# Session Overview ## Aims To provide a critical and nuanced introduction to the debate around the relationship that income, economic growth, inequality have on population health (and vice-versa). To discuss and illustrate the implications of this debate for policymaking, as well as how the evidence and arguments around income and health are used to support or critique different political arguments and policy approaches. ## Learning outcomes - Appreciate and critically assess the evidence on the links between income, inequality and health. - Be aware of the influence of this evidence and related debate(s) on public policy - Be able to hypothesize and reflect about the possible impacts of specific evidence in different contexts - Identify and critically discuss the influences and causal assumptions around income, inequality and health and how these impact policy choices # 1. Introduction and motivation ## 1.1 Why are we interested in understanding the relationship between income and health? There is a large body of literature examining the relationship between income, inequality and health outcomes from the fields of economics, public health and sociology. ## 1.2 The socioeconomic health gradient The existence of a socioeconomic “gradient” in health outcomes is a well-established fact (Adler et al., 1994). More specifically, we tend to observe that individuals who have a higher socioeconomic status have, on average, better health outcomes. Income is not the only relevant variable in determining socioeconomic status but plays an important role. This relationship holds for many different health outcomes, including life expectancy, overall burden of disease, and specific conditions from maternal mortality to cardiovascular disease. This is also true across a wide range of contexts (although with some exceptions), both within-country and across countries. Importantly, we can still observe these differences in countries with universal health coverage (Decker and Rentier, 2004). ## 1.3 What explains the socioeconomic gradient in health outcomes? > So it is clear that there is relationship between socioeconomic status and health outcomes. Higher socioeconomic status, better health outcomes. Ill health negatively impacts income and livelihoods. The existence of such clear statistical relationships between socioeconomic status and health outcomes highlights the importance of the social and economic determinants of health. However, the nature of the relationship is complex, and it is unclear which mechanisms dominate or play the most important role. Some studies have focused on education as a key factor, where people or families on higher incomes invest more in education, which in turn improves health. Other mechanisms that can play an important role in explaining the observed statistical correlations include the negative impacts that ill health can have on income and livelihoods, as well as factors that can simultaneously affect health and income in a highly correlated way (e.g., improvements in nutrition or certain public health interventions). A large number of studies have also argued that, in some contexts, [[it is not income or material poverty that causes the socioeconomic gradient we observe, but that the existence of inequality itself is the problem]]. One influential set of theories focuses on psychosocial stress, experienced by individuals with lower socioeconomic status in hierarchical, unequal societies. Other research has highlighted, among others, the impacts that high levels of inequality have on the political process and particularly on the provision of public goods. ## 1.4 What are the policy implications? [[Open Question]] [[What are the policy implications of clear relationship between wealth and health?]] - To what extent should countries focus on policies that promote growth if they want to increase health and wellbeing? - To what extent can income redistribution policies be expected to improve health? - Can economic arguments be used to incentivise the financing of health interventions? - Would it be more efficient to focus on the provision of non-health interventions such as education or social protection that might trigger virtuous circles, improving both health and inequality? ## Activity 1: Influence of the income and health debate on policy discourse and intervention What might be the different purposes and intentions behind these references, in their context? What seem to be the underlying assumptions? Revisit your answers after reading the session and briefly reflect. To what extent do these references align with the existing evidence on the links between health and growth? ### Extract 1 from [[World Bank (WB)]], World Development Report, 1993 Improving the economic environment for healthy households: > Advances in income and education have allowed households almost everywhere to improve their health. In the 1980s, even in countries in which average incomes fell, death rates of children under age 5 declined by almost 30 percent. But the child mortality rate fell more than twice as much in countries in which average incomes rose by more than 1 percent a year. Economic policies conducive to sustained growth are thus among the most important measures governments can take to improve their citizens' health. Of these economic policies, increasing the income of those in poverty is the most efficacious for improving health. The reason is that the poor are most likely to spend additional income in ways that enhance their health: improving their diet, obtaining safe water, and upgrading sanitation and housing. And the poor have the greatest remaining health needs, as Figure 3 illustrates for Porto Alegre, Brazil. ==Government policies that promote equity and growth together will therefore be better for health than those that promote growth alone==. In the 1980s many countries undertook macroeconomic stabilization and ==adjustment programs== designed