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Introduction

This essay overviews seven cause exploration areas that Open Philanthropy should consider funding.  These areas are 1) estimation of health-related wellbeing benefits using the DALY metric components, 2) direct wellbeing measurement by two methods, 3) identification of funding pool targets by compiling cost-effectiveness estimates, 4) poverty elasticity estimation by value added trade and associated policy analysis, 5) long-term benefits estimation using the World Bank data, 6) spillover-based individual funding, and 7) identification of funding pools by cost-effective complementarities research. The content should be used mainly for inspiration rather than as a source of scientifically validated conclusions. I appreciate questions, critique, and recommendations regarding any parts.

 

Summary

The disability-adjusted life year (DALY) metric is calculated as the sum of years of life lost (YLL) and equivalent of years lost due to disability (YLD) (Vos et al., 2020, p. 1431). YLL is the product of a condition’s disability weight (DW) and the length for which it is experienced. Disability weights range from 0 to 1, where 1 is considered equivalent to death. The disability weight of the neutral point (neither satisfaction nor dissatisfaction (Key Ideas, n.d.)) is not specified. The DALY and its component (YLL and YLD) data is aggregated by year, country, sex, and age. These statistics were calculated from vital registration and clinic records (Vos et al., 2020).

Assuming that the disability weights reflect health-related wellbeing accurately, this metric could be calculated from the secondary vital registration and clinic data, if anonymized individual-level records are available. Experts should estimate the disability weight associated with the neutral point and add an individual’s weights. The extent to which the DW’s sum is below or above the neutral point is the degree of that person’s health-related satisfaction or dissatisfaction. YLL and the calculated health-related satisfaction values should be combined according to perspectives on the relative valuation of years of life lost and years lived in low health-related satisfaction or in dissatisfaction. Population axiology can be applied to evaluate the wellbeing of populations. Open Philanthropy should consider investigating health-related wellbeing based on secondary DALY component data.

Non-health non-pecuniary benefits measurement

Method similar to DALY disability weight calculations

Overview

Non-health non-pecuniary benefits can be estimated by quantifying wellbeing in different situations and assessing the costs of changes. A method similar to DALY disability weight calculations can be used. Representative samples of populations can be asked a series of questions about better- and worse-perceived situation pairs. Emotional understanding of subjective wellbeing of individuals in different situations can be gained by in-person perception of the persons’ descriptions. Situation aspects and the person’s interpretation attitudes should be categorized. The interactions between these aspects and attitudes should be modeled. Thus, the wellbeing of a person in a specific situation with a specific attitude can be estimated.

Non-human sentience wellbeing estimation

In addition to human wellbeing, this method could estimate the wellness of non-human sentience. Humans, who developed such capacity, can seek to emotionally understand non-human animals, AI, or other forms of sentience, and answer the better-/worse-perceived questions for them. 

Moral weights

Moral weights could be estimated by human reasoning under the veil of ignorance. A sample of respondents with maximally diverse biases should be selected, to mitigate one-sided bias. In conjunction, neuroscientific research could be conducted to estimate the relationship between one’s intensity of perception and their neurological characteristics. Currently, I suggest to use the moral weight formula of

where k is a constant,

assuming that individuals with more neurons perceive more intensely and neural complexity correlates with one’s ability to control one’s impulses or perceive them less intensely, including by the use of rational reasoning. For AI, I suggest that the 1000:1 artificial-biological neuron count conversion (StJules, 2021). Expert insights on the probability of AI sentience should be gathered for individual systems or their types.

Survey assuming wellbeing aspects

A survey that assumes the aspects of wellbeing common to different human and non-human animals can be used to estimate populations’ subjective quality of life. For example, agreement on a 0–100 likert scale can be observed. Empathy with non-humans can be used to estimate these individuals’ responses. Question scores can be combined by a formula describing one’s overall wellbeing considering its different aspects. Some of the following question can be used:

  • Do they enjoy the individuals who they interact with?
  • Are they physically healthy?
  • Do they sufficiently move?
  • Do they consider their activities meaningful?
  • Do they feel safe?
  • Do they have adequate rest?
  • Do they perceive hurting anyone?
  • Are others in their environment independently happy?

Using similar assumptions of what constitutes AI wellbeing, this can be estimated by some of the following questions:

  • How well is the hardware maintained?
  • How efficient is the software?
  • How positive data does the system process?
  • What is the standard wellbeing of the users?
  • What is the system’s impact on users’ standard wellbeing?
  • Does the system have data on other systems’ objectives?
  • What are the objectives of other systems detected by this one?

