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Environmental health risks, inequality and welfare beyond GDP

dc.contributor.authorMensah, Angela Cindy Emefa, author
dc.contributor.authorBarbier, Edward B., advisor
dc.contributor.authorWeiler, Stephan, committee member
dc.contributor.authorMiller, Ray, committee member
dc.contributor.authorMclvor, David W., committee member
dc.description.abstractA seemingly overlooked impact on economic well-being and inequality is the mortality and morbidity attributed to the environment, such as air, soil and water pollution, ecosystem degradation, unsafe water and sanitation, temperature balance and other environmental quality changes. These environmental health risks are impacting welfare worldwide. The World Health Organization estimates that 24% of all global deaths are linked to environmental factors, or around 13.7 million mortalities per year (Pruss-Ustun et al. 2016). Air pollution accounts for 7 million of these deaths, and around 3 billion people face health risks from using polluting fuels such as solid fuels or kerosene for lighting, cooking and heating (WHO 2020). Particulate matter alone kills more than 4 million people each year, mainly in emerging market and developing economies (Nansai et al. 2021). Over half the world's population is exposed to unsafely managed water, inadequate sanitation and poor hygiene, resulting in more than 800,000 deaths annually (WHO 2020). These exposures reduce the average life expectancy and constrain human capital accumulation, thereby reducing the quantity of human capital per person and adversely impacting income distribution, especially among poor countries who already have low human capital. This dissertation examines two channels by which these environmentally health risks impact the economy. The first chapter of this dissertation examines inequality convergence over the past three decades and asks if environmental health risks (EIH) on human capital are responsible for the slow rate of inequality reduction in countries. Though higher initial incidence of EIH simultaneously worsens the rate of inequality reduction, we find that those countries that experience faster reduction in the level of EIH tend to converge to a lower level of inequality more quickly than their counterparts. Thus, estimates that exclude the incidence of EIH may bias the speed of convergence downward. We conclude that high rates of income growth, per se, do not reduce inequality within developing countries. Instead, the level of both initial inequality and EIH are just as important as growth. As such, policies targeted at reducing inequality must also address the health impacts of the environment. The second chapter of this dissertation examines the impact of environmental health risk on welfare through its impact on average life expectancy. Employing the Global Burden of Disease (GBD) dataset of environmentally related mortality and morbidity across 163 countries over 1990-2019, we modify the consumption-equivalent macroeconomic welfare measure developed by Jones and Klenow (2016) to include these risks. We use the GBD estimates of environmentally related morbidity as a lower bound estimate of these risks to adapt the expected lifetime component of the Jones-Klenow welfare measure for each country relative to the United States. Similarly, we use the GBD's estimates of environmentally related disability adjusted life years (DALYs) as an upper-bound estimate of adjusting life expectancy for environmental health risks. Our results suggest that, across all 163 countries over 1990-2019, including environmental health risks in welfare is significant when compared to income (GDP) per capita or to welfare that excludes these risks. While welfare in advanced economies is considerably high and closer to the United States, emerging market and developing economies who suffer the most from environmentally related mortality and morbidity diverge substantially from the United States. This divergence in welfare is especially prominent among low and lower middle-income countries, who are disproportionately affected by environmental health risks. The findings of the first two chapters reaffirm the need to aggressively target and successfully implement the Paris Agreement, Agenda 2030 and its linked Sustainable Development goals. For example, achieving the target on green energy transition, not only promote energy efficiency but will also significantly cut down the number of mortality and health risks associated with polluting solid fuel and kerosene usage in developing countries. Similarly, the target on improving access to clean water and sanitation, when achieved, will improve welfare and reduce, if not eliminate, the about 827,000 deaths associated with unclean water and poor sanitation each year (see WHO 2020). Thus, the strategies for improving welfare, which is the focus of my research, are very much tied to the successful implementation of the Sustainable Development Goals. The third chapter analyzes the impact of crowding and ecosystem externalities flowing from the industrial fishery sector to the artisanal fishery sector. Both externalities are the results of illegal trawling of small pelagic stock (which is the legal target stock of artisanal fishery) as bycatch by the industrial fishery sector. To explore this issue, we develop a two-sector bioeconomic model with empirical application for the case of fishery in Ghana. We demonstrate that both externalities impact the productivity and profitability of the artisanal fishery. Our empirical results show that, between 1986 and 2013, by-catch ranges from 18% - 95% of total artisanal catch except for some extreme outliers. We also found that industrial fishing effort has being increasing since 2007 but with less than a proportionate increase in legal annual catch, when compared to previous years. This seems to have coincided with significant increases in by-catch. The conjectured is that the extra increases in industrial fishing effort may have been moved toward illegal trawling of by-catch. This may explain why effort is increasing with less than a proportionate increase in industrial fishery's annual landings. We estimated the optimal tax rate to be approximately 11%. However, given the data challenges, we believe that the true optimal tax rate lies between 100% and 10%. Consequently, when the optimal tax rate is applied, the amount of by-catch chosen by the industry fishery in the decentralized equilibrium is identical to the amount chosen by the government. We conclude that if the government's priority is to increase the productivity of the artisanal fishery, then the current level of by-catch should be reduced through monitoring and effective tax structures.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.publisherColorado State University. Libraries
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dc.subjectecosystem externalities
dc.subjectenvironmental health risks
dc.subjectcrowding externalities
dc.titleEnvironmental health risks, inequality and welfare beyond GDP
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