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on Post Keynesian Economics |
| By: | Assa, Jacob; Morgan, Marc |
| Abstract: | This paper argues that fiscal space is not given in absolute terms, but is relative to a country’s macroeconomic conditions. We first identify the relativity of fiscal space in the ideas of John Maynard Keynes, drawing an analogy to the theory of special relativity by Albert Einstein. We then present the outline of a ‘general relativity’ theory of fiscal space based on our interpretation of functional finance. Building on our theory we operationalize four macroeconomic determinants of relative fiscal space – two cyclical factors (unemployment and inflation) and two structural factors (productive capacity and monetary sovereignty) – using available data for 151 countries. Among the structural factors, we contribute a novel Monetary Sovereignty Index (MSI). We use this index, alongside UNCTAD’s Productive Capacities Index (PCI), the rate of unemployment, and the inflation rate, in a principal component analysis (PCA) to compute a Fiscal Space Index (FSI) for each country. We find that there is a wide variety of fiscal space across countries, and even within regions – with some developing economies scoring high, and some developed economies scoring low on the index. We illustrate the dynamic operation of our framework with mechanical simulations covering three scenarios for the case study of Malawi, the country with the fifth lowest fiscal space in our sample. We find that the functional finance paradigm of fiscal space outperforms the sound finance paradigm for a range of macroeconomic variables, including the FSI, and more so when government spending is targeted to easing import dependence, rather than just targeting increased productive capacity. We end by discussing key political economy and geopolitical constraints and identify various reforms that would facilitate countries to fully use or increase their fiscal space. |
| Keywords: | Fiscal Space, Relativity, Sound Finance, Keynes, Functional Finance, Development |
| JEL: | B50 C38 E00 E61 E62 H6 H87 O23 O50 O55 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:gnv:wpaper:unige:176185 |
| By: | Jonathan Colmer; Suvy Qin; John Voorheis; Reed Walker |
| Abstract: | This paper explores the relationships between air pollution, income, wealth, and race by combining administrative data from U.S. tax returns between 1979-2016, various measures of air pollution, and sociodemographic information from linked survey and administrative data. In the first year of our data, the relationship between income and ambient pollution levels nationally is approximately zero for both non-Hispanic White and Black individuals. However, at every single percentile of the national income distribution, Black individuals are exposed to, on average, higher levels of pollution than White individuals. By 2016, the relationship between income and air pollution had steepened, primarily for Black individuals, driven by changes in where rich and poor Black individuals live. We utilize quasi-random shocks to income to ex-amine the causal effect of changes in income and wealth on pollution exposure over a five-year horizon, finding that these income-pollution elasticities map closely to the values implied by our descriptive patterns. We calculate that Black-White differences in income can explain ~10 percent of the observed gap in air pollution levels in 2016. |
| Keywords: | income, inequality, air pollution |
| Date: | 2024–11–12 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2051 |
| By: | McKenzie, Rex; Koutny, Christian |
| Abstract: | This paper develops the concept of the hyper-capitalist urban node (HCUN) as a new theoretical framework for understanding how financial capitalism transforms contemporary cities. Through systematic analysis of London's economic, social, and technological changes from 1992 to 2023, we demonstrate that global financial centres have evolved beyond established models of the global city. Drawing on comprehensive longitudinal data, we identify six distinctive characteristics of HCUNs: financial sector dominance, extreme income polarisation, housing financialisation, the political-financial nexus, technological acceleration, and social-spatial transformation. London's empirical evidence demonstrates how these characteristics manifest in concrete terms through dramatic shifts in employment structure, housing markets, income distribution, and spatial organisation. We argue that HCUNs represent not merely a quantitative intensification of existing urban processes, but rather a qualitative shift in how cities function within global capitalism. The analysis reveals fundamental contradictions within the HCUN model, explaining why conventional urban policies often fail to achieve their intended outcomes. Through this research, we advance both theoretical understanding of contemporary urban transformation and methodological approaches to studying it, including the development of the HCUN Index. Our findings demonstrate the need for fundamental innovation in urban theory and governance to address the distinctive challenges posed by financial capitalism's intensifying influence over urban development. |
| Date: | 2024–11–15 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:fhk2c |
| By: | Researcher, AS Independent |
| Abstract: | "Divided We Fall: A Multidisciplinary Analysis of Polarization, Social Divides, and the Fragility of Unity in Human Societies" explores the escalating threat of polarization and tribalism in modern human societies. By examining historical case studies, such as Nazi Germany and McCarthyism, alongside contemporary events like Brexit and the U.S. elections of 2016 and 2024, the paper identifies recurring patterns in how societal divisions are exploited for political and ideological gain. The analysis integrates insights from social psychology, highlighting cognitive biases like confirmation bias, in-group/out-group dynamics, and heuristic-driven decision-making, which leave individuals vulnerable to manipulation. The paper also delves into the role of emerging technologies, such as social media and AI-driven propaganda, in amplifying divisions, creating echo chambers, and eroding democratic norms. Beyond diagnosing the problem, it explores opportunities for fostering unity, drawing on historical examples of collective action, such as post-WWII reconstruction and the global response to the COVID-19 pandemic. The findings underscore the fragility of social cohesion and emphasize the urgent need for proactive leadership, media responsibility, and grassroots mobilization to counter polarization. This multidisciplinary framework aims to provoke discussion on how humanity can navigate its growing divides and build resilience against future existential threats. The paper also explores how modern technologies—such as social media algorithms and artificial intelligence—amplify polarization, creating echo chambers and eroding trust in democratic processes. Insights from social psychology, including heuristics, cognitive biases, and tribalism, highlight the vulnerabilities that make societies susceptible to manipulation. Finally, the paper discusses pathways to unity through shared goals, historical examples of successful collaboration, and the necessity of ethical leadership and robust institutions. The findings underscore the urgent need for proactive measures to counteract polarization, emphasizing education, transparency, and collective action as essential tools for preserving democracy and fostering global unity in the face of existential threats such as climate change and technological disruption. |
| Date: | 2024–11–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:wzm5d |
| By: | Augusto Cerqua; Marco Letta; Gabriele Pinto |
| Abstract: | Machine Learning (ML) is increasingly employed to inform and support policymaking interventions. This methodological article cautions practitioners about common but often overlooked pitfalls associated with the uncritical application of supervised ML algorithms to panel data. Ignoring the cross-sectional and longitudinal structure of this data can lead to hard-to-detect data leakage, inflated out-of-sample performance, and an inadvertent overestimation of the real-world usefulness and applicability of ML models. After clarifying these issues, we provide practical guidelines and best practices for applied researchers to ensure the correct implementation of supervised ML in panel data environments, emphasizing the need to define ex ante the primary goal of the analysis and align the ML pipeline accordingly. An empirical application based on over 3, 000 US counties from 2000 to 2019 illustrates the practical relevance of these points across nearly 500 models for both classification and regression tasks. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09218 |