to deal with severe economic imbalances and move the countries onto sustainable growth paths. Such adjustment is clearly needed for long run health gains. But during the transitional period, and ==especially in the earliest adjustment programs, recession and cuts in public spending slowed improvements in health==. This effect was less than originally feared, however.. [[Insights]] From Extract 1, I sensed that the World Bank wanted to captivate the reader to the idea that economic growth is equal to improvement in health. They emphasized sustainable growth, even during severe economic imbalances, the government needs to take up the macroeconomic stabilization and adjustment programs (from WB and IMF) to sustain the growth, even if they have to cut public spending on health. ### Extract 2 - speech by Angel Gurria, [[OECD]] Secretary-General, delivered at the China Development Forum 2008 > Ladies and Gentlemen. > I am delighted to share with you some experiences on health care systems in OECD countries, which are facing some similar challenges to yours and may provide interesting insights for China. > Improving human health and providing access to affordable, high quality health care is a key concern of all countries. ==It is not only an ethical and social imperative; it is also a necessary ingredient for the sustainable long-term development of our economies and societies.== Good health improves people’s wellbeing. Healthy workers are more productive and healthy students learn better. In many OECD countries, health care is one of the most important and dynamic growth sectors in the economy. > ... > Even if we know what works to improve the performance of health systems, this is not an easy task for policymakers. ==Health policy decisions have considerable economic consequences and reforming the system can be extremely difficult, especially if the various stakeholders do not embrace reform==. Given the speed of developments in medicine and the evolution of health-care goals, [[reform of health systems is necessarily an ongoing, iterative process]]. > Increasing value for money in health systems requires experimentation and performance measurement, using actionable and accurate indicators. Benchmarking within and across countries and sharing information can help. Bringing experience, evidence and new ideas together, will help policy makers meet the challenges they face. The OECD stands ready to help China address its health policy challenges by sharing our own countries’ experiences and working together to find the most appropriate “Chinese” solutions. > [[Insights]] - The difference between Extract 1 from WB and Extract 2 from OECD is that OECD's central idea is reforming healthcare as the precondition for economic growth. It is an essential ingredient for sustainable growth. However, the OECD also acknowledges that reforming healthcare is difficult due to its economic consequences, which is why they highlighted the importance of increasing its value for money. ### Extract 3 - [[International Monetary Fund (IMF)]] publication; Health, Trade, and Finance Roadmap to End the Pandemic and Secure a Global Recovery, 2021 > IMF, WB, WHO, WTO principals call for $50 billion investment to generate $9 trillion in global economic returns by 2025 and boost manufacturing capacity, supply, trade flows and the equitable distribution of diagnostics, oxygen, treatments, medical supplies and vaccines. > ... > Leaders of the four agencies said: “By now it has become abundantly clear there will be no broad-based recovery without an end to the health crisis. Access to vaccination is key to both.” > The joint statement draws on a recent IMF staff analysis, which stated that $50 billion in new investment is needed to increase manufacturing capacity, supply, trade flows, and delivery, which would accelerate the equitable distribution of diagnostics, oxygen, treatments, medical supplies and vaccines. This injection would also give a major boost to economic growth around the world. > [[Insights]] IMF tied up WB, WHO, and WTO to call for macroeconomic investments for economic returns. Their central message is investment in health to prepare for future pandemics. Even leaders of the four agencies, incl. WHO, agreed that improved access to vaccination will give a major boost to economic growth around the world. #to-read 1. World Bank. 1993. World Development Report 1993 : Investing in Health. New York: Oxford University Press. © World Bank. https://openknowledge.worldbank.org/handle/10986/5976 License: CC BY 3.0 IGO. 2. OECD Secretary General speech, China Development Forum 2008: https://www.oecd.org/health/developingahealthcaresystembenefitingall.htm 3. Health, Trade, and Finance Roadmap to End the Pandemic and Secure a Global Recovery, 2021 https://www.imf.org/en/News/Articles/2021/06/01/pr21150-new-billion-health-trade-finance-roadmap-end-pandemic-secure-global-recover > [!Box] Box 1: Is global inequality increasing or decreasing? > Understanding whether human societies are becoming more unequal or not has profound political, societal and even philosophical implications. Therefore, this topic has been the subject of many studies. > > However, assessing whether or not inequality is increasing at a global level is far from straightforward and it has been common over the years for different studies to find conflicting results. This is mainly due to the deficiencies in the underlying data as well as different ways of measuring inequality. > > ==Relatively recent studies show that global income inequality has increased in absolute terms since the 1970s but decreased in relative terms. That is, countries with lower incomes have tended to grow faster but, given that growth rates have been high overall and the differences were large to begin with, the degree of convergence has been insufficient to reduce gaps. Moreover, inequality within countries has increased very rapidly in some of the fastest growing economies, such as India and China (Niño‐Zarazúa, 2017).