ABEAI



Proceedings

of the Sixteenth Annual Conference

of the

Applied Business and Entrepreneurship Association International

Conference Chair

Lisa Andrus

Program Co-Chairs

Bahram Adrangi

Arjun Chatrath

Pamplin School of Business Administration

The University of Portland

November 2019

Kauai, Hawaii, U.S.A.

Articles published in this Conference Proceedings are accepted based on the double-blind peer-review process.

Table of Contents

Screening Leaders for Success in Turbulent Environments………….5

Use of Alternative Data in Consumer Lending Models: The Case of “Upstart”…………………………………………………. …………….10

Child Labor in Globalized Economy: Strategies to Combat

the Problem……………………………………………………………..17

Passively Active Investing – A Five Year Test…………………28

Screening Leaders for Success in Turbulent Environments

Phillip L. Hunsaker, School of Business, University of San Diego, 5998 Alcala Park, San Diego, CA 92110, Phone: (619 985-8600, philmail@sandiego.edu

Abstract

The success of task-oriented organizations is highly dependent on the individuals selected to assume responsibility for leadership. Because of the high costs involved in leadership training, and the costs related to future consequences, it is important to ensure that individuals who can profit from training and perform successfully in the criterion environment are selected as candidates. The purpose of the present study was to test the efficacy of a unique personality variable, the General Incongruity Adaptation Level, as a predictor of success in OCS leadership training. The results of the study confirmed that a higher proportion of high GIAL candidates successfully complete the OCS program, which provides support for the basic GIAL hypothesis concerning the relationship between GIAL and environmental turbulence. Exposure to the tremendous turbulence in the OCS program resulted in a significant increase of the mean GIAL score of candidates completing the program. Low GIAL candidates also reacted more strongly to environmental turbulence than high GIAL candidates, emphasizing the importance of controlling for individual differences when investigating the effects of exposure to incongruent environments. Implications for OCS programs of this nature (i.e., producing turbulent-field conditions) include that the GIAL Self- Description Inventory appears to have high potential as a screening device, and that this type of program is instrumental in increasing the adaptation levels of low GIAL candidates.

Introduction

It has been established for some time that the success of task-oriented organizations is highly dependent on the individuals selected to assume responsibility for leadership (Williams, and Leavitt, 1947). Because of the high costs involved in leadership training, and the costs related to future consequences, it is important to ensure that individuals who can profit from training and perform successfully in the criterion environment are selected as candidates. Consequently, the determination of effective selection devices is highly desirable.

This need is especially acute in the Army Officer Candidate School (OCS) where over one-third of the entering class does not graduate, despite an initial screening examination which eliminates approximately 75 percent of all enlisted personnel from OCS consideration (Lippitt and Petersen, 1967), When examining personality characteristics as possible screening criteria, studies have found few significant correlations related to success in OCS leadership training, (Richardson, 1969; Williams and Leavitt, 1947) Although Petersen and Lippitt (1968) found that some OCS candidates have a greater propensity to successfully complete training programs than others, their results were confounded by a variety of design problems making their conclusions only tentative.

Theoretical Framework, Purpose and Hypotheses

The purpose of the present study was to test the efficacy of a unique personality variable as a predictor of success in OCS leadership training. The General Incongruity Adaptation Level (GIAL) has been proposed by Driver and Streufert (1965) as an important predictor of responses to turbulent situations (i.e., constantly changing, highly uncertain and ambiguous). Basically, the GIAL is an average expectation of all types of incongruity (e.g., stress, conflict, failure and ambiguity, etc.). Individuals differ in GIAL depending upon their previous experience with incongruity, i.e., the more, incongruity experienced in one's past, the higher his G IAL. Environments that provide too little or too much incongruity (i.e., very high or low degree of turbulence) will be disliked, and the individual will attempt to maintain the desired level of environmental turbulence within the range of his GIAL via physical or psychological

avoidance, changing the nature of his environment, or the use of other internal defense mechanisms.

Since the OCS leadership training program is designed to expose candidates to

turbulence similar to that encountered in actual combat, they are constantly subjected

to mental, physical, and emotional stress (Petersen and Lippitt, 1968). Within this environment, the following relationships with the GIAL concepts were investigated:

Hypothesis 1: A greater proportion of high GIAL candidates than low GIAL candidates will successfully complete OCS (Hunsaker, 1975).

Hypothesis 2: Experience in OCS will increase candidates' expectations of incongruence.

Hypothesis 3: The OCS experience will elicit greater increases in the incongruity expectations of low G IA L candidates than high GIAL candidates.

Hypothesis 4: High GIAL candidates will be more effective leaders than low GIAL candidates in the OCS environment.

Method

Eighty-five cadets of the Wisconsin Army National Guard and Army Reserve completed the GIAL Self-Description Inventory (Driver and Streufert, 1967), immediately prior to, and immediately after, the two-week OCS training program conducted at the Wisconsin

Military Academy. For comparison, a (nonequivalent) control group consisting of 29

undergraduate students enrolled in the Administrative Organization course at the

University of Wisconsin-Milwaukee completed the GIAL inventory on the same dates. No

significant differences existed between the mean scores of the control group and experimental group on the pre-test administration of the GIAL inventory. Comparisons of before and after scores provided evidence of the effects of differences in environmental turbulence on both groups’ GIALs. Quartile comparisons provided estimates of the variation of these effects between low and high GIAL subjects.

Rosters of candidates withdrawing from the training program, and the reasons for these withdrawals, were obtained from the OCS administrative officers. The proportions of high GIAL candidates (i.e., scores above the mean) and low GIAL candidates dropping out was determined after eliminating withdrawals due to extraneous reasons such as physical injury. Leadership scores, based on observations of the candidates' ability to accomplish assigned missions, were obtained from peer rankings and evaluations by the Tactical Department Officers [Tac officers) who made certain that each candidate was given ample opportunity to exercise leadership skills

in turbulent environments. Leadership ranks were correlated with GIAL scores to determine the relationship of GIAL level to leadership effectiveness.

Results

The proportion of high GIAL candidates dropping out of the OCS program was .09, while the proportion of low GIAL candidates dropping out was .18. The difference between these proportions was significant (Z = l.76, p .04), resulting in acceptance of the first hypothesis that the proportion of high GIAL candidates successfully completing the program is greater than the proportion of low GIAL candidates completing the program.

The mean GIAL score of the OCS candidates was 44.87 before exposure to the two-week training program, and 48.12 after completion. This 3.25-point difference represents a significant increase (t = 4.12, df = 65, p < .001) in the mean GIAL score. The before and after difference between mean GIAL scores for the control group was not significant, and the second hypothesis that subjection to the highly turbulent environment of OCS would result in increases in incongruity expectations was accepted.

Quartile comparisons revealed significant differences in the changes of incongruity expectations for low and high candidates in OCS, but not in the control group. Although OCS candidates in the first (top) quartile and second quartile manifest no significant changes, the mean GIAL scores for candidates in the third quartile increased significantly (t = 2.98, df = 15, p < .01) as did those for candidates in the fourth quartile (t = 6.59, df = 16, p < .001). Because of these differences the third hypothesis that the incongruity expectations of low G IAL candidates would increase by a greater degree than those of high GIAL candidates was accepted.

Pearson product-moment correlations between GIAL scores and leadership rankings by peers did not yield significant results. Correlations between GIAL scores and Tac officers’ leadership rankings also failed to be significant. Consequently, the hypotheses suggesting a positive relationship between GIAL scores and leadership in the OCS environment were rejected. A significant, negative correlation was found between the leadership rankings of peers and Tac officers (r = .59, Z = 4.72, p < .0001). Since the numerical values in ranking schemes for peers and Tac officers were reversed, the significance of this correlation indicates that both types of judges agreed on candidates' relative leadership capabilities.

Discussion and Conclusions

The positive results confirming the first hypothesis, that a higher proportion" of high GIAL candidates than low GIAL candidates would successfully complete the OCS program, provides support for the basic GIAL hypothesis concerning the relationship between GIAL and environmental turbulence. The proposition is that whenever the environment provides either too much or too little turbulence relative to the individual's GIAL, the negative effect associated with this incongruence will motivate the individual to change or avoid it. Since an OCS candidate can do little to modify the nature of his environment, an active response alternative for overloaded

individuals is to withdraw from the program. Consequently, low GIAL candidates behave in accordance with traditional dissonance theory and choose to sacrifice the future rewards of becoming an officer in order to avoid the surplus of immediate dissonance relative to their expectations. High GIAL candidates, on the other hand, find less discrepancy between this turbulent environment and their expectations. Consequently, they have little difficulty enduring the dissonant occurrences and successfully completing the program.

Support of the second hypothesis suggests an addition to the GIAL model. Exposure to the tremendous turbulence in the OCS program resulted in a significant increase of the mean GIAL score of candidates completing the program. Thus, when subjected to a situation where they can neither significantly alter the nature of dissonant inputs, nor escape from the situation without considerable cost, it appears that the successful candidates experience at least temporary increases in their incongruity expectations, allowing them to endure the situation, Research is currently in process to determine whether these shifts in expectations arc temporary or

permanent.