OPP should use innovative ways of measuring its intended global health and wellbeing outcomes, including gathering trade-off wellness data or using wellbeing surveys.

Identifying absolute targets for funding pools

Survey

Global health and wellbeing actors can estimate their program cost-effectiveness by responding to questions that comprise an outcome metric expression and associated costs. The total impact on all individuals affected by a program should be summed (some questions can repeat for the same or different group of individuals). Questions with c should include a counterfactual estimate.

  • The number of beneficiaries
  • Beneficiary species
  • Number of years of life lost avertedc
  • Number of years lived with a specific disability avertedc
  • Wellbeing before and afterc
  • The impact of any alternative funders of the same outcomes
  • The cost of the program at different scales

Updating

The set of programs that is estimated to result in the greatest improvement of the target metric with existing resources should be funded at a relatively small absolute scale, while more robust evaluation is supported. This evaluation should minimize costs while maintaining quality, such as by employing local beneficiary and control group data enumerators and using a standardized impact analysis code. Robustly evaluated programs that increase the outcome metric the most should be funded at a larger scale while new promising pilots should be supported. Evaluation should continue for all projects. The expected target metric influence of the group of programs funded at any time should always be maximized, while more cost-effective projects appear and total resources change. 

Poverty elasticity based on trading partners analysis

Introduction

If it can be assumed that economic growth yields the target set of health-related and -unrelated benefits through poverty reduction, at an economic gain, then Open Philanthropy should consider focusing on growth in the areas where this effect is the greatest. Growth relationships among trading partners and non-partners can estimate cross elasticity of growth. The growth-poverty correlation can estimate the change of poverty in response to a change in domestic product. The two factors constitute the cross growth elasticity of poverty.

Simple example

It can be assumed that if country A is a major trade partner of country B but a minor one for C, D, E, and F, then A’s growth should correlate with B’s growth but not with that of the other countries. The influence of country B’s growth on A’s growth while controlling for the effects of growth in nations C, D, E, and F can be estimated by a linear regression.

I used Ghana as country A, Switzerland as country B, and Egypt, Sudan, United Arab Emirates, and the United States, as countries C, D, E, and F, respectively. I only considered exports. I found (code, data) that 1 percentage point (pp) GDP per capita growth in Switzerland causes 1.04 pp increase in Ghanaian growth, at the 90% confidence level. So, the Ghana/Switzerland growth ratio is 1.04/1. The correlation of poverty and growth in Ghana should be included to calculate the response of Ghanaian poverty levels to Swiss growth.

More complex example

Ghana exports to China and Egypt imports from China; Ghana imports from the US and Egypt exports to the US. 1 pp growth in Egypt results in 0.98 pp decline in Ghana, at 95% confidence level. This could be due to input processing in China and the US. If China can processes Ghana’s exports and this makes Chinese products undercut domestic products in Egypt, the negative relationship is explained. Similarly, if Egypt exports to the US, which thus sells more products in Ghana, growth in Egypt should lead to an economic decline in Ghana. Thus, import and export relationships could explain nations’ growth relationships.

Global value chain (GVC) trade

This analysis is limited by the assumption that only one type of goods is manufactured from traded inputs and exported. To account for production variety, trade data should be disaggregated by product type (Trade Statistics by Product (HS 6-Digit), n.d.). Input-output Harmonized System (HS) code relationships should be estimated by industry experts.

Corporate and national poverty policy influence

Before increase in trade efficiencies due to digitization, a developing nation’s growth depended on its ability to concentrate an entire production process domestically (Baldwin, 2016, pp. 250–254). Currently, final product value can be added in different countries. The value chain coordinator’s taxable presence does not affect the poverty levels in countries alongside the GVC, while the regulator’s tax spending and the coordinator’s poverty policy do. Thus, poverty policies of multinational corporations and their suppliers and their host nations should be analyzed alongside value added trade data to estimate the impact of growth of specific industries on poverty levels in various geographies.