== > > Another important phenomenon is the increase in within-country inequality that has taken place in the global North since the 1980s. Piketty (2015, 2018) provides a comprehensive analysis of this phenomenon, focusing on how the return to capital has been higher than the growth rate. Therefore, simply owning capital will cause your income to grow relative to the economy as a whole, and individuals in higher income and higher socioeconomic status groups tend to own more capital. Other factors feeding into this vicious circle of inequality include rapid increases in executive compensation (especially in the USA) and looser financial regulation putting money into the hands of financiers. There are other potential explanations which have been discussed in the literature, such as the impacts of certain monetary policy strategies since the 2008 crisis, but these are beyond the scope of this course and will not be covered here (Lenza et al., 2015). > [!Box] Box 2: Case study. COVID-19 and economic inequality > Suggested topic for discussion: What impact do you think COVID-19 has had on inequality? And vice versa, would you expect more unequal societies to have had higher or lower mortality rates? What other factors might have mediated this relationship? What might be the implications of this? > > ==A study published in 2020 found a positive association, across 84 countries, between income inequality and COVID-19 mortality, and further explored potential impacts of other factors such as social trust and trust in state institutions==(Elgar, 2020). > > Research has also found important inequalities in health outcomes resulting from the economic crisis brought on by COVID-19 in South Africa (Nwosu and Oyenubi, 2021) > > However, the most recent and comprehensive evidence suggests that COVID-19 has reduced global income inequality at a country level, with higher-income countries experiencing more deaths and a larger loss in income. > > Contrary to some of the narratives encountered in the media, there appears to have been no “trade-off” between health and economic growth, and higher numbers of deaths have been associated with larger loss of income (Deaton, 2021). # 2. The impact of income on health ## 2.1 What is the [[Preston Curve]]? ![[CleanShot 2024-02-21 at 11.35.19.png]] > [[Insights]]While [[Preston Curve]] demonstrates higher incomes cause better health, it fails to account international health benefit of higher incomes in any given country is might have the positive effect on other countries' growth, or because of greater investment in health research. Although, strictly speaking, the curve demonstrates association rather than causality, it lends support to the narrative that higher incomes cause better health. By comparing the relationship at different points in time Preston demonstrated that income per se did contribute to longevity, although was only able to explain 10–25% of growth in life expectancy. This is likely to be an underestimation, however, as ==it fails to account for the international health benefit of higher incomes in any given country; for example, due to the positive effect that this might have on other countries’ growth, or because of greater investment in health research.== Preston also showed that the curve tends to shift upward over time, meaning that countries are generally able to achieve a given life expectancy at lower levels of income than they were in the past. If the curve is causal, then this all suggests that a) there are (diminishing) health gains to higher incomes at a given point in time, and b) that health tends to improve over time for the same level of per-person income. Finally, despite suggestions that income and life expectancy become less causally connected over time, Preston’s analysis found the opposite to be true. This could be because of the emergence of new health technologies which can more easily be afforded by those with higher incomes. ## Reflection Point Take a few moments to look at the curve above. At a first glance, do you think that the relationship is causal? If so, in what direction does the causality run? Where do(es) your own home country(ies) fall on this curve? Is the life expectancy higher or lower than what would be predicted by the Preston Curve? What might cause this to be the case? Do you see any important outliers on this curve? [[Insights]] [[Preston Curve]], in my opinion, is casual. The steep curve in early curve, suggested, at least to me, the fragile early years of life. As proposed by [[Hans Rosling]] in Factfullness, children under-5 mortality is high on lower-income countries and might contribute to the steepness of the curve. When countries take care of their basic healthcare needs, the improvement might be dramatic. The other end of the curve might speak about the longevity, the end-of-life improvement, which required cutting-edge, new medical technologies, to extend only a few years of life with huge cost. I think USA is an outlier, despite having high GDP, their life expectancy falls below the Preston Curve. ## 2.2 Influence of the Preston Curve in the policy debate This can justify policies which are intended to increase GDP, based on the assumption that it will increase the holistic wellbeing of a population (including health) – this includes trickle-down economics, for example: Thatcher’s tax cuts from 1979–87 (especially for top earners), and Reagan’s and Bush’s tax cuts in the USA. To keep in mind: - [[Average GDP per capita is less reflective of typical income per person when income inequality is greater]] - GDP is the amount of economic activity in a country – in the case of capital flight (see Glossary) or foreign-owned productive assets, overall economic activity may have little relationship to typical incomes (e.g., mineral-rich countries like Canada, Australia and South Africa experiencing