The results supporting the third hypothesis that low CIAL candidates react more strongly to environmental turbulence than high GIAL candidates, emphasizes the importance of controlling for individual differences when investigating the effects of exposure to incongruent environments. These results also substantiate the GIAL hypothesis that low GIAL individuals will be disturbed by much less turbulence than high GIAL individuals, who may actually seek more incongruence at the same level of environmental turbulence that causes low GIAL individuals to avoid it.

In terms of the resulting increases in adaptation levels, the largest increase occurred for candidates in the fourth quartile (i.e., lowest CIAL scores), and the second largest for candidates in the third quartile. No significant changes occurred for candidates in the top two quartiles (a slight decrease was noted for candidates in the first quartile and a slight increase was noted for candidates in the second quartile). These results suggest that the low GIAL candidates were encountering a degree of environmental incongruity exceeding their adaptation levels, and since withdrawal from the OCS program may have been even more costly (in terms of dissonance experienced) than enduring it, the outcome was an increase in their incongruity expectations.

High GIAL candidates, on the other hand, may have found the dissonance of OCS training to be congruent with their expectations and, therefore, had no need to adapt. Had the level of environmental turbulence been even greater, so that the resulting incongruity exceeded the expectations of both high and low GIAL candidates, the result could have been an increase in the expectations of candidates in all quartiles.

The lack of significant results regarding the fourth hypothesis indicates that differences in GIAL's are not enough by themselves to predict leadership success rankings in OCS environments. Since a significant correlation was found between the leadership rankings of peers and experienced officers, it seems that this is another case, similar to that reported by Williams and Leavitt (1947), where the cadet's fellow candidates are better predictors of leadership effectiveness than personality tests. Further research to determine the criteria utilized by these raters, controlling for their own personality make-up, is needed to suggest other personality variables related to leadership success in OCS.

Implications for OCS programs of this nature (i.e., producing turbulent-field conditions) include the following: (1) the GIAL Self- Description Inventory appears to have high potential as a screening device. (2) this type of program is instrumental in increasing the adaptation levels of low GIAL candidates (at least temporarily), (3) although common leadership rankings are produced by peer groups and superior officers, more research is needed to determine the personality and behavioral characteristics contributing to leadership effectiveness.

References

Driver. M. and S. Streufert (1965), The General Incongruity Adaption Level (GIAL) Hypothesis: An Analysis and Integration of Cognitive Approaches to Motivation (W. Lafayette, lnd: Purdue

University Institute for Research in the Behavioral. Economic and Management Sciences.

Driver. M. and S. Streufert (1967), Purdue-Rutgers Prior Experience Inventory II (GIAL Self-Description Test, Purdue University.

Hunsaker, P.L. (1975), "Incongruity Adaptation Capability and Risk Performance in Turbulent Decision-Making Environments," Organizational Behavior and Human Performance, Vol. 14, No. 2, pp. 173-185.

Hunsaker, P.L. (1972). "The Effects of Environmental Incongruity and General Incongruity Adaptation Level on Risk Perception and Risk Preference," Proceedings of the 1972 Annual Convention of the American Psychological Association.

Hunsaker, P.L., Mudgett, W.C. and Wynne, B.E. (1975), "Assessing and Developing Administrators for Turbulent Environments," Administration and Society, Vol. 17, No. 3, pp. 312-327.

Hunsaker, P.L., Wynne, B.E. and Mudgett, W.C. (1974), "A Preliminary Model for Developing Managerial Capabilities for Coping with Environmental Turbulence," Proceedings, Midwest Division of the Academy of Management, pp. 217-234.

Lippitt, G. and P. Petersen (1967), "Development of a Behavioral Style in Leadership Training." Training and Development Journal, pp. 9-17.

Petersen, P. and G. Lippitt (1968), "Comparison of Behavioral Styles Between Entering and Graduating Students in Officer Candidate School." Journal of Applied Psychology, Vol. 52, No.1, pp. 66-70.

Richardson, J. (1969), "The Relationship of Some Measures of Candidate Personality and Selection by OTU Board," Australian Military Forces Research Report, Vol. 69, pp. 1- 26.

Tannenbaum, R. I., Weschler, R. I and F. Massarik, Leadership and Organization: A Behavioral Science Approach (New York: McGraw-Hill, 1961).

Williams, S., and H. Leavitt (1947), "Group Opinion as a Predictor of Military Leadership," Journal of Consulting Psychology, Vol. II, pp. 283-291.

Use of Alternative Data in Consumer Lending Models: The Case of “Upstart”

Naveen Gudigantala, Robert B. Pamplin School of Business Administration, The University of Portland, 5000 N. Willamette Blvd., Portland, OR 97203, Phone: (503) 943-8457

gudigant@up.edu

Abstract

This work discusses the case of a fin-tech company called Upstart, which specializes in using AI/ML based platform to provide credit to traditionally underserved populations. Upstart’s AI platform uses alternative data in addition to the traditional FICO scores in its algorithms. This alternative data includes borrowers’ educational data and occupational data. Upstart’s data shows that a majority of traditionally underserved populations was able to obtain more credit and at better terms using their credit scoring system.

Introduction

Issues surrounding the fairness of algorithms are attracting much attention from the researchers (Saxena et al., 2019). The goal of this case study is to discuss the opportunities and challenges in using alternative data for credit scoring modeling. The case study uses a fin-tech company “Upstart Network, Inc.” (called “Upstart” from here on) and an analysis of Upstart’s AI practices in lending to address the questions of algorithmic fairness in consumer lending. In specific, this work will look at how do different approaches to the development of machine learning models can either help or hinder fairness in consumer lending.

This case study is intended for researchers in AI and financial services, students learning analytics/AI, and for practitioners doing AI/Data science work. The issues discussed in this case will help students better evaluate the implications of models they learn to create as part of analytics curriculum; for the researchers to continue investigating the problems raised in this study; and for data science practitioners to reflect on issues of algorithmic fairness.

Consumer Lending and Problems Addressed by Upstart

Upstart is an online lending platform, launched by ex-Google employees in 2014, with an aim to provide credit to people with limited credit or work history. Consumers in need of credit approach Upstart, a website embedded with Artificial Intelligence (AI)/ Machine Learning (ML) technologies, to inquire and create a loan application. Upstart automated the underwriting technology for credit scoring, meaning, given the information provided by the consumer, a model will decide whether to give or reject loan and loan terms. The use of a model – as opposed to a human – for decision-making refers to AI and the model itself may be developed using one or many machine learning (ML) algorithms. The Upstart’s website is cloud-based, meaning the consumer data and underwriting technologies operate on the Internet by providing online services to the consumers.

Upstart learned from an early study that although 83% of Americans have never actually defaulted on a loan, only 45% have access to bank-quality credit. Upstart notes this “45% vs. 83% gap” as unfair and sets out to create an AI platform that can make ingenious use of alternative data in expanding credit to underserved groups (Girouard, 2019). Girouard (2019), the CEO of Upstart, suggests that FICO score - a measure of credit risk available through credit reporting agencies such as Equifax, Experian, and Transunion – is limited in its predictive ability of consumer risk because it focuses exclusively on a consumer’s past credit history. Therefore, traditional lenders who rely almost exclusively on FICO score and traditional modeling techniques ignore some important predictive information about potential borrowers. This is one of the reasons contributing to the “45% vs. 83% gap” (Girourad, 2019).

To overcome this problem, Upstart’s underwriting model, in addition to using FICO scores, uses alternative data for borrowers, such as educational attainment and work history as predictors. Using the model with alternative data, Upstart claimed that 27% more loans are approved which also lowered interest rates by an average of 3.57% (Girourad, 2019). Although Upstart used education and work history as alternative data, other pieces of data such as payment history concerning rent, electricity, gas and telecom bills, repayments to payday lenders can be considered as alternative data. The major U.S. credit reporting agencies are initiating attempts to include alternative data in their credit scoring system, but they face several hurdles in capturing this information fully (Malik, 2019). Therefore, opportunities emerge for companies such as Upstart to ascertain creditworthy individuals with near prime FICO scores and create a business model around such customers.

