A multidisciplinary study was conducted to investigate the environmental and economic impact of the Grand Ethiopian Renaissance Dam (GERD). Focusing on Egypt, we estimate projected losses in Egypt’s annual water allocation from the Blue Nile under the 3, 7, and 10-year GERD reservoir filling scenarios, which are part of an array of scenarios currently under consideration. We then examine the resultant losses in Egypt’s agricultural land and the corresponding impact on selected macroeconomic variables relative to a baseline (no GERD) scenario. For the 3-year filling period, in particular, we estimate projected losses in Egypt’s annual water allocation to be 51.29 ± 2.62%. This translates into annual losses of agricultural land of 52.75 ± 2.44% relative to the baseline, with a resultant decline in food production of 38.47 ± 2.18% and an overall decline in agricultural sector output by 17.51 ± 0.99%. This contributes to a rise in the national unemployment rate of 11.24 ± 1.77 percentage points above the baseline. Moreover, we estimate projected annual losses in real Gross Domestic Product (GDP) per capita to be 8.02 ± 0.45% relative to the baseline, which translates into an annual loss in real GDP of $26.30 ± 2.81 billion and a loss in welfare of 12.83 ± 0.73% annually, relative to the baseline.

This paper assesses whether the impact of monetary policy uncertainty on the U.S. economy has changed over time. Estimating a Time-Varying Parameter Vector Autoregressive model on U.S. data from 1985Q1 to 2022Q3, we find that uncertainty shocks have larger negative effects on output during the COVID-19 recession than during other periods. However, financial market variables, such as stock prices and dividends, responded more significantly to uncertainty shocks during the Asian crisis of the late 1990s, the IT bubble of the 2000s, and the Great Recession. We then develop a Dynamic Stochastic General Equilibrium model with monetary policy uncertainty. Based on the calibrated model, we conduct several counterfactual exercises to demonstrate that the effects of uncertainty shocks depend on the state of the economy, which is consistent with the empirical evidence. These findings provide new insights into the time-varying nature of the impact of economic uncertainty.

This study explores how financial development and openness influence the effectiveness of fiscal and monetary policies. An analysis of data from about 100 countries between 1980 and 2018 reveals that both financial openness and development weaken the impact of monetary and fiscal policies. Our results further show that financial development in a country diminishes policy effectiveness depending on the country’s level of financial development; specifically, the more developed a country, the less effective the policies would be. Additionally, through a detailed examination employing a dynamic panel GMM approach, the study investigates the global repercussions of economic downturns in the US and how financial maturity shapes policy effectiveness during these times. We also discuss some policy implications that show that the positive impacts of monetary policy on output growth are lessened during crisis periods, and policymakers should act accordingly.

In this paper, we study the effects of uncertainty shocks in a quantitative framework where firms in the corporate sector are constrained by credit. Specifically, we formulate borrowing constraints as a nested function that features both earnings and capital as alternative instruments for assessing credit worthiness, in line with recent trends in corporate finance. We find that the quantitative framework that incorporates only one instrument (capital or earnings) in the borrowing constraint falls short in matching the business cycle properties of the US economy in terms of the behavior of output, inflation, and the price markup which are an essential part of the literature on uncertainty shocks. Rather, a hybrid formulation of the borrowing constraint which accounts for both capital and earnings helps us bring the results in the quantitative model closer to the data.