given sufficient adherence to data quality and meaningfulness requirements, today’s scaling methods allow these indicators to be combined into a single composite measure—from which each individual observation can be inferred.

Elaborating this second point a bit further, I noted that some speakers at the 2011 conference in Washington thought reducing data volume is a matter of limiting the number of indicators that are tracked. This strategy is self-defeating, however, as having fewer independent observations increases uncertainty and risk. It would be far better to set up systems in which the metrics are designed so as to incorporate the amount of uncertainty that can be tolerated in any given decision support application.

The third area I have in mind deals with the diverse spectrum of varying interests and preferences brought to the table by investors, beneficiaries, and other stakeholders. Contemporary approaches in measurement make it possible to adapt the content of the particular indicators (counts or frequencies of events, or responses to survey questions or test items) to the needs of the user, without compromising the comparability of the resulting quantitative measure. This feature makes it possible to mass customize the content of the metrics employed depending on the substantive nature of the needs at that time and place.

Fourth, it is well known that different people judging performances or assigning numbers to observations bring different personal standards to bear as they make their ratings. Contemporary measurement methods enable the evaluation and scaling of raters and judges relative to one another, when data are gathered in a manner facilitating such comparisons. The end result is a basis for fair comparisons, instead of scores that vary depending more on which rater is observing than on the quality of the performance.

Fifth, much of the discussion at the conference in Washington last year emphasized the need for shared data formatting and reporting standards. As might be guessed from the prior four areas I’ve described, significant advances have occurred in standard setting methods. It is suggested in the CDIR proceedings that the Treasury Department should be the home to a new institute for social impact measurement standards. In a series of publications over the last few years, I have suggested a need for an Intangible Assets Metric System to NIST and NSF (see below for references and links; all papers are available on request). That suggestion comes up again in my third-prize winning entry in the 2011 World Standards Day paper competition, sponsored by NIST and SES (the Society for Standards Professionals), entitled “What the World Needs Now: A Bold Plan for New Standards.” (See below for link.)

Sixth, as noted by Salamon (p. 43), “metrics are not neutral. They not only measure impact, they can also shape it.” Though this is not likely exactly what Salamon meant, one of the most exciting areas in measurement applications in education in recent years, one led in many ways by my colleague, Mark Wilson, and his group at UC Berkeley, concerns exactly this feedback loop between measurement and impact. In education, it has become apparent that test scaling reveals the order in which lessons are learned. Difficult problems that require mastery of easier problems are necessarily answered correctly less often than the easier problems. When the difficulty order of test questions in a given subject remains constant over time and across thousands of students, one may infer that the scale reveals the path of least resistance. Individualizing instruction by targeting lessons at the student’s measure has given rise to a concept of formative assessment, distinct from the summative assessment of accountability applications. I suspect this kind of a distinction may also prove of value in social impact applications.

Relevant Publications and Presentations

Fisher, W. P., Jr. (2002, Spring). “The Mystery of Capital” and the human sciences. Rasch Measurement Transactions, 15(4), 854 [http://www.rasch.org/rmt/rmt154j.htm].

Fisher, W. P., Jr. (2004, Thursday, January 22). Bringing capital to life via measurement: A contribution to the new economics. In  R. Smith (Chair), Session 3.3B. Rasch Models in Economics and Marketing. Second International Conference on Measurement in Health, Education, Psychology, and Marketing: Developments with Rasch Models, The International Laboratory for Measurement in the Social Sciences, School of Education, Murdoch University, Perth, Western Australia.

Fisher, W. P., Jr. (2005, August 1-3). Data standards for living human, social, and natural capital. In Session G: Concluding Discussion, Future Plans, Policy, etc. Conference on Entrepreneurship and Human Rights [http://www.fordham.edu/economics/vinod/ehr05.htm], Pope Auditorium, Lowenstein Bldg, Fordham University.

Fisher, W. P., Jr. (2007, Summer). Living capital metrics. Rasch Measurement Transactions, 21(1), 1092-3 [http://www.rasch.org/rmt/rmt211.pdf].

Fisher, W. P., Jr. (2008, 3-5 September). New metrological horizons: Invariant reference standards for instruments measuring human, social, and natural capital. Presented at the 12th International Measurement Confederation (IMEKO) TC1-TC7 Joint Symposium on Man, Science, and Measurement, Annecy, France: University of Savoie.

Fisher, W. P., Jr. (2009, November). Invariance and traceability for measures of human, social, and natural capital: Theory and application. Measurement, 42(9), 1278-1287.

Fisher, W. P.. Jr. (2009). NIST Critical national need idea White Paper: Metrological infrastructure for human, social, and natural capital (Tech. Rep., http://www.nist.gov/tip/wp/pswp/upload/202_metrological_infrastructure_for_human_social_natural.pdf). Washington, DC: National Institute for Standards and Technology.

Fisher, W. P., Jr. (2010). The standard model in the history of the natural sciences, econometrics, and the social sciences. Journal of Physics: Conference Series, 238(1), http://iopscience.iop.org/1742-6596/238/1/012016/pdf/1742-6596_238_1_012016.pdf.

Fisher, W. P., Jr. (2011). Bringing human, social, and natural capital to life: Practical consequences and opportunities. In N. Brown, B. Duckor, K. Draney & M. Wilson (Eds.), Advances in Rasch Measurement, Vol. 2 (pp. 1-27). Maple Grove, MN: JAM Press.

Fisher, W. P., Jr. (2011). Measuring genuine progress by scaling economic indicators to think global & act local: An example from the UN Millennium Development Goals project. LivingCapitalMetrics.com. Retrieved 18 January 2011, from Social Science Research Network: http://ssrn.com/abstract=1739386.

Fisher, W. P., Jr. (2012). Measure and manage: Intangible assets metric standards for sustainability. In J. Marques, S. Dhiman & S. Holt (Eds.), Business administration education: Changes in management and leadership strategies (pp. 43-63). New York: Palgrave Macmillan.

Fisher, W. P., Jr. (2012, May/June). What the world needs now: A bold plan for new standards. Standards Engineering, 64(3), 1 & 3-5 [http://ssrn.com/abstract=2083975].

Fisher, W. P., Jr., & Stenner, A. J. (2011, January). Metrology for the social, behavioral, and economic sciences (Social, Behavioral, and Economic Sciences White Paper Series). Retrieved 25 October 2011, from National Science Foundation: http://www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=36.

Fisher, W. P., Jr., & Stenner, A. J. (2011, August 31 to September 2). A technology roadmap for intangible assets metrology. In Fundamentals of measurement science. International Measurement Confederation (IMEKO) TC1-TC7-TC13 Joint Symposium, http://www.db-thueringen.de/servlets/DerivateServlet/Derivate-24493/ilm1-2011imeko-018.pdf, Jena, Germany.

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