In credit risk modeling, an important and universally used predictor is FICO score/credit score. The FICO scores range from 300 to 850. Many lenders consider borrowers with FICO scores of at least 720 to be “prime”. The next classification, “near-prime” generally falls in an interval of mid-to-high 600s to the low 700s. The third classification of “sub-prime” includes borrowers whose scores fall below 620 (Andriotis, 2016). The FICO score distribution of U.S. population as of April 2018 is shown in Table 1. As per this data, the individuals with credit scores between 300-600 don’t qualify for bank-quality credit (Dornhelm, 2018). Individuals with credit scores above 700 (58.2% of population) usually qualify for best possible terms. The “near prime” from this table can be loosely categorized as the percentage of people between scores 600 and 700, and they stand at 22.6% of U.S. population. This segment of population can be considered as “traditionally underserved” in terms of credit.

|FICO Score |Percentage of U.S. population |

|300-600 |19.1% |

|600-649 |9.6% |

|650-699 |13% |

|700-749 |16.2% |

|750 and above |42% |

Table 1. Distribution of FICO scores across U.S. population as of April 2018 (Dornhelm, 2018)

What is the problem with FICO score as an important predictor? The FICO credit scores are unduly impacted by the length of credit history of an individual. Even the other components that go into the calculation of FICO scores such as payment history, new credit, credit mix, and credit utilization also favor individuals with longer credit history. In conclusion, FICO scores inherently create bias against younger borrowers or recent immigrants with fewer accounts (called “thin files”), lower credit limits, and fewer years of making payments. These types of borrowers have substantially lower credit scores compared to older borrowers. Another interesting point noted by Upstart is that a majority of traditional lenders use the length and breadth of borrowers’ credit files as independent criteria in making determination of loans (Upstart, 2017). Please see the data in table 2 and a scatterplot in Figure 1 showing the positive linear relationship between Age and FICO score (Dornhelm, 2018).

|Age Range of U.S. Individual |Average FICO Score |

|18-29 |659 |

|30-39 |677 |

|40-49 |690 |

|50-59 |713 |

|60+ |747 |

Table 2. Data of Age and Credit Score as of April 2018 (Reference: Dornhelm, 2018)

[pic]

Figure 1. Relationship between Age and Credit Score as of April 2018 (Reference: Dornhelm, 2018)

So what happens if a model predominantly uses FICO score to assess creditworthiness of an individual? Upstart conducted a study in September 2016 with a random sample of their borrowers during the years 2014-16. It used two models to do the comparison: a limited model with no alternative data (used FICO score and length of credit history) and an Upstart’s model with alternative data. The results are presented in Table 3 and show that the use of alternative data in credit modeling results in better credit terms and also improves the predictive accuracy of the model.

|Limited Model (No alternative data; use of |Upstart Model (traditional variables plus |

|traditional variables) |alternative data) |

|Model recommended average APR of 23.5% |Model recommended average APR of 16.7% |

|Model has lower R^2 for predicting default rate |Model has higher R^2 for predicting default rate |

Table 3. Results from comparison of credit models by Upstart (Upstart, 2017)

Benefits of Upstart’s AI models for underwriting

Dave Girouard (2019), CEO of Upstart, presented the following benefits of using AI system to the House Committee taskforce:

1. Upstart’s models approved 27% more consumers and lowered interest by average of 3.57% compared to traditional models.

2. For a near-prime consumers (620-660 FICO), Upstart’s models approved 95% more consumers and reduced interest rates by an average of 5.42% compared to traditional models.

3. Upstart’s model provided higher approval rates and lower interest rates for every traditionally underserved demographic.

Upstart reported to have facilitated 80,000 loans totaling over $1 billion. The loans typically fall in the range of $1,000 to $50,000 with repayment periods between 3 and 5 years. The average age of the borrower is 28 years with the APR rates ranging between 4% and 25.9% (Upstart, 2017).

An interesting aspect of these statistics is that the average age of borrower for Upstart’s services is 28 years. What do Upstart’s consumers do with this money? A majority of Upstart’s borrowers paydown higher interest credit card balances, use them to consolidate payday loans, reduce student loans, or to pay tuition for graduate education (Upstart, 2017).

Limitations of Upstart’s AI models for underwriting

Any AI model that is developed within ‘certain constraints’ will not work well outside of that specific environment. In this instance, Upstart appears to focus on relatively young borrowers with limited credit history but good educational background and work history. Looking at it from another perspective, Upstart’s model focuses more on future financial potential of their borrowers – mostly appearing to be students and recent graduates – than the traditional models which look at the past credit history of borrowers. Therefore, Upstart (2017) acknowledges that their underwriting model may not be equally predictive across all demographic groups, meaning that the benefits similar to those offered by Upstart to their “thin file” consumers may not be as attractive to older borrowers.

Summary and Conclusions

In conclusion, the use of alternative data offers much promise in our efforts offer bank quality credit to millions of underserved Americans. Such promise is possible because of Big Data and the use of AI and ML technologies. However, there is also a great danger that lurks in the corner if companies don’t exercise due diligence in employing this new generation of tools and technologies. This work attempts to show the efforts of an innovative company, Upstart, in making strides in the use of AI/ML to expand credit, and also given the challenges concerning this nascent phenomenon, calls for further research.

References

Andriotis, Annamaria (2016). Banks Have a New Phrase for Risky Customers: ‘Near Prime’, Wall Street Journal Blogs. Retrieved from (Current August 15, 2019).

CFPB consumer laws (2013). Equal Credit Opportunity Act (ECOA). Retrieved from current August 15, 2019.

Dornhelm, Ethan (2018). Average U.S. FICO Score Hits New High. FICO/Blog. Retrieved from (Current August 15, 2019).

Girouard (2019). Examining the Use of Alternative Data in Underwriting and Credit Scoring to Expand Credit Access. Testimony of Dave Girouard, CEO and Co-Founder, upstart Nework, Inc. Before the Taskforce on Fintech, United States House Committee on Financial Services, Retrieved from: (Current December 19, 2019).

Hayashi, Yuka (2019). Where You Went to College May Matter on Your Loan Application. The Wall Street Journal. Retrieved from: (current August 15, 2019).

Malik, Sanjay (2019). Alternative Data: The Great Equalizer To Lending Inequalities? Forbes. Retrieved from: (Current August 15, 2019).

Saxena, N. A., Huang, K., DeFilippis, E., Radanovic, G., Parkes, D. C., and Liu, Y. (2019). How Do Fairness Definitions Fare?: Examining Public Attitudes Towards Algorithmic Definitions of Fairness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 99-106.

Upstart (2017). Request for No-action letter, Consumer Financial Protection Bureau. Retrieved from: (Current December 19, 2019).

Child Labor in Globalized Economy: Strategies to Combat the Problem

Foad Derakhshan, Management Department, California State University-San Bernardino,

CA 92407-2397 Phone: (909)880-5734 Fax: (909)880-5994, der@wiley.csusb.edu

and

Benjalux Sakunasingha, Business Administration Division, Mahidol University International College (MUIC), 999 Buddha Monthon Sai 4 Road, Salaya, Nakornpathom Thailand 73170 benjalux.sak@mahidol.ac.th

Abstract

Although statistics on use of child labor are not always accurate, an estimated 100-250 million children work globally (Child Labor Facts 2019) of which about 100 million work in agriculture, mining and domestic work under hazardous conditions, being under age, being underpaid and subject to inhumane treatments (Human Rights Watch 2019). Historically, although not new, child labor reached new extremes during industrial revolution (Child Labor- History 2019). During the 19th and 20th centuries, reformer acted on the problem in developed countries pushing for social and legislative actions which ultimately caused almost extinction of the problem in these countries. However, these actions were not duplicated in less developed parts of the world and globalization of the economies, which followed the collapse of the Soviet Union, actually intensified the problem. The use of subcontracting to small local operators, a cost saving measure favored by many multinational companies particularly aggravated the problem. Human right organizations launched negative publicity campaigns in developed-consuming countries as well as pushing local governments of producing countries. With limited success these efforts forced large companies to check on the ethical practices of their subcontractors.

Main contributors to the use of child labor are poverty, exacerbated by uneven distribution of wealth, wars, lack of proper education and insufficient governmental willingness and legal mechanisms to fight the problem. Insufficient consideration of the ethical concerns by MNCs as well as poorly constructed contracts with their subcontractors often leaves room for the use of child labor. Societal and government indifference has also perpetuates the problem.

Strategies to combat the child labor problem also can come from various sources and in different forms. Some corporations have voluntarily established codes of conduct and labeling to inform consumers about this problem. Governmental and international non-profit agencies have acted in form of trade restrictions and regional agreements. Societal actions include consumer boycotts and marches to protest the use of child labor.

Introduction

The estimated number of children used for child labor ranges between 100 to over 200 million (Child Labor Facts 2019). Of these children, many work in hazardous conditions, are subject to abuse and miss schools (Human Rights Watch 2019).

Cost-saving benefits of globalization has pushed large companies to subcontractors which use child labor. This has intensified the problem. The use of child labor is neither a new problem nor restricted to the less-developed part of the world. Children have historically been a part of the work force throughout human civilization. Before the Industrial Revolution, children worked as apprentices with the justification that such experiences was a preparation for adult life (Mendelievich, 1979). In early stages of industrial revolution children worked in factories carrying menial and repetitive tasks for minimal pay without any special concern for their needs and safety. As industrial revolution progressed in developed economies and the abuses of children were better known, social groups and unions pressured for child labor legislations to address the issue which consequentially nearly abolished such practices.

