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We compute government spending multipliers for the Euro Area (EA) contingent on the interestgrowth differential, the so-called r-g. Whether the fiscal shock occurs when r-g is positive or negative matters for the size of the multiplier. Median estimates vary conditional on the specification, but the difference between multipliers in the negative and positive r-g regimes differs systematically from zero with very high probability. Over the medium run (5 years), median cumulated multipliers range between 1.22 and 1.77 when r-g is negative, and between 0.51 and 1.26 when r-g is positive. We show that the results are not driven by the state of the business cycle, the monetary policy stance, or the level of government debt, and that the multiplier is inversely correlated with r-g. The calculations are based on the estimates of a factor-augmented interacted panel vector-autoregressive model. The econometric approach deals with several technical problems highlighted in the empirical macroeconomic literature, including the issues of fiscal foresight and limited information.

Macroeconomics --- Public Finance --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- Diffusion Processes --- State Space Models --- Multiple or Simultaneous Equation Models: Models with Panel Data --- Classification Methods --- Cluster Analysis --- Principal Components --- Factor Models --- Fiscal Policy --- Debt --- Debt Management --- Sovereign Debt --- National Government Expenditures and Related Policies: General --- Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data) --- Public finance & taxation --- Economic growth --- Expenditure --- Public debt --- Fiscal multipliers --- Business cycles --- Fiscal stance --- Expenditures, Public --- Debts, Public --- Fiscal policy --- Austria

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Direct measurement of corruption is difficult due to its hidden nature, and measuring the perceptions of corruption via survey-based methods is often used as an alternative. This paper constructs a new non-survey based perceptions index for 111 countries by applying sentiment analysis to Financial Times articles over 2005–18. This sentiment-enhanced corruption perception index (SECPI) captures not only the frequncy of corruption related articles, but also the articles’ sentiment towards corruption. This index, while correlated with existing corruption perception indexes, offers some distinct advantages, including heightened sensitivity to current events (e.g., corruption investigations and elections), availability at a higher frequency, and lower costs to update. The SECPI is negatively correlated with business environment and institutional quality. Increases in the perceived incidence or scope of corruption influences economic agents’ behaviors, and thus economic dynamics. We found that when the SECPI is at least one standard deviation above the mean, the growth per capita falls by 0.65 percentage point on average, with more pronounced impacts for emerging market and low income countries.

United Kingdom --- Macroeconomics --- Economics: General --- Criminology --- Finance: General --- Corporate Finance --- Bureaucracy --- Administrative Processes in Public Organizations --- Corruption --- Institutions and the Macroeconomy --- Classification Methods --- Cluster Analysis --- Principal Components --- Factor Models --- General Financial Markets: General (includes Measurement and Data) --- Financial Institutions and Services: General --- Economic & financial crises & disasters --- Economics of specific sectors --- Corporate crime --- white-collar crime --- Finance --- Crime --- Emerging and frontier financial markets --- Financial markets --- Business environment --- Economic sectors --- Currency crises --- Informal sector --- Economics --- Financial services industry --- Business enterprises --- White-collar crime

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The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.

Macroeconomics --- Economics: General --- International Economics --- Econometrics --- Intelligence (AI) & Semantics --- Foreign Exchange --- Industries: Financial Services --- Informal Economy --- Underground Econom --- Classification Methods --- Cluster Analysis --- Principal Components --- Factor Models --- Technological Change: Choices and Consequences --- Diffusion Processes --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- State Space Models --- Monetary Systems --- Standards --- Regimes --- Government and the Monetary System --- Payment Systems --- Economic & financial crises & disasters --- Economics of specific sectors --- Econometrics & economic statistics --- Machine learning --- Currency --- Foreign exchange --- Computer applications in industry & technology --- Factor models --- Econometric analysis --- Technology --- Time series analysis --- Spot exchange rates --- Mobile banking --- Currency crises --- Informal sector --- Economics --- Econometric models --- Banks and banking, Mobile --- South Africa

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The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.

South Africa --- Macroeconomics --- Economics: General --- International Economics --- Econometrics --- Intelligence (AI) & Semantics --- Foreign Exchange --- Industries: Financial Services --- Informal Economy --- Underground Econom --- Classification Methods --- Cluster Analysis --- Principal Components --- Factor Models --- Technological Change: Choices and Consequences --- Diffusion Processes --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- State Space Models --- Monetary Systems --- Standards --- Regimes --- Government and the Monetary System --- Payment Systems --- Economic & financial crises & disasters --- Economics of specific sectors --- Econometrics & economic statistics --- Machine learning --- Currency --- Foreign exchange --- Computer applications in industry & technology --- Factor models --- Econometric analysis --- Technology --- Time series analysis --- Spot exchange rates --- Mobile banking --- Currency crises --- Informal sector --- Economics --- Econometric models --- Banks and banking, Mobile

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We measure the impact of frequent exogeneous shocks on small ECCU economies, including changes to global economic activity, tourism flows, oil prices, passport sales, FDI, and natural disasters. Using Canonical-Correlation Analysis (CCA) and dynamic panel regression analysis we find significant effects of most of these shocks on output, while only fluctuations in oil prices have significant effects on inflation. Results also suggest a significant impact of FDI and passport sales on the external balance, a link that CCA identifies as the strongest among all analyzed relations. The model also shows how Covid-19 related shocks lead to substantial contractions in output in all ECCU countries and deterioration of the current account balance in most of them, depending on countries’ tourism dependency.

Macroeconomics --- Economics: General --- Exports and Imports --- Industries: Hospital,Travel and Tourism --- Natural Disasters --- Multiple or Simultaneous Equation Models: Models with Panel Data --- Classification Methods --- Cluster Analysis --- Principal Components --- Factor Models --- Economic Growth of Open Economies --- Macroeconomic Aspects of International Trade and Finance: Forecasting and Simulation --- Energy: Demand and Supply --- Prices --- Sports --- Gambling --- Restaurants --- Recreation --- Tourism --- International Investment --- Long-term Capital Movements --- Current Account Adjustment --- Short-term Capital Movements --- Climate --- Natural Disasters and Their Management --- Global Warming --- Economic & financial crises & disasters --- Economics of specific sectors --- Hospitality, leisure & tourism industries --- Finance --- International economics --- Natural disasters --- Oil prices --- Economic sectors --- Foreign direct investment --- Balance of payments --- Current account balance --- Environment --- Currency crises --- Informal sector --- Economics --- Investments, Foreign --- Dominica