Archive for the ‘impact investing’ Category

Self-Sustaining Sustainability, Once Again, Already

August 12, 2018

The urgent need for massive global implementations of sustainability policies and practices oddly and counterproductively has not yet led to systematic investments in state of the art sustainability metric standards. My personal mission is to contribute to meeting this need. Longstanding, proven resources in the art and science of precision instrumentation calibration and explanatory theory are available to address these problems. In the same way technical standards for measuring length, mass, volume, time, energy, light, etc. enable the coordination of science and commerce for manufactured capital and property, so, too, will a new class of standards for measuring human, social, and natural capital.

This new art and science contradicts common assumptions in three ways. First, contrary to popular opinion that measuring these things is impossible, over 90 years of research and practice support a growing consensus among weights and measures standards engineers (metrologists) and social and psychological measurement experts that relevant unit standards are viable, feasible, and desirable.

Common perceptions are contradicted in a second way in that measurement of this kind does not require reducing human individuality to homogenized uniform sameness. Instead of a mechanical metaphor of cogs in a machine, the relevant perspective is an organic or musical one. The goal is to ensure that local uniqueness and creative improvisations are freely expressed in a context informed by shared standards (like DNA, or a musical instrument tuning system).

The third way in which much of what we think we know is mistaken concerns how to motivate adoption of sustainability policies and practices. Many among us are fearful that neither the general population nor its leaders in government and business care enough about sustainability to focus on implementing solutions. But finding the will to act is not the issue. The problem is how to create environments in which new sustainable forms of life multiply and proliferate of their own accord. To do this, people need means for satisfying their own interests in life, liberty, and the pursuit of happiness. The goal, therefore, is to organize knowledge infrastructures capable of informing and channeling the power of individual self-interest. The only way mass scale self-sustaining sustainable economies will ever happen is by tapping the entrepreneurial energy of the profit motive, where profit is defined not just in financial terms but in the quality of life and health terms of authentic wealth and genuine productivity.

We manage what we measure. If we are to collectively, fluidly, efficiently, and innovatively manage the living value of our human, social, and natural capital, we need, first, high quality information expressed in shared languages communicating that value. Second, we need, to begin with, new scientific, legal, economic, financial, and governmental institutions establishing individual rights to ownership of that value, metric units expressing amounts of that value, conformity audits for ascertaining the accuracy and precision of those units, financial alignments of the real value measured with bankable dollar amounts, and investment markets to support entrepreneurial innovations in creating that value.

The end result of these efforts will be a capacity for all of humanity to pull together in common cause to create a sustainable future. We will each be able to maximize our own personal potential at the same time we contribute to the greater good. We will not only be able to fulfill the potential of our species as stewards of the earth, we will have fun doing it! For technical information resources, see below. PDFs are available on request, and can often be found freely available online.

Self-Sustaining Sustainability

Relevant Information Resources

William P. Fisher, Jr., Ph.D.

Barney, M., & Fisher, W. P., Jr. (2016). Adaptive measurement and assessment. Annual Review of Organizational Psychology and Organizational Behavior, 3, 469-490.

Fisher, W. P., Jr. (1997). Physical disability construct convergence across instruments: Towards a universal metric. Journal of Outcome Measurement, 1(2), 87-113.

Fisher, W. P., Jr. (1999). Foundations for health status metrology: The stability of MOS SF-36 PF-10 calibrations across samples. Journal of the Louisiana State Medical Society, 151(11), 566-578.

Fisher, W. P., Jr. (2000). Objectivity in psychosocial measurement: What, why, how. Journal of Outcome Measurement, 4(2), 527-563.

Fisher, W. P., Jr. (2002). “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). The mathematical metaphysics of measurement and metrology: Towards meaningful quantification in the human sciences. In A. Morales (Ed.), Renascent pragmatism: Studies in law and social science (pp. 118-153). Brookfield, VT: Ashgate Publishing Co.

Fisher, W. P., Jr. (2004). Meaning and method in the social sciences. Human Studies: A Journal for Philosophy & Social Sciences, 27(4), 429-454.