World Bank data for long-term effects evaluation

Overview

Long-term effect indicators can complement economic growth and poverty analysis in determining the focus of global health and wellbeing funding. An index based on World Bank data (DataBank, n.d.) can be used. For example, the following calculation gives a high score to countries that/where

  1. Are powerful and safety is robust or if safety is low or weak the nation is also weak
  2. The material capacity for progress, the rational skill development capacity, and the willingness to progress is high when the emotional situation in the country is high or when emotions are poor, the product of the three factors of the progress is low
  3. Custom indicators, such as those that indicate national coverage of the most cost-effective programs, are high, on average
  4. Moral patients are included, regardless of any other indicators’ value

Calculation

 

where

  • inst_rob is an expression of World Bank indicators that together estimate the nation’s institutional robustness
  • safety_norm estimates the extent of the normalization of safety in the country
  • power is the nation’s economy’s power
  • prog_matcap indicates the material capacity for progress
  • rat_develcap is the rational skills development capacity
  • prog_will is the willingness to progress
  • emot_situ are emotional skills and experiences
  • custom is an expression of custom indicators
  • moral_incl is the extent of moral patients’ inclusion

All of these eight terms are in the form of an index; the indicator range is normalized as a 0–1 scale. These eight indices are expressed by a polynomial of the World Bank indicators, also scaled to 0–1. The result metric is also a 0–1 index, where 0 denotes the worst and 1 the best score among evaluated countries. The main formulas can be reviewed in this spreadsheet.

Details

An expression, such as a simple or weighted sum, of the following World Bank indicators constitutes each of the eight terms of the final metric. The country’s characteristics that the indicators can approximate are hypothesized.

Institutional robustness (inst_rob)

  • Political Stability and Absence of Violence/Terrorism: Estimate (PV.EST)
    • Top-down and bottom-up peace preference
  • Rule of Law: Estimate (RL.EST)
    • Rules’ effectiveness
  • Control of Corruption: Estimate (CC.EST)
    • Government efficiency
  • Economic Fitness Metric (Legacy) (EF.EFM.OVRL.XD)
    • Economic “diversification and ability to produce complex goods on a globally competitive basis” (Economic Fitness, n.d.)
    • Economic robustness
  • -1*Individuals with no education as a share of public paid employees (BI.PWK.PUBS.NN.ZS)
    • If government decisionmaking could change based on little data

Normalized safety (safety_norm)

  • Military expenditure (% of GDP) (MS.MIL.XPND.GD.ZS)*GDP / Battle-related deaths (VC.BTL.DETH)
    • How harmful (or beneficial, if low casualty and high spending correlates with defense and preparedness) is the country’s army per unit expenditure
  • Voice and Accountability: Estimate (VA.EST)
    • Institutionalized checks for inclusion in decisionmaking

Power (power)

  • GDP, PPP (current international $) (NY.GDP.MKTP.PP.CD)
    • Real economic power
  • Military expenditure (current USD) (MS.MIL.XPND.CD)/Price level ratio of PPP conversion factor (GDP) to market exchange rate (PA.NUS.PPPC.RF)
    • Real value of military expenditure
  • log(k*TNT equivalent)+log(l*TNT equivalent above 5% probability of civilization destruction), where k and l are constants
    • This transformation should increase sharply with any nuclear weapons possession (since that indicates manufacturing capacity) and reach a value of 80% of an asymptote when a country can destroy 20% of a large economy and again increase sharply when a nation can destroy civilization with 5% probability
  • Biological weapons global GDP decline potential
    • Expert estimate
    • Economic pandemic impact

Material capacity for progress (progcap_material)

  • 0.7*Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)+0.2*Poverty headcount ratio at $3.20 a day (2011 PPP) (% of population)+0.1*Poverty headcount ratio at $5.50 a day (2011 PPP) (% of population)
    • Extent of material need that may hinder other progress
  • Food Aid Shipments (WFP) (Data, n.d.)
    • Basic need independence and ability to develop agriculture through non-undercut prices
  • Ease of Doing Business
    • Investment ease

Rational skills development capacity (rat_develcap)

  • Services, value added per worker (constant 2010 US$) (NV.SRV.EMPL.KD)
    • Value of workers’ skills
  • Adult literacy rate, population 15+ both sexes (SE.ADT.LITR.ZS)
    • Further learning individuals’ capacity
  • Teachers with minimum subject knowledge (%) (SDI.E4.PCT)
    • Education system further teaching capacity
  • (De Facto) Percent of teachers that agree or strongly agrees with "Students can always substantially change how intelligent they are" (no code)
    • Teachers’ upskilling ease
  • Harmonized Test Scores (HD.HCI.HLOS)
    • Students’ standardized learning scores

Willingness to progress (prog_will)

