Internalising Externalities: Macro Methods for Tracking and Optimizing Social Welfare and Sustainability

I investigate the systemic problem of economic externalities from multiple angles with the goal of improving the way that we formulate, quantify, evaluate, and advance economic welfare and environmental sustainability./nExternalities are costs (or benefits) that result from economic activities and transactions which are not paid (received)1 by those directly involved. Some aspect of the cost or harm is external to the direct parties, which means that the market price is lower than it would otherwise be, with some part being ‘paid’ by others, usually in the form of some harm suffered. An example is air or water pollution caused by a factory: the firm has direct costs (materials, equipment, infrastructure, labour etc) which it, and potentially its customers, pay, but downwind and downstream communities suffer negative health and quality of life impacts that are not part of the costs paid by the producer or the consumer.Externalities undermine the theoretical optimum and welfare maximizing function of competitive markets, fundamentally skewing outcomes and causing major harm as a result of economic activity. Without accounting for externalities, the profit maximising and cost minimising impetus of markets guarantees harm, because it is always cheaper to undertake activities wherein some of the costs are not paid by the firm, while the revenues are realised directly. Issues of sustainability can be understood as negative externalities that impact the welfare of people in the future (Brundtland 1987)./nIn my investigations, I generate novel analyses and tools for explaining, quantifying, and evaluating externalities. I complete assessments and analyses of the Genuine Progress Indicator (GPI) for California and the United States, and suggest improvements to its methodologies. The GPI is a macroeconomic indicator that incorporates assessments of a number of significant externalities in order to construct a more comprehensive measure of welfare. I produce a cross-sectional analysis of the Ecological Footprint (EF) efficiency in national welfare provision. EF measures the anthropogenic draw on renewable biological resources, the externality of unsustainable impact on natural resources. Finally, as a foundation for ongoing work examining externalities and related policy questions, I build an open source and free software package, MPSGE.jl, that facilitates easier Computer General Equilibrium (CGE) modelling. The equilibrium framework of CGE models lends itself to applications accounting for externalities, such as the imposition of Pigouvian taxes2.1 While there are both negative and positive externalities, in general I will default to the negative case for discussion throughout for readability, unless explicitly described otherwise. 2 Pigouvian taxes and subsidies (Pigou 1920) imposed and provided by the government, can adjust market prices to incorporate the externality cost, to more fully reflect and optimise social welfare./nMy GPI work, presented in Chapters 1 and 2, includes calculating and analysing the GPI of California and the United States in order to measure truer social welfare over time, while I also analyse the indicator and its strengths and weaknesses.In Chapter 1, we estimate the GPI for California for a five-year period, 2010-2014. Within this relatively short time period, which includes the recovery from the Great Recession, we examine how inequality, nonmarket activities, and environmental degradation, affects the GPI of California. We also evaluate our estimation of the California GPI (CA-GPI) in two specific ways. First, we compare California’s GPI to an alternative indicator of social welfare, the Human Development Index (HDI) for California. Our comparison points to the GPI as a more holistic measure of sustainable economic well-being, although the HDI is useful in evaluating educational attainment, life expectancy, or earnings across regions or demographic groups in the state. Second, we compare our estimation of CA-GPI to the California results from a GPI estimation for all fifty states for 2011, and evaluate how different methodological decisions and data selection affect the results. Comparison of the two GPI assessments shows how the use of region-specific data compared to scaled national data increases the accuracy of estimates. However, using data and methods that prioritizes standardization is essential for comparing the GPI across regions, though the trade-off is diminished regional accuracy. The chapter concludes with a discussion of the uses of the GPI to evaluate policies, and suggests fruitful steps forward. Chapter 2 argues that important improvements in the Genuine Progress Indicator can be made by directly calculating the loss of natural resources, the benefits of leisure, and adjusting for inequality using a global norm, rather than using local, historical benchmarks. Local benchmarking is an obstacle to the standardisation and comparability of the GPI. We provide alternative methods for the five components that have used benchmarking in the standard GPI. We present new empirical estimates for the GPI of the United States and California over the period 1995 through 2017, calculated with and without the alternative methods. Using the alternative methods, we show that some differences between the GPI of the U.S. and CA are artefacts of the benchmark methods. Implementing the alternative methods narrows the gap between the U.S. and CA GPI, as it reduces the U.S. environmental costs, and removes the artificial differential between the U.S. and CA in the cost of inequality. We find that the GPI provides insight into net welfare not reflected in GDP, both with and without the benchmarking methods. Overall, we suggest that the GPI can be significantly improved with these high priority revisions without changing the fundamental approach or theoretical framework./nIn Chapter 3, I present a novel data analysis and visualization that combines indicators of different of national welfare in conjunction with each country’s associated Ecological Footprint, generating a measure of Ecological Footprint Efficiency, that is, the efficiency in providing economic services while minimizing the externality of ecological impact. The Ecological Footprint (EF) measures humanity’s draw on renewable biological resources. I use a specific set of data from a model which tracks the EF through the global supply chain to seven categories of final consumption. Using that EF data enables a direct association with selected data from the Sustainable Development Goals Indicator (SDGI) tracking the quality of the welfare benefits, under each of the seven categories./nIn Chapter 4, I detail my research contribution in developing MPSGE.jl, a novel, open-source software platform to facilitate simpler, error-reducing, fast, and free Computable General Equilibrium (CGE) modelling. The package is an evolution of the Mathematical Programming System for General Equilibrium analysis (MPSGE, Rutherford 1987) in the open-source Julia programming language. MPSGE.jl provides free and open-source access to the succinct-form model-construction functionality of its predecessor, and provides additional features such as an algebraic print of the model equations and easy integration with the growing ecosystem of other packages in the Julia programming language. I describe the package’s structure and use, and illustrate its benefits as part of a general-use and increasingly popular scientific programming language with an application. I use Monte Carlo methods to perform sensitivity analyses on a variety of parameters within a national model of the United States, and generate visualisations of the results with a standard plotting package.

Advisor: Anthoff, David