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. (2009, November 19). Draft legislation on development and adoption of an intangible assets metric system. Living Capital Metrics blog: https://livingcapitalmetrics.wordpress.com/2009/11/19/draft-legislation/.

Fisher, W. P., Jr. (2009). Invariance and traceability for measures of human, social, and natural capital. 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 (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, 22 November). Meaningfulness, measurement, value seeking, and the corporate objective function: An introduction to new possibilities. LivingCapitalMetrics.com, Sausalito, California.

Fisher, W. P., Jr. (2010). Measurement, reduced transaction costs, and the ethics of efficient markets for human, social, and natural capital. Bridge to Business Postdoctoral Certification, Freeman School of Business, Tulane University (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2340674).

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), 012016.

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. (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). 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. (2015). A probabilistic model of the law of supply and demand. Rasch Measurement Transactions, 29(1), 1508-1511 [http://www.rasch.org/rmt/rmt291.pdf].

Fisher, W. P., Jr. (2015). Rasch measurement as a basis for metrologically traceable standards. Rasch Measurement Transactions, 28(4), 1492-1493 [http://www.rasch.org/rmt/rmt284.pdf].

Fisher, W. P., Jr. (2015). Rasch metrology: How to expand measurement locally everywhere. Rasch Measurement Transactions, 29(2), 1521-1523.

Fisher, W. P., Jr. (2017, September). Metrology, psychometrics, and new horizons for innovation. 18th International Congress of Metrology, Paris, 10.1051/metrology/201709007.

Fisher, W. P., Jr. (2017). A practical approach to modeling complex adaptive flows in psychology and social science. Procedia Computer Science, 114, 165-174.

Fisher, W. P., Jr. (2018). How beauty teaches us to understand meaning. Educational Philosophy and Theory, in review.

Fisher, W. P., Jr. (2018). Separation theorems in econometrics and psychometrics: Rasch, Frisch, two Fishers, and implications for measurement. Scandinavian Economic History Review, in review.

Fisher, W. P., Jr., Harvey, R. F., & Kilgore, K. M. (1995). New developments in functional assessment: Probabilistic models for gold standards. NeuroRehabilitation, 5(1), 3-25.

Fisher, W. P., Jr., Harvey, R. F., Taylor, P., Kilgore, K. M., & Kelly, C. K. (1995). Rehabits: A common language of functional assessment. Archives of Physical Medicine and Rehabilitation, 76(2), 113-122.

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).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.

Fisher, W. P., Jr., & Stenner, A. J. (2016). Theory-based metrological traceability in education: A reading measurement network. Measurement, 92, 489-496.

Fisher, W. P., Jr., & Wilson, M. (2015). Building a productive trading zone in educational assessment research and practice. Pensamiento Educativo: Revista de Investigacion Educacional Latinoamericana, 52(2), 55-78.

Pendrill, L., & Fisher, W. P., Jr. (2013). Quantifying human response: Linking metrological and psychometric characterisations of man as a measurement instrument. Journal of Physics Conference Series, 459, 012057.

Pendrill, L., & Fisher, W. P., Jr. (2015). Counting and quantification: Comparing psychometric and metrological perspectives on visual perceptions of number. Measurement, 71, 46-55.

 

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On social impact bonds and critical reflections

May 5, 2018

A new article (Roy, McHugh, & Sinclair, 2018) out this week in the Stanford Social Innovation Review echoes Gleeson-White (2015) in pointing out a disconnect between financial bottom lines and the social missions of companies whose primary objectives concern broader social and environmental impacts. The article also notes the expense of measurement, increased administrative burdens, high transaction costs, technical issues in achieving fair measures, the trend toward the negative implications of managing what is measured instead of advancing the mission, and the potential impacts of external policy environments and political climates.