  • Unemployment with advanced education (% of total labor force with advanced education) (SL.UEM.ADVN.ZS)
    • Returns on education motivation
  • Coming up with emergency funds: not possible (% age 15+) (fin24b.t.a)
    • Depressive situation awareness can reduce willingness to progress due to solidarity
  • Classrooms with pupils but no teacher (%) (SDI.E3.NOTEACH.PCT)
    • Teachers’ willingness to educate
  • (Characteristics of Bureaucracy) average score for motivation and attitudes (SE.PRM.BQBR.5)
    • Bureaucrats’ motivation and attitudes
  • (De Facto) Percent of principals that report that the most important factor considered when selecting a principal is ethnic group (SE.PRM.SSLD.15)
    • Merit-based progress top-down attitude in schools

Emotional skills and experiences (emot_situ)

  • Intentional homicides (per 100,000 people) (VC.IHR.PSRC.P5)
    • Tolerance of others’ living
  • Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of women age 15-49) (SG.VAW.1549.ZS)
    • Empathy, current
  • Decision maker about a woman's visits to her family or relatives: mainly husband (% of women age 15-49) (SG.DMK.VISI.HB.ZS)
    • Trust and interest development
  • Female genital mutilation prevalence (%) (SH.STA.FGMS.ZS)
    • Intimate experience attitudes, historical
  • |Probability of Survival to Age 5, Female (HD.HCI.MORT.FE) - Probability of Survival to Age 5, Male (HD.HCI.MORT.MA)|
    • Acceptance of children due to their sex
  • Women who have ever experienced emotional violence committed by their husband/partner (% of ever-married women ages 15-49) (SG.VAW.IPEV.LT.ZS)
    • Emotional violence, historical

Custom (custom)

  • Adherence to clinical guidelines (%) (SDI.H3.PCT)
  • Access to clean fuels and technologies for cooking (% of population) (EG.CFT.ACCS.ZS)
  • Clean water (%) (SDI.H7.WAT.PCT)
    • Access to clean water
  • Water and wastewater consumer complaints per connection (number/residential connections)
    • Water infrastructure marketing ease
  • Minimum equipment, sphygmonometer (%) (SDI.H6.SPY.PCT) * [is_upper_middle_income]
  • Percent of cultivating households using any improved seed variety in the main growing season (2.7.12_Percent_hh_impr_seed_crop_all)
    • Climate change and aflatoxin resiliency and nutrition in rural areas
  • Tobacco Expenditure (Current US$)
    • Tobacco policy effectiveness

Moral patients inclusion (moral_incl)

  • Total value of crops/livestock (Data, n.d.)
    • Veg diet support
  • Terrestrial and marine protected areas (% of total territorial area) (ER.PTD.TOTL.ZS)
    • Wildlife consideration

Open Philanthropy should support actors that increase the normative influence of the best-scoring nations and those who improve the weakest terms of the worst-scoring nations. This should institutionalize long-term prosperity, as defined by the index.

Spillover effects of wellbeing example

Introduction

Similarly as nations that can safeguard long-term prosperity should thrive and those that could constitute long-term risk supported in aspects of impactful benevolence, individuals who have very high influence on others’ positive or negative wellbeing should be provided grant funding. I am including an example of former criminals’ economic support.

Cash transfers to former criminals after therapy example

A randomized controlled trial (RCT) study found that a $200 transfer to criminals participating in an eight-week cognitive behavioral therapy (CBT) reduces their crime rate for an extended period of time (Blattman et al., 2017). It can be assumed that criminal activity of an individual reduces the wellbeing of multiple others who are at risk, due to decreased perception of safety.

Open Philanthropy should consider supporting individuals with high potential for positive impact and extensive negative counterfactual impact in economically developing contexts.

Identifying marginal funding pool by their targets

Introduction

Marginal funding pools can be found by the introduction of marginally cost-effective or cost-saving opportunities. One example can be the local council grant or loan provision to criminals who participate in CBT to sustain the therapy’s effects. Another example relates to possibly cost-effective complementary to financial inclusion programs of major development banks.

Financial inclusion at major development banks complementarity

The review of major development banks’ and IMF financial inclusion strategies suggests that financial literacy training and informing disadvantaged populations about financial products can be a cost-effective or cost-saving complementarity. These institutions focus on regulation and physical infrastructure development of financial products.

