While HDI is not a useful metric when attempting to quantify the impact, it can be used as a benchmark when investigating the quality of life standards in different countries. HDI could serve to help provide important context to investors who are looking to help combat global inequality. As seen in the video below, if investors filter opportunities by country, they compare HDIs. For example, if an investor is choosing between charities that operate in Uganda and Norway, they will be able to see that Uganda has a much lower national HDI. This HDI benchmark will allow investors to make a more informed investment decision.

Due to some of the issues we uncovered with HDI we wanted to use some alternative metrics to still offer investors a broad look at the elements of HDI, the standard of living, education, and overall health. Using our new core metrics allows investors to more tangibly measure and scale impact. These new metrics would be presented together on the ImpactX site as a suggested follow-up for users after they have used the geographic search element to see the HDI of a country and search local non-profits. 

As an alternative measure to GNI to measure the impact of an NGO on the standard of living, we decided to focus on the element of housing. As a measure of housing, we decided to focus on access to affordable and safe housing. This metric still provides a broad overview of housing similar to GNI, while allowing for a more scalable and functional measurement. It was decided to use the shelter poverty indicator to address this particular area. The shelter poverty indicator measures the amount of income left over after paying for housing costs and whether it is sufficient enough to cover a market basket of non-housing goods.

This metric gives investors a good look at the burden that housing costs in a market put on a household. Looking at housing costs is one of the most important indicators of higher standards of living particularly in depressed areas. By providing affordable housing in communities you are able to protect against a variety of other vulnerabilities. More access to housing leads to better security for families and contributes to a lower crime rate.

Housing boosts school attendance and student performance. Housing in Africa in particular can also cut the chances of contracting malaria in half. Lowering the burden of housing costs on households is a metric that allows investors to see exactly how a project is able to provide more disposable income available to individuals and families to afford other essentials.

This metric still allows for a more general assessment of the standard of living in an area while also giving investors a tangible goal to provide households with more post-housing cost income. There are other measures used in addressing affordability such as the ratio of income spent on housing but, this measure has more limitations and using the SPI investors are able to see the effect of residual income on all aspects of the households quality of life.

In order to better address education, we decided to provide the dropout rate as a broad alternative metric to expected years of schooling. The dropout rate provides a similarly broad application of an educational system that allows the user to see how an investment is shifting the larger framework. Moreover, the dropout rate for our purposes is the number of children who decide or are forced to discontinue their education before completing high school. As opposed to expected years of education, the dropout rate provides a more substantive look at the impact and in many situations can be an important indicator of the success of a policy or project investment.

The dropout rate is an important way to evaluate success over time similar to expected years of education as it can easily be viewed as a trend and taken as a measure of how many kids are actually able to receive an adequate education. As opposed to expected years of education, the dropout rate provides a similar look at education availability but can be more easily connected to a project and used as a measure of impact on a year-to-year basis and in a long multi-year trend. These qualities can allow users on ImpactX to better measure the success of their investment and see a more localized effect on a particular school system in both the short and long term. 

Ideally, the population health measurement of a country should be a holistic representation of the dynamic and specific conditions within a certain area. The life expectancy metric currently feeding into the HDI value gives a generalized view of an entire country. Regional characteristics are overlooked as this number is more of a macro summary rather than a microlens into the varying conditions of people.

Our suggestion to use a condition-based mortality rate rather than life expectancy stems from the need for a more descriptive and smaller scale measurement. Condition-based mortality rate would allow NGOs to develop interventions to target the specific conditions that are causing the decrease in life expectancy within a smaller area. Having information on the exact cause of mortality provides insight that ImpactX can use to target illnesses such as malaria or diarrheal diseases, which are leading causes of death in the middle to lower-income countries.

It also highlights the most pressing health issues within communities. The availability of this metric would be an attractive feature to NGOs and investors using the ImpactX database, more so than life expectancy, as many of these organizations passionately target one specific issue. In terms of measurement, the condition-based mortality rate is an easily reported and quantifiable metric that clearly shows the direct impact, whereas life expectancy may take into account external factors that lead to inaccuracies in reporting NGO-specific impact. 

The interpretation key serves as a comparison between the multiple frameworks of the HDI-adjacent framework and IRIS. The key is necessary since it allows the comparison of terminology from one framework to another. An equivalent IRIS metric for each HDI metric and an explanation for the comparison is provided. Despite the comparison, this does not mean that one metric directly correlates to another. As previously mentioned, the HDI provides a poor framework for testing NGOs. An example of this is the life expectancy component of HDI. To evaluate an NGO on their life expectancy improvements is almost impossible, yet is a core fundamental for HDI. As a result, the metrics were shifted to our own custom framework, which can be used to evaluate NGOs. 