The authors contend that social impact bonds are popular and proliferating for ideological reasons, not because of any evidence concerning their effectiveness in making the realization of social objectives profitable. Some of the several comments posted online in response to the article take issue with that claim, and point toward evidence of effectiveness. But the general point still stands: more must be done to systematically align investors’ financial interests with the citizens’ interest in advancing their financial, social, and environmental quality of life, and not just with the social service providers’ interest in funding and advancing their mission.

Roy et al. are correct to say that to do otherwise is to turn the people served into commodities. This happens because governance of, accountability for, and reporting of social impacts are shifted away from elected officials to the needs of private funders, with far less in the way of satisfactory recourse for citizens when programs go awry. The problem lies in the failure to create any capacity for individuals themselves to represent, invest in, manage, and profit from their skills, health, trust, and environmental service outcomes. Putting all the relevant information into the hands of service providers and investors, and making that information as low quality as it is, can only ever result in one-sided effects on people themselves. With no idea of the technologies, models, decades of results, and ready examples to draw from in the published research, the authors conclude with a recommendation to leave well enough alone and to pursue more traditional avenues of policy formation, instead of allowing the “cultural supremacy of market principles” to continue advancing into every area of life.

But as is so commonly the case when it comes to technical issues of quantification, the authors’ conclusions and criticisms skip over the essential role that high quality measurement plays in reducing transaction costs and supporting property rights. In general, measurement standards inform easily communicated and transferable information about the quantity and quality of products in markets, thereby lowering transaction costs and enabling rights to the ownership of specific amounts of things. The question that goes unasked in this article, and in virtually every other article in the area of ESG, social impact investing, etc., is this: What kind of measurement technologies and systems would we need to be able to replicate existing market efficiencies in new markets for human, social, and natural capital?

That question and other related ones are, of course, the theme of this blog and of many of my publications. Further exploration here and in the references to other posts (such as Fisher, 2011, 2012a, 2012b) may prove fruitful to others seriously interested in finding a way out of the unexamined assumptions stifling creativity in this area.

In short, instead of turning people into commodities, why should we not turn skills, health, trust, and environmental services into commodities? Why should not every person have legal title to scientifically and uniformly measured numbers of shares of each essential form of human, social, and natural capital? Why should individuals not be able to profit in both monetary and personal terms from their investments in education, health care, community, and the environment? Why should we allow corporations to continue externalizing the costs of social and environmental investments, at the expense of individual citizens and communities? Why is there so much disparity and inequality in the opportunities for skill development and healthy lives available across social sectors?

Might not our inability to obtain good information about processes and outcomes in the domains of educational, health care, social service, and environmental management have a lot to do with it? Why don’t we have the information infrastructure we need, when the technology for creating it has been in development for over 90 years? Why are there so many academics, researchers, philanthropic organizations, and government agencies that are content with the status quo when these longstanding technologies are available, and people, communities, and the environment are suffering from the lack of the information they ought to have?

During the French revolution, one of the primary motivations for devising the metric system was to extend the concept of universal rights to individual commercial exchanges. The confusing proliferation of metrics in Europe at the time made it possible for merchants and the nobility to sell in one unit and buy with another. Universal rights plainly implied universal measures. Alder (2002, p. 2) explains that:

“To do their job, standards must operate as a set of shared assumptions, the unexamined background against which we strike agreements and make distinctions. So it is not surprising that we take measurement for granted and consider it banal. Yet the use a society makes of its measures expresses its sense of fair dealing. That is why the balance scale is a widespread symbol of justice. .. Our methods of measurement define who we are and what we value.”

Getting back to the article by Roy, McHugh, and Sinclair, yes, it is true that the measures in use in today’s social impact bonds are woefully inadequate. Far from living up to the kind of justice symbolized by the balance scale, today’s social impact measures define who we are in terms of units of measurement that differ and change in unknown ways across individuals, over time, and across instruments. This is the reason for many, if not all, of the problems Roy et al. find with social impact bonds: their measures are not up to the task.

But instead of taking that as an unchangeable given, should not we do more to ask what kinds of measures could do the job that needs to be done? Should not we look around and see if in fact there might be available technologies able to advance the cause?