falls in GDP after the end of the 2001–2012 Commodity Supercycle without experiencing much change in median incomes) - [[Insights]] #to-write This can justify [[pro-rich policies]] which increase GDP with muted impacts on incomes in the lower quartiles – this is especially relevant to health because [[the health of people in lower-income quartiles may be more responsive to increases in income]] - [[If the Preston Curve is not causal, or the mechanisms that work cross-country don’t work within countries, then efforts to increase mean income with an eye to improving health are misguided]] – this is especially relevant given the endogenous relationship (see Glossary) between health and income The empirical relationship between income and health also has relevance to development economics, as discussed in Preston (1975). [[Big Push economists|'Big Push' economists]] have argued that ==small and incremental increases in income per person will reduce mortality, creating excessive population growth and driving per-person income down again==. This suggestion is used to justify the need for a ‘big push’ of investment to generate fast growth across all sectors simultaneously. Population pessimists such as Malthus even claim that falls in life expectancy in low-income countries are always bad, resulting in overpopulation and harming per-person incomes (this claim is dismantled in Preston’s paper). Arguments from economists who have themselves lived through demographic transition differ from this considerably. Ha-Joon Chang notes consistently that ==virtually every country which has achieved comprehensive economic transformation has done so by taking advantage of the rapid population growth caused by the fall in mortality which arises from an increase in living standards== (also called the ‘[[demographic dividend]]’). In his ground-breaking and timeless 1954 article Economic Development with Unlimited Supplies of Labour, Sir William Arthur Lewis outlines the mechanism by which low- and middle-income countries (LMIC) can take advantage of this. ## 2.3 What explains the shape of the Preston curve? The concave shape of the curve suggests that there are diminishing health gains to increased income, as Preston states in his original paper. Why could this be? Some possible reasons are: 1. [[Although higher incomes may improve health, there are also health burdens associated with higher incomes]]. The prevalence of some non-communicable diseases (diabetes, obesity, lifestyle-related cancers) may increase with income. Higher incomes may allow people to afford more antibiotics, contributing to antimicrobial resistance (see Glossary). People may consume more highly processed foods in higher-income countries. All of these factors can contribute to the so-called ‘double burden’ of disease (see Glossary), which often affects countries as they transition from low- to middle-income. 2. [[Countries at the highest income levels may contribute more-than-proportionally to the research and development of health technologies, generating positive externalities for other countries]]. Thus, for LMIC, increases in per-person income mean that previously unaffordable existing technologies can now be deployed. Innovating entirely new health technologies may become easier at higher income levels but is slower and more costly. However, this suggested mechanism has been widely criticised. Preston notes that health technologies have always spread between countries at all income levels. A rich literature exists on the spread of health (and other) innovations from LMICs to high-income countries, often termed ‘reverse innovation’ (Govindarajan and Trimble, 2012) [[Insights]] For LMICs transitioning to MICs or possibly HICs, like Indonesia, we have to take the necessary steps to afford existing technologies before capturing entirely new technology, which is easier at higher income levels but usually slower and more costly. However, there is a leapfrog way. What if during the steps, we focus on speed and lowering cost. Thus, when attracting investment to become the epicenter, pioneering the new technology, we can utilize the [[demographic dividend]]. Or, we can start attracting early, to enable reverse innovation. #to-write 3. Finally, ==lower-income countries may stand to gain more life years from increases in income==. The interventions with the historically greatest impact on life expectancies are those which significantly reduce the infant mortality rate. These interventions tend to become more widespread at lower levels of income. Especially in higher income countries which may have undergone more epidemiological transition, improvements in healthcare aided by per-person income growth may derive their main benefit from extending the lives of older people, having a smaller impact on life expectancies. ## 2.4 How exactly does the level of income contribute to health? Causal mechanisms, hypothesis and evidence At the individual level. Having a higher income may allow a person to live a longer, healthier life. This makes sense if we consider that people at lower income levels often have to spend a larger portion of their income on subsistence needs (rent, food, childcare, debt). This leaves less to be spent on health-seeking behaviours (time off work, health foods, private healthcare, high-quality baby products, housing in low-air-pollution areas with parks, gym membership, psychotherapy). Even when people on low incomes do pay for healthcare and health-seeking behaviours, the quality may be lower than that of more expensive health-related goods. --- ### Reflection Point We have named some health-seeking behaviours here. What other health-seeking behaviours can you name? Which of these cost money? Physical activities, incl. private trainers, phyisotherapy. Supplements to enhance health and recovery. Wearable devices to track health. Everything cost money. --- Especially in countries without free and