The Nanhi Kali Project by the Mahindra Foundation is a non-profit NGO designed to equip women in India with an education that will enable them to contribute to a more equal society. The project attempts to provide girls in India with 10 years of formal schooling and an eventual goal that education will allow women to break free from the societal and religious norms in India.

Their mission is more important than ever, as the COVID pandemic has disproportionately affected young girls, who have been expected to stay home and help their families while boys are sent back to school. Since its inception in 1996, The Nanhi Kali project has helped over 450,000 girls by providing adaptive learning software and tablets, trained female tutors, sports curriculum, and general school supplies.

Charity Navigator gives Nanhi Kali a failing Finance and Accountability score, as it does not provide results of independent audit and review. However, they do receive high grades on every other aspect of their finance and accountability, primarily regarding their administrative practices. No other sections have scores on Charity Navigator. GuideStar provides little to no information regarding their finances and goals, however, Nanhi Kali’s website provides all of this information.

The impact of this NGO has a primary effect on the dropout rate among students (particularly girls) but also has downstream effects reaching into categories such as health, employment, and innovation. The results of running The Nanhi Kali Project through the HDI, IRIS+, and our own metrics show interesting results. As this project is not focused on a particular region of India, the scale of HDI is not a problem as it would be for more localized NGO efforts. From 2016 to 2017, India showed an HDI increase of 0.016, going from 0.624 to 0.640.

Breaking down the impact further, life expectancy and school life expectancy showed little to no gains, while GNI increased by nearly $400 per capita. What these results show is that HDI inadequately represents the impact created. HDI might catch some of the employment and compensation benefits of female education, but we cannot be sure. Using the IRIS+ metrics, we are mainly focused on the education metrics. For this run, we will focus on the School Enrollment total. This metric would indicate improvement in enrollment, increasing nearly 2% from 2016 to 2017.

Finally, utilizing the three metrics we found as viable substitutes for HDI, we finally found some valuable impact for measuring the effectiveness of the project. While we only see a very slight decrease in the percentage of Indians living in inadequate housing, there is a 1.5% decrease in the secondary school dropout rate. We use this metric to gauge educational attainment. We also see a decrease in condition-specific mortality rates for malaria, which is an indirect benefit of education. Health education is one of the most valuable resources that children, particularly girls, miss out on if they are not educated. In a country like India, where malaria and other viruses run rampant, a prime way to prevent incidence is education.

Malaria No More (MNM) is an NGO focused on prioritizing Malaria on the global health agenda and global resources to the long-lasting Malaria epidemic that is still prevalent in many developing countries. While they operate in several African countries, for the purpose of collecting impact we will focus on their efforts in India, where 3% of global malaria persists. Malaria No More has a vision of a future without Malaria, one in which economies can operate more effectively and people of the developing world do not die from a curable disease.

According to Charity Navigator, MNM has an overall score of 84.5 out of 100, which tells donors to “give with confidence”. MNM’s financial metrics suffer from a low working capital ratio and liabilities to assets ratio. They receive a 92 out of 100 for their accountability, which is highlighted by their transparent governance practices.

When we put MNM through each set of metrics, HDI again fails to properly measure impact. Where we expect to see large changes are in the life expectancy metric, where we see only a 0.3 increase from 2016 to 2017, which is good but not indicative of the specialized Malaria efforts. GNI and schooling are not largely affected in the same time period. Under the IRIS+ metrics, we would be focused on the health metrics, specifically disease addressed.

As this NGO is focused on Malaria, we would look at the data surrounding the prevalence of Malaria. The WHO published a 24% decrease in Malarial infection from 2016 to 2017. Once we put MNM through our new three metrics, we find the impact that is worthwhile. When MNM is isolating their efforts towards a specific disease, as many NGOs do, it is most effective to display impact when you isolate the data as well. When we look at the conditional mortality rate of Malaria in India, we see a 0.15 decrease per 100,000 people from 2016 to 2017.

This is more valuable than life expectancy as a whole because MNM is not trying to solve a country’s entire health system, they are focusing on a disease that killed 22,786 people in 2016 and kept many others from work and school. The secondary effects such as schooling and work can be seen by the improvements in the dropout rate and access to adequate housing. For MNM, our metrics show impact better than HDI due to the fact that our metrics are more focused than HDI’s.

Smart Havens Africa (SHA) provides low-cost sustainable homes, along with legal ownership of the land that the homes are on, to low-income women and families in need. Additionally, SHA provides new homeowners with financial literacy and property management training so that they can develop important long-term skills for maintaining their homes. While the provision of affordable homes is their primary focus, SHA also seeks to provide work opportunities to women throughout the construction process.