As Soviet Union collapsed and the global markets became more accessible, the globalization trend called for cost saving measures by decentralization of production. Local subcontractor were used by multinational companies (MNCs) in less developed parts of the world where cheaper labor was abundant. An appealing source of cheap labor was child labor. Less developed economies lacked child labor protection laws and government enthusiasm to deal with the problem. However, the child labor problem is a not limited to the less developed part of the world. A survey of a thousand of working schoolchildren in the north-east of England found that 25% were under legal working age of 13, 44% had suffered injury at work during the past year, one in seven worked over maximum hour-limit and they earned as little as 33 pence an hour ( BBC News, Feb. 10, 1998.) The picture is much grimmer in less-developed countries. In Pakistan, many children under age of 10 work for less than 10 pence an hour stitching soccer balls that are exported (BBC News, Oct. 29, 1997.) In Brazil, children work in hazardous production of sisal with the risk of serious damage to their lungs (BBC News, Oct. 30, 1997). In Pakistan, children are used in the booming carpet manufacturing business with menial pay and for long hours. India has the largest number of children engaged in labor, estimated at 100 million (BBC News, Oct. 3, 1998). According to the BBC, an estimated 250 million children worked around the world (BBC News, January 17, 1998).

Lack of proper data gathering problem is perpetuated by governments’ reluctance to provide such information. International Labor Organization (ILO) puts the estimate of the number of working children between 100 - 200 million, with about 95% living in less developed and developing countries. Geographically speaking, Africa has the highest proportion, with one in every three children working. Being the world's most populated region, Asia provides 50% of the children working.

Child labor is used in family farming, services (domestic servants, restaurants, street vending, etc.), manufacturing (carpets, footballs, garments, furniture, etc.) and prostitution. Most of such activities are in the underground economy which is not regulated by laws. Subcontracting industries such as garments, shoes and carpets makes a distinction between formal and informal economies even more difficult. Many export industries in less developed countries which employ children include garments, carpets, shoes, small-scale mining, gem-polishing, food-processing, leather tanning, and furniture. Government polices to promote exports of low-skilled, labor intensive products, such as garments and carpets, without addressing the child labor problem have actually intensified the demand and use of child labor. Without strong international pressure and assistance, this problem will not resolve itself.

Complex subcontracting arrangements, with layers of middlemen between the exporter and the primary production unit, frequently hide or least disguise the use of child labor. For instance, in the garment and shoe industries, parts fabricated by children in one country are sent to a second country for assembly before being exported.

The following section outlines major reasons for the use of child labor.

Causes of Child-Labor Problem

Poverty. Uneven distribution of wealth has traditionally been a major source of the use of child labor in oligarchical societies. Global economic growth in recent decades has done little to address this problem. Wide spread poverty leaves families with no choice but to use their children as a source of income. Employers provide less pay and exhibit abusive practices without the fear of retaliation or legal retributions. The most devastating form of child labor abuse, child prostitution, is often ignored and sometimes encouraged by cultural values (BBC News, Oct. 23, 1998 and Jan. 4, 1999). For these children, work is often a substitute for education. This fact perpetuates their poverty and reduces their chances to break out of this vicious cycle. Governments and international agencies have traditionally ignored the problem due to their indifference or inadequacies. Lack of proper social security networks in most less developed countries aggravates this problem. In some cases, burdened by heavy debt, families sell their children to debtors to cover their debt. (International Labor Conference, 1996). In India, some parents exchange their children for use as child labor to local moneylenders for an average of two thousand rupees (Human Rights Watch, 1996). This modern form of slavery often continues after payment of the family debt since the child who has known no other forms of work or alternative continues working for his/her owner for the rest of his/her life. In Pakistan, Iqbal Masih was sold into slavery at the age of four by his parents for only $16. For six following years, he was shackled to a carpet-weaving loom, tying tiny knots for ten hours a day (Ridder, 1996). For children and their families, poor education aggravates the lack of awareness of their rights which makes it more difficult to scape this cycle of slavery.

Poor Education and Other Societal Issues. Lack of education and low literacy rates are major contributing factors to child labor. It is suggested that the pressing need for the child's earnings as well as low perceived advantages of schooling causes parents to send their children to work (Badiwala, 1998). Additionally, other cultural traditions and history also contribute to this problem. History of some indigenous societies, former slave families and other minority groups allow children to work in exploitative environments in conditions close to slavery similar in their ancestral history. A by United Nations paints a horrifying picture of child prostitution in North-West of Pakistan where the sexual abuse of young boys is a matter of pride (BBC News, January 4, 1999.)

Historically, the child labor problem did not receive much attention at the international level until more recent decades. Increased globalization, in the last several decades, forced international organizations and government agencies to begin paying more attention to the plight of working children. International Labor Organization’s (ILO’s) mandates related to working children, including the Convention (No 138, 1973) entitled Minimum Age for Admission to Employment and the Forced Labor Convention (No 29, 1930) that is used to examine practices of child slavery, was never ratified by all 156 member states (Lee-Wright, 1990). Consequently, there has been a call for the work done by the ILO to be more widely accepted and its recommendations implemented in governmental policies. However there are problems associated with government inadequacies and corruption, in countries where child labor is used are major obstacles. The shortage of skilled and technically-competent inspectors combined with their inadequate authority to check factories and businesses who engage in child labor are serious problems. Corruption of government officials has also been an obstacle, many officials use the pressure for economic growth as a justification for use of child labor.

Economics of the Production Demands. Cost-saving measures generated by the use of child labor are the main reason behind employers in using this source of employment. From the economic perspective, three main reasons attract employers to use children as workers: They cost less and they have irreplaceable skills (nimble fingers) and are easier to manage. The ILO does not consider the first two reasons as valid Anti-Slavery measures (ILO Web Site- 1998.) It also does not consider these as legitimate reasons.In regards to cost-saving justification, a survey done by the ILO has concluded that the cost savings between paying an adult or a child's wage is minimal. The survey found a difference of about 5-I0 per cent, which if it exists, it can be easily absorbed by retailers and wholesalers. As far as the second reason, irreplaceable skills, the survey found that are children mostly work side by side with adults in industries such as carpet-making, glass manufacturing, the mining of slate, limestone and mosaic chips, lock-making and gem polishing. In most cases, children are assigned to do unskilled works, while adults do most of the skilled work. The third reason that children are more vulnerable and easier to manage is based on mostly unethical reasons. The U.S. Department of Labor identifies children’s lack of awareness of their rights, their lack of questioning authority, that they are more compliant, more trustworthy and less likely to be absent from work (U.S. Department of Labor, 1994; Brecher Web Site, 1998; FIET Web Site, 1998).

Work is not necessarily a harmful matter for children if proper protective mechanism are in place. Children often work for training and as a step to prepare them for their adult lives (Fyfe, 1989). However, the exploitation of children at work place composes a moral dilemma for economies of developing nations with some responsibility placed on shoulders of industrial world through globalization of production and consumption of final goods. Appropriate strategies can be implemented from several angles to deal with this problem. In addition the actions taken by government agencies, in both producing and consuming countries, international agencies and non-profit groups, social and educational organizations and business organizations can develop strategies to tackle this problem.

Strategies to Deal with Child-Labor Problem

Three groups have initiated strategies to deal with child labor problems. Multinational Companies (MNCs), governments and international agencies and societal initiatives have been used to deal with the problem.

Multinational Companies’ Strategies: MNC’s use subcontractors in less-developed countries to reduce the production cost. This is often achieved through the use of child labor. These subcontractors are usually small firms or families that use child labors. Multinationals who have been indirectly involved in use of child labor to produce their goods have had to deal with resulting negative public sentiment in recent decades. Levi Strauss, Sears, Reebok, and Nike (Anti-Slavery web Site, 1998) have witnessed such negative publicity in recent years. In response to public pressure, some companies have developed codes of conduct, policies and taken actions to deal with the issue of child labor (ILO Web Site, 1998; Grootaert, 1995)Levi Strauss has incorporated minimum working conditions and a pledge to set up educational facilities in its company code of practices. Other companies have also formulated such codes of practice (ILO, 1998; Ansari, 1998). A code of conduct that was negotiated between the International Confederation of Free Trade Unions (ICFTU) and the International Federation of Football Association (FIFA) concerning social conditions which were to be respected in the manufacturing of soccer balls bearing the FIFA logo. This was done as a response to the ICFTU’s accusation that FIFA used soccer balls which were manufactured by the exploitation of children in the Sialkot region of Pakistan. The code of conduct includes a statement prohibiting the use of child labor in manufacturing soccer balls and sets up a system of monitoring the production sites as well as education and training programs for children (ILO Web Site, 1997; Littlefield, 1996). In response to the same problem, as an joined initiative of UNICEF, Save the Children, the World Federation of the Sporting Goods Industry (WFSGI), the Soccer Industry of America and the ILO (and others) a plan of action was set up to ensure the gradual phase out of child labor being used in the manufacture of soccer balls in Pakistan. This plan involves an educational and social program for the children to provide them with other alternatives as well as a system for independent monitoring to ensure that the use of child labor was being phased out. This program was also supported by over 50 sporting goods brands including Adidas, Mitre, Nike, Puma and Reebok. They pledged their support to purchase soccer balls produced in Pakistan only from manufacturers who were participating in the monitoring program (Save the Children Office, 1997).