universal healthcare, health expenditures are often ‘lumpy’ (i.e., they require large payments at once) and unexpected, making them difficult to afford for people living paycheck-to-paycheck and with little access to credit. Finally, people on lower wages may need to work excessive hours in order to afford to live, which can have a negative effect on mental and physical health. At the geographical level, incomes may be linked to population health in different locations within countries. For example, food deserts may exist in poorer neighbourhoods where insufficient demand exists for fresh food, limiting people's ability to eat healthily. In cases where local-level public services are funded by locally collected taxes (such as the council tax system in the UK), ==wealthier areas will tend to have better-funded public services==. This can lead to more parks and green spaces, better education (including about health-seeking behaviours), and better road safety. [[Insights]] Like Jakarta? High APBD, so we have better-funded public services such as MRT. But yeah, it still has the worst air pollution in the world and densed areas making it difficult to access parks and green spaces. --- ### Suggested extra reading #to-read Check out the work of The Centric Lab, which has produced research on health determinants and inequalities in urban environments. --- At the national level, higher economic output per person can also be linked to better health outcomes. For one, wealthier people will be more able to afford health-seeking behaviours as discussed earlier, and this fact will still be true on aggregate. ## 2.5 How does income inequality contribute to population health? We have discussed how the level of income can contribute to population health, but not its distribution. Intuitively, it makes sense to imagine that a society having greater income equality may cause it to have better health outcomes, but why might this be the case? #to-write The simplest argument, and that cited by Preston, is that there are diminishing health returns to income at the individual level as there are at the population level. Put simply, an extra £1,000 should make more difference to the health of a poor person than to that of a rich person, and even the richest people in the world cannot ‘buy’ more than a certain amount of healthy life years. Perhaps the most influential work on the [[relationship between income inequality and population health]] is Angus Deaton’s 2001 paper, which discusses several further proposed reasons for this relationship. Some of these are: - Income inequality may be related to investment in health and education - For people at low levels of income, income and nutrition may be causally related to each other - Income inequality may result in a higher crime rate - We may be evolutionarily predisposed toward fairly equal societies, and so income inequality may harm our mental health - Income inequality may impede the ability of disadvantaged people to engage in the political process and advocate for their needs - Where there is greater income inequality, more people may be discriminated against in credit markets (i.e., they are less able to borrow) - Conversely, poor population health may contribute to income inequality, for example if sick people become unable to work or must sacrifice time and money to look after their health Interestingly, ==Deaton does not conclude that income inequality per se harms population health, or at least that relationships between income inequality and population health are mediated by other factors==. He is careful to point out: 1. that non-income-inequality factors which influence population health are still linked to equality and fairness 2. that non-income inequalities and the social environment are still important for health 3. that his findings do not mean that improving income inequality will not also improve health, merely that income inequality itself is not a factor responsible for health ## Reflection Point [[Open Question]] [[Do you agree with Deaton’s findings? Do you think that improving income inequality in your home country(ies) would improve population health? Why/why not?]] Yes it would. Because then the system would be adjusted to serve equal treatment to all socioeconomic levels with narrower gaps. In a country like Indonesia, where the differences are stark between higher-income population with lower-income population in terms of access and coverage, exacerbated by geographical challenges, improving income will dramatically increase the lower-income population to overcome the barriers in accessing healthcare. ## 2.6 Health system design and population health There are many examples of countries with starkly different health outcomes at the same level of income per person, or better health outcomes despite lower incomes. For example, Cuba has a higher life expectancy than the USA despite having only one-seventh of the nominal income per person. Outcomes like this can be explained in part by the structure of the healthcare system. In the USA, many low-income, unregistered and unemployed people do not have access to healthcare at all due to the privatised healthcare system, and even those who do have health insurance may only receive coverage for certain treatments or those up to a certain monetary value, discouraging health-seeking behaviours. Loose regulation of pharmaceutical firms can result in extortionate prices of essential medicines such as insulin (Thibault, 2020). In addition, high levels of public investment in healthcare in Cuba mean that there are more than three times as many physicians per capita there than in the USA. > Because much avertible morbidity and mortality occurs in people who have insufficient access to healthcare, and because ==the public sector can ensure very few people experience this lack of access with a fairly moderate level of income per person (as in Cuba), then healthcare system design