Theory and evidence have, in fact, been brought to bear in formulating approaches to instrument calibration that reproduce the balance scale’s fair and just comparisons of weight from data like that from tests and surveys (Choi, 1998; Massof, 2011; Rasch, 1960, pp. 110-115). The same thing has been done in reproducing measures of length (Stephanou & Fisher, 2013), distance (Moulton, 1993), and density (Pelton & Bunderson, 2003).

These are not isolated and special results. The methods involved have been in use for decades and in dozens of fields (Wright, 1968, 1977, 1999; Wright & Masters, 1982; Wright & Stone, 1979, 1999; Andrich, 1978, 1988, 1989, 2010; Bond & Fox, 2015; Engelhard, 2012; Wilson, 2005; Wilson & Fisher, 2017). Metric system engineers and physicists are in accord with psychometricians as to the validity of these claims (Pendrill & Fisher, 2015) and are on the record with positive statements of support:

“Rasch models belong to the same class that metrologists consider paradigmatic of measurement” (Mari and Wilson, 2014, p. 326).

“The Rasch approach…is not simply a mathematical or statistical approach, but instead [is] a specifically metrological approach to human-based measurement” (Pendrill, 2014, p. 26).

These statements represent the attitude toward measurement possibilities being applied by at least one effort in the area of social impact investing (https://www.aldcpartnership.com/#/cases/financing-the-future). Hopefully, there will be many more projects of this kind emerging in the near future.

The challenges are huge, of course. This is especially the case when considering the discontinuous levels of complexity that have to be negotiated in making information flow across locally situated individual niches, group-level organizations and communities, and global accountability applications (Fisher, 2017; Fisher, Oon, & Benson, 2018; Fisher & Stenner, 2018). But taking on these challenges makes far more sense than remaining complicitly settled in a comfortable rut, throwing up our hands at how unfair life is.

There’s a basic question that needs to be asked. If what is presented as measurement raises transaction costs and does not support ownership rights to what is measured, is it really measurement? How can the measurement of kilowatts, liters, and grams lower transaction costs and support property rights at the same time that other so-called measurements raise transaction costs and do not support property rights? Does not this inconsistency suggest something might be amiss in the way measurement is conceived in some areas?

For more info, check out these other posts here:

https://livingcapitalmetrics.wordpress.com/2015/05/01/living-capital-metrics-for-financial-and-sustainability-accounting-standards/

https://livingcapitalmetrics.wordpress.com/2014/11/08/another-take-on-the-emerging-paradigm-shift/

https://wordpress.com/post/livingcapitalmetrics.wordpress.com/1812

https://wordpress.com/post/livingcapitalmetrics.wordpress.com/497

References

Alder, K. (2002). The measure of all things: The seven-year odyssey and hidden error that transformed the world. New York: The Free Press.

Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43(4), 561-573.

Andrich, D. (1988). Sage University Paper Series on Quantitative Applications in the Social Sciences. Vol. series no. 07-068: Rasch models for measurement. Beverly Hills, California: Sage Publications.

Andrich, D. (1989). Constructing fundamental measurements in social psychology. In J. A. Keats, R. Taft, R. A. Heath & S. H. Lovibond (Eds.), Mathematical and theoretical systems. Proceedings of the 24th International Congress of Psychology of the International Union of Psychological Science, Vol. 4 (pp. pp. 17-26). Amsterdam, Netherlands: North-Holland.

Andrich, D. (2010). Sufficiency and conditional estimation of person parameters in the polytomous Rasch model. Psychometrika, 75(2), 292-308.

Bond, T., & Fox, C. (2015). Applying the Rasch model: Fundamental measurement in the human sciences, 3d edition. New York: Routledge.

Choi, E. (1998). Rasch invents “ounces.” Popular Measurement, 1(1), 29. Retrieved from https://www.rasch.org/pm/pm1-29.pdf

Engelhard, G., Jr. (2012). Invariant measurement: Using Rasch models in the social, behavioral, and health sciences. New York: Routledge Academic.