Poor enforcement of established codes is also a problem. The insufficient or total absence of monitoring mechanisms is a major obstacle to enforcement of these codes of conduct. Sub-contractors are often not informed of these codes, or even if they were, there is a tendency to blatantly disregard the codes. MNC’s cannot solve the problem alone. Governments’ initiatives in both producing countries, where child labor is used, as well as in industrial countries, which often consume the final products, are equally important. These government initiatives require cooperation if they are to lead to fruitful results.

Governmental and International Agencies Initiatives: Historically, the most prevailing government reaction to child labor problems has been that of denial. Child labor was mostly illegal, therefore, was not reported by governments. More recently public pressures caused by media reporting as well as pressure from international bodies such as the ILO, UNICEF, Save the Children and other nongovernmental organizations (NGOs) have caused governments in developing countries, where children are exploited, to act.. Governments have reviewed their legislations and have enacted policies on enforcing the laws on child labor. Increasing the adult wages and building educational facilities to encourage children to stay at school have been used in Brazil, India, Indonesia, Thailand, Kenya, Nepal, Pakistan, United Republic of Tanzania, and Zimbabwe are few countries that have taken such measures (Anti-Slavery web Site, 1998). Government of India passed legislation and enacted policies with the goal to eradicate child labor by the beginning of the 21" century (Ansari, 1998). However, the results are not clear and despite actions, the child labor still remains as problem in India and other countries.

Government agencies have to tackle various aspects of the child-labor problem. Legislation and policies concerning minimum age restrictions and minimum work conditions have been enforced in last several decades (Grootaert, 1995; Act No. 677/1991; China Labor Newspaper, Jan. 1994). The problem has been tackled by various government departments and agencies such as the education departments, health departments and welfare departments.

Cooperative international initiatives have come from many sources in many forms. Industrial countries have used international agencies, legislation or trade sanctions to pressure developing countries to reduce that use of child labor. Historically, the United States Government and the European Union have been at the forefront of developing legislation aimed at reducing incidences of child labor. In 1993, Senator Tom Harkin introduced the Child Labor Deterrence Act which allowed the prohibition of certain imports, such as minerals or manufactured goods, into the United States if they were produced by child labor (U.S. Congress, 1995). United States Government also has included the “respect for workers' rights” as a condition in its Generalized System of Preferences (GSP) which links the granting of trade preferences to foreign countries. Some of its trade preferences of Pakistan was removed due to the use of child labor in 1995 (Littlefield, 1996.) During 1995-1998 period, the European Union (EU) included extra provisions regarding the temporary removal of all or part of the benefits of the scheme if goods were produced by prison or slave labor (EU Web Site 1998). Following this arrangement, the EU established further incentives and preferences which included provisions to inhibit the use of child labor.

As an advocate of rights of working children, ILO proposed inclusion of clause trade agreements regarding the respect of certain worker's rights including the right of children not to be forced to work before certain age. To facilitate implementation of its proposal, the ILO set up the Working Group on the Social Dimension of the Liberalization of International Trade in 1994. Through series of conventions, this group reached a broad agreements to include a minimum age for admission to employment in the international labor standards (ILO Web Site 1998).

Societal Movements and Initiatives: Societies in developed as well as developing countries have played major roles in addressing the child-labor problem. A strong force in developed countries is the consumers and their boycott of the products made by child labor. The consumer boycott of such products does not always lead to the desired outcomes, however. Children who lose their original job, may be forced to take up employment in more dangerous environments (e.g. construction, agricultural work with the risk of chemical pollution) or situations far more despicable than the mere assembly of products (e.g. prostitution, slavery or "bonded labor"-Fyfe, 1997.) Consumer power is an important tool which is best to be used in conjunction with government and corporate programs.

Raising social awareness of child labor problem can be instigated via a number of channels, namely media, labeling and international publicity campaigns. News organizations can play a major role in increasing public awareness of child labor problems by allocating more time on coverage of the related issues particularly on television. A television report on the World in Action program showed the Sicom, a subcontractor of Marks and Spencer (M&S), employing girls under age of 15 in a Moroccan garment factory. Even though the M&S Company later denied the allegations, the report successfully generated a panic in the industry to force them into reviewing their supply chains operations. A flood of calls into the ILO's London office was following the program reconfirmed the social impact of the news cast (Littlefield, 1996.)

Another channel information channel is the computer web sites. Websites can provide up-to-date information but mostly to the more educated part of the public. The credibility problem associated with this source has improved by major newscasters, government agencies and reputable nonprofit groups using web sites to report child labor cases. Beginning 1994, product labeling was used by the companies voluntarily as a means of guaranteeing that the product had been manufactured without the use of child labor, or if children were used in the manufacture of such products, they did so under strict adherence to laws. Germany, USA, India, Nepal and Netherlands have set up the Rugmark system to identify carpets that are woven and made by exploitation of children. Other labeling systems are also used in the carpet industry, the Fair and Care System which is operating in Germany and the Step system is used in Switzerland. Examples of labeling systems for other products include the Double Income Project system, based in Switzerland, for the textile and garment industry and a voluntary labeling system for the footwear industry in the State of Sao Paulo in Brazil. Labeling programs are used in both industrial and developing countries. Among the industrialized countries, U.S., Germany and Switzerland use labels. Among developing countries, Brazil, Kenya, India, Nepal and Pakistan have initiated such systems. Labeling systems are usually initiated by companies and are on voluntary basis (Hilowitz, 1997.) Credibility of labels, whether labels are being exploited as a marketing tool, channeling of revenues to address the problem, potentials and limits of labeling as a tool in eradicating child labor and, protectionism are some issues regarding the use of labels.

Various nonprofit organizations have also effectively initiated publicity campaigns to increase awareness of child labor problems. A major organizer of global marches is the International Steering Committee of the Global March. A list of international organizations which participate in organizing marches include the African Network for the Prevention and Protection Against Child Abuse and Neglect, Anti-Slavery International (UK), Education International (Belgium), and International Labor Rights Fund (USA). (Kochar, 1998) Organizers of marches come from a variety of organizations including education organizations (American Federation of Teachers); religious organizations (United Methodist Church); women groups (Gemal Federation of Women's Club); child labor groups (Child Labor Coalition.) These groups have organized events, rallies and demonstrations in Manila, Rio de Janeiro, Geneva, and California to name a few. An on-line march on a number of web sites related to child labor was also organized (National Consumer League, 1998).

Conclusion

Insufficient and unreliable data makes it an obstacle in determine the exact extent of the use of child labor globally; however, it is certain globalization has contributed to the expansion of this problem (New York Times, Feb. 6,2019); NYT Nov. 22, 2018. A common belief of the advocates of child labor is that it provides less developed economies with a competitive advantage in global economy. However, studies suggest that there is little or no evidence that these countries benefit from lower labor standards to allow use of child labor. On the contrary, child labor impedes the development of children which in turn serves as an obstacle to future economic development. The claim that low cost of child labor creates competitive advantage is not substantiated. However, a trend in budget cuts of monitoring agencies is a disturbing occurrence in tackling this problem (The Atlantic, December 2014).

Globalization can in fact be used as a vehicle to reduce incidents of child labor. Developing countries investment in educating children is an important vehicle for creating the foundation for future economic growth. Multinational companies can participate in this investment which will provide a reliable and cost-effective source of labor for their future operations. Furthermore companies can benefit from improved image by avoiding countries where child labor exists.

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Passively Active Investing – A Five Year Test

Jeffry Haber, Penta School of Business, Iona College , jhaber@iona.edu

Abstract

This paper took five years of the Council on Foundations-Commonfund study of foundations and using the average asset allocation of large, private foundations replaced the active managers with ETFs with a similar strategy to form a replacement portfolio. Over the five years the active portfolio outperformed the replacement portfolio in four of the five years, and also produced superior returns in Fixed Income, Non-US Equities and Alternative Strategies. The replacement portfolio produced similar returns to the active portfolio in US Equities and Short-term Securities/Cash.

The five year return of the actively managed portfolio was 8.1% versus 4.8% for the replacement portfolio. The replacement portfolio was rebalanced annually to the new average asset allocation, and all ETFs chosen were based on Morningstar ratings and/or designation as an “all-star.” When removing an ETF (due to rebalancing) the last one in the list was removed, not the worst performing. The ETFs chosen also had to have expense ratios of less than 20 basis points, except for alternative strategies, which could have an expense ratio of up to 80 basis points.

Introduction

In investment circles one of the longest running debates is whether active management can outperform (net of fees) passive investing. The literature contains ample evidence of the persistence of this argument. Both the academic and professional literature has countless examples of papers written on the subject. The papers usually take a focus that follow one of a few paths:

A. Whether active management outperforms passive on an annual basis[1]

B. Whether active management outperforms passive on a cumulative return basis[2]

C. Whether a passive index is still attractive when subjected to the diligence that an active manager would be[3]

This paper looks at the debate in a different way. The Council on Foundations and the Commonfund produce an annual report that compiles the investment allocations and returns from a number of foundations. This paper uses the last five years (2013 – 2017) that the report was produced. The 2013 report included just private foundations, whereas starting in 2014 the report included private and community foundations. This paper takes the average asset allocation reported for the large ($500 million and over in endowment) private foundation and uses exchange-traded-funds (ETFs) to populate the asset allocations.

ETFs were chosen over indices because not all indices are investable, and most reported indices are gross of fees. To use the investable indices (even if imputing a reasonable management fee) would require also filling some asset allocations with a different type of security. Using all ETFs provides a homogenous security type for replacing the asset allocations in the average portfolio.

Procedure

A comparative portfolio will be developed comprised of ETFs that are intended to mirror the asset allocation of the average, large ($500 million and above) private foundation (the “replacement portfolio”). Each year the replacement portfolio will be rebalanced to the new asset allocation. The initial $500 million replacement portfolio will use the asset allocation reported in the 2013 Study. The returns will be applied and the ending balance of the asset allocations in the replacement portfolio at the end of 2013 will be used in the asset allocation for 2014. This will continue for each of the years.

The E-Trade website was used to select the ETFs that will replace managers in the portfolio. In order to try and select passive ETFs, rather than active investment management provided in an ETF framework, a maximum expense ratio of 20 basis points was initially chosen. Additionally, since the initial portfolio is set beginning on January 1, 2013, the ETF has to have a history going back at least that far.

For some broad groupings the Study had unspecified allocations, using the term “other.” This happened with alternatives and cash. For cash, the allocation to “other” was simply added to the allocation of cash. For alternatives, the allocation to “other” was reallocated to the specified alternative allocations within the alternative grouping on a pro-rata basis.

As a general rule, no individual allocation could be greater than 5% of the portfolio balance. It became apparent that it would be difficult to find ETFs to replicate alternative managers for 20 basis points. So the cap was raised to 80 basis points for alternative managers (with a priority on using lower cost ETFs where they existed). The ETFs utilized were:

| | | | |Expense |

|Asset Class |ETF |Name |Ratio |

| | | | | |

|US equities | | | |

| |Active |MGK |Vanguard Mega Cap Growth Index |0.07% |

| |Active |VYM |Vanugard High Dividend Yield |0.08% |

| |Active |VOE |Vanguard Mid-Cap Value |0.07% |

| |Active |VOT |Vanugard Mid-Cap Growth |0.07% |

| |Passive |VOO |Vanugard S&P 500 |0.04% |

| | | | | |

|Fixed income | | | |

| |US investment grade (active) |VCIT |Vanguard Intermediate Term Corporate Bond |0.07% |

| |US investment grade (passive) |BIV |Vanugard Intermediate Term Bond |0.07% |

| |US non-investment grade |VMBS |Vanguard Mortgage-Backed Securities |0.07% |

| |Non-US investment grade |BWX |SPDR Int'l Treasury Bond |0.35% |

| |Emerging markets |EMAG |VanEck Emerging Markets Aggregate Bond |0.35% |

| | | | | |

|Non-US equities | | | |

| |Active MSCI, EAFE |IEFA |iShares Core MSCI EAFE |0.08% |

| |Active MSCI, EAFE |IXUS |iShares Core MSCI Total International Stock |0.10% |

| |Active MSCI, EAFE |VEU |Vanguard Int'l Equity Ind FD FTSE All World ex US |0.11% |

| |Passive index MSCI, EAFE |VXUS |Vanguard Total Int'l Stock Index |0.11% |

| |Emerging markets |VWO |Vanguard Emerging Markets FTSE |0.14% |

| |Emerging markets |IEMG |iShares Core MSCI Emerging Markets |0.14% |

| | | | | |

|Alternative investments | | | |

| |Private equity |BDCS |UBS Securities Linked Wells Fargo Business |0.85% |

| |Private equity |BDCL |UBS 2x Leveraged Long Linked Wells |0.85% |

| |Private equity |QAI |IndexIQ Hedge Multi-Strategy Tracker |0.79% |

| |Marketable alternatives |MNA |IndexIQ Merger Arbitrage |0.78% |

| |Marketable alternatives |HDG |ProShares Hedge Replication |0.95% |

| |Marketable alternatives |PBP |Invesco S&P 500 Buy Write |0.49% |

| |Marketable alternatives |MRGR |ProShares Merger |0.75% |

| |Venture capital |IWC |iShares Micro Cap |0.60% |

| |Venture capital |IPO |Renaissance IPO |0.60% |

| |Private real estate |USRT |iShares Core US REIT |0.08% |

| |Energy, natural resources |VAW |Vanguard Materials |0.10% |

| |Commodities and managed futures |GSP |Barclays Traded Notes Linked to GSCI |0.75% |

| |Distressed debt |ANGL |VanEck Fallen Angel High Yield |0.35% |

| | | | | |

|Cash, short-term |VGSH |Vanguard Short Term Treasury |0.07% |

Where multiple ETFs were available to fill allocations the selection criteria was to use the Morningstar rating and/or whether the ETF was classified as an “all-star.” No information about returns was used (prior or future). And once an ETF was selected it remained in the portfolio unless an allocation change required that it be dropped. Where an allocation decreased and there were multiple ETFs covering that mandate, the last ETF in the list was dropped, regardless of the prior reported returns.

Private foundations have a distribution requirement, but since the ETFs are all liquid, any distribution could be done pro-rata across all allocations, thereby not affecting the reported returns. The study uses a minimum assets under management of $500 million to define the “large” foundation class, so it is probable that the average large foundation in the study had an endowment that was greater than $500 million. Given how the comparative portfolio is developed it would not matter how large the assumed beginning portfolio balance was.

Average Asset Allocation

The average asset allocation shown in the 2013-2017 studies were:

| | | |Asset Allocation Percentages |

| | | |2013 |2014 |2015 |

| |Active |19 |18 |18 |17 |18 |

| |Indexed |3 |5 |5 |5 |4 |

| | | | | | |

| |Domestic investment grade - active |3 |5 |5 |4 |5 |

| |Domestic investment grade - passive |1 |2 |3 |1 |2 |

| |Domestic non-investment grade |1 |0 |0 |1 |0 |

| |International bonds |1 |1 |0 |0 |0 |

| |Emerging markets |1 |0 |0 |0 |0 |

| | | | | | |

| |Active MSCI EAFE |11 |8 |10 |9 |14 |

| |Passive/index MSCI/EAFE |2 |4 |2 |5 |2 |

| |Emerging markets |7 |6 |5 |6 |5 |

| | | | | | |

| |Private equity |10 |10 |9 |9 |9 |

| |Marketable alternative strategies |17 |18 |20 |19 |18 |

| |Venture capital |5 |6 |7 |9 |8 |

| |Private equity real estate |5 |5 |5 |4 |4 |

| |Energy and natural resources |5 |5 |3 |4 |4 |

| |Commodities and managed futures |0 |0 |1 |0 |1 |

| |Distressed debt |4 |3 |3 |4 |4 |

| | | | | | |

| | | | | | | |

| | | | | | | |

Using the 2013 asset allocation and a $500 million starting balance the 2013 portfolio was:

| | | | |Beginning | |Ending |

| | | |ETF |Balance |Return |Balance |

|Domestic equities | | | | |

| |Active |MGK |23,750,000 |27.99% |30,397,316 |

| |Active |VYM |23,750,000 |23.50% |29,332,144 |

| |Active |VOE |23,750,000 |32.59% |31,490,361 |

| |Active |VOT |23,750,000 |28.62% |30,546,352 |

| |Indexed |VOO |15,000,000 |27.24% |19,085,678 |

|Subtotal | |110,000,000 |28.05% |140,851,850 |

| | | | | | | |

|Fixed income | | | | |

| |Domestic investment grade - active |VCIT |15,000,000 |-6.05% |14,091,787 |

| |Domestic investment grade - passive |BIV |5,000,000 |-7.31% |4,634,672 |

| |Domestic non-investment grade |VMBS |5,000,000 |-2.43% |4,878,585 |

| |International bonds |BWX |5,000,000 |-5.38% |4,731,236 |

| |Emerging markets |EMAG |5,000,000 |-11.24% |4,437,970 |

|Subtotal | |35,000,000 |-19.04% |28,336,280 |

| | | | | | | |

|International equities | | | | |

| |Active MSCI EAFE |IEFA |18,333,334 |17.63% |21,566,332 |

| |Active MSCI EAFE |IXUS |18,333,333 |10.59% |20,274,634 |

| |Active MSCI EAFE |VEU |18,333,333 |9.38% |20,053,000 |

| |Passive/index MSCI/EAFE |VXUS |10,000,000 |9.31% |10,931,000 |

| |Emerging markets |VWO |17,500,000 |-8.84% |15,953,000 |

| |Emerging markets |IEMG |17,500,000 |-5.86% |16,474,500 |

|Subtotal | |100,000,000 |5.25% |105,252,465 |

| | | | | | | |

|Alternative strategies | | | | |

| |Private equity |BDCS |25,000,000 |6.15% |26,536,643 |

| |Private equity |BDCL |25,000,000 |10.95% |27,736,411 |

| |Marketable alternative strategies |MNA |21,250,000 |6.08% |22,540,967 |

| |Marketable alternative strategies |HDG |21,250,000 |2.63% |21,809,348 |

| |Marketable alternative strategies |PBP |21,250,000 |4.69% |22,247,099 |

| |Marketable alternative strategies |MRGR |21,250,000 |-6.69% |19,828,067 |

| |Venture capital |IWC |25,000,000 |38.06% |34,515,714 |

| |Private real estate |USRT |25,000,000 |-5.99% |23,503,586 |

| |Energy and natural resources |VAW |25,000,000 |19.78% |29,944,896 |

| |Distressed debt |ANGL |20,000,000 |0.15% |20,029,630 |

|Subtotal | |230,000,000 |8.13% |248,692,361 |

| | | | | | | |

|Short-term securities, cash, other | | | | |

| |Short-term securities, cash |VGSH |25,000,000 |0.05% |25,012,325 |

| | | | | | | |

|Total | | |500,000,000 |9.63% |548,145,281 |

Taking the ending balance from 2013 and using it as the beginning balance in 2014, and then applying the average asset allocation to the beginning balance and applying the returns produced a portfolio of:

| | | | |Beginning | |Ending |

| | | |ETF |Balance |Return |Balance |

|Domestic equities | | | | |

| |Active |MGK |24,666,538 |12.50% |27,749,855 |

| |Active |VYM |24,666,538 |10.66% |27,294,777 |

| |Active |VOE |24,666,538 |12.41% |27,726,602 |

| |Active |VOT |24,666,538 |12.91% |27,851,616 |

| |Indexed |VOO |27,407,264 |11.79% |30,638,631 |

|Subtotal | |126,073,415 |12.05% |141,261,481 |

| | | | | | | |

|Fixed income | | | | |

| |Domestic investment grade - active |VCIT |27,407,264 |4.04% |28,513,357 |

| |Domestic investment grade - passive |BIV |10,962,906 |3.57% |11,354,438 |

| |International bonds |BWX |5,481,453 |-3.66% |5,281,052 |

|Subtotal | |43,851,623 |2.96% |45,148,847 |

| | | | | | | |

|International equities | | | | |

| |Active MSCI EAFE |IEFA |21,925,811 |-7.89% |20,195,403 |

| |Active MSCI EAFE |IXUS |21,925,811 |-6.63% |20,473,058 |

| |Passive/index MSCI/EAFE |VXUS |21,925,811 |-6.73% |20,449,425 |

| |Emerging markets |VWO |16,444,358 |-1.21% |16,245,451 |

| |Emerging markets |IEMG |16,444,358 |-3.90% |15,803,028 |

|Subtotal | |98,666,151 |-5.57% |93,166,366 |

| | | | | | | |

|Alternative strategies | | | | |

| |Private equity |BDCS |27,407,264 |-15.20% |23,240,791 |

| |Private equity |BDCL |27,407,264 |-28.21% |19,676,304 |

| |Marketable alternative strategies |MNA |24,666,538 |5.72% |26,077,389 |

| |Marketable alternative strategies |HDG |24,666,538 |1.83% |25,117,936 |

| |Marketable alternative strategies |PBP |24,666,538 |0.14% |24,702,286 |

| |Marketable alternative strategies |MRGR |24,666,538 |-2.08% |24,154,315 |

| |Venture capital |IWC |16,444,358 |2.52% |16,858,260 |

| |Venture capital |IPO |16,444,358 |4.20% |17,135,021 |

| |Private real estate |USRT |27,407,264 |25.12% |34,291,725 |

| |Energy and natural resources |VAW |27,407,264 |4.15% |28,544,914 |

| |Distressed debt |ANGL |16,444,358 |-3.59% |15,853,798 |

|Subtotal | |257,628,282 |-0.77% |255,652,739 |

| | | | | | | |

|Short-term securities, cash, other | | | | |

| |Short-term securities, cash |VGSH |21,925,811 |0.02% |21,929,413 |

| | | | | | | |

|Total | | |548,145,281 |1.64% |557,158,845 |

procedure for 2015:

| | | | |Beginning | |

| |Active |MGK |25,072,148 |1.68% |25,492,729 | |

| |Active |VYM |25,072,148 |-3.36% |24,229,997 | |

| |Active |VOE |25,072,148 |-4.49% |23,946,562 | |

| |Active |VOT |25,072,148 |-2.31% |24,492,445 | |

| |Indexed |VOO |27,857,942 |-1.25% |27,510,619 | |

|Subtotal | |128,146,534 |-1.93% |125,672,352 | |

| | | | | | |

| |Domestic investment |VCIT |27,857,942 |-2.47% |27,169,733 | |

| |grade - active | | | | | |

| |Domestic investment |BIV |16,714,765 |-1.91% |16,394,998 | |

| |grade - passive | | | | | |

|Subtotal | |44,572,708 |-2.26% |43,564,731 | |

| | | | | | |

| |Active MSCI EAFE |IEFA |27,857,942 |-2.16% |27,256,475 | |

| |Active MSCI EAFE |IXUS |27,857,942 |-7.38% |25,803,276 | |

| |Passive/index MSCI/EAFE |VXUS |11,143,177 |-6.68% |10,398,608 | |

| |Emerging markets |VWO |27,857,942 |-18.12% |22,810,083 | |

|Subtotal | |94,717,004 |-8.92% |86,268,442 | |

| | | | | | |

| |Private equity |BDCS |25,072,148 |-11.44% |22,203,632 | |

| |Private equity |BDCL |25,072,148 |-26.49% |18,429,297 | |

| |Marketable alternative |MNA |27,857,942 |-0.04% |27,848,021 | |

| |strategies | | | | | |

| |Marketable alternative |HDG |27,857,942 |-0.05% |27,844,711 | |

| |strategies | | | | | |

| |Marketable alternative |PBP |27,857,942 |-0.82% |27,628,936 | |

| |strategies | | | | | |

| |Marketable alternative |MRGR |27,857,942 |-0.55% |27,705,713 | |

| |strategies | | | | | |

| |Venture capital |IWC |19,500,560 |-6.64% |18,205,236 | |

| |Venture capital |IPO |19,500,560 |-0.21% |19,459,445 | |

| |Private real estate |USRT |27,857,942 |-12.54% |24,363,413 | |

| |Energy and natural |VAW |16,714,765 |-37.33% |10,474,908 | |

| |resources | | | | | |

| |Commodities and managed |GSP |5,571,588 |-37.33% |3,491,714 | |

| |futures | | | | | |

| |Distressed debt |ANGL |16,714,765 |-6.31% |15,660,064 | |

|Subtotal | |267,436,246 |-9.02% |243,315,091 | |

| | | | | | |

| |Short-term securities, |VGSH |22,286,354 |-0.23% |22,235,121 | |

| |cash | | | | | |

| | | | | | | |

And for 2016:

| | | | |Beginning | |Ending |

| | | |EFT |Balance |Return |Balance |

|Domestic equities | | | | |

| |Active |MGK |22,144,869 |6.80% |23,651,784 |

| |Active |VYM |22,144,869 |15.20% |25,511,885 |

| |Active |VOE |22,144,869 |14.68% |25,395,396 |

| |Active |VOT |22,144,869 |7.64% |23,836,694 |

| |Indexed |VOO |26,052,787 |11.72% |29,106,479 |

|Subtotal | |114,632,262 |11.23% |127,502,239 |

| | | | | | | |

|Fixed income | | | | |

| |Domestic investment grade - active |VCIT |20,842,229 |1.91% |21,241,278 |

| |Domestic investment grade - passive |BIV |5,210,557 |-0.18% |5,201,166 |

| |Domestic non-investment grade |VMBS |5,210,557 |-0.98% |5,159,494 |

|Subtotal | |31,263,344 |1.08% |31,601,937 |

| | | | | | | |

|International equities | | | | |

| |Active MSCI EAFE |IEFA |23,447,508 |0.30% |23,517,671 |

| |Active MSCI EAFE |IXUS |23,447,508 |4.21% |24,434,772 |

| |Passive/index MSCI/EAFE |VXUS |26,052,787 |3.15% |26,872,794 |

| |Emerging markets |VWO |15,631,672 |12.37% |17,565,993 |

| |Emerging markets |IEMG |15,631,672 |10.49% |17,271,434 |

|Subtotal | |104,211,147 |5.23% |109,662,664 |

| | | | | | | |

|Alternative strategies | | | | |

| |Private equity |BDCS |23,447,508 |13.85% |26,694,629 |

| |Private equity |BDCL |23,447,508 |25.48% |29,421,396 |

| |Marketable alternative strategies |MNA |24,750,147 |4.98% |25,982,778 |

| |Marketable alternative strategies |HDG |24,750,147 |2.70% |25,419,230 |

| |Marketable alternative strategies |PBP |24,750,147 |4.62% |25,894,522 |

| |Marketable alternative strategies |MRGR |24,750,147 |-2.38% |24,160,536 |

| |Venture capital |IWC |23,447,508 |20.73% |28,308,496 |

| |Venture capital |IPO |23,447,508 |0.34% |23,527,184 |

| |Private real estate |USRT |20,842,229 |4.63% |21,808,100 |

| |Energy and natural resources |VAW |20,842,229 |21.20% |25,261,402 |

| |Distressed debt |ANGL |20,842,229 |18.44% |24,684,747 |

|Subtotal | |255,317,311 |10.12% |281,163,021 |

| | | | | | | |

|Short-term securities, cash, other | | | | |

| |Short-term securities, cash |VGSH |15,631,672 |0.21% |15,665,101 |

| | | | | | | |

|Total | | |521,055,737 |8.55% |565,594,962 |

And finally for 2017:

| | | | |Beginning | |Ending |

| | | |EFT |Balance |Return |Balance |

|Domestic equities | | | | |

| |Active |MGK |25,451,773 |27.15% |32,361,191 |

| |Active |VYM |25,451,773 |12.33% |28,590,258 |

| |Active |VOE |25,451,773 |13.73% |28,946,527 |

| |Active |VOT |25,451,773 |19.92% |30,523,009 |

| |Indexed |VOO |22,623,798 |18.68% |26,850,162 |

|Subtotal | |124,430,892 |18.36% |147,271,148 |

| | | | | | | |

|Fixed income | | | | |

| |Domestic investment grade - active |VCIT |28,279,748 |2.27% |28,921,793 |

| |Domestic investment grade - passive |BIV |11,311,899 |1.23% |11,451,232 |

|Subtotal | |39,591,647 |1.97% |40,373,025 |

| | | | | | | |

|International equities | | | | |

| |Active MSCI EAFE |IEFA |26,394,432 |22.71% |32,387,820 |

| |Active MSCI EAFE |IXUS |26,394,432 |24.34% |32,820,042 |

| |Active MSCI EAFE |VEU |26,394,432 |23.05% |32,478,149 |

| |Passive/index MSCI/EAFE |VXUS |11,311,899 |23.10% |13,924,789 |

| |Emerging markets |VWO |28,279,748 |26.89% |35,885,109 |

|Subtotal | |118,774,942 |24.18% |147,495,909 |

| | | | | | | |

|Alternative strategies | | | | |

| |Private equity |BDCS |25,451,773 |-8.79% |23,215,238 |

| |Private equity |BDCL |25,451,773 |-16.50% |21,252,877 |

| |Marketable alternative strategies |MNA |25,451,773 |5.65% |26,889,335 |

| |Marketable alternative strategies |HDG |25,451,773 |5.15% |26,762,442 |

| |Marketable alternative strategies |PBP |25,451,773 |0.56% |25,594,962 |

| |Marketable alternative strategies |MRGR |25,451,773 |1.71% |25,887,273 |

| |Venture capital |IWC |22,623,798 |10.19% |24,928,894 |

| |Venture capital |IPO |22,623,798 |35.49% |30,654,052 |

| |Private real estate |USRT |22,623,798 |1.99% |23,074,972 |

| |Energy and natural resources |VAW |22,623,798 |20.90% |27,351,846 |

| |Commodities and managed futures |GSP |5,655,950 |5.79% |5,983,217 |

| |Distressed debt |ANGL |22,623,798 |3.85% |23,494,548 |

|Subtotal | |271,485,582 |5.01% |285,089,656 |

| | | | | | | |

|Short-term securities, cash, other | | | | |

| |Short-term securities, cash |VGSH |11,311,899 |-0.63% |11,241,037 |

| | | | | | | |

|Total | | |565,594,962 |11.65% |631,470,774 |

Analysis

The returns from the Studies versus the returns from the replacement portfolio were:

| | |Replacement |

|Year |Study |Portfolio |

| | | |

|2013 |11.9% |9.6% |

|2014 |7.1% |1.6% |

|2015 |1.1% |-6.5% |

|2016 |6.7% |8.6% |

|2017 |14.3% |11.7% |

| | | |

|5-year return |8.1% |4.8% |

The actively managed portfolios from the various Studies outperformed the replacement portfolio is four of the five years, and the cumulative recalculated 5-year return was also superior.

The returns can also be compared based on the asset class allocation:

|US Equities |

| | |Replacement |

|Year |Study |Portfolio |

| | | |

|2013 |31.8% |28.1% |

|2014 |9.9% |12.2% |

|2015 |-1.3% |-1.9% |

|2016 |10.2% |11.2% |

|2017 |21.5% |18.4% |

|5- year |13.9% |13.1% |

|Fixed Income |

| | |Replacement |

|Year |Study |Portfolio |

| | | |

|2013 |-0.7% |-19.0% |

|2014 |4.2% |3.0% |

|2015 |0.1% |-2.3% |

|2016 |2.9% |1.1% |

|2017 |3.8% |2.0% |

|5- year |2.0% |-3.4% |

|Non-US Equities |

| | |Replacement |

|Year |Study |Portfolio |

| | | |

|2013 |15.9% |5.3% |

|2014 |0.2% |-5.6% |

|2015 |-5.0% |-8.9% |

|2016 |4.6% |5.2% |

|2017 |28.2% |24.2% |

|5- year |8.1% |3.4% |

|Alternative Strategies |

| | |Replacement |

|Year |Study |Portfolio |

| | | |

|2013 |7.3% |8.1% |

|2014 |14.2% |-0.8% |

|2015 |-2.1% |-9.0% |

|2016 |7.1% |10.1% |

|2017 |9.8% |5.0% |

|5- year |7.1% |2.5% |

|Short-term Securities/Cash |

| | |Replacement |

|Year |Study |Portfolio |

| | | |

|2013 |0.1% |0.5% |

|2014 |0.4% |0.2% |

|2015 |0.1% |-0.2% |

|2016 |1.2% |0.2% |

|2017 |0.8% |-0.6% |

|5- year |0.5% |0.0% |

The returns in US Equities and Short-term Securities/Cash was fairly close. The larger differentials were in Fixed Income (5.4%), Non-US Equities (4.7%) and Alternative Strategies (4.6%). It should not be overlooked that the replacement portfolio was an entirely naïve portfolio – all ETFs selected for the portfolio were done without reference to past returns, and once selected the ETF remained in the portfolio unless a future asset allocation required the removal of an ETF in a particular allocation. The ETF to be removed was the last one listed, not the worst performer. It should also be pointed out the ETFs that were selected were based on a higher Morningstar rating and/or being designated an “all-star,” so the extent to which this represents a better performing ETF then the replacement portfolio may not have been so naïve.

The take-away is that from the data in this study, passive investing produces comparable returns to active investing for allocations to US Equities and Short-term Securities/Cash, and active investing outperforms passive for allocations to Fixed Income, Non-US Equities and Alternative Strategies.

References

Council on Foundations-Commonfund, “2013 Study of Investments for Private Foundations.”

Council on Foundations-Commonfund, “2014 Study of Investment of Endowments for Private and Community Foundations.”

Council on Foundations-Commonfund, “2015 Study of Investment of Endowments for Private and Community Foundations.”

Council on Foundations-Commonfund, “2016 Study of Investment of Endowments for Private and Community Foundations.”

Council on Foundations-Commonfund, “2017 Study of Investment of Endowments for Private and Community Foundations.”

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[1] Haber, Jeffry, “The Failure of Equity Managers to Beat Their Benchmark: Lord, Is It I (Or Is It the Benchmark)?” Journal of International Business Management & Research, Volume 4, Issue 11, pp 122-129

[2] Haber, Jeffry, “Resolving the Dichotomy between Investors and Managers About Whether Active Management Beats the Index,” Journal of Business and Economics, Volume 4, Number 10, October 2013, pp 1033-1037

[3] Haber, Jeffry, “Can Active Management Outperform a Benchmark: Let’s Stop the Madness – The Benchmark is an Unattractive Investment,” American Journal of Management, Volume 15(1) 2015, pp 101-110

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