which ensures widespread access may be just as important as income per person in determining health outcomes==. # 3. The impact of health on income, growth and inequality ## 3.1 Motivation and policy relevance The fact that, by improving health, income and livelihoods can also be improved, has been frequently used in policy discourse to encourage and justify investments in healthcare and public health interventions are concerned about a country’s economic prospects and under pressure to ensure economic growth (Ashraf, 2008). ==The underlying logic is that increased health budgets should be prioritised, particularly in low-income contexts, and that they will be financially sustainable, leading to increases in economic activity and, potentially, increased tax revenue==. It is also important to understand the potential impacts of health interventions at a societal level, to inform social and economic policy in these settings. Malaria eradication is a frequently cited and debated example: > “malaria has slowed economic growth in African countries by 1.3% per year as a result of which GDP for African countries is now 37% lower than it would have been in the absence of malaria.” Abuja Declaration, 2005 Signed by 53 African Heads of State (cited in Ashraf, 2008). The links between health, economic development and global security have also been emphasised in public arenas: > “in today’s world, poor health has particularly pernicious effects on economic development in sub‐Saharan Africa, South Asia, and pockets of high disease and intense poverty elsewhere” (WHO 2001, 24) and “extending the coverage of crucial health services … to the world’s poor could save millions of lives each year, reduce poverty, spur economic development and promote global security” > — World Health Organization, 2001 (cited in Acemoglu and Johnson, 2007). ## 3.2 Evidence and mechanism Investment in education has also been identified as an important causal mechanism. In some contexts, increased life expectancy and improved expected health can create more incentives to invest in education, as a higher return on the investment can be expected. In other words, people might be likely to experience more gains in terms of lifetime earnings for each dollar they invest in education if they are in better health (Weil, 2007). However, from the evidence on individual-level impacts we cannot directly assume that improvements in population health will immediately lead to increased economic growth or to reduced income inequality at a national level. This is because changes in population health can affect population growth, age distribution, investment patterns and fertility, all of which can affect economic activity. Additionally, in the short term at least, improved health can increase the amount of people available to work, but this might not lead to increased growth or income if there are high levels of unemployment in the economy. A good number of studies have analysed the links between health and incomes at aggregate levels (average income, GDP per capita, economic growth and levels of inequality). In aggregate terms, the literature tends to find a positive impact of health on GDP and growth. ==These studies need to be interpreted with caution==, as it can be very difficult to determine whether the relationship between two variables is causal in a context where the interaction and variable measurement themselves are so complex (Weil, 2007). Recent studies, using longer time series and a wider range of models (Sharma, 2018; Ogundari, 2018), have found that health improvements, measured using life expectancy as a proxy, seem to have contributed to important increases in income per capita across high-, middle- and low-income countries. However, the authors warn that this relationship might not hold in future for countries with increasing non-communicable disease burdens and ageing populations. [[Open Question]] [[What is the impact of health on inequality, and can we expect public health interventions to reduce socioeconomic inequalities?]] However, this is not always the case, and in many contexts, at least initially, improvements in public health and medical treatment have tended to widen income inequalities, as they have been implemented or adopted earlier by the higher income groups. Anticipating and monitoring potential inequalities arising from health interventions can help implement strategies to mitigate these, including redistributive policies or targeted health interventions for lower income groups. ## 3.3 Can we really expect investments in health alone to reduce poverty significantly? A critique of the individualist approach ==Some critics of the “health for development” stance have pointed out that this stems from an individualistic view of society, which considers that development and poverty reduction can be achieved by making individuals healthier, more educated and therefore more productive, rather than considering the economic and social context in which these supposedly isolated individuals live==. However, according to critics, this approach tends to under-emphasise structural factors including investment in productive capacity and the creation of stable and functional institutions. Without these structures in place, productivity cannot improve substantially and individual improvements in health and education can fail to result in reduced poverty. Such thinkers maintain that improvements in material living standards and the eradication of poverty are the result of structural transformation (see Glossary) in an economy, i.e., the movement from low-value-added activities (see Glossary) to high-value-added activities and the accumulation of productive capacity over time, which provides countries with the material means to improve health outcomes and other aspects of wellbeing. Without this process, they argue, some health gains can be made from improvements in worldwide technological