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.

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. (2017). A practical approach to modeling complex adaptive flows in psychology and social science. Procedia Computer Science, 114, 165-174. Retrieved from https://doi.org/10.1016/j.procs.2017.09.027

Fisher, W. P., Jr., Oon, E. P.-T., & Benson, S. (2018). Applying Design Thinking to systemic problems in educational assessment information management. Journal of Physics Conference Series, pp. in press; [http://media.imeko-tc7-rio.org.br/media/uploads/s/wfisher@berkeley.edu_1497049869_781396.pdf].

Fisher, W. P., Jr., & Stenner, A. J. (2018). Ecologizing vs modernizing in measurement and metrology. Journal of Physics Conference Series, pp. in press [http://media.imeko-tc7-rio.org.br/media/uploads/s/wfisher@berkeley.edu_1496875919_204672.pdf].

Gleeson-White, J. (2015). Six capitals, or can accountants save the planet? Rethinking capitalism for the 21st century. New York: Norton.

Mari, L., & Wilson, M. (2014, May). An introduction to the Rasch measurement approach for metrologists. Measurement, 51, 315-327.

Massof, R. W. (2011). Understanding Rasch and Item Response Theory models: Applications to the estimation and validation of interval latent trait measures from responses to rating scale questionnaires. Ophthalmic Epidemiology, 18(1), 1-19.

Moulton, M. (1993). Probabilistic mapping. Rasch Measurement Transactions, 7(1), 268 [http://www.rasch.org/rmt/rmt71b.htm].

Pelton, T., & Bunderson, V. (2003). The recovery of the density scale using a stochastic quasi-realization of additive conjoint measurement. Journal of Applied Measurement, 4(3), 269-281.

Pendrill, L. (2014, December). Man as a measurement instrument [Special Feature]. NCSLi Measure: The Journal of Measurement Science, 9(4), 22-33.

Pendrill, L., & Fisher, W. P., Jr. (2015). Counting and quantification: Comparing psychometric and metrological perspectives on visual perceptions of number. Measurement, 71, 46-55.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests (Reprint, with Foreword and Afterword by B. D. Wright, Chicago: University of Chicago Press, 1980). Copenhagen, Denmark: Danmarks Paedogogiske Institut.

Roy, M. J., McHugh, N., & Sinclair, S. (2018, 1 May). A critical reflection on social impact bonds. Stanford Social Innovarion Review. Retrieved 5 May 2018, from https://ssir.org/articles/entry/a_critical_reflection_on_social_impact_bonds?utm_source=Enews&utm_medium=Email&utm_campaign=SSIR_Now&utm_content=Title.

Stephanou, A., & Fisher, W. P., Jr. (2013). From concrete to abstract in the measurement of length. Journal of Physics Conference Series, 459, http://iopscience.iop.org/1742-6596/459/1/012026.

Wilson, M. (2005). Constructing measures: An item response modeling approach. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Wilson, M., & Fisher, W. (2017). Psychological and social measurement: The career and contributions of Benjamin D. Wright. New York: Springer.

Wright, B. D. (1968). Sample-free test calibration and person measurement. In Proceedings of the 1967 invitational conference on testing problems (pp. 85-101 [http://www.rasch.org/memo1.htm]). Princeton, New Jersey: Educational Testing Service.

Wright, B. D. (1977). Solving measurement problems with the Rasch model. Journal of Educational Measurement, 14(2), 97-116 [http://www.rasch.org/memo42.htm].

Wright, B. D. (1999). Fundamental measurement for psychology. In S. E. Embretson & S. L. Hershberger (Eds.), The new rules of measurement: What every educator and psychologist should know (pp. 65-104 [http://www.rasch.org/memo64.htm]). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

Wright, B. D., & Masters, G. N. (1982). Rating scale analysis: Rasch measurement. Chicago, Illinois: MESA Press.

Wright, B. D., & Stone, M. H. (1979). Best test design: Rasch measurement. Chicago, Illinois: MESA Press.

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