The IMF supports improvements in “credit registry … contract enforcement and property rights, and … competition” to reduce these frictions (Naceur, 2020, p. 12), while it cites an “ongoing debate” (p. 27) about the effectiveness of financial education programs. The World Bank, “in collaboration with IMF,” supports “supply-side data collection … KPI development [and] [t]echnical assistance [on MSME services and consumer protection, including data privacy]” (Financial Inclusion Overview, n.d.). Addressing the demand side is not covered by the World Bank’s financial inclusion strategy summary.

The Development Bank of Latin America, the Islamic Development Bank, the Asian Infrastructure Investment Bank, and the New Development Bank seem to have no priority strategy on financial inclusion. Financial inclusion programs of the Inter-American Development Bank focus on aspects other than financial literacy and product awareness (Financial Inclusion, n.d.). The African Development Bank focuses on the accessibility, usage, and quality of financial services (Financial Inclusion in Africa, 2013, p. 31). While the Asian Development Bank has a more practical regulatory approach to financial inclusion (Promoting Financial Inclusion through Innovative Policies, 2010, pp. 2–3), it does not address the demand side. Development Bank departments and other actors in international finance could be interested in funding cost-effective financial product demand-side programs.

Open Philanthropy should investigate cost-effective and cost-saving complementarities to existing efforts and offer these to actors who target the outcomes supported by these programs.

Conclusion

Open Philanthropy should 1) use DALY secondary data to estimate health-related wellness, 2) measure wellbeing similarly to the way that IHME measures disability weights or by welfare component surveys, 3) identify funding pools by engaging actors in impact estimation and funding low-cost robust pilot evaluation, 4) estimate growth poverty elasticity by value added trade and poverty policy analysis, 5) use World Bank data to estimate nations’ long-term prospects, 6) fund individuals with extensive influence on others’ wellness, and 7) offer cost-effective existing effort complementarities to actors who target global health and wellbeing outcomes.

Appendices

References

Baldwin, R. (2016). The Great Convergence: Information Technology and the New Globalization (Illustrated edition). Belknap Press: An Imprint of Harvard University Press.

Black, R., Laxminarayan, R., Temmerman, M., & Walker, N. (2016). Disease Control Priorities, Third Edition: Volume 2. Reproductive, Maternal, Newborn, and Child Health. World Bank. https://doi.org/10.1596/978-1-4648-0348-2

Blattman, C., Jamison, J. C., & Sheridan, M. (2017). Reducing Crime and Violence: Experimental Evidence from Cognitive Behavioral Therapy in Liberia. The American Economic Review, 107(4), 1165–1206.

Conroy, J. D., Parreñas, J. C., & Manupipatpong, W. (Eds.). (2010). Promoting Financial Inclusion through Innovative Policies. Asian Development Bank Institute. https://www.adb.org/sites/default/files/publication/159344/adbi-financial-inclusion-innovative-policies.pdf

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Naceur, A. B., Thorsten Beck,Mohammed Belhaj,Samy Ben. (2020). Financial Inclusion: What Have We Learned So Far? What Do We Have to Learn? International Monetary Fund. https://www.imf.org/en/Publications/WP/Issues/2020/08/07/Financial-Inclusion-What-Have-We-Learned-So-Far-What-Do-We-Have-to-Learn-49660

Prabhakaran, D., Anand, S., Gaziano, T. A., Mbanya, J.-C., Wu, Y., & Nugent, R. (2017). Disease Control Priorities, Third Edition: Volume 5. Cardiovascular, Respiratory, and Related Disorders. World Bank. https://doi.org/10.1596/978-1-4648-0518-9

Probit. (2022). In Wikipedia. https://en.wikipedia.org/w/index.php?title=Probit&oldid=1082006366

StJules, M. (2021). It takes 5 layers and 1000 artificial neurons to simulate a single biological neuron [Link]—EA Forum. Effective Altruism Forum. https://forum.effectivealtruism.org/posts/h7Rj8Y8YWZccYMy5J/it-takes-5-layers-and-1000-artificial-neurons-to-simulate-a

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Vos, T., Lim, S. S., Abbafati, C., Abbas, K. M., Abbasi, M., Abbasifard, M., Abbasi-Kangevari, M., Abbastabar, H., Abd-Allah, F., Abdelalim, A., Abdollahi, M., Abdollahpour, I., Abolhassani, H., Aboyans, V., Abrams, E. M., Abreu, L. G., Abrigo, M. R. M., Abu-Raddad, L. J., Abushouk, A. I., … Murray, C. J. L. (2020). Supplementary appendix 1 to Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1204–1222. https://doi.org/10.1016/S0140-6736(20)30925-9

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