capacity and more efficient use of existing resources, but transformative change is not possible. Poor countries only appear to have been held back by diseases because they have not had the economic growth necessary to overcome that burden. [[There is a strong bi-directional causality between economic growth and health]]. The assumed impact of malaria on economic growth has also been questioned, and it has been suggested that there is a strong bi-directional causality. For example, Ha-Joon Chang (2016) has argued that today's poor countries only appear to have been held back by malaria and other diseases because they have not had the economic growth necessary to overcome that burden. That is, today's rich countries suffered heavily from vector-borne diseases (e.g., West Nile virus in the USA; malaria in the Mediterranean and parts of Japan and Korea; dengue in Singapore) but became wealthy and thus were able to significantly reduce the burden of those diseases. Now, those diseases no longer have a significant impact on income growth. In countries which have not achieved the high incomes necessary to curb these disease burdens (e.g., Nigeria with malaria), such diseases continue to have a negative impact on growth, creating the illusion that it is primarily the health burden that has created the low incomes. # 4. Looking beyond income and health - Further issues ## 4.1 Intersectionality, group identity and non-income factors which can affect health People from marginalised ethnicities, races, social classes and other groups may have lower incomes on average, they may be discriminated against in employment, or governments may dedicate proportionally lower amounts of funding toward areas where they live. Here are some examples of health risks which affect some people exclusively or disproportionately because of their race, class, gender or other group identities: - Health resources only being available in certain languages - Discrimination or dismissive treatment by healthcare professionals - Hate crime - Medicines, procedures and guidelines designed for white bodies - Lack of awareness of, funding for and attention to health issues which disproportionately affect people of colour, e.g., hypertension in black people, coronary heart disease in South Asian people (BHF, 2021), and diabetes in both groups (Diabetes UK, 2021) - Differences in vaccine hesitancy rates by ethnicity - Transgender health issues. Transgender people have much lower life expectancies than cisgender people. Transgender health issues include gender dysphoria and a higher risk of mental ill health and suicide; a higher risk of sexually transmitted infections; a higher risk of substance abuse; a higher risk of emotional, physical, and psychological abuse; a higher risk of homelessness; lack of understanding of transgender health issues by health professionals; health risks associated with prescribed hormones; and many others (VUMC, 2021; Mayo Clinic, 2021) ## 4.2 Alternative measures of prosperity and health Typically, we use indicators to measure outcomes – economic outcomes with nominal GDP per capita – and we measure health outcomes with life expectancy at birth: what does each of these mean? [[Question]] What is the limitation of [[Gross Domestic Product (GDP)]] per capita as a measure for health? Nominal GDP per capita is the total amount of value added (see Glossary) within the territory of a polity (both by entities from that polity and from without) over a given time period, divided by the total number of people living in that polity, expressed in units of a common currency. Briefly, some key limitations of nominal GDP/capita as a measure are: 1. It does not account for income inequality, and is less reflective of typical living conditions in the presence of greater inequality 2. It is nominal, and doesn’t reflect purchasing power (see Glossary) in the relevant context 3. It doesn’t account for domestically owned overseas assets, or foreign-owned domestic assets 4. Given the diminishing health gains to income, and the link between poverty and ill health, we may be more concerned with the median income, the poverty rate, or the incomes of the lower quantiles rather than the mean [[Question]] What is the limitation of life expectancy at birth as an indicator? Life expectancy at birth is the expected age at death for a synthetic cohort (see Glossary) of people of various ages. Briefly, some issues with this indicator are: 1. As a measure, it is based on a synthetic cohort and thus does not reflect the actual expected lifespan of anyone (Modig et al., 2020) 2. Issues with age standardisation (ibid) 3. It does not reflect the health of life-years lived, and so measures such as quality-adjusted life expectancy and disability-adjusted life expectancy may be preferable [[Question]] What are the alternative measures for wellbeing? Discussion of alternative measures of wellbeing is outside the scope of this session, as we are introducing you to commonly used measures. However, some examples of holistic measures of wellbeing include: 1. The human development index (HDI; see Glossary) 2. The multidimensional poverty index 3. Urban health index (Centric Lab, 2021) 4. Individual indicators more relevant to the situation. It may not be the case that there is a straightforward relationship between ‘prosperity’ and ‘health’. Both prosperity and health have many components, and these components may be related to each other in a range of ways. In addition, in certain situations we may be particularly concerned with more specific kinds of prosperity and health, e.g., infant mortality, incidence of a particular illness, the poverty rate, or proportion of people with electricity # 5. Summary [[Open Question]] The relationship between income and health is highly complex and bi-directional. A large body of research has investigated the following questions: To what extent, in what context and how do income