Since its start, SHA has built over 80 homes and put a roof over the heads of over 400 people. In doing so, they not only created 2000 jobs for women and youth who would normally struggle to find work, but they also generated numerous other positive impacts including having transferred a total of $1,584,000 to women and low-income households for their work on SHA homes, as well as saving 4,050 rare trees from being cut down due to their brick building technique.

Unfortunately, SHA is not on Charity Navigator or GuideStar due to these information services being geared toward US non-profits, leaving little in the way of third-party assessments of the NGO. That said, in piecing together information from the various media outlets that SHA has been featured in, interviews with the founder, and SHA’s website, one can obtain a significant amount of information that would normally be found on these sites which indicates that it is investment worthy.

When we apply the lens of each framework and its respective metrics, HDI is once again unable to properly measure SHA’s impact. Its previously mentioned limitations are particularly relevant when assessing the standard of living because the HDI framework uses GNI as its sole measurement. Because GNI is calculated on a national scale, in order to determine the HDI impact of a single organization, that organization must only work in a single country.

This is not the case for SHA as its name implies they serve low-income communities across the continent of Africa. Transitioning to IRIS+, we would focus on their closest equivalent to GNI and their metrics which pertain to SHA’s goals; the former being employees earning a living wage or higher and the latter being those that fall under the IRIS+ housing/real estate category. Now turning to our proposed alternative, an HDI-adjacent framework with substitute metrics, we would measure SHA’s impact through the Shelter Poverty Indicator (SPI).

As stated earlier, SPI measures the amount of income left over after paying for housing costs and whether it is sufficient enough to cover a market basket of non-housing goods. Unlike HDI, using the SPI metric to measure SHA’s impact is feasible; it requires looking at SHA’s measurements of the amount of additional annual income that homeowners who have bought an affordable SHA house, now have to spend on various non-housing goods as a result of not having to pay the traditional, more costly amount for a house.

This total, as measured by SHA, is $3,535 in additional income per year which is extremely significant given the average per capita income being $762 (including countries such as South Africa and Seychelles which raise this average by approximately $200). Translating this total reveals that SHA is increasing the annual income of those that buy their homes by over 462%. Getting to see these data points more clearly helps demonstrate the impact that SHA is making, and would not have been possible with the rigidity of the GNI metric that is part of the HDI framework.

Helping Other People (HOPe) is a non-sectarian humanitarian organization that includes efforts in healthcare, education, water and food, and work programs.  For this test case, we will focus on their work in Honduras, where they have been collaborating with the Sisters of Choloma in Choloma, Honduras. In this particular project, HOPe provides funding for uniforms and books in schools and operational support for clinics. We will now run HOPe through the HDI, IRIS+, and new methodology. HOPe does not have information on Guidestar but has an 85 overall score on Charity Navigator with a passing score. 

When we look at the HDI approach, Honduras had an HDI of 0.633 in 2018 and 0.634 in 2019. The only movements in the components are an increase in life expectancy from 75.1 years to 75.3 years and a $70 increase in GNI per capita. This demonstrates how poorly HDI captures impact. When we put it through IRIS+, the categories involve education, and health, but also the SDG metric of Good Health and Well-Being.

The best metric within Well-Being would be individuals provided new access; this can be education, health, or resource related. This displays a downfall of IRIS+ though, as we do not have definitive data on individuals who gained access to these clinics and schools.  When examined through the lens of our metrics, coronary heart disease is the leading cause of premature death in Honduras. The conditional mortality rate for heart disease in 2018 was 120.29 per 100,000. In 2019, this number was 105.35 per 100,000.

This decrease can be attributed to enhanced clinics and screening procedures. The dropout rate in Honduras is difficult to calculate, however, they have had significant struggles in maintaining high enrollment rates due to high violence rates. This may be a weak spot in our metrics when calculating for developing countries with weak data. In 2018, 38.6% of the population lacked adequate housing. This decreased very slightly. These metrics highlight the significance of the clinics set up by HOPe. 

While our alternative HDI-adjacent framework is a more useful metric when attempting to quantify the impact, HDI can be used as a benchmark in determining the quality of life in different countries. HDI can also provide important context to investors who are looking on a global scale. This is part of the reason why we incorporated it into our prototype via the landing page that investors are directed to upon expressing interest in a specific country. On this page which provides a country-level analysis, HDI is prominently displayed to help give the investor a sense of the nation’s standards which will allow them to make a more informed investment decision.