COVID-19

COVID-19 Impact on Micro, Small, and Medium-Sized Enterprises under the Lockdown: Evidence from a Rapid Survey in the Philippines ArXiv ID: ssrn-3807080 “View on arXiv” Authors: Unknown Abstract The novel coronavirus disease, COVID-19, has brought significant change to people’s lives and business activities nationally, regionally, and globally. The Phil Keywords: COVID-19, Supply Chain, Economic Resilience, Business Operations, Corporate Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The paper relies on descriptive statistics and basic econometric models (e.g., linear probability) without advanced mathematical derivations, while its empirical work is based on a single rapid survey without code, backtests, or robust implementation details. flowchart TD A["Research Question:<br>How did the COVID-19 lockdown impact MSMEs?"] --> B["Methodology: Rapid Survey<br>in the Philippines"] B --> C["Data Inputs:<br>Survey Responses &<br>Business Metrics"] C --> D["Computational Process:<br>Descriptive Statistics &<br>Economic Impact Analysis"] D --> E["Key Findings:<br>Revenue Loss, Supply Chain Disruption<br>& Need for Digital Resilience"]

Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain ArXiv ID: ssrn-3586723 “View on arXiv” Authors: Unknown Abstract Since the first death in China in early January 2020, the coronavirus (COVID-19) has spread across the globe and dominated the news headlines leading to fundame Keywords: COVID-19, Volatility, Market Turbulence, Risk Management, Crisis Economics, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 9.0/10 Quadrant: Street Traders Why: The paper uses advanced econometric methods (e.g., time-series regressions with fixed effects) but is fundamentally an empirical study relying on a massive proprietary transaction dataset (23 million transactions) to analyze real-world consumer behavior, with no code/backtests presented but heavy data and implementation details. flowchart TD A["Research Goal:<br>Assess UK consumer<br>spending volatility<br>amid COVID-19"] --> B["Data Source:<br>UK Finance Admin Data<br>(n = 70M accounts)"] B --> C["Methodology:<br>Panel Regression &<br>Time-Series Analysis"] C --> D["Computational Process:<br>Compare Pre/Post-<br>Pandemic Spending Trends"] D --> E["Key Finding 1:<br>Immediate spending<br>contraction (Mar 2020)"] D --> F["Key Finding 2:<br>Shift from services<br>to durable goods"] D --> G["Key Finding 3:<br>Volatility spiked;<br>uncertainty persisted"]

DigitalFinance& The COVID-19 Crisis ArXiv ID: ssrn-3558889 “View on arXiv” Authors: Unknown Abstract The COVID-19 coronavirus crisis is putting unprecedented strain on markets, governments, businesses and individuals. The human, economic and financial costs are Keywords: COVID-19, Market Volatility, Systemic Risk, Economic Impact, Cross-Asset Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a high-level policy and regulatory analysis with no mathematical models or empirical backtesting, focusing on conceptual strategies and qualitative recommendations. flowchart TD A["Research Goal: Impact of COVID-19<br>on Digital Finance Markets"] --> B["Data Collection"] B --> C["Methodology: Cross-Asset Analysis"] C --> D["Computational Process:<br>Volatility & Risk Modeling"] D --> E["Key Findings"] subgraph B ["Data/Inputs"] B1["Market Volatility Data"] B2["Systemic Risk Indicators"] B3["Economic Impact Metrics"] end subgraph E ["Outcomes"] E1["Increased Market Volatility"] E2["Systemic Risk Transmission"] E3["Cross-Asset Correlation Spike"] end