growth and/or inequality reduction lead to improvements in population health? And vice-versa? Under what circumstances and via what mechanisms are investments in health or health interventions likely to increase incomes? The evidence from these studies, and the different assumptions and interpretations surrounding it, have been used to inform policy decisions, as well as to justify or critique different policy approaches, particularly in the areas of development and investment in health systems. This debate is likely to remain relevant, while increasingly broadening its scope to acknowledge the importance of context, and to include issues such as intersectionality and alternatives to GDP and income to measure prosperity. # 6. Glossary *Externalities* Positive externalities are societal benefits resulting from the consumption or production of a good for which the producer or consumer is not compensated in a free market. For example, providing (‘producing’) a university module in the economics of global health policy provides a benefit to the wider society beyond that enjoyed by the students who pay to take the module in the form of more informed policymakers. Negative externalities are societal harms from the production or consumption of a good for which the producer or consumer does not compensate others in a free market. For example, buying and using a car harms others by polluting the environment and contributing to the ongoing climate crisis. *Health-seeking behaviours* Any action or inaction undertaken by individuals who perceive themselves to have a health problem or to be ill for the purpose of finding an appropriate remedy (Olenja, 2004). *Food desert* An area where residents have limited access to (especially affordable) healthy, fresh or nutritious foods. *Structural transformation* The change in the distribution of economic activity within an economy over time; typically away from labour-intensive and low-value-added activities (see ‘value added’) such as agriculture, and toward more complex capital- and knowledge-intensive high-value-added activities such as services and manufacturing. *Endogeneity* > Related: [[ceteris paribus]] This is a term used in statistics and, in particular, in econometrics. From a technical point of view, you have endogeneity in a regression model when at least one of the explanatory variables is correlated with the error term. In this case, the estimated coefficients will be biased and we will get the wrong conclusions. Intuitively, there might be different causes for endogeneity. One common cause it that there is at least one variable that is not included in the model, and that “affects'' the variable you are trying to predict (e.g., health status) as well as the explanatory variables (e.g., income growth). If the area of study had an increase in the frequency of flooding or another natural disaster, which affected both income and health, but in our study we omitted weather variables, we could end up concluding that income affects health, or vice versa. ==Measurement error or reverse causality (see below) are other possible causes of endogeneity.== *Reverse causality* Reverse causality or simultaneity happen when, in a statistical model, the independent variable is causing the explanatory variable (i.e., generally both variables are affecting each other). If we ignore this when estimating the model, the coefficients will be biased. *[[Human Development Index (HDI)]]* HDI was developed as a critique to the exclusive use of GDP as a measure of societal progress. It is based on the concept and theory of “capabilities” developed by Amartya Sen. The UNDP provides a full explanation. We include here a brief extract from their website. For those interested in learning more about HDI, the link below is a good place to start. http://hdr.undp.org/en/content/human-development-index-hdi “The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The health dimension is assessed by life expectancy at birth, the education dimension is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. The standard of living dimension is measured by [[GNI per capita]]. The HDI uses the logarithm of income, to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean.” (UNDP, nd) *Capital flight* Capital flight happens when investors sell their assets or money held in a country’s currency rapidly, normally due to an event that reduces how much they think the assets in that country will be worth in the future. This can be triggered by events such as the country defaulting on its debt, or by increases in tax rates on capital, or political events. There is often a speculative and self-reinforcing element, where investors think that other investors will sell their assets in that country, and that will cause them to further lose value. This leads to depreciation of the currency and increases in the price of imports in the local currency. *Synthetic cohort* A synthetic cohort is an imaginary group of people who experience the “demographic conditions” of a specific period throughout their life. Life expectancy at birth in a specific period is the expected average age at death for a synthetic cohort that experiences the mortality risks of that period, which are reflected in age-specific death rates. “Cohort” life expectancy at birth, on the other hand, refers to age-specific death rates of a specific group of people (e.g., those who were born in a specific year). # 7. References ## 7.1 [[Essential readings]] [The Centric Lab](https://www.thecentriclab.com/) - researches and maps health and health determinants from an intersectional and holistic perspective. [[@deatonHealthInequalityEconomic2003]] Page 113-158 [[@prestonChangingRelationMortality2003]] Page 833-841 [Multidimensional poverty index](https://ophi.org.uk/multidimensional-poverty-index/) – an influential and holistic metric of material deprivation which goes beyond income ## 7.